- Montfort Mlachila, Ahmat Jidoud, Monique Newiak, Bozena Radzewicz-Bak, and Misa Takebe
- Published Date:
- September 2016
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.
|Financial development index|
|Sahay and others (2015b)|
|Financial institutions index||Sahay and others (2015b)|
|Financial markets index||Sahay and others (2015b)|
|Real GDP growth||IMF WEO database|
|Growth volatility||Authors’ calculation. Five-year rolling standard deviation of real GDP growth|
|Investment volatility||Authors’ calculation. Five-year rolling standard deviation of growth of investment-to-GDP ratio|
|GDP per capita||IMF WEO database|
|Education enrollment, primary||World Bank (2015), World Development Indicators|
|Share of government consumption in GDP||Penn World Tables, version 7|
|Agriculture value added (percent of GDP)||World Bank (2015), World Development Indicators|
|Investment-to-GDP ratio||IMF, World Economic Outlook database|
|Trade openness||Sahay and others (2015b)|
|Financial openness||Sahay and others (2015b)|
|Energy exports (percent of GDP)||World Bank (2015), World Development Indicators|
|Volatility of foreign growth weighted by export||Sahay and others (2015b)|
|Volatility of regional gross inflow/GDP ratios-winsorized (5 percentile, 95 percentile)||Sahay and others (2015b)|
|Volatility of term of trade changes||Sahay and others (2015b)|
|Real aid to GDP growth||IMF (2015b)|
|Institutional quality||Polity IV database, Marshall and others (2010)|
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.
|Financial development index||See Box 1.||Sahay and others (2015b)|
|Capital account openness||Sum of international assets and liabilities as a share of GDP, indicating the country’s de facto degree of capital account openness||IMF WEO database|
|Trade openness index||Sum of exports and imports of goods and services as a share of GDP||IMF WEO database|
|Real GDP per capita||…||IMF WEO database|
|Inflation||Annual inflation rate||IMF WEO database|
|ICRG country risk rating||International Country Risk Guide country risk rating, which covers political, financial, and economic risks||PRS Group|
|SSA (dummy)||A dummy variable with value 1 for sub-Saharan African countries, as defined by the IMF||IMF WEO database|
|Financial development index’s distance to the benchmark||Derived from the results in chapter 1||IMF|
|Institutional quality index||The 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 economies||Global Competitiveness Index Pillar 1 (Institution) by the World Economic Forum (WEF)|
This Annex provides an overview of the data sources used in the empirical analysis of the drivers of income inequality in Chapter 4.
|Income Inequality||The 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 Participation||The 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 gap||The 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 capita||The logged GDP per capita (in constant 2011 international dollars).||World Bank (2015), World Development Indicators|
|Financial development||This 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 economies||An 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 GDP||The value-added share of agriculture as a percentage of GDP||World Bank (2015), World Development Indicators|
|Government consumption expenditure||Government consumption expenditure as a percentage of GDP||World Bank (2015), World Development Indicators|
|GDP per capita squared||The squared value of the GDP per capita variable||World Bank (2015), World Development Indicators|
|Education gap||The 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 differential||From 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 index||The 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 rate||The 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 year||World Bank (2015), World Development Indicators|
|SSA (dummy)||A dummy variable with value 1 for sub-Saharan African countries, as defined by the IMF||IMF classification|
|SSA * Financial inclusion gap||An interaction term of the Financial inclusion gap with the SSA dummy variable||IMF classification|
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