How has your life changed for you compared to your parents or grandparents when they were your age? How do you see your children’s lives and possibilities compared to your own? To find out we’ve kicked off a social media campaign to highlight the issue of intergenerational mobility. And we invite you to take part in the #InheritPossibility campaign and share your stories.
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.
We know that fiscal policy can be harnessed to reduce inequality in low- and middle-income countries, but until now, we knew less about its ability to reduce poverty. Our recent volume looks at the revenue and spending of governments across eight low and middle income countries (Armenia, Ethiopia, Georgia, Indonesia, Jordan, Russia, South Africa and Sri Lanka), and it reveals that fiscal systems, while nearly always reducing inequality, can often worsen poverty.
Inequality can be both good and bad for growth, depending on what inequality and whose growth. Unequal societies may be holding back one segment of the population while helping another. Similarly, high levels of inequality may be due to a variety of factors; some good, some bad for growth.
Long one of the world’s most unequal countries, Brazil surprised pundits by recording a massive reduction in household income inequality in the last couple of decades. Between 1995 and 2012, the country’s Gini coefficient for household incomes fell by seven points, from 0.59 to 0.52. (For comparison, all of the inequality increase in the United States between 1967 and 2011 amounted to eight Gini points – according to this study.)
In the ongoing debate about the benefits of trade, we must not lose sight of a vital fact. Trade and global integration have raised incomes across the world, while dramatically cutting poverty and global inequality.
Within some countries, trade has contributed to rising inequality, but that unfortunate result ultimately reflects the need for stronger safety nets and better social and labor programs, not trade protection.
This is the third of three blog posts on recent trends in national inequality.
In earlier blogposts on recent trends in inequality, we had referred to measurement issues that make this exercise challenging. In this blogpost we discuss two such issues: the underlying welfare measure (income or consumption) used to quantify the extent of inequality within a country, and the fact that estimates of inequality based on data from household surveys are likely to underreport incomes of the richest households. There are a number of other measurement challenges, such as those related to survey comparability, which are discussed in Poverty and Shared Prosperity 2016 – for a focus on Africa, also see Poverty in a Rising Africa, published earlier in 2016.