The quest for pro-poor and inclusive growth: the role of governance

ABSTRACT

This paper analyses the role of good governance in fostering pro-poor and inclusive growth. Using a sample of 112 countries over 1975–2012, it shows that growth is generally pro-poor. However, growth has not been inclusive, as illustrated by a decline in the bottom 20 percent of the income distribution. While all features of good governance support income growth and reduce poverty, only government effectiveness and the rule of law are found to enhance inclusive growth. The investigation of the determinants of pro-poor and inclusive growth highlights that education, infrastructure improvement, and financial development are the key factors in poverty reduction and inclusive growth. Relying on the panel smooth transition regression (PSTR) model, the paper identifies a nonlinear relationship between governance and pro-poor growth, while the impact of governance on inclusive growth appears to be linear.

KEYWORDS:

• Pro-poor growth
• inclusive growth
• governance
• PSTR

JEL CLASSIFICATION:

• C23
• G28
• O11
• O57

Disclosure statement

The findings, interpretations, and conclusions expressed in this paper are entirely those of the author and should not be attributed in any manner to the World Bank.

Acknowledgement

The author is grateful to Jean-Bernard Chatelain, Jean-Louis Combes, Philippe de Vreyer, Ivailo Izvorski, Tidiane Kinda, Aart Kraay, participants to the African Development Bank and UNDP 2015 Annual Economic Conference and to the two anonymous referees whose comments led to substantial improvements in the paper. I would like to thank Romaric Sodjanhin, Fousseynou Bah and Thanh Thi Thanh Bui. I am also thankful to David Dollar, Aart Kraay and Tatjana Kleineberg for making their data on the income of the poor available.

Notes

¹ Appendix A1 presents the list of countries.

² The latter period is the mean of the two previous years. The results remain unchanged if the baseline results are reproduced without the two previous years. This approach has been used in the literature. For instance, Giuliano and Ruiz-Arranz (2009) split their sample period into six nonoverlapping five-year periods, except for the last period, which is the average of the three previous years.

³ Growth in average income can shift the income distribution, while variations in inequality can also change the shape of income distribution. Both of these effects can impact the income of the poor and the poverty headcount ratios.

⁴ See Dollar, D., Kleineberg T., and Kraay, A., 2016. ‘Growth Still Is Good for the Poor.’ European Economic Review, Elsevier, vol. 81(C): 68–85.

⁵ In the paper, per capita income and per capita GDP are equivalent.

⁶ The point estimates range from −2.5 (weak governance) to 2.5 (strong governance).

⁷ In column (1), the log of GDP per capita and the square of GDP per capita are used as explanatory variables to consider the Kuznets relationship (Barro 2008; Woo et al. 2017). In the literature, the existing evidence of the Kuznets curve is mixed. Our data do not support evidence of the Kuznets inverted U-shaped link between GDP per capita and inequality (measured by the income shared of the poorest 20 percent).

⁸ The index is constructed through principal component analysis.

⁹ This paper does not find a significant effect of trade openness on the income of the poor. Results in the empirical literature are mixed on this. For instance, Lopez (2004) suggested that the impact of trade openness on the poor might vary according to the sectors in which the poor are concentrated. Measuring trade openness as the volume of trade adjusted by a country’s size and population, he found that while trade openness appears to increase poverty in the short run, it is negatively correlated with poverty in the long run.

¹⁰ The quality of governance is captured by two indicators: the aggregated governance indicator and control of corruption.

¹¹ The paper retains control of corruption as a proxy for good governance in pro-poor growth regressions.

¹² This paper relies on an approach that consists in splitting the sample below and above the median of variables of interest in order to capture the potential nonlinear relationship (see Giuliano and Ruiz-Arranz 2009). Unlike regressions with interaction terms, this approach relies on a threshold point, which is the median level of variables of interest (log of GDP per capita and control of corruption).

¹³ The transition function depends upon the governance indicator: control of corruption.

¹⁴ The paper retains government effectiveness because this is the main significant variable in the inclusive growth regressions.

¹⁵ Appendix A4 presents correlations for Table 1 (Benchmark results) in which the paper uses only lag 1 as instruments.