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References

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Appendix 1. Country Coverage

This section specifies the countries used in the empirical analyses in the paper. Owing to data constraints, not all the countries are included in all the regressions.

High income countries, as defined by the World Bank:

United States, United Kingdom, Austria, Belgium, Denmark, France, Germany, Italy, Luxembourg, Netherlands, Norway, Sweden, Switzerland, Canada, Japan, Finland, Greece, Iceland, Ireland, Malta, Portugal, Spain, Australia, New Zealand, Chile, Uruguay, Barbados, Trinidad and Tobago, Cyprus, Israel, Hong Kong SAR, China, Korea Rep., Singapore, Russian Federation, Czech Republic, Slovak Republic, Estonia, Latvia, Lithuania, Croatia, Slovenia, and Poland.

Middle income countries are those defined by the World Bank as “Upper Middle Income countries”:

Turkey, South Africa, Argentina, Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Panama, Peru, Venezuela RB, Belize, Jamaica, St. Lucia, Suriname, Iran Islamic Rep., Iraq, Jordan, Malaysia, Maldives, Thailand, Algeria, Angola, Botswana, Gabon, Mauritius, Seychelles, Namibia, Tunisia, Fiji, Marshall Islands, Azerbaijan, Belarus, Albania, Kazakhstan, Bulgaria, China, Turkmenistan, Serbia, Montenegro, Hungary, Macedonia FYR, Bosnia and Herzegovina, and Romania.

Low income countries are those defined by the World Bank as “Lower Middle Income” or “Low Income” countries:

Bolivia, El Salvador, Guatemala, Haiti, Honduras, Nicaragua, Paraguay, Guyana, Syrian Arab Republic, Egypt Arab Rep., Yemen Rep., Afghanistan, Bangladesh, Bhutan, Cambodia, Sri Lanka, India, Indonesia, Lao PDR, Nepal, Pakistan, Philippines, Vietnam, Djibouti, Burundi, Cameroon, Cabo Verde, Central African Republic, Chad, Comoros, Congo Rep., Congo Dem. Rep., Benin, Ethiopia, The Gambia, Ghana, Guinea-Bissau, Guinea, Côte d'Ivoire, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Morocco, Mozambique, Niger, Nigeria, Zimbabwe, Rwanda, São Tomé and Príncipe, Senegal, Sierra Leone, Sudan, Swaziland, Tanzania, Togo, Uganda, Burkina Faso, Zambia, Papua New Guinea, Micronesia Fed. Sts., Armenia, Georgia, Kyrgyz Republic, Moldova, Tajikistan, Ukraine, Uzbekistan, and Mongolia.

1

The authors thank Céline Allard, Christine Dieterich, Clara Mira, David Robinson, Abebe Aemro Selassie, Alun Thomas and other IMF colleagues for helpful comments, Cleary Haines and Azanaw Mengistu for excellent research assistance, and Natasha Minges and Charlotte Vazquez for excellent editorial support. The research underlying this paper was initiated in the context of the preparation of the October 2015 sub-Saharan Africa Regional Economic Outlook chapter on “Inequality and Economic Outcomes in Sub-Saharan Africa.” All remaining errors are our own.

2

The findings on the regional trends in inequality are broadly robust to the use of inequality estimates based on household survey data. Income inequality displayed for Latin America and the Caribbean is based on income measures which tend to exhibit higher levels of inequality than measures based on consumption. Sala-i-Martin (2002) shows that poverty and inequality increased in sub-Saharan Africa between 1970 and 1998.

3

It should be noted that the gap between male and female labor force participation rates is on average 15 percentage points lower in sub-Saharan Africa than in the rest of the world. This mainly reflects the generally low female labor force participation gaps in low-income and fragile economies where women have to work for subsistence, often in the low-productivity agricultural sector. At higher income levels, the gap increases as women may face the tradeoff between homemaking and joining the labor force. The poor ranking of low-income sub-Saharan African countries in terms of the GII in Figure 5 despite relatively low gender differences in labor force participation rates suggest that other aspects of gender inequality in education, health, and empowerment play a substantial role in these countries.

4

This paper uses the World Bank’s classification of countries. However, the group of low-income countries includes lower-middle income countries, given their many similarities.

5

The Xtabond2 package for STATA (Roodman 2009) is used to estimate the system-GMM regressions.

6

The analysis uses interaction terms to capture non-linearity in the inequality-growth relationship. However, the estimated effects of the income and gender inequality variables are broadly robust to limiting the sample only to developing countries and to reducing the number of control variables. The finding of significant effects of income and gender inequality after controlling for variables that may be interrelated with the inequality variables is consistent with Berg and Ostry (2011) and Ostry and others (2014).

7

Many studies rightly note the significance of the value added to the economy by women from family-related activities, which are not measured in GDP.

8

The next section of the paper finds per capita GDP growth to have been associated with contemporaneous increases in income inequality for sub-Saharan African countries. The findings in this section are based on regressions techniques that address possible endogeneity and account for a common set of control variables that could be associated with higher growth and increases in income inequality.

9

The finding that the removal of gender-related restrictions affects growth positively in the oil exporting countries may reflect correlation rather than causation given that oil exporting countries can, if conditions are right, grow without much labor effort as oil and minerals are capital intensive. This would be the case if gender equality is correlated with other conditions, such as better property rights, or a greater integration with developed-country capital markets, that make it easier for foreign companies to exploit mineral reserves.

10

The effect of the initial level of income per capita on changes in inequality is not statistically significant (not reported here). There is a high degree of multicollinearity between the initial level of inequality and GDP per capita.

Inequality, Gender Gaps and Economic Growth: Comparative Evidence for Sub-Saharan Africa
Author: Ms. Dalia S Hakura, Mr. Mumtaz Hussain, Ms. Monique Newiak, Mr. Vimal V Thakoor, and Mr. Fan Yang