Back Matter

Back Matter

Author(s):
Montfort Mlachila, Ahmat Jidoud, Monique Newiak, Bozena Radzewicz-Bak, and Misa Takebe
Published Date:
September 2016
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    Annex 1. Financial Development, Growth, and Economic Volatility

    We examine the impact of financial developments on growth, with first the composite measure and then its components, the measures of financial institutions and financial markets. Both financial institutions and markets are assessed based on their depth, access, and efficiency. Following Sahay and others (2015b), a dynamic system generalized method of moments (GMM) estimator is used with additional control variables including per capita GDP, education enrollment, and share of government consumption in GDP. The share of agriculture in GDP is added to better reflect its significance in sub-Saharan Africa.

    The basic estimation equation is:

    where y is real GDP growth, averaged over non-overlapping five-year periods for country i at time t. The key regressors are financial development indicator (FD or its component index), its squared term, and additional interaction terms. X includes the other likely determinants described above. The model is estimated for the 1980-2013 period and all sub-Saharan African countries, but data availability varies by variable.

    The quadratic form is chosen to allow for a possible non-linear impact (Arcand, Berkes, and Panizza 2012; Sahay and others 2015b). The interaction term is used to examine the impact in sub-Saharan Africa, our region of our interest. This approach also allows us to use more data from the global sample to mitigate data constraints in sub-Saharan African countries. However, given the weak coefficient on the square terms, and that most sub-Saharan African countries are at the relatively low level of financial development—and therefore below the threshold to exhibit a negative growth impact as discussed in the text—the model is run only on the level of the index in favor of parsimony.

    Similarly, we estimate the financial development’s impact on economic volatility, and then extend to investment volatility and to different components of financial development. Based on the literature and the specific context of sub-Saharan Africa, additional control variables include: five-year lags of GDP per capita, trade and financial openness, energy exports (percent of GDP), volatility of foreign growth, gross capital inflows in the region excluding country in question, terms-of-trade changes, polity index, growth in GDP per capita, government consumption, and aid to GDP volatility for 1995–2013.

    The large size of the coefficients of the financial development variables on investment volatility could be explained by the substantial volatility of investment across countries and over time. In addition, although the coefficient is positive for the global sample, the financial development coefficient for sub-Saharan African countries (FD + FD * SSA) is statistically significant and negative while the coefficient on the square term is statistically significant and positive. Similar results were obtained using a two-stage least square estimator.

    Table A.1.1Financial Sector Development, Growth, and Volatility: Key Variables and Sources
    VariableSource
    Financial development index
    Sahay and others (2015b)
    Financial institutions indexSahay and others (2015b)
    Financial markets indexSahay and others (2015b)
    Real GDP growthIMF WEO database
    Growth volatilityAuthors’ calculation. Five-year rolling standard deviation of real GDP growth
    Investment volatilityAuthors’ calculation. Five-year rolling standard deviation of growth of investment-to-GDP ratio
    GDP per capitaIMF WEO database
    Education enrollment, primaryWorld Bank (2015), World Development Indicators
    Share of government consumption in GDPPenn World Tables, version 7
    Agriculture value added (percent of GDP)World Bank (2015), World Development Indicators
    Investment-to-GDP ratioIMF, World Economic Outlook database
    Trade opennessSahay and others (2015b)
    Financial opennessSahay and others (2015b)
    Energy exports (percent of GDP)World Bank (2015), World Development Indicators
    Volatility of foreign growth weighted by exportSahay and others (2015b)
    Volatility of regional gross inflow/GDP ratios-winsorized (5 percentile, 95 percentile)Sahay and others (2015b)
    Volatility of term of trade changesSahay and others (2015b)
    Real aid to GDP growthIMF (2015b)
    Institutional qualityPolity IV database, Marshall and others (2010)
    Annex 2. Drivers and Impediments of Financial Development

    This annex examines the drivers of financial development using the generalized method of moments (GMM) to mitigate endogeneity problems. The model used panel regressions of data for 1980-2013 for about 90 developing countries (excluding oil exporters), although the number of observations varies depending on the variables.

    The estimation equation is:

    where FD is the financial development index for country i at time t. CA stands for de facto capital account openness, TO for de facto trade openness (ratio of the sum of imports and exports to GDP), GDP for real GDP per capita, I for inflation, CR for country risk rating, and SSA for a sub-Sahara Africa dummy variable, and ε is the error term.

    All the coefficients of the main variables show expected signs. Interaction terms with the sub-Saharan Africa dummy variable should be interpreted as the incremental impact for the regions’ countries (Table 3 in the main text). The overall impact for the region’s countries should be evaluated by the sum of the coefficients for all developing countries and the coefficients on interaction terms. For example, the overall impact of capital account openness of the region’s countries should become 0.014 (= 0.005 + 0.009). A robustness test was conducted including oil exporters, but the main results were not changed.

    The second section examines the impediments of financial development focusing on institutional quality. The purpose is to investigate why some countries’ financial development is less advanced than others’ compared to the benchmark. A simple ordinary least squares (OLS) regression was run with each country’s financial development distance to its benchmark. The distance to the benchmark is the average in the period of 2011 and 2013 and institutional quality is as of 2013. The results should be interpreted in such a way that institutional quality with a higher coefficient is more influential to the distance to the benchmark (Table 4 in the main text). Of the 21 institutional quality indices, 14 have an expected sign while the remaining 7 indices do not but these were statistically insignificant. Robustness tests were conducted with various institutional quality indicators such as the Country Policy and Institutional Assessment (CPIA), the World Bank’s Doing Business Indicators, and institutional indicators of Polity IV. The results are qualitatively similar.

    Table A.2.1Drivers and Impediments of Financial Development: Data Sources and Description
    VariableDescriptionSource
    Financial development indexSee Box 1.Sahay and others (2015b)
    Capital account opennessSum of international assets and liabilities as a share of GDP, indicating the country’s de facto degree of capital account opennessIMF WEO database
    Trade openness indexSum of exports and imports of goods and services as a share of GDPIMF WEO database
    Real GDP per capitaIMF WEO database
    InflationAnnual inflation rateIMF WEO database
    ICRG country risk ratingInternational Country Risk Guide country risk rating, which covers political, financial, and economic risksPRS Group
    SSA (dummy)A dummy variable with value 1 for sub-Saharan African countries, as defined by the IMFIMF WEO database
    Financial development index’s distance to the benchmarkDerived from the results in chapter 1IMF
    Institutional quality indexThe index consists of 21 categories and is constructed mainly based on the WEF’s Executive Opinion Survey, which captures the opinions of over 14,000 business leaders in 144 economiesGlobal Competitiveness Index Pillar 1 (Institution) by the World Economic Forum (WEF)
    Annex 3. Gender Gaps in Financial Inclusion and Income Inequality

    This Annex provides an overview of the data sources used in the empirical analysis of the drivers of income inequality in Chapter 4.

    Table A.3.1.Data Sources and Description
    VariableDescriptionSource
    Income InequalityThe traditional Gini measure of inequality. In this paper, we use “net” Gini but find similar results with “market” Gini. This is the dependent variable in the analysis on the gender determinants of income inequality. A value of 0 represents perfect equality.Solt (2014).
    Labor Force ParticipationThe ratio of labor force participation rate of females to males. A value of 1 represents perfect equality.World Bank (2015), World Development Indicators
    Financial inclusion gapThe result of a principal components analysis (PCA) estimate on five Findex time series variables if an individual: (1) has an account at a financial institution, (2) has a credit card, (3) has a debit card, (4) saved at a financial institution, and (5) borrowed from a financial institution. Each variable is disaggregated by gender. We first calculated ratios of female to male inclusion for each component before performing PCA on these ratios; the final variable is the fitted value using the principal component. A value of 1 represents perfect equality.World Bank (2014b), Global Findex 2014
    GDP per capitaThe logged GDP per capita (in constant 2011 international dollars).World Bank (2015), World Development Indicators
    Financial developmentThis is an index published by the IMF in a Staff Discussion Note (SDN) that aims to measure financial development. The index takes on values in the continuum between 0 and 1, where 1 represents maximum development. Please refer to the SDN for details on the methodology.Sahay and others (2015b)
    Financial development * advanced economiesAn interaction term of financial development with a dummy variable that takes on value 1 for advanced economies (as per IMF definition)Sahay and others (2015b)
    Agriculture share of GDPThe value-added share of agriculture as a percentage of GDPWorld Bank (2015), World Development Indicators
    Government consumption expenditureGovernment consumption expenditure as a percentage of GDPWorld Bank (2015), World Development Indicators
    GDP per capita squaredThe squared value of the GDP per capita variableWorld Bank (2015), World Development Indicators
    Education gapThe difference between the mean years of schooling for females and males across all educational attainment levels. A positive value represents more female schooling. The source data is the UN HDR 2013.Barro and Lee (2013), UNESCO Institute for Statistics (2013), and Human Development Report Office estimates based on data on educational attainment from UNESCO Institute for Statistics (2013) and on methodology from Barro and Lee (2013)
    Marriage age differentialFrom the UN World Marriage Database, we extract the Singulate Mean Age at Marriage (SMAM), which is the average length of single life expressed in years among those who marry before age 50. We take the difference of this between male and female SMAM, such that a positive value is how much (on average) older the male spouse is compared to the female.United Nations, Department of Economic and Social Affairs, Population Division, World Marriage Data 2012
    Equal rights to get a job (dummy)This is a dummy variable that takes on value 1 when the answer to the WBL question “Can a married woman get a job or pursue a trade or profession in the same way as a married man?” is “Yes.”World Bank (2013)
    Female legal rights indexThe sum of 10 binary indicators representing existence of selected (unmarried and married) women’s legal rights. Takes on value of 0 (no rights) to 10 (all selected rights). Rights include obtaining identification, signing contracts, inheritance, ownership of property, and favorability of the default marital regime.World Bank (2013), and IMF (2015b).
    Fertility rateThe number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified yearWorld Bank (2015), World Development Indicators
    SSA (dummy)A dummy variable with value 1 for sub-Saharan African countries, as defined by the IMFIMF classification
    SSA * Financial inclusion gapAn interaction term of the Financial inclusion gap with the SSA dummy variableIMF classification
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    Dependent variable: overall index of financial development and index of financial institutions (see Box 1). The explanatory variables include: log of real GDP per capita and its square term to account for non-linearities, population, population density, the age-dependency ratio to account for different savings behaviors across income groups, dummies for oil exporters and legal origin, and time dummies to capture the global macroeconomic environment. IMF (2015b) follows a similar approach with a different financial development index. Barajas and others (2013) used similarly structured regression to benchmark countries’ private sector credit-to-GDP ratios against a statistical benchmark, which has been applied to specific country groups in various cases (for example, Alter and Yontcheva 2015;Newiak and Awad 2015). Similar results were obtained when examining financial institutions and financial markets separately.

    Evidence from Arcand, Berkes, and Panizza (2012) shows an adverse impact of finance on growth above a private credit-to-GDP threshold of 100 percent, far above the actual level of sub-Saharan African countries.

    As identified in the literature on finance and growth (for example, Levine 2005 and Beck 2008), the empirical analysis faces significant endogeneity issues. Following the literature, our analysis uses the panel generalized method of moments (GMM) estimator that uses lagged variables as instruments to minimize the problem.

    See Annex 1 for details on data and the econometric specification.

    We report only the results for our most parsimonious model, as the results show that the parameter on the square term of financial development is not significant.

    To obtain the growth impact from increasing the baseline financial development level to a higher one—while holding other conditions equal—we calculate a new growth rate by using the new index and coefficients in Table 1, while the baseline growth rate is calculated using the existing index. Thus the difference between the two growth rates can be considered as a one-off impact owing to the improvement of the financial development index. The estimates for country groups are based on a median index for the corresponding group.

    Government debt has increased from 18 percent to 22 percent of banks’ total assets between 2011 and 2014 in the WAMEU region.

    See Annex 2 for details on data and econometric specification.

    The GMM estimation method was employed to mitigate the problems caused by variables’ endogeneity. Nevertheless, the lack of consensus on the theory of the factors driving financial development suggests some uncertainty in model specification, and thus the results from this exploratory analysis should be interpreted with due caution.

    While there are many other candidates for a proxy for macroeconomic stability, inflation is the most widely used measure of macroeconomic stability.

    The institutional quality index was obtained from the Global Competitiveness Index’s Pillar 1 (Institution) by the World Economic Forum. Using alternative indicators, such as the World Bank Country Policy and Institutional Assessment index of the World Bank’s Doing Business indicators, yields similar results.

    The same exercise was conducted with the World Bank’s Doing Business indicators, and the institutional indices of Polity IV. The results are similar but less robust than those from the Global Competitiveness Index.

    Refer to Kammer and others (2005) for a comprehensive description of Islamic finance products.

    Senegal and Côte d’Ivoire have recently issued Sukuks of US$164 million and US$246 million, respectively, to finance infrastructure projects, while South Africa issued US$500 million with the aim to become the Islamic finance hub for sub-Saharan Africa’s infrastructure financing.

    M-Shwari and M-Kesho are banking platforms that enable customers to save, earn interest and access small amounts of credit via mobile phone. M-Kopa—originally set up as a company that sold small solar panels to rural population for which it allowed customers to make daily micro-payments—overtime became a provider of mobile money services.

    This section is based on Deléchat, Newiak, and Yang (forthcoming).

    We construct an index of formal financial inclusion using data from the World Bank (2014b) Global Findex database, using a principal components analysis approach. The index covers the following dimensions, defined as ratio of female to male, as a share of the total population ages 15 and older: having a bank account at a formal financial institution, having a debit card, having a credit card, saving in a formal financial institution, and borrowing from a formal financial institution.

    This section is based on Kinda, Mlachila, and Ouedraogo (2016).

    Countries included in the sample are net exporters of a nonrenewable commodity, where that commodity represents at least 10 percent of the country’s total exports in 2005, the base year, and for which sufficient financial sector data are available. Sub-Saharan countries are Angola, Botswana, Burundi, Cameroon, Côte d’Ivoire, Ethiopia, Gabon, Ghana, Guinea, Equatorial Guinea, Mali, Mozambique, Namibia, Niger, Nigeria, South Africa, Sudan, Tanzania, Togo, Uganda, Zambia, and Zimbabwe.

    The mean comparison T-test shows that the differences are statistically significant for NPLs, provisions to NPLs, return on equity, and banking crises.

    The alternative measure of commodity price shocks follows Arezki and Brückner (2012) and Brückner and Ciccone (2010), and measures commodity price shocks by changes in prices.

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