# Gender Parity Index

UNESCO defined the Gender Parity Index (GPI) as a socioeconomic index usually designed to measure the relative access to education of males and females. It is used by international organizations particularly in measuring the progress of developing countries. For example, some UNESCO documents consider gender parity in literacy.
UNESCO describes attempts to eliminate gender disparities in primary and secondary education and emphasize on the plight of girls in unequal access in third world countries.
GPI is often used in order to identify nations and regions that are in need of economic development and equality.
The World Economic Forum's Global Gender Gap Report 2022 allows users to look at and compare country GPI data, calculate their own country's gender parity and explore global patterns.
World GPI has consistently increased toward parity since 1980.

## Definition and calculation

The Institute for Statistics of UNESCO also uses a more general definition of GPI: for any development indicator one can define the GPI relative to this indicator by dividing its value for females by its value for males.
In its simplest form, GPI is calculated as the quotient of the number of females by the number of males enrolled in a given stage of education (primary, secondary, etc.).
A GPI value that is less than one is an indication that gender parity favor males while a GPI value greater than one designates that gender parity is in favor of females. The closer a GPI is to one, the closer a country is to achieving equality of access between males and females. A nation is said to have achieved gender parity when its GPI value falls within the range of 0.97 and 1.03.

## Application
### Economics

The utilization of Gender Parity Index (GPI) by economists enables comprehensive monitoring and assessment of a nation's economic progress from a gender equality perspective. It is believed by many economists that gender inequality results in economic consequences such as increased unemployment, decreased output, and vast income inequality. As such, the monitoring of GPI by international organizations, such as UNESCO and the World Bank, serves the purpose of discerning the economic development deficiencies in nations and regions that are caused by gender inequality.
There is a present link between low-income countries a low GPI, particularly in secondary education and tertiary education. For example, many low-income, sub-Saharan countries have large gaps between male and female access to education.
Household income level is also a determining factor in secondary and tertiary GPI. In underdeveloped countries such as Cameroon, Guinea, Pakistan, and Yemen, gender parity existed among the top 20% of earners; yet low GPI levels were present among the bottom 20% of earners. As such, policymakers and other stakeholders utilize GPI in order to target regions in which economic development is a primary cause of gender disparity.

#### Education

In the education space, GPI is used to measure gender equality in education access. Governments and international organizations such as UNESCO and the United Nations monitor the gender gap in education access, using GPI to identify sectors where intervention is needed to address gender disparities.
A low GPI at the primary education level is an indication of economic, cultural, or other structural barriers that prevent women from receiving education, and a low GPI at the tertiary education level is an indication of structural barriers preventing women from breaking into specialized, advanced career fields.
GPI is also used to measure literacy equality, particularly by UNESCO. Gender disparities, specifically in primary education, have a drastic effect on literacy rates. For example, a low primary GPI is an indication that women in a particular nation or region are restricted from acquiring basic literacy skills.

## World gender parity index (1970–2020)

World gender parity index (as reported by World Bank) :
| Year | World GPI |
| 1970 | 0.8 |
| 1971 | 0.8 |
| 1972 | 0.8 |
| 1973 | 0.8 |
| 1974 | 0.8 |
| 1975 | 0.82 |
| 1976 | 0.84 |
| 1978 | 0.85 |
| 1979 | 0.84 |
| 1980 | 0.85 |
| 1981 | 0.85 |
| 1982 | 0.85 |
| 1983 | 0.84 |
| 1984 | 0.85 |
| 1985 | 0.85 |
| 1986 | 0.87 |
| 1987 | 0.87 |
| 1988 | 0.87 |
| 1989 | 0.88 |
| 1990 | 0.88 |
| 1991 | 0.89 |
| 1992 | 0.9 |
| 1993 | 0.9 |
| 1994 | 0.91 |
| 1995 | 0.91 |
| 1996 | 0.91 |
| 1997 | 0.92 |
| 1998 | 0.92 |
| 1999 | 0.92 |
| 2000 | 0.92 |
| 2001 | 0.93 |
| 2002 | 0.93 |
| 2003 | 0.95 |
| 2004 | 0.95 |
| 2005 | 0.95 |
| 2006 | 0.96 |
| 2007 | 0.96 |
| 2008 | 0.97 |
| 2009 | 0.97 |
| 2010 | 0.97 |
| 2011 | 0.98 |
| 2012 | 0.98 |
| 2013 | 1 |
| 2014 | 1 |
| 2015 | 1 |
| 2016 | 1.01 |
| 2017 | 1 |
| 2018 | 0.98 |
| 2019 | 0.98 |
| 2020 | 0.98 |

## By country

Gender parity index by country (as reported by World Bank) :
| Country | Most recent year of calculation | Most recent value |
| Afghanistan | 2017 | 0.42 |
| Albania | 2021 | 1.02 |
| Algeria | 2020 | 0.97 |
| Andorra | 1983 | 0.96 |
| Angola | 2018 | 0.94 |
| Antigua and Barbuda | 2019 | 0.98 |
| Argentina | 2020 | 1.01 |
| Armenia | 2021 | 1.01 |
| Austria | 2020 | 0.99 |
| Azerbaijan | 2021 | 1.01 |
| Bahrain | 2019 | 0.99 |
| Bangladesh | 2021 | 1.02 |
| Barbados | 2021 | 0.97 |
| Belarus | 2021 | 1 |
| Belgium | 2020 | 1.01 |
| Belize | 2021 | 0.96 |
| Benin | 2021 | 0.93 |
| Bhutan | 2021 | 1.04 |
| Bolivia | 2020 | 1 |
| Bosnia and Herzegovina | N/A | No Data |
| Botswana | 2021 | 0.98 |
| Brazil | 2020 | 0.95 |
| Brunei | 2020 | 1 |
| Bulgaria | 2020 | 0.99 |
| Burkina Faso | 2021 | 1.02 |
| Burundi | 2020 | 1.01 |
| Cabo Verde | 2019 | 0.94 |
| Cambodia | 2021 | 0.98 |
| Cameroon | 2019 | 0.9 |
| Canada | 2020 | 0.97 |
| Central African Republic | 2017 | 0.77 |
| Chad | 2021 | 0.8 |
| Channel Islands | N/A | No Data |
| Chile | 2020 | 0.97 |
| China | 2021 | 1.01 |
| Colombia | 2020 | 0.97 |
| Comoros | 2018 | 1 |
| Congo | 2018 | 0.97 |
| Costa Rica | 2021 | 0.99 |
| Côte d'Ivoire | 2021 | 0.95 |
| Croatia | 2020 | 1 |
| Cuba | 2021 | 0.98 |
| Cyprus | 2020 | 0.99 |
| Czech Republic | 2020 | 1 |
| Denmark | 2020 | 1 |
| Djibouti | 2021 | 0.92 |
| Dominica | 2021 | 0.95 |
| Dominican Republic | 2021 | 0.97 |
| DR Congo | 2020 | 0.95 |
| Ecuador | 2021 | 1.02 |
| Egypt | 2019 | 1.01 |
| El Salvador | 2014 | 0.96 |
| Equatorial Guinea | 2015 | 0.99 |
| Eritrea | 2019 | 0.86 |
| Estonia | 2020 | 1 |
| Eswatini | 2019 | 0.92 |
| Ethiopia | 2021 | 0.91 |
| Fiji | 2021 | 0.96 |
| Finland | 2020 | 1 |
| France | 2020 | 0.94 |
| French Guiana | No Data | No Data |
| Gabon | 2019 | 0.97 |
| Gambia | 2021 | 1.13 |
| Georgia | 2021 | 1.01 |
| Germany | 2020 | 1.03 |
| Ghana | 2020 | 1.02 |
| Gibraltar | 2021 | 1.12 |
| Greece | 2020 | 1.01 |
| Grenada | 2018 | 0.98 |
| Guatemala | 2021 | 0.98 |
| Guinea | 2020 | 0.85 |
| Guinea-Bissau | 2020 | 0.73 |
| Guyana | 2019 | 0.99 |
| Haiti | 1998 | 0.97 |
| Holy See | No Data | No Data |
| Honduras | 2012 | 1 |
| Hong Kong | 2021 | 1.08 |
| Hungary | 2020 | 0.98 |
| Iceland | 2020 | 1.01 |
| India | 2021 | 1.04 |
| Indonesia | 2018 | 0.99 |
| Iran | 2020 | 1.06 |
| Iraq | 2007 | 0.84 |
| Ireland | 2020 | 1.01 |
| Isle of Man | No Data | No Data |
| Israel | 2020 | |
| Italy | 2020 | 1.01 |
| Jamaica | 2007 | 1 |
| Japan | 2019 | 1.02 |
| Jordan | 2021 | 0.99 |
| Kazakhstan | 2020 | 1 |
| Kenya | 2020 | 1 |
| Kuwait | 2019 | 1.25 |
| Kyrgyzstan | 2021 | 1 |
| Laos | 2021 | 0.97 |
| Latvia | 2020 | 1 |
| Lebanon | 1985 | 0.91 |
| Lesotho | 2019 | 0.95 |
| Liberia | 2020 | 1 |
| Libya | 2006 | 0.96 |
| Liechtenstein | 2020 | 0.97 |
| Lithuania | 2020 | 1 |
| Luxembourg | 2020 | 0.99 |
| Macao | 2021 | 0.98 |
| Madagascar | 2019 | 1.02 |
| Malawi | 2019 | 1.03 |
| Malaysia | 2019 | 1.03 |
| Maldives | 2019 | 1.01 |
| Mali | 2020 | 0.91 |
| Malta | 2020 | 1 |
| Mauritania | 2020 | 1.07 |
| Mauritius | 2021 | 1.02 |
| Mayotte | No data | No data |
| Mexico | 2020 | 1.02 |
| Moldova | 2021 | 0.99 |
| Monaco | No Data | No Data |
| Mongolia | 2021 | 0.98 |
| Montenegro | 2021 | 1 |
| Morocco | 2021 | 0.97 |
| Mozambique | 2020 | 0.94 |
| Myanmar | 2018 | 0.96 |
| Namibia | 2021 | 0.97 |
| Nepal | 2021 | 0.97 |
| Netherlands | 2020 | 1 |
| Nicaragua | 2020 | 0.99 |
| Niger | 2021 | 0.91 |
| Nigeria | 2019 | 1.01 |
| North Korea | 2019 | 1.01 |
| North Macedonia | 2020 | 1 |
| Norway | 2020 | 1 |
| Oman | 2021 | 1 |
| Pakistan | 2019 | 0.88 |
| Panama | 2021 | 0.99 |
| Paraguay | 2015 | 0.97 |
| Peru | 2021 | 0.97 |
| Philippines | 2021 | 0.98 |
| Poland | 2020 | 0.96 |
| Portugal | 2020 | 0.99 |
| Qatar | 2021 | 1.02 |
| Réunion | No Data | No Data |
| Romania | 2020 | 0.99 |
| Russia | 2019 | 0.99 |
| Rwanda | 2021 | 0.99 |
| Saint Helena | No Data | No Data |
| Saint Kitts and Nevis | 2020 | 1 |
| Taiwan | 2021 | 0.90 |