Is artificial intelligence the future for economic development? Earlier this month, a group of World Bank staff, academic researchers, and technology company representatives convened at a conference in San Francisco to discuss new advances in artificial intelligence. One of the takeaways for Bank staff was how AI technologies might be useful for Bank operations and clients. Below you’ll find a full round-up of all the papers and research-in-progress that was presented. All slides that were shared publicly are linked here, as well as papers or other relevant sites.
In chapter 1 of book 1 of Adam Smith’s foundational economics book, The Wealth of Nations, he explains the concept of the division of labor. He uses the example of a pin factory.
To take an example, therefore, from a very trifling manufacture, but one in which the division of labour has been very often taken notice of, the trade of a pin-maker: a workman not educated to this business (which the division of labour has rendered a distinct trade, nor acquainted with the use of the machinery employed in it (to the invention of which the same division of labour has probably given occasion), could scarce, perhaps, with his utmost industry, make one pin in a day, and certainly could not make twenty.
Many of today’s increasingly complex development challenges, from rapid urban expansion to climate change, disaster resilience, and social inclusion, are intimately tied to land and the way it is used. Addressing these challenges while also ensuring individuals and communities are able to make full use of their land depends on consistent, reliable, and accessible identification of land rights.
Small differences in the time and cost to trade can determine whether or not a country participates in global value chains. In this respect, the World Trade Organization’s (WTO) Trade Facilitation Agreement (TFA), which came into force on February 22, 2017, is a landmark achievement given its comprehensive coverage of the issues around cutting red tape and promoting efficiency and transparency, as well as the fact that it is the first multilateral agreement since the establishment of the WTO in 1995. Coincidentally, the Trading Across Borders (TAB) indicator of Doing Business measures the efficiency of national regulations in trade facilitation and keeps track of relevant reforms, allowing us to analyze how the provisions of the TFA are related to the reform efforts of governments around the world.
Many insights from behavioral science apply directly towards better understanding and addressing inequalities between men and women, in education and health, ownership of assets, access to more and better jobs, and the capacity to act on one’s own behalf and interests.
Here are three insights that stand out as critical to closing these inequalities by 2030.
Non-energy prices advanced by over 1 percent. Agricultural prices increased almost 2 percent, largely on higher prices for soybean meal (+12 percent), cocoa (+9 percent), maize and sorghum (+5 percent each). Fertilizer prices rose 2 percent, led by phosphate rock (+6 percent), DAP, and Urea (+2 percent each).
Machine learning methods are increasingly applied in the development policy arena. Among many recent policy applications, machine learning has been used to predict poverty, soil properties, and conflicts.
In a recent Policy Research Working Paper by Paolo Brunori, Paul Hufe and Daniel Mahler (BHM hereafter), machine learning methods are utilized to measure a popular understanding of distributional injustice – the amount of unequal opportunities individuals face. Equality of opportunity is an influential political ideal since it combines two powerful principles: individual responsibility and equality. In a world with equal opportunities, all individuals have the same chances to attain social positions and valuable outcomes. They are free to choose how to behave and they are held responsible for the consequences of their choices.
In the wake of the Global Financial Crisis (GFC), many wondered whether the strong pre-crisis trend toward greater internationalization in banking would be reversed and, more immediately, whether local state-owned banks had to assume a larger role in restoring banking stability and ensuring the delivery of credit. We revisit those conjectures in the light of new data on bank ownership and research on the post-Crisis period (Cull, Martinez Peria, and Verrier, 2018).