This handy pocket guide is a quick reference for users interested in the gender statistics. The book presents gender-disaggregated data for more than 200 countries in an easy country-by-country reference on demography, education, health, labor force, political participation and the Millennium Development Goals. The book's summary pages cover regional and income group aggregates.

This handy pocket guide is a quick reference for users interested in gender statistics. The book presents gender-disaggregated data for more than 200 economies in an easy country-by-country reference on demography, education, health, labor force, political participation and the Millennium Development Goals. The book’s summary pages cover regional and income group aggregates

This handy pocket guide is a quick reference for users interested in gender statistics. It presents gender-disaggregated data for more than 200 countries in an easy country-by-country reference on demography, education, health, labor force, political participation and the Millennium Development Goals. The book s summary pages cover regional and income group aggregates.

This handy pocket guide is a quick reference for users interested in the gender statistics. The book presents gender-disaggregated data for more than 200 countries in an easy country-by-country reference on demography, education, health, labor force, political participation and the Millennium Development Goals. The book s summary pages cover regional and income group aggregates.

The Little Data Book on Gender in Africa 2012/2013 presents gender-disaggregated data for Sub-Saharan Africa countries in an easy country-by-country reference on demography, education, health, labor force, political participation and the Millennium Development Goals. The book s summary pages cover regional and income group aggregates.

In 2011 the World Bank—with funding from the Bill and Melinda Gates Foundation—launched the Global Findex database, the world's most comprehensive data set on how adults save, borrow, make payments, and manage risk. Drawing on survey data collected in collaboration with Gallup, Inc., the Global Findex database covers more than 140 economies around the world. The initial survey round was followed by a second one in 2014 and by a third in 2017. Compiled using nationally representative surveys of more than 150,000 adults age 15 and above in over 140 economies, The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution includes updated indicators on access to and use of formal and informal financial services. It has additional data on the use of financial technology (or fintech), including the use of mobile phones and the Internet to conduct financial transactions. The data reveal opportunities to expand access to financial services among people who do not have an account—the unbanked—as well as to promote greater use of digital financial services among those who do have an account. The Global Findex database has become a mainstay of global efforts to promote financial inclusion. In addition to being widely cited by scholars and development practitioners, Global Findex data are used to track progress toward the World Bank goal of Universal Financial Access by 2020 and the United Nations Sustainable Development Goals. The database, the full text of the report, and the underlying country-level data for all figures—along with the questionnaire, the survey methodology, and other relevant materials—are available at www.worldbank.org/globalfindex.

A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.