Annex I. Assessing Inclusiveness of Growth: Some Theoretical Considerations
Inclusive growth should simultaneously reduce poverty and inequality. Growth reduces poverty if the mean income of the poor rises. Growth reduces inequality if it helps straighten the Lorenz curve, which plots the percentage of total income earned by various portions of the population when the population is ordered by the size of their incomes. More formally, starting from Ravallion and Chen (2003), the growth incidence curve, which traces out variability of consumption or expenditure growth by the percentile of the population, can be defined as
gt(p) = γt, if
: growth at each decile of incidence curve will be equal to the average growth of the distribution at each decile of population, if the slope of the Lorenz curve does not change over time.
gt(p) > γt, if
: growth at each decile of the incidence curve will be higher than the average growth of the distribution at each decile of population, if the slope of the Lorenz curve increases.
gt(p) < γt, if
: growth at each decile of the incidence curve will be lower than the average growth of the distribution at each decile of population, if the slope of the Lorenz curve decreases.
The slope of the incidence curve is positive if
The slope of the incidence curve is negative if
Therefore, based on the incidence curve, pro-poor and inclusive growth can be derived as follows. Assuming for simplicity of illustration that the incidence curve is linear (Figure 12), (i) pro-poor growth shifts the mean expenditure (or consumption) of the poor up; the slope of the incidence curve is irrelevant and may be positive, suggesting that growth is not inclusive; (ii) pro-poor inclusive growth shifts the mean expenditure up while the incidence curve is negatively sloped; (iii) accelerations of pro-poor growth just shift the median income further up, while the slope of the incidence curve may remain positive, suggesting the growth remains noninclusive; (iv) an increase in the inclusiveness of growth suggests that the incidence curve becomes negatively sloped (g), the slope increases (g’) and/or the whole curve shifts to g” as inequality declines and
From an operational perspective, to assess inclusiveness of growth a country should take a number of actions: (i) establish the slope of the incidence curve based on the information of at least two sequential household surveys; (ii) if the slope is positive, suggesting that growth has not been inclusive, identify measures that could increase income and spending of the lowest deciles, while increasing the mean growth rate, that is, not at the expense of higher deciles; (iii) if the slope of the incidence curve is negative, suggesting growth has been inclusive, identify measures to increase the slope by making growth of consumption of lower deciles even faster, without hampering any other deciles; (iv) alternatively or in addition, find a measure to reduce inequality in the Lorenz curve coefficient in the next period that would shift the entire incidence curve up.
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)| false , Azam, J., M. Dia, C. Tsimpo, and Q. Wodon 2007, “ Has Growth in Senegal After the 1994 Devaluation Been Pro-Poor?” in Growth and Poverty Reduction: Case Studies from West Africa, World Bank Working Paper No. 79, January, pp. 45– 67( Washington: World Bank).
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The author is grateful to H. Joly, C. Kolerus, D. Ross, and R. Garcia-Verdu for careful reading and helpful comments. The author thanks World Bank colleagues (Y. Batana, P. English, S. Radyakin, R. Swinkels, D. Ndoye) for providing the databases and useful discussions. Research assistance from D. Shapiro is gratefully acknowledged. Any remaining errors are the author’s. The findings of this paper were presented in Dakar to the Senegalese authorities in September 2012 during the 2012 Article IV Consultations and were issued as an appendix to IMF Country Report No. 12/337.
Most comparisons in this paper are based on the data from household surveys. The most recent survey for Senegal was conducted in 2011, whereas for most SSA countries the latest surveys were published in 2005–10.
Methodological differences between national and internationally comparable poverty-related estimates are documented and discussed in detail on the World Bank PovCalNet site (http://iresearch.worldbank.org/PovcalNet).
Based on data from income, expenditure, household, and budgetary surveys conducted by the Senegalese authorities in 1991–2011 and processed by the World Bank through PovCalNet, an online poverty calculation tool (http://iresearch.worldbank.org/PovCalNet).
The squared poverty gap index averages the squares of the poverty gaps relative to the poverty line. It takes into account not only the distance separating the poor from the poverty line (the poverty gap), but also the inequality among the poor because it places a higher weight on households further away from the poverty line.
The Watts index is defined as a logarithm of the quotient of the poverty line and a geometric mean of an income standard applied to the censored distribution.
An index of inequality is given by the mean across the population of the log of the overall mean divided by individual income.
PPP-based calculations. The Gini index and income shares may differ from the aggregates used for the national poverty lines. The Gini index based on ESAM 2001-2002, ESPS 2005-2006 and ESPS 2011 household surveys was 39.2 in 2001, 38.1 in 2005, and 37.8 in 2011. All income/consumption shares by decile are based on estimated Lorenz curves. Households are ranked by income or consumption per person. Distributions are population (household-size and sampling expansion factor) weighted.
Lt(p) is the fraction at time t of total income that the holders of the lowest pth fraction of incomes possess. This varies from zero to one, 0 ≤ p ≤ 1, presented as the inverse of the cumulative distribution function.