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Africa is on time

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Abstract

We present evidence that the recent African growth renaissance has reached Africa’s poor. Using survey data on African income distributions and national accounts GDP, we estimate income distributions, poverty rates, and inequality indices for African countries for the period 1990–2011. We show that: (1) African poverty is falling rapidly; (2) the African countries for which good inequality data exists are set to reach the Millennium Development Goal (MDG) poverty target on time. The entire continent except for the Democratic Republic of Congo (DRC) will reach the MDG in 2014, one year in advance, and adding the DRC will delay the MDG until 2018; (3) the growth spurt that began in 1995, if anything, decreased African income inequality instead of increasing it; (4) African poverty reduction is remarkably general: it cannot be explained by a large country, or even by a single set of countries possessing some beneficial geographical or historical characteristic. All classes of countries, including those with disadvantageous geography and history, experience reductions in poverty. In particular, poverty fell for both landlocked as well as coastal countries; for mineral-rich as well as mineral-poor countries; for countries with favorable or with unfavorable agriculture; for countries regardless of colonial origin; and for countries with below- or above-median slave exports per capita during the African slave trade.

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Notes

  1. By “reasonable amount of data” we mean at least two consumption or income surveys conducted between 1990 and 2011.

  2. Young (2012) has recently argued that traditional sources of national accounts data understate African growth by several percentage points of GDP per year. However, Alwyn Young does not use this finding to compute poverty or inequality estimates for Africa. The novelty of our contribution is to show that not only is Africa growing rapidly, but this growth is translating into poverty reduction fast enough to achieve the MDGs at or close to the target date of 2015. In particular, we show that if Alwyn Young’s growth estimates for Africa are extrapolated after 1990, poverty reduction is even more striking and the MDG has already been achieved.

  3. Bloom and Sachs (1998) suggest that landlocked countries, or countries with unfavorable agriculture have poorer performance than geographically advantaged countries. Porta et al. (1999) argue that the identity of the colonizer may matter for subsequent economic development. Nunn (2008) presents evidence that the impact of the African slave trade was highly persistent, and affected recent African performance.

  4. For example, the Nigerian survey mean in the Chen–Ravallion dataset declines by \(-0.04\) % per year between 1992 and 2010, while Nigerian GDP grows by 2.2 % per year during the same time period in the World Bank’s GDP series. For Ethiopia, Tanzania and Mozambique, survey mean growth rates are one-third to two-thirds of the national account growth rates.

  5. Various definitions of the $1-a-day poverty line have been used in the literature; we use the $1.25-a-day line in 2005 PPP, which is currently used by the World Bank.

  6. This argument is made in Easterly (2009).

  7. Another partial explanation for the discrepancy between national accounts-based poverty estimates and those made on the basis of Alwyn Young’s growth findings is that the sample of countries considered by Alwyn Young tended to have faster poverty reduction. Figure 13 shows that once we consider poverty reduction in the Alwyn Young sample only (this time, without restricting ourselves only to the Group A countries in that sample to maintain comparability to Alwyn Young’s results) the discrepancy between our national accounts-based series and the series based on Alwyn Young’s findings shrinks by 25 %.

  8. For consistency with our baseline results, we will present regional results for Group A countries only. However, the regional analysis of all African countries, if anything, strengthens the patterns we find.

  9. For the mineral-rich/mineral-poor breakdown, as well as for the favorable/unfavorable agriculture breakdown, omitting the countries not classified by Nijkam (2008) from the analysis does not qualitatively change the results

  10. All of these plots show inequality between African citizens within and across countries. It may be argued that while growth accrued to the poorest African countries, within-country inequality in Africa may have risen. While the Gini cannot be decomposed into between and within components, we have looked at (Group A) African within-country inequality as measured by the Atkinson inequality index and the Generalized Entropy index. Within-country Group A African inequality declines between 1990 and 2011 for parameters of the Atkinson index between 0.5 and 2, and for parameters of the GE index between \(-1\) and 1.5.

  11. For Africa as a whole, inequality declines but trivially (by a few tenths of a percentage point), and rises (also trivially) between 1990 and 2006 if PWT 7.1 GDP is used.

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Acknowledgments

We are very grateful to Oded Galor and two anonymous referees for outstanding suggestions and comments. Pinkovskiy would like to thank the Paul and Daisy Soros Foundation for New Americans for intellectual stimulation, and the NSF GRFP and the Institute of Humane Studies for funding. This paper solely represents the views of the authors and not necessarily of the organizations listed above, of the Federal Reserve Bank of New York or of the Federal Reserve System. All errors are our own.

Author information

Authors and Affiliations

  1. Federal Reserve Bank of New York, New York, NY, USA

    Maxim Pinkovskiy

  2. Columbia University and NBER, New York, NY, USA

    Xavier Sala-i-Martin

Authors
  1. Maxim Pinkovskiy
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  2. Xavier Sala-i-Martin
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Correspondence to Xavier Sala-i-Martin.

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Appendices

Appendix I: Construction of combined PWT GDP

Johnson et al. (2013) caution against using recent versions of the Penn World Tables to obtain purchasing-power-parity adjusted estimates of GDP for prior years. In particular, they suggest assessing relative GDPs of countries in a given year by using the PWT created closest to that year. In light of this suggestion, we construct our GDP measure as follows:

  1. 1.

    We start with the 2011 estimate of GDP from PWT 7.1 (base year 2005). These are computed by taking the 2010 estimates and updating them with the growth rates for 2010-2011 from the World Development Indicators, since there is no data for 2011 in PWT 7.1

  2. 2.

    We generate estimates for prior years with growth rates computed as follows:

    1. a.

      PWT 7.1 growth rate for years 2005 and later.

    2. b.

      Average of PWT 6.2 and PWT 7.1 growth rates for years between 2000 and 2004.

    3. c.

      Average of PWT 6.1 and PWT 6.2 growth rates for years between 1996 and 1999.

    4. d.

      Average of PWT 5.6 and PWT 6.1 growth rates for years between 1991 and 1995.

Recently, PWT 8 has been released at the University of Groningen. However, its methodology is considerably distinct from that of the first 7 iterations of the PWT, so we do not use any growth rates from it for our computations.

Appendix II: Consumption adjustment

To adjust consumption surveys in order to use them in our analysis, we adapt the procedure of Bhalla (2002). We select all country-years from the UNU-WIDER World Income Inequality Database for which both income and consumption surveys are available, and manually select which income and consumption surveys of those available for a given country-year to use. We base our selection on 1) similarity of source, and 2) similarity of income sharing units, units of analysis and equivalence scales. Altogether, we have 100 pairs of income and consumption surveys.

We then estimate the system of seemingly unrelated equations:

$$\begin{aligned} q_{ijI} =\beta _j q_{ijC} +u_{ij} , J=1,\ldots 5 \end{aligned}$$

where \(q\) is the quintile share, \(I\) and \(C\) index income and consumption, \(i\) indexes observations (country-years), and we allow the \(u_{ij}^{{\prime }s}\) to be correlated across \(j\) (since quintile shares must sum to unity, the errors in the above regression are probably correlated across quintile shares). We exclude a constant from estimation. Our estimates are as follows:

 Seemingly unrelated regression

Equation

Obs

Parms

RMSE

“R-sq”

chi2

P

 

q1_I

100

1

1.785765

0.8624

1498.20

0.0000

 

q2_I

100

1

2.237161

0.9321

3755.21

0.0000

 

q3_I

100

1

2.337126

0.9662

7431.34

0.0000

 

q4_I

100

1

2.709944

0.9812

9206.86

0.0000

 

q5_I

100

1

8.073047

0.9801

10443.53

0.0000

 
  

Coef.

Std. Err.

z

\(\hbox {P}>{\vert }\hbox {z}{\vert }\)

[95% Conf. Interval]

q1_I

q1_C

.8273436

.0213748

38.71

0.000

.7854498

.8692374

q2_I

q2_C

.8973646

.0146437

61.28

0.000

.8686634

.9260658

q3_I

q3_C

.9321035

.0108126

86.21

0.000

.9109111

.9532958

q4_I

q4_C

.9756106

.0101677

95.95

0.000

.9556824

.9955388

q5_I

q5_C

1.072232

.0104922

102.19

0.000

1.051668

1.092797

 Correlation matrix of residuals

 

q1_I

q2_I

q3_I

q4_I

q5_I

q1_I

1.0000

    

q2_I

0.9217

1.0000

   

q3_I

0.7526

0.9033

1.0000

  

q4_I

0.3906

0.5839

0.7973

1.0000

 

q5_I

\(-\)0.7649

\(-\)0.8790

\(-\)0.9198

\(-\)0.7769

1.0000

Breusch-Pagan test of independence: chi2(10) = 616.843, Pr = 0.0000

Hence we see that the residuals are highly correlated across \(j\), so the SUR procedure made sense. We then multiply all consumption quintile shares for the surveys we use by these estimates, and renormalize the resulting shares to sum to unity. (In practice, the shares sum very close to unity even without renormalization).

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Pinkovskiy, M., Sala-i-Martin, X. Africa is on time. J Econ Growth 19, 311–338 (2014). https://doi.org/10.1007/s10887-014-9103-y

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