This study examines the relationship between microfinance and income inequality at the macro level using cross-country and panel data. We show that a country with higher MFIs’ gross loan portfolio per capita tends to have lower income inequality, which confirms the beneficial role of microfinance in reducing income inequality at the macro level. Our results suggest that microfinance loans can lead to improve the relative income position of the poor in developing countries, albeit slowly. In the light of the foregoing outcomes, some important recommendations are suggested to the policymakers in order to reducing income inequality. Microfinance becomes an even more popular tool for fighting poverty; reducing income inequality; institutions innovate in their products and programs at a rapid pace
Research
Does Microfinance Program Innovation reduce income inequality? A cross-country analysis
https://doi.org/10.21203/rs.3.rs-267504/v1
This work is licensed under a CC BY 4.0 License
You are reading this latest preprint version
Microfinance
income inequality
loan portfolio
instrumental variables
macro-level analysis
Though some negative issues especially those about over indebtedness and high interest rates are discussed as well, microfinance remains as an effective and innovative measure for reducing poverty and income inequality.
The subject of poverty and inequality still one of the most problems that constitute a major anxiety in the topic of economic development, in particular by poor developing countries during the last decades. For that reason, understanding the key role played by microfinance program innovation in reducing poverty and inequality is a question of great concern for developing countries’ poor population where providing financial access, like microfinance, to the poorest seems to be a panacea for reducing poverty (Johnson & Rogaly, 1997; Armendariz & Morduch, 2005; Beck, 2007; Hossain & Knight, 2008; Bakhtiari, 2011; Gibbons & Meehan, 2002, Imai et al. 2012; Roodman & Morduch, 2014; Bel hadj Miled & Ben Rejeb, 2018) and income inequality (Beck et al., 2004; Kai & Hamori, 2009; Tchouassi, 2011; Hermes, 2014; Bangoura et al., 2016; Lacalle-Calderon et al., 2019). The microfinance industry carries every sign of an innovation in its take-off phase (Mersland & Strøm, 2012).
Indeed, the dynamics of microfinance program innovation can be recognized from social and economic perspectives. From the social perspective, microfinance can help the poor by reducing barriers to access credit, thereby increasing access of the poor to financial services, providing safety-net and consumption smoothening and increasing women’s self-employment opportunities and access to education (Kabeer, 2005; Fishman, 2012; Kumar, 2016).
From the economic perspective, microfinance institutions which are profit-seeking institutions, play a significant role in the fight against both poverty and inequality (Beck et al., 2004; Ahlin & Jiang, 2008) that was one of the United Nations Millennium Development Goals (MDGs) development agenda until the year 2015.
One important channel through which poverty can be reduced is by tackling income inequality, because the income distribution can affect the level of poverty through its impact on economic growth (Soubbotina and Sheram, 2000; Besley and Burgess, 2003; Bourguignon, 2004; Ravallion, 2005; Bhargava, 2006). Pro-poor growth can be achieved when the incomes of the poor grow at a higher rate than
the incomes of the non-poor (Jalilian and Kirkpatrick, 2005; Niels.H, 2014)
Several studies have been argued that the lack of access to finance by the poor is one of the most barriers that impede that state to eradicate its level of poverty or income inequality (Hulme & Mosley, 1996; Beck & Demirgüç-Kunt, 2008; McKenzie & Woodruff, 2008). So that, it can be argued that microfinance represents an extremely powerful tool to achieve pro-poor growth as they disproportionately benefit the poor.
Microloans is an alternative to traditional finance which gives to the poor the opportunity to increase their incomes since it can be used for self-employment and other income-generating activities (Ahlin and Jiang, 2008;
Roodman & Morduch, 2014; Banerjee & Jackson, 2017; Castells-Quintana et al., 2019)
The provision of microloans gives the poorest segments of the world population access to funds allowing them to maintain their standard of living and economic activities or create new ones (Ahlin & Jiang, 2008; Banerjee and al., 2015; ). Since, the modern microfinance model offers a wide range of potentially beneficial products (e.g., savings, insurance, money transfers) that can lead to enhanced access to health and education (Morduch & Haley, 2002). Besides, the provision of microloans to the poor gives them the opportunity to increase their incomes because it can be used for self-employment and other income-generating activities (Ahlin & Jiang, 2008). Because microfinance in developing countries is mainly focused on the poor, it can be argued that the provision of microfinance helps reducing the level of income inequality as it disproportionately raises the incomes of the poor as compared to the rich.
Although there are various success stories of microfinance, it has been the subject of many criticisms that have sparked an important debate among academics and practitioners about its influence and sustainability (Banerjee & Newman, 1993; Ghosh, 2013; Hermes, 2014; Ali & Ghoneim, 2019; Castells-Quintana et al., 2019). The nexus between microfinance and income inequality still remain controversial as the true relationship has not been identified. Therefore, the aim of this paper is to further explore the hypothesis that a country with higher microfinance institutions (MFIs) gross loan portfolio per capita tends to have lower income inequality.
This study contributes to the existing literature in the following ways. First, we consider microfinance as a financial system that directly affects income inequality. Second, we provide a more in-depth discussion of the outcomes regarding the linkage between microfinance and income inequality. Third, we use cross-country panel data covering 57 developing countries for 1132 microfinance institutions which has the advantage of incorporating individual dimension by a two-period 2006 and 2013 (income inequality data for the panel were constructed by taking averages for 2000–2006 and 2007–2013) and apply ordinary least square (OLS), pooled ordinary least square (POLS) and instrumental variable (IV) estimations in order to overcome potential endogeneity in the equation. We found strong evidence that microfinance has a significant negative impact on income inequality, revealing that countries with high level of microloans provision are generally associated with lower levels of income inequality.
The rest of the paper is organized as follows. Section 2 draws an overview of the literature on the relationship between microfinance intensity and income inequality. Section 3 describes the data, the variables and the econometric framework. Section 4 discusses the empirical results. Section 5 concludes and suggests some policy implications.
The microfinance industry carries every sign of an innovation in its take-off phase. We trace the innovations in microfinance for instance group lending, loans to women, and their financing. Thus, microfinance gives poor people and small businesses access to financial services (Mersland & Strøm, 2012. While the existing literature reveals that though research on the linkage between microfinance and poverty using different set of countries, data and estimation techniques are voluminous, less attention has been paid to inequality and there are a few recent works that have addressed the impact of microfinance program innovation on income inequality at the macro level. For example, Ahlin and Jiang (2008) examined the long-run effects of microcredit on development, measured by per capita income, inequality and poverty, by using an occupational choice model proposed by Banerjee and Newman (1993). The empirical results revealed that microcredit contributes to lower long-run inequality and poverty by making subsistence payoffs less widespread and increasing the income of the poor people. By employing the computable general equilibrium (CGE) model from 1999 to 2000, Mahjabeen (2008) found that microfinance in Bangladesh reduces inequality and improves social welfare. This study suggested that microfinance is one of the required critical interventions for empowering the poor people. Kai and Hamori (2009), and Tchouassi (2011) used a cross-country empirical study to investigate the effect of microfinance on income inequality in developing countries, including those in Africa. The authors measured the degree of microfinance intensity by both the number of MFIs and the number of active borrowers from MFIs. The empirical results revealed that income inequality is negatively and significantly influenced by microfinance intensity. Their studies suggested that microfinance intensity plays a key role in creating a financial system endowed with the equalizing effect. Hermes (2014) examined the impact of microfinance on income inequality in 70 developing countries over the period 2000–2008 by using Ordinary Least Squares (OLS) and instrumental variable (IV) estimations, and two different measures of microfinance intensity, i.e. the number of active borrowers divided by the country’s total population and the total value of microfinance loans to GDP ratio. The empirical evidence showed that the effects of microfinance on reducing income inequality are relatively small due to the small size of the microfinance sector in these countries. It is concluded that microfinance should, therefore, not be seen as a panacea for bringing down income inequality in a significant way. Using heterogeneous panel causality techniques, Bangoura et al. (2016) examined the relationship between microfinance and its effects on poverty and inequality for a panel of 52 developing countries over the period 1996–2011. The effect of microfinance is measured through two intensity indicators, i.e. the number of active borrowers from MFIs and the volume of loans. The empirical results showed that microfinance intensity has a significant negative effect on income inequality, suggesting that countries with high level of microfinance intensity, is generally associated with a decreased level of income inequality. Their findings further suggested that providing access to loans through microfinance offers to the poor the possible for income-generating activities. More recently, Lacalle-Calderon et al. (2019) found that microfinance has an egalitarian effect on income inequality in 85 countries over the period 2001–2012. The study further indicated that an increased in the macro-scale of microfinance activities in a country could be one effective tool for reducing country's inequality, among others. Arif et al. (2019) examined the effect of microfinance on poverty reduction and inequality for 33 provinces in Indonesia from 2011 to 2016 and found that higher level of microfinance significantly reduces poverty but it cannot be done to reduce income inequality. Using cross-country analysis data for 30 developing countries from 2013 to 2015, Ali and Ghoneim (2019) analyzed the effect of microfinance on income inequality by including two different measures of microfinance intensity, that is, the number of active borrowers and the value of microfinance loans. Their empirical results revealed that both measures of microfinance are still too weak to reduce income inequality. Castells-Quintana et al. (2019) examined the relationship between aid, microfinance and its effects on income inequality using a panel dataset covering 87 developing countries during the period 1995–2012. The empirical evidence revealed that microfinance seems not to be a panacea for reducing income inequality. It was further suggested that the effect of microfinance intensity on inequality is extensively depending on the country-specific context.
In the present study, we use cross-sectional data covering 596 microfinance institutions for 2013, supplemented by a two-period (span from 2000 to 2006 and 2007 to 2013)[1] panel data of 57 developing countries in 1132 microfinance institutions with high levels of informational transparency. The sample period is selected according to the microfinance data availability. Hence, we focused exclusively on those 3-5 diamonds levels which are the highest level of disclosure to its outreach, impact and financial data, audited financial statements and rating/evaluations. The sample countries include 12 countries in Europe and Central Asia (ECA), 10 in East Asia and Pacific (EAP), 13 in Latin America and Caribbean (LAC), 3 in South Asia (SA), 16 in Sub-Saharan Africa (SSA), and 3 in Middle East and North Africa (MENA).[2]
Data on the number of MFIs and gross loan portfolio divided by the country’s total population (as proxies to measuring microfinance) are taken from Microfinance Information Exchange (MIX) market database. Total gross loan portfolio of MFIs aggregated for each country is adjusted for write-offs and inflation. Following preceding studies (Li et al., 1998; Beck et al., 2004, 2007; Kim & Lin, 2011; Younsi & Bechtini, 2018; Younsi et al., 2019), we consider our dependent variable as Gini coefficient (income inequality). In line with earlier studies (Dollar & Kraay, 2002; Herzer & Vollmer, 2012; Stewart & Moslares, 2012; Stiglitz, 2012; Delbianco et al., 2014; Shahbaz et al., 2017), real GDP per capita (in constant international dollars) is considered. Furthermore, country-level control measures are included, namely, trade openness (Milanovic, 2002; Reuveny & Li, 2003; Zhu & Trefler, 2005; Silva, 2007; Dreher & Gaston, 2008; Kai & Hamori, 2009; Franco & Gerussi, 2013) and domestic credit to private sector (Beck & Levine, 2002; Beck et al., 2007; Kim & Lin, 2011; Jauch & Watzka, 2016). All data were obtained from the World Development Indicators (WDI) online database (http://data.worldbank.org/indicator). Moreover, Latin America and Caribbean Dummy are included as dummy variables for the Latin America and Caribbean region, which takes the value 1 if a country belongs to this region and 0 otherwise. This region is considered to comprise countries with lower levels of income inequality in the developing regions for the year 2013. Summary statistics for the dependent and explanatory variables are given in Table 1. For each of the variables, we computed the mean and median statistics by region.
3.2. Econometric model
We estimate a specification model similar to that of Milanovic (2002), Kai and Hamori (2009), Tchouassi (2011), and Hermes (2014), where variation in income inequality across countries is regressed on microfinance and a set of control variables:
where i = 1, 2, … , N is the country indicator. Eq. (1) represents the income inequality (INEQ) measured by Gini coefficient, while Eq. (2) represents gross loan portfolio per capita (GLF), as a proxy for microfinance intensity (after adjusting for inflation)Xi. represents a vector of control variables including trade openness (TRADE), measured by the sum of exports and imports divided by GDP, per capita GDP (in constant 2000 international dollars), domestic credit to private sector by banks to GDP ratio (DCP), and a set of regional dummy variables, u and Ω are the stochastic disturbance terms.
We, thus, empirically analyze how a change in MFIs’ GLF can affect INEQ. In order to address the problem of endogeneity, we employ the instrumental variable technique to determine each parameter. Eq. (2) is the reduced form to test the existence of endogenous variables. However, we use enforcing contracts at the country level (CE) and the weighted 5 year average lag of GLF, which is weighted by the number of MFIs for each country (lnw5lagGLF), while Y is the vector of other explanatory variables considered in Eq. (1).
We adopt ordinary least squares (OLS) and two-stage least squares (2SLS) or instrumental variables (IV) estimates to assesses the impact of MFIs’ GLF per capita on income inequality. 2SLS involves two stages: GLF per capita of MFIs is estimated by instrumental variables and other covariates in the first stage, while the second one estimates the INEQ by the predicted GLF per capita and other covariates, a technique for solving endogeneity problems associated with the bi-casual relationship between GLF per capita and INEQ levels in a country. This reverse causality from INEQ to GLF per capita may arise, for instance, if INEQ-oriented development partners and governments provide more funds to MFIs located in poorer countries (Imai et al., 2012). Given the difficulty in finding a valid instrument that satisfies “an exclusion restriction”, that is, correlates with GLF per capita but does not have a direct causal effect on INEQ, this exercise uses two types of instrument, cost of enforcing contract and a lag of 5-year average of GLF weighted by the number of MFIs for each country[3]. However, treatment with the STATA 12 allows a resolution using OLS and 2SLS estimators.
Figure 1 plots the variation of median GLF for the selected different regions (after adjusting for inflation). From this graph, is shown that the median GLF increases for all regions over the sample period, and as can be observed, the variation is remarkably for the two sub-periods: 2007-2008 and 2011-2012. An interpretation of the trend over these sub-periods needs to take into consideration the potential impact of the global financial crisis on the microfinance industry. In addition to that, Eastern Europe and central Asia, and Middle East and Nord African regions have experienced a sharper increase in the GLF than other regions. Until 2007, the largest MFIs were located in Latin America and the Caribbean. Nevertheless, in 2008, MFIs in Middle East and North Africa experienced a sharp increase in their GLF per capita.
Tables 2 and 3 present the results on the impact of microfinance intensity (GLF) on income inequality (INEQ). Table 2 reports the result based on cross-sectional data with OLS and IV estimations, while Table 3 reports the result based panel data using pooled OLS, EF and RE regressions.
Referring to Tables 2 and 3, we conclude that GLF per capita negatively and significantly affects income inequality in all regressions at the 1%, 5% and 10% levels respectively, except for FE regression. The results confirm that microfinance intensity plays a decisive role in bringing down income inequality in a significant way. So, providing access to loans through microfinance offers to the poor the potential for income-generating activities. Moreover, the results indicate that countries with high level of microfinance intensity, is generally associated with a lower level of income inequality, which involve the potential of microfinance on reducing income inequality at macro-level and, thus, reinforce the case for channeling funds from development finance institutions and governments of developing countries into microfinance institutions. The results corroborate the studies of Mahjabeen (2008), Kai and Hamori (2009), Tchouassi (2011), Bangoura et al. (2016), and Lacalle-Calderon et al. (2019). Yet, our empirical results are inconsistent with the findings of Hermes (2014), Arif et al. (2019), Ali and Ghoneim (2019), and Castells-Quintana et al. (2019) suggesting providing financial access to the poor cannot reduce the level of income inequality.
With regard to the control variables, the impact of some variables is sensitive to the data and estimation techniques, whether cross-sectional data or panel data regressions. For example, in Table 2, GDP per capita has a negative and a statistically significant impact on income inequality at the 1% level. On the other hand, in Table 3, GDP per capita positively and significantly affects income inequality except for regression (2). Moreover, domestic credit (% of GDP) has a negative but insignificant impact on income inequality, except for regressions (2) and (4). On the other hand, in Table 3, it has a negative and a statistically significant effect on income inequality at the 5% level only in regression (1). The relatively small impact of financial development on income inequality is probably due to the inequality of financial development between countries and regions. The financial sector in low-income countries is not yet sufficiently developed and illiquid, limiting access to long-term financing, and therefore reduces the ability of different countries of the sub-group to be financed by local debt. The results also show that trade openness aggravates income inequality, as shown in Table 2 it has a positive and a statistically significant impact on income inequality at the 1% level only in regressions (1) and (2). While in Table 3, it shows that trade openness negatively and significantly impairs income inequality at the 10% level only in regression (3). The fairly small impact of trade openness on income inequality can be explained by the increase in manufacturing exports which reinforced the amplitude of inequalities, consistent with the results of Hamori and Hashiguchi (2012) suggesting that trade openness has not a beneficial effect on reducing income inequality.
As for the impact of regional dummy variables on the incidence of income inequality (see Table 2), we show that MFIs’ GLF per capita and GDP per capita remain statistically significant after inclusion of regional dummy variables. However, the inclusion of the regional dummy variables reveals that Middle East and North Africa (MENA) with South Asia (SA) as the reference case, have a negative and significant coefficient (at the 1% level as in the case of OLS and IV estimation). In addition, Latin America and Caribbean (LAC) and Sub-Saharan Africa (SSA) dummies are positive in the OLS estimation. This implies that LAC and SSA have higher income inequality levels relative to SA.
As discussed earlier, the endogeneity may be due to a bi-causal relationship between income inequality and gross loan portfolio per capita. In terms of a bi-causal relationship between GLF per capita and income inequality, we allude to the fact that investors who are inclined to income inequality reduction might direct their financial resources to countries and regions where income inequality is high. In our study, we check the validity of our instruments by using two robust tests for identification such as: weak-identification and under-identification tests (Kleibergen & Paap, 2006) as shown in Table 2 (columns 3 and 4). This does not compromise the Sagan’s over-identification test as we fail to reject the null hypothesis that the instruments are valid, that is, uncorrelated with the error term.
Table 4 shows the first stage IV regression results, which offers a justification for the validity of our instruments. We use two kinds of instrument, that is, the cost of enforcing contracts and weighted 5-year lag of average GLF. The selection of the first instrument is based on the ground that the decision of microfinance commercial investors, especially international funders, on whether to invest in a particular country is likely to depend on the extent to which the country has a good institution (e.g., represented by a low cost of enforcing contracts) that would facilitate economic activities (Imai et al., 2012). In this context, the cost of enforcing contracts is supposed to have a significant and negative correlation with MFIs’ GLF per capita (see Table 4).
In summary, our main findings show that MFIs improve income inequality. This hypothesis is further corroborated by the pooled OLS and RE regressions in Table 3. The empirical findings confirm that microfinance intensity contributes directly and positively to not only narrowing the gap between the poor and the rich, but also reducing income inequality at the macro level. On the other hand, an increase in the degree of GLF per capita significantly reduces, and therefore improves, income distribution in developing countries. This result is somewhat logical since, according to the literature on the subject, informal financial sector, especially small-scale non-collateral loans, affects standards of living for low-income households. However, we believe that the differences recorded in terms of the impact of microfinance on income inequality between regions may be mainly due to two reasons. First, we think that it can be explained by the differences in the degree of financial development and economic growth, which are influenced by other factors, such as the legislative tradition of the country, the nature of political systems, available in an economy and other factors specific to each country. Second this region has difficulty in controlling social and ethnic unrest, which eventually turns into violence and conflict (Imai et al., 2012; Bel hadj Miled & Ben Rejeb, 2018).
Identifying the vital role played by MFIs in reducing both poverty and inequality is a matter of great concern for the poor countries – where combating poverty/inequality is the 2030 development agenda of the United Nations Millennium Development Goals (MDGs). Many researchers (e.g., Li et al., 1998; Clarke, Xu, & Zou, H.-f., 2006; Hossain & Knight, 2008; Gimet & Lagoarde-Segot, 2011; Fishman, 2012; Banerjee et al., 2015), have argued that because formal financial systems in developing countries are still incomplete and deficient, the majority of people do not have access to the basic financial services. Moreover, many people worry that financial development benefits only the rich. Since, financial markets are fraught with adverse selection and moral hazard problems, borrowers need collateral. The poor, who do not have this, might, therefore, find it difficult to get loans even when financial markets are well developed, it might worsen inequality. In these circumstances, microfinance may play an important role to benefit poor people and making more money available to low-income households.
Therefore, in this paper, we attempt to examine the effectiveness of microfinance participation on income inequality by using cross-sectional data covering 596 MFIs for 2013, supplemented by a two-period (2000-2006 and 2007-2013) panel data of 57 developing countries in 1132 MFIs. The empirical evidence is based on OLS and IV estimations. The results confirm the hypothesis that higher microfinance participation is associated with lower income inequality. In fact, a country with a higher MFIs’ GLF per capita tends to have lower income inequality, after controlling for the effects of other factors influencing. The results would be useful for development agencies, governments, and other practitioners in developing countries. Indeed, microfinance can play a potentially crucial role for reducing that country's inequality, and opening an opportunity for low-income borrowers to play a significant role in economic development and thus to increase their income and well-being.
These findings lend support to the suggestion that microfinance is an appropriate tool to reducing income inequality gap between the poor and the rich in developing countries. It thus seems to have the potential to help poor people directly, as it enables them to engage in self-employment and play an active role in the economy. Therefore, policy intervention should do more direct contact with microfinance and the banks for facilitate institutions providing financial services to the poor.
ENDNOTES
1 Gini coefficient data for the panel were constructed by taking averages for 2000 to 2006 and 2007 to 2013.
2 ECA region includes: Albania Azerbaijan, Bosnia and Herzegovina, Georgia, Kazakhstan, Moldova, Tajikistan, Kyrgyz Republic, Romania, Russian Federation, and Serbia. EAP region includes: Argentina, Bolivia, Cambodia, Chile, China, Dominican Republic, India, Indonesia, Philippine, and Vietnam. LAC region includes: Brazil, Colombia, Costa Rica, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, and Peru. SA region includes: Bangladesh, Nepal, and Pakistan. SSA region includes: Benin, Burkina Faso, Cameroon, Congo Dem. Rep, Ethiopia, Madagascar, Malawi, Mali, Mozambique, Nigeria, Rwanda, Senegal, Sierra Leone, Tanzania, Uganda, and Zambia. MENA region include: Egypt, Jordon, and Morocco.
3 This index passes the statistical validity of a valid instrument as it shows a high correlation with GLF per capita and a low correlation with the Gini index
DATA AVAILABILITY
The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.
Afrane, S. (2002). Impact assessment of microfinance interventions in Ghana and South Africa: A synthesis of major impacts and lessons. Journal of Microfinance, 4(1), 37-58
Ahlin, C., & Jiang, N., (2008). Can micro-credit bring development? Journal of Development Economics, 86, 1-12.
Ali, I. A. M., & Ghoneim, H. (2019). The effect of microfinance on income inequality: Perspective of developing countries. Journal of Economics and Management, 35(1), 41-62.
Arif, M., Ismail, M., & Santoso, D. B. (2019). Does Microfinance Affect Poverty Reduction and Inequality in Indonesia? International Journal of Scientific and Technology Research, 8(4), 122-125.
Armendariz de Aghion, B., & Morduch, J. (2005). The Economics of Microfinance. Massachusetts London, England: The Mit Press Cambridge, pp. 352.
Bakhtiari, S. (2011). Microfinance and Poverty Reduction: Some International Evidence. International Business and Economics Research Journal, 5(12), 65-71.
Banerjee, A. B., & Jackson, L. (2017). Microfinance and the business of poverty reduction: Critical perspectives from rural Bangladesh. Human Relations, 70(1), 63-91.
Banerjee, A. B., Duflo, E., Glennerster, R., & Kinnan, C. G. (2015). The miracle of microfinance? Evidence from a randomized evaluation. American Economic Journal: Applied Economics, 7(1), 22-53.
Banerjee, A.V., & Newman, A.F. (1993). Occupational choice and the process of development. Journal of Political Economy, 101(2), 274-298 .
Bangoura, L., Mbow, M. K., Lessoua, A., & Diaw, D. (2016). Impact of Microfinance on Poverty and Inequality A Heterogeneous Panel Causality Analysis. Revue d'Economie Politique, Dalloz, 126(5), 789-818.
Barnes, C. (1996). Assets and the impact of micro-enterprise finance programs. AIMS paper. Washington DC: Management Systems International.
Beck, T. & Demirgüç-Kunt, A. (2008). Access to finance: an unfinished agenda. World Bank Economic Review, 22, 383-96.
Beck, T., & Levine, R. (2002). Industry growth and capital allocation: Does having a market- or bank-based system matter? Journal of Financial Economics, 64, 147-180.
Beck, T., Demirguc-Kunt, A., & Levine, R. (2004). Finance, inequality and poverty: Cross-country evidence. World Bank Policy Research Working Paper 3338. Washington DC: World Bank.
Beck, T., Demirguç-Kunt, A., & Levine, R. (2007). Finance, inequality and the poor. Journal of Economic Growth, 12(1), 27-49.
Bel hadj Miled, K.., & Ben Rejeb, J. (2018). Can Microfinance Help to Reduce Poverty? A Review of Evidence for Developing Countries. Journal of the Knowledge Economy, 9, 613-35.
Berrebi, Z. M. & Silber, J. (1987). Regional differences and the components of growth and inequality change. Economics Letters, 25, 295-98.
Besley, T. and Burgess, R. (2003) Halving global poverty, Journal of Economic Perspectives, 17, 3–22
Bhargava, V. (2006) Global Issues for Global Citizens: An Introductory to Key Development Challenges, World
Bank, Washington, DC.
Bourguignon, F. (2004) The poverty-growth-inequality triangle, New Delhi Working Papers No 125. Available at http://www.icrier.org/pdf/wp125.pdf (accessed 11 November 2013).
Canavire-Bacarreza, G., & Rioja, F., (2009). Financial development and the distribution of income in Latin America and the Caribbean. Well-Being and Social Policy, 5(1), 1-18.
Castells-Quintana, D., Larrú, J. M., & Lacalle-Calderón, M. (2019). Aid, Microfinance and Income Inequality: A Global View. Revista de Economía Mundial, 52, 143-174.
Clarke, G. R., Xu, L. C., & Zou, H-F. (2006). Finance and Income Inequality: What Do the Data Tell Us? Southern Economic Journal, 72(3), 578-596.
Delbianco, F., Dabús, C., & Caraballo, M. A. (2014). Income inequality and economic growth: New evidence from Latin America. Cuadernos de Economia, 33(63), 381-398.
Dollar, D., & Kraay, A. (2002). Growth Is Good for the Poor. Journal of Economic Growth, 7(3), 195-225.
Dreher, A., & Gaston, N. (2008). Has globalization increased inequality? Review of International Economics, 16(3), 516-536.
Fishman, J. (2012). Microfinance – is there a solution? A survey on the use of MFIs to alleviate poverty in India. Denver Journal of International Law & Policy, 40(4), 588-619.
Franco, C. & Gerussi, E. (2013). Trade, foreign direct investments (FDI) and income inequality: empirical evidence from transition countries. The Journal of International Trade & Economic Development, 22(8), 1131-1160.
Gaston, N., & Nelson, D. (2002). Integration, foreign direct investment and labour markets: Microeconomic perspectives. The Manchester School, 70(3), pp.420-459.
Ghosh, J. (2013). Microfinance and the challenge of financial inclusion for development. Cambridge Journal of Economics, 37, 1203-19.
Gimet, C., & Lagoarde-Segot, T. (2011). A closer look at financial development and income distribution. Journal of Banking & Finance, 35, 1698-1713.
Hamori, S., & Hashiguchi, Y. (2012). The effect of financial deepening on inequality: some international evidence. Journal of Asian Economics, 23(4), 353-359.
Hermes, N. (2014). Does microfinance affect income inequality? Applied Economics, 46(9), 1021-1034.
Herzer, D., & Vollm er, S. (2012). Inequality and growth: Evidence from panel cointegration. Journal of Economic Inequality, 10(4), 489-503.
Hossain, F., & Knight, T. (2008). Financing the Poor: Can microcredit make a difference? Empirical observations from Bangladesh. Brooks World Poverty Institute (BWPI) Working Paper No. 38.
Imai, K. S., Gaiha, R., Thapa, G., & Annim, S. K. (2012). Micro-finance and Poverty: A Macro Perspective. World Development, 40(8); 1675-1689.
Jalilian, H. and Kirkpatrick, C. (2005) Does financial development contribute to poverty reduction?, Journal of Development Studies, 41, 636–56.
Jauch, S., & Watzka, S. (2016). Financial development and income inequality: A panel data approach. Empirical Economics, 51, 291-314.
Kai, H., & Hamori, S. (2009). Microfinance and Inequality. Research in Applied Economics, 1(1), 1–12.
Kim, D.-H., & Lin, S.-C. (2011). Nonlinearity in the financial development–income inequality nexus. Journal of Comparative Economics, 39, 310-325.
Kleibergen, F., & Paap, R. (2006). Generalized reduced rank tests using the singular rvalue decomposition. Journal of Econometrics, 127, 97-126.
Kumar, A. (2016). Does microfinance redefine identity, income and insecurity among rural women? A model of women’s empowerment. Enterprise Development and Microfinance, 27(3), 192-203.
Lacalle-Calderon, M., Larrú, J. M., Garrido, S. R., & Perez-Trujillo, M. (2019). Microfinance and income inequality: New macro-level evidence. Review of Development Economics, 23(2), 860-876.
Li, H., Squire, L., & Zou, H-F. (1998). Explaining International and Intertemporal Variations in Income Inequality. The Economic Journal, 108 (446), 26-43.
Mahjabeen, R. (2008). Microfinancing in Bangladesh: Impact on households, consumption and welfare. Journal of Policy Modeling, 30(6), 1083-1092.
McKenzie, D. & Woodruff, C. (2008). Experimental evidence on returns to capital and access to finance in Mexico. World Bank Economic Review, 22, 457–82.
Mersland.R. & Øystein Strøm.R, (2012), “The Past and Future of Innovations in Microfinance”, Oxford
Handbooks Online, DOI: 10.1093/oxfordhb/9780195391244.013.0028
Milanovic, B. (2002). Can we discern the effect of globalization on income distribution? Evidence from household budget survey. World Bank Policy Research, Working Paper 2876.
Morduch, J., & Haley, B. (2002). Analysis of the Effects of Microfinance on Poverty Reduction, NYU Wagner Working Paper No. 1014, June.
Odell, K. (2010). Measuring the impact of microfinance: Taking another look. Washington, DC: Grameen Foundation.
Ravallion,M. (2005) Inequality is bad for the poor, Policy research working paper 3677, World Bank, Washington, DC.
Reuveny, R. & Li, Q. (2003). Economic openness, democracy, and income inequality. Comparative Political Studies, 36(5), 575-601.
Roodman, D., & Jonathan, M. (2014). The Impact of Microcredit on the Poor in Bangladesh: Revisiting the Evidence. Journal of Development Studies, 50(4), 583-604.
Shahbaz, M., Bhattacharya, M., & Mahalik, M. K. (2017). Finance and income inequality in Kazakhstan: Evidence since transition with policy suggestions. Applied Economics, 49(52), 5337-5351.
Silva, J. A. (2007). Trade and income inequality in a less developed country: The case of Mozambique. Economic Geography, 83(2), 111-136.
Soubbotina, T. and Sheram, K. (2000) Beyond Economic Growth: Meeting the Challenges of Global Development,World Bank, Washington, DC.
Stewart, R., & Moslares, C. (2012). Income inequality and economic growth: The case of Indian states 1980-2010. Cuadernos de Economia, 31(57), 41-64.
Stiglitz, J. E. (2012). The price of inequality: How today’s divided society endangers our future (p. 2012). New York W.W. Norton & Company.
Tchouassi, G. (2011). Microfinance, inequality and vulnerability: Empirical analysis from Central African countries. Journal of Development and Agricultural Economics, 3(4), 150-156.
Wade, R. H. (2004). Is globalization reducing poverty and inequality? World Development, 32(4), 567-589.
Wood, A. (2002). Globalization and wage inequalities: A synthesis of three theories”. Weltwirtschaftliches Archiv, 138(1), 54-82.
Younsi, M., & Bechtini, M. (2018). Economic Growth, Financial Development, and Income Inequality in BRICS Countries: Does Kuznets’ Inverted U-Shaped Curve Exist? Journal of the Knowledge Economy, pp. 1-22. https://doi.org/10.1007/s13132-018-0569-2.
Younsi, M., Khemili, H., & Bechtini, M. (2019). Does foreign aid help alleviate income inequality? New evidence from African countries. International Journal of Social Economics, 46(4), 549-561.
Zhu, S. C., & Trefler, D. (2005). Trade and inequality in developing countries: A general equilibrium analysis. Journal of International Economics, 65(1), 21-48.
World Bank. (2013). World Development Indicators database, World Bank, Washington DC. Available at: https: //data.worldbank.org/data-catalog/world-development-indicators.
TABLE 1 Summary statistics
|
|
Gini index |
GLF per cap. |
GDP per cap. |
DCP |
TRADE |
No. of MFIs |
|||||||
|
2006 |
2013 |
2006 |
2013 |
2006 |
2013 |
2006 |
2013 |
2006 |
2013 |
2006 |
2013 |
||
|
MENA |
Mean |
37 |
35.7 |
2.8 |
13.7 |
1841 |
2270 |
61.7 |
59 |
93.4 |
83 |
9 |
3 |
|
Median |
39 |
35.7 |
2.2 |
18 |
1948 |
2432 |
51 |
71 |
70 |
84 |
10 |
8 |
|
|
SA |
Mean |
35 |
32 |
2 |
6 |
478 |
569 |
30 |
39 |
39.6 |
43 |
37 |
33 |
|
Median |
33 |
32 |
0.9 |
4 |
421 |
568 |
28.6 |
48 |
39.6 |
41 |
28 |
34 |
|
|
SSA |
Mean |
54 |
43 |
2.4 |
4.7 |
449.5 |
500 |
12.7 |
16 |
59 |
62 |
9 |
11 |
|
Median |
55 |
43 |
1 |
2.5 |
375 |
405 |
11 |
17 |
61.4 |
62 |
8 |
9 |
|
|
EAP |
Mean |
38.7 |
38 |
3.5 |
19 |
1019 |
1479 |
46 |
61 |
88.7 |
84 |
36 |
44.7 |
|
Median |
39.5 |
37.7 |
2.7 |
5.5 |
970.5 |
1258 |
34 |
41 |
75 |
61 |
23.5 |
32.5 |
|
|
LAC |
Mean |
54 |
51.8 |
12 |
57 |
3619 |
4099 |
31 |
38 |
68.5 |
75 |
14.75 |
24 |
|
Median |
55 |
51.6 |
7.8 |
43 |
3203 |
3930 |
23 |
36 |
59 |
69 |
13 |
21 |
|
|
ECA |
Mean |
35 |
34 |
15 |
72 |
2549 |
3280 |
22 |
37 |
92 |
95 |
13.5 |
12.6 |
|
Median |
35 |
34 |
13.7 |
84 |
2810 |
343 |
21.7 |
38 |
96 |
88 |
8.5 |
11 |
|
Note. MENA: Middle East and North Africa, SA: South Asia, SSA: Sub-Saharan Africa; EAP: East Asia and Pacific, LAC: Latin America and Caribbean, ECA: Europe and Central Asia.
Source: Authors’ compilations from WDI and MIX databases.
TABLE 2 Results based on cross-sectional regressions
|
Variables |
OLS (without region) (1) |
OLS (with region) (2) |
VI (without region) (3) |
VI (with region) (4) |
||
|
GLF |
-5.00*** (-4.19) |
-2.5** (-2.32) |
-3.61** (-2.39) |
-0.83* (-1.62) |
||
|
GDP |
-13.72*** (-6.81) |
-7.31*** (-3.04) |
-14.65*** (-7.08) |
-6.99*** (-3.25) |
||
|
DCP |
-0.02 (-0.33) |
0.03 (0.66) |
-0.03 (-0.47) |
0.014 (0.36) |
||
|
TRADE |
0.71*** (2.80) |
0.31*** (1.70) |
-0.03 (-0.47) |
0.014 (0.36) |
||
|
MENA |
- |
-13.40*** (-3.81) |
- |
-10.80*** (-4.9) |
||
|
LAC |
- |
6.62 (0.91) |
- |
8.2 (1.30) |
||
|
SSA |
- |
8.30*** (3.24) |
- |
9.60*** (4.34) |
||
|
EAP |
- |
0.38 (0.44) |
- |
0.80 (1.35) |
||
|
ECA |
- |
-0.98*** (-3.14) |
- |
-1.14*** (-4.56) |
||
|
Constant |
135.60*** (10.41) |
74.68*** (3.93) |
138.80*** (10.74) |
67.13** (4.11) |
||
|
R-sq. |
0.70 |
0.80 |
0.83 |
0.89 |
||
|
Under id (p-value) |
- |
- |
33.5 (0.000) |
12.1 (0.0023) |
||
|
Weak id (p-value) |
- |
- |
4.3 (0.0178) |
23.4 (0.000) |
||
|
Over id (p-value) |
- |
- |
4.8 (0.028) |
3.208 (0.070) |
||
|
Hausman test (Prob>chi2) |
|
|
2.18 (0.700) |
3.87 (0.900) |
||
|
Observations |
57 |
57 |
57 |
57 |
||
Note. GLF and GDP variables are in logarithm. OLS denotes Ordinary Least Squares. Figures in brackets show t-statistic. Regional dummies with South Asia being the reference region: MENA: Middle East and North Africa, SA: South Asia, SSA: Sub-Saharan Africa; EAP: East Asia and Pacific, LAC: Latin America and Caribbean, ECA: Europe and Central Asia.
*, ** and *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
TABLE 3 Results based on panel data regressions
|
Variables |
POLS (1) |
FE (2) |
RE (3) |
|
GLF |
-0.00028** (-50.06) |
-0.0100 (-1.18) |
-0.0100* (-1.72) |
|
GDP |
0.037** (12.59) |
0.0300 (0.48) |
0.040* (1.81) |
|
DCP |
-0.001** (-5.36) |
-0.0005 (-0.47) |
-0.00079 (-0.93) |
|
TRADE |
0.0006 (3.51) |
-0.00007 (-0.16) |
-0.06* (-1.8) |
|
2013 year dummy |
-0.006 (-2.40) |
0.00005 (0.01) |
0.0006 (0.07) |
|
MENA |
-0.33** (-43.10) |
- |
- |
|
LAC |
-0.35*** (-31.29) |
- |
- |
|
SSA |
-0.16** (-12.85) |
- |
- |
|
EAP |
-0.16*** (-138.9) |
- |
- |
|
ECA |
-0.44 (0.30) |
- |
- |
|
Constant |
3.65*** (81.96) |
3.49*** (6.15) |
3.44*** (19.07) |
|
R-sq. |
0.62 |
- |
- |
|
Hausman test (Prob>chi2) |
- |
- |
0.43 (0.91) |
|
R-sq. within |
- |
- |
0.06 |
|
R-sq. between |
- |
- |
0.04 |
|
R-sq. overall |
- |
- |
0.05 |
|
Prob>chi2 |
- |
- |
0.21 |
|
Observations |
114 |
114 |
114 |
Note. GLF and GDP variables are in logarithm. 2013 year dummy (2013= 1, other = 0). POLS denote pool Ordinary Least Squares. FE denotes fixed-effects regression. RE denotes random-effects regression. Figures in brackets show t-statistic. Regional dummies with South Asia being the reference region: MENA: Middle East and North Africa, SA: South Asia, SSA: Sub-Saharan Africa; EAP: East Asia and Pacific, LAC: Latin America and Caribbean, ECA: Europe and Central Asia.
*, ** and *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
TABLE 4 First stage IV regression results
|
Variables |
Coefficients |
|
w5lag_GLF
|
0.20*** (3.41) |
|
CE
|
-0.04*** (-3.22) |
|
GDP |
-0.19 (-0.64) |
|
DCP |
0.007 (-0.97) |
|
TRADE |
0.016** (2.54) |
|
Constant |
3.54 (1.37) |
|
Observations |
57 |
Note. Dependent variable: lag of GLF per capita. w5lagGLF is the weighted 5 year average lag of gross loan portfolio. CE is the enforcing contracts at the country level. w5lag_GLF and GDP variables are in logarithm. Figures in brackets show t-statistic.
** and *** indicate significance at the 5% and 1% levels, respectively.