Further reflections on shared prosperity…and regress

Posted in: Poverty

By Mohd Umair Khan

The World Bank recently published the Poverty and Shared Prosperity (SP) report for 2020 (see this CDS blog). In this blog, I reflect further on the adoption of SP as the second core goal for the World Bank to accompany its other goal of extreme poverty reduction. I argue that while the SP indicator can be criticised in various ways its adoption is still historically important.

In 2013, the World Bank (WB) introduced SP as one of its twin goals, the other goal being the reduction of extreme poverty to less than 3% (Nadkarni, 2015). The WB defines Shared Prosperity as growth in the average income or consumption of the poorest 40% of the population of a country (World Bank, 2013). To monitor and report the progress on both the goals, the WB publishes a report every two years. Unlike the goal of extreme poverty reduction, the SP measure is a relative measure of wellbeing, because the poorest 40% in each country fall in different global income deciles. For example, a person below the 40th income percentile in the United States of America is richer than a person with the same position in South Africa.

The SP goal aims for growth of the incomes of the poorest 40% of the population to be faster than the income growth of the total population (World Bank, 2013). The Shared Prosperity Premium (SPP) is measured by subtracting the income growth rate of the total population of a country from the income growth rate of the poorest 40% of the population[i]. With regards to the availability of data and consistent tracking of progress, the data for SP measurement is extracted from the household surveys which are periodically conducted in, not all, but many countries. In its 2020 SP report, the World Bank has included 91 economies. For the poorest populations, the income growth data is available for a relatively large number of developing economies than the data on non-income measures of inequality.

Scholars have discussed the concept of SP for a long time, and it can be linked particularly to the work of John Rawls (Jolliffe, 2014). One of the pioneering papers on SP is by Basu (2001), and draws inspiration from Rawls’ (1971) “difference principle”. This states that “social and economic inequalities are to be arranged so that they are…to the greatest benefit of the least advantaged” (p. 302). Basu was the Chief Economist at the WB at the time of the introduction of this idea of SP in 2013.

The idea of SP is linked to inclusive or pro-poor economic growth, which can be defined in two distinct ways. One is that growth in the income of poorer people is more than that of average income growth, and the other is that economic growth should be accompanied by a fall in chosen poverty indicators (Ravallion, 2004). The World Bank’s SP indicator is based on the first and relative definition. It is not entirely new to World Bank policy. For example, the 2015 and 2020 reports on SP mention excerpts of a speech by Robert McNamara in 1972, in which he advocated policies to reduce the deprivation of the bottom 40% of the population (World Bank Group, 2015). Nevertheless, its official adoption as an overarching goal for the institution alongside absolute poverty reduction represented an important shift.

Shared Prosperity has a number of limitations which are highlighted in my dissertation with the help of two country cases. I will look at two of these: reliance on income as a single dimension of wellbeing and the issue of the weak transfer axiom. Reliance on income as an indicator has been questioned by many economists, including Sen (1997) and Stewart (2000). Sen (1997) highlights how the complexities of individual circumstances affect the conversion of income and other resources into functioning’s (being and doings). Stewart (2000) emphasises how social structure and identities (including location, class, religion, race, and gender) mean that people on similar incomes can experience very different levels of wellbeing.

As per the 2018 SP report, Brazil and China have experienced the SPP of 1.6% and 1.7% respectively. That is, the incomes of the poorest 40% are growing faster than that of the rest of the population. A comparison of both the countries illustrates how non-income factors can help to explain the persistence or reduction of inequality in countries experiencing income growth among the poorest. Employing the Multidimensional Inequality Framework (MIF) Domains 1, 3 and 4 (focusing on Life & Health, Education & Learning, and Financial Security & Dignified Work respectively), my research revealed that SP (i.e. faster per capita economic growth in the incomes of the poorest 40%) between 2010-2015 in Brazil and China coincided with persistent inequalities in these domains, and in some subdomains inequality even increased (McKnight et al., 2019). In Brazil, the drivers of persistent inequality were race, gender, and location; while for China the driving forces were location and socio-economic status. Brazil experienced persistent inequality in all the three domains and most measures of the MIF, whereas China experienced inequality in the measures of domains 1 and 4. Rising number of deaths due to non-communicable diseases, location disparity in terms of education completion rate, and lack of social protection schemes were some of the common themes in both the countries.

A second limitation of SP is that even if we consider income to be a “reasonable first proxy for standard of living” (Crow et al., 2009, p. 1053), the World Bank SP measure does not satisfy the ‘weak transfer principle’ of income distribution. This states that within the population below 40%, if a transfer takes place then it should impact the inequality measurement (Rosenblatt and McGavock, 2013; Bosmans et al, 2009). This can be illustrated with an example: imagine there are two people in South Africa named A and B having income of $5 and $11 respectively. Person B decides to transfer $3 (out of his $11) to person A so the new distribution in South Africa will be $8 and $8 for A and B respectively. The average income of South Africa in both situations is the same ($8), but there is definitely less inequality after the transfer. The SP measure takes into account only the average income growth, which remains the same. It therefore misses out on the distributional aspect. Therefore, even with reference to income dimension the SP measure has serious limitations when it comes to understanding inequality.

Against these arguments, the SP does have the advantage of simplicity. By focusing on the single dimension of income it is more comprehensible for policy makers, and builds on the consensus among economists that income growth is a necessary if not sufficient condition for poverty reduction. Moreover, Basu (2013) argues that income “in reality is a highly multidimensional metric”. It consists of real income, and as such is an aggregation of expenditure on food, clothing, housing and other items. He goes on to say that the debate between income and multidimensional measures of wellbeing often ignores the fact that income also reflects a degree of multidimensionality. Viewed in this way, the SP is simple yet comprehensive and multidimensional at the same time. The ease with which SP can be understood and interpreted also goes some way to offset its limitations in reflecting the effects of all income transfers, when compared to the Gini coefficient, for example.

As explained above, the SP combines twin objectives: first is to achieve income growth of poorest 40% and second is to maximise the SPP. The second is a potentially more powerful goal than the first. Striving to maximize the SPP could be given more emphasis over merely reaching the SP threshold, given that the World Bank releases data on both. And it is a relatively small additional step to monitor policy against the SPP not only the bottom 40% but also for the bottom 20% and 10%, as well as the corresponding indicators for other individual components of multidimensional wellbeing (Basu, 2013).

The COVID-19 shock has shed light on another important takeaway from the idea of SP. The dual of SP is that the poorest 40% should not lose out disproportionately during downturns. The Shared Prosperity report for 2020 reports that the pandemic (combined with conflict and climate change effects) have reversed the gains in poverty reduction (World Bank, 2020). In the baseline scenario, the global per capita GDP is set to decline by 5 percentage points, and poverty would increase 1.4 percentage points in 2021 (World Bank, 2020). The global poverty shock will be uneven, with South Asia being the hardest hit followed by Sub-Saharan Africa. Employment and food security shocks mean the poorest people in many countries have suffered disproportionately losing during crisis.

SP has limitations as a lead indicator of welfare alongside absolute poverty, and needs to be supplemented by other measures inequality in income and other dimensions of wellbeing. However, these arguments do not diminish the historical significance of the shift in the World Bank’s priorities and goals towards a vision of development that gives more emphasis to relative poverty as well as absolute poverty, and hence to the importance of social relationships and absolute resource scarcity.



[i] Shared Prosperity Premium (SPP) = G40 - Gaverage (Here, G40 stands for the per-capita income growth rate of the bottom 40% of the population and Gaverage stands for the per-capita income growth rate of the entire country).


Mohd Umair Khan was a Commonwealth Shared Scholar at the University of Bath (2019-2020) and graduated with a MSc International Development with Economics. This blog is based on research from his master’s dissertation. 



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Posted in: Poverty


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