Real and Financial Sectors in Post-Pandemic Central and Eastern Europe: The Impact of Economic, Monetary, and Fiscal Policy (Contributions to Economics) 3030998495, 9783030998493

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Contributions to Economics

Bojana Olgić Draženović Vesna Buterin Stella Suljić Nikolaj   Editors

Real and Financial Sectors in Post-Pandemic Central and Eastern Europe The Impact of Economic, Monetary, and Fiscal Policy

Contributions to Economics

The series Contributions to Economics provides an outlet for innovative research in all areas of economics. Books published in the series are primarily monographs and multiple author works that present new research results on a clearly defined topic, but contributed volumes and conference proceedings are also considered. All books are published in print and ebook and disseminated and promoted globally. The series and the volumes published in it are indexed by Scopus and ISI (selected volumes).

Bojana Olgić Draženović • Vesna Buterin • Stella Suljić Nikolaj Editors

Real and Financial Sectors in Post-Pandemic Central and Eastern Europe The Impact of Economic, Monetary, and Fiscal Policy

Editors Bojana Olgić Draženović Faculty of Economics and Business University of Rijeka Rijeka, Croatia

Vesna Buterin Faculty of Economics and Business University of Rijeka Rijeka, Croatia

Stella Suljić Nikolaj Faculty of Economics and Business University of Rijeka Rijeka, Croatia

ISSN 1431-1933 ISSN 2197-7178 (electronic) Contributions to Economics ISBN 978-3-030-99849-3 ISBN 978-3-030-99850-9 (eBook) https://doi.org/10.1007/978-3-030-99850-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

This book is the result of extensive research and efforts of members of the research team of three scientific projects funded by the University of Rijeka, and Faculty of Economics and Business, as well as other scientists and experts in economic policy analysis, who are engaged in the study of the financial and economic landscape of the Republic of Croatia. The scientific projects, whose leaders are also the editors of the monograph, are listed below: – “The Efficiency and Regulation of Financial Institutions in the Function of the Development of the Croatian Economy” – “Model of Optimal Institutional Growth of the Republic of Croatia in the Crisis Caused by the Pandemic COVID-19” – “Banking Regulation and Deposit Insurance System in Achieving Banking and Financial Stability” The outbreak of the COVID-19 pandemic caused a supply and demand shock in the economy and led to a decline in economic activity. The severe consequences occurred within a short period of time and required timely and decisive macroeconomic activities. Subsequently, the economies required consistent action by governments, regulators, and all market actors to manage this “new normal” environment and ensure economic and financial stability. Central banks responded by lowering their policy rates and injecting liquidity into financial systems, and governments launched unprecedented stimulus packages. Although this type of crisis, which is simultaneously a health, financial, and economic crisis, is affecting the global economy, it is particularly difficult to measure and understand its impact on the European economy. This book offers new insights and perspectives on the real and financial sectors in the post-pandemic period in the EU, with specific insights into the countries of Central and Eastern Europe, with special reference to Croatia. The authors examine the timeliness, justification, and appropriateness of the measures taken in response to the deteriorating economic conditions and the associated outcomes, but they also v

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address various aspects of economic, financial, and energy policies, making a valuable contribution to the field. The contribution of this book has two main themes. The first is an analysis and assessment of the financial development and performance of the real sector. The second theme provides insights into the institutional dimensions of the COVID-19 pandemic obstacles and opportunities for recovery in the near future. The authors offer new insights into recent developments and challenges related to the pandemic outbreak, the response of economic policymakers, and ultimately the new financial architecture in Croatia and other European countries. Institutional changes and their macroeconomic impact on economic growth are also examined, as well as ways to create an economically stimulating institutional environment at the time of sudden and unexpected changes in the economy due to the pandemic crisis. The magnitude and strength of the economic impact of the crisis will depend on the duration of the pandemic, which, despite numerous estimates, cannot yet be estimated with certainty. Therefore, the role of the government and institutions in the pandemic crisis is crucial to mitigate the negative impact of the crisis from the perspective of institutional investors, and examine the institutional framework of fiscal policy and CNB monetary policy to counteract the negative impact of the pandemic crisis. This volume is interdisciplinary and covers a wide range of topics: unconventional monetary policy and unprecedented fiscal policy during the pandemic, financial cycles, institutional development and institutional quality, banking system, real estate markets, pension systems, financial regulation, energy market, ESG factors, and agricultural policy. Therefore, this book can be seen as a critical insight into the general economic situation as well as some specific areas from an EU perspective, with a focus on post-transition countries. The monograph fills a gap in the academic literature by providing a comprehensive and systematic overview of economic policies and actions taken in the emergence of the recent health, economic, and financial crises triggered by the onset of the pandemic, and is a valuable contribution to identifying the guidelines for possible improvements in economic performance. The volume should be used as a relevant resource for academics, postgraduates, practitioners, financial experts, and policymakers to better understand Croatia’s economic situation and the environment of the current health and economic crises. Rijeka, Croatia

Bojana Olgić Draženović Vesna Buterin Stella Suljić Nikolaj

Contents

From Pandemics to the Unconventional: Monetary Policy in EMs: The Case of Croatia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Boris Vujčić Financial Cycle Convergence: Evidence on Financial Cycles Synchronisation in the European Union and the European Economic and Monetary Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marinko Škare and Malgorzata Porada Rochon Fiscal Response to the COVID-19 Shock in Croatia . . . . . . . . . . . . . . . . Frane Banić, Milan Deskar-Škrbić, and Hrvoje Šimović

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The Impact of Financial Integration on Sectoral Polarization between Croatia and Eurozone Countries . . . . . . . . . . . . . . . . . . . . . . . . Mario Pečarić, Ante Tolj, and Helena Blažić

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Foreign-Owned Banks and Real Estate Markets in Croatia: A Panel Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ana Rimac Smiljanić and Blanka Škrabić Perić

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Financial Cooperatives Development in Croatia: Social Capital Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ingrid Omerzo and Jakša Krišto

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EU Tax and Agricultural Policy in the Wine Sector . . . . . . . . . . . . . . . . 109 Jana Katunar, Maja Grdinić, and Dario Maradin Integration as an Indicator of (under) Development of the Croatian Capital Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Sanel Haistor Ramić, Dario Silić, and Denis Buterin Perspectives and Challenges in the Development of the Croatian Digital Startup Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Mirjana Grčić Fabić vii

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Pension Funds Regulation in the Context of Investment Climate Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Ivana Bestvina Bukvić, Dražen Novaković, and Ivan Kristek Challenges of Energy Policy within Decarbonisation: Evidence of the European Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Barbara Fajdetić

From Pandemics to the Unconventional: Monetary Policy in EMs: The Case of Croatia Boris Vujčić

Abstract The emergence of the pandemic has once again highlighted the possibilities of unconventional monetary policy and enabled the expansion of monetary policy instruments in emerging and developing economies. For most of these European countries, the interest rate channel of monetary policy may not be very strong; they are used to balance sheet policies. The balance sheet of the Croatian National Bank (CNB) has also increased in lockstep with the balance sheet of the European Central Bank over the past decade, as have the excess reserves of the banking system. Although the fuel for these increases has been the accumulation of international reserves rather than the purchase of domestic securities, the banking liquidity channel has operated in the same manner. Since the COVID -19 crisis, the CNB has acted within the framework of various measures taken by the government as well as other regulatory institutions. At the same time, the CNB intervened heavily in the foreign exchange market and used a number of monetary policy operations to support kuna liquidity. In addition to standard structural and regular operations and the reduction of the reserve requirements, the CNB initiated for the first time a program to purchase government securities through the secondary market. The CNB used all conventional and unconventional instruments available to central banks in emerging markets, adapted them to specific domestic circumstances and successfully fulfilled its mandate.

1 Introduction One of the best years in terms of economic performance was 2019: it was marked by strong growth, rising employment and wages and reducing external and internal imbalances against the background of favourable financing conditions and stable

B. Vujčić (*) Croatian National Bank, Zagreb, Croatia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_1

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Fig. 1 The real activity was initially reduced to a standstill. Source: CNB

prices. Outlook for 2020 was optimistic and confident. In retrospect, it was hard to imagine a more drastic turn of events. The pandemic abruptly stopped economic and social activities and played havoc with the financial and foreign exchange markets. In addition, two devastating earthquakes hit Croatia, while intensive preparations for joining the European Exchange Rate Mechanism, close cooperation with the ECB’s Single Supervisory Mechanism and the Single Resolution Mechanism were under way (Fig. 1). Faced with extraordinary circumstances, the CNB acted quickly with a broad set of measures. Some policy tools were adapted to the new circumstances and used to an unprecedented extent, while others, some of which could be dubbed unconventional, were used for the first time. The monetary policy proved extraordinarily effective in stabilising the financial markets and containing spill-overs into the real economy, while supervisory measures were crucial for alleviating liquidity pressures on households and firms and allowing the banking system to cushion the temporary blow on incomes. The financial markets, international and domestic, were the first to react when the pandemic hit, and they did so immediately and strongly. With large fluctuations of most assets prices, certain segments of the financial markets were almost frozen in the period immediately after the outbreak, with demand for cash and liquid assets shooting up. The demand for foreign currencies rose sharply, as it always does in Croatia under high uncertainty, prompting depreciation pressures on kuna. Turbulence in the financial markets also triggered large redemptions from investment funds. To pay off investors, they sold assets, mostly government securities. Ensuing fire sale led to falling bond prices and rising yields, and, due to weak demand, posed the risk of a trade freeze in that segment of the financial market. Rising bond yields and freezing of the government bond market threatened to worsen financing conditions for all domestic sectors. A large chunk of assets withdrawn from investment funds was converted to foreign currency deposits, increasing even further the demand for foreign currency. These shocks were intertwined and reinforced each other. While

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Fig. 2 The CNB balance sheet, absolute change compared to end of 2019. Source: CNB

the depreciation of the exchange rate would directly worsen the debt servicing capacity of all sectors, deteriorating financing conditions would add further pressure on borrowers across the board (Fig. 2). The pandemic also heavily affected the balance of payments. Current and capital account surplus decreased, as tourism receipts plummeted, although less initially feared. Exports declined, but less than imports and the use of EU funds was somewhat greater than initially expected, which mitigated the worsening of external accounts. Although the combination of fiscal, monetary, and supervisory measures reduced the adverse pandemic effects, the contraction of economic activity combined with the Government’s countercyclical measures has inevitably led to declining general government revenues and strong growth in the government’s funding needs. After three consecutive years of budget surpluses and 5 years of declining public debt-toGDP ratios, in 2020, due to shrinking revenues and rising pandemic-related costs, fiscal accounts deteriorated as a large budget deficit emerged while public debt sharply increased. Nonetheless, the Republic of Croatia maintained its investmentgrade rating, and the government risk premium remained relatively low (Fig. 3).

2 CNB Monetary Policy Measures The nature of the shocks posed a policy dilemma for the CNB since a response to each shock required a response that threatened to reinforce the other one. Large FX interventions, necessary to meet the growing demand for foreign currency and stabilise the exchange rate, would reduce kuna liquidity of the financial system and exacerbate market freeze and deteriorating financial conditions. On the other hand, using various instruments to inject additional kuna liquidity into the system

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Fig. 3 Worsening of fiscal indicators. Source: CBS and CNB

could have added to depreciation pressures and drained the international reserves even further. Still, several factors made the job a bit easier. First and foremost, as current and capital accounts were expected to remain in surplus, depreciation pressures were the result of de-anchoring of exchange rate expectations rather than fundamental pressures. Thus, communication was a crucial part of the policy package as breaking expectations of FX depreciation was essential to exit the vicious cycle. Further on, ample international reserves gave headroom to intervene in the FX market. Finally, the CNB entered into the swap agreement with the European Central Bank on 15 April, courtesy of the expected ERM-II entry, which made another EUR 2bn available to Croatia. The initial agreement was subsequently extended with the current expiration set on March 2022. With such calculations in mind, CNB simultaneously intervened heavily in the FX market and used a range of monetary operations to support kuna liquidity. In addition to standard structural and regular operations and reduction of reserve requirements, the CNB for the first time initiated a program of government securities purchase through the secondary market. Also, the range of counterparties to these operations was expanded as injections of liquidity did not reach parts of the financial system where the market strain was originating. Given the tailwinds from a strong external position of the country and swap arrangement as well as successful communication of commitment to exchange rate stability, monetary policy has managed to preserve both the stable exchange rate and liquidity of the government securities market, as well as favourable financing conditions for all sectors. On the back of inflows of EU funds and some external borrowing of the government, international reserves quickly rebounded towards the end of 2021 creating ample room to react in case of a need (Fig. 4).

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Fig. 4 Large FX interventions in support of exchange rate stability. Source: CNB

3 CNB Supervisory Measures Equally important to monetary policy were supervisory measures designed to ease the liquidity squeeze of the non-financial corporates and households. Banks were allowed to delay downgrading of the good debtors distressed due to the pandemic until the uncertainty created by the pandemic subsided. Banks could grant a moratorium, restructure existing and approve new loans to these debtors without regulatory scrutiny. Banks were also allowed to temporary operate below the required liquidity coverage ratio (LCR) of 100%, although this option has not been used by any bank. To further enhance financial stability and lending capacity, banks were prohibited from distributing dividends and other bonuses. As most debtors remained in good financial health as moratoria gradually expired, the dividend ban was lifted in September 2021. Also, in order to ease the operational burden on credit institutions, many supervisory activities have been postponed, such as stress testing, on-site examination, and compliance with certain supervisory measures. This was critical for minimising the social and economic consequences of the pandemic and created some relief for credit institutions faced with extreme uncertainty. Banks were able to keep the credit flowing towards households and businesses, cushioning the pandemic blow to liquidity (Fig. 5). It is important to stress that the CNB undertook actions in the context of various measures implemented by the government as well as other regulatory institutions. Although each regulator acted independently within its competence, the CNB, the Government of the Republic of Croatia, and HANFA coordinated their activities and acted in cooperation with European regulators, the European Central Bank, the European Banking Authority and the European Systemic Risk Board. Measures were successful due to their unique combination of speed and breadth. Also, the purchase of government securities, although large in scale, ended after only about 3 months, lessening the concerns about exit from such unconventional policy.

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Fig. 5 Response to the Coronavirus crisis in Croatia during 2020. Source: CNB

The whole package of monetary and supervisory measures as well as other economic policies, such as fiscal support to heavily hit enterprises, had broad effects that permeated the whole economy. Also anchoring the FX expectations during the most acute phase of the crisis was, in the end, an exercise in effective communication. Broadening the policy space showed that the CNB was standing ready to act upon its words and to do “whatever it takes”. The swap arrangement with the ECB lent additional credibility to the CNB, although, like the original, it was never used, but the markets “believed, and it was enough”.

4 The CNB’S Unconventional Measures in the International Context The use of unconventional monetary policy measures (UMP) by the advanced economies (AE) is “tale as old as time”. The Japan case comes to mind first, but other advanced economies used them as well, especially in the context of navigating zero lower bound and liquidity trap (Krugman 2000; Eggertsson and Woodford 2003; Bernanke et al. 2004). The macroeconomic context was different, whether it was stagflation, “global savings glut”, financial super-cycles, TFP decline,

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demographic slowdown. . . whenever the conventional monetary policy was rendered less efficient than was the case in “Great Moderation”. Emerging markets and developing economies (EM) were awakened to the possibility of unconventional monetary policy in the aftermath of the great financial crises. The IMF in 2009 provisionally classified unconventional measures used in these countries (Yehoue et al. 2009). In the follow-up paper, Yehoue (2009) extended the research to assess the effectiveness of these measures on a panel of developing countries. In the past decade, the use of unconventional measures in the EM grew in importance. BIS theoretically grounded the unconventional measures both for AE and EM and contextualised them in the “overall monetary transmission system” (Borio and Disyatat 2010). Along those lines, Bofinger and Debes (2010) in their “Primer on unconventional monetary policies” used conventional monetary models and applied them to the realm of unconventional policies. They argued that the unconventional measures are, as a rule, reflected on the central banks’ balance sheet. Today, we call them asset purchase programs (APPs). The use of unconventional policies in EMs evolved far beyond those early efforts. One area of UMP application in EMs was countering the spillovers from global financial conditions to EMs, mainly driven by the US policy actions and the global risk appetite. “When the source countries (i.e., advanced economies) move to exit unconventional policies, some recipient countries (i.e., emerging markets) are (left) leveraged, imbalanced, and vulnerable to capital outflows.” (Rajan 2015: Competitive monetary easing). Global financial conditions are important for monetary and exchange rate policies in EMs and, consequently, for asset purchase programs. There is a relatively large literature documenting evidence on spillovers from global financial conditions to EMs. A recent IMF study (Sever et al. 2020) extended this literature with similar findings in the case of the recent COVID-19 episode, albeit in a positive direction. They show that the QE announcement by the Fed helped EMs improve bond yields, currencies, and equities, whereas weaker global risk aversion had an opposite impact on those markets. Indeed, the nature of the COVID-19 crisis, monetary easing in AE and fiscal policy support have globally helped calm global financial markets and helped central banks in EMEs to orient their monetary policy towards domestic objectives despite large capital outflows and currency depreciations (Fig. 6). Since the COVID-19 crisis, about 20 central banks from emerging markets (EMs) engaged in APPs, a practice they have so far steered clear of (Sever et al. 2020). APPs can be a useful tool to mitigate specific constraints. A very recent study confirmed that APPs were successful in significantly reducing bond yields in EMs, and these effects were even stronger than those of policy rate cuts (Fratto et al. 2021). However, for EMs, there are limits and pitfalls in undertaking APPs. Among others, they are not issuing a reserve currency, they are often more sensitive to foreign funding and FX volatility and sometimes they have a shorter credible policy track record. Given the confluence of factors needed to make QE type of policies a success, such a policy will always have to be weighed carefully against

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Fig. 6 The CNB supported the financial markets with unprecedented liquidity. Source: CNB

vulnerabilities and buffers, such as foreign reserves, as well as global financial conditions and spillovers from monetary policies of major central banks. Although APPs have clearly served EMs well during the pandemic, less clear are the conditions in which to use them in the future.

5 Conclusion Taking everything into account, it is encouraging to see the broadening of the scope of monetary policy instruments in EMs. After all, while the interest rate channel of monetary policy may not be very strong in many EMs, they are used to balance sheet policies. The balance sheet of the CNB increased over the past decade more or less in lockstep with the balance sheet of the European Central Bank, just as excess reserves of the banking system increased in parallel. Although the fuel behind those increases has not been purchases of domestic securities, but rather the accumulation of international reserves, the bank liquidity channel has operated in the same way. In essence, the CNB used all the conventional and unconventional tools available to central banks in emerging markets, adapted them to the specific, domestic circumstances and successfully delivered its mandate.

References Bernanke B, Reinhart V, Sack B (2004) Monetary policy alternatives at the zero bound: an empirical assessment. Brook Pap Econ Act 2(2):1–100

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Bofinger P, Debes S (2010) A primer on unconventional monetary policy Borio C, Disyatat P (2010) Unconventional monetary policies: an appraisal. Manch Sch 78:53–89 Fratto C, Harnoys Vannier B, Mircheva B, de Padua D, Poirson H (2021) “Unconventional monetary policies in emerging markets and frontier countries”, IMF Working Paper WP/21/14 Eggertsson G, Woodford M (2003) The zero bound on interest rates and optimal monetary policy. Brook Pap Econ Act 1:139–233 Krugman P (2000) Thinking about the liquidity trap. J Jpn Int Econ 14:4 Rajan R (2015) Competitive monetary easing: is it yesterday once more? Macroecon Financ Emerg Mark Econ 8:1–2 Sever C, Goel R, Drakopoulos D, Papageorgiou E (2020) Effects of emerging market asset purchase program announcements on financial markets during the COVID-19 pandemic, IMF Working Paper 20/292, Washington: International Monetary Fund Yehoue EB (2009) Emerging economy responses to the global financial crisis of 2007–09-an empirical analysis of the liquidity easing measures. International Monetary Fund Yehoue EB, Ishi K, Stone MR (2009) Unconventional central bank measures for emerging economies. International Monetary Fund

Financial Cycle Convergence: Evidence on Financial Cycles Synchronisation in the European Union and the European Economic and Monetary Union Marinko Škare and Malgorzata Porada Rochon

Abstract Financial cycles are an established phenomenon in economics. An increasing collection of studies captures and quantifies financial cycles, their average duration and amplitude. Here we examine the national dimension of financial cycles in the euro area and their relationship to real GDP cycles. We use data on bank lending to businesses and households, as well as house prices in the European Union (EU) and the European Monetary Union (EMU) over a sample period of about 20 years. Financial cycles behaviour is more synchronised and convergent in the (EMU) area compared to the dynamics the authors reveal in the (EU) area. Germany and Luxembourg show a divergent financial cycles path and have a different relative transition path than the other clubs. In summary, we find empirical evidence that financial cycles dynamics vary across the (EU) Member States. The dynamics depend on the national debt level and monetary sovereignty position. There is no evidence of one distinct financial cycle convergence on the (EU) level. Moreover, we find evidence on two distinct convergence clubs with country members listed above. The identified convergence clubs show two distinct financial cycles patterns, one clearly more homogeneous for countries in club 1. The second one, in convergence club 2, is far more heterogeneous and divergent from one in Club 1. Also, the financial cycle dynamic in Club 2 is far more heterogeneous than the one we evidence for country members in Club 1.

M. Škare (*) Faculty of Economics and Tourism, Juraj Dobrila University of Pula, “Dr. Mijo Mirković”, Pula, Croatia e-mail: [email protected] M. P. Rochon Faculty of Management and Economics of Services, University of Szczecin, Szczecin, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_2

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1 Introduction Financial cycles are currently established phenomena in the field of economics. An increasing collection of studies captures and quantifies financial cycles, particularly their average duration and amplitude. Financial cycles are a crucial component of modern economies’ macroeconomic dynamics and considerably impact actual economic activity worldwide (Schularick and Taylor 2012). Several financial crises with far-reaching consequences have occurred during the past few decades, ranging from the early 1990s asset market catastrophe to the Asian financial upheaval of 1997 to the 2008 global financial crisis. Consequently, using empiricism and theory to characterise financial cycles has grown in importance as a research subject. Drewhmann et al. (2014) attribute financial cycles to changes in credit and property values. Financial cycles are inextricably linked to financial crises and exhibit a high degree of correlation with business cycles. Furthermore, there is a strong correlation between financial cycles and various economic policies, including monetary policy (Borio 2014) and fiscal policy (Borio et al. 2017). According to a study by Drehmann et al. (2012), financial cycles have a more extended memory than business cycles, lasting sixteen or more years. Another critical component of financial cycles that has garnered considerable attention in recent years is their cross-country influence. According to Rey (2015), financial cycles and the VIX move in lockstep (a measure for market uncertainty and risk aversion). The effects of financial cycles on capital flows, credit growth, and global banks are investigated using a vector autoregressive (VAR) approach. Juselius et al. (2017) investigate the comovement of financial cycles, business cycles, and monetary policy in the United States using the same method. Nonetheless, Wen et al. (2019) investigated the time-varying effect of financial cycles on oil prices using a time-varying parameter (TVP-VAR). They discovered that the 2008 global financial crisis enhanced the relationship between financial markets and crude oil prices. Brem et al. (2020), Pagan and Robinson (2014), and Yépez (2018) all have relevant studies on the link between oil prices and financial crisis (2018). However, few studies examine the relationship and causality of financial cycles in the economy and finance. In this research, the critical point is the search for a steady-state relative transition path between the European Union (EU) and the European Economic and Monetary Union (EMU) country members. Another research by Kunovac et al. (2018) looks at financial cycles in the euro area. Their results of three distinct empirical techniques on the cross-country dimension of the financial cycle in eurozone nations are highly consistent in total. Authors find that variables that reflect the prices or returns on financial assets (long-term interest rates, the term spread, and actual stock prices) exhibit a degree of cross-country synchronisation that is as significant as real GDP cycles. In general, the results for real estate prices and credit factors indicate a relatively weaker cross-country synchronisation. Real estate prices, particularly real bank loans to private consumers, of which mortgage loans are the largest, exhibit poor cross-country correlations. The study does not show any evidence of

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a typical discernible cycle in the real estate sector. However, there is a typical cycle between countries for actual bank loans to non-financial firms between the credit variables. One possible explanation for this result is that, as Scharnagl and Mandler (2019) show for the four largest eurozone countries, bank loans to non-financial firms have typical cycles with real activity, such as real GDP and real investment at frequencies far above standard business cycle frequencies. Thus, typical cycles and bank lending to companies are likely to reflect typical cycles in actual activity in all countries. Research by Scharnagl and Mandler (2019) uses wavelet analysis to investigate the dimension of financial cycles within the country in the four largest economies of the eurozone. Except for Germany, the variables contain significant typical cycles within each country, near the upper limit of the business cycle and beyond which financial cycles can be understood. They identify comovements in credit aggregates and house prices in all countries, except Germany, at the frequency of the financial cycle. Domestic financial cycles are inextricably associated with real GDP cycles, with economic cycles preceding financial. Authors examine the national dimension of financial cycles in the euro area and their relationship to real GDP cycles, using data on bank lending to businesses and households, as well as on house prices in Germany, Spain, France, and Italy over a sample period of about 20 years before and after the introduction of the euro. Studies demonstrate that these typical credits and home prices at the country level correlate strongly with similar cycles in real production growth in Spain, France, and Italy. A study by Adarov and The Vienna Institute for International Economic Studies (2019) provides additional empirical evidence showing the significance of the inherent cyclicality of financial markets in the context of European economies. His findings align with the idea that financial markets are exposed to persistent cyclical dynamics associated with the formation of imbalances, followed by contractions that often lead to macroeconomic downturns. The research is based on assessing a synthetic measure of financial cycles, using relevant price, quantity, and risk characteristics in important financial market segments. The above study empirically demonstrates the importance of financial cycles as a driver of business cycles and a significant factor in the dynamics of public debt in Europe, as well as the extent of cross-country financial synchronisation, spillovers, and the significant role of the typical Europe-specific cycle and the global financial cycle in shaping the dynamics of national financial markets. The degree of financial cycles convergence in the EU and EMU area attracted minimum focus. This gap can be attributed to the assumption that euro, financial integration, and growth convergence will lead to financial cycle convergence by default. Our study is the first (to our knowledge) to look at the financial cycle convergence, specifically in the EU and EMU areas. Using a non-linear time-varying factor model, this study analyses convergence in financial cycles and isolated convergence club for European economies. Previous studies focus on patterns and dynamics in financial cycles. They do not test for possible convergence between the cycles. Most studies attempt to empirically identify and provide quantitative knowledge on the link between financial and business cycles. Supported by the acquired

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empirical knowledge, this line of research tries to identify leading and lagging behavior between financial and real cycles. We can identify three distinct lines of research on financial cycles. The first includes studies on the financial cycle existence and pattern (phases, duration, amplitude, variability). The second one looks at the connection between financial and real cycles (level of synchronisation, coincidence, leading and lagging behaviour, convergence between financial and real cycles). The last one, looking at the global financial cycle, attempts to identify global, persistent financial cycles driving major economic forces. The possible divergence (decoupling) between the financial cycles has not been explored in the literature so far. Convergence theory is at the heart of the EU and particularly the EMU idea. Policymakers are looking at the economic growth convergence and real cycles issues, typical currency stability, price, inflation convergence, interest rates convergence, and fiscal harmonisation. However, no study has searched for convergence in financial cycles and their role as critical driving forces in economic and monetary union convergence. Here we address this issue by searching for empirical evidence on financial cycle convergence to check how important they are in the coming together or drifting apart processes we witness in the (EU) and (EMU). This study adds to the body of knowledge on financial cycle convergence by utilising a modern panel club convergence and clustering approach established by Phillips and Sul (2007a, b, 2009). This technique addresses biases (stationarity, linearity) and constraints on studies on financial cycles convergence. We can measure and test for convergence in financial cycles in the EU and EMU using these techniques. Furthermore, we employ the credit-to-GDP gap as a proxy for financial cycles in our study, similar to Claessens et al. (2012), who used the credit-to-GDP ratio (or total credit) and housing prices as proxies for similar medium-term cycles. As a result, the creditto-GDP ratio may be used as a reliable proxy for financial cycles, and we employ it in our study of financial cycles convergence. The following are the problems with utilising the credit-to-GDP gap as a measure of financial volatility; according to Drehmann and Tsatsaronis (2014): (1) the credit gap is ineffective as a buffer reference because it can lead to decisions that contradict the goal of countercyclical capital buffers (CCB); (2) the credit gap is not the best early warning indicator (EWI) for banking crises, particularly in emerging economies, and (3) the credit gap show estimation biases (Behn et al. 2016). Additionally, Drehmann and Juselius (2014) dismissed concerns about the creditto-GDP gap, demonstrating that it may be used as a viable proxy for financial crises. In line with the initial finding and Claessens et al. (2012), the credit-to-GDP ratio data reveal a medium-term similarity to the financial cycle pattern described in Škare and Porada-Rochoń (2020). Due to the empirical findings of the two studies, we conclude that the credit-to-GDP gap displays comparable medium-term dynamic patterns and can therefore be used as a proxy for financial cycles.

Financial Cycle Convergence: Evidence on Financial Cycles. . .

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2 Financial Cycles in Review Financial cycles are becoming increasingly important due to their widespread use in macroeconomic policy. Mainly due to boom-and-bust cycles forecasting (Borio 2014). In general, the results of the study confirm that financial cycles operate on more extended frequencies than business cycles (Borio 2014), (Drehmann et al. 2012), (Rünstler and Vlekke 2018), (Skare and Porada-Rochoń 2019) as well as a larger amplitude than business one (Drehmann et al. 2012), (Galati et al. 2016), (Schüler et al. 2017), (Skare and Porada-Rochoń 2019). Furthermore, an essential finding of the research is that financial cycles lead to business cycles (Škare and Porada-Rochoń 2020). However, there are still many issues to be explored, such as convergence and the synchronisation of the financial cycle. Unfortunately, far fewer publications address the topic of convergence than synchronisation, showing a gap in this area of science (Table 1). Gammadigbe (2021) analysed both convergence and the synchronisation of the financial cycle in the West African Economic and Monetary Union (WAEMU) between 2005Q1-2020Q4. The study results negated the synchronisation of financial cycles (essential differences in the duration and amplitude) and the lack of convergence of financial cycles in WAEMU. Aldasoro et al. (2020) investigate the domestic and global financial cycles, i.e., the cycles’ degree of synchronicity across countries. The results show that the link between them is relatively weak, although they tend to come together during the crisis. According to Schüler et al. (2017), as a result of applying a general method of constructing complex financial cycles, synchronisation of financial cycles has been proved, but not for all countries. Exceptions are Germany and Japan. Škare and Porada-Rochoń (2020) search for the synchronisation between financial and business cycles over a long period. Using the data for the UK from 1270Q1 to 2016Q4, they find that both cycle series move along over the medium-term spectrum. Synchronisation between financial cycles as well as between business cycles has been proven by (Kunovac et al. 2018). Taking six European countries (BE, DE, ES, FR, IT, NL) in the period 1970Q1–2016Q4, the research results indicate cycles in equity prices and interest rates are more strongly synchronised across countries than cycles in actual economic activity. In contrast, the synchronisation of credit cycles and, in particular, cycles in house prices are weaker than for real GDP. The synchronicity of the financial cycle in Belgium, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Spain, and the United Kingdom for the period from 1980Q1 to 2012Q4 were investigated by Stremmel (2015). The evidence shows that the financial cycle is more synchronised during stress times than in boom periods.

M. Škare and M. P. Rochon

16 Table 1 Most important research on financial and business cycles

Authors/year of publishing Gammadigbe (2021)

Period 2005Q1– 2020Q4

Aldasoro et al. (2020)

1996Q to 2018Q4

Skare and PoradaRochoń (2020) Mandler and Scharnagl (2019)

1270Q– 2016Q4

UK

1983Q1 – 2007Q2

G7

Kunovac et al. (2018)

1970Q1*– 2016Q4 depends on the country 1970Q1 –

6 euro area countries

Rünstler and Vlekke (2018)

1973 Q1 to 2014 Q4

Stremmel (2015) Crowley (2008)

1980Q1 to 2012Q4 1971Q1– 2007Q1

The U.S. and the five largest economies in Europe 11 European countries. EU

Schüler et al. (2017)

Country/ Countries West African Economic and Monetary Union 40

G7

Synchronisation of the financial cycle or financial and business cycle No

Financial cycle convergence/ business cycle convergence No

Synchronisation between the global financial cycle and domestic financial cycle is weak Synchronisation between financial cycle and business cycle

No

Synchronisation between financial cycle and synchronisation between business cycle Synchronisation between financial cycle and synchronisation between business cycle

x

Synchronisation between financial cycle—strongly across countries for most of them Synchronisation between financial cycle and the business cycle (exception: Germany) Synchronisation between financial cycle Synchronisation between business cycle

x

x

x

x

x Convergence between business cycle

Source: Authors’ review

Mandler and Scharnagl (2019) also search for synchronisation in G7 countries from 1971–2018. Two findings are presented: cycles in equity prices and interest rates across countries are at least as synchronised as cycles in real GDP, but cycles in credit and house prices are less synchronised across countries than those in real GDP. Synchronisation between the financial cycle and the business cycle was investigated by Rünstler and Vlekke (2018) for the US and the five largest economies in Europe in the period from 1973 Q1 to 2014 Q4. Except for Germany, long cycles in

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credit and house prices are highly correlated with a medium-term component in GDP cycles. Crowley (2008) analyses convergence and synchronicity of business cycles in the euro area between 1971Q1 and 2007Q1 and finds convergence and synchronisation behaviors, although synchronisation has different degrees and is non-linear. The analysis shows that the results of the tests are diverse, both in terms of results considering synchronisation of the financial cycle and synchronisation of the business cycle. This is due to the study’s methodology like research sample, period, different methods, and others.

3 Data and Methods Here we attempt to measure financial cycles convergence for the (EU) and (EMU) area and compare them across dynamic patterns. To quantify the financial cycle dynamics, we use credit-to-GDP ratios from the Bank for International Settlements (BIS, January 2020). The credit-to-GDP gap is the difference between the credit-toGDP ratio’s long-run trend and the current level. The BIS database spans 44 economies dating back to 1961. The credit-to-GDP ratio summarises the total credit to the non-financial private sector (total borrowing from all domestic and foreign sources). We derive two samples based on data availability on credit-to-GDP ratios for the EU and EMU region for the member countries with data. The EMU region consists of all country members using euro as currency with the EU region, including Bulgaria, Croatia, the Czech Republic, Denmark, Hungary, Poland, Romania, and Sweden as non-euro-area members. Our assumption relies on the premise that a high level of financial cycles synchronisation across countries results from solid convergence in the credit-toGDP ratios. Moreover, convergence in the credit-to-GDP ratios is a necessary condition for financial cycles synchronisation. We expect to find more divergence in credit-to-GDP time series data for the EU area than the EMU area sharing euro as their currency with more harmonised monetary policy across members. Strong financial cycle convergence is expected in countries sharing euro and having a high level of monetary policy coordination (prices, interest rates convergence). The concordance between financial cycles across country members is a function of the relative transition path to the steady state (convergence). The study of Crowley (2008) uses cross recurrence plots to compare real GDP growth in the euro area to a sample of European member states. Synchronicity and convergence were strong between the majority of core euro area member states, implying that monetary policy does not have a significant differential effect on these nations and that they are increasingly suited to monetary union membership. However, both within and outside of this core, several member states lack stability in their synchrony or convergence to the euro area growth rate. To test for convergence in financial cycles, we follow Phillips and Sul (2007a, b, 2009); Sichera and Pizzuto (2019) in the form

M. Škare and M. P. Rochon

18 Table 2 Panel data statistical properties Variable austria belgium switzerland czechrepublic germany denmark spain finland france unitedkingdom greece hungary ireland italy luxembourg netherlands norway poland portugal sweden

Obs 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104 104

Mean 133.359 174.032 208.581 79.076 130.888 213.983 158.868 150.269 164.551 153.981 91.957 84.748 220.845 101.088 271.117 242.564 200.964 58.326 171.861 193.762

Std. Dev. 13.122 38.068 23.035 10.813 9.66 44.628 47.381 28.56 28.99 22.006 36.019 26.481 89.866 21.412 113.255 33.084 37.521 21.38 39.262 43.796

Min 102.7 106.4 183 59.1 117.3 140.4 78.5 108.4 127.9 108.1 34.8 43.8 89.6 66.6 103.2 180.4 139.7 20.6 92.9 128.2

Max 153 233.8 270.1 93.1 148 268.8 226.8 193.3 239.9 186.3 135.5 136.7 416.2 126.9 428.4 294.3 270.1 86.6 231.6 270.7

Source: Authors’ analysis

log Coit = δoit log Cot þ eit where log C oit stands for financial cycle convergence indicator for the country ith in the panel, log Cot representing a common trend for the countries in the panel with idiosyncratic business cycle components (eit). First, we test for convergence in financial cycles for the sample (EU) and (EMU). Table 2 presents the statistical properties of the panel data time series we use in the analysis. Our panel includes data on credit-to-GDP ratios for selected members of the (EU) and (EMU). Country inclusion in the sample is based on data availability. Since credit-to-GDP ratios are not monitored for all the (EU) or (EMU) members, for the sample we selected in testing for convergence in financial cycles, we only used countries with data on the credit-to-GDP. After testing for convergence in

Financial Cycle Convergence: Evidence on Financial Cycles. . .

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financial cycles for the whole sample, we tested for convergence across the countries in the sample. Under the assumption of convergence in financial cycles, X it ¼


 git þ ait ut ¼ δit ut ut

we use the log t regression equation of the form
 H1 log  2 log Lðt Þ ¼ bc þ b b log t þ b ut Ht under the hypothesis of convergence in the form H0 : δi ¼ δ and α  0 after running log t regression testing log


 H1  2 log f log ðt Þg ¼ a þ blogðt Þ þ εt Ht for t ¼ ½rT 1 ½rT  þ 1, . . . , T with r > 0

Following relative transition component as identified in Sichera and Pizzuto (2019). H t ¼ N 1

XN i¼1

ðhit  1Þ2 ! 0, t ! 1:

Firstly, we classify countries according to their initial financial cycle indicator, checking for associated relative transition components. Moreover, we identify initial convergence clubs using the above testing procedure, further testing for possible clubs merging. k ¼ arg max ft k g subject to min ft k g > 1:65 k

Checking t statistics regression results to the equation 1 sequentially for all countries, we can identify convergence groups and subgroups to derive the final financial cycles convergence clubs’ empirics. The data entered in the t-test are pre-filtered using the form (Hodrick and Prescott 1997). m

fgtgTt2 2 1

nXT

ð y 2 gt Þ 2 þ λ t=1 t

XT

½ ð gt 2 gt 2 1 Þ 2 ð gt 2 1 2 gt 2 2 Þ  2 t=1

o

Moreover, we determine cross-sectional means between convergence clubs using relative transition parameters (plotting relative transition curves between clubs). Plots of relative transition parameters within and between clubs demonstrate a trend toward convergence or divergence within and between clubs. Finally, to

M. Škare and M. P. Rochon

20

correct a possible bias, the following data are adjusted by proper calibration (Hamilton 2018). The following section presents financial cycle convergence and synchronisation results in the (EU) and (EMU) areas.

4 Financial Cycles Convergence and Synchronisation in (EU) and (EMU): Results We found no statistical evidence that financial cycles converge at the (EU) or (EMU) levels. Log t regression results both for the (EU) and the (EMU) region rejects the hypothesis that financial cycles are converging to their steady states for all members of the (EU) or the (EMU). Moreover, we found a clear distinction in financial cycles convergence results between the (EU) and the (EMU). After rejecting the null of convergence in financial cycles both for the (EU) and the (EMU), we checked for the possibility of financial cycles clustering among country members. Tables 3 and 4 show log t regression results of financial cycles convergence for the (EU) and the (EMU). The coefficient estimates for financial cycles convergence in the (EU) are negative with large negative t-statistics, so the null convergence hypothesis is promptly rejected. The same holds for the (EMU) region (Table 4). Therefore, we found no evidence on financial cycles convergence among country members in the (EU) and the (EMU).

4.1

Financial Cycles Convergence Clubs in the EU

After rejecting the hypothesis of financial cycles convergence in the EU, we used country members for our convergence cluster analysis to check for financial cycles club convergence. Although country members differ in economic structure, growth model, institutional policy, or monetary sovereignty, we investigated the possibility Table 3 Log t regression results: European Union (EU)

r-value in log t regression (panel EU), r ¼ 0.30 bγ 0.448 65.182 tbγ Source: Authors’ analysis

Table 4 Log t regression results: European Union (EMU)

r-value in log t regression (panel EMU), r ¼ 0.30 bγ 0.403 88.802 tbγ Source: Authors’ analysis

Financial Cycle Convergence: Evidence on Financial Cycles. . .

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Table 5 Initial convergence club Classification (20 Countries) Club Club 1 [9]

bγ 0.164

tbγ 8.476

Club 2 [8]

0.6497

9.4577

Club 3 [2] Divergent [1]

0.3851

1.9199

Member countries Sweden, Switzerland, Norway, the Netherlands, Denmark, Ireland, France, Belgium, Poland Finland, United Kingdom, Portugal, Spain, Austria, Greece, Italy, the Czech Republic Germany, Hungary Luxembourg

Source: Authors’ analysis

Table 6 Club merging test—final convergence club classification (20 Countries) Club Club 1 + Club 2

bγ 0.164

tbγ 8.476

Club 2 + Club 3

0.294

11.131

Divergent [1]

Member countries Sweden, Switzerland, Norway, the Netherlands, Denmark, Ireland, France, Belgium, Poland Finland, United Kingdom, Portugal, Spain, Austria, Greece, Italy, the Czech Republic, Germany, Hungary Luxembourg

Source: Authors’ analysis

of financial cycle clusters. Following Phillips and Sul (2007a, b, 2009); Sichera and Pizzuto (2019), we applied the stepwise log t regression to check for the existence of convergence clubs for financial cycles. Table 5 presents initial convergence results for club classification (20 countries). The clustering procedure, after initial classification, identifies three convergence clubs with one divergent country (Luxembourg). Estimated fitted log t regression slope coefficients are significantly positive, supporting the convergence hypothesis for club convergence. The list of convergence clubs with country members is displayed in Table 5. Financial cycle convergence clubs—countries that share common financial cycle dynamics list three different clubs with financial cycles converging to their own steady-state. To check for the possible bias in the clustering procedure, we use Phillips and Sul (2009) to test for possible clubs merging after initial club classification (Table 6). Table six shows that clubs 2 and 3 can be merged to form a new convergence club from the initial club classification. Two countries, Germany and Hungary, are merged to the initial club 2 to form a new club, with Luxembourg having a distinct financial cycle relative transition path. Figure 1 shows the relative transition path across the identified convergence clubs. We observe a heterogenous dynamic pattern for financial cycles within and across convergence clubs. In Club 1, financial cycles display a similar pattern across countries except for Poland. That is expected since Poland retains monetary sovereignty.

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Fig. 1 Financial cycles transition paths for the member states in convergence clubs—initial classification. Source: Authors

Club 2 presents high heterogeneity in financial cycles across members. In the long run, countries in the club converge to their steady states, with the Czech Republic, Greece, and Italy showing a slow convergence path compared to other countries in the club. Moreover, financial cycles appear to be highly influenced by monetary sovereignty and the country’s level of debt.

Financial Cycle Convergence: Evidence on Financial Cycles. . .

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Average transition paths - All clubs

0.95 1.00 0.90

Relative transition path

club1 club2 club3

0

50

100

Time

Fig. 2 Average transition paths—all initial clubs. Source: Authors

Club 3 presents Germany and Hungary (retains monetary sovereignty) with a relative transition path different from other clubs we identified during the clustering procedure. The results support results from Fig. 1 on an average relative transitional path for different clubs (see Fig. 2). Moreover, it is evident that financial cycles dynamics in Club 1 significantly differ from the one in Club 2, and 3 displays more close patterns in financial cycles. In conclusion, financial cycles behaviour in Club 1 is significantly different from the dynamics of the cycle we identified for Clubs 2 and 3. Figure 3 presents the final club identification after checking for merging procedure in clustering. After testing for the possible merging of clubs, we display a final club classification for financial cycles convergence in Fig. 3. Now we have two new country members in the final Club 2, including Germany and Hungary. Once again, significant heterogeneity in financial cycles in the second club, especially after the inclusion of Germany and Hungary, is visible. Financial cycles behaviour in the EU Member States of Clubs 1 and 2 is notably different, showing a high level of asynchronicity in the EU Member States. The same conclusion is supported by displaying average transitional paths for Clubs 1 and 2 in Fig. 4. In summary, we found empirical evidence that financial cycles dynamics vary across the EU Member States. The dynamics depend on the national debt level and

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Fig. 3 Financial cycles transition paths for the member states in convergence clubs—final classification. Source: Authors

monetary sovereignty position. There is no evidence of one distinct financial cycle convergence at the EU level. Moreover, we found evidence on two different convergence clubs with country members listed above. Identified convergence clubs show two distinct financial cycles patterns, one more homogeneous for countries in Club 1. In convergence Club 2, the second one is far more heterogeneous and divergent from Club 1. Also, the financial cycle dynamic in Club 2 is far more heterogeneous than the one we evidence for country members in Club 1.

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Average transition paths - All clubs

1.00 0.98 0.94

0.96

Relative transition path

1.02

1.04

club1 club2

0

50

100

Time

Fig. 4 Average transition paths—all final clubs. Source: Authors

4.2

Financial Cycle Convergence Clubs in the EMU

Here we present financial cycle convergence test results for the EMU area. Our empirical results show that financial cycles behaviour is more synchronised and convergent in the EMU area than the dynamics we revealed in the EU area. Therefore, financial cycles dynamics and patterns significantly differ between the EU and the EMU. As in the EU area, we found no statistical evidence that financial cycles are converging at the EMU level. Moreover, log t regression results for the (EU) (EMU) region rejects the hypothesis that financial cycles are converging to their steady states for all members of the EMU, see Table 4. Following the initial categorisation, the clustering algorithm identifies two convergence clubs with two diverging countries (Luxembourg, Germany). The estimated fitted log t regression slope coefficients are positive, corroborating the club convergence hypothesis. Table 7 is a list of convergence clubs with country members. Clubs of financial cycle convergence—countries with typical financial cycle

M. Škare and M. P. Rochon

26 Table 7 Initial convergence club classification (16 countries) Club Club 1 [10]

bγ 0.152

tbγ 4.139

Club 2 [4] Divergent [2]

0.51

18.31

Member countries Switzerland, Norway, the Netherlands, Denmark, Ireland, France, Belgium, Finland, Greece, Poland Portugal, Spain, Austria, Italy Luxembourg, Germany

Source: Authors’ analysis

Table 8 Club merging test—final convergence club classification (16 Countries) Club Club 1 + 2

bγ 0.042

Divergent [2]

tbγ 1.573

Member countries Switzerland, Norway, the Netherlands, Denmark, Ireland, France, Belgium, Finland, Greece, Poland, Portugal, Spain, Austria, Italy Luxembourg, Germany

Source: Authors’ analysis

dynamics—mention two unique clubs whose financial cycles have converged to their steady-state. To assess possible cluster bias in the clustering process, we use Phillips and Sul (2009) to investigate possible club mergers after the initial club classification (Table 8). As shown in Table 8, Clubs 1 and 2 can be combined to form a new convergence club based on their initial club classification. Portugal, Spain, Austria, and Italy are united in the initial Club 1 to form a new club. Luxembourg and Germany display a distinct financial cycles pattern and dynamics compared to other EMU members. The relative transition path between selected convergence clubs is shown in Fig. 5. Within and across convergence clubs, we identify a heterogeneous dynamic pattern for the financial cycle. Except for Poland and Greece, the financial cycles in Club 1 follow a similar pattern in all nations. Given Poland’s continued monetary sovereignty and Greece’s debt issue, this is expected. On the other hand, Club 2 members have a high degree of diversity in their financial cycles. In the long run, club members converge to their respective stable states, with Austria and Italy showing weaker convergence than the rest of the club. Moreover, it appears that monetary sovereignty has a substantial impact on the financial cycle and debt levels. Luxembourg and Germany show a divergent financial cycles path and have a different relative transition path than the other clubs identified through the clustering process. The results in Fig. 5 are confirmed by data on the average relative transition path for various clubs (Fig. 6). In addition, we can observe that the dynamics of financial cycles in Club 1 are significantly different from Club 2, which exhibit more similar patterns in financial

Financial Cycle Convergence: Evidence on Financial Cycles. . .

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Fig. 5 Financial cycles transition paths for the member states in convergence clubs—initial classification. Source: Authors

cycles. In summary, the behaviour of financial cycles in Club 1 differs significantly from the dynamics of financial cycles in Club 2. Following a test for prospective club consolidation, we present a final club categorisation for the convergence of the financial cycle in Fig. 7. Portugal, Spain, Austria, Italy have now joined the final club. Once again, significant diversity in financial cycles is accompanied in the second club, especially when Poland, Greece, and Italy are included. In addition, the behaviour of financial cycles in the EMU member states is noticeably different, indicating a level of asynchronicity in the EMU member states. Finally, Fig. 8 confirms the same conclusion describing the average transition paths for Club 1.

M. Škare and M. P. Rochon

28 Average transition paths - All clubs

0.99 0.98 0.97 0.95

0.96

Relative transition path

1.00

1.01

club1 club2

0

50

100

Time

Fig. 6 Average transition paths—all initial clubs. Source: Authors

We can observe financial cycles in the EMU move more closely compared to the EU area. The difference we observe is expected since the EMU area is a single currency space. However, convergence testing results reveal that a single currency area is necessary but not sufficient for financial cycle synchronisation.

5 Discussion We found no statistical evidence of the convergence of financial cycles at the level of the EU or the EMU. While we acknowledge that country members differ in economic structure, growth model, institutional policy, and monetary sovereignty, our study examines the possibility of clusters in the financial cycle. The dynamics are determined by the level of debt of the country and the position of monetary sovereignty. At the EU level, there is no indication of a single distinct financial cycle convergence. The empirical results of this research show that the behaviour of the financial cycle in the EMU region is more synchronised and

Financial Cycle Convergence: Evidence on Financial Cycles. . .

29

Fig. 7 Financial cycles transition paths for the member states in convergence clubs—final classification. Source: Authors

convergent than the dynamics revealed in the EU area. As a result, the dynamics and pattern of financial cycles significantly diverge between the EU and the EMU. In the long run, club members converge to their respective stable states, with Austria and Italy showing less convergence than the rest. Additionally, it appears that monetary sovereignty has a significant impact on a country’s financial cycle and debt levels. Our findings align with the fact that the EMU area is a single currency zone. On the other hand, convergence testing demonstrates that a single currency region is a necessary but not sufficient condition for financial cycle synchronisation. Luxembourg and Germany follow divergent financial cycles paths from the rest of the EU and the EMU. Our results align with (Franks et al. 2018) (Hennani and Theal 2019). Germany’s financial cycle is becoming more detached from the rest of Europe. Unlike its eurozone counterparts, Germany’s bilateral financial concordance declined in the pre-and post-crisis eras to the lowest level ever recorded for the EA-12. This is partly due to Germany’s unconditional financial cycle, which has a standard deviation of about 40% below the eurozone average. This discrepancy reflects Germany’s unique credit and housing price dynamics, which differ from

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M. Škare and M. P. Rochon

Fig. 8 Average transition paths—all final clubs. Source: Authors

most EA-12 countries. Between 2004 and 2011, lending to the private non-financial sector remained steady, avoiding a subsequent overhang of private debt and reducing credit supply. While convergence in business cycles, inflation, and interest rate appears strong among the EU and the EMU, such condition does not hold for financial cycles (Canarella et al. 2011). Euroarea banking convergence is behind financial cycles dynamics in Europe (Affinito 2011). Together, these results suggest that new entrants in the eurozone must be carefully selected, as they can jeopardise the convergence achieved by the first joiners. On the other hand, a successful entry of a country into the eurozone increases banking convergence, which in turn seems to improve per capita convergence. Bank competition and credit risks affect financial cycles dynamics patterns (Karadima and Louri 2020), (Grandi and Bozou 2019). It should be stressed that the convergence of the variables under consideration is a necessary but insufficient condition for the euro region to achieve financial unity. According to our study, the exact condition seems to hold for financial cycles in the EU and the EMU. Financial fragmentation could also have a significant effect on financial cycles dynamics and convergence (Dor 2019), we believe after comparing identified financial cycles convergence in the EU and the EMU.

Financial Cycle Convergence: Evidence on Financial Cycles. . .

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Although the timing of the cycle phases was relatively uniform in each country, the amplitudes varied significantly. For example, in Belgium and Finland, mortgage cycles are comparable in amplitude to total credit cycles and move in the same direction, but non-financial credit cycles move in the opposite direction (Samarina et al. 2017). Ireland and Spain have larger amplitudes for non-financial business loans than for mortgage loans, but Estonia, France, and Portugal have similar amplitudes for non-financial business loans and mortgage loans. Thus, credit cycles of all types continue in lockstep in most periphery countries of the eurozone (Estonia, Spain, Ireland). Such findings support our evidence on financial cycles convergence in the EU and the EMU.

6 Convergence or Synchronisation? A Conclusion The authors address this problem by examining empirical evidence of the convergence of the financial cycle to determine its significance in bringing together or distorting processes observed in the EU and the EMU. According to the previous conclusion and Claessens et al. (2012), credit-to-GDP ratio data indicate a mediumterm resemblance to the financial cycle pattern outlined by Škare and PoradaRochoń (2020). Financial cycles are becoming increasingly important as they are used in macroeconomic policy. However, the findings indicate that the link between them is doubtful, although they prefer to band together during times of stress. Synchronicity and convergence between the majority of core member states of the eurozone have been found robust, which means that monetary policy has no different impact on these countries and is becoming increasingly appropriate to membership in the monetary union. However, many member states, both inside and outside of this core, lack consistency in their synchrony with or convergence with the eurozone’s development rate. The authors found no statistical evidence of the convergence of financial cycles at the EU or the EMU level. While the authors acknowledge that country members differ in economic structure, growth model, institutional policy, and monetary sovereignty, they examine the possibility of clusters in the financial cycle. The dynamics are determined by the level of debt of the country and the position of monetary sovereignty. At the EU level, there is no indication of a single, distinct convergence of the financial cycle. The empirical results of this research show that the behaviour of the financial cycle in the EMU region is more synchronised and convergent than the dynamics revealed in the EU area. As a result, the dynamics and patterns of financial cycles significantly diverge between the EU and the EMU. In the long run, club members converge to their respective stable states, with Austria and Italy showing less convergence than the rest. Moreover, it appears that

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monetary sovereignty has a significant impact on a country’s financial cycle and debt levels. The authors’ observation is in line with the EMU area as a single currency zone. On the other hand, convergence testing demonstrates that a single currency region is a required but insufficient requirement for financial cycle synchronisation. Acknowledgement This work was funded within the project line ZIP UNIRI of the University of Rijeka, for the project ZIP-UNlRl-130-5-20.

References Azarov A, The Vienna Institute for International Economic Studies (2019) Financial cycles in Europe: dynamics, synchronicity and implications for business cycles and macroeconomic Imbalances. URL https://wiiw.ac.at/financial-cycles-in-Europe-dynamics-synchronicity-andimplications-for-business-cycles-and-macroeconomic-imbalances-p-5051.html Affinito M (2011) Convergence clubs, the Euro-area rank and the relationship between banking and real convergence Aldasoro I, Avdjiev S, Borio CEV, Disyatat P, et al. (2020). URL https://ssrn.com/abstract¼35992 71 Behn MD, Detken C, Peltonen T, Schudel W, et al. (2016) Predicting vulnerabilities in the EU banking sector: the role of global and domestic factors. Monetary economics: financial system & institutions eJournal Borio C (2014) The financial cycle and macroeconomics: what have we learnt? J Bank Financ 45(8):182–198 Borio C, Lombardi M, Zampolli F et al (2017) Fiscal sustainability and the financial cycle. https:// doi.org/10.1017/9781316675861.013 Brem A, Nylund P, Viardot E et al (2020) The impact of the 2008 financial crisis on innovation: a dominant design perspective. J Bus Res 110:360–369. https://doi.org/10.1016/j.jbusres.2020. 01.048 Canarella G, Miller SM, Pollard SK et al (2011) The global financial crisis and stochastic convergence in the Euro area. Int Adv Econ Res 17:315–315 Claessens S, Kose MA, Terrones ME et al (2012) How do business and financial cycles interact? J Int Econ 87(1):178–190. https://doi.org/10.1016/j.jinteco.2011.11.008 Crowley PM (2008) Analyzing convergence and synchronicity of business and growth cycles in the euro area using cross recurrence plots. The European Physical Journal Special Topics 164(1): 67–84. https://doi.org/10.1140/epjst/e2008-00835-3 Dor E (2019) The financial fragmentation of the Euro area is the main challenge for the ECB and the sustainability of the monetary union. ERN: Monetary Policy Objectives; Policy Designs; Policy Coordination (Topic) Drehmann M, Juselius M (2014) Evaluating early warning indicators of banking crises: Satisfying policy requirements. Int J Forecast 30(3):759–780. https://doi.org/10.1016/j.ijforecast.2013. 10.002 Drehmann M, Tsatsaronis K (2014) The credit-to-GDP Gap and countercyclical capital buffers: questions and answers. BIS Quarterly review special features series Drehmann M, Borio C, Tsatsaronis K, et al. (2012) Characterising the financial cycle: don’t lose sight of the medium term! Drewhmann M, Borio C, Tsatsaronis K et al (2014) Can we identify the financial cycle? In: Evanoff D, Holthausen C, Kaufman GG, Kremer M (eds) The role of central banks in financial stability: how has it changed? vol 30. World Scientific Publishing Co. Pte. Ltd, Singapore,

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World Scientific Studies in International Economics, pp 131–156. https://doi.org/10.1142/ 9789814449922_0007 Franks MJR, Barkbu MBB, Blavy MR, Oman W, Schoelermann H, et al. (2018) Economic convergence in the euro area: coming together or drifting apart Galati G, Hindrayanto I, Koopman SJ, Vlekke M et al (2016) Measuring financial cycles in a model-based analysis: empirical evidence for the United States and the euro area. Econ Lett 145 (C):83–87 Gammadigbe V (2021) Financial cycles synchronization in WAEMU countries: implications for macroprudential policy. Financ Res Lett 102281. https://doi.org/10.1016/j.frl.2021.102281 Grandi P, Bozou CN (2019) Bank competition and firm access to credit: bank-firm level evidence from Europe. ERN: Commercial Banks (Topic) Hamilton JD (2018) Why you should never use the Hodrick-Prescott filter. Rev Econ Stat. https:// doi.org/10.1162/rest_a_00706 Hennani R, Theal J (2019) Characterizing the Luxembourg financial cycle: alternatives to statistical filters Hodrick RJ, Prescott EC (1997) Postwar US business cycles: an empirical investigation. J Money Credit Bank 29(1):1–16 Juselius M, Borio C, Disyatat P, Drehmann M et al (2017) Monetary policy, the financial cycle, and ultra-low interest rates. Int J Cent Bank 13(3):55–89 Karadima M, Louri H (2020) Bank competition and credit risk in Euro area banking: fragmentation and convergence dynamics. J Risk Financ Manage 13(3):57–57. https://doi.org/10.3390/ jrfm13030057 Kunovac D, Mandler M, Scharnagl M, et al. (2018) Financial cycles in euro area economies: a cross-country perspective Mandler M, Scharnagl M (2019) Financial cycles across G7 economies: a view from wavelet analysis Pagan A, Robinson T (2014) Methods for assessing the impact of financial effects on business cycles in macroeconometric models. J Macroecon 41:94–106. https://doi.org/10.1016/j.jmacro. 2014.04.002 Phillips PC, Sul D (2007a) Some empirics on economic growth under heterogeneous technology. J Macroecon 29(3):455–469. https://doi.org/10.1016/j.jmacro.2007.03.002 Phillips PC, Sul D (2007b) Transition modeling and econometric convergence tests. Econometrica 75(6):1771–1855. https://doi.org/10.1111/j.1468-0262.2007.00811 Phillips PCB, Sul D (2009) Economic transition and growth. J Appl Econ 24(7):1153–1185. https:// doi.org/10.1002/jae.1080 Rey H (2015) Dilemma not Trilemma: the global financial cycle and monetary policy independence Rünstler G, Vlekke M (2018) Business, housing, and credit cycles. J Appl Econ 33(2):212–226. https://doi.org/10.1002/jae.2604 Samarina A, Zhang L, Bezemer D et al (2017) Credit cycle coherence in the EurozoneEurozone: was there a euro effect? J Int Money Financ 77:77–98. https://doi.org/10.1016/j.jimonfin.2017. 07.002 Scharnagl M, Mandler M (2019) Real and financial cycles in euro area economies: results from wavelet analysis. Jahrbücher für Nation- alökonomie und Statistik 239(5–6):895–916 Schularick M, Taylor AM (2012) Credit booms gone bust: monetary policy, leverage cycles and financial crises. Am Econ Rev 102(2):1029–1061 Schüler YS, Peltonen, Tuomas A, Hiebert P, et al. (2017) Coherent financial cycles for G-7 countries: why extending credit can be an asset. ESRB Working Paper Series 43 Sichera R, Pizzuto P (2019) Convergence clubs: a package for performing the phillips and Sul’s. Club Convergence Clustering Procedure R Journal 11(2):142–151 Skare M, Porada-Rochoń M (2019) Tracking financial cycles in ten transitional economies 20052018 using singular spectrum analysis (SSA) techniques. Equilibrium Quarterly Journal of Economics and Economic Policy 14(1):7–29

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Skare M, Porada-Rochoń M (2020) Multi-channel singular-spectrum analysis of financial cycles in ten developed economies for 1970–2018. J Bus Res 112:567–575. https://doi.org/10.1016/j. jbusres.2019.10.047 Škare M, Porada-Rochoń M (2020) URL https://doi.org/10.3846/tede.2020.12567 Stremmel H (2015) Capturing the financial cycle in Europe. SSRN Electron J. https://doi.org/10. 2139/ssrn.2577211 Wen F, Zhang M, Deng M, Zhao Y, Ouyang J et al (2019) Exploring the dynamic effects of financial factors on oil prices based on a TVP-VAR model. Physica A: Statistical Mechanics and its Applications 532:121881–121881 Yépez CA (2018) Financial intermediation and real estate prices impact on business cycles: a Bayesian analysis. The North American Journal of Economics and Finance 45:138–160. https:// doi.org/10.1016/j.najef.2018.02.00

Fiscal Response to the COVID-19 Shock in Croatia Frane Banić, Milan Deskar-Škrbić, and Hrvoje Šimović

Abstract In this paper, we analyse the effects of the COVID-19 shock on fiscal developments in Croatia. In the first part of the paper, we analyse the effects of adverse economic developments and discretionary policy measures on different (structural) fiscal indicators. In addition, we evaluate the evolution of fiscal space in Croatia before and during the COVID-19 crisis. In the second part of the paper, we analyse the policy response to this exogenous shock by fiscal policy makers and compare the size and structure of instruments used with other EU countries. Our analysis shows that fiscal policy makers in Croatia relied primarily on the so-called direct “above-the-line” fiscal measures and that fiscal response in Croatia was powerful in local terms but relatively modest compared to other EU countries.

1 Introduction The outbreak of the COVID-19 pandemic caused an unprecedented global public health crisis, which entailed a severe decline in economic activity in countries around the world. In most countries, economic activity contracted more than in previous global crises, such as Great Depression or Great Recession (IMF 2020). Looking at economic developments in the European Union (EU), the COVID-19 crisis (C19C) was more severe compared to both Global Financial Crisis (GFC) and European Sovereign Debt Crisis (ESDC) that hit the EU economy in the past decade

F. Banić Croatian National Bank, Zagreb, Croatia e-mail: [email protected] M. Deskar-Škrbić (*) Zagreb School of Economics and Management, Zagreb, Croatia e-mail: [email protected] H. Šimović Faculty of Economics and Business Zagreb, University of Zagreb, Zagreb, Croatia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_3

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and a half.1 In Croatia, in 2020, GDP fell by 8.1%, which is the strongest contraction since the beginning of the 1990s, marked by the disintegration of the common market, loss of main export partners, institutional transition shock, and the Homeland War (Škreb 1998). Such unprecedented exogenous shock led to an unprecedented reaction by policy makers in both central banks.2 and ministries of finance.3 As Deskar-Škrbić and Milutinović (2021b) point out, fiscal authorities responded to the disruptions caused by COVID-19 with various above-the-line measures (e.g., a large expansion of health-related and other public expenditures and revenue deferrals), below-the-line measures (e.g., equity injections, liquidity loans, debt assumptions) and guarantees, thus designing the strongest “fiscal bazooka” in history. With respect to the EU countries, total fiscal support to the economy from the beginning of the pandemic has ranged from around 7% of GDP in Croatia to above 45% in Italy. Such fiscal support in the EU would not be possible without the built-in flexibility of EU fiscal rules. More precisely, EU policy makers used the flexibility of European fiscal rules under the Stability and Growth Pact (SGP) and activated the so-called “general escape clause” in 2020. In this sense, Larch et al. (2021) indicate that the European Commission and the EU Member States swiftly agreed to avail themselves of the extra flexibility allowed by the EU fiscal rules in the event of a severe economic downturn. The combined effort of national governments and EU institutions was instrumental in mitigating the socioeconomic impact of the pandemic. As a result, deficit and debt-to-GDP ratios recorded the largest ever increase in a single year in post-WWII history. European Commission’s model simulations indicate that the fiscal response of EU Member States have cushioned the contraction in GDP in 2020 by around 4.5 percentage points (European Commission 2021a). The aim of this paper is to analyse the effects of the COVID-19 shock on fiscal developments in the EU countries and national fiscal responses, with a focus on Croatia. The paper is structured as follows. After the introduction, Section 2 presents key fiscal indicators for EU countries during the pandemic. Section 3 analyses the fiscal space in Croatia before and after the outbreak of the COVID-19 crisis. Section 4 brings a detailed analysis of fiscal response in Croatia to the COVID-19 shock, while Sect. 5 concludes.

1

Eurostat data show that EU GDP contracted by 4.3% in 2009, 0.7% in 2012, and 5.9% in 2020. Central banks in significant markets expanded existing asset purchases programmes and launched new ones, while some emerging markets launched emergency asset purchases programmes for the first time (Croatia, among others). 3 According to the European Commission (2021a), the global fiscal response to the COVID-19 pandemic amounted to around EUR 6 trillion of direct budgetary support in 2020 (close to 7½% of world GDP), with most support coming from the G20 countries. 2

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2 Effects of the COVID-19 Shock on Fiscal Developments in Croatia and the EU As previously explained, the crisis caused by the COVID-19 pandemic has resulted in the implementation of fiscal stimulus packages in the EU, which have consequently reflected on the general government budget developments. With regard to lockdown and restriction of movement in order to prevent the spread of coronavirus, governments have provided fiscal stimulus primarily through subsidies for job preservation and for maintaining corporate liquidity. Given that write-offs and deferrals of tax revenues and social contributions adversely affected the revenue side of the budget, while subsidies and health expenditures put pressure on the expenditure side of the budget, the general government balance deteriorated significantly in 2020. Thus, in Croatia, the general government budget deficit-GDP ratio in 2020 stood at 7.5%, given the simultaneous sharp decline in economic activity and the widening of deficit. When it comes to comparing Croatia with other EU-27 Member States, it is evident that the general government deficit-GDP ratio is somewhere below the middle of the EU-27, which reflects the insufficient fiscal space, but also the structure of the economy and service-oriented economy (Fig. 1). If we compare the effect of the ESDC (average 2010–2012), the GFC (2009), and the COVID-19 shock (2020), it is noticeable that EDSC and GFC had a significantly weaker effect on fiscal developments in most countries, especially Luxembourg, Sweden, Estonia, Finland, Denmark, Italy, Austria, and Belgium, than the impact of C19C. On the other hand, the adverse effect of EDSC and GFC on fiscal developments was stronger in Greece, Ireland, Portugal, Spain, Latvia, Lithuania, and Slovakia compared to the effects of C19C. This is mainly because most of these countries, primarily Greece, Spain, Portugal, and Ireland, have not been able to refinance the public debt or bail out over-indebted banks without the assistance from a third party (IMF, ECB, or other EA Member State).

GFC

ESDC

C19C

Fig. 1 General government balance (% of GDP). Source: AMECO

Greece

Spain

Latvia

Lithuania

Poland

Croatia

Cyprus

Belgium

Netherlands

Hungary

Malta

Denmark

Estonia

Luxembourg

5 0 -5 -10 -15 -20

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Primary balance

Spain

Romania

Belgium

Austria

Slovenia

Poland

Estonia

Finland

Slovakia

Latvia

Cyprus

Bulgaria

Portugal

Denmark

2 0 -2 -4 -6 -8 -10 -12

Interest payments

Fig. 2 Primary balance and interest payments (% of GDP). Source: AMECO

If we look at the primary balance, it is evident that interest expenditures contributed most to the deterioration of the fiscal position in Portugal, Italy, Greece, Cyprus, and Spain due to the EDSC and the impossibility of refinancing public debt in the previous period. Consequently, the accumulated debt at a relatively higher interest rate compared to other EU Member States. In the context of the primary balance, Croatia is in the middle of the EU-27 when it comes to the primary deficit in 2020, with a relatively high share of interest expenditures in total expenditures. However, it is important to note that in 2009 during the GFC, the crisis spilled over from the financial sector to the real sector, while in the case of C19C, the situation was reversed given that counter-pandemic measures constrained the real sector (Fig. 2). When it comes to comparing the impact of the cyclical component and discretionary measures, the predominant effect of discretionary measures is noticeable at the EU level. Namely, the cyclical component reflects the sensitivity of the budget depending on the cyclical movements of the economy, i.e., depending on the output gap. Given the importance of automatic stabilisers, which largely explain the impact of the cyclical component on fiscal developments, it can be seen that the cyclical component in 2020 is most pronounced in Greece, Portugal, Germany, Luxembourg, France, Italy, and Spain, which reflects adverse pandemic effects on economic developments. On the other hand, the strong impact of discretionary measures, which reflect the direct impact of fiscal policy makers on the budget once the automatic stabilisers are removed, is noticeable in Ireland, Romania, Austria, Cyprus, Poland, Lithuania, Hungary, the Czech Republic, Slovakia, Poland, and Croatia (Fig. 3). As adverse economic developments have affected fiscal movements, this has consequently resulted in increased government borrowing needs due to the financing of a significant budget deficit. The largest increase in public debt was recorded in Greece, Italy, Cyprus, Spain, Hungary, but Croatia can also join this group, given that the General Government Debt-GDP ratio in 2020 increased by 16.2 percentage

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Discreonary measures

Spain

Greece

Belgium

Romania

Slovenia

Lithuania

Poland

Czechia

Cyprus

Ireland

Latvia

Germany

Bulgaria

Denmark

2 0 -2 -4 -6 -8 -10 -12

Cylclical component

Fig. 3 Breakdown of change of general government budget balance (pp of GDP). Source: AMECO, authors’ calculations

250.0 200.0 150.0 100.0 50.0

2019

Greece

Portugal

France

Cyprus

Austria

Hungary

Germany

Netherlands

Poland

Latvia

Romania

Denmark

Luxembourg

Estonia

0.0

2020

Fig. 4 Public debt-to-GDP ratio (% of GDP). Source: AMECO

points or 87.3% from 71.1% in 2019. Also, it is important to emphasise that since the recession ended in 2014, the public debt-GDP ratio has been on a downward trajectory until 2020. However, nominal GDP also plays an important role in the relative fiscal indicator, given that in the period of expansion, i.e., before C19C, it had a favourable effect on the public debt-GDP ratio, which is also known as the denominator effect, since the absolute level of debt did not decrease significantly (Fig. 4). When it comes to comparing the increase in public debt-GDP ratio during the GFC, ESDC, and C19C, it is noticeable that during the GFC (percentage change 2009 compared to 2008), there was a noticeable increase in the public debt-GDP ratio in Lithuania, Greece, Spain, Portugal, and France. Furthermore, during the

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GFC

ESDC

Spain

Italy

France

Belgium

Slovenia

Austria

Poland

Lithuania

Finland

Denmark

Latvia

Sweden

Luxembourg

35 30 25 20 15 10 5 0 -5 -10

C19C

Fig. 5 Change in the public debt-to-GDP ratio (in pp of GDP). Source: AMECO; authors’ calculations

ESDC (percentage change in 2012 compared to 2010), the largest increases in public debt-GDP ratio were recorded in Spain, Cyprus, Greece, Portugal, Slovakia, and Croatia. Finally, during the C19C (percentage change 2020 compared to 2019), which is still ongoing, the largest increases in public debt-GDP ratio were recorded in Greece, Spain, Cyprus, and Italy, although the remaining countries also recorded a significant increase (Fig. 5).

2.1

Effects of COVID-19 Shock on Fiscal Developments in Croatia: More Details

Looking at the breakdown of the changes in general government balance in Croatia in the period from 2014 to 2020, interest expenditures in the mentioned period recorded a decline after Croatia’s exit from recession, but interest rate movement were also influenced by global trends in the context of relatively low interest rates. The structural and cyclical components have the largest impact on the overall fiscal balance, primarily in 2020. As already pointed out, the cyclical component is strongly influenced by economic trends, while the movement of the structural component is largely due to tax changes in the corporate tax system but also written off tax revenues (Fig. 6). With regard to Croatia’s fiscal stance in 2020, the largest contribution comes from temporary measures related to the crisis and, to a lesser extent, from changes in the financing of primary current expenditures and expenditures financed from EU instruments, while the slightest change was recorded in nationally financed investments. However, unlike previous crises, in the COVID-19 crisis, public investments played a stabilising role, i.e., fiscal policy makers did not cut public investments in

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4 2 0 -2 -4 -6 -8 -10

2014

2015

2016

2017

Economic cycle Structural primary balance Total fiscal balance

2018

2019

2020

One-offs Interest

Fig. 6 Breakdown of the change in general government budget balance (pp of GDP). Source: AMECO; authors’ calculations

Fig. 7 Fiscal stance in Croatia in 2020 (pp of GDP). Source: European Commission (2021b)

order to contain pressures on the growing deficit (for discussion of pro-cyclical fiscal policy in Croatia see Deskar-Škrbić and Milutinović 2021a) (Figs. 7 and 8). Observing the average breakdown of changes in the public debt-GDP ratio from 2015 to 2019, it is noticeable that the favourable change was contributed mainly by economic growth but also by the primary balance and inflationary effect. Interest expenditures acted in the opposite direction, which had an adverse effect on the change in the public debt-GDP ratio. When it comes to 2020, a significant positive change in the public debt-GDP ratio is noticeable. Namely, only the inflationary effect mitigated a strong increase in the public debt-GDP ratio. On the other hand,

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20 15 10 5 0 -5 -10

Average 2015-2019

2020

0

2.5

Inflation effect

-0.7

-0.3

Growth effect

-2.3

6.3

Interest expenditure

2.7

2

Primary balance

-2.1

5.4

Change in public-debt-to GDP ratio

-2.4

15

SFA

Fig. 8 Breakdown of the change in the public debt-to-GDP Ratio (pp of GDP). Source: AMECO; authors’ calculations

the crisis caused by the coronavirus pandemic thus reflected a sharp decline in economic activity, which contributed most to the positive change in the public debt-GDP ratio. Then, to a somewhat lesser extent, the primary balance influenced the increase of public debt-GDP ratio, as well as stock-flow adjustment and interest expenditures.

3 The Assessment of Fiscal Space in Croatia In this section, fiscal space will be assessed using the primary balance sustainability gap. According to Creel (2020), the primary balance sustainability gap represents the gap between the actual primary balance and the primary balance-to-GDP ratio that would stabilise the targeted debt-to-GDP ratio. Calculating fiscal space is intuitive and simple, whereby the calculation formula is: 

 ig  pbsgt ¼ pbt  d : 1þg

ð1Þ

pbsgt is primary balance sustainability gap, pbt primary balance-to-GDP ratio, i the long-term interest rate on public debt, g the nominal growth rate of the economy in percent, and d* the debt-to-GDP target (Creel, 2020). Debt-to-GDP target (60% of GDP) is set according to the Stability and Growth Pact. The paper uses the primary balance to extract the impact of interest rates on fiscal developments since they are not influenced by current fiscal policy. When it comes to the primary balance

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sustainability gap intuition, positive values indicate fiscal space and the possibility for more intensive government spending. In contrast, negative values indicate the absence of fiscal space, which will be reflected in increased public debt given the increased needs of the state to finance the budget, but also in refinancing overdue liabilities. Fiscal discipline and sustainable public debt management are crucial for creating fiscal space (Žigman and Cota 2011). If, during expansion periods, fiscal policy makers focus on unproductive public spending, they will not be able to create fiscal space and will have to pursue fiscal consolidation during recessionary periods to achieve sustainable public finance. Consequently, investors will evaluate fiscal policy in accordance with the achievements of public finances. In other words, investors will negatively value a government bond whose fiscal policy is unsustainable, which will be reflected in an increase in government spreads (Žigman and Cota 2011). The data used in the calculation were taken from the AMECO database, with advantages and disadvantages in the fiscal space simulation. On the one hand, a precise calculation requires a detailed analysis at a more disaggregated level when it comes to macro-fiscal assumptions and the projection horizon. On the other hand, the simplicity and clear economic intuition of the calculation provide important information to fiscal policy makers about fiscal sustainability, i.e., about the possibilities for fiscal tightening or loosening. The analysis of fiscal and public debt sustainability in Croatia was conducted by Šimović and Batur (2017), Mihaljek (2005), Banić (2021), and Deskar-Škrbić and Grdović Gnip (2020), whereby the authors of the latter two papers assessed the fiscal reaction function in Croatia. The results of the empirical research (Banić 2021; Deskar-Škrbić and Grdović Gnip 2020) indicate fiscal sustainability by applying linear models from 2002 to 2020, i.e., 2019, while robust models (Deskar-Škrbić and Grdović Gnip 2020) by introducing structural breaks determined fiscal sustainability exclusively for the period from 2012 to 2015, i.e., during fiscal consolidation. According to research by Šimović and Batur (2017), fiscal and public debt sustainability indicators point to a significant deterioration in the recession period from 2009 to 2014 and an improvement in the fiscal position in 2015, which is primarily the result of overcoming the long recession. According to Fig. 9, the estimates of the fiscal space for Croatia in the period from 2006 to 2020 are calculated according to formula (1). Negligible fiscal space was observed during the pre-recession period (2006–2008), and at the beginning of the Global Financial Crisis, due to a strong negative shock on the economic activity at the global level, which spilled over to Croatia, whereby the fiscal space sharply decreased and amounted to 11.8% of GDP. The unfavourable fiscal performance results from a simultaneous deterioration in economic trends and increased borrowing costs due to the deterioration of the credit rating. Consequently, there was a snowball effect, which represents the interaction of economic growth, borrowing costs, and public debt, which came to the fore in the recession period from 2009 to 2015. Only in 2016, fiscal space was created (2% of GDP), reflecting the economic recovery and reduction of the trajectory of the public debt-to-GDP ratio. Sustainable fiscal policy is noticeable in 2017 (4.5% of GDP), 2018 (4% of GDP), and 2019

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6 4 2 0 -2 -4 -6

-8 -10 -12 -14 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Fig. 9 Primary balance sustainability gap (Fiscal Space). Source: Authors’ calculations

(4.3% of GDP). However, the perennial accumulated fiscal space was fully utilised and negative in 2020 when the crisis caused by the coronavirus pandemic occurred. Thus, the primary balance sustainability gap in 2020 amounted to 10.9% of GDP. In other words, in order to approach the targeted public debt of 60%, it was necessary to achieve a more favourable balance by 10.9% of GDP since the primary balance deficit amounted to 5.4% of GDP.

4 Fiscal Policy Response to COVID-19 Shock in the EU and Croatia As previously noted, EU Member States have provided an unprecedentedly large amount of fiscal support to their economies during the coronavirus crisis. In 2020, taking automatic adjustments in tax payments and social benefits (so-called ‘automatic stabilisers’) together with additional fiscal measures, support is estimated to have been around 8% of GDP in the EU (Fig. 10), considerably more than the support provided during the 2008–2009 crisis. The bulk of new measures in 2020 consisted of additional spending. This included emergency spending on health care, for example, to increase the capacity of health systems, provide protective equipment, and set up testing and tracing systems. Expenditure measures in other areas consisted of compensations to specific sectors for income losses, as well as shorttime work schemes and other items. EU governments also used various tax relief measures (European Commission 2021a). These measures are usually called “above the line measures” as they directly affect (general) government budget balance. The effect of these measures on GDP depends on the size of fiscal multipliers.

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25.0 20.0 15.0 10.0 5.0

Greece

Germany

Malta

Austria

Italy

Ireland

Hungary

Czechia

Latvia

Spain

Portugal

Croatia

Finland

Romania

0.0

Accelerated spending / deferred revenue Non-health sector expenditures Health sector expenditures Fig. 10 Above-the-line fiscal measures in the EU in 2020 (% of GDP). Source: IMF Fiscal Policies Database

Although all EU Member States reacted strongly to the COVID-19 shock in 2020, there were notable differences in both the size and the structure of fiscal packages. As Fig. 10 shows, direct fiscal support to the economy in the EU ranges from below 5% of GDP in Romania to above 20% in Greece. Although strong and unprecedented in local terms, fiscal support in Croatia was relatively modest compared to other EU countries as the share of above-the-line measures in GDP in Croatia was among the lowest in the EU. As for the structure of above-the-line measures, Fig. 10 indicates that most EU countries relied on non-health-sectorrelated measures (e.g., support for the preservation of jobs), except Sweden, Denmark, and Luxembourg, which mainly relied on accelerated spending/deferred revenue. Table 1 gives deeper insight into the structure of above-the-line measures in Croatia. Figures in the table indicate that the subsidies for the preservation of jobs, tax deferrals, write-offs, and expenditures for medical equipment had the strongest fiscal impact. Besides these measures, fiscal policy makers in Croatia also used other fiscal policy measures, such as subsidies to specific sectors, benefits for temporary inability to work, social transfers in kind, and capital injections. EU Member States also provided sizeable liquidity support to companies in 2020 through quasi-fiscal operations, guarantees, equity injections, loans, asset purchases, and debt assumptions. These measures are usually labelled as “below-the-line measures” as they do not affect budget balances but (can) affect public debt trajectory. Figure 11 shows that there were also notable differences in the size and structure of below-the-line measures among the EU Member States. The share of these measures in GDP was lowest in Croatia, below 2% of GDP, and largest in Italy

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Table 1 Above-the line measures in Croatia in 2020 (% of GDP) Measures Tax deferrals and write-offs Personal income tax Social security contributions (healthcare and pension security) Corporate income tax Subsidies to agriculture, transport, culture, sport, and tourism sectors Subsidies for the preservation of jobs Benefits for temporary inability to work Social transfers in kind (institutions outside the public sector) Capital injections (capital transfers) to Croatia airlines Medical equipment Total fiscal impact

Fiscal impact 0.8 0.0 0.8 0.0 0.1 2.1 0.0 0.1 0.2 0.5 3.8

Source: Croatia Convergence Program 2021

Italy

Denmark

France

Belgium

Finland

Slovenia

Luxembourg

Sweden

Poland

Slovakia

Romania

Ireland

Lithuania

Croatia

40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0

Quasi-fiscal operations Guarantees Equity injections, loans, asset purchase or debt assumptions Fig. 11 Below-the-line fiscal measures in the EU in 2020 (% of GDP). Source: IMF Fiscal Policies Database

(35% of GDP). Most EU Member States relied on guarantees, except Ireland, Estonia, and Denmark, where policy makers relied on other measures. Quasi-fiscal operations4 were introduced only in Bulgaria, Luxembourg, Finland, and Spain. 4 Quasi-fiscal operations are any activities undertaken by state-owned banks and enterprises, and sometimes by private sector companies at the direction of the government, where the prices charged

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Table 2 Below-the-line measures in Croatia in 2020 (% of GDP) Measures COVID-19 liquidity loan portfolio insurance program and COVID-19 exporter liquidity loan insurance program Loan insurance approved under HBOR’s lending program “COVID-19 working capital for SMEs in tourism” Loan insurance approved under HBOR’s lending program “COVID-19 working capital for entrepreneurs in wood processing and furniture production” COVID-19 loan guarantees in the maritime sector, transport, transport infrastructure, and related activities COVID-19 loan guarantees in tourism and sport COVID-19 loan guarantees in culture and creative industries Total

The maximum amount of contingent liabilities 0.8

Estimated take-up 0.2

0.01

0.01

Na

0.02

0.3

0.04

0.4 0.1

0 0.01

1.5

0.3

Source: European Commission (2021b)

Table 2 shows below-the-line measures implemented in Croatia in 2020. The table confirms the previous observation that fiscal policy makers in Croatia primarily relied on guarantees/contingent liabilities. Largest liquidity packages refer to the “COVID-19 liquidity loan portfolio insurance program and COVID-19 exporter liquidity loan insurance program”, “COVID-19 loan guarantees in tourism and sport”, and “COVID-19 loan guarantees in the maritime sector, transport, transport infrastructure, and related activities”. These programmes were aimed at sectors mostly hit by the COVID-19 shock. However, the table also indicates that companies did not use full packages, as the estimated take-up stands at only 20% of the maximum amount of contingent liabilities.

5 Conclusions In his influential 2004 paper, Nobel laureate Robert Solow (Solow 2004) posted two “big fiscal questions”: Is fiscal policy possible? Is it desirable? COVID-19 crisis showed that the answer to both of these questions is more than affirmative as countries worldwide used strong fiscal policy tools to cushion the effects of this unprecedented shock. Without such a policy reaction, the economic crisis triggered are less than usual or less than the “market rate”. Examples include subsidised bank loans provided by the central bank or other government-owned banks and noncommercial public services provided by state-owned enterprises. A typical example would be state-owned enterprises providing fuel, electricity, or water at below-market prices, thus providing an implicit price subsidy (IBP 2011).

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by the COVID-19 shock in the EU (and the rest of the world) would be deeper and long-lasting (Verwey and Monks 2021). Nonetheless, the fall of economic activity, automatic stabilisers, and discretionary fiscal measures led to widening and expanding public deficit and debt ratios in all EU Member States. High debt ratios are expected to persist, remaining above pre-pandemic levels over the next decade, despite the recovery of EU economies. Thus, fiscal policy makers in EU countries should shift their focus from stabilisation of economic to fiscal sustainability in the mid-run but without endangering stillfragile economic recovery and low potential growth prospects. Reforms and investments channelled through the Next Generation EU package and especially the Recovery and Resilience Fund should help fiscal policy makers in the EU countries safely bring public debts to a stable trajectory and keep the economic momentum. The fiscal policy response in Croatia to the COVID-19 shock can be assessed as adequate and appropriate, i.e., it can be understood as a textbook example of countercyclical fiscal policy. This is especially important because fiscal policy makers in Croatia have a history of inadequate policy choices, which had led to pro-cyclical effects of fiscal policy and self-defeating fiscal consolidation outcomes (Deskar-Škrbić and Raos 2018; Deskar-Škrbić and Milutinović 2021a). However, as Croatia is preparing to introduce the euro in 2023, public finance sustainability in Croatia is even more critical than in other EU countries. Thus, fiscal policy makers should now show that in the post-COVID-19 recovery period, they can steer fiscal policy so that it can also serve as a textbook example of a successful growth-friendly fiscal consolidation episode.

References Banić F (2021) Stohastička analiza javnog duga: primjer Hrvatske. Ekonomska politika u:291–320. Available at: http://www.hde.hr/ekonomskapolitikahrvatske/publikacija/eph2001/010_ Banic.pdf Deskar-Škrbić M, Grdović Gnip A (2020) Obilježja fiskalne politike u Hrvatskoj. In G. Družić et al., eds. Održivost javnih financija na putu u monetarnu uniju. Zagreb: HAZU: Ekonomski fakultet, pp. 121–139. Available at: https://ideas.repec.org/h/zag/financ/2008.html Deskar-Škrbić M, Milutinović D (2021a) Design of fiscal consolidation packages and model-based fiscal multipliers in Croatia. Public Sector Economics. 45:1. Available at: https://hrcak.srce.hr/ ojs/index.php/pse/article/view/15501 Deskar-Škrbić M, Milutinović D (2021b) Macro and micro effects of fiscal policy–experience from the COVID-19 pandemic: introduction to the thematic issue of public sector economics. Public Sector Economics 45:4. Available at: http://www.pse-journal.hr/en/archive/macro-and-microeffects-of-fiscal-policy-experience-from-the-covid-19-pandemic-introduction-to-the-thematicissue-of-public-sector-economics_7903/ Deskar-Škrbić M, Raos V (2018) Karakter fiskalne politike i politička ekonomija fiskalne konsolidacije u Hrvatskoj u post-kriznom razdoblju. In: Blažić H, Grdinić M (eds) Tax policy and fiscal consolidation in Croatia: project ¼ Porezna politika i fiskalna konsolidacija u Hrvatskoj: project. University of Rijeka. Available at: https://www.bib.irb.hr/947429/ download/947429.Tax_Policy_and_Fiscal_Consolidation_in_Croatia.pdf

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European Commission (2021a) Communication from the commission to the council: one year since the outbreak of COVID-19: fiscal policy response. Available at: https://ec.europa.eu/info/ sitesdefault/files/economy-finance/1_en_act_part1_v9.pdf European Commission (2021b) Commission staff working document statistical annex providing background data relevant for the assessment of the 2021 stability and convergence programmes. Available at: https://ec.europa.eu/info/sites/default/files/economy-finance/swd-2021-501_en_ v2.pdf Hrvatska narodna banka (2020) Redovne publikacije. Makroekonomska kretanja i prognoze. Available at: https://www.hnb.hr/documents/20182/3973321/hMKP_10.pdf/fd6d26e1-a556115e-7f5a-82cad6391a22 IBP (2011) Guide to transparency in public finances: looking beyond the core budget – quasi-fiscal activities. International Budget Partnership IBF (2021) Guide to transparency in public finances: looking beyond the core budget. International Budget Partnership. Available at: https://internationalbudget.org/wp-content/uploads/LookingBeyond-the-Budget.pdf IMF (2020) Annual report 2020. International monetary fund. Washington. Available at: https:// www.imf.org/external/pubs/ft/ar/2020/eng/ Larch M, Malzubris J, Santacroce S (2021) Economic growth as the ultimate constraint: EU fiscal policies in 2020. VoxEU column. Available at: https://voxeu.org/article/eu-fiscal-policies-2020 Mihaljek D (2005) Sustainability of Croatia’s public and external debt. Croatian Economic Survey 7:11–52. Available at: https://hrcak.srce.hr/6311 Šimović H, Batur A (2017) Fiskalna održivost i održivost javnog duga u Hrvatskoj. Financije na prekretnici: Imamo li snage za iskorak:271–287. Available at: https://www.bib.irb.hr/900912 Škreb M (1998) Iskustvo tranzicije u Hrvatskoj: pogled iznutra, HNB Pregled. Available at: https:// www.hnb.hr/-/iskustvo-tranzicije-u-hrvatskoj-pogled-iznutra Solow RM (2004) Is fiscal policy possible? Is it desirable? In: Solow RM (ed) Structural reform and economic policy. Palgrave Macmillan, London, pp 23–39 Verwey M, Monks A (2021) The EU economy after COVID-19: implications for economic governance. VoxEU column. Available at: https://voxeu.org/article/eu-economy-after-covid-1 9-implications-economic-governance Žigman A, Cota B (2011) The impact of fiscal policy on government bond spreads in emerging markets. Financial Theory and Practice 35(4):385–412. Available at: https://hrcak.srce.hr/ clanak/116480

The Impact of Financial Integration on Sectoral Polarization between Croatia and Eurozone Countries Mario Pečarić, Ante Tolj, and Helena Blažić

Abstract The purpose of this paper is to analyse the impact of financial integration, as measured by bilateral FDI, on sectoral structures in Croatia and eurozone countries. The research objective is to determine whether there is a “bad” specialisation, i.e., a separation of sectoral structures that increases the costs of monetary integration of Croatia. The “bad” specialisation is the result of the inefficient allocation of productive resources to sectors with lower productivity. The research results show that financial integration promotes Croatia’s specialisation and makes the country more vulnerable to the occurrence of idiosyncratic shocks, which increases the costs of Croatia’s monetary integration. Moreover, the process of structural divergence shifts the specialisation of the Croatian economy to non-tradable sectors, making real convergence with more developed euro area countries more difficult. The impact of financial integration on the sectoral structures of Croatia and eurozone countries in the period 2001–2019 is analysed using a panel model. The panel results show that the impact is stronger if we exclude the crisis years. This suggests a stronger polarisation of sectoral structures between Croatia and eurozone countries during periods of larger FDI inflows to Croatia.

M. Pečarić (*) Faculty of Economics, Busines and Tourism, University of Split, Split, Croatia Faculty of Economics and Business, University of Rijeka, Rijeka, Croatia e-mail: [email protected] A. Tolj Faculty of Science, University of Split, Split, Croatia e-mail: [email protected] H. Blažić Faculty of Economics and Business, University of Rijeka, Rijeka, Croatia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_4

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1 Introduction In the theory of optimal currency areas (OCA), a diversified sector structure is one of the most important prerequisites for the acceptance of a common currency. Kenen (1969) points out that: (1) a productively diversified economy is not affected by changes in terms of trade as often and as severely as specialised economies; (2) a fall in demand for the main export product has a smaller impact on unemployment in a diversified economy than in a specialised export economy—not only does diversification of production reduce the likelihood of shocks, but also mitigates the negative impact of shocks on employment and aggregate output; (3) in a diversified economy the link between foreign and domestic demand and between export dynamics and investment levels is weaker. If a country is sufficiently diversified, it can accept lower costs of giving up its currency in return for the benefits of a common currency. Countries with a more similar composition of sectoral structures respond more similarly to the occurrence of common shocks. Moreover, they are more similarly affected by sectoral shocks because their sectors are equally exposed to these types of shocks. Therefore, more similar sectoral structures imply more synchronised business cycles and thus the more efficient implementation of the common monetary policy. Similarly, the composition of the sectoral structure is important for countries in the process of monetary integration, such as Croatia. A sectoral structure more similar to that of core euro area countries implies a lower risk of idiosyncratic shocks and thus lower costs for monetary integration.1 The country’s productivity and competitiveness depend not only on the effectiveness of the implementation of the common monetary policy but also on the sectoral structure. Over the last 15–20 years, euro area countries have experienced a gradual change in their sectoral structures. Mongelli et al. (2016) distinguish between two basic forms of country specialisation: “good” and “bad” specialisation. They point out that “good” specialisation is desirable because it is a consequence of exploiting comparative advantages. Moreover, it is often accompanied by different risk-sharing channels. On the other hand, “bad” specialisation is the consequence of an inefficient allocation of productive resources to sectors with lower productivity. Within the euro area itself, there is a divergence of countries’ sectoral structures, with the more developed core countries associated with “good” specialisation and the less developed countries in the periphery with “bad” specialisation. This specialisation (North–South) is problematic because it makes real convergence of the 1

If there are mechanisms that perfectly absorb idiosyncratic shocks, sectoral specialisation does not necessarily lead to divergence of business cycles, i.e., it makes it more difficult to implement a common monetary policy. However, previous research shows that there are price and wage rigidities (Kunovac and Pufnik 2015) and constraints in the implementation of fiscal policy in Croatia (Šimović et al. 2014). Research for euro area countries confirms that risk-sharing channels are not strong enough to fully absorb idiosyncratic shocks (Cimadomo et al. 2018; Furceri and Zdzienicka 2015; Afonso and Furceri 2008; Asdrubali et al. 1996), and such measures should not be neglected. Specialisation which increases the risk of idiosyncratic shocks and thus the costs of monetary integration.

The Impact of Financial Integration on Sectoral Polarization. . .

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peripheral countries difficult. Krugman (1993) highlights the problem of specialisation raising the costs of monetary integration but does not mention “bad” specialisation. Following Krugman’s hypothesis and taking Mongelli et al. (2016) into account, this paper highlights the emergence of “bad” specialisation within the dynamic approach of modern OCA theory. While the structure of the sector has its own path dependence dynamics, it is also strongly influenced by financial flows. This paper aims to analyse the impact of financial integration on the sectoral structures of Croatia and euro area countries, i.e., to determine whether under the influence of foreign direct investment (FDI) there is a “bad” specialisation that increases the costs of monetary integration and prevents Croatia from achieving real convergence with the eurozone countries. Given the nature of the relationship between financial integration and sectoral specialisation, a model was constructed that was partly adopted from Imbs (2004). The independent variables used in the model were: financial integration, trade integration, country size, and level of country development, where financial integration was measured by the cumulative inflow of foreign direct investment, trade integration by imports and exports of goods, country size by the product of countries’ GDPs and country development by the absolute difference in GDP per capita between Croatia and eurozone countries. The dependent variable is the similarity of sectoral structures measured by the sum of absolute differences in sectoral gross value added between the two countries. All variables are constructed in bilateral form. Garcia Herrero and Ruiz (2008) method was used to detect the channels of influence, modified to provide better insight into the time dimension, i.e., to make the approach to the problem more dynamic. The main thesis of the paper is that, under the influence of financial integration, as measured by bilateral FDI, “bad” specialisation is emerging in Croatia, i.e., a separation of sectoral structures that increases the costs of Croatia’s monetary integration. The results of the research confirm that financial integration, as measured by FDI, promotes “bad” specialisation in Croatia, thus increasing the costs of monetary integration, making it difficult to achieve real convergence with the core eurozone countries and requiring the creation of specific industrial policies in line with public investment and flexible fiscal policies. The second part gives an overview of the literature and the results of similar studies. The third part presents the methodology, the variables used with the expected theoretical features and the research results. Finally, the fourth part concludes with recommendations for future research.

2 Literature Review The traditional theory of OCA emphasises financial integration as one of the key criteria of monetary integration. Ingram (1962) notes that financial integration reduces the need for exchange rate adjustments and allows the absorption of temporary adverse disturbances through capital inflows—borrowing from surplus

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areas or reducing net foreign assets. With a high degree of financial integration, even small changes in interest rates trigger equilibrium capital movements across partner countries, reducing long-term interest rate differentials, facilitating the financing of external imbalances and promoting efficient resource allocation (Mongelli 2008). Through capital mobility, financial integration facilitates adjustment to emerging macroeconomic shocks, as countries can more easily withdraw funds from a common pool of savings2 (Jappelli and Pagano 2008). Moreover, countries with higher growth potential can make additional investments even if domestic households do not increase their savings accordingly. When capital markets are integrated, regions and countries can hedge against asymmetric shocks and thus better exploit comparative advantages arising from differences in technology, factor availability or economies of scale (Kalemli-Ozcan et al. 2003). However, traditional OCA theory ignores the potential problem of structural polarisation, i.e., the emergence of “bad” specialisation concentrated in sectors with lower productivity, which ultimately makes true convergence difficult. Contemporary OCA theory focuses on a dynamic approach, in which two important hypotheses stand out: the endogeneity hypothesis (Frankel and Rose 1998) and the specialisation hypothesis (Krugman 1993). Frankel and Rose (1998) point out that the criteria of OCA are jointly endogenous, which means that the decision on the monetary integration of countries cannot be made on the basis of the analysis of historical data, since the structure of these economies is likely to change drastically by joining the currency area. Indeed, countries joining the currency area may fulfil the criteria of OCA ex post, although they did not fulfil them ex ante. In this way, the boundaries of the currency area can be expanded in the expectation that simply joining the currency area will increase the integration and consistency of business cycles. Endogeneity is associated with progress on many OCA criteria, and De Grauwe and Mongelli (2005) extend it to three additional sources: (i) endogeneity of financial integration, i.e., capital market insurance; (ii) endogeneity of shock symmetry and output consistency; (iii) endogeneity of labour market flexibility. On the other hand, Krugman (1993) posits the specialisation hypothesis, which states that a higher degree of integration leads to specialisation in the production of goods and services in which countries have comparative advantages. In this way, countries become more vulnerable to supply shocks and their incomes are less correlated. According to Krugman, greater integration moves countries away from the optimal currency area, i.e., they move closer to the area where the advantages of using their own currency and autonomous monetary policy prevail. Numerous theoretical models show that financial integration leads to specialisation (Saint-Paul 1992; Acemoglu and Zilibotti 1997; Fenney 1999). Kalemli-Ozcan et al. (2003) point out that inter-country income insurance induces higher specialisation in production. On the other hand, Obstfeld (1994) points out that financial

2

For example, if a country faces a decline in national savings due to an increase in the government deficit, it may be easier to draw on foreign savings to maintain the level of national investment.

The Impact of Financial Integration on Sectoral Polarization. . .

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integration shifts investment to riskier projects, allowing countries to specialise according to their comparative advantages. Benigno and Fornaro (2014) point out that tradable sectors are the engine of growth and that productivity increases with the share of workers employed in tradable sectors. In this context, specialisation by non-tradable sectors is “bad” specialisation that ultimately leads to different growth rates. Grabner et al. (2020) point out that there is structural polarisation within the euro area and the development of two growth models: export-led growth in core countries and debt-led growth in peripheral countries, and the main causes are a lack of technological capacity and poor business performance in the periphery countries. The explanation of specialisation as a structural development phenomenon is found in various approaches of heterodox economics, especially in post-Keynesian doctrine. According to this approach, strong economic growth is based on foreign savings, i.e., inflows of mainly foreign capital, which are accompanied by lower real interest rates, increased demand in the recipient country and higher wages, especially in the construction, tourism and financial sectors. In such a situation, a positive inflation differential arises, mainly from rising prices of non-tradable goods (which are not affected by foreign competition), the reverse Ballas-Samuelson effect and the appreciation of the real exchange rate, which is particularly pronounced in countries with fixed exchange rates. An overvalued currency affects trade flows (imports increase) leads to a current account deficit and affects external debt growth. Not only do countries become more vulnerable to external shocks, but the large inflow of foreign capital into emerging markets also affects specialisation in non-tradable sectors. Trade orientation affects capital movements, especially foreign direct investment. The shift of capital to the non-tradable sector also means lower GDP per capita growth rates, as these sectors are technologically less developed and have lower value-added. Although, in theory, FDI should have a number of positive effects for the recipient country, the development of “bad” specialisation in emerging economies means that FDI inflows are mainly in the classical non-tradable sectors. In this way, financial integration promotes the emergence of structural polarisation. This means that financial integration alone is not sufficient to achieve real convergence or to reduce the costs of monetary integration, as the traditional theory OCA assumes too optimistically. In the recent literature, several papers analyse the relationship between financial integration and sectoral specialisation. Most of them consider sectoral specialisation as the indirect channel through which financial integration affects the synchronisation of business cycles. However, the results of these studies are inconclusive. Moreover, none of them emphasises the problem of “bad” specialisation. Imbs (2004) analyses the impact of financial integration on the business cycle synchronisation of 24 industrialised countries between 1980 and 2000 and confirms that: (i) countries with more similar sectoral structures have more synchronised business cycles; (ii) financial integration promotes specialisation. The author points out that most forms of specialisation are independent of financial integration. Similarly, Siedschlag (2010) shows that similarity of economic

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structures was associated with higher correlations of business cycles between euro area countries and the 8 new EU countries, emphasising that countries at different stages of development had more dissimilar economic structures. In contrast to Imbs (2004), Cerqueira and Martins (2009) point out that the degree of industrial similarity of 20 OECD countries between 1970 and 2002 does not explain business cycle synchronisation, suggesting that specialisation is not a transmission channel through which financial integration affects business cycle synchronisation of the observed countries. Similarly, in a sample of over 100 countries between 1970 and 1997, Baxter and Kouparitsas (2005) show that the similarity of industrial structures is not robustly related to the degree of business cycle synchronisation. Their findings are also confirmed by Bower and Guillemineau (2006), who analyse the aggregate specialisation of 12 euro area countries. However, when measured by differences in shares in industrial sectors, specialisation has a significant impact on the synchronisation of business cycles over the whole period observed (1980–2004). In addition, some papers analyse the specialisation pattern of countries affected by financial integration. For example, Kalemli-Ozcan et al. (2003) examined whether higher levels of insurance were associated with greater specialisation in a sample of 12 countries over the period from 1977 to 1993, using income and consumption-based measures of insurance. The results of their study show that financial integration (through risk-sharing) promotes specialisation in production, and the result is robust even when trade determinants such as geographic distance and factor availability are included. Similar results are confirmed by Xing and Abbott (2007) in a sample of 15 OECD countries from 1984 to 2003 and by Lee and Azali (2010), who analyse the nature of the relationship between financial integration, trade integration, specialisation, and business cycle synchronisation to investigate the suitability of monetary integration of East Asian countries. By contrast, Garcia Herrero and Ruiz (2008) show that financial integration leads to the creation of more similar production structures, which ultimately favours the synchronisation of business cycles of the observed countries. From a methodological point of view, two works are interesting: Imbs (2004) and Garcia Herrero and Ruiz (2008). Imbs (2004) uses a system of simultaneous equations that allows the separation of direct and indirect channels of the influence of financial integration on the business cycle synchronisation, i.e., the identification of the specialisation channel. Garcia Herrero and Ruiz (2008) analyse the direct and indirect effects of financial and trade linkages on the synchronisation of Spain’s business cycles (benchmark countries) with the EU, the G7 and Latin American (109 countries in total). Unlike most papers of this type, the authors use a small open economy (Spain) as the benchmark country, reducing the problem of omitted variables and allowing conclusions to be drawn for a specific country. Both papers used cross-sectional data in which the time component was neglected. Research for Croatia on this topic is rare. Čondić-Jurkić (2011) examines the extent to which the bilateral synchronisation of the business cycles of EU countries and Croatia (1999–2009) can be explained by trade and sectoral determinants. The author finds that the most similar sectoral structures are found in the EU-15

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0,34 0,33 0,32 0,31 0,3 0,29 0,28 0,27 0,26 0,25

CORE EUROZONE COUNTRIES

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

0,24

PERIPHERAL EUROZONE COUNTRIES

Fig. 1 Similarity index of Croatia’s sectoral structures with core and peripheral Eurozone Countries. Source: Author’s calculation according to Eurostat data

countries, very similar sectoral structures are recorded by pairs of new EU member states and the differences in sectoral structures are largest for pairs of old and new EU member states. The results of their study show that the similarity of sectoral structures promotes the business cycle synchronisation. The specialisation pattern of the Croatian economy can be seen in Fig. 1, which shows the index of similarity of Croatia’s sectoral structures with those of the core and peripheral eurozone countries in the period from 2001 to 2019. The index was constructed following Krugman (1991) by calculating the average similarity with the above groups of countries in each year in order to see the trend more clearly. It is evident that the sectoral structure of Croatia is moving away from the sectoral structures of the core eurozone countries and is becoming closer to the sectoral structure of the peripheral eurozone countries. This suggests that there is specialisation in non-tradable sectors in Croatia, i.e., “bad specialisation”, which increases the risk of idiosyncratic shocks and thus increases the costs of monetary integration. Looking at the sectoral structure at a lower level of aggregation of economic activity, Croatia achieves the largest share of sectoral gross value added in total gross value added in manufacturing (16,43%), wholesale and retail trade (12,43%), real estate activities (9,25%), construction (6,28%), and financial and insurance activities (6,10%).3 However, due to the different growth dynamics of the individual sectors, there were significant changes in the sector structure in Croatia during the observed period.

3

Author’s calculation according to Eurostat data (average from 2001 to 2019)

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For example, the share of sectoral gross value added in total gross value added grew by 122,57% in administrative and support service activities; arts, entertainment, and recreation grew by 82,43%; accommodation and food service activities grew by 74,04%; professional, scientific, and technical activities grew by 54,38%; electricity, gas, steam, and air conditioning supply grew by 53,69%. At the same time, the share of gross value in total gross value added decreased in some sectors: activities of households as employers by 81,34%, agriculture, forestry, and fishing by 41,01%, mining and quarrying by 28,54%, public administration and defence by 27,46%, and manufacturing by 26,45%. It is evident that there is stronger growth in the non-tradable sectors, i.e., a shift from tradable to non-tradable sectors.4 In a situation where the economy is moving towards specialisation, there must be mechanisms for private and public risk-sharing that allow the smooth functioning of the monetary area. Cimadomo et al. (2018) distinguish between two basic channels of private (market) risk-sharing: the credit channel and the capital channel. The capital channel operates through the capital market and provides ex-ante insurance against the occurrence of idiosyncratic shocks through the mutual ownership of property, interest and rents. It is primarily related to foreign direct investment and portfolio investment. The credit channel provides an ex-post adjustment to idiosyncratic shocks through borrowing/lending in the credit market. On the other hand, the public risk-sharing mechanism is based on interregional fiscal transfers, and the institutional architecture of the euro area does not have a central macroeconomic stabilisation function, making smoothing through this channel negligible at the euro area level (Cimadomo et al. 2018). When these mechanisms do not exist or do not work, consumption smoothing is difficult in the case of idiosyncratic shocks. This is also true for Croatia as it affects the cost of its monetary integration. In addition, the issue of specialisation of the economy is also important to achieve real convergence. In this context, the “bad” specialisation that goes into non-tradable sectors is particularly problematic. The European Commission (2015) points out that Croatia had relatively weak productivity growth in 2002–2013, masked and partially offset by strong capital inflows and an optimistic external environment before the crisis. However, the weak growth base has fully emerged with the decline in positive demand factors. The problem of weak productivity growth is exacerbated by specialisation from tradable to non-tradable sectors. Such “bad” specialisation leads to different growth rates in Croatia and eurozone countries. Gelo and Družić (2015) point out that, given the state of the Croatian economy, it is necessary to increase both the share and productivity of the sector with internationally tradable goods. Although the domestic economy relied more on debt capital, which was about 54% in the observed period, foreign direct investment was also a strong source of financing (Bukovšak et al. 2017). In this context, it is interesting to examine how FDI has affected the composition of the Croatian sectoral structure, i.e., the specialisation pattern compared to euro area countries.

4

The division into tradable and non-tradable sectors was modeled on Gelo and Družić (2015).

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3 Models and Results The impact of financial integration on the sectoral structures of Croatia and euro area countries over the period 2001–2019 is analysed using a panel model. Compared to cross-sectional data used in most previous studies, panel data are more desirable because they contain much more information, which allows for greater precision in estimation (Hoechle 2007). In addition, panel data can reduce the effects of parameter bias due to missing data, and estimators in panels are more robust to incomplete model specifications. For the purposes of this paper, we used a static panel with a fixed effect, similar to Hsu et al. (2011). Consistent with the theoretical framework and based on previous research, sector structure was explained by four variables: financial integration (FIijt), trade integration (TIijt), level of country development (RZijt) and country size (VZijt): SSijt ¼ β0 þ β1 FIijt þ β2 TIijt þ β3 RZijt þ β4 VZijt þ εijt , where ijt stands for the index of country pairs (i, j) in period t and ε is the disturbance term. Financial integration leads to specialisation due to better cross-border income insurance (Kalemli-Ozcan et al. 2003) or a shift of investment to riskier projects (Obstfeld 1994). In this way, it separates countries’ sectoral structures. On the other hand, financial integration can promote specialisation in the same productive activities, for example in a situation where FDI is concentrated in sectors where the source country has comparative advantages, replicating its productive structure to another country (Garcia Herrero and Ruiz 2008). In this case, there is a convergence of the countries’ production structures. The expected sign of the parameter β1 is therefore ambiguous. The Ricardo/Heckscher-Ohlin theorem predicts that trade integration promotes specialisation among countries according to comparative advantages arising from differences in technology/factor availability. This form of specialisation leads to divergence in the sectoral structures of countries. On the other hand, if intra-industry trade predominates (Linder’s hypothesis), then trade integration is associated with more similar sectoral structures. The expected sign of the parameter β2 is thus ambiguous. Imbs and Wacziarg (2003) point out that specialisation is not linear but U-shaped and varies with the level of development of countries. At lower levels of development, countries reduce the degree of specialisation to avoid the negative effects of sector-specific shocks, while at higher levels of development, countries re-specialise to take full advantage of comparative advantages. Accordingly, the expected sign of the parameter β3 is ambiguous. Larger countries generally have a more diversified sectoral structure and thus a more similar sectoral composition. Therefore, the expected sign of the parameter β4 is positive.

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Following Krugman (1991), the similarity of sectoral structures (SSijt) was measured by the sum of absolute differences in sectoral gross value added between the two countries: SSijt ¼

N  X  snit  snjt  n¼1

where snit is the gross value added of sector n of the country and in period t, measured as a share of GDP. The variable SSijt ranges from zero to two, where a value of two indicates a completely different structure of Croatia and the observed eurozone countries (higher degree of specialisation in different sectors) and a value of zero indicates an identical sectoral structure. Sectors are systematised according to the Classification of Economic Activities, which consists of 21 sectors NACE Rev. 2. Eurostat was used as the source for the sectoral gross value-added data. Financial integration (FIijt) is measured by the share of bilateral foreign direct investment in the countries’ gross domestic product: FI ijt ¼

fiijt þ foijt Y it þ Y jt

where fiijt is the cumulative inflow of FDI from country i to country j in period t, foijt is the cumulative outflow of FDI from country i to country j in period t and Yit is the nominal GDP of country i in period t. A higher value of the indicator FIijt indicates a higher degree of financial integration between Croatia and the individual euro area countries. Data on cumulative inflows and outflows of FDI are taken from the WIIW database (Vienna Institute for International Economic Studies). Trade integration (TIijt) was measured by the share of bilateral imports and exports of goods in the countries’ GDP,5 following the model of Frankel and Rose (1998): TI ijt ¼

xijt þ mijt , Y it þ Y jt

where xijt is the export from country i to country j in period t, mijt is the import of country i from country j in period t and Yit is the nominal GDP of country i in period t. A higher value of the indicator indicates a higher degree of trade integration between Croatia and the observed euro area country. Data on countries’ imports and exports of goods are obtained from the official database of international trade statistics—UN Comtrade. The level of development of countries (RZijt) is measured by the absolute difference of GDP per capita and the size of countries (VZijt) is measured by the Although comprehensive trade flows should include services, data on bilateral trade in services between Croatia and the eurozone countries are not available. Accordingly, services are not included in the calculation of the degree of trade integration.

5

The Impact of Financial Integration on Sectoral Polarization. . .

−15

−10

−5

0

61

−2

−1.5 logFI

logSS

−1

−.5

Fitted values

Fig. 2 Scatterplot FI-SS (2001–2019). Source: Author’s calculation according to Eurostat data

product of GDP. The data on GDP and GDP per capita were taken from the Eurostat database. The econometric analysis was carried out with a spatial sample comprising Croatia and the eurozone countries and consisting of 19 bilateral observations.6 The temporal dimension of the sample covers the period from 2001 to 2019, resulting in a total sample of 381 (n ¼ 19 * 19) observations. Before the panel analysis of the impact of financial integration on the sectoral structures of Croatia and the euro area countries, scatter plots were constructed to provide a basic insight into the relationship between the variables. Figure 2 shows a scatter plot for the period from 2001 to 2019, which indicates that the regression direction of the relationship between financial integration and the similarity of sectoral structures has a positive slope. Taking into account the way the similarity of sectoral structures is measured, the negative linear relationship between financial integration and the similarity of sectoral structures is confirmed. This indicates that financial integration promotes the divergence between the Croatian sectoral structure and the sectoral structures of the eurozone countries. The results of the panel analysis are shown in Table 1. Two models are included, the first covers the period 2001–2019, the second covers the same period but excludes the crisis years (2008–2012). The panel results show that financial integration reduces the similarity between the sectoral structures of Croatia and the eurozone countries. Thus, FDI promotes the specialisation of the Croatian economy and the eurozone countries in different sectors. Moreover, the specialisation of the Croatian economy is shifting towards the non-tradable sectors. With the polarisation of sectoral structures, countries

6

Croatia with every eurozone country.

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Table 1 The impact of financial integration on the similarity of the sectoral structure Similarity of sectoral structures Financial integration (β1) Trade integration (β2) Level of country development (β3) Country size (β4) Constant

2001–2019

2001–2019 exl. Crisis years

0,0196923** (0,0090019) 0,0011709 (0,0158526) 0,0887292*** (0,0282712) 0,0066637 (0,0037159) 2,114889*** (0,3133932)

0,221155** (0,0107725) 0,0108264 (0,0193735) 0,0857132*** (0,0326362) 0,0050618 (0,004793) 2,029274*** (0,0107725)

Standard errors are shown in parentheses. , , Significance at 1%,5%,10% Source: Authors’ calculation

*** ** *

become more vulnerable to the occurrence of idiosyncratic shocks, indicating an increase in the costs of monetary integration for Croatia. Bukovšak et al. (2017) point out that almost one-third of cumulative FDI inflows between 1993 and 2016 went to manufacturing, while the rest went to real estate, trade, and telecommunications (i.e., mostly non-tradable sectors). FDI inflows in Croatia contribute to the relocation of production and export activities and promote “bad” specialisation, which also makes real convergence more difficult. The panel results show that the effects are stronger if we exclude the crisis years, suggesting a stronger polarisation of sectoral structures between Croatia and euro area countries during periods of larger FDI inflows to Croatia. It is clear that most FDI in Croatia flows into the non-tradable sector, which “feeds” the debt-led growth model with all its negative effects. Nominal convergence is not sufficient to achieve real convergence, and in this context, the need for structural convergence arises as a basic condition for achieving a real convergence. FDI inflows, especially in the non-tradable sectors, cannot contribute to the achievement of structural convergence. Therefore, it is necessary to achieve higher levels of technological capacity and better institutional conditions to shift FDI to tradable sectors. Moreover, the panel’s findings show that trade integration promotes divergence of sectoral structures. This is consistent with previous research confirming that the majority of Croatian trade is inter-industry (Botrić and Broz 2016; Derado 2006), which promotes specialisation in different sectors. Although the sign of the effects is consistent with the theoretical framework, the effects are not statistically significant. In the tested models, the development of countries has a positive sign, which means that a larger difference in the level of development between Croatia and eurozone countries implies a larger difference between their sectoral structures. This shows the importance of real convergence in achieving structural convergence. Finally, the size of the countries has a positive sign, which means that it has a negative effect on the similarity of the sectoral structures of the observed countries.

The Impact of Financial Integration on Sectoral Polarization. . .

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This effect can be explained by the fact that larger countries are more diversified, in contrast to smaller countries like Croatia, which are more specialised.

4 Discussion and Conclusion The motivation for Croatia’s accession to the EU and the eurozone is to accelerate economic growth and catch up with industrialised countries through economic and, above all, financial integration. The developmental goal of European integration is thus to achieve real convergence between countries. Although the theory of OCA explains the possibility of countries with different levels of development participating in the monetary union, the process of divergence of growth models between core and peripheral countries, accompanied by structural polarisation and macroeconomic divergence, is increasingly noticeable. Structural polarisation is reflected in the emergence of two growth models—export-led and debt-led—leading to macroeconomic divergence and development imbalances. By specializing in low value-added sectors, mostly non-tradable sectors with low productivity and low technological capabilities, the country is doomed to remain in a kind of development trap. The sectoral structure of the country is important for both the implementation of the common monetary policy and for the achievement of real convergence. The results of this study show that a gradual change in sectoral structures is taking place in Croatia, with Croatia structurally diverging from eurozone countries. The divergence of sectoral structures is a consequence of “bad specialisation”. The research results show that financial integration promotes Croatia’s specialisation and makes the country more vulnerable to the occurrence of idiosyncratic shocks, which increases the costs of Croatia’s monetary integration. Moreover, the process of structural divergence shifts the specialisation of the Croatian economy to non-tradable sectors, making real convergence with more developed euro area countries more difficult. The impact of financial integration on Croatia’s sectoral structures, as measured by the similarity between the sectoral structures of Croatia and eurozone countries in the period 2001–2019, is analysed using a panel model. The results of the panel show that financial integration reduces the similarity between the sectoral structures of Croatia and the eurozone countries. The specialisation of the Croatian economy shifts to the non-tradable sectors and becomes more vulnerable to the occurrence of idiosyncratic shocks. Croatia needs to achieve a higher level of technological capacity and better institutional conditions to shift FDI to tradable sectors. The study also underlines the importance of real convergence for achieving structural convergence. Although the proposals to solve this situation go beyond the aim and scope of this paper, it must be emphasised that structural polarisation and the emergence of poor specialisation as a development phenomenon cannot be solved by financial integration. At the core of sector specialisation are technological capabilities and structural

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competitive advantages that lead to growth as a structural shift towards higher value-added sectors. This process needs to be supported by an active industrial policy of the peripheral countries.

References Acemoglu D, Zilibotti F (1997) Was Prometheus unbound by chance? Risk, diversification and growth. J Polit Econ 105(4):709–751 Afonso A, Furceri D (2008) EMU enlargement, stabilization costs and insurance mechanisms. J Int Money Financ 27:169–187 Asdrubali P, Sorensen BE, Yosha O (1996) Channels of interstate risk sharing: United States 1963-1990. Q J Econ 111(4):1081–1110 Baxter M, Kouparitsas M (2005) Determinants of business cycle comovement: a robust analysis. J Monet Econ 52:113–157 Benigno G, Fornaro L (2014) The financial resource curse. Scand J Econ 116(1):58–86 Botrić V, Broz T (2016) Industry-specific trade patterns with Eurozone and economic crisis: Bosnia and Herzegovina, Croatia and Serbia. Zbornik radova Ekonomskog fakulteta Sveučilišta u Mostaru 22:7–25 Bower U, Guillemineau C (2006) Determinants of business cycle synchronisation across euro area countries. European Central Bank. http://www4.fe.uc.pt/jasa/m_i_2010_2011/determinants_ business_cycle_synchronisation_across_euro_area_countries.pdf. Accessed 13 Jun 2021 Bukovšak M, Lukinić Čardić G, Ranilović N (2017) Structure of capital flows and exchange rate: the case of Croatia. Hrvatska narodna banka. https://www.hnb.hr/documents/20182/2030174/ w-052.pdf/23163f57-2b6f-4f39-81b9-014c4304892b. Accessed 29 Mar 2021 Cerqueira PA, Martins R (2009) Measuring the determinants of business cycle synchronization using a panel approach. Econ Lett 102(2):106–108 Cimadomo J, Hauptmeier S, Palazzo A, Popov A (2018) Risk sharing in the euro area. European Central Bank https://www.ecb.europa.eu/pub/pdf/other/ecb.ebart201803_03.en.pdf. Accessed 28 Mar 2021 Čondić-Jurkić I (2011) Trgovinske i sektorske determinante usklađivanja poslovnih ciklusa Hrvatske i zemalja Europske unije. Zbornik Ekonomskog fakulteta u Zagrebu 9(1):105–122 De Grauwe P, Mongelli F P (2005) Endogeneities of optimum currency areas: what brings countries sharing a single currency closer together? European Central Bank. https://www.ecb.europa.eu/ pub/pdf/scpwps/ecbwp468.pdf?29304b3978d4e29741b276bce4430688G. Accessed 20 Mar 2021 Derado D (2006) The effects of trade liberalisation among the south eastern European countries. Tourism and hospitality management 12(1):1–17 Europska Komisija (2015) Izvješće za Hrvatsku 2015. S detaljnim preispitivanjem o sprječavanju i ispravljanju makroekonomskih neravnoteža. Europska komisija. https://publications.europa.eu/ en/publication-detail/-/publication/308d43cb-bdb2-11e4-bbe1-01aa75ed71a1/language-hr Accessed 12 Mar 2021 Fenney J (1999) International risk sharing, learning by doing, and growth. J Dev Econ 58:297–318 Frankel JA, Rose A (1998) The Endogeneity of the optimum currency area criteria. Econ J 108: 1009–1025 Furceri D, Zdzienicka A (2015) The euro area crisis: need for a supranational fiscal risk sharing mechanism? Open Econ Rev 26(4):683–710 Garcia Herrero A, Ruiz JM (2008) Do trade and financial links Foster business cycle synchronization in a small open economy. Moneda y Credito 226(1):187–226 Gelo T, Družić M (2015) Ukupna faktorska produktivnost sektora hrvatskoga gospodarstva. Ekonomska misao i praksa 2:327–344

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Foreign-Owned Banks and Real Estate Markets in Croatia: A Panel Data Analysis Ana Rimac Smiljanić and Blanka Škrabić Perić

Abstract This paper analyses the role of real estate markets and foreign-owned banks in bank credit growth in Croatia from 1999 to 2008 by applying panel data analysis. This paper gives a more profound explanation of host countries’ variables influencing bank orientation towards housing credit in the home county. We explain the channel through which the foreign-owned banks can facilitate domestic demand for housing and push the housing bubble, motivated by determinants outside the host country. Our results suggest that foreign-owned banks’ orientation to real estate markets increased credit supply to all private sectors in Croatia during the credit boom in 1999–2008.

1 Introduction This paper analyses two new aspects of bank credit growth to the private sectors in Croatia, external bank liabilities and real estate markets, throughout 1999–2008. Namely, it is documented that excessive macroeconomic imbalances, created by external borrowing by banks and state, caused deeper and longer recessions in some CEE countries after the global financial crisis (Ehlers and McGuire 2017; Brkić 2019). Additionally, capital flows directed to the real estate sector had a more significant impact on changes in GDP than inflows in other sectors in the new EU member countries (Mitra 2011). Moreover, research confirms that some banks were more exposed to the real estate markets during boom episodes (Akin et al. 2014; Martín et al. 2021). We assume that, in Croatia, differences in bank exposure toward real estate markets were evident in the behaviour of foreign-owned banks from 1999 to 2008. Therefore, we decided to investigate the role of foreign banks in real estate and the credit cycle more in depth.

A. R. Smiljanić (*) · B. Š. Perić Faculty of Economics, Business and Tourism, University of Split, Split, Croatia e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_5

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Firstly, we wanted to find whether foreign-owned banks stimulated the growth of housing credit in a country. In the considered period, foreign-owned banks started to dominate the Croatian banking market and external liabilities of the banking sector and economy increased. In addition, the boom in real estate markets occurred accompanied by a significant increase in housing credit. The previous studies focusing on credit cycles found that cross-border credit significantly contributed to rapid credit growth in the period before the global financial crisis (de Haas and Naaborg 2005; Borio et al. 2011; Ehlers and McGuire 2017; Niţoi et al. 2021). External credit is not restricted as much as domestically funded credit by a domestic deposit base or capital controls (Duijm 2019). Additionally, it is documented that shocks to banks’ leverage can generate boom and bust cycles in the economy, especially in house prices (Arslan et al. 2021). We doubt it was more pronounced in CEE countries because foreign-owned banks dominated the market. They had comparative advantages due to their possibilities to raise funds on external capital markets (Buch and Goldberg 2020; Belton et al. 2021). Foreign-owned banks use external funding to finance credit growth in countries with a higher return (de Haas and Van Lelyveld 2010). We doubt that in Croatia these funds were largely distributed to the real estate sector. Housing credit is considered low risk for the creditor. For foreign-owned banks, other market segments of the economy can be perceived as more unfamiliar and risky due to a lack of knowledge of the market and legal surroundings (Bruno and Hauswald 2013). The situations with less available information might encourage banks to lend more to the best credit-risk borrowers. Additionally, real estate markets can attract banks because they can, in difficult times, sell assets more profitably, the value of which can be more easily evaluated by outsiders (Dell’Ariccia and Marquez 2004). In the considered period, Croatia became very attractive for international investors in real estate, and domestic demand grew due to tourism. Did the advantages in accessing external capital markets and the safety of real estate markets attract foreign banks to finance housing loans as they perceived it a safe market niche and created internal and external macroeconomic imbalances? Secondly, at the considered period, Croatian real economy started to rise, especially investments in the real estate sector, and it significantly contributed to GDP growth in Croatia. Due to the importance of real estate markets and connected sectors for Croatian households and companies, the perceptions of systematic risk decrease in-country and additionally stimulate investment and credit boom. Because of the poor legal protection of creditors, real estate collateral was significant in the credit assessment of potential borrowers for all types of credits (OECD 2003; Galac 2005; Ivičić et al. 2008). Therefore, we doubt that higher house prices might have stimulated credit growth in other sectors by providing secure collateral for financing other attractive investments and consumption. Similar episodes of slipovers of credit growth from the real estate sector to other sectors have been documented (Jiménez et al. 2020; Martín et al. 2021). Namely, if the housing bubble lasts, the net worth and the bank capital rise so they can expand credit supply to other sectors (Herring and Wachter 1999). Higher bank exposure to the real estate market encourages banks in credit expansion and higher risk tolerance. Banks give more relaxed credit

Foreign-Owned Banks and Real Estate Markets in Croatia: A Panel Data Analysis

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standards for existing borrowers. In addition, the banks fight for new clients and give more credit to first-time borrowers that substantially have more problems in repaying the debt (Jiménez et al. 2020). We doubt that impact of real estate collateral is more pronounced in countries with insecure laws where real estate is seen as additional creditor insurance. The credit boom in Croatia began in 1999 and ended in 2009 when the consequences of the global financial crisis (GFC) became evident. It was fuelled with direct and indirect cross-border credit via foreign-owned banks. A boom in asset markets, especially in real estate prices, arose in the considered period, ending with GFC. The research indicates the positive effects of foreign banks’ entry in Croatia like competition growth, falling interest rates, new innovative products and services, and improvement of the existing ones, which resulted in higher quality and technologically advanced operations (Kraft 2003). Besides those positive changes, did the entrance of foreign banks encourage indebtedness and cause credit and real estate cycle? In this paper, we argue that that was due to the connection of banks external liabilities, real estate markets and perception of systematic risk in parent bank home country and Croatia. Previous research on credit growth in Croatia indicated an increase in financial liberalisation, institution building, better corporate governance, capital inflows, exchange rate, and the entry of foreign banks as sources of credit growth (Kraft and Jankov 2005). Additionally, banks’ orientation to “safer” credit was identified as the source of credit growth of the household sector (Kraft 2006). The study results point out the strong growth of housing credit, emphasising the importance of collecting long-term sources of funds. In the considered period, most bankers in Croatia were using foreign funds via foreign-owned banks because the term structure of domestic sources was inappropriate, and the growth of domestic deposits was too slow (Ivičić et al. 2008). In addition, Lang and Krznar (2004) point out that the monetary policy had a lesser effect on the loan supply of foreign-owned banks because they can borrow from parent banks and thus, to some extent, avoid the actions of the Croatian National Bank (CNB). Furthermore, the share of long-term liabilities in total liabilities for Croatian banks was an important determinant to the banks’ credit growth in Croatia Bambulović and Valdec (2018). The above indicates the importance of external funding for foreign banks’ credit expansion in Croatia. The results of research for CEE countries also support that view. Cottarelli et al. (2005) provide evidence that the credit growth in Croatia resulted from financial deepening that followed the entry of foreign banks. In CEE countries, parent banks strongly influenced the foreign-owned banks in the capital allocation and credit steering mechanisms (de Haas and Naaborg 2005). Via internal capital markets, international banks allocate resources in countries that generate the highest yield compared to the risk (de Haas and Van Lelyveld 2010). Therefore, for foreign-owned banks, conditions in the home and host country and the safety of investment significantly affect their lending policies in the host country (de Haas and van Lelyveld 2006). Recent research shows that cross-border bank claims and foreign-owned bank credit are strongly connected in CEE countries but show pro-cyclicality with a clear difference during regular and crisis periods (Niţoi et al.

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2021). In good times, the credit cycle can start imported from the home to the host country. Consequently, increased liquidity can start the rising cycle in real estate prices (Ludvigson et al. 2013). According to Ferrero, A. (2015, pp. 1.) “One of the most striking features of the period before the Great Recession is the strong positive correlation between house price appreciation and current account deficit... in countries that have subsequently experienced the highest degree of financial turmoil”. In the literature, connections between foreign banks, credit, perception of systematic risk, and real estate cycles in CEE are rare. Mihaljek (2007) analysed bank credit trends to private sectors in CEE countries and stressed that it is strongly driven by events on residential real estate markets in those countries. He concludes that all the banks participated in the credit expansion, regardless of ownership, but that foreign banks, at the aggregate level, contributed to the growth of foreign borrowing. To capture the effect of a real cycle on credit growth in Croatia, Bambulović and Valdec (2018) estimate the macro factor variable constructed from GDP, real estate prices, stock exchange index, and wages growth. They found that real estate prices have a more substantial influence on the credit growth of foreign-owned banks and the relationship is present only in the boom phase of the economic cycle, which ends with a crisis period. Following the idea from the previous research of the home-host effect on the foreign banks’ credit policies (De Haas and van Lelyveld 2006, 2010; Arakelya 2018; Niţoi et al. 2021), our research extends and upgrades conclusions of the studies presented in Mihaljek (2007), Bambulović and Valdec (2018), Duijm (2019) and Niţoi et al. (2021). We upgraded our model with the real estate prices, bank external liabilities, and perception of systemic risk in the parent bank home country and Croatia. This approach gives more insight into foreign bank credit behaviour influenced by host and home country effects. We add to the literature in several aspects. Firstly, this paper contributes to the existing literature on home and host effects on international banking by considering in more depth the role of foreign banks in a credit cycle funded by external sources. More precisely, we found empirical evidence that bank credit in Croatia was determined with the perception of systematic risk in home and host country, banks external funding, real estate markets, and corruption, regardless of bank ownership. Secondly, we found the importance of bank liquidity and external funding for bank housing credit only for foreign-owned banks. Additionally, parent bank size and liquidity are important a source of growth of housing credit in Croatia. Thirdly, we identified real estate prices as the determinant of banks’ credit growth in the country with insecure laws. Banks’ external funding can stimulate a boom in the specific asset market in such countries because banks flee to more safe clients because of legal insecurity. Through banks, a rising cycle from one asset market can be transited to other sectors of the economy. This paper is organised as follows. Section 2 describes used data, methodological approach, and the analysis description. The empirical results are presented and discussed in Sect. 3. Section 4 concludes the paper with a summary and states the potential policy implications.

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2 Empirical Methodology and Data This section describes data, used methodology and specification of models.

2.1

Data and Variable Definition

We used the micro- and macroeconomic yearly data for the data panel construction. The sample was selected from primary and secondary data from the period from 1999 to 2008. By choosing that period, a sample begins with the occurrence of a significant entry of foreign banks in Croatia and ends with ending the boom phase in the credit and real estate cycle. Studies documented that external credit has been an important source of credit growth in CEE countries before the GFC and after the entry of foreign banks into that market (de Haas and Naaborg 2005; Borio et al. 2011; Ehlers and McGuire 2017). In addition, previous studies on CEE countries found that foreign-owned banks changed their funding strategy from external financing towards more local bank financing after the outbreak of GFC (Niţoi et al. 2021), which further argues the choice of the research period. In constructing our data set, we use microeconomic data taken from the CNB for all banks that operate in Croatia and BankScope database for the parent banks. For the initial years, the data for each parent bank were not available in the database BankScope; therefore, we collected them from the databases: Reuters and/or Bloomberg and/or directly from the specific bank. The macroeconomic data for Croatia were taken from the website of the CNB. The spread between AAA and BBB Merrill Lynch corporate bond index is taken as an indicator of the perception of systemic risk on credit markets in parent banks’ countries. The data were extracted from Bloomberg’s database. The same indicator is used for all parent banks’ countries, because it is assumed that banks of that size operate and are financed on the European single market. Therefore, their decisions are affected by the perception of risk from a single corporate bond-credit market. As an indicator of banks’ perception of systemic risk in Croatia, we took an interest rate spread between banks housing loans in the euro area and Croatia.1 The ratio of government domestic debt to banks to the total bank credit in Croatia is used to indicate the influence of government debt on bank credit policies. By including that indicator, we wanted to capture the possible crowding-out effect of the private sector by state. The Corruption Perception Index of Transparency International was taken as an indicator of the legal environment. Table 1 provides the list of all micro- and macroeconomic data used in constructing the data panel.

1

The housing credit is a homogeneous product, and Croatian banks collect the funds for its financing from the same market as the banks in the eurozone. In addition, the competition in the Croatian market is similar to the EU markets. Therefore, we can state that interest rates differential can be used as a good proxy of banks’ perception of systematic risk in Croatia.

A. R. Smiljanić and B. Š. Perić

72 Table 1 Definitions of variables and data sources Variable Definition Bank specific indicators Housing Bank housing credit in total bank credit (%) Credit Credit Natural logarithm of total bank credit Bank specific Size Capital Liquidity Profitability BankExternal Funding ShareExternal Funding Ownership

Bank assets to total bank sector asset (%) Equity to total bank assets (%) Liquid assets to total bank assets (%) Return of average bank assets (%) Bank external liabilities to total bank assets (%)

Bank external liabilities to external liabilities of all banks (%) Dummy variable. Value 1 in year t if the bank is in the majority foreign ownership, 0 otherwise. parentBank Dummy variable. Value 1 in year t if the bank has parent bank, 0 otherwise. Parent bank-specific indicators pbSize Bank assets to total bank sector asset of parent country (%) pbCapital Equity to total parent bank assets (%) pbLiquidity Liquid assets to total parent bank assets (%) pbProfitability Return of average parent bank assets (%) Macroeconomic indicators from parent bank country pSystematic Spread between AAA and BBB Merrill lynch corRisk porate bond index (%) pInterestRate The interest rate on housing loans in the parent country (%) pShare Share of housing credit in total bank credit in HousingCredit parent (home) country (%) Macroeconomic indicators from Croatia HousePrices House prices index External External liabilities of all banks to total assets of all Funding banks (%) Corruption Corruption index Systematic Indicator of banks perception of systemic risk in Risk (host country) Croatia (%) Government Government domestic debt to commercial banks to Domestic total credit of commercial banks (%) Dept

Source: Authors

Source

Exp. Sign

CNB

Dep. Var. Model (1) Dep. Var. Model (2)

CNB

CNB CNB CNB CNB CNB

+ + + + + +

CNB

+

CNB

+

Bankscope, ECB Bankscope Bankscope Bankscope

+

Bloomberg’s




ECB

+/


ECB




CNB CNB

+ +

Trans. Int’l CNB





CNB




+ + +

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Table A1 in the appendix provides descriptive statistics of all data sets. In the next step, we conducted a t-test of mean differences of banks operating in Croatia concerning their ownership. The results of a t-test for the sample are shown in the Appendix Table A2. It is evident from Table A2 that there is a difference between domestic and foreign-owned banks regarding the size of the bank, external liabilities, and capital. Foreign-owned banks are larger and use more external funding than domestic. Additionally, the data clearly showed that foreign-owned banks have a higher ratio of credit to the private sectors in total bank assets, especially housing credit. In the next step, we formulated two models. In the first model, the dependent variable was the share of banks’ housing credit in total bank credit (HousingCredit). In the second model, the dependent variable was banks’ total credit to the private sectors (Credit). With the first model, we wanted to capture the banks’ exposure to the real estate markets in their credit portfolio regarding the possibility of their external funding. With the second model, we wanted to test the effect of external liabilities, real estate markets, and perception of systematic risk in home and host country on the total bank credit. To find the answer to research questions and estimate proposed models, we had to choose an adequate methodology.

2.2

Econometric Methodology

The collected data is comprised of macroeconomic data from several countries and microeconomic data on more banks in several countries for 10 years. Therefore, it is necessary to employ panel data methodology. Panel data models have advantages over multiple regressions because they consider the spatial and temporal components of data. Therefore, to estimate the proposed model, we will use the dynamic panel estimator proposed by Arellano and Bond (1991). The current value of Housing Credit and Credit depends on its value from the previous year. A dynamic panel model can be written as: yit ¼ μ þ γyi,t
1 þ β1 xit1 þ β2 xit2 þ . . . þ βK xitK þ αi þ εit , i ¼ 1, . . . N, t ¼ 1, . . . , T

ð1Þ

where i denotes individual, t denotes time, μ is an intercept, γ is a parameter of lagged dependent variable and β1, β2,..., βK are the parameters of exogenous variables. It is assumed that εit are identically and independently distributed error terms. Unobservable individual-specific effect αi is correlated with yi,t-1 because αi is part of yi,t-1. But in Eq. (1), both of them are on the right side of the equation. In this case, usual OLS estimators and GLS estimator become biased. Therefore, Arellano and Bond (1991) proposed a new estimator for the dynamic panel model. To solve the problem of endogeneity, this estimator includes additional instrumental variables. In the first step, this estimator uses the equation in the first differences. It can be written as:

A. R. Smiljanić and B. Š. Perić

74

Δyit ¼ γΔyi,t
1 þ β1 Δxit1 þ Δβ2 xit2 þ . . . þ βΔK xitK þ Δεit , i ¼ 1, . . . N, t ¼ 1, . . . , T:

ð2Þ

By using the equation in the first differences αi is removed from the Eq. (2), but endogeneity still exists. The problem of correlation between Δεit ¼ (εit
εi, t
1) and Δyi, t
1 ¼ (yi, t
1
yi, t
2) appears. Arellano and Bond (1991) proposed introducing instruments to solve this problem. Valid instruments for Δyi, t
1 are yi, t
2 and earlier lags. Additionally, they also proposed valid instruments for potential endogenous independent variables. xi, t
2, k and earlier lags are valid instruments for endogenous xi, t
2, k. After the estimation of the model, two diagnostic tests are performed. The problem of endogeneity in the model is tested by the Sargan test (Baltagi 2008). The first and second order of autocorrelation in differenced residuals are tested by two tests AR(1) test and AR(2) test. The firstorder autocorrelation in the differenced residuals is expected while the existence of the second-order autocorrelation is not allowed (Arellano and Bond 1991). Two-step, Arellano and Bond GMM estimator, is used for model estimation because it is robust on heteroscedasticity and more efficient. (Baltagi 2008).

3 Empirical Analysis and Results We started our empirical analysis by forming a basic model of bank housing credit with three groups of variables: bank-specific characteristics, parent bank-specific and variables from the financial market in the country of the parent bank. With this model, we wanted to test whether the share of housing credit in the bank’s portfolio depended on the characteristics of the bank, its parent bank, and conditions in its home markets. In the first step of empirical testing, we estimated the basic model, which is written by the following equation: HousingCredit i,t ¼ μ þ γHousingCredit i,t
1 þ β2 BankSpecifici,t þ β3 ParentBankSpecifici,t þ β4 FinancialMarketParentBanki,t þ αi,t þ εi,t i ¼ 1, . . . N, t ¼ 1, . . . T,

ð3Þ

Where HousingCrediti,t is the share of bank i housing credit in total bank credit in year t. HousingCrediti,t-1 is the share of bank i housing credit in total bank credit in year t-1. BankSpecifici,t is the matrix of bank-specific indicators: size, capital, liquidity, profitability, and share of bank i external liabilities to external liabilities of all banks (%) in time t. pBankSpecifici,t is the matrix of parent bank-specific: size, capital, liquidity, the profitability of the parent bank i in time t. FinancialMarketParentBanki,t is the matrix of variables from financial markets

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in the parent bank’s country: perception of systematic risk, interest rates on housing credit, and share of housing credit in total bank credit in parent country i in time t. αi,t is the specific error for each bank, while εi,t is the remaining part of the error term. N represents the number of banks and T is the number of periods. γ is the coefficient of lagged dependent variable while β 1, β 2, β 3, β 4 are matrices of parameters. The results of the estimated basic model are given in Table 2, Model (1). For the robustness check, the same model is tested for the shorter period, 2001–2008 (Table 2, Model (2)). With shortened period, we capture the period of significant domination of foreign-owned banks after the consolation they had done in the first years after entering the market. We also wanted to test the difference between domestic and foreign-owned banks in housing credit, considering different foreign and domestic banks’ credit policies (de Haas and van Lelyveld 2006; Bruno and Hauswald 2013; Perić et al. 2018; Skała 2021). Therefore, we split the sample into foreign-owned and domestic-owned banks. According to the Sargan test, the estimated models are not valid; we therefore had to reduce the model. First, to ensure that the results for parent bank variables are not consequences of multicollinearity with variables from financial markets from the country of the parent bank, we estimated the model without them. Model (3) gives results for all banks and Model (4) only for the foreign-owned bank (Table 2). In the next step, we upgraded our model with variables from the Croatian economy to find whether the share of housing credit in the bank’s portfolio depends on the real estate prices and legal environment in Croatia. The equation of the described model is specified as follows: HousingCredit i,t ¼ μ þ γHousingCredit i,t
1 þ β2 BankSpecifici,t þ β3 ParentBankSpecifici,t þ β4 FinancialMarketParentBanki,t þ β5 MacroCroatiai,t þ αi,t þ εi,t i ¼ 1, . . . N, t ¼ 1, . . . T,

ð4Þ

Where MacroCroatiai,t is the matrix of variables from the Croatian economy: house prices and corruption index in time t. Variables from parent bank’s country: interest rates on housing loans and share of housing loans in total bank credit are omitted due to the validity of the estimated model. The results of this equation are given in Table 2, Model (5). Additional robustness check of model results is presented in Table 2, Model (6). Model (6) is estimated without variable perception of systematic risk in parent bank’s home country. Diagnostic tests for all model specifications, Models (1)–(6), are presented in Table 2. Sargan, AR (1) and AR (2) test indicate that all models are well specified.2 The results presented in Table 2, Model (1)–(6), indicate that the lagged dependent variable ‘bank housing credit’ (L.HousingCredit) is statistically significant and 2

The results for the Sargan test indicate the validity of the chosen instruments. The AR (2) test rejects the existence of autocorrelation in the second order of differenced residuals. The AR (1) test results indicate the presence of autocorrelation in the first order of differenced residuals.

pbProfitability

pbLiquidity

pbCapital

ShareExternal Funding Parent bank-specific pbSize

Profitability

Liquidity

Capital

Size

Bank specific L.HousingCredit 0.582* (0.026)
0.058 (0.063) 0.017*** (0.010) 0.011 (0.009) 13,050* (3.067) 0.581** (0.025) 1.31e
15* (4.50e-16) -1.25e-10 (1.04e-10) 0.011 (0.009) 12.106 (8.532)

1.31e
15* (4.50e
16) -1.25e-10 (1.04e-10) 13.050* (3.067) 12.106 (8.532)

Model (2) 2001–2008 HC

0.581* (0.026)
0.057 (0.062) 0.017*** (0.010) 0.011 (0.009)
0.079** (0.040) 0.581** (0.025)

Model (1) HC

Table 2 Results of the housing credit model (HC)

7.91e-16*** (4.11e-16) -1.02e-10* (2.25e-11) 15.871* (4.322) 21.818*** (12.240)

0.611* (0.333)
0.101 (0.078) 0.005 (0.012) 0.013 (0.009)
0.909** (0.040) 0.084 (0.556)

Model (3) HC

1.98e-15* (5.27e-16)
2.37e
10* (4.46e
11) 13.374* (2.609) 71.490* (10.619)

0.633* (0,033)
0.070 (0.094) 0.002 (0.015) 0.042* (0.018)
0,042 (0.041) 0.053** (0.023)

Model (4) Foreign banks HC

1.84e-15** (8.08e-16) -1.4e-10* (2.61e-11) 15.102* (2.239) 33.935* (7.899)

0.558* (0.018) 0.008 (0.347) 0.013 (0.009) 0.023* (0.007)
0.086** (0.349) 0.671* (0.162)

Model (5) HC

1.82e-15* (6.91e-16) -1.48e-10* (1.83e-11) 14.659* (2.161) 38.275* (6.947)

0.558* (0.022) 0.008 (0.356) 0.009 (0.009) 0.034* (0.005)
0.077** (0.032) 0.086* (0.165)

Model (6) HC

76 A. R. Smiljanić and B. Š. Perić

0.281* (0.046) 260 41 0.208 0.101 0.930 0.281* (0.046) 260 41 0.208 0.101 0.930

0.280* ((0.055) 260 41 0.216 0.045 0.763

Notes: *,**,*** indicate significance at 1%, 5% and 10%; Standard errors in parentheses Source: Authors

N Number of banks Sargan test (p value) AR(1) AR(2)

Constant

Corruption

Financial market characteristics in parent bank country pSystematic
0.200*
0.200* Risk (0.077) (0.077) pInterestRate 0.198* 0.198* (0.047) (0.047) pShareHousing
0.029*
0.029* Credit (0.008) (0.008) Characteristics of the Croatian economy HousePrices

0.629* (0.098) 117 24 0.433 0.067 0.985

0.020* (0.003)
0.022* (0.004) 0.201* (0.041) 260 41 0.128 0.091 0.881


0.206* (0.492)

0.017* (0.004)
0.022* (0.005) 0.216* (0.037) 260 41 0.178 0.102 0.964

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A. R. Smiljanić and B. Š. Perić

positive, as expected. The variable representing the share of bank’s external liabilities into external liabilities of all banks in Croatia (ShareExternalFunding) is positive and statistically significant in all models presented in Table 2, except the in Model (3). In addition, banks profitability (Profitability) is statistically significant and negative in six models, and only in Model (2) has a positive sign. Moreover, profitability is not statistically significant in Model (4), tested only on foreign-owned banks. The variable ‘bank capital’ (Capital) is statistically significant in Model (1) and Model (2), while variables ‘bank’s size’ (Size) is not statistically significant. Variable liquidity (Liquidity) is statistically significant in the model tested only on foreign-owned banks (Model (4), Table 2) and models where variables from financial markets of parent banks are omitted, Models (5)–(6). From parent bank variables, parent bank size (pbSize) and liquidity (pbLiquidity) are statistically significant and positive in all estimated models presented in Table 2, except liquidity in Model (2). Parent capital and profitability are statistically significant in models where variables from financial markets of parent banks are omitted (Models (3)–(6), Table 2). Capital has a negative and profitability a positive sign. From financial market characteristics in the parent bank country, all variables are statistically significant (Models (1), (2), and (5)). Variable interest rates on housing credit in home countries have positive signs. In contrast, the share of housing credit in total banks credit and perception of systematic risk in home countries have a negative sign. As assumed, results for house prices in Croatia are statistically significant and positive, while results for corruption are statistically significant and have a negative sign (Models (5) and (6), Table 2). From the presented results of the Models (1)–(4) in Table 2, where we test the model from Eq. (3), it is evident that bank share in external funding (ShareExternalFunding) is an important determinant of bank orientation to the housing credits in the bank credit portfolio. This result is in line with the results of Dujim (2019), which provided evidence that foreign-funded credit is associated with a household’s credit booms in the post-2000 period. An important result arises from the reduced Model (4), tested only on foreign-owned banks, where variable ‘bank share in external funding’ (ShareExternalFunding) is significant and positive. In Model (3), tested on all banks, that variable is not significant. These results suggest that foreign-owned banks had a strong orientation to the real estate markets, measured by the share of housing credit in total bank credit. We can conclude that external funding of foreign-owned banks facilitated that boom in the housing credit by providing external sources of funding. Additionally, the positive significance of parent bank indicators size and liquidity support the view that international banks, via the internal capital market, direct funds to a country with a higher yield (de Haas and Van Lelyveld 2010). Results for housing credit, interest rates, and perception of systematic risk in a home country also support those arguments. They are in line with previous research about home and host effects on the credit supply of foreign-owned banks in home countries (De Haas and van Lelyveld 2006, 2010; Niţoi et al. 2021). Still, the results from this paper give a more profound explanation of host countries’ variables influencing bank orientation towards housing credit in the host county. We explain that the

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channel through which the foreign-owned banks can facilitate domestic demand for housing and push the creation of the housing bubble is motivated by determinants outside the host country. Namely, the housing supply is rigid, and it takes time to create a new housing supply (Rimac Simljanić 2005). This process is more pronounced in countries with higher corruption (Koumpias et al. 2021). These results are more important because previous studies prove that credit-financed housing price bubbles have emerged as a hazardous phenomenon to the economy (Jordà et al. 2015). The results from models (5) and (6) suggest that corruption in the host country decreases banks’ orientation to the long-term credit, measured by the share of housing credit in banks credit portfolios. As expected, higher house prices in the host country attracted banks to increase their share of the housing credit. The results of Jiménez et al. (2020) and Martín et al. (2021) indicate that higher ex-ante bank real-estate exposure, measured by the share of mortgage credit in total bank credit, increases credit supply to non-real-estate firms. Therefore, in the next step, we formulated a new model presented in Eq. (4). With the model of total bank credit to the private sectors, we wanted to capture the effect of banks’ external liabilities, the perception of systemic risk in the home and host country, house prices, and legal surroundings on banks’ credit expansion to private sectors. In that, we can test whether the external bank funding and boom in real estate prices affected the credit cycle in Croatia. In line with theoretical assumptions, the bank credit to the private sector model consists of four groups of variables: bank-specific characteristics, parent bank-specific, perception of systematic risk on financial markets in the home country and selected macroeconomic variables from Croatia. The following Equation writes the model: Credit i,t ¼ μ þ γCredit i,t
1 þ β2 BankSpecifici,t þ β3 ParentBankSpecifici,t þ β4 PerceptionSystematicRiski,t þ β5 MacroCroatiai,t þ αi,t þ εi,t i ¼ 1, . . . N, t ¼ 1, . . . T,

ð5Þ

where Crediti,t is the natural logarithm of bank i total credit to the private sector in year t. BankSpecifici,t is the matrix of bank-specific indicators: size, capital, liquidity, profitability, and bank external liabilities of bank i in time t.3 pBankSpecifici,t is the matrix of parent bank-specific: size, capital, liquidity, the profitability and external liabilities of the bank i in time t. PerceptionSystematicRiski,t is an indicator of the perception of systemic risk on credit markets in the home countries. MacroCroatiai,t is the matrix of variables from the Croatian economy: house prices, corruption index, banks perception of systematic risk in Croatia, external liabilities of all banks and government credit by banks in time t.

3

The ratio of external bank liabilities to the total bank asset (BankExternalFunding) is used due to the high correlation between ShareExternalFunding and ExternalFunding variables.

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The results of the model presented in Eq. (5) are given in Table 3, Model (8). Additionally, as a robustness check, the model is estimated for the shorter period, 2001–2008, (Model (9), Table 3). According to the diagnostic tests, all model specifications are well specified (Sargan test, AR (1), and AR (2) test). The result of Models (8)–(9), from Table 3, indicate that lagged dependent variable bank credit (L.Credit) is statistically significant and has a positive sign. All other bank-specific variables are also statistically significant. Capital and liquidity have a negative sign, while size and profitability are positive. In line with the previous findings, they indicate that banks’ higher assets and higher profitability positively affect credit growth. However, the obtained result for the bank’s liquidity and capital is negative. This can be explained by the fact that Croatian banks largely kept the minimum level of capital prescribed by the regulator. The primary sources of their financing were collected deposits and received loans. Additionally, this is supported by the conclusions of De Haas and Naaborg (2005). They claim that parent banks transmit on to their subsidiaries in CEE countries as much capital as the local regulator requires them to and that the rest of the business funds are passed on to them through lending. This argument is confirmed with our result for the variable ‘external bank liabilities’ (BankExternalFunding). The result implies that banks with higher foreign funding have higher credit growth, i.e., that banks finance credit expansion cross-border funding. From the parent bank indicators, statistically significant are results for the variables ‘parent size’ and ‘liquidity’. The negative sign for parent bank size can be explained by the fact that banks that entered the Croatian market had different business strategies, i.e., some approached the expansion, and some were more cautious in the market. The change in the liquidity of the parent bank is statistically significant and positive, i.e., it is fair to say that banks direct excess liquidity to foreign markets where they can achieve a higher rate of return. This result is in line with the results obtained for external bank funding. The obtained results for the perception of systemic risk on the parent bank home market indicate that funds are transferred via wholesale funding for a credit growth of a subsidiary in Croatia in a situation in which they need to grow out of risk on the home parent market. All Croatian macroeconomic variables: house prices, corruption, banks perception of systematic risk, and external liabilities of all banks, are statistically significant. Statistical insignificance of the variable ‘government debt’ (GovermentDomesticDep) is unexpected. As evident in Table 3, house prices, eternal liabilities of banks, and corruption have a positive sign, while bank perception of systematic risk has a negative sign. In accordance with the theoretical assumptions, empirical results indicate that real estate prices affect bank credit growth. These results confirm the results of Bambulović and Valdec (2018). In addition, our results suggest that an increase in the bank’s perception of systemic risk in Croatia decreases bank credit growth. The estimated parameters for the variable ‘foreign funding of the banking system’ (ExternalFunding) have the most significant impact on bank credit growth. Namely, an individual bank can borrow directly abroad from the parent bank or with its help and direct the funds to a subsidiary in Croatia. Subsidiarity can give loans directly to its clients. Still, it can also lend them to other banks that do not have direct opportunities or access to foreign credit markets under such favourable conditions.

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Table 3 Results of the Model Total Bank Credit to the Private Sector Model (8) Bank credit

Model (9) Bank credit 2001–2008

Characteristics of a Croatian bank Credit i,t-1 0.449* 0.536* (0.023) (0.040) Size 0.162* 0.135* (0.023) (0,026) Capital
0.017*
0.015* (0.003) (0.004) Liquidity
0.018*
0.018* (0.002) (0.025) Profitability 0.028* 0.029* (0.005) (0.006) BankExternal 0.337** 0.443* Funding (0.152) (0.211) Characteristics of the parent bank pbSize
4.76e-16*
5.93e-16* (1.49e-16) (2.17e-16) pbCapital 4.19e-12
4.07e
12 (5.21e-12) (9.48e-12) pbLiquidity 1.048* 1.061** (0.356) (0.361) pbProfitability
0.098 0.996 (0.995) (1.260) Perception of systematic risk on European credit markets pSystematicRisk 0.040* 0.057* (0.124) (0.020) Characteristics of the Croatian economy HousePrices 0.002* 0.006* (0.000) (0.001) SystematicRisk
0.160*
0.218* (0.025) (0.034) Corruption 0.008* 0.011* (0.013) (0.001) GovermentDomestic
0.012
0.015 Dept (0.010) (0.014) ExternalFunding 2.151* 3.333* (0.426) (0.560) Constant
0.189
0.762* (0.021) (0.032) Observations 259 259 No. banks 41 41 Sargan test 0.413 0.157 (p-value) AR (1) 0.455 0.419 AR (2) 0.921 0.901

Model (10) Bank credit

Model (11) Bank credit Foreign banks

0.363* (0.237) 0.195* (0.027)
0.030* (0.004)
0.018* (0.002) 0.026* (0.006) 0.313 (0.177)

0.601* (0.057) 0.137* (0.021)
0.034* (0.002)
0.018* (0.002) 0.025* (0.005)
0.461 (0.303)


6.00e-16* (8.64e-17) -1.00e-11 (8.36e-12) 2.024** (0.540)
1.809 (1.500)


4.76e-16* (1.46e-16)
5.38e-16 (1.45e-11) 1.797* (0.849) 5.452*** (2.945)

0.001 (0.001)
0.143* (0.013) 0.004* (0.001)

0.003* (0.001)
0.062* (0.015) 0.003* (0.001)

13.816* (0.546) 259 41 0.1436

8.783* (1.201) 116 24 0.889

0.5946 0.6817

0.241 0.628

Note: *significance at 1%, ** significance at 5%, *** significance at 10% Source: Authors

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A. R. Smiljanić and B. Š. Perić

In the next step, we wanted to test the difference between domestic and foreignowned banks. Therefore, we split the sample into foreign-owned and domesticowned banks. According to the Sargan test, the estimated models were not valid. Consequently, we had to reduce the model. Estimated reduced models with omitted variables are presented in Table 3, Models (10)–(11), and according to the diagnostic tests, all model specifications are well-specified. Model (10) is tested on all banks and Model (11) on foreign-owned banks. All results obtained for the bank-specific variables in Model (10) and Model (11) are the same except for the variable ‘bank external funding’, which is insignificant. Variables from Croatian economy: ‘corruption’ and ‘systemic risk’ are statistically significant and have the same sign as in the basic Model (8). The variable ‘house prices’ is insignificant in Model (10) tested on all banks, while it is significant and positive in Model (11) tested only on foreign banks. These results indicate that real estate markets have more influence on the credit growth of foreign banks than of domestic ones. This might be the case because of lower knowledge and trust in the legal system or the consequences of foreign bank assessment of the current and future importance of the sector. The variable ‘corruption’ has a positive and statistically significant effect regardless of the bank ownership. The obtained results confirm that home and host country’s macroeconomic characteristics play a significant role in the bank credit expansion in Croatia. However, our results suggest a new approach to understanding home and host country effects in international banking. We provide evidence that banks’ perception of systematic risk in home and host country affect credit growth in Croatia. Therefore, we expand the findings of previous research about macroeconomic determinants from home and host countries (de Haas and van Lelyveld 2006; Arakelya 2018). Additionally, our result obtained for house prices indicates that specific asset markets can stimulate bank credit expansion, and it should be considered as an additional macroeconomic host variable to determine bank lending in the host country. The importance of house prices for foreign-owned banks implies that in countries with the domination of foreign-owned banks, credit is more connected with real estate cycles. Considering the importance of external funding and parent bank liquidity for bank credit growth, we can conclude that, in such countries, cycles in real estate prices can be started by forces outside of the country, via banks’ wholesale funding, and encourage credit and real estate cycles.

4 Conclusion In this paper, we examined the role of foreign banks in the credit and real estate cycle in Croatia. We analysed how the credit policies of banks in Croatia are affected by the perception of systematic risk on home and host markets, and house prices, corruption, and external bank funding in Croatia. Our findings indicate that the foreign liabilities of all banks are significant for credit growth in Croatia. In addition, we found the importance of the perception of systemic risk in the home and host

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market and parent bank liquidity for the banks’ credit policies. Results, as well, point out the importance of real estate markets for bank credit policies, especially foreignowned banks. We provide evidence that banks directed foreign credit toward the housing market in a country with insecure laws. This result implies that in countries with the domination of foreign-owned banks, credit and real estate cycles are more connected. Considering the importance of external funding and parent bank liquidity for bank credit growth, we can conclude that in such countries, cycles in real estate prices can be started by forces outside of the country, via bank wholesale funding, and encourage credit and real estate cycle. The main limitation of this research is that we cannot trace the interest rates that a specific bank charges for the credit given to the real estate sector or secured by real estate collateral compared to the other unsecured credit. That would provide us with a more detailed answer about banks’ importance in the real estate sector. In the future, it would be interesting to investigate the impact of real estate price movements on credit risk assessment in banks considering the importance of the real estate sector for the Croatian economy and banks’ credit policy.

Appendix

Table A1 Descriptive statistics Variable HousingCredit Credit Size Capital Liquidity Profitability BankExternalFunding ShareExternalFunding pbSize pbCapital pbLiquidity pbProfitability pSystematicRisk pInterestRate pShareHousingCredit HousePrices Corruption SystematicRisk ExterbalFunding StateDomesticDept Source: Authors

Obs 383 383 383 383 369 369. 383 383 383 351 351 351 449 372 372 450 449 351 351 449

Mean 8.336 20.290 2.275 17.022 26.041 0.886 0.055 2.303 2.275 4.1E+07 0.039 0.004 1.101 2.101 9.897 107.160 6.088 0.039 0.004 1.101

Std. Dev. 9.532 1.737 4.568 11.767 9.018 2.724 0.098 5.454 4.568 4.4E+08 0.350 0.023 0.468 2.787 13.671 14.045 8.526 0.350 0.023 0.468

Min 0.000 15.490 0.009
1.975 4.159
22.353 0.000 0.000 0.009 0 0.000
0.011 0.669 0.000 0.000 95.600 Vlj.70 0.000
0.011 0.669

Max 41.581 24.622 23.894 84.571 93.953 7.226 0.643 33.275 23.894 5.16E+09 5.710 0.417 2.072 12.310 45.300 139.200 50.90 5.710 0.417 2.072

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84

Table A2 T-Test indicators for foreign-owned and domestically-owned banks Variable Size BankExternalFunding Capital Liqudity Profitability Credit HousingCredit

v¼0 v¼1 v¼0 v¼1 v¼0 v¼1 v¼0 v¼1 v¼0 v¼1 v¼0 v¼1 v¼0 v¼1

Number 220 163 220 163 220 163 208 161 208 161 220 163 220 163

Mean 0.675 4.433 0.017 0.104 19.111 14.200 25.896 26.226 1.127 0.574 19.586 21.266 4.877 13.003

Std. Dev. 1.926 6.002 0.032 0.128 12.422 10.193 9.364 8.574 2.454 3.017 1.176 1.889 7.067 10.420

T-test* 0.000* 0.000* 0.000* 0.364 0.027* 0.000* 0.000*

Note: Indicator of bank domestically owned 6¼an indicator of a foreign-owned bank: p-value of “one-sided” t-test equality of mean; H1 ¼ mean of indicator of a domestically owned bank 6¼ mean of indicator of a foreign-owned bank; bold results are statistically significant—* significant at 5%, ** significant at 10% Source: Authors

References Akin O, Montalvo JG, García Villar J et al (2014) The real estate and credit bubble: evidence from Spain. SERIEs 5(2):223–243. https://doi.org/10.1007/s13209-014-0115-9 Arakelya M (2018) Foreign banks and credit dynamics in CESEE. IMF Working Paper (WP/18/3). International Monetary Fund. Available via https://www.elibrary.imf.org/view/ journals/001/2018/003/article-A001-en.xml. Accessed 21 August 2021 Arellano MA, Bond SR (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58:277–297 Arslan Y Guler B Kuruscu B (2021) Credit supply driven boom-bust cycles. University of Liverpool. Working Paper in Economics No 202115. Avalible via https://www.liverpool.ac. uk/media/livacuk/schoolofmanagement/research/economics/Credit,Supply,Driven,Boom-Bust, Cycles.pdf. Accessed 24 October 2021 Baltagi BH (2008) Econometric analysis of panel data. Wiley & Sons, New York Bambulović M Valdec M (2018) Determinants of credit cycle–case of Croatia. Avalible at: https:// www.hnb.hr/documents/20182/%202101839/13-yes-bambulovic-valdec.pdf/7bd8e5c8-89fc-4 dc3-9469-bda518ebba48. Accessed 20 October 2021 Belton D, Gambacorta L, Kokas S, Minetti R (2021) Foreign banks, liquidity shocks, and credit stability. Rev Corp Finance Stud Published online October 2021. https://doi.org/10.1093/rcfs/ cfab020 Borio C, McCauley RN, McGuire P (2011) Global credit and domestic credit booms. BIS Q Rev: Available via https://www.bis.org/publ/qtrpdf/r_qt1109f.htm. Accessed 21 August 2021 Brkić M (2019) “Banking distress in Europe in the context of the global financial crisis–the role of capital flows,” Surveys 36, The Croatian National Bank, Croatia. Avalible at: https://www.hnb. hr/repec/hnb/survey/pdf/s-036.pdf. Accessed 20 October 2021

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Financial Cooperatives Development in Croatia: Social Capital Perspective Ingrid Omerzo and Jakša Krišto

Abstract Financial cooperatives are member-owned institutions that operate on seven principles, all of which depend on social capital. The paper aims to establish the relationship between social capital and the depth of the financial cooperative sector, analyse both concepts and assess the development prospects of financial cooperatives in Croatia from a social capital perspective. Also, the purpose of the paper is to highlight the role of social capital—that per se generates positive externalities on economic growth—in the development of financial cooperatives, which further foster regional development, mitigate intertemporal risks, provide greater community resilience and have a positive impact on GDP growth. Crosscountry analysis will be performed using descriptive statistics to present secondary data related to both concepts, for the Republic of Croatia and the rest of the EU-27 countries. One could conclude that Croatia has low levels of both bridging and linking social capital, and at the same time, that cooperative and mutual sector are poorly developed. Therefore, to boost economic growth, public and local policies should concentrate on the development of social capital in the region, which would also increase people’s willingness to form financial cooperatives, as well as other forms of social economy necessary to enhance sustainable economic growth.

1 Introduction According to Piaget (1931), co-operation is the encouragement to work together. Co-operation can also be defined as the “association of persons for common benefit” (Merriam—Webster 2021), as well as “doing something together or working

I. Omerzo Catholic University of Croatia, Zagreb, Croatia Croatian Bureau of Statistics, Zagreb, Croatia J. Krišto (*) Faculty of Economics & Business, University of Zagreb, Zagreb, Croatia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_6

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together towards a shared aim” (Oxford Learner's Dictionaries 2021). Both definitions include acting together towards a shared goal which, according to Tuomela (2000), requires sociality in a very strong sense, since it is based on joint intention and shared goal. Dasgupta (2006) claims that a more cooperative society would have better economic performance. In a free-market system, investors-owned companies are not the only type of ownership. Even for the United States, examples of employee or consumer-owned companies are not unconventional (Hansmann 1996). In today’s free-market society, the third sector coexists with the private and public sectors. It consists of civil society organizations which include “all non-state, not-for-profit structures, membership-based, cause-based and service-oriented CSOs, community-based organisations, non-governmental organisations, faithbased organisations, foundations, research institutions, and cooperatives, through which people organise to pursue shared objectives and ideals, whether political, cultural, social or economic” (European Commission 2012; 3). Financial cooperatives are part of the third sector, they are defined as self-sustaining savings and lending businesses that create social impact through their provision of inclusive and affordable financial services (Social Finance 2016). Cuevas and Fischer (2006) and Krišto et al. (2020a, b) indicate that cooperative financial institutions include different intermediates owned by their members; credit unions, saving and credit cooperatives and cooperative banks, all of which are not-for-profit organisations with the primary goal to serve the needs of its members. These institutions are also considered to be “community development financial institutions (CDFIs) and help address the financial needs of under-served, predominantly low-income communities. CDFIs include community development banks, credit unions, business and microenterprise loan funds, and venture capital funds” (Krišto et al. 2020a, b:141). In the paper, we will also include in this classification mutual credit institutions, as well as mutual and cooperative insurances and call them all financial cooperatives. Since these institutions have clearly defined values and are based on democratic principles, they depend on a sense of democracy existing in communities. Stiglitz (1990) indicates that peer monitoring and social sanctions are an important factor in ensuring prudent borrowing behaviour and debt repaying, and that financial cooperatives effectively prevent opportunistic behaviour on the part of borrowers (Hansmann 1996). Since financial cooperatives are people-driven institutions and as such promote both social capital and active participation of individuals, their business depends on a close link between members. This means that the development of social capital and financial cooperatives is interlinked (Volkova and Baltaca 2013). Financial cooperatives and other forms of social economy institutions serve as indicators of the level of social capital existing in a community/region/country (Putnam 1993; Scrivens and Smith 2013; Wallace and Pichler 2007). Moreover, social capital in a country or a region is the basis for civil society development (Cooper et al. 2005). Social capital is defined by Putnam (1993:163) as ‘features of social organizations, such as networks, norms and trust that facilitate action and cooperation for mutual benefit“. In the paper, we will approach the potential development of financial cooperatives from a social capital perspective. Since these institutions contribute to economic growth and local community development,

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it is important to explore methods by which the development of these institutions in Croatia will be encouraged. Many researchers have proven the importance of social capital and its components for the successful business of financial cooperatives, as well as for community and regional development. Dasgupta (2005:1) indicates that “rotating savings and credit associations, irrigation management systems, credit arrangements, civic associations, mutual insurance arrangements make social capital a productive asset and that social capital is frequently identified with the workings of civil society”. Financial cooperatives have positive effects on members’ human capital, they finance small and local farmers and producers and have a positive impact on the local community; they are committed to the community and invest in philanthropic projects (Périlleux et al. 2016). Shore et al. (2011) indicate how quality social relations foster norms of reciprocity and that trust is an underlying mechanism of social exchange, which ensure a lasting relationship of exchange (Barraud-Didier et al. 2012). Smyth and Pryke (2008) claim that trust is necessary for all exchanges and that it grows in collaborative relationships. Barraud-Didier et al. (2012:5) have proven a connection between social capital and participation in decision-making in a cooperative. Saz-Gil and Díaz-Foncea (2021) indicate that cooperatives are social enterprises in which trust and cooperation are basic pillars and that by strengthening social capital, cooperatives can achieve internal and external objectives. There is no comprehensive study or literature review that observes the development of financial cooperatives through lenses of social capital in the Republic of Croatia, and this paper aims to fill this gap. Also, we will investigate the concept of social capital and the depth of the financial cooperative sector, try to determine the relationship between them and by identifying social groups with the highest level of social capital in Croatia, discuss development prospects of financial cooperatives from a social capital perspective. A comprehensive literature review will be presented, and methods of descriptive statistics will be used to process, present and analyse data from three different sources: European Association of Co-operative Banks (EACB), World Council of Credit Unions (WOCCU), and International Cooperative and Mutual Insurance Federation (ICMIF). Although we stress that these are not all financial institutions in Europe, we can conclude that these organisations include most European financial cooperatives. The second concept we will explore is social capital, but since there is no consensus over its appropriate measurement, our approach will be based on Scrivens and Smith (2013), Wallace and Pichler (2007), and Putnam’s (2001) research and suggestions. Official statistics data published by Eurostat will be used to assess levels of bridging and virtual social capital in a specific country, combined with data from a World Value Survey that presents the level of trust and confidence in a particular institution. Correlation analysis will be used to examine the relationship between the two concepts. Furthermore, various types of bridging social capital in each of the EU-27 countries will be presented, to explore social groups with the greatest potential for the establishment and development of financial cooperatives. The study is in line with the current literature; a positive connection between social capital and depth of the financial cooperative sector was recorded. Also, virtual social capital proved to be one of the areas on which further policies should concentrate while planning financial cooperative development in Croatia.

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Financial cooperatives, the concept of social capital and the relationship between them is presented in the first chapter, Introduction. The main principles, history, purpose, pros and cons of financial cooperatives will be discussed in the next chapter. After that, the concept of social capital, its classifications, importance, and measurement will be presented in the third chapter. Data regarding financial cooperatives in Europe and social capital in the EU-27 countries will be explored in the fourth chapter. The fifth and last chapter will be dedicated to discussion and suggestions for further policies and researches.

2 Financial Cooperatives: Characteristics and Literature Review To co-operate, all participants need to participate willingly and actively in a particular activity. Cooperatives are part of social economy and are defined as “autonomous associations of persons united voluntarily to meet their common economic, social, and cultural needs and aspirations through a jointly-owned and democraticallycontrolled enterprise” (International Cooperative Alliance (ICA) 1995) as well as through “mutual action and sharing of economic returns based on individual participation” (Shaffer 1999:39). Since there is no consensus over the division of cooperatives by the type of ownership, several different approaches are offered. McLeod (2006) distinguishes five types of cooperatives: consumer cooperatives, worker, producer, purchasing/service, and housing cooperatives. On the other hand, Williams (2007) offers broader division and includes the term ‘financial cooperatives’. In other literature, financial cooperatives are often perceived as a type of consumer cooperative. The cooperative movement has begun as a response of workers and farmers who were not satisfied with the current state of play and their economic and social position during the Industrial Revolution. It dates back to 1844 when the Rochdale Society of Equitable Pioneers in the UK was founded. As Shaffer (1999) emphasises, the Rochdale Society was the first to define cooperative principles which have changed on several occasions since, but their core content remained the same. The final and current version of these principles was defined in 1995 by the ICA and contains the following: voluntary and open membership, democratic member control, member economic participation, autonomy and independence, education, training and information, cooperation among cooperatives, and concern for the community. “Cooperatives are based on the values of self-help, self-responsibility, democracy, equality, equity, and solidarity. In the tradition of their founders, cooperative members believe in the ethical values of honesty, openness, social responsibility and caring for others” (ICA 1995). After the establishment of the first consumer cooperative in Europe, strong development of cooperatives in financial sectors followed. Various types of financial

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cooperatives differ in terms of business management, legal form and profit orientation, but prioritise serving its members’ needs, members’ cooperative ownership, one member –one vote rule, taking care of society and community, social role, socially responsible business operations, are all characteristics common to all of them. The origin of financial cooperatives comes from Italy, when the Catholic Church developed a network of Monte di Pieta, which were a form of pawnshops and charities, followed by saving banks. After that, the first financial cooperatives were developed in Germany based on self-responsibility and self-help principles (Kalmi 2017; Poli 2019). Based upon the same cooperative principles, cooperatives were rapidly developed in all Western countries. The first institution in Croatia based on the model was the Mutual Aid Fund, established on Korčula in 1864 upon the same principles of self-help and selfresponsibility, without any interference of the state. One feature of social capital was crucial for its development; the norm of reciprocity, i.e., “today I give to you, tomorrow it will be reverse”. In 1880, there were 19 financial cooperatives in Croatia, with a capital of 4602 forints, and in 1910 the number increased to 744, with the capital of 64,521 forints (Mataga 2005). Since then, the cooperative sector in Croatia has been poorly developed. As Njavro et al. (2020) point out, cooperatives in all post-socialist countries are perceived as a communist form of organisation. In this sense, it is important to stress that financial cooperatives were developed on completely other ideologies than communism. The ideas of SchultzeDelitsch were based on libertarian principles of self-help, self-responsibility, voluntary membership, and refusal of any kind of state involvement. (Spangenberg 2015; Poli 2019). On the other hand, Raiffesien’s idea was based on moral Christian principles. Due to underdeveloped legislation and regulation of the cooperative sector in general, as well as the fact these forms of institutions are not targeted by the public policies, these forms of institutions cannot be developed or perceived seriously in public in Croatia (Njavro et al. 2020). The original purpose of financial cooperatives is to economically and socially develop communities. Their founders specified that these institutions should make loans for productive purposes, to help anyone go into business and earn a living. They should encourage buying tools that would provide further value and increase communities’ economic growth. Many financial cooperatives specialised in consumer lending, not economic development in the sense of creating new value. On the other hand, there is a large potential for financial cooperatives, not to serve only microlending to consumers but to have an important role in economic development, and to ease the pursuit of vocational careers. Volkova and Baltaca (2013) indicate an example of Latvia’s credit unions and divide them into two groups; urban and rural. While in urban credit unions loans are mostly made for consumption, in rural areas, they are both for consumption and entrepreneurial needs, which supports communities’ economic activity. Financial cooperatives have closer relations with their customers and are more capable of transmitting relational information than larger banks. They also foster regional development, by saving in good times, accumulating capital and mitigating intertemporal risks, enjoying greater earnings stability and having a positive impact on GDP growth (Ayadi et al. 2010; Coccorese and Shaffer 2021).

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Cooperative organisations have some benefits in comparison to jointly owned companies. Périlleux et al. (2016) point out that commercial banks do not have much information about their borrowers, and sometimes due to lack of collateral, some vulnerable groups are financially excluded. Social sanctions and strong social interconnectedness between members ensure easy punishment for each of them in the case of a bad payer. Commercial banks are based on financial capital, while financial cooperatives are based upon social capital. When individuals do not have any financial capital, it is possible to trust them and use social collateral as a kind of insurance that would ensure fair and timely repayment of the loan. Sometimes individuals from underdeveloped areas collect funds and offer loans based on information they know of each other (Valentinov 2004). “The urban banks did not possess required information about the creditworthiness of small-scale farmers, merchants, and businessmen living there, and therefore could not offer them the required loans. The supply of loans was therefore monopolised by the local usurers, who invested significant resources in acquiring (learning) this information. The inhabitants of these areas, however, managed to internalise the loan supply transactions by creating local credit cooperatives, which effectively utilised the pool of local information and the intimate knowledge that members had of each other and charged on this basis acceptable interest rates” (Valentinov 2004:2). Small financial cooperatives cooperate with the same aim and develop a network between them, usually at a regional or national level to enable economics of scale, provide strategic and operational help, offer education and “know-how” training, ensure liquidity, always using bottom-up approach since they are managed by their members’ representatives (Poli 2019). Associations of cooperatives are particularly active in promoting entrepreneurship and job creations by mobilising grassroots communities, delivering services and stimulating income-generating activities for the poor and marginalised, including both social and environmental factors in stimulating economic growth (European Commission 2012; 3). On the other hand, The tragedy of the commons, The prisoner’s dilemma, and The logic of collective action are all concepts that put the free-rider problem in the centre that also characterises the cooperative model. Whenever one person cannot be excluded from the benefits that others provide, each person is motivated not to contribute to the joint effort, but to free-ride on the efforts of others” (Ostrom 1990:22). Although literature in most cases proposes either central authority solutions to this problem or privatisation, Ostrom suggests a way in which participants voluntary consent to the contract by which they need to act (Ostrom 1990). She suggests the establishment of local organisations and the building of social capital in the community to overcome the free-rider problems. Also, self-managing institutions affect physical and human capital which positively affects productivity and growth. The situation in which individuals voluntary agree to respect and accept themselves due to their mutual respect and the rules they have created presents a democratic spirit (Piaget 1931).

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3 Social Capital in the Context of Civil Society Financial cooperatives are usually developed due to the lack of access to formal financial services, and unfulfillment of community needs, greater control and willingness to make social relations and connections in the community stronger (Mason 2014). Social capital in the community, as well as between members, is crucial for their development and successful business. As Paxton (2002) points out, social capital affects democracy and provides a place for voluntary associations to grow. In order to act cooperatively, individuals should follow and integrate prosocial values and develop social capital. Social capital is the term that covers trust, common norms, and values that are important for the maintenance of democracy and for societies and economies to develop (Fukuyama 1995), Putnam (1993). Coleman (1988:98) also claims that. “social capital is defined by its function. It is not a single entity but a variety of different entities, with two elements in common: they all consist of some aspect of social structures, and they facilitate certain actions of actors-whether persons or corporate actors-within the structure. Like other forms of capital, social capital is productive, making possible the achievement of certain ends that in its absence would not be possible”. Fukuyama (1995) indicates how important it is to cultivate values such as cooperative behaviour, trust, trustworthiness, and responsibility for others. The argument he uses to verify this is that, in the case when people are not capable to organise together independently, the government becomes involved. People then stop being aware of the fact that they are supposed to cultivate social relations with others, helping each other and creating innovations for further development of society together. Also, he adds that prosperous societies have high levels of social capital and spontaneous sociability. “Social capital can therefore be defined as norms, values, and trust embodied in the specific structural forms (e.g. cooperatives, networks, associations, groups, etc.)” (Valentinov 2004:7). “Not just any set of instantiated norms constitutes social capital; they must lead to cooperation in groups and therefore are related to traditional virtues like honesty, the keeping of commitments, reliable performance of duties, reciprocity, and the like” (Fukuyama 1995:3). Research on social networks began in 1930, and in 1970 and 1990 number of theories that focus on social behaviour (such as collective action) increased rapidly. The term ‘social capital’ became popular and is often used in many fields. Social capital builds upon social exchange and exist only in social relations. Literature differs two types of social capital, bonding and bridging (Putnam 2000), while Woolcock (2001) adds a third type, linking social capital. Bonding social capital is exclusive, connected to in-group relations and strong ties. On the other hand, bridging social capital is connected to weak ties, is inclusive and creates bridges across different individuals and groups. Also, bridging social capital increases generalised trust and makes relations in the community stronger. As Halpern (2005) points out, developed and industrialised countries such as Scandinavian

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countries or the USA have high levels of bridging, as well as high levels of bonding social capital. Areas of recent modernisation and urbanisation have high levels of bridging and low levels of bonding social capital. South of Italy or Sub-Saharan Africa, which are famous for closed communities and families, have the opposite, low levels of bridging and high levels of bonding social capital. Tribes in Uganda, for example, have both low levels of bonding, as well as bridging social capital. Social capital and social norms include activities directed towards various types of associations and mutual trust, which serve to build strong relations in the community and civic loyalty. Bridging and linking social capital contributes to the development of democracy and democratic values, and even Tocqueville has claimed that voluntary associating is an indicator of democracy. Putnam (1993) indicates that, in the absence of civic virtue, individuals tend to maximise their one short run and material advantage for themselves and their nuclear family. Relationships in the civic community are based on horizontal relations of reciprocity and co-operation, not by vertical relations of authority and dependency. Local associations are crucial for rural development. Economically advanced regions are so due to their civicness. He has also concluded that in regions with weaker civic communities, clientelism is a more common practice. Also, the least civic regions are prone to corruption more than more civic ones, and civic life is eased because of expectations that others will follow the rules. Civic life in modern Italy has started early on, based on mutual assistance and economic cooperation. As civil order was met, rapid growth in commerce was noticed. An increase in bridging social capital was crucial for the flourishing of trust and confidence. Not legal obligations, but a sense of honesty and belonging to the community has made people participate with their savings with other people in the productive process. Voluntary co-operation depends on the broader social context within which it is played, and communities with higher levels of social capital manage to form various types of civil society institutions. Voluntary and spontaneous organisations, such as rotating credit associations and financial cooperatives, can succeed without too much deviation due to strong norms and dense networks of reciprocal engagement and generalised trust. However, at the same time, these institutions strengthen social capital in the community, which is why they are often rather social than economic institutions. All forms of social capital, such as trust, norms, and networks are kinds of moral capital which means they increase by usage, and not decrease (Ostrom 1999). Putnam claims that social capital is a public good and that it is a by-product of other social activities. According to him, without trust, there is no cooperation. For every transaction and every game, trust is necessary. Norms also facilitate co-operation, especially reciprocity, and communities where this norm is strong can easily resolve the problem of collective action. It is very important that each member of the institution, but also society in general, respects the rules and acts accordingly. For a rule of behaviour to be a social norm, it must be in the interest of everyone to act in accordance with the rule if all others were to act in accordance with it” (Dasgupta 2005:6). Norms of reciprocity underpin collaboration, which means “if I do something for you now, I expect something not now, but in the future”. “Ways of life are made viable by classifying certain behaviours as worthy of

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praise and others as undesirable, or even unthinkable. In a post-communist country, “amoral familism, clientelism, lawlessness, ineffective government, and economic stagnation—seems likelier than successful democratization and economic development” (Thompson et al. 1990). The general agreement is that social capital is beneficial up to a certain point, but after that, it can be harmful and turn into corruption. In some cases, bonding social capital can be exclusive and discriminate other groups and individuals (Mafia and cartels are examples of institutions with strong social capital). To avoid this, it is important to foster the development of the civil sector and institutions that offer voluntary associating and open membership that boost cooperation with other individuals and groups. The establishment of third-sector institutions can reduce identity politics and tribal behaviour (Brown 2019), which means that these institutions have a positive impact on society in general. Globalisation, social media, and social networks have made us reconsider what defines community and how social capital is built. “On the one hand, online social networks have been described as ‘virtual communities’ to highlight their socially beneficial qualities and as an indicator of renewed ‘community’. On the other hand, negative claims have been made that heavy social network site users are more likely to be socially isolated than occasional users and that new technology leads to a breakdown of traditional community” (Chambers 2013). In any case, large social networks serve as a resource of information, connection between people with similar interests and values, and build trust among site users. Sharing economy and collaborative finance are successful examples.

4 Methodology and Results Research is conducted using quantitative, already existing and publicly available data, that are summarised, collected and analysed to conduct cross-country analysis of the EU-27 countries, with particular emphasis on Croatia. To estimate the depth of the financial cooperative sector in each country, we combined three secondary data sets from the main European cooperative/mutual financial organizations; EACB, WOCCU, and ICMIF. Main categories of aggregated financial cooperatives balance sheets at country level are presented, and the relation between the depth of the financial cooperative sector and other macroeconomic and financial data is established. The depth of the financial cooperative sector is estimated by indicators such as total assets, deposits, and loans of financial cooperatives in each of the EU-27 countries. Data published by Eurostat and the World Value Survey (WVS) are collected to obtain information about levels of social capital in a country. These data were assessed by questionnaires, while methodology for the measurement of social capital is based on the Organisation for Economic Co-operation and Development (OECD) (Scrivens and Smith 2013) and Wallace and Pichler’s (2007) papers. Namely, the authors suggested using specific indicators for exploring levels of social capital in a

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specific geographical area, but due to the significant increase of virtual social activities, we have added indicators of virtual social capital to the analysis. Onsite formal and informal voluntary activities, active citizenship, level of generalised trust and confidence in formal institutions are indicators of bridging social capital. On the other hand, participating in social networks, taking part in online consultations or voting to define civic or political issues and posting opinions on civic or political issues via websites are used as indicators of virtual social capital. The relationship between financial cooperative sector development and levels of social capital at a country level is also examined and a correlation analysis is performed. Credit unions are the only cooperative/mutual institutions in the financial sector existing in Croatia. Currently, there are 17 credit unions in Croatia (Croatian National Bank 2021) and no cooperative banks. Mutual/cooperative insurances also do not exist in Croatia. Table 1 presents data on total assets, deposits, and loans of cooperative banks and credit unions in Europe. Although these results are not mutually comparable due to the various sizes of the financial sector and economy of each country, they provide us with information about countries that have well-developed credit cooperative/ mutual sectors and those that do not. Six countries do not have financial cooperatives’ representatives in neither EACB nor WOCCU. Table 2 presents data concerning credit unions’ and cooperative banks’ assets for each country, which were summed up and put in relation to the nominal GDP of each country. Also, the membership penetration rate was calculated as a number of financial cooperative membership relative to the total active population of a country. It is important to stress that one can be a member of many financial cooperatives, so this percentage indicates membership, not the percentage of individuals. It is clear that the financial cooperative sector is poorly developed in Croatia with regard to financial cooperatives. Regarding mutual insurances, the total market share in 2017 in Europe was 32.7% compared to 24.3% in 2007, which indicates a growing trend and development of the mutual insurance sector in Europe. Also, while the 10-year compound annual growth rate for the total market in Europe for the period 2007–2017 was
1.6%, for the mutual insurance market it was 1.4% (ICMIF 2019). Figure 1 provides information about cooperative banks’ deposits and loan market shares, as well as mutual insurance market share in European countries. It is clear that in some countries these institutions play an important role in the financial markets. These are all well-developed countries with real GDP per capita above average (Eurostat 2021). In 7 out of 27 EU countries, mutual insurance market share is around 50%, which indicates the importance of cooperative institutions in the insurance industry. Since Putnam (1991), measures social capital in a region by a number of cooperatives present in the territory, it is to be expected that France, Finland, Austria, the Netherlands, Denmark, Germany, and Luxembourg are countries with the highest levels of social capital, which we will explore below (Putnam 1991, 1993, 2001). The relationship between the two variables (number of cooperatives and social capital) is expanded in two directions: first is the one that assumes that social capital affects the creation and development of cooperatives, and another is that

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Table 1 Financial cooperatives in the EU-27

BE BG CZ DK DE EE IE EL ES FR HR IT VY LV LT LU HU MT NL AT PL PT RO SI SK FI SE

Cooperative banks Assets Deposits from customers – – 3122 2775 – – 215,556 11,428 1,384,088 880,398 – – – – 3158 2672 154,571 117,338 4,279,946 1,854,856 – – 220,557 153,000 – – – – 452 396 8912 7748 7795 4522 – – 590,598 417,914 347,159 236,530 47,243 32,630 19,362 15,205 308 233 1018 889 – – 147,024 63,998 – –

Loans to customers – 1444 – 181,090 844,552 – – 2750 91,883 2,096,914 – 128,200 – – 348 6478 4170 – 416,025 243,597 20,667 10,555 221 747 – 91,463 –

Eur mio Credit unions Assets Deposits from customers – – – – – – – – – – 173 172 17,024 5787 – – – – – – 66 67 – – – – 4 28 1009 713 – – – – – – 9 8 – – 2362 1748 – – 98 88 – – – – – – – –

Loans to customers – – – – – 4 3146 – – – 4 – – 3 86 – – – 1 – 146 – 12 – – – –

Source: EACB (2019), WOCCU (2019)

cooperatives contribute to the development of social capital (Bauer et al. 2012). Our intention is not to explore causality regarding these two variables, but to check by methods of descriptive statistics whether the assumptions of correlation between them are valid in the case of the EU-27 countries. According to the published literature, social capital is necessary for financial cooperatives to develop, and the development of these institutions builds social capital. According to Fig. 2, it is clear that countries with the highest levels of civic engagement in the sense of volunteering activities and active citizenships are the ones with the most developed financial cooperative sector; the Netherlands, Finland, Denmark, Ireland, Luxembourg, France, and Austria. On the other hand, Malta, Cyprus, Romania, Bulgaria, Greece, and Croatia are countries with underdeveloped financial cooperatives sectors, as well as below-average real GDP per capita.

98 Table 2 Assets to GDP ratio and penetration rate of financial cooperatives in EU-27 countries

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France Austria Netherlands Denmark Finland Germany Luxembourg Spain Italy Poland Portugal Hungary Bulgaria Lithuania Slovenia Greece Estonia Romania Croatia Latvia

Assets to GDP Ratio 175.6% 87.3% 72.6% 69.4% 61.2% 39.8% 14.2% 12.4% 12.3% 9.3% 9.0% 5.3% 5.1% 3.0% 2.1% 1.7% 0.6% 0.2% 0.1% 0.0%

Penetration Rate 96.5% 52.6% 21.1% 33.9% 75.0% 43.7% 12.1% 13.2% 5.3% 14.0% 8.6% 0.1% 0.2% 19.2% 0.0% 4.0% 2.3% 8.6% 2.5% 2.0%

Source: Authors’ calculations based on data from the EACB (2019), WOCCU (2019), Eurostat (2021)

Fig. 1 Financial cooperatives market share, EU in 2019/2017. Source: EACB (2019), ICMIF (2019)

From theoretical background and as we have previously specified, for the establishment and development of cooperatives, high levels of generalised trust and trust in society and institutions is necessary.

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Fig. 2 Participation in Voluntary Activities and Active Citizenship, 2015, %. Source: Eurostat 2021

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12.0

70

10.0

60 8.0

50

6.0

40 30

4.0

20 2.0

10 0

The Government

Croatia

Bulgaria

Slovenia

Poland

Romania

Greece

The Civil Services

Czech Rep.

Italy

Cyprus

Spain

Parliament

France

Lithuania

EU-22

The Political Parties

Slovakin

Hungary

Estonia

Portugal

Germany

Austria

Netherlands

Finland

Sweden

Denmark

0.0

Generalized trust

Fig. 3 Levels of Trust, 2017–2020. Source: Authors’ calculations based on data from WVS Table 3 Correlation between financial cooperative sector strength and social capital Informal volunteering Formal volunteering Active citizenship Most people can be trusted The Civil Services

Penetration Rate 0.30 0.50 0.74 0.50 0.61

Mutual Insurance Market Share 0.41 0.44 0.33 0.36 0.39

Source: Authors’ calculations based on data from Eurostat, ICMIF, EACB, and WOCCU

Croatia is, compared to the rest of EU-22, a country with the lowest levels of trust in the government, political parties, parliament, and civil services (Fig. 3). Regarding financial cooperatives and civil society in general, very low levels of trust in civil services are particularly disturbing. The same countries that do not have developed civil society are the ones with the lowest level of generalised trust. Croatia is at the very bottom of the table, with low levels of generalised trust, which is crucial for any institution of civil society to develop. As presented in Table 3, correlation analysis was used to measure the relationship between the strength of the credit cooperative sector on the one hand, and bridging social capital on the other. Financial cooperatives’ membership ratio to total active population (penetration) was used as an indicator of financial cooperative sector development. Membership in credit cooperatives correlates positively with all bridging and linking social capital indicators at the country level, as well as mutual insurance market share. The development of social networks has made space for the creation of online (virtual) social capital. According to the data, Croatia is, concerning all individuals, near the EU average. However, as presented in Table 4, young individuals, those between 16 and 24 years

GEO (Labels) EU-27 Belgium Bulgaria Czech Republic Denmark Germany Estonia Ireland Greece Spain France Croatia Italy Cyprus Latvia Lithuania Luxembourg Hungary Malta Netherlands Austria Poland 142 142 250 75 33 117 83 150 83 50 83 117 142 50 167 117 133 50

150 170 260 70 30 110 90 100 70 40 60 100 160 50 160 90 90 60

150 98 120 119 106 109 78 107 78 133 120 113 117 128 132 124 104 98

113 104 109 104 107 105 86 113 85 114 112 111 100 108 108 107 109 107

Taking part in online consultations or voting to define civic or political issues All Individuals, 16 to 24 years Individuals old 100 100 50 67 40 33 60 75

Participating in social networks All Individuals, 16 to 24 years individuals old 100 100 141 111 98 95 109 112

Table 4 Internet use, 2019

182 109 100 91 91 118 73 100 100 118 91 109 136 100 118 73 55 109

129 114 114 71 100 107 57 129 114 121 107 93 114 107 71 86 57 150 (continued)

Posting opinions on civic or political issues via websites All Individuals, 16 to 24 years individuals old 100 100 46 57 82 121 100 93

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Source: Authors’ calculations based on data from Eurostat

GEO (Labels) Portugal Romania Slovenia Slovakia Finland Sweden

Participating in social networks All Individuals, 16 to 24 years individuals old 111 112 111 99 96 104 109 104 124 100 133 105

Table 4 (continued) Taking part in online consultations or voting to define civic or political issues All Individuals, 16 to 24 years Individuals old 120 125 30 42 50 67 50 58 150 158 130 192

Posting opinions on civic or political issues via websites All Individuals, 16 to 24 years individuals old 118 121 73 79 36 29 91 86 73 79 127 86

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of age, are in all three categories above the EU-27 average. Regarding the category “Participating in social networks”, Croatia is 12.8% above the EU-27, regarding “Taking part in online consultations or voting to define civic or political issues”, it is 50% above the EU-27 average, and regarding “Posting opinions on civic or political issues via websites” 28.6% above the EU-27 average. These data represent the level of virtual social capital, which is at high levels in the case of young adults in Croatia.

5 Discussion To boost the financial resilience of households in Croatia, a country with the highest percentage of households not being able to face unexpected financial expenses (Eurostat 2021), it is important to diversify the financial sector, its services and products (Krišto et al. 2020b). In 2017, only 10% of individuals have borrowed money to start, operate, or expand a farm or business, which is 14 percentage points below the EU-27 average (World Bank 2021). 3.5% of individuals have financial assets, which is, according to HFCS, less than in any other European country, while 9.5% of people have an outstanding balance of credit line/overdraft, which is 7.86 percentage points above average. 19.6% of people who applied for credit in the last 3 years were refused or got only reduced credit, and also 19.6% of people in Croatia do not apply for credit due to perceived credit constraints. While in advanced economies the main source of an emergency fund is saving, in Croatia it is money from family and friends (World Bank 2021). Financial cooperatives could play an important role in increasing levels of financial inclusion in Croatia, which is, as the above-mentioned data suggest, an important step in building financial resilience. For financial cooperatives to succeed in the long term, bonding social capital must exist between members. To keep a mutually advantageous course of action (in the form of a financial cooperative) and to co-operate, an equilibrium strategy has to be developed. Each individual needs to believe that the other person is trustworthy and would act in accordance with expectations. The “Foundation of cooperation” is the ability of two or more individuals to respect each other (Piaget 1931). Valentinov (2004) points out that one of the advantages of the social capitalbased financial cooperative organisation is the elimination of opportunistic behaviour within the group and direction of action towards a common goal, for which internalisation of group goals is necessary. In this sense, individual gain is achieved through mutual self-help objectives. Fukuyama (1995) stresses that the ability to co-operate is based on habit and practice and that social capital arises as a result of repeated interaction. Also, he claims that social networks used to develop information for microlending are an existing form of social capital. Antiethical behaviour in financial cooperatives can be overcome by policies within financial cooperatives that do not encourage enrollment only to get a loan, but to repeatedly save, vote and participate actively in decision making. Since credit unions in Croatia do not follow the cooperative principle and often serve as one-time-lending institutions, as indicated by Krišto et al. (2020b), it can be assumed that social capital within it will not

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develop. However, if the members are obliged to act according to cooperative principles, one can expect social capital to emerge. The stronger the community, the more important the role of social collateral. The previously discussed social capital theory indicates that continuous communication and interaction between members would force them to act in a more prudent way which would prevent the failure of financial cooperatives. To establish and develop a cooperative, one has to be aware of (and also stick to) cooperative principles. Social capital can only be built upon a voluntary approach which is the first cooperative principle (alternative to hierarchical authority, which replaces voluntary action by a directed one). Individuals in Croatia do not participate in voluntary activities as they do in countries with the developed cooperative sector. Open membership is the second principle, and social capital does not decrease if it is shared by an additional person—a member of a cooperative can become anyone who shares local social capital (norms and values). The voting rule (one member—one vote) is a direct expression of social capital since it is determined by a number of personal identities of its individual bearers and each bearer can only have one identity. Autonomy and independence are also examples of the importance of a high level of social capital in the financial cooperative—it stresses a voluntary approach and prevents attempts of hierarchical authority to occupy the place of social capital. Both voting rule and autonomy and independence are connected to the index of democracy in a region/country. Unfortunately, it is very low in Croatia since individuals in Croatia have yet to fully embrace democracy and its principles. Education, training, and information are the next principle. Relations are crucial for a financial cooperative to succeed; among financial services, 90% of financial cooperatives offer non-financial support, including education, training, consulting and mentoring, followed by group support (European Commission 2020). This means they are actively involved in the provision of financial knowledge and changing financial attitudes. DeVries (1997) concluded, based on exploring Piaget’s social theory, that surrounding oneself with high levels of social capital has a positive impact on learning and behaviour, which means that education in institutions where people trust each other has a positive impact on learning outcomes. Concern for the community is the last principle; developed financial cooperatives sector increases bridging social capital in the whole community, which is important for avoiding negative effects of social capital that can be manifested when relations within a group are too strong and at the expense of others outside the group. According to data, preconditions for the establishment of financial cooperatives in Croatia are not fully developed and this requires joint effort because it is not only an economic but even a very important social issue. When people lose their trust in the existing social institutions, there is nothing to connect them anymore. Due to the study conducted by Krišto et al. (2020a) that has investigated the prospects for the development of mutual insurance companies in the CEE countries, unfamiliarity with the mutual model, low levels of trust in mutuals, low levels of financial literacy skills among citizens, not adhering to the “bottom-up” approach, the fact that mutuals are small and marginal actors, lack of resources for the development of services, the impossibility of raising enough capital, regulation, communist

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reputation, lack of popularity, and the fact that development in one sector does not lead to development in other sectors, are main reasons these institutions are not developed in Croatia. Also, Croatia has a low level of trust in civil society institutions such as political parties, trade unions, and the rest of the civil society sector, but also in the government and the Parliament. This may cause further division between people and society, and financial cooperatives can help bring people together again. Many research have shown that the density of local civil society organisations play a crucial role in social capital development and consequently, economic development (Cooper et al. 2005).

6 Conclusion When considering appropriate policies and the next steps regarding development perspectives of financial cooperatives, it is important to use the principles of the cooperative sector, which is why building trust and other forms of social capital for its development is important. Other incentives that are not connected to the core principles of cooperatives could work in other directions and attract members for the reason of external incentives and no internal will for voluntary co-operation. Although it is often proposed that public policy should move in the direction of interference in the cooperative sector development, we would not support that. Public policies should only create a fair and transparent legal and regulatory system, and as Pejnović et al. point out, the creation of a corresponding legal framework aligned with the best examples of positive practice of more-developed states of the European Union will increase the level of trust in these institutions and provide them with credibility. Transparent, fair, and well-defined legal and regulatory framework for financial cooperatives and encouragement of education about this concept should be the first step in introducing this concept to the public. According to Rogers (1983), for an innovation to succeed, the first stage in decision making (buying the product) is knowledge about it (understanding how it functions). Interestingly, Croatia is below the EU-27 average in all categories of bridging and linking social capital, except virtual one. High levels of participation in various forms of social networks and civic engagement have been recorded, especially in the case of young adults. Further research could explore the level of trust and strength of relationships between members of the existing financial cooperatives in the EU-27, which will be an indicator of bonding social capital in cooperatives. Also, it is important to explore which affinity groups have the highest level of social capital that would present a potential for future development of financial cooperatives. Virtual social capital cannot fully replace face-to-face interactions, but if levels of trust online are higher than face-to-face, financial cooperatives can, while using fintech and modern technologies, play a crucial role in making a bridge between online and onsite offering of financial services.

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EU Tax and Agricultural Policy in the Wine Sector Jana Katunar, Maja Grdinić, and Dario Maradin

Abstract The European Union’s (EU) agricultural sector faces a number of challenges due to climate changes and depopulation of rural areas, which affects biodiversity, land and water quality, and food supply. For those reasons, the agricultural sector and agricultural policy, which have been constantly changing in the recent decades to tackle existing challenges, attract the attention of scientists and the public. The wine sector is an important segment of the EU agriculture. The EU, as a world leader in the wine industry with 65% of the world wine production, 60% of global consumption, and 70% of exports, under the Common Agricultural Policy (CAP) focuses on improving the competitiveness of the wine sector. Since the New World countries entered the EU market, maintaining and increasing wine sector competitiveness has become more important than ever. The main goal of this paper is to analyse how tax and agricultural policies affect the EU wine sector competitiveness. The empirical research was conducted on the panel data on wine production, consumption, exports, excise duties on wine, and VAT for 15 selected EU Member States, for the period 2005–2019. We estimated the econometric fixedeffect model. Our results indicate that tax and agricultural policy have an impact on the wine sector competitiveness.

1 Introduction Considering the importance of the agriculture sector and the agricultural producers in the economy of each country, not just for producing enough products for a country to be self-sufficient so it can guarantee the security of food supply, but also because of rural development and for the return of the population to the rural areas, research of the agricultural sector attracts public attention and is in a media focus of all European countries. 95.2% of companies in the agricultural sector in the EU are

J. Katunar (*) · M. Grdinić · D. Maradin Faculty of Economics and Business, University of Rijeka, Rijeka, Croatia e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_7

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small family farms (https://ec.europa.eu) with lower output and productivity levels than bigger companies. Given the lower productivity and lower ability to achieve economies of scale, a company’s competitiveness in the agricultural sector declines. Also, the agricultural sector is capital intensive, dependent on farm capacity, weather conditions, climate change, and the depopulation of rural areas. Innovation became the key factor for increasing productivity and meeting consumers’ needs. The European Union (EU) seeks to encourage the development of the agricultural sector and rural areas through financial support. This paper focuses on wine producers and the impact of the EU agricultural policy and tax policy on the development and competitiveness of the wine sector. The EU is a world leader in the wine sector with 65% of the world wine production, 60% of global consumption, and 70% of exports. Although the data suggest EU superiority in wine production, the EU wine industry is at a turning point where EU policies dictate further development. On the one hand, the EU countries have a long tradition of wine production that has overcome many natural and economic challenges, while on the other hand, the wine industry faces a number of challenges, such as lack of a work force and expensive workforce (Popescu et al. 2021), globalisation of the wine industry with the “New World” countries entering the global wine market (Thorpe 2009) with sophisticated marketing strategies (Campbell and Guibert 2006), emigration from the rural areas, large differences in land prices in the Member States (Silvis and Voskuilen 2018), small vineyard areas (Popescu et al. 2021), and scattered vineyard positions, tax policies, etc. Given the many problems and challenges facing the agricultural sector, and thus the wine sector, understanding the causes of these problems are the basis for finding solutions and increasing the EU wine sector competitiveness. This paper analyses 15 EU countries, traditionally engaged in wine production, which produce more than 92% of the EU-27 wine production (OIV 2021). The focus of the paper is to investigate the impact of the EU tax policy and CAP policy on wine consumption and wine exports in the analysed countries. The paper is structured as follows. After a brief introduction, the second part of the paper presents a literature overview. Data and methodology are described in the third part of the paper. The results are presented in the fourth part, while the paper ends with a discussion and conclusion.

2 Literature Review 2.1

The Impact of Common Agricultural Policy on the Wine Sector

Globalisation in the world wine market and an increase in wine consumption have been present in recent decades, which also affected the increase in exports. According to Anderson and Nelgen’s (2011) research, since the 1970s, only 10%

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of the world’s wine production was exported, while after the latest globalisation wave, it accounted for more than 35%. Globalisation and the emergence of the „New World“ countries on the world wine market have also led to significant changes in terms of European dominance in wine production and wine export. According to the OIV (2020a), vineyard surface area decreased in the period 2003–2020 by 6.5% and in 2020 it was 7.3 mha. During the same period, wine production fluctuated, while in 2020 it was at the level of 2003, when it was 260 mhl. Annual oscillations in wine production are expected given that grape production depends on weather conditions. In addition to the emergence of new wine-producing forces in the world wine market, changes are also taking place in consumer habits (Casttellini et al. 2017; Castellini and Samoggia 2018; Hledik and Harsanyi 2019) and consumer purchasing power (Radovanović et al. 2017). The easiest access to information on consumer opinions and the increase in consumer purchasing power has led to an increased desire for consuming high-quality products/wines. Following the trends and consumer habits, EU wine producers are building a reputation based on high quality. The increased competition due to “New World” countries has also contributed to this trend. According to Eurostat data for the EU wine production, in 2005/2006 quality wines accounted for less than 64% of total wine production, while in the year 2019/ 2020, it was more than 88% (EC – DG AGRI 2021). Based on the analysed data, the trend of increasing the share of quality wine is evident, and the EU Agricultural Policy contributes to it. Despite the increase in the quality of wine, there is a downward trend in the EU wine production, while wine production is stable worldwide (in some years even growing). In the year 2000, the EU produced 68% of total wine production, while in the year 2018, the share was 62% (OIV 2021). All the EU Member States are under the EU Common Agricultural Policy (CAP), which also includes the EU wine policy. CAP was launched in 1962 and since then it aims to „support farmers, improve agricultural productivity, ensure a stable supply of food, keep the rural economy and rural areas alive and help tackle climate change and the sustainable management of natural resources“ (European Commission 2021a). Given the high investment costs (capital intensive industry), high sunk cost, time gap between investment and income, gap between consumer demand and farmer supply, lower farmer income than in other non-agricultural sectors, high weather and climate influence, etc., the importance of EU resources through the CAP is very significant. In 2019, support for EU farmers was 57.9 billion €, while the overall EU budget was 160.28 billion €, which makes EU farmers’ support almost 36% of the overall EU budget. The above data indicate the importance of agriculture in the EU. Since it was created, CAP has undergone several reforms and adjustments. The wine sector, as part of the CAP, is also involved in the reform processes in parallel with wine policy changes and the wine market has developed considerably. Changes in the CAP in recent decades are related to a shift in focus from strict control of production in quantitative terms to increasing the competitiveness of European wineproducing countries by increasing wine quality (Pomarici and Sardone 2020; Meloni and Swinnen 2013; Corsinovi and Gaeta 2017).

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The CAP reform adopted in 2013 for the period 2014–2020 had three main goals (AIDV 2020): making EU wine producers even more competitive; making the market-management rules simpler, clearer, and more effective and preserving the best traditions of European wine growing and boosting its social and environmental role in rural areas. To achieve these goals and strengthen the competitiveness of the wine sector, EU wine producers can use financial support through the five measures: restructuring and conversion of vineyards, investments, innovation, by-product distillation, and promotion (Pomarici and Sardone 2020). Measures increase the EU wine producers’ competitiveness through increasing the quality of wine and through promotion. On December 2, 2021, a new CAP policy for the period 2023–2027 was adopted, along with the 9 objectives (European Commission 2021b): to ensure a fair income to farmers; to increase competitiveness; to rebalance the power in the food chain; climate change action; environmental care; to preserve landscapes and biodiversity; to support generational renewal; vibrant rural areas, and to protect food and health quality. Many scientific studies have addressed the impact of the CAP on the wine sector production (Katunar et al. 2021), vineyard cultivation (Martinez-Casasnovas et al. 2010), sustainability of the wine producers (Obi et al. 2020), etc. In this paper, we will investigate the changes in the EU wine quality, among other things caused by changes in CAP, and their impact on wine consumption.

2.2

The Impact of Tax Policy on the Wine Sector

In recent years, the volume of alcohol consumption has increased worldwide. Anderson et al. (2018) in their research state that consumption has increased by 25% between 2001 and 2015. On the other hand, the World Health Organization (WHO) has estimated that approximately 2.3 billion people are current consumers of some form of alcoholic products. More than half of the population within the Americas, Europe, and the Western Pacific is consuming alcohol. According to data published by the WHO (2018), 45% of total recorded alcohol consumed worldwide is in the form of spirits, followed by beer (34%) and the third most common is wine (12%). Therefore, the wine sector is an important part of total alcohol consumption which is especially evident in the European Union, where the EU comes first in terms of the number of consumers, producers, exporters, and importers of wine in the world. The EU accounts for 44% of the world’s winegrowing areas (with also 2.5 million wine growers), 61% of total production (Spain, France, and Italy account for 76% of EU areas under vines), 53% of global wine consumption and 67% of the total exports (OIV 2019). In 2015 Global Agricultural Information Network (GAIN 2015: 1) reported information regarding wine production in the European Union and, according to that report, the EU Member States France, Italy, and Spain are major world wine producers and leading wine importers

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and exporters. Also, GAIN has reported that the United States is the leading EU export market. In the EU, excise duties for alcohol are regulated through two main Directives. Directive 92/83/EEC prescribes rules about the structure of excise duties, the list of products they apply to and the basis on which excise duties are calculated. The second one is Directive 92/84/EEC through which are defined harmonised minimum rates for each category of products, above which the Member States are free to set their rates according to their needs. The minimum rate imposed for wine and sparkling wine is currently 0 EUR/hl. Generally, countries around the world can apply tariffs on wine according to various aspects: (a) (b) (c) (d)

ad valorem (according to the price of the wine); volume-based; alcohol contents (per liter of alcohol in the product); or typologies (such as still, sparkling, bottled, and bulk).

Against this background, the question arises as to how a particular form of tax rates affects the consumption and production of alcoholic beverages. Goodhue et al. (2009) studied how different tax rates affect wine supply. In their research, they used a dynamic model to examine four different tax rates (ad valorem sales, volumetric sales, ad valorem storage, and volumetric storage tax). The results show that increasing any of the four taxes reduces the quantity of wine produced, but their impact on quality is not clear. Wine taxes are either specific/volume-based (e.g., excise duty per hectoliter) or ad valorem (e.g., VAT). Different taxation methods affect the consumption and production of wine. Cnossen (2009) found that specific taxes may lead to an increase in consumer prices and a decrease in wine consumption, which in turn leads to a decrease in tax revenues. Cnossen (2009) also found that specific taxes on wine do not affect producers’ decisions to invest in the production of better quality wines. On the other hand, Barzel (1976) concluded that ad valorem taxation increases consumption and tax revenues but induces firms to reduce prices, lower product quality, and reduce advertising and marketing costs. In general, the choice of the type of tax rate and method of taxation depends on the policy objectives of governments. Besides the excise duties, European Union Member States are using a broad consumption goods and services tax, i.e., Value Added Tax and wine is included as part of the tax base on the consumption and taxed together with most other types of goods and services to raise government revenue. Also, as mentioned before, Member States impose additional specific taxes (excise duty) on wine. In accordance with the harmonised system of VAT in the EU Member States, domestically produced wine is taxed by the standard VAT rate and a low or zero excise duty on still and sparkling wine by reference to the number of hectolitres of the finished product. The reason why wine is taxed with both VAT and excise duties is twofold. The first reason for applying excise duty on wine is the fiscal reason in the context of increasing government revenues and correcting externalities. Given that wine has a highly inelastic demand, a small increase in price due to an increase in excise duties has a

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very small effect on wine consumption. Another reason justifying the taxation of wine by excise duties is that it corrects external costs that are not part of the market prices of products, such as health costs. Specific tariffs based on volume are the most popular in 15 EU countries which are the subject of research in this paper (see the list of countries below). Among the analysed countries, France is the only country that has an excise duty for still wines higher than 0 EUR/hl, while for sparkling wines six countries have an excise duty higher than EUR/hl. Moreover, all the analysed countries apply the standard VAT rate to still and sparkling wines, with the sole exception of Portugal, which applies a reduced VAT rate to still wines. According to Anderson (2020), comparing the excise duties on wine, beer and spirits from 2008 to 2018 across 42 countries, wine is taxed slightly less than beer and spirits, although taxes for all three products have risen during the period studied. Although there is a considerable amount of scientific research on the impact of various policies on the consumption and production of alcoholic beverages, the research dealing with the impact of taxes and tax policies on the consumption and production of wine in the European Union is rather scarce. The vast majority of studies refer to the United States, Australia, New Zealand, and only some EU Member States such as France and Italy.

3 Data and Methodology The empirical research was conducted on data for 15 EU Member States that are traditionally wine-producing countries: Austria, Bulgaria, Croatia, Cyprus, the Czech Republic, France, Germany, Greece, Hungary, Italy, Portugal, Romania, Slovakia, Slovenia, and Spain. Our panel data are constructed from publicly available data regarding wine production, consumption, wine export, excise duty, and VAT. In Table 1 we show descriptive statistics for all variables, where we used original variables. Based on the theory and literature review, we developed two empirical models:

Table 1 Descriptive statistics for the database VARIABLES StWC SpWC EDSpW EDStW VAT EXP

N 210 210 210 210 210 210

Source: Authors’ calculation

Mean 6625.12 469.30 28.54 0.54 19.97 1,073,442,944

sd 8577.978 606.182 42.566 2.522 3.131 2,107,543,840

min 130.886 8.7612 0 0 12 1,244,343

max 31699.26 2146.68 136 20 27 9,346,117,757

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StWCit ¼ β0 þ β1 EDStW þ λt þ uit

ð1Þ

SpWCit ¼ β0 þ β1 EDSpW þ λt þ uit

ð2Þ

According to OIV (2020b), from the beginning of the century, global consumption of sparkling wine has significantly increased, from 5% of total wine consumption in 2002 to 8% of total wine consumption in 2018. The annual growth rate was 3%. Due to the lack of data on the ratio of sparkling and still wine consumption in selected countries, we used the global ratio from the OIV report (OIV 2020b) to calculate the consumption of sparkling wine in the observed EU countries. In 2005, 5.4% of total world wine consumption was sparkling wine consumption. We predicted a linear growth of 3% over the next 14 years in all observed countries, and in 2018 sparkling wine was estimated at 8.01% of total wine consumption in all 15 countries. Data gathered for this research cover the 2005–2018 period in which we have still wine consumption (StWC) (in 000 hl) in all 15 countries, sparkling wine consumption (in 000 hl), excise duty on still wine (in EURO) for 1 hl (ECStW), excise duty on sparkling wine (in EURO) for 1 hl (ECSpW), VAT (in percent) and wine export (in EURO). λt stands for time fixed effects and uit is the error term. Using a fixedeffect model we estimated the influence of excise duty on wine consumption and the influence of VAT on wine export.

4 Results Using a fixed-effect model we estimated the influence of excise duty on wine consumption. We estimated two models, first (1) for still wine and second (2) for sparkling wine. For sparkling wine, the results of the conducted panel analysis, although not significant ( p-value is greater than 5%) are in line with our expectations based on the previous empirical studies. An increase in excise duty for sparkling wine by 1 euro/ hl leads to a decrease in sparkling wine consumption by 330 hl. We have yearly effect data with 2005 as a basic year. It is noticeable in Table 2 that consumption of sparkling wine is growing every year (compared with 2005). Therefore, in the last observed year, 2018, sparkling wine consumption was higher by 125,000 hl than in 2005. Also, before 2008 (before the global economic crisis) and after 2014 (when the global economy recovered) we can see that the impact of excise duty on sparkling wine consumption was significant (p-value was less than 5%). For still wine, the results of the conducted panel analysis were also in line with our expectations and are significant at the level of 5%. Our results show that an increase in excise duty for still wine by 1 euro/hl reduces the still wine consumption by an average of 22,440 hl. We can notice the negative trend of still wine consumption. In the last observed year 2018, the average still wine consumption was lower by 1,047,900 hl.

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Table 2 Results of the model estimation

Excise duty (EUR) Still wine

(1) Still wine consumption (000 hl) 22.44*

(2) Sparkling wine consumption (000 hl)

(2.24) Excise duty (EUR) Sparkling wine Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 _cons N R2

0.337

38.35 (0.63) 1.339 (0.01) 167.3 (0.64) 503.6 (1.23) 670.0 (1.61) 976.9 (1.88) 958.7 (1.72) 1095.8 (1.83) 1172.6 (1.74) 1119.3 (1.85) 1095.4 (1.87) 1069.2 (1.85) 1047.9 (1.73) 7345.4*** (17.73) 210 0.223

t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 Source: Authors’ calculation

(1.18) 11.51 (1.98) 27.19* (2.62) 31.71* (2.42) 24.88 (1.51) 27.55 (1.72) 20.72 (1.17) 36.55 (1.70) 41.21 (1.68) 52.69 (1.80) 71.76* (2.30) 87.12* (2.53) 106.1* (2.88) 125.0* (2.96) 431.5*** (28.83) 210 0.303

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5 Discussion Although the tradition of wine production and wine consumption has been known for thousands of years, wine consumption is dependent on the global trend of alcohol consumption. According to the OIV (2020a) Italy, France, and Spain are traditionally the world’s biggest wine-producing countries with 34.7% of vineyard surface area and 52.5% of world production in 2020, but a decline in production is noticeable in the last 14 years. In addition to global trends, wine (and other alcohol) consumption is also greatly influenced by the economic situation. In times of economic crisis, such as the 2008 recession, there is a phenomenon of poor consumers getting poorer and rich people getting richer. This phenomenon leads consumers with lower incomes and less security to change their behaviour more than consumers with higher incomes. In this respect, wealthier consumers are less aware of the negative consequences of the economic crisis in times of financial crisis and do not significantly change their lifestyle and consumption of products they bought before the crisis. The interpretation of alcohol consumption in times of crisis is also related to the „income effect“, which states that alcohol consumption decreases during the economic crisis because there is less income available to buy alcohol. This effect implies a decrease in alcohol consumption in the whole population (both the poor and the rich), but especially in the group of people with lower incomes, who are most affected by income losses. Sparkling wines are traditionally more expensive than still wines (OIV 2020b) and their consumption is mainly associated with people of higher-income groups, who are usually the least affected by the negative impact of economic crises. According to the OIV report, in 2002, the average export price for still wine was 2.5 €/l while by the end of 2018, it almost doubled to 4 €/l. The export price of sparkling wine in the period 2002–2018, except in the years of economic crisis when the demand for premium and ultra-premium sparkling wines dropped, was 7 €/l. Although the average export price of still wine har risen, mainly due to the increase in wine quality, the average export price of sparkling wine is still almost double. The current trend is in favour of sparkling wine consumption, which no longer has seasonal character in terms of consumption mainly during the New Year’s celebration. Consumer habits have changed in the last decade as demand for high quality and more expensive wines has grown and the international trade in the volume of sparkling wine doubled in the period 2002–2018. The impact of excise duties on still wine consumption is significant in all observed years while sparkling wine consumption is significant only in the preand post-crisis years. We plan to investigate the reason for this in further scientific work. Since we have increased wine consumption linearly by year, in line with the average global trends, we believe that the data are accurate enough for the general conclusions we have reached. However, for a more concrete analysis of the impact of tax policy on sparkling wine consumption, we need more detailed data, which are currently not available.

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The production and consumption of sparkling wine are currently experiencing a „boom“ due to a number of phenomena already explained: the seasonal adjustment of sparkling wine consumption, the increase in the supply and price level of sparkling wine, the increasing purchasing power of consumers. Although sparkling wine consumption still represents a very small share of total wine consumption, sparkling wine production and consumption are increasing every year and the largest producers are significantly expanding their production capacity. In 2008, according to the OIV (2020b), sparkling wine production accounted for 7% of total world wine production, which is an increase of 2% in total production compared to 2002. Italy, France, and Germany are the largest producers of sparkling wine, accounting for 64% of world production in 2018. Of these countries, Italy is leading the way in the future development of sparkling wine consumption, as over the period from 2008 to 2018, France and Germany have reduced their production, while Italy has more than doubled its sparkling wine production. The economic crisis that began in 2008 led to a slight increase in excise duties on sparkling wines, and most countries also increased the standard VAT rate to increase tax revenues. Such a tax policy in the area of consumption tax has led to an increase in product prices, including the price of sparkling wine. Simultaneously with the recovery of the economy begins a period when sparkling wines from the third countries (e.g. Mexico, Canada, Brazil, Australia, Chile), whose prices are significantly lower compared to sparkling wines produced in the EU (even after excise duties and VAT), enter the EU. During this period, there is an increase in the consumption of sparkling wine in the analysed EU countries, which can be explained by the fact that, despite increasing excise duties, excise duties on sparkling wine are not too high and have an impact on the consumption of imported sparkling wine and the overall consumption of sparkling wine.

6 Conclusion Given the importance of the agricultural sector in the economy of the EU Member States, increasing the competitiveness of the agricultural sector is a strategic goal of the EU. The analysis of the factors influencing the consumption and the competitiveness of agricultural products, thus the wine products, contributes to the understanding of global trends and the behaviour of producers and consumers. The results of our research indicate the impact of CAP and tax policy on the consumption in the wine sector, which also indicates that policy makers involved in wine sector regulation play a crucial role in terms of the development of the sector in countries where wine trade and wine production are important. It also helps policy makers in the development of future strategies. In our research, we also planned to investigate the impact of VAT on wine exports. We expected that in countries with lower VAT rates wine industry will develop faster, which will lead to higher cost efficiency and greater competitiveness, thus, consequently, to an increase in wine exports and to a lower import/export ratio.

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Our hypothesis was not proven in this research. Given the small number of countries covered by this research and that they are all EU Member States, we plan to repeat our research on a larger sample of countries that are wine producers, traditionally engaged in wine production and New World countries. The main limitation of our research is the available data. Since we did not have data on the still and sparkling wine consumption ratio, we applied global consumption trends to obtain the consumption ratio in the observed countries. Also, for future research, to get a better picture of the factor influencing the agricultural sector, it is necessary to include other macroeconomic variables, as well as extend the analysis period on the years marked by the COVID-19 crisis. Unfortunately, the COVID-19 pandemic has not bypassed the wine industry. Job insecurity and reduced incomes are just some of the reasons, while together with logistical problems and suspension of tourist travelling, caused a decline in wine trade and consumption. We expect that the impact of COVID-19 would be greater on the still wine trade than on the sparkling wine trade, given the category of consumers who are more sensitive to prices during the crisis, which is the recommendation for future research. Acknowledgments This work has been supported by the University of Rijeka, project 20–39.

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Integration as an Indicator of (under) Development of the Croatian Capital Market Sanel Haistor Ramić, Dario Silić, and Denis Buterin

Abstract Acquisition processes are considered to be the acquisition of a company or a significant share in the equity of other companies, regardless of whether this was achieved by purchasing the property or ownership share of the acquired company or by pooling ownership interests. A takeover is considered hostile if management officially rejects the takeover bid. In countries whose capital markets are dominated by banks, including Croatia, companies are significantly more protected from hostile takeovers compared to economies with developed financial markets. The authors explore the features of integration processes, with special emphasis on hostile takeovers in the context of the development of the Croatian capital market. The results of primary research indicate its relative underdevelopment, which can be related to the achieved degree of institutional stability as one of the preconditions for its development.

1 Introduction A takeover is an integration process when the acquiring company aims to take over a majority stake and acquire ownership rights in another company. Given the attitude of the target company’s management about this process, the takeover can be considered friendly or hostile. These are very expensive processes whose costs increase significantly if they are not friendly. The intensity and success of such processes are largely determined by the development of the country’s financial market but also by the level of overall institutional development of the country.

S. H. Ramić · D. Buterin (*) Polytechnic of Rijeka, Rijeka, Croatia e-mail: [email protected]; [email protected] D. Silić Swiss School of Business and Management, Geneva, Switzerland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_8

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In the Republic of Croatia, the market is bank-centric, which means that apart from banks, the capital market has a relatively minor role in the long-term financing of business processes of Croatian companies. In addition, Croatia is still characterised by relatively weak institutional development (Buterin 2015, 2020, 2021). Therefore, the results of a survey conducted on Croatian companies on the attitudes of managers towards integration processes, which indirectly indicate the underdevelopment of the Croatian capital market, are not surprising. The paper is structured as follows. After the introduction, Sect. 2 presents the key features of integration processes. Section 3 analyses the strategy of hostile takeovers and the possibility of defending against such attempts. Section 4 provides an analysis of the attitudes of Croatian company managers about mergers and acquisitions, while Sect. 5 concludes.

2 Features of Takeover Processes Integration processes are usually initiated by larger companies taking over smaller target companies in order to strengthen market share and a more stable position in target markets. The acquired companies thus become part of stronger and larger companies on the market. The difference between a merger and a takeover is that the merged company ceases to exist as a legal entity, while the result of the takeover process is the continued operation of both companies (Filipović 2011). If management recommends accepting the takeover bid, the bid is considered friendly; if management officially rejects the offer, the takeover is considered hostile. At the same time, the attitude of the management sometimes changes during negotiations (Sjåfjell 2010; Haistor Ramić et al. 2021). With a friendly takeover, there are direct negotiations between the management of the company interested in the takeover and the management of the target company. In the case of the decision to continue negotiations, the company that is the subject of the takeover must provide accurate data for review and evaluation of the company’s value. A series of activities precede these negotiations to gather information and compile a list of potential takeover companies (Boone and Mulherin 2008). In other words, in a friendly transaction, the buyer company can obtain data from the target company, and the results of the in-depth analysis will influence the decision on whether to proceed with the takeover process or not. With such a takeover, there are generally no difficulties and conflicts between the acquiring company’s management and the target company’s management, and no special takeover or defense strategies are implemented. In the context of the Croatian economy as a post-transition economy, it is important to emphasise that mergers and friendly takeovers are the most common forms of foreign direct investment in the economy. Namely, foreign direct investments are realised by taking over or merging a domestic company with an existing company abroad. Direct investments through the privatisation process in transition countries also fall into this group, and their most common forms in Croatia so far

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have been reflected in takeovers or mergers with foreign companies. In the first two decades of post-transition, the share of completely new investments in processes was significantly lower (Buterin and Blečić 2013). Among the main motives for initiating a takeover is the need to expand, increase the range of customers, products, or geographical reach. Corporations that strive for horizontal or vertical expansion seek to diversify and acquire attractive resources or manage risks more effectively. Hostile takeovers are mostly a battle for corporate control and are most often initiated by an external entity, usually another corporation trying to reach the target company’s shareholders through various mechanisms (Gaughan 2017). Also, among the important reasons for initiating such processes may be access to technology and know-how, acquisition of intellectual property rights, and increase of specialised resources for investments in research and development processes (DePamphilis 2010). However, a hostile takeover is an expensive undertaking and is mainly opted for in the event of a significant underestimation of the firm. The takeover price is, among other things, defined by the costs of engaging funds intended for shareholder payments. These are mostly significant amounts of money, as shareholders are often offered a price higher than the market price. Takeover transactions of companies in which a significant share of debt is primarily used are called leverage buyouts. In leverage, the acquirer finances the company’s purchase by a combination of debt and capital. After the takeover, the capital structure of the acquired company changes significantly, and the debt ratio is much higher than before the takeover. Lever takeovers are often used when taking over a company by managers but also by employees (Ayash 2020; Kaplan and Stromberg 2009). Modern forms of financing are constantly evolving, so mezzanine financing is becoming an increasingly common form of takeover financing. Mezzanine loans are a hybrid form of financing that combines the characteristics of capital and debt. Mezzanine loans, therefore, enable companies with good expansion prospects to obtain financial resources to expand and implement integration processes (Giurca Vasilescu 2010). Mezzanine financing enables the financier of the acquiring company to convert the debt into equity if a situation arises where the acquiring company is not sufficiently liquid and thus not capable of repaying its financial obligations properly. This form of financing is not secured, i.e., creditor insurance is based on the conversion of debt into equity and is therefore only available to companies with significant financial credibility and a good reputation in the market.

3 Strategies of Hostile Takeovers and Defense In a hostile takeover, the acquirer can rely on a number of strategies, each of which has certain advantages and disadvantages, and each can be used in different ways and different circumstances. However, the most common strategies in such cases are bear hugs, proxy fights, and tender offers (Puziak and Martyniuk 2012). Likewise,

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the attacked company also has several defense strategies at its disposal, depending on the financial capabilities and the specific situation in which it finds itself. Bear Hug is one of the main strategies of enemy takeover. The essence of a bear hug is to make offers to buy the company by the acquiring company in which the offer is to buy back a share at a price that is significantly higher than the market price. This takeover strategy is almost always used when the acquirer estimates that the target company’s shareholders are willing to sell the shares at the offered price (Betton et al. 2008). A bear hug can be classified as a hostile takeover attempt by a bidding company because it is designed to put the target company in a position where it cannot refuse a purchase. However, unlike some other forms of hostile takeovers, a bear hug often leaves shareholders in a favourable financial situation. The acquiring company may offer additional incentives to the target company to increase the likelihood of accepting the offer. Therefore, a bear hug can be extremely expensive for the acquirer and will take more time than usual to achieve the desired return on investment (Schwert 2000). Another strategy used to realise a hostile takeover is a proxy fight. In this case, there is interest and aspiration of a particular group of shareholders to acquire a majority stake and management rights, which means that the change of the existing ownership structure is initiated in the company’s internal environment. A proxy fight or proxy wote refers to a situation in which a group of shareholders in a company joins forces in an attempt to oppose and outvote the current management. It is a hostile takeover strategy used in companies where there is a high level of conflict and tension between the interests of management and the interests of certain groups of shareholders or all shareholders. Voting in the assembly is a fundamental tool on the basis of which the goal of a hostile takeover is achieved. In many corporations, executive bonuses are paid out in share packages in order to turn managers into co-owners of the company and, in this way, to dedicate themselves to the realisation of shareholder and ownership interests. When the share of voting rights of existing target board members is too high, a proxy fight may not be effective because of a group of shareholders seeking to change ownership structure can hardly achieve a majority vote in the assembly (Whittaker and Hayakawa 2007). It is usually difficult to win in the fight against intermediaries due to corporate governance safeguards and restrictive acts, and therefore most proxy fights are unsuccessful (Huang et al. 2018). Any intention to take control of a company is announced through a public offering in the capital market. The hostile tender offer bypasses the company’s management and aims at the rapid accumulation of shares in order to acquire a controlling stake and ownership rights over the company even when management resists the takeover. If a hostile takeover cannot be undertaken using other tactics, the takeover company makes a hostile tender offer (Gaughan 2017). Here the costs are significantly higher than in a friendly takeover. Namely, launching a hostile tender offer to the acquirer does not guarantee success in taking over the target company because other acquirers may get involved in the fight for the same company, which is reflected in the growth of costs.

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Anti-takeover strategies can be divided into two categories: preventive and reactive. Preventive strategies are introduced in order to reduce the likelihood of a financially successful hostile takeover, while active measures are implemented after the hostile offer is sent to the target company (Pearce and Robinson 2004). Among them, the most used are Poison Pill, Crown Jewels, Pacman Defense, and White Knight. A defense strategy against a hostile takeover in which the target company seeks to make its shares as unattractive as possible to a potential acquirer is called a poison pill. This strategy is trying to create a kind of shield against takeovers. The target company entitles the shareholders to purchase the shares at a price lower than the market price and thus devalues them in order to increase the acquirer’s costs. The two basic types of poison pills are (Cremers et al. 2018): • Flip-in poison pills allow existing shareholders (other than those belonging to the company that is the potential acquirer) to buy more shares at a better price. • Flipover poison pills allow shareholders to buy shares of the acquiring company at lower prices after the takeover. Both types of poison pills aim to reduce the share of the potential acquirer in the target company, i.e., reduce its business power and make the acquisition more difficult and expensive. The extreme form of the poison pill is called a suicide pill, in which case the target company decides on business moves that will lead to the termination of its business, i.e., bankruptcy. The advantage of poison pills is reflected in the fact that its application can be adopted by the board of directors of the target company without the special consent of the shareholders of the target company, as the board can subsequently change the rights from poison pills (Sunder 2013) However, companies should be careful in developing strategies for poison pills. As a strategy, poison pills are effective only as a scarecrow. When introduced, they often create potentially devastatingly high costs and are usually not in the best long-term interest of shareholders. Selling valuable assets or crown jewels is another strategy by which the target company can defend itself against takeovers. By selling assets, the company becomes less attractive to take over. On the other hand, applying this strategy may lead to a fall in the market price of the target company’s shares, which would be desirable for the acquirer. This is an extreme and risky defense measure to the detriment of the target company itself. Crown jewels refer to the most valuable units in a corporation defined by characteristics such as profitability, asset value, and prospects. A company can use this defense by creating anti-takeover clauses that force the sale of their gems in the event of a hostile takeover. This deters potential acquirers from attempting to take over the company, as the acquirer would not receive the desired operations or resources if it continued to take over (Dalal 2011). Selling a company’s crown jewels is often a drastic attempt to prevent a hostile takeover or get rid of a company’s previously incurred financial debt. In both cases, the best business assets of the company are sold, which basically changes the whole nature of the company and leaves it with a different set of prospects for growth and shareholder support. When a company is too overburdened with debt, it may be

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forced to sell gems to avoid possible bankruptcy. Sales of crown jewels generally leave the company’s remnants in less attractive or slower-growing markets. The Pacman defense method is named after a video game where the characters try to eat each other. In this strategy, the target company must buy enough acquirer shares at a high price. The target company should have sufficient funds at its disposal to purchase a sufficient stake in the acquirer to jeopardise the acquirer’s control over its company. The essence of this method is that the target company offers to buy the company of the attacker in response to his offer. This method is often threatened, but it is rarely used. This is because this requires additional financial resources the strategy to be implemented, which often makes it very impractical (Haistor Ramić 2021). The target company can organise its defense against a hostile takeover of the company by contacting one of the friendly companies with the desire to appear as a new acquirer after the announcement of the tender offer for the purchase of shares by the enemy company. In practice, a friendly company is often called a “white knight”, hence the name of this strategy (Monks and Minow 2008). Namely, the management of the target company, when it receives a takeover offer that it does not want to accept, asks for a knight, i.e., a friendly company that will offer a higher price for their shares than the current bidder or will allow management to continue the current strategy. Although the target company is still losing its independence, a friendly investor is still more favorable to management. Compared to other strategies, this one is the most commonly applied and is very effective.

4 Survey on the Attitudes of Croatian Company Managers The target population for the implementation of the primary survey was represented by joint stock companies operating in the territory of the Republic of Croatia and listed on the Zagreb Stock Exchange. The sample for conducting the research is a sample of N ¼ 150 joint stock companies active in the Republic of Croatia, and contact data were collected from the Zagreb Stock Exchange database. A total of N ¼ 61 joint stock companies responded to the call for research. One employee from each company participated in the research. The instrument used to collect primary data is a structured questionnaire with closed questions. The questions were structured in such a way that respondents could choose from multiple choice answers or express views on individual questions related to mergers and acquisitions on a scale of 1 to 3. Respondents on a scale of 1 to 3 expressed the likelihood of certain events and circumstances related to integration processes. Some numbers on the scale have the following meanings: – 1—probability is less than 50%, – 2—probability is 50%, – 3—probability is greater than 50%.

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8%

Managerial

92%

Non managerial

Fig. 1 Respondents by position in the organisational structure of the company. Source: Authors’ research

The structured questionnaire consisted of a total of 10 questions, of which three questions related to the characteristics of respondents and companies in which they are employed, five questions related to the implementation of mergers and acquisitions and views on these processes, and two questions to factors in Croatia affecting the likelihood of mergers and acquisitions in accordance with the views of the respondents. Figure 1 shows the structure of respondents with respect to the position within the company’s organisational structure. The data in Graph 1 show that the share of respondents in a managerial position in the company is relatively high and amounts to a high 92%, while the share of respondents employed in other positions is 8%. The business life, the business cycle of the company and the phase in which the company is significantly determining the business development strategy and the possibility of using organic, internal growth, or the level of need for mergers and acquisitions given the maturity of the market, products and the fact that companies can not further spread exclusively by organic growth. The business life of the companies involved in the research is shown n Fig. 2. The largest share of respondents works in joint stock companies whose business life is longer than 15 years. The share of such companies is 41%. They are followed by companies with a life cycle of 11–15 years and a share of 34%. A total of 20% of companies have been operating for 6–10 years, and 5% have been operating in the Republic of Croatia for less than five years. The sectoral structure is also important data for conducting primary research on mergers and acquisitions, given that integration processes are significantly driven by trends within related industrial sectors, i.e., key competitive forces in an industry. The structure of enterprises with regard to the industrial sector in which they operate is shown in Fig. 3.

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45%

41%

40% 34%

35% 30% 25% 20% 20% 15% 10% 5% 5% 0% Under 5 years

6-10 years

11-15 years

Longer than 15 years

Fig. 2 The business life of the company. Source: Authors’ research

26%

14% 13%

26%

21%

manufacturing industry construction trade

tourism financial sector

Fig. 3 Sectoral structure of the company. Source: Authors’ research

Joint stock companies from the manufacturing industry, construction, trade, tourism, and the financial sector participated in the research. Companies from the financial sector and tourism participated in the survey with shares of 26% each. The share of companies operating in the trade sector was 21%, while companies from the manufacturing industry were 14% in the total survey sample. The last sector is represented in the structure of surveyed companies in the construction sector with a share of 13%.

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24%

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37% Yes, successfully Yes, unsuccessfully

39%

No

Fig. 4 Share of companies that have experienced integration processes in their business so far. Source: Authors’ research

The share of companies that have implemented integration strategies with other companies in the course of their business so far, whether they are mergers, acquisitions, or takeovers, is shown in Fig. 4. Of the observed companies, 37% have successfully implemented the strategy of mergers, acquisitions, or takeovers in their business so far, and 39% have unsuccessfully tried integration processes. Less than a quarter of companies, or 24%, have not encountered integration processes in their operations so far. In the following question, the respondents from the companies that participated in the integration processes named the type of integration process: merger, friendly takeover, or hostile takeover. Of the 61 joint stock companies observed, 46 and 75.4%, respectively, participated in one of the integration processes. Of these, 30 mergers, 13 friendly takeovers, and three hostile takeovers were successful or unsuccessful. The integration processes shown in Fig. 5 are additionally classified according to the success criteria of the implementation of the integration process. The data in Fig. 5 show that the largest share in the successful implementation of integration processes was achieved through the merger process, with a share of 57%, while 43% of mergers were unsuccessful. Of the 13 companies involved in the friendly takeover process, 33% were successful, and as many as 67% did not result in a compromise solution. Based on the research results, it can be concluded that hostile takeovers are currently the least represented form of integration processes in Croatian economic practice. Out of a three hostile takeover attempts, one (31%) was successful and two (69%) unsuccessful. The next question was related to the subjective assessment of the probability of initiating integration processes by the surveyed company in the next five years of operation, and the results are shown in Fig. 6. Respondents rated the probability of initiating each of these forms of integration.

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100% 90% 80%

60% 50%

69%

67%

70% 57% 43%

40%

33%

31%

30% 20% 10% 0% Merger

Friendly takeover

Yes, successfully

Hosle takeover

Yes, unsuccessfully

Fig. 5 Types of integration processes in the companies covered by the research with regard to the criterion of integration success. Source: author’s research

31%

3 (MORE THAN 50% PROBABILITY)

65% 2 (50% PROBABILITY) 4.00%

1 (LESS THAN 50% PROBABILITY)

0 Hosle takeover

0.2

0.4

Friendly takeover

0.6

0.8

1

Merger

Fig. 6 Probability of initiating the integration process by the surveyed company in the next five years. Source: Authors’ research

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HOSTILE TAKEOVER

10% 6.00%

FRIENDLY TAKEOVER

8%

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21% 57%

MERGER

0%

10%

20%

3 (more than 50% probability)

30%

40%

50%

60%

2 (50% probability)

1 (less than 50% probability) Fig. 7 The probability that the company will be the target of integration processes in the next five years. Source: Authors’ research

The answers show that the surveyed companies are most inclined to initiate mergers as integration processes, and 65% of them estimate that they are very likely to start the merger process in the next five years. The probability of initiating friendly takeovers is also very likely for 31% of companies. The companies surveyed are the least prone to initiating hostile takeovers. Only 4% of respondents said that their company could initiate a hostile takeover, but with a probability of less than 50%. The estimation of the probability that an individual surveyed company will be the subject or goal of a merger, friendly or hostile takeover process is shown in Fig. 7. Respondents estimated the probability that the company will be the target of a particular integration process on a scale of 1 to 3. The results show that 57% of respondents believe that there is more than 50% probability that their company will be the target of the integration process of the merger. In 29% of companies, it is estimated that there is a certain probability of a friendly takeover. According to the answers received, the probability of a hostile takeover is even lower and is expected by only 16% of respondents. Respondents’ attitudes about the success of individual defense strategies against hostile takeovers are shown in Fig. 8, which shows how many companies would decide on the offered defense strategies. The opinion of the largest number of respondents (76%) is that the strategy of the white knight in which the company relies on a friendly company is the most successful and least risky in the fight against possible hostile takeovers. The poison pill is considered successful by 13% of respondents because it risks the position of shareholders. The Pacman defense strategy requires exceptional financial resources to buy a significant stake in the ownership of the acquiring company and is advocated by 6% of respondents.

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76%

13% POISON PILL

5% CROWN JEWELS

6% WHITE KNIGHT

"PACMAN" DEFENSE

Fig. 8 Respondents’ attitude about the success of particular defence strategies against enemy takeover. Source: Authors’ research

Only 5% of respondents favour selling the company’s key assets as crown jewels since this endangers the resource base of the company’s long-term competitiveness. However, these results should be taken with caution due to the respondents’ lack of knowledge and experience, which is due to the small number of hostile takeovers in the Croatian economy and the small number of known successfully implemented defence strategies. Furthermore, the attitude of the respondents about the factors influencing the probability of a hostile takeover on the Croatian market was analysed. A total of two factors were analysed: the probability of a hostile takeover given the development of the capital market (Fig. 9) and the likelihood of a hostile takeover given the extent to which a firm depends on capital market financing (Fig. 10). As many as 91% of respondents think that the level of development of the capital market and its almost insignificant role in financing is the reason why the probability of a hostile takeover is low. Further on, 6% of respondents believe that this probability is medium, and 3% consider the probability high. Such responses are not surprising given the already mentioned small number of hostile takeovers so far. The assessment of the likelihood of hostile takeovers is also influenced by the low degree to which a company depends on capital market financing. The coincidence of the answers with the answers to the previous question is noticeable. Namely, for 90% of respondents, the probability of a hostile takeover is low given the company’s dependence on financing through the capital market. For 6% of respondents, the probability of a hostile takeover is moderate due to companies’ dependence on financing through the capital market, while 4% of respondents rate this probability as high.

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91%

LOW PROBABILITY

6%

3%

MEDIUM PROBABILITY

HIGH PROBABILITY

Fig. 9 Respondents’ attitudes about the probability of hostile takeovers with regard to the development of the capital market in the Republic of Croatia. Source: Authors’ research

90%

LOW PROBABILITY

6%

4%

MEDIUM PROBABILITY

HIGH PROBABILITY

Fig. 10 Respondents’ attitude about the likelihood of a hostile takeover given the extent to which the company depends on financing through the capital market. Source: Authors’ research

The research shows that most of the surveyed companies participated in integration processes, based on which it can be concluded that this strategy of company growth and expansion is recognised in the Republic of Croatia and somewhat follows global trends, especially given the limited opportunities for organic growth

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in markets marked by intense competition. The results also show that not all types of integration processes are marked by the same degree of success. The highest level of success was recorded in mergers, followed by friendly takeovers, while enemy takeovers operated successfully in the least number of cases. This empirically confirms the theoretical knowledge that the problems associated with overestimating the value of the company and the asymmetry of information represent very real risks that the acquiring company should take into account when deciding on an expansion strategy through hostile takeovers. Further research results related to the probability of initiating integration processes by the surveyed companies, in the long run, indicate that the surveyed companies in the Republic of Croatia are not prone to hostile takeovers. Several factors can justify such an attitude. The first factor is certainly the low level of success in implementing hostile takeovers due to the relationship of distrust towards the acquiring company. Other factors relate to the situation on the capital market, which in Croatia does not play such a role in financing companies in relation to commercial banks and other financial institutions. In bank-centric economies, such as the Croatian economy, companies are significantly more protected from hostile takeovers than in open, Anglo-Saxon corporate governance systems. The obtained results are in line with the results of research conducted by Buterin et al. (2017), who point out that the stability of institutions is the foundation of capital market development, and this prerequisite in Croatia has not yet been met. In the Croatian capital market, investment funds recorded a rapid growth trend, but also a significant decline during the recession (Olgić Draženović et al. 2018). The research results show that the respondents do not consider the capital market in the Republic of Croatia to be sufficiently developed. In line with the above conclusions is the respondents’ assessment of the likelihood that their company will be the target of hostile takeovers, which can generally be assessed as low. It is interesting to note that respondents still consider the probability that their company will be the target of hostile takeovers more than the probability that they will find themselves in the role of initiators of hostile takeovers. If a hostile takeover occurred, most respondents would choose to defend using the White Knight strategy because other hostile takeover strategies are destructive to the company’s resource base and weaken its competitiveness base within the sector in which it operates.

5 Conclusion The primary research results show that mergers are the most frequent and most accepted form of integration processes by Croatian joint stock companies. It is also the integration process with which the companies in the observed sample had the highest degree of success. The share of hostile takeovers is the smallest and most unsuccessful, which can be attributed to the fact that hostile takeovers pose a high risk of overpaying the target company, but also the risk of destroying the value of the target company. As key factors influencing the respondents’ attitudes about the low

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value of hostile takeovers, the level of (under) development of the capital market and the low level of dependence of corporations on financing in the capital market stand out because the corporate system in Croatia is still highly bank-centric. Also, another key factor influencing possibly the respondents’ attitudes about the low value of hostile takeovers is the fact that due to Covid 19 there is a negative impact on future cash flow’s projections, especially with respect to terminal values, which makes company valuations difficult for sellers or acquirers. In such unpredictable framework, many sellers wait better circumstances and higher multiples or terminal vales except if they are obliged to sell their company due to serious deterioration of financial situation last years, loss of internal financing and lack of external financing. A limitation in this paper is the sample size, and, therefore, the results obtained should be viewed in this context and interpreted with caution. As with most such studies, great care must be taken in generalising the results obtained. Nevertheless, mergers, acquisitions, and takeovers are phenomena that have been the focus of corporations, science, and legislative and regulatory bodies in the economies of many countries for years. Based on that, and the basis of the relative unknownness of these terms among Croatian managers determined by this research, it is possible to point out the need for further study of this topic. Acknowledgement This work was funded within the project line ZIP UNIRI of the University of Rijeka for the project ZIP-UNlRl-130-5-20.

Appendix: Business Merger and Acquisition Survey 1. Your position in the company is: (a) Managerial (b) Non-managerial 2. The company operates: (a) (b) (c) (d)

Less than 5 years 6–10 years 11–15 years More than 15 years

3. In what sector does your company operate? __________________ 4. Has your company applied a strategy to integrate business with another company so far? (a) Yes, successfully (b) Yes, unsuccessfully (c) Not

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5. If the answer to the previous question is YES, what kind of integration it was: (a) Merge (b) Friendly re-ional (c) Hostile takeover 6. How likely is it that your company will initiate some of the following forms of business integration in the future (5 years old)? Forms of integration Merge Friendly takeover Hostile takeover

1 (less than 50% Probability)

2 (50% probability)

3 (more than 50% probability)

7. How likely is it that your company will receive an offer in the future (5 years) or be the subject of some of the following forms of business integration? Forms of integration Merge Friendly takeover Hostile takeover

1 (less than 50% probability)

2 (50% probability)

3 (more than 50% probability)

8. If there is a likelihood that your company would be subject to a hostile takeover, what strategy would you use to prevent such a takeover? (a) (b) (c) (d)

Poison pill Crown jewels White Knight “Pacman” defense

IN MORE DETAIL ABOUT THE FACTORS AFFECTING THE LIKELIHOOD OF A HOSTILE TAKEOVER: 9. How do you assess the probability of a hostile takeover with regard to the development of capital markets in the Republic of Croatia? (a) Low (1) (b) medium-term (2) (c) high-level (3) 10. How do you assess the likelihood of a hostile takeover given the dependence of external financing of your company through capital markets in the Republic of Croatia? (a) Low (1) (b) medium-term (2) (c) high-level (3)

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Perspectives and Challenges in the Development of the Croatian Digital Startup Sector Mirjana Grčić Fabić

Abstract Startups are an inevitable part and active drivers of the digital economy. The number of digital startups is growing rapidly and their business model has proven successful in doing business in the context of the global health and economic crisis. The attributes of high innovation and significant potential for achieving rapid growth and a scalable economic model make them relevant participants in creating contemporary economic conditions, especially in the digital economy. The purpose of this paper is to point out the importance of the development of the digital startup sector as one of the crucial drivers of economic growth and development, and the achievement of both economic and social benefits. The research objective is to study and discuss the current state of the Croatian digital startup sector and to identify perspectives and challenges, as well as to put forward recommendations for its further development based on available secondary data. The analysed data suggest that the main determinant of the entrepreneurial ecosystem is its insufficient adaptation to the specific needs of startups, and hence the digital startup sector in Croatia is at a low level compared to other countries in the region of the same development group.

1 Introduction Digital transformation in the last two decades stimulates the development of digital entrepreneurship and leading political institution (European Commission 2015a, 2020a), and a plethora of actors belonging to the wide entrepreneurial ecosystem (Cavallo et al. 2019) are calling for research on how to foster digital entrepreneurship. In academia, a recent theoretical framework that describes and interprets the relationship between digital technologies and the entrepreneurial process is the

M. Grčić Fabić (*) Faculty of Economics and Business, University of Rijeka, Rijeka, Croatia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_9

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concept of digital entrepreneurship (Sahut et al. 2021; Giones and Brem 2017; Nambisan 2017). Sahut et al. (2021) define digital entrepreneurship as a process in which the entrepreneur creates digital value through the use of various sociotechnological enablers to support the efficient acquisition, processing, distribution, and consumption of digital information. The application of digital technologies transforms the nature of the entrepreneurial process, which is inherent in nonlinearity, complexity, experimentation, risk-taking, improvisation and effectual logic (Alsos et al. 2020, Lindholm-Dahlstrand et al. 2019, Kerr et al. 2014; Duening et al. 2012; Goel and Karri 2006; Sarasvathy 2001). In general, startups are the leading actors playing a crucial role in creating the digital economy and transforming the benefits of digital technology for economic activities and society. These players are also more likely to scale up their businesses and become high-growth companies. It is the digital technology that enables startups to achieve a scalable business model (Huang et al. 2017; Nambisan 2017; Van Welsum 2016) and, consequently, a significant impact on the growth and development of the economy (Ivanović-Đukić et al. 2019; Steve and Dorf 2014; Moreno and Casillas 2007). Startups are also very risky and unsteady, with a high probability of failure or destined to remain a small business. In the latter scenario, the contribution to regional growth can be very limited. Taking this into account, the incoming digital technologies in the realm of entrepreneurship represent a new challenge for entrepreneurs and policy makers, which is especially pronounced in doing business during the COVID-19 pandemic, which has only accelerated the transition to a digital economy. The purpose of this paper is to point out the importance of the development of the digital startup sector in Croatia as one of the crucial drivers of economic growth and development, and the realisation of both economic and social benefits. Also, the paper aims to emphasise the role of digitalisation for the functionality and vitality of the entrepreneurial ecosystem in the context of achieving a larger share of startups, i.e., high-growth entrepreneurs in the Croatian economy. The paper is structured in four sections. After introducing the context and the purpose of the paper, the theoretical background was set in Sect. 2. This is followed by Sect. 3, where the analysis of the digital entrepreneurship system in Croatia is presented. Finally, Sect. 4 discusses the results and concludes by setting recommendations as well as limitations and guidelines for future studies.

2 Literature Review Digitalisation of the economy and the benefits that can be acquired from the digital transformation is is very interesting to policy makers. The development of the digital economy is one of the crucial priorities of the European Union (EU) (European Commission 2015b, 2016, 2018) and the benefits it brings have been covered by research studies (Zhang et al. 2021; Ivanović-Đukić et al. 2019; Steve and Dorf 2014). Still, defining a digital economy is a notable challenge. Academic literature offers a multitude of different ways of perceiving the notion of the digital economy,

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which stems from different research interests and issues, and the many aspects that define the concept of the digital economy and the digital sector, making them thus more or less specified and detailed. Bukht and Heeks (2017) emphasise several perspectives in defining the digital economy; resource perspective, process/flow perspective, structural perspective, business model perspective. Furthermore, policy representatives struggle trying to bring forward a definition that will be able to support a broad political discussion and precise economic measurement at the same time. OECD (2020) in a recent study about defining a common framework for measuring the digital economy set out a comprehensive definition of the digital economy to serve different policy measurement needs: The Digital Economy incorporates all economic activity reliant on, or significantly enhanced by the use of digital inputs, including digital technologies, digital infrastructure, digital services and data. It refers to all producers and consumers, including the government, that are utilising these digital inputs in their economic activities (OECD 2020). Due to the ubiquitous impact of digitisation on all economic and social activities, Ahmad and Ribarsky (2018) refer to the multidimensional nature of the digital economy phenomenon, which crystallised a problem of clarity, and also a problem of scope in an attempt to embrace the concept. A pragmatic approach that serves primarily the purposes of statistical coverage, and is often present in the literature, defines the digital economy as an ICT sector, included in ISIC Rev. 4. However, such a narrow approach overlooks some desirable components, such as intermediary platforms, i.e., platforms delivering intermediary or zero-priced services, which certainly form an important part of digital economy activities. In this regard, Bukht and Heeks (2017) introduced a three-scope approach to understanding the digital economy, sometimes also referred to as flexible approach (OECD 2020, Fig. 1); (1) The digital sector: more often called the “IT sector” or the “ICT sector” represents a core element of the digital economy and currently covers ISIC industrial codes, ISIC Rev. 4, Section J, Information and Communication; (2) The digital economy: includes production of digital technologies (digital sector) and also their extensive application (activities not covered by ISIC Rev. 4, such as digital services, retail activities, content activities, and also platform activities); (3) The digitalised economy: corresponds to a wide range of ICT-enabled business activities and covers e-business, e-commerce (sub-set of e-business), algorithmic decision-making in business, use of digitally-automated technologies in manufacturing and agriculture including Industry 4.0, etc. For the purpose of this paper, the definition brought by Bukht and Heeks (2017) as a result of the aforementioned tiered approach, according to the extensive or intensive application of digitisation, will be accepted as a competent one, as follows: “that part of economic output derived solely or primarily from digital technologies with a business model based on digital goods or services”. The author believes that this framework is broad enough and can cover all the activities of the digital startups, the leading vehicles that convey the benefits of digital technology into the economy and society.

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Fig. 1 The scope of the digital economy. Source: Bukht, R., Heeks, R., 2017, Defining, Conceptualising and Measuring the Digital Economy, Development Informatics Working Paper no. 68, Centre for Development Informatics, Global Development Institute, SEED, University of Manchester

The concept of a startup is not clear-cut. One of the first and best-known definitions of a startup was set by Steve Blank (2005): “a startup is a temporary organisation searching for a repeatable and scalable business model”. The definition is very concise, yet broad enough to encompass the essence of these actors. However, in the literature, the term itself was often conceived in the beginning as an emerging organisation, sometimes equating the startup to any newly established company. Nevertheless, today it is gaining consensus in conceptualisation by the academic community, as well as by policy representatives. European Startup Monitor (ESM), the leading institution that deals with the development and promotion of startups defines a startup by three characteristics (European Startup Monitor 2019/2020): entity younger than 10 years has to have an innovative product and/or service and/or business model and has to aim to scale up (intention to grow the number of employees and/or turnover and/or markets in which they operate). It is precisely the characteristic of innovation and intention to grow and scale business activities that is crucial for the differentiation of startups from other newly established entities. As Blank (2013) argues, a startup must strive to be at the same time “repeatable” (the ability to be sustainable with recurring profit) and “scalable” (ability to serve profitably an increasing number of customers, that is, the ability to rapidly improve performance in the presence of low cost). Due to these features, the risk of failure is even more accentuated in comparison with the traditional new formed company (Bortolini et al. 2018; Kuester et al. 2018; Spender et al. 2017). Krishnan et al. (2020), in their study about distinguishing innovative startups among micro, small and medium-sized enterprises, reveal that startups are addressed in the literature by different names, such as hi-tech firms, university

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spin-offs, innovative start-ups, lean start-ups, Silicon Valley start-ups and new technology-based firms. According to the ESM, five of the top seven sectors in terms of new firms in 2018 were digital sectors, which indicates that digital startups account for the majority of new businesses created and, as claimed by Steigertahl and Mauer (2018), this share is growing. These entities are more likely to scale up their businesses and become high-growth companies, because the process of growth and development of digital companies differs significantly from the growth of traditional organisations, especially in the context of unpredictability and nonlinearity of the business process (Huang et al. 2017; Nambisan 2017). Digital technologies, such as artificial intelligence, digital 3D printing, social media platforms, big data, cloud technology, and mobile technology open new sources of efficiency and effectiveness of the entrepreneurial process, and provide a wide range of opportunities in the modalities of entrepreneurial activities (Von Briel et al. 2018). Digital business mostly implies lower start-up costs, large-scale access to markets and easier entrance to potential buyers and investors on a global scale, improved customer relations through social media and, consequently, the potential to grow and scale across borders very quickly (OECD/European Union 2019; Van Welsum 2016), i.e., affordances to quickly scale (Autio et al. 2018; Bukht and Heeks 2017), all of which represent the advantages of applying digital technology in business activities. This scaling ability allows digital startups to quickly transition to scaleups and become high-growth companies. Identification of scaleups is maybe even more important than startups. This claim is based on the cognition that startup is a fuzzy concept, which can be attributed to reasons of a subjective nature; it is often interpreted in different ways and sometimes equated with any newly formed organisational subject and objective reasons; startup refers to the beginning of a business activity marked with the shortage of resources, where the initial concept is still vague and ambiguous, both to interested stakeholders and to the entrepreneurs themselves, therefore, it is difficult to predict the potential for exceptional business growth and “pick winners” (Shane 2009). Some of these causes may explain the limited results of entrepreneurship development policies, related in particular to the contribution to local economic growth, innovation, firms’ survival and dissemination of knowledge and competencies, and go in favour of criticism of many policy programs and the approach of policy representatives (Brown et al. 2017; Hathaway and Litan 2014; Storey and Greene 2010; Shane 2009). Shane (2009) elaborates that policy representatives should stop promoting and subsidising the formation of the newly established companies without any undifferentiated approach to the prospects of value generation and job creation of these companies, and focus on the subset of businesses with growth potential. In the wake of such implications is the research discussion about the notion of quality of entrepreneurship and the cognition that “entrepreneurs are a small fraction of the business owners” (Carland et al. 1984). Scaleups represents firms that manage to pass through the startups’ early troublesome stage of development and achieve high growth in sales or employees, and are therefore often also called high growth firms, which make them a crucial source of economic growth and employment, especially in the digital economy. As Autio

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(2016) stated, digital startups are typically committed to testing and validating their business model, while digital scaleups already show significant traction on customers, a validated business model, and they have been funded through a first Series ‘A’ round – over one million $. Studies that deal with this phenomenon consider a high-growth company the one that has experienced an average annualised growth of 20% per annum over three years (European Commission 2020b). The majority of these companies refer to the high-tech sector, from which most startups emerge. Klincewicz (2005) delineates the high-tech industry as the one that covers companies developing digital technology based on advanced technical knowledge, such as software, hardware, and telecommunications. Also, this sector allocates a greater share of the budget to research and development, has a higher share of engineers and scientists in the employee structure, and focuses on technical specialisation. One of the indicators of digital startup ecosystem development (Senyo et al. 2019; Ojaghi et al. 2019) is the capital raised by the startups, especially the level of investment activity at the earliest stages of startup development, and also accelerated pace that this capital accumulates. For example, according to the study The State of European Tech, European tech companies founded in 2010 had raised a total of $2.8B by the end of their fourth year, while companies founded in 2015 had raised $14.4B by the end of their fourth full year, which indicates their ability to scale rapidly. The feature of rapid growth makes these high-growth companies a key source of economic growth and employment, bearers of innovation (Ivanović-Đukić et al. 2019; Steve and Dorf 2014; Valliere and Peterson 2009; Moreno and Casillas 2007), and also of special policy interest. The share of high-growth enterprises among SMEs in the EU-27 in 2020 was around 17% (European Commission 2020b) and this share has remained quite stable over the past few years. Due to their importance for the economy, it is certainly useful to statistically cover and monitor this group of organisational entities, but methodologically it is not an easy task. As stated before, there is a heterogeneity of the concept and, consequently, the criteria that would define such an organisational phenomenon. Rostek and Skala (2018) argue that there is a need for more quantifiable criteria that differentiate this population of enterprises that will allow for a clear distinction between startups and non-startups. According to such issues, many researchers opt for the approach that identifies startups by applying the method of self-identification (Jáki et al. 2019). The number of studies and research papers dealing with the analysis of national sectors of digital startups is very modest; specifically when it comes to the case of Central and Southeast Europe. This can probably be attributed to the reasons given in the previous section. State of European Tech (2020) reports about start-up activity that varies significantly at the country level across Europe and points to Estonia as the European capital of start-ups on a population-adjusted basis with 4.6 times more startups per capita than the European average. These results are not random but are a reflection of investing in a startup-friendly environment that is crucial for a startup activity to flourish (Ojaghi et al. 2019). Latvia is at the European average, Slovenia, Spain, and Austria are slightly lower than the average, while Ukraine, Albania, and Moldova are at the very bottom. The relative maturity of private capital markets is one of the signals of startup activity and a favourable

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startup ecosystem. One such indicator is the ratio of startups per 1 M population with the level of per capita investment, which also points to the density of entrepreneurial activity. Data generally follow the pattern; the denser the relative number of startup activity within a country, the greater the level of per capita investment. According to this indicator, Estonia shows underinvestment relative to the density of startup activity in the country, while Sweden has higher levels of investment per capita relative to the density of startup activity (State of European Tech, 2020). Studies about the country startup sector mainly involve research about the startups’ ecosystem, because of their dependency on appropriate context (Jáki et al. 2019; Ojaghi et al. 2019). Mason and Brown (2014) set a comprehensive definition of an entrepreneurial ecosystem as a „set of interconnected entrepreneurial actors, entrepreneurial organizations, institutions and entrepreneurial processes which formally and informally coalesce to connect, mediate and govern the performance within the local entrepreneurial environment“, where the dynamic and systemic nature of the concept was highlighted. According to the European Commission (Autio et al. 2020), entrepreneurial ecosystems are regional communities of entrepreneurs, business angels, accelerators, and other stakeholders who specialise in facilitating business model experimentation and related knowledge spill-over among new stand-up, start-up, and scale-up ventures. Skala (2016) presented the first research on the startups’ ecosystem and digital startups in Poland. The number of startups was estimated using the appropriate definition of a startup and by obtaining startup names from the following sources: venture capital funds, accelerators, entrepreneurship incubators, training companies, organisers of startup competitions, subsidy lists, lists from industry media sites, and the private rankings and databases of “startup activists”. The estimated number of startups was 2432, which is small in relation to the total population of the state. As reported by the State of European Tech, the European average for the startups per 1 M population is approximately 190 startups. The study also deals with the general characteristics of the Polish startups, such as startup profiles, business models and growth, resources, employment, export and innovation. A similar study was conducted in Hungary by the authors Jáki et al. (2019), where they presented research results on the Hungarian startup ecosystem. The focus was on the features of startup entrepreneurs, but also other stakeholders of the startup ecosystem, such as venture capital investors, incubator houses, accelerators, corporations, and co-working spaces. The identification of startups was made using the startup database Crunchbase, and the survey was prepared for the 200 startups from Hungary in August of 2017. Also, they identified 26 venture capital investors, 25 incubator houses and accelerators. A wide range of issues was covered by the research, such as demographic characteristics, motivational factors, scaling strategy, job creation, and financing concerns of the startupers, and also the evaluation of startup ecosystem factors. The results of the research revealed that social events (such as meetups and networking) are the strongest characteristic of the Hungarian ecosystem, while the access to entrepreneurial education, favourable tax environment for entrepreneurs, advanced entrepreneurial culture and a favourable level of required administration for entrepreneurs were found to be the weakest

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characteristics of the Hungarian startup ecosystem. Besides, the authors point out the important role of the Hungarian government in the development of the startup ecosystem. It especially refers to the fact that the development of the startup ecosystem has been covered by the government in its strategic documents and that the government established the biggest government-owned venture capital fund in the country. There is no similar research study for Croatia, except for the study that was conducted by Dukić et al. (2019) on the Croatian high-tech sector. Digital startups mainly operate in the high-tech sector, so the results of this study can contribute to the understanding of the development potential of the digital startup sector in Croatia. The observation period in the study was 2008–2017. The authors presented two separate groups of data; one for the number of high-technology manufacturing enterprises and the knowledge-intensive high-tech services. The latter group achieved better results in terms of the number of enterprises and number of employees, indicating that knowledge-intensive high-tech services have greater development potential compared to the high-tech manufacturing enterprises. High-technology manufacturing enterprises realised better results of turnover and production value indicators. The authors conclude that Croatia has not provided an environment and institutional framework that encourages high-tech investments. Furthermore, it was emphasised that Croatia had achieved a shortage of experts in many high-tech areas, which can probably be attributed to the education system that is inadequate in providing the business sector with the required workforce profile.

3 Digital Entrepreneurship System in Croatia Digitisation and use of information and communication technology tools imply recognising the specifics of the digital way of communication and potentially different dynamics of resource acquisition and achieving effects for the digital startups. The development of a supportive environment for startup entrepreneurship requires consideration of the specifics of the business development process of these entities, which especially refer to the startups in the digital realm. Given their distinctive attributes, the development of entrepreneurial ecosystem (Fredin and Lidén 2020; Ojaghi et al. 2019; Audretsch and Belitski 2016; Stam 2015) as a community of stakeholders that support the efficient and effective development of entrepreneurship, require different conditions for digital startups in comparison with the low-tech companies. According to the European Commission (2018), digital startups can be especially hindered by the difficulty in accessing talent, funding, and new markets. For example, these ecosystems cannot exist without competent, highly skilled human resources, which implies that the universities and other research and higher education institutions are a crucial part of these ecosystems. Context analysis is very important for understanding the digital startup sector because it provides insight into the processes that generate these activities.

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Metrics and data on individual startup ecosystems are more recent, and only starting from 2018 are these data monitored more systematically. This is most often a state-level analysis (for example, The European Index of Digital Entrepreneurship Systems or the Global Startup Ecosystem Index) or a regional analysis, such as a city-level analysis (for example Global Startup Ecosystem Index or The Global Startup Ecosystem Report). The European Commission has issued The European Index of Digital Entrepreneurship Systems (EIDES) metrics in 2018 to monitor framework conditions for digital entrepreneurship in the EU Member States. It is the most comprehensive systemic framework performance analysis focused on digital entrepreneurial ecosystems at a country-level in the EU. It monitors three kinds of framework conditions; general, systemic and digital framework conditions. General framework conditions apply broadly to the general context of doing business in a particular country and it consists of culture and informal institutions, formal institutions and regulatory framework, market conditions, and physical infrastructure. Systemic framework conditions specifically refer to the stand-up, start-up, and scale-up stage of the entrepreneurial life-cycle and include human capital and talent, knowledge creation and dissemination, finance, and networking and support. Digital framework conditions present the general level of digitalisation of the economy regarding entrepreneurial activity. The index is composed of a total of 16 pillars and it also calculates a measure of the digital context for each index pillar. Overall, EIDES value is the arithmetic mean of the measures for general and systemic framework conditions. According to the data, countries are classified into four groups, where scale can range from 0 (lowest) to 100 (highest); leaders (EIDES score above 60), followers (EIDES score above 45 and up to 60), catchers-up (EIDES score above 35 and up to 45) and laggards (EIDES score below 35). The data reveal that there are significant differences in the digital entrepreneurship ecosystem among the EU countries, even amongst the leading EU members. Taking into account 2020 data (Autio et al. 2020) the leader and follower group of countries are mostly comprised of the Nordic EU-27 countries and the Western European countries plus Estonia, while the catchers-up and laggards mainly consist of a mix of Southern European and new Member States, plus Greece. Table 1 shows 2020 EIDES digital scores for the laggards group, which includes Croatia, the EU-27 and the UK average. There are significant deviations of the digital scores of the countries in the laggards group in relation to the EU-27 and the UK average. Regarding Croatia, the best scores are in the start-up system, and this indicator is slightly higher than the average of the laggards group. Overall, Croatia significantly lags behind its EU peers. Slovenia, the Czech Republic, and Poland are in the catchers-up group with scores ranging from 37.3 to 43.1. What is interesting is that countries in the higher group (leaders and followers) generally have better results for the stand-up and scaleup system, while countries in the lower groups (catchers-up and laggards) generally have better results for the start-up system. Table 2 presents changes in EIDES scores in the period from 2018 to 2020 for the laggards group and the EU-28 average. Croatia achieved growth of EIDES indicator in the observed period and the average EIDES scores improved from 26.5 points in 2018 to 30.8 points by 2020

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Table 1 2020 EIDES digital scores for the laggards group, the EU-27 and the UK average

Country Hungary Latvia Slovakia Croatia Romania Greece Bulgaria Laggards EU-27 and UK average

Stand-up system Score Rank 32.5 23 33.5 22 32.4 24 29.9 26 30.1 25 27.4 27 26.3 28 30.3 48.6

Start-up system Score Rank 35.9 22 35.6 23 35.1 24 33.3 25 29.4 26 29.1 27 27.4 28 32.3 48.3

Scale-up system Score Rank 34.6 21 33.7 22 31.7 24 29.2 25 28.9 26 26.0 28 26.9 27 30.1 48.4

EIDES Score 34.3 34.3 33.1 30.8 29.5 27.5 26.9 30.9 48.4

Rank 22 23 24 25 26 27 28

Source: Adapted by the author based on Autio, E., Szerb, L., Komlósi, É., Tiszberger, M. (2020) The European Index of Digital Entrepreneurship Systems. Nepelski, D. (ed), EUR 30250 EN. Publications Office of the European Union, Luxembourg. https://publications.jrc.ec.europa. eu/repository/handle/JRC120727 Accessed 28 Sept. 2021 Table 2 EIDES scores and ranking, 2018–2020, for the Laggards group and the EU-28 average Country Hungary Latvia Slovakia Croatia Romania Greece Bulgaria EU-28 average EU-28 max. EU-28 min.

EIDES 2018 Score Rank 26.7 24 29.0 23 30.4 22 26.5 25 21.7 28 23.9 26 22.4 27 42.7

EIDES 2019 Score Rank 31.7 23 31.7 22 29.7 24 27.5 25 25.3 26 24.8 27 23.7 28 44.9

EIDES 2020 Score Rank 34.3 22 34.3 23 33.1 24 30.8 25 29.5 26 27.5 27 26.9 28 48.4

Change in Score, 2018–2020 7.6 5.3 2.6 4.2 7.8 3.6 4.4 5.8

69.7 (Denmark)70.9 (Sweden) 78.3 (Denmark) 10.2 (Ireland) 22.4 (Romania)23.7 (Bulgaria) 26.9 (Bulgaria) 1.5 (Malta)

Source: Adapted by the author based on Autio, E., Szerb, L., Komlósi, É., Tiszberger, M. (2020) The European Index of Digital Entrepreneurship Systems. Nepelski, D. (ed), EUR 30250 EN. Publications Office of the European Union, Luxembourg. https://publications.jrc.ec.europa. eu/repository/handle/JRC120727 Accessed 28 Sept. 2021

(4.2 index points). Certain countries moved to another category. For example, Slovenia and Poland moved from the laggards group to the cathers-up group. Table 3 presents a more detailed insight into the components of the digital entrepreneurial ecosystem, namely individual pillar values for the laggards group, the EU-27 and the UK average, which creates a broader background for the interpretation of the results achieved. EIDES framework implies partial nonsubstitutability across individual pillars, which means that an individual pillar can only partly substitute for

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Table 3 Pillar values of the EIDES 2020 for the Laggards group, the EU-27 and the UK average

Country Hungary Latvia Slovakia Croatia Romania Greece Bulgaria Laggards EU-27 and UK average

Culture and informal institutions 21.9 31.4 27.2 16.6 19.9 18.3 17.0 21.7

Form-al insti-tutions, regulation and taxa-tion 29.6 35.2 26.3 34.8 37.9 28.6 35.7 32.6

49.0

47.4

Market conditions 49.2 28.2 45.8 35.3 19.4 26.7 15.3 31.4

Physical infrastructure 50.0 52.1 38.0 44.7 58.7 30.2 39.0 44.7

Human capital 32.9 34.5 30.4 32.3 22.0 33.1 25.0 30.1

Knowledge creation and dissemination 36.8 22.0 37.8 25.5 29.2 26.8 24.1 28.9

56.3

55.4

50.7

46.7

Finance 38.8 47.4 38.1 42.0 35.5 40.5 33.0 39.3

Networking and support 33.8 34.7 29.8 34.6 35.7 32.4 37.7 34.1

EIDES 2020 score 34.3 34.3 33.1 30.8 29.5 27.5 26.9 30.9

49.4

48.2

48.4

Source: Adapted by the author based on Autio, E., Szerb, L., Komlósi, É., Tiszberger, M. (2020) The European Index of Digital Entrepreneurship Systems. Nepelski, D. (ed), EUR 30250 EN. Publications Office of the European Union, Luxembourg. https://publications.jrc.ec.europa. eu/repository/handle/JRC120727 Accessed 28 Sept. 2021

gaps in another individual pillar so each pillar may act as a bottleneck that holds back the performance of the entire system of general and systemic framework conditions. The first four pillars present general framework conditions of the digital entrepreneurship ecosystem. Croatia achieved the lowest value for the culture and informal institutions pillar in relation to all pillars, and also within laggards countries group. Generally, the average value of the culture and informal institutions pillar for the laggards group of countries has the lowest average value compared to other conditions, which is not the case for the EU-27 and the UK average. According to the variables that make up this indicator, it can be concluded that, for the laggards countries, pronounced level of corruption has a negative impact on entrepreneurial activity. Also, sociocultural norms reflect the highly present fear of failure among citizens in these countries. The physical infrastructure pillar has the highest value for Croatia. The EIDES pillar consists of two types of physical infrastructure; electricity infrastructure and transportation infrastructure, while the digital pillar complements it and reflects quality-related features, such as affordability, speed, security, and coverage of the digital infrastructure. The condition of physical infrastructure has the highest value of all the pillars for the laggards group, but also the same pattern is evident for the EU-27 and the UK average. The next four pillars refer to systemic framework conditions and they can be more directly related to the stimulating environment for the different stages of the

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entrepreneurial life cycle, i.e., the stand-up, the start-up and the scale-up stage. The same pillars are used for each stage but different indicators are taken into account in computing each stage sub-index, and also for its digital counterparts. Overall, Croatia scores best for the finance pillar and ranks second in the laggards group. The lowest value was achieved for the knowledge creation and dissemination pillar. Considering that Croatia achieves the highest digital scores for the start-up system index (Table 1), it can be concluded that finance is the most appropriate pillar for stimulating new digital startups, in relation to other conditions, such as knowledge creation and dissemination and human capital. This certainly does not mean that it is at a sufficient level of development. The financial stimulating environment is largely related to the accessibility of different forms of formal and informal investors to startups and scaleups, such as venture capitalists, business angels, new forms of alternative finance such as crowdfunding and peer-to-peer markets. The availability of funding is crucial for building the startup community, and especially for the scaleups to achieve their growth potential (Cavallo et al. 2019, Hellmann and Puri 2002). Relative maturity of the private capital markets represents one of the signals of startup activity development. The private capital market is not developed in Croatia and is small compared to its peer countries in the CEE region, especially compared to Croatia’s share in total regional GDP. According to the OECD (2021), private equity investment and divestment in Croatia account for 1.7% and 0.66% of the total private equity activity in the CEE region, which is significantly lower compared with Croatia’s 3.5% share in total regional GDP. Regarding the venture capital (VC) investments, the aggregate amount invested by venture capital funds in the past five years was only EUR 11 million, amounting to only 5% of the total investments in Croatia (OECD 2021), which is very low compared to the European and CEE averages. Most venture capital investments are concentrated in the start-up stage (91%), with only 3% in the seed stage and 6% in later stages. Also, government agencies are the largest source of capital for Central and Eastern Europe (CEE) (Invest Europe 2021), which also applies to Croatia. As the startup ecosystem develops and matures, the share of venture capital funding from government agencies is declining. In Europe’s most mature markets the share of VC funding from the government accounts for less than 10% of VC funds raised (Atomico 2020). For digital scaleups, which belong mainly to the high-tech sector, and are dominated by software companies, fintech and healthech, private equity investment have an important role. As reported by the State of European tech report (Atomico 2020) most of the private equity target companies are likely to be unknown to the dominant venture capital-backed European startups and scaleups ecosystem. Sometimes they are called ‘hidden giants’ and are often characterised by the use of bootstrapping scaling strategies. One such example of a large-scale tech company from Croatia is Infobip, the first Croatian unicorn, a startup company worth a billion dollars or more, emerged in the crisis year of 2020. For example, Germany has 15 such companies, France 8, and Japan 4 (Number of unicorns worldwide as of April 2021 2021). In view of these patterns, it is interesting to note that according to 2020 data (European Commission 2020a, b, Survey on the access to finance of

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enterprises) among the EU-27 countries, Croatian SMEs most often experience high growth (35%), and 17% of all SMEs in the EU-27 were considered a high-growth enterprise. Positive examples of digital startups and scaleup companies certainly imply the existence of a significant potential for further development of the digital startup sector in Croatia.

4 Discussion and Conclusion According to the presented data, it can be concluded that Croatia does not have a developed environment, i.e., a stimulating ecosystem for the development of digital startups. Croatia is in the laggards group of countries and at the bottom of the scale within the same group. As for the general factors of the entrepreneurial ecosystem, the biggest limitation relates to culture and informal institutions, and also formal institutions, regulation, and taxation. Physical infrastructure is the most developed factor and corresponds to the average level of the laggards group. Regarding the more specific factors of a certain phase of the entrepreneurial life cycle; stand-up, start-up and scale-up, the weakest factors are knowledge creation and dissemination and a closely related human capital factor, while better results were achieved for the finance and networking and support. These results are consistent with the research results of Skawinska and Zalewski (2020), where they found that the gap in key success factors of startups in the EU between highly developed and catching-up countries can be attributed to human capital and institutions. It can be concluded that the role of the government in creating stimulating policies in Croatia is still very weak and should become active to take advantage of the development of the digital startup sector as an opportunity for growth for the Croatian national economy. Recognising the specifics of the resource acquisition and business development, the dynamics of digital startups is the starting point in creating an appropriate ecosystem for digital startups and scaleups. The role of the government in creating this startup ecosystem is crucial, and is even more highlighted in the realm of digital entrepreneurship. The implications of new digital technologies on the entrepreneurial process are manifested in a much greater fluidity and porosity of borders in terms of the spatial and temporal dimension of entrepreneurial activities and at the same time a higher level of unpredictability and nonlinearity, a larger and more diverse number of actors and democratiation of entrepreneurship (Aldrich 2014). The role of economic policy makers in such conditions is in recognising these characteristics and creating an environment in which they will connect these numerous segments. Croatia does not have a specific strategic institutional framework for the development of an ecosystem that will be suitable to the actual needs, life-cycle and growth potential of digital startup and scaleup companies. For example, the Hungarian government has issued The Digital Startup Strategy of Hungary (Digital Success Programme 2016). Regarding the innovation policy in general, Croatia has adopted The Croatian Smart Specialisation Strategy (2016) to increase innovation capacity and research excellence of the Croatian economy, which can be linked to efforts to

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increase the share of innovative startup companies. As specified by the World Bank (2021) in its report on the analysis of the Croatian Smart Specialisation Strategy, there are significant limitations in the overall intervention logic of the strategy itself. Also, progress is not monitored for a significant number of indicators and contribution is not estimated. Croatia should certainly keep this in mind when planning a new smart specialisation strategy. Also, it should be noted that Croatia is quite absent from the modern world and European programs and projects in the field of development of adequate startup ecosystems. For example, 24 EU Member States and Iceland signed the declaration on March 19, 2021, on Startups Nations Standard of Excellence, to support startups across Europe in each stage of their development, and Croatia does not participate. Data on the availability of funding for the needs of startups and scaleups in Croatia support the still dominant bank-centric financial system, which does not meet the needs of innovative and risky startups, especially in the early stage and the scale-up phase. Venture capital investments in the past five years accounted for only 5% of the total investments in Croatia (OECD 2021), which is very low compared to European and CEE averages. There is also the potential to use other innovative sources of funding, such as business angels and crowdfunding. The share of venture capital funding from government agencies is a government policy tool to encourage the early stages of venture capital market development. Croatia also follows such an approach, and the establishment of the Croatian Venture Capital Initiative in 2018 by the Croatian government and the European Investment Fund can be highlighted as a positive example. Furthermore, the EIF and the Croatian Bank for Reconstruction and Development founded the venture capital fund Fil Rouge Capital, with EUR 42 million in fund capital (OECD 2021). Another example of a policy tool to support innovative and growing startups and scaleups is tax incentives to promote business angel and venture capital investment by reducing the cost of investing in such funds. According to the European Commission, Croatia does not operate such incentives. A further example of a government incentive for supporting digital startup activity is regulatory sandboxes, which represent a framework by which providing a structured context for experimentation allows, where appropriate, the testing of innovative technologies, products, services or approaches in a real environment in a limited period and a limited part of a sector or area under regulatory supervision, and thus ensuring the establishment of appropriate safeguards, especially in the context of digitization (Attrey et al. 2020). The first such initiative was created in 2012 and the first launching under this term was in 2015 in the U.K., and its application has been most evident in fintech. The emergence of digital startups due to their high innovation and knowledgeintensive products and services are closely linked to the presence of educational and research institutions, which represent a fruitful environment for spotting and creating new opportunities and launching them through digital startups. The lowest-rated Croatian results of the system conditions of the digital entrepreneurial ecosystem are for knowledge creation and dissemination, which implies the need for Croatia to achieve a higher level of participation in multistakeholder networks and innovation-intensive collaborations. There is plenty of room to improve and apply

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more innovative teaching methods that would be more adequate for a complex environment, such as student centered and experiential teaching methods to strengthen students’ entrepreneurial competencies. In this way, it is certainly possible to act in the direction of achieving positive change in the context of a positive perception of entrepreneurship in society and its greater affirmation. The development of the digital startup sector is particularly pronounced in challenging economic crisis periods, such as doing business in the COVID-19 pandemic. The COVID-19 crisis has accelerated the process of digital transformation of economies and encouraged entrepreneurs to create innovative digital solutions. Digital startups are the source of significant potential to increase the level of innovation and competitiveness of national economies and achieve economic growth and development necessary for the economic recovery in the pandemic (Skawinska and Zalewski 2020). In Croatia, the availability of data on the number and business activities of digital startups is at a very low level and requires comprehensive field research, and in this regard emphasises the need to implement a more exploratory approach in research studies. The lack of a unified record of the number of startups in Croatia and the absence of Croatia in the modern world and European programmes and projects in the field of analysis of startups, their features and trends of further development, was a difficulty in researching and drawing conclusions about the characteristics and quality of the digital entrepreneurship ecosystem. Also, in further research of the development potential of digital startups, it is preferable to focus more on the regional level, given the significant differences in the development of individual regions and the specifics of environmental factors. Acknowledgments This work is a part of the project „Development of management in the entrepreneurial economy and society“ supported by the University of Rijeka, grant number: 18-44 117.

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Pension Funds Regulation in the Context of Investment Climate Development Ivana Bestvina Bukvić, Dražen Novaković, and Ivan Kristek

Abstract Many Central European countries, including Croatia, have still not reached the desired regulatory and institutional level of the financial sector required for the development of a favourable business environment. The aim of this paper is to emphasise the key impact of financial system regulation on the development of investment climate by analysing mandatory pension funds (MPF) as an evergrowing element of the Croatian financial system. The objective of this paper is to show the quantitative impact of potential changes in the regulation of MPFs on investments in Croatia and, consequently, on the improvement of the business environment as an important element of economic growth. For this purpose, a comparative analysis of the regulation of MPFs in Croatia and the European Union (EU) was conducted and projections of a potential rise in investments were made, taking into account possible alignment with the trends in the EU-15 through regulation relaxation. The results clearly show that Croatia is slowly following EU trends, and it can be concluded that a change (in terms of relaxing) in MPF investment regulation can contribute to investment growth, improvement of the investment climate and business environment, and it can thus stimulate economic development, but with the careful assessment and balance of potential risk and returns of MPF portfolios.

1 Introduction The external business environment “comprises a wide range of influences—economic, demographic, social, political, legal, technological, etc.—which affect business activity in a variety of ways and which can impinge not only transformation process itself, but also on the process of resource acquisition and the creation and

I. Bestvina Bukvić (*) · D. Novaković · I. Kristek Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Osijek, Croatia e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_10

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consumption of output” (Worthington and Britton 2009) Consequently, the business environment or investment climate can be defined as a set of factors, policies and institutions (Aterido et al. 2009) that affect businesses, their performance and growth in different ways. As a positive business environment has a positive impact on firms, a poor one tends to harm the growth of businesses of all sizes (Aterido et al. 2009). A hostile business environment may arise in an extreme economic distress (Bosio et al. 2020) in the form of difficult access to finance and uncertainties that affect confidence in the steadfastness of business partners, the economy, and the government. Moreover, the massive legislative changes which change the financial and cost objectives are the main reason for the instability of the business environment (Koišová et al. 2017). Together with complex rules and regulations, restrictions on institutional financing and extreme bureaucracy and regulatory constraints in raising capital, tempt entrepreneurs to turn to corrupt activities to obtain financial support (Tonoyan et al. 2010 and Missaoui et al. 2018 according to Cepel et al. 2019). Consequently, finance, crime, and political inconsistency are binding constraints that are directly related to the business growth rate (Ayyagari et al. 2008). It is obvious that financial opportunities are a key determinant of the business environment, but at the same time, an important obstacle for most companies (Cepel et al. 2019) which can be reduced by providing access to capital through equity funds supported by released liquidity from mandatory pension funds (MPFs). Obviously, the question of adequate evaluation of risk and return on such investments which should be conducted by institutional investors is, at the same time, extremely important (Kaminker and Stewart 2012; Knežević 2021). Through literature review and the analysis of the differences in regulation among the EU countries, the following research question was posed: What is the potential of deregulation of MPFs investment opportunities in terms of their eventual contribution to the growth of business sector investments? This chapter is organised as follows: The following subchapter provides a literature review on the topic of business environment and investment climate, MPFs regulation, and investment opportunities. The third subchapter describes research methodology, followed by the results of the research conducted on the Croatian market. Through this subchapter, the EU-10 and EU-15 countries’ pension funds’ investments in alternative assets were compared with the level of the investment of the same type in Croatia, and the projection of the value of the additional funds available for investments in private equity and projects was made. Also, for the purpose of the full context overview, the liquidity trends were taken into account. The obtained results were used to synthesise the conclusions presented in the last chapter.

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2 Literature Review Recently, there has been an increased interest of institutional investors and academics in alternative investment opportunities (Aubry et al. 2017; Bonizzi and Churchill 2017; Choudhary and Papanikolaou 2017; Segal 2020; Gillers 2021). There are many common reasons for the shift from traditional forms of investment towards alternative opportunities, but they can, as well, vary depending on the type of investor under consideration (Aubry et al. 2017; Peng and Wang 2019; Shergold 2021). Given their exceptional role in the Croatian financial market and investment potential in SME financing, the focus is on pension funds as institutional investors facing frequent reviews and proposals for change. Not only do the improvements in MPFs investment opportunities have an impact on their investors (citizens), but also companies, investment funds (equity, hedge, etc.) and the economy as a whole through improved investment climate by released liquidity of MPFs, which has the power to improve the business sector investments. The economic (macroeconomic environment of the SME segment, monetary policy and interest rates, financing, consumption, and changes in income) and non-economic factors affect the perception of the quality of the business environment (Cepel et al. 2019). Ayyagari et al. (2007) define business environment indicators as “easiness of industry entry and exit, sound contract enforcement, effective property rights, registration, and access to external finance” which can influence the development of the business sector in the sense that they can indicate the opportunities or possible constraints and threats for business growth. According to the research conducted by the World Bank, there has been an increase in confidence in the importance of the following business environment elements: access to finance, infrastructure, and human capital (Zhenwei Qiang et al. 2020). It is obvious that financial opportunities are a key determinant of the business environment, but at the same time, an important obstacle for most companies (Cepel et al. 2019) where access to finance and legal stability appear to have an even stronger impact as the economy matures (Xu 2011). It is undeniable that ‘business environment’ and ‘investment climate’ are closely related concepts and sometimes even used as a synonym (Aterido et al. 2009). The investment climate is defined by Stern (2002) as “policy, institutional, and behavioural environment, both present and expected, that influences the returns, and risks, associated with investment”, where improvements in investment climate can lead to productivity gains (Escribano et al. 2019). Escribano et al. (2019) classify investment climate into the following blocks: Infrastructure, Bureaucracy, Corruption and Crime, Finance and Quality, Innovation, and Workforce Skills, referring to this model as a constrained Cobb-Douglas production function. Together with other elements, financial sector reforms, with a purpose to ease financing constraints, these are probably the most effective means for economic growth (Ayyagari et al. 2008). Access to external finance for investment financing “has a larger positive effect in the growth of small, medium and large firms, particularly in economies with more developed financial systems or better rule of law” (Aterido et al. 2009). In this

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context, one can speak of a favourable investment climate, which is the basis for increasing investment capacity and the subsequent growth of reproduction, the efficiency of use of the investment opportunities and development at all levels (Irtyshcheva et al. 2020). Regardless of the regulatory constraints on institutional investors activities, MPFs as its sub-segment undoubtedly represent the potential for investments in private equity. This is also underlined by the fact that the number and activity of individual investors are insufficient, especially in small and underdeveloped capital markets (Kovač et al. 2018) to meet its liquidity needs. Allowing institutional investors to play a greater role in the capital markets of small and medium-sized companies, one can realise a double impact of institutional investors. Fist relates to the realisation of higher benefits for their investors, as institutional investors are more sophisticated investors than individuals, and the other relates to the companies through an advisory role and monitoring and thereby reducing the probability of occurrence of crises in the companies held by institutional investors (Parthiban and Kochhar 1996). This helps to improve the investment climate, the business environment, and the economy as a whole. Naturally, to invest in the equity markets, institutional investors must have an appropriate risk-return profile (Kaminker and Stewart 2012), as is the case with their other market-oriented investments. The increase in pension funds’ exposure to equities at a global level is related to the fact that they benefited from the positive performance of equity markets between 2014 and 2017. In terms of asset management in Europe, pension funds lead with 29%, insurance companies with 25%, banks with 2%, and others with 17%. Turkey, the United Kingdom, Greece, and Denmark were the leading European countries with 55%, 45%, 30% and 27% respectively of pension funds’ contribution to asset management. At the European level, 28% of pension funds’ assets are invested in equities, 42% in bonds, 6% in cash, and 24% in other investments (EFAMA 2020). The results of the survey, conducted among 100 private equity funds on their investment plans in private equity over the next 12 months (forecast 2021), show that 45% of respondents plan to invest more and 39% plan to maintain the current level of investments (Bain & Company; Private Equity International 2021 according to Statista 2021). Looking at the level of Croatia, pension funds accounted for only 5% of asset management at the end of 2018, while investments by all institutional clients were 28%, led by insurance companies at 10% and followed by banks (EFAMA 2020). The European Commission (2020) identifies the causes of Croatia’s productivity deficit in the lack of skilled labour, low investment in R&D, inflexible business environment, and shortcomings in state administration. The ability to increase private sector investment is constrained by unremitting high levels of corporate sector debt and the complex business environment. The inflow of investment into Croatia is hindered by the institutional weaknesses combined with the high share of state-owned enterprises. Moreover, the business environment in Croatia is burdened by excessive restrictions and regulations, which limits competition and hinders investment. However, a number of measures have been taken to reduce excessive administrative obligations and liberalise services, which has led to some

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improvements. High kuna liquidity in the banking sector continued to grow, supporting a favourable financial environment (EC 2020). The Croatian Recovery and Resilience Plan should improve the business environment in Croatia and remove obstacles to growth and investment, as repeatedly noted in the Council’s recommendations (European Union Council 2021). Although access to finance has improved and sources of finance have been diversified, 55% of medium-sized and 77% of small businesses believe that the current financing conditions in Croatia make it difficult to operate (Vujčić 2019). In contrast, by the World Bank’s 2019 Enterprise Survey, 68.3% of the 404 Croatian companies claimed that they do not need a loan, and only 6.1% claimed that access to finance is a major obstacle as they are mainly internally financed (85.6% of respondents). Compared to other EU-10 countries (Bulgaria, Romania, Poland, the Czech Republic, Slovakia, Lithuania, Latvia, Hungary, Estonia, and Slovenia), where on average 10.2% of enterprises report access to finance as a major obstacle, and 8% of enterprises having their last loan application rejected (3% in Croatia), these results seem better for the Croatian investment climate. If we compare the results on Croatian with European and East Asian levels, the difference is even greater (World Bank 2019). The question, therefore, arises whether access to finance is an objective barrier for business development in Croatia or whether the lack of quality investment opportunities and uncertainty in the business environment is preventing companies from investing more and expanding.

2.1

Pension Funds

Before analysing the research on pension fund investments, it is necessary to provide an overview of the research on alternative investments in general. The main problem is to determine what is meant by alternative investments. A simple approach is to determine what is not an alternative investment—stocks, bonds, and cash (Aubry et al. 2017; Bonizzi and Churchill 2017), regardless of whether these securities are held directly or indirectly through mutual funds. All other types of investments are alternative investments, with four main categories—hedge funds, private equity, real estate, and commodities (Rose and Seligman 2016; Aubry et al. 2017). Hedge funds enable investments that provide a hedge against market risk, i.e., market movements or increase market exposure to generate higher returns (Aubry et al. 2017). Private equity funds refer to transactions involving private capital or debt, or private companies that are not listed on a stock exchange (Peng and Wang 2019), while commodities include all real assets with intrinsic economic value, such as energy, currency, gold, metals, livestock, etc. (Aubry et al. 2017). Real estate refers to direct or indirect investments in commercial and residential properties (Aubry et al. 2017). Investments in infrastructure projects can be considered as a special category (Kowalczyk-Rólczyńska and Rólczyński 2016; Gillers 2021). Alternative investments are characterised by a long-term investment horizon, illiquidity risk and a

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private character, as they are not traded on the public market (Shergold 2021; Gillers 2021). Among the advantages of this type of investment, the effect of diversification or reduction of portfolio volatility is certainly noteworthy, as the correlation between the returns of traditional and alternative investments is low (Aubry et al. 2017; Bonizzi and Churchill 2017). Of course, there is also the potential to generate higher returns over the long term due to the risk premium and liquidity premium (Aubry et al. 2017; Peng and Wang 2019; Shergold 2021), as well as the possibility of inflation hedging (Shergold 2021). However, there are also important challenges. Firstly, alternative investments require a proper understanding of their complex nature and risks (Aubry et al. 2017). Secondly, because they are often illiquid and not publicly traded, it is not easy to assess their value (Peng and Wang 2019; Gillers 2021). Thirdly, due to their complex nature, a financial intermediary is often involved, leading to higher transaction fees and costs (Peng and Wang 2019). Finally, illiquidity itself can pose a risk to investors (Aubry et al. 2017). Alternative investments became popular in the early part of the century as traditional equities seemed unable to continue generating relatively high returns while falling interest rates made bonds less and less attractive investment options (Aubry et al. 2017). In addition to the changes in financial markets, pension systems have faced other difficult challenges in recent decades. A rising proportion of the elderly population combined with declining birth rates is pushing pension systems towards individual funded savings schemes (Norrestad 2021). Moreover, driven by market developments and having experienced two recessions in two decades, pension funds are increasingly directing these capitalised savings into alternative instruments (Rose and Seligman 2016; Choudhary and Papanikolaou 2017; Segal 2020; Gillers 2021). There are several reasons for this investment strategy. The most important one is to achieve higher expected returns combined with possible lower volatility, as mentioned earlier, which also reduces the beta of the portfolio (Rose and Seligman 2016; Stalebrink 2016; Shergold 2021). As globalisation and the interconnectedness of world markets mean that geographic diversification is no longer optimal, funds have turned to diversification by investing in new instruments (Stalebrink 2016). This trend is also encouraged by the prudent investor rule, as it leads to herding effect— funds follow the tactics and strategies of others (Rose and Seligman 2016). The fact that pension funds are long-term investors reduces the risk of investing in illiquid assets (Defau and De Moor 2021; Shergold 2021). However, caution is needed as product information is not as readily available as for traditional exchange-traded instruments (Rose and Seligman 2016). In addition, the market for alternative investments is not as deep and broad as for traditional markets, and increasing investor interest will make it more difficult to find profitable investments over time (Peng and Wang 2019). However, forecasts of financial market developments do not suggest that this trend of increasing exposure to alternative investments is over (Gillers 2021). Rose and Seligman (2016) found that the US pension plans held an average of 3.62% of assets in alternative investments over the period 2001–2011, rising to 21.8% of plan assets (8.3% real estate, 7.2% hedge funds, 6.3% private equity) by 2013 and 24% by 2015 (Aubry et al. 2017). Japanese pension funds also

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plan to increase their exposure to alternative assets in the medium term by investing in infrastructure, real estate, and private equity funds, but only to 1.6% of their portfolios (Yamaguchi 2021). Kowalczyk-Rólczyńska and Rólczyński (2016) analysed the Polish voluntary pension scheme and found that investments in residential real estate, silver and gold as a supplement to the traditional portfolio significantly affect the control of investment risk, as there is no (or a negative) correlation between traditional and alternative investments. Using US data from 2001 to 2014, Peng and Wang (2019) found a positive effect of private equity (and alternative investments in general) on investment performance, although the effect was not large or sustainable. Defau and De Moor (2021) concluded, based on data from 2000 to 2015, that the trend toward diversification, rather than the low-interestrate environment, was the key driver of the growing interest in alternative investments. Using data from Swedish pension funds, Stalebrink (2016) analysed the reasons for the discrepancy between the observed level of alternative investments and the level predicted by modern portfolio theory, as many public pension systems have no or only a small portion of their assets invested in alternative investments. Possible causes include investment restrictions, limited access to alternatives, the characteristics of alternatives, as well as political considerations.

2.2

Regulation

The analysis of previous research has already shown that regulation is crucial to the strategic direction of the pension fund industry and can lead to prosperity or the complete closure or freezing of the individual capitalised savings model as such (Altiparmakov 2018). Studies analysing the impact of regulation and its changes on the situation in the industry are widespread in foreign literature, while they are less represented in Croatia. The analysis conducted by Boon et al. (2018) shows that regulation strongly influences the allocation of funds, regardless of whether they are public or private. An older study by Srinivas et al. (2000) comparing Latin American, Eastern European and OECD countries generated conclusions that are still relevant today. Although regulation is often initially very strict due to the risk constraint, in the medium term, only its mitigation can lead to the successful development of the pension system and investment climate. Thus, domestic restrictions initially protect against the fragility and inexperience of the system and the shallow and underdeveloped market, but may later limit market development because pension funds hold a significant portion of the instruments offered as long-term investments, thereby undermining market liquidity. Therefore, Grubišić Šeba (2017) calls for an active role of both the regulator and the stock exchange to ensure that there is always a supply of corporate securities in the market. This is particularly important in bank-centric economies such as Croatia, where the population invests almost exclusively in bank deposits and real estate despite low-interest rates. This fact is even more unusual when taking into account the state’s generous

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incentives for voluntary pension saving (Olgić Draženović 2021). High-quality regulation should provide a transparent and secure basis for investment (Chohan 2018) and encourage new capital market processes such as privatisation or other market offerings (Volarević et al. 2021). Otherwise, it may have a restrictive effect on larger and longer-term investments by pension funds in the domestic market (Sopta et al. 2021). In this context, it is important not to encourage the holding of highly liquid instruments that do not yield high returns and to support the establishment of domestic platforms and investment funds so that capital does not leave the country (Evans and Harper 2021). Allowing frequent changes in the selected fund also negatively affects long-term investment orientation, as does monitoring based on short-term and relative performance indicators and rewarding managers for shortterm results (Morales et al. 2017). Matek and Galić (2017) analysed the impact of regulatory changes in Croatia in 2014 and concluded that funds continue to invest predominantly in Croatian government bonds, partly due to the generous supply and partly due to the significantly lower yields of developed country debt instruments. On the other hand, Ridzak and Žigman (2020) point to the opportunities for sustainable green investments by pension funds and the importance of a harmonised regulatory approach across the EU countries. The topic of voluntary pension funds and the management of their domestic and foreign assets has been studied by Volarević et al. (2021), as well as by Olgić Draženović (2021), who highlight the lack of financial literacy and education that prevents the further development of voluntary pension saving. Matek and Galić (2017) studied the mechanism of guaranteed return in mandatory pension funds, and Olgić Draženović et al. (2019) studied their effectiveness using the DEA method and concluded that all mandatory pension funds are equally relatively inefficient and behave like a herd due to the set of regulatory investment framework and the lack of competition in the market. Sopta et al. (2021) studied pension fund investments in the Croatian tourism sector and found that both sides benefited from them—the funds made a sustainable, positive long-term investment, and the tourism sector found the partner needed to finance future growth.

2.3

Croatian Mandatory Pension Funds Investment Opportunities

A novelty on the capital market, further encouraged by the economic consequences of the COVID-19 pandemic, is the step taken by the Croatian Financial Services Supervisory Agency (HANFA) and the establishment of the Stability Fund, in which pension funds invested about HRK 500 million to help investment funds and insurance companies in need of additional liquidity (Žigman 2020). Thus, pension funds and HANFA show that the second pillar of pension insurance has entered a phase of playing a more active role in the economy and that funds can participate in economic recovery and possible future privatisation. In the future, more emphasis

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will be placed on green investments and HANFA will strive to increase its attractiveness by law (Žigman 2020). Furthermore, in June 2021, HANFA issued a decision on the establishment of a closed-end alternative investment fund with a private offering of a combined investment strategy ‘The CRO Value Fund’. This is a fund that can invest in a broader range of assets, such as companies that are not present in the capital market and require additional investment and business development (HANFA 2021). In countries with a more developed sector of non-bank financial intermediaries, alternative funds are important providers of funds for the growth and development of some companies and are an important complement to bank financing, thus contributing to the development of investment climate and business environment while seeking to increase the value of the target companies. It is planned that the CRO Value Fund will have an asset size of one billion kunas (with a possible maximum of up to 1.5 billion) and pension funds as main investors. The fund will invest in the capital of small, medium-sized and large companies and influence the improvement of their business by strengthening management skills and introducing operational improvements. Under the fund’s rules, project financing will include financing the development of new products and technologies, financing capital expenditures, expanding or restructuring the company’s operations. At the same time, the fund will be able to invest up to 40 percent of its assets in strengthening construction infrastructure through public-private partnerships, making it the first such fund on the Croatian market (HANFA 2021). However, the importance of regulation has come to the fore again. Namely, the problem is the fact that MPFs have investment limits, according to which no pension fund can invest in more than 10% of the total value of a single equity fund (Knežević 2021). So, pension funds can still invest in the Cro Value Fund, but not in the amount currently planned. The announced legislative changes provide for broader investment opportunities. It was expected that HANFA would grant pension funds a temporary exemption from investment limits for one year, i.e., until the legislative amendments are passed. In any case, it is hard to believe that none of the parties involved knew about the existing legal provisions (Knežević 2021). In the long run, however, the announced legislative changes and liberalisation of pension fund investments are important. There are amendments announced to three laws in this area in 2022—the Law on Mandatory Pension Funds, the Law on Voluntary Pension Funds, and the Law on Pension Insurance Companies (Knežević 2021). Liberalisation of pension fund investments is planned through increased participation in infrastructure projects and greater investments in companies owned by the Republic of Croatia, which is also planned as part of the implementation of the National Plan for Recovery and Resilience 2021–2026. Additional administrative relief of pension fund costs is also envisaged (Knežević 2021). The proposal also includes the possibility of increasing pension funds’ investments in alternative investment funds. However, it will require pension funds to increase their safeguards with HANFA as a momentous monitoring institution.

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3 Methodology and Results of the Research To determine the potential of flexibilisation of the MPFs investment options in terms of increasing their impact on business sector investments growth, the research of secondary sources of information was conducted. Information from the OECD, Croatian Bureau of Statistics and Eurostat, HANFA, and research results of other authors were primarily analysed. The descriptive statistics was used for comparing the level of investments of pension funds of the EU-10 and EU-15 countries in alternative assets with the level of investments of the same type in Croatia, and extrapolating the value of additional funds available for investments in private equity and projects. To determine the absorption potential of the Croatian economy, data on the level of liquidity were taken into account. A comparative analysis of the investment structure of pension funds should always begin with an analysis of assets relative to the size of the economy, as shown in Table 1. Indeed, the historical and economic development of the EU Member States was very different. Therefore, when analysing the specific elements, one should always keep in mind the importance of the pension fund industry in a given country, as this also influences the regulation. Given the large differences between the EU countries, the comparative analysis includes only data from the transition countries categorised as EU-10, as they are historically and economically comparable to Croatia. Also, the comparison with EU-15 is shown to find solutions for the needs of MPFs based on a good practice carried out in developed countries. In most countries, bonds and equities were the two main asset classes at the end of 2020. The combined share of bonds and equities was highest in Romania (98.6%), while Croatia (95.1%) was among the top 10 countries (OECD 2021b). Alternative investments had a relatively small share of pension assets in most countries. As shown in Table 1, there is considerable variation across the old EU Member States in pension fund investments in land and buildings, hedge funds, and private equity funds. However, the focus is on investments in land and buildings, while hedge funds are the least represented. The differences between the new (transitional) EU Member States are even greater. The same trend can be seen in the structure of investment as in the old members, but the amounts invested in relation to assets are many times lower. An explanation for such an investment structure is provided by the analysis of pension fund investment regulation presented in Table 2. The constraints shown in Table 2 are very different across the observed countries, as well as the data on alternative investments from Table 1. Direct investment in real estate for pension funds is prohibited in many countries, including Croatia. However, in most countries, only direct investments are not allowed. Indirect investment may be allowed to some extent through bonds and shares of property companies or real estate investment trusts (OECD 2021b). Some countries have recently relaxed investment limits and encouraged investment in infrastructure, long-term projects, and other alternative investments. For example, while maintaining very stable general regulation and contribution levels to the second pillar (Altiparmakov 2018), Croatia has expanded investment opportunities for mandatory pension

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Table 1 Pension assets as a percentage of GDP and share of alternative investments in MPFs assets in 2020 in the EU member states Country Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden United Kingdom EU-15 Bulgaria Czech Republic Estonia Hungary Latvia Lithuania Poland Romania Slovakia Slovenia EU-10 Croatia

% of GDP 6.6 9.5 58.3 56.4 2.5 8.2 1.0 33.4 9.8 2.9 212.7 11.4 10.5 4.2

14.8 9.5 19.8 4.0 2.1 9.5 6.5 7.4 14.4 6.6

% of assets Land and buildings 0.2 0.4 0.2 11.7 2.9

0.7 0 8.2 7.4 0.1 1.7 1.6 2.3 1.6 0.3 0.0 0.5 1.6 0.0

1.1 0.5

34.6

Hedge funds 0

Private equity funds 2.4

0.1

1

0 1.4 0.9 1.2

0 3.9 0 0.9

2.3 0.4

0.8 0.6

0.0 0.0

0.0 0.0

0.0 0.0 0.0 0.0 0.5

Source: Authors’ work (processing by OECD (n.d.))

funds, allowing them to invest in infrastructure projects directly and in alternative investment funds (OECD 2021b). A closer look at the structure of pension fund investments in Croatia over the last five years shows that changes have been very cautious and slow. As the data in Table 3 show, the majority of funds are invested directly or indirectly in bonds and equities. Since the share of the main classes changes only slowly, it is more interesting to analyse the trends in the share of alternative investments, more precisely investments in alternative investment funds. Table 4 shows that in the less conservative funds of category A, investments are almost exclusively in foreign funds, with significant growth, albeit with very small amounts in total assets.

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Table 2 MPFs portfolio ceilings in 2020 for two alternative investment types in the EU-10 countries (Systems with MPFs) and Croatia Country Bulgaria Croatia

Real estate Supplementary MPFs
5% (direct) MPFs—Category A, B and C: 0%

Estonia

MPFs: 40% (Total) Comments: 10% to single property State MPFs: 0% (Direct) Pension Asset Preservation Fund, 0% The target group pension funds and supplementary accumulation for pension in pension funds: 0% (Direct)

Latvia Lithuania

Poland

Open pension funds, Employee pension funds and Employee Capital Plans: 0% (Direct)

Romania

Private pension fund—second pillar: 3% (Direct)

Slovakia

Privately managed MPFs: the mortgage bonds—not more than 50% of the net asset value (Direct) Not more than 10% of the net asset value to a single property. Max 20% direct

Slovenia

Private investment funds Supplementary MPFs—0% (direct) MPFs—Category A: 15% of the NAV (Direct). MPFs—Category B: 10% of the NAV (Direct). Limit for alternative investment funds with a private offering (marketed to institutional investors only). MPFs fund—Category C: 0% MPFs: 100% (Direct) State MPFs: 15% (Direct). Non-UCITS. Pension Asset Preservation Fund: 0%. The target group pension funds: 20% (Direct). According to law II, pillar pension funds can invest up to 20% to non-UCITS (or similar) funds. Supplementary accumulation for pension in pension funds: 30% (Direct) Open pension funds and Employee pension funds: 0% (Direct) Employee Capital Plans: limit for the life-cycle fund: 10%—refers to a closedended fund Private pension fund—second pillar: 10% (Direct). This limit refers to private equity. Privately managed MPFs: 0%.

Max 30% in non-UCITS open-end funds, 5% financial instruments issued/ guaranteed by the EIB.

Source: Authors’ work (processing by OECD (2021a))

The relations between the observed variables are not significantly different; not even for category B funds. The Croatian economy cannot be satisfied with the fact that only HRK 46 million was invested in domestic alternative funds, and even more serious is the fact that the value of domestic investments has declined over the past three years. Given the pronounced bank-centricity of the economy, which is evident from the data in Table 5, pension funds, as the strongest institutional investors, need to be empowered and encouraged to turn to alternative sources and provide the Croatian economy with a stable source of funding outside the banking sector.

Pension Funds Regulation in the Context of Investment Climate Development Table 3 Structure of assets of croatian pension funds (%)

Type of assets/Year Cash and Deposits Bills and Bonds (public and private) Loans Equity Land and Buildings Mutual funds (CIS)—of which: Cash and deposits Bills and bonds Equity Land and buildings Other Hedge funds Private equity funds Other

169 2015 1.9 73.2 .. 19.0 .. 5.6 15.7 .. 84.3 .. .. .. 0.2 0.1

2020 4.1 68.9 .. 18.5 .. 7.8 .. 20.3 79.2 0.4 0.1 .. 0.5 0.3

Source: Authors’ work (processing by the OECD (n.d.)) Table 4 Investments of A and B mandatory pension funds in Alternative Investment Funds (AIFs) (000 HRK) Type of Assets Domestic AIFs—A Foreign AIFs—A Total assets—A Domestic AIFs—B Foreign AIFs—B Total assets—B

Dec. 2015 0 194 423,824 179,738 9529 72,452,964

Dec. 2018 0 1437 657,089 186,801 121,566 93,012,721

Dec. 2020

Oct. 2021

0 6247 986,920 79,088 498,032 111,364,775

82 7621 1,365,330 46,612 862,197 121,543,450

Source: Authors’ work (processing by HANFA (n.d.)) Table 5 Structure of the croatian financial sector Type of intermediary Banks, housing savings banks and credit unions Pension funds Insurance and reinsurance undertakings Investment funds Leasing societies Other

Total assets (HRK billion) 450.3

Share (in total) 68.1%

40 15

119.1 46.6

18.0% 7.1%

29.6% 11.6%

136 15 33

21.9 20.7 1.7

3.3% 3.1% 0.3%

5.4% 5.2% 0.4%

Number 44

Share (in GDP) 111.9%

Source: Ridzak and Žigman (2020)

By OECD (n.d.), Croatian MPFs are investing 0.46% of their assets in private equity and projects (private equity, hedge funds, lands and buildings). The EU-10 countries on average invest 0.50% and the EU-15 3.34% of their assets in the same

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Table 6 Potential for investment growth (in 000 HRK)

Year 2017 2018 2019 2020

Gross investment in new and existing fixed assets (excluded activities connected to statea) 44,605,640 45,646,711 51,365,492 45,919,522

% of annual change

Investment potential (2.89% of total A and B category MPFs assets)

Potential for investment growth

2% 13%
11%

2,691,703 3,085,906 3,225,848

5.90% 6.01% 7.03%

a

Activities primarily connected to state: Human health and social work activities and public administration and defence; compulsory social security; education Source: Authors’ work (processing by OECD (n.d.), Croatian Bureau of Statistics (2021) and MPFs (2021) data)

Table 7 Total liquid assets in croatia (000000 HRK) Period Dec. 2018 Dec. 2019 Dec. 2020 Oct. 2021

Total liquid assets (M4) 324,030.2 333,306.5 364,466.2 399,515.6

Percentage of growth in comparison to the previous year 5.5% 2.9% 9.3% 9.6%

Source: Authors’ work (processing by the CNB (n.d.))

types of assets. Table 6 shows the results of the analysis of investment growth potential based on additional liquidity that could be directed towards private sector equity from MPFs in the case of MPFs regulation reform. According to the Croatian Bureau of Statistics (2021), there was a negative investment trend of 6.2% in 2020 as of economic consequences of the COVID-19 pandemic. Additional liquidity directed towards private equity from MPFs could contribute to growth in investments of the private sector. Table 7 gives information about trends of total liquid assets in the Croatian monetary system. Despite the economic consequences of the COVID-19 pandemic, the banking sector increased its liquidity and capitalisation which, together with positive trends on international financial markets, resulted in favourable financing options for the business and public sector (CNB 2021). Liquidity is many times higher than the total value of investments made (Table 6) and therefore, the lack of financing does not seem to be a problem, but the reasons for the low investments probably lie in other economic factors.

4 Discussion and Conclusion The analysis carried out shows that the investment potential of mandatory pension funds is untapped. The louder and more consistent attitude of all stakeholders should be seen as positive. MPFs, the regulator, the academic community, the relevant

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ministry, and even the public are publicly advocating for regulatory changes that would allow pension funds to make riskier, alternative investments that would benefit both pension fund members and the Croatian economy through improving the investment climate. According to research by Sopta et al. (2021), MPFs have sufficient funds and are also actively seeking opportunities to invest in the Croatian real sector. It is emphasised that the money should not be sought abroad, that it is also available for infrastructure projects and that any such investment has a direct impact on GDP growth, which was one of the desired externalities for the introduction of capitalised savings in the pension system 20 years ago, which is why the issue of regulation remains controversial as a key element. Through this research, it was found that the foregone growth potential of investment in the business sector from 2018 to 2020 is 5.90%, 6.01%, and 7.03% respectively. The value of MPFs domestic investment has also declined over the past three years. This is a direct result of delays in the implementation of MPF reforms. Capital markets are experiencing historically low interest rates and high liquidity combined with general instability and currently rising inflation. In these circumstances, pension funds require assets that offer a more appropriate return at an acceptable level of risk and are in line with regulatory constraints (UMFO 2019). To expand investment opportunities for mandatory and voluntary pension funds in Croatia in alternative investment vehicles such as private equity and venture capital funds, municipal and project bonds, and green energy investments, the regulations restricting their investments need to be amended. The existing legislation in Croatia allows up to 10 percent of fund assets to be invested in alternative investments, which includes not only alternative funds but also direct investments in companies and various other investments (UMFO 2019), but investments cannot exceed 15% of the individual asset (e.g. equity funds). As most companies in Croatia fall into the category of micro, small and medium-sized enterprises, a more active presence of private equity funds would have a positive impact on them, with MPFs’ pension funds acting as initiators and main institutional investors. Citizens’ money accumulated in both pension funds and bank savings accounts is substantial, but the economy does not benefit directly from it. This situation has been noticed by all stakeholders. The announced changes in the pension funds combined with the expressed interest of MPFs in non-traditional investments such as private equity, renewable energy, real estate, and state projects such as airports and further privatisation of state-owned enterprises (Vlaić 2019) show that Croatia is following the trends in more developed markets and adapting the situation to its circumstances, but, unfortunately, with a greater time lag compared to the EU Member States. At the same time, this means that the share of the portfolio of investments in which MPFs do not have significant experience is increasing. Therefore, these competences need to be strengthened to protect investors’ funds while achieving adequate returns. Therefore, in addition to deregulation, there is a need to consider the availability of appropriate investment options that ensure acceptable returns at an acceptable level of risk. The fact is that the current capital market is characterised by high liquidity at low interest rates, so there is an undeniable opportunity to finance business projects from various sources. However, the question is whether there is

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a sufficient number of suitable projects in which pension funds would be willing to invest, either directly or through equity funds, taking into account the risk. This paper has not considered the economic impact of the COVID-19 pandemic on certain observed categories such as investment and liquidity, which should be the subject of future research. It would also be desirable to analyse the potential impact of regulatory changes on the achievement of the European Union’s green and digital technology investment and climate change objectives.

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Challenges of Energy Policy within Decarbonisation: Evidence of the European Union Barbara Fajdetić

Abstract Decarbonisation is a serious and comprehensive process of reducing carbon emissions that affects all areas of life, especially energy policy. This study aims to analyse the role of renewable energy sources, electricity prices and the level of economic development on final energy consumption and greenhouse gas emissions. For this purpose, a panel data analysis with fixed and random effects was carried out for 27 EU Member States over the period 2019–2009. The results show that, as economic activity increases, energy consumption also increases, but GHG emissions per capita decrease. The use of renewable energy sources reduces GHG emissions per capita but also has the effect of decreasing final energy consumption. Electricity prices showed no statistically significant relationship with total energy consumption or GHG emissions per capita. Decarbonisation definitely affects the energy strategy by encouraging the use of renewable energy sources in all sectors— transport, household and industry. Renewable energy sources, especially in electricity production, will be the backbone of energy strategies in the EU Member States.

1 Introduction The question that has arisen in recent years is the future of energy use in the decarbonisation process. In recent years, there has been increasing talk about the process of decarbonisation, which is the reduction of carbon emissions through the use of low-carbon energy sources, such as renewable sources. The world is at a turning point, as the question arises of how to increase production and achieve higher economic indicators without compromising the stability and quality of the environment. Increasingly evident global climate change has prompted world leaders to make serious changes and take action to combat climate change. The frequent

B. Fajdetić (*) University of Rijeka, Faculty of Economics and Business, Rijeka, Croatia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Olgić Draženović et al. (eds.), Real and Financial Sectors in Post-Pandemic Central and Eastern Europe, Contributions to Economics, https://doi.org/10.1007/978-3-030-99850-9_11

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natural disasters–hurricanes, floods, major droughts, and rising sea levels—are an alarm signal. It is becoming increasingly clear that the economy has been striving for only one goal all these years, and that is to achieve a high gross domestic product, low unemployment, and the development of technology. In this race of technological development between economies, a very important segment has been forgotten, without which people cannot survive—a healthy and sustainable environment. Without the use of energy, it would not be possible to produce, to carry out basic life activities, to work, but also not to live. Energy is the engine of the economy. Since the industrial revolution, the use of fossil fuels such as coal and oil still make up the majority of energy sources. Fossil fuels still account for 70% of the gross available energy in the European Union (Eurostat 2021a). Instabilities in the energy supply chain can significantly affect the availability of individual energy sources as well as their price. It happens that countries rich in certain energy sources can condition their partners to a certain extent and determine the price at which energy products are sold. Therefore, within the framework of the energy transition in which it is currently engaged, the European Union must find stable sources of energy, guarantee an uninterrupted supply of energy and, at the same time, be affordable. After all, energy prices ultimately have a major influence on the formation of other prices. It is particularly important to emphasise that the European Union is the economy that is moving towards low-carbon development. Specifically, the EU is aiming for climate neutrality by 2050 and a reduction in CO2 emissions of almost 80% compared to 1990 levels. Right now, the EU is making good progress. By 2018, CO2 emissions had been reduced by 23%, while GDP had increased by 61%. From this data, it can be concluded that the EU has turned to sectors with lower energy intensity. The countries of the European Union are at a turning point set by the EU Green Deal—an EU strategy under which national strategies to reduce the carbon footprint are to be developed and adopted. Decarbonisation is a process that will have a major impact on the economy as we have known it until now, and the transport, manufacturing, and energy sectors are certainly one of the sectors where the change will take place the most. Under the EU Green Deal, greenhouse gas emissions must be significantly reduced. This will also mean structural changes in all areas of life but mostly in energy policies. All members of the European Union must adapt their energy policies to the EU’s Green Plan. This will certainly mean finding new, cleaner forms of energy that can replace existing sources just as effectively. In particular, the European Union is turning to the increasing use of renewable energy sources such as solar, wind and hydro. However, the use of nuclear energy and hydrogen also has a significant role in the green transition. Certainly, considerable investment will be needed in the near future to make these plans a reality. The aim of this study is to investigate the impact of renewable energy sources and electricity prices on final energy consumption. Moreover, the impact of renewable energy sources and electricity prices on GHG emissions per capita is also examined. It is crucial to detect the role of renewable energy sources in the decarbonisation process since future energy policies will rely on renewables. Furthermore, as electrification is the future, electricity prices definitely have an impact on final energy consumption. This research aims to detect this relationship. The paper is organised

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as follows. After the introduction, data on energy consumption and prices in the European Union are presented. It also presents existing research on the impact of energy consumption on economic growth, the impact of energy consumption on the environment and the role of renewable energy consumption. Following the methodology and research findings, policy implications and concluding remarks are given.

2 Decarbonisation and Energy Policy in the EU To prevent cities such as Amsterdam, Glasgow, and Copenhagen from being completely flooded, the European Union has taken the initiative and adopted a series of ambitious measures to reduce greenhouse gases. More specifically, the goal is to become a carbon-neutral economy by 2050. With the existing measures, it is possible to reduce emissions by 60% by 2050, but this is not good enough. Since the unveiling of the EU Green Deal, several strategies and policies have been adopted that further regulate certain sectors, such as the European Climate Law, EU Biodiversity Strategy for 2030, Organic Action Plan, and many more. The decarbonisation process refers to reducing CO2 emissions using low carbon power sources. Bernstein and Hoffmann (2018) have listed two ways to think about decarbonisation. First, there is deep decarbonisation that starts with the goal after the set of measures and policies had been developed to achieve it. The second way is to start with the experiments and analyse the politics they produce that can lead to different pathways. Geels et al. (2017) stated that the deep and rapid decarbonisation process requires the transformation of “sociotechnical systems”, more precisely changes in technology, infrastructure, organisations, regulation, and user practices. Decarbonisation is the process that has a significant impact on the EU energy policy. In the energy mix in the European Union, fossil fuels still have the biggest share— around 70% while renewables are on 15% of total available energy. However, across the European Union, the total energy available varies among states. Petroleum products account for 90% of total energy available in Cyprus and 87% in Malta. Solid fossil fuels are the most significant in Estonia—60% and Poland—43%. Nuclear energy accounts for 40% of total energy available in France and 31% in Sweden. Renewables are most significant in Scandinavia. To decarbonise these sectors, large funds need to be invested in technology development and the transition process. The aim is to increase energy efficiency, reduce losses in electricity transmission and increase the use of renewable energy sources. Each member state can determine from which sources they would generate electricity for the industry as well as for households, as long as they are green and clean energy sources. Figure 1 gives an overview of GHG emissions in the European Union in 2018. Three quarters of GHG emissions come from three sectors—the energy sector, transportation, and fuel combustion. Fuel combustion includes manufacturing industries, construction, and households. The remaining one quarter is from agriculture, industrial processes and waste. The European Union is an economy that is not rich in fossil fuels to the extent that it can meet the current needs of its population.

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Fig. 1 Share of GHG emissions by sector in the EU in 2018. Source: Eurostat (2021b)

Therefore, it is an import-dependent economy. The source countries are mainly Russia and the United States. According to Eurostat data (2021c), most fossil fuels are imported from Russia. More precisely, 27% of crude oil, half of gas and coal imports are imported from Russia. It can be concluded that the European Union is highly dependent on energy from Russia both because of its proximity and because of Russia’s great energy wealth. However, on the other hand, this poses a problem for the stability of the European Union’s energy system, as it is vulnerable and sensitive to political decisions. Energy dependence undoubtedly contributes to political and economic insecurity and instability. The average dependency ratio in the European Union was 61% in 2019, meaning that more than half of its energy needs were met by imports. Malta, Luxembourg, and Cyprus have the highest percentage of energy dependence with over 90% energy dependence rate. To achieve the green transition in the timeframe envisaged, the European Union faces a major challenge by 2050. Extensive research and investment by governments and corporations are required to make the plans a reality. Therefore, the importance of this and similar research is great because, from a scientific point of view, it helps economic policymakers determine in which direction the green transition should be implemented. A more detailed view is provided by Fig. 2, which shows the structure of electricity generation by fuel type. Electrification of all sectors is the key to a green transition and a reduction in the carbon footprint. Indeed, fossil fuels are the most widespread in almost all countries of the European Union. Sweden, where renewables are most prevalent, and France, which leads with nuclear energy, have the smallest share. Cyprus, Malta, and Poland are the three countries with the highest use of fossil fuels in electricity generation. In the near future, this trend will have to change. The European Union has proposed to increase the share of renewable energy to 40% in 2021 by 2030. The fact is that electricity from clean green sources will be the main backbone of the low-carbon transition. Electrification of the transport sector and industry will require

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Fig. 2 EU production of electricity by Source in 2019. Source: Eurostat (2021d)

both increased electricity production and the construction of new power plants. The high dependence on fossil fuels and the slow transition in some countries is a concern. To make the transition to a low-carbon economy, renewable energy sources must become more affordable than fossil fuels. The price of energy from solar and wind power has fallen dramatically over the last decade. The price of electricity from solar power plants has fallen by almost 90%, while the price of electricity from wind power plants has fallen by 70% (Our World in Data 2020). The question is how the price of renewable energy may be falling so fast. After all, generating electricity from fossil fuels and nuclear energy depends on two factors—the price of the fuel used and the cost of operation. Renewable power plants, such as solar and wind, on the other hand, have initial construction costs and operating costs, while the “fuels” are self-generating. The process of decarbonisation and the increasing awareness of environmental protection is having a significant impact on the development of energy strategies and policies around the world. More funds are invested in renewable energy as well as technologies that are using them. All these factors affect the consumption of renewable energy. Indeed, the decarbonisation process determines

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the energy policies in the EU Member States by encouraging the use of clean and green energy sources. However, extensive resources and knowledge are needed to make the transition to a low-carbon economy as painless and easy as possible.

3 Literature Review Since renewable energy sources are the most important link in the green transition and in setting energy strategies, the following authors have examined the relationship between the use of renewable energy sources and carbon emissions. Murad et al. (2019) investigated the relationship between technological innovations, energy consumption, energy prices, and economic growth in Denmark in the period 1970–2012. The results showed that real GDP has a positive and statistically significant impact on energy consumption while technological innovations and energy prices have a negative impact on energy consumption. They concluded that Denmark is not an energy-dependent country so the transition toward low carbon would not be challenging. Denmark has also proposed carbon taxes to discourage the use of fossil fuels. Dong et al. (2020) analysed 120 countries in the period 1995–2015 to test the effect of renewable energy consumption on carbon emissions within different income levels. They concluded that renewable energy has a negative impact on carbon emissions for global panels and four subpanels. This effect is found to be statistically insignificant although renewables can reduce carbon emissions and the mitigation effect will increase with the economic growth and renewable energy consumption. Kahia et al. (2019) concluded that renewable energy deployment could significantly improve environmental quality in the Middle East and North African countries. Kirikkaleli and Adebayo (2021) explored the effects of financial development and renewable energy consumption on environmental sustainability. They found that financial development has a negative impact on carbon emissions while renewable energy consumption improves environmental quality. Furthermore, they found that global economic growth deteriorates environmental quality. Yuping et al. (2021) evaluated the effects of globalisation, renewable and non-renewable energy consumption on carbon emissions in Argentina in the period 1970–2018. They revealed that globalisation and renewables inhibit carbon emissions while non-renewables increase carbon emissions. Moreover, they have verified the EKC hypothesis for Argentina. Several authors were dealing with the total energy consumption and carbon emissions. Accordingly, Lotfalipour et al. (2010) investigated the relationship between economic growth, fossil fuels consumption, and carbon emissions. The results indicate that reducing energy consumption would lead to a reduction in carbon emissions. Analysis indicates that fossil fuel consumption does not lead to higher economic growth so that transition toward low carbon does not pose an issue. Furthermore, Pao and Tsai (2010) analysed the determinants of carbon emissions in BRIC countries. Results revealed a long-run equilibrium relationship between emissions, energy consumption and real output for the panel of BRIC countries and

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concluded that higher energy consumption responds to changes in emissions. Apergis and Payne (2014) concluded that, in the short run, an increase in carbon dioxide emissions per capita increases renewable energy consumption per capita. At the same time, an increase in per capita renewable energy consumption reduces per capita carbon dioxide emissions. In the short run, an increase in real oil prices increases per capita renewable energy consumption. Haseeb et al. (2018) investigated the impact of energy consumption, globalisation, financial development, and economic growth on carbon emissions for BRICS countries. The EKC hypothesis was validated in the BRIC countries. Moreover, energy consumption and financial development showed a positive relationship with carbon emissions. Scientists were dealing with the effect of energy consumption on economic growth like Esen and Bayrak (2017) and Pearson (2021). Since energy is a driver of economic activities, the question is what is its impact on economic growth. Esen and Bayrak (2017) have examined the effects of energy consumption on economic growth in 75 net energy import countries in the period 1990–2012. They have divided countries based on their energy dependence ratio and income level. The results showed that the effect of energy consumption on economic growth is greater in those countries with an energy dependency ratio of less than 50%. Moreover, energy consumption has a positive effect on economic growth and that effect is decreasing as the development level increases. This indicates that countries use their energy resources more effectively. Pearson (2021) investigated the effects of renewable energy on economic growth in Croatia in the period 1996–2018. She has found that renewable energy consumption has a positive effect on the Croatian economy indicating that renewables are a good path in reducing vulnerability to fossil fuel prices volatility. Sadorsky (2009), Farhani and Ben Rejeb (2012), and Saidi and Hammami (2015) examined the effect of economic development on energy consumption. Sadorsky (2009) concluded that, in the long run, an increase in real GDP per capita and CO2 emissions per capita increases renewable energy consumption in G7 countries. He found that oil price has a negative impact on renewable energy consumption. On the other hand, Farhani and Ben Rejeb (2012) investigated the link between energy consumption, economic growth, and carbon emissions in the MENA region. They found that high economic growth leads to high energy demand. Moreover, a short-run causality was found leading from energy consumption to economic growth and CO2 emissions. Saidi and Hammami (2015) examined the effect of economic growth on energy consumption. The results indicate that increase in economic growth increases energy consumption and CO2 emissions. This research completes the existing research in this field since it examines the impact of renewable energy sources on final energy consumption and GHG emission level. Since the European Union is fighting against global warming, these relationships should be identified.

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4 Data and Methodology As the European Union strives to become a climate-neutral continent by 2050, serious changes should be made, especially in the field of renewable energy and energy efficiency. The aim of this study is to investigate how economic development, the share of renewable energy, and electricity prices affect final energy consumption and per capita greenhouse gas emissions. The analysis was carried out in the 27 EU Member States over the period 2009–2019. The focus of this research is whether the European Union is on track in reducing greenhouse gas emissions. To answer the research questions, two models were introduced: Final energy consumptionit ¼ α þ β1 GDPpcit þ β2 RESit þ β3 Electricity pricesit þ eit,

ð1Þ

GHGpcit ¼ α þ β1 GDPpcit þ β2 RESit þ β3 Electricity pricesit þ eit,

ð2Þ

In the first model, the dependent variable is final energy consumption. This indicator measures the energy by end-users expressed in million tons of oil equivalent. In the second model, the dependent variable is GHG pc, i.e., total greenhouse gas emissions in tonnes per capita. It consists of CO2, N2O in CO2 equivalents, CH4 in CO2 equivalents, HFC in CO2 equivalents, PFC in CO2 equivalents, SF6 in CO2 equivalents, and NF3 in CO2 equivalents. RES are renewable energy sources expressed as a percentage of total energy sources. GDP per capita is an indicator of a country’s economic development and is expressed in euros per capita. Electricity prices for final consumers are expressed in euro per kilowatt-hour. Table 1 shows descriptive statistics for the observed data. According to the analysis, the average value of greenhouse gasses per capita in the European Union is 8.7 tons per capita. The highest value was recorded in Luxembourg in 2010 and the lowest in Sweden in 2016. The average final energy consumption in the EU is 51 million tons of oil equivalent (Mtoe). The highest consumption was detected in Germany in 2010. Germany, France, and Italy account for almost half of total final energy consumption in the European Union during 2019. The average GDP per capita in the European Union is 26873.5 euros. Luxembourg is the country with the highest GDP per capita in 2019, while the lowest value was recorded in Bulgaria in 2009. Renewable energy sources are the future of the green transition in the European Union. However, in the observed period, the average share of renewable energy sources is 19.3%. The highest share of renewables was recorded in Sweden in 2019 and the lowest in Malta in 2009. The highest electricity prices were recorded in Cyprus in 2012 while Estonia has the lowest electricity prices in 2010. To conduct panel data analysis, fixed and random effects were conducted. Afterwards, Hausman tests were performed to see which is the best test for the analysed data.

Million tonnes of oil equivalent

Percentage

Euro

Euro

Final energy consumption

RES

Electricity prices

GDP pc

Source: Author

Unit Tonnes per capita

Variable GHG pc

Table 1 Descriptive statistics Category Overall Between Within Overall Between Within Overall Between Within Overall Between Within Overall Between Within 26873.5

0.090945

19.2559

36.33266

Mean 8.684848

Standard deviation 4.052436 4.022128 0.8895275 50.22947 51.06704 1.805571 11.55854 11.49292 2.444393 0.0251612 0.022196 0.0125326 18402.88 18346.19 3668.033

Minimum 1.7 2.172727 5.784848 0.5 0.5727273 29.37811 0.221 4.492545 10.35008 0.0573 0.0645273 0.0393082 4930 6421.818 13763.5

Maximum 26.4 21.89091 13.19394 223 215.2545 45.66902 56.391 51.48327 27.60508 0.2171 0.1579455 0.1610082 100,890 91,490 49727.14

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5 Results and Discussion Results of the fixed and random effects, as well as the Hausman test, were given in Table 2. Hausman test has confirmed that fixed effects are a better fit for this dataset. Obtained results showed a positive and statistically significant relationship between GDP per capita and final energy consumption. This indicates that higher economic development and activity results in higher final energy consumption. These results are aligned with Farhani and Ben Rejeb (2012) and Saidi and Hammami (2015). Farhani and Ben Rejeb (2012) concluded that higher economic activity leads to higher energy demand while, similarly, Saidi and Hammami (2015) stated that increased economic growth increases energy consumption. Consequently, it leads to higher carbon emissions. A higher share of renewable energy sources is found to have a negative and statistically significant effect on final energy consumption. Moreover, higher electricity prices have a negative but statistically insignificant impact on final energy consumption. After conducting the Hausman test, random effects suits the first model better. In the second model, the test results showed a negative and statistically significant relationship between GDP per capita and GHG emissions per capita in the European Union. These results are aligned with the existence of the EKC curve. It implies that better economic development of a country means lower GHG emissions. These results are aligned with Yuping et al. (2021), who verified the EKC hypothesis for Argentina, and Haseeb et al. (2018), who confirmed it for the BRIC countries. Furthermore, renewable energy sources show a negative and statistically significant relationship with GHG emissions per capita, indicating that a higher share of renewable energy sources in total energy production decreases GHG emissions per capita. Similarly, Murad et al. (2019), Dong et al. (2020), Kahia et al. (2019) and Kirikkaleli and Adebayo (2021) concluded that renewable energy consumption improves environmental quality and reduces carbon emissions. Electricity prices, however, show a negative but statistically insignificant impact on GHG emissions per capita. Nevertheless, this study has shown that electricity prices potentially affect final energy consumption as well as per capita greenhouse gas emissions. It is important to emphasise that the price of electricity and other energy sources are an important component of the prices of final goods—food, electronic equipment, transportation services, etc. The prices for electricity from renewable energy sources were higher than the prices for electricity from fossil fuels in earlier periods. However, as technologies have developed and the cost of generating electricity from renewable energy sources has decreased, cost prices have converged. The growing demand for renewable energy sources as well as the policies of the EU countries lead governments and companies to invest more and more in renewable energy sources. Although the initial cost is not low, as technology develops and energy efficiency increases, the cost-effectiveness of renewable energy power plants increases. In general, maintenance and operation costs are low after the installation of such plants, which brings potential benefits in the future. This analysis has shown that the use of renewable

Source: Author

GDP per capita RES Electricity prices Constant Descriptive statistics Tests for model significant F-statistic (fixed effects) p-value > F-statistic Wald statistic (random effects) p-value > Wald-statistic Observations Minimum Maximum Average Hausman test Test statistic p-value > test statistic Decision

Table 2 Fixed and random effects

p-value 0.000 0.000 0.167 0.000

297 11 11 11 Fixed vs. random effects 0.31 0.8572 Random effects

7.35 0.0001

Model 1 Fixed effects Coefficients 0.0001184 0.2435903 12.65519 38.99214

22.75 0.0000 297 11 11 11

Random effects Coefficients 0.0001197 0.2461033 12.82678 30.02095 p-value 0.000 0.000 0.159 0.000

p-value 0.093 0.000 0.535 0.000

297 11 11 11 Fixed vs. random effects 14.77 0.0006 Fixed effects

14.54 0.0000

1. Model 2 Fixed effects Coefficients 0.000029 0.1107294 2.69248 11.84255

59.32 0.0000 297 11 11 11

2. Random effects Coefficients p-value 0.0000256 0.000 0.1631679 0.000 1.870082 0.675 11.30823 0.000

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energy sources in the European Union has a positive impact on lowering GHG emissions, putting renewables at the top priority in the energy mix. Some Member States still mostly use fossil energy sources like oil, coal, and natural gas. According to Eurostat (2021b), Poland produces 96% of the hard coal in the European Union and remains the largest consumer of hard coal. However, nuclear power plants are seen as the future. As reported by Reuters (2021), Electricite de France has offered to build nuclear reactors in France to make Poland’s energy sector green and sustainable. France is the country with the largest share of nuclear power in electricity generation and has a developed infrastructure. Although the environmental impact of nuclear power plants is not as direct as that of conventional thermal power plants, which have a significant carbon footprint, nuclear power plants pose an indirect threat to the environment and the people around them. Therefore, the European Union has not yet clearly defined its position on nuclear power plants. On the one hand, they are a good source of energy, the method of energy production is developed, and the infrastructure is already persistent in many European countries. On the other hand, in the event of a disaster, large amounts of radiation are released into the environment and human health could be harmed. The European Union advocates the production of energy from renewable sources such as solar energy, wind energy, and hydroelectric power. The Nordic countries are rich in natural resources. According to Nordic Energy Research (2021), the Nordic countries have achieved their 2020 plans under the EU Renewable Energy Directive. Compared to the EU average, the share of renewable energy in the Nordic countries is five times higher. In Denmark, wind energy is the most widely used, accounting for over 40% of the electricity mix. Norway, Sweden, and Iceland are strong in hydropower, Finland in biomass. The rest of the European Union still depends on fossil fuels, but the South has the potential to develop solar power plants and Central Europe has the potential to develop wind farms. Nevertheless, the European Union is in the midst of a challenging development that requires large financial investments and the development of technologies. According to the current strategy, by 2030 all EU countries want to reduce greenhouse gas emissions by 40% compared to 1990, use at least 32% renewable energy, and increase energy efficiency by 32.5%. All this is done to achieve climate neutrality and sustainable economic growth in the European Union. It remains to be seen whether the European Union will support nuclear energy as a clean source of energy or strictly rely on the use of renewable energy sources.

6 Conclusion Decarbonisation is a complex process that involves reducing the carbon footprint and switching to lower energy intensity energy sources. This process requires many technological changes and changes in people’s lifestyles. The European Union has set a goal to become a carbon-neutral economy by 2050. Renewable energy sources and energy efficiency play a key role in achieving these goals. The electrification of

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economies will increase the consumption of electricity generated from renewable energy sources. However, fossil fuel consumption is high today in the energy sector, in the manufacturing industry, and also in households. According to the latest data, the European Union is reducing its greenhouse gas emissions and is on track to meet its targets. The aim of the study was to establish the relationship between per capita greenhouse gas emissions and primary energy consumption, with the share of renewable energy, electricity prices, and the level of economic development. The study showed that renewable energy sources have a significant effect on reducing GHG emissions but also in reducing final energy consumption. Moreover, the results indicate that higher economic development means higher final energy consumption, but at the same time, the level of GHG emissions per capita was reduced. It can be concluded that more developed countries use much cleaner and environmentally friendly energy sources. Although the European Union is on a good path to reduce GHG emissions and become carbon-neutral, the effect on a global scale is minimal if other leading economies do not show the same effort. Acknowledgments This work has been supported by the University of Rijeka under the project Uniri-drustv-18-27 and ZIP-UNlRl-130-6-20.

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