Syndicate content

Sweden

Machine learning and the measurement of injustice

Daniel Mahler's picture

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.

Why Didn’t the World Bank Make Reducing Inequality One of Its Goals?

Jaime Saavedra's picture

The World Bank Group (WBG) has established that its mission, endorsed by the governors of its client countries, is centered around the goals of sustainably ending extreme poverty and promoting shared prosperity.  Extreme poverty is monitored by the percent of people living below the $1.25-a-day threshold.  The Bank’s mission thus gives a clear message:  Extreme poverty, hunger, destitution must come to an end.

To monitor progress in shared prosperity, the WBG will track the income growth of the bottom 40 percent of the population in each country.  The clear signal the WBG wants to give is that the institutional mission is about reducing poverty, fostering growth and increasing equity, so we need to monitor what happens to welfare of the less well off in every country.  Improving averages is not enough; a laser focus on those who are at the bottom of the distribution at all times, everywhere, is needed.

Protecting the vulnerable during crisis and disaster: Part II Ethiopia’s Productive Safety Net Program

Matt Hobson's picture

The following post is a part of a series that discusses 'managing risk for development,' the theme of the World Bank’s upcoming World Development Report 2014.

Despite more than 19 episodes of severe food shortage in Ethiopia since 1895, it was the dramatic images of famines in 1972 and 1984 which came to the world’s attention and (wrongly) made Ethiopia synonymous with drought and famine. Despite consistent food shortages in Ethiopia for decades, it only became clear in the run-up to the 2002/3 drought that, while the humanitarian system appeared to be saving lives, it was proving to be ineffective in saving livelihoods and managing risks effectively. In essence, rural Ethiopians had faced chronic food insecurity for decades, but were receiving ‘treatment’ for transitory food insecurity. In part as a result of this misdiagnosis, rural Ethiopians were becoming increasingly less resilient to drought and were unable to manage risks effectively. This realization prompted the birth of the Productive Safety Net Programme (PSNP).