Our Gender Data Portal is the World Bank Group’s comprehensive source for historical and current data disaggregated by sex. This data site brings together high-quality, curated data on women and men (and girls and boys) in an easy-to-use platform that covers a wide range of topics such as demography, education, health, economic opportunities, public life and decision-making, and agency. The Gender Data Portal is the go-to place for reliable data disaggregated by sex for countries and regions around the world.
Here are 5 things you can do in our Gender Data Portal:
The launch of the Human Capital Project has galvanized global action to close human capital gaps, and has highlighted the importance of investments in the knowledge, skills, and health that people accumulate throughout their lives, to realize their potential as productive members of society.
Improving both the quantity andquality of education is pivotal to empowering young people to fulfill their potential. Science, Technology, Engineering, and Mathematics (STEM) education is critical not only for fulfilling the needs of the future workforce, but also for producing researchers and innovators who can help to solve intractable challenges.
The underrepresentation of women and girls in STEM gets a lot of attention, but the data on access to, and quality of, education shows that the story is more nuanced.
At primary school level globally, there is gender parity in both enrollment and completion–a remarkable achievement of recent times. Gender gaps emerge in a number of low-income countries, mostly in Sub-Saharan Africa, and in some Latin American countries there are ‘reverse’ gender gaps (with boys less likely to attend or complete primary school). Overall, gender gaps (where they exist) are modest in comparison to the gaps between rich and low-income countries.
When it comes to academic performance, girls often do as well as, or better than, boys in science and mathematics.
In primary schools, there are no gender differences in science achievement in more than half of the 47 countries where performance is measured (Figure 1). Girls score higher than boys in 26 percent of the countries. The difference in achievement is almost three-times higher when girls score more than boys compared to when boys score more than girls. Results for mathematics achievement are similar. There are no gender differences in about half of the countries with data, but boys score better than girls in 37 percent of the countries.
Figure 1: Primary-school girls perform as well as boys in science and mathematics
A new guidebook published by the World Bank and the UNESCO Institute for Statistics (UIS) casts light on how to measure the heavy burden of education spending that falls on the world’s families. Measuring Household Expenditure on Education: A Guidebook for Designing Household Survey Questionnaires will help countries report on SDG 4 indicator 4.5.4: education expenditure per student by level of education and source of funding. The guidebook also aims to ensure proper representation of education expenditures in consumption-based poverty and inequality measures, and enable more micro-econometric research on resource allocation in households.
The burden of education spending by families
We already know that the burden on families can be heavy. UIS data released in 2017 found that families in low-income countries pay more for their children’s education: households in many developing countries spend a far greater share of average GDP per capita on education than those in developed countries. Household spending on secondary education amounts to 20-25% of average GDP per person in Benin, Chad, Côte d’Ivoire, Guinea, and Niger, and more than 30% in Togo. In stark contrast, the share does not exceed 5% in almost all high-income countries.
The data also reveal that families—including the poorest—are providing much of the world’s education spending. For example, households provide about one-quarter of education expenditure in Viet Nam, one-third in Côte d’Ivoire, half in Nepal, and more than half in Uganda.
Improving the capacity of national statistical systems (NSSs) has long been a part of the global development agenda. The NSSs play an important role in modern economies. They provide stakeholders, ranging from policy makers to stock market analysts and the general public, with the data on the country’s socioeconomic developments. At the international level, monitoring global initiatives such as the Sustainable Development Goals (SDGs) requires high-quality data that are produced consistently across different national statistical systems.
In 2004, the World Bank developed the Statistical Capacity Index (SCI) to measure progress in statistical capacity building. The SCI was based on publicly available data and was designed to assess a country’s statistical capacity in an internationally comparable and cost-effective manner. Several international and national agencies have adopted the SCI for measuring progress in statistical capacity building and related investments (United Nations, 2016).
In 2015, leaders of 193 countries formed an ambitious plan to guide global development action for the next 15 years by agreeing on a set of Sustainable Development Goals (SDGs). Four years after their launch, the World Bank’s expertise in development data and its large repository of development indicators has played an important role in helping track progress made towards the achievement of the SDGs.
How does SDG monitoring work and how is the World Bank involved?
To monitor the 17 goals and 169 associated targets, a framework of 230+ indicators was developed by the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs), a group of UN Member States with international agencies as observers. Different international agencies were assigned as “custodians” of the SDG targets. In this capacity, the custodian agencies work with national statistical offices to develop methodologies for indicators to measure progress on the SDGs. The agencies also work with countries to compile data for SDG indicators, which they submit to the UN Statistics Global SDG database.
The World Bank participates in IAEG-SDGs as an observer and is a custodian or co-custodian (with other agencies) for 20 indicators, and is involved in the development and monitoring of an additional 22 indicators. Altogether, the World Bank is formally engaged with the monitoring of 42 of the 230+ indicators. The indicators cover a wider range of topics in which the World Bank has expertise, including poverty and inequality, social protection, gender equality, financial access, remittances, health, energy, infrastructure, and so on.
Globally, 56 percent of children live in countries with Human Capital Index (HCI) scores below 0.5. As these countries gear up to improve their human capital outcomes, it is vital to set a target that is ambitious enough to prompt action and realistic enough to be achieved. One way to get at this is to examine the historical rate of progress that countries demonstrated to be possible.
Using time-series data between 2000 and 2017, we estimated countries' progress in the health components of HCI (fraction of children not stunted, child survival and adult survival) using a non-linear regression model.  Our measure of progress is the fraction of gap to the frontier that is eliminated every year- the frontier being 100 percent child and adult survival, and no stunting.,
We address the following two questions:
What is the typical progress in the health components of HCI observed globally?
In 2004, my colleague Zurab Sajaia and I submitted a maximum likelihood routine to the Stata SSC archive. The program was quickly propelled by the Stata user community to the top 10 most downloaded Stata files; it is still in use now. While experimenting with similar algorithms to develop test procedures (five years after the program’s release), we uncovered an error in the routine. Hundreds, if not thousands, of econometricians had used our program and looked at our code, but no one raised any concerns.
Open Source Software (OSS) is quickly gaining popularity in the corporate world as a practical alternative to costly proprietary software. 78% of companies are now using OSS extensively and open source components are found in more than half of all proprietary software. The rationale is simple: OSS lowers development costs, decreases time to market, increases developer productivity, and accelerates innovation.
We’re living in a time of disruptive technologies evolving at an exponential pace. Today, you can enjoy an Impossible Burger (meat industry disrupted) delivered by Caviar (food delivery disrupted) to your AirBnB (hotel industry disrupted) while you’re on FaceTime (telecommunication industry disrupted) urging your teenager to get back to lessons on Khan Academy (education industry disrupted). And all the while, you’re leaving a trail of digital data points.
A multinational conglomerate uses artificial intelligence (AI) algorithms to gather intelligence about the news you peruse, social media activity, and shopping preferences. They choose the ads you passively consume on your newsfeed and throughout your social media accounts, your internet searches, and even the music you hear, creating an incrementally increasingly customized version of reality specifically for you. Your days are subtly influenced by marketers, behavioral scientists, and mathematicians armed with cloud supercomputers. All of this is done in the name of maximizing profit to influence what you’re thinking, buying, and whom you will be electing…
Sound familiar? Apocalyptic prognoses of the impact of AI on the future of human civilization have long been en vogue, but seem to be increasingly frequent topics of popular discussion. Elon Musk, Bill Gates, Stephen Hawking, Vint Cerf, Raymond Weil, together with a host of other commentators and—of course—all the Matrix and Terminator films, have expressed a spectrum of concerns about the world-ending implications of AI. They run the gamut from the convincingly possible (widespread unemployment) to the increasingly plausible (varying degrees of mind control) to the outright cinematic (rampaging robots). François Chollet, the creator of a deep neural net platform, sees the potential for “mass population control via message targeting and propaganda bot armies.” Calls for study, restraint, and/or regulation typically follow these remonstrations.
The ICP blog series explores ideas and issues under the International Comparison Program umbrella – including innovations in price and data collection, discussions on purpose and methodology, as well the use of purchasing power parities in the growing world of development data. Authors from across the globe, whether ICP practitioners or researchers making use of ICP data, are encouraged to submit relevant blogs for consideration to [email protected].
It has been over three years since countries adopted the UN’s 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals. From the outset, a number of targets were identified to help pinpoint the desired outcomes within these broad areas – 169 in total. Monitoring progress towards each of these targets relies on data originating in countries, and which are often collected in partnership with regional and international organizations. The World Bank’s Atlas of Sustainable Development Goals used such data to visualize trends and comparisons across the globe, drawing on data from World Development Indicators and many other sources.
Purchasing Power Parity (PPP) data, from the International Comparison Program, play an important role in this monitoring: by eliminating the effect of price level differences between countries they allow us to measure living standards and other economic trends in real, comparable terms. PPPs are utilized in a number of the official SDG indicators, but also in other associated indicators, which help us to explore the underlying issues and impacts of the goals and targets more deeply. The four charts presented here exemplify the crucial insights PPPs help provide in SDG monitoring and analysis.