Back Matter

References

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Appendix A. Gender-Related Data1

This appendix provides information on data with specific disaggregation by sex, drawn from World Bank databases (whose primary sources are documented in each database). The data series are classified under five broad categories: economic opportunity, education, health, political opportunity, and violence against women. Table A1 lists the indicators under each of the categories, with a description of the earliest availability of the data and whether it was available on a sex-disaggregated basis.

Table A1.

List of Gender Indicators

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Sources: All data are obtained from the World Bank’s databank.

Data availability

Below are snapshots of data coverage for certain key indicators of gender-related data, for 188 countries, grouped by their level of income. For each indicator, the data are treated as “available” if a country has at least 50 percent of the data for the years 1980-2014. While more indicators would have met the threshold if we had used a shorter time period or lower threshold, our preference was to encompass the period in which reforms in gender equality became a focus of international efforts. For each bar, a darker shade implies greater data availability for countries with at least 50 percent of the data. That is, a full dark bar indicates complete data coverage and a full light bar indicates no data coverage. Indicators are then ranked from left to right, by number of countries with 50 percent of the data available.

Of the 21 identified gender indicators for economic opportunity (Figure A1), only labor force participation rate and youth unemployment have significant global data coverage. Over 93 percent and 90 percent of countries have data on labor force participation rate and youth unemployment rate, respectively. Data coverage on low-income countries is particularly high for the two variables mentioned: out of 60 LIDCs, 58 countries have data for labor force participation rate and 56 countries have data for youth unemployment rate. Data coverage for other indicators—wage work, employment by category, female employers, and own account workers—are highly skewed towards high-income countries.

Figure A1.
Figure A1.

Data Availability-Economic Opportunity, 1980-2014

Citation: IMF Working Papers 2016, 021; 10.5089/9781475592955.001.A999

Sources: World Bank databank and IMF staff estimates.

Figure A2 shows that among education indicators, gross secondary enrollment has the highest coverage: 130 countries have data, accounting for 70 percent of the total. Of the LIDCs, 57 percent have data from 1980-2014. Aside from gross enrollment rates, the following indicators also have reasonable data coverage—primary completion rates, gross tertiary enrollment, net primary enrollment, and survival rate in school to grade 5.

Figure A2.
Figure A2.

Data Availability-Education, 1980-2014

Citation: IMF Working Papers 2016, 021; 10.5089/9781475592955.001.A999

Sources: World Bank databank and IMF staff estimates.

Health indicators, where collected, have strong data coverage (Figure A3). Data for the first four indicators—crude birth rate, adolescent fertility rate, life expectancy, and under-5 mortality rate—are available in 97 percent of countries. Meanwhile, data on HIV prevalence is available for 56 out of 60 LIDCs. Maternal mortality data are available only every five years.

Figure A3.
Figure A3.

Data Availability-Health, 1980-2014

Citation: IMF Working Papers 2016, 021; 10.5089/9781475592955.001.A999

Sources: World Bank databank and IMF staff estimates.

Out of the four gender indicators for political opportunity (Figure A4), only the share of female seats in parliament has a reasonable amount of coverage globally: 127 out of 188 countries have reported data. Other indicators, namely, the share of female legislators, senior officials and managers, the share of female judges, and the share of female police officers, have scarce data availability.

Figure A4
Figure A4

Data Availability - Political Opportunity, 1980-2014

(Darker shades imply availability)

Citation: IMF Working Papers 2016, 021; 10.5089/9781475592955.001.A999

Sources: World Bank databank and IMF staff estimates.

There are a number of initiatives on data underway, which have an exclusive or significant focus on improving gender-related statistics. We provide a list of these in Table A2 below.

Table A2.

Data Initiatives

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Appendix B. List of Countries by Region and LIDC Classification

Table B1 provides a list of countries included in the sample of this study. Their regional classification is also presented below. Bolded countries indicate LIDCs.

Table B1.

Countries and Their Classification

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Appendix C. Data on Gender Indices

Table C1 provides details on each index discussed in the text, the developer, the source of data, whether published form or web link, the year of the data, corresponding to publications or available on the web, and country coverage in the latest variant of the data.

Table C1.

Index Data Sources and Availability

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The number of countries reflects the sample size for the latest listed year of data.

The old GDI was discontinued after the 2007 index, which was reported in the 2009 Human Development Report.

The revised GDI was introduced in the 2014 Human Development Report, using 2013 data.

Appendix D. Replication, Extension, and Revision of the GDI and GII

In this appendix, we provide details on the extension of the UNDP’s new GDI and the GII back to 1990 and the sensitivity of the GDI index to replacement or re-estimation of some of the variables in the index, following suggestions by Klasen (2014) and Dijkstra (2002). We refer to our replicated and extended series as time consistent (TC) versions.

Construction of the GDI, TC version

The UNDP’s newly introduced GDI begins in 2014, limiting the ability to do time series and panel data analysis with this index. Given that gender equality is a long-term objective and many of the index’s indicators are available over a longer period, we have extended the series to provide a consistently constructed series. Dilli, et al. (2015) and Gonzales, et al. (2015) have done something similar, the former, with their own gender equality index and the latter, with the UNDP’s GII. Table D1 provides information on the various indicators included in the GDI calculation. Although data for many of the indicators are available as far back as 1950, some indicators have limited data and, rather than impute data for variables other than wages, we extend the index back only to 1990.

Table D1.

Overview of the Indicators Included in the GDI

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Sources: World Bank; Barro and Lee (2014); UNESCO; ILO; and IMF staff estimates. F refers to female.

The indicator expected years of schooling has limited availability across time. 2 For many countries, there might only be one observation available between the years 1970-2013. To include as many countries possible, the UNDP uses observations from as far back as 2002 to calculate the 2013 GDI. This is problematic when measuring gender equality because while expected years of schooling is an indicator that may not change drastically year by year, we see that the cumulative effect over a decade shows significant change in a country. Using data for all countries with at least two observations we calculate the average growth rate in expected years of education. We find the average yearly growth rate for expected years of schooling for females is 2.3 percent and for males 1.7 percent, while the average ten year growth rate is 16.8 percent for females and 11.4 percent for males. Figure D1 depicts the gender gap in expected years of schooling. From 1970-2013 we can see the gap narrowing; the two lines converge around 2000. Therefore using data from 1990 to represent a country’s gap in 2000, for example, may not be an accurate representation. This raises the question of whether it is useful to include this indicator in the index at all.

Figure D1.
Figure D1.

Female and Male Expected Years of Schooling

Citation: IMF Working Papers 2016, 021; 10.5089/9781475592955.001.A999

Sources: UNESCO; and IMF staff estimates.

The UNDP uses two measures to create the “knowledge” sub-index: expected years of schooling and mean years of schooling. UNDP argues that it is necessary to include both measures of educational attainment because they measure educational attainment in two different age groups. Expected years of schooling refers to children and their chances of receiving education, while mean years of schooling refers to the adult population who have completed formal schooling. Yet there are other gender indices in which the educational attainment sub-index was limited to data on the adult population, namely the UNDP’s Gender Inequality Index. Moreover expected years of education and mean years of education are highly correlated (with a correlation coefficient of about 0.9); thus, the benefit of including both indicators is minimal.

Of all the indicators in the GDI, ratio of female to male wage has the poorest data coverage, with data on this indicator, in the year with highest data coverage, available for only 68 countries. Forty-four percent of the observations cover Europe and Central Asia, compared to only 0.6 percent of the observations for sub-Saharan Africa. Table D2 shows the regional distribution of data.

Table D2.

Wage Data Coverage by Region

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Sources: International Labor Organization; and IMF staff estimates.

To deal with the missing wage data, UNDP uses the global weighted wage ratio average of 0.8. This is a poor substitute for a number of reasons. First, Europe and Central Asia is over-represented in the sample based on population. Using the global average assumes that the wage ratio in countries with missing data is the same as the average in countries outside the same region and/or income group. Second, using the global average wage ratio penalizes countries with data where the reported wage ratio is below the global average.

The wage data are only available from 1995-2011. To cover the period 1990-2013 and fill in the series for countries with missing data, we impute the missing data using interpolation for those countries that have a reasonable amount of data (at least 5 years) and for those that do not, we use a regional average in place of the UN’s global average ratio.

After the wage interpolation, our data cover 146 countries from 1990-2013. To construct the GDI, TC version, we followed the steps of computing the GDI, as described in the HDR 2014 technical notes.3 Our calculations vary slightly from the UNDP’s because data have been updated since the UNDP’s original calculations. Given the problem of missing wage data, we also try substituting the labor force participation rate as an indicator in the “standard of living” sub-index. The most significant deviation from the UNDP’s GDI is that the index value for all countries is reduced. In fact, while the UNDP’s GDI shows a number of countries with gender disparity in favor of women, we see that all countries now have an index value below one.

Table D3 provides a correlation matrix using Spearman’s rank correlation between the UNDP’s GDI; the GDI, TC version, with wages; and the TC version with the labor force participation rate in the standard of living sub-index. It is apparent that while the two ranks are still strongly correlated they do differ slightly.

Table D3:

Spearman Rank Correlation Between Different Calculations of the GDI

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***, **, and * denote significance at the 1, 5, and 10 percent levelSources: UNDP Human Development Reports; World Bank; UNESCO; ILO; and IMF staff estimates.

Construction of the GII, TC version

Our data cover 141 countries from 1990-2013. Five indicators are included in our calculation of the GII: maternal mortality ratio, adolescent fertility rate, share of female seats in national parliaments, educational attainment at secondary and tertiary levels, and labor force participation rate.

To have a complete time series we needed to interpolate and extrapolate data for years of missing data. The methods used are as follows. Data for maternal mortality ratio (MMR) are available beginning from 1990 in five-year intervals. Data for in-between years are interpolated using linear interpolation.

Data for adolescent fertility rate are from United Nations Department of Economic and Social Affairs and are available through the World Development Indicators database for most years in our time period (1990-2013). In general, data coverage for this indicator is good with a consistent time series. Countries with missing data tend to be smaller states.

The indicator, female seats in parliament, provided by the Inter-Parliamentary Union, is available beginning in 1990. However, for most countries, data from 1991- mid/late 1990s are missing. Although the gap in data for some countries is large (around eight years), the share of women in parliaments does not change drastically over a short period of time and thus we use linear interpolation to fill in years of missing data.

Educational attainment for the purposes of the GII is defined as attainment at secondary and higher education levels, or as referred to by UNESCO “population over the age of 25 with at least secondary education.” To create this variable, we use data from two sources: Barro and Lee (2014) and UNESCO Institute for Statistics.

Using the Barro and Lee data, we add the “percent of secondary schooling attained in population” to the “percent of tertiary schooling attained in population” to estimate the population with at least secondary education. To supplement missing data, we use UNESCO’s indicator “population with at least secondary education (+25).” We use linear interpolation when the missing data are between two points of available data. However for recent years where linear interpolation is not possible, we use the most recent year of data as a substitute. For some countries data from as far back as 2010 is used in the calculation of the most recent year of the index.

Finally, data for labor force participation rate are provided by the ILO and are available through the World Bank Indicators database. In general, countries with data have a complete time series from 1990-2013. For countries where data are missing in the most recent years we use the most recent available year of data to fill in the missing data points.

To reconstruct the GII, we followed the steps as described in UNDP’s Human Development Report 2014 technical notes.4 To compare our calculations of the GII to UNDP’s calculations we calculated the Spearman’s rank correlation, the most recent year of the index. We found that our calculations come very close to the UNDP’s calculations with Spearman’s rho of 0.99 and significant at the 1 percent level. The slight variation is due to updates in the indicators used.

*

We are grateful to Mark Blackden, Diane Elson, Stephan Klasen, Jenny Lah and IMF colleagues, Andy Berg, Nen Gang, La-Bhus Fah Jirasavetakul, Kalpana Kochhar, Pritha Mitra, Monique Newiak, Cathy Pattillo, Victoria Perry, Jesmin Rahmin, and Genevieve Verdier, for useful suggestions, Carla Intal for excellent research assistance, and Jing Wang and Biva Joshi for excellent administrative support. This paper is part of a research project on macroeconomic policy in low-income countries supported by the U.K.’s Department for International Development (DFID), and it should not be reported as representing the views of the International Monetary Fund or of DFID.

4

See Klasen (2004) and Casarico and Profeta (2015) for further discussion.

7

Agenor and Canuto (2015) incorporate into a model of growth how a lack of infrastructure affects women’s allocation of time to formal labor markets.

8

In our sample, the gross enrollment rate has a correlation of about 0.9 with net enrollment and completion rates in the overall and developing country samples. Nonetheless, there is need for more comprehensive data.

9

An important goal should be to improve civil registration and vital statistics in countries with poor maternal mortality data to ensure the availability of more accurate data on this key variable.

10

Another useful variable would be unemployment, but again the data are lacking for this cross-country sample.

11

Appendix B provides details.

12

Few countries change their broad income classification over time. From sample beginning to end, only 10 countries move from the developing and emerging category to the advanced, and all of these with the exception of Korea are small countries, and altogether they have a minimal effect on the population-weighted averages.

13

If data are available for only a portion of the five years, we generate the average from the years for which the data were available. Because the indicators change slowly over time, this methodology does not introduce any significant bias; to check, we calculated the average using only countries that had at least half the annual observations for each variable (for child and maternal mortality, all countries would be dropped due to lack of data availability) and found no significant difference in the results.

14

The bump in the Americas and Caribbean reflects a variation resulting from one country and a small sample size and is thus not meaningful.

15

The data are available for 4 years, stretched out over an approximate 10-year interval, for each country. We have smoothed the series to construct 5-year intervals.

16

See Mithra and Farid (2013) for discussion of this issue.

17

Appendix 3 provides details on data availability and country scope.

18

The Women’s Empowerment in Agriculture Index (IFPRI, 2012), the African Gender Equality Index (AFDB, 2015), and the Gender Equality Index 2015 (European Institute for Gender Equality, 2015) are three other indices with focus on a particular sector or region, respectively.

19

See UNDP (1995) for details.

20

Table 1 of their paper provides a useful summary of the methodological framework for the evaluation of indices.

21

Our calculation suggests this estimate is high.

22

Permanyer (2013, p. 7) points out that the developers of the GII fail to recognize that because there are some variables in the index for women only, the index will not take a value of 0 when men and women are equal in other dimensions.

23

Social Watch has also developed a Gender Equity Index, which we do not review here but is discussed in Gaye et al. (2010) and Hawken and Munck (2013).

24

Dilli, Rijpma, and Carmichael (2015) and Gonzales, Jain-Chandra, Kochhar, Newiak, and Zeinullayev (2015) also replicate gender indices backward in time in a consistent manner. We term our replications “TC” to signify that they are time consistent and to distinguish them from the UNDP’s own indices.

25

We split the sample using the World Bank income group classification and then test for unit roots using the Levin, Lin, and Chu and Im, Pesaran, and Shin panel unit root tests in Stata. We do not reject the null of a unit root. We next test for a cointegrating relationship among the variables using Pedroni panel cointegration tests and reject the null of no cointegration. Finally, we run panel dynamic OLS regressions and find that the results vary by income group. The results are available from the authors upon request.

1

This appendix was prepared by Carla Intal.

2

Data indicated with – are collected but not yet available.

1

The number of countries reflects the sample size for the latest listed year of data.

2

Referred to by UNESCO as “School Life Expectancy: Primary to Tertiary.”

Trends in Gender Equality and Women’s Advancement
Author: Ms. Janet Gale Stotsky, Sakina Shibuya, Ms. Lisa L Kolovich, and Suhaib Kebhaj