Figure 1
Change in Average Schooling Years Between 1960 and 2010.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD. Female years of schooling is the average educational attainment among adult women aged 15 and over; male years of schooling is the average educational attainment among adult men aged 15 and over. For each country, the arrow connects the average level of educational attainment in 1960 to the average level of attainment in 2010. Countries are assigned to regions based on the World Bank's classifications. The dashed line is the 45 degree line.
Figure 2
Change in Gender Gaps in Educational Attainment.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD. The gender gap is the difference between average educational attainment (years of schooling) among adult women and average educational attainment among adult men. Orange indicates countries where women's educational attainment grew more slowly than men's between 1960 and 2010; light blue indicates countries where women's educational attainment grew faster than men's. Countries are assigned to regions based on the World Bank's classifications.
Table 1
Ratio of Females to Males at Various Education Levels in 2010.
| Region | Ratio of females to males | ||
|---|---|---|---|
| No formal education | Complete primary | Complete secondary | |
| East Asia and Pacific | 1.89 | 0.99 | 0.93 |
| Europe and Central Asia | 2.13 | 0.99 | 0.94 |
| Latin America and Caribbean | 1.48 | 0.97 | 1.02 |
| Middle East and North Africa | 1.79 | 0.91 | 1.08 |
| South Asia | 1.84 | 0.73 | 0.88 |
| Sub-Saharan Africa | 1.52 | 0.86 | 0.77 |
[i] Note: No formal education denotes the ratio of percent of the female population with no schooling divided by percent of the male population with no schooling. Complete primary denotes the female–male ratio of percent of the population that completed at least primary education. Complete secondary is defined analogously. The source of the data for this analysis is the Barro–Lee educational attainment dataset, and data come from all 126 countries that were not founding members of the OECD.
Figure 3
Regional Change in Gender Gaps in Average Schooling Years, 1960–2010.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD.
Figure 4
Change in Gender Gap in Average Schooling Years Given Schooling Levels in 1960.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD.
Table 2
Regression of Female Schooling and Gender Gap in Schooling on Key Variables.
| Female schooling | Female–Male gap | |
|---|---|---|
| Male years of schooling | 1.121*** (0.028) | 0.121*** (0.028) |
| Log GDP per capita (PPP-adjusted) | 1.985*** (0.155) | 0.428*** (0.066) |
| Poverty index | −0.104*** (0.010) | −0.016*** (0.005) |
| Life expectancy at birth | 0.298*** (0.024) | 0.052*** (0.013) |
| Infant mortality index | −0.126*** (0.010) | −0.025*** (0.005) |
| Corruption index | 0.107*** (0.011) | 0.019*** (0.005) |
[i] Notes: Male and female years of schooling are taken from the Barro–Lee educational attainment dataset, using data from 2010. Log GDP per capita is PPP-adjusted to reflect 2011 dollars, using the most recent available year from the World Bank's World Development Indicators data. Poverty index is the poverty headcount ratio (so that smaller numbers indicate less poverty), using the most recent available year from the World Bank's World Development Indicators data. Infant mortality index and life expectancy at birth data are for the most recent available years from the World Bank's World Development Indicators data. Corruption index is measured using the Corruption Perceptions Index 2018 from Transparency International, with lower numbers indicating higher corruption. Standard errors are listed in parentheses below the point estimates. The sample size ranges from 121 to 126, depending on availability of data, except for the poverty regression, for which we have data for only 103 countries.
Figure 5
The Number of High-Education Countries by Year.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD. “High education” indicates countries where men have an average of more than eight years of education. “Gender gap” indicates a difference in male vs. female educational attainment (mean years of schooling) that is greater than one year.
Figure 6
Change in Average Schooling Years between 1960 and 2010 for Younger Cohort.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD. Female years of schooling is the average educational attainment among adult women aged 20–24; male years of schooling is the average educational attainment among adult men aged 20–24. For each country, the arrow connects the average level of educational attainment in 1960 to the average level of attainment in 2010.
Figure 7
Change in Gender Gaps in Educational Attainment for Younger Cohort.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD. The gender gap is the difference between average educational attainment (years of schooling) among women aged 20–24 and average educational attainment among young men aged 20–24. Orange indicates countries where women's educational attainment grew more slowly than men's between 1960 and 2010; light blue indicates countries where women's educational attainment grew faster than men's.
Figure 8
Gender Gaps in Education and Labor Force Participation.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD. Data on labor force participation comes from the World Development Indicators database. Gender gaps are calculated in the difference in levels between female and male labor force participation and educational attainment. The change is the difference between the gender gap in 2010 and the gender gap in 1990. Positive changes indicate that the gender gap shrunk over time.
Figure A1
Year of Worst Gap Among Countries Where It Got Worse Before It Got Better.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD. Countries are classified as experiencing the “worse before better” phenomenon if the year of the worst gap is after 1960 and the gap in 2010 is smaller than the worst gap. Countries where the gap “did not get worse before better” either had their worst gap in 1960 or 2010.
Figure A2
Countries' Transition to and from the High Education and Big Gender Gap Status.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD. “High education” indicates countries where men have an average of more than eight years of education. “Gender gap” indicates a difference in male vs. female educational attainment (mean years of schooling) that is greater than one year.
Figure A3
Schooling Years and Gaps.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD. “Large gender gap” indicates a difference in male vs. female educational attainment (mean years of schooling) that is greater than one year.
Figure A4
Regional Change in Gender Gaps in Average Schooling Years for Younger Cohort, 1960–2010.
Notes: Sample includes 126 countries, all those included in the Barro–Lee educational attainment data set that were not founding members of the OECD.
Table A1
Barro–Lee Sample Compared to UN Member State Sample.
| Barro–Lee Sample | UN Sample | Difference | |
|---|---|---|---|
| GDP per capita (Mean) | 20535 | 18453 | 2082 |
| GDP per capita (SE) | 1764 | 1432 | 2250 |
| Number of countries | 139 | 182 | |
| Literacy (Mean) | 86 | 85 | 1 |
| Literacy (SE) | 2 | 1 | 2 |
| Number of countries | 109 | 143 |
[i] Note: Barro–Lee sample includes all the countries from the Barro–Lee dataset that have available data on PPP-adjusted GDP per capita (constant 2011 international $) (2017) or literacy (most recent year available since 2008) in the World Bank's World Development Indicators. Similarly, UN sample includes all such countries that are members of the United Nations. The calculation excludes from the Barro–Lee sample Hong Kong, Macao, and Taiwan—the only three states that are originally in the Barro–Lee sample but not in the UN sample.
Table A2
Change in Female Schooling Years.
| Region | Female schooling | Slope | ||
|---|---|---|---|---|
| 1960 | 2010 | Change | ||
| East Asia and Pacific | 3.04 | 8.67 | 5.63 | 1.22 |
| Europe and Central Asia | 5.07 | 11.08 | 6.00 | 1.18 |
| Latin America and Caribbean | 3.37 | 8.34 | 4.97 | 1.07 |
| Middle East and North Africa | 1.24 | 7.63 | 6.39 | 1.13 |
| South Asia | 1.09 | 5.08 | 3.99 | 0.86 |
| Sub-Saharan Africa | 1.05 | 4.90 | 3.85 | 0.95 |
[i] Note: The figures in the 1960 column denote average schooling years for the female population in 1960. The figures in the 2010 column denote average schooling years for the female population in 2010. Change is calculated by subtracting female schooling years in 1960 from female schooling years in 2010. Slope is calculated by dividing the change in female schooling years between 1960 and 2010 by the change in male schooling years over the same period. The source of the data for this analysis is the Barro–Lee educational attainment dataset, and data come from all 126 countries that were not founding members of the OECD.
Table A3
Top Three Countries for Male Schooling Years by Region.
| Region | Country | Male Schooling Years in 1960 | Gap in 1960 | Male Schooling Years in 2010 | Gap in 2010 |
|---|---|---|---|---|---|
| East Asia and Pacific | South Korea | 5.57 | −2.62 | 12.76 | −1.30 |
| Hong Kong | 6.43 | −3.09 | 11.77 | −0.77 | |
| Japan | 8.16 | −1.37 | 11.69 | −0.24 | |
| Europe and Central Asia | Czech Republic | 8.80 | −0.81 | 12.89 | −0.18 |
| Slovakia | 8.86 | −0.82 | 12.80 | −0.03 | |
| Hungary | 7.66 | −0.46 | 11.89 | −0.07 | |
| Latin America and Caribbean | Belize | 7.74 | −0.31 | 11.23 | 0.11 |
| Trinidad and Tobago | 5.84 | −0.39 | 10.64 | 0.00 | |
| Cuba | 3.94 | 0.11 | 10.32 | −0.29 | |
| Middle East and North Africa | Israel | 8.37 | −1.45 | 12.32 | 0.01 |
| Malta | 4.81 | −1.04 | 10.77 | −0.60 | |
| Jordan | 3.50 | −2.30 | 9.94 | −0.69 | |
| South Asia | Sri Lanka | 4.70 | −1.49 | 10.32 | −0.35 |
| India | 1.72 | −1.21 | 7.59 | −2.78 | |
| Maldives | 3.81 | −0.78 | 6.29 | −0.42 | |
| Sub-Saharan Africa | South Africa | 4.38 | 0.03 | 9.72 | −0.08 |
| Botswana | 1.43 | 0.06 | 9.68 | −0.26 | |
| Mauritius | 4.34 | −1.55 | 9.36 | −0.89 |
[i] Notes: Male schooling years in 1960 denotes average schooling years for the male population in 1960. Gap in 1960 denotes the female-male gap in average schooling years in 1960. Male schooling years in 2010 denotes average schooling years of the male population in 2010. Gap in 2010 denotes the female-male gap in average schooling years in 1960. The source of the data for this analysis is the Barro–Lee educational attainment dataset, and data come from all 126 countries that were not founding members of the OECD.
Table A4
Ratio of Females to Males at Various Education Levels in 2010 (25–29 Year Olds).
| Region | Ratio of males to females | ||
|---|---|---|---|
| No formal Education | Complete Primary | Complete Secondary | |
| East Asia and Pacific | 1.27 | 1.03 | 1.03 |
| Europe and Central Asia | 0.84 | 1.00 | 1.06 |
| Latin America and Caribbean | 4.56 | 0.99 | 1.10 |
| Middle East and North Africa | 1.64 | 0.97 | 1.24 |
| South Asia | 1.80 | 0.78 | 0.93 |
| Sub-Saharan Africa | 3.06 | 0.90 | 0.79 |
[i] Notes: No formal education denotes the ratio of percent of 25–29-year-old female population with no schooling divided by percent of 25–29-year-old male population with no schooling. Complete Primary denotes the female-male ratio of percent of population aged 25–29 that completed at least primary education. Complete Secondary is defined analogously. The source of the data for this analysis is the Barro–Lee educational attainment dataset, and data come from all 126 countries that were not founding members of the OECD.
Table A5
Countries Where The Gender Gap Got Worse Before It Got Better.
| Country | Gap in 1960 | Worst Gap | Year of Worst Gap | Gap in 2010 |
|---|---|---|---|---|
| Brunei Darussalam | −2.69 | −2.71 | 1965 | −0.26 |
| Honduras | −0.31 | −0.36 | 1965 | 0.03 |
| Kazakhstan | −1.22 | −1.25 | 1965 | −0.17 |
| Philippines | −0.55 | −0.57 | 1965 | 0.59 |
| Singapore | −2.38 | −2.38 | 1965 | −0.88 |
| Myanmar | −0.75 | −0.86 | 1965 | 0.50 |
| Qatar | −1.18 | −1.43 | 1965 | 1.46 |
| Trinidad and Tobago | −0.39 | −0.43 | 1965 | −0.00 |
| Vietnam | −1.56 | −1.71 | 1965 | −0.68 |
| Guyana | −0.55 | −0.79 | 1965 | 0.96 |
| Barbados | −0.36 | −0.46 | 1965 | 0.51 |
| Bahrain | −0.92 | −1.36 | 1970 | 0.48 |
| Australia | −0.59 | −1.12 | 1970 | 0.12 |
| Jamaica | 0.15 | 0.05 | 1970 | 0.46 |
| Fiji | −1.00 | −1.18 | 1970 | −0.16 |
| Czech Republic | −0.81 | −1.59 | 1970 | −0.18 |
| Mongolia | −0.78 | −1.47 | 1970 | 0.59 |
| Slovakia | −0.82 | −1.43 | 1970 | −0.03 |
| Jordan | −2.30 | −2.66 | 1970 | −0.69 |
| Albania | −1.02 | −1.20 | 1970 | −0.47 |
| Saudi Arabia | −3.00 | −3.26 | 1970 | −0.54 |
| Russia | −1.02 | −1.42 | 1970 | −0.22 |
| Ukraine | −1.11 | −1.54 | 1970 | −0.04 |
| Indonesia | −1.23 | −1.53 | 1970 | −0.90 |
| Reunion | 0.29 | 0.12 | 1970 | 0.87 |
| Ecuador | −0.58 | −0.71 | 1970 | −0.04 |
| Poland | −0.59 | −0.62 | 1970 | −0.04 |
| Chile | −0.30 | −0.35 | 1970 | −0.26 |
| Mauritius | −1.55 | −1.99 | 1970 | −0.89 |
| Lithuania | −0.91 | −0.93 | 1975 | 0.02 |
| Libya | −1.16 | −2.65 | 1975 | 1.60 |
| Nicaragua | −1.11 | −2.54 | 1975 | 0.44 |
| Colombia | −0.26 | −0.35 | 1975 | −0.10 |
| Romania | −1.11 | −1.89 | 1975 | −0.60 |
| Peru | −1.26 | −1.47 | 1975 | −0.98 |
| Tajikistan | −1.71 | −1.96 | 1975 | 0.50 |
| Syria | −1.37 | −2.56 | 1975 | −1.45 |
| Rwanda | −0.95 | −1.27 | 1975 | −0.24 |
| Moldova | −1.03 | −1.12 | 1975 | −0.11 |
| Burundi | −0.61 | −1.23 | 1975 | −0.79 |
| Mexico | −0.48 | −0.82 | 1980 | −0.29 |
| China | −1.38 | −1.57 | 1980 | −0.81 |
| Iran | −0.81 | −1.92 | 1980 | −0.39 |
| South Africa | 0.03 | −0.55 | 1980 | −0.08 |
| Rep. of Congo | −1.41 | −2.36 | 1980 | −1.29 |
| New Zealand | −0.11 | −0.52 | 1980 | 0.83 |
| Bangladesh | −1.32 | −1.94 | 1980 | −0.52 |
| Tanzania | −1.83 | −2.43 | 1980 | −0.80 |
| Cameroon | −1.24 | −1.80 | 1980 | −1.00 |
| Estonia | −0.11 | −0.32 | 1980 | 0.51 |
| Dominican Republic | 0.03 | −0.65 | 1980 | 0.59 |
| Kenya | −1.39 | −2.16 | 1980 | −0.89 |
| Laos | −1.62 | −2.18 | 1980 | −0.92 |
| Bolivia | −1.44 | −2.02 | 1980 | −1.15 |
| Mozambique | −0.94 | −1.27 | 1980 | −1.00 |
| Malta | −1.04 | −1.22 | 1980 | −0.60 |
| Egypt | −0.99 | −2.44 | 1985 | −1.47 |
| Zimbabwe | −0.84 | −1.57 | 1985 | −0.44 |
| Uganda | −1.20 | −1.86 | 1985 | −0.94 |
| Cambodia | −1.34 | −2.22 | 1985 | −1.73 |
| Tunisia | −1.01 | −2.22 | 1985 | −1.20 |
| Papua New Guinea | −0.43 | −1.64 | 1985 | −1.29 |
| Cuba | 0.11 | −0.61 | 1985 | −0.29 |
| Algeria | −0.55 | −2.50 | 1985 | −0.66 |
| Sudan | −0.64 | −1.40 | 1985 | −1.06 |
| Ghana | −1.08 | −3.31 | 1985 | −2.03 |
| Iraq | −0.60 | −2.68 | 1985 | −1.92 |
| Zambia | −1.19 | −2.28 | 1985 | −0.61 |
| Dem. Rep. of Congo | −1.32 | −2.60 | 1985 | −2.15 |
| Finland | −0.16 | −0.92 | 1990 | −0.00 |
| Togo | −0.70 | −3.24 | 1990 | −3.24 |
| Hungary | −0.46 | −0.94 | 1990 | −0.07 |
| Nepal | −0.21 | −2.41 | 1990 | −1.46 |
| Uruguay | −0.04 | −0.98 | 1995 | 0.37 |
| Liberia | −0.75 | −2.88 | 2000 | −2.40 |
| Morocco | −0.30 | −1.84 | 2000 | −1.67 |
| Malawi | −0.87 | −1.64 | 2000 | −0.87 |
| Gambia | −0.34 | −1.57 | 2000 | −1.29 |
| Benin | −0.63 | −2.35 | 2000 | −2.15 |
| Niger | −0.62 | −1.21 | 2000 | −1.10 |
| Latvia | −0.45 | −0.60 | 2000 | −0.03 |
| Yemen | −0.03 | −2.33 | 2005 | −1.94 |
| Afghanistan | −0.54 | −3.62 | 2005 | −3.43 |
| Maldives | −0.78 | −0.83 | 2005 | −0.42 |
| Sierra Leone | −0.40 | −1.75 | 2005 | −1.65 |
| Central African Republic | −0.49 | −2.35 | 2005 | −2.13 |
| Pakistan | −1.35 | −2.62 | 2005 | −2.48 |
| Haiti | −0.49 | −2.50 | 2005 | −2.40 |
| Eswatini | −0.39 | −1.34 | 2005 | 0.06 |
| El Salvador | −0.40 | −0.98 | 2005 | −0.39 |
| Guatemala | −0.44 | −1.04 | 2005 | −1.00 |
| Mauritania | −0.33 | −1.97 | 2005 | −1.42 |
| Costa Rica | −0.10 | −0.15 | 2005 | 0.06 |
| Mali | −0.20 | −0.76 | 2005 | 0.11 |
| India | −1.21 | −3.05 | 2005 | −2.78 |
| Cote d’Ivoire | −0.83 | −2.08 | 2005 | −1.87 |
[i] Notes: Gap in 1960 denotes the female-male gap is average schooling years in 1960. Worst Gap denotes the magnitude of the largest gap in favor of men among countries where the largest gap is after 1960 and gap in 2010 is smaller than the “worst” gap. Year of Worst Gap denotes the year when the largest gap in favor of men appears. Gap in 2010 denotes the female-male gap in average schooling years in 2010. The source of the data for this analysis is the Barro–Lee educational attainment dataset, and data come from all 126 countries that were not founding members of the OECD.
Table A6
Schooling Characteristics in the Year of Widest Gender Gap for Countries Where Gender Gap Got Worse Before It Got Better.
| 25th Percentile | 50th Percentile | 75th Percentile | |
|---|---|---|---|
| Female schooling (15+) | 2.07 | 3.20 | 4.93 |
| Male schooling (15+) | 4.12 | 4.93 | 6.40 |
| Female schooling (20–24) | 3.27 | 4.83 | 7.09 |
| Male schooling (20–24) | 5.59 | 6.74 | 8.62 |
[i] Notes: Male and female years of schooling are taken from the Barro–Lee Educational Attainment Dataset, excluding OECD countries. The sample is restricted to the 96 countries where the gender gap got worse before it got better between 1960 and 2010. The statistics are for the year where the gender gap was widest between 1960 and 2010. The numbers in the parentheses indicate the age group of the sample.