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.