Challenges in Fiscal and Monetary Policies in Mongolia 9811993645, 9789811993640

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New Frontiers in Regional Science: Asian Perspectives 66

Hiroyuki Taguchi Osamu Ito Koki Hirota Takeshi Osada   Editors

Challenges in Fiscal and Monetary Policies in Mongolia

New Frontiers in Regional Science: Asian Perspectives Volume 66

Editor-in-Chief Yoshiro Higano, University of Tsukuba, Tsukuba, Ibaraki, Japan

This series is a constellation of works by scholars in the field of regional science and in related disciplines specifically focusing on dynamism in Asia. Asia is the most dynamic part of the world. Japan, Korea, Taiwan, and Singapore experienced rapid and miracle economic growth in the 1970s. Malaysia, Indonesia, and Thailand followed in the 1980s. China, India, and Vietnam are now rising countries in Asia and are even leading the world economy. Due to their rapid economic development and growth, Asian countries continue to face a variety of urgent issues including regional and institutional unbalanced growth, environmental problems, poverty amidst prosperity, an ageing society, the collapse of the bubble economy, and deflation, among others. Asian countries are diversified as they have their own cultural, historical, and geographical as well as political conditions. Due to this fact, scholars specializing in regional science as an inter- and multi-discipline have taken leading roles in providing mitigating policy proposals based on robust interdisciplinary analysis of multifaceted regional issues and subjects in Asia. This series not only will present unique research results from Asia that are unfamiliar in other parts of the world because of language barriers, but also will publish advanced research results from those regions that have focused on regional and urban issues in Asia from different perspectives. The series aims to expand the frontiers of regional science through diffusion of intrinsically developed and advanced modern regional science methodologies in Asia and other areas of the world. Readers will be inspired to realize that regional and urban issues in the world are so vast that their established methodologies still have space for development and refinement, and to understand the importance of the interdisciplinary and multidisciplinary approach that is inherent in regional science for analyzing and resolving urgent regional and urban issues in Asia. Topics under consideration in this series include the theory of social cost and benefit analysis and criteria of public investments, socio-economic vulnerability against disasters, food security and policy, agro-food systems in China, industrial clustering in Asia, comprehensive management of water environment and resources in a river basin, the international trade bloc and food security, migration and labor market in Asia, land policy and local property tax, Information and Communication Technology planning, consumer “shop-around” movements, and regeneration of downtowns, among others. Researchers who are interested in publishing their books in this Series should obtain a proposal form from Yoshiro Higano (Editor in Chief, [email protected]) and return the completed form to him.

Hiroyuki Taguchi • Osamu Ito • Koki Hirota • Takeshi Osada Editors

Challenges in Fiscal and Monetary Policies in Mongolia

Editors Hiroyuki Taguchi Graduate School of Humanities and Social Sciences Saitama University Saitama-shi, Saitama, Japan

Osamu Ito Graduate School of Humanities and Social Sciences Saitama University Saitama-shi, Saitama, Japan

Koki Hirota Graduate School of Humanities and Social Sciences Saitama University Saitama-shi, Saitama, Japan

Takeshi Osada Graduate School of Humanities and Social Sciences Saitama University Saitama-shi, Saitama, Japan

ISSN 2199-5974 ISSN 2199-5982 (electronic) New Frontiers in Regional Science: Asian Perspectives ISBN 978-981-19-9364-0 ISBN 978-981-19-9365-7 (eBook) https://doi.org/10.1007/978-981-19-9365-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

This book provides quantitative evidence on the issues in fiscal and monetary policies in Mongolia and presents necessary policy recommendations for policymakers and academic circles. Mongolia belongs to a natural resource-based, transition economy, and thus has faced the risk of the so-called resource curse— including the “Dutch Disease” and immaturity in market-based systems, particularly in financial markets. Consequently, reformations of resource allocation and policy governance in fiscal and monetary fields have been required. So far, however, there have been only a very limited number of quantitative studies in the Mongolian economy among the vast literature of Asian studies. This book applies scientific approaches to address fiscal and monetary issues, such as data-oriented and econometric methods (a structural vector auto-regression model, a spatial econometric model, and panel estimation with fixed effects, among others). In this manner, the book enriches empirical evidence in academic literature and also contributes to evidence-based policymaking. All the authors are young leaders of government officials in the Ministry of Finance, Financial Regulatory Commission, and National Statistics Office in Mongolia, who have been trained in academic research methodologies at Saitama University, Japan, on the Project for Human Resource Development Scholarship (JDS) by Japan International Cooperation Agency (JICA). Thus, academic researchers and policymakers will be prominent members of the target audience for this work. Chapter 1 (Analysis of the “Dutch Disease” Effect and Public Financial Management in Mongolian Economy) diagnosed Mongolian economy on whether the economy has suffered from the Dutch Disease by applying a vector auto-regression model. The study found the existence of the Dutch Disease such that the boom in the mining sector has crowed out manufacturing and has deteriorated capital accumulation. The strategic implication for the public financial management is that the part of the existing resource fund should be used for public investment in the fields of education, health, and economic infrastructure. Chapter 2 (Enhancement in Governance Capacity of the Sovereign Wealth Funds of Mongolia) examined the current modality of the Sovereign Wealth Funds (SWF) v

vi

Preface

of Mongolia and investigated the ways to enhance their management and governance capacity. The study applied a comparative (SWOT) analysis by referring to the best practices in the SWF of Norway and Chile. The study also conducted a quantitative analysis of the relationship between the scale of SWF and fiscal balance fluctuation and showed that the SWF accumulation has contributed to the stability of fiscal balance. Chapter 3 (Government Financial Supports for Small- and Medium-sized Enterprises (SMEs) in Mongolia) examined the current status of policy supports for SMEs in Mongolia and extracted lessons on SME supports from Japanese experiences. The study verified geographical concentration and spatial externality of SMEs in Ulaanbaatar through the spatial econometric method. The study also argued that the accessibility to finance is a major issue for SME supports and that the Japanese framework of the subsidized loans named “Fiscal Investment and Loan Program” could be a good reference for Mongolia. Chapter 4 (Macroprudential Policy to Manage Systemic Risk Deriving from Financial Institutions in Mongolia) identified the level of systemic risk in financial market in Mongolia by using the method of “systemic expected shortfall (SES).” The study measured the SES by regression analysis employing financial statement data in terms of cross section and time series. The analysis identified the cross-dimension systemic risk that financial institutions would contribute to and the forecasting ability of time-series systemic risk, which implies the manageability of macroprudential policy. Chapter 5 (Inflation Targeting and the Pass-through Effect in Mongolia) provided the empirical evidence on the relationship between inflation targeting (IT) and the pass-through effect from exchange rate to consumer prices in Mongolia by using a vector auto-regression model. The analysis identified the existence of the passthrough effect during the pre-IT period (before 2007) and the loss of the effect during the post-IT period (after 2007). This result implied an IT benefit such that domestic agents’ expectations under IT would follow the target instead of exchange rate shocks. Chapter 6 (Stock Market Development and Macroeconomic Policies in Mongolia) examined the relationship between stock market and macroeconomic policies in Mongolia by using a vector auto-regression model, under the hypothesis that the recent biases of fiscal and monetary policies would distort stock-price formation. The analysis showed that the cumulative public debt and high policy interest rate have stagnated stock prices. It suggested the policy requirements to ensure budget consolidation and to address a fear of floating in monetary policy management in Mongolia. Chapter 7 (The Development Stage of Bond Market in Mongolia among Asian countries) investigated the determinants of bond market development with a focus on Asian economies, and also identified the impediment factors to prevent its development in Mongolian economy. The panel estimation with fixed-effect model demonstrated that the two manageable variables, namely, institutional quality

Preface

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and level of interest rate, are major determinants for bond market development. The result also showed that these two are the main factors to prevent the Mongolian bond market from developing. Saitama-shi, Japan

Hiroyuki Taguchi Osamu Ito Koki Hirota Takeshi Osada

Contents

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3

Analysis of the “Dutch Disease” Effect and Public Financial Management of the Mongolian Economy . . . . . . . . . . . . . . . . . . . . . Ganzorig Bulgankhuu

1

Enhancement in Governance Capacity of the Sovereign Wealth Funds of Mongolia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Naranchimeg Luvsansharav

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Government Financial Support for Small- and Medium-Sized Enterprises (SMEs) in Mongolia . . . . . . . . . . . . . . . . . . . . . . . . . . . . Naranzul Tsaschikher

87

4

Macroprudential Policy to Manage Systemic Risk Deriving from Financial Institutions in Mongolia . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Narantuya Natsagdorj

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Inflation Targeting and Pass-Through Effect in Mongolia . . . . . . . . . 181 Bolortuya Jambaldorj

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Stock Market Development and Macroeconomic Policies in Mongolia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Enkhbaatar Namjil

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Development Stage of the Bond Market in Mongolia Among Asian Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Bolormaa Ganbold

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Chapter 1

Analysis of the “Dutch Disease” Effect and Public Financial Management of the Mongolian Economy Ganzorig Bulgankhuu

Abstract This study aims to analyze whether the Mongolian economy has been suffering from the “Dutch disease” or not by employing a vector autoregression (VAR) model with quarterly data from 2000 to 2017 of the National Statistical Office, Mongolia (NSO). Our results show that the Mongolian economy has probably been suffering from the “Dutch Disease” through the resource movement effect in that the mining sector boom has crowded out manufacturing activities. Moreover, our results indicate that the mining sector boom has not contributed to or even deteriorated the capital accumulation effect that alleviates the Dutch disease. Therefore, the Government of Mongolia should strengthen its public financial management to avoid the resource “curse.” Moreover, the current natural resource funds, named the “Future Heritage Fund” of Mongolia, could be one of the successful solutions for the Dutch disease. Conversely, the “fiscal stabilization fund” needs reform and independence from political pressures. Hence, as a policy recommendation, resource revenues should be utilized for education projects, human resource development, and economic infrastructure to accumulate capital and promote economic diversification and the future development of Mongolia. Keywords Dutch disease · Public financial management · Public investment

1.1

Introduction

Mongolia is a landlocked, middle-income developing country with vast agricultural and mineral resources. Nevertheless, the country’s national income is highly dependent on the mining sector and mineral resource exports. Income from mining flows is instrumental in the Mongolian economy and its growth. The Government of Mongolia prioritizes exporting mineral products instead of implementing good industrialization policies. Mongolia has world-class mining deposits such as the “Tavan

G. Bulgankhuu (✉) Ministry of Finance of Mongolia, Ulaanbaatar, Mongolia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Taguchi et al. (eds.), Challenges in Fiscal and Monetary Policies in Mongolia, New Frontiers in Regional Science: Asian Perspectives 66, https://doi.org/10.1007/978-981-19-9365-7_1

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Tolgoi” coking coal site and “Oyu Tolgoi” copper and gold mining, which are both strong enough to upgrade the Mongolian economy to upper-income class. However, the 2012–2016 recession in the country driven by a fall in commodity prices and decline in foreign direct investment (FDI) in the mining sector shows that the Mongolian economy is extremely vulnerable and in an urgent need of an analysis of the economic effects of mineral recourse dependence. This study assumes that there would be the “Dutch disease” effects on the Mongolian economy and that policymakers should reconsider comprehensive public financial management to avoid the “Dutch disease” in the context of a “resource curse” and transform it into a “resource blessing.” The International Monetary Fund (IMF) (2013) defined a “resource curse” as a complex phenomenon through which abundant revenues from natural resources translate into economic stagnation, waste, corruption, and conflict. A country “cursed” by its resources tends to have less economic growth and development than other countries with fewer natural resources. One typical con of being resourcerich is the “Dutch disease,” which is an economic fact wherein revenues from natural resources could damage a country’s manufacturing sectors by real exchange appreciation and an increase in labor demand and wages of the booming sector. Hence, the manufacturing sector would become less competitive in the international market. The extreme result of the “Dutch disease” is crowding out of manufacturing activities and declining output of tradable goods through resource movement and spending effects through the real appreciation of domestic currency contributes (Corden and Neary 1982). To avoid the “Dutch disease,” the Government of Mongolia should strengthen its public financial policy and management, including the establishment of natural resource funds (NRFs) to support manufacturing and higher value-added activities. Figure 1.1 shows that Mongolian GDP growth has highly fluctuated over the last two decades and has been severely affected by changes in commodity prices in the international market since the 2000s. The Parliament of Mongolia approved a “Minerals Law” in 1997 and has enacted this since 1998 to boost its mining sector’s production and to attract FDI on mining. Mongolia was one of the member countries of the Soviet Union from 1924 to 1989. In 1990, Mongolia became a Democratic country and shifted to a marketbased regime after the Soviet Union’s collapse. However, Mongolia faced economic difficulties and had negative economic growth until 1993. Subsequently, the economy recovered yearly and maintained steady growth until the global financial crisis. Mongolian economic growth subsequently reached its peak in 2011. However, a fall in international market prices of mineral resources caused a sharp drop in the economy from 2012 to 2016. At the beginning of 2017, the Government of Mongolia announced that Mongolia was going to take an “Extended Facility Fund” Program of the IMF to recover its economy until 2019. According to statistics from the National Statistical Office (NSO), Mongolia, the economy remains in recovery in 2019, while growth rates were 5.3% in 2017 and 6.9% in 2018, respectively. Despite the improving growth trend, structural challenges (e.g., the limited export diversification) remained and worsened the vulnerability of the

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Analysis of the “Dutch Disease” Effect and Public Financial Management. . .

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21.8

100 90 80

16.8

70 60

11.8

50 40

6.8

30 20

1.8

10 0

(3.2) Minerals ratio to Total export (secondary axis)

GDP growth

Fig. 1.1 Percentage of resource export to total export and GDP growth of Mongolia between 1993 and 2017. Source: National Statistical Office, Mongolia 3,500.0

35% 33%

3,000.0

30% 27% 25% 21%

20%

22%

24% 21%

21%

2,500.0 22% 2,000.0

16%

15%

1,500.0 1,000.0

10%

500.0

5%

-

0% 2011

2012

2013

2014

2015

Mining sector's revenue mil.MNT (right axis)

2016

2017

2018*

2019**

Share in total fiscal revenue

Fig. 1.2 Revenue of the mining sector in the total fiscal revenue (million MNT). Source: Ministry of Finance, Mongolia, as of November 2018. *-preliminary performance, **-expected

economy to commodity price or other external shocks, given its high reliance on the mining sector according to the economic outlook of the World Bank (2018). Figure 1.2 shows that the share of the mining sector revenue in total fiscal revenue is expected to increase in 2019 (27%), which means that the Mongolian economy is relying more on the mining revenue.

4 Table 1.1 Major mineral resources reserve of Mongolia as of 2014

G. Bulgankhuu # 1. 2. 3. 4. 5. 6. 7.

Minerals Coking coal Copper Gold Zinc Iron Oil Uranium

Measurements Million metric ton Thousand metric ton Metric ton Thousand metric ton Million metric ton Million barrels Thousand metric ton

Reserves 175,500.0 57,000.0 2493.0 1740.0 1166.0 2438.0 170.0

Source: Ministry of Mining, Mongolia

According to the IMF (2007), an indicative threshold for resource revenue dependency is in the range of 20–25% of total fiscal revenue, and the fiscal year of 2019 is expected to be beyond the threshold owing to the “Oyu Tolgoi” mining’s underground production. Since 2011, average mining sector revenue in the total fiscal revenue of Mongolia has been around 23%, which has been in the range of the IMF threshold. Mongolia has had large-scale mineral reserves (Table 1.1). The highest-ranked mineral reserve of Mongolia is coking coal, but the main national revenue source is from the second largest reserve, copper sites of “Erdenet” and “Oyu Tolgoi.” Therefore, utilizing these nonrenewable resource revenues is crucial, according to the policy implications and recommendations for resource-rich developing economies proposed by international organizations such as JICA, WB, and IMF. The study of IMF (2013) on pubic financial management (PFM) shows that a few countries that successfully avoided the “resource curse” had strong institutional and PFM systems. Mongolia currently has a weak institutional system and needs PFM reforms (e.g., an efficient public investment policy). Figure 1.3 shows that Mongolia’s budget deficit has been increasing rapidly since 2011 compared to that of its previous period. Additionally, development on monitoring and evaluation of public financing has decreased. The budget deficit remains high, and its growth rate remains in the lower rate under the period of high commodity price. Although budget revenue has been limited after a fall in international resource prices, budget expenditure has been continuously expanding. Moreover, public financing decision-making process is getting more centralized in the Parliament. Hence, public financing is highly risky in the context of Mongolia’s political interests. For example, some political parties promised very expensive and inefficient social welfare programs during the election to secure a majority of the Parliament. This situation also indicates that the PFM should be immediately renewed for independence from political influences. Resource-rich developing countries must select special operational mechanisms for public financing and resource revenue management. Resource revenues should be saved and invested in a timely manner so that returns on optimal investment establish a permanent income stream (Coutinho 2011). Natural resource funds comprise the most frequently used mechanisms in PFM for allocating resource revenues (NRFs) (IMF 2012). Demachi and Kinkyo (2014) indicate that natural

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Analysis of the “Dutch Disease” Effect and Public Financial Management. . .

5 500.00

8,100.00 7,100.00

-

6,100.00 (500.00)

5,100.00 4,100.00

(1,000.00)

3,100.00 2,100.00

(1,500.00)

1,100.00 (2,000.00) 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

100.00

Budget Revenue mil.USD

Budget Expenditure mil.USD

Budget Balance mil.USD

Fig. 1.3 Budget balance of Mongolia between 1990 and 2017. Source: National Statistical Office, Mongolia

resource funds, despite its different names among countries—ranging from “oil fund” and “stabilization fund” to “future generation fund”—are crucial for resource-rich developing countries like Mongolia. Moreover, NRFs are considered a policy tool for PFM with two different timeframes: managing short-run high-price volatility and stabilizing the macroeconomy to avoid a resource curse in the long run. The Parliament of Mongolia approved the Law on Fiscal Stability in 2010. According to this law, a fiscal stabilization fund (FSF) as one of the NRFs was established for macroeconomic and fiscal sustainability. Moreover, the Law on Future Heritage Fund (FHF) was enacted by the Parliament in 2016 and was enacted for saving natural resource income for future generations since 2017. To address the questions “Is Mongolia blessed or cursed by its resources?” and “Could current NRFs of Mongolia be a successful case or not?”, this study uses a vector autoregression (VAR) model and employs quarterly data between 2000 and 2017 (72 data) from the NSO under the “Dutch disease” hypothesis. This study selected 2000 as the first year of the estimation based on resource export percentage to total export. Figure 1.1 indicates that the ratio of minerals to total export has increased rapidly since 2000, and it is around 80.0% of the total export in the recent years. Moreover, the “Minerals Law” contributed to attracting FDI in the mining sector, and this sector has been booming since 1998. As we have the “Dutch disease” hypothesis for the study, its effects on the economy could be estimated in the period of minerals export booming. The remainder of this paper is organized as follows. Section 1.2 presents the literature review. Section 1.3 describes a theoretical framework. Section 1.4 conducts an empirical analysis. Section 1.5 extracts policy implications and discusses the public financial management issues of Mongolia. Finally, Section 1.6 concludes the paper.

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1.2

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Literature Review

In this section, we review the literature related to the “Dutch disease” hypothesis and its effects on the economy including studies on Mongolian cases and the role of PFM in resource-rich developing countries. Moreover, we emphasize this study’s contribution to the topic.

1.2.1

Literature on the “Dutch Disease”

The Economist first coined “Dutch disease” in 1977 to describe the fact that the decline of the manufacturing sector was caused by the discovery of big natural resources in the Netherlands in the late 1950s. However, most academic studies related to the “Dutch disease” have cited Corden and Neary (1982), who introduced the “resource movement effect” (RME) or “direct de-industrialization” and “spending effect” (SE) or “indirect de-industrialization” in a small open economy. They observed that the high wage and labor demand in the mining sector cause labor movement from the manufacturing sector and cause “direct de-industrialization.” Subsequently, more spending on consumption is triggered by higher real income resulting from the booming sector, leading to real appreciation defined by the relative price of services to traded manufacturing goods. Moreover, this leads to further reduction in manufacturing employment and production, namely, “indirect de-industrialization.” In the long run, the mining sector boom would further lead to a decline in the higher value-added manufacturing sector and a constraint to the main source of technological progress in the economy. Hence, an extreme result of the “Dutch disease” is a crowding out of manufacturing activities both in the short and long term. Sachs (2007) added the longer-term intertemporal perspective, namely, the “capital accumulation effect” (CAE), to the “Dutch Disease” framework. Sachs (2007) also argued that the Dutch disease could be avoided if natural resource revenues were not used for consumption but public investment, as the positive benefits of increased public investment in the non-mining traded sector through productivity improvement would outweigh any negative consequences of the Dutch disease. The studies of Corden and Neary (1982) and Sachs (2007) will be illustrated and used for the theoretical framework section of this study. The Dutch disease hypothesis is mostly examined in the context of a real exchange appreciation and its effects on the manufacturing sector of a certain country or region during the booming period of mineral resources. For instance, Sachs and Warner (2001) showed that resource-rich countries, “cursed” by minerals, tended to have less economic development compared to resource-poor countries owing to their high prices derived from the booming of their resource sector. Moreover, there is less evidence of geographical and climate variables’ influence on the economies. They confirmed that high-priced economies finally crowded out their high-value-added manufacturing activities. Ismail (2010) revealed four main

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Analysis of the “Dutch Disease” Effect and Public Financial Management. . .

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facts about oil-exporting countries using the structural factor model. First, oil-exporting countries have experienced a reduction in manufacturing output caused by the oil boom. Second, in these countries, which are more open to foreign investment, oil windfall shocks have greater adverse impacts on the manufacturing sector. Third, if windfall increases, the relative price of labor to capital will appreciate. Finally, labor-intensive tradeable sectors are affected more negatively by windfall shocks compared to capital-intensive ones. Therefore, developing capitalintensive manufacturing could help avoid commodity price volatility. Many studies examine the Dutch disease hypothesis in Asian resource-rich developing countries. Results differ among countries/regions and periods of resource booming. Taguchi and Lar (2016) examined the “resource-curse” hypothesis for 37 Asian countries and showed different results of two periods using the VAR model with panel data. The existence of the Dutch Disease effect was first observed in 1980–1995, and while no evidence of the effects on those economies in 1995–2014 exists, evidence of the capital accumulation effect in that period has been found. Moreover, Taguchi and Khinsamone (2018) examined selected five resource-rich countries of ASEAN and found that the forerunners of Malaysia and Indonesia have no Dutch Disease effects on the economy from 1997 to 2015. Conversely, latecomers Myanmar and Lao PDR have experienced the Dutch Disease over the sample period. From this result, they extracted the following lessons from the forerunners’ experiences to allow latecomers to escape from the Dutch disease: to establish some funding system of resource allocation for investment projects, diversify the economy by improving domestic industries and business environments, and improve institutional quality to strengthen resource control. Moreover, resource-rich developing economies (RRDEs) have studied how windfall revenue from booming sectors should be managed to sustain long-term growth and the types of macroeconomic and fiscal policy and management. Coutinho (2011) showed that some resource-rich countries (e.g., Nigeria, Saudi Arabia, and some countries in Latin America) are considered as countries that have poorly managed their resource revenues. Moreover, the governments of these countries typically spend more money on overly ambitious and inefficient projects and implemented inadequate strategy and policy for protecting manufacturing and maintaining real exchange rate from the shocks of resource booms. Cases of failure also showed that policy and investment in education and human resource are insufficient for improving labor productivity. Conversely, very few economies managed their resource revenues in the way of “blessing,” such as Norway with the “bird-in-hand” rule. In Asian countries, Malaysia and Indonesia, among lessindustrialized economies, are examples of successful cases. According to the economic theory, which focuses on natural resources, gathering all cases into a single theory is impossible; however, we should understand specific cases and experiences of successful stories. Common actions of successful cases showed that they have implemented a policy for supporting manufacturing and economic diversification and have managed a pegged exchange rate policy or devaluation of its currency and used optimal PFM for windfall revenues with political stability. Moreover, the government prioritized human capital in the best cases. One of the most successful

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policies for avoiding the “resource curse” and managing windfall revenue is the establishment of NRFs. Essentially, they saved and invested mineral revenues as NRFs in productive periods and are assured a permanent income stream in down periods.

1.2.2

Literature on Public Financial Management for Resource-Rich Countries

Another issue related to the management of resource revenues is how well-designed PFM with NRFs and fiscal policy are playing a massive role in resource-rich countries to avoid the “resource curse” and escape from Dutch disease. Baunsgaard et al. (2012) showed that a flexible fiscal policy, which considers country-specific characteristics (e.g., resource horizon, development needs (capital scarce or ample), and resource dependency) should be developed and should address both volatility and windfall shocks. Demachi and Kinkyo (2014) also discussed macroeconomic management for RRDEs in the JICA report of Myanmar’s economic development program. They argued that many RRDE governments with poor institutional power could not perform the expected economic growth despite having resource funds financed by windfall revenues. Establishing NRFs is not so flexible for all RRDEs for long-term fiscal sustainability if they have a weak institution and high political influences and corruption. Successful experiences in preventing the Dutch disease demonstrated that countries’ circumstances and institutional conditions were favorable for NRFs in facing the boom. Therefore, many international organizations including IMF, JICA, and WB have developed PFM and fiscal policy for RRDEs and recommended certain policy priorities and implications that RRDEs should follow depending on a country’s characteristics. PFM, as defined by the IMF (2013), involves managing government finances, estimating economic conditions and prospects, allocating public money, and reporting financial results. The report also suggested that optimal PFM and macroprudential policy are essential for RRDEs. Moreover, IMF guided some important fiscal policy frameworks in RRDEs as follows: • Sustainability of the fiscal policy that enhances capital accumulation. • Appropriate fiscal anchors, such as non-resource current balance rules, which are essential for short- and midterm policy. • Scaling up of growth-enhancing spending financed by resource revenue. • Addressing of volatility and uncertainty of resource revenue, which are critical for policymakers to achieve long-term fiscal sustainability. • Well-designed resource fund(s). Japan International Cooperation Agency (2016) also recommended sound fiscal and PFM policies for resource-rich countries (RRCs) through three case studies,

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Analysis of the “Dutch Disease” Effect and Public Financial Management. . .

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including Mongolia. According to the report, four important recommendations for fiscal and PFM policies for RRCs are the following: increase non-resource fiscal revenues by tax-base expansion; strengthen public debt management and fiscal discipline through public debt-GDP ratio ceiling and fiscal deficit reduction; enhance the transparency of resource-revenue flow, which means central control of revenue information; and understand and develop an optimal resource revenue allocation through NRFs and special account system. World Bank (2016) also emphasized that many RRCs have established resource funds for fiscal and macroeconomic stabilization, future generation savings, and national development and portfolio management. This WB report indicated that the Mongolian FSF has not yet achieved financial wealth since 2010. In summary, all international organizations and some studies above reviewed have a common idea for RRDEs wherein the allocation of resource revenue is one of the most important factors in any PFM and fiscal priorities and policies.

1.2.3

Mongolian Studies and Contributions

This study focuses on the Dutch disease effects on the Mongolian economy and attempts to examine the current PFM including the NRFs of Mongolia that could help in successfully avoiding the Dutch Disease. Few studies analyzed the effect of the Dutch Disease on the Mongolian economy. Batsukh and Avralt-Od (2012) evaluated the economic impact of the massive capital inflow in the Mongolian mining sector using a New Keynesian Dynamic Stochastic General Equilibrium (DSCE) model. Through resource movement and spending effects, this study showed that an increase in wages and marginal product in the nonmanufacturing sector leads to an increase in labor demand and production of non-tradable goods and, thereby, a decline in manufacturing production. Hence, this study proved that the inflow shocks of FDI and the sharp increase in the commodity price of the mining sector generate Dutch disease effects on the economy. In contrast, Ragchaasuren et al. (2016) argued that rapid expansion in the mining sector has no strong negative effects on the production of the other sectors. Hence, they did not confirm any severe Dutch disease effects on the economy and indicated only that there are small negative effects on other export goods through real exchange rate appreciation. This study used a single-country static computable general equilibrium (CGE) model calibrated to a 2010 Mongolian social accounting matrix (SAM). However, Khan and Gottschalk (2017), economists at the WB, showed that development of big mining projects is leading the Mongolian economy to the Dutch disease using a CGE model. The mining sector demand for domestic factor inputs explains two-thirds of the appreciation of the real exchange rate as the strongest channel for explaining the Dutch disease. They suggested that to expand the economy’s long-term sustainability, policymakers should aim to reduce the usage of domestic labor in the mining sector and channel resource revenue toward public investment. Battogtvor (2018) presented evidence of the Dutch disease effect on the Mongolian economy through

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an estimation of the vector error correction model (VECM) and showed that a 1% increase in the mining sector’s output creates a 2% decline in manufacturing output using the framework of Corden and Neary (1982). This study contributes to literature reviewed in this section by enriching evidence on the applicability of the Dutch Disease hypothesis to the Mongolian economy among limited previous studies with different results on the topic. For an empirical analysis, it applies a VAR model with quarterly data as the VAR model allows for potential endogeneity between variables of concern and for tracing out the dynamic responses of variables to exogenous shocks. Moreover, this study extracts some policy implications to avoid the Dutch disease risk (i.e., the policy suggestions for transforming the PFM including current NRFs from resource-curse form to resourceblessing one for the future development of Mongolia).

1.3

Theoretical Framework

The theoretical framework for the Dutch Disease analysis of the empirical study is based on the description of the “resource movement effect” (RME) and the “spending effect” (SE). Dutch disease effects are initially defined by Corden and Neary (1982) and also the “capital accumulation effect” (CAE) added by Sachs (2007) from an intertemporal perspective. Four main assumptions are considered under the Dutch disease hypothesis in the framework of Corden and Neary (1982). First, a small open economy produces three types of goods. Two of them (mining1 and manufacturing) are traded at a given international market price. The third good includes non-traded goods or services, and its price is established by the equilibrium of domestic supply and demand. Each of the three sectors uses labor, which is perfectly mobile between sectors. Second, a mining sector boom originates from a once-and-for-all Hicks-neutral improvement in technology. 2 Third, the models are purely real ones and ignore monetary considerations—only relative prices (expressed in terms of the given prices of traded goods) are determined, and national output and expenditure are always equal. Hence, trade is always balanced overall. Fourth, we assume that the commodity or factor markets have no distortions. Particularly, real wages are perfectly flexible, ensuring that full employment is constantly maintained. Based on these assumptions, we explain the pre-boom equilibrium and the Dutch disease effects (RME, SE, and CAE) as follows.

1

Originally, the two goods were energy and manufacturing. However, since the Mongolian mining sector is booming, it was changed to “mining” for further understanding. 2 Hicks’ view on neutrality is “an invention which raises the marginal productivity of labor and capital in same proportion.” Thus, a technical change is neutral if the ratio of marginal product of capital to that of labor remains unchanged at constant capital labor ratio (www. economicsdiscussion.net).

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RME: SE: Fig. 1.4 Effect of the boom on the labor market. Source: Author description based on Corden and Neary (1982)

1.3.1

Pre-Boom Equilibrium

This model begins by describing the pre-boom equilibrium. Figures 1.4 and 1.5 present the labor and commodity markets, respectively. In the labor market (Fig. 1.4), wage rate in manufacturing is measured on the vertical axis. Total labor supply is given by the horizontal axis OSOT. Labor input into services is measured by distance from OS, while the distance from OT measures labor input into two traded goods sectors. LM denotes labor demand for the manufacturing sector, and LT is obtained by adding to LM the initial labor demand for the mining sector (LT = LM + Lmining). Pre-boom labor demand for the two traded goods sector is combined by LT. Initial labor demand for the services sector is drawn by LS. Thus, initial full-employment equilibrium is at A. Here, LT intersects with LS, and the initial wage rate is w0. Fig. 1.4 does not present a complete picture of the initial equilibrium because the profitability of producing services and the location of LS depend on the initial price of services, which is not exogenous but determined as part of the complete general equilibrium of the model. For the commodity market in Fig. 1.5, traded goods aggregating mining and manufacturing output are measured on the vertical axis, and services are on the

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Fig. 1.5 Effect of the boom on the commodity market. Source: Author description based on Corden and Neary (1982) and Sachs (2007)

horizontal axis. The pre-boom production possibility curve is shown by TS and the highest attainable indifference curve is I0. Hence, the initial equilibrium is at point a, where TS is tangential to I0.3 Initial real exchange rate (defined by the relative price of services to traded goods) is provided by the slope of the common tangent to the two curves at a.

1.3.2

Effects of a Boom: Resource Movement Effect

Considering the resource movement effect (Fig. 1.4), the mining sector’s labor demand schedule shifts upward by an amount proportional to the extent of technological progress of the form of Hicks-neutral in the mining sector under the assumption that the real exchange rate is constant. This makes composite labor demand schedule LT shift upward to L0T . Hence, a new equilibrium at B is attained through an increase in the wage rate to w1. This effect thus causes labor to abandon both the manufacturing and services sectors for the mining sector. As employment in

3

Indifference curves summarize aggregate demands and ignore changes in income distribution.

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13

manufacturing falls from OTM to OTM′, the resource movement effect can be considered “direct de-industrialization.” Considering Fig. 1.5, the boom does not change maximum output of services, which remains as OS. However, booming raises the maximum output of traded goods from OT to OT′. Therefore, the production possibility curve shifts out asymmetrically to T′S, and the resource movement effect at a constant real exchange rate is represented by the movement of the production point from a to b. Movement of labor out of both manufacturing and services sectors leads to a fall in their outputs. Therefore, point b lies on the left side of point a in the figure.

1.3.3

Effects of a Boom: Spending Effect

The description turns to the spending effect (Fig. 1.5). Provided that the demand for services increases with income (i.e., services are normal goods), its demand at the initial real exchange rate moves along an income-consumption curve such as On, which intersects T′S at point c. As excess demand for services exists under the initial real exchange rate at point b, real appreciation must occur. However, the new equilibrium must lie somewhere between j (a point with the income elasticity of demand for services being zero) and c, namely, at point g, so the output of services is raised compared to the initial situation. Hence, the new equilibrium at g is a higher relative price of services than that of the initial equilibrium at a. As explained in Sect. 1.3.2, resource movement effect tends to lower the output of services, while spending effect raises it. Figure 1.4 shows that the service sector’s labor demand schedule shifts upward to L0S owing to an increase in the relative price of services to traded goods (i.e., real appreciation). Hence, the final equilibrium is attained at point G. Thus, wages increase to w2, causing employment in manufacturing to fall further from OTM′ to OTM′′. The result is a further reduction in manufacturing output, and the spending effect can be considered “indirect de-industrialization.” In summary, mining sector boom facilitates both “direct de-industrialization” reflected in the fall from OTM to OTM′ through the resource movement effect and “indirect de-industrialization” reflected in the fall from OTM′ to OTM′′ through the spending effect.

1.3.4

Intertemporal Effect: Capital Accumulation Effect

Corden and Neary (1982) explained that the Dutch disease involves a short-term sectoral dispute. Conversely, Sachs (2007) added the longer-term intertemporal perspective, namely, the “capital accumulation effect,” to the Dutch disease framework. Sachs (2007) argued that the production possibilities curve, T′S, could be

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shifted outward to T′′S′ (Fig. 1.5), if the resource revenues were utilized for the public investment projects on infrastructure (e.g., roads, power, telecoms). This raises productivity in both the traded goods and services sectors. The Dutch disease becomes critical if mining revenues are used to finance consumption rather than investment. Public investment financed by windfall revenues typically aims to transform a resource-based economy into a capital-intensive and knowledge-based economy. Sachs (2007) also emphasized that the mining sector boom could even lead to a real exchange rate depreciation at point k if public investment financed by the mining earnings raised the productivity of the nontraded sector (e.g., by financing improved seed varieties for smallholder farmers in developing countries). In summary, from a sectoral dimension, the mining sector boom might sacrifice manufacturing production under the Dutch disease case. However, from an intertemporal dimension, this sectoral repercussion of the boom might be offset through capital accumulation financed by the mining sector.

1.4

Empirical Analysis

In this section, we conduct an empirical analysis of the Dutch disease hypothesis on the Mongolian economy using a VAR model with quarterly data from 2000 to 2017 from the NSO of Mongolia. This study selects the year 2000 as the first year of the estimation as the mineral ratio to the total exports has increased rapidly since 2000 and is around 80% of the total export in recent years. The Dutch disease effects could be noticed in the economy during the booming period of the mining sector. Our empirical analysis is based on the theoretical framework presented in the previous section. Hence, the resource movement effect (RME), the spending effect (SE), and the capital accumulation effect (CAE) from mining sector shocks are examined by the model to check whether the Mongolian economy is suffering from the Dutch disease. This section represents key variables and data, a methodology of the estimation, and estimation outcomes with their interpretation.

1.4.1

Key Variables and their Data

The study identifies the following five key variables for a VAR model estimation to examine the RME, SE, and CAE: manufacturing-GDP ratio (moy), mining and quarrying (mup), consumer prices (cpi), investment-GDP ratio (ioy), and real GDP per capita (ypc) (for the variable list, see Table 1.2). The first variable of manufacturing ratio as GDP (moy) is a key variable used for examining RME with a combination of mining and quarrying (mup), which is the second variable expressed in terms of the 2010 constant price of million Mongolian tugrik (MNT). Manufacturing-GDP ratio (moy) is expressed as “manufacturing” as a

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Table 1.2 Key variables of the estimation and their usage in the model Symbols in estimation moy

# 1.

Variables Manufacturing-GDP ratio

2.

Mining and quarrying at 2010 constant price

mup

3. 4.

Consumer price index (2010 = 100) Investment-GDP ratio

cpi ioy

5.

Real GDP per capita

ypc

Usage of variables Resource movement effect (RME) Resource movement effect (RME); Spending effect (SE); Capital accumulation effect (CAE) Spending effect (SE) Capital accumulation effect (CAE) Control variable

Source: Author’s description

percentage of GDP; conversely, mining and quarrying (mup) represents production activity in the mining sector. The third variable is a consumer price index (cpi) with the base year of 2010. This is a substitute for a real exchange rate as one of the key variables for examining the spending effect. Using consumer prices as a proxy is justified as the exchange rate in Mongolia has been highly controlled. According to Ilzetzki et al. (2011), the Mongolian authority has adopted the “De facto crawling band and peg to the US dollar (USD)” as the currency regime since 1997. In selecting proxy variables of real exchange rate, Frankel (2010) argued in the context of the Dutch disease that the real appreciation in the currency takes the form of money inflows and inflation if the country has a fixed exchange rate, whereas it takes the form of nominal currency appreciation if the country has a floating exchange rate. For the estimation, the combination of mup and cpi is used for examining the “spending effect” that leads to “indirect de-industrialization”: the effect of mining and quarrying on consumer prices. The fourth variable of investment-GDP ratio (ioy) is used to check the CAE. The ratio is expressed as “gross fixed capital formation” as a percentage of GDP, derived from the NSO in the category of GDP by type of expenditure. However, the NSO started calculating GDP by expenditure type since the first quarter of 2005, and quarterly data on investment-GDP ratio is only available from that time. The combination of mup and ioy is used for estimating the CAE. The last variable of real GDP per capita (ypc) is a control variable in a VAR model estimation since the development stage of a country might affect manufacturing and investment to GDP ratios. Data for the mining and quarrying (mup) and real GDP per capita (ypc) variables are processed by seasonal adjustment (x12). For the VAR model estimation, data for all variables are converted into a natural logarithm form to avoid heteroskedasticity in the error terms.

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12.00

1200000

10.00

1000000

8.00

800000

6.00

600000

4.00

400000

2.00

200000

0

-

Manufacturing ratio as GDP

Mining and quarrying (mil.MNT) (secondary axis)

Fig. 1.6 Quarterly time-series data of the manufacturing ratio as GDP and mining and quarrying (mil. MNT at the 2010 constant price) of Mongolia between 2000 and 2017 with seasonal adjustment. Source: National Statistical Office, Mongolia

Figure 1.6 shows that the linear trends of mup and moy might have an ultimate effect on the Dutch disease, which is a crowding out of manufacturing. However, it should be evaluated statistically using the VAR model. We would like to find what kinds of macroeconomic and public financial policies should be implemented to escape from the crowding-out effect on the manufacturing sector, if the Mongolian economy is in the beginning stage of Dutch disease. Figure 1.7 simply displays the five variables above with seasonal adjustment. The manufacturing-GDP ratio does not appear to change considerably over the selected period, while mining and quarrying production shows a sharp increasing trend. The hike in consumer prices is also noticeable, thereby seeming to exhibit the Dutch disease phenomenon. However, the statistical test should be used to check the dynamic correlations of variables in a more sophisticated way, as variables are interacting with each other. Therefore, conducting a VAR model estimation in the subsequent section is necessary.

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1200000 1000000 800000 600000 400000 200000 0

2000-I 2001-I 2002-I 2003-I 2004-I 2005-I 2006-I 2007-I 2008-I 2009-I 2010-I 2011-I 2012-I 2013-I 2014-I 2015-I 2016-I 2017-I

Mining and quarrying (million MNT at 2010 constant prices)

Activity and Expenditures (percent of GDP) 100.000 -

2000-I 2001-I 2002-I 2003-I 2004-I 2005-I 2006-I 2007-I 2008-I 2009-I 2010-I 2011-I 2012-I 2013-I 2014-I 2015-I 2016-I 2017-I

50.000

Manufacturing ratio as GDP (moy) Investment ratio as GDP (ioy)

CPI (2010.IV=100) 200.00 150.00 100.00 -

2000-I 2001-I 2002-I 2003-I 2004-I 2005-I 2006-I 2007-I 2008-I 2009-I 2010-I 2011-I 2012-I 2013-I 2014-I 2015-I 2016-I 2017-I

50.00

15,00,000.00 10,00,000.00 5,00,000.00 -

2000-I 2001-III 2003-I 2004-III 2006-I 2007-III 2009-I 2010-III 2012-I 2013-III 2015-I 2016-III

GDP per capita (million MNT at 2010 constant prices)

Fig. 1.7 Time series of the variables used in the estimation (with seasonal adjustment). Source: National Statistical Office, Mongolia (www.1212.mn)

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1.4.2

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Methodology for a VAR Model Estimation

This section clarifies the methodology for a VAR model estimation. The reason why the study adopts a VAR model for the Dutch disease analysis is that the VAR model allows for potential and highly likely endogeneity among five key variables and also for tracing out the dynamic responses of variables to the structural shocks. Endogeneity can then be described, for instance, in the interaction between the mining sector’s production and manufacturing activity: a boom in the mining sector may crowd out manufacturing activity as the Dutch disease effect, and the manufacturing activity itself may also affect an economy’s dependence on the mining sector. In that case, a single-equation regression usually causes an estimation bias. A VAR model, instead, allows for potential endogeneity and lets the data determine the “impulse responses” of variables to the structural shock of the mining sector’s production. The VAR model estimation thus enables the comprehensive exploration of the Dutch disease effects (the resource movement effect and the spending effect) and the capital accumulation effects from the mining sector shock. Before the VAR model estimation, the unit root test should be conducted for each endogenous variable to examine the data stationarity, and, if needed, a cointegration test for a set of variables’ data should be checked. The unit root test is conducted with the null hypothesis that individual data is a unit root at their level and/or the first difference. This study uses the augmented Dickey-Fuller (ADF) test (Said and Dickey 1984) for examining the stationary property of data. If each endogenous variable is not stationary at their “level” but stationary at the first difference (i.e., I (1)), the Johansen cointegration test (Johansen 1995) could be further examined on the “level” data of set variables. We could finally use the “level” data for a VAR model estimation if the cointegration test shows that a set of variable data have cointegration. Table 1.3 reports the result of both unit root and cointegration tests. For the data of all four endogenous variables, the unit root test identified a unit root in their levels but rejected it in their first differences at the conventional level of significance, thereby the variables following the case of I(1). Hence, the cointegration test was conducted further on the combination of variables for examining the Dutch disease effect (the resource movement effect and the spending effect) and the capital accumulation effect. Both the trace test and the maximum-eigenvalue test implied that the level series of a set of variables’ data were co-integrated. Thus, utilizing level data for a VAR model estimation is appropriate. A VAR model for estimation is expressed as follows: y t = μ þ V 1 yt - 1 þ V 2 z t þ εt ,

ð1:1Þ

where yt is a column vector of the endogenous variables with year t, that is, yt = (mupt moyt)′ for examining the resource movement effect, yt = (mupt cpit)′ for the spending effect, and yt = (mupt ioyt)′ for the capital movement effect; μ is a constant vector; V1 and V2 is a coefficient matrix; yt - 1 is a vector of the lagged

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Table 1.3 Unit root ADF and Johansen cointegration test results Endogenous variables Mup

Cpi

Moy

Ioy

ADF unit root test Level (intercept and trend) Statistics Result t-stat -0.609 Nonstationary p0.861 value t-stat -2.757 Nonstationary p0.218 value t-stat -1.746 Nonstationary p0.404 value t-stat -1.742 Nonstationary p0.404 value

First difference (intercept) Statistics Result t-stat -8.966 Stationary p0.000 value t-stat -7.933 Stationary* p0.000 value t-stat -8.775 Stationary p0.000 value t-stat -13.280 Stationary p0.000 value

*With trend Set of variables

mup & moy mup & cpi mup & ioy

Johansen cointegration test Trace t-stat p-value 48.173 0.000 26.134 0.007 53.203 0.000

Max-Eigen t-stat 31.071 17.405 41.318

p-value 0.000 0.029 0.000

Source: author’s estimate

endogenous variables; zt is a vector of the control variable of real GDP per capita (ypc); and εit is a vector of the random error terms in the system. Lag length (-1) is selected using the Schwarz Information Criterion with a maximum lag equal to (-6) under a limited number of observations. Based on the reduced-form VAR model estimation (1), the study examines the impulse responses of variables to exogenous shocks: the response of moy to the mup shock for examining the resource movement effect, the response of cpi to the mup shock for the spending effect, and the response of ioy to the mup shock for the capital accumulation effect. In examining impulse response under the assumption of contemporaneous interaction between the pair of variables, structural shock should be identified by imposing some restrictions in the VAR model specification. Generally, to identify structural shocks, there are several approaches to implement restrictions: short-run and long-run restrictions. This study, based on the theoretical framework presented in Sect. 1.3, employs the Cholesky restriction as one of the short-run restrictions with the following recursive orders: from mup to moy for the resource movement effect, from mup to cpi for the spending effect, and from mup to ioy for the capital accumulation effect. By imposing the Cholesky restriction, the error term of the reduced-form Eq. (1.1) could be linked with the structural shock in the model. In the estimated results, the negative response of moy to the mup shock would imply the

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existence of a resource movement effect, the positive response of cpi to the mup shock would suggest the existence of the spending effect, and the positive response of ioy to the mup shock would indicate the existence of the capital accumulation effect.

1.4.3

Estimation Outcomes and Interpretation

Table 1.4 reports the lag selection criteria of the VAR model, and the Schwarz information criteria suggests the choice of 1-year lag. Tables 1.5 and 1.6 and Fig. 1.8 present the estimated outputs of the VAR model, respectively. Regarding the Dutch disease effect, the manufacturing-GDP ratio (moy) responds negatively to the shock of mining and quarrying (mup) at the 95% significance level in the second to fourth quarter after the shock, implying the existence of the resource movement effect. Consumer prices (cpi) respond slightly positively to the shock of mining and quarrying (mup) after a 1-year (first to fourth quarter) lag. However, impulse response shows a statistically insignificant result of spending effect at the 95% significance level. Therefore, we are unable to discuss SE in this study. The fact is, however, that during the mining sector boom, the Mongolian economy experienced two-digit inflation, and the FDI inflows increased sharply. As for capital accumulation effect, there is no positive response, but a negative response of investment-GDP ratio (ioy) to the shock of mining and quarrying (mup) was found. Thus, our estimation outcomes here imply that the Mongolian economy may have been suffering from the Dutch disease through the resource movement effect, such that manufacturing activities were reduced by the mining sector boom and the boom has not contributed to or even deteriorated the capital accumulation effect that alleviates the Dutch disease. Moreover, the result of the empirical analysis can be supported by actual cases in the Mongolian economy. For the period from 2001 to 2008 and from 2010 to 2013, the Mongolian economy entered two booming stages in the mining sector and accepted inward FDI in that sector. In this situation, many workers shifted to the mining sector (the resource movement effect). Simultaneously, the Government of Mongolia launched the cash handout to the public from the Human Development Fund (HDF) financed by the mining sector’s revenues, accelerating inflation through the consumption expansion and the loss of capital accumulation.

Table 1.4 Lag selection criteria of the VAR model VAR model mup & moy mup & cpi mup & ioy

Schwarz information criteria 1a 1a 1a

Source: author’s estimate Selected lag length for the estimation

a

Akaike information criteria 6 1 6

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Analysis of the “Dutch Disease” Effect and Public Financial Management. . .

Table 1.5 Estimated VAR model

Resource movement effect mup(-1) moyl(-1) ypc Adj. R-squared Spending effect mup(-1) cpi(-1) C Adj. R-squared Capital accumulation effect mup(-1) ioyl(-1) ypc Adj. R-squared

mup 0.579*** [7.201] -0.098** [-2.187] 0.420*** [5.258] 0.948 mup 0.629*** [6.924] 0.274*** [3.877] 3.696*** [4.079] 0.942 mup 0.567*** [6.281] -0.061** [-2.281] 0.434*** [4.801] 0.927

21

Moyl -0.376** [-2.064] 0.513*** [5.047] 0.430** [2.376] 0.227 cpi 0.009 [0.275] 0.991*** [40.713] -0.054 [-0.173] 0.996 ioyl -1.633*** [-3.593] 0.039 [0.290] 1.813*** [3.987] 0.211

Note: ***, **, and * denote rejection of the null hypothesis at the 99%, 95%, and 90% significance levels, respectively. Numbers in [ ] denote the t-value Source: author’s estimate

1.5

Policy Implications: Strengthening Public Financial Management

The previous section showed that the Mongolian economy, although expected to sustain economic growth as a middle-incomer, would fall into the “resource curse” in terms of the Dutch disease. The mining sector boom attracted the labor force from the manufacturing sector, and the resource movement effect of the Dutch disease was found through VAR model estimation. Simultaneously, during the booming period of mining, windfall revenues were used by the Government of Mongolia to finance social welfare programs, especially cash handout to the public. Thus, this process negatively affected the capital accumulation of the country as indicated by the empirical analysis. Therefore, the answer to the question of how Mongolia could escape from the Dutch disease is obviously to strengthen its PFM and transform the country from a “resource-curse” to a “resource-blessed” economy. Then, the issue in

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Table 1.6 Estimated impulse responses of the model

Period First quarter Second quarter Third quarter Fourth quarter Fifth quarter Sixth quarter Seventh quarter Eighth quarter Ninth quarter Tenth quarter

moy response to mup shock RME -0.015 (0.024) -0.041** (0.020) -0.041** (0.019) -0.034** (0.017) -0.026 (0.015) -0.020 (0.013) -0.015 (0.011) -0.011 (0.009) -0.008 (0.007) -0.006 (0.006)

cpi response to mup shock SE -0.002 (0.004) -0.001 (0.005) -0.000 (0.006) -0.000 (0.007) 0.000 (0.008) 0.000 (0.008) 0.000 (0.008) 0.000 (0.008) 0.000 (0.008) 0.000 (0.008)

ioy response to mup shock CAE -0.071 (0.056) -0.134** (0.036) -0.087** (0.028) -0.063** (0.026) -0.045* (0.023) -0.032 (0.020) -0.023 (0.017) -0.016 (0.014) -0.012 (0.011) -0.008 (0.009)

Note: ** denotes rejection of the null hypothesis at the 95% significance level. Numbers in () are standard errors Source: author’s estimate

this section is how to strengthen the current PFM, including NRFs of Mongolia as international organizations and the literature suggested. According to the IMF (2007) classification, if a country’s resource revenue is larger (20–25%) than the total fiscal revenue, it is considered a resource-dependent country. As mentioned in the literature review, Baunsgaard et al. (2012) developed a decision tree to determine the public financing policy priorities for RRDEs based on countries’ specific features and characteristics as follows. Figure 1.9 suggests that those RRDEs, which have a temporary resource revenue and capital scarce economy like Mongolia, should implement a fiscal and public financial policy and management with the following three priorities. First, Mongolia should engender macrostability, which is a common strategy needed for all resource-rich countries. Second, the country must save money for future generations. Third, to promote industrial diversification, Mongolia should develop capital accumulation, specifically in education, human resource development, and economic infrastructure. We herein discuss these three priorities as follows. Regarding top priority of macro-stability, many international organizations and scholars argued that fluctuation of resource prices causes unpredictable resource

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moy Response to mup Shock 0.04 0.02 0 -0.02

1

2

3

4

5

6

7

8

9

10

-0.04 -0.06 -0.08 -0.1

cpi Response to mup Shock 0.02000 0.01500 0.01000 0.00500 0.00000 -0.00500 -0.01000 -0.01500 -0.02000

1

2

3

4

5

6

7

8

9

10

8

9

10

ioy Response to mup Shock 0.10000 0.05000 0.00000 -0.05000

1

2

3

4

5

6

7

-0.10000 -0.15000 -0.20000 -0.25000

Fig. 1.8 Estimated impulse responses. Note: Dotted lines above represent a 95% error band. Source: author’s estimate

revenue and uncertainty for macroeconomic policy. The boom-and-bust cycles of the economy have occurred in Mongolia since the 2000s, and its economic growth has followed the serious fluctuation of international resource prices. Moreover, public financing would be influenced by short-term political interests and rentseeking activities, which would lead to overspending on wasteful consumption and low-return investments (ultimately leading to the Dutch disease). Hence, insulating fiscal spending from these volatilities and political pressures is necessary. For

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Is the resource revenue large to total revenue? 㼅㼑㼟 Is the resource revenue permanent (long lasting) or temporary? Permanent

Temporary

Is the economy capital scarce? Yes

No

Is the economy capital scarce? No

Yes

Priorities:

Priorities:

Priorities:

Priorities:

-Macro-stability

-Macro-stability

1.Macro-stability

-Macro-stability

-Development

2. Future

-Future

Examples:

generations

generations

Iraq, Nigeria

Examples: Norway, Netherlands

Fig. 1.9 Decision tree to determine the public financial management priorities. Source: author description based on Baunsgaard et al. (2012)

that purpose, setting up natural resource funds (NRFs) could be useful in public financial management (PFM) in resource-rich countries. Demachi and Kinkyo (2014) showed that the NRFs are considered a policy tool in PFM with two different timeframes: managing high-price volatility and stabilizing the macroeconomy to avoid the resource curse. In Mongolia, the FSF was established according to the Fiscal Stability Law (FSL) enacted by Parliament in 2010. FSF primarily aims to manage and handle windfall mining revenues and improve macroeconomic stability and fiscal discipline. The income from the FSF is derived from the difference between actual income and the calculated income based on the benchmark prices, which are defined as a 16-year backward and forward moving average of mineral prices. The past 12-year actual prices and current and next 3-year future prices are included in the moving average calculation. The FSF receives income transfers when actual income is higher than the calculated benchmark income. This rule is applied despite the budget deficit in the FSL. Conversely, the World Bank (2016) reported that no net financial wealth has been accumulated in the FSF since 2010. Notably, FSF money has been used to finance promissory notes by the government with political influences in 2015 and 2016. The current situation of the FSF (Table 1.7) shows that the net financial accumulation will be around 40.0 million USD (104.2 billion MNT) in 2019. Within 10 years since its establishment, the FSF’s

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Analysis of the “Dutch Disease” Effect and Public Financial Management. . .

Table 1.7 Current situation of the fiscal stabilization fund (billion MNT)

Revenue and expenditures Total revenues Royalties and taxes Interest income Government fund amount Total expenditures FSF accumulation

2016 10.1 – 10.1 – – 332

2017 345.6 325.7 14.6 5.3 572.1 104.2

25 2018 122.7 122.7 – – 122.7 104.2

2019 322 322 – – 322 104.2

Source: Ministry of Finance, Mongolia

accumulation is relatively low compared to neighboring countries. For instance, Kazakhstan has a national wealth fund established in 2008 with a total asset of 74.1 billion USD, which is 1800 times larger than the Mongolian FSF. Therefore, the Mongolian FSF could not sustain its economy during the recession derived by the fall of mineral prices from 2012 to 2016. Moreover, domestic and foreign debts with high interest rate were utilized for public financing. In summary, on the top priority of macro-stability, Mongolia has the FSF for stabilizing its economy from the fluctuations of resource revenues. However, this fund has no net financial wealth currently and could not perform well against volatility during the recession. This is evidence that the FSF management as a tool of PFM needs rethinking and strengthening for optimal performance. The FSF management could be reformed only if decision-makers and political parties understand that Mongolia is beginning to suffer from the Dutch disease and that inefficient projects and programs stopped reducing budget deficits that are financed by the FSF over the last decade. On second policy priorities, savings for the future generation had never been done in Mongolia before a law on the FHF was approved in 2016 and enacted in 2017, which established one of the current NRFs named FHF. The FHF is now paying off the debt of HDF (2009–2017), which replaced the Mongolian Development Fund (2007–2008). As previously mentioned, previous funds, particularly the HDF, had been utilized for the cash handout to the public for the social welfare purpose, raising consumption but not investment and leading to two-digit inflation. To provide 1.5 million MNT (now it is around 600 USD) per citizen of Mongolia after the election, 500 thousand MNT financed by HDF was provided in cash to every citizen during the 2010–2012 period. The remaining 1.0 million MNT was granted to citizens who were provided with two options. The first option was that those who met special requirements such as age limit and poor living condition could have it in cash by the Resolution #116 of the Government of Mongolia in 2012. Incidentally, this could be the main reason why the Mongolian economy could not accumulate capital. All money was poured into consumption, accelerating into the Dutch disease. This indicates how the PFM in RRDEs is important in macroeconomic policies. The second option was that the remaining 1.0 million MNT was paid by 1072 shares of the “Erdenes Tavantolgoi,” a state-owned joint company that holds the most strategic mining licenses if a citizen has an account in broker/dealer companies registered in the Mongolian Stock Exchange (MSE). The second option remains ongoing as not

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Table 1.8 Current situation of the FHF (billion MNT) Revenue and expenditures Total revenues Dividends Royalties and taxes (65%) Total expenditures Budget financing (according to the law) Payment for the debt of HDF Debt amount of HDF FHF accumulation

2016 HDF

1071.9 –

2017 357.7 – 357.7 357.6 160.0 197.6 875.0 –

2018 508.7 47.8 460.9 508.7 100.0 408.7 465.4 –

2019 1068.5 300.9 767.6 515.4 50.0 465.4 553.1

Source: Ministry of Finance, Mongolia

all citizens have sufficient knowledge about shares and accounts related to stock exchanges. Since 2017, no wealth has been accumulated in the FHF as the debt shifted from the HDF was paid by the revenues of the FHF as mentioned previously. Table 1.8 shows that, in 2019, more than 70% of the revenue of FHF will originate from royalties and tax income from mining and quarrying activities. The remaining 30% will be transferred from dividends of state-owned companies in the mining sector, and 553.1 billion MNT is expected to be saved in the fund in 2019. This is the first time to raise savings from natural resource revenue for future generations of Mongolia. According to the Law on Future Heritage, the money accumulated in the FHF will not be spent until 2030, unless efficient allocation in the form of domestic and foreign investments and assets is approved by the government. Then the question arises on whether Mongolian natural resource funds (NRFs), FSF and FHF, have been successful or not. Since there will be no utilization but only savings until 2030, FHF could be a rather neutral solution against the Dutch disease, while the FSF requires reformation and independence from the political pressures and interests. However, to answer the question above more correctly, we should discuss the third priority. The third PFM priority of “development” is directly related to the allocation of the resource revenues in RRDEs. Basically, there has been a controversy on whether the funds should be saved or invested. This depends on the development stage of a country, and in the case of developing countries including Mongolia, the funds should be used for investments. Sachs (2007) argued that resource earnings should be used for public investment to facilitate capital accumulation. Japan International Cooperation Agency (2016) also emphasized that domestic investment could yield higher benefits compared to investment in international capital markets, owing to capital scarcity in developing countries. Baunsgaard et al. (2012) also argued that low-income countries usually have less capital, which might be below the “steadystate level.” Under capital scarcity, rate of return to capital is likely higher than that of financial assets and investing more resource revenues domestically could raise the potential non-resource growth in their countries.

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The concept of the funds for investments is also consistent with the argument of Sachs (2007) with an emphasis on the role of public investment. On funds for investment, then, the types of investments to be promoted would be another critical question. Coutinho (2011) represented the investment strategies for managing resource revenues, which could be drawn as common lessons from successful practices in Botswana, Indonesia, Malaysia, and Chile, as follows. Resource revenues should first be invested in human resource development and education as a way of boosting permanently incomes, increasing labor productivity and also spreading benefits across generations. Second, revenues should be invested in economic infrastructure to diversify the economy so as to insulate it from external shocks in the mining sector in the mid and long term. Finally, this section provides the following policy implications and suggestions on the current PFM in Mongolia. In managing FHF, part of the fund should be allocated for domestic investment (Sachs 2007). Specifically, investment in the projects on human resource development and education and economic infrastructure is necessary to facilitate industrial diversification (Coutinho 2011). Particularly, industrial diversification is key for the Mongolian economy to sustain its growth by enhancing its resilience against the resource sector’s volatilities. Another suggestion is that investment allocation for the NRFs needs some framework independent from political pressure. Auty (2007) proposed the establishment of an independent unit that would evaluate the rate of return of each public investment project. Coutinho (2011) emphasized the roles of the “technocrats” in Indonesia and “Chicago boys” in Chile in resource revenue allocation and economic management.

1.6

Conclusions

This study determined whether the Mongolian economy is suffering from the Dutch disease by applying a VAR model with quarterly data from 2000 to 2017 of the National Statistical Office (NSO), Mongolia. The study also extracted some policy implications for transforming public financial management from the form of a resource curse into a resource blessing. Results of a VAR model estimation found the Mongolian economy has been suffering from the Dutch disease through resource movement effects. Evidently, manufacturing activities were reduced by the mining sector boom, and the sector boom has not contributed to or even deteriorated the capital accumulation effect that alleviates the Dutch disease. The strategic policy implications for the current Mongolian public financial management are that part of the existing resource fund should be used for public investment to facilitate capital accumulation, specifically, for projects on education, human resource development, and economic infrastructure to promote industrial diversification. Particularly, industrial diversification is a vital requirement for the Mongolian economy to sustain its growth by enhancing its resilience against the resource sector’s volatilities. Another suggestion is that the allocation of investment

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of the current natural resource funds requires some framework of independence from political pressure.

References Auty R (2007) Natural resources, capital accumulation and the resource curse. Ecol Econ 61:627– 634 Batsukh T, Avralt-Od P (2012). Risk assessment of “Dutch disease” in Mongolia due to a major resource and expected massive capital inflow. ERI Discussion Paper Series No 1, 25–84 Battogtvor E (2018) Gains from trade in transition economies: the cases of Mongolia and Vietnam. Dissertation of Yokohama National University. Yokohama, Kanagawa Baunsgaard T, Villafuerte M, Poplawski-Ribeiro M, Richmond C (2012) Fiscal frameworks for resource rich developing countries. IMF Staff Discussion Note, SDN/12/04 Corden WM, Neary JP (1982) Booming sector and de-industrialization in a small open economy. Econ J 92:825–848 Coutinho L (2011) The resource curse and fiscal policy. Cyprus Econ Policy Rev 5(1):43–70 Demachi K, Kinkyo T (2014) Macroeconomic management in resource-rich developing economies. Kokuminkeizaizassi 210:55–67 Frankel J (2010) The natural resource curse: a survey. NBER Working Paper Series, No.15836 Ilzetzki E, Reinhart CM, Rogoff KS (2011) The country chronologies and background material to exchange rate arrangements into the 21st century: will the anchor currency hold? Q J Econ 134(2):599–646 International Monetary Fund (IMF) (2007) Guide on resource revenue transparency International Monetary Fund (IMF) (2012) Macroeconomic policy frameworks for resource-rich developing countries International Monetary Fund (IMF) (2013) Public financial management and its emerging architecture Ismail K (2010) The structural manifestation of the ‘Dutch Disease’: the case of oil exporting countries. International Monetary Fund Working Paper, 10/103 Japan International Cooperation Agency (2016) Study on economic and fiscal policies in resourcerich countries. Final Report, KRI International Corporation and Mitsui Mineral Development Engineering Co., Ltd. Johansen S (1995) Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press, Oxford Khan TS, Gottschalk J (2017) Investigating the transmission channels behind Dutch Disease effects: lessons from Mongolia using a CGE model. World Bank Policy Research Working Paper, 8183 Ragchaasuren G, Tsolmon B, Munkh-Ireedui B, Nasantogtokh N, Telmen T, Tuvshintugs B (2016) A static CGE model of the Mongolian economy. Partnership for Economic Policy (PEP), Working Paper 2016–03 Sachs JD (2007) How to handle the macroeconomics of oil wealth. In: Hamphreys M, Sachs JD, Stiglitz JE (eds) Escaping the resource curse. Columbia University Press, New York Sachs JD, Warner AM (2001) Natural resources and economic development: the curse of natural resources. Eur Econ Rev 45:827–838 Said SE, Dickey DA (1984) Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika 71(3):599–607

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Taguchi H, Khinsamone S (2018) Analysis of the ‘Dutch disease’ effect on the selected resourcerich ASEAN economies. Asia Pac Policy Stud 5(2):249–263 Taguchi H, Lar N (2016) The resource curse hypothesis revisited: evidence from Asian economies. Bull Appl Econ 3(2):31–42 World Bank (2016) Fiscal management in resource-rich countries. World Bank Group, Washington, D.C. World Bank (2018) Mongolia economic update: fiscal space for growth—the role of the public investment spending efficiency. World Bank Group, Washington, D.C.

Chapter 2

Enhancement in Governance Capacity of the Sovereign Wealth Funds of Mongolia Naranchimeg Luvsansharav

Abstract The purpose of this study is to examine the learnings from past Mongolian wealth management, particularly in sovereign wealth fund (SWF) activities, to explore the key development features of governance of selected SWF best practices, and to identify appropriate and prudent policy implications for strengthening fund management. From international experiences, it is recommended that the government institutions, which can help stabilize the economy and exchange rate and flatten the inflation rate over generations, are the key to sustainable development in resourcerich countries. Sovereign wealth funding is a modern approach to sustainable development. In resource-rich countries, in particular, it is considered to prevent the Dutch disease and rent-seeking caused by limited democracy, poor fiscal discipline, and corruption of bureaucracy. The study involves quantitative and qualitative methods to answer a research question using a strengths, weaknesses, opportunities, and threats (SWOT) analysis and regression analysis. The selection of case studies is based on SWF indicators (scale of the fund, transparency, and accountability rate) and the country’s resource similarities that contributed significantly to the government budget. This study covers Norwegian and Chilean cases and investigates the main factors behind the success of SWF management. After obtaining the key concepts and principles for the success of SWF, we examine the Mongolian case and investigate the main reasons for its failure. Subsequently, the pros and cons of the Mongolian SWF in terms of internal and external factors are studied using SWOT analysis. The quantitative analysis is conducted in two batches of statistical estimation techniques to (1) examine the relationship between governance indicators and the scale of SWF and (2) verify the assumption that the SWF can contribute to the government’s fiscal activities. We believe that natural resources contribute to well-being, poverty elimination, job creation, and prosperity through fiscal income. Resource-rich countries should be concerned about how resource revenues drive other forms of capital that can achieve sustainable development. However, there are

N. Luvsansharav (✉) Ministry of Finance of Mongolia, Ulaanbaatar, Mongolia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Taguchi et al. (eds.), Challenges in Fiscal and Monetary Policies in Mongolia, New Frontiers in Regional Science: Asian Perspectives 66, https://doi.org/10.1007/978-981-19-9365-7_2

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several challenges related to governing issues and development difficulties arising from capacity building in developing nations. Several studies indicate that resourcerich countries tend to fail in capacity building, thus causing vulnerable economies and weak social development over generations, particularly in emerging and developing countries. However, not all endowed nations have resource-management problems. Some have achieved sound resource management, which enables them to provide good public service and social benefits on a transparent base, develop innovative and modern technology, improve productive capacity, and increase revenue from surplus for safe and responsible management for future generations. In this study, we address the importance of SWF strengthening through sound governance, attempt to clarify the current situation of SWFs in Mongolia, and suggest further improvements. Mongolia, a natural resource-rich country, can convert wealth into well-being if wealth management is significantly improved. Due to the democratic regime, the nation has the potential to implement good international practices and introduce them into national wealth management under effective and efficient governance. The Government of Mongolia has established several sovereign funds through policy reforms to achieve the international organizations’ requirements for the sustainable stabilization of the economy. Some funds are terminated early due to weak governance and legislation, while others achieve their initial objectives by updating policy and legal documents through reforms. The main challenges of SWF management are political interference in decisionmaking; weak institutional capacity, including human capital, technology, industry standards, and internal ethical guidelines; and poor transparency performance. The failures of previous SWFs were due to unclear rules for its mission and unclear delegation from owners to managers, inadequate design and governance, excess withdrawal by political decisions, lack of operational freedom (political interference), and mismanagement (to finance directly to government expenditure). This study analyzes the measures of national governance correlated with the scale of SWF to provide evidence suggesting that countries with a higher (lower) degree of government effectiveness tend to accumulate more (less) wealth for generations. We also present an empirical analysis of how the growth in SWF asset accumulation contributes to national fiscal balance fluctuations. We show that if nations accumulate more financial wealth from government revenue surplus, the fiscal deficit tends to fluctuate in a stable manner. Keywords Sovereign wealth fund · Governance · Institutional capacity building · Transparency and accountability · Inclusive growth · Sustainable development · Stabilizing the economy · Fiscal deficit

2.1

Introduction

This study aims to examine learnings from past Mongolian wealth management, particularly in sovereign wealth fund (SWF) activities, to investigate key development features of governance of SWF best practices that have been chosen by this

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research, and to identify appropriate and prudential policy implications that can enhance fund management. Natural resources can contribute to improving well-being, eliminating poverty, and creating jobs and prosperity through increased fiscal income, and they must be sustained for national development. Countries aiming to stabilize their economy and ensure better well-being and quality of life should be concerned about how resource revenues drive other forms of capital to achieve sustainable development. There are many aspects of sustainable development, such as sound institutional capacity, a strong legislative system, transparency, trade policy and partners, capital investment policy, and better social and geographical opportunities. We should learn from both the cursed and blessed experiences of resource management and not repeat the same mistakes that others did but learn from their success. Several studies indicate that resource-rich countries tend to fail in capacity building, thus causing vulnerable economies and weak social development over generations, particularly in emerging and developing countries. However, not all endowed nations have resourcemanagement problems. Some have achieved sound resource management, which enables them to provide good public services and social benefits transparently, develop innovative and modern technology, improve productive capacity, and increase revenue from resource surplus to secure and responsible management for future generations. These countries include Norway and Chile. Mongolia, a natural resource-rich country, can convert wealth into well-being if wealth management can be significantly improved. Owing to the democratic regime, the nation has the potential to conduct good international practices and implement them in national wealth management under effective and efficient governance. More precisely, the main difference between resource “winners” and “losers” is the quality of their institutions (Mehlum et al. 2006). From international experiences, it is recommended that public institutions, which can help stabilize the economy and exchange rate and flatten the inflation rate over generations, are the key to sustainable development in resource-rich countries. The SWF is a modern approach to sustainable development, particularly because in resource-rich countries, it is considered to prevent Dutch disease and rent-seeking, which is caused by less democracy, poor fiscal discipline, corruption of bureaucracy, and so on. Resource-rich countries tend to have poorer economic policies, insecure societies, high corruption, inequality, and poverty issues than resource-poor countries. Mongolian economic growth attained 17.5% when mineral product price was higher in 2011; afterward, the GDP growth dropped by approximately 5–7%, caused by lower growth in China. The Mongolian economy is vulnerable to external factors, particularly mineral product price fluctuations in the world market, and simultaneously contains fiscal risk. To reduce vulnerability, the Government of Mongolia established two types of sovereign funds under the laws “Stabilization Fund” and “Human Development Fund ((HDF), later named Future Heritage Fund (FHF))” in 2010 and 2009. However, since the launch of the two funds, many political interferences were associated with the fund’s activity, causing their weaker economic performance. One of the worst examples was the cash distribution to individuals during the

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parliamentary elections in 2008 and 2016, rocketing national debt and leading to a dried-up HDF. By the end of 2016, after this populist action, the HDF recorded a significant debt burden of ₮1071 billion, and the government spent 3 years taking over for it. The mining-dependent economy was not resilient to this populist decision and eventually caused a large number of loans, leading the nation to confront the debt crisis.1 Behind these populist decisions was a weak legislative background, and sensible macroeconomic policies and implementation led to a vulnerable economic trajectory for its nation’s development. For future prosperity in the long term, proper wealth management policies and approaches are needed. Ensuring qualified institutions’ capacity and good governance in resource management is an essential driver of long-term development in Mongolia. From this perspective, this study conducts a qualitative and quantitative analysis of natural resource management, particularly the SWF. In Sect. 2.2, we introduce key features of natural resource management, such as international experiences of SWF, its basic concept, international guidelines, and good practices of SWF that should be adopted in Mongolia. In particular, we focus on the cases of Norway and Chile. Section 2.3 mainly focuses on the Mongolian development of the SWF to present policy implications and reforms based on historical data. Sections 2.4 and 2.5 conduct the qualitative and quantitative analyses of SWF, respectively, and finally, we summarize the conclusions and propose our policy recommendations in Sect. 2.6.

2.2 2.2.1

International Benchmarks: Norway and Chilean SWFs General Understanding of SWF

There is no single definition for SWF. The definition by Andrew Rozanov 2 in 2005 is “state-owned investment funds that make the long-term domestic and international investment in search of commercial returns.” He classified SWFs into five categories: stabilization funds, endowment funds, pension reserve funds, development funds, and government holding funds. There are a few other definitions of SWF. Edwin Truman defines “SWF is a separate pool of state-owned or governmentcontrolled financial asset that includes some international assets” (Bortolotti et al. 2015). The International Monetary Fund (IMF) defines it as “Special investment funds created or owned by governments to hold foreign assets for long-term purposes.” There are two classification criteria for SWF: (1) the source of sovereign wealth and (2) their policy objectives (International Monetary Fund 2007). The source is

1

Debt to GDP peaked nearly 90% in 2016 from 30% in 2008. Managing Director of State Street Corporation, an American financial service and bank holding company and one of the largest asset management companies in the world.

2

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Table 2.1 Different criteria in types of SWFs Stabilization fund Short-term

Saving fund Long-term

Highly liquid assets

Broader asset classes

Performance benchmarks

Minimizing expenditure volatility & maintaining adequate liquidity

Risk tolerance

Low riskreturn profile

Achieving real expected returns for long-term periods to maintain the long-term purchasing of the wealth Higher riskreturn profile

Asset & liability management

Ensuring the sustainability of future fiscal expenditure

Investment horizon Asset composition

Maximizing the net value of the fund considering the correlation between asset price and liabilities

Pension reserve Fund Long-term

Development Fund Long-term

Proper balance in liquid & equities Minimizing government fiscal burden from future pensionrelated expenditure volatility

Broader asset classes Investing in socioeconomic projects (infrastructure)

Higher riskreturn profile

Higher riskreturn profile

Offset rising pension costs

Improve socioeconomic development

Reserve investment corporation Long-term High shares in equities To reduce the negative carry costs of holding reserves

Higher risk-return profile Higher return by high allocation in equities

Source: summarized by the author from Al-Hassan et al. (2013)

commodity, non-commodity, oil, and minerals such as copper, and the policy objectives are classified as stabilization funds, savings, development funds, pension reserve funds, and reserve investment corporations. The performance of SWFs is significantly affected by national fiscal and monetary policies as well as the global financial market. The key factors of successful SWFs are transparent governing bodies with clear roles and accountabilities and clear ruling in legitimacy, such as deposit, withdrawal, independence of decision-making, prevention of fraud and mismanagement, and communication with stakeholders. Table 2.1 shows that asset allocation differs by SWF type based on its objectives, regardless of origin. Except for stabilization funds, SWFs tend to invest in long-term projects that can meet the country’s strategic development purposes with high-risk tolerance, and all types of funds aim to achieve an increase in asset value, which is the underlying performance benchmark. In this study, we use asset value as a general indicator to measure SWF’s performance because it can only be considered a criterion to evaluate all funds’ performance commonly due to data limitations. Many countries set their SWF regulations in different legal forms, but in general,

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SWFs are organized as separate legal entities, state-owned corporations (SOC), or units under the Ministry of Finance or Central Bank. However, in a wide range of legal frameworks across countries, good governance and a clear ruling system matter in view of the overall trend of SWF performance. This study highlights two countries’ cases in terms of governance, including independence in decision-making, institutional capacity, reporting, and transparency. The benchmarks for Mongolia were Norway and Chile. Norway has the largest SWFs in the world and is considered to have a good practice of in-housing asset management with a well-insulated political system (hybrid political system) in ethical matters. Chile, a richly endowed country having copper reserves (coal and other minerals as well), has established an effective macroeconomic linkage of SWFs that could be a good reference for Mongolia.

2.2.2

Sound Portfolio Management with Hybrid Political Structure and Ethical Manner in Norwegian SWFs

Norway has two SWFs: (1) the Government Pension Fund Global (GPFG), also known as the oil fund, established in 1990 to invest revenue surplus from petroleum, and (2) the Government Pension Fund Norway (GPFN), established in 1967 from the national insurance system, as shown in Table 2.2. The latter is smaller than the GPFG and mainly holds Norwegian companies’ stock in the domestic and Scandinavian markets, while the GPFG invests in the international market. Both funds are owned by the state and managed by the Central Bank and a specified private investment company, respectively, under the mandate of the Ministry of Finance (Government of Norway 2019). They are managed for both savings and stabilization purposes. This study mainly focuses on GPFG funding from natural resources rather than that based on the national insurance scheme since our aim is to learn good practices of natural resource management (Fig. 2.1).

Governance The GPFG and GPFN seek maximum return with an acceptable level of risk based on the mandate of the MOF. Both funds make investment decisions independently and pay attention to environmental and sound corporate governance in decisionmaking. Both the Norges Bank and Folketrygdfondet apply international standards (Organisation for Economic Cooperation and Development [OECD], United Nations [UN], etc.) and norms in their management guidelines. The Council on Ethics, the core concept of responsible investment, provides guidelines for observation and exclusion to the GPFG, and the Central Bank publishes a list of excluded companies based on the recommendation of the Council. Norges Bank refers to

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Table 2.2 Asset allocation characteristics of GPFG and GPFN

Owner Establishment Originate

Government Pension Fund Global State (MOF) 1990 Oil (surplus of petroleum sector)

Institutional framework

Managed by Central Bank (Norges bank) under the mandate of MOF

Market Investment portfolio Revenue

Outside of Norway Equity: 70%, Fixed income: 30% Tax, interest, dividend, and stock sales relating to petroleum activities Based on parliament’s decision, 3% of funds each year is transferred to gov. budget (shall not fund gov. budget expenditure directly) Independent of the local economy Higher risk-return profile

Withdrawal

Risk tolerance Performance benchmarks Evaluation

Ethical investment (green) with the highest return that ensures petroleum wealth benefits both current & future generation MOF

Government Pension Fund Norway State (MOF) 1967 Surplus of national insurance scheme Managed by a private company (Folketrygdfondet) under the mandate of MOF Inside Norway Equity: 60%, Fixed income: 40% Surplus of national insurance scheme Transfer to gov. budget

Dependent on the local economy Less risk-return profile than a global fund Seek to achieve the highest possible return after management costs over time MOF

Source: summarized by the author from the Norwegian Ministry of Finance (2019)

Fig. 2.1 Institutional framework of SWFs in Norway. Source: author description according to the Norwegian Ministry of Finance (2019)

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responsible investment policy, such as establishing principles, exercising ownership, and investing in sustainability (Norwegian Ministry of Finance 2019). Norges Bank prioritizes promoting good corporate governance and responsible business practices based on dialogue with companies, which is inseparable from the understanding of companies. Norges Bank emphasized sustainability, board accountability and effectiveness, executive remuneration, and shareholder rights in 2018 in accordance with international standards and norms under the mandate. The function of the Council on Ethics is expressed in the following explanation with regard to objectives, actions, outcomes, and the relationship between managers and owners.

GPFG GPFG has a safe, responsible, and sound management, resulting in the world’s firstranked SWF with regard to the market value showing a 6.1% annual return over the last two decades. Norway has achieved a relatively stable exchange rate, owing to the accumulation of foreign reserves through its monetary policy. In 1990, the parliament passed legislation for the GPFG to allow investment in the domestic market to avoid imbalances in the economy. The country’s economy then grew significantly, and the GPFG made overseas investments only after the domestic market became saturated. The country confronted an increase in its surplus from the massive production in the oil sector, and investment strategy, regulation, and guidelines were set for SWFs. The first withdrawal was set to the government budget by a parliamentary decision 26 years after its inception. Now GPFG has three significant guidelines that other countries may learn and apply to enhance their performance: (1) ethical guidelines, (2) responsibility, and (3) transparency.

Ethical Guidelines Norway has the ethical act “Guidelines for Observation and Exclusion from the Government Pension Fund Global,” which aims to avoid investing in violencerelated products such as weapons and tobacco, finite resource production,3 and power producers. The Council on Ethics provides recommendations to the fund manager, based on which the manager makes investment decisions and excludes the listed companies in making further portfolio investments. The Council has five members appointed by the owner based on the manager’s nomination for 4 years. The ethical guidelines suggest investing in green products in power and other sectors. Under Section 5 of the Guidelines, the Council on Ethics regularly monitors the Fund’s portfolio to ensure that the involved companies behave ethically. Section 3:

3

The GPFG supports green energy and green production rather than an exhausted one.

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1. Serious or systematic human rights violations, such as murder, torture, deprivation of liberty, forced labor, and worst forms of child labor, 2. Serious violations of the rights of individuals in the situation of war or conflict, 3. Severe environmental damage, 4. Act or omissions that on an aggregate company level lead to unacceptable greenhouse gas emissions, 5. Gross corruption, 6. Other particularly serious violations of fundamental ethical norms.

One thing that should be addressed here is that the Council on Ethics provides companies with a chance to explain how their behavior involves either observation or violation. The manager considers the company’s characteristics, focusing on whether the company has the capacity to reduce the risk of expected violations through sound governance, particularly in increasing green investments. The relationship between the owner, manager, ethic council, and companies invested in is clearly ruled by “Guidelines for Observation and Exclusion from the Government Pension Fund Global.” When the manager publishes a decision regarding this guideline, the Council on Ethics must disclose its recommendations (Norwegian Ministry of Finance 2014).

Responsibility The GPFG encourages companies to invest in improving governance and mitigating social and financial risks. The GPFG believes that it will be evaluated by the sustainable and sound governance of the companies invested, thus requesting responsible investments. Around 9000 companies in over 70 countries are being invested in by the GPFG, and this supports the invested company’s sound and responsible governance through its responsible investment policy. Conceptually, if companies in the fund’s portfolio achieve their desired growth with sound management, the fund’s value would improve. This is an inseparable idea for the future development of both the companies and GPFG. The GPFG expects companies to comply with international standards and Sustainable Development Goals, including children’s rights, climate change, water management, human rights, tax compliance and transparency, anticorruption, and ocean sustainability. Political responsibility can be described as a hybrid structure in which the GPFG is well segregated from political pressure, accepting ownership by the state with the disengagement of the management. Norway could be a model in terms of political insulation with SWF through its acts on other SWFs, which face the difficulty of less effectiveness caused by political interference. More precisely, acts and guidelines for fund management must clarify what politicians can or cannot do.

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Transparency Norway has a good model of the legal system in terms of the investment decisionmaking process, which can serve as a benchmark for other countries, including emerging economies (Clark and Monk 2011). There are several restrictions that politicians can impose to ensure that the manager makes transparent investment decisions independently from politicians under a mandate from the fund owner in order to achieve its strategic objective. According to the management mandate for the GPFG (Chap. 6) and the Guidelines for Observation and Exclusion from the GPFG, the report shall be disclosed to the public on a regular basis in a user-friendly format4 on open-data sources. The reports include: • Norges Bank’s performance of management on SWFs under the mandate • Investment strategy (portfolio ratio, risk assessment, etc.) every third year • Semi-annual reports (performance and risk assessment, environment-related investment, etc.) shall be made public no later than 2 months after the end of the relevant 6-month period • Annual reports (performance and risk assessment, etc.) shall be made public no later than 3 months after the end of the relevant 6-month period • Guidelines for Observation and Exclusion from the GPFG (list of companies excluded by funding in an ethical manner) • Changes in calculation methods on a report explaining why changes need to be made • The Executive Board’s guidelines (the result of mandate) Furthermore, a full list of the investing companies and voting results (under GPFG Management Mandate, Sections 2–4 (12) “The Norges bank shall not hold more than 10 percent of voting shares of any company”) must be available on the website. Stone and Truman (2016) studied SWFs in Norway as the most transparent funds in terms of transparency and accountability among the 69 SWFs across countries. Moreover, Norway has the highest score in applying Santiago Principles,5 which promote transparency, good governance, accountability, and prudent investment practices in SWF activities.

4

Computer-readable format Generally Accepted Principles and Practices (GAPP) which was released by International Working Group of the sovereign wealth funds in Santiago of Chile, September 22, 2008 – called the “Santiago Principles”

5

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Calling Santiago Principles of SWF

In recent decades, SWFs have proliferated in over 60 countries, and leading global institutions have been launched to share good practices and capacity building for good governance. SWFs cannot be governed and managed with a single rule or standard, and each country applies its own legislative and governing style depending on its circumstances. However, there are generally accepted practices and principles that countries can incorporate into their policies and governance. For instance, consider the Santiago Principles. In 2008, the International Forum of Sovereign Wealth Funds (IFSWF) was launched by the leading international sovereign investors under the International Working Group (IWG) of SWFs. Gathering international practitioners from leading global institutions, such as the Group of Twenty (G20) and IMF, the US Department of Treasury drafted Generally Accepted Principles and Practices (GAPP) and released it in Santiago in Chile (International Working Group 2008), later known as the Santiago Principles, to share the knowledge of SWFs’ institutional governance and risk management frameworks. The IFSWF plays the role of bridging member countries with their sound governance and accountability structures and practices and supports the implementation of the Santiago Principles. The IFSWF has 34 full members who have agreed to implement the Santiago Principles and 4 associated members who need to build the legislative framework of SWF to become full members after completing the necessary amendments. Norway withdrew its membership in the IFSWF based on the decision of the Norwegian Finance Ministry and canceled the sponsorship. Membership is entirely voluntary, and Norway, from outside the Forum, still plays a vital role in the IFSWF’s activities. Three main principles out of all 24 Santiago Principles were agreed on for implementation. They are: 1. Legislation, objectives, and association with macroeconomic policies 2. Institutional capacity building in governance 3. Investment management (risk assessment) These three arguments are fundamental concerns for SWFs. This study mainly covers institutional capacity building for governance, which is related to our research question.

Legislation, Objectives, and Association with Macroeconomic Policies (GAP Principles 1–6) The overall framework of the legal background of SWFs can be described by three components under the GAPP, as shown in Fig. 2.2. As previously mentioned, there is no single ruling framework for managing SWFs to meet the nation’s strategic plan. Although different management styles of SWFs should be applied in different circumstances, the GAPP proposes a minimal standard for building a legal

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Fig. 2.2 General legal framework structure of SWFs. Source: summarized by the author from the International Working Group (2008)

environment. These include clear amendments to national law; disclosure of legal acts, including investment mandates; and relationships with stakeholders.

SWFs Support Sound Macroeconomic Policy The Santiago Principles stated that SWFs have both negative and positive macroeconomic policy characteristics. Investing in domestic boundaries may crowd out the local market but has a positive impact on fiscal policy, particularly on the government balance. The financial performance of the SWF should be calculated and disclosed to the public, thereby promoting accountability. In this study, we analyze the correlation between SWFs and fiscal deficits using the econometric regression model in Sect. 2.4.

Institutional Capacity Building in Governance (GAP Principles 6–17, Table 2.3) The GAPP suggests that general perception improves sound governance regardless of the country’s membership in the IFSWF. However, not all SWFs have overcome the difficulties in managing resources because of the lack of institutional capacity. Dixon and Monk (2011) agreed that “the success of SWFs, however, depends on the organization’s design, as the local inputs have to come together to form an effective global investor.” They found that investment governance plays a vital role in success of SWFs. The SWF is designed for specific investment in the financial market through its unique position, differentiated from traditional government agencies and departments (Clark and Monk 2013). These distinctive characteristics help build a proper capacity to compete with the private sector, which already occupies the financial market. If there are no significant differences between the performance of SWFs and the government, this specific funding tool is no longer beneficial.

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Table 2.3 Institutional framework and governance structure GAP Principles 6–17 1 1.1

1.2

1.3

2 3

Effective division of roles Owner (state/ (a) Set clear legitimacy: Objectives, mandate, acceptable MOF) level of risk, ethical standard. (b) Zoom in on a true picture of SWFs through a reporting system. (c) Appointing board members. Manager (board (a) Set strategy and policy. members) (b) Draw governing structure (committees). (c) Appoint and remove the operational manager. (d) HR policy, including recruiting scheme. (e) Act in the best interest of SWF. (f) Define ethical manner for both employers and business. (g) Select external operational manager. (h) Disclosure of performance in host country ensuring compliance with all applicable regulatory. (i) Independent decision-making from the owner. Operational (a) Independent decision-making from the owner to manager (CEO) ensure Fund’s strategy within the mandate. (b) Applying internal control system. Accountability: Auditing both external and internal with an independent base Reporting: Annual and quarterly reports with descriptions based on international standards shall be audited (international auditing standards) regularly and should be disclosed publicly

GAPP 6, 7, 13

GAPP 8, 13, 14, 15, 16

GAPP 9, 10

GAPP 10 GAPP 11, 12, 17;

Source: summarized by the author from the International Forum of Sovereign Wealth Fund (2014)

Furthermore, the political interest in feeding their pocket projects is most harmful to the success of the SWF (Dixon and Monk 2011), as we know of several cases of SWF failure caused by over-populistic activities. Papua New Guinea’s Mineral Resource Stabilization Fund and Mongolia’s HDF are relevant examples. What design and governance apply to successful resource management in terms of SWF management? According to the IWG, there are generally accepted practices and principles for the sound governance of SWFs.

Investment Management and Risk Assessment GAP Principles 18–24 We ensure that investment management gets a higher return with minimal risk as asset management. Using internal human and capital resources requires paying specific attention to enhancing knowledge and innovatively improving technology. To enhance knowledge, we should ensure an understanding of the investment strategy and risk mitigation, which is defined by the owner according to the investment mandate. Investment strategy works in the circumstance where highlevel professionals in the financial market pursue ambitious maximization of the

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long-term return from the fund investment within acceptable risk exposure in a transparent manner, as shown in GAP Principle 18. Except for financial and economic purposes, the ethical approach is important for investment decisions under the management guidelines approved by the owner GAP Principle 19. This means that some countries, such as Norway, have an ethical act like “Guidelines for Observation and Exclusion from the Government Pension Fund Global,” which aims to avoid violations related to products, as described before. Moreover, SWFs ensure that the information is segregated from privilege or inappropriate influence on investment decisions as shown in GAP Principle 20. Domestic laws impose restrictions on access to information. For example, information access within departments of Norges Bank is documented in the job description of workers. Furthermore, SWFs respect shareholders’ rights and protect the financial value of their investment as shown in the GAP Principle 21. In Norway, the management mandate for GPFG Sections 2–4 (12) states that “Bank shall not hold more than 10 percent of the voting shares of any one company.” The annual general meetings vote for the number of resolutions, and after 1 business day of general meetings, the data must be publicly disclosed. Risk assessment management in both operational and financial risks is an inseparable component of investment GAP Principles 22. Internal and external auditing are essential to verify the effectiveness of the risk management framework, review whether the governing system of the fund worked or missed targets, give advice, and report recommendations on the improvement of the control system. The performance of the SWF’s investment should be measured and reported to the owner under the proper standards of GAP Principle 23. For the Norwegian case, the management mandate (Sect. 2.3) clearly defines the benchmarks, including reporting requirements, to ensure that asset allocation and risk tolerance are properly in accordance with their mandate. Finally, countries should engage in further improvements in GAPP by implementing the GAP Principle 24. The IFSWF publishes membership in the Santiago Principles on its website.

2.2.4

Coordination of Macroeconomic Policy Objectives in the Case of Chilean SWFs

Chile, the richest country in terms of copper reserves and its largest producer worldwide, providing for 37% of world consumption, is a high-income developing economy. Since Chile signed the agreement to become an OECD member as the first South American country in 2010, there have been significant improvements in macroeconomic factors. The quality of life in Chile has improved significantly in terms of well-being, job and earnings, work-life balance, and health status. A monetary easing policy and supportive fiscal stance are fundamental to substantial

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economic recovery (OECD 2018). According to the international organizations’ view, Chile has sound macroeconomic management, including inflation targeting, a rule-based fiscal framework, and a free-floating exchange rate. Historically, the national regime for copper production has been updated and modernized. In the late twentieth century, the revenue from copper was used for military purposes following the nationalization of the mining sector after taking it over from foreign investors (mainly American companies). During the transition period, the copper price fluctuated significantly, fiscal revenue was volatile depending on the copper price, and copper became a financial simulator tool for stabilizing the economy. After several reforms in 2006, the Fiscal Responsibility Law was approved, and the country established the Pension Reserve Fund (PRF) to support fiscal pension obligations and the Economic and Social Stabilization Fund (ESSF) to cover the fiscal deficits and government debt payments in 2006 and 2007, respectively. The funds are managed on a non-strategic basis to promote the Santiago Principles. The Ministry of Finance has played an important role as a member of the International Working Group of Sovereign Wealth Funds (IWG-SWF) since Chile established it in 2008. Chile can be a model country because it uses these funds to prevent the Dutch disease as copper prices fluctuate significantly, weakening competitiveness. Chile’s rule-based fiscal framework is key to success during financial difficulties, as it has proven to be a good buffer system for responding to internal and external shocks. Chilean funds are managed under fiscal policies such as structural balance ruling, ceiling government debt, and fiscal deficit. The structural balance rule means that “the balance that the central government would have achieved if the economy was operating at potential, i.e., excluding the effect that the cyclical fluctuations in economic activity, the copper price, and other factors of similar nature, may have in the government revenues and expenditures” (Daban 2010), which is the strength of Chile’s fiscal policy. The management rule is provided by the guidelines, particularly focusing on the stabilization of the fluctuation of the economy when procyclical shocks occur. The funds play the role of automatic stabilizers in the budget under certain rules in the stabilization process. Approximately 60% of the GDP volatility reduction is related to the fiscal rule (Solimano and Guajardo 2017). This study emphasizes the allocation of fiscal surplus from the funds generated by the implementation of this rule-based fiscal framework. The revenue accumulation of SWFs is well controlled by the Fiscal Responsibility Law, but withdrawals from SWFs are not specified and controlled by the Ministry of Finance decree when needed (see Table 2.4).

Governance The funds are the property of the General Treasury of Chile under the Fiscal Responsibility Law. The Minister of Finance makes decisions on investing,

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Table 2.4 Asset allocation characteristics of PRF and ESSF

Owner Establishment Originate Institutional framework

Market

Investment portfolio

Revenue

Withdrawal

Risk tolerance Performance benchmarks Criteria

Pension Reserve Fund (PRF) State (MOF) 2006

Economic & Social Stabilization Fund (ESSF) State (MOF) 2007 based on Copper Stabilization Fund 1980 Commodity Managed by Central Bank with sub-contract international companies: BlackRock & Mellon (custodian: JP Morgan) Abroad Short-term investment horizon

Commodity Managed by Central Bank with subcontract international companies: BlackRock & Mellon and Allianz (custodian: JP Morgan) Abroad Medium and long-term investment horizon Equities 40%, sovereign bonds 23%, corporate bonds 13%, high-yield bonds 8%, US Agency MBS 6%, real estate 5%, inflation indexed sovereign bond 5% Up to 0.2% of the previous year’s GDP (even in case of overall deficit), +if actual fiscal surplus exceeds that amount, the fund can receive up to 0.5% of GDP Based on fiscal objectives (formula) fund only covers payment of pension and welfare system liabilities Moderate

It can be used anytime when fiscal deficit occurs, payment difficulties in debt, and contribute PRF by MOF Low risk

To generate resources to finance part of the fiscal pension liabilities Higher return with low risk

Seeks to maximum market value with low credit risk High liquidity readiness

Banking assets 15%, treasury bills and sovereign bond 74%, inflation indexed sovereign bond 3.5%, equities 7.5%

Fiscal surplus after contribution to PRF when effective balance > structural balance

Source: summarized by the author from Solimano and Guajardo (2017)

managing,6 and creating a unit within the ministry. The unit acts as a technical secretariat for the Financial Committee, in charge of monitoring SWF’s performance, preparing reports, and submitting them to Congress (Fig. 2.3). The responsibilities of the Financial Committee include the following: (1) advising the Minister on the analysis and design of SWF investment and strategy; (2) recommending investment and custody, tender process, selection of managers, and structure and content of reports to the Minister; and (3) providing the assessment to the Minister. The responsibilities of the Central Bank include the following: (1) managing the portfolio, (2) selecting external manager and completing nominations from the MOF, (3) carrying out funds’ accounts with the register of

Whether central bank or external manager get granted to operate choosing the field of investment portfolio, the general management is carried by MOF.

6

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Fig. 2.3 Institutional framework of funds. Source: summarized by the author from the 2018 Annual report of Sovereign Wealth Funds of Chile

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Congress

MOF

Financial committee

(SWF unit)

Treasury

Central bank

External manager (BlackRock, Mellon. Allianz)

Custodian (JP Morgan)

transactions, (4) managing custodian services, (5) evaluating custodian and external managers’ performance, (6) preparing reports of external services; and (7) providing payments as needed. The responsibilities of the Treasury include the following: (1) providing accounting services, (2) making financial statements with audit reports, (3) reviewing compliance with investment guidelines, and (4) providing fiscal services. The responsibilities of the external manager include providing professional financial services for managing funds’ equity and corporate bond investment portfolios for both funds. The custodian, JP Morgan, provides middle office financial services such as calculating fund returns, reporting investment portfolios with risk assessment, and monitoring compliance with investment limits (Chilean Ministry of Finance 2018).

Transparency Chilean SWFs are considered one of the most transparent funds according to the Truman scoreboard7 and Linaburg-Maduell8 transparency index. Stone and Truman

7 Truman scoreboard shows the degree of governance of SWFs in terms of transparency and accountability. 8 Linaburg-Maduell transparency index is the method of rating transparency in respect to SWFs.

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(2016) examined funds’ transparency and accountability and showed that ESSF is ranked sixth, followed by PRF, out of 60 SWFs worldwide. The Linaburg-Maduell transparency index also evaluates both funds as one of the most transparent among the 48 SWFs. Moreover, the government of Chile conducts a self-assessment of the compliance of Santiago Principles for both funds and publicly discloses the results every 2 years. We can retrieve timely reports (detailed investment performance and financial statements), investment policy, financial situation, self-assessment report of the Santiago Principles of each fund, and members of the finance committee and its reports on the webpage of the Ministry of Finance of Chile; however, the legislative information in English is limited to the public.

2.3 2.3.1

SWFs of Mongolia Outline of SWFs of Mongolia

Mongolia, with its rich natural resources (gold, silver, copper, gold, crude oil, etc.), is a democratic middle-income developing country. In the last three decades, the social regime has been dramatically changed from a centrally planned socialist system, based on agriculture, to an open-market economy relying on mining, owing to democracy. During this transition period, the economy was volatile, bringing both opportunities and challenges to the country’s macroeconomic profile (Fig. 2.4). Exporting dozens of tons of raw minerals at a favorable price gave a

Fig. 2.4 Macroeconomic framework of Mongolia. Source: Webpage of National Statistical Office of Mongolia retrieved from http://www.1212.mn/

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Fig. 2.5 Evolution of SWFs in Mongolia. Source: summarized by the author from financial statements of the funds 2007–2019

windfall to fiscal revenue, and public expenditures increased significantly during the boom period after 2009. The commodity-dependent environment leads to low competitiveness among domestic manufacturers. Capital and labor are accumulated mainly in mining companies, while other sectors have been noticeably abandoned, particularly during the boom period. As a result, the country faces a high fiscal deficit and debt and a decline in productivity. In Mongolia, based on the positive expectations of ambitious commodity prices, the budget is largely associated with political interests and tends to involve weak governance and corruption. To prevent this risk, international organizations such as the World Bank (WB), IMF, and Asian Development Bank (ADB) recommend enhancing public finance in resource management for the sustainable and inclusive development of Mongolia. Several projects, such as technical assistance through international SWF experts, have been implemented by ADB, WB, and IMF. Some experts recommend the Norwegian SWF model, while others recommend the Chilean model, as both are good references in SWF governance. The Government of Mongolia has adopted these recommendations to achieve national interests, develop a strategy plan, and attempt to improve resource management by absorbing good practices. For this purpose, the Government of Mongolia set up several funds and sought an efficient way to implement national strategic policy (Fig. 2.5). The Ministry of Finance guides the general management of SWFs. Assets are deposited in an account with the treasury in national currency, whereas foreign currency assets are deposited in an account with the Central Bank. The reports are prepared by the treasury department of the MOF and consolidated into government financial statements. These funds are fiscal components of the government budget. However, they are ruled by relevant laws as separate accounts in the treasury and central bank, although the overall management is taken care of by the MOF. The next section of this study will explore more deeply each of the Mongolian SWFs, such as legislation background, governance including institutional framework, rules

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of contribution and withdrawal, transparency and accountability, and reporting mechanism.

2.3.2

Overview of Government Policy on SWFs of Mongolia

This section discusses the characteristics of each fund, including the legal framework and policy implications. Some funds were terminated at an early stage because of weak governance and legislation. In contrast, some achieved their initial objectives, although the initial policy and legal documents were modified after several reforms (Table 2.5).

Table 2.5 Policy approaches of the Government of Mongolia for SWF development Year 2007 2008 2009 2009 2010 2010 2010 2013

2014

2017 2017

2019

Policy implications Mongolian Development Fund was established, desiring to reach ambitious development goals Due to the global financial crisis, the country needed international assistance to cope with fiscal difficulties and strengthen resource management Standby Agreement signed with IMF for encouraging public financial management, particularly in resource management Human Development Fund was created with multiple strategies, distributing the country’s wealth to a nation and covering social development issues Regulations related to wealth and cash distribution were approved by the Cabinet (decree of the government: 99/2009, 347/2010, 3/2010, 84/2010, 67/2010) International assistance by ADB, IMF, and WB supported to pass Fiscal Stability Law, which is based on the Chilean rule-based fiscal framework Fiscal Stabilization Fund is established for the stabilization of the economy Capacity development project “Public finance resource management” was implemented to MOF for drafting the Future Heritage Fund Law, supporting the update of Human Development Fund more efficiently, and encouraging capacity building on both skill training and preparing strategic documents National audit reported that “Spending fund asset for direct distributing cash bill was mismanaged and the management of Human Development Fund was not adequately implemented” Political wind was given to the long-term fund, so FHF was established to replace HDF Capacity development project in public financial management and governance, “Establishing Sovereign Wealth Fund Management Institution,” implemented by ADB. The project will be terminated by the end of 2020 when a few policy recommendations and drafts of core documents for capacity building will be expected FHF Corporation was established officially, but it will effectively function when the MOF responds to operational management through its unit

Source: summarized by the author from related documents

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Mongolian Development Fund The Parliament of Mongolia passed the Mongolian Development Fund Law and launched the Mongolian Development Fund in 2007, whose sources include tax from specific products’ price increases and a fiscal surplus. Withdrawal from this fund was ruled out for financing contingency payments related to fiscal deficit, small- and medium-sized enterprise development, and social welfare payments to children and families. The Ministry of Finance of Mongolia is authorized to manage the funds and is responsible for the following: (1) reporting as a sub-financial statement of consolidated government balance with the audit report, (2) submitting the report to the cabinet and parliament, and (3) monitoring operational performance under this law. The fund accumulation reached ₮1286.5 billion before its assets dried up because of financing fiscal deficit and government expenditures related to social welfare. These funds were dissolved in 2009.

HDF The movement of Mongolia toward the establishment of SWFs embraced the next fund aimed at achieving people’s development status at the same level as that of the developed nations. That national ambition entails creating an HDF based on the HDF Law in 2009. Contribution rule: (a) (b) (c) (d)

The dividend of state-owned shares in mining 65% of mining royalties Net profit of fund’s financial activities Unearned revenue or borrowing related to manufacturing strategical mining reserve (e) Issuing sovereign bonds or treasury bills may cover funds’ annual deficits and can be allowed from this fund. Withdrawal rule:

(a) Payment of borrowing (b) Dividends and wealth distribution to citizens of Mongolia (social spending) HDF had accumulated ₮3,119,430.15 million from basic operation income, ₮1,806,567.26 million from issuing bonds and borrowing from the government budget. The fund performed poorly. The expenditure included ₮223,013.14 million for untargeted social spending, ₮2,722,179.88 million for cash handouts to citizens, ₮1,528,207.42 million for payment of borrowing, and ₮452,596.98 million for other expenditures (Table 2.6 and Fig. 2.6).

Source: summarized by the author from financial statements of the Human Development Fund 2010–2019

Table 2.6 Statement of cash flows of the Human Development Fund in million togrog (₮)

52 N. Luvsansharav

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Fig. 2.6 Cash flows of HDF in percentage. Source: summarized by the author from financial statements of Human Development Fund 2010–2019

Fig. 2.7 Comparison of HDF’s debt and government debt. Source: Ministry of Finance of Mongolia retrieved from the webpage https://mof.gov.mn/ and World Bank webpage retrieved from http://tradingeconomics.com

Aiming at a favorable political environment repeatedly over time has led to political parties’ competition9 fueled by their political promises (such as distributing cash handouts by modifying the public laws from their pet project decisions). However, this wasteful spending ironically overheated the economy while deteriorating the government’s fiscal conditions (Robbins 2013). This phenomenon might be one of the reasons for the deterioration of the main fiscal indicators, such as a dramatic increase in government debt and fiscal deficits (Fig. 2.7 and Table 2.7). In accordance with the HDF Law, almost half of the fund’s resources were borrowed to cover untargeted social spending and political promises rather than achieving the original policy goals that the IMF initially recommended. The Parliament changed the HDF Law several times, in 2010, 2011, 2012, 2015, and 2016. Unsurprisingly, the fund was leveraged by additional debt, and we can clearly see the negative equity ratio:

“World bank engagement with Mongolia’s Sovereign Wealth Fund” available at http:// documents1.worldbank.org/curated/en/450721468060545662/pdf/858180BRI0REPL00Box38214 7B00PUBLIC0.pdf

9

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Table 2.7 Statement of financial position of the Human Development Fund in million togrog (₮)

Source: summarized by the author from financial statements of the Human Development Fund 2010–2019

Table 2.8 Equity ratio of HDF Equity ratio

2010 -1.44

2011 -15.83

2012 -113.61

2013 -709.33

2014 -6.91

2015 -3.78

Source: summarized by the author from financial statements of the Human Development Fund 2010–2019

Equity ratio =

Total net equities Total asset

The equity ratio value should be above 0.50 because the fund will not be able to cover its debt if this ratio is below this level. An equity ratio value higher than 0.50 means that the fund is managed effectively; thus, financial market investors seek companies with higher equity ratio values to minimize risks. In the case of HDF, the equity ratio is far from the conservative range and is even negative in its active period (Table 2.8). A negative value indicates that the fund liabilities exceeded fund assets due to the annual accumulated loss from negative earnings retained in the balance sheet. Furthermore, there were significant losses due to overborrowing and mismanagement.

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Fig. 2.8 Institutional framework of HDF. Source: summarized by author from the Human Development Fund Law

Governance The Ministry of Finance grants HDF to manage fund accumulation and withdrawal through the Treasury departments under the fund’s annual budget, which is approved by the Parliament (Fig. 2.8). Risk assessment is carried out by the MOF. Investment in the international and domestic financial markets for sovereign bonds and bank deposits (savings) is permitted. The creation of the fund’s contingency reserve can be permitted in a specific account in the Central Bank. Moreover, the SOC hold the state’s share of strategic mining manufacturers, and the citizens of Mongolia have the right to hold SOC shares and receive dividends and benefits from the fund. Citizens cannot sell their shares or transfer them to anyone else.10 The SOC has no right to the financial operations of the fund. They only carry out mining operations on the resources. The SOC is ruled down by the Company

10

According to Human Development Fund Law Section 8.4.

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Law, and its own rules are set by the Cabinet. The SOC has a board that consists of nine members appointed by the Cabinet, such as Central Bank (1), Financial Regulatory Commission (1), Ministry of Finance (1), and government (6) for a period of less than 6 years. The head of the board shall be appointed by the Cabinet based on the request by the board. The operational manager shall be selected and released by the Cabinet at the request of the board. The Control Committee has five members, including one from the Standing Committee of Economy; one from the Standing Committee of Social, Education, Culture, and Science; one from the Standing Committee of Justice; and two from the Standing Committee of Budget. The Control Committee is regulated by the Corporation’s Rule, is responsible for regular monitoring of the fund’s performance, and submits its annual report directly to the parliament. External managers can incorporate partial or full portfolio management of the strategic mining industry in accordance with the management contract by the Ministry of Finance, based on the mutual decisions of the Central Bank and Financial Regulatory Commission.

Transparency and Reporting The Ministry of Finance is in charge of reporting and submitting a financial statement and performance report of the funds, which is one of the government’s consolidated financial statements submitted to the Parliament. The semi-annual and annual reports of SOC include financial statements, environmental activities, and the performance of the board members and are published via mass media every 6 months. The National Audit Office is responsible for independent auditing of HDF activities and submitting its audit report to the Parliament. The cabinet must invite international bodies to audit the fund’s financial statements and performance reports and submit its results to the Parliament. Both national and international audit reports must be disclosed to the public through the mass media. At the end of 2016, HDF was dissolved because of the cancelation of the HDF Law, and ₮1071.8 billion debt was transferred to the FHF. What should we learn from the HDF? Clear delegation rules, operational freedom from political interests, and sound governance, including transparency and accountability, are crucial for SWF efficiency. The failure of the HDF was due to unclear delegation rules, untargeted objectives for inclusive growth, and mismanagement. The worst case of fund mismanagement was the debt from overborrowing. The original function of the fund was to save and distribute wealth to citizens, but it did not achieve this objective, and the debt burden was transferred to the next generation because of excess withdrawals.

Stabilization Fund Since the global financial crisis, nations have recognized that resource management needs to improve as it is an important contributor to sustainable growth and well-

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being. This attention to resource management, particularly in SWF, leads to capacity-building challenges. For the stabilization of the economy as a result of several consultations with foreign experts dispatched from the IMF based on a standby agreement that expired in September 2010, the Fiscal Stabilization Fund (FSF) was built in accordance with the Fiscal Stability Law, which was designed as a rule-based fiscal framework in 2010. The Ministry of Finance acts as a manager of this fund, and the Minister is responsible for ruling the fund in an attempt to accumulate resources efficiently and make withdrawals for fiscal policy requirements under the Fiscal Stability Law. The objectives of fund investment aimed at implementing macroeconomic and fiscal stabilization policies include a stable single-digit inflation rate and a stable exchange rate. The fund is regulated to invest in railways, crude oil manufacturing, and power stations in the domestic market and export-oriented goods with the euro standard in the foreign market. Domestic investment is limited to purchasing stateowned bank bonds. The Ministry of Finance is allowed to delegate its operational management to the Central Bank or SOC based on the FHF Law. The portfolio requirements are as follows: (a) Eligible issuers must operate in the international financial market to exclude locally registered entities or partners holding shares in Mongolian entities. (b) The selection requirement for risk must be equal to or above a risk rating, which is defined by international risk rating agencies. (c) The financial investment shall ensure liquidity. (d) Using fund assets for collateral purposes, lending, accepting guarantees for borrowing, and other financial obligations are not permitted. (e) Any agreements on the financial obligation are not permitted. (f) Financial derivatives may be used only for covering investment risk. (g) Borrowing may only be allowed in a situation of payment shortage related to investment transactions in a short time (not exceeding a week). According to the Fiscal Stability Law and Sovereign Wealth Fund Law, the rules for revenue accumulation are (a) fiscal surplus, (b) the remaining balance of the Government Reserve Fund (GRF)11 from the previous year, (c) revenue of the fund’s financial activities, and (d) other resources. The rules for withdrawal are as follows: (a) Fiscal deficit caused by commodity price volatility exceeds 4% of GDP and an acceptable level (current fiscal deficit >4% GDP + acceptable level of deficit). (b) The fiscal requirement in case the GDP growth rate is equal to zero or negative. (c) National emergency budget exceeds 5% of GDP. (d) Price of main mining products and production size declined by over 20%. (e) The current fiscal deficit. The withdrawal rules are decrees of the Cabinet except for the withdrawal under rule “c.” When there is a fiscal requirement for a national emergency, the 11 Government Reserve Fund is another sovereign fund for contingency whose revenue is sourced from tax, not from natural resources.

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Table 2.9 Contributions and withdrawals in million togrog (₮)

Years 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total

Contribution Revenue (a + b + d) 241,019.88 94,656.46 47,745.97 40,532.32 5515.95 10,069.45 330,991.52 197,826.39 100,642.59 1,069,000.57

Interest earned (c)

Withdrawal

29.35 1537.38 1666.31 7450.72 13,260.05

572,106.50

5714.31 29,658.13

322,000.00 1,012,306.50

118,200.00

Year-end balance 241,019.88 335,676.35 383,451.68 307,321.39 314,503.66 332,023.84 104,168.91 301,995.31 86,352.21

Fund asset as share of GDP (percentage) 0.002 0.002 0.002 0.001 0.001 0.001 0.000 0.001 0.000

Note: a, fiscal surplus; b, remaining balance of GRF from the previous year; c, revenue of the fund’s financial activities; d, other resources. GDP is calculated as the current price of the national currency Source: summarized by the author from financial statements of the Stabilization Fund 2011–2019, National Statistical Office webpage retrieved from http://1212.mn

parliament is responsible for the amendment of the Fiscal Law based on the request of the MOF. The law authorizes the government to increase its debt under the debt ceiling in case the fund cannot cover fiscal withdrawal under the rule “a.” The balance of the FSF at the year-end of 2019 was ₮86,352.21 million. The fund has accumulated ₮1,069,000.57 million from basic operation income, ₮29,658.13 million from banking deposits, and the three withdrawals amounted to a total of ₮1,012,306.50 million since its inception. Until 2014, the funds were not used. The first withdrawal was made for financial fiscal requirements for stabilization purposes by the Cabinet decree amounting to ₮118,200.00 million, ₮572,106.50 million, and ₮322,000 million in 2014, 2017, and 2019, respectively (Tables 2.9 and 2.10). When the fund asset reaches over 10% of GDP, the excess reserve can be invested in the domestic and foreign markets by the joint decisions of the Central Bank and the Ministry of Finance for long-term financial return. Table 2.8 shows that there has never been an excess amount according to this criteria. The asset remains in cash (equity ratio is always equal to assets), and there are no investment instruments from financial statements. FSF acts not only as a stabilizer for the economy but also as a disciplinary tool to insulate overspending by political interference during the boom and burst cycles. It also contains international norms and standards for enhancing governance in public finance by strengthening the policy environment. Oyunzul and Nyambaatar (2020) published their paper “The impact of SWF’s of Mongolia on the economy” and evaluated the impacts of FSF and FHF on national development factors using econometric methods. The study concluded that FSF works properly in its original function.

Source: Summarized by the author from financial statements of the Stabilization Fund 2011–2019

Table 2.10 Statement of financial position of the Fiscal Stabilization Fund in million togrog (₮)

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FHF The government of Mongolia attempted to enhance HDF’s effectiveness and create intergenerational equity from finite resources through a sustainable mechanism. Thus, the FHF was established based on the HDF in accordance with the FHF Law in 2016. A more advanced and updated legal framework is being introduced by applying international best practices after several discussions on technical assistance projects. The ADB has extended a series of technical assistance to the Ministry of Finance of Mongolia to promote institutional capacity building and policy advisory on resource management since 2010. After transforming the HDF to FHF, capacity building and preparing and approving fundamental documents and guidelines are carried out progressively by the associated team of the SWF of the MOF and international consultants. The consultants are Ashby H.B Monk and Malan Rietveld, leading experts on SWF’s management and governance. After the FHF Law was passed in 2016, the SWF team worked on preparing and validating the Future Heritage Fund Corporate Rule, which is one of the core documents for fund management in accordance with FHF Law Sections 13.3, 18.1, and 18.7. The FHF corporate rule was authorized by the Cabinet in July 2019. At present, the team has been working on an investment mandate, regulation of the selection Control Committee, Operational Rule of Control Committee, and Selection Board. Drafts of these documents were submitted to the Minister of Finance for approval. According to the ADB report (2017), technical assistance was terminated at the end of 2020, and a few prepared documents are expected to bring financial sustainability (business plan, investment mandate, and financial mechanisms and criteria) and prudential governance and accountability (internal regulation and guidelines). Regarding the FHF law, the contribution rules are: (a) Earnings of dividends in state-owned shares (b) Sixty-five percent of the remaining amount of taxes and royalties after FSF’s contribution ((taxes and royalties in mining-FSF’s contribution) *65%) (c) Financial investment revenue after covering the operational cost by FHF Corporation (d) Fifty percent of additional tax income related to mining law amendment (e) Twenty percent of the surplus annual mining revenue (except contribution rule: a, b, c, d) Withdrawal rule: (a) The operational cost of SOC and payment for independent auditing. (b) Any withdrawals except for “a” are not allowed until 2030. (c) Ten percent of net financial investment income shall transfer to the government budget after 2030. The fund received ₮1071,891.34 million in debt from the HDF when it was established. It faced a takeover of debt from the previous fund under the FHF Law and other public laws. The fund took 3 years to pay off the debt completely. Now,

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Table 2.11 Statement of financial position of the Future Heritage Fund in million togrog (₮)

Source: summarized by the author from financial statements of the Future Heritage Fund 2017–2019

the fund has saved ₮636,744.87 million contributed from its operating income in accordance with FHF Law at the end of 2019. The fund’s equity ratio has been treated noticeably, from negative to positive, since its inception with new policies and approaches (Tables 2.11 and 2.12). Withdrawals from the fund were used for payment of the previous fund’s debt only. Until 2030, the fund’s assets are not permitted to be withdrawn. Currently, the fund’s assets are allocated in the national currency and deposited in cash in the treasury account. According to MOF’s prediction, “Oyu Tolgoi,” one of the biggest exploration companies in the world, will start underground production, and the influx of mining revenue will be deposited with the government by 2020. A certain ratio of mining revenue is expected to constitute fiscal revenue. Beyond this fiscal revenue, FHF’s contribution is expected to increase12 in accordance with the FHF Law. Currently, the government has taken action for documentation and training of workers for the capacity development of the FHF Corporation (Fig. 2.9).

12

According to the FHF Law Section 7.1.2, 7.1.5 (FHF contribution rules “a” and “e”).

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Table 2.12 Statement of cash flows of the Future Heritage Fund in million togrog (₮)

Source: summarized by the author from financial statements of the Future Heritage Fund 2017–2019

Fig. 2.9 Fiscal expectations of FHF revenue. Source: Ministry of Finance of Mongolia retrieved from the web page https://mof.gov.mn/

Institutional Framework of FHF The owners of the FHF are Mongolia’s citizens. The fund is a fiscal component, and fund management is carried out by a 100% state-owned corporation underinvestment mandate (Fig. 2.10). The Ministry of Finance (MOF) has been granted the power for approval or cancelation of the investment mandate and is responsible for reporting to the Parliament about the implementation of investment mandates; selecting and contracting with the board, advisory team, and control committee; and reviewing the reports of FHF. Furthermore, the MOF ensures the performance quality of the

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Fig. 2.10 Investment management structure of FHF. Source: Ministry of Finance of Mongolia retrieved from the webpage https://mof.gov.mn/

fund and that independent international audits in the FHF Corporation are conducted every 2 years. The Control Committee consists of five members appointed by the MOF (Candidate Committee may refer to a selection procedure based on the Minister’s decision) within 5 years. They monitor the board’s functions and tasks. The Control Committee has a number of key roles in ensuring compliance with laws, mandates, investment strategy implementation, governance, transparency, accountability, and documenting conclusions and recommendations to the board and MOF. The advisory team advises the Minister on mandate approval, amendment, evaluation of the fund’s performance, and recommendations for further activities relating to the fund. Research and development of the fund will be distributed by this team. The Corporation is an operational manager of the fund. The top management consists of a board and has to follow its own rules decided by the cabinet and make investment decisions independently for efficient and sound management per the mandate. Corporations have separate accounts in both foreign and national currencies from the funds in the central bank. Internal control, investment, and risk assessment units are organized within the corporate structure to ensure sound governance and accountability. The operation of the corporation may be extended to manage the FSF if needed. The Ministry of Finance creates a candidate committee based on the collected data, storing a large amount of information about a capable person for a member. The candidate committee organizes the selection process for the board and control committee members and submits its results to the Minister of Finance. The Minister decides on contracting the members. After the selection process, the candidate committee is closed with a termination report (the result of selection). The board is at the top of corporate management and retains the final responsibility for ensuring investment management. The board can also help ensure sound governance and is responsible for investment management through its investment strategy, qualified people, and culture (ethical manner). An operational manager or

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team is appointed by the board under the agreement to commit efficient management to implement an investment strategy in accordance with the mandate through the representative warranty of the corporation.

Transparency and Reporting The FHF Corporation must periodically report its performance with audited financial statements to the MOF, which will be submitted to the Parliament within 60 days. The MOF must disclose the following documents related to the fund management publicly: (a) Decisions of the parliament and cabinet (within 10 days after the release of disclosure documents) (b) Mandate (within 10 days after the release of disclosure documents) (c) The recommendation of the Advisory team (within 5 days after the release of the disclosure documents) (d) Annual report of performance and financial statements (within 10 days after the release of disclosure documents) (e) An investment portfolio with the evaluation of return and risk assessment (updated monthly) (f) Modification of board members (updated monthly) (g) Custodian and external manager’s list and profiles (updated monthly) A recent study demonstrated that the FHF is a mechanism to prevent overspending the taxpayers’ money and has the ability to create wealth for future generations (Oyunzul and Nyambaatar 2020).

2.3.3

Current Challenges of SWFs of Mongolia

Since 2007, Mongolia has attempted to achieve sustainable development by strengthening resource management and SWFs. Within such a wide range of reforms, the types of fund frameworks were designed for specific purposes, mainly for the stabilization of the economy and distribution of wealth to the citizens. Our first assumption was that a lower rate of saving resources due to the significant debt from political pet projects through wealth distribution is the main weakness of SWFs in Mongolia. After researching SWFs in Mongolia, we found that our difficulties were more complicated than in the previous case of failure. What we learn from the past is the importance of (1) a well-defined rule-based legislation mechanism, (2) institutional capacity development, particularly in human resource management, in an ethical manner, and (3) political insulation schemes on what they can and cannot do for resource management.

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1. The high dependence on natural resource revenues causes uncertainty in fiscal policy. Commodity prices are affected frequently by the global market and are almost impossible to predict with certainty. At present, the budget is prepared based on historical data covering the previous 12–20 years and estimates the possible range of prices in the short- and medium-term fiscal periods. If sudden changes in the global economy occur, such as the Asian financial crisis, or a global pandemic caused by the coronavirus disease, the nationwide challenges would be exacerbated unless we have prudential public financial management. Global uncertainties may have a substantial effect on SWF management. 2. Less emphasis on institutional capacity development, such as a weak legal system and governance, will lead to a limited number of professionals and fewer skills needed for efficient asset management of the funds. Even though there is no proper promotion system in place for human resources in the public sector, the private sector is willing to absorb the educated people who have successfully graduated from the world’s top universities, such as Harvard, Oxford, and Berkeley. In addition, obtaining local experience is challenging because the national financial market is operated by domestic players, not by foreign banks and investment funds. This means that there is no platform for practicing to become a financial expert (Robbins and Smith 2014). A few people have international financing experience when they work overseas; however, they have no interest in the public sector, which offers a low salary. Another important aspect of sound governance is the professional investment standards for operational controls. In Mongolia’s public finance arena, internal auditing was developed with the assistance of JICA projects. Conversely, internal control was established in 2015 in accordance with the Budget Law. However, new legal acts and institutional frameworks of internal control are still weak due to a lack of human resources and core documents, which dragged down the responsibilities of governance. 3. Overdistribution of wealth without proper savings for future generations is a common issue. This means that policy implications associated with political interference not only impact fund returns and market value but also deteriorate governance and accountability. Several studies13 and surveys have published that the main challenges of SWF’s performance are weak governance associated with political interest, non-transparent decision-making followed by irresponsible consequences, and lack of institutional capacity. According to WGI 2019, Mongolia is ranked below the world average for government effectiveness (114), the rule of law (confidence level of law-abiding agents and society, 116), regulatory quality (the ability of the government to formulate and implement sound policies and regulations that permit or promote private sector development, 95), and control of corruption (126) out of 209 nations.

13

Robbins 2013, Robbins and Smith 2014, U.S. Embassy in Mongolia 2018

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2.4 2.4.1

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Comparative and Qualitative Analyses Literature Review

Transparency Is Vital for Governance There are two similar indicators to measure the transparency and disclosure of data. Truman introduced his scoreboard for SWFs, proposing transparency and accountability scores across countries (Truman 2007). He created a scoreboard that explains the core scheme and operation of SWFs, including (1) structure, (2) governance, (3) transparency and accountability, and (4) behavior. Each category is scored by yes/no questions for 25 elements in total (later updated to 33 elements in 2013). The score is “1” for yes and “0” for no. In addition, partial scores of 0.25, 0.50, and 0.75 are accepted. All data were sourced from published reports (IMF, WB, etc.), and the information was collected through ad hoc interviews. His first study covered 32 SWFs and was updated in 2009, 2012, and 2016 (Stone and Truman 2016). Bagnall and Stone were associated with him in 2012 and 2016, respectively, for the updates. Truman’s study shows a comparison of progress in IFSWF member and nonmember fund scoreboards since 2007, and the Santiago Principles have been applied since 2012. Santiago Principles are also used for 25 and 16 elements out of all SWF scoreboards, and the correlation with all SWF scoreboards is 0.9912 for 25 elements and 0.9609 for 16 elements (Bagnall and Truman 2013). A few words were seen about SWFs a decade ago, but recently, the concept has become more common. However, there are a limited number of studies on SWF governance. A variety of literature on economic impact, governance, transparency, and accountability has been discussed rather recently. Monk (2010) criticized, “Truman is apparently arguing that the Santiago Principles fall well short of international standards of best practice, as he defines them. He said that a fund that achieves 100% compliance with the Santiago Principles (which, by the way, no fund has achieved) would represent only 76% compliance with his Scoreboard.” He continued his opinion that the biggest weaknesses of Truman’s scoreboard were excessive respect for accountability and transparency. Stone and Truman (2016) conclude that “transparency and accountability are the core of good governance.” Monk (2010) also addressed that; however, all performances of Truman’s scoreboard involve weakness, saying that “Santiago Principles may be weak and toothless, but they have actually been effective” between them. Both Truman and Monk agreed that the Santiago Principles can be improved and implemented in a better manner. The second measurement of transparency was developed by Carl Linaburg and Michael Maduell as the “Linaburg-Maduell Transparency (LMT) Index,” representing the view of the Sovereign Wealth Fund Institute (SWFI) on SWF’s transparency. The SWFI is a global organization designed to analyze public asset owners of SWF, pensions, engagements, and supranational funds as well. In 2008, their index was proposed as a benchmark for the global standard, based on ten

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essential principles of SWF transparency. Each element was evaluated by 1 point (overall: 10 points), as in the website: https://www.swfinstitute.org/research/ linaburg-maduell-transparency-index. The SWFI recommends that SWFs require a minimum value of 8 to comply with adequate transparency. By April 2020, 15 countries had a score of 10, and almost half of the funds received 8 or higher scores out of 49 funds.

SWFs Contribute to Corporate Governance Ungureanu (2014) found that SWFs can significantly relate to corporate governance in two ways. On the one hand, the “proactive” governance approach is considered for companies to engage in corporate governance and environmental and social matters through guidelines. On the other hand, the “reactive” approach relates to shareholder rights engagement. There are transactions that approve or disapprove of noticeable corporate activities (mergers, acquisitions, etc.) through voting. According to Ungureanu (2014), SWF’s investment characteristics have changed from riskless assets to high-return investments with an acceptable level of risk allowance under sustainable and sound governance. This might be significant support for how strongly companies have improved their management. Her study continues the political balance of domestic and foreign interest because SWFs make investments for strategic purposes in a political manner (maximization of economic welfare) rather than purely commercial objectives. Her study suggests that “Regulators can promote engagement and responsible sovereign investment by being more transparent in their regulatory effort, whilst SWFs can smooth the checks and pressure from politicians by enhancing the transparency of their intentions relative to the governance of the target firm.” The study concludes that “SWFs may play an increasing role in financial markets, and this will lead to a new era in shareholder activism” (Ungureanu 2014).

SWF Design and Development Are a Long-Term Process Another aspect of SWF governance is its design and development (Dixon and Monk 2011). Their study addressed three main principles: institutional coherence, people, and processes and containing politics. SWFs may face a lack of institutional capacity, which could result in their failure in many countries. Although investment strategy is a crucial stage in the establishment of SWF, robust governance structure and principles are also important in the first step. Building robust designs and principles requires institutional coherence, people, processes, and containing politics by adopting good practices. Moreover, they suggested that “a separate entity can be established that operated on par with other sophisticated institutional investors operating on global markets.” They believe that this framework can establish operational freedom from political interference, mismanagement, and corruption in the long term.

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Institutional Capacity Is Core to Winners Mehlum et al. (2006) emphasized that the main reason for the differences between resource “winners” and “losers” is the quality of their institutions. Dixon and Monk (2011) mentioned a similar idea that institutional capacity is essential for designing a good framework of governance. They all agreed that many countries tend to fail in the use of in-housing services, while others hired outsourcing services due to a lack of capable human resources and technology.

Governance Is the Key to Success One way to explain the importance of governance is what regulators should perform in-house asset management, which is a similar concept to SWF management. Clark and Monk (2013) studied 20 institutional cases worldwide and developed several principles and policies. The importance of governance is vital in their study at the fundamental level, and the board should encourage the following pyramid in each strategic lever of success for institutional investment organizations. They addressed why managers are concerned with internally sourced asset management. Five arguments have been provided for in-house management. 1. Access: If third-party vehicles are no longer attractive, a direct basis can be effective for accessing target assets or markets.14 2. Alignment: In-housing asset management is an approach that can reduce agency costs. 3. Capabilities: Once it’s decided to apply internal management, it could improve institutional capacities in several ways, such as high professional human resources and the latest technologies for further diagnosing market situations. 4. Performance: The main reason to choose internal management is to maximize investment return. 5. Sustainability: Managing assets using an internal source provides the ability to obtain critical thinking about sound portfolio management to achieve strategic objectives. Notwithstanding the positive view of in-house asset management, the board must ensure the capability of value-added through the internal source. If there is no better internal source, outsourcing should be the solution until it is ready to be utilized.

14 “There are instances where the third-party vehicles are not attractive, and access to a given asset or market can be more effectively achieved on a direct basis” Clark and Monk (2013).

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Methodology

To answer the research question, a comparative analysis was conducted. Determining the weaknesses of SWFs in Mongolia and suggesting an effective approach to resolve this problem are great challenges. More precisely, a workable mechanism for improving the governance of SWFs in Mongolia is implied in this section rather than simply answering the question, “is it true?” An analysis of the contemporary challenges using comparative and assumption techniques is proposed through a strengths, weaknesses, opportunities, and threats (SWOT) analysis.

2.4.3

Result of Comparative and Qualitative Analysis

This study incorporated worldwide good practices that can be adopted into relevant national laws, policies, and guidelines from the viewpoint of sustainable development. As explained previously, Norway’s sound governing approaches may provide a solution. Furthermore, a rule-based fiscal framework may provide an optimal solution for fiscal planning, as explained in the previous section. Countries should have a careful dialogue through their policy corridor when adopting international practices.

Comparative Analysis of Legislation The central banks of Norway and Chile protect sovereign immunity without separate legal entities. The legal bases for the SWF of both nations are established in public laws. The legislation of SWF in Norway provides a clear delegation from owner to manager through a hybrid political structure. More precisely, the acts clearly define the politician’s role in SWF as what they can and cannot do, which is essential to grant independent operational authority to the managers. Norwegian SWF acts are well insulated from political interference in the proper delegation process for fund management. Chilean legal documents on SWFs are not published in English; thus, this study could not analyze these documents. The Acts of Norway and Mongolia are similar in holding the company’s share: GPFG shall not hold more than 10% of the voting shares of any company, whereas FHF has limitations in holding shares that are lower than 51% of any company. Norway ensures responsible management through the Council on Ethics to exclude companies with violence, child rights, weapons, production using finite resources, and other environmental restrictions. The FHF Law includes certain ethical concerns such as restrictions on violence, weapons, drugs, alcohol, and tobacco production.

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The legal base of the SWF in Mongolia has progressed through the application of good practices (US Embassy in Mongolia 2018). Delegation functions, transparency, and reporting under the recently approved law of FHF were significantly improved from previous acts. The fund has revised its legal framework and significantly outperformed it. The FHF Law allocates independent decision-making functions to a separate entity (FHF Law, Section 18.2) to insulate political interference. However, this provision is limited by the investment mandate. If mandates change frequently, the fund has mismanagement issues. There are no requirements for changing the mandate in the currently available legal documents. This means that the MOF has the full authority to change the investment mandate without any legal requirements. The fund’s competitive situation might be negatively affected by the threat of political interference via the MOF in the long run. The Fiscal Stabilization Law Section 17.3 allows the FHF to invest in domestic and foreign markets. A domestic investment allows only buying a Development Bank’s (DB) bond, which invests in manufacturing in the domains of railway, petroleum, and power. Allowing only DB15 can make SWFs the target for rentseeking, as several mismanagement issues were once revealed by independent audition.16 According to a report from the National Auditing Office, the DB has performed poorly with significant financial losses, untargeted loans (out of mandate), and noticeable nonperforming loans due to mismanagement. This rent-seeking problem may deteriorate fund governance in the long term.

Comparative Analysis of Contribution and Withdrawal Rule Chile and Mongolia have established different stabilization and saving objectives, whereas Norway has multifunctional funds for stabilization and saving for future generations (Fig. 2.11). Chile has a rule-based fiscal framework in which mining revenue is initially transferred to the government budget, as in Mongolia, and a certain surplus goes to SWFs under public law and regulations (Robbins and Smith 2014). Both countries’ fiscal policies are similar: the volatility in commodity prices frequently heats government revenue, the government sets a minimum price estimated on the basis of historical fiscal planning data, and a certain ratio of the revenue from the resources is allocated to SWFs under related laws. The Chilean accumulation rule is clearly defined in the Fiscal Responsibility Law, whereas it is unclear whether the Ministry of Finance has full authorization to spend it.

15 Development Bank is a state-owned bank for financing local investment projects under the national investment strategy. 16 The National Auditing Office of Mongolia published a report about the 2019 performance of the Development Bank. The report available at https://api.audit.mn:4444/pages/c62d1c5c0ebf33e9bac9216a70c90ac2/watermarked.pdf.

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Fig. 2.11 Fund’s rule for contribution and withdrawal. Source: summarized by the author from related documents

Norway differs from the other two countries in which petroleum revenue is entered directly into SWF, and the withdrawal is made by the parliamentary decision: 3% of funds can be transferred annually to the government budget. SWFs do not directly fund government expenditure (see Table 2.2). The fund assets in Norway and Chile have invested in financial derivatives and earned high returns since their inception, whereas Mongolian SWFs have not yet started financial investment measures. The Mongolian government has not yet developed investment guidelines or investment strategies. This may be one of the weaknesses of SWF in Mongolia. GPFG (Norway) and FHF (Mongolia) require investment abroad, whereas GPFN (Norway) and FSF (Mongolia) invest only in the domestic market. There is a lack of capital accumulation in Mongolia owing to underinvestment in maintenance.17 The infrastructure index of Mongolia has risen steadily from 1.93 in 2007 to 3.26 in 2017, although it does not compare with the world average or with that of East Asia and the Pacific (Davaadorj 2019). This means that there is a potential to invest in the local market. In particular, health, education, and social capital require improvements to improve jobs and income in the long run. These basic infrastructures are needed to promote social development and accelerate economic growth. There is also persistent criticism of overseas investments by FHF. Some strongly argue that the FHF should contribute to national development instead of financing in a well-functioning market. Others believe investing abroad can avoid the rentseeking problem of nations facing governance issues. It is too early to judge by the cover of the views, and there might be a need for a specific study for further decision-making.

17

OECD iLibrary available at https://www.oecd-ilibrary.org/sites/f983b68a-en/index.html?itemId=/content/component/f983 b68a-en

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Comparative Analysis of Institutional Framework The institutional frameworks of the SWFs in the three nations differ significantly. The funds of Norway, Chile, and Mongolia belong to the “Manager model,” classified by Al-Hassan et al. (2013) and the operational management sections differ slightly, as shown in Figs. 2.1, 2.4, and 2.11. The GPFG (Norway), PRF (Chile), and ESSF (Chile) are managed by the central banks under the MOF mandate. The central banks of Chile and Norway work in part with external managers for equity portfolio management. Chile uses external services to manage its equity. However, Norway has terminated several external managers and handled it internally, while there are external portfolio managers for small-scale companies in emerging markets to enhance local market governance through the encouragement of the invested company’s governance. Small companies in emerging markets need to be concerned with external managers, in contrast to Norway which operates in well-functioning markets for responsible investment. The GPFN (Norway) and FHF (Mongolia) have similarities in fund management as the funds’ assets are managed by private investment companies and SOC and guided by a mandate given by the MOF. There are weaknesses in Mongolia’s institutional capacity, including human capital, professional investment industry standards, technology, and transparency. The lack of human capital development leads to the outsourcing of management to minimize risk and help achieve its objectives. Outsourcing is more expensive than internal management (Clark and Monk 2011). Both Norway and Chile have betterdesigned, in-house investment mechanisms that Mongolia does not have in terms of human capital and institutional capacity. Despite a lack of expertise, the accumulation of FSF (Mongolia) assets has not exceeded 10% of GDP, which is the optimal level of investment per the Fiscal Stabilization Law.

Comparative Analysis of Transparency and Accountability As mentioned in this section, the Truman Scoreboard and LMT Index are two leading methodologies for determining the transparency of SWF. The funds from Norway and Chile are ranked top places in both methods, while funds from Mongolia are not included. We attempted to evaluate the transparency and accountability of Mongolian SWFs using the latest updated Truman Scoreboard methodology (a new version of the scoring board with 33 elements) introduced in this section. The results are shown in Table 2.13, which illustrates the transparency and accountability of SWFs in Mongolia, and it seems clear that neither fund is actively investing, with a score of 0. Structure and governance are better in FHF compared with FSF, and both funds contributed to fiscal management as originally designed. There is an impressive establishment in reporting as a requirement of international accounting standards and norms; however, the pattern of fund behavior is unclear.

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Table 2.13 Truman’s scoreboard for the SWFs in Mongolia

Source: summarized by the author from related documents such as the Future Heritage Fund Law and the Fiscal Stabilization Law of Mongolia

The FSF and FHF scores were 13 and 16 out of 33, respectively, corresponding to 39% and 48%, respectively. The main reason for the lower score is the insufficient development of investment guidelines, as previously mentioned. Currently, the government is preparing core documents for investment measures. In addition to the investment documentation process, the table shows low scores on internal ethical

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standards for its management and staff, which are crucial to sound governance. There are internal ethical requirements for the manager and staff in the FHF Corporation rule Sections 16.3 and 17.1; however, the fund’s internal ethical guidelines have not yet been established in Mongolia.

Findings of Qualitative Analysis for Mongolia Table 2.14 summarizes our qualitative analysis of Mongolian SWFs, compared with the two international practices, using the SWOT framework. All components in each frame have been pointed out previously. According to the main findings of our qualitative study, Mongolia has several weaknesses and threats rather than strengths and opportunities. The government has attempted to improve wealth management through several reforms, as mentioned in Sect. 2.3. Public awareness of wealth management has increased significantly, and NGOs are urged to take action and develop solutions for well-being and efficient resource management. Foreign experts have been invited by international organizations requested by the government to enhance SWF management, and after a long discussion, the political consensus turned out to improve wealth management through SWF. Fiscal revenue estimation from mining provides a wide scope for discussion among Mongolian citizens and now expects wealth benefits and social development. Recently, the government prepared and introduced legal documents on SWF to solve internal difficulties related to institutional capacity building. However, investment management is still questionable because of unclear guidelines. As pointed out by Dixon and Monk (2011), SWF’s design and development are a long-term process, and threats might arise in the long journey of SWF development. Mongolia has been placed on a gray list of anti-money laundering by the Financial Action Task Force. The economy tightens under strong monitoring of foreign transactions and Table 2.14 SWOT analysis of SWFs in Mongolia Strengths • Strong growth in mining production. • Creation of wealth for the next generation. • Open-source international practices. • International assistance (experts). • New legal environment. Opportunities • Potential capacity in local investment. • Public awareness of saving. • Strong state encouragement in the long run. Source: summarized by the author

Weakness • Lack of transparency (decision-making and performance). • Lack of human capital. • Lack of professional investment standards. • Lack of technology for in-house investment (software, hardware). • Unclear investment guidelines. Threats • Commodity price volatility. • Political interference. • Rent-seeking problem. • International gray list by financial action task force agency.

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investments, and further uncertainty will continue in the next few years. Moreover, correlations with global trends are vital for making decisions not only in investment but also in policy actions. Polishing strengths and optimal use of opportunities should be prioritized in development strategies. Therefore, both preparing for externalities and clear guidelines to deal with internalities are crucial for the fund’s governance.

2.5 2.5.1

Importance of Governance in SWFs Using Panel Data Regression Analysis Literature Review

Few empirical analyses of SWFs governance have been conducted to date. Aizenman and Glick (2008) conducted a comparative analysis of the SWF governance and degree of transparency from the viewpoint of international norms and behavior. They examine three aspects of SWF’s characteristics. First, they depict the effect of a country’s characteristics on the newly established SWF in 2007–2008 using probit regressions and conclude that countries with more surplus from the petroleum sector tend to launch their SWFs to invest resource revenue at moderate risk in the long run. Second, they study the impact of national governance on SWF establishment using Worldwide Governance Indicators (WGI). They introduced the Kaufmann, Kraay, and Mastruzzi (KKM) indicator, which is an average of six subindices of WGI, and concluded that KKM and the other five indicators, except for Voice and Accountability, have positive effects on the likelihood of SWF. Government effectiveness was significantly correlated. Moreover, they stated that “developing countries that have better governance, particularly in terms of government effectiveness, regulatory quality, and control of corruption, are more likely to have SWFs.” The third field of study is an empirical analysis using the Truman scoreboard with regard to the correlation of WGI (KKM and subindices) with the existence of SWFs (Aizenman and Glick 2008). We can see clearly that the KKM national governance measurement and Truman governance measurement for the SWFs are less correlated, with a coefficient of 0.28 and a probability value of 0.16. The indicator of Voice and Accountability is significantly correlated in all elements of the Truman scoreboard. Furthermore, Aizenman and Glick (2008) compared the correlation of SWF asset/GDP with the KKM and Truman scoreboards and showed that the national governance indicators of KKM are more correlated than Truman scores with SWF asset size to GDP for the 26 selected countries that established SWFs.

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2.5.2

Methodology

Panel regression is applied to detect and measure the causal effect using both crosssectional and time series data. The ordinary least squares (OLS) approach is first applied. Using this approach, we examine how the main observatory variables of “SWF” can be explained by worldwide governance indicators over the chosen period. We tested heteroscedasticity and autocorrelation consistency using the Newey-West estimator in order to minimize the error terms in this model. The study uses the weighted least squares (WLS) econometric method, which uses both linear and nonlinear parameters to determine the influence of the SWF value on the fiscal deficit in a given period. Fiscal deficit is one of the main indicators of government fiscal activity that can determine government effectiveness. We assume that the fiscal deficit can be stabilized by SWF in the long run if countries achieve constant growth of SWF from their surplus fiscal activities. We examine whether this assumption is true using the WLS method.

2.5.3

Theoretical Background

Based on this approach, the simple equation of the basic regression model with no lags can be written below (2.1). The model can be adjusted depending on statistical significance: Y i = β 0 þ β 1 x i þ ui

ði= 1, . . . , nÞ

ð2:1Þ

where ui is an iid random error term Yi = observator(dependent) variable in t - period β0 = intercept(constant)coefficient β1 = slope of coefficient xi = explanatory variable (selected indicator in i period) ui = the error term The OLS regression equation may be written below: yi = b β0 þ b β 1 xi þ b ui

ði= 1, . . . , nÞ

yi = the observator variable in t - period b β0 = the OLS estimator of intercept ðconstant Þcoefficient β0 b β1 = the OLS estimator of slope of coefficient β1 xi = the selected indicator/variable in t - period for example, in our panel data government effectiveness, rule of law, etc. b ui = the OLS residual the error term

ð2:2Þ

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Since our regression model explains SWF by Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption, it has a high possibility of containing heteroskedasticity problems; thus, we estimate the parameters of the equation using the White cross-section approach in regression analysis. Depending on the result of the test, we may use a fixed effects modeling approach to estimate country-specific effects. The model assumed that the intercept across the country is different, but slope coefficients are constant (Hank et al. 2019). The model can be expressed as follows: Y it = αi þ βc xit þ uit ðt= 1, 2, . . . , TÞ ði= 1, . . . , NÞ

ð2:3Þ

Yit = dependent variable observed for individual i in t - period αi = individual specific intercept xit = the selected indicator/variable in t - period βc = constant slope of coefficient uit = error term WLS is an econometric regression method that can be applied to small-scale data. When heteroskedasticity occurs, an efficient result from the OLS regression is no longer expected. We alter the modeling approach to WLS to improve our regression because we have a sign of heteroskedasticity in OLS regression when we estimate the slope of the SWF’s effect on government fiscal activities. Wasserman (2006) show that the theoretical approach of WLS can be explained by Eqs. (2.3) and (2.4). The residual sum of squares (RSS) tells you how much of the dependent variable’s variation your model does not explain. It is the sum of the squared differences between the actual Y and the predicted Y: RSSðβÞ =

Xn i=1



→

yi - x i ∙ β

2

ð2:4Þ

RSS(β) = residual sum of squares yi = the ith value of the variable to be predicted xi = the ith value of the explanatory variable β = estimated value of the slope coefficient n = number of observations We can minimize the weighted sum of squares (WLS is an estimation technique that weighs the observations proportional to the reciprocal of the error variance for that observation and thus overcomes the issue of nonconstant variance):   Xn  2 → → WSS β ω = ω y x ∙ β i i i=1 i   → WSS β ω = weighted sum of squares

ð2:5Þ

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ωi = the weights variable xi = the ith value of the explanatory variable β = estimated value of the slope coefficient n = number of observations. After testing the data and collecting error statistics on the result, the proper weights are applied to the regression to solve the heteroskedasticity problem.

2.5.4

Data

The data consist of two groups of unbalanced panel data over time, depending on the estimation purpose: (1) to obtain correlation in governance indicators and SWF volume and (2) SWF’s role in government fiscal activities. When there is more than one fund per country, each SWF takes our panel data.

Governance Indicator and SWF’s Volume In the first part of the empirical analysis, we look at governance indicators over the period 2011–2018 to estimate the most correlated governance indicators with SWF assets per GDP. We have chosen the latest reported WGI by the WB aggregated from 200 countries and territories over the period 2011–2018 in six dimensions: (1) Voice and Accountability, (2) Political Stability and Absence of Violence, (3) Government Effectiveness, (4) Regulatory Quality, (5) Rule of Law, and (6) Control of Corruption. We select these six indicators for the countries where SWF data are available because some countries do not have consistent data for a selected year. They might not have established a government fund structure or released the data to the public. Table 2.15 presents the general outlook of the Mongolian governance indicators compared with the world average. They are significantly lower than the world average, such as Government Effectiveness, Rule of Law, and Control of Corruption. This may be explained by Mongolia’s substandard public services, high political intervention in the decision-making of government stakeholders, and incoherent policy approaches through government bodies. Moreover, there is less confidence in the abidance of law and rules in society, particularly the enforcement of a contract, property rights, the police, and the court. Over the past decade, public power has been misused for personal interests, including nontransparent government procurement and corruption around natural resource wealth management. Therefore, this study investigates the assumptions on which indicators are most significantly correlated to SWF activity using econometric methods.

Notes: Some countries have several types of funds depending on their purpose, and SWT ranks the total value of funds. The GDP is calculated based on the current price. Government effectiveness is indicated by governance performance of approximately -2.5 (weak) and 2.5 (strong). Source: International Monetary Fund, World Bank database, Ministry of Finance dataset, hand-collected data, annual report “Sovereign wealth fund” by ESADE

Table 2.15 Comparison of the Mongolian governance indicators and world average

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SWF’s Volume in Fiscal Activities Following the empirical analysis of SWF and governance, our study investigates the effects of SWF’s role in government fiscal activities, particularly in the fiscal deficit, because one of the functions of SWF is stabilization through spending in the case of resource revenue decrease and increase in the short and medium term. We aimed to demonstrate the impacts of the stabilization function of SWF in cases where government income shortage occurs by using the world economic outlook data of IMF for over 200 countries. Data are available from 1980 to the present, along with projections for the next 2 years. The data are open for national accounts, inflation, unemployment rates, the balance of payments, fiscal indicators, trade for countries, and commodity prices. We retrieved fiscal indicators (payment balance, government expenditure, and government revenue) for our estimation.

2.5.5

Result of Regression

The results of the first part of the empirical analysis are shown in Table 2.16; it verifies the most significant indicator of governance for the SWF. Before applying the regression, we checked for multicollinearity in the panel data. The result of the variance inflation factor (VIF) in the model shows that there is no significant multicollinearity because it is less than the critical value of 5. Because our pooled model has a lower R-squared value of 0.17, we cannot rely on this model, and we use a fixed effects modeling approach based on the fixed effects and the Hausman test. Both tests confirmed the application of the fixed effect model. The Hausman test showed a chi-square value of 18.36 with six degrees of freedom and a probability value of 0.005. The fixed effect test has an F statistic of 20.23 with a probability value of 0, a chi-square statistic of 601.73 with a probability value of 0, and above the critical value F statistic of 4.82 is a chi-square statistic of 11.34. Hence, it accepts the alternative hypothesis that the fixed effects model is appropriate. After running the fixed effect model, the R-squared value improved significantly to 0.83, and we can use this modeling approach for our panel data estimation. Regarding policy implications, the intercept value is different for each country depending on the country’s circumstances. Table 2.16 shows the result of our econometric analysis. The estimation result presents that the most significant governance indicator of SWF’s performance is Government Effectiveness, while Rule of Law, Political Stability, and Absence of Violence, and Voice of Accountability have a negative influence on SWF performance. Control of Corruption and Regulatory Quality do not have a statistically significant contribution with different signs of coefficient. Our empirical analysis differs from Aizenman and Glick (2008) in terms of not only the selected period but also the statistical methodology used; they used crosssectional data in their estimations, while we applied panel data analysis. Moreover,

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Table 2.16 Result of OLS econometric analysis (dependent variable SWF/GDP)

Regressor

OLS (fixed effect model)

Control of Corruption

-0.268 (0.186) [2.060]

Government Effectiveness

1.065*** (0.156) [2.391]

Political Stability and Absence of Violence

-0.301 (0.190) [1.435]

Regulatory Quality

0.184 (0.225) [1.694]

Rule of Law

-0.484 ** (0.214) [1.433]

Voice and Accountability

-0.037 (0.298) [1.555]

Cons.

0.435 *** (0.100) [N/A]

R-squared

0.839

Adjusted R-squared

0.803

F-Statistic

23.292

Sample

2011-2018

Period Included

8

Cross-section Included

62

Observations

367

Note: This table illustrates the results of a set of dynamic panel estimations aimed at showing the relationship between the sovereign wealth fund and the characteristics of the governance indicators. The regression is estimated with annual data from 2011 to 2018 using panel least squares with correction of the White cross-section approach. P value of t statistics are in parentheses, with ***p > 0.0001; **0.0001 < p < 0.05; *0.05 < p < 0.1. The variance inflation factor (centered VIF) and standard error are in parentheses with [...] and (...), respectively. Data source: International Monetary Fund, World Bank database, Ministry of Finance of Mongolia dataset, hand-collected data, annual report “Sovereign wealth fund” by ESADE

they studied the correlation between governance indicators and SWF likelihood, while our study covered the scale of SWF (SWF assets as a share of GDP) and governance indicators. The results of the WLS econometric analysis in Table 2.17 illustrate the relationship between the SWF and fiscal deficit. OLS does not verify our assumption; SWF asset growth has imperfect relation to fiscal deficit fluctuation with a correlation value of 19.783, while WLS shows our expected correlation to fiscal deficit fluctuation with a correlation value of -6.385 after the White test. The White test shows significant heteroskedasticity below the critical value of 5. Table 2.17 shows that if SWF growth increases by 1, the fluctuation of a fiscal deficit is expected to decrease by more than six times. The WLS regression model gives the equation as the probability value of 0.06 for the explanator variable (slope of SWF to GDP), indicating that the growth of SWF to GDP can be a significant variable in the fluctuation of fiscal deficit. The overall feature of the results of WLS for countries

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Table 2.17 Result of WLS econometric analysis (dependent variable: St. Dev_ fiscal deficit)

Regressor

OLS

WLS

Slope of SWF to GDP

19.783** (7.351)

-6.385** (3.327)

4.034**

16.424***

(1.290)

(2.319)

Slope of SWF to GDP^2 Cons. R-squared

0.139

-1.627

Adjusted R-squared

0.119

-1.685

F-Statistic

7.241

White Test F-Statistic

5.006

Prob. F(3, 43)

0.005

Observations

47

47

Note: This table illustrates the results of a set of dynamic panel estimations aimed at showing the relationship between the sovereign wealth fund and the characteristics of the government payment balance. All regressions are estimated with annual data from 2011 to 2018 using panel least squares and weighted least squares with a heteroskedasticity White test. P value of t statistics are in parentheses, with ***p > 0.0001; **0.0001 < p < 0.05; *0.05 < p < 0.1. Data source: International Monetary Fund, World Bank database, Ministry of Finance of Mongolia dataset, handcollected data, annual report “Sovereign wealth fund” by ESADE

is that the constant growth of SWF’s assets can contribute to a reduction in fiscal deficit in the long run. After running WLS, we checked heteroskedasticity problems in our model, whether it has been removed by WLS or not, by using the White test once again. We set the null hypothesis residuals as homoscedastic. Our test result indicates that probability values of 0.19 and 0.18 for the F-statistic and Obs*R-squared, respectively, cannot reject the null hypothesis.

2.6 2.6.1

Concluding Remarks and Recommendations Conclusion

The regulatory architecture of the SWF in Mongolia has not been fully developed to manage the funds’ assets with strong returns in the financial market. There have been significant improvements in public laws desiring sound governance, such as well-

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designed institutional frameworks, a clear delegation from owners to managers, and clear contribution and withdrawal rules. These improvements could contribute to anticorruption measures and improve fiscal discipline through a clear rule-based framework. We would like to further improve stakeholders’ activities in two ways. First, as mentioned in the Sect. 2.4, allowing the purchase of only state-owned banks’ bonds might involve rent-seeking problems in FSF. Diversifying investment in the local market by FSF ensures equal opportunities to other domestic competitors. Second, the FHF policy may contain sensitive practices from frequently changing acts in the absence of legal requirements for modification. State parties may agree to change them at any time without legal requirements. In fact, we learned a great lesson from the HDF that the relevant laws frequently changed with changes in political interests. The failures of previous SWFs were attributed to unclear rules regarding the mission and delegation from owner to the manager, poor design and governance, excess withdrawal by political decisions, limited operational freedom (political interference), and mismanagement and waste (to finance directly to government expenditure). Furthermore, strengthening institutional capacity is crucial for the sound governance of SWFs. There are four fundamental aspects to the establishment of strong institutions in sovereign wealth management: (1) capable human capital available for sovereign management, (2) clear institutional structure, (3) proper standards on ethics, and (4) technology used for financial analysis. How can human capital be efficiently prepared? Most experts agree that the first component of the governance model is people and that concerns require hiring professionals with an acceptable remuneration scheme. Simultaneously, human resource development is crucial for supporting employees in developing their skills in practical and academic research. Presently, overseas training involving nations with sound governance in sovereign fund management may be one of the solutions to obtain the required knowledge and abilities for the SWFs in Mongolia. Another solution involves human resource development in SWF by providing cost-free training programs with experts for employees seeking personal and organizational skills in SWF management. Both approaches support human resource development in SWF management. They recognize investment industry standards and technology and manage SWF as fully developed financial institutions. The remuneration system is also important for maintaining capable managers recognized globally. We hope that the additional documents will cover the proper remuneration scheme that is currently being processed. In addition, the transparency of SWFs in Mongolia was below the median of the global average based on the Truman scoreboard approach. A law on transparency of public finance in Mongolia to strengthen efficiency introduced the “Glass Account Law” in 2015. According to the law, public budgetary entities must disclose their financial transactions and audited statements regularly, except for salary and social reductions. Although the implementation of the law on SWFs has improved since it was first introduced by the parliament, the 2018 financial statements of SWFs were not disclosed publicly.

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According to Transparency International’s Corruption and Perceptions Index 2019, Mongolia ranks 106 out of 180 nations with a score of 35 out of 100, which means that public sector corruption is a significant concern for future policy development. In addition, the quantitative analyses provided supportive results on the research question. The first result from an empirical analysis of the relationship between the World Governance Indicators and SWFs as a share of national GDP revealed that SWFs depend on the quality of public policy formulation and implementation. More precisely, Government Effectiveness is a significant explanatory variable for the scale of SWFs. It can be explained that the accumulation of sovereign funds will increase if nations deal with disciplinary problems properly, such as weak and bureaucratic public services, decision-making that depends on political will, overspending, and conflict with personal interests. The second result of the theoretical consideration of how the SWF contributes to government fiscal activities verifies our assumption that the SWF stabilizes fiscal deficits in the long run. Thus, if countries establish respectful and useful services for their citizens, quality policy formulation and implementation without political interference lead to more accumulation of financial wealth and a reduction in the fiscal deficit.

2.6.2

Recommendation for the Government of Mongolia

In view of these circumstances, the Mongolian government needs to consider the following aspects to strengthen SWF governance. • Policy acts related to SWFs must be improved to prevent rent-seeking problems and political interference. To insulate political interests, Norway’s hybrid policy approach may be a good model for Mongolia. This discussion should be applied to the Mongolian act, and policymakers should consider the context and preference for adopting good practices more deeply. • Investment guidelines should be developed by adopting the appropriate practices. Chile and Norway have well-developed investment guidelines, and thus during the current transition period of SWF management in Mongolia, the Chilean external portfolio management might be a good model for Mongolia. The government does not need to have all the capacity in place at the establishment stage, and a systematic approach is crucial for sustainable development. In the long run, Mongolia will need to develop an in-house investment scheme that can reduce costs and strengthen human capital development. • Mongolia needs to consider the development of human resources that can manage fund assets efficiently and comply with investment guidelines in a sustainable manner. The inclusion of countries that have good practices in SWF management to meet industry standards and policy implications may be a solution for Mongolia.

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• Internal ethics guidelines on funding staff need to be developed. A country with full scores on the internal ethics of fund professionals, such as Norway, is a model for Mongolia. • The quality of law abidance on transparency should be improved to meet public necessities since the fund was designed to create wealth and opportunities for all. Finally, we found that the main reasons for the failure of SWF are weak policy formulation and implementation, mismanagement due to lack of institutional capacity, and questionable performance associated with politics. If these issues are resolved significantly by modifying the acts related to freedom of decision-making, strengthening the capacity of the institution with a prudential policy on human resource development, and transparency related to fiscal performance, we would significantly improve the performance of SWF governance in the near future.

References Aizenman J, Glick R (2008) Sovereign wealth funds: Stylized facts about their determinants and governance. National Bureau of Economic Research Working paper 14562. Al-Hassan A, Papaioannou M, Skancke M et al (2013) Sovereign wealth funds: aspect of governance structure and investment management. International Monetary Fund Working Paper WP/13/231 Bagnall AE, Truman EM (2013) Progress on sovereign wealth fund transparency and accountability: an updated SWF scoreboard. Peterson Institute for International Economies Bortolotti B, Fotak V, Megginson WL (2015) The rise of sovereign wealth funds: definition, organization, and governance. In: Caselli S, Corbetta G, Vecchi V (eds) Public private partnerships for infrastructure and business development. Palgrave Macmillan, New York Chilean Ministry of Finance (2018) Annual report: sovereign wealth funds. Ministry of Finance Clark GL, Monk AH (2011) Principles and policies for in-house asset. J Financial Perspect, EY Global FS Institute 1(3):39–47 Clark G, Monk A (2013) Principles and policies for in-house asset management. J Financ Perspect 1 (3):39–47 Daban T (2010) Strengthening Chile's rule-based fiscal framework. Working paper. International Monetary Fund Davaadorj B (2019) Infrastructure financing in Mongolia. United Nations Economic and Social Commission for Asia and Pacific (ESCAP) Dixon AD, Monk AH (2011) The design and governance of sovereign wealth funds: principles and practices for resource revenue management. University of Bristol Government of Norway (2019) Management mandate for government pension funds globally. https://www.regjeringen.no/en/topics/the-economy/the-government-pension-fund/the-guide lines-for-the-management-of-the/id434605/ (Accessed 10 Jan 2022) Hank C, Arnold M, Gerber A et al (2019) Introduction to econometrics with R. Department of Business Administration and Economics University of Duisburg-Essen, Essen, Germany International Forum of Sovereign Wealth Fund (2014) Santiago principles: 15 case studies, How IFSWF members implement the Santiago principles. IFSWF, Doha International Monetary Fund (2007) Global financial stability report: sovereign wealth fund. International Monetary Fund International Working Group (2008) Generally accepted principles and practices-Santiago principles. International Working Group of Sovereign Wealth Funds Mehlum H, Moene K, Torvik R (2006) Institutions and the resource curse. Econ J 116(508):1–20

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Monk A (2010) Santiago principles Vs. Truman scoreboard. University of Oxford. https:// oxfordswf.wordpress.com/2010/07/19/santiago-principles-vs-truman-scoreboard/ (Accessed 10 Jan 2022) Norwegian Ministry of Finance (2014) Guidelines for observation and exclusion from Government Pension Fund Global. https://www.regjeringen.no/contentassets/9d68c55c272c41e99f0bf4 5d24397d8c/guidelines-for-observation-and-exclusion-from-the-gpfg%2D%2D-01.09.2019. pdf (Accessed 10 Jan 2022) Norwegian Ministry of Finance (2019) Meld. St. 20 (2018-2019) Report to the Sorting: the government pension fund. Norwegian Ministry of Finance. https://www.regjeringen.no/ contentassets/8996cca30e5741a788218d417762a52c/en-gb/pdfs/stm201820190020000 engpdfs.pdf (Accessed 10 Jan 2022) OECD (2018) Economic survey: Chile. OECD Oyunzul T, Nyambaatar B (2020) The impacts of sovereign wealth fund on economy. https://www. mongolbank.mn/documents/news/communication/%D0%AD%D0%A8%D0%91%D0%A5/2. %20%D0%9C%D0%BE%D0%BD%D0%B3%D0%BE%D0%BB%20%D0%A3%D0%BB% D1%81%D1%8B%D0%BD%20%D0%B1%D0%B0%D1%8F%D0%BB%D0%B3%D0% B8%D0%B9%D0%BD%20%D1%81%D0%B0%D0%BD%D0%B3%D1%83%D1%83%D0 %B4%D1%8B%D0%BD%20%D1%8D%D0%B4%D0%B8%D0%B9%D0%BD%20%D0% B7%D0%B0%D1%81%D0%B0%D0%B3%20%D0%B4%D0%B0%D1%85%D1%8C%20% D0%BD%D3%A9%D0%BB%D3%A9%D3%A9.pdf (Accessed 10 Jan 2022) Robbins AB (2013) The geographic challenges of establishing sovereign wealth funds in frontier market. The Fletcher School. TUFTS University Robbins AB, Smith G (2014) Engagement with Mongolian's sovereign wealth fund. World Bank Solimano A, Guajardo DC (2017) The copper sector, fiscal rules, and stabilization funds in Chile. WIDER Working paper 2017/53. United Nations University Stone SE, Truman EM (2016) PB 16-18 Uneven progress on sovereign wealth fund transparency and accountability. Peterson Institute for International Economies Truman EM (2007) Scoreboard for sovereign wealth funds. Peterson Institute for International Economies U.S. Embassy in Mongolia (2018) Mongolia investment climate 2018. https://mn.usembassy. gov/2018-investment-climate-statement-mongolia/ (Accessed 10 Jan 2022) Ungureanu MC (2014) The corporate governance of sovereign wealth funds. Harvard Law School Forum on Corporate Governance Wasserman L (2006) All of nonparametric statistics. Springer-Verlag, Berlin

Chapter 3

Government Financial Support for Small- and Medium-Sized Enterprises (SMEs) in Mongolia Naranzul Tsaschikher

Abstract This study aims to examine the current state of policy support for smalland medium-sized enterprises (SMEs) in Mongolia. Accordingly, it identifies implications regarding policy actions and recommendations for the Mongolian government based on the Government of Japan’s SME-supporting measures from a historical perspective. Regardless of the different functions of SMEs implemented in these countries, Japan has faced various critical economic conditions. Thus, Mongolia could learn lessons from Japan’s past experience and enhance the contribution of its SME industry. The research was conducted using qualitative and quantitative approaches, and descriptive and spatial econometric methods were adopted. In Japan, priority industries were heavily supported following World War II, when it tried to minimize war-driven economic devastation. After a successful economic reconstruction process, priority industries matured and were able to operate by themselves. Therefore, the Government of Japan shifted its attention to SME-supporting policies more aggressively. The framework for supporting the SME sector was built during the reconstruction period and held a huge domestic financing capacity. Additionally, it financially supported the Government of Japan and implemented other measures for SMEs through management, fiscal, commerce, and regional frameworks. The Government of Japan formulated the SME-supporting policy according to the national development policy, and its characteristics were changed according to the development state of the economy. One of the reasons for the success of these policies was the Fiscal Investment and Loan Program. Through this system, the Government of Japan supported weak sectors and regions, successfully avoided bottlenecks, and achieved nationwide balanced development. The solution to many of the problems in Mongolia could lie in the establishment and promotion of SMEs, especially in the creation of a policy framework that helps build a balanced development structure in accordance with the “regional development strategy” and improves educational and financial capacity. Nurturing and growing this important segment could help Mongolia diversify its economy away from its

N. Tsaschikher (✉) Ministry of Finance of Mongolia, Ulaanbaatar, Mongolia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Taguchi et al. (eds.), Challenges in Fiscal and Monetary Policies in Mongolia, New Frontiers in Regional Science: Asian Perspectives 66, https://doi.org/10.1007/978-981-19-9365-7_3

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dependence on mining-based exports. Similar to many other countries, access to finance for SMEs remains a major issue. SMEs face a wide range of difficulties in accessing finance, including high-interest rates, collateral requirements, size and maturity of loans, and complex application procedures. Along with the commercial bank loan, the Government of Mongolia and other international organizations are implementing a loan program to support SMEs in large cities and rural areas. However, the share of the project loan amount to the total outstanding loans was 3.5% in 2018. Considering that the commercial bank penetration rate in local areas is very low, access to subsidized loans for local SMEs is limited. According to the data used in this study, SMEs from 122 soums (a total of 339 soums) have bank loans amounting to a total of 397.8 trillion MNT, of which the bank loans of SMEs operating in Ulaanbaatar city account for 89.4%. Therefore, there is a significant concentration of SME financing activity in Ulaanbaatar city. However, SMEs operating in regional areas have extremely limited access to additional financial services. In this study, the excessive concentration of economic activity and SMEs in the capital city of Ulaanbaatar has been examined and verified using the spatial econometric method. The spatial econometric method has been applied in theoretical econometrics and is getting more attention from researchers. Using this method, we identify the spatial interaction (spatial autocorrelation) and structure (spatial heterogeneity) in regression models for cross-sectional and panel data. The results obtained from the spatial statistical analysis clearly verify the statistically significant concentration of SMEs in the capital city of Ulaanbaatar and its neighboring areas. The neighboring area of Ulaanbaatar shows the highest economic performance. Following the theoretical concept of the key features of the spatial econometric approach, the coefficient of the spatial lag of output (sales revenue) identifies the magnitude of output spillover from the neighboring provinces. The spatial lag model affirms that there exists a positive spatial externality of output with a magnitude of 0.24, and the output spillover is revealed as a key factor of the SME concentration in the area surrounding the capital city and induces firms to locate closer to each other. Therefore, the firms operating in these areas can increase their output or sales revenue. In the spatial error model, the coefficient of the spatial error model λ affirms that there exists an indirect influence, a magnitude of 0.7, operating through the spatially linked error terms. Accordingly, this outcome reveals that the firms operating in these areas can indirectly affect other firms. Considering the empirical results, the Government of Mongolia needs to address the concentration of the economic activity in the central city and mining-based provinces to achieve balanced development across the country. Nationwide balanced development is vital for the sustainable development of the country, as seen in Japan’s development history. Keywords Government support · Small- and medium-sized enterprises (SMEs) · Spatial data analysis

3

Government Financial Support for Small- and Medium-Sized. . .

3.1

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Introduction

Small- and medium-sized enterprises (SMEs) play a pivotal role in the global economy. The development of SMEs has played an important role in reducing poverty and income inequality, boosting employment for the growing population, revitalizing local economies, and supporting inclusive growth. The development of SMEs remains a top policy priority across developing and developed countries, including Japan. The global economy has changed dramatically over the years, and the characteristics of SMEs that affect their development vary for each country, such as compliance with the development state. Developed countries are facing new challenges in revitalizing SMEs and improving productivity, whereas access to finance is the major challenge in developing countries. Therefore, it is necessary to formulate a country-specific strategy that creates a suitable environment for SMEs and identifies mechanisms that help promote their development. The solution to many of the problems of Mongolia could lie in the establishment and promotion of SMEs, especially in the creation of a policy framework that helps build a balanced development structure in accordance with the “regional development strategy” and improves educational and financial capacity. Nurturing and growing this important segment could help Mongolia diversify its economy away from its dependence on mining-based exports. As most value-added across non-extractive sectors are created by SMEs, their development will be critical for governments to achieve their top priorities, diversify economic activity, and reduce their exposure to commodity price fluctuations. Thus, this study examines the current state of policy support for SMEs in Mongolia. Accordingly, it identifies implications for policy actions and recommendations for the Mongolian government based on the Government of Japan’s SME-supporting measures from a historical perspective. However, the most difficult but essential part is taking the appropriate actions. Each country has unique characteristics reflecting its society and history. For instance, Japan and Mongolia have followed different logical mechanisms to support the SME sector. Moreover, the establishment of the policies has been affected by many internal and foreign factors. Therefore, this study carefully compares the conditions in Japan during its modernization to the ongoing experience of Mongolia. Furthermore, it identifies the lessons that are transferable to the Mongolian setting. Regardless of the different functions of SMEs implemented in both countries, Japan has faced several critical economic conditions. Thus, Mongolia could learn lessons from Japan’s past experience and enhance the contribution of its industry. The research was conducted using qualitative and quantitative approaches, and descriptive and spatial econometric methods were adopted. The remainder of the study is organized as follows. The next chapter discusses the characteristics of governmental support for SMEs in Japan. Section 3.2 explains the characteristics and development state of Mongolian SMEs. Section 3.3 explains the data, empirical methodology, and results. Section 3.4 concludes the study.

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Characteristics of Government Support for SMEs in Japan

This chapter discusses the characteristics of governmental support for SMEs in Japan.

3.2.1

Definition of SMEs in Japan

SMEs definition varies across countries and organizations, as countries set their own guidelines for defining SMEs. There is no generally accepted definition of SMEs, but most countries define SMEs through indicators of the firm size, such as the number of employees, capital, and annual turnover. In Japan, SMEs are defined by the Small and Medium-sized Enterprise Basic Act enacted in 1963. SMEs are categorized into the following four types of industries: 1) Manufacturing, construction, transportation, and other industries (excluding 2–4). 2) Wholesale (textiles, food, building materials, machinery, etc.) 3) Services (educational, medical, real state, etc.) 4) Retail (food, machinery, restaurants, accommodation, etc.) Additionally, the agricultural industry is excluded from SMEs because these organizations are not legally incorporated. Before 1999, SMEs were roughly categorized, as shown in Table 3.1. The Small and Medium-sized Enterprise Basic Act was thoroughly amended in December 1999, and the registered capital requirement was relaxed. The definitions of SMEs after December 1999 are as provided in Table 3.2.

Table 3.1 SMEs Basic Act of Japan before 1999 Industry 1) Manufacturing and others 2) Wholesale 3) Services 4) Retail

Capital Up to ¥100 million Up to ¥30 million Up to ¥10 million Up to ¥10 million

Number of regular employees Up to 300 Up to 100 Up to 50 Up to 50

Source: National Association of Small and Medium Enterprise Promotion Organizations, White paper on Small and Medium enterprises in Japan, 2018, https://www.chusho.meti.go.jp/sme_ english/index.html

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Table 3.2 SMEs Basic Act of Japan after 1999

Industry 1) Manufacturing, construction, transport, other industries (excluding 2–4)a 2) Wholesale

3) Servicesa

4) Retail

SMEs (meet one or more of the following conditions) Number of regular employees Capital Up to 300 Up to ¥300 million Up to 100 Up to ¥100 million Up to Up to 100 ¥50 million Up to 50 Up to ¥50 million

Small enterprises included among SMEs at left Number of regular employees Up to 20

Up to 5

Up to 5

Up to 5

[SMEs] 1) Manufacturing: rubber product manufacturing industry – up to ¥300 million in capital or up to 900 regular employees 2) Services • Software industry and information service industry: up to ¥300 million in capital or up to 300 regular employees • Hotel industry: up to ¥50 million in capital or up to 200 regular employees [Small enterprises] 3) Services • Accommodations industry and industries: up to 20 regular employees Source: National Association of Small and Medium Enterprise Promotion Organizations, White paper on Small and Medium enterprises in Japan, 2018, https://www.chusho.meti.go.jp/sme_ english/index.html a Notes: The following industries are separately stipulated, as shown below, based on the government ordinance related to SME legislation:

3.2.2

Importance of SMEs in Socioeconomic Development

SMEs occupy an important position in the Japanese economy. This sector has comprised most business establishments over the decades (Fig. 3.1), accounting for over 99% of all establishments in Japan. This stimulated economic growth as SMEs contribute to social wealth by generating employment opportunities and new creating businesses (Honjo and Harada 2006). SMEs, defined as enterprises with less than 20 employees, play a dominant role, and this sector is important for several reasons. First, all companies start as SMEs. Even large and successful companies started their business as small enterprises, such as Sony Corp., Kyocera Corp., etc. Using the help of appropriate governmental policies, SMEs can promote competition, create a suitable environment, and foster innovative enterprises at the right time. Second, promoting competition among SMEs can improve the quality of products

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95%

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90% 50,00,000

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40,00,000

80% 75%

30,00,000

70% 65%

20,00,000

60% 10,00,000

55%

-

50%

Share of SMEs

Share of Large companies

Number of SMEs

Fig. 3.1 Number of business establishments by size (except nonprimary industry). Source: data for 1947–1994 from the National Association of Small and Medium Enterprise Promotion Organizations, White paper on Small and Medium enterprises in Japan, 1998, and data for 1996-2006 from the White paper on Small and Medium enterprises in Japan, 2011

and services. Therefore, the positive effect of diversification among industries is seen. Third, because of their large share in economic activity, SMEs can revitalize the Japanese economy. Hence, the SME-supporting policy has been more sophisticated in Japan than in most European countries. It was used to guide the industrialization process before and after World War II. This role has gradually been shifted to the private sector, especially to Japanese multinational corporations that hold an important position. Figure 3.1 illustrates the trend in the number of business establishments by size. As shown in the figure, the number of SMEs increased until 1991, and between 1947 and 1991, the number of SME establishments in the nonprimary sector increased by 2.6 times, from 2.6 million to 6.6 million. The increase was most notable in the 1960s when the Japanese economy experienced rapid growth. Considering the working force, between 1963 and 1996, employment in SMEs increased by 2.0 times, from 23.6 million to 46.8 million employees (Fig. 3.2). There is a substantial amount of literature demonstrating the importance of the SME for sustainable economic and social development in the Organisation for Economic Co-operation and Development (OECD) and non-OECD member countries (Unleashing Entrepreneurship: Making Business Work for the Poor 2004). This observation is also valid for Japan in terms of historical and future developments (Regnier 2006). A considerable amount of literature considers the SME public policy of Japan from various different angles, qualitatively and quantitatively. On the one hand, many studies and researchers have tried to explain the SME policy system and its contents. Toru Imajoh (2012) compared the financial history of small businesses and explained the condition of small business financing in Japan, from

Government Financial Support for Small- and Medium-Sized. . .

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Share of SME employees

Share of Large company employees

NUmber of employees in SMEs

Number of employees in Large companies

Fig. 3.2 Trend in number of employees. Source: National Association of Small and Medium Enterprise Promotion Organizations, White paper on Small and Medium enterprises in Japan, 2011

pre-war to high-growth periods. They concluded that essential institutions and policies supporting small businesses during high economic growth stemmed from the post-war reconstruction era. Seki Tomohiro (2008) explained the SME policies and measures taken by the Japanese government between 1940 and 2000 in simple terms. Additionally, they pointed out the policy difference between post-war and high-growth periods. On the other hand, other studies have examined the relationship between the SME sector and economic growth and the relationship between public intervention and SME sector development. Yuji Honjo and Nobuyuki Harada investigated the effects of public policy and financial structure on the growth of SMEs (Honjo and Harada 2006). They examined whether or not the SME Creative Business Promotion Law (CBPL) and financial structure affect firm growth. It was found that SMEs approved by prefectural governors under this law tend to increase assets. Furthermore, they provided evidence that the CBPL and cash flow affect the growth of younger SMEs. Iichiro Uesugi and Hiroshi Uchida (植杉威一郎、内田 浩史、水杉裕太 2014) were the first to examine the determinants and effects of lending by government-affiliated financial institutions to the SMEs in Japan. They also described the information production functions of the Japan Finance Corporation (JFC) in a quantitative and comprehensive manner. They concluded that there is no clear evidence that JFC lending improves corporate performance. In 2016, they also analyzed the impact of the introduction of unguaranteed loans by JFC’s Small Business Division, a major government-affiliated financial institution in Japan, on firms’ funding and performance in the fiscal year 2004. It was found that the ex-post performance of enterprises using unguaranteed loans was excellent following the introduction of the system. However, it was inferior in terms of the probability of financial crisis and profit rate in the fiscal year 2005 (植杉威一郎、内田浩史、岩 木宏道, 無保証人貸出の導入と企業の資金調達・パフォーマンス, 2016). However, their result contradicted the case of unsecured loans, which was introduced by JFC’s Small Business Division in August 2008. Unsecured lending eased firms’ funding constraints and deteriorated their ex-post performance (植杉威一 郎、内田浩史、岩木宏道, 無担保貸出と企業の資金調達・パフォーマンス, 2015).

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Japanese Government Policy on SMEs from Post-war Period Through High-Growth Period

Pre-war and Wartime Periods Before explaining the governmental policy on SMEs in the post-war period, understanding the government’s role in supporting SMEs during the wartime and post-war reconstruction periods, tracing back to the Edo era, is also important. The Japanese government duly organized the SMEs to strengthen product competitiveness and credit in the pre-war period. In 1900, the Ministry of Agriculture and Commerce enacted the first governmental law regarding the organization of small entrepreneurs, the Manufacturers’ Cooperatives Act for Important Goods (Jyuyo Bussan Dogyo Kumiai Ho 重要物産同業組合法). In addition, the government recommended the establishment of credit associations as a measure against the prevalent financial issues. Nevertheless, these measures were not fully implemented due to World War I. Additionally, because the connection between policy and finance was weak, government assistance in utilizing credit cooperatives had a limited effect. The governmental administration in the pre-war period did not set up governmental financial institutions to aid small businesses and did not provide any particular support for small business loans through private sector financial institutions. However, Imajoh (2012) demonstrated that many small businesses actively used informal transactions, such as loans given by traders, family members, or private institutions, by showing the various types of lenders in Japan’s five major cities in the 1930s. During wartime, in the 1930s, Japan decisively turned to militarism, and wartime economic planning was adopted. Therefore, the government forced business establishments to manufacture munitions or face closure. To facilitate changes or closures in small businesses, a governmental financial institution was established. The government subsidized the SMEs that changed their businesses into munition manufacturers and export industries or helped businesses unable to make such conversion close down and relocate unemployed workers to defense industries. Table 3.3 illustrates the main principles of policy actions from the pre-war period until now.

Post-war Reconstruction Period Following the war, Japan’s economic base was destroyed (Fig. 3.3). Many SMEs tried to begin operation but faced various problems such as the lack of materials for production and severe inflation caused by the shortage. Japan lost almost all of its military and commercial ships, along with the means to transport energy and materials. Due to the lack in inputs, production was halted. Therefore, productivity had reached its minimum level because of the lack of inputs and not the lack of capacity (Ohno 2006). During the post-war reconstruction period, the government established a special governmental institution for small business policy. Accordingly, it undertook many

3

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Table 3.3 Japan’s SME policies in relation to the country’s economic development Time period Before 1937 Wartime period 1937–1945 Reconstruction period 1945–1954 High-growth period (first stage) 1955–1962

High-growth period (second stage) 1963–1972

Stable-growth period 1973–1984

Transition period (first stage) 1985–1999 Transition period (second stage) 2000–present

Stance of the government • Improve product competitiveness and creditworthiness through the organization. • Change business to munitions industry or close down. • Improvement of basic tools for SME policies (finance/organizational upgrading/management diagnosis & guidance). • 1948 establishment of the small and medium Enterprise Agency • Rectification of dual structure (gaps between SMEs and large enterprises). – Systematization of SME policy (finance/organizational upgrading/management diagnosis & guidance). – Response to the structure of the division of labor among subcontracting enterprises. • Modernization of SME. – 1963 establishment of “small and medium Enterprise basic law” – Intensification of policies for rectifying disadvantages – Measures for small-scale enterprises – Measures for enriching equity capital (small and medium business investment and consultation co., ltd.) – SME modernization promotion law • Knowledge intensification. • Enriching intangible managerial resources. – Institute for Small Business Management and Technology. – Small business information Center. – SME regional information Center in districts. • Structural change and industrial agglomeration. • Measures for supporting startups and new businesses. – Temporary law concerning measures for the promotion of the creative business activities of small and medium enterprises. • 1999 amendment of “small and medium Enterprise basic law” – Promoting diverse and vigorous growth and development of independent SMEs – Promoting business innovation and new business startups – Strengthening the management base of SMEs – Facilitating adaptation to economic and social changes

Source: Small and Medium Enterprise Agency. https://www.chusho.meti.go.jp/sme_english/ outline/01/01.html

organizational and financial measures, exceeding the number of measures in the pre-war period. The government’s recognition of the importance of SMEs in the Japanese economy, and their relatively disadvantageous position against large firms, led to active SME promotion policies in the post-World War II period. In the early post-war period, chemical industries were heavily promoted, and the basic principle behind SME policies was to protect SMEs from larger firms. In 1947, the Act on Prohibition of Private Monopoly and Maintenance of Fair Trade (the Japanese Antimonopoly Act) and the Law on Elimination of Excessive

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Fig. 3.3 Industrial production index. Source: Management and Coordination Agency, Historical Statistics of Japan, Vol. 2, 1988

Concentration of Economic Power were enacted to implement measures that introduced a democratic economy. As part of the measures for SMEs and to prevent economic centralization, the Small and Medium Enterprise Agency was established in 1948, as an extra-ministerial bureau of the Ministry of Commerce and Industry, under the guidance of GHQ1 (the General Headquarters of Allied Powers). Accordingly, Japan’s measures for SMEs were initiated and have continued ever since (Small and Medium Enterprise Agency 2019). During this period, SMEs faced general issues such as low management level, especially in financial accounting, lack of technology and funding capacity, aimless investment and production, etc. Thus, a new industrial policy was created to include foreign exchange budget, capital control, control of technology imports, preferential tax treatment for specific industries, creation of policy banks, and a number of laws that promoted SME rationalization (Ohno 2006). The basic tools for SME measures were prepared in line with these issues, including financial resources, cooperatives, and management consulting and guidance. • Financial resource: Along with private financial institutions, government financial institutions were also established as supplements. These institutions included the Shoko Chukin Bank, established in 1936, and the National Life Finance Corporation, established in 1949. These institutions extended loans to SMEs facing difficult financial situations. Furthermore, the Japan Finance Corporation 1

After the war, Japan was occupied by the USA. The occupying force was called the Supreme Commander of the Allied Powers (SCAP), alternatively, the General Headquarters (GHQ—this term was more popular among Japanese). The GHQ was headed by US Army General Douglas MacArthur.

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for Small Business was established in 1953 with government equity to facilitate long-term funding for SMEs. To strengthen the financial weakness of SMEs, such as a lack of security, the complementary credit support system was established through the Small and Medium Enterprise Credit Insurance Law in 1950 and the Credit Guarantee Association Law in 1953 (Small and Medium Enterprise Agency 2019). • Cooperatives: The Law on the Cooperative Association of Small and Medium Enterprises was enacted in 1949 to correct the social and economic disadvantages of SMEs and uplift their social status. Accordingly, cooperatives and groupings of SMEs proceeded. Furthermore, in 1953, the organization and functions of the Chamber of Commerce and Industry, an incorporated body based on the Civil Code of that time, were strengthened by the provision of a legal status based on the Chamber of Commerce and Industry Law. • Management consulting and guidance: Management consulting and guidance are important in modernizing and rationalizing SME management. Thus, the Management Consulting System and Consulting Desk for SMEs were established in 1948. The Registration System of SMEs Consultant was started in 1952, and subsidies for municipal governments were provided to promote the guidance program. • Taxation: An important system in this category, the “Blue Returns,” was initiated. Since the old official assessment was replaced by the self-assessed taxation system after the war, SMEs were in disorder caused by incomplete bookkeeping and the fear of over-taxation. To resolve this situation, Blue Returns was established in 1949. It allowed certain tax merits if a tax return was filed using a “certain formula of quick bookkeeping.” This system resulted in the improvement of financial accounting and the strengthening of financing systems for SMEs (Small and Medium Enterprise Agency 2019).2 By the mid-1950s, this principle had changed from protection to promotion and modernization (Itoh and Urata 1994). A reason for this change in emphasis was the productivity disparity between Japan’s large firms and SMEs. Therefore, Japanese industries had to strive for efficiency and competitiveness. The days of economic planning and physical expansion were over, and the government wanted to eradicate productivity disparity to achieve further economic expansion.

The High-Growth Period Through the Reconstruction Period and by the late 1950s, the Japanese economy nearly recovered to its pre-war levels, and SMEs became very active. The average real growth was approximately 10% from the mid-1950s to the early 1970s (Ohno 2006). Due to the high and sustained growth, national income increased 2 Outline of policy actions for SMEs on this section is based on Small and Medium Enterprise Agency’s website (https://www.chusho.meti.go.jp/sme_english/outline/04/01_06.html).

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significantly, and capital investments called for further increase. However, because of the priority production system implemented during the post-war reconstruction period, scarce resources were to be selectively used among a few strategically important industries. This measure was introduced to restart an expansionary reproduction cycle which led to two different paces of growth, that is, large-scale enterprises and SMEs. Thus, arguments began to prevail on the “Dual Industrial Structure” of “advanced large-scale enterprises” and “delayed SMEs” with different levels of productivity, wages, technology, and financing ability. The government started to implement measures to resolve the Dual Industrial Structure. The following measures were taken: • Financial resources: The Law on Financial Assistance for Promoting Small and Medium Enterprises was enacted in 1956 to support improvement in productivity by applying modern equipment. Municipal governments extended loans under the said law for modernizing the equipment of SMEs. • Management consulting and guidance: In this area, the Law on Organizing Commerce and Industry Association was enacted in 1960 to improve the management of small-scale enterprises through management consulting. Regarding guidance programs, the Small and Medium Enterprise Guidance Law was enacted in 1963 to prepare a systematic and efficient scheme of guidance for the rationalization of management, and improvement in technology for SMEs, whereby municipal governments can plan and efficiently undertake the guidance provided (Small and Medium Enterprise Agency 2019). During the 1960s, the government’s stance was to open the market for trading and to stimulate foreign investors. To achieve a balanced development of the national economy, SMEs were promoted by upgrading the industrial structure and strengthening international competitiveness. Accordingly, the Small and Medium Enterprise Basic Law (the SME Basic Law) was enacted in 1963. The SME Basic Law was designed for the following reasons: (a) to eliminate SMEs’ disadvantages derived from economic and social restrictions, (b) to support their self-help efforts, (c) to improve their productivity and trading conditions to rectify the dual-structured gap, and (d) to improve the social status of their employees. The SME Basic Law established a basic idea of measures for SMEs. Furthermore, specific measures were decided and implemented under the SME Basic Law. Additionally, the Small and Medium Enterprise Modernization Promotion Law (the Promotion Law) was enacted in 1963 to promote the upgrading of the industrial structure by improving the productivity of SMEs. The Promotion Law was designed to exercise the measures by projecting a modernizing plan across industries and combining various measures in an overview, such as strengthening the international competitiveness of the industry.

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The Stable Growth Period (1973–1984) With the first oil crisis in 1973, the Japanese economy transitioned from a highgrowth period to a stable growth period. Along with this change, measures for SMEs also changed direction, from aiming at modernization of equipment for upgrading productivity and expansion of management scale to moving the industrial structure toward intellectual industrialization. Thus, the need for “soft” management resources was discussed, which required technical skills, human resource, and information. Considering these needs, the Institute for Small Business Management and Technology was established in 1980 to develop human resources. To provide information services for management improvement, the Information Center for Small and Medium Enterprises was opened in the Small Business Promotion Corporation, and Regional Information Centers for Small and Medium Enterprises were opened in prefectures.

Transition Period, the First Stage (1985–1999) From 1985, Japan experienced a drastic yen revaluation and economic depression. The yen revaluation damaged the competitiveness among particular industries and regions in which such industries agglomerated. The Temporary Law concerning Measures for Changing Business for Specific Small and Medium Enterprises was enacted in 1986 to specify the type of industries for SMEs and help them transform into other businesses (Small and Medium Enterprise Agency 2019). Furthermore, the Temporary Law concerning Measures for Small and Medium Enterprises of Specific Regions was enacted to promote the conversion of SME businesses in specific regions which were heavily influenced by the economic depression and yen revaluation. After the collapse of the economic bubble in 1992, the inflexibility of the Japanese economy became the subject of discussion. The startup rate decreased, the closure rate increased, and unemployment also showed an increasing trend. To promote startups and new SMEs in such circumstances, the Temporary Law Concerning Measures for the Promotion of the Creative Business Activities of Small and Medium Enterprises was enacted in 1995. This law was designed to help SMEs and individuals to inaugurate new businesses or invest in research and development without specifying a particular type of industry (Small and Medium Enterprise Agency 2019).

Development of Measures to Support Business Innovation In the 1980s and 1990s, the environment surrounding SMEs in Japan underwent major changes caused by the increasingly intense competition, progress in information technology (IT), and other factors in the global economy. Each SME had to

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change its direction from reduced costs (“cost-down”) competition, which has few future prospects, to management issues such as greater product quality and improvement in marketability. In such circumstances, to strengthen and support business innovation, the Small and Medium Enterprise Modernization Promotion Law was enacted in 1963. It encouraged industry-wide large-scale benefits (“scale merit”) and modernization of equipment. Similarly, the Temporary Law concerning Measures for Smooth Adaptation to Structural Changes in Economy by Advancement of Specific Small and Medium Enterprises to New Fields was enacted in 1993; however, its subjects of assistance were limited. These two laws were integrated to create the new Law on Supporting Business Innovation of Small and Medium Enterprises (1999).

Transition Period, the Second Stage (2000–Present) The Japanese government developed its SME policies under the former SME Basic Law enacted in 1963. The core points of the policies were “to remedy disadvantages in business activities” for modernizing SMEs across all types of industries. In the past, the focus was to pursue the scale merit of SMEs while developing uniform modernization policies for each industry. However, the environment surrounding SMEs has undergone various changes since then, and the conventional idea of SMEs and past policy tools no longer fit the actual situation of SMEs. These changes included the growth and maturation of the economy, diversification of consumer needs, IT revolution, and progress of globalization. These factors, among many others, reduced the importance of trying to eliminate the scale gap, increased the number of enterprises engaged in diverse businesses within an industry, encouraged a shift from mass production of standardized products to small-lot production of a variety of products, increased business opportunities, and intensified competition. In the current dynamic economic environment, SMEs are beginning to make the most of their advantages of “mobility and flexibility.” Additionally, the recent decline in the startup rate, which has dropped below the closure rate, is a provoking concern that may impede the metabolism and labor-absorbing capacity of the economy (Small and Medium Enterprise Agency 2019). In these circumstances, the Japanese government has recognized the importance of assisting the activities of all kinds of SMEs, from venture businesses to small enterprises. They are also encouraging SMEs’ self-help efforts based on a new concept of positively acknowledging the merits of SMEs. Accordingly, the government fundamentally revised and restructured conventional SME policies, including the SME Basic Law, during the “SMEs Diet” in December 1999.

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Fiscal Investment and Loan Program

The government absorbed large quantities of funds through the publicly administered postal savings and other types of insurance and pension systems and channeled them into public financial intermediaries. Furthermore, these funds became the principal resource for the government’s Fiscal Investment and Loan Program (FILP) activities, such as capitalization, lending, and underwriting of bonds of private sector industries (Suzuki 1989). The FILP is a system unique to Japan and a major component of the Japanese economy measured by its size relative to its economy and the financial system. The FILP works in close coordination with the general budget planning procedure and complementarity. This began to evolve in the mid-1880s in response to banking problems associated with the failure of the national banking system. It was primarily introduced to provide a stable financial and monetary framework that supports industrialization. Currently, it is managed by the Deposit Bureau of the Ministry of Finance (Trust Fund Bureau established in 1885) (Table 3.4).

Resource The major source of the FILP was the Postal Savings System, which is part of Japan’s postal system that offers deposit services at 24,700 post offices throughout Japan, with over 400 offices located in Tokyo. The post offices also sold life insurance. The collected funds (postal deposits and insurance premiums) were transferred to the FILP. The FILP combined the funds received with funds from other sources (Figs. 3.4 and 3.5) and distributed these funds to the government’s financial institutions, quasi-government corporations, special accounts, local governments, and special firms. In addition to private sector financial institutions, government financial institutions played a major role in the Japanese financial system during the post-war period, especially in the early 1950s, when Japan was reindustrializing, rebuilding, and recovering from the devastation of the war. Among the various entities, government banks were the most controversial because they were heavily dependent on funds provided by the Postal Savings System (PSS), accounting for the largest proportion of funds provided by the FILP system. They also directly competed with private banks and allegedly interfered with monetary policy (Cargill and Yoshino 2003). Previously, there were eleven government banks, but following several consolidations in 1999 and 2008, the number of government banks has been reduced.

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Table 3.4 Chronological table of Fiscal Investment and Loan Program Early Meiji Era

Reserve Funds Handling Bylaws

1876

#

1878

1885

Deposit regulation

Mid- to late Meiji era to Taisho era

#

1925

Deposits section deposit act

Around WWII

#

1946

Laws concerning special treatment of losses incurred by deposits section of the Ministry of Finance #

Under US occupation 1951

Trust fund bureau fund act

Miscellaneous incomes other than tax incomes accumulated as “savings” and later as “reserves” Deposits of funds to the government bond Bureau of the Ministry of finance, which was also responsible for their management Postal savings deposited with the government bond bureau for management Depositing funds to the Ministry of Finance was legalized, and the deposits section was set up. Initially, the ministry focused on custody business instead of investments Investments by the deposits section shifted from government bonds to bonds issued by industrial banks and special-purpose banks. In the early Taisho era, some loans became irrecoverable, similar to the Nishihara loan. Improvement of the deposits section system became necessary to ensure proper custody and management of funds Basic principles of “management in secure and efficient ways” and “for the benefit of the state and public” were clarified. “Deposits section fund management committee” was established With the country involved in the war, investment of funds gradually shifted to state-backed entities and war industries and focused on China. Therefore, the investments resulted in a huge loss Liquidation of assets and liabilities in the deposits section GHQ ordered that the recipient of the deposits section funds should be limited to the state and local governments For post-war restoration, demand for long-term funds arose among industrial circles • unified management of state funds • contribution to the promotion of public interest • investment of funds in secure and efficient ways (continued)

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Table 3.4 (continued) 1973

#

1987

Revision of trust fund bureau fund act

2001

# Fiscal loan fund act

The act on special measures on the long-term management of the trust fund bureau fund and postal life insurance reserve was enforced Development of interest deregulation and other changes in the economic and financial environment • the legal system for interest rates on deposits was amended and entrusted to government decree • foreign government bonds were added as targets for asset management of trust fund bureau fund Reflecting changes in the environment, the focus of policies shifted from industry to living environment. • elimination of the requirement that all postal savings and pension reserves were to be deposited with the trust fund bureau (the FILP reform) • introduction of policy cost analysis • enhanced information disclosure • market-based fund-raising

Source: Ministry of Finance, Fiscal Investment and Loan Program Report, p-11, 2018 100%

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Government-guaranteed bonds and government-guaranteed borrowed money Postal life insurance fund Withdrawn money, etc. Welfare pension and national pension Postal savings Special account for industrial investment Total FILP financial resources (left scale)

Fig. 3.4 Trend in total FILP resources and share of financial resources in trillion yen. Source: Budget Bureau, Financial Bureau and Information Systems Department, Policy Research Institute, Ministry of Finance

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10% 0%

Fig. 3.5 Trend in total FILP resources and share of financial resources in trillion yen (from 1955 to 1974). Source: Budget Bureau, Financial Bureau and Information Systems Department, Policy Research Institute, Ministry of Finance

Allocation of the Fund Funds obtained through the FILP are subsidized for the borrowers who cannot obtain sufficient funding from the private-banking system, thereby promoting a smooth flow of funds. This helps to solve social and economic problems and creates demand and employment. The program provides long-term, fixed, and low-interest loans and long-term risk money for projects. These funds are invested in the capitalization, lending, and underwriting of securities to various bodies that act in the public interest, such as special accounts of the government, public corporations, public banks, public finance corporations, public bodies, local government, and special public companies. These funds are utilized for various purposes such as cash flow support for SMEs and microenterprises, scholarship provision, and supplying stable resources and energy security, contributing to people’s life and economic growth in Japan. The allocation of funding across industries reflected the following two specific objectives of the FILP system: to encourage industries that had the potential to contribute to national development and to compensate industries adversely impacted by national development. Funding was directed to encourage industries that the government believed would contribute to national development, had the capacity to compete internationally, or were deemed important foundations for domestic investment and export-oriented growth. Accordingly, petroleum, metals, machinery, transportation, communication, and electricity supply depended heavily on governmentsubsidized bank credit. The FILP system was also used to compensate industries that

3

Government Financial Support for Small- and Medium-Sized. . .

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Table 3.5 FILP target cases during postwar reconstruction through high economic growth Major areas Housing

FILP agencies Japan public housing corporation

SME support

Small business finance corporation

Social capital development

Japan highway public corporation, Japanese National Railways, new Tokyo international airport authority

Industry

Electric power development co. Japan development Bank

FILP utilization cases Tama new town and Takashimadaira housing complex development Lending to Sony Corp., Kyocera Corp., and other companies in their startup and development phases Construction of Tomei, Meishin, and other expressways, Tokaido and Sanyo Shinkansen bullet train lines, and Narita international airport Construction of dams for electricity supply (Miboro dam), provision of long-term loans for basic industries (coal, steel, shipping, electricity)

Source: Ministry of Finance, Fiscal Investment and Loan Program Report, p-5, 2018 Table 3.6 FILP target cases during stable growth through economic bubble/post-bubble Major areas Housing

Life environment development, local development

FILP agencies Housing loan corporation Residential land development corporation Housing and urban development corporation Japan regional development corporation Water resources development public corporation

SME support

Small business finance corporation, People’s finance corporation

Social capital development

Japan railway construction public corporation, airport development special account, etc.

FILP utilization cases Loans for housing construction in Chiba new town development, etc. Urban redevelopment (Minato Mirai 21), academic new town (Tsukuba) development, etc. Iwaki new town and Nagaoka new town development, rural city redevelopment Construction of Naramata, Sameura, and other dams for developing and utilizing water resources Lending to SMEs and other firms that face difficulties in receiving loans from private financial institutions Construction of Nagano and other Shinkansen bullet train lines, offshore expansion and re-expansion of Tokyo international airport (Haneda airport)

Source: Ministry of Finance, Fiscal Investment and Loan Program Report, p-5, 2018

could not compete and consequently contribute to the general objective of mutual support pervasive throughout Japan’s economic institutions during the postwar period. During this period, the government’s financial institutions were established in terms of sector base and in line with the governmental development strategy, as shown in Tables 3.5 and 3.6.

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Target Fields The FILP system is wide-ranging in terms of funded projects and activities. The target fields can be grouped into the following six broad functional categories of FILP lending: (1) strengthening key industries, industry, and innovation, (2) trade/ economic cooperation, overseas investment, and loans, (3) regional development, agriculture, forestry, and fisheries, national land development, and local governments, (4) infrastructure, social capital, road construction, and transportation/communications, (5) modernization of low-productivity sectors, SMEs, and microenterprises, (6) improvement in living standards, housing, living environment, welfare/health, and education (Cargill and Yoshino 2003; FILP report 2018). As illustrated in Fig. 3.6, supporting SMEs and the modernization of low-productivity sectors has been a continuing goal of the FILP system. Allocation of FILP lending to SMEs has gradually increased over time, accounting for 8 to 19%. It reached its highest point in 1981, with lending to SMEs accounting for 19.6% of the fund received. Furthermore, this amount gradually decreased to 12% by 1999. In the postwar reconstruction period, FILP prioritized nurturing important industries (coal, steel, shipping, electricity, etc.). When the economy recovered and started to grow rapidly, the focus gradually shifted to housing purchases and projects that could improve living standards, especially to infrastructure development, where 100% Foreign trade and economic cooperation Industry and technology

90% 80%

Local area development

70%

Transport and communication Road

60%

National land security and disaster restoration Agriculture forestry and fisheries Small and medium enterprises Culture and education

50% 40% 30% 20%

Welfare

10%

Life environment improvement Housing 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999

0%

Fig. 3.6 Trend in the distribution of FILP funds by target fields. Source: Budget Bureau, Financial Bureau and Information Systems Department, Policy Research Institute, Ministry of Finance

3

Government Financial Support for Small- and Medium-Sized. . .

107

Japan was lagging behind Western countries. Furthermore, FILP supported SMEs and public works, and it became an important goal. The number of FILP agencies increased to cover these areas. Industrial development matured, and financing this sector was no longer a major area of FILP allocation. It constituted less than 6% of the funds allocated after 1970. Therefore, following the recovery, priority industries had strengthened and could develop by themselves. Thus, FILP funds were primarily allocated to “weak” sectors and regions for realizing “balanced” development. From the late 1970s to the early 1980s, FILP financing for housing and SMEs expanded. Housing, SME financing, and life environment development (urban development) accounted for 60% of the FILP. The FILP was used for the largescale new town and academic development in major urban regions, local industrial base construction, and other areas where profitability was not necessarily high. In the 1990s, following the burst of the economic bubble, FILP financing for housing expanded and accounted for one-third of FILP, as public works were promoted to stimulate the economy. As the economy matured with progress in the market mechanism development, the government implemented thoroughly reformed the FILP system in 2001. In addition to that, other issues regarding FILP fund disbursement, such as financial resources collected without actual policy requirements, and lack of efficient management, were highlighted. Subsequently, the government reformed FILP projects from 2004 to 2005 to complement private sector projects, political needs, and financial soundness. Furthermore, FILP fundraising was shifted to the market through the issuance of FILP bonds, enabling efficient fundraising to meet the demand. Therefore, the initial FILP size for the fiscal year 2008 shrank to approximately one-third of the peak on a flow basis. Moreover, a policy cost analysis was introduced.

FILP and National Budget The FILP is determined together with the national budget each fiscal year. The trend in FILP budget and its share in general account expenditure is shown in Fig. 3.7. The government offices with jurisdiction over FILP agencies compile budget requests to the General Account and FILP agencies. These requests are submitted to the Ministry of Finance. Later, the FILP Plan is formulated along with the budget. The Financial Bureau of the Ministry of Finance screens the requests of FILP agencies while hearing opinions from the Fiscal System Council. In screening the requests, the bureau utilizes policy evaluations and other approaches, such as examining the necessity of the policy, whether it will complement the private sector, and if the provided funds will be securely repaid. Until the fiscal year 1972, Diet approval was necessary for only a portion of FILP, such as the portion that was accounted for under the General Account of the budget and special accounts within the budget. These included lending and investment by Industrial Investment Special Account and the special accounts concerning government-guaranteed bonds and loans. The largest portion of the funds, which

108

N. Tsaschikher 100

70.0%

90

60.0%

80 50.0%

70 60

40.0%

50 30.0%

40 30

20.0%

20 10.0% ¥0.3

10

0.0%

FILP Budget

General account Budget

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

0

FILP/General account (left scale)

Fig. 3.7 FILP budget and its share in general account expenditure in trillion yen. Source: Budget Bureau, Financial Bureau and Information Systems Department, Policy Research Institute, Ministry of Finance

came from Trust Fund Bureau Funds and Postal Insurance Annuity Assets, did not require Diet approval. However, after 1972, the scale of the FILP expanded, and its influence on the national economy greatly increased. From the fiscal year 1973, a new law was established in this regard, the Law Concerning Special Measures for Long-Term Investment of the Trust Fund Bureau Funds and Funds Accumulated from Postal Annuities and Postal Life Insurance Annuity (enacted in March 1973). Under the new law, the investment of the Trust Fund Bureau Funds and Postal Insurance Annuity Assets for periods of over 5 years required the government to obtain Diet approval for the amounts intended for each use under the general provisions of the budget, for special account expenditures. Accordingly, each element within the FILP should be listed in the budget at some point and is a part of the budget plan that must be approved by the Diet (Suzuki 1989).

3.2.5

Government Financial Institutions for SMEs

As mentioned previously, the Small and Medium Enterprise Agency is the main body that formulates SME policy. It was established in 1948. Government institutions that were in charge of the financial support of SMEs were established in the 1950s. Over the years, there have been several consolidations in governmental institutions according to the changes in the economic structure and development state, as illustrated in Fig. 3.8. Therefore, the JFC is the only institution that currently holds all rights and obligations of the consolidated agencies.

3

Government Financial Support for Small- and Medium-Sized. . .

109

People’s Finance Corporation (Jul. 1949) Environmental Sanitation Business Financing Corporation (Sep. 1967)

National Life Finance Corporation (Oct. 1999)

Agriculture, Forestry, Fisheries Finance Corporation (Apr. 1953)

Japan Finance Corporation (Oct. 2008)

Japan Finance Corporation for Small Business (Aug. 1953) Small Business Credit Insurance Corporation (Jul. 1958)

Japan Small and Medium Enterprise Corporation (Jul. 1999)

Fig. 3.8 Chronological consolidations of the SME financing institutions. Source: author’s description

People’s Finance Corporation During the post-war period, private banks continued to focus on lending to large enterprises and corporations. Small business firms were unable to raise funds from the market and were forced to resort to unofficial or black markets for needed funds. Small firms found that credit was further restricted in 1948, as the Dodge Plan austerity programs were initiated to deal with the triple-digit inflation and large government deficits. The lack of credit for SMEs combined with the high inflation rate imposed extreme hardships on SMEs. To reduce the pressure on SMEs, the People’s Finance Corporation was established in 1949. It extended loans to SMEs facing difficult financial situations. Furthermore, the People’s Finance Corporation initiated education loans to support the parents of high-school and university students.

Environmental Sanitation Business Finance Corporation Sanitation deficiencies posed a serious problem for many years after the war, especially in the areas of public baths, laundries, small restaurants, butcher shops, etc. Families and small firms operated these facilities, and their small-scale operation made it difficult to modernize their equipment. The Environmental Sanitation Business Finance Corporation was established in 1967 to maintain and improve

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public sanitation by providing funds to these facilities and upgrading their sanitation equipment.

National Life Finance Corporation In September 1997, the Cabinet decided on the Reorganization and Rationalization Plan for Special Public Corporations by integrating the People’s Finance Corporation with Environmental Sanitation Business Financing Corporation. Therefore, in accordance with the partial revision of the People’s Finance Corporation Act, the People’s Finance Corporation changed its name to the National Life Finance Corporation and inherited all rights and duties of the dissolved Environmental Sanitation Business Finance Corporation in 1999.

Agriculture, Forestry, Fisheries Finance Corporation Immediately following the end of the war, inflation and the subsequent Dodge Plan made it difficult for farmers to obtain private sector financing. Additionally, the land reform implemented by the Allied Occupation resulted in a large number of small farms. This led to a further increase in the agricultural demand for private sector loans. To tackle these issues and provide long-term loans, the Agriculture, Forestry, Fisheries Finance Corporation was established in 1953.

Japan Finance Corporation for Small Business Sogo3, which are the regional banks, and shinkin4 banks were established in 1951 to assist SMEs because the large private city banks primarily focused on large enterprises and corporations. Despite these new institutions, many SMEs had difficulty obtaining credit, especially long-term credit. The Japan Finance Corporation for Small Business was established in 1953 using government equity to provide long-

3

Originally, member banks were established as joint stock companies under the Sogo Bank Law of 1951 and were referred to as “Sogo Banks.” However, over the years, the types of business conducted by the Sogo and ordinary commercial banks became similar. Therefore, the Sogo banks were quickly converted to regional banks, which were classified as ordinary commercial banks from 1989 onward. 4 Shinkin banks were founded in 1951. They were created to serve some of the same functions as credit unions; however, they can accept deposits from nonmembers without limitation and give loans to “graduated” members. They are cooperative regional financial institutions serving small and medium enterprises and local residents. Anyone who lives, works, or has an office in the region served by the bank can become a member. However, companies with over 300 employees are prohibited from membership.

3

Government Financial Support for Small- and Medium-Sized. . .

111

term funding to SMEs whose assets were less than 100 million yen or employed fewer than 300 employees.

Small Business Credit Insurance Corporation To strengthen the financial weakness of SMEs, such as the lack of security, the complementary credit support system was initiated with the Small and Medium Enterprise Credit Insurance Law in 1950 and the Credit Guarantee Association Law in 1953. In 1958, the Small Business Credit Insurance Corporation was established. In 1984, the credit insurance operations for SMEs and loan operations were transferred from the Small and Medium Enterprise Agency to Small Business Credit Insurance Corporation, and it took over machinery credit insurance operations from the Ministry of International Trade and Industry. Following the Reorganization and Rationalization Plan for Special Public Corporations, Japan Small and Medium Enterprise Corporation (JASMEC) was established in 1999 and took over the operations of Small Business Credit Insurance Corporation.

Japan Finance Corporation Subsequently, in October 2008, JFC was established based on the Japan Finance Corporation Act. Excluding the assets inherited by the government, the JFC inherited all rights and obligations of the National Life Finance Corporation, Agriculture, Forestry, Fisheries Finance Corporation, Japan Finance Corporation for Small Business, and Japan Small and Medium Enterprise Corporation. These corporations are now operating together as an independent unit of the JFC. In conclusion, the “most important” feature of the FILP system was supporting “weak” sectors and regions for the Government of Japan, therefore successfully avoiding bottlenecks and leading to “balanced” development. Additionally, it had other important features. First, the FILP budget is large by any reasonable standard and regarded as a “second budget.” As illustrated in Fig. 3.7, FILP’s budget proportion to General Account gradually increased over time, from 29.4% in 1955 to 59.4% in 1999. The second most important feature is that the FILP system reduces the tax burden. It uses the funds raised from the public. The largest portion of the FILP depends on the funds on which interest and principal must be repaid, such as postal savings, welfare pensions, and national pensions. For that reason, the investors cannot ignore the profitability aspect and must invest in undertakings that promise a reasonable profit and have certainty of return and redemption. Moreover, their investment must serve public goals, such as the improvement of national welfare and economic or social development (Suzuki 1989). Third, the FILP system covers almost every economic sector, carrying out a resource allocative function by traveling through two particular routes. The first route is the supply of FILP funds to industrial undertakings such as the Japanese

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National Railways or Public Housing Corporations and thereafter supply by these institutions the goods and services they produce to the population. The second route is the supply of FILP funds to financing institutions such as the Japan Development Bank or People’s Finance Corporation and the subsequent lending of such funds to private sector enterprises. Although socially beneficial, these enterprises cannot respond to needs using only private sector financing for reasons such as a long repayment window, high risk, and low profitability (Suzuki 1989). Fourth, along with resource allocation, the FILP system also functions as a policy tool to weaken the effect of long-term financial market failures. Therefore, because of the flexible system of additions or carry-overs in the FILP budget, it is possible to either speed up or slow down project implementation. Therefore, compared with the budget of the General Account, FILP funds are easier to use in counter-cyclical policy because of their mobility and flexibility. Finally, the FILP budget is determined in relation to the national budget; consequently, the FILP system plays an important role in Japan’s political institutions because the distribution of funds plays a key role in maintaining and enhancing political power (Cargill and Yoshino 2003). On the one hand, this program may have been successful because of the people’s trust in their government. Owing to public trust, the Government of Japan was able to raise massive funds from the public. On the other hand, the government has been wisely and successfully using these funds to implement the objectives of the national development policies at the right time.

3.3 3.3.1

Characteristics and Development State of the SMEs in Mongolia Definition of SMEs

In Mongolia, SMEs are measured by their number of employees and annual sales and revenue specified in the basic SMEs act “Law on Small and Medium Enterprises,” which was adopted on June 27, 2007. Table 3.7 illustrates the criteria of SMEs based on their industry type, employee number, and annual revenue amount, following the basic SMEs act. The “Law on Small and Medium Enterprises” of Mongolia defines a small and medium enterprise as an entity under the following circumstances: (1) it has no more than 199 employees with 1.5 MNT billion income from production and trading business, (2) it has no more than 149 employees with 1.5 MNT billion income from the service industry, and (3) it has no more than 19 employees with MNT 250 million income from production, trading, and service industries.

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Table 3.7 SME classification in Mongolia Classification Small Medium

Industry type Service industry Manufacturing Service industry Wholesale Manufacturing Retail business

Employees 9 or fewer 19 or fewer 49 or fewer 149 or fewer 199 or fewer 199 or fewer

Annual sales revenues Up to MNT 250 million Up to MNT 250 million Up to MNT 1.0 billion Up to MNT 1.5 billion Up to MNT 1.5 billion Up to MNT 1.5 billion

Source: Ministry of Finance, Fiscal Investment and Loan Program Report, p-5, 2018

3.3.2

Outline of the SME Sector in Mongolia

In Mongolia, SMEs account for 86.0% of all companies, 53.9% of all employees,5 more than 17% of value-added, and 31.2% of GDP.6 Nurturing and growing this important segment could help Mongolia diversify its economy away from the dependence on mining-based exports. As SMEs create the most value-added across non-extractive sectors, their development is critical to achieving the government’s top priorities, diversifying economic activity, and reducing exposure to commodity price fluctuations. Without this vital sector, Mongolia would be constantly limited by the commodity cycle and left with only the hope of raising the commodity prices. In addition, this sector is also considered a major base for employment creation and income generation. By supporting this sector, the government could achieve its other developmental goals, such as the sustainable development goals; it could also accomplish the following goals: (1) reduce income inequality and have 80% of the population in middle- and upper middle-income classes and (2) be ranked among first 40 countries by the Doing Business Index, and among first 70 countries by the Global Competitiveness Index. Therefore, policies that build educational and financial capacity could be the key to diversifying the economy. However, it is a major challenge for executing bodies to build a policy that encourages competitiveness among SMEs, boosting their investment and financing opportunities and human resources capacity. We will discuss this topic later in the study. Table 3.8 illustrates the number of active entities classified based on the number of employees from 2013 to 2018, as registered in the business register by the National Statistics Office of Mongolia. The annual increase in the number of entities with one to nine employees is much smoother than others, approximately 10.2% a year. Regarding the number of entities with 10–19, they show employees more ups and downs over time. Considering the number of entities with 20–49 employees, their annual average rate of increase is 3.7% except for the sudden increase of 22.1%

5 6

National Statistics Office of Mongolia web page: www.1212.mn Annual Report of SME Development Fund (2017)

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Table 3.8 Active entities by the number of employees Number of employees 1-9 10-19 20-49 50+ Total

2013-IV 45,684 4452 2990 1803 54,929

2014-IV 50,282 4603 3134 1824 59,843

2015-IV 52,402 5680 3827 2392 64,301

2016-IV 59,794 5952 3979 2457 72,182

2017-IV 65,983 6094 4050 2458 78,585

2018-IV 73,929 5164 4171 2485 85,749

Source: Ministry of Finance, Fiscal Investment and Loan Program Report, p-5, 2018

90,000 78,585

80,000

72,182

70,000

64,301 59,843

60,000 50,000

57,276

54,929 47,722

50,431

52,678

43,624

40,000 30,000 20,000 10,000 2013

2014

Total active entities

2015

2016

2017

Number of SMEs

Fig. 3.9 Trend in number of SMEs (from 2013 to 2017). Source: data from 2013 to 2015 are from the Ministry of Food, Agriculture, and Light industry, and data from 2016 and 2017 are from the Business register database, National Statistics Office of Mongolia

in 2015. The number of entities with 50+ employees increased by an average of 1.3% a year, except for the sudden increase of 31.1% in 2015. As of 2017, the number of active entities was 78,585, of which 57,276 or 72.9% are classified as SMEs, as shown in Fig. 3.9. This sector employs approximately 800,000 people. If we classify active entities by their number of employees, 97% of all entities employ fewer than 50 people, which means that the majority of entities operate on a small scale. SMEs employing up to 9 people account for 86.2% of all SMEs, those with 10–19 employees account

3

Government Financial Support for Small- and Medium-Sized. . .

2018-IV

115

86.2%

6.0%4.9%

2017-IV

84.0%

7.8% 5.2%

2016-IV

82.8%

8.2% 5.5%

2015-IV

81.5%

8.8% 6.0%

2014-IV

84.0%

7.7% 5.2%

2013-IV

83.2%

8.1% 5.4%

0%

10%

20%

1-9

30%

40%

10-19

50%

60%

20-49

70%

80%

90%

100%

50+

Fig. 3.10 Share of active entities by the number of employees (from 2013 to 2018). Source: business register database, National Statistics Office of Mongolia

for 6%, those with 20–49 account for 4.9%, and entities with more than 50 employees account for 2.9% of all SMEs in 2018 (Fig. 3.10).7 Most large companies operating in Mongolia are concentrated in the mining sector, with less than 1% of SMEs classified as mining and quarrying entities. However, SMEs are the main players in all other economic sectors, especially agriculture, manufacturing, construction, and wholesale and retail trade, as shown in Fig. 3.11. As of 2018, wholesale and retail trade accounts for 39.2%, construction for 7.6%, manufacturing for 7.2%, and agriculture for 4.5% of all SMEs, respectively.

3.3.3

Overview of Governmental Policy for SMEs in Mongolia

After the transition to a market mechanism in the early 1990s, the Government of Mongolia and international organizations started implementing projects and programs to improve the SME sector and its development. During that time, the scope and number of projects were limited. Under the agreement “Agriculture from the United States to Mongolia on Agriculture Products” between the Government of 7

There is not enough data on employment in SME sector. Thus, we use National Statistics Office’s business register database.

116 100% 90%

N. Tsaschikher

9.6%

9.6%

9.6%

9.7%

2.9% 3.5% 3.1%

2.8% 3.6% 3.0%

2.8% 3.6% 3.0%

2.7% 3.8% 2.8%

4.9%

4.6%

3.9%

4.0%

10.8%

10.2%

3.1% 3.5% 3.7%

2.8% 3.8% 3.4%

80% 70%

4.5%

4.1%

4.3% 4.0%

4.9% 3.7%

4.9%

4.0%

60%

Education

Real estate, renting and business activities

Financial intermediation

Transport, storage and communications

50%

Hotels and restaurants

39.0%

37.8%

37.4%

38.1%

38.4%

39.2%

40%

Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and household goods Construction

30% 20%

6.4%

10%

7.6% 0.6% 5.9%

0%

Health and social work

7.9%

8.1%

8.2%

7.8%

7.6%

Manufacturing

8.3%

8.0% 0.9% 5.3%

7.3% 0.8% 5.0%

7.3% 0.8% 5.0%

7.2% 1.0% 4.5%

Mining and quarrying

0.9% 5.8%

Agriculture, hunting and forestry

2013-IV 2014-IV 2015-IV 2016-IV 2017-IV 2018-IV

Fig. 3.11 Active entities with 1–49 employees by economic activities. Source: business register database, National Statistics Office of Mongolia

Mongolia and the United States, the Ministry of Trade and Industry (formerly known as the “Small and Medium Enterprises Agency”) was established in 1993, to support SMEs that sold ghee and butter.8 This was the first ever known governmental support scheme for SMEs. The Government of Mongolia has been progressively implementing policies since the early 2000s. Table 3.9 illustrates the policy actions and measures implemented by the Mongolian Government since 1994. As illustrated in Table 3.9, there are two kinds of support schemes for SMEs, namely, domestic and international support. The government has been supporting SMEs by establishing a regulatory framework, establishing policy implementation through SME agencies (currently, SME agencies are working under the Ministry of Food, Agriculture, and Light Industry), and implementing training and financial assistance programs. Additionally, the Government of Mongolia is providing tax and VAT incentives to SMEs. However, the concept of industrial policy was only recently established, around 2014. Before 2014, the government was focused on priority projects and regarded the following sectors as important sectors: • Heavy industrial sectors such as metal and coking coal utilize the mining resources.

8

Annual Report of SME Development Fund (2017)

3

Government Financial Support for Small- and Medium-Sized. . .

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Table 3.9 Policy actions of the Government of Mongolia for SME development Year 1994 1996 1997 1999 2002 2005 2006 2007 2008 2009

2010

2011 2012

2014

2015

2016

2017 2019

Activities Government of Mongolia started implementing the “poverty reduction project” in collaboration with the World Bank Savings and credit association was established. UNDP started implementing the “micro-start project” through six NGOs in Mongolia First nonbank financial institution was established “Law on non-bank financial institution” was enacted Government of Mongolia announced 2005 as a year of “microfinance” and commenced the “SME development program” Financial regulatory Commission of Mongolia was established to regulate the nonbanking sector “Law on small and medium enterprises” was passed Government established a SME agency in charge of implementing SME policies Government has established a fund for SME development. To promote SME development and improve the business climate, the government announced 2009 as a year of supporting industrial production and commenced “guidelines for developing industrial production in the local area” Government of Japan started financing 75 billion MNT for developing SMEs in Mongolia, USAID approved $25 million credit for SMEs, and European Commission granted €four million to increase competitiveness in the SME sector. The government announced 2010 as a year of improving the business environment The Soum (local administration unit) development fund was established to promote local SME development. The government announced 2011 as a year of supporting employment “Law on credit guarantee fund” was passed in 2012 to provide a 60% credit guarantee for small- and medium-sized enterprises that do not have sufficient collateral. The government announced 2012 as a year of supporting household development The SME Development Program (2014–2016) was commenced by the government. In accordance with the implementation of the program policy, actions were taken to enhance SMEs’ competitiveness and create employment “Law on supporting manufacturing industry” was passed. State policy on industrialization was commenced. The purpose of the policy was to motivate industrialization and services that used advanced techniques, high technology, and competitiveness and develop the industrial sector as the priority sector that provides sustainable development for Mongolia Supporting domestic production by customs and tax policies according to the government of Mongolia’s action plan for 2016–2020. The government announced 2016 as a year of “promoting domestic production and sales” The issue of amending SME basic law was passed in the great Khural state (parliament of Mongolia) The SME Development Program (2019–2022) was commenced by the government. Almost all objectives were similar to that of the previous program. However, the following two measures were added to enhance regional SMEs’ competitiveness by organizing the “One village-One Product” campaign and developing the consulting service

Source: author’s description

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• Agricultural sector: government subsidies to crop, livestock, and agro-processing products such as meat, dairy, and textiles, including wool and cashmere. • Infrastructure projects, including power plants, roads, water supply, and construction. • New projects (except for those in the mining sector) such as IT, tourism, and environmental protection. The government has formulated around 400 development policies and plans since 1990. Among these, more than 100 policies and plans have been effective (Data collection survey on business environment and investment promotion in Mongolia, 2017). However, current development policies and plans are not consistent with each other. The goal and measurement of the policies established in general aspects are not clear, and insufficient financial resources make it more difficult to achieve the determined goals. As mentioned previously, plenty of non-mining-related projects have been established. Moreover, approximately 80% of the foreign direct investment between 1993 and 2012 was used for development projects related to mining, and only 1% of the amount was utilized in manufacturing projects. Thus, establishing an industrial policy that strengthens the manufacturing industry and other non-mining sectors was an urgent issue faced by the government. Since 2015, the government has enacted a new law, “Law on Supporting Manufacturing industry,” to support export-oriented and import-substituting companies. Additionally, the State Policy on Industrialization was commenced in June 2015. The policy’s purpose was to motivate industrialization and other services that use advanced techniques and technology and incentivize competitiveness. It aimed to develop the industrial sector as the priority sector that provides the sustainable development of Mongolia. Furthermore, the government enacted the “Law on development policy and plan” in December 2015.

State Industrial Policy The industrial sector accounts for a major part of the economy that is based on the relationship between the state, science, and private sector. Thus, the purpose of the policy is to develop export-oriented, high-technology, and competitive industrialization and services. The goal of the State Industrial Policy of Mongolia is to establish integrated activities for knowledge-creation and skill-driven manufacturing of highvalue-added products and services in the agricultural raw materials and mining industries. The government formulated the following main principles to implement the above purpose: • To promote a healthy, safe, and environment-friendly manufacturing industry. • To support the industry that manufactures export-oriented, import-substituting, and competitive final products, maintaining the national and international standards. • To promote and develop an economically efficient manufacturing industry based on advanced techniques, high technology, and innovation.

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Government Financial Support for Small- and Medium-Sized. . .

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Table 3.10 State industrial policy projects Projects Budget (mL $) Total number of projects411 Top priority To approve To consider Low priority Production type

Heavy industry 44,628.38 (99.5%) 107

Light industry 164.2 (0.4%) 41

16 13 7 71 – Oil production. – Coke and coal chemical. – Copper smelting. – Steel production. – Cement industry.

19 4 13 5 – Leather and hide production. – Cashmere production. – Wool. – Wood.

Small and medium industry 65.1 (0.1%) 83 23 – 17 43 – Dairy production. – Construction material production. – Food production. – Biopreparations. – Information technology.

Source: Ministry of Food, Agriculture and Light Industry, 2015

• To base the industrial sector development on effective state institutions, science, and private sector collaboration. • Provide equality and fair competition to stakeholders of industrial sectors. The government has listed a total of 231 projects. Among these, 58 are considered a top priority, with a total budgeted cost of 44.9 billion dollars. However, as illustrated in Table 3.10, only 0.1% of the total budget is allocated to the SME sector.

Taxation The Mongolian government’s Action Plan for 2016–2020 has stated that it will be “supporting domestic production by customs and tax policies.” The following three main tax changes have been included within this framework: • Regional discount tax: In remote AIMAG9 and soums located more than 550 km from the capital city of Ulaanbaatar, a tax exemption of 50% is provided. For soums more than 1000 km from the capital city, an income tax exemption of 90% is provided, starting from January 1, 2017. • Imposing a 1% income tax on SMEs with an annual income of up to 1.5 billion MNT, including the following four sectors: food production, manufacturing of

9 Administration unit of Mongolia. Officially, Mongolia is divided into three administrative tiers, with different types of administrative units in each tier. The first tier is divided into 21 provinces and the capital city, and the second tier is divided into 332 soums and 9 districts, as of 2018. The second tier is further subdivided into bags and subdistricts.

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textile and apparel, manufacturing of construction materials, and crop and livestock production. • Exemption from customs tax is provided to SMEs producing equipment and spare parts. According to the Government Resolution No. 168 of 2017, SMEs producing equipment and spare parts are exempted from paying customs duty until 2019.

International Support Regarding international support, a wide range of international institutions have shown the initiative to build SMEs and entities in the microcredit sector. These organizations provide financial support and technical assistance for strengthening management capacity and technology and producing more value-added products. In 1998, the UN began to support microcredit in Mongolia through its MicroStart program. Accordingly, XacBank emerged from this program. Since then, a number of nations, such as the Netherlands, Japan, Germany, and the United States, have offered credit guarantee schemes. The European Union (EU) has also provided funding. These programs allow banks to provide loans at competitive rates and over longer tenors to borrowers who lack security. Along with financial assistance, they also provide a wide range of assistance to SMEs. They help relevant organizations and institutions in developing capabilities important to SMEs (such as the Chamber of Commerce, banks, the Mongolian Management Consulting Institute, and the Institute of Finance and Economics), research and training, consulting, and direct advice to women and young entrepreneurs. Currently, 12 international projects are being implemented. In January 2012, the European Bank for Reconstruction and Development (EBRD), along with the EU, began providing a wide range of assistance to SMEs in a 5-year, €3.8 m program. This program provides assistance in improving procurement procedures so that small enterprises have a better chance of securing government contracts. One product of this initiative is an online course offered by the Mongolian Banking and Finance Academy. It teaches bank loan officers about the best international practices in lending to SMEs. The training series went live in late October 2013. The EBRD is also working in cooperation with the International Finance Corporation (IFC), the Swiss State Secretariat for Economic Affairs, and the Mongolian Bankers Association (MBA) to encourage secured transaction reforms. These reforms will help develop the legal infrastructure to allow loans to be backed by movable collaterals. Hopefully, a registry can be created for collaterals of this type (such as farm equipment) so that current information on an asset can be accessed to prevent competing claims against the same asset. Potential borrowers tend to be relatively poor, predominantly agricultural, and sometimes nomadic. Therefore, they often have to pledge movable assets. It is also important to note that, historically, it has not been easy to secure a loan using land. This is because the market for land is

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new, and property rights are not well-established. Moreover, a clear title is not always easy to prove.

3.3.4

Current Basic Challenges for SMEs

Over the years, the presence of an SME financing system has emerged, combining various public and private programs. However, on the demand side, small firms and entrepreneurs struggle to access financial services and face numerous challenges. A considerable amount of surveys conducted by several international organizations reveal the current challenges faced by Mongolian SMEs. The challenging factors can be categorized as supply-side or demand-side problems. The main supply-side issues include unattractive and costly loans, high and universal requirements for immovable assets as collateral, and overly complicated administrative procedures. On the other side, low financial literacy, low-level of profitability, and lack of collateral are the main demand-side problems. Moreover, the lack of coherent and accessible data on SMEs and their financing is an additional general issue faced by policymakers (EBRD and World Bank 2015). According to the results of the survey on “Small and medium enterprises’ development and financial access” conducted by the Central Bank of Mongolia (BOM),10 social and political conditions (-0.59), macroeconomics (-0.40), financing (-0.52), and legal and institutional conditions (-0.35) are the most challenging factors in the business environment (BOM, 2018). The latest Business Environment and Enterprise Performance Survey (BEEPS V), a joint survey by the EBRD and World Bank, also found that access to finance was the most frequently cited barrier for SMEs (31% of respondents), followed by tax rates (12%) and inadequate education of the workforce (9%). In addition, 56% of Mongolian SMEs do not apply for loans because they are discouraged by credit conditions. The OECD peer review note “Enhancing access to finance for micro, small and medium-sized enterprises in Mongolia” outlines five main challenges Mongolian SMEs face in accessing loans. These include the following: (1) the lack of data for sound policymaking in favor of SMEs, (2) loans that are not adapted to SMEs’ needs, (3) pervasive and restrictive collateral requirements, (4) cumbersome application processes, and (5) poor financial literacy of SMEs. The following section closely examines some challenges faced by SMEs.

10

The Central Bank of Mongolia has annually conducted a nationwide survey on small and medium enterprises’ development and financial access from 2011, to better understand the current development, business characteristics, challenges, and financial access and develop policy recommendations for the Government of Mongolia. In 2018, the survey covered 1922 SMEs operating in Mongolia.

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60 50 40 30 20 10 0

Fig. 3.12 Lending rate in Mongolia. Source: International Monetary Fund, International Financial Statistics, and data files

Access to Finance as a General Issue for SMEs Generally, access to financial services in Mongolia appears to be high when measured using composite indicators of financial inclusion. Approximately 93.0% of the Mongolian population over 15 years has a bank account. Moreover, 21.9% of the population demonstrates high usage of mobile banking, and the number of bank branches per 100,000 adults is 70.3 (ranked third among 264 countries). This shows that the financial inclusion in Mongolia is on par with developed countries. However, the drawbacks include a lower amount of savings, high-cost financial resources, and the lack of knowledge related to savings and insurance, according to the Global Findex Database. Similar to many other countries, access to finance for SMEs remains a major issue. SMEs face a wide range of difficulties in accessing finance, including highinterest rates, collateral requirements, size and maturity of loans, and complex application procedures. Moreover, according to the BEEPS survey, SMEs’ lack of financial knowledge hinders banks’ ability to trust SMEs and extend credit based on the interviews with financial institutions operating in the market (EBRD and World Bank 2015). In the abovementioned survey, high interest rates were found to be a major financing obstacle in Mongolia. However, they have substantially dropped since the late 1990s, as illustrated in Fig. 3.12. Currently, there are 13 commercial banks operating in Mongolia. Among these, 1 is public, and 12 are privately owned. The largest five banks, namely, the Khan Bank, Trade and Development Bank, Golomt Bank, XacBank, and State Bank, hold approximately 90% of the total deposits in the country and have administered 85% of the total loans in the banking system. They operate 1512 bank branches nationwide; among these, one-third of the branches are located in Ulaanbaatar city. Only the Khan and State Banks have branches throughout the country. However, the

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Table 3.11 Number of branches and units of commercial banks

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Bank name Arig Bank Bogd Bank Golomt Bank Capital Bank* Capitron Bank Credit Bank National Investment Bank XacBank Khan Bank Trade and development Bank Chingis Khaan Bank State Bank Transbank Ulaanbaatar city bank Total

Branch 1 4 33 23 20 0 9 35 32 52

Settlement center 13 0 45 18 10 0 0

Currency exchange center 1 3 25 3 9 0 4

Other units 1 0 23 0 0 0 0

Total number of branches 16 7 126 44 39 0 13

44 502 3

8 3 1

0 1 44

87 538 100

0

3

0

5

35 1 9

446 1 7

11 0 25

1 0 1

493 2 42

256

1089

96

71

1512

2

Note: *Capital bank dissolved on April 8, 2019 Source: Central Bank of Mongolia, as of December 31, 2018

Golomt, XacBank, and Trade and Development Banks operate only in the central area of the provinces (Tables 3.11 and 3.12). As shown in Table 3.13, the number of SMEs registered as borrowers comprises 6776 or 12.9% of the total 52,276 SMEs. As of 2018, they account for 19.9% (including 44,481 individual microbusiness loans) of the total outstanding loans. In addition to commercial bank loans, the Government of Mongolia and other international organizations are implementing a loan program to support SMEs from large cities and rural areas under several projects. These subsidized concessional loans are extended through commercial banks, and their interest rates are lower than the market rate, ranging from 3 to 10%, with a longer loan period. Furthermore, some projects do not require collateral, and some provide loan guarantees for businesses that lack the necessary collateral. However, the share of the project loan amount to total outstanding loans for SMEs was 25.8%, constituting 32.4% of the total borrowers, as illustrated in Table 3.14 (row number 2, except for small business loans for individuals). Additionally, the share of project loans for SMEs to the total outstanding loan was 3.5% in 2018. Considering that the commercial bank penetration rate in the local area is very low, access to subsidized loans for local SMEs is limited. Moreover, there have been incidents involving enormous amounts of project loans. Specifically, loans from the SME Development Fund and Soum Development Fund were illegally extended to acquaintances of politicians,

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Table 3.12 Number of branches and units of commercial banks by province

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Province Ulaanbaatar Arkhangai Bayan-Ӧlgii Bayankhongor Bulgan Govi-Altai Govisümber Darkhan-Uul Dornogovi Dornod Dundgovi Zavkhan Orkhon Uvurkhangai Umnugovi Sukhbaatar Selenge Tӧv Uvs Khovd Khuvsgul Khentii Foreign country Total

Branch 141 4 6 5 4 4 3 10 10 6 3 4 7 5 8 4 9 4 5 5 5 4 0

Settlement center 241 40 33 47 34 42 6 35 42 35 31 54 28 48 49 31 55 55 44 41 53 45 0

Currency exchange center 67 0 0 2 0 0 0 1 8 2 0 0 1 1 5 0 5 0 0 3 0 1 0

Other units 66 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3

Total number of branches 515 44 40 54 38 46 9 47 60 43 34 58 36 54 62 35 69 59 49 49 58 50 3

256

1089

96

71

1512

Source: Central Bank of Mongolia, as of December 31, 2018 Table 3.13 Access to Finance Statistics 2018

Total outstanding loans (trillion MNT) Average lending rate for loans in MNT Central Bank policy rate Deposit rate Outstanding loans to SMEs (% of total loans) –Establishments –Individuals* Number of SME borrowers: –Establishments –Individuals*

17,082.4 16.9% 11% 12.0% 19.9% 12.9% 7.0% 6776 44,481

Note: * BOM classifies the total outstanding loans to households (individuals) as mortgage loans, SME loans, consumer, salary, pensions, and other loans. SME loan data cover outstanding loans to households and private nonfinancial corporations Source: Central Bank of Mongolia

111,894 423,470

179,818 507,698 1,195,502 36,125 52,452 70,071 501,489

Outstanding loan 2,207,312 129,767 513,731 341,767 534,531

4485 23,651

32,010 80,124 79,205 3766 9106 4804 33,393

Of which: project loan 568,561 71,068 269,823 30,148 85,388

4289 16,835

889 1812 44,481 2849 2861 477 17,170

Number of borrowers 6776 416 1252 635 1772

493 849

278 446 2813 234 433 38 766

Of which: project loan borrowers 2193 207 661 92 509

48 16

42 27 24 30 31 33 30

WA loan period (month) 37 42 35 41 38

20.3 18.6

19.6 15.1 18.6 18.5 19.6 20.0 18.3

0.0 10.0

7.6 7.6 10.9 0.0 0.0 0.0 12.0

WA lending rate (annual) Foreign MNT currency 16.9 9.2 18.6 0.0 16.6 12.5 17.7 10.1 17.0 9.4

Note: * BOM classifies the total outstanding loans to households (individuals) as mortgage loans, SME loans, consumer, salary, pensions, and other loans. SME loan data cover both outstanding loans to households and private nonfinancial corporations Source: Central Bank of Mongolia. WA weighted average

2.5 2.6

1.5 1.6 2 2.1 2.2 2.3 2.4

1 1.1 1.2 1.3 1.4

Classification Loan for SMEs Agriculture, hunting, and forestry Manufacturing Construction Wholesale and retail trade, and motor vehicle, motorcycle maintenance service Service Others Small business loans for individuals Agriculture, hunting, and forestry Manufacturing Construction Wholesale and retail trade, and motor vehicle, motorcycle maintenance service Service Others

Table 3.14 Outstanding loan for SMEs and individuals, 2018 (in million MNT)

3 Government Financial Support for Small- and Medium-Sized. . . 125

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highlighting the urgent need to improve transparency and monitoring of the fund allocation process. SMEs from 122 soums (a total of 339 soums) have bank loans amounting to a total of 397.8 trillion MNT, of which the bank loans of SMEs operating in Ulaanbaatar city account for 89.4% (Table 3.15). This means there is a significant concentration of SME financing activity in Ulaanbaatar city. Therefore, SMEs operating in regional areas have extremely limited access to additional financial services. Moreover, the interest rate is higher than that in Ulaanbaatar city. In Chapter 4, we examine the concentration of economic activity of SMEs using the spatial econometric method. Table 3.15 summarizes the data used in this study. Budget investment constituted 8.8% of the total investment amount, of which over 50% was allocated to electricity, gas, steam, and ventilation sectors. Moreover, the budget investment in Ulaanbaatar city accounts for 70.7% of the total budget investment.

Cumbersome Loan Application Process SMEs face a complex process when applying for a subsidized loan from the SME Development Fund. According to the SMEs, the overall process takes between 6 and 9 months. Moreover, the uncertainty along the various steps can be discouraging and make their application irrelevant. With limited support, SMEs must prepare a total of 30 documents for the 2 institutions (the private bank and SME Development Fund). Among these, seven are actually administrative documents that the SME must submit to different public agencies.

3.3.5

Lack of Data Access

Data on Mongolian SMEs are gathered by a number of public institutions, with limited or no coordination among them. Data collection and analysis are shared by several organizations. For example, data on financing are collected by the Central Bank of Mongolia, and data on number, size, sector, and geographical location are collected by the National Statistics Office. The ministries and their agencies, such as the Mongolian Credit Guarantee Fund, internally register information on loan guarantees and SME guaranteed loans, and the SME Development Fund collects the information on financial needs, participation in government programs, etc. Additional data on SMEs are gathered by the Chamber of Commerce through surveys conducted by its members. The international organizations conduct their own surveys, such as the Business Environment and Enterprise Performance Survey conducted by the EBRD and World Bank. These independent surveys offer a specific service, disseminating data to the public and researchers (OECD 2018). This lack of coordination makes it difficult to present a clear, transparent picture of SMEs’ situation. Moreover, institutions gather data based on different definitions of

Sales revenue 25,731.5 345.0 862.3 1159.1 364.9 23,000.3

Capital 21,416.7 436.3 728.2 1787.1 282.1 18,183.1

Investment 2744.9 38.6 104.5 145.6 40.6 2415.6

Source: National establishments’ census 2016, National Statistics Office of Mongolia

Country total Western region Khangai region Central region Eastern region Ulaanbaatar city

Number of SMEs 43,753 3978 4595 5055 1794 28,331

Table 3.15 Summary of national establishments census (in trillion MNT) Budget investment 242.0 17.9 32.4 12.4 8.2 171.0

Bank loan 397.8 4.1 22.4 13.3 2.6 355.4

FDI 78.8 0.1 0.2 20.0 0.1 58.4

Securities 17.9 0.0 0.0 – – 17.9

3 Government Financial Support for Small- and Medium-Sized. . . 127

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SMEs, creating inconsistency among data on the various aspects of their activities and structure.

Instability in Government Actions and Bureaucracy The Government of Mongolia established the SME Agency in 2008 and the SME Development Fund in 2009 to support financing for SMEs. Their roles include providing long-term concessional loans for SME operations, helping SMEs to access production equipment through financial leasing, promoting the activities of subsidized SMEs, organizing workshops and training, and offering double guarantees for credit. However, following every election, the new government changes the status, location, and responsibilities of the SME Development Fund, leading to instability in its function and structure. Until 2012, it was an agency within the Ministry of Agriculture and Light Industry; however, in 2014, it became a department within the Ministry of Industry. Currently, it operates under the Ministry of Food, Agriculture, and Light Industry. The structural uncertainty and frequent changes weaken the policy implementation and monitoring. In some cases, important operations are left behind, and no progress is achieved. Additionally, there is less development in monitoring and evaluating public financing and subsidized loans for SMEs. These issues prove that SMEs are left behind because the economy is highly dependent on the mining sector. The government needs to increase its attention to developing the SME sector. In addition to supporting SMEs through tax policy, the development of a comprehensive legal framework for SMEs is required. Moreover, a comprehensive government policy aimed at improving educational and financial capacity by creating favorable financial, loan, and investment conditions will be a promising step toward a greater result. Moreover, the Government of Mongolia needs to address the concentration of the economic activity in the central city and mining-based provinces to achieve nationwide balanced development. Although the government has formulated various policies and plans to develop local SMEs based on the “regional development strategy” since 2009, no progress has been seen. In the next chapter, using the spatial econometric method, we examine and prove that the economic activity of SMEs has been concentrated in central cities and mining-based provinces.

3.4

Identifying SME Density and Performance Distribution in Mongolia Using Spatial Data Analysis

In this study, we apply nighttime light data to estimate the spatial distribution of sales revenue (output) and SMEs density in Mongolia. The night-time light (NTL) data has been proven to be capable of providing a strong estimation of population, GDP,

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and electricity consumption, based on the strong correlation between lights and human activities.

3.4.1

Methodology

Spatial econometrics is a subfield of econometrics that deals with spatial interaction (spatial autocorrelation) and structure (spatial heterogeneity) in regression models for cross-sectional and panel data (Anselin 1988). The spatial econometric method has been applied in theoretical econometrics and is gaining increasing attention from researchers. The observations could represent performance level, income, employment, population levels, tax rates, infrastructure, construction, flood and vegetation levels, etc. in the specific regions. Conventional regression models commonly used to analyze cross-sectional and panel data assume that observations are independent of one another. However, it is commonly observed that sample data collected for regions or points in space are not independent but rather spatially dependent. This means that observations from one location tend to exhibit values similar to those of nearby locations (Lesage 2008). The next section explains the methods for estimating models and spatial connectivity structures.

3.4.2

Theoretical Background

In this study, to estimate the spatial distribution of output across firms, we use the trans-log form of the modified Cobb-Douglas production function, which has been widely used in economic growth literature to measure the rate of technological progress. Equation (3.1) is the mathematical form representing the relationship between a firm’s output and input: ln SR = β0 þ β1 ln L þ β2 ln K þ β3 ln X þ e

ð3:1Þ

where: lnSR = the vector of the natural logarithm of sales revenue. lnL = the vector of the natural logarithm of total labor. lnK = the vector of the natural logarithm of capital. lnX = the vector of the natural logarithm of the controlling variables. e = residual. Specifically, the controlling variables include the value of investment, government budget investment, bank loan, foreign direct investment (FDI), and the revenue from issuing securities. Additionally, the national establishments’ census of Mongolia does not provide information that exactly matches the concept of the

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output. Numerous studies have used total sales as a proxy for output. We used total sales revenue as a proxy for the output of individual firms.

Spatial Statistics (Moran’s I) The localized association between the NTL and main variables obtained from the survey has been quantitatively examined in this study. Specifically, the spatial autocorrelation statistic (Moran’s I) has been applied for validating localized correlation. Equation (3.2) represents the mathematical representation of the Moran’s I test:
 n n  Þ Xj - X  n Σⅈ = 1 Σj = 1 W ij ðX i - X Moran s I = W0  Þ2 Σn ð X i - X 0

ð3:2Þ

ⅈ=1

with the normalizing factor W o = Σnⅈ = 1 Σnj= 1 W ij

ð3:3Þ

where: Xi = variable of interest.  = mean of Xi. X N = number of spatial units indexed by i and j Wij = spatial weight matrix  Þ = deviation of Xi from its mean ð
X i- X   = deviation of Xj from its mean X j- X The obtained value by Moran I quantitatively identifies the correlation between the pairs of X located within the area specified by the spatial weight matrix Wij. Fundamentally, the computation of Moran I is based on the concept of correlation. Hence, the value obtained by Moran I ranges between -1 and 1. A value close to 1 indicates that there exists a clustering value of X in most areas. Conversely, a value of Moran I close to -1 specifies a dispersion pattern in which neighboring areas have the opposite characteristics. It is noted that Moran’s I identifies any similarity in the whole data set. However, there is a limitation in identifying the specific location of correlation. Therefore, Local Moran’s I or local indicators of spatial association (LISA) have been developed as an alternative methodology, enabling the identification of the specific location of correlation. The mathematical representation of Local Moran’s I (LISA) is shown in the following equation:

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Local Moran s I i =

Þ ðX i - X

P

j W ij Si 2




 Xj - X

131

 ð3:4Þ

where: P
Si = 2

 Xj - X ð n - 1Þ

2

j

Wij = spatial weight matrixn = number of spatial units The value of Local Moran’s Ii obtained from computation based on Eq. (3.4) indicates the correlation of X in area i, and those in the neighboring areas. Similar to the methodology for the conventional correlation test, the statistical significance test of Local Moran’s Ii can be obtained. The outcome of the test, conventionally identified as p-value, empowers the application of Local Moran’s Ii in the analysis of spatial data, allowing identification of the localized correlation.

Spatial Econometrics Spatial dependence reflects a situation where values observed in one region depend on the values of neighboring observations in nearby areas. Anselin (1988) introduced the techniques of spatial econometrics with two specifications, namely, the spatial lag model (SLM) and the spatial error model (SEM). The former pertains to spatial correlation in the dependent variable, while the latter refers to the error term. Hence, it has become convenient to distinguish between SLM and SEM specifications. Under the assumption of normality distribution and independent and identical distribution of disturbances, both the SLM and SEM can be estimated using maximum likelihood (ML) estimation. Spatial Lag Model SLM is the extension of regression models. The spatial dependence is incorporated into the conventional linear regression as an additional independent variable. They allow observations of the dependent variable y in area i (i = 1, . . ., n) to depend on observations in neighboring areas j ≠ i. The basic SLM takes the form given in the following equation: yi = ρΣnj= 1 W ij yj þ ΣQ q = 1 X iq βq þ εi

ð3:5Þ

In matrix notation, Eq. (3.5) may be written as: y = ρWy þ xβ þ εi

ð3:6Þ

The abovementioned equation follows the standard specification, in which the independent variable X explains the variation of the dependent variable Y. Moreover,

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the disturbance, εi, randomly and marginally affects Y. The key modification is that, with a row standardized spatial weight matrix W (i.e., the weights are standardized such that ∑jWij = 1 for all i), this amounts to including the average of the neighbors as an additional variable in the regression specification. This variable, Wy, is referred to as the incorporation of influence from the dependent variables in neighboring areas. The variation of Y is a combination of the effect of independent variables located in the host area and influence from the neighbors. An example of the matrix representation of this specification is shown in the following equation: ½ y 1 y2 y3  = ρ½ðw11 y1 þ w12 y2 þ w13 y3 Þ ðw21 y1 þ w22 y2 þ w23 y3 Þ ðw31 y1 þ w32 y2 þ w33 y3 Þ  þ ½x11 x12 x13 x21 x22 x23 x31 x32 x33 ½β1 β2 β3  þ ½ε1 ε2 ε3  ð3:7Þ The above matrix representation of SLM clearly shows the significant role of spatial weight W. Particularly, the specification of all elements of the spatial weight matrix governs the spillover across locations, and in general, the attribute of matrix Wis based on either the adjacency or radius of distance: ln SR = ρW ln y þ β1 ln K þ β2 ln L þ β3 ln X þ ε

ð3:8Þ

In this study, Eq. (3.8) is the SLM estimated using the soum- and district-level data obtained from the official establishments’ census conducted in 2016. The magnitude and statistical significance of coefficient ρ is a parameter (to be estimated) identifying the strength of the spatial autoregressive relation of the cross-regional productivity. Spatial Error Model The second specification of the spatial econometric method is the SEM. Fundamentally, this approach is based on the assumption that spatial influence is the omitted variable, and the error term is correlated across locations. Equation (3.9) indicates the mathematical form of SEM: y = Xβ þ ε; ε = λWε þ u

ð3:9Þ

Also, this relationship is represented in matrix form, as shown in the following equation: ½y1 y2 y3  = ½x11 x12 x13 x21 x22 x23 x31 x32 x33 ½β1 β2 β3  þ ½ε1 ε2 ε3 ; ½ε1 ε2 ε3  = λ½ðw11 y1 þ w12 y2 þ w13 y3 Þ ðw21 y1 þ w22 y2 þ w23 y3 Þ ðw31 y1 þ w32 y2 þ w33 y3 Þ  þ ½ u1 u 2 u 3  ð3:10Þ The abovementioned representation exhibits the role of the spatial weight matrix, connecting the cross-location effect via error terms. Specifically, the magnitude and

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statistical significance of λ are the key determinants of identifying the existence of the mechanism influencing propagation through spatially linked error terms: ln y = β1 ln K þ β2 ln L þ β3 ln X þ ε; ε = λWε þ u

ð3:11Þ

In this study, the estimation based on SEM has the function form shown in Eq. (3.11). The standard production function has been extended to incorporate the spatial error relationship.

3.4.3

Data

In this study, we used two different sources of data, namely, National Statistics Office ground data and spatial NTL data. The official establishments’ census 2016 conducted by the National Statistics Office of Mongolia is the main data source of data in this study. The nationwide survey was conducted in 2016, collecting all information related to establishments in 2016. Among the 103,079 firms, 55,638 firms were actively operating with 639,235 employees. In this study, we used soum (territorial administrative unit of Mongolia) and district level’s SME aggregated data, including sales revenue, capital, number of employees, investment divided according to budget investment, bank loans, FDI and securities, and number of SMEs in the analysis. In 2016, 43,753 SMEs (78.6% of total active establishments) were operating with 368,347 employees (57.6% of total employees). The NTL data for 2013 is the globally collected NTL of the Earth’s surface, from 8:30 to 10:00 PM. These data were procured by the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and administrated by the US Air Force. To eliminate noise and irrelevant information, the raw data were cleaned and processed by the National Geophysical Data Center (NGDC) under the administration of the National Oceanic and Atmospheric Administration (NOAA). The processed data have been publicly available since 1992. Each pixel of this global data represents an area of 0.86 km2 with the value of light intensity scaling between 0 and 64. Many scholars have used this data as an index to represent urban density and economic activity. Various studies have documented the statistically significant NTL index. First, we computed the NTL data in QGIS software to calculate the NTL Index. NTL index will be used as a variable to indicate establishment density, as illustrated in Figs. 3.13 and 3.14. Figure 3.18 illustrates the NTL index of Mongolia on the soum and district levels. It shows that the Bayangol district in Ulaanbaatar has the highest density index, with a value of 61.7, while the rest of the index is lower than 30. Table 3.16 illustrates the administration units with the highest NTL Index values (NTL mean). There are 16 central cities in the provinces, out of the 21 total provinces, excluding Ulaanbaatar, that show high NTL values than that of other soums, ranging between 8.29 and 29.13. The NTL index values for some central

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Fig. 3.13 Night-time light data of Mongolia by province for 2013. Note: NTL data computed using QGIS software by author

soums are as follows: Sainshand, 4.14 (Dornogovi Province); Yesonbulag, 4.10 (Govi-Altai Province); Ulaangom, 4.38 (Uvs Province); Sumber, 4.09 (Govisümber Province); and Saintsagaan, 3.96 (Dundgovi Province). The final soum shows almost the same index value as other remote soums. Other 312 soums, except the soums illustrated in Table 3.16, have an NTL index value between 3.35 and 4.73. Furthermore, we checked the correlation between sales revenue, number of employees, capital, and bank loans using the NTL Index y. As shown in Fig. 3.15, a statistically significant positive correlation indicates that the NTL data can be applied to estimate the spatial concentration of key variables.

3.4.4

Spatial Statistical Analysis Result (LISA)

Figures 3.20, 3.21, and 3.22 illustrate the result of LISA between NTL and firms’ sales revenue. The low value of Moran’s I (0.09) shows that the firms’ nationwide concentration of sales revenue is low (Fig. 3.16). However, the outcome of LISA (Fig. 3.17) shows the main result of identifying a specific area of concentration. Specifically, provinces indicated with red color in Fig. 3.21 are areas in which both NTL and sales revenue are statistically higher than in those in other provinces. Conversely, provinces with dark blue color indicate that NTL and sales revenue are statistically lower than those in other provinces. The results of the LISA indicate that there exists a cluster of high values in provinces painted with red color, which include all 9 districts from Ulaanbaatar city, 13 soums from Tӧv Province, 13 soums from Selenge Province (agricultural cluster), all 4 soums from Darkhan-Uul Province, 2 soums from Bulgan Province, 1 soum from Khentii Province, and 1 soum from Govisümber Province. This indicates that highly productive (has the highest sales revenue) companies are located in these areas. The light red areas denote that there exists a cluster of high value of sales

Fig. 3.14 Night-time light index by province for 2013. Note: NTL data computed using QGIS software by author

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Table 3.16 Soums and districts with the highest NTL index # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Province Ulaanbaatar Darkhan-Uul Ulaanbaatar Umnugovi Selenge Orkhon Zavkhan Tӧv Khovd Bayankhongor Uvurkhangai Ulaanbaatar Ulaanbaatar Bayan-Ulgii Arkhangai Bulgan Khuvsgul Ulaanbaatar Dornod Ulaanbaatar Sukhbaatar Ulaanbaatar Darkhan-Uul Ulaanbaatar Dornogovi Ulaanbaatar Hentii

District Bayangol Darkhan* Chingeltei Dalanzadgad* Sukhbaatar* Bayan-Ӧndӧr* Uliastai* Zuunmod* Jargalant* Bayankhongor* Arvaikheer* Sukhbaatar* Xan-Uul Ӧlgii* Erdenebulgan* Bulgan* Moron* Songinokhairkhan Kherlen* Bayanzurkh Baruun-Urt* Bagakhangai Sharyngol Nalaix Zamyn-Üüd Baganuur Bor-Ӧndӧr

NTL count 41 181 156 39 91 483 74 34 127 106 86 371 873 176 105 162 186 2091 496 2137 94 269 171 1192 776 1074 252

NTL sum 2530 5273 4269 930 1968 9396 1420 642 1955 1532 1238 5312 11,670 1915 1104 1646 1843 20,030 4512 19,174 779 1936 1143 7432 4592 6336 1373

NTL mean 61.71 29.13 27.37 23.85 21.63 19.45 19.19 18.88 15.39 14.45 14.40 14.32 13.37 10.88 10.51 10.16 9.91 9.58 9.10 8.97 8.29 7.20 6.68 6.23 5.92 5.90 5.45

Note: * represents the capital of the province Source: author’s calculation

revenue companies; however, most of these areas have low NTL values. These areas include four soums from Umnugovi Province, where Tavan Tolgoi and Oyu Tolgoi mining areas are located. Additionally, one of the important border-crossings between Mongolia and China, which is a free economic zone, Zamyn-Üüd in Dornogovi Province, has a high concentration of sales revenue but a low NTL value. Figure 3.18 correspondingly illustrates the probability value, where dark green indicates the p-value at 1%, and light green indicates the p-value at 5%. This outcome clearly specifies the statistically significant concentration of NTL and sales revenue in the central areas. These areas include Ulaanbaatar city and its neighboring areas, along with areas with mining and agricultural activities. Figures 3.19, 3.20, and 3.21 illustrate the result of LISA between NTL and the number of employees. Figures 3.22, 3.23, and 3.24 illustrate the result of LISA between NTL and capital. Similar to firms’ sales revenue, the nationwide

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Fig. 3.15 Correlation between NTL Index and other variables

concentration of the number of employees and capital is low. However, there exists a cluster of high values in the capital city Ulaanbaatar, and its neighboring provinces, including Tӧv, Selenge, Darkhan-Uul, Bulgan, Khentii, and Govisümber.

3.4.5

Regression Result

The results obtained from the spatial statistical analysis clearly verify the significant concentration in the capital city Ulaanbaatar and its neighboring areas. The neighboring area of Ulaanbaatar city hosts the highest economic performance. This section applies the spatial econometric technique to the nationwide establishments’ census. Specifically, this empirical test is based on the theoretical background discussed in Sect. 3.4.2. Table 3.17 lists the results obtained from the three estimation techniques, namely, the ordinary least square (OLS) regression, SLM, and SEM. Following the conventional form of the modified Cobb-Douglas production function as the main

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Fig. 3.16 Moran scatterplot

Fig. 3.17 Cluster map between NTL and sales revenue

specification, the first column of Table 3.17 illustrates the result obtained from OLS regression. Most results are consistent with those demonstrated in the literature. The labor (lnL) and capital (lnK) factors have a positive and statistically significant

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Fig. 3.18 Significance map ( p-value)

Fig. 3.19 Moran scatterplot

contribution to the creation of output. Bank loans positively influence firms’ performance. However, the involvement of FDI, budget investment, and securities do not

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Fig. 3.20 Cluster map between NTL and the number of employees

Fig. 3.21 Significance map ( p-value)

have a statistically significant impact. The second and third columns in Table 3.17 list the results obtained from the estimation based on the SLM and SEM. The SLM and SEM show similar results and affirm the positive contribution of labor, capital, loans, FDI, budget investment, and securities on firms’ productivity. However, similar to OLS results, FDI, budget investment, and securities do not have a statistically significant impact. Following the theoretical concept of the key features of the spatial econometric approach, the coefficient of the spatial lag of sales revenue (W _ LN _ SalesR) identifies the magnitude of output spillover from the neighboring provinces. In this study, the result of SLM affirms that there exists a positive spatial externality of output with a magnitude of 0.24. This outcome reveals that the output spillover is a key factor generating agglomeration in the area nearing the capital city of Ulaanbaatar, which includes the firms located closer to each other. Therefore, firms operating in those areas can increase their output and sales revenue. Thus, the SMEs are major industrial establishments, and labor is induced to cluster within the area of the capital city of Ulaanbaatar. These statistical results jointly explain why we can observe the spatial concentration as shown in spatial statistical analysis in the previous section. The result of SEM and coefficient of the SEM λ affirm that there

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Fig. 3.22 Moran scatterplot

Fig. 3.23 Cluster map between NTL and capital

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Fig. 3.24 Significance map ( p-value)

exists a mechanism influencing propagation through spatially linked error terms with a magnitude of 0.7. This outcome reveals that the firms operating in those areas can be indirectly affected by other firms.

3.5 3.5.1

Concluding Remarks and Recommendations Concluding Remarks

SMEs play an important role in economic activity by fostering economic growth and generating employment and income, which are integral to the economic transformation process. Moreover, they help reduce poverty and income inequality, diversify economic activity, revitalize local economies, and support inclusive growth. In Japan, priority industries were supported heavily after World War II when Japan was trying to escape from war-driven economic devastation. After a successful economic reconstruction process, priority industries matured and were able to operate on their own. Therefore, the Government of Japan shifted its attention to SME-supporting policies. The framework for supporting the SME sector was built during the reconstruction period, given the huge domestic financing capacity. Additionally, it financially supported the Government of Japan and implemented other measures for SMEs through management, fiscal, commerce, and regional framework. The Government of Japan formulated its SME-supporting policy according to the national development policy, and its characteristics have been changed according to the development state of the economy. One of the reasons these policies have been successful is the Fiscal Investment and Loan Program. Through this system, the Government of Japan supported weak sectors and regions, successfully avoided bottlenecks, and achieved nationwide balanced development. Mongolia has shifted from the centrally planned economy at a significant pace since the early 1990s. In the past decade, Mongolia has ranked as one of the fastestgrowing economies in the world, given its wealth of natural resources. However, the

Government Financial Support for Small- and Medium-Sized. . .

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Table 3.17 Result of spatial econometric analysis (dependent variable lnSR)

lnK lnL ln_loan ln_FDI ln_Securities ln_Budget investment Constant

OLS 0.31 (0.03)* 0.68 (0.06)* 0.02 (0.01)* 0.01 (0.01) 0.03 (0.02) 0.01 (0.01) 11.04 (0.48)*

ρ (W_LN_SalesR) λ Statistical detail F-stat R-squared Pseudo-R-squared Log likelihood AIC Moran’s I (error) Lagrange multiplier (lag) Robust LM (lag) Lagrange multiplier (error) Robust LM (error) Lagrange multiplier (SARMA) Number of observations

Spatial econometric models SLM SEM 0.26 0.21 (0.03)* (0.03)* 0.72 0.83 (0.06)* (0.05)* 0.02 0.02 (0.01)* (0.01)* 0.01 0.01 (0.01) (0.01) 0.02 0.02 (0.02) (0.02) 0.01 0.004 (0.007) (0.01) 6.99 12.45 (0.91)* (0.49)* 0.24 (0.05)* 0.704 (0.08)*

289.80 0.84 -426.57 867.14 10.19* 30.99* 6.23* 84.77* 60.01* 91.00* 339

0.85 -413.71 843.43

0.87 -399.62 813.25

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Notes: The numbers in parenthesis are the standard error. ***, **, and * indicate the level of statistical significance at 1, 5, and 10%, respectively. OLS = ordinary least square, SLM = spatial lag model, SEM = spatial error model, AIC = Akaike information criterion, robust LM lag = robust Lagrange multiplier test for the spatial lag model, and robust LM error = robust Lagrange multiplier test for the spatial error model

Government of Mongolia has been more focused on the mining sector than on other sectors, especially the SME sector. As a result, SMEs face various challenges in accessing financial resources. Moreover, social and political conditions, macroeconomics, and legal and institutional uncertain conditions are the most challenging factors faced by SMEs. These issues prove that SMEs have been left behind because the economy is highly dependent on the mining sector. To realize its full economic potential, the government needs to make growth sustainable and become less

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vulnerable to commodity price shocks. This can be achieved if growth is broadbased across various sectors and business segments, ranging from large corporations to SMEs. In addition to supporting SMEs through tax policy, the development of a comprehensive legal framework for SMEs is required. Additionally, a comprehensive government policy aimed at improving educational and financial capacity will be a promising step toward a greater result. Moreover, the Government of Mongolia needs to address the concentration of the economic activity in the central city and mining-based provinces to achieve nationwide balanced development. Nationwide balanced development is vital for the sustainable development of the country, as seen in the Japanese developmental history. The excessive concentration of economic activity and SMEs in the capital city of Ulaanbaatar has been examined and verified using the SEM. Moreover, using the statistical data, we have verified that regional financial support and other measures are very low. Thus, we need to consider the experience of Japan’s Fiscal Investment and Loan Program to support weak sectors or SMEs. Although the government has formulated various policies and plans to develop regional SMEs according to the “regional development strategy” since 2009, the laws that support industrialization were issued and adopted in Mongolia. Therefore, very few of them were implemented and produced outcomes. Many laws were adopted, such as the Law on Industry and the Law on Technology, SMEs, Investment, and Innovation, and many decisions were made, such as the governmental resolution on the “New Era of Industrialization,” the project to industrialize Mongolia, and Law to Support Exports. However, none of them have shown notable results so far. Furthermore, the Government of Mongolia established a Soum Development Fund to financially support regional SMEs in 2011. The rights were given to regional government offices to allocate the fund directly on their own, without transferring them through commercial banks. This could be one reason for irresponsible financial management.

3.5.2

Recommendations and Policy Implications for the Government of Mongolia

To improve its SME supporting measures and pursue nationwide balanced development, the Government of Mongolia needs to address the following issues: • Develop an internal loan assessment tool for the SME Development Fund, as implemented by the JFC. The regional staff in charge of loan assessment lack knowledge. Loan staff should be able to assess loans appropriately. Furthermore, using this tool could help reduce the disparity between regional loan committee decisions. • Improve the SME Development Fund’s financial management by restructuring the monitoring of the loan repayment schedules and improving transparency.

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Currently, only direct loans from SME Development Fund disclose the information of loan applicants, names of legal entities (individual and establishments), loan amounts, loan period, interest rate, and name of the project. However, most funds have been extended through commercial banks, and the information has not been disclosed to the public. If we improve the financial management and transparency, the irresponsible allocation of the SME Development Fund will be eliminated. The SME Development Program (2014–2016) aims to establish a businessincubation center in each province and support its activities. This has not been implemented due to instability in government actions and the three structural changes made during this period. In the National SME Development Program (2019–2022), the measure was changed to establish business incubation centers and promote their human resource skills without providing an exact number of incubation centers that will be built. Thus, to promote regional SMEs, the government should address the measures related to the regional development of SMEs. Establish an educational and technological cooperative system connecting large enterprises with medium enterprises and medium enterprises with small enterprises. To create a suitable cooperative environment for them, as implemented in Japan’s cooperatives. To attract skillful human resources to regional areas, establish a promotion policy for SME regional staff and all public sector human resources, especially for health and education. To sustain SME policy implementation, the government needs to consider establishing an independent SME agency and address instability in policy actions to improve the general credibility of the public sector. To improve policy effectiveness, the government should establish a policy effectiveness cycle including the following steps: diagnosis (the identification of functional problems), assessment (the manifestation of power asymmetries and bargaining arrangements), and targeting (how incentives and preferences can reshape the policy arena) (World Bank 2017).

Finally, to tackle these issues, effective collaboration of all stakeholders is necessary, including public and private sectors and scientific and international organizations. If we successfully implement the regional SME development policy by improving their competitiveness and educational and financial capacity, Mongolia will no longer be dependent on the mining sector in the near future.

References 植杉威一郎、内田浩史、水杉裕太 (2014) 日本政策金融公庫との取引関係が企業パフォー マンスに与える効果の 検証. 経済産業研究所 RIETI Discussion Paper Series, 14-J-045 植杉威一郎、内田浩史、岩木宏道 (2015) 無担保貸出と企業の資金調達・パフォーマン ス. 神戸大学経済経営学 会『国民経済雑誌』212, 第212号, 21–37

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植杉威一郎、内田浩史、岩木宏道 (2016) 無保証人貸出の導入と企業の資金調達・パフォ ーマンス. 日本金融学 会『金融経済研究』16-J-023, 第40号, 27–53 Anselin L (1988) Spatial econometrics: methods and models. Springer Science Business Media, Dordrecht Cargill T, Yoshino N (2003) Postal savings and fiscal investment in Japan: the PSS and the FILP. Oxford University Press, New York Central Bank of Mongolia (2018) Small and medium enterprises’ development and financial access. Research and Statistics Department, Ulaanbaatar EBRD, World Bank (2015) Business environment and enterprise performance survey (BEEPS) V country profile: Mongolia. European Bank for Reconstruction and Development, World Bank, Washington DC, London Ministry of Finance (2018) Fiscal investment and loan program report. Ministry of Finance, Tokyo Honjo Y, Harada N (2006) SME policy, financial structure and firm growth: evidence from Japan. Small Bus Econ 289(27):4–5 Imajoh T (2012) Small business financing in Japan, from the prewar to high-growth periods: an international comparison of the financial history of small businesses. Kyoto Econ Rev 81:14–27 Itoh M, Urata S (1994) Small and medium-size enterprise support policies in Japan. Policy Research Working Paper 1403, World Bank Japan International Cooperation Agency (2017) Data collection survey on business environment and investment promotion in Mongolia. JICA, Japan Economic Research Institute, Tokyo Lesage J (2008) An introduction to spatial econometrics. Rev Econ Ind 123:19–44. https://doi.org/ 10.4000/rei.3887 National Statistics Office of Mongolia (n.d.). Retrieved from http://www.1212.mn/ OECD (2018) Enhancing competitiveness in Central Asia, competitiveness and private sector development. OECD Publishing, Paris Ohno K (2006) The economic development of Japan: the path traveled by Japan as a developing country. GRIPS Development Forum, Tokyo Regnier P (2006) Japanese small enterprise development cooperation overseas: linkages with Japan’s industrial organization and ties with Japanese SMEs Seki T (2008) What are the SME policies and measures in Japan?: the outline of SME promotion policies in Japan. 阪南論集. 社会科学編 44(1):173–190 Small and Medium Enterprise Agency (2019). Retrieved from https://www.chusho.meti.go.jp/sme_ english/outline/04/01_06.html SME Development Fund (2017) Annual report of SME Development Fund. SME Development Fund, Ulaanbaatar Suzuki Y (1989) The Japanese financial system. Oxford University Press, New York United Nations Development Programme (2004) Unleashing entrepreneurship: making business work for the poor. United Nations Development Programme, New York World Bank (2017) World development report 2017: governance and the law. World Bank, Washington DC

Chapter 4

Macroprudential Policy to Manage Systemic Risk Deriving from Financial Institutions in Mongolia Narantuya Natsagdorj

Abstract This study presents a measurement model of systemic risk in the frontier market that fully employs financial statement data. The Financial Times Stock Exchange survey notes that frontier markets exist in more than 150 countries. The sum of systemic risk in the frontier markets can hinder economic stability in terms of a herd of risk. Frontier markets are likely to imply unpredicted systemic risk if we think based on previous lessons learned from being unable to foresee the early signals of the Lehman Brothers shock. The reasons for the rarely studied systemic risk in these markets might be that the markets are systemically unimportant, and there is a lack of publicly available data access. Therefore, I gave it my best shot to capture systemic risk in the frontier market entirely using financial statement data. It is sometimes said that the frontier market is systemically not important because of its small size; however, the research argued that the interconnection between financial institutions is highly likely to raise systemic risk even if it is small. The fact is the majority of financial institutions in developed, emerging, and frontier markets are deeply interconnected with each other via a network. For instance, the financial market in Mongolia is representative of frontier markets. As a financial regulatory aspect, financial conglomerates are increasing and deepening their interconnection in bank-dominated and underdeveloped capital markets. Hence, the intuition is to capture the systemic uncertainty behind increasing conspiracies in financial conglomerates to negatively impact financial stability. To accomplish this, the methodology measures financial institutions’ contribution to systemic risk. Financial institutions’ contribution to systemic risk can be computed by the systemic expected shortfall. The systemic expected shortfall would be dependent on the marginal expected shortfall and can be explained by financial leverage and liabilities as the predicting power increases. Moreover, financial statements are situational mirrors of financial institutions. Based on these assumptions, we empirically measured the time and cross-dimensions of systemic risk using financial statement data. Potential

N. Natsagdorj (✉) Financial Regulatory Commission of Mongolia, Ulaanbaatar, Mongolia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 H. Taguchi et al. (eds.), Challenges in Fiscal and Monetary Policies in Mongolia, New Frontiers in Regional Science: Asian Perspectives 66, https://doi.org/10.1007/978-981-19-9365-7_4

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variables from financial statements were tested to identify variables that could forecast systemic risk. Stock returns and capital market data have been frequently experimented with in previous literature, and financial statement data of frontier markets are new for systemic risk measurement. As a result of the analysis, the systemic expected shortfall could explain cross-dimension systemic risk, which is financial institutions’ contribution to systemic risk. Subsequently, the time series of the marginal expected shortfall can forecast the amount of systemic risk in the next two periods. Eventually, macroprudential policy, a policy tool for systemic risk, can be easily developed after forecasting financial institutions’ contribution to systemic risk. Keywords Systemic risk · Macroprudential policy · Systemic expected shortfall · Marginal expected shortfall · Financial leverage · Value at risk · Optimal tax

4.1

Introduction

Historically, economic crises have been prevalent and generated adverse follow-on effects. Prototypical examples include the economic meltdown of 2008, the Great Depression of 1929, the oil shock of 1979, and the Asian financial contagion of 1997. The main negative effects of such events often entail decreased purchasing power, mortgage foreclosures, job losses, and food scarcity, especially for destitutes. Admittedly, a wealth of information about economic malaise and its attendant systemic risk exists. Financial institutions face manifold risks, including credit, market, operational, strategic, reputational, and systemic. Concurrently, they are also risk-creators. For instance, when all institutions in a financial market are in distress, the economy is hampered, hindering economic growth. The existing financial systems have systemic risks. Such risk affects numerous participants, including households, investors, governments, businesses, and intermediaries in financial markets, and can result in dislocations in an economy. In today’s globalized era, owing to world trade and technological advances, economies of scale help reduce costs considerably, thus enhancing countries and their economies. Likewise, financial markets are highly interconnected, using technology to provide financial services. This study focuses on systemic risk derived from financial enterprises in Mongolia. Macroprudential policy is a new tool for managing systemic risk and enhancing financial stability. The fundamental focus is to assess the contribution of financial institutions to systemic risk in enhancing financial stability. An additional attempt is to explain financial stability, systemic risk, its management referring to macroprudential policy, and its difference from microprudential policy to capture academic descriptions. The Mongolian financial market is bank-dominated and is in an early stage of stock market development. However, the financial market is a frontier market. The market dominated by commercial banks is transferring to financial conglomerates,

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and similarly, their interconnections are increasing. This provides a signal of the increasing likelihood of facing unrecognizable systemic risk. Generally, systemic risk in the frontier market needs to be mandatorily managed. To the best of our knowledge, no study has considered any evaluation of systemic risk or the implementation of macroprudential policy in the frontier market. To resolve this research gap, I measure the systemic risk in the frontier market by drawing available data from financial statements. As a result, this study might become a potential source of encouragement for other frontier markets. The remainder of this study is organized as follows. Section 4.2 presents the basic concept of systemic risk and macroprudential policy. This section aimed to express the definitions of financial stability, systemic risk, financial instability, and macroprudential policy, which are argued by research. Section 4.3 provides a literature review of methodologies for estimating financial institutions’ contribution to systemic risk. In general, this section covers several types of methodologies of conditional value-at-risk, its extensions, maximum likelihood estimations, extreme value theory, and Shapley value. Section 4.4 offers the methodology employed for evaluating the systemic risk contribution of Mongolian financial institutions. To evaluate this, an economic model formulated a systemic expected shortfall, which would be explained by the marginal expected shortfall and financial leverage. Section 4.5 presents an empirical analysis consisting of two sections pertaining to Mongolian financial markets and the primary evaluation. Specifically, Mongolian economic and financial stability is cyclical. To find potential data, alternative variables from financial statements are tested. The analysis consists of a time series and cross-sectional to evaluate the two stems of time- and cross-dimensional systemic risk. Finally, Section 4.6 reveals our conclusions.

4.2

The Concept of Financial Stability and Systemic Risk

The role of the financial market in economic growth is to provide efficient capital allocation and play the role of an intermediary. Similarly, the flat and continuous growth of the financial market supports economic stability. Nonetheless, the overall financial market has not developed consistently throughout history, whereas collapses, booms, and crises have worsened the economy and social well-being. Achieving and maintaining financial stability has become increasingly important for regulators, policymakers, and market participants for the pleasant lives of human beings in terms of sustainable development goals. The implementation of systemic risk management and its policy guidance have grown in recognition since the 2008 Lehman Brothers shock to prevent shocking fluctuations in the entire nation. Since then, macroprudential policies have been actively implemented worldwide. In addition, the risk management perspective extended markedly from individual risk to systemic risk to build tolerance against frequently occurring crises.

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Maintaining financial stability is not only the responsibility of the government, but supranational regulators are also aware of it with respect to one of their basic roles. At the beginning of this research, I would like to answer the following questions: What is financial stability? How does systemic risk engulf this? What mechanisms are involved in the implementation of macroprudential policy?

4.2.1

Financial Stability

Some scholars critically dig out to draw a big picture of financial stability, and then they can describe it to some extent. Subsequent paragraphs are scholars’ delineations of financial stability. Schinasi (2006) highlighted that financial stability is to be able to implement its rules in an economy, which means allocating capital efficiently, managing its risk, maintaining its capacity to perform its functions, and being flexible to external shocks. Alternatively, the financial system can dissipate the financial imbalances that arise endogenously from adverse and unanticipated events. Additionally, Schinasi (December Schinasi 2009) characterized globally that the financial system has built cross-border linkages through a triad of institutions, markets, and infrastructure from one country to another and that one country’s problems are transmitted to others via these linkages. Moreover, the stability of financial institutions and markets makes up a stable financial system, market participants tend to be highly confident with their intermediaries that they could achieve their contractual obligations, and the market must be provided to be far away from extreme short-term volatilities. In summary, financial stability means that the financial market facilitates its function of accelerating an economy by allocating assets to efficient investment opportunities. To achieve this, financial institutions and markets are able to internalize external shocks, be flexible to contagion effects, and emphasize the importance of participants’ confidence in the financial market. In addition, the financial system ought to create capacity and mechanisms to manage its risks at systemic and individual levels in a globally interconnected environment. Financial stability is long-term consistent growth, in contrast to not being sudden up and down changes in financial activities. Based on these definitions, systemic risk is one of the causes of the volatility of financial stability, which is a lesson learned from the 2008 economic crisis.

4.2.2

Systemic Risk

Systemic risk harms financial stability and propagates contagion effects. Moussa (2011) concluded that systemic risk could occur as a consequence of aggregate

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negative shocks affecting all institutions in the system and that it differs from the risk in a financial institution by its spillover effect. Galati and Moessner (2013) illustrates these three main sources of systemic risk. First, the cross-sectional dimension of systemic risk arises from the contagion effects of the financial market, institutions, and infrastructure in a networked and interconnected system. Financial institutions that fail have set out a concentration in the financial market by extending their business. Moreover, systemic risk does not solely come from systemically important entities, but the accumulated effects of minor financial institutions also trigger the system as an aspect of the herd of a risk. “Spillovers can come in the form of direct contractual spillovers or indirect spillovers. Direct spillovers include interconnectedness and domino or network effects. Indirect spillovers include information spillovers following bank failures or after policy actions and pecuniary externalities arising from fire sale externalities” (Freixas, Xavier, Luc Laeven, and José-Luis Peydró. Systemic risk, crises, and macroprudential regulation). Second, the time dimension of systemic risk is that long-term accumulated risks coincide with the trough stage of a business cycle or are hit by external shocks. The cyclicality of risk usually arises from the economic cycle, which means that the financial market is affected by economic shocks. Alternatively, these two sources are referred to as endogenous and exogenous systemic risk. “The key drivers of such forms of systemic risk are considered to be financial innovation, financial deregulation, financial globalization, competition policy, and monetary policy” (Freixas, Xavier, Luc Laeven, and José-Luis Peydró. Systemic risk, crises, and macroprudential regulation). Finally, capital flows in international trade could systemically hinder the financial market when the shortage of capital inflows increases. The first two systemic risk resources come directly from the financial market. The following sections highlight these factors. Asymmetric information: Systemic risk can increase under conditions of increasing asymmetric information in the market, which causes moral hazard, adverse selection, and free-riding information. Globalization: Globalization inherently implies systemic risk through all sectors, such as financial markets, supply chains, and international trade. The Lehman Brothers shock was the first worldwide systemic risk with respect to globalization. Globalization in the financial market lasted for 10 years, namely, the Golden decade 1998–2007. Its first step coincided with the computerization of computer penetration in financial activities, including stock order and business-accelerated transactions. The negative aspect was technological risks, such as technical failure, cybercrime, and increasing cases of operational risks, followed by human error in communicating with the newly computerized environment. Regulators and policymakers released rules and opened the market internationally. It actively accelerates crossborder capital flows and increases interdependence in correspondence with technological advancements.

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Financial deregulation: Financial markets have increased dramatically worldwide because of strong competition among countries to attract investors. First, the United States deregulated in 1970; subsequently, the United Kingdom and other countries persuaded regulators to release a strict ruling environment as a consequence of investment banks. Hence, competition between financial hubs grew and increased profitability, tax revenue for the government, as well as GDP, as a result of every part of financial participants satisfied, including the government. Unfortunately, the value-added situation was not consistent, and everyone was cheated by short-term upstream because the financial market was not stable and inherently implied systemic risk. Deposit insurance: Government guarantee incentivizes mega-financial conglomerates to take excessive risk and is conducted at high concentrations. The global financial market has become increasingly complex, massively interconnected (horizontally and vertically connected throughout the world), interdependent, but non-managed at the systemic level. New financial products: Newly issued products, namely, shadow banking, alternative investment products, and hedge funds, are not regulated like traditional products. In particular, securitization, collateralized debt, credit default swaps, asset-backed securities, and special-purpose vehicles buy risky assets from investment banks and clear banks’ balance sheets (statement of financial position) from bad debt. Rating agencies: Big credit rating agencies exaggerated the worthiness of fixedincome securities until the bubble burst in 2008. This is because a few rating agencies are monopolistic. The primary issue is that financial regulation mandates that rating agencies are a central source of bond creditworthiness. The second is the Securities and Exchange Commission’s (SEC) established category of nationally recognized statistical rating organizations, which first approved only three rating agencies and, so far, only increased to 10. Moreover, the SEC set up a protective entry barrier for other agencies. Third, the conflict of interest between rating agencies and security issuers caused the changed model from investor pay to issuer pay as informed creditworthiness. Direct and indirect connections: Goldin and Mariathasan (2015) examined the direct and indirect linkages of systemic risk. Direct linkage refers to the interbank market of financial institutions, and its complex and interconnected environment adheres to the contagion effect. Additionally, systemic risk emerges from indirect linkages, which means that financial institutions diversify their risk individually, but in fact, all of them invest only one basket of assets at the systemic level. Alternatively, Acharya et al. (2009a, 2009b) define the systemic risk of joint failure risk arising from the correlation of returns on the asset side of the bank balance sheet. The last originator of systemic risk is information spillover (informational contagion), which overlaps with herding behavior. According to Acharya et al. (2016), systemic risk can be thought of as widespread failures of financial institutions or freezing up of capital markets, which can substantially reduce the supply of capital to the real economy.

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Lastly, systemic risk is highly likely to occur in the absence of an appropriate policy when unexpected losses are raised and triggered, but its influence on the financial market depends on the risk’s dispersal of impact and transmission effects. Systemic risk is triggered by the contagion effect, followed by a loss of confidence and increased uncertainty, instigating a huge negative impact on the real economy. Hence, systemic risk wouldn’t occur when it hasn’t any possibility to transmit via contagion effect across financial markets. The contagion effect is a risk movement from one institution to another through market and information channels, payment systems, interbank linkages, and liquidity. Systemic risk is defined in several ways. For instance, the European Systemic Risk Board has defined it as follows: “systemic risk means a risk of disruption in the financial system with the potential to have serious negative consequences for the internal market and the real economy. All types of financial intermediaries, markets, and infrastructure may be systemically important to some degree.” According to Goldin and Mariathasan (2015), systemic risk is the risk or probability of breakdowns in an entire system, as opposed to breakdowns in individual parts and components, and is evidenced by co-movements (correlations) among most or all parts. Regulating bodies also tend to have different perspectives on systemic risk definition, depending on their regulatory framework and legislation, particularly supranational- and country-level regulators.

4.2.3

Financial Instability

Financial instability is a condition when the financial market doesn’t function and is not able to fulfill its purpose. Mishkin (2007) described that this comes from asymmetrical information, which is macroeconomic uncertainty and inherently exists as an adverse selection and moral hazard. On the other hand, it becomes harder to make decisions that raise information disruptions in the financial market. Four categories of factors lead to financial instability: increasing interest rates, increasing uncertainty, asset markets affecting balance sheets, and problems in the banking sector.

4.2.4

Macroprudential Policy

The main tool for managing systemic risk is a macroprudential policy. The macroprudential policy has grown in recognition since 2008 as an extension of discovering policy papers in terms of the Tinbergen principle. Since the economic crisis of 2008, macroprudential policy has been actively developed by central authorities in each country’s policymakers and international regulatory bodies: FSB, BIS, IOSCO, and IAIS since the Economic Crisis of 2008. The IMF-FSB-BIS (August 31, 2016) defined the policy objectives as follows:

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1. “increase the resilience of the financial system to aggregate shocks by building and releasing buffers that help maintain the ability of the financial system to function effectively, even under adverse conditions; 2. contain the build-up of systemic vulnerabilities over time by reducing procyclical feedback between asset prices and credit and containing unsustainable increases in leverage, debt stocks, and volatile funding. 3. control structural vulnerabilities within the financial system that arise through interlinkages, common exposures, and the critical role of individual intermediaries in key markets that can render individual institutions “too-big-to-fail”. On the other hand, macroprudential regulation has attracted the attention of regulators during the past two decades. The Federal Reserve has examined the possibility of creating a systemic risk authority whose responsibility is to “(1) monitor large or rapidly increasing exposures across institutions and markets, rather than only at the level of individual institutions; (2) assess the potential changes in the markets and products that could increase systemic risk; (3) assess the risk of contagion between financial institutions within and across markets, such as the mutual exposures of highly interconnected institutions; and (4) identify possible regulatory gaps”. The macroprudential approach limits the significant macroeconomic costs that come from system-wide distress and shock effects in the financial market (Hanson et al. 2011). Kemp (2017) added to the research field that macroprudential policy is implemented by forward guidance to obtain feedback or abruptly to market financial institutions. However, forward guidance is preferable precisely because it helps the regulator update its policy based on the idea of the public. On the other hand, it is effective for institutions to change their working way of giving time to respond. The IMF concluded that the implementation of the macroprudential policy has already spent two decades, and it was time to assess the policy in all advanced, developing, and emerging markets; as a result, they organized a central macroprudential policy database. In accordance with this assessment, macroprudential policy has become a crucial toolkit for policymakers worldwide. In conclusion, macroprudential policy complements public policy, such as monetary, fiscal, and microprudential policies, to provide financial stability and achieve economic stability.

4.3

Literature Review

Systemic risk has been measured and managed at the country and supranational regulator levels. Internationally, the Financial Stability Board (FSB), Bank for International Settlement (BIS), International Organization of Securities Commission (IOSCO), and International Association of Insurance Supervisors (IAIS) usually develop guidance to manage systemic risk based on member countries’ experiences.

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Recently, the International Monetary Fund (IMF) has become a research maker in this field and has disclosed financial stability reports semiannually. European Union established the European Systemic Risk Committee to manage interconnected systemic risks across its member countries and provide guidance. Countries with rich experience in systemic risk management are the United States, Japan, Great Britain, Mexico, and South Korea. Systemic risk’s stem comes from any shocks, and then its cause of becoming more and more is triggered through financial institutions’ interlinkages, their balance sheet connection, and systemically non-diversified assets. Systemic non-diversification implies that each financial institution manages its portfolio by diversifying; however, it is not diversified in the entire system because of the cost of information and so forth. Hence, the risk is transferred by these channels through financial institutions, namely, the contagion effect, domino effect, spillover effect, and trigger event. The probability of financial institution failure causing a specific institution default or macro-shock has been actively measured by scholars, and several methodologies have been formulated. The following sections briefly explain current academic research in the field of the systemic contribution and interconnection of financial firms. The contribution of financial institutions to systemic risk is the main driver of the cross-sectional systemic dimension. The amount of contribution was measured using a variety of methods. ΔCoVaR: Tobias and Brunnermeier (2016) evaluated conditional value at risk and aimed to measure systemic risk under both conditions of sector-specific shock and spillover effects, then formulated it to measure how the exposure of financial institutions affects system-wide, and then raised systemic risk with respect to the cross-sectional dimension. The delta conditional value at risk (ΔCoVaR) to forecast systemic risk is different from the conditional value at risk at the median level and a definite quantile level. Hereafter, the opposite condition is also considered: how much financial institutions deteriorated due to the financial crisis and estimated by exposure-ΔCoVaR. Moreover, this formulation is extended to measure the procyclicality and future probabilities of systemic risk using forward ΔCoVaR by formulating an intercept and macroeconomic and institutional variables. Lastly, the predicted ΔCoVaR was analyzed to forecast future systemic risk by using panel data lagged by one quarter, 1 year, and 2 years. They employed variables of publicly traded companies’ weekly stock returns, loans, leverage, maturity mismatches, and risk-free investment products from financial statements for panel and cross-sectional series. The quantile regression method was evaluated by regressing at confidence levels of 95, 99, and 99.9%. Bayesian inference for CoVaR: Bernardi et al. (2013) estimated the dynamic co-movement of two institutions while changing over time using Bayesian inference for CoVaR. Data were collected from publicly traded companies belonging to different sectors, such as financials, consumer goods, energy, industrials, technologies, and utilities. The primary assumption is that future tail behaviors of systemic risk can be forecasted as time series of its past movements on an asymmetric Laplace

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distribution. This study analyzed the dynamic co-movement of two institutions and the tail movements between an individual institution and the whole system. A further extension is Segoviano and Goodhart’s (2009) systemic impact index (SII), which is the probability that at least one bank becomes distressed. SRISK: Brownlees and Engle (March 2017) quantified the systemic risk contribution of financial firms and formulated SRISK, a model weighing the expected capital shortfall of financial institutions throughout the time series dimension. The viewpoint is that systemically risky institutions develop minor systemic risks over a long period. The highest SRISK is more likely to make the largest contribution to the undercapitalization of the financial system; the result of summing all institutions’ SRISK draws the total amount of systemic risk that might hinder the financial system. The SRISK methodology formulated a regression model consisting of explanatory variables of leverage and long-run marginal expected shortfall, fractioned by the percentage of capital adequacy. The long-run marginal expected shortfall simulates a single institution’s loss in the occurrence of market distress. Panel data are composed of daily stock returns, daily market capitalization, and quarterly accounting data (total assets, debt, and equity) from financial statements for 10 years (2003–2012). Multivariate extreme value theory: Zhou (2009) measures extreme co-movements in a set of tail behaviors, covering cases of both financial institutions’ default and systemic shock. It considers three sets of assumed formulations. First, the probability that at least one bank becomes a distressed (PAO) model to capture the probability of at least one extra financial institution’s default in the condition of another particular institution’s failure simultaneously. Second, the PAO model extension is a systemic impact index that captures the total number of expected defaults in the event of a particular institution’s failure. Finally, the vulnerability index analyzes the failure of financial institutions in the case of systemic distress. The PAO model and vulnerability index provide information for ranking systemically important institutions, whereas the systemic impact index estimates the systemic impact of a financial institution’s failure. The data are daily stock returns, total assets, total equity, and total debt. The scholar concluded that overly big financial institutions do not directly become systemically important; instead, if their business strategy, portfolio, and balance sheet are interlinked to others, they are highly likely to become systemically important institutions. Alternatively, too-connected small financial institutions could become systemically riskier than large ones. Financial institutions become too big to fail if their diversified portfolios are systemically too connected and interconnected with their balance sheets, although they are managed well individually. Shapley value approach: Drehmann and Nikola (2013) estimate each bank’s incremental contribution to systemic risk using a participation and contribution approach based on game theory. The participation approach assumes that shocks propagate systemically to other banks through the interbank network. On the other hand, the contribution approach measures the amount of the bank’s own risk that influences the systemic level. Hence, the Shapley value is an estimation of the total contribution of interconnected banks to systemic risk. The data are loans and

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liabilities (size, interbank liabilities, and interbank assets) on balance sheets, off-balance sheets, and the probability of bank default. Specifically, banks’ interconnections were analyzed by creating a correlation matrix of co-movement using the independent variables of interbank and non-bank assets. The International Monetary Fund (IMF) introduced four complementary approaches to assess systemic linkages in the financial sector in the publication of the Financial Stability Report 2009. The four approaches are explained as follows: 1. The network approach: The safety and soundness of financial institutions are not only important, but their linkages to others also need to be managed in order to maintain financial stability. Hence, this approach considers two related shocks: first, an analysis of the domino effect. The domino effect is estimated as the probability of the transmission of defaulted financial institutions using credit risk linkages. Second, financial institutions are burdened by liquidity shortages when borrowing from defaulted institutions. To measure these, the IMF used data on cross-country bilateral exposures published by the BIS and estimated simulations between cross-institutions. 2. Co-risk model: The purpose is to measure the co-movement (co-risk or conditional distribution) of financial institutions in the case of risk events at one institution. Estimating co-risk across financial institutions, the model was formulated as a nonlinear relationship, and quantile regression of daily credit default swap spreads from 2003 to 2008. 3. The distress dependence matrix is organized to capture linear and nonlinear interdependence among financial institutions, and the volatility of the economic cycle implies an evolving conditional probability. Alternatively, this model draws a correlation matrix between financial institutions using their equity options. 4. The default intensity model describes the default rate of the spillover effect by measuring linear and nonlinear linkages. Huang et al. (2012) use a stress test to judge the systemic contribution of financial institutions in imposing insurance premiums. The main indicators are the probability of default (PD), loss given defaults (LGDs), correlation, and liability weights. The Monte Carlo simulation method was employed to compute the expected credit losses of financial institutions under the condition of the market total loss exceeding a given threshold. Adams et al. (2014) estimate spillover effects among financial institutions by extending a state-dependent sensitivity VaR model. The model is constituted as the total market VaR is dependent on the VaR of three indices: the US REIT index, GSCI Commodity index, and index of the US nonfinancial index, plus the dependent variable’s own lag period. The indices cover data on commercial banks, insurance companies, hedge funds, and investment banks. Engle and Manganelli (2004) assume that the distribution of return volatility in the stock market is autocorrelated and developed conditional autocorrelated value at risk (CaViaR) by regression quantiles. Studies of financial institutions’ contribution to systemic risk were mostly made in advanced markets, and data access could be compiled from the capital market file.

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In contrast, data availability is confined to the frontier market, thus making the research to face some kind of challenge. This field is conducted to bridge the data shortage to strengthen the analysis of systemic risk in the frontier market.

4.4

Methodology

Financial systemic risk engulfs the entire economy, causing defaults in most financial institutions. The risk burdens the government to pay recovery costs to the defaulted system through taxpayers’ money. However, financial institutions can internalize external shocks by measuring their contribution to systemic risk during systemic distress instead of insuring the government. Managing systemic risk is a different process for an individual, which means computing the total expected loss of a highly concentrated and interconnected financial market systemically. To accomplish this, how to measure systemic contribution of financial institutions? Hence, this methodology is based on the assumption of imposing an optimal tax on financial institutions. The amount of taxation depends on the financial institution’s contribution to systemic risk. Based on this assumption, the contribution is estimated by employing systemic expected shortfall (SES) (Acharya et al. 2016), which is dependent on marginal expected shortfall (MES) and financial leverage. Subsequent parts are written about explanations of economic model proof.

4.4.1

Basics of Systemic Contribution Model

The contribution to the systemic risk model is based on a standard risk measure at the firm level. The standard risk measures are “value at risk (VaR)” and “expected shortfall (ES).” VaR is the standard risk measurement for financial institutions, as well as financial regulators, to measure market, credit, and operational risk, respectively. Subsequently, the expected shortfall was developed as an extension of VaR to estimate the tail risk. The VaR and ES are: The probability of random loss increases from random variable X at a confidence level of α. Alternatively, the VaR model was developed to assess the amount of risk at the definite confidence level at the normal distribution in the 1980s and has become the standard measurement of market and portfolio risk for entities. The expected shortfall (Artzner et al. 1997) is the conditional expectation of loss, given that the loss is beyond the VaR level. VaR measures only the risk of an individual institution, whereas estimation of systemic risk requires an additional extension. Alternatively, the VaR model evaluates the amount of risk at the confidence level of a normal distribution. In contrast,

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systemic risk increases significantly at the outlier of a normal distribution. Then, ES during crisis extended to marginal expected shortfall (MES) due to eqs. 4.1 and 4.2, which measure the total risk in the system added to the financial institution’s overall risk. ESα = -

X1

y E½r i jR ≤ i i

- VaRα 

∂ESα = - E½r i jR ≤ - VaRα   MESiα ∂yi

ð4:1Þ ð4:2Þ

where R is financial institution i’s return, α is the percentage of extreme events, yi is the weight of group i, and ri is the return of each group. Hereafter, measuring each institution’s systemic expected shortfall as a dependent variable follows three factors: banks’ incentives to do business, negative externality, and optimal taxation.

4.4.2

Financial Institutions’ Incentives

Financial institutions intermediate in allocating cash flows from one part who planned to spend in the future to others who are demanding at the moment. Therefore, they run businesses to gain profits, such as fees and interest rates. Their financial position statement stands at the balance of the credit and debt sides. Financial institutions manage assets in terms of managing risk by creating a diversified portfolio. In addition, they reserve a definite number of assets to prevent predicted risks. Table 4.1 shows the economic model of banks’ incentives to engage in business. The model assumes that N financial institutions operate during two periods. Eq. (4.3) explains financial institutions’ investments in acquiring target assets at time 0. Investments come from two sources: debt and equity. Equation (4.4) depicts the total market value at time 1, in which financial institutions earn returns from their investment. Their total market value is equal to the difference in pre-distress income (return) and the costs of financial distress. Distress costs depend on the market value of financial institutions and the face value of outstanding debt. In addition, costs could occur even if the institution does not default; however, they are restricted by total assets. The government guarantees the costs to limit the market value of total assets. Equation (4.5) shows the fractions of government guarantees. Eq. (4.6) explains the institution’s net worth. Net worth is the residual amount to the institution’s owners after paying debt claims, which are owners’ required returns (opportunity cost) and their utilized assets. According to Table 4.1, financial institutions leverage to increase their own capital, which lies behind the fact that they take risks by collateralizing their own

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Table 4.1 Bank’s incentives No. 1

Times t=0

Equations XJ ai = xi ðj = 1Þ j

2

t=1

yi = ybi - ϕi XJ ybi = r i xi j = 1 j j (4.4)   ϕi = ϕ ybi , f i

3

Government guarantee on debt t=1 Net worth

bi = αif i + (1 - αi) E[min( fi, yi)](4.5)

Explanations ai : total asset J: Number of assets invested by each bank i = 1, 2, . . . .. N, number of financial firms xij : Each bank’s investment into available assets t = 0, 1 wi0 : Equity bi: Debt yi : total market value of asset ybi :Pre-distress income фi : Costs of financial distress f i : outstanding debt αi: Government guarantee

wi1 = ybi - ϕi - f i n o wi0 bi max; xij ;      c wi0 - wi0 - τi þ E u 1½wi   wi1 1>0 (4.6)

wi1 : equity at time 1 wi0 :Remaining equity ui(x):owner’s utility at time 1 τi : tax c: Opportunity cost

ai = wi0 þ bi

4

(4.3)

Source: author’s description based on Acharya et al. (2016)

capital. Nevertheless, they burden by paying outstanding debt corresponding to distressed costs when a crisis occurs. Looking through historical cases, financial institutions tend to take more excessive risks than the government-guaranteed amount, which is why the government is in charge of their risks (Calomiris 2009). The government needs to recover the defaulted system as a guarantor because of the lender of the last resort, deposit insurer, and provide economic stability at the trough of the cycle. In this case, the government’s risk fund might be insufficient to cover the default cost; thus, taxpayers’ money will be substituted. The result will direct an economic downturn and further instability. By contrast, the government aims to maximize welfare by building capital buffers, as shown in Table 4.2. A regulator that represents the government makes an effort to maximize the welfare function. The welfare function consists of three parts, as shown in Eq. (4.7). “p1” in Eq. (4.8) is the sum of utilities of all financial institutions as shown in Eq. (4.4) of Table 4.1, and it represents risk tolerance. “p2” in Eq. (4.9) is the expected cost of the debt guarantee which is covered by the government. “p3” in Eq. (4.10) is the main focus of an analysis which formulates the condition of occurring of systemic risk. Systemic risk occurs when aggregate equity falls below aggregate assets throughout the system.

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Table 4.2 Government welfare Regulator’s goal: Maximize the welfare function Sum of the utilities of all the bank owners The expected cost of the debt insurance program The externality of financial crisis

Choose a tax system based on ex ante regulation

Equations p1 + p2 + p3 (4.7) p1 =

PN



i = 1c

(4.8)

h

p2 = E g 

 hP  i N i i wi0 - wi0 - tax þ E i = 1 u 1½wi > 0  w1 1

PN

i = 1 1½wi1 < 0 α

i

i

wi1 (4.9)

g: Administrative costs (costs of tax collection) (cost is paid conditional on default by firm i)   p3 = E e  1½W 1 < zA  ðzA- W 1 Þ (4.10) e: Measures severity of the externality imposed on the economy P A = Ni= 1 ai : Aggregate assets in the system P w1 = Ni= 1 wi1 : aggregate banking capital to support it at t = 1 zA > w1 : systemic crisis occurs when the aggregate capital in the financial system falls below a fraction z of the asset A. P τ = τi (4.11) i

Source: author’s description based on Acharya et al. (2016)

Hence, the regulator imposes a tax “τ ” as shown in Eq. (4.11). The amount of tax depends on each financial institution’s financial leverage and flexibility to internalize external shocks. Initially, a tax is imposed at time 0; further, the regulator balances its capital buffer with lump-sum taxes from time 1. In summary, the government squeezes taxes from financial institutions as they increase leverage to restrict excessive risk. Table 4.3 shows the model of optimal taxation using the building capital buffer in Table 4.2. The model depends on each bank’s expected shortfall and systemic expected shortfall. The expected shortfall is explained earlier, whereas the systemic expected shortfall in Eq. (4.13) is the difference between the fraction of assets and capital equity in the condition that the owner’s utility becomes lower than the riskadjusted assets in terms of the regulatory principle. Finally, the optimal tax imposed on each financial institution is the total sum of the expected shortfall, systemic expected shortfall, and lump-sum tax (Eq. 4.14). The first part of Eq. (4.14) measures the financial institution’s risk. It is estimated as the financial institution’s probability of default times the expected losses. The second part of Eq. (4.14) depends on the probability of systemic risk; thus, it forces financial institutions to internalize the externality from aggregate distress. A financial institution’s contribution to systemic risk is computed by SES, but this is weighed by the severity “e” divided by costs of capital “c.” To build these three formulations in Table 4.3, it is important to capture the amount of individual institutions’ contribution to systemic risk. As demonstrated in Eq. (4.15), the main key to measurement is the systemic expected shortfall:

162 Table 4.3 Optimal taxation Expected shortfall Systemic expected shortfall Optimal tax

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 ESi = - E wi1 j wi1