Key Challenges and Policy Reforms in the MENA Region: An Economic Perspective (Perspectives on Development in the Middle East and North Africa (MENA) Region) 3030921328, 9783030921323

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Perspectives on Development in the Middle East and North Africa (MENA) Region

Mohamed Sami Ben Ali   Editor

Key Challenges and Policy Reforms in the MENA Region An Economic Perspective

Perspectives on Development in the Middle East and North Africa (MENA) Region Series Editor Almas Heshmati, Jönköping University, Jönköping, Sweden

This book series publishes monographs and edited volumes devoted to studies on the political, economic and social developments of the Middle East and North Africa (MENA). Volumes cover in-depth analyses of individual countries, regions, cases and comparative studies, and they include both a specific and a general focus on the latest advances of the various aspects of development. It provides a platform for researchers globally to carry out rigorous economic, social and political analyses, to promote, share, and discuss current quantitative and analytical work on issues, findings and perspectives in various areas of economics and development of the MENA region. Perspectives on Development in the Middle East and North Africa (MENA) Region allows for a deeper appreciation of the various past, present, and future issues around MENA’s development with high quality, peer reviewed contributions. The topics may include, but not limited to: economics and business, natural resources, governance, politics, security and international relations, gender, culture, religion and society, economics and social development, reconstruction, and Jewish, Islamic, Arab, Iranian, Israeli, Kurdish and Turkish studies. Volumes published in the series will be important reading offering an original approach along theoretical lines supported empirically for researchers and students, as well as consultants and policy makers, interested in the development of the MENA region. More information about this series at https://link.springer.com/bookseries/13870

Mohamed Sami Ben Ali Editor

Key Challenges and Policy Reforms in the MENA Region An Economic Perspective

Editor Mohamed Sami Ben Ali College of Business and Economics Qatar University Doha, Qatar

ISSN 2520-1239     ISSN 2520-1247 (electronic) Perspectives on Development in the Middle East and North Africa (MENA) Region ISBN 978-3-030-92132-3    ISBN 978-3-030-92133-0 (eBook) https://doi.org/10.1007/978-3-030-92133-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1 Terrorism Impact on Public Debt and Government Borrowing Cost: New Empirical Evidence from Long-Run Relationship in MENA Countries������������������������������    1 Lamia Jaidane Mazigh and Islem Khefacha 2 Digital Gaps and Economic Inequalities in MENA Countries: An Empirical Investigation��������������������������������   23 Ewa Lechman 3 Assessing the Determinants of Capital Flight from Tunisia: An ARDL Investigation Framework������������������������������   45 Hajer Dachraoui and Maamar Sebri 4 Assessing Macroeconomic, Distributive, and Environmental Impacts of Energy Subsidy Removal in Tunisia with Input–Output Modeling������������������������������������������������������������������   65 Aram Belhadj, Ahlem Dakhlaoui, and Rania Gouider 5 Remittances, Income Inequality, and Brain Drain: An Empirical Investigation for the MENA Region������������������������������   85 Hajer Kratou and Najeh Khlass 6 Digital Divide and External Trade Liberalization in the MENA Region: A Theoretical and Empirical Investigations������������������������������������������������������������������  103 Xiaoqun Zhang 7 The Relationship Between Money Laundering and Economic Growth in the MENA Region—A Simultaneous Equation Model����������������������������������������������������������������������������������������  123 Mehrez Ben Slama and Arij Gueddari 8 The Institutional Approach to Financial Development: Panel Study for the MENA Region��������������������������������������������������������  143 Samouel Beji and Aram Belhadj v

About the Editor

Mohamed Sami Ben Ali  is Professor of Economics at Qatar University. Previously, he was head of the Department of Economics and member of the scientific board at HEC Business School, Tunisia. He holds a H.D.R. degree, the highest European qualification for research. Previously, he received a Ph.D. in economics with high honors from the University of Lille, France, an M.Phil. (D.E.A.) in international finance and international trade, and a B.A. in business economics. Dr. Ben Ali is serving as associate editor for Springer and De Gruyter journals and editor for Palgrave, Springer, and Routledge (Taylor and Francis) books series. He has been teaching for the past years at graduate and undergraduate levels in Tunisia, Qatar, and France where he was a visiting professor. He has published numerous articles in French and in English in international refereed academic journals. His research and publications focus mainly on economic development and international monetary macroeconomics. He is actively participating in and chairing numerous international conferences.

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Chapter 1

Terrorism Impact on Public Debt and Government Borrowing Cost: New Empirical Evidence from Long-Run Relationship in MENA Countries Lamia Jaidane Mazigh and Islem Khefacha

1.1  Introduction The Middle East region has been a conflict environment for a long time. Political violence, vast oil wealth, water stress, strategic location, and huge population are serious factors, which enhance the conflict risk of these countries (Böhmelt et al., 2014). Since the 1990s, the region has become a favorable ground for fundamentalist terrorist activities, especially after the decline of left-wing terrorism in Europe, Latin America, and the USA.  This phenomenon worsened further after the 9/11 attacks and has been increasing significantly since Arab Spring uprising. The global MENA’s share of terrorist attacks jumped up from 9.8% during 1970–1989 to 36.1% during 2002–2018 (Kim & Sandler, 2020). The number of deaths from terrorism reached more than 96,000 deaths between 2002 and 2019 (IEP, 2020). Several considerations explain the geographical shift of terrorism events to MENA countries. Foremost, the region has been the birthplace of numerous terrorist organizations namely Islamic State, al-Qaida, and al-Nusra. Furthermore, various MENA countries are involved in civil wars both within and between nations. Over 96% of deaths from terrorism in 2019 occurred in countries already in conflicts (IEP, 2020). Finally, political instability caused by Arab Spring and the transition to democracy

L. J. Mazigh (*) University of Monastir, FSEG Mahdia, Hiboun, Tunisia UR DEFI, University of Tunis, Tunis, Tunisia e-mail: [email protected] I. Khefacha University of Monastir, FSEG Mahdia, Hiboun, Tunisia LaREMFiQ, University of Sousse, Sousse, Tunisia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. S. Ben Ali (ed.), Key Challenges and Policy Reforms in the MENA Region, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-3-030-92133-0_1

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reinforce terrorism. Indeed, Gaibulloev et al. (2017) showed that there is an inverted U-shaped relationship between terrorism and the type of regime, so countries in democratic transition will be characterized by much higher levels of terrorism than autocratic regimes or even old democracies. The proliferation of terrorist attacks in several MENA countries for many years had cast dark shadows on their economies. Indeed, beyond the destruction of infrastructure, terrorism has seriously affected the key sectors of several economies in the region. The deadly attacks in Egypt, Tunisia, Turkey, and Morocco have drastically plummeted tourism receipts and fled foreign investment, causing major problems for these countries. Likewise, violent activities have often disrupted oil exports and destabilized several financial markets. Faced with these disastrous consequences, the countries of the region are forced to further increase their security expenditure, which is already quite high (Ortiz et al., 2021). Compared to other countries, those of MENA region had the highest average of military burden. In 2019, the Middle East is estimated to have accounted for about 9.4% of the global military spending in 2019, ranked as the fourth region after North America, Asia-Oceania, and Europe (SIPRI, 2020). The strain on public finances, from which most MENA countries are suffering (Acikgoz & Ben Ali, 2019), increases debt needs to support economic growth or to finance weapons importation. This debt problem can get even worse when the cost of borrowing in the financial markets is very high. Indeed, political instability and terrorism are among the major determining factors of sovereign credit rating established by different agencies. What this research attempts to do is to investigate whether terrorist attacks affect public debt in the case of MENA countries. This study contributes to the existing literature in three dimensions. Firstly, it is the first to focus on the direct relationship between terrorism and both ratio and cost of the public debt. Secondly, it employs alternative panel data estimation techniques addressing econometric issues, such as heterogeneity and cross-sectional dependence between countries, and the Dumitrescu–Hurlin panel causality test to examine the causal association among the variables and the fully modified ordinary least-squares (FMOLS) model to estimate the long-term coefficients. Finally, it uses recent data with new terrorism indicator. The remainder of the study is organized as follows. Section 1.2 describes a brief overview of the studies on the nexus between terrorism growth and public debt. Section 1.3 introduces data and method. Section 1.4 presents main findings. Finally, the study is concluded with a summary of the main results and policy recommendations.

1.2  L  iterature Review: Terrorism, Economic Growth, and Public Borrowing The multidimensionality of terrorism makes its definition very difficult to specify. Indeed, various legal systems and government agencies use different definitions. The Council Resolution of Safety of the United Nations defines terrorism as

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“criminal acts, including against civil, made in the intention to cause death, of the serious body lesions or the taking of hostages, with an aim of causing a state of terror in general public or a group of people or people in particular or of forcing a government or an international organization to make or to abstain from any act.” More specifically, the Institute for Economics and Peace (IEP) considered it as “the threatened or actual use of illegal force and violence by a non-state actor to attain a political, economic, religious, or social goal through fear, coercion, or intimidation.” Of relevance to this chapter is the latter definition as it is the most frequently used in the literature. Following the First World War, Keynes (1920) and Pigou (1940) confirmed the strong nexus between peace, violent activities, and economic growth. In the same vein, several economists tried to study the effect of terrorism, given its heavy consequences on the economic and financial sphere, especially after 11/9/2001. The global economic impact of terrorism in 2019 amounted to US$26.4 billion (IEP, 2020). The MENA region is ranked in third position accounting for $4.7 billion, after the Sub-Saharan Africa ($12.5 billion) and South Asia ($5.6 billion). In the short run, direct costs of terrorism include deaths, property damage, injury deterioration of transport, communication, and electricity infrastructure. Although these direct costs can sometimes be quite substantial (as is the case with World Trade Center attacks), the indirect and long-term costs of terrorism are always much greater for the targeted country. Indeed, terrorism slows growth, reduces trade, redirects FDI, and destabilizes the financial markets. Bilgel and Karahasan (2017) analyzed the economic performance of the Turkish economy with and without terrorism, during the period 1988–2008. They conclude that Turkey’s per capita GDP would have been 21.4% higher on average if there were no terrorist attacks. Employing the LM bootstrap panel cointegration to study growth peace and terrorism nexus in 18 MENA countries over the period 2008–2014, Bayar and Gavriletea (2018) showed the negative impact of terrorism on economic growth, which was relatively higher than the positive impact of peace on economy. Furthermore, their study affirmed that violent attacks could harm strategic sector namely transport and tourism. This effect is more pronounced in growth economy based on tourism, as in the case of several MENA countries. Since the 1990s, Enders et al. (1992) demonstrated that terrorism deter tourism not only in the target country but also in neighboring nations. More recently, Afonso-Rodrıguez and Santana-­ Gallego (2018) showed that the Arab Spring and terrorist attack have diverted tourists from the MENA region to Spain. In addition, terrorism can limit investment due to increasing uncertainty and insurance premiums, which reduce profits and productivity. Otherwise, a context of violence reduces the attractiveness of the target country and redirects foreign direct investment flows to safer locations. Enders and Sandler (1996) revealed that violent attacks in Spain have reduced FDI by an average of 13.5% per annum from 1975 to 1991. Using the Spatial Econometric Panel Data Approach, Seifi et al. (2020) examined the link between terrorism and capital outflow in MENA countries during the period 2002–2016. The authors concluded that terrorism has a significant and positive impact on capital flight between these countries and dramatically influences the flow of goods and resources. Certainly, the need of strengthening border protection enhances the cost transaction of trade and

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can, in some cases, make it even impossible. In that way, Nitsch and Schumacher (2004) showed that terrorism impedes trade by increasing transaction costs and indirectly by harming economic growth and production capacity. By analyzing a panel of 177 countries during the period 1968–1999, Blomberg and Hess (2006) concluded that terrorist attack is equivalent to as much as a 30% tariff on trade. It should be noted that these negative effects of terrorism depend on the level of the target country economic development. Blomberg et al. (2004) showed that terrorism is more widespread in developing countries than in developed countries. Gaibulloev and Sandler (2008) concluded that developed countries, as opposed to developing countries, can absorb terrorism without negatively affecting economic performance. The main reason is that developed countries have more diversified economy and terrorism simply reallocates resources to more secure sectors. In addition, it appears that terrorism impact is more pronounced if there are a large number of victims (Tavares, 2004) and when terrorists belong to well-known organizations (Sandler & Enders, 2008). Contrary to the abundant analyses of the economic effects of terrorism, cited in part above, scant attention has been devoted to study its impact on public finances and specifically its consequences on debt (Jaidane-Mazigh et  al., 2019; Abid & Sakrafi, 2020). The objective of this chapter is to make progress in this area. We consider that terrorism affects budget balance on both the revenue and the expenditure side. On the one hand, terrorism tends to reduce the tax base and tax revenues by discouraging consumption and investment and slowing down economic activity. On the other hand, counterterrorism activities increase government’s spending for security, especially military expenses. Terrorism can also raise the volatility of the discretionary component of fiscal policy and divert resources away from spending on socially and economically productive sectors. To maintain financial balance, several countries particularly those with severe financial constraints resort to borrowing, when taxation or seigniorage is not workable. This effect is even more pronounced when the sovereign rating of the country targeted by terrorist attacks is downgraded by the various rating agencies, like Moody, Fitch, and Standard and Poor. Political events impact financial markets, and the literature on sovereign debt shows a strong link between political and sovereign risks (Afonso, 2003). Indeed, a lower rating increase sovereign spread and consequently escalate the cost of debt. The harmful terrorism effects caused by terrorism on target countries are theoretically obvious. However, the empirical investigations carried out by several economists have not reached the same consensus. Using the ICRG measure for internal conflict and terrorism for 66 low- and middle-income countries, Gupta et al. (2004) concluded that terrorism contract the government revenues from taxes, increase the public expenditure and weaken economic growth. Gaibulloev and Sandler (2008) found that acts of terrorism lead to an increase in government spending in European countries. They show that an additional domestic incident per million persons causes an increase in the government spending share by 0.2%. Yogo (2015) revealed that terrorism aggravates the volatility of fiscal policy in countries of small size and much less in more democratic countries. In the same vein, Drakos and Konstantinou (2014) showed that terrorism significantly increases public order spending on

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European countries; however, the magnitude of this effect is small and fades out after only one year. As for Cevik and Ricco (2015), they suggest that terrorism has only a marginal negative effect on tax revenue. This effect is also not robust to alternative specifications and empirical strategies. Edeme and Nkalu (2019) concluded that terrorism does not seem to have impact on government revenue independent of its effect on growth in the case of Nigeria. Otherwise, the impact of terrorism on public debt is not limited on its effect on the real economy, and some studies have sought to determine its indirect long-term repercussions on the capital market. The seminal analysis of Haddad and Hakim (2007), using a panel of five MENA countries (Egypt, Lebanon, Morocco, Saudi Arabia, and Turkey), showed that during 2002–2006, these countries’ sovereign spread increased on average by 135 bps after the 9/11 attacks. Procasky and Ujah (2016) extended this analysis to 102 countries. Their study supports the hypothesis that terrorism results in a higher cost of debt for sovereigns, and this effect is more pronounced in developing as opposed to developed countries. The authors find that two-point increase in terrorism on average downgrades the sovereign credit rating by an entire notch. In the same vein, the study of Moody’s Investor Services (2015) using data for 156 countries concluded that terrorism increases the sovereign borrowing cost by about 65 bps in the year of the attack and by a further 51–81 bps the following year. Unlike the results of several studies, Haddad and Hakim (2008) analysis revealed that, with exception of Turkey, terrorist event like the suicide bombings in Casablanca (May 2003) or the attacks on Sharm el-Sheikh (July 2005) has no impact on the sovereign spread on most MENA counties. The authors assumed that “the region’s Eurobond markets have become somewhat immune to war and terrorism” (p: 249). Contradictory results from different analyses of terrorism effect on public debt, and the scarce published work on the relationship between violent attacks and the cost of borrowing, explain our keen interest in prolonging the research in this area, specifically for MENA countries.

1.3  Research Methodology 1.3.1  Data Description and Econometric Model This section discusses the construction of the empirical model specification to examine the causal relationship between terrorism and debt for eleven MENA countries, namely Algeria, Bahrain, Egypt, Lebanon, Jordan, Kuwait, Qatar, Saudi Arabia, Morocco, Tunisia, and Turkey between 2002 and 2018. To avoid the problem of missing values and to the extent that it allows us to have a continuous and comprehensive source of data, we decided to retain only 11 countries in this specific period. The dataset was obtained from different sources (see Table 1.1).

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Table 1.1  Variable description Variables DTG SCR GTI TAX GDPTH LNTOTR TB PS DS

Role Dependent variable Dependent variable Main independent variable Control variables

Definition Debt-to-GDP ratio Sovereign credit rating Global terrorism index

Source IMF Moody IEP

Taxes on income GDP growth Log of total reserves minus gold (current US$) Trade balance Political stability Default sovereign

IMF WDI WDI WDI WDI Bank of Canada—CRAG

IMF International Monetary Fund, IEP Institute for Economics and Peace, WDI World Development Indicator

Global terrorism index is the first terrorism index, which tries to rank countries on the impact of terrorist activities by including both economic and social dimension. GTI is produced by the Institute for Economics and Peace (IEP) and based on data from the Global Terrorism Database1 (GTD). Analytically, it is a composite index that combines several factors weighted differently as shown in Appendix 1. Sovereign credit rating is an assessment of both the ability and the willingness of a country to honor its debt. Given the qualitative nature of these ratings (Annex 2), we used the method of Procasky and Ujah (2016) by converting letters to scalar index from 1 (default) to 22 (Aaa). For reasons of data availability, we choose rating from Moody’s agency (Appendix 3). Two major empirical research questions are analyzed in this study: First is terrorism cointegrated with the ratio and the cost of debt? If the long-run equilibrium relationship is identified, what is the magnitude of the impact of terrorism in this specific case of MENA countries? Second, for the short run what are the main channels through which terrorism can impact debt ratio? To this end, and in line with Dunne (2003) for the first mode and Afonso (2003) for the second model, we try to estimate two regressions expressed as follows:

DTG i , t   0   1 GTI i , t   2  X i , t    i , t





SCR i , t   0   1 GTI i , t   2  zi , t    i , t



(1.1) (1.2)

where i corresponds to the countries, and t, the time dimension.

1  Codified over 104,000 cases of terrorism, the GTD is considered one of the most comprehensive datasets on terrorist activity.

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DTGi,t and SCRi,t are the two dependent variables representing, respectively, the national debt and sovereign rating, GTIi,t represents Global Terrorism Index, and Xi,t is the vector of control variables that include TAXi,t, PSi,t, DSi,t, ln(TOTRi,t), GDPTHi,t, and TBi,t, νi,t = δift + εi,t where ft refers to the unobserved common heterogeneous factor loadings δi, and εi,t is the residual term. Our main parameters of interest are α1 and β1, describing the percentage point change in debt-to-GDP and sovereign credit rating as a reaction to one percentage point increase in the index of terrorism. Figure 1.1 shows a similitude between the evolutions of terrorism and cost and debt ratio in MENA region, hence expecting a causal relationship between them.

Fig. 1.1  GTI, debt-to-GDP ratio, and sovereign credit rating in MENA Countries (mean levels), 2002–2018

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1.3.2  Econometric Methodology As it is known, panel data include both the cross-sectional and time-series dimensions. However, the use of panel data in the regressions also presents some problems due to the presence of trending variables referring to the concept of non-stationarity. Consequently, conventional panel data estimations, such as fixed and random effects, will be spurious. To avoid these dangerous techniques of estimation, the analysis of the empirical relationship for the determinants of debt-to-GDP ratio and sovereign rating should follow four steps. First, to choose the right estimation method, it is necessary first to test the contemporaneous correlation across countries in the panel (CD test). Consequences of cross-sectional dependence might be severe, resulting in estimator efficiency loss and invalid test statistics due to the residual dependence (Pesaran, 2007). Many tests for cross-section dependence in the literature were developed. We apply the widely cross-sectional dependence test in economic literature based on the Lagrange multiplier (LM), which is widely to decide the presence of cross-sectional dependence among the panel of countries. Initiated by Breusch and Pagan (1980), this test is a more appropriate test for panels with adequate large T and relatively small N. The LM test is modeled using the following empirical equation:

yit   i   i xit  it

for i  1, 2,.., N ; t  1, 2,, T

cross-sectional and time dimensions are shown, respectively, by i and t, and the vector of impendent variables is represented by xit. The null hypothesis of no cross-­ sectional dependence and alternate hypothesis of cross-sectional dependence are stated as follows: H 0 : cov  it , jt   0

H a : cov  it , jt   0

forall t and i  j forallleast onepair of i  j



To test these hypotheses, the LM statistic test is calculated as follows:



N 1 N 2  N  N  1  LM  T   Æ  ij   2 i 1 j  i 1  

where ρˆij corresponds to the correlation coefficient for each i. Another preliminary test, known as delta test (∆) developed by Pesaran and Yamagata (2008), must be used to decide whether the slope coefficient is homogeneous or heterogeneous. The null hypothesis tests the homogeneity of the slope coefficient and heterogeneous in the alternative hypothesis. In case of large-scale panel, and under the null hypothesis of homogeneity, the ∆ is distributed as asymptotic standard normal:

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 1 ˜  ˆ  N  N S  k   2k   



However, Pesaran and Yamagata (2008) proposed to improve the Δ test for the small sample properties by introducing a bias-adjusted version of the mean and the variance:

ˆ adj

 1 ˜ ˜  N S  E  Z iT   N ˜  var Z iT  

     

where



˜  ˜  E  Z it   k, var  Z it   2 k  T  k  1 /  T  1    

The second step in our empirical methodology is based on the fact that if the hypothesis of cross-sectional dependence is accepted when tested across sections, we must implement unit root tests with special specifications to subtract cross-­ sectional means or allow for cross-sectional dependence. To consider the possible cross-sectional dependence of errors and the heterogeneity of parameters, we use the second-generation panel unit root tests, notably the CIPS unit root test (Pesaran, 2007). This test is based on the mean of individual DF (or ADF) t-statistics of each unit in the time-series data set. To eliminate problems arising from cross dependence, the CIPS test includes estimation of the separate cross-sectionally augmented Dickey–Fuller (CADF) regressions, allowing a difference between autoregressive parameters for each member of the panel. The test is calculated as follows: N



CIPS  N 1 CADFi i 1



where CADFi statistic represents the ith cross-sectional unit given by the OLS t ratio of ρi in the above CADF regression. Once the non-stationarity of the variables is tested and confirmed, the subsequent step is to examine the long-run relationship among cost and the ratio of public debt, terrorism, and control variables with Westerlund (2007) cointegration test. In fact, concerning heterogeneity and cross-sectional dependency, the first-generation panel cointegration tests are not appropriate (Pedroni, 2007). In so doing, we use the second-­generation panel cointegration tests developed by Westerlund (2007) to avoid the problem of common factor restrictions. Based on the error correction

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model (ECM), four-panel cointegration tests, Gτ, Gα, Pτ, and Pα, are applied. The two first tests (called group tests) are applied to assess the alternative hypothesis that at least one cross-section is cointegrated, while the other two tests (called panel tests) are used to test the alternative hypothesis that the whole panel is cointegrated. Gτ and Pτ are estimated in a standard way calculated with the standard errors of the intercept term, while Gα and Pα are based on the Newey and West (1994) adjusted standard errors for heteroskedasticity. The rejection of the null hypothesis allows the existence of the cointegration relationship (Westerlund, 2007). Moreover, to obtain reliable results in the presence of slope heterogeneity and cross-sectional dependencies, we use the bootstrapped distribution method (Persyn & Westerlund, 2008). In the third step and having shown the non-stationary of the panel series but also the presence of cointegration, we turn to generate long-run estimates for regressions (1.1) and (1.2). Based on the nonparametric method, Phillips and Moon (1999, 2000) propose a variation for the MG estimator, which they call the group mean panel fully modified OLS (FMOLS) and then further modified by Pedroni (2000, 2007). Apart from its capacity to consider heterogeneity among the panel units, the FMOLS method has an advantage over the traditional ordinary least squares (OLS) by eliminating the correlation between the explanatory variable and the random interference term (Khefacha & Belkacem, 2016). Finally, if a panel long-run relationship between both cost and the ratio of public debt and terrorism is established, this indicates that there must be causality in at least one direction (Granger, 2003). Considering the heterogeneity and cross-­ sectional dependency, Dumitrescu and Hurlin (2012) proposed a test to detect the direction of causality. The short-term causal relationship test is based on the individual Wald statistic averaged across the cross-section units following the Granger (1969) non-causality hypothesis in a heterogeneous panel model.

1.4  Results and Discussion 1.4.1  C  ross-Sectional Dependence, Homogeneity, and Unit Root Test The presence of cross-sectional dependence and the non-stationarity of the variables affect the causal estimates between the dependent and independent variables. Table 1.2 illustrates the results of cross-sectional dependence tests of Breusch and Pagan (1980) appropriate for a small N. The results confirm the presence of spatial effect across the panel of 11 MENA countries for the first model and across the panel of 10 MENA countries for the second model at 1% significance level2. 2  Alegria was removed from the MENA countries’ sample because of the absence of sufficient data for the sovereign credit rating.

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1  Terrorism Impact on Public Debt and Government Borrowing Cost… Table 1.2  Cross-sectional dependence Breusch–Pagan LM test

Model 1: DTG Statistic 165.1464

Model 2: SCR Statistic P-value 183,844 0.0000

P-value 0.0000

Source: Authors’ calculations Table 1.3  Homogeneity test

∆ ∆adj

Model 1 : DTG Statistic 3.757 5.164

P-value 0.0000 0.0000

Model 2 :SCR Statistic 4.630 6.363

P-value 0.0000 0.0000

Source: Authors’ calculations

Since this study is conducted over a small panel, the ∆adj statistic of Pesaran and Yamagata (2008) should be interpreted. Our findings as shown in Table 1.3 revealed that null hypothesis of homogeneity was rejected, and hence, the cointegrating coefficients were found to be heterogeneous at the 1% significance level. To examine the stationarity properties, we apply the CIPS unit root test, and the reported results are shown in Table 1.4. We find that the null hypothesis is accepted, and it is decided that most of the series are not stationary at the level, for both the model with intercept and the model with intercept and trend. In simple words, both dependent and independent variables are integrated, i.e., I(1) except for the default sovereign (DS), which is I(0). Therefore, there is evidence of a long-run interaction between the series.

1.4.2  Findings of Panel Cointegration Tests The non-stationarity of the variables leads to test for cointegration among the dependent and independent variables. Given the heterogeneity of the panel and the existence of cross-sectional dependence, we employ the error correction-based panel cointegration test developed by Westerlund (2007) to detect the long-run relationship between public debt and government borrowing cost and terrorism. Table 1.5 reports the results of panel cointegration tests. Following Persyn and Westerlund (2008), we consider the bootstrapped p-values robustness, which accounts for the presence of common factors affecting the cross-sectional units. The results revealed the rejection of the null hypothesis of no cointegration for the fourpanel statistics at 1% level. As economic implication, we can conclude that there is a stable equilibrium long-run relationship among the variables for a sample of MENA countries over the period 2002–2018.

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Table 1.4  Pesaran (2007) panel unit root test analysis

DTG SCR GTI LNTOTR GDPTH TB PS DS TAX

Constant Level −2.242 0.415 −1.679 −2.124 −2.234 −1.804 −2.568 −7.905*** −1.363

Constant and trend Level First difference −2.064 −3.171*** 3.236 −1.941*** −2.611 −3.787*** −2.640 −3.907*** −2.461 −4.748*** −2.173 −3.224*** −2.403 −3.613*** −3.793*** −12.491*** −3.366*** −4.558***

First difference −2.960*** −4.871*** −3.786*** −3.956*** −12.571*** −3.268*** −3.771*** −8.060*** −4.642***

Source: Authors’ calculations Note: *** indicates rejection of null hypothesis at 1% level Table 1.5  Westerlund (2007) cointegration test analysis

Gt Ga Pt Pa

Model 1 : DTG Value z-value −1.723 2.286 −1.300 4.992 −4.888 1.811 −1.741 3.131

Robust p-value 0.000 0.000 0.000 0.000

Model 2 :SCR Value z-value −2.108 0.985 −2.027 4.485 −3.306 2.862 −1.463 3.086

Robust p-value 0.000 0.000 0.000 0.000

Source: Authors’ calculations

1.4.3  F  indings from Fully Modified Ordinary Least Square (FMOLS) Following the lead of Pedroni (2000), the long-run association among the variables is examined by using the FMOLS technique. According to Table 1.6, it appears that terrorism has a positive and statistically significant effect on the ratio of public debt. A 1% rise in the terrorism index generates a significant increase of 0,82% in the debt-to-GDP ratio. Our result is consistent with Jaidane-Mazigh et al. (2019) and Abid and Sakrafi (2020). Many transmission channels can explain the positive impact of terrorism on public debt such as the increased security spending, the crowding out private investment, the slowing economic growth, the loss of tax revenue, and the disruption of foreign trade. This hypothesis is based on findings of several studies (Gupta et  al., 2004; Drakos & Konstantinou, 2014). Furthermore, regarding the control variables, our results show that GDP growth, tax revenue, and total reserves negatively impact the public debt ratio in accordance with Smyth and Narayan (2009), Ahmed (2012), and Shahbaz et al. (2013). It is obvious that fast economic growth rates and increases in tax revenue allow countries to pay off the debt and reduce the need to borrowing in the future (Dunne et al., 2004). A country with large foreign reserves will have better abilities to refund external debt (Azam & Feng, 2015).

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Table 1.6  Results of long-run analysis through FMOLS Variable GTI LNTOTR GDPTH TB SP TAX DS

Model 1 : DTG Coefficient t-Statistic 0.823427 10.77233 −0.546475 −8.169373 −0.533534 −5.613663 0.667018 8.123986 0.828936 9.788170 −1.260450 −13.00132 – –

Prob. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 –

Model 2 :SCR Coefficient −3.857548 5.769672 −0.972177 19.40569 4.174643 – −3.302228

t-Statistic −36.99132 68.69550 −8.639680 310.8722 43.39685 – −29.42217

Prob. 0.0000 0.0000 0.0000 0.0000 0.0000 – 0.0000

Source: Authors’ calculations

Interestingly, unlike the results of several studies that have tried to verify the different variables that determine public debt (Berg & Krueger, 2003; Ali & Ahmed, 2017), we notice that openness and political stability impact positively the ratio of public debt in the case of MENA countries. It turns out that an open country characterized by political stability can benefit from donor confidence and raising of the debt ceiling. This finding is supported by the results of our second model, which show that openness and political stability impact positively the sovereign credit rating. The second regression show, as anticipated, that terrorism index impact negatively sovereign credit rating. The resultant coefficient of GTI suggests that on average, a one-unit increase in terrorism index would decrease by 3,86 the sovereign rating at 1% significance level, which is approximately equivalent to four-notch downgrade. This result supports those of Haddad and Hakim (2007) and Procasky and Ujah (2016) and shows that the impact of terrorism on sovereign ratings is more pronounced in our study. In effect, descriptive statistics (Appendix 4) indicate that the average of the rating for this region during the period studied is 13.39, which corresponds to the rating Baa3 (Appendix 2). The downgrading of almost four-­ notch suggested by our model corresponds to a shift from lower medium grade to non-investment grade speculative Baa3 to B1 (Appendix 3). This comparatively higher finding corresponds well to the reality of some countries in our sample, such as Tunisia, Jordan, Egypt, and Turkey, which saw their scores deteriorate by 4 notches and more since 2010. Admittedly, several other factors explain this deterioration, but the multiple terrorist attacks, which occurred in these countries during this period, certainly contributed to this situation. By comparing the results of the two models, it appears that the impact of terrorism on the cost of debt (−3.85) is much more pronounced than on the debt ratio (0.823). These findings confirm the idea that financial markets are much more sensitive to bad news than the real economy. Additionally, it emphasizes the amplified pro-cyclical role of rating agencies who “upgrading countries in good times and downgrade them in bad times” (Kaminsky & Schmukler, 2002, p: 172).

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1.4.4  R  esults of Dumitrescu and Hurlin (2012) Panel Causality Test The causal relationship between cost and the ratio of public debt with terrorism is analyzed with the panel Granger causality test of Dumitrescu and Hurlin (2012), which considers both cross-sectional dependency and heterogeneity. The results reported in Table  1.7 show that terrorism (GTI) causes both the borrowing cost (SCR) and the ratio (DTG) of the public debt. To detect possible transmission channels that can explain the effect of terrorism on public debt on the long run, we test the Granger causality between terrorism, and the different control variables. Table 1.8 shows causality running from terrorism to growth, tax, openness, and public stability. These results are consistent with those of Gupta et al. (2004) and Chuku et al. (2019).

1.5  Conclusions and Policy Implications This study investigates the long-term effect of terrorism on the public debt in the MENA countries. We used the second-generation panel cointegration test, which includes cross-dependence among countries (Westerlund, 2007) and FMOLS estimators. Our results can be summarized in three main findings. First, terrorism has a positive and statistically significant effect on the debt-to-GDP ratio. Second, the causality test confirms that tax, growth, political stability, and openness are the main channels through which terrorism can impact the public debt. Third, the results indicate that a one-unit increase in terrorism index would decrease by 3,86 the sovereign rating at 1% significance level, which is approximately equivalent to four-­ notch downgrade. Hence, terrorism not only affects debt through increased security spending and the deterioration of major macroeconomic magnitudes, but also through the downgrading of the sovereign rating credit of the target country. This downgrade, in turn, increases the spread and thus increases the cost of borrowing in international markets and discourages the influx of foreign investment. Hence, through its effect on sovereign ratings, terrorism aggravates the debt problem even in countries that have no constraints on repaying their debts. Several policy insights can be drawn from this analysis. First, it is obvious that target countries are employing all the necessary measures to thwart terrorism considering its multiple effects. Nonetheless, the authorities should not overspend on defensive counterterrorism strategies, which can destabilize their public finances. It Table 1.7  Dumitrescu and Hurlin (2012) Granger causality analysis ΔGTI does not Granger cause ΔDTG/ΔSCR Model 1: ΔDTG z-statistics 13.669 0.0000 Source: Authors’ calculations

Model 2: ΔSCR 5.348 0.0000

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Table 1.8  Dumitrescu and Hurlin (2012) Granger causality analysis ΔGTI does not Granger cause ΔGDPTH z-statistics 10.1584 ΔGTI does not Granger cause ΔTAX z-statistics 2.9968 ΔGTI does not Granger cause ΔTB z-statistics 4.5406 ΔGTI does not Granger cause ΔSP z-statistics 2.9149 ΔGTI does not Granger cause ΔLNTOTR z-statistics 1.2094

0.0000 0.0027 0.0000 0.0036 1.2094

Source: Authors’ calculations

will be better to improve the management of public spending to prevent terrorism rather than just fight it. Second, the countries of the region are called upon to continue their efforts in political and economic reforms to ensure internal stability and a better integration into the world economy. An improvement in political rights, an implementation of good governance practices, and more open economy can discourage rating agencies from “over-downgrading” these countries. Lastly, security measures, which can certainly reduce terrorist attacks in a country, can also shift these activities to the rest of the region. Terrorism is a “world public scourge” that must necessarily be fought jointly. Indeed, the MENA countries must improve their coordinated efforts to combat terrorism jointly and limit the burden of defensive measures, which crowd-out investment and growth with great cost for present and future generations, especially against the backdrop of COVID-19.

Appendices Appendix 1 The score of GTI at time t can be calculated as following:



 

 

 

Score  t   X 1  Y 3  Z 0,5  W  2



(X) the total number of terrorist incidents in a given year; (Y) the total number of fatalities caused by terrorist incidents in a given year (weight of three); (Z) the total number of injuries from terrorist attacks in a given year; and (W) a measure of the total property damage from terrorism in a given year (weight of two) (Table 1.9). In order to take into account the remaining effect terrorist attacks on a society in terms of fear and subsequent security response, the GTI combines the events of previous years as having a bearing on a country’s score in the current year by weighting the country’s previous scores using the values shown in Table 1.10.

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Table 1.9  Indicator weights used in the GTI Indicator Incidents (X) Deaths (Y) Injures (Z) Property damage (W)

Weight 1 3 0,5 2

Table 1.10  Weighting of historical scores Year % of Score

Current year 52%

Previous year 26%

Two years ago 13%

Three years ago 6%

Four years ago 3%

Finally, given these percentages, the country for that year would be assessed as having a raw impact of terrorism score as:



GTIt  52%Score  t   26%Score  t  1  13%Score  t  2  6%Score  t  3  3%Score  t  4 



Appendix 2 Table 1.11 provides the bond rating conversion codes for the Moody’s and S&P ratings that we use in the analysis.

1  Terrorism Impact on Public Debt and Government Borrowing Cost… Table 1.11  Bond rating numerical conversions Conversion no. 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Source: Klock et al. (2005)

Ratings Moody’s Aaa Aa1 Aa2 Aa3 A1 A2 A3 Baa1 Baa2 Baa3 Ba1 Ba2 Ba3 B1 B2 B3 Caa1 Caa2 Caa3 Ca C Default

S&P AAA AA+ AA+ AA− A+ A A− BBB+ BBB BBB− BB+ BB BB− B+ B B− CCC+ CCC CCC− CC C D

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Appendix 3

L. J. Mazigh and I. Khefacha

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Appendix 4 Descriptive Statistics TDE Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis JarqueBera Probability Sum Sum Sq. Dev. Observations

GTI

LNTOTR GDPTH

0.558980 3.375273 23.83315 4.647783 0.460700 3.560000 23.73228 3.803522 1.851900 7.500000 27.31894 26.17025

TAX

SP

TB

SCR

0.150729 −0.510724 0.020695 13.39412 0.155016 −0.510508 −0.027019 13.00000 0.473000 1.223623 0.484522 20.00000

0.015800 0.000000 21.17230 −7.076056 0.000000 −2.116773 −0.415008 6.000000 0.414448 2.140773 1.361245 4.112838

0.109271 0.755674

0.199090

4.219281

1.066019 0.004316 0.543143 1.576103

0.625355 0.351497

0.430061

0.141061

3.770321 1.869120 3.035727 8.801576 40.04122 9.965261 9.204256 339.6752

3.508095 2.756512 14.19979 4.312593

2.468391 7.966338

1.906517 9.033358

0.000000 0.006856 0.010030 0.000000

0.000825 0.115753

0.018627

0.010925

104.5293 631.1760 4456.799 869.1354 31.94865 852.4208 344.6560 3146.271

28.18640 −95.50542 3.870050 2.220883 106.2140 7.372443

2277.000 3008.594

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187

170

187

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Chapter 2

Digital Gaps and Economic Inequalities in MENA Countries: An Empirical Investigation Ewa Lechman

2.1  Introduction Observed globally, digital technologies’ fast-growing penetration rates shall inevitably lead to cross-country diminishing gaps in this respect. The unique technology convergence shall be observed. Rapid advances in digital technologies access to and use of observed at national level shall automatically generate cross-country convergence process and so that the development gaps between the “ICT-poor” and the “ICT-rich” should be gradually eradicated. It seems that the nature and unprecedentedly rapid diffusion of digital technologies constitute a perfect prerequisite for cross-country ICT gap elimination. This technology convergence potentially may be converted into economic convergence. New information and communication technologies bring huge potential of enhancing economic growth and development; hence, the question arises whether overwhelming technology adoption and falling digital technology gaps process if followed falling economic disparities among countries. This research contributes to the present state of the art by examining changes in cross-country inequalities in digital technologies deployment and economic performance. Our empirical target builds on hypothesis that rapid global diffusion of digital technologies unequivocally leads to gradual eradication of cross-country digital gaps and cross-country inequalities in terms of deployment of ICT; the technology convergence occurs. Bearing in mind the fact that digital technologies’ growing access and use boost country’s economic performance, we confront changes in cross-country inequalities in terms of economic development. We hypothesize that E. Lechman (*) Faculty of Management and Economics, Gdansk University of Technology (a member of Fahrenheit Universities), Gdańsk, Poland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. S. Ben Ali (ed.), Key Challenges and Policy Reforms in the MENA Region, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-3-030-92133-0_2

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gradual elimination of digital gaps shall be followed by diminishing gaps in economic wealth. Our country sample covers Middle East and North Africa (MENA) countries, and the time span of analysis covers the period 1990–2020. To test changes in digital development inequalities, we use 4 core ICT indicators: mobile cellular telephony, fixed broadband and mobile broadband networks, and Internet users, while regarding economic performance, we consider gross per capita income and Human Development Index. Our statistical data are entirely extracted from World Telecommunication/ICT Indicators Database 2020 and World Band Development Indicators 2021. Our empirical strategy combines visual identification of shifts in digital and economic development using kernel density functions and Lorenz curves to test change cross-country inequalities. The graphical evidence is then enriched by the use of sigma convergence approach and inequality indices— Gini and Theil entropy indices.

2.2  Context and Background Apparently, the speed of new digital technologies’ diffusion, growing adoption, and usage are increasing rapidly since the emergence of these technologies in the 1970s of the twentieth century. What is important is these innovative technologies are spreading not only in highly developed and materially rich countries but also in those more economically, socially, and/or infrastructural backward. ICTs, due to its unique characteristics, are easily distributable in geographically remote areas and easily adoptable even in poorly educated and low-income societies. Broad adoption and usage of digital technologies can effectively support achieving socioeconomic development targets and hence support state-designed policies in fulfilling their long-run goals. Digital technologies support promoting education and empowerment (Samarakoon et al., 2017); better and more effective healthcare system (Ben Ali & Selmi, 2020), institutional quality and effectiveness; reducing corruption (Ben Ali & Gasmi, 2017; Sassi & Ben Ali, 2017; Ben Ali et al., 2020), different forms of exclusion (Kondrateva & Ben Ali, 2021), henceforth may enforce economic growth and development process, macroeconomic stability and competitiveness and democratic regimes (Ben Ali, 2020), external shocks resilience. During last few decades, all world economies were experiencing fast and usually unbounded diffusion of digital technologies (ICT). These technologies were spreading globally, both in materially well and in materially deprived countries, regardless of institutional regulations, types of telecommunication market organization and competition, and the whether prices of accessing and using different forms of digital technologies were dropping or shifting (Lechman, 2016, 2017). Such dynamic and unlimited diffusion of new technological solutions was mostly driven by emerging strong network effects that allowed boosting the spread of ICT among economic agents (Vicente & Suire, 2007; Lechman, 2018). In effect, fast increasing demand for ICT along with growing adoption and usage offers to economic agents unbounded connection with others, usually at relatively

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low cost (Kaur et al., 2017). Looking back in history, the technology gap has been persistent, and technological innovations have been adopted and being diffused at extremely slow pace (Lechman, 2017). However, the historically unprecedented rapid spread of new information and communication technologies has opened windows of opportunity to technologically backward countries for them to technologically catch up, leapfrogging other countries and escaping from their previous, usually historically conditioned, technological marginalization (Perez & Soete, 1988; Perez, 2003). Along with dynamic spread of digital technologies across countries, we hopefully shall observe gradually decreasing digital and technology gaps among societies and economies. This process of gradual cross-country gap eradication we label “technology convergence” (Lechman, 2012a, b). The process of technology convergence allows decreasing cross-country differences in access to and use of digital technologies, relying on basic assumption that initially technologically more backward countries assimilate new technologies at annually higher average rate compared to countries initially better off in this respect. In this work, I adopt the definition of ICT convergence proposed by Lechman (2016) and define it as the process “whereby initially technologically poor countries tend to grow faster (in terms of average annual growth rates) compared to countries that were initially technologically better off” (Lechman, 2016, pp. 142–143). The elementary statistics above suggest that for the last three decades progress in terms of accessibility and use of ICT has been rapid in both developed and developing countries, inevitably leading to diminishing cross-country inequalities in this respect. However, this approach to technology convergence analysis is not very common in either the theoretical or the empirical literature. Nevertheless, some evidence may be gleaned from studies by Comin and Hobijn (2004), Comin et al. (2006), Castellaci (2006, 2011), and Lechman (2012a, b, 2016). Using the historical cross-country technology adoption dataset (HCCTAD), Comin and Hobijn (2004) provide an extensive analysis of technology convergence over the period 1788–2001. Their study covers 20 technologies in 23 different countries, and their major contribution consists in tracing the determinants of the rate of countries’ technology adoption. They also test the technology convergence hypothesis by applying beta- and sigma-convergence procedures. Their major observation is that after World War II convergence for technologies like the telephone, cars, trucks, aviation, and electricity was faster than before. This is demonstrated by more rapidly decreasing variation coefficients, which was unprecedented for pre-World War II technologies. Testing the beta-convergence hypothesis, they find that both on average and within technology groups the acceleration of the speed of convergence is evident—there is a change from 10% to 13.7% between the pre- and post-World War II periods. Their general conclusion is that the rate at which technologically behind countries caught up with technological leaders increased significantly after 1945. Comin et al. (2006) present similar evidence. They test beta- and sigma-convergence using the cross-­ country historical adoption of technology (CHAT) dataset.1 In the cited

 Available at http://www.nber.org/papers/w15319.pdf

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research, they use the sample of 115 different technologies in over 150 countries over the last 200 years, and their findings support the supposition that the level of technological development is a critical factor differentiating the economic performance of countries. Castellaci (2006) tests the unconditional technology β-convergence between 1990 and 2000 in an empirical sample of 130 countries. For technological development proxies, he uses, inter alia, numbers of patents, numbers of scientific articles, and Internet and fixed telephone penetration rates. Using simple cross-country regressions, he finds a faster β-convergence rate for Internet penetration, around 6.6% annually. Interestingly, he shows that for all the remaining proxies the speed of convergence was below 1% per year. He employs quantile regression models, treating Q-convergence as a refinement of β-convergence models. Estimating the speed of convergence of the 20th, 40th, 60th, and 80th quantiles of the distributions for each technology indicator used, he finds a higher speed of convergence in the upper quantiles of consecutive distributions. Analyzing the technology dynamics in 131 developed and developing countries during the period 1985–2004, Castellaci (2011) reports more evidence on technology convergence. As in the research presented in Castellaci (2006), technology convergence is investigated unconditionally using β-convergence and Q-convergence. For each technology, the β-parameter estimated is negative, suggesting the existence of technology β-convergence patterns. The β-parameters reported demonstrate the highest speed of convergence for Internet and mobile telephony infrastructure—6.6% and 6% per year, respectively. As for Q-convergence, the results are again similar to the previous ones in Castellaci (2006), with a higher rate of technology convergence in the upper quantiles. Lechman (2016) reports on technology convergence exclusively for information and communication technologies. In Lechman (2012a), the analysis covers the period between 2000 and 2010 and includes 145 countries. Technology convergence is tested using five core ICT indicators: fixed telephony, mobile telephony, fixed narrowband Internet networks, fixed broadband Internet networks, and Internet use. The empirical analysis involves technology β-convergence, σ-convergence, and quantile convergence specifications. Her major findings are consistent with those reported in Comin et al. (2006) and Castellaci (2006, 2011), with faster rates of convergence for mobile telephony and Internet use. The β-coefficients returned are −8.14 and −5.4, respectively, but only −1.9 for fixed telephony. This again shows that the speed of diminishing inequalities is much higher in the case of new information and communication technologies than for older technologies. As in Castellaci (2006, 2011), Lechman (2012b) additionally finds evidence of higher technology quantile convergence in the upper quantiles. As for technology σ-convergence, there are radical drops in the coefficients for the variations calculated for 2000 and 2010, with the most significant falls reported for mobile telephony and Internet use. More detailed evidence on technology convergence is reported in Lechman (2015). This study covers 113 countries and verifies the technology convergence hypothesis for the period between 2000 and 2012 for four ICT indicators (mobile telephony, fixed narrowband Internet networks, fixed broadband Internet networks, and Internet use). As in Lechman (2012b), the technology convergence hypothesis is tested using β-convergence, quantile

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27

convergence, and σ-convergence specifications. The technology convergence β-parameters estimated for mobile telephony, fixed narrowband Internet networks, fixed broadband Internet networks, and Internet use are, respectively, 17.6%, 10.7%, 25.3%, and 14.8%. For each technology, there are gradually falling values of the σ-convergence coefficients, showing diminishing cross-­country disparities in access to and use of new information and communication technologies. Furthermore, the highest rate of technology quantile convergence is again reported for the highest quantiles for each ICT indicator. More recent works regarding digital divides and various forms of digital inequalities regard more specific fields and usually mezzo- and/or micro-level. Among the most contributive findings, we may refer to, for instance, Alvarez-Galvez et  al. (2020), Koiranen et al. (2020), Mehra et al. (2020), Pick and Sarkar (2020) or Pérez-­ Amaral et al. (2021), and Reggi and Gil-Garcia (2021). So far, we did not trace empirical evidence that would be dedicated to examining the digital divides and digital inequalities in MENA countries. Our analysis discussed below fills this gap.

2.3  Data and Empirical Settings 2.3.1  Data Our research covers 16 MENA countries,2 and the time span for the analysis is set for 1990–2019 and is selected on the basis of data accessibility—this is the sole period for which a balanced dataset is available for the majority of the sample countries. To run the empirical analysis, we have selected ICT indicators, gross per capita income, and Human Development Index. As for ICT, we use four types of data. First, we use mobile cellular telephony (MCSy,c) penetration rates showing the share of a country’s population having access to and using mobile cellular telephony infrastructure. Next, we use two indicators of approximating infrastructural development regarding digital technologies: active mobile broadband (AMBy,c) subscriptions that refer to the sum of standard mobile broadband and dedicated mobile broadband subscriptions to the public Internet and fixed broadband subscriptions (FBSy,c) that show fixed subscriptions to high-speed access to the public Internet at downstream speeds equal to or greater than 256 kbit/s. Finally, we use Internet users (IUy,c), as the “proportion of individuals who used Internet from any location in the last three months.” To examine changes in cross-­ country economic development disparities, we rely on gross domestic product per capita (GDPy,c) and Human Development Index developed by United Nations Development Programme. All data used in this research are extracted from World

2  Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, and United Arab Emirates.

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E. Lechman

Development Indicators 2021 database, World Telecommunication/ICT Indicators database (June 2020 Edition), and United Nations Development Programme.

2.3.2  Methodological Approach To check whether in sample countries digital and economic gaps are diminishing or rather growing, we rely on several analytical techniques. To examine visually the statistical relationships between variables, we adopt the exploratory data analysis method—locally weighted polynomial smoother. Applied scatterplot smoothing is a robust, nonparametric, and flexible approach to data exploration (Cleveland, 1979; Cleveland & Devline, 1988) allowing finding the functional relationships between variables. A local polynomial smoother, using weighted least-squares regression minimizing the weighted least-squares function, fits a locally weighted regression at each point of variable x to produce the estimate—the response variable—y, at each x. Weighted least square is down-weight observations with more variability, and thus, the graphical estimates become more robust (Loader, 2012). In order to examine how cross-country distribution of ICT and other tested variables are changing, we use nonparametric density estimator—kernel density estimator. Kernel density curves, generated by the nonparametric estimation, allow for drawing probability density function having a general form: f  x 

d F  x, dx

(2.1)

where F(x) shows continuous distribution of variable X. To estimate kernel density function f(x), its discrete derivative is adopted and then the nonparametric estimator holds a general form as follows: f  x 

1 n  Xi  x  , k nh i 1  h 

(2.2) 

where k(u) stands for kernel function satisfying the condition of our study, we adopt kernel Epanechnikov as follows: 3 1  u2 4 .  u  1





 k  u  du  1 . In





(2.3)

To learn about cross-country inequalities and divides, we use two inequality measures Gini coefficient and Theil entropy index. Gini coefficient (Dorfman, 1979; Milanovic, 1997) reports on the inequality of income distribution or any other

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variable. For given population attributed to values yi, i = 1…n, if (yi ≤ yi + 1), the general formula for Gini coefficient is as follows (Gini, 1912): i 1  n  1  i  yi 1  n  1  2 n in1 yi n



   (2.4)

The values of Gini coefficient range from 0 to 1, where 0 yields perfect equality and 1 yields perfect inequality. The Theil entropy index of inequality, stemming from the information theory (Gray, 2011), follows the general formula:



Yi  1  i 1 Y T     n  n ln  Yi Y 

,    (2.5)

where n is the size of population for which the Theil index is calculated, Yi represents the value of examined variable (e.g., welfare) for given unit (economic agent) i, and Y stands for the mean value of given variable in considered population. The Theil entropy index is a scale-free measure, and its value does not have any intuitive nor direct economic interpretation; still higher values of the index indicate higher inequalities among population units. Finally, we intend to examine the process of convergence both regarding digital technologies and economic development; we convergence concept stemming from neoclassical growth theory (Solow, 1956) claiming that entities examined tend to converge, which leads to achieving a common and stable equilibrium (Barro & Sala-i-Martin, 1992). As noted in the earlier section, the existence of the convergence suggests that poor countries demonstrate faster growth rates than rich countries, and hence, the catching-up process takes place leading to gaps gradual eradication. The convergence process is formally examined adopting two empirical strategies: sigma (σ)-convergence and beta (β)-convergence. The σ-convergence is tested using a standard deviation (absolute approach) or coefficient of variation (relative approach), while the coefficient of variation is specified as follows:

 i ,t

 i ,t

, (2.6)

where θi, t is the mean of considered variable in the empirical sample. The σ-convergence hypothesis is verified positively if σi, t  →  0 is satisfied. The β-convergence is empirically verifiable relying on the neoclassical growth model. The unconditional β-convergence is tested using the following equation:

30



E. Lechman





gi  a  b vc , y0   i ,

(2.7)

vc, y0

where c denotes country, y0 is the initial year, is level of examined variable in y0, and εi is random error term. The b convergence coefficient tells about the speed of the process. Negative value of b coefficient supports the hypothesis on convergence process, while positive value rather suggests divergence tendencies. Using the b coefficient, we calculate the rate of convergence as follows:

   ln 1  b  / T ,

(2.8)

and the time span necessary for present cross-country disparities to be cut by half is given by:

HLi    ln  2   /  ,

(2.9)

The regression in Eq. (2.7) is conventionally estimated applying OLS, however bearing in mind that tested variable may not be normally distributed, returned coefficients are biased. In this case, following Koenker and Basset (1978), it is recommended to rely on nonparametric quantile regression if considered variable distribution is asymmetric. The quantile regression estimator uses non-central locations (Koenker, 2004; Hao & Naiman, 2007) to estimate regression coefficients. In our approach, we additionally rely on quantile regression, applying estimates for 20th, 40th, 60th, and 80th quantiles.

2.4  Empirical Analysis Results Continuous and dynamic shifts in ICT accessibility and usage between 1990 and 2019 potentially may lead to the unique process of technology convergence implying that initially wide gaps between examined countries are being gradually eradicated. The baseline assumption that underlies these empirical results presented in the below section is that diminishing digital gaps that occur due to process of technology (ICT) convergence enhances drops in economic development inequalities at the same time. Analogous process shall be hypothetically observed in regard to economic development process if we assume digital technologies’ increasing deployment positively affects economic growth and devilment. Hence, technology and economic convergence processes shall continue simultaneously. The following section answers the question of whether in MENA countries in the period 1990–2019 we observe the process of digital gaps diminishing that is accompanied by economic convergence. With this aim, we apply 4 ICT indicators—to check for the technology convergence and gross per capita income and Human Development index—to approximate economic development convergence. Our

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31

empirical analysis reports on the process of digital and economic development gaps diminishing using 2 different approaches. First, we tackle the issue using density representations, Gini and Theil entropy indices, which allow examining changes in cross-country inequalities regarding ICT and economic development in MENA economies. Second, we examine the process applying classical approach to convergence analysis deriving from neoclassical growth theory (Solow, 1956), and we use sigma- and beta-convergence models. Figure 2.1 visualizes changes in variables distribution, while Fig. 2.2 shows time trends regarding changes in cross-country inequalities (approximated by Gini and Theil entropy indices). Additionally, Table 2.2 in Appendix reports on specific values for all indicators between 1990 and 2019. Considering changes and shifts in ICT access to and use of, we observe the most dynamic growths in case of mobile telephony usage and access to active mobile broadband networks facilitating data transfer. A brief look at raw data regarding MCSy,c shows that on average, the mobile telephony usage shifted from 0.44 per 100 inhab. in 1990 to 125.3 per 100 inhab. in 2019, which accounts for high average annual growth rates. The top performing country in this respect was United Arab Emirates achieving slightly more than 200 per 100 inhab., while the most laggard country was Lebanon—61.8 per 100 inhab. Index. MENA countries. Period 1990-2019. .0002

.05

.00015

.03

Density

Density

.04

.02 .01

.0001 .00005

0 0

50 AMB

100 IU

FBS

150 MCS

0 22000 24000 26000 28000 30000 32000 34000 GDP

Density

8

6

4

2 .6

.65

.7 HDI

.75

.8

Source: Author`s elaboration. Note – bandwidth set as default. Fig. 2.1  Density representations for ICT indicators, GDP per capita, and Human Development Index. MENA countries. Period 1990–2019. (Source: Author’s elaboration. Note: bandwidth set as default)

32

E. Lechman .01

1.5

.08

.8

.6 Theil_HDI

.07 Gini_HDI

.008

1

.4

.006

.5

.06 .2

.004

0 1990

2000 Theil_GDP Theil_FBS

2010 Theil_IU Theil_AMB

2020

.05

0 1990

Theil_MCS Theil_HDI

2000 Gini_GDP Gini_FBS

2010 Gini_IU Gini_AMB

2020 Gini_MCS Gini_HDI

Source: Author`s elaboration. Fig. 2.2  Gini and Theil indices changes. MENA countries. Period 1990–2019. (Source: Author’s elaboration)

As for the AMBy,c, we observe analogously radical shifts—from 0.26% in 2007 (on average) to 104.5% in 2019, with the best performer—again United Arab Emirates achieving 240%, and the worst—Lebanon—40%. Regarding FBSy,c and IUy,c, analogously fast shifts are observed, although not so massive cross-country differences in absolute terms have been generated as in case of mobile telephony and mobile broadband networks. Changes in distribution of variables visualized in Fig. 2.1 support our claim in fast and undisrupted diffusion of ICT across MENA economies. Respective density curves suggest massive changes, in terms of both increasing ICT penetration rates and growing absolute differences among economies in this respect. If gross per capita income and HDI are considered, obviously observed changes are not that massive and dynamic (especially regrinding HDI) as in case of ICT. Still in case of both variables, increases are reported suggesting that MENA countries follow stable and dynamic development pattern if economic developments are taken into account. The reported process of growing ICT access to and use of, as well as increases in, the level of economic development resulted in gradual eradication of cross-­ country relative inequalities. In our case, these are examined using the Gini and Theil entropy indices and Fig.  2.2 displays over time changes in this respect.3 Bearing in mind that the most dynamic increases are reported for mobile telephony also in this case drops in cross-country relative inequalities are observed. For MCSy,c, the Gini and Theil entropy indices fell from 0.64 to 0.14 and from 0.78 to 0.03 accordingly; henceforth, the downward trend is easily traceable. Analogously radical decreases in cross-country relative inequalities are visible in case of Internet

 For year-specific values—see Table 2.2 in Appendix.

3

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33

penetration rates—IUy,c; Gini and Theil entropy indices fell from 0.54 to 0.17 and from 0.60 to 0.12, respectively. These calculations both for mobile cellular telephony and Internet penetration rates evidently show that between 1990 and 2019 across MENA countries digital inequalities in this regard were being gradually eradicated. In early 2000s, the process of digital technologies accelerated despite high heterogeneity of these economies, especially in terms of social and economic development. Needless to claim that massive diffusion of mobile cellular telephony and increasing usage of Internet network are a positive consequence of state policies being pursued to support digitalization. As for mobile telephony use by economic agent except two countries (Egypt and Lebanon), in all remaining 14 economies MCSy,c exceeded 100 per 100 inhabitants. Regarding Internet penetration rates, massive drops in cross-country relative inequalities are observed, although in this respect in 2018 and 2019 MENA countries differed significantly in absolute terms indicating that various infrastructural shortages limit unbounded access to World Wide Web by its inhabitants. In 2019 in only 4 out of 16 examined countries, almost 100% of its population could benefit from access to the Internet network. In another 4 economies, it was about 50% of population. The latter shows that probably infrastructural prerequisites ensuring access to the Internet are still not enough and are not distributed well enough to eradicate unequal opportunities in this respect. This supposition is supported by the data on fixed broadband infrastructure development that ensures access to high-carrying capacity network. Looking at country-level data, we see that in only 2 countries—Israel and United Arab Emirates—FBSy,c penetration rates achieve approximately 30 per 100 inhab., while in remaining economies the state of development and within-country distribution of fixed broadband infrastructure are still poor. Henceforth, the provision of adequate infrastructure shall become one of the state policy priorities not to allow these countries and their societies to lag behind in terms of digital developments. In 7 economies (Algeria, Egypt, Jordan, Kuwait, Lebanon, Libya, and Morocco), the FBSy,c does not exceed even 10 per 100 inhab., which significantly hinders society unlimited digitalization. Changes in cross-country inequalities regarding access to and use of digital technologies are accompanied by changes in inequalities in terms of level of economic development. According to our calculations, between 1990 and 2019, on average gross per capita income shifted from 22,900 US to 29,400 US. Cross-country relative inequalities also fell, although bearing in mind the “nature” of economic growth these changes are not that abrupt as in case of ICT indicators. As for GDP per capita, the Gini index dropped from 0.53 to 0.41. Regarding HDI values reporting on income and non-income development level, Gini index fell from 0.08 to 0.04. Apparently ups and downs in gross per capita income—especially in terms of HDI—are relatively slow and demonstrate high overtime inertia. Still, what is openly visible between 1990 and 2019 in MENA economies falling relative cross-country inequalities in digitalization level is accompanied by—although slow but stable—eradication of economic inequalities; henceforth, MENA countries are gradually becoming more homogenous in these two aspects.

34

E. Lechman

The above evidence is enriched by providing evidence on technology and economic convergence. We adopt formal specification on convergence analysis deriving from neoclassical growth theory, and we rely on sigma- and beta-approach. Figures 2.3 and 2.4 visualize σ-convergence and β-convergence processes accordingly, while Table  2.1 summarizes regression estimates for β-convergence. Calculated coefficients of variation reporting on the process of σ-convergence are summarized in Table  2.3 in Appendix. Technology and economic σ-convergence process are unveiled; it allows concluding directly on changing distribution of examined variables (Young et al., 2008) in the sample. As argued in Quah (1993), adoption of σ-convergence allows reporting directly on dropping or falling cross-­ entities inequalities and hence in more conclusive and informative than β-convergence analysis. Figure 2.3 displays evidence on technology and economic σ-convergence using coefficient of variation as basic a measure. Starting from the year 1990, observably calculated coefficients of variation steadily decrease. The only exception reported is in regard to fixed broadband infrastructure, for which data start late 90s of twentieth century. Initially, calculated coefficients of variation increased abruptly, but then since 2006 it falls gradually following the general trend observed. In case of remaining 5 indicators, downward trends in coefficients of variations are detectable. For MCSy,c, IUy,c, and AMBy,c calculated coefficients of variation fell from 133.9 to 28.3, from 186.5 (in 1993) to 17.8, and from 134.6 to 45.8, respectively. Regarding gross per capita income, this drop is from 129 to 83.5, and for

16

250

200 14

HDI

150

100

12

50 10 0 1990

2000

2010

GDP

IU

AMB

HDI

MCS

2020 FBS

Source: Author`s elaboration. Fig. 2.3  σ-convergence (Coefficient of variation). MENA countries. Period 1990–2019. (Source: Author’s elaboration)

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.5

Growth_rate_FBS

1

0

1 0 -1 -2

-.8

-.6 -.4 HDI_1990

-.2

9

10 GDP_1990

11

20

-1

0

1 2 AMB_2012

3

4

10

-5

FBS_2003

0

5

40

35 30 25 20

0

20

-10

Growth_rate_MCS

Growth_rate_IU

40

30

12

40

60

40

0 8

80 Growth_rate_AMB

50

2 Growth_rate_GDP

Growth_rate_HDI

1.5

35

30

20

10 -6

-4 -2 IU_1995

0

-6

-4

-2 MCS_1990

0

2

Source: Author`s elaboration. Note: for visualization – nonparametric approximation applied.

Fig. 2.4  β-convergence. MENA countries. Period 1990–2019. (Source: Author’s elaboration. Note: for visualization, nonparametric approximation applied)

Human Development Index from 15.7 to 9.5. These results support our supposition on existence of σ-convergence in MENA countries, which additionally confirms our initial results regarding decreasing cross-country relative inequalities in terms of digital and economic development. Finally, we test the existence of absolute β-convergence using both graphical evidence and numerical evidence. Figure  2.4 visually tests this process for each consecutive indicator, and Table  2.1 summarizes regression estimates. Charts in Fig. 2.4 plot average annual growth rates versus initial value regarding considered variable, and negative statistical relationship between these two suggests that the process of absolute β-convergence is reported. In fact, this is the case for all tested variables—initial values of variable correlate negatively with average annual growth rates, which shows countries initially lagging behind grow faster in terms of ICT deployment that leads to gradual equalizing cross-country relative disparities. Analogous absolute β-convergence tendencies are reported as gross per capita income and HDI; hence, the graphical evidence supports the hypothesis on existing β-convergence tendencies that proceed parallelly in digital and economic development. In the final step, we test absolute β-convergence formally using regression analysis. With this aim, we follow regression formalized in Eq. 2.7 under the assumption that b F 0.00 0.00 Quantile regression estimates HDI GDP 20_quantile Coefficient −2.7 −1.02 SE 0.87 0.27 Pseudo-R2 0.32 0.52 40_quantile Coefficient −1.7 −1.00 SE 0.64 0.23 Pseudo-R2 0.36 0.44 60_quantile Coefficient −12.7 −0.77 SE 0.37 0.23 Pseudo-R2 0.46 0.41 80_quantile Coefficient −1.6 −0.77 SE 0.29 0.24 Pseudo-R2 0.53 0.41

MCS −3.29 0.13 0.98 0.09 7.6 16

FBS −3.73 0.24 0.94 0.09 7.13 16

AMB −10.6 1.22 0.86 0.15 4.52 16

IU −3.75 0.13 0.95 0.09 7.11 16

MCS −3.22 0.11 0.00

FBS −3.73 0.26 0.00

AMB −8.2 0.82 0.00

IU −3.64 0.11 0.00

MCS

FBS

AMB

IU

−3.46 0.24 0.86

−3.84 0.47 0.71

−9.97 4.7 0.34

−3.65 0.54 0.76

−3.31 0.19 0.87

−4.09 0.33 0.76

−8.35 1.74 0.53

−3.61 0.12 0.83

−3.26 0.14 0.89

−3.81 0.37 0.77

−9.71 1.62 0.65

−3.62 0.13 0.87

−3.14 0.12 0.91

−3.58 0.27 0.81

−11.41 1.76 0.71

−3.63 0.16 0.88

Source: Author’s estimates. Note: for OLS estimates, robust standard errors applied; constant included—not reported; for quantile regression—bootstrapped quantile regression 100 replications, for robust regression—biweight iteration = 7 (set as default), all estimates at 5% level of statistical significance

respective ICT and economic development variable growth behavior of the average of the distribution, but is poorly informative regarding changes in this variable distribution over the sample period. To tackle the issues of estimates in skewed distributions, we adopt nonparametric quantile regression (Koenker & Basset, 1978). In quantile regression approach, we predefined location of variable distribution and we estimate consecutive regressions examining the β-convergence process at different distribution quantiles. Here below, we estimate regressions for 20th, 40th, 60th, and 80th of each test variable distribution.

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Table 2.1 summarizes regression analysis providing cross-country evidence on unconditional β-convergence in regard to digital and economic convergence. The results show that the hypothesis on technology and economic β-convergence is confirmed, as all estimated coefficients are negative and statistically significant. Estimates generated from the robust regressions are very close in value to those obtained from OLS estimates, which confirms the validity of the later. As expected, the highest estimated coefficient—both for OLS and robust estimates—is reported for active mobile broadband network diffusion. Bearing in mind raw data, in this respect we observe the most dynamic shifts over the sample in relatively short time period.4 Returned coefficients are (−10.6) and (−8.2) for OLS and robust regression accordingly. High estimated coefficient for AMBy,c implies relatively short half-­time indicating the number of years needed to reduce cross-country gaps by half—in case of mobile broadband networks, it is estimated for 4.5 years. As for returned coefficients for remaining three ICT indicators—mobile telephony, fixed broadband network, and Internet penetration rate, their values are very close suggesting that in these areas the process of cross-country gap eradication proceeds are similar pace in MENA economies. In effect, estimated half-time is 7.6 years, 7.1 years, and 7.1 years for MCSy,c, FBSy,c, and IUy,c. Robust regression estimations return analogous results, which may be interpreted as OLS results that are not heavily biased by outliers in the sample. Estimates for gross per capita income and HDI variables report much slower convergence process than reported for ICT indicators. Regression coefficients for GDPy,c are (−0.91) from OLS and robust regression, which implies 17.1  years needed to reduce cross-countries economic disparities by half. The process of combating inequalities and development gaps is, however, faster when HDI is considered—10.7 years of estimated half-time. Still as mentioned before, the process of economic development usually proceeds slower compared to dynamic and disrupted digital technologies diffusion. What is seminal to conclude in our case is that both technology and economic convergence processes proceed parallelly in MENA economies. Finally, we re-confirm our results on absolute β-convergence applying quantile regression for selected quantiles—see Table 2.1. Generally, these results support evidence from OLS and robust regressions, although, as expected, results for different quantiles differ, which shows skewness of variables distribution, and thus, estimated coefficients for different quantiles vary. Still, in all cases, estimated coefficients for 80th quantile are close in value for estimates over whole distribution.

2.5  Concluding Remarks and Policy Implications Countries classified as MENA economies are heterogeneous group. These countries differ not only in terms of economic performance, but also regarding legal regulations and quality of institutions, social norms and attitudes, development of

 Note that between 2007 and 2019, the average AMBy,c grew from 0.26 to 104.5 per 100 inhabitants.

4

38

E. Lechman

backbone infrastructure, population density, urbanization, and geographical conditions. This paper aimed at examining if MENA countries are becoming gradually more homogenous in terms of digital technologies adoption and hence whether digital technologies convergence process is observed and if that process is accompanied by economic convergence. The empirical part regarded 16 MENA economies, and the time span of analysis covered 1990–2019. To examine the process of technology convergence, we have applied four basic ICT indicators—mobile telephony, fixed broadband and mobile broadband networks, and Internet penetration rates. The period of analysis is characterized by dynamic and stable digital technologies growing access to and use of. The decade between 1990 and 2000 was quite unique regarding the process of digital technologies diffusion. In early 90s of twentieth century, the state of ICT penetration was negligible, but at the end of this decade and in the beginning of 2000, digital technologies adoption boosted and all MENA countries started dynamically using these technologies. Apparently, after abrupt take-off observed across all MENA economies, further deployment of digital technologies was enhanced or hindered by various elements, like infrastructural shortages. Despite the fact that on average basic data on increasing ICT access to and use report radical increases, still in 2019 some countries significantly lagged behind. Nevertheless, provided empirical evidence strongly supports our initial hypothesis on gradual eradication of cross-country inequalities in terms of both digital technologies adoption and usage and economic development. Calculation of Gini and Theil entropy indices demonstrates downward trends in respect to each consecutive variable, and the hypothesis on technology and economic convergence (sigma and beta version) was confirmed. At the same time, we confirm the importance of technological progress for economic development. Needless to claim that from the state policy perspective providing solid fundaments, especially institutional and legal, to ensure stable and equal digital technologies diffusion is essential for social, economic, cultural, and political advancements. Critical for effective and productive use of digital technologies in terms of enhancing economic development is to equip all segments of societies in these technologies and facilitate them adequate skills acquisition. In this context, elimination of various exclusions that certain societal groups may suffer is critical. So far in MENA region, opportunities offered by digital technologies are not equally distributed, and these inequalities are also visible not only among, but within countries and societies. Limited access to and use of ICT by female population are probably one of the issues to be challenged. Basic ITU data for 2018 regarding Internet usage discriminated by age and gender report on significant gender inequalities in this respect. For instance, in Algeria or Egypt, the gender gap in Internet usage for population aged 25–74 is 14% and 13% accordingly. Luckily, in several MENA countries, gender disparities in this respect are negligible, for instance, in Qatar or Saudi Arabia. Within society, disparities disabling exposing full potential of digital technologies to be converted in economic development are also visible in data on Internet usage broken by educational status. Apparently, those at lower (primary and secondary) levels of education use Internet network less, compared to those at tertiary education level. Such disparities are massive again in Algeria and Egypt, but also in Iran.

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Finally, observable inequalities in digital technologies’ usage are reported between rural and urban regions, additionally broken by gender. Not surprisingly women in rural areas suffer from significant deprivation regarding access to and use of ICT. For instance, in Algeria, only 17% of women (ITU, 2018) in rural areas have access to Internet network. Uneven within-society and within-region development of ICT may significantly impede boosting economic development through ICT adoption. Unique characteristics of MENA economies and their country-specific impediments and shortages, like access to financial and natural resources, geographic features including sparsely populated and infrastructural underserved regions, population size, density and urbanization, underlying social norms and socioeconomic structure, and ongoing conflicts, are key challenges for strategic policy planners (OECD, 2021). “Along with fit-for-purpose policy, human capacity and ICT skills, ICT infrastructure is ultimately at the heart of this historical transformation and the predominant enabler of the Arab States region’s future competitiveness and economic diversification. It is important not to lose sight of the fact that improving ICT infrastructure is more than a goal for operators and consumers. It does much more than support mobile and broadband connections: it serves as the backbone for global and regional supply chain integration; facilitates the innovative use of critical health information; gives the opportunity for citizens to improve their options in the workforce; enables students to acquire previously out-of-reach skillsets; and offers many more positive externalities that are changing the course of history. Indeed, future history will look back at this early era of technological development to see how policies and governance approaches reinforced the resilience and responsiveness of societies, while assessing for risks, protecting consumers and enabling positive outcomes for citizens” (ITU, 2021, s. 3). Acknowledgements  This work was supported by the National Science Centre in Poland under Grant No. 2015/19/B/HS4/03220.

Time 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Gini_HDI 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.07 0.07 0.07 0.07 0.06 0.06 0.06 0.06 0.05 0.05 0.05 0.05 0.05 0.05 0.05

Theil_HDI 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.009 0.008 0.007 0.007 0.007 0.006 0.006 0.005 0.005 0.005 0.004 0.004 0.004 0.004

Gini_GDP 0.53 0.55 0.54 0.53 0.52 0.52 0.51 0.51 0.49 0.47 0.48 0.48 0.48 0.49 0.48 0.47 0.46 0.45 0.43 0.42 0.44 0.42 0.42

Table 2.2  Gini and Theil indices

Appendix

Theil_GDP 0.54 0.53 0.51 0.49 0.49 0.46 0.44 0.44 0.41 0.38 0.4 0.39 0.9 0.9 0.39 0.36 0.36 0.33 0.31 0.28 0.32 0.29 0.29

Gini_IU Theil_IU Gini_MCS 0.64 0.65 0.65 0.54 0.6 0.66 0.75 1.2 0.65 0.7 0.95 0.63 0.67 0.87 0.64 0.61 0.68 0.64 0.62 0.71 0.61 0.59 0.63 0.61 0.58 0.6 0.59 0.54 0.51 0.53 0.45 0.34 0.53 0.42 0.31 0.49 0.34 0.2 0.43 0.36 0.22 0.32 0.36 0.22 0.25 0.36 0.22 0.22 0.34 0.2 0.2 0.32 0.18 0.18 0.31 0.17 0.17 0.29 0.15 0.15 0.26 0.12 0.14

Theil_MCS 0.78 0.8 0.8 0.83 0.86 0.78 0.78 0.79 0.71 0.69 0.61 0.49 0.5 0.41 0.31 0.18 0.1 0.07 0.07 0.05 0.04 0.03 0.03 0.49 0.55 0.77 0.81 0.77 0.7 0.68 0.63 0.59 0.55 0.51 0.49 0.46

0.66 0.57 1.3 1.5 1.3 1.03 0.96 0.78 0.66 0.54 0.46 0.42 0.37

0.47 0.15 0.34 0.49 0.38 0.38

0.58 0.03 0.24 0.43 0.24 0.36

Gini_FBS Theil_FBS Gini_AMB Theil_AMB

40 E. Lechman

Gini_HDI 0.05 0.05 0.05 0.05 0.05 0.05 0.05

Theil_HDI 0.004 0.004 0.004 0.004 0.004 0.004 0.004

Source: Author’s calculations

Time 2013 2014 2015 2016 2017 2018 2019

Gini_GDP 0.41 0.42 0.43 0.43 0.42 0.41 0.41

Theil_GDP 0.28 0.3 0.31 0.31 0.29 0.29 0.29

Gini_IU 0.17 0.24 0.18 0.2 0.17 0.13 0.08

Theil_IU 0.12 0.1 0.06 0.07 0.05 0.03 0.01

Gini_MCS 0.14 0.15 0.15 0.16 0.15 0.14 0.14

Theil_MCS 0.03 0.03 0.03 0.04 0.03 0.03 0.03

Gini_FBS 0.47 0.46 0.44 0.4 0.38 0.36 0.36

Theil_FBS Gini_AMB Theil_AMB 0.38 0.34 0.2 0.36 0.35 0.21 0.32 0.32 0.18 0.25 0.31 0.15 0.24 0.28 0.13 0.22 0.25 0.11 0.22 0.22 0.08

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Table 2.3  σ-convergence (Coefficient of variation) Time 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 2018 2019

HDI 15.7 16.2 15.7 15.4 15.3 15.5 15.1 14.9 14.5 14.1 13.2 12.8 12.6 12.6 11.9 11.2 10.8 10.63 10.3 10.1 9.8 9.6 9.7 9.6 9.7 10.1 10.2 9.8 9.6 9.5

GDP 129.1 124.9 120.5 116.6 116.9 107.7 106.3 106.5 102.1 99.5 98.3 97.2 97.5 97.4 97.1 93.1 92.2 88.4 85.4 81.6 82.2 88.1 83.8 84.2 85.7 86.6 85.8 83.6 83.2 83.5

IU – – 127.4 186.5 173.1 150.1 120.4 131.1 125.6 128.1 111.3 91.7 82.4 65.2 69.4 70.3 68.1 62.8 58.1 56.0 53.7 48.2 49.4 45.1 34.3 37.9 32.4 25.1 17.8 –

MCS 133.9 135.1 133.7 137.1 134.2 125.4 136.5 139.8 129.9 129.6 124.2 107.8 105.0 94.2 82.1 60.5 46.5 40.0 37.9 32.8 31.8 28.2 26.3 26.5 27.4 27.2 31.7 28.6 28.1 28.3

FBS – – – – – – – – – – 140.2 119.7 220.1 260.7 236.2 202.6 189.3 159.8 144.1 122.7 111.3 107.1 99.2 99.4 93.1 87.5 79.2 76.6 73.7 73.8

AMB – – – – – – – – – – – – – – – – – 134.6 32.2 72.4 94.1 73.7 71.2 63.1 65.6 59.3 58.1 57.5 53.3 45.8

Source: Author’s calculations

References Alvarez-Galvez, J., Salinas-Perez, J. A., Montagni, I., & Salvador-Carulla, L. (2020). The persistence of digital divides in the use of health information: A comparative study in 28 European countries. International Journal of Public Health, 65(3), 325–333. Barro, R., & Sala-i-Martin, X.  X. (1992). Convergence. Journal of Political Economy, 100, 223–251. Ben Ali, M.  S. (2020). Does ICT promote democracy similarly in developed and developing countries? A linear and nonlinear panel threshold framework. Telematics and Informatics, 50, 101382. Ben Ali, M.  S., & Gasmi, A. (2017). Does ICT diffusion matter for corruption? An economic development perspective. Telematics and Informatics, 34(8), 1445–1453.

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Ben Ali, M.  S., & Selmi, N. (2020). ICT for education and health care systems: Potentialities and discrepancies in low-and high-income countries. In Society and technology (pp. 95–107). Routledge. Ben Ali, M.  S., Fhima, F., & Nouira, R. (2020). How does corruption undermine banking stability? A threshold nonlinear framework. Journal of Behavioral and Experimental Finance, 27, 100365. Castellacci, F. (2006). Convergence and divergence among technology clubs. DRUID Conference, Copenhagen, 30(07), 06–21. Castellacci, F. (2011). Closing the technology gap? Review of Development Economics, 15(1), 180–197. Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 596–610. Comin, D., & Hobijn, B. (2004). Cross-country technology adoption: Making the theories face the facts. Journal of Monetary Economics, 51(1), 39–83. Comin, D., Hobijn, B., & Rovito, E. (2006). Five facts you need to know about technology diffusion (No. w11928). National Bureau of Economic Research. Dorfman, R. (1979). A formula for the Gini coefficient. The Review of Economics and Statistics, 61, 146–149. Gini, C. (1912). Variabilità e mutabilità. Reprinted in Memorie di metodologica statistica (Ed. Pizetti E, Salvemini, T). Libreria Eredi Virgilio Veschi. Gray, R. M. (2011). Entropy and information theory. Springer. Hao, L., & Naiman, D. Q. (2007). Quantile regression quantitative applications in the social sciences (Vol. 149). Härdle, W. (1984). Robust regression function estimation. Journal of Multivariate Analysis, 14(2), 169–180. ITU. (2018). World Telecommunication/ICT Indicators Database 2017. Geneve. ITU. (2021). Digital trends in the Arab States region 2021 information and communication technology trends and developments in the Arab States region, 2017–2020. ITU. Kaur, H., Lechman, E., & Marszk, A. (Eds.). (2017). Catalyzing development through ICT adoption: The developing world experience. Springer. Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74–89. Koenker, R., & Bassett, G., Jr. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 46, 33–50. Koiranen, I., Koivula, A., Saarinen, A., & Keipi, T. (2020). Ideological motives, digital divides, and political polarization: How do political party preference and values correspond with the political use of social media? Telematics and Informatics, 46, 101322. Kondrateva, G., & Ben Ali, M. S. (2021). ICTs for women’s poverty alleviation. In E. Lechman (Ed.), Technology and women’s empowerment. Routledge. Lechman, E. (2012a). Technology convergence and digital divides. A country-level evidence for the period 2000–2010. Ekonomia, Rynek, Gospodarka, Społeczeństwo, No. 31. Lechman, E. (2012b). Catching-up and club convergence from cross-national perspective a statistical study for the period 1980-2010. Equilibrium, 7, 95–109. Lechman, E. (2015). ICT diffusion in developing countries. SPRINGER INTERNATIONAL PU. Lechman, E. (2016). ICT diffusion in developing countries. Springer International PU. Lechman, E. (2017). The diffusion of information and communication technologies. Routledge. Lechman, E. (2018). Networks externalities as social phenomenon in the process ICT diffusion. Economics and Sociology, 11(1), 22–43. Loader, C. (2012). Smoothing: Local regression techniques. In Handbook of computational statistics (pp. 571–596). Springer.

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Mehra, B., Sikes, E. S., & Singh, V. (2020). Scenarios of technology use to promote community engagement: Overcoming marginalization and bridging digital divides in the Southern and Central Appalachian rural libraries. Information Processing & Management, 57(3), 102129. Milanovic, B. (1997). A simple way to calculate the Gini coefficient, and some implications. Economics Letters, 56(1), 45–49. OECD. (2021). Middle East and North Africa investment policy perspectives. OECD Publishing. Pérez, C. (2003). Technological change and opportunities for development as a moving target. In Trade and development: Directions for the 21st century (p. 100). Perez, C., & Soete, L. (1988). Catching up in technology: Entry barriers and windows of opportunity. In Technical change and economic theory (pp. 458–479). Pérez-Amaral, T., Valarezo, A., López, R., & Garín-Muñoz, T. (2021). Digital divides across consumers of internet services in Spain using panel data 2007–2019. Narrowing or not? Telecommunications Policy, 45(2), 102093. Pick, J. B., & Sarkar, A. (2020). Geographies of global digital divides. In Geographies of the internet (pp. 115–135). Routledge. Quah, D. T. (1993). Galton’s fallacy and tests of the convergence hypothesis. The Scandinavian Journal of Economics, 95, 427–443. Reggi, L., & Gil-Garcia, J. R. (2021). Addressing territorial digital divides through ICT strategies: Are investment decisions consistent with local needs? Government Information Quarterly, 38(2), 101562. Rousseeuw, P. J., & Leroy, A. M. (2005). Robust regression and outlier detection (Vol. 589). Wiley. Sala-i-Martin, X.  X. (1996). Regional cohesion: evidence and theories of regional growth and convergence. European Economic Review, 40(6), 1325–1352. Samarakoon, S., Christiansen, A., & Munro, P. G. (2017). Equitable and quality education for all of Africa? The challenges of using ICT in education. Perspectives on Global Development and Technology, 16(6), 645–665. Sassi, S., & Ben Ali, M.  S. (2017). Corruption in Africa: What role does ICT diffusion play. Telecommunications Policy, 41(7–8), 662–669. Solow, R. M. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70(1), 65–94. Vicente, J., & Suire, R. (2007). Informational cascades versus network externalities in locational choice: Evidence of ‘ICT clusters’ formation and stability. Regional Studies, 41(2), 173–184. Young, A. T., Higgins, M. J., & Levy, D. (2008). Sigma convergence versus beta convergence: Evidence from US county-level data. Journal of Money, Credit and Banking, 40(5), 1083–1093.

Chapter 3

Assessing the Determinants of Capital Flight from Tunisia: An ARDL Investigation Framework Hajer Dachraoui and Maamar Sebri

3.1  Introduction The relatively old question of capital flight (Erbe, 1985; Cuddington, 1987; Dooley, 1988; Pastor, 1990) has gained a renewed interest among scholars, politicians, and public opinion these years. While it is argued that this issue is universal, developing countries are more prone to substantial outflows of capital. In particular, the sub-­ Saharan African countries have largely suffered from capital flight consequences such as loss of international reserves, greater pressure for devaluation of the domestic currency, high level of external indebtedness, and acute poverty. North African countries are less affected by capital flight than the sub-Saharan countries, but the volume of illicit capital outflows is still harmful (Ndikumana & Boyce, 2012). In particular, the estimated capital flight from Tunisia is the lowest among the North African countries, but its consequences on social and economic development are significant. Political elites and their business associates are the main ones responsible for these lost funds. According to PERI (2021) estimates, capital flight from Tunisia reaches $25 billion (in constant 2018 dollars) over the period 1980–2018. Moreover, 89% of this cumulative capital flight was recorded during the Ben Ali regime (1987–2010). Figure 3.1 shows the evolution of capital flight from Tunisia during the period 1980–2018. Identifying the main drivers of capital flight is still among the central strands of the related literature. External borrowing, macroeconomic fundamentals, and H. Dachraoui Higher Institute of Finance and Taxation of Sousse and MOFID, University of Sousse, Sousse, Tunisia M. Sebri (*) Faculty of Economics and Management and LaREMFiQ, University of Sousse, Sousse, Tunisia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. S. Ben Ali (ed.), Key Challenges and Policy Reforms in the MENA Region, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-3-030-92133-0_3

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Fig. 3.1  Capital flight from Tunisia over the period 1980–2018 (in constant 2018 dollars). (Source: Based on data from PERI, 2021)

institutional quality are the main determinants of capital flight, according to the empirical literature (Ndikumana & Boyce, 2003, 2011; Collier et al., 2004; Alam & Quazi, 2003; Boyce & Ndikumana, 2001; Lensink et al., 2000; Lester, 1996; Boyce, 1992). More recently, natural resources are also advocated as a significant determinant (Ndikumana & Sarr, 2019; Kwaramba et al., 2016; Ndikumana et al., 2015; Demachi, 2014; Ndikumana & Boyce, 2011). Most of these studies are undertaken on the resource-rich countries in sub-Saharan Africa. Resource rents and fuel exports are the main employed variable proxies of natural resources endowment, while the effect of some disaggregated types such as oil, natural gas, minerals, and the forest is also investigated separately. Most studies conclude that natural resources exert a positive effect on capital flight. That is, the more resource rents the higher capital flight. This effect is more pronounced in countries with bad governance and institutions. However, an adequate political environment with good governance can mitigate the curse feature of natural resources. The relationship between capital flight and natural resources has many interrelated transmission channels from which corruption, trade misinvoicing, and rent-­ seeking are the most significant (Ndikumana & Sarr, 2019). First, rent-seeking indicates the embezzlement of a significant share of resource returns by corrupt elites and their associates by relying on political manipulations. The latter include giving privileges and exclusive rights to some multinationals, tax evasion, and securing lucrative contracts. The embezzled assets are generally hidden abroad and constitute a significant share of capital flight. Second, foreign companies and local firms, which operate in the natural resource sector, may manipulate resources

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exports by misinvoicing. Their official accounts do not meet what they exactly gain due to their sophisticated and complex financial systems (Ndikumana & Sarr, 2019). Although there exist numerous panel data and time series studies on the determinants of capital flight for the sub-Saharan countries, little attention has been paid to study such a topic for individual North African countries. The few existing studies consider these countries into a panel of African countries. This study attempts to fill that void by estimating an ARDL model that analyzes the determinants of capital flight from Tunisia over the period 1980–2018 while putting a greater emphasis on the role of natural resources. The econometric methodology and analysis include (i) testing the integration properties of variables in the presence of structural break stemming in the series, (ii) investigating the presence of a level relationship based on ARDL bounds testing approach, and (iii) providing the results on both the short run and long run. The ensuing sections of the chapter are as follows: Sect. 3.2 provides an overview of Tunisia’s key natural resources. On the one hand, there has been a belief, particularly since the 2011 uprising, concerning the country’s natural resource wealth and its ostensible role in resolving several economic difficulties. On the other hand, it has been argued that resource rents in Tunisia have been mismanaged and have served, to some extent, as a source of capital flight. Section 3.3 describes the data and econometric methodology; Sect. 3.4 presents the empirical results and discussion; and finally, Sect. 3.5 summarizes the principal findings and draws policy implications.

3.2  Natural Resources in Tunisia: A Close Look Despite being sandwiched between two resource-rich countries (Algeria and Libya), Tunisia’s natural resources are limited. Table 3.1 shows that Tunisia is ranked 8th in Africa and 48th globally in 2017, with proven oil reserves of 0.42 billion barrels (bnbls). The proven gas reserves do not exceed 0.06 trillion cubic meters (tcm) putting the country in the 12th and 56th rank in Africa and the world, respectively. These proven reserves for both oil and gas are much lower than those of the two neighboring countries, Algeria and Libya. Concerning oil resources, the first discoveries dated to 1964 in the extreme south of the country. Oil exploration peaked in the early 1980s, when more than 30 oil wells were drilled (Figs.  3.2 and 3.3); Tunisia became an exporting country as a Table 3.1  Proven oil and gas reserves in 2017 Country Libya Algeria Tunisia

Proven oil reserves Global rank Africa rank 9th 1st 16th 3rd 48th 8th

Source: Nakhle and Lassourd (2019)

Size (bnbls) 48.36 12.20 0.42

Proven gas reserves Global rank Africa rank 30th 8th 17th 1st 56th 12th

Size (tcm) 1.48 4.45 0.06

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Fig. 3.2  Tunisia’s proven oil and gas reserves over the period 1980–2016. (Source: Nakhle & Lassourd, 2019)

Fig. 3.3  Tunisia’s oil and gas production over the period 1980–2017. (Source: Nakhle & Lassourd, 2019)

result, with oil exports accounting for more than half of the country’s currency earnings, whereas from the beginning of the 2000s, the country has become a net energy importer due mainly to the increasing local demand and the depletion of some fields. The Tunisian government has inked contracts with multinationals to develop the oil discoveries and extraction, while it collects fees and taxes following the agreed accords. The majority of the country’s crude oil is exported (87% in 2017). Simultaneously, it imports the majority of its refined fuels (85% in 2017) (Nakhle & Lassourd, 2019). The situation for natural gas is not significantly different from oil. Tunisia’s reserves are limited compared to its neighboring resource-rich countries. The peak of proven gas reserves was reached in the early 1980s (Fig.  3.2). Regarding the natural gas production, it significantly increased following the discovery of the

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Miskar gas field in 1996, reaching a peak of 3 billion cubic meters in 2008 (Fig. 3.3). Since then, a continuous slump in production is recorded. The produced natural gas is sold in the local market accounting for 38% of demand. The rest is coming from Algeria (49%) and the royalty levy (13%) on the gas transit from Algeria to Italy (Nakhle & Lassourd, 2019). Tunisia is also endowed with mineral resources (phosphate, zinc, lead, barite, and iron) with phosphate occupying the lion’s share since the country has substantial reserves of phosphate rock. Its mining began in the nineteenth century (in 1885). Since 1975, the state-owned phosphate producer, Compagnie des Phosphates de Gafsa (CPG), operates as a monopoly on the extraction and recovery of phosphate in various mineral fertilizers. Phosphate provides jobs for roughly 30,000 people. A significant share of this workforce was recruited in the aftermath of the revolution in 2011 because of recurring protests and sit-ins asking for jobs in the CPG. Tunisia has also dense and rich forests in the north and to a less extent in the center-west. They cover 1.3 million hectares (ha) in 2015, which represents 8% of the country’s total land area (FAO, 2016). Over time, the size of the forest has grown. It grew from 643,000 hectares in 1990 to 1,041,000 hectares in 2015 (FAO, 2016). For decades, Tunisia had been mainly exporting the paper pulp made from esparto grass. However, due to dwindling international demand and the redevelopment of the paper pulp facility, its value has fallen in recent years, reaching 3.4 million US dollars in 2014, compared to 8 million US dollars in 2000 (FAO, 2016).

Fig. 3.4  Aggregated and disaggregated natural resource rents in Tunisia over the period 1980–2018

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Despite Tunisia’s meager natural resources in comparison to its neighbors, they have played an important part in the country’s economic and social development over the years. Figure 3.4 shows total, oil, natural gas, mineral, and forest rents from 1980 to 2018. Oil rentals account for the lion’s share of total rents. Mineral revenues as a percentage of overall GDP also contributed significantly in the late 2000s. Natural gas and forest rents have the lowest percentages.

3.3  T  he Empirical Framework: Data, Model, and Methodology The current empirical framework is based on data from Tunisia during the period 1980–2018. Data on the capital flight are published by the Political Economy Research Institute at the University of Massachusetts at Amherst (PERI, 2021). These data are computed and periodically updated by Ndikumana and Boyce for most African countries using the residual method of the World Bank (1985). Accordingly, capital flight is defined as the difference between capital inflows (the sum of net increases in external debt and net foreign direct investment) and capital outflows (the sum of current account deficit and net additions to the stock of foreign reserves). In a series of papers, Ndikumana and Boyce implemented some adjustments to capital flight computation to refine the obtained estimates, such as the adjustment for trade misinvoicing, the adjustment for exchange rate fluctuations, the adjustment for debt write-offs, and the adjustment for underreporting of remittances.1 In the current empirical framework, the capital flight variable is included as a percentage of GDP. The explanatory variable of interest is the total natural resource rents as a share of GDP (Rents). It is used as a proxy for the abundance of natural resources. We intended to consider also the disaggregated types of total natural resources: oil rents, natural gas rents, mineral rents, and forest rents, in separate regressions, but the shares of the last three types are very small compared to the oil rents. Therefore, we felt like using the aggregated total natural resources to consider disaggregated types instead. As discussed earlier in the introductory, the relationship between natural resources wealth and capital flight may be established through many channels, including corruption, rent-seeking, trade misinvoicing, and tax evasion. In the regression model, we include also the squared term of Rents variable (Rents^2) to account for nonlinearity in the relationship between capital flight and natural resources. The other explanatory variables incorporated in the regression model are selected based on the literature on the determinants of capital flight. We include external debt stock, external debt flow, inflation rate, foreign direct investment, and institutional

1  The detailed algorithm of computing the capital flight series can be found in Ndikumana and Boyce (1998, 2003, 2010, 2011, 2012).

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variables. These variables are selected from a wide set of variables based on the general-to-specific method in the econometric estimation to come with a parsimonious model that is consistent with different steps of the cointegration approach and satisfies the statistical significance of the estimated coefficients as well. For instance, two variables related to external borrowing are considered, but they are used to check for two different hypotheses largely discussed in the debt-capital flight literature: debt-driven capital flight and debt-fueled capital flight (Boyce, 1992; Ndikumana & Boyce, 2003, 2011; Ndikumana & Sarr, 2019). First, we include the stock of external debt to GDP ratio to assess the debt-driven capital flight. This situation indicates that following a massive government borrowing, domestic investors and private agents move their assets abroad because they expect lower overall returns to domestic investment and distortionary measures such as heavy taxes and a decline in after-tax returns to productive public investment (Boyce, 1992). Second, the annual flow of external debt will capture the possibility of debt-fueled capital flight. This situation occurs when the inflow of capital in the form of externally borrowed funds constitutes both the motive and the means for re-exporting it overseas as private assets, especially by corrupt leaders and elites (Boyce, 1992). Concerning inflation, a positive relationship is expected between capital flight and the inflation rate. In a country with high inflation, the value of money is reduced and uncertainty is increased. This discourages the willingness to invest domestically while encourages investors to seek a more favorable investment climate and transfer their funds abroad. Another potential determinant of capital flight is the foreign direct investment (FDI) inflows. The latter may exhibit a positive or a negative effect on capital flight. On the one hand, FDI may fuel capital flight if associated profits are transferred out of the country. On the other hand, it may be seen as an indicator of higher investment returns, encouraging the willingness to invest domestically. It has been argued that capital flight is also affected by the quality of governance and institutions. To this end, we employ the well-known Polity2 index provided by the Polity IV project database. It ranges from −10 (strongly autocratic) to 10 (strongly democratic). The wide range dataset used in this study does not enable us to consider the well-known six indexes of the World Bank’s Worldwide Governance Indicators (WGIs) because they are calculated only from 1996 with some missing years, while our data start from 1980. Descriptive statistics of variables and the correlation matrix are shown in the Appendix (Tables 3.7 and 3.8). Before estimating the regression model relating capital flight to its potential determinants, it is common to start by examining the integration properties of the variables to avoid spurious regression. Therefore, numerous available unit root tests can be applied to test the stationarity properties of the variables such as ADF by Dickey and Fuller (1979), PP by Philips and Perron (1988), KPSS by Kwiatkowski et  al. (1992), DF-GLS by Elliott et  al. (1996), and Ng-Perron by Ng and Perron (2001). In the current study, the ADF test is implemented. However, this type of test does not allow controlling for potential structural breakpoints that occurred in the series which is common in long time series. Therefore, in addition to the ADF test, we conduct the conventional Zivot–Andrews unit root test with a structural break (Zivot & Andrews, 1992).

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The subsequent step after testing the stationarity properties of the variables is to examine the long-run (or the cointegration) relationship between them. Many cointegration approaches exist in the economic literature, including Engle and Granger (1987), Johansen (1991), Phillips and Ouliaris (1990), and Banerjee et al. (1998). In addition to the background theory and the goal of the empirical study, the choice of the suitable cointegration approach is done following the results of the unit root tests discussed above. In particular, the autoregressive distributed lag (ARDL) bounds testing approach to cointegration developed by Pesaran et  al. (2001) is applicable whether the variables are stationary I(0), non-stationary I(1), or mutually cointegrated. This is a value-added compared to Johansen’s technique, for example, which requires that all the variables should be integrated of order one. Indeed, it has many advantages that (i) it performs better with small samples size, (ii) it deals with the endogeneity issues of some variables, and (iii) it allows assessing simultaneously both the short- and long-run effect of a particular variable and separating short-run effect from long-run effect (Bentzen & Engsted, 2001). Thanks to its nice features, the ARDL modeling approach is being increasingly used in empirical studies (e.g., Martinez-Zarzoso & Bengochea-Morancho, 2004; Santana-­Gallego et al., 2011; Bangake & Eggoh, 2012; Kennedy & Palerm, 2014; Sebri & Ben-Salha, 2014; Ben Ali & Acikgoz, 2019; Ben Mim & Ben Ali, 2021). Kripfganz and Schneider (2020) point out that the concept of a level relationship is broader than that of cointegration because it is not required that the variables should be individually integrated of order one when using the ARDL bounds testing approach. Using the Kripfganz and Schneider (2020) notations, the ARDL (p, q,….,q) model is expressed as follows: p

q

i 1

i 0

yt  c0  i yt i   i xt i  ut



(3.1)

p ≥ 1. For simplicity, assume the same lag order q ≥ 0 for all the independent variables included in the k × 1 vector xt. The error term ut has an independent normal distribution, ut~IN(0, ωuu). To test the null of no level relationship between the variables, it is common to reparameterize Eq. (3.1) into an unrestricted error correction model (UECM) as follows: p 1

q 1

i 1

i 0

yt  c0    yt 1   xt    yi yt i   xi xt i  ut

p

(3.2)

with   1   j is the speed of adjustment coefficient, while the long-run coefj 1

ficients are given by  

q

 j 0



j

.

An alternative reparameterization can be written also as follows:

3  Assessing the Determinants of Capital Flight from Tunisia…

53

p 1

q 1

i 1

i 0

yt  c0    yt 1   xt 1    yi yt i  wxt   xi xt i  ut



(3.3)

From Eq. (3.3), the hypothesis to be tested is the absence of any level relationship between yt and xt, and it is investigated based on the F-test and t-test resulting from some restrictions on the coefficients. The Pesaran et al. (2001) procedure for this hypothesis testing consists of the following steps. q

• Testing

the

hypothesis

H 0F :   0 and



j

 0 against

the

alter-

j 0

q

F native H1 :   0 or  j  0 . j 0

• If H cannot be rejected (the F-statistic is below the lower bound), this implies that there is no level relationship. F • However, if H 0 is rejected (the F-statistic is above the upper bound), use t-­ statistic to test another null hypothesis H 0t :   0 against the alternative H1t :   0. t • If H 0 cannot be rejected (the t-statistic is below the lower bound), conclude that there is no level relationship; • If H 0t is rejected (the t-statistic is above the upper bound), at least asymptotically, a level relationship between yt and xt exists. F 0

Critical values and approximate p-values from Kripfganz and Schneider (2020) are used to decide on the test. These critical values depend on the number of independent variables, their integration order, the number of short-run coefficients, and the inclusion of an intercept or a time trend (Kripfganz & Schneider, 2020). On the one hand, there is no level relationship between the variables if both F- and t-­ statistics are closer to zero than critical values for I(0) variables (lower critical bound), i.e., if p-values are higher than the desired level for I(0) variables. On the other hand, there is a level relationship if both F- and t-statistics are more extreme than critical values for I(1) variables (the upper critical bound), i.e., if p-values are lower than the desired level for I(1) variables. The reliability of the ARDL estimates is assessed via the CUSUM and other diagnostic tests that check for the residuals normality, serial correlation, heteroscedasticity, autoregressive conditional heteroscedasticity, and functional form misspecification issues.

3.4  Results and Discussion The results of testing for the integration order are presented in Table 3.2. According to the ADF test, there is a mixture of I(0) and I(1) variables, but none is I(2). The Zivot–Andrews unit root test confirms these findings in the presence of structural

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H. Dachraoui and M. Sebri

Table 3.2  Unit root tests Variable Flight

ADF test Level −4.714(0)***

1st difference −5.466(3)***

Debt stock

−2.119(2)

−4.431(0)***

Debt flow

−4.775(0)***

−4.772(6)***

Inflation

−3.742(0)***

−8.745(0)***

FDI

−4.050(0)***

−10.173(0)***

Rents

−3.148(0)**

−6.061(0)***

Rents^2

−4.877(0)***

−5.877(0)***

Polity

−0.090(0)

−5.614(0)***

Zivot–Andrews test Level 1st difference −4.086(3)* −4.859(4)** [2005] [1991] −2.673(2) −5.542(0)** [1995] [1988] −5.252(1)** −5.722(2)*** [2001] [2001] −4.504(0) −5.380(4)** [2008] [1998] −4.860(0)*** −10.925(0)** [2011] [2007] −2.408(3) −7.792(0)*** [2013] [2009] −5.721(0)*** −7.654(0)*** [1986] [2009] −6.481(0)** −7.337(0)*** [2011] [2011]

***, **, and * indicate the statistical significance at the 99%, 95%, and 90% confidence levels, respectively. For both tests, values in parentheses indicate the lag length. For the Zivot–Andrews test, values in brackets indicate the time break

breaks. All of the variables are found stationary after the first difference in the presence of structural breaks occurring in the series. The structural break periods for different variables are shown in brackets. In particular, following the objective of the current study, we are interested in the structural break for the capital flight variable which occurred in 2005. This year coincides with some major economic and political reforms implemented by the Tunisian government and devoted to accelerating economic growth while consolidating macroeconomic stability. The main focuses of these reforms consist in further integrating Tunisia into the world economy, strengthening the private sector, export development, increasing investments, and educational reform (AfDB et al., 2013). The finding of a mixture of I(0) and I(1) variables means that it is appropriate to apply the ARDL bounds testing approach to test for the presence of a level relationship between the variables. It is worth noting that a dummy variable corresponding to the year 2005 is added to the model to allow controlling for the structural break information based on the Zivot–Andrews unit root test. The Schwarz–Bayesian information criterion (SBIC) is used for the optimal lag order selection of the ARDL model. The results of the ARDL bounds test are reported in Table 3.3 for three different specifications. The first specification includes all the aforementioned variables except the institutional indicator (Polity). The latter is added in the second specification to assess the role of the quality of institutions in reducing capital flight. In the third specification, an interaction term between the Rents and Polity variables is incorporated in the regression to test whether the quality of institutions tends to mitigate the impact of natural resource rents on capital flight from Tunisia. The

3  Assessing the Determinants of Capital Flight from Tunisia…

55

Table 3.3  ARDL bounds test for the existence of a level relationship Specification 1 Critical values 1% 5% 10% Specification 2 Critical values 1% 5% 10% Specification 3 Critical values 1% 5% 10%

F-statistic 6.692*** I(0) 4.066 2.823 2.324 14.501*** I(0) 3.751 2.688 2.249 13.387*** I(0) 3.881 2.776 2.318

I(1) 6.237 4.464 3.750 I(1) 5.644 4.169 3.557 I(1) 5.732 4.230 3.604

t-statistic −6.317*** I(0) −3.654 −2.853 −2.466 −7.709*** I(0) −3.638 −2.887 −2.522 −6.982*** I(0) −3.637 −2.894 −2.533

I(1) −5.591 −4.571 −4.083 I(1) −5.703 −4.751 −4.289. I(1) −5.542 −4.618 −4.168

*** and * indicate statistical significance at the 99% and 90% confidence levels, respectively. Critical values are from Kripfganz and Schneider (2020)

bounds test results indicate that for the three specifications, the F- test and t- statistics exceed (in absolute terms) the upper critical bounds (I(1)) at the 1% significance level. These findings imply that the null of no level relationship is rejected in all specifications, and there is evidence of a long-run relationship in the presence of structural breaks in the variables. Once the ARDL bounds testing approach confirms the existence of a level relationship, the long- and short-run coefficients may be estimated. The long-run estimates are shown in Table  3.4. The estimation results are robust across the three specifications. Starting with the two external borrowing variables, which are included to test the debt-driven and debt-fueled capital flight hypotheses stated earlier in the data section. The estimation results exhibit no statistically significant coefficient of the stock of debt variable, suggesting that the accumulation of external borrowing does not significantly affect capital flight. However, a positive and strongly significant coefficient of the change in external debt variable is estimated in the three specifications, supporting, therefore, the debt-­fueled capital flight hypothesis (Boyce, 1992; Ndikumana & Boyce, 2003, 2011; Ndikumana & Sarr, 2019). The latter, which is linked to the revolving door phenomenon that has been extensively addressed in the debt-capital flight literature (see, for example, Boyce, 1992; Ndikumana & Boyce, 2003, 2011; Dachraoui et al., 2020), states that externally borrowed money are re-exported offshore as private assets, hence increasing capital flight. According to the estimation results, more than 60 cents of every dollar borrowed by the Tunisian government leaves the country each year in capital flight. The inflow of capital in the form of externally borrowed funds has constituted both the motive and the means for re-exporting it abroad as private assets, especially by

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Table 3.4  Long-run estimates Variable Debt stock

Polity

Specification 1 0.046 (0.0762) 1.356*** (0.2324) 0.103 (0.2004) −0.743*** (0.2452) 0.351 (0.5844) 0.351 (0.0468) _

Rents*polity

_

Specification 2 −0.025 (0.0643) 0.602*** (0.1771) 0.277* (0.1532) −0.460** (0.2293) −0.034 (0.4647) −0.035 (0.0319) −0.299*** (0.0915) _

Year 2005

−2.899 (2.4105)

−0.746 (2.4557)

Debt flow Inflation FDI Rents Rents^2

Specification 3 −0.070 (0.0685) 0.659*** (0.1940) _ −0.510* (0.2756) 0.104 (0.5461) −0.059 (0.0385) _ −0.054** (0.0207) −1.241* (2.8906)

***, **, and * indicate the statistical significance at the 99%, 95%, and 90% confidence levels, respectively. The inflation variable is discarded in Specification 3 because of collinearity issues

corrupt leaders and elites. Despite that Tunisia, like most North African countries, could not be classified as a highly indebted country, its debt burden has increased over time (Ndikumana & Boyce, 2012). In Particular, during the period of the Ben Ali regime, the external indebtedness has sharply increased and reached high levels. This period coincides with the increasing openness of the Tunisian economy in the context of international trade liberalization and capital markets globalization (Ben Mimoun, 2013). The results indicate that inflation shows a positive and significant impact on capital flight (Specification 2). As explained earlier in the data section, inflation is an indicator of macroeconomic instability. The latter reduces assets attractiveness denominated in domestic currency compared to those denominated in foreign currency. As a result, investors who want to invest in the country are sent mixed signals, as excessive inflation may be interpreted as the government’s failure to successfully handle macroeconomic policy (Ndikumana & Boyce, 2011). During 1990s, Tunisia had succeeded to reduce progressively inflation and preserve competitiveness, but since 2002, a continuous increase in the inflation rate has been occurred due to many factors, including the high energy prices in international markets and the depreciation of the Tunisian dinar against the main foreign currencies. Since the 2011 uprising, the continuous protests in different economic sectors, the disturbance in the distribution channels, and the salary increases have made the bad situation even worse. The current findings of the impact of inflation on capital flight are

3  Assessing the Determinants of Capital Flight from Tunisia…

57

in line with those of Asongu et al. (2019), Ndikumana and Boyce (2011), Pastor (1990), and Cuddington (1986). Foreign direct investment inflows appear to decrease the capital flight from Tunisia, as a negative and statistically significant coefficient for the FDI variable is estimated. This indicates that foreign direct investment inflows have helped to curb capital flight from Tunisia. The influx of private capital has served as a signal of higher returns to investment in the Tunisian economy, encouraging investors to keep their money in the country. According to OECD (2020), Tunisia has experienced its FDI highs between 2005 and 2009. Since then, it has steadily declined, especially after the 2011 uprising with some terroristic attacks, and economic and political instability. However, since 2017, FDI inflows have seen a slight rising trend despite that COVID-19 tends to dampen this gain. According to the empirical literature, the impact of FDI on capital flight is mixed. For instance, Lessard and Williamson (1987) argue that a negative relationship exists between FDI and capital flight especially when the investment climate is improved. Ndikumana and Sarr (2019) find a positive effect between FGD and capital flight and refer to it as FDI-fueled capital flight. The authors explain this by claiming that private capital inflows could be a source of capital flight financing. Lensink et al. (2000) find no significant influence of FDI capital flight. Concerning institutional factors, the Polity variable shows a negative and statistically significant coefficient (Specification 2). This suggests that capital flight decreases with better quality of institutions. Political stability coupled with good governance generally leads to attract foreign investments as well as encourage domestic investments and consequently reduce capital flight. The quality of institutions, according to Ndikumana and Sarr (2019), can be used as a proxy for the government’s ability to set a suitable framework for efficient natural resource management, therefore minimizing the potential for natural resources to fuel capital flight. Our empirical results corroborate those of previous studies including Ndikumana and Sarr (2019), Efobi and Asongu (2016), and Ndikumana and Boyce (2003, 2011). With regard to the effect of natural resources on capital flight, estimation results show that neither the level nor the squared term of the Rents variable is statistically significant in the three specifications. This means that in the case of Tunisia, capital flight is not significantly fueled by natural resource rents. The current empirical evidence is not in line with few studies conducted on the subject (Dachraoui et al., 2021; Ndikumana & Sarr, 2019; Ndikumana & Boyce, 2011; Kwaramba et  al., 2016) which reach out that an increase in natural resource rents results in accelerated capital flight. This could be explained by the fact that Tunisia is not as rich with natural resources as neighboring or some other African countries. Ndikumana and Sarr (2019) point out that no robust evidence on the relationship between natural resources and capital flight could be retrieved from the empirical literature. To assess the effect of natural resources according to the institutional quality, we have incorporated in Specification 3 an interaction term between Rents and Polity. The estimated coefficient associated with this term is negative and statistically significant, suggesting that during the years of transition to democracy, natural resources exert a negative impact on capital flight. Tunisia has been undergoing a democratic

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transition since the 2011 uprising after deposing a long time autocrat, and citizens today have unparalleled political rights and civil liberties. For a long time, there has been a strong link between natural resource management and contentious politics in Tunisia (Walsh, 2021). Since 2011, these disputes take the shape of regular protests, sit-ins, and production disruptions asking for jobs and a share in revenue from natural resources. These movements have forced, successive governments to further monitor the sector and associated multinationals, limiting, to some extent, the capital flight. The estimation results in the short run provided in Table 3.5 strongly support the results in the long run in terms of explanatory power and coefficient signs. As per the estimated error correction term (ECT), its estimated coefficient in the three specifications is negative and statistically significant, confirming further the established long-run relationship between capital flight and its determinants. In particular, the estimated coefficients range from −1.08 to −0.972. This indicates that capital flight does not monotonically converge to its long-run equilibrium since the ECT coefficients are not always between −1 and 0. Nonetheless, the ECTs make some dampened oscillations in capital flight about its equilibrium before converging relatively quickly. The estimated ARDL specifications have passed a series of diagnostic tests of normality, heteroscedasticity, serial correlation of the estimated residuals, autoregressive conditional heteroscedasticity, functional form misspecification, and the cumulative sum (CUSUM) test for parameter stability (see Table 3.6). For each test, the null hypothesis is rejected against the alternative in both specifications. This indicates that the residuals are normally distributed with a constant variance. There are no problems of serial correlation of residuals or autoregressive conditional heteroscedasticity. The functional forms of the ARDL models are well specified, and there are no omitted variable issues. The ARDL estimates are reliable and consistent as suggested by the CUSUM test of parameter stability over time. This is confirmed graphically in Fig.  3.5, where the CUSUM plots for the three specifications are inside the 99% confidence bands.

3.5  Conclusions and Policy Implications Like the rest of North African countries, Tunisia would be debt-free if it could recuperate only a part of its assets that were illicitly transferred abroad. The capital flight reached its high levels during the Ben Ali regime (1987–2010) to represent 89% of the cumulative capital flight from Tunisia between 1980 and 2018. This study employs the recently updated data on the capital flight provided by PERI (2021) to identify the drivers of capital flight from Tunisia over the period 1980–2018. The econometric methods apply the structural break unit root tests and ARDL bounds testing approach to estimate the long-run relationship and the short-run dynamic error correction model. Empirical results indicate that there exists a level relationship among the variables in the presence of structural break. The empirical evidence

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59

Table 3.5  Short-run estimates Variable ECT

Δ Polity

Specification 1 −1.080*** (0.1710) −0.045 (0.1135) 0.889*** (0.1339) 0.111 (0.2200) −0.803*** (0.2665) −0.380 (0.6190) −0.057 (0.0490) _

Δ Rents*Polity

_

Specification 2 −1.086*** (0.1199) −0.027 (0.0684) 0.654*** (0.1465) 0.301* (0.1656) −0.500* (0.2589) −0.037 (0.5059) −0.039 (0.0386) −0.326*** (0.1178) _

Δ Year2005

−3.132 (2.6238) −3.381 (4.6431) 0.815 −67.318

−0.811 (2.6790) 2.570 (4.1638) 0.766 −80.438

Δ Debt stock Δ Debt flow Δ Inflation Δ FDI Δ Rents Δ Rents^2

Constant Adj. R-squared Log-likelihood

Specification 3 −0.972*** (0.1392) 0.068 (0.0634) 0.641*** (0.1392) _ −0.496* (0.2787) 0.101 (0.5297) −0.057 (0.0378) _ −0.053** (0.0227) −1.207 (2.8209) −6.112 (4.2571) 0.728 −84.002

***, **, and * indicate the statistical significance at the 99%, 95%, and 90% confidence levels, respectively

Table 3.6  Diagnostic tests Test

Normality Heteroscedasticity Autocorrelation ARCH RESET CUSUM

Specification 1 Test statistic (p-value) 0.96 (0.327) 0.07 (0.790) 0.965 (0.325) 0.011 (0.918) 0.567 (0.451) 0.412

Specification 2 Test statistic (p-value) 0.59 (0.443) 4.12** (0.042) 0.194 (0.659) 0.077 (0.781) 0.92 (0.444) 0.292

Specification 3 Test statistic (p-value) 0.71 (0.398) 1.92 (0.165) 0.082 (0.774) 0.404 (0.525) 0.98 (0.417) 0.597

The critical values for the cumulative sum test for parameter stability are 1.1430, 0.9479, and 0.850 for the 1%, 5%, and 10% levels, respectively

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Recursive cusum plot of D.Flight

Recursive cusum plot of D.Flight

4

with 99% confidence bands around the null

-4

-4

-2

-2

0

0

2

2

4

with 99% confidence bands around the null

1980

1990

2000

2010

2020

1980

1990

2000

2010

2020

Specification 2

Specification 1 Recursive cusum plot of D.Flight

-4

-2

0

2

4

with 99% confidence bands around the null

1980

1990

2000

2010

2020

Specification 3 Fig. 3.5  Recursive CUSUM plots for the three specifications

suggests that capital flight from Tunisia is significantly affected by external borrowing, inflation, foreign direct investment inflow, and natural resource rents interacted with the quality of institutions. These results have several implications for designing effective policies that help to curb capital flight. First, the effect of external borrowing was assessed via two variables: the debt stock and debt flow to check for the debt-driven capital flight and debt-fueled capital flight hypotheses, respectively. According to the findings, every dollar borrowed from foreign loans induces almost 60 cents as a capital flight each year through the flowing back process (debt-fueled). Hence, external borrowing was a primary source of capital flight from Tunisia, especially during the Ben Ali regime. The government should therefore pursue strategies that make external borrowing beneficial to the country’s development by promoting responsible lending and accountable debt management on both the government’s and debtor’s sides. Second, one of the most significant characteristics of a favorable business climate is macroeconomic stability, and corporate investors employ a variety of measures to assess national macroeconomic stability, with inflation remaining a key indicator. The current inflation rate and its situation in the future are key elements in determining prevailing interest rates and investing strategies. Third, promoting a favorable business climate boosts the inflow of foreign direct investments while reducing capital

61

3  Assessing the Determinants of Capital Flight from Tunisia…

flight. In fact, the government should not follow a discriminatory policy that favors foreign investors over domestic investors through, for example, preferential taxation (tax holidays) and investment or exchange rate guarantees (Kant, 1996, 1998). Such measures lead to discourage domestic investment and bring capital flight. Fourth, the findings indicate that establishing an adequate institutional environment that enforces transparency and accountability, and the effectiveness of mechanisms and institutions help curb capital flight. Finally, natural resource rents have been demonstrated to exert a negative impact on capital flight during periods of enhanced governance and institutions. This confirms the estimates that high levels of capital flight were recorded during the Ben Ali regime. The latter was plagued by corruption (especially during its last decade) which afflicted both public and private institutions. Over decades, Tunisia has suffered from capital flight waves and incurred “odious” debts at the expense of economic and social development. The tracing and repatriation of the Tunisian assets hidden abroad are still an urgent priority for the post-revolutionary governments. Since 2011, recurring protests have occurred against multinational corporations, asking for jobs in the natural resource sector and more transparency and accountability in the management of the natural resources revenues. Implementing sound macroeconomic policies and efficient judicial and political institutions and transparency would be, therefore, imperative to control capital flight and establish an adequate climate of social and economic development in Tunisia.

Appendix Table 3.7  Definition and descriptive statistics of variables Variable Flight Debt stock Debt flow Inflation

Description The ratio of capital flight to GDP Total outstanding debt as a percentage of GDP Change in the stock of debt as a percentage of GDP GDP deflator (annual %)

FDI

Foreign direct investment net inflows as a percentage of GDP Total natural resource rents as a percentage of GDP Polity2 index

Rents Polity

Source Mean PERI institute 2.462 World Bank 58.447 (WDI) World Bank 3.479 (WDI) World Bank 5.688 (WDI) World Bank 2.275 (WDI) World Bank 5.774 (WDI) −2.692 Polity IV project database

Std. dev. 3.854 9.947

Min Max −7.612 10.081 40.408 88.296

3.997

−5.015 12.825

3.373

2.107

16.007

1.635

0.600

9.424

3.772

1.674

16.730

4.942

−8

7

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Table 3.8  Correlation matrix Flight Flight Debt stock Debt flow Inflation FDI Rents Rent^2 Polity Rents*Polity

1 0.359 0.698 0.100 −0.334 −0.223 −0.203 −0.251 −0.021

Debt stock

Debt flow

1 0.500 −0.295 −0.321 −0.557 −0.555 0.418 0.460

1 −0.260 −0.183 −0.129 −0.127 −0.017 0.031

Inflation FDI

Rents

1 −0.015 0.593 0.636 −0.321 −0.601

1 0.969 1 −0.475 −0.455 1 −0.831 −0.853 0.808 1

1 0.097 0.089 −0.024 −0.032

Rents^2 Polity Rents*Polity

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Dachraoui, H., Smida, M., Sebri, M. (2020). Role of capital flight as adriver of sovereign bond spreads in Latin American countries. International Economics, (162), 15–33. Dachraoui, H., Sebri, M., & Dwedar, M. (2021). Natural resources and illicit financial flows from BRICS countries. Biophysical Economics and Sustainability, 6, 3. https://doi.org/10.1007/ s41247-­021-­00085-­8 Demachi, K. (2014). Capital flight from resource-rich developing countries. Economic Bulletin, 34(2), 734–744. Dickey, D., & Fuller, W. A. (1979). Distribution of the estimates for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427–431. Dooley, M. (1988). Capital flight: A response to differences in financial risks. IMF Staff Papers, 35(3), 422–436. Efobi, U., & Asongu, S.  A. (2016). Terrorism and capital flight from Africa. International Economics, 148, 81–94. Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64, 813–836. Engle, R.  F., & Granger, C.  W. J. (1987). Cointegration and error correction representation: Estimation and testing. Econometrica, 55(2), 251–276. Erbe, S. (1985). The flight of capital from developing countries. Intereconometrics 20(6), 268–275. FAO. (2016). State of the World’s forests: Tunisia case study. http://www.fao.org/3/a-­c0185e.pdf. Accessed 12 May 2020. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551–1580. Kant, C. (1996). Foreign direct investment and capital flight. International Finance Section Department of Economics of Princeton University. Kant, C. (1998). Capital inflows and capital flight-individual countries experience. Journal of Economic Integration, 13(4), 644–661. Kennedy, M., & Palerm, A. (2014). Emerging market bond spreads: The role of world financial-­ market conditions and country-specific factors. The Journal of International Money and Finance, 43(C), 70–87. Kripfganz, S., & Schneider, D. C. (2020). Response surface regressions for critical value bounds and approximate p-values in equilibrium correction models. Oxford Bulletin of Economics and Statistics. Kwaramba, M., Mahonye, N., & Mandishara, L. (2016). Capital flight and trade misinvoicing in Zimbabwe. African Development Review, 28(S1), 50–64. Kwiatkowski, D., Phillips, P., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationary against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54, 159–178. Lensink, R., Hermes, N., & Murinde, V. (2000). Capital flight and political risk. Journal of International Money & Finance, 19, 73–92. Lessard, D.  R., & Williamson, J. (1987). Capital flight and third world debt. Institute for International Economics. Lester, H. (1996). Capital flight from beautiful places: The case of three Caribbean countries. International Review of Applied Economics, 10, 263–272. Martinez-Zarzoso, I., & Bengochea-Morancho, A. (2004). Pooled mean group estimation of an environmental Kuznets curve for CO2. Economics Letters, 82, 121–126. Nakhle, C., & Lassourd, T. (2019). Assessing Tunisia’s upstream petroleum fiscal regime. Natural Resource Governance Institute. https://resourcegovernance.org/sites/default/files/documents/ assessing-­tunisia-­upstream-­petroleum-­fiscal-­regime.pdf. Accessed 11 May 2020. Ndikumana, L., & Boyce, J. K. (1998). Congo’s odious debt: External borrowing and capital flight in Zaire. Development and Change, 29(2), 195–217. Ndikumana, L., & Boyce, J. K. (2003). Public debts and private assets: Explaining capital flight from sub-Saharan African countries. World Development, 31(1), 107–130.

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Ndikumana, L., & Boyce, J. K. (2010). Measurement of capital flight: Methodology and results for sub-Saharan African countries. African Development Review, 22(4), 471–481. Ndikumana, L., & Boyce, J. K. (2011). Capital flight from sub-Saharan African countries: Linkages with external borrowing and policy options. International Review of Applied Economics, 25(2), 149–170. Ndikumana, L., & Boyce, J.  K. (2012). Capital flight from North African countries. Research Report, Political Economy Research Institute. University of Massachusetts, Amherst. Ndikumana, L., & Sarr, M. (2019). Capital flight, foreign direct investment and natural resources in Africa. Resources Policy, 63, 101427. Ndikumana, L., Boyce, J. K., & Ndiaye, A. S. (2015). Capital flight from Africa: Measurement and drivers. In S. I. Ajayi & L. Ndikumana (Eds.), Capital flight from Africa: Causes, effects, and policy issues (pp. 15–54). Oxford University Press. Ng, S., & Perron, P. (2001). Lag selection and the construction of unit root tests with good size and power. Econometrica, 69, 1519–1554. OECD. (2020). OECD review of foreign direct investment statistics: Tunisia. Pastor, M. (1990). Capital flight from Latin America. World Development, 18(1), 1–18. PERI. (2021). Capital flight from African countries, 1970–2018. https://peri.umass.edu/capital-­ flight-­from-­africa. Accessed 30 July 2021. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326. Phillips, P. C. B., & Ouliaris, S. (1990). Asymptotic properties of residual-based tests for cointegration. Econometrica, 58(1), 165–193. Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335–346. Santana-Gallego, M., Ledesma-Rodríguez, F., & Perez-Rodríguez, J. V. (2011). Tourism and trade in OECD countries. A dynamic heterogeneous panel data analysis. Empirical Economics, 41, 533–554. Sebri, M., & Ben-Salha, O. (2014). On the causal dynamics between economic growth, renewable energy consumption, CO2 emissions and trade openness: Fresh evidence from BRICS countries. Renewable and Sustainable Energy Reviews, 39(2014), 14–23. Walsh, A. (2021). The contentious politics of Tunisia’s natural resource management and the prospects of the Renewable Energy transition (K4D Helpdesk Report). Institute of Development Studies. https://doi.org/10.19088/K4D.2021.048 World Bank. (1985). World development report 1985. World Bank. Zivot, E., & Andrews, D. W. K. (1992). Further evidence of the great crash, the oil price shock, and the unit root hypothesis. Journal of Business and Economic Statistics, 10(3), 251–270.

Chapter 4

Assessing Macroeconomic, Distributive, and Environmental Impacts of Energy Subsidy Removal in Tunisia with Input– Output Modeling Aram Belhadj, Ahlem Dakhlaoui, and Rania Gouider

4.1  Introduction Following the adoption of the Paris Agreement (COP21), all the signatory countries, including the countries of the Middle East and North Africa region (MENA), have undertaken to reduce their volume of GHG emissions (CO2, CH4, N2O) by around 35%. The main goal is to achieve a long-term objective of limiting global warming to 2 °C. These changes reflect the need to guarantee survival, improve economic life, and insure environmental security and sustainable development. Under this agreement, each state must make public its national efforts in the context of the fight against climate change through an intended nationally decided contribution (INDC). MENA is not the largest emitting region, but it seems to be already under severe heat and water stress with little margin for adaptability. Compared to the rest of the world, this region will suffer disproportionally from extreme heat, hence the importance of its climate commitment. Tunisia was among the MENA countries engaged in the COP21 process. Following this commitment, the public authorities must consolidate the efforts undertaken in terms of GHG reduction. To do this, Tunisia must succeed in the energy transition favoring energy efficiency and renewable energy projects, in A. Belhadj (*) Faculty of Economics and Management of Nabeul & University of Orléans, LR18ES48, Economics Department, University of Carthage, ENVIE, Nabeul, Tunisia A. Dakhlaoui Faculty of Economics and Management of Nabeul, Polytechnic School of Tunisia, LEGI, Industrial Economics Department, University of Carthage, Nabeul, Tunisia R. Gouider Faculty of Economics and Management of Nabeul, Polytechnic School of Tunisia LEGI, Economics Department, University of Carthage LEGI, Nabeul, Tunisia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. S. Ben Ali (ed.), Key Challenges and Policy Reforms in the MENA Region, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-3-030-92133-0_4

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particular in energy-intensive and high-GHG-emitting sectors such as electricity, transport, and cement. Such a transition is not obvious because, despite Tunisia’s efforts to preserve the environment, the energy mix remains heavily dependent on polluting fossil fuels, the use of which is subsidized by public finances. In addition, the volatility of international energy prices, the constant and significant evolution of energy consumption, and its strong dependence on imports are factors that can thwart the commitments undertaken by Tunisia in terms of GHG reduction. To honor its stated commitments (INDC), the government must mobilize significant sums over the period 2015–2030, a good part of which will be oriented toward investments in the energy sector. The main objective is not only to improve energy efficiency but also to promote investment in renewable sources of energy, especially in energy-intensive sectors with high GHG emissions (electricity, cement, and transport). One of the possible options in terms of economic policy is to undertake a reform of energy subsidies and allocate a part to private investors to promote the renewable energy sector. In Tunisia, despite the new political orientation toward the gradual elimination of energy subsidies following the adoption of new pricing for electricity, gas, and petroleum products from 2014 onwards, studies on the economic impact of this decision are very rare. Some examples are those of ITCEQ (2017), the World Bank (2013), and Dakhlaoui et al. (2017). This latest analysis assessed the macroeconomic and environmental impacts of removing the energy subsidy in the transport sector with its three modes, using a computable general equilibrium model. It is useful to remember that analysis of the economic impact of energy subsidies reform has just started to develop in many countries, notably China, Oman, Kuwait, Iran, Malaysia, Egypt, Jordan, etc. In the economic literature, the impact of eliminating the energy subsidy is assessed using five different approaches: the price gap approach,1 the computable general equilibrium approach,2 the input–output approach,3 the econometric approach,4 and the partial equilibrium approach.5 The main objective of our study is to assess the macroeconomic, distributive, and environmental impacts of subsidy reform in the energy sector in Tunisia by using a methodology based on the price gap approach coupled with the input–output and partial equilibrium approaches. The numerical simulation exercises take the form of three scenarios of potential reforms in the energy sector. The first scenario corresponds to the total removal of all subsidies throughout the energy sector (electricity,  See among others Corden (1957), Larsen and Shah (1992), IEA (1999), Koplow (2009).  See among others Khan (2019), Glomm and Jung (2015), Elshennawy (2014), Gharibnavaz and Waschik (2015), Gelan (2018), Solaymani and Kari (2014), Magne et al. (2014), Saunders and Schneider (2000), and Farajzadeh and Bakhshoodeh (2015). 3  See among others Li and Jiang (2016), Jiang et  al. (2015), Hong et  al. (2013), Jiang and Tan (2013), Sasana et al. (2017), and Yulia and Klaus (2013). 4  See among others Al Iriani and Trabelsi (2015) and Acharya, R.H. and Sadath (2017). 5  See among others Breton and Mirzapour (2016) and Moshiri et al. (2018). 1 2

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gas, and oil refining). The second represents a policy aimed at removing all subsidies granted only to the electricity industry. Finally, the third scenario takes the form of the removal of subsidies in both the electricity and gas industries. The rest of the paper is organized as follows: The second section presents an overview of the subsidy policy and the electricity industry in Tunisia, the pricing, and the current role of the private sector. The third section presents the main results of the three scenarios of partial or total removal of energy subsidies. Finally, the last section is devoted to the conclusions and some policy implications.

4.2  Subsidy Policy and Energy Sector in Tunisia 4.2.1  The Subsidy Policy and the Energy Sector Subsidies are one of the economic policy instruments used by the Tunisian government to achieve social, economic, or environmental objectives. In Tunisia, the government has adopted a subsidy policy for commodities (foodstuffs, hydrocarbons) since December 31, 1970 (date of creation of the compensation fund, law no. 65-1970). This policy aims to support the poorer class by providing access to basic goods and services at affordable prices, help industrial competitiveness in international markets, and stabilize prices following volatility in the global prices of energy and food products. In Tunisia, direct subsidies target more particularly energy products, commodities, and transport. In 2019, for example, the total subsidy scheduled in the budget amounts to 4789 MD, distributed between 1800 MD for basic products, 451 MD for transport, and 2538 for the energy sector.6 This total was only 1500 MD in 2010, reflecting the magnitude of the pressures faced by the budget and the burden on the taxpayers. In recent years, spending on all subsidies has grown. This trend can be explained in part by the remarkable increase in world prices of energy and food products, the depreciation of the dinar against the dollar and the euro, and the growing consumption of these products in Tunisia. It is also notable from Fig. 4.1 that energy has often been in first position, capturing a large portion of the allocated subsidies, followed by the agrifood industry and transportation. Direct subsidies allocated exclusively to the energy sector have experienced a series of ups and downs since 2010. In fact, Fig. 4.2 shows that these subsidies have increased significantly, with an explosion in 2013. There was then a continuing decline until 2016, but it picked up again immediately afterward, with a new peak in 2018. As shown in Fig. 4.3, the energy sector in Tunisia is therefore characterized by a strong presence of the state, which conducts a policy of subsidizing and administering prices on several levels. These policies are implemented through the four public  Complementary Budget Law, 2018, Ministry of Finance.

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Fig. 4.1  Subsidy expenses by product, 2008–2019 (in MD)

Fig. 4.2  Energy subsidies 2004–2019 (in MD)

operators in the energy sector in Tunisia, namely the Tunisian Company of Petroleum Activities (ETAP), the Tunisian Company of Refining Industries (STIR), the National Company of Petroleum Distribution (SNDP), and the Tunisian Company of Electricity and Gas (STEG). Until January 2013, public operators STIR and STEG benefited from two types of subsidy: direct subsidies and indirect ones. The indirect subsidies came from the supply of oil to STIR and natural gas to STEG through ETAP at a fixed price, regardless of the world prices. Indeed, ETAP imports crude oil from Libya and then sells it to STIR at a fixed price equivalent to $31/barrel. Similarly, it imports natural gas from Algeria and sells it to STEG at $56/toe. As for direct subsidies, they take the form of transfers made directly by the state to STEG and STIR to cover their deficit. This deficit is the result of the difference between the cost of producing electricity and gas compared to the consumer administered prices, in the case of STEG, and the cost of refining oil compared to various administered petroleum products prices, in the case of STIR (see Fig. 4.3). On January 1, 2015, the activities of importing crude oil were transferred to STIR; from 2014 to 2015 ETAP sold natural gas from Algeria to STEG at a price equal to the import price. As of July 01, 2015, the import of natural gas was

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Ministry of Finance subsidy

Import of petroleum products

Fixed tariff by the State

Export of local crude oil Natural Gas

Fixed price

STIR

Import Algeria with PW

Fixed margin

Crude oil

Need gas 75%

Natural gas Crude oil

with PW

90.8 D/Toc (56$)

Gas activity

• Gasoline • Diesel

Local producers of gas

Natural Gas

Need gas 25% Ministry of Finance subsidy

Gas consumers • High pressure • Medium pressure • Lower pressure

STEG

Gaz naturel Price IN

Imports

Consumers of petroleum products

50 D/Bail (31$)

ETAP

Import Libva

Local distributors of petroleum products

Electricity activity Fixed price

+

Independent Private Producers IPP

Transformation/Transport/Distribution

Electricity

Electricity consumers • High pressure

Discriminatory pricing

• Medium pressure Progressive pricing by • Lower pressure tranche Final consumption

Source: Authors (2020)7

Fig. 4.3  Energy sector in Tunisia: the complete value chain (until 2013). (Source: Authors (2020)

transferred to STEG at 639 Dt/toe. The entirety of the natural gas produced from Tunisian fields is sold to STEG.

4.3  Electricity Industry, Pricing Policy, and Subsidies Since 1962, the electricity sector has been owned by a public company, the Tunisian Electricity and Gas Company (STEG), which has exclusively produced, transported, and distributed electricity in Tunisia. This industry is organized as a vertically integrated public monopoly. Afterward, a structural reform was promulgated by decree no. 96-1125 of June 20, 1996, which transformed the industry from a monopolistic structure at the level of the production of high voltage to a monopsony organization. Following this reform, the production of electrical energy by independent private producers (IPP) became possible, with the provision that sales have to be made exclusively to STEG following a long-term purchase agreement concluded between the two parties. As for the transport and distribution of electricity, they have always been monopolized by this public company. The first experiment with the participation of the private sector in the production of electricity was made following the promulgation of law no. 96-27 on April 1, 1996. Since 2001, there have been a small number of auto-producers of electricity and co-generators linked to operators consuming large quantities of electricity that are authorized to produce electricity for their own consumption. However, these operators have to sell their surplus exclusively to STEG through an electricity transfer agreement. Despite this reform, the participation of private producers in 2019 did not exceed 15.19% of the national

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production of high voltage, while the rest was provided mainly by STEG with 84.12%, followed by the self-producers with 0.69%.7 The Tunisian electricity field is very little diversified, with a very low percentage of renewable energy sources. The electricity generation technology in Tunisia is based on combined cycle technology, with a share amounting to 66.4%, followed by thermal gas (16.8%) and thermal steam (14%). Renewable sources make only a limited contribution, with a share of 2.8% (STEG, 2019).8 Tunisia’s electricity mix depends mainly on fossil fuels, particularly gas, which is becoming increasingly scarce (61% imported from Algeria). Because of this situation, the state is forced to subsidize the use of this resource in order to guarantee accessible prices to the various consumers of electricity, and this accounts for 10% of the budget and 4% of GDP. Such a situation is untenable in the long term following the rapid increase in electricity demand in Tunisia. Structural reform of STEG is necessary in order to rebalance the current electricity mix based on exhaustible energy sources. Such reform has to be in favor of the growing use of renewable sources, especially photovoltaic and wind. With the government’s announcement in December 20179 of a target of achieving 30% of electricity generation from renewables by 2030, the transition was commenced through the adoption of the Tunisian Solar Plan (TSP). To accelerate the implementation of this energy transition, several legal instruments have been passed: the law of renewable energy production on May 11, 2015, the decree of August 24, 2017, stating the conditions of project operations and sales to STEG, and the decree of July 26, 2017, relating to the operating and organization of the energy transition fund. The objective of the TSP is to achieve the following distribution of the renewable energy mix by 2030: 15% for wind turbine technology, 10% for solar photovoltaic technology, and 5% for thermodynamic solar technology. For that reason, the TSP plans to open up the renewable energy sector to different types of potential investors (private, public, national, and international) with access regimes compatible for each of these investors. However, under these different regimes, STEG continues to maintain its position as the single buyer of the surplus electricity from independent producers with a diversified pricing policy. Over the period 2000–2018, electricity consumption has evolved continuously at a rapid pace. This remarkable growth can be explained by the increase in the number of subscribers, the improvement of the standard of living of households, as well as by economic and social development. The cost of producing electricity from power plants has risen considerably since this production requires a quantity of fuel that continues to increase from one year to another. Moreover, given that the use of gas by STEG is subsidized, the state bears the cost of the difference between the international gas import price (which is often increasing) and the purchase price of inputs for electricity production (which is fixed). Consequently, international fuel price volatility has exacerbated the state’s fiscal burden.  Activity report of STEG (2019).  In 2014, the electricity generation technology in Tunisia was based on thermal gas, with a share amounting to 36.9%, followed by combined cycle technology (35.15%) and thermal steam (21.7%). Renewable energy sources had a share of only 6.15%. 9  National conference on renewable energy, Tunisia, December 7–8, 2017. 7 8

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Finally, the tariff policy adopted does not make it possible to reduce the increasing consumption of electricity. Indeed, the electricity sales prices applied by STEG cover only part of the production and distribution costs, which explains the permanent deficit of this public company. For the period 1976–2004, STEG adopted an hourly rate for high voltage. However, since 2005, it has adopted the increasing block pricing method for the low voltage. Electricity consumption rates are adjusted according to a post-hourly system for the high and medium voltage, where STEG adopts a third-degree price discrimination policy. It segments its market into different consumer groups for which prices are different. More clearly, it is a question of binomial pricing that consists of a differentiation of the price of kWh according to the level of consumption. It is composed of two parts: a fixed part (flat rate subscription) equivalent to the entry fees on the market (fixed at 500 millimes per KVA per month), and a variable and adjustable part, which constitutes the invoicing for the quantity consumed. This pricing system is aimed at ensuring an equitable distribution of these energy resources, rationing demand, and fighting against energy insecurity, especially for small consumers (STEG).

4.4  Methodology Our empirical analysis consists of three steps and is based on the methodology of Ogarenko and Klaus (2013). We first estimate the subsidy using the price gap approach. Then, we assess the macroeconomic impacts of the reduction of the energy subsidy using the input–output model, before applying the partial equilibrium model to complement the results.

4.4.1  Assessing the Subsidy Based on the Price Gap Approach In the economic literature, there are two approaches to estimate subsidies: a top-­ down approach based on the price gap and the bottom-up approach based on inventory purpose. The first approach has been used by the IEA and IMF to quantify subsidies for petroleum, gas, and electricity products that are granted by governments in various countries.10 However, the second approach uses the results of inventories made by the state according to some specific methods. In the first step of our empirical analysis, we use the price gap approach to estimate subsidies allocated to final consumers of electricity and gas in Tunisia. This approach is based on the calculation of the difference between the price paid by the final consumers Pci of the energy product or service i and its reference price ( Pri ). Thus, the unitary subsidy of the energy product or service is defined as follows:

 

10

 See Corden (1957); IEA (1999); Koplow (2009); Larsen and Shah (1992).

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si  Pci  Pri



The reference price of petroleum energy products (diesel, petrol, LPG, kerosene) for a small net importer of fossil fuels is reduced, according to the IEA (1999), to the international price of the product at the nearest market, increased by the cost of freight and fuel and by the costs of insurance, distribution, internal marketing, and value-added tax. Thus, the reference price of petroleum energy product i is defined as follows:

Pri  CAFprice i  internaldistribution cost i  VATi



For the non-tradable energy products such as electricity, their reference price is reduced to the costs of production, transmission and distribution, plus the value-­ added tax. In the electricity sector, the reference price is based on long-run marginal generation costs, including capital costs, over the expected life of power plants. Thus, the reference price of the non-tradable energy products i is defined as follows:

Pri  Production cost i  distribution andtransmission cost i  VATi



Then, the subsidy is evaluated by weighting the difference between the two prices si,by the quantity consumed of each energy product or service. The subsidy rate of each energy product or service is the ratio of this difference to the price.

4.4.2  A  ssessing the Price and Distributional Impacts of Energy Subsidy: Input–Output Analysis The input–output analysis serves to evaluate the impact on prices. This analysis is indeed considered to be a form of macroeconomic modeling, tracing the functioning of an economy while taking into account the interdependence between the different sectors of activity and economic agents (the state, firms, households, and the rest of the world). It has the advantage of quantifying the direct short-term impact of an economic shock and analyses its indirect and induced effects in order to guide economic policy without resorting to complex modeling as in the computable general equilibrium model. We refer to Leontief’s price model, which is a system with n linear equations and n unknowns, with n the number of products in a given economy. Each equation defines the price of a unit of one product produced by a branch of industry as being equal to its total cost of production including its primary and intermediate inputs. Given the fundamental assumption of the fixed proportion of intermediate demand of a branch at its level of production through the defined technical coefficients aij  =  zij/xj, the price system of the reference year is written as follows:

P   P A  Vc



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P   I  A  Vc  L Vc

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1





with P′ = (P1, P2, …., Pn) being a line vector of product prices for each sector of activity. Vcrepresents the column vector of primary inputs expressed in a monetary unit. (I − A′)−1 = L′represents the transpose of the inverse matrix of Leontief, I the identity matrix, and A the matrix of the technical coefficients. This last equation shows that the price indices are determined by the exogenous values of the costs of primary inputs. The removal of the energy subsidies induces an increase in the price of different energy inputs, and the direct and indirect effects of this shock on prices run through the variation in the added value in the electricity, gas, and oil sectors (Ogarenko & Klaus, 2013). Thus, price changes following the removal of the energy subsidies can be assessed as follows:

P  L Vc (4.1)

To quantify the impact of this price change (which is the result of the removal of energy subsidies) on the consumer price index (CPI) of various categories of household classified according to their income level, we use the Laspeyres price index, which regards the composition of the basket of good and service consumption as fixed. This methodology, which is the most widely used in the world (Ilo, 2004), is compatible with the approach used since the input–output price model makes it possible to quantify the short-term impacts during the period in which economic agents will not have had time to alter their behavior. Otherwise, the structural adjustment and substitution of energy goods and services, which have seen an increase in prices following the removal of the energy subsidies, do not occur. Indeed, this index allows the calculation of the variation in the total cost of purchasing a basket of goods and services compared to a base period (Ilo, 2004; Ogarenko & Klaus, 2013). It is defined as follows:



IPC  sih Pi , h  1,, H i

(4.2)

sih represents the share of household income that household category h spent to purchase goods i. Households are classified into nine categories according to their level of income (Q1, Q2…Q9) where the first group has the lowest income, while the ninth group has the highest. Similarly, to assess the geographical impact of the removal of the energy subsidies, we calculated the change in the CPI in both rural and urban areas.

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4.4.3  I mpact on Final Demand, Production, Energy Consumption, and CO2 Emissions: Partial Equilibrium Model We evaluate the impacts of the removal of the energy subsidy on the final demand for products in different sectors (ΔY) by using the partial equilibrium model (Ilo, 2004; Ogarenko & Klaus, 2013), which establishes a link between the variation in the prices and the final demand as follows:

  Y  Py0

 where ∆P , ε , and y0 represent, respectively, a matrix of price changes by sector, a matrix of price elasticity of demand, and the final demand vector before the adoption of the reform policy. In the final step, we use the input–output model of demand (Leontief’s model) to evaluate the impact of the variation in demand on the total output for each sector and its associated effects on the consumption of energy and thus CO2 emissions. According to Miller and Blair (2009), Ilo (2004), and Ogarenko and Klaus (2013), the variation in output of each industry following the decline in final demand is calculated as follows:

X  L y (4.4)

The variation in production will cause a variation in energy consumption and consequently in CO2 emissions. According to the same authors, the hybrid matrix that integrates the physical coefficients is defined as the ratio of the emissions (or inputs)  of each activity sector by its output θ . Thus, the environmental and socioeconomic impacts are calculated as follows:

 e   X (4.5)

4.5  Empirical Results and Economic Implications 4.5.1  Subsidy Rates of Electricity and Gas in Tunisia In the first stage of our empirical analysis, we use the price gap approach to assess subsidies allocated only to final consumers of electricity and gas in Tunisia, because data on reference prices of some petroleum energy products are not available. The subsidy rates are computed at the national level and by level of voltage for electricity (high voltage (HV), medium voltage (MV), low voltage (LV)) and level of pressure for gas (high pressure (HP), medium pressure (MP), and low pressure (LP)).

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Table 4.1  Estimation of subsidy rates of electricity and gas in Tunisia (2013 and 2019) Type of energy Electricity (millimes/kWh)

Gas (DTN/Tep)

National HV MV LV National HP MP LP

Pci2013

Pci2019

Pri2013

Pri2019

174.93 157.20 172.91 180.76 406.61 470.17 358 349.96

290.36 267.99 319.63 269.65 714.24 800.04 729.95 562.28

316.54 277.98 307.73 333.68 1018.76 993.06 1017.57 1071.07

379.85 276.32 360.69 415.91 1210.35 1159.77 1210.59 1231.98

Subsidy rate (2013) 44.73% 43.45% 43.81% 45.83% 60.11% 52.67% 64.82% 67.32%

Subsidy rate (2019) 23.56% 3.01% 11.38% 35.21% 40.99% 31.02% 39.70% 54.36%

Source: Authors’ calculations (2020)

Table 4.1 presents the results of the evaluation of the subsidy rates for electricity and gas for the 2013 reference year11 and for 2019. We note that these two types of energy are subsidized at very high rates: 43–46% in 2013 (3–35% in 2019) for electricity, especially low voltage, and 52–68% in 2013 (40–55% in 2019) for natural gas, with the highest rate for the low pressure. Then, we calculated the evolution of electricity and gas subsidy rates over the period 2000–2017 using the same approach. Figure 4.4, which shows the evolution of the subsidy rate for electricity and natural gas over the period 2000–2017, proves that the trend is upward, with a record high of 62.5% for natural gas and 48.7% for electricity in 2012. This is mainly due to a sharp increase in the import price of oil and natural gas, on the one hand, and the pricing policy adopted by STEG, which is disconnected from actual production costs, on the other hand. A considerable drop in subsidy rates is recorded in 2016 with, respectively, 5.9% and −2.6% for natural gas and electricity, mainly due to a considerable drop in the gas import price that affected the reference prices of both kinds of energy. We note that the gas subsidy rate is still higher than that of electricity, with the exception of the period 2002–2004. Since 2014, the state has implemented a new policy of phasing out the subsidy and adopted a new pricing system for electricity and gas. The evolution of subsidy rates presented in Fig. 4.4 below is explained not only by the pricing policy but also by the fluctuation in the production costs of the STEG company. Since the energy subsidy policy is expensive and has not achieved its social equity objectives, it is necessary to undertake reforms in the energy sector. In addition, it is very useful to evaluate the macroeconomic, distributive, and environmental impacts of the reform of energy subsidies in Tunisia and to judge its feasibility. We use the input–output analysis and the partial equilibrium approach. The numerical simulation exercises take the form of three scenarios of potential reforms in the  The choice of reference year is justified by availability of data on the reference price by voltage and pressure levels.

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Fig. 4.4  Evolution of electricity and natural gas subsidy rates for the period 2000–2017. (Source: Authors’ calculation 2020)

energy sector. Scenario 1 corresponds to the total removal of all subsidies throughout the energy sector (electricity, gas, and oil refining). Scenario 2 corresponds to a policy aimed at removing all subsidies granted only to the electricity industry. Scenario 3 takes the form of the removal of subsidies in both the electricity and gas industries.

4.5.2  Price and Distributional Impacts We use equation (1) and the data from the input–output table of the reference year 2013 collected from the National Institute of Statistics (INS). Table 4.2 shows the impact of the removal of energy subsidies on prices in the various economic sectors in Tunisia for three simulation exercises. Obviously, it is the removal of all subsidies in all energy sectors (electricity, gas, and oil refining) (scenario 1), which will most affect the prices in the different economic sectors (indirect effect). It affects more the prices in sectors impacted directly by this reform (direct effect). Indeed, according to this scenario, the final prices will be driven upward. Some sectors will be directly affected by the removal of these subsidies, while others will suffer indirect effects. The oil refining sector is the most impacted, with a price increase effect of 36.02%, followed by the electricity and gas, water, and mining sectors, with respective increases of 17.9%, 13.85%, and 12.56%. Other sectors will experience a smaller effect, such as ceramic, glass, and building materials (7.9%), tobacco (7.08%), and oil and gas extraction (6.91%).

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Table 4.2  Price impact in different economic sectors (in %) Sectors Agriculture and fishing Food industry Tobacco Textile, clothing, leather Various industries Oil refining Chemical industry Building materials, ceramic, glass Mechanical and electrical industry Oil and gas extraction Mine Electricity and gas Water Civil engineering and buildings Maintenance and repair Trade Hotel and catering Transport Telecommunications and post Financial services Other commercial services Public administration

Scenario 1 1,41 1,68 7,08 2,27 3,31 36,02 4,43 7,90 1,48 6,91 12,56 17,90 13,85 2,99 2,36 1,64 1,51 3,70 1,56 0,84 0,65 0,34

Scenario 2 0,04 0,15 0,44 0,31 0,28 0,18 0,31 0,83 0,24 0,18 0,87 13,25 2,51 0,20 0,30 0,12 0,11 0,06 0,16 0,06 0,09 0,03

Scenario 3 0,29 0,52 1,73 0,83 1,08 5,98 1,31 2,43 0,62% 6,17 3,00 16,93 4,99 0,81 0,82 0,44 0,65 0,67 0,44 0,50 0,25 0,09

Source: Authors’ calculations (2020)

This effect is most of the time an indirect effect linked to the use of energy as intermediate consumption or the importance of services like transport in the distribution activities of these sectors. The level of the energy subsidies for these sectors explains this price reaction. However, scenario 2 has only a small impact on prices in the different sectors, except for the electricity and gas sector, directly concerned with the subsidy (price increase of 13.25%). This shows that the removal of the electricity subsidies remains a plausible scenario at low cost, provided that the authorities follow a gradual and controlled approach that does not affect the purchasing power of households and the production costs of companies benefiting from this type of subsidy. Scenario 3 implies a slightly larger impact on prices in the different sectors compared to scenario 2. Indeed, the simultaneous removal of the subsidies in the electricity and gas industry will directly affect not only prices in this industry (16.93%) but also the prices of petroleum refining (5.98%), oil and gas extraction (6.17%), and water (4.99%). This demonstrates that it is not advisable to remove the subsidies for electricity and gas simultaneously and that it is necessary to proceed in stages. To assess the impact of subsidy reforms on CPI, we use equation (2). The data on the share of household income that household category h spent to purchase goods

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are collected from the household survey published by the National Institute of Statistics (INS). Households are classified into nine categories according to their level of income (Q1, Q2…Q9) where the first group has the lowest income, while the ninth group has the highest one. The calculation results are represented as follows (Fig. 4.5): With respect to the price impact on the different consumption categories by socioeconomic group, it is clear that the scenario of total removal of the subsidy in the three sectors (scenario 1) has the greatest impact, followed by the scenario of the removal of the subsidy in the electricity and gas industries (scenario 3), then the scenario of the removal of the subsidy of electricity only (scenario 2). More precisely, scenario 1 has a heavy impact on housing and food and transportation for a range of socioeconomic groups. This is understandable, as these categories of consumption are highly dependent on subsidies on electricity, gas, and oil refining activities, whether directly (housing and food) or indirectly (transport). Scenario 3 will affect the same consumption categories for the different socioeconomic groups but to a lesser degree, since the removal of the subsidy concerns, this time, electricity, and gas. The results presented in Table 4.3 above do not change too much with the breakdown of the price impact by zone. Indeed, scenario 1 has the greatest impact, followed by scenarios 3 and 2. On the other hand, in the different scenarios, the impact is greater in urban than in rural areas. This is understandable to the extent that the aforementioned consumption categories, which are highly dependent on subsidies for electricity, gas, and petroleum refining activities, are more important in urban than in rural areas. Transport, food, housing, and clothing are more in demand in urban than in rural areas and are therefore naturally more affected by subsidy reform.12

Fig. 4.5  Impact on CPI for 9 consumption categories by socioeconomic group. (Source: Authors’ calculation 2020)

 These results confirm that the higher income classes are the main beneficiaries of the subsidy, while the disadvantaged classes benefit very little.

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Table 4.3  Impact on CPI, by zone (in %)

Urban Rural

Variation of CPI Scenario 1 228,27 225,98

Scenario 2 17,40 17,75

Scenario 3 64,71 64,54

Source: Authors’ calculation (2020)

4.5.3  I mpact on Final Demand, Production, Energy Consumption, and CO2 Emissions The application of the methodology presented in Sect. 4.4.2 to Tunisian data for three scenarios shows the following results (Table 4.4). The results of the simulation exercises show that the rise in the prices of goods from different sectors following the adoption of scenarios 1, 2, and 3 helps to curb the demand for electricity, gas, and fuel. The decline in the consumption of petroleum products is most remarkable in scenario 1 (7.2%), whereas it is no higher than 0.04% and 1.20%, respectively, in scenarios 2 and 3. As for the demand for electricity and gas, it responds negatively to price variation with approximately the same magnitude (3%) in all three scenarios. On the other hand, most of the activities where electricity, gas, and oil are strongly present (ceramic, glass and building materials, mines, buildings, tobacco) also experience a drop in demand. These results clearly reflect the negative effect on demand of the total removal of subsidies, in accordance with the law of demand. Table  4.5 states the main results of assessing the effects of energy sector reforms on production. These results show that the effect on supply takes the same direction as that on demand. Scenarios 1, 2, and 3 have indeed a negative impact on production. The causation is clear: A decline in supply is an adjustment response to a decline in demand. The exception here is that the magnitude is much larger and concerns many sectors. More specifically, in the case of the removal of all subsidies, the effect is significant on the tobacco industry, mines, chemical industry, petroleum refining, oil and gas extraction, miscellaneous industries and building materials, ceramic, and glass. As for the effect on energy consumption, the results show that it is negative in all three scenarios, as long as supply falls in each of the scenarios. More specifically, following the total removal of all forms of subsidy (scenario 1), electricity consumption decreases in almost all sectors. It is mainly the construction materials industry, miscellaneous industries, the chemical and oil industries, agriculture and water, and the extractive industries that are most affected by this reform. The extent of this consumption decline diminishes in the case of the removal of the subsidies in both the electricity and gas industries (scenario 3) but also and especially in the case of the removal of subsidies granted only to the electrical industry (scenario 2). Similarly, the consumption of gas decreases in the first three scenarios, except that this decrease affects the industrial sector, whereas it does not affect the agriculture and services sectors as much. The building material industry, together with the

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Table 4.4  Impact of the removal of energy subsidy on final demand, by sector (in %) Sectors Agriculture and fishing Food industry Tobacco Textile, clothing, leather Various industries Oil refining Chemical industry Building materials, ceramic, glass Mechanical and electrical industry Oil and gas extraction Mine Electricity and gas Water Civil engineering, buildings Maintenance and repair Trade Hotel and catering Transport Telecommunications and post Financial services Other commercial services Public administration

Scenario 1 −0,28 −3,36 −4,96 −1,59 −2,31 −7,20 −3,10 −5,53 −1,04 −1,38 −2,51 −3,58 −2,77 −2,09 −1,65 −1,31 −1,51 −2,59 −1,09 −0,67 −0,33 −0,17

Scenario 2 −0,01 −0,31 −0,31 −0,22 −0,19 −0,04 −0,22 −0,58 −0,17 −0,04 −0,17 −2,65 −0,50 −0,14 −0,21 −0,10 −0,11 −0,04 −0,12 −0,05 −0,05 −0,01

Scenario 3 −0,06 −1,03 −1,21 −0,58 −0,75 −1,20 −0,92 −1,70 −0,44 −1,23 −0,60 −3,39 −1,00 −0,57 −0,57 −0,35 −0,65 −0,47 −0,31 −0,40 −0,12 −0,05

Source: Authors’ calculation (2018)

chemical and petroleum industries, suffers the biggest decline in consumption. This is probably related to a much heavier dependence of these industries on gas (Table 4.6). Given the data availability regarding CO2 emissions by sector, we have harmonized our results for four aggregated sectors, namely the energy sector, the manufacturing sector, the transport sector, and the “other” sector. The main results are as follows (Table 4.7): The numerical simulation exercise shows that the three scenarios all have positive impacts such as a decrease in CO2 emissions, especially for the energy and manufacturing sectors. Clearly, scenario 1 is much more favorable to the environment, followed by scenario 3 and then scenario 2. This is predictable in Tunisia, where energy subsidies are almost universal. Indeed, most of the energy-intensive sectors benefit from these subsidies: industrialists, hotels, and even oil and energy companies (STIR, ETAP, and STEG) themselves. Thus, eliminating these subsidies will only rationalize the consumption of energy and consequently reduce the negative impact on the environment.

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Table 4.5  Effect of energy sector reforms on production, by sector (in %) Sectors Agriculture and fishing Food industry Tobacco Textile clothing, leather Various industries Oil refining Chemical industry Building materials, ceramic, glass Mechanical and electrical industry Oil and gas extraction Mine Electricity and gas Water Civil engineering, buildings Maintenance and repair Trade Hotel and catering Transport Telecommunications and post Financial services Other commercial services Public administration

Scenario 1 −5,71 −6,33 −36,91 −5,16 −11,92 −20,74 −24,07 −11,63 −8,12 −18,31 −28,48 −7,07 −5,95 −2,36 −3,33 −1,31 −1,62 −4,75 −3,32 −2,98 −1,81 −0,17

Scenario 2 −0,49 −0,57 −2,28 −0,69 −1,08 −0,62 −2,00 −1,14 −1,14 −1,14 −2,33 −2,72 −0,85 −0,16 −0,35 −0,10 −0,12 −0,22 −0,35 −0,28 −0,19 −0,01

Scenario 3 −1,73 −1,97 −9,05 −1,88 −3,86 −4,33 −7,49 −3,53 −3,14 −6,05 −8,51 −4,28 −2,05 −0,65 −1,08 −0,35 −0,69 −1,14 −1,07 −1,19 −0,61 −0,05

Source: Authors’ calculation (2020)

4.6  Conclusion and Some Policy Recommendations The comparison of the simulation results of the different scenarios shows that the removal of all energy subsidies has negative economic effects on most macroeconomic aggregates because of a consequent increase in prices. However, this reform is beneficial for budget balances and for the environment. Clearly, the removal of all subsidies would be a negative shock for consumers and producers. On the other hand, the removal of the electricity subsidy remains a plausible scenario at a low cost, if the authorities follow a gradual and controlled approach that does not affect the purchasing power of households and the production costs of companies that rely on this kind of subsidy. In light of all these results, it is clear that the authorities must ensure efficiency in the production of electricity and gas through cost control. They should conduct an effective pricing policy that takes into account social, economic, and environmental aspects. At the same time, they have to follow a subsidy targeting policy through tariff reforms and direct transfers to low-income citizens to counteract the negative effects of removing energy subsidies.

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Table 4.6  Impact of the reform on electricity and gas consumption (by sector) Variation of electricity consumption (Gwh) Sectors Scenario 1 Scenario 2 Scenario 3 Extractive industries −59,27 −3,69 −19,58 Food and tobacco −48,19 −4,22 −14,67 industries Textile and clothing −27,37 −3,65 −9,96 industries Chemical and −104,52 −6,71 −39,03 petroleum industries Construction −185,86 −18,24 −56,44 materials industries Basic metallurgical −24,05 −3,37 −9,30 industries Miscellaneous −123,99 −11,28 −40,16 industries Agriculture and −69,50 −6,07 −21,20 water −13,16 −0,80 −3,43 Transport and communications Tourism −9,78 −0,73 −4,15 Services −11,53 −1,10 −3,82 Total −677,21 −59,86 −221,73

Variation of gas consumption (Ktep) Scenario 1 Scenario 2 Scenario 3 −1,83 −0,11 −0,60 −5,89 −0,52 −1,79 −3,11

−0,42

−1,13

−27,99

−1,80

−10,45

−76,48

−7,50

−23,22

−0,51

−0,07

−0,20

−5,83

−0,53

−1,89

−0,81

−0,07

−0,25

−0,25

−0,02

−0,07

−0,95 −0,26 −123,91

−0,07 −0,02 −11,13

−0,40 −0,09 −40,09

Source: Authors’ calculations (2020) Table 4.7  Variation in CO2 emissions, by sector Sectors Energy sector Manufacturing sector Transport sector Others

Variation in emissions of CO2 (%) Scenario 1 Scenario 2 −17,19 −1,39 −9,7 −1,03 −4,75 −0,21 −2,15 −0,18

Scenario 3 −5,34 −3,29 −1,14 −0,68

Source: Authors’ calculation (2020)

However, in removing subsidies, they have to follow a gradual approach. This could be done by avoiding the removal of the electricity and gas subsidies at the same time and then by proceeding gradually.13 On the other hand, a clear and effective strategy that ensures the involvement of the private sector in the new orientation toward renewable sources of energy (as part  Note that this latter recommendation has already been proposed in the World Bank (2013) study. The authors of this study recommended the reform first of subsidies that have little impact and where it is easy to make compensation before reforming more complex products. The authors also proposed starting with the reform of subsidies going to LPG and gasoline, then those to diesel, before the restructuring of electricity prices.

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of the adoption of the TSP) is needed. This could guarantee a successful transition to renewable energy, based on the public–private partnership. For that, some measures will have to be taken, such as a review of energy sector regulation and institutions, an adoption of modern and efficient public transport, as well as a strengthening of the interconnections with neighboring countries, in order to consolidate power grids and benefit from the comparative advantages of the Maghreb zone.

References Acharya, R. H., & Sadath, A. C. (2017). Implications of energy subsidy reform in India. Energy Policy, 102, 453–462. Al Iriani, M. A., & Trabelsi, M. (2015). The economic impact of phasing out energy consumption subsidies in GCC countries. Journal of Economics and Business, 87, 35–49. Breton, M., & Mirzapour, H. (2016). Welfare implication of reforming energy consumption subsidies. Energy Policy, 98, 232–240. Corden, M. (1957). The calculation of the cost of protection. The Economic Record, 33(64), 29–51. Dakhlaoui, A., Abbassi, A., & Daldoul, M. (2017, April 27). Assessing impacts of energy subsidy reform on Tunisia economy and transport sector with a computable general equilibrium model In GIZ conference on macroeconomic modeling for energy scenario in Tunisia, Tunis, Tunisia. Elshennawy, A. (2014). The implications of phasing out energy subsidies in Egypt. Journal of Policy Modeling, 36, 855–866. Farajzadeh, Z., & Bakhshoodeh, M. (2015). Economic and environmental analyses of Iranian energy subsidy reform using Computable General Equilibrium (CGE) Model. Energy for Sustainable Development, 27, 147–154. Gelan, A. (2018). Economic and environmental impacts of electricity subsidy reform in Kuwait: A general equilibrium analysis. Energy Policy, 112, 381–398. Gharibnavaz, M., & Waschik, R. (2015). Food and energy subsidy reforms in Iran: A general equilibrium analysis. Journal of Policy Modeling, 37, 726–741. Glomm, G., & Jung, J. (2015). A macroeconomic analysis of energy subsidies in a small open economy (CESifo Working Paper, No. 5201). CESifo. Hong, L., Liang, D., & Di, W. (2013). Economic and environmental gains of China’s fossil energy subsidies reform: A rebound effect case study with EIMO model. Energy Policy, 54, 335–342. IEA. (1999). World energy outlook insights, looking at energy subsidies: Getting the prices right. OECD Publishing. ILO. (2004). Consumer price index manual: Theory and practice. International Labour Office. ITCEQ report. (2017). What strategy for reforming energy subsidies in Tunisia? Tribune de ITCEQ, n° 19 Jiang, Z., & Tan, J. (2013). How the removal of energy subsidy affects general price in China: A study based on input–output model. Energy Policy., 63, 599–606. Jiang, Z., Ouyang, X., & Huang, G. (2015). The distributional impacts of removing energy subsidies in China. China Economic Review, 33, 111–122. Khan, M. A. (2019). Welfare and distributional effects of the energy subsidy reform in the Gulf Cooperation Council Countries: The case of Sultanate of Oman. International Journal of Energy Economics and Policy, 9(1), 228–236. Koplow, D. (2009). Measuring energy subsidies using the price-gap approach: What does it leave out? International Institute for Sustainable Development. Larsen, B., & Shah, A. (1992). World energy subsidies and global carbon emissions (Research paper 1002). World Bank.

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Li, K., & Jiang, Z. (2016). The impacts of removing energy subsidies on economy-wide rebound effects in China: An input-output analysis. Energy Policy, 98, 62–72. Magne, B., Chateau, J., & Dellink, R. (2014). Global implications of joint fossil fuel subsidy reform and nuclear phase-out: An economic analysis. Climatic Change, 123, 677–900. Miller, R. E., & Blair, P. D. (2009). Input–output analysis: Foundations and extensions (2nd ed.). Cambridge University Press. Moshiri, S., Martinez, S., & Alfonso, M. (2018). The welfare effects of energy price changes due to energy market reform in Mexico. Energy Policy, 113, 663–672. Ogarenko, I., & Klaus, H. (2013). Eliminating indirect energy subsidies in Ukraine: Estimation of environmental and socioeconomic effects using input-output modeling. Journal of Economic Structures, 2(7), 1–27. Report activity of STEG. (2019). Sasana, H., Setiawan, F., Ariyanti, F., & Ghozali, I. (2017). The effect of energy subsidy on the environmental quality in Indonesia. International Journal of Energy Economics and Policy, 7(5), 245–249. Saunders, M., & Schneider, K. (2000, June 7–10). Removing energy subsidies in developing and transition economies. In ABARE conference paper, 23rd annual IAEE international conference, International Association of Energy Economics, Sydney. Solaymani, S., & Kari, F. (2014). Impacts of energy subsidy reform on the Malaysian economy and transportation sector. Energy Policy, 70, 115–125. World Bank. (2013, November). Towards better equity: Energy subsidies, targeting and social protection in Tunisia, Report n ° 82712-TN. Yulia, O., & Klaus, H. (2013). Eliminating indirect energy subsidies in Ukraine: Estimation of environmental and socio-economic effects using input-output modeling. Journal of Economics Structures, 2(7), 1–2.

Chapter 5

Remittances, Income Inequality, and Brain Drain: An Empirical Investigation for the MENA Region Hajer Kratou and Najeh Khlass

5.1  Introduction The economic intuition behind reducing inequality through the flow of money from migrant workers to their homelands, known as migrant remittances, found its origins in the UN 2030 agenda, the so-called “Sustainable Development Goals (SDGs).” SDG 10 target (7), covering reduced inequalities, sets out a target to assure safe, responsible, and well-managed migration policy and mobility of people. Including the question of migration in the SDGs is of considerable interest because income differences may cause a growth gap across countries (Acikgoz & Ben Ali, 2019). Thus, to reduce inequalities, governments have to pay attention to left-behind and vulnerable populations and provide them a universal economic, social, and political policies. Despite that the target does not distinguish between migrant skill composition, the migration of high skilled is typically thought to lead to serious social losses for those left behind, which also contributes to an increase in inequality (Docquier & Marfouk, 2006). The belief is that high level of brain drain leads to a deficiency in the number of skilled workers and capitals in the source country and might result in increased wage inequality (Bhula-or, 2019). As such, reducing the brain drain with initiatives to support short-term guest migration for less-skilled workers may reduce inequality across nations. Relaxing the constraint of migration for less-skilled migrants have a more proportionate benefit in helping to close the income gap between the have (skilled) and the have nots H. Kratou (*) Department of Finance, Ajman University, Ajman, UAE e-mail: [email protected] N. Khlass Department of Economics ISCAE, University of Manouba, Manouba, Tunisia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. S. Ben Ali (ed.), Key Challenges and Policy Reforms in the MENA Region, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-3-030-92133-0_5

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(low skilled), and it is among the useful mechanisms for mitigating poverty in developing countries. This is because unskilled migrants are likely to stay less but to remit more and on a frequent basis compared with their skilled compatriots (Faini, 2007; Adams, 2009; Niimi, 2010). Further, this means more income goes to particularly poor families, which lessens the income disparity within communities. The MENA region witnesses a key challenge with the rise in the level of brain drain to high-income OECD countries (Fig. 5.2). The USA ranked the top destination of high-skilled migrants from the region. Three out of the top five descent groups that hold the highest levels of education in the USA are from the region, with Egypt is ranked the first, followed by Iran. Turkey occupies the fifth rank (US Census Bureau, 1990; Chaichian, 2012). Iran is among the highest level of brain drain in the world. Most of their skilled migrants are working as physicians and engineers in the USA (Chaichian, 2012). Turkey is ranked 24th among top source countries of skilled migrants (UNESCO, 2006). Political tensions, anti-freedom policies, and socioeconomic conditions are among the incentives which result in migration of the high skilled, but also developed countries implement immigration policies that favor the absorption of foreign skills. Thus, the main goal of this research is to explore the role that less-skilled migrants play in making remittances to be sent to the lowest deciles of the income distribution. As argued by some researchers, such as Kratou and Gazdar (2016) and Ben Mim and Ben Ali (2021), remittances are a financial flow crucial to achieving macroeconomic goals in MENA region, like economic growth and equitable income distribution. This reduction in inequality is the reason why many migrants tend to send money home (Dasgupta & Kanbur, 2011). Focusing on the migration skill composition allows us to test if this tool can diminish the gap between the rich and the poor in the countries receiving remittances. The structure of the paper is as follows. Section 5.2 discusses the theoretical framework of brain drain inequality channels and discusses the assumption of a negative relationship between the brain drain and the high flow of remittances. Section 5.3 discusses the methodology and the empirical results on the effect of remittances on income distribution. This latter probably depends on migrant skill composition. Section 5.4 concludes and emphasis on key policy implications.

5.2  Remittances: A Literature Overview Brain drain, also known as “human capital flight,” is defined as the international emigration, mostly permanent, of skilled people, of which their home society carried out a substantial educational investment (Wickramasekara, 2002). The primary literature on the brain drain and international agencies such as the ILO, the OECD, and even the World Bank, among others corroborates on the fact that the emigration of the educated elite will unavoidably lead to many jeopardizing consequences and put a damper on source countries’ development path (Bhagwati & Hamada, 1974; Rizvi, 2005).

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Literature discussed a set of channels of transmission over which the migration of elites and qualified would affect the source country. This section discusses two strands of literature. The first wave of literature relies on channels of transmission between brain drain and income inequality in the source country. The second wave of literature discusses that brain drain is not necessarily accompanying with high remittances, arguing that the pessimistic view on the influence of brain drain would not necessarily be compensated by migrants’ remittances. Early literature argues that brain drain is derived from the world capitalist system, which is effectively built on socioeconomic gap between developed, namely wealthy countries (core) and the developing, mainly poor countries (Periphery) (Frank, 1980; Wallerstein, 1974). More recent scholars corroborate with Frank and Wallerstein approach such as Rizvi (2005): “in the capitalist system, there is a strong division of international labor. Such kind of system encourages high skills and qualified individuals from the periphery to relocate to the core which strengthening the latter position and enhance pattern of global inequality.” A more recent wave of literature attempts to investigate the effect of the migration of elites on their home country, more precisely they discuss channels of transmission on income inequality: First, developing countries are deprived from their human capital. The decrease of these scarcest resources concretely limits the ability of the source country to innovate and to adopt recent technologies and knowledge. This lack in technology’ access widens the gap of income distribution at the international level, with an increase of rich countries’ income at the expense of the deprived countries (Capuano & Marfouk, 2013). This is because highly skilled workers contribute to enhance productivity and technological diffusion (Le & Bodman, 2011; Hornung, 2014; Hübler, 2015). The smaller is the level of human capital in the migrants’ home countries, the higher is the gap with the leading economies (Docquier & Marfouk, 2006). Second, frequently elite migrants are an important fiscal contributor. Depriving the source country from this fiscal revenue could be a loss for their home countries (Capuano & Marfouk, 2013). The fiscal loss may deprive developing countries from holding fiscal buffers and government would not be able to provide social transfers to vulnerable and low-income families. Third, human capital is a vital determinant of growth. The depart of elites’ individuals would probably jeopardize the performance of source country in terms of growth prospects. This is said because the less educated unavoidably replace the more educated in the home country (Bhagwati & Hamada, 1974; Piketty,1997). Thus, wage inequality (redistributive effects from less- to high-skilled workers) can be widened due to an insufficient number of qualified and highly skilled workers. Fourth, there is another important channel through which brain drain may influence inequality is the “high return to education.” Literature on brain gain such as Vidal (1998) and Beine et al. (2001, 2008) had been arguing that earnings of highly educated migrants are far better in the destination compared with home. The earning gap of human capital creates an incentive to invest more in education. However, better-off households in income-­ unequal countries (frequently educated) are among the few that can invest in tertiary-­level education (Gungor & Tansel, 2007). Thus, high return to education would be only beneficial for those with better financial capacity. This corroborates

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with the hypothesis that unequal access to education and unequal postgraduation opportunity could unfavorably impact income inequality and contribute to widen it (Nabassaga et  al., 2020). Fifth, while developing countries incurs a heavy social cost of education spent on skills who end up to migrate, these public resources could be invested in other sectors of the economy and provide better productivity, generating higher income and a better distribution of nation wealth. The above pessimistic view on the influence of brain drain would not necessarily be compensated by migrants’ transfers. This is because of the assumption of a negative correlation between the brain drain and the high volume of remittances. Actually, there is no indication on the data acquired on remittances providing information on the level of skills among MENA emigrants. While looking to the World Bank (2006) remittances’ definition, a clear statement on remittance sender population mentions that remittances are private transfers carried out by senders/migrants for the benefit of receivers/households endeavor to ameliorate household conditions and welfare. It is widely observed that these person-to-person transactions of relatively small amount are mainly carried out by poor, less-educated migrants (e.g., Suro et al., 2002). Thus, the level of household poverty is a crucial determinant of remittances (Adams, 2009). Using Docquier and Marfouk’s (2004) data on brain drain in developing countries, a growing number of researchers have asserted that there is few evidence to justify the claim that skilled workers remit more money than the lesser skilled (Faini, 2007; Niimi, 2010) and, further, others claim that there is only limited evidence indicating that the number of remittance transactions is disposed to increase with increasing levels of skill (Johnson & Whitelaw, 1974; Rempel & Lobdell, 1978; Borjas & Bratsberg, 1996; Faini, 2007). This is perhaps not surprising given that, although skilled workers typically get better payment and so might be anticipated to remit in greater amounts, they are probably from relatively better-off families and they have lower tendency to remit (Faini, 2007). This is the case of Turkish professionals and Iranian immigrants in the USA that come from relatively well-to-do families and hold financial funds to go abroad (Gungor & Tansel, 2007; Chaichian, 2012). In addition, there is a fact that skilled migrants originated from developing countries possibly convert into a permanent residency in the developed receiving countries (Kuehn, 2007; Al Ariss & Özbilgin, 2010). So, they tend to emigrate with their families (Docquier & Marfouk, 2006). Or it could lead that migrants’ families join him in the host country, which they can probably afford to do (Niimi, 2010). Or they spend a longer time abroad and may also weaken their family relationships in the home country. There is also evidence that high-­ skilled return intentions are weaker, as they have better work environments and greater professional opportunities in the destination country, specifically those working as academics and medical professionals (Gungor & Tansel, 2007; Chaichian, 2012). These scenarios align with findings that skilled migrants are possibly transferring less compared with their unskilled compatriots. One of the noteworthy limitations of the literature is the lack of consideration given to the impact of low-skilled migrants as a conditional factor in making remittances to be sent to the lowest deciles of the income distribution. While previous studies have not explored the factors that might deter remittances to poor families,

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such as the distinguish between migrant skill composition (as we address in this paper), we claim that the less-skilled migrant is highly influential. More specifically, we conjecture that low-skilled migrants are positively correlated to recorded remittances to poor families, which, in turn, contributes to mitigate the income gap in recipient countries.

5.3  D  o Remittances Affect Income Inequality in MENA Countries? Our analysis on the distributional effect of recorded remittances uses Gini coefficient data spanning 14 MENA countries covering the period 1995–2020. The choice of countries was also depending on the availability of the following data. Cross-­ country studies on income inequality typically favor the Gini coefficient (WDI, 2020)—as the measure of inequity over other indexes due to the widespread availability of Gini-based data and tools. The variable is distributed from zero to 100%. For the purpose of further robustness, we use a substitute indicator of income inequality, the “ratio of 40” defined as the income share of the bottom 50% compared with that of the middle 40%. The series pertaining to our analysis were personal transfers/remittances and employee compensation measured in terms of amount. The former includes transfers “in cash” or “in-kind” carried out by migrant member to households in home country. The latter includes short-term and seasonal border workers active in the destination economy (WDI, 2020). The limitation of this data source is that it does not record remittance flows through informal channels. Many studies on remittances have noted this shortcoming, i.e., that data on informal remittance flows are scarce and too patchy to be able to construct time series data for an analysis with any degree of reliability. Likewise, we were only able to include remittances flowing through formal channels in our analysis. The preliminary analysis, illustrated in Fig. 5.1 indicates that formal remittances are negatively correlated with the Gini coefficient in MENA countries, aligning with previous findings, such as Zhu and Luo (2008). Lebanon is one of the highest in developing countries dependent on remittances, of which the Gini coefficient is relatively less compared with the other MENA countries. Countries with relatively high level of income inequality show the lowest level of remittances to GDP ratio. Large size countries, such as Turkey and Iran, are distinguished by high level of migration and migration of high skilled, respectively (Fig.  5.2). The number of skilled migrants in Iran is higher compared with their unskilled compatriots. This corroborates with the assumption developed in literature review that skilled migrants’ tendency to remit is relatively lower. In terms of control variables, the literature on the determinants of income disparity shows that changes in a nation’s income inequality may be driven by many factors, including the level of economic development, inflation, government expenditures and trade, etc. Details on each of the control variables follow. The first

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Fig. 5.1  Gini coefficient per average number of remittances in MENA region 1995–2020

Fig. 5.2  Migrants from MENA countries, per skill level, 2010

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is GDP per capita (from WDI), which is a measure of economic development. The general trend goes that during a country’s initial stages of economic development, income inequality tends to be high (i.e., the GDP per capita (log) is expected to have a positive coefficient), which is the price a country pays for growth (Meschi & Vivarelli, 2009). Macroeconomic instability is another influence over income inequality, which is assumed to have a detrimental influence on the poor (i.e., a positive coefficient) because only the rich can invest in capital, land, foreign investments, and other such assets (Bulíš, 2001). To capture this effect, we used the GDP deflator from WDI as a measure of inflation. Conversely, governments with propoor expenditure policies should be ameliorating income inequality (Tanzi, 2001). This variable is the general government final consumption expenditure (from WDI). The level of openness to trade is an important indicator of the export and import size of an economy and its capacity to acquire foreign income. According to the Heckscher-Ohlin-Samuelson (HOS) trade model, it is supposed to reduce income inequality. Other control variables included age dependency and level of democracy. Following Ebeke and Le Goff (2010), we controlled for age as the proportion of people under 15 or over 64 against people of working age with data sourced from WDI. A high ratio suggests a wider income gap. The influence of political regimes over income inequity and, more specifically, the level of democracy was measured using the Polity IV index—a qualitative variable on a scale of 0–10 points. This indicator is broadly used in political science and economics (e.g., Bjørnskov, 2010). We used it to check the assumption that democracy ensures an equal distribution of wealth and reduces income inequality between citizens. A negative coefficient confirms the assumption. Another crucial explanatory variable, following Card (2001), we distinguished between different groups of migrant skills. We included statistics on the ratio of unskilled migrants versus skilled migrants from each of the MENA country to 20 OECD countries. Data on brain drain were sourced from Brücker et  al. (2013) and categorized by educational attainment (primary, secondary, and tertiary), which corresponds to low, medium, and high skilled, respectively. Unfortunately, this database and most of the produced database on migrant skill composition do not capture information on migration per skill to the Gulf Cooperation Council (GCC) countries, which is a main destination of migrants originated from MENA region. Also, it does not differentiate between short- and long-stay migrants, rather than it is related to working age and foreign-born. Notably, these data are not entirely accurate due to the difficulties in gauging migration by levels of education or skills at the macrolevel, but they are accurate enough to provide a reasonable proxy. A higher ratio indicates less-income inequality, based on Docquier and Marfouk’s (2006) assumption that restricting migration for the less skilled or making it difficult reduces the chance for this category of migrants to improve their economic and social welfare. The main relationships modeled included the distributional effect of recorded, as formulated in Eq. (5.1), and the influence of migrant skill composition on that correlation, as formulated in Eq. (5.2):

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Gini it   0  1 Rem it 1   2  it 1   it

(5.1)



Gini it   0  1 Rem it 1   2  it 1   3 Rem it 1  Migrant Skill Ratioit 1   it

(5.2)



where Gini denotes the Gini coefficient of the remittances to the recipient country i, and Rem denotes the annual volume of international remittances received by country i, per capita, per migrant and as a percentage of its GDP over the period t in US dollars. Migrant Skill Ratio denotes the ratio of low-skilled to high-skilled migrants. Migrants are originated from each of the MENA countries (country i) to 20 OECD countries over period t. Rem*Migrant Skill Ratio is the variable of multiplication between remittances Rem and the ratio of unskilled to skilled migrants associated with capturing the distributional effect of remittances when Migrant Skill composition varies. X is a vector of the explanatory variables, i.e., the economic/demographic/political influences, for country i at time t. Ɛ is the error term. The instrumental variable (IV) technique was used to handle the issue of endogeneity, coming from three sources. First, data measurement related to remittances. As noted above, the amount of remittances channeled by unrecorded channels is high (Maïga et al., 2016). Second, the reverse causality between income inequality and remittances. From one side, a high level of income gap in home country may attract more remittances. Migrant has a high incentive to remit when the income gap within household is high. From the other, remittances may enlarge the income gap if channeled to rich families. Third, source of endogeneity is associated with the omitted variables (such as exogenous shock). Empirical results are presented in Tables 5.1, 5.2, 5.3, and 5.4. We present in Table  5.1 the first-stage results for the migrant remittance instrumental variable, where the two external instruments are significant for predicting remittances. As Table 5.1  First-stage instrument variable estimates for remittances Instruments ∑Remittances –remittances i Income differential Exogenous variables In GDP/Capita Age dependency Inflation Government expenditure Trade Democracy Constant Observations Number of countries F-statistics

−4.10***(1.40) −3.07***(0.92) 4.29***(0.97) −7.01**(2.99) −0.04(0.06) −4.47(2.66) −2.94***(1.27) −0. 26*(1.49) 198.37***(35.02) 41 14 27.56

Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. Robust t-statistics are in parentheses

Remittance (Log) In GDP/Capita Age dependency Inflation Government expenditure Trade Democracy Constant Statistics Observations Number of countries R-squared Kleibergen-Paap (under-identified test) Cragg-Donald Wald F statistic (weak identification test)

−1.66(1.69) −0.35*(0.19) 4.25(25.25)

41 14

0.36 11.56

15.87

−6.38***(2.08) −0.45**(−2.10) 16.08(28.54)

41 14

0.38 15.75

24.33

Remittance −0.45***(1.51) 4.90***(1.43) −1.26(4.57) 0.06 (0.09) 8.77**(3.79)

(Dependent variable: Gini coefficient) Remittance (%GDP) −2.19***(0.58) 2.49(1.57) −0.29 (4.09) 0.05(0.08) 8.73**(3.63)

(Dependent variable: Gini coefficient)

Table 5.2  Distributional Effect of Recorded Remittances

20.63

0.33 14.25

41 14

−5.68***(1.96) −0.44**(0.21) −2.16(25.49)

(Dependent variable: Gini coefficient) Remittance (per capita) −0.60**(2.84) 5.00***(1.39) 0.63(4.27) 0.04(0.09) 7.90**(3.52)

20.79

0.33 14.90

41 14

−5.56***(1.87) −0.47**(0.21) 4.38(26.59)

(Dependent variable: Gini coefficient) Remittance per migrant −0.49***(0.18) 4.37***(1.44) 0.44(4.33) 0.06(0.09) 7.80**(3.61)

37.58

0.213 24.58

87 14

0.05*(0.02) −0.003(0.002) −0.34(0.26)

18.48

0.53 19.26

87 14

0.07**(0.03) −0.13**(0.00) −218***(−4.04)

(Dependent (Dependent variable: ratio variable: ratio 40) 40) Remittance Remittance (%GDP) ***(0.00) 0.01 0.03***(0.00) −0.000(0.01) 0.09***(0.02) 0.001*(0.00) 0.08* (0.04) −0.001(0.00) −0.00 (0.00) −0.02(0.03) 0.03(0.04)

31.81

0.14 22.59

87 14

0.02(0.02) −0.003(0.002) −0.34(0.26)

(Dependent variable: ratio 40) Remittance (per capita) 0.01***(0.00) −0.004(0.01) 0.001(0.00) −0.001(0.00) −0.00(0.03)

(continued)

32.09

0.60 24.63

87 14

0.03(0.02) −0.003(0.002) −0.01(0.22)

(Dependent variable: ratio 40) Remittance per migrant 0.01***(0.00) 0.009(0.01) 0.02(0.03) −0.001(0.00) 0.00(0.03)

5  Remittances, Income Inequality, and Brain Drain: An Empirical Investigation… 93

(Dependent variable: Gini coefficient) Remittance (per capita) 20.54

6.37

(Dependent variable: Gini coefficient) Remittance (%GDP) 18.50

0.92

5.19

(Dependent variable: Gini coefficient) Remittance per migrant 24.59

5.56

0.61

(Dependent (Dependent variable: ratio variable: ratio 40) 40) Remittance Remittance (%GDP) 34.21 25.77

11.01

(Dependent variable: ratio 40) Remittance (per capita) 28.54

6.51

(Dependent variable: ratio 40) Remittance per migrant 37.42

Note: ***, **, and * denote significance at, respectively, the 1, 5, and 10% levels. Robust t-statistics are in parentheses. Data are an average of three years

Remittance 24.72 Kleibergen-Paap (weak identification test) 4.62 Hansen J Statistic (over-identification test)

(Dependent variable: Gini coefficient)

Table 5.2 (continued)

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Table 5.3  Migration skill level, remittances, and income inequality (Dependent variable: Gini coefficient) Remittance (Log) Remittance * ratio of low- to high-skilled migrants1980 In GDP/Capita Age dependency Inflation Government expenditure Trade Democracy Constant Statistics Observations Number of countries R-squared Kleibergen-Paap (under-­ identified test) Cragg-Donald Wald F statistic (weak identification test) Kleibergen-Paap (weak identification test) Hansen J Statistic (over-­ identification test)

Remittance Remittance (%GDP) −0.21(0.2) 4.69*(2.69) ***(0.27) −0.93 −9.16*(5.39)

Remittance (per capita) 1.98*(1.01) −5.50***(1.77)

Remittance per migrant 0.18(0.25) −1.80***(0.55)

3.33***(1.09) 0.04(3.76) 0.08* (0.04) 4.24*(2.20) −7.35***(1.86) −0.50***(1.76) 50.15***(15.92)

0.68(2.00) 9.76 (5.34) 0.07*(0.04) 6.05(3.77) −2.48(2.26) −0.50**(0.19) 77.87**(33.48)

5.01***(1.19) 1.05(2.42) 0.05*(0.07) 7.34***(2.45) −8.52***(1.77) −0.45**(0.19) 11.13(18.58)

3.69***(1.07) 1.66(2.23) 0.05(0.03) 4.07(2.48) −7.19***(1.62) −0.47***(0.17) 29.39*(16.97)

41 14 0.24 25.96

41 14 0.42 17.41

41 14 0.14 20.78

41 14 038 38.40

303.41

6.75

26.35

327.0

295.82

9.46

16.53

356.9

14.87

4.24

15.83

14.97

Note: ***, **, and * denote significance at, respectively, the 1, 5, and 10% levels. Robust t-­statistics are in parentheses. The interaction variable between remittances and the ratio of migrants’ skills is instrumented by the interaction between each instrument of remittances and the migrants’ skills, respectively. Data are an average of three years

expected, the overall trend of international remittances with all the developing countries in our sample subtracting the country remittances is negatively related to the remittances for each country. Furthermore, the income disparity between the two countries (host and home) is negative and statistically significant. This widening gap in income signals a high cost of living in the host country. The reasoning is that migrants in the lower income brackets of expensive countries, which is likely, have decreased purchasing power and, therefore, send less money home (Mourao, 2016). The F-statistics is over 27. On the whole, these results imply the instruments are relevant. Hence, we used them in the second-stage results presented in Table 5.2. The estimated results of the IV for the four remittance variables are shown in Table 5.2. These include (a) volume of remittances; (b) the remittance to GDP ratio; (c) remittances per capita; and (d) remittances per migrant. The interpretation of the coefficients is consistent: the remittances and most of the control variables are statistically significant and persistent across the specifications no matter the measure of remittances used. As expected, the coefficient related to remittances is negative

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Table 5.4  Robustness checks (Dependent variable: Gini coefficient) Remittance (Log) Remittance * number of low-skilled migrants In GDP/Capita Age dependency Inflation Government expenditure Trade Democracy Constant Statistics Observations Number of countries R-squared Kleibergen-Paap (under-­ identified test) Cragg-Donald Wald F statistic (weak identification test) Kleibergen-Paap (weak identification test) Hansen J Statistic (over-­ identification test)

Remittance Remittance (%GDP) −0.08(1.71) −0.18**(0.08) ***(0.00) −0.02 −0.06***(0.02)

Remittance (per capita) −0.10(0.28) −0.03***(0.00)

Remittance per migrant −0.11(0.19) −0.02***(0.00)

4.13***(1.50) 1.36(3.76) 0.06 (0.07) 7.62**(3.10) −1.53(2.43) −0.42**(0.20) −12.94(27.55)

5.64***(1.19) 3.68 (3.73) 0.065(0.08) 7.66**(3.01) −4.14*(2.22) −0.35(0.21) −24.80(23.30)

4.09***(1.50) 1.73(3.66) 0.06(0.07) 7.41**(3.15) −1.23(2.29) −0.42**(0.21) −16.47(25.64)

4.10***(1.49) 1.55(3.56) 0.06(0.07) 7.70**(3.01) −1.55(2.22) −0.42**(0.20) −14.24(25.79)

41 14 0.38 7.25

41 14 0.42 9.01

41 14 0.39 6.29

41 14 0.39 6.51

8.17

9.17

8.74

8.30

6.91

15.88

6.80

6.77

1.89

11.99

1.89

1.83

Note: ***, **, and * denote significance at, respectively, the 1, 5, and 10% levels. Robust t-­statistics are in parentheses

(positive) and significant for the different specifications, when the dependent variable is the Gini coefficient (ratio 40). A 1% growth in remittances diminishes the Gini coefficient by about 2 units, which is more than a proportional reduction. This result highlights the vitality of these flows to decrease the income disparity in the recipient countries and is consistent with previous findings (World Bank, 2006; Dasgupta & Kanbur, 2011). Remittances provide additional revenues for low-­ income families in MENA region and help to finance their children education, which contributes to reduce children work and dedicate more time to school (Ben Mim & Ben Ali, 2012). The results also show support for the hypothesis that economic growth seems to be accompanied by economic and social inequality. Income inequality increases with initial levels of development, which explains the positive and significant coefficient associated with GDP per capita This corroborates with previous findings (e.g., Milanovic, 2005; Kratou & Goaied, 2016). Initial stages of development benefit skilled labor given its complementarity with capital and technology but, over time, once the level of wealth and development starts to spread, a more egalitarian distribution begins to emerge. The government expenditure coefficient is positive, with a statistical significance at 5% in all of the four regressions on Gini. This could be explained first by ineffective government spending (inefficient government

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subsidies) that may result in widening the income gap. Second, Saha and Ben Ali (2017) argue that MENA countries are characterized by a large size government, which leads to corruption. The trade size reflects the capacity to acquire foreign income and mitigate the income gap in developing countries. The variable holds a negative coefficient and shows robustness across the four regressions on Gini. The distributional influences of political systems discussed in earlier studies have been mixed at best. The coefficients related to democracy are negative and significant for Gini specifications. This confirms findings advocating the pro-poor system in democratic countries and contradicts with Bjørnskov’s (2010) findings that democracy is not essentially a pro-poor political arrangement. We undertook several tests to check the validity of the instrument. We use the Cragg and Donald (1993) and Kleibergen and Paap (2006) tests to check the validity of the instruments. The Hansen J statistics of over-identification suggest that the instruments are exogenous to remittances in all specifications. Therefore, use of these instruments in our model is warranted. The next regression consists on checking what some consider to be an important channel for reducing inequality—migration of the less skilled to improve the economic conditions in the home country. The latter probably belongs to the bottom income distribution in a given country. Following Card (2001), we distinguished between different groups of migrant skills and conducted a new estimation, controlling for the interaction between remittances and the ratio of unskilled to medium and high skilled for the year 1980. The results (Table 5.3) yielded a negative and significant coefficient. A 1% rise in the number of unskilled migrants compared with the number of skilled stimulates more remittance operations to be channeled to low-income families, which reduces income inequality by 1 unit in the home country. This supports two categories of previous findings. The first category by Grabel (2008), Adams (2009), and Prokhorova (2017) and others, who find higher remittances by unskilled and semi-skilled migrants than from better-educated migrants. As mentioned, they attribute the reason to the inclination of skilled workers to settle in the destination country permanently, which weakens links with their home country. The second category of previous findings by Ebeke and Le Goff (2010) and Kratou and Goaied (2016) argue that remittances would not have a favorable consequence on income distribution if the home country is featured by a high level of brain drain. In a panel of 66 developing countries, the latter shows that the threshold percentage of brain drain below which the distributional effect of remittances would be carried out is 22% in developing countries. They also argue that compared with other regions, the stereotype of migrant in MENA region is relatively less skilled. As a further robustness check, we substitute the ratio of unskilled to skilled migrants of 1980 with the current number of low-skilled migrant. This test assumes that the high is the number of unskilled migrants, the high is the volume of remittances to be sent to low-income families and the less is the income gap. Studies discussed in the literature review, such as Adams (2009), corroborate the assumption that remittances are basically sent by workers migrants, of which their migration purpose is to support household in home countries on a frequent basis and during a temporary stay. Results (Table 5.4) show a negative and highly significant coefficient for the variable of interaction and validate the above assumption.

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5.4  Conclusions and Policy Implications We approached the effect of remittances on income inequality from the premise that this relationship depends on the migrant’s skill composition in the source country. This is the first research to test the basis of SDG 10 by studying the importance of target 10(7): facilitate migration and mobility of people, with a special interest in the migration of the less skilled. We applied the instrument variable technique to tackle endogeneity issues and found an increase in remittances has a mitigating effect on income inequality in 14 MENA countries, over the period 1995–2020. Our result hints at the possibility that the sender population for remittances is generally made up of recent immigrants with low-skill levels and low incomes who frequently send remittances to support their families left behind. This confirms with other researches in the field such as the World Bank (2006) and Akobeng (2016). In developing countries, the rich often receive less remittances because they tend to take their families with them and are less motivated to return or invest in their home country (Adams, 2009). Our results on MENA region are consistent with previous empirical findings highlighting the pro-poor effects of migrant remittances in increasing the disposable income of recipient households in sub-Saharan Africa (Akobeng, 2016). We conclude the paper with the following policy implications. First, as migration is probably continuing to raise, reconsidering policy design and unifying international practices for migration policies is crucial. This could be achieved by mitigating the unfavorable discrimination among migrants’ skill level and origin. Second, the loss of skilled workers is typically thought to lead to serious social losses for those left behind, which also contributes to an increase in inequality. As such, reducing the brain drain with initiatives to support short-term guest migration for less-­ skilled workers is likely to lead to greater remittance flows than those encouraging permanent settlement and may reduce inequality across nations. Third, these could be coupled with efforts by source country to incentivize return of skilled migrants. For instance, countries with high level of brain drain might implement a tax on brain for the profit of the home country. In addition, international cooperation might endeavor to address the illegal migration of the unskilled and promote a better matching of low-skilled migrants with labor-intense activities in developed destinations. Further, providing secure living conditions and working environment and providing legal identity are also needed for migrant workers, specifically that a significant proportion of low-skilled women migrants operate in informal employment sector (domestic workers). Such reforms could be coupled with reducing the recruitment costs incurred by migrant workers and establishing bilateral labor agreements to facilitate job access, skills training, and social services. Lastly, a commitment to make transaction costs affordable means the more of that income will be at the disposal of low-skilled migrants’ families.

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US Census Bureau. (1990). CPH-L-149 selected characteristics for persons of Iranian ancestry. available at: www.census Vidal, J.-P. (1998). The effect of emigration on human capital formation. Journal of Population Economics, 11(4), 589–600. Wallerstein, I. (1974). The modern world system: Capitalist agriculture and the origins of the European world economy in the sixteenth century. Academic Press. Wickramasekara, P. (2002). Policy responses to skilled migration: Retention, return, and circulation. Social protection sector. International Migration Programme, International Labor Office. World Bank. (2006). Global economic prospects: Economic implications of remittances and migration. World Inequality database. World Bank (2020). Zhu, N., & Luo, X. (2008). The impact of remittances on rural poverty and inequality in China (World Bank Policy Research Working Paper No. 4637). World Bank.

Chapter 6

Digital Divide and External Trade Liberalization in the MENA Region: A Theoretical and Empirical Investigations Xiaoqun Zhang

6.1  Introduction During the past decades, the Internet and broadband have diffused rapidly across the world. These digital information and communication technologies (ICTs) have significant impacts on various aspects of the contemporary global society. The United Nations Development Program (UNDP) argued that ICTs are the requisite for economic and human development (UNDP, 2001). In addition, the United Nations (U.N.) advocated that Internet access is one of the basic human rights in the contemporary society (U.N., 2006). The research of the International Telecommunication Union (ITU) suggested ICTs promote innovation, increase productivity, and attract foreign investment (ITU, 2003). This chapter is focused on the digitalization of the Middle East and North Africa (MENA) region. This is the region that stretches across two continents and consists of 19 countries/territories (The World Bank, n.d.-a). Compared with other countries, MENA countries have more youthful populations and higher rates of unemployment (Gelvanovska et al., 2014). MENA countries also have well-educated populations. But these highly skilled populations have not fully employed for their economic development. Roughly 30% of the unemployed people are high-skill university graduates (Saliola & Connon, 2018). With these special socioeconomic characteristics, MENA countries face a pressing and unique challenge: How to create sufficient job opportunities for their well-educated populations and promote their economic growth?

X. Zhang (*) Department of Media Arts, College of Liberal Arts and Social Science, University of North Texas, Denton, TX, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. S. Ben Ali (ed.), Key Challenges and Policy Reforms in the MENA Region, Perspectives on Development in the Middle East and North Africa (MENA) Region, https://doi.org/10.1007/978-3-030-92133-0_6

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ICTs, especially the Internet and broadband, would be one of the important instrumental means that MENA countries can utilize to meet this challenge because these technologies enable people and firms to exploit underused physical and human capacity (Saliola & Connon, 2018). Researchers have found that ICTs improve education effectiveness and healthcare efficiency, promote democracy, alleviate women’s poverty, and work as an effective tool to control corruption (e.g., Ben Ali, 2020; Ben Ali & Gasmi, 2017; Sassi & Ben Ali, 2017; Ben Ali & Sassi, 2017, 2020; Kondrateva & Ben Ali, 2021). Nevertheless, the diffusion of Internet and broadband in this region is uneven. There is a big digital divide, i.e., the gap between the people who have access to the ICTs and those who have not (Gunkel, 2003), in this region. Section 6.2 of this chapter compares the penetrations of the Internet, fixed broadband, and mobile broadband of this region with those of other groups of countries and measures the digital divide among the countries in this region. The digital divide in this region prohibits MENA countries work collaboratively to address their pressing and unique challenges. Section 6.3 of this chapter investigates the impacts of the Internet and broadband on external trade liberalization in this region. Trade liberalization is regarded as one crucial issue in the economic growth literature. Although there are a large number of published articles on the relationship between ICTs and economic growth, the research of the impacts of ICTs on trade liberalization is scarce. This chapter advances the literature by estimating the correlations between the penetrations of the Internet, fixed broadband, and mobile broadband and the indexes of trade liberalization and openness. The significant correlations found in this chapter suggest the Internet and broadband facilitate the trade liberalization of this region. The investigations of the digital divide and the impacts of Internet and broadband on trade liberalization have policy implications for MENA countries. Section 6.4 discusses these policy implications. Specifically, the countries with low Internet and broadband penetrations need to implement proactive policies to reduce the digital divide between them and other MENA countries. These policies can help them not only address the unemployment problems but also facilitate trade liberalization. Both these positive effects would promote their economic growth.

6.2  The Digital Divide in the MENA Region The concept of digital divide was coined in the mid-1990s and refined by the US Department of Commerce (Gunkel, 2003). Initially, it was defined as the social gap between the people who have access to the information and communication technologies (ICTs) and those who have not (National Telecommunications and Information Administration, 1995). However, this binary definition has been criticized to be reductive as it does not reveal the multifaceted differences in various ICT adoption and use behaviors. Based on this criticism, Organization for Economic Co-operation and Development (2001) refined this concept: “digital divide refers to the gap between individuals, households, businesses and geographic areas at

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different socioeconomic levels with regard both to their opportunities to access ICT and to their use of the Internet for a wide variety of activities” (p. 32). This refined definition reveals two levels of digital divide: the gap between “has” and “has no,” which is regarded as the first level; the inequality in terms of resources and capacities in using the digital technologies, which is regarded as the second level (Albarran, 2013). These two levels of digital divide interact with each other: The decreased gaps at the first level would help alleviate the gaps at the second level. Hypothetically, the first-level digital divide will eventually disappear when the Internet becomes universally accessible, while the second level will exist after this takes place (Vehovar et al., 2006). As the first-level digital divide still exists across countries, the analysis of this chapter is focused on this level. Previous studies used various ICTs to measure the first-level digital divide including landline telephones, mobile phones, computers, the Internet, and broadband (e.g., International Telecommunication Union, 2010; Kyriakidou et  al., 2011; National Telecommunications and Information Administration, 1995; Vehovar et al., 2006). Meanwhile, this chapter is focused on the measures of the Internet and broadband as these are the fundamental ICTs in the contemporary global society.

6.2.1  C  omparisons of the Penetration Levels of Internet, Fixed Broadband, and Mobile Broadband Between MENA Countries and Other Countries As the penetration rates, i.e., the percentages of people using ICTs, have been used in the literature as the measures of digital divide, this chapter also uses the penetration rates of the Internet and broadband to investigate the digital divide in the MENA region. Multiple previous studies showed that the digital divide exists across the world (e.g., Dewan et  al., 2005; Guillen & Suarez, 2005; International Telecommunication Union, 2010; Zhang, 2013, 2017). Therefore, it is necessary to first compare the penetration rates of the Internet and broadband of the MENA countries and other groups of countries. All the data used in this comparison are obtained from the database of International Telecommunication Union. Figure 6.1 is the comparison of the average number of individuals using the Internet between MENA countries and other groups of countries. It shows that the average Internet penetration rate of MENA countries in 2005 is much lower than that of the developed countries and even lower than the world average. But it is higher than the average of the developing countries. In 2019, the average Internet penetration rate of MENA countries is as the same high as that of the developed countries, and much higher than the world average and the average of the developing countries. This pattern suggests that the Internet diffusion rate of MENA countries, i.e., the growth rate of new Internet users, has been higher than that of other groups of countries during the period of 2005–2019. The digital divide between MENA countries and the developed countries has been bridged in this regard.

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100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 -

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 World

Developed

Developing

MENA

Fig. 6.1  Comparison of the average number of individuals using the Internet per 100 inhabitants. (Data source: International Telecommunication Union, n.d.) 35.0 30.0 25.0 20.0 15.0 10.0 5.0 -

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 World

Developed

Developing

MENA

Fig. 6.2  Comparison of the average number of fixed broadband subscriptions per 100 inhabitants. (Data source: International Telecommunication Union, n.d.)

Figure 6.2 is the comparison of the average number of fixed broadband subscriptions per 100 inhabitants between MENA countries and other groups of countries. It shows that the average fixed broadband penetration rate of MENA countries in 2005 is much lower than that of the developed countries and also lower than the world average. This pattern has not changed over the next 15 years. In 2019, the average fixed broadband penetration rate of MENA countries is much lower than that of the developed countries as well as the world average. This indicates that the

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140.0 120.0 100.0 80.0 60.0 40.0 20.0 0.0

2007

2008

2009

2010 World

2011

2012

Developed

2013

2014

Developing

2015

2016

2017

2018

2019

MENA

Fig. 6.3  Comparison of the average number of mobile broadband subscriptions per 100 inhabitants. (Data source: International Telecommunication Union, n.d.)

diffusion rate of fixed broadband of MENA countries has been almost the same as that of other groups of countries. And the digital divide in terms of the fixed broadband between MENA countries and developed countries has not reduced. This dimension of digital divide also exists between MENA countries and many other countries as the average fixed broadband penetration rate of MENA countries is lower than the world average. Figure 6.3 is the comparison of the average number of mobile broadband subscriptions per 100 inhabitants. It shows that the mobile broadband started to diffuse in MENA countries very late. In 2007, the average penetration rate of mobile broadband in MENA region is almost zero, much lower than that of the developed countries, and lower than the world average as well as that of the developing countries. Nevertheless, in 2019, the average penetration rate of mobile broadband in MENA region is much higher than the world average and that of the developing countries. This pattern suggests that the diffusion rate of mobile broadband in MENA region has been much higher than those of other countries. The digital divide in this dimension has been bridged to a great extent. In summary, the digital divide in terms of the Internet access between MENA countries and developed countries has been successfully bridged. There is still a big gap in terms of the fixed broadband between MENA countries and developed countries. There is even a gap of fixed broadband penetration between MENA countries and the world average. The gap of the mobile broadband penetration between MENA countries and developed countries has been reduced to a large extent. The average mobile broadband penetration rate of MENA countries is higher than the world average. MENA countries exceed the developing countries in all these three dimensions.

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6.2.2  The Digital Divide Within the MENA Region Although MENA countries as a whole has achieved successes in bridging the digital divide compared with other countries, the digital divide still exists within MENA region. Figure 6.4 shows the penetration rates of the Internet, fixed broadband, and mobile broadband of 11 MENA countries. Other MENA countries are not included in this comparison because the data are not available in 2019. This figure suggests that the gaps of Internet penetration and broadband penetration exist among MENA countries. For example, the penetration rate of mobile broadband of United Arab Emirates is 240 in 2019, which is much higher than that of other MENA countries. To further investigate the digital divide within the MENA region, this chapter creates a compound index to comprehensively measure the penetration rates of the Internet, fixed broadband, and mobile broadband. Previous research developed multiple compound indexes to measure the digital divide, including the Digital Access Index (International Telecommunication Union, 2003), Networked Readiness Index (Dutta & Jain, 2004), the Technology Achievement Index (United Nations Development Program, 2001), the Information Society Index (IDC, 2001), the Internet Connectedness Index (Jung et al., 2001), the Infostate (Sciadas, 2005), and the Digital Divide Index (Hüsing & Selhofer, 2004). This chapter uses a compound index proposed in a recently published article (Sidorov & Senchenko, 2020). This compound index has the advantage of excluding the subjective factor in determining the weights of particular indicators. This compound index is developed by combining different indexes that are measured within different boundaries. The first step is to apply normalization process that transforms different units to a common measurement scale. The compound

300.00 250.00 200.00 150.00 100.00 50.00 0.00

Bahrain

Egypt

Israel

Kuwait

Internet

Malta Morocco Oman

Fixed broadband

Qatar

Saudi Arabia

Tunisia United Arab Emirates

Mobile broadband

Fig. 6.4  Comparison of the penetration rates of the Internet, fixed broadband, and mobile broadband among MENA countries in 2019. (Data source: International Telecommunication Union, n.d.)

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index is calculated based on the normalized values of multiple indexes using the following formula: l 1

v   l  l



w



where v is the compound index ηl is the weighting coefficient of the normalized value of the source index εl is the value of the normalized value of the source index w is the number of source indexes

l 

l lw1  l

where σl is calculated by the following formula:

  l





in eil  e l n 1



where σl is the standard deviation estimate for the l-th index eil is a normalized value of the l-th index for the i-th country e l is the average value of the l-th index for the aggregate of all i-th country n is the number of countries Table 6.1 reports the assessment of digitalization of MENA countries using the data of 2017. This year is selected because the data of most MENA countries are available. There are 19 MENA countries in this table. The values of compound index of digitalization suggest there is a big digital divide in this region. The highest value of the compound index is 0.884 (United Arab Emirates). The lowest value is 0.025 (Yemen). The standard deviation is 0.241. Based on uniform breakdown of the compound index score range, these countries are categorized into five groups (greater than 0.71—high level; 0.54–0.70—above average level; 0.37–0.53—average level; 0.20–0.36—below average level; and less than 0.19—low level). There are two countries in the high-level group, five countries in the above average-level group, two countries in the average-level group, seven countries in the below average-­level group, and three countries in the low-level group. The average compound index of high-level group is 0.804; For the above average-level group, it is 0.613; for the average-level group, it is 0.412; for the below average-level group, it is 0.266; and for the low-level group, it is 0.073. The compound index clearly shows the digital divide in the MENA region.

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Table 6.1  Assessment of digitalization of MENA Countries (2017) United Arab Emirates Malta Israel Bahrain Saudi Arabia Qatar Kuwait Oman Iran (Islamic Republic of) Jordan Tunisia Morocco Algeria Iraq Egypt Djibouti Syrian Arab Republic Libya Yemen

Compound index 0.884 0.724 0.647 0.644 0.625 0.595 0.553 0.455 0.368 0.343 0.291 0.289 0.272 0.257 0.206 0.201 0.130 0.064 0.025

Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Level of assessment of digitalization High

Above average

Average

Below average

Low

6.3  D  igitalization and External Trade Liberalization in the MENA Region 6.3.1  ICTs and External Trade Liberalization The Internet and broadband are regarded as a driving force for economic growth as they encourage innovation, increase productivity, and attract foreign investment (International Telecommunications Union, 2003). This proposition has been supported by accumulative evidence that shows the positive correlations between the diffusion of these technologies and economic growth or productivity (e.g., Akerman et al., 2015; Crandall et al., 2006; Farooqui & van Leeuwen, 2008; Ghosh, 2017; Gruber et al., 2014; Haller & Lyons, 2015; Jung & Lopez-Bazoc, 2020; Koutroumpis, 2009; Kumar et al., 2016). On the other hand, the relationship between trade liberalization and economic growth has remained as one of the important issues in the economic growth literature. Theoretically, trade liberalization positively affects economic growth because it promotes efficient allocation of resources, increases competition, and facilitates knowledge diffusion and technology transfer (Young, 1991). As the empirical studies regarding this relationship obtained inconsistent results, economists argued that the differences in technological capacities across

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countries may be associated with the possible different outcomes of trade liberalization (Grossman & Helpman, 1993). The literature in both areas suggests it is necessary to explore the connection between ICTs and trade liberalization as they may jointly affect economic growth. There are a number of studies that focused the impacts of ICTs on external trade in the literature. Researchers proposed theoretical explanations on how ICTs affect external trade. They argued that the investment in ICT infrastructure and the diffusion of ICTs would reduce the transaction costs, which include entry costs to foreign markets, coordination costs related to the production processes, interaction costs between the firm and the customer, and information costs for consumers to compare prices of different products (e.g., Adjasi & Hinson, 2009; Demirkan et al., 2009; Freund & Weinhold, 2002; Jungmittag & Welfens, 2009). There is also an argument that ICTs spur innovations that lower production costs and transaction costs (Ghalazian & Furtan, 2007; Grossman & Helpman, 1995). Researchers found empirical evidence that support the influence of ICTs use and external trade. For example, Clarke and Wallsten (2006) found a positive effect of Internet use on external trade using the data of 98 trading countries in 2001. Lin (2015) found a positive effect of Internet use on exports using the data of international trade within 200 countries during the period 1990–2006. Moreover, there are also empirical findings that support the influence of ICT infrastructure on external trade. Portugal-­ Perez and Wilson (2012), for example, found a positive effect of ICT infrastructure on exports using a weighted ICT-based index for a sample of 101 countries. Researchers also investigated the different impacts of ICT use and infrastructure on external trade. Abeliansky and Hilbert (2017), for example, compared the impacts of ICT use and infrastructure on trade for 122 countries during 1995–2008. They found ICT use had higher impacts for developed countries, while ICT infrastructure had higher impacts for developing countries. Besides the Internet penetration and ICT infrastructure, researchers also included other control variables in their models including real effective exchange rate index, and total population (e.g., Alcalá & Ciccone, 2004; Clarke & Wallsten, 2006; Harrison, 1996; Lin; 2015; Wade, 2012; Yousefi, 2018) While there are a number of studies exploring the impacts of ICTs on external trade, the published research on the impacts of ICTs on external trade liberalization is scarce. External trade liberalization is usually measured by the change of tariff of a nation. There are a few published articles that explored the relationship between broadband penetration and tariff. Different from other studies that examined the impacts of ICTs on external trade, these studies tested the impacts of the degrees of tariff diversity on broadband adoption (e.g., Haucap et  al., 2016; Lange, 2017). Nevertheless, the impacts of broadband on tariff have not been investigated in these studies. This chapter is an attempt to fill this gap in the literature. It investigates the relationships between the penetrations of the Internet and broadband and the indexes of external trade liberalization in the MENA region.

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6.3.2  The External Trade Liberalization in the MENA Region External trade liberalization is the process of removing or reducing of restrictions or barriers on the exchange of goods between nations. Figure  6.5 presents the average rate of tariffs of MENA countries during the period 1995–2017. The y-axis is the simple mean applied tariff, which is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods (The World Bank, n.d.-b). The pattern shown in this figure reveals that the tariff rates of MENA countries have declined during the past two decades. In 1995, the average tariff rate is 27.19%. This rate declined gradually and reduced to 7.35% in 2017. It is obvious that MENA countries have gone through an external trade liberalization process. Trade openness is another concept that is closely related to external trade liberalization. It is usually measured by trade intensity variables, such as export and import to GDP ratio (Alcalá & Ciccone, 2004). Figure 6.6 presents the average values of this trade intensity variable of MENA countries during 2000–2019. The change pattern of this variable shows an uprising trend overall but with some extents of fluctuation. In 2000, the imports and exports of goods and services (% of GDP) are 76.5%. In 2019, the value of this variable rose to 86.0%. This uprising pattern corresponds the declined pattern of the tariff rates of MENA countries and reflects the process of external trade liberalization process in this area.

30 25 20 15 10 5 0

Fig. 6.5  Average tariff rate of MENA countries during 1995–2017. (Data source: The World Bank, n.d.-b)

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120 100 80 60 40 20 0

Fig. 6.6  Average imports and exports of goods and services (% of GDP) of MENA countries. (Data source: The World Bank, n.d.-b)

6.3.3  T  he Impacts of Internet and Broadband on the External Trade Liberalization in the MENA Region To examine the impacts of Internet and broadband on the external trade liberalization in the MENA region, the simple mean applied tariff and the trade intensity variable (export and import to GDP ratio) are used as dependent variables. The independent variables are the penetration rates of the Internet and broadband. The ICT goods import variable is also added to the model as this variable is related to the digitalization of an economy. Other independent variables—GDP per capita, GDP per capita growth rate, gross fixed capital formation (% of GDP), real effective exchange rate index, and total population—in the previous studies are used as controlled variables (Alcalá & Ciccone, 2004; Clarke & Wallsten, 2006; Harrison, 1996; Lin; 2015; Wade, 2012; Yousefi, 2018). The panel data regression is performed using the following regression model:



ETL i ,t   ICTi ,t   ICTIMi ,t   GDPPi ,t   GDPG i ,t   GFCFi ,t   EXCHi ,t   POPUi ,t   i ,t



where ETLi, t denotes the variable of external trade liberalization, measured by the simple mean applied tariff and import and export to GDP ratio. ICTi, t denotes the penetration variable of the Internet and broadband. Three ICT variables are used in the analysis: INTi, t is the Internet penetration rate; FBi, t is the fixed broadband penetration rate; and MBi, t is the mobile broadband penetration rate. These three

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X. Zhang

ICT variables not only reflect the ICT infrastructure but also the diffusions of digital ICTs in an economy. ICTIMi, t is ICT goods imports (% total goods imports). GDPPi, t denotes GDP per capita; and GDPPi, t denotes GDP per capita growth rate. GFCFi, t denotes gross fixed capital formation (% of GDP). EXCHi, t denotes the real effective exchange rate index (2010 = 100). POPUi, t denotes the logarithm transformation of total population. εi, t denotes random error. The structure of εi, t depends on whether the model is estimated by fixed effect model (FE) or random effect model (RE). The data of the Internet, fixed broadband, and mobile broadband are obtained from the database of International Telecommunication Union (ITU, n.d.). The data of external trade and other economic variables are obtained from the database of the World Bank (The World Bank, n.d.-b). Most data of Yemen and Malta are not available. These two countries are excluded from regression analysis. The time period of the analysis is 2000–2019 when the Internet and fixed broadband are used as the independent variables. The time period of the analysis is 2007–2019 when mobile broadband is used as the independent variables because the data of mobile broadband are available since 2007. Table 6.2 reports the results of the panel data regression when the simple mean applied tariff is used as the independent variable. Model 1 shows the Internet penetration rate is significantly and negatively correlated with the simple mean applied tariff (α = −0.09, p < 0.001). Model 2 shows its coefficient is still significant and negative (α = −0.17, p < 0.001) when other controlled variables enter the regression model. Model 3 shows the fixed broadband penetration rate is significantly and negatively correlated with the simple mean applied tariff (α = −0.21, p < 0.01). Model 4 shows the coefficient of fixed broadband becomes not significant when other controlled variables enter the regression model. Model 5 shows the mobile broadband penetration rate is significantly and negatively correlated with the simple mean applied tariff (α = −0.01, p < 0.05). Model 6 shows the coefficient of mobile broadband is still significant (α = −0.05, p < 0.001) when other controlled variables enter the regression model. Table 6.3 reports the results of the panel data regression when the import and export to GDP ratio is used as the independent variable. Model 7 shows the Internet penetration rate is significantly and positively correlated with the import and export to GDP ratio (α = 0.12, p < 0.01). Model 8 shows its coefficient is still significant and positive (α = 0.20, p < 0.01) when other controlled variables enter the regression model. Model 9 shows the fixed broadband penetration rate is significantly and positively correlated with the import and export to GDP ratio (α = 0.58, p < 0.01). Model 10 shows its coefficient is also significant (α = 0.62, p < 0.01) when other controlled variables enter the regression model. Model 11 and Model 12 show the mobile broadband penetration rate is not significantly correlated with import and export to GDP ratio.

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Table 6.2  Results of panel data regression for estimating the impacts of ICTs on external trade liberalization (independent variable: simple mean applied tariff) Constant INT

Model 1 13.15*** (24.57) −0.09*** (−7.70)

Model 2 8.19 (1.19) −0.17*** (−7.09)

FB

Model 3 10.29*** (22.24)

Model 4 2.56 (0.27)

−0.21** (−3.53)

−0.18 (−1.51)

MB ICTIM

1.17*** (4.03)