The Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa

Adeleye, Ngozi; Osabuohien, Evans; Bowale, Ebenezer Article The Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa Journal of Contextual Economics – Schmollers Jahrbuch Provided in Cooperation with: Duncker & Humblot, Berlin Suggested Citation: Adeleye, Ngozi; Osabuohien, Evans; Bowale, Ebenezer (2017) : The Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa, Journal of Contextual Economics – Schmollers Jahrbuch, ISSN 2568-762X, Duncker & Humblot, Berlin, Vol. 137, Iss. 1-2, pp. 173-192, https://doi.org/10.3790/schm.137.1-2.173 This Version is available at: https://hdl.handle.net/10419/292513 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ The Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa* By Ngozi Adeleye**, Evans Osabuohien***, and Ebenezer Bowale**** Abstract This study contributes to the literature on income inequality by providing evidence that financial development not only impacts income distribution, but the effects can improve when there is a strong institutional framework. Using the system-generalised method of moments (sys-GMM) technique on a sample of 42 Sub-Saharan African (SSA) countries from 1996 to 2015, our major findings are summarised as follows: (1) inequality is persistent in the region (2) financial development does not significantly reduce income inequality; and (3) the control of corruption and its interaction with domestic credit exhibit an inverted-U relation with income inequality. Thus, policies that will reduce income inequality require that corruption be controlled given increase in domestic credit. JEL Codes: F36; G21; O15 Journal of Contextual Economics 137 (2017), 173 –192 Duncker & Humblot, Berlin Journal of Contextual Economics 137 (2017) 1–2 *The authors appreciate comments from participants at the: 3 rd Witten Conference on “Institutions in Development Research –New Buzzword or Real Impact?”, 1 st & 2 nd December, 2016, Witten/Herdecke University, Witten, Germany and Covenant University College Seminar, 27 th October, 2016, where the earlier versions of the paper were presented. The authors acknowledge funding support from Covenant University Centre for Research, Innovation and Discovery (CUCRID) during the preparation of this manuscript. ** Dept. of Economics and Development Studies, Covenant University, 10 Idiroko Rd, Ota, Nigeria. The author can be reached at [email protected]. ng. *** Dept. of Economics and Development Studies, Covenant University, 10 Idiroko Rd, Ota, Nigeria. The author can be reached at evans.osabuohien@covenantuniversity. edu.ng. **** Dept. of Economics and Development Studies, Covenant University, 10 Idiroko Rd, Ota, Nigeria. The author can be reached at ebenezer.bowale@covenantuniversity. edu.ng. OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 1. Introduction Between 1980 and up to the beginning of the millennium, most African countries embarked on various reforms required to restructure their economies and liberalise their financial markets. The outcomes, among others, include the strengthening and development of the financial sector evidenced by intense financial intermediation with growth in credit leading to faster long-run economic growth. However, despite the resurgence of growth in more than two decades, extreme poverty has not been decreasing in the continent (Asongu and Nwachukwu 2017). According to African Development Bank-AfDB (2012), in addition to being one of the poorest regions in the world, Africa is also the world’s second most inequitable region after Latin America (Solt 2009; UNDP 2011 and 2013; United Nations 2013; Milanovic 2014). On the growth-inequality mix, AfDB (2012) finds that: “In the 2000s, six of the world’s ten fastest-growing countries were in Africa, but this has not significantly helped to equal incomes or to redistribute wealth.”Ironically, by 2010, six of the ten countries in the world with the most unequal income distribution were in the region (AfDB 2012), and the countries with the most unequal income distribution include Namibia, Comoros, South Africa, Angola, Botswana, Lesotho and Swaziland, with the sub-region of Southern Africa showing a striking concentration of countries which suffer from remarkably high income inequality levels. The terms “financial liberalisation”and “financial development”connote different interpretations; although, there is an intrinsic relationship between them, they are often used interchangeably in empirical studies. While financial liberalisation relates to the progressive removal or elimination of restrictions, such as the liberalisation of capital accounts allowing for international movements of funds and encouraging financial innovation, financial development refers to the upgrading of the quality of financial transactions within the national context with the observance of financial depth in an economy (Gehringer 2013; Osabuohien et al. 2017). According to Mishkin (1999), the liberalisation of financial markets is central to the efficient functioning of financial markets by enabling the channelling of funds to those with the most productive investment opportunities. In essence, deregulation is beneficial in the long-run because it leads to more financial efficiency and deepening evidenced with increased lending and economic growth (Adeleye et al. 2017). Given that in African countries the richest capture the largest share of income, could the type of institutional framework prevalent in the region be driving inequalities? This is because if institutions are weak, financial development may neither improve economic welfare nor decrease inequality due to discriminatory lending, lopsided distribution of credit, lack of judicial protection for the poor (Chong and Gradstein 2007) and pervasive corruption (Gupta, Davoodi and Alonso-Terme 2002) which allows the oligarchy or established interests to 174 Ngozi Adeleye, Evans Osabuohien and Ebenezer Bowale Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 sway finance away from the poor. Another plausible conjecture is that weak institutions tend to alter the ability of financial intermediaries to channel resources to finance productive activities efficiently. Consequently, where institutions are strong and unbiased, financial development may reduce inequality, allowing the poor to invest in human and physical capital (Demirguc-Kunt and Levine 2008). Thus, this paper presents an investigation of such evidence by analysing the influence of institutional quality on financial development in mitigating income inequality. In an attempt to address the gap observed above and contribute to the current empirical literature, this study: (i) employs an up-to-date dataset on the Gini index (the measure of income inequality); (ii) includes wider coverage of SSA countries; (iii) extends the institutional dimensions to five, namely: control of corruption, government effectiveness, political stability, rule of law and political rights; and (iv) underscores the channel of influence by interacting the financial development variable with each of the institutions variable. The remainder of the paper is structured as follows: section 2 reviews the theoretical and empirical literature, section 3 outlines the data and methodology. Section 4 details empirical analysis, results and robustness checks while section 5 concludes with some policy recommendations. 2. Brief Insights from the Literature The empirical review is conceptualised within the two theoretical frameworks that informed this study. First is the extensive margin theory which states that financial development might operate on the extensive margin by increasing the availability and use of financial services by individuals who had not been employing those services because of price and other barriers. In essence, financial development will expand the economic opportunities of disadvantaged groups and reduce the intergenerational persistence of relative incomes (Becker and Tomes 1979 and 1986; Greenwood and Jovanovic 1990). Second is the discrimination theory of Becker (1957) which states that discrimination is cheaper when there is little competition, but such is competed away if the barriers of entrance of new firms are sufficiently low. Therefore, financial sector reforms that intensify competition will reduce discrimination and expand the economic opportunities of disadvantaged groups thereby reducing income inequality. Empirical reviews reveal that while a voluminous extant literature has documented a strong link from financial development to economic growth, a relatively small group of recent studies have explored the link between finance and income inequality (Demirgüç-Kunt and Levine 2009; Agnello, Mallick and Sousa 2012; Saran 2013). From the empirical survey, Batuo, Guidi and Mlambo (2010) examine financial development and income inequality in 22 African countries and find eviThe Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa 175 Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 dence that finance has an equalising effect on income. Their findings are corroborated by Agnello and Sousa (2012) on the study of 62 OECD and nonOECD countries from 1980 to 2006. They find access to the banking sector helps to reduces inequality and that inequality rises some periods before a banking crisis and declines afterwards. In a similar vein, Agnello, Mallick and Sousa (2012) extend the works of Abiad and Mody (2005) with an unbalanced panel data on 62 countries from 1973 to 2005 and find that financial liberalisation reduce income inequality corroborating the works of political-economy researchers (Benmelech and Moskowitz 2010; Rajan and Ramcharan 2011) while the existence of minimum amount of lending to certain ‘priority’sectors1and trade openness exacerbates income inequality. Similarly, Asongu (2013) investigates how financial reforms address the problem of income inequality in Africa through financial sector competition and informal sector inclusion (known as financial inclusion). The author finds that improvement of the formal financial sector reduces inequality, improvement of the semi-formal financial sector increases inequality, improvement of the informal financial sector2reduces inequality and improvement of the semiformal financial sector reduces inequality. From the foregoing, it is obvious that finance has significant impact on income distribution. Financial reforms can improve the efficiency of the domestic financial systems (Čihák et al. 2013) and influence the distribution of income, as rising inequality generally reflects an unequal access to productive opportunities. Also, they can lead to a better allocation of risk and socialization of costs, which is particularly relevant during financial crises (Claessens and Perotti 2007). These outcomes, in turn, can help mitigate income inequality. On the role of institutions, political and economic institutions are interwoven and can either be inclusive or extractive (Acemoglu and Robinson 2013). Inclusiveness fosters economic growth while the exclusive variant hampers economic growth. Under an inclusive system, the financial sector, which comprises the money and capital markets, is regulated by the central banks and exchange commission respectively, who ensure that the ethics and dictates of these markets are followed by players. Such a system supports economic activity, making it possible for firms and individuals to borrow money from banks and financial markets. Inferences can be made on the fact that if, for instance, a central bank fails in its duty to enforce rules governing money market operations, 176 Ngozi Adeleye, Evans Osabuohien and Ebenezer Bowale Journal of Contextual Economics 137 (2017) 1–2 1Priority sectors are those sectors that are selected for lending by the central government, e.g. sectors involved in the production or importation of the military and defence apparatus. Such that if lending is disproportionately geared, it may hinder credit flows to small and medium scale enterprises, thus aggravating income inequality. 2The author defined “informal”financial sector as those whose deposits is the net difference of money supply and financial deposits (% of GDP); while semi-formal financial sector as those whose deposits is the net difference of financial and bank deposits (% of GDP). OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 the tendency of deposit money banks to circumvent loans or credit access to poor households will arise. The five institutional indicators encapsulate “inclusiveness”in the sense that they measure both economic (control of corruption and government effectiveness) and political (political stability, rule of law and political rights) inclusiveness. However, empirical studies on impact of quality institutions are wide-ranging, mixed and diverse. To some, higher levels of institutional quality are associated with economic growth (Zak and Knack 2001; Easterly, Ritzen and Woolcock 2006; Green 2011) via support to small and medium scale enterprises (Rus and Iglič2005) and the promotion and enforcement of contracts, which enhances innovation and human and physical capital accumulation (Latusek and Cook 2012; Andriani and Sabatini 2015). Others argue differently but reaching the same conclusion that the absence of quality institutions (i.e., the absence of the rule of law and the prevalence of corruption) is the ‘grease’that oils the wheels of economic growth (i.e., a blessing), that is, a way of getting around burdensome bureaucratic regulations or circumventing bureaucratic inefficiencies (Kaufmann 1997; Holmes 2000). However on a study of 19 South Asian countries given a shadow economy framework (which is one in which economic activities are often criminal, unreported, unrecorded, illegal or at least ‘informal’), Kar and Saha (2012) argue that extra-legal dimensions of the shadow economy are capable of cornering resources and raising inequality. Since these countries cannot ignore the economic trade-offs, strong economic and political tension exists between maintaining extra-legal activities, lowering growth and lowering inequality, which are posed against economic growth via proliferation of the formal economy where the rules are stricter. This approach supports the “sand”hypothesis. Thus, in this study we propose to test the role of institutions using SSA data for the period 1996 to 2015. The aim is to examine whether the developments taking place in the financial sector as a result of financial reforms can reduce the persistent level of inequality given the quality of institutions prevalent in the region. To our knowledge, this study is one of the very studies that explores the relationship between financial development and income inequality to explore this path of analysis restricted only to data on SSA countries. 3. Methodology and Data 3.1 Empirical Model and Estimation Technique Following Beck, Levine and Loayza (2000) and Calderón and Servén (2004) that have used this estimation approach in solving the endogeneity problem by exploiting the time series variation in the data, controlling for unobserved group-specific effects, and allowing for the inclusion of a lagged dependent The Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa 177 Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 variable, we first run the estimation using annual data, which is then transformed to an average of five-year periods due to fact that inequality data are highly persistent. Since our focus is on the long run trend rather than on the behaviour over the business cycle, using data averaged over a period of five years would smooth out short term fluctuation. Lastly, an attempt was made to identify the relevant determinants of inequality, as measured by the Gini index, first with a baseline model, the subsequent inclusion of the institutions variables and their interactions with financial development in chronological order. The empirical model specification is given as: lnginiit ¼lnginiit1þFDit þZ0it þ’X0it þiþ@tþeit:ð1Þ where lngini, is the natural logarithm of the Gini index; lnginiit1is the natural logarithm of the lagged Gini index, FD is the proxy for financial development; Z0is the vector of institution variables; X0is the vector of control variables;  is the unobserved country-specific fixed effects; @is the time trend; ,,and ’are parameters; iis the number of cross-sections (=1,…,N); tis the number of time series (=1,…,T) and eis the error term. The inclusion of the control variables is to determine whether the effect of financial development on inequality still holds true after considering the effects of these covariates on inequality. In this model specification,3the endogenous variable is the lagged log of Gini index and others are treated as weakly exogenous (FD) and strictly exogenous (Z0;and X0). Since a static4model will not capture the short and long-run impacts of the regressors on the dependent variable, we use a dynamic model and the sysGMM estimator to capture the persistent nature of income inequality, address the problems of omitted variables, measurement error, endogeneity, and country-specific heterogeneity. The consistency of the system-GMM estimator is assessed by two specification tests. The Hansen test of over-identifying restrictions tests for the overall validity of the instruments and the second test examines the null hypothesis that the error term is not serially correlated. Failure to reject both null hypotheses gives support to the model (Arellano and Bond 1991; Arellano and Bover 1995; Blundell and Bond 1998; Osabuohien, Efobi and Gitau 2015). This study reports p-values of Arellano-Bond AR(2) tests as contained in section 4. 178 Ngozi Adeleye, Evans Osabuohien and Ebenezer Bowale Journal of Contextual Economics 137 (2017) 1–2 3The following instrumental variables are used: the GMM instrument is one-period lagged values of the logged Gini index; other instruments are broad money supply (% of GDP), gross fixed capital formation (% of GDP) and government final consumption expenditure (% of GDP). 4In the initial version, we started with the use of the static model; thanks to one of the anonymous reviewers who advised us to concentrate on the dynamic model with a view of capturing the proper influence of the variables. OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 3.2 Data and Descriptive Statistics The Gini index which is drawn from Lahoti, Jayadev and Reddy (2016) is the measure of income inequality, the outcome variable. It is the most used measure of income equality and ranges from 0 (perfect income equality) to 1 (perfect income inequality), and countries with Gini indices closer to 0 (European countries) are more equal than those closer to 1, i.e., Latin America and African countries (UNDP 2011 and 2013). The index is widely used, perhaps owing to its easy computation and availability relative to other income measures. It can be measured using gross income or net income, income or expenditure, data per capita or data per household (Milanovic 2014; Solt 2016). However, variations in defining the measure of the Gini index itself can undermine international and intertemporal comparability of any data (Deininger and Squire 1996; Milanovic 2014; Solt 2014; Lahoti, Jayadev and Reddy 2016). The undermining may be as a result of different income definitions (gross income or net income) used across or even within countries and different reference units. The financial development and control variables are from the World Development Indicators (WDI) of the World Bank (2016a) while the measures of institutional quality are from World Governance Indicators (WGI) of the World Bank (2016b) and Freedom House (2017). We point out here that most institutional indicators and their respective databases have been criticized for biases and are not completely free of errors. However, these are still the best data sets available for cross-country studies (Kar and Saha 2012). Domestic credit to the private sector is the proxy for financial development, the control variables are inflation rate, per capita gross domestic products (GDP) and age dependency ratio, while the measures of institutional quality are control of corruption, government effectiveness, political stability, rule of law and political rights. On the a priori expectations, domestic credit to private sector (% of GDP), which is the measure of financial efficiency and development, shows the ability of banks to transform their mobilised deposits into household credits, and in line with the discussion of inequality, this would allow individuals at the lower end of the income distribution to have easier access to credit to fund investment ideas more efficiently, thus reducing inequality. The per capita GDP controls for the level of economic development and it is expected that inequality falls as the average income of the population increases. The age dependency ratio measures the ratio of the working age group between 15–64 years and as the ratio increases, we expect a reduction in income inequality. GDP growth rate and the inflation rate are expected to exert a negative effect on inequality. Lastly, for all the governance variables, empirical evidence supports that quality institutions enhance government’s role in resource allocation, increases the level of social services available to the poor, increases growth and investment, and that policies that improves institutional quality will most likely reduce inThe Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa 179 Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 equality. The description of each variable and data sources are in Appendix Table A3 while we show the summary statistics and a priori expectations in Table 1. Table 1 Variables Statistics and a priori Expectations Variables Obs Mean SD Min Max a priori Gini Index 817 0.437 0.079 0.296 0.875 NA Dom. Credit by Banks 796 17.534 16.558 0.154 108.022 negative Financial System Dep. 751 22.694 18.160 1.690 97.820 negative GDP per capita 831 1556.211 2367.205 72.527 15695.898 negative Age Dependency 840 86.500 14.150 40.618 112.973 negative GDP Growth Rate 831 4.890 6.138 –37.011 106.270 negative Inflation Rate 830 21.474 211.41 –29.69 5399.51 negative Control of Corruption 838 –0.572 0.602 –2.386 1.250 negative Govt. Effectiveness 838 –0.673 0.596 –1.980 1.040 negative Political Stability * 840 –0.521 0.947 –2.990 1.190 negative Rule of Law 840 –0.654 0.653 –2.230 1.060 negative Political Rights 840 4.217 1.852 1.000 7.000 negative Note * : The ranking of the institutional variables are –2.5 (very weak) to +2.5 (very strong), but we observed some countries below the “very weak”rank for political stability. The countries are: Congo, DR: –2.99 (1997), –2.83 (1996), –2.78 (1999), –2.58 (2000); Central Afr. Rep.: –2.69 (2014); Sudan: –2.65 (2009, 2010), –2.51 (1996); Liberia: –2.55 (1996); Burundi: –2.51 (2004). Source: Authors’computations. From Table 1, a cursory look at the data reveals that countries in the Southern African sub-region make up the largest proportion of those with high inequality. South Africa recorded the highest Gini index in the sample (0.875 for 2009) and has the highest number of index above 0.65 followed by Namibia and Lesotho. The country with the lowest Gini index was Ethiopia (with 0.296 for 2004) and also has the highest number of index below 0.30 followed by Sao Tome and Principe. On institutional quality, the majority of the countries did poorly with few having good ratings with some specific measures. The correlation matrix (Table 2) informs the study about potential relationships between inequality and other variables. Domestic credit and financial system deposit exhibit a positive correlation with the Gini index; of the four control variables, age dependency and GDP growth rate exhibit a negative correlation while out of the five institutional variables only political rights indicate a negative correlation. This may be due to the different computational dynamics used by the different data sources. 180 Ngozi Adeleye, Evans Osabuohien and Ebenezer Bowale Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 contributing significantly to the reduction of inequality. Likewise, all the measures of institutional quality are statistically insignificant although they possess negative coefficients. On the role of quality institutions, our results show the tendency for improved income distribution if institutions are efficient. This assertion is supported with the statistical significance of the inverted-U relationship of corruption and its interaction with domestic credit on the Gini index. The governments of SSA need frantic efforts and policies directed towards reducing inequality. This needs to ensure that credit is not exclusively meant for the rich (as it currently stands). Although comprehensive, there are further research questions relating to both income inequality and credit growth that remain to be answered. Data limitations can restrict the ability to test a range of hypotheses, and identifying causal effects is a serious challenge. It is important to test the impact of other financial variables, such as the number of branch networks of banks, the liquidity ratio, the cash reserve ratio and international capital flows. This can be taken up in subsequent research. References Abiad, A. and A. Mody. 2005. “Financial Reform: What Shakes It? What Shapes It?” The American Economic Review 95 (1): 66–88. Acemoglu, D. and J. A. Robinson. 2013. Why Nations Fail. Vol. 2. London, Great Britain: Profile Books Ltd. Adeleye, N., E. Osabuohien, E. Bowale, O. Matthew, and E. Oduntan. 2017. “Financial Reforms and Credit Growth in Nigeria: Empirical Insights from ARDL and ECM Techniques.”International Review of Applied Economics. http://dx.doi.org/10.1080/ 02692171.2017.1375466. AfDB. 2012. “Income Inequality in Africa.”Accessed September 23, 2016. https://www. afdb.org/fileadmin/uploads/afdb/Documents/Policy-Documents/FINAL%20Briefing %20Note%205%20Income%20Inequality%20in%20Africa.pdf. Agnello, L., S. K. Mallick, and R. M. Sousa. 2012. “Financial Reforms and Income Inequality.”Economics Letters 116 (3): 583–587. Agnello, L. and R. M. Sousa. 2012. “How Do Banking Crises Impact on Income Inequality?”Applied Economics Letters 19 (15): 1425–1429. Andriani, L. and F. Sabatini. 2015. “Trust and Prosocial Behaviour in a Process of State Capacity Building: The Case of the Palestinian Territories.”Journal of Institutional Economics 11 (4): 1–24. Arellano, M. and S. Bond. 1991. “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment.”Review of Economic Studies Limited 58: 277–297. Arellano, M. and O. Bover. 1995. “Another Look at the Instrumental Variable Estimation of Error-Components Models.”Journal of Econometrics 68 (1): 29–51. The Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa 187 Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 Asongu, S. A. 2013. “How Do Financial Reforms Affect Inequality Through Financial Sector Competition? Evidence from Africa.”Economics Bulletin 33 (1): 401–411. Asongu, S. A. and J. C. Nwachukwu. 2017. “Foreign Aid and Inclusive Development: Updated Evidence from Africa, 2005–2012.”Social Science Quarterly 98 (1): 282– 298. Batuo, E. M., F. Guidi, and K. Mlambo. 2010. “Financial Development and Income Inequality: Evidence from African Countries.”Munich Personal RePEc Archive https://mpra.ub.uni-muenchen.de/25658/. Beck, T., R. Levine, and N. Loayza. 2000. “Finance and the Sources of Growth.”Journal of Financial Economics 58: 261–310. Becker, G. S. 1957. The Economics of Discrimination. 2nd ed. Chicago: University of Chicago Press. Becker, G. S. and N. Tomes. 1979. “An Equilibrium Theory of the Distribution of Income and Intergenerational Mobility.”Journal of Political Economy 87 (6): 1153– 89. —1986. “Human Capital and the Rise and Fall of Families.”Journal of Labour Economy 4 (3): 1–39. Benmelech, E. and T. J. Moskowitz. 2010. “The Political Economy of Financial Regulation: Evidence from US State Usury Laws in the 19th Century.”The Journal of Finance 65 (3): 1029–1073. Blundell, R. and S. R. Bond. 1998. “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models.”Journal of Econometrics 87: 115–144. Calderón, C. and L. Servén. 2004. “Trends in Infrastructure in Latin America.”Central Bank of Chile Working Papers Nr. 269. Chong, A. and M. Gradstein. 2007. “Inequality and Institutions.”The Review of Economics and Statistics 89 (3): 454–465. Čihák, M., A. Demirgüč-Kunt, E. Feyen, and R. Levine. 2013. “Financial Development in 205 economies, 1960 to 2010.”National Bureau of Economic Research Working Paper 18946. Accessed on 09 Nov 2017. http://www.nber.org/papers/w18946. Claessens, S. and E. Perotti. 2007. “Finance and Inequality: Channels and Evidence.” Journal of Comparative Economics 35 (4): 748–773. Deininger, K. and L. Squire. 1996. “A New Data Set Measuring Income Inequality.” World Bank Economic Review 10: 565–591. Demirguc-Kunt, A. and R. Levine. 2008. “Finance and Economic Opportunity.”Policy Research Working Paper 4468. Washington, D.C.: World Bank. —2009. “Finance and Inequality: Theory and Evidence.”National Bureau of Economic Research Working Paper 15275. Accessed on 9 Nov 2017. http://www.nber.org/papers/w15275. Easterly, W., J. Ritzen, and M. Woolcock. 2006. “Social Cohesion, Institutions and Growth.”Economics & Politics 18 (2): 103–120. 188 Ngozi Adeleye, Evans Osabuohien and Ebenezer Bowale Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 Gehringer, A. 2013. “Financial Liberalization, Financial Development and Productivity Growth –An Overview.”Economics Discussion Papers, Kiel Institute for the World Economy 2013–46. Green, A. 2011. “Institutions Matter, but in Surprising Ways: New Evidence on Institutions in Africa.”Kyklos 64 (1): 87–105. Greenwood, J. and B. Jovanovic. 1990. “Financial Development, Growth and the Distribution of Income.”Journal of Political Economy 98 (5): 1076–1107. Gupta, S., H. Davoodi, and R. Alonso-Terme. 2002. “Does Corruption Affect Income Inequality?”Economics of Governance 3: 23–45. Holmes, L. 2000. “Funktionen und dysfunktionen der korruption und ihrer bekämpfung in Mittelund Osteuropa.”In Zentrum für Europaund NordamerikaStudien, Politische Korruption, 117–144.Opladen: Leske + Budrich. Kar, S. and S. Saha. 2012. “Corruption, Shadow Economy and Income Inequality: Evidence from Asia.”The Institute for the Study of Labor (IZA) IZA Discussion Paper No. 7106. Kaufmann, D. 1997. “Corruption: The Facts.”Foreign Policy: 114–131. Lahoti, R., A. Jayadev, and S. G. Reddy. 2016. “The Global Consumption and Income Project (GCIP): An Overview.”Accessed on 09 Nov 2017. https://ssrn.com/abstract= 2480636. Latusek, D. and K. S. Cook. 2012. “Trust in Transitions.”Kyklos 65 (4): 512–525. Law, S. H., H. B. Tan, and W. N. W. Azman-Saini. 2014. “Financial Development and Income Inequality at Different Levels of Institutional Quality.”Emerging Markets Finance & Trade 50 (1): 21–33. Levine, R. 2004. “Finance and Growth: Theory and Evidence.”National Bureau of Economic Research Working Paper 10766. Accessed on 09 Nov 2017. http://www.nber. org/papers/w10766. —2008. “Finance and the Poor.”The Manchester School Supplement 76 (s1): 1–13. Milanovic, B. 2014. “Description of All The Ginis Dataset.”Accessed April 23, 2015. www.worldbank.org/en/research/brief/all-the-ginis. Mishkin, F. 1999. “Lessons from the Tequila Crisis.”Journal of Banking and Finance 23: 1521–133. Osabuohien, E., U. R. Efobi, and C. M. Gitau. 2015. “Environment Challenges in Africa: Further Dimensions to the Trade, MNCs and Energy Debate.”Management of Environmental Quality: An International Journal 26 (1): 118–137. Osabuohien, E., U. Efobi, J. Odebiyi, and F. Fayomi. 2017. “Financial Development, Trade Costs and Bilateral Trade Flows: Connecting the Nexus in ECOWAS.”In Investment and Competitiveness in Africa, edited by D. Seck, 153 –176. Geneva: Springer International Publishing. Rajan, R. and R. Ramcharan. 2011. “Land and Credit: A Study of the Political Economy of Banking in the United States in the Early 20th Century.”The Journal of Finance 66 (6): 1895–1931. The Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa 189 Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 Rus, A., and H. Iglič. 2005. “Trust, Governance and Performance. The Role of Institutional and Interpersonal Trust in SME Development.”International Sociology 20 (3): 371–391. Saran, S. 2013. “Quantitative Easing: Impact on Emerging and Developing Economies.” Accessed Sep 18 2014. http://www.ipsnews.net/2013/06/quantitative-easing-impacton-emerging-and-developing-economies/. Solt, F. 2009. “Standardizing the World Income Inequality Database.”Social Science Quarterly 90 (2): 231–242. —2014. “The Standardized World Income Inequality Database.”Working Paper: SWIID Version 5.0, October 2014. —2016. “The Standardized World Income Inequality Database.”Social Science Quarterly Bulletin 97: SWIID Version 5.1, July 2016. UNDP. 2011. “Towards Human Resilience: Sustaining MDG Progress in an Age of Economic Uncertainity.”United Nations Development Programme. Accessed Sep 15 2016. http://www.undp.org/content/dam/undp/library/Poverty%20Reduction/Towards_ SustainingMDG_Web1005.pdf. —2013. “Humanity Divided: Confronting Inequality in Developing Countries.”United Nations Development Programme. Accessed Sep 15, 2016. http://www.undp.org/ content/dam/undp/library/Poverty%20Reduction/Inclusive%20development/Human ity%20Divided/HumanityDivided_Full-Report.pdf. United Nations. 2013. “Inequality Matters.”United Nations, Accessed Sep 12, 2016. www.un.org/esa/socdev/documents/reports/InequalityMatters.pdf. World Bank. 2016. “Worldwide Governance Indicators.”World Bank, Accessed July 02 2017. http://databank.worldbank.org/data/reports.aspx?source=worldwide-governanceindicators. —2017. “World Development Indicators.”World Bank, Accessed July 20 2017. https://data.worldbank.org/data-catalog/world-development-indicators. Zak, P. J. and S. Knack. 2001. “Trust and Growth.”The Economic Journal 111 (470): 295–321. 190 Ngozi Adeleye, Evans Osabuohien and Ebenezer Bowale Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 Appendix Table A1 Variables Description, Sources and Scope Variables Description Sources, Scope Gini Index Measure of income inequality. Ranges between 0 (perfect equality) and 1 (perfect inequality). Lahoti et al., (2016), 1996 –2015 Dom. Credit by Banks Domestic credit to private sector by banks refers to financial resources provided to the private sector by other depository corporations (deposit taking corporations except central banks), such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. WDI (2016), 1996 –2015 Financial System Dep. Demand, time and saving deposits in deposit money banks and other financial institutions as a share of GDP. WBGFDD (2016), 1996 –2015 GDP per capita (US$) This is gross domestic product divided by midyear population. WDI (2016), 1996 –2015 Age Dependency Age dependency ratio is the ratio of dependents–people younger than 15 or older than 64–to the working-age population–those ages 15-64. –do – GDP Growth Rate (%) Annual percentage growth rate of GDP at market prices based on constant local currency. –do – Inflation Rate Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. –do – Control of Corruption Reflects perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture“of the state by elites and private interests. Ranges from –2.5 (very corrupt) to +2.5 (least corrupt) WGI (2016), 1996 –2015 Govt. Effectiveness Reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. Ranges from –2.5 (least effective) to +2.5 (most effective) –do – Continued next page The Role of Institutions in the Finance-Inequality Nexus in Sub-Saharan Africa 191 Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52 Table A1 continued Variables Description Sources, Scope Political Stability Political Stability and Absence of Violence/ Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. Ranges from –2.5 (least stable) to +2.5 (most stable). –do – Rule of Law Reflects perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Ranges from –2.5 (very weak) to +2.5 (very strong). –do – Political Rights Measure of political rights of the citizenry. Ranges from 1 (highest degree of freedom) to 7 (lowest degree of freedom). FIW (2017), 1996 – 2015 Source: Authors’. A2 List of Countries Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Democratic Republic of Congo, Congo Republic, Cote d’Ivoire, Ethiopia, Gabon, Gambia Ghana, Guinea and Guinea Bissau. Others are: Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Seychelles, Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda and Zambia. 192 Ngozi Adeleye, Evans Osabuohien and Ebenezer Bowale Journal of Contextual Economics 137 (2017) 1–2 OPEN ACCESS | Licensed under | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.137.1-2.173 | Generated on 2023-01-16 13:37:52