In 1966, Japan experienced a sudden drop in its fertility rate—for just that year. During the 1960s, the fertility rate was about 2.0 to 2.1 children per woman, but in 1966 it dropped dramatically to 1.6 children per woman (Chart 2). The number of births in 1966 was much lower than in surrounding years, as can also be seen in Japan’s population pyramid, where there’s a big dent for people born in 1966 (the highlighted bars). This isn’t an error in the data, it’s real.
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.
Goal 1 seeks to eradicate poverty in all its forms by 2030. Extreme poverty is measured using the international poverty line of $1.90 a day using 2011 PPPs. The use of PPPs ensures that the poverty line represents the same standard of living in every county. Higher poverty lines used by the World Bank better measure poverty in lower-middle and upper-middle income countries. Using these poverty lines, we can visualize the shifts in population living at various standards of living.
Our interviewers are still in the field, we need more time to complete the survey, could you extend our server for two more months? We receive such requests every day. Why do so many of our users fail to estimate the timing of their fieldwork?
Survey Solutions is a free platform for data collection developed by the World Bank and used by hundreds of agencies and firms in 143 countries. Many users of the Survey Solutions host data on free cloud servers provided by the World Bank. A user requests a server by filling in a form where he indicates the duration of the planned survey, the number of cases to be collected, and provides other relevant information. We impose no restrictions on how long a user can use the servers. Any survey end date is accepted. Over the last six years we have accumulated data on more than 2,000 surveys. We use information about surveys that collected 50 or more cases for this analysis.
How well can people conducting surveys follow the survey schedule?
Of the world’s 736 million extreme poor in 2015, 368 million—half of the total—lived in just 5 countries. The 5 countries with the highest number of extreme poor are (in descending order): India, Nigeria, Democratic Republic of Congo, Ethiopia, and Bangladesh. They also happen to be the most populous countries of South Asia and Sub-Saharan Africa, the two regions that together account for 85 percent (629 million) of the world’s poor. Therefore, to make significant continued progress towards the global target of reducing extreme poverty (those living on less than $1.90 a day) to less than 3 percent by 2030, large reductions in poverty in these five countries will be crucial.
Combinatorial innovation is driving innovation in satellite-based economic measurements at unprecedented resolution, frequency and scale. Increasing availability of satellite data and rapid advancements in machine learning methods are enabling a better understanding into the fundamental forces shaping economic development.
Why satellite data innovations matter
The desire of human beings to “think spatially” to understand how people and objects are organized in space has not changed much since Eratosthenes—the Greek astronomer best known as the “father of Geography”—first used the term “Geographika” around 250 BC. Centuries later, our understanding of economic geography is being propelled forward by new data and new capabilities to rapidly process, analyze and convert these vast data flows into meaningful and near real-time information.
International Migrants Day is a call to disseminate information on international migration and look toward further understanding its intersection with economic growth and socioeconomic wellbeing. Here we draw on data from the World Bank Gender Data Portal to highlight four big facts about women AND international migration. We focus on the “international migrant stock” which is the number of people born in a country other than that in which they live. Women, men, boys and girls experience migration differently. Accurate and timely sex-disaggregated data on international migration is critical for uncovering the specific needs and vulnerabilities of women and men and for shaping migration policy.
Globally, women are on the move: they comprise slightly less than half of all international, global migrants. In fact, the share of women among global, international migrants has only fallen slightly during the last three decades, from 49 percent in 1990 to 47 percent in 2017.
The record-high number of forcibly displaced people today—refugees, asylum seekers and internally displaced persons (IDPs)—has underscored the need to improve the way the global community addresses these situations. The new global compact on refugees adopted at the UN General Assembly on December 17th will guide these efforts.
It is widely acknowledged that statistics are critical to inform our response, but until recently, there were no global standards. Lacking international guidance, different institutions produced data on forced displacement without due coordination or transparency. Terminology was inconsistent, making data incomparable. Statistical capacity varies between countries, and refugees and asylum seekers were not included in national censuses or regular migration and population statistics.
The World Bank data catalog is an ongoing effort to provide a “one-stop shop” for all Bank data related to development. That aspiration took a big step forward this week as we completed the addition of datasets from the World Bank’s ENERGYDATA.INFO platform. ENERGYDATA.INFO continues to provide public access to hundreds of datasets from over a dozen organizations on topics such as solar and wind measurement data, electricity transmission networks and energy access. Users may now search for and download those same datasets from the Bank-wide catalog.
This integration is similar to previous efforts to provide greater data access. The World Bank’s finances platform, microdata platform, and open data catalog have all been added to the data catalog. For data users and Bank staff alike, there is a clear benefit in being able to search and access all available data from a single online location. The data catalog also provides a consistent approach to data licensing, so users can understand which datasets are open data, which are subject to third-party terms, and which may carry other restrictions.
Shared prosperity is one of the World Bank Group’s Twin Goals, introduced in 2013. Progress toward this goal is monitored through an indicator that measures the annualized growth rate in average household per capita income or consumption among the poorest 40 percent of the population in each country (the bottom 40), where the bottom 40 are determined by their rank in household per capita income or consumption. Chapter 2 of the 2018 Poverty & Shared Prosperity Report provides an update on the recent mixed progress on shared prosperity around the world in about 2010-15.
The shared prosperity indicator was proposed as a means to shine a constant light on the poorest segments of the population in every country, irrespective of their level of development. Shared prosperity has no target or finish line, because the aim is to continuously improve well-being. In good times and in bad, in low and high-income economies alike, the bottom 40 percent of the population in each nation would be monitored. Tracking the bottom 40’s absolute growth as well as their growth relative to the mean is a way to remind us to always consider distributional impacts and strive for equitable outcomes.
An important but challenging goal to monitor
Despite its importance and universal relevance, shared prosperity is more challenging to monitor than global poverty. While one household survey is sufficient to calculate poverty, shared prosperity measurement requires two recent comparable surveys.
The implication of this stronger data requirement is that 91 out of the 164 economies with an international poverty rate measured in PovcalNet are included in the 6th edition of the Global Database of Shared Prosperity (GDSP).