Poverty and Shared Prosperity: Let’s Move the Discussion Beyond Growth^*

* The views expressed in this paper are those of the authors and do not necessarily represent those of any of their affiliations, the World Bank Group or their member countries.

Abstract

Some authors argue that it is enough to focus on growth to achieve lower poverty and greater shared prosperity. Policy-makers are warned that any effort to make growth more equal would be a distraction at best and could even be detrimental. Achieving the World Bank target of a 3% poverty rate by 2030 will require, however, more targeted policies favoring the poorest segments of the population. But what would be these policies? While studies investigating determinants of GDP growth have been numerous, less is known about factors influencing household incomes at the lowest segments of the income distribution. This paper estimates income drivers for the poorest two income quintiles drawing on a panel of 117 countries over the period 1967–2011. Its results suggest that maintaining macroeconomic stability as well as investing in human and physical capital would not only be associated with faster overall economic growth, but also with even faster income growth for the poorest segments of the population. This paper confirms the central role overall economic growth should play in any strategy to reduce poverty. Its results suggest, however, that in addition policy-makers may have instruments to tweak the distribution of the benefits of faster economic growth in favor of the households at the bottom of the income distribution. There thus need not be a trade-off between inequality and growth.

Keywords:

• growth
• inequality
• poverty
• education
• infrastructure
• macroeconomic stability
• panel data

JEL Classifications:

• O11
• D31
• O54

Acknowledgments

The authors wish to thank Roland Kpodar, David Cal McWilliams, Ambar Narayan, Vinaya Swaroop and Cédric Tille for their helpful comments on earlier drafts and Aart Kraay for sharing his data, as well as the participants at the ASSA Conference, 5–8 January 2017, in Chicago.

Notes

* The views expressed in this paper are those of the authors and do not necessarily represent those of any of their affiliations, the World Bank Group or their member countries.

¹ The World Bank has adopted the Twin Goals of ending extreme poverty (defined as a fall of the percentage of people living with less than $1.25 a day to no more than 3% globally by 2030) and promoting shared prosperity (defined as income growth of the bottom 40% of the population in every country).

² Ravallion (2013).

³ World Bank (2015).

⁴ See, inter alia, Lübker, Smith, and Weeks (2002) and Amann, Aslanidis, Nixson, and Walters (2006) for a critical assessment of the Dollar and Kraay (2002) paper.

⁵ See for instance Le Goff and Singh (2014) or Singh and Huang (2015).

⁶ POVCALNET data are either income or consumption, LIS data is disposable income. We still refer to “income” in our paper.

⁷ From the International Country Risk Guide (ICRG) rating, the Democratic Accountability indicator goes from 0 to 6, the highest score for responsive governments. Generally, higher scores are assigned to Alternating Democracies, while the lower scores assigned to Autarchies.

⁸ Dollar et al. (2016) use Bayesian Model Averaging to try to deal with model uncertainty. Ciccone and Jarociński (2010) have, however, questioned this approach, arguing that the results could be too sensitive to small changes in the dependent variable. To overcome this difficulty, Rockey and Temple (2016) recommend to include initial incomes and regional or country fixed effects. While Dollar et al. (2016) include the former, they do not include the latter although they recognize statistically significant differences in the growth-poverty elasticities between regions. Against this backdrop, we preferred adopting here the more traditional OLS framework with the appropriate controls.

⁹ The robustness of the results has been investigated by imposing various sample restrictions and adding control variables. For instance, only the post-1980 period was considered, excluding the period where only LIS data are available, fewer and covering only OECD countries. Additional potential control variables were also added such as terms of trade, government debt, government consumption, urbanization, different governance indicators, and the share of agricultural GDP. In all cases, the results reported above remain unchanged.

¹⁰ Increased health might have effects on growth via various channels. See e.g. the contributions by Knowles and Owen (1995), Bloom, Canning, and Sevilla (2003), Well (2007), and Jayachandran and Lleras-Muney (2009).

¹¹ See Singh and Huang (2015), for instance, for a review of this literature.