Toward a New Definition of Shared Prosperity: A Dynamic Perspective from Three Countries

By the standards of a very austere international poverty line, such as the World Bank’s $1.25 per person per day in 2005 Purchasing Power Parity (PPP) dollars, global poverty has fallen rapidly in recent decades (World Bank, 2015a). In many countries of the world, absolute poverty defined in these terms no longer affects significant segments of the population. This is a remarkable achievement that is rightly celebrated — even though it is clear that in certain countries and certain parts of the world, extreme poverty by this standard remains both widespread and stubbornly resistant to change. But poverty is not only thought of in absolute terms, and on the basis of an international standard. Most countries of the world assess poverty in their societies on the basis of national poverty lines that are largely anchored to the standards, expectations, and aspirations of their own societies. With social progress and economic growth, these standards typically evolve and consequently the poverty thresholds underpinning national poverty analysis also tend to rise (Ravallion and Chen, 2011). In this context, attention in many countries is shifting away from merely a focus on the rate of income growth among the poorer population groups towards also conducting an assessment of the quality of this growth. In particular, a key question that resonates in many countries is whether the poor are able to participate to the same degree and extent as the non-poor in a given country’s growth process; whether they are sharing equally in the country’s rising prosperity. In an attempt to provide a quantifiable measure that engages with these concerns, the World Bank has recently proposed a definition of “shared prosperity” as growth in the income of the bottom 40 percent of the income distribution over time (for example, Basu, 2013; Jolliffe et al., 2015). 1 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.