I nequality trends in SSA, 1991-2011: divergence, determinants, consequences

1. Inequality trends in SSA, 1991-2011: divergence, determinants, consequences Giovanni Andrea Cornia University of Florence and SITES ------------------------------------------------------------- SITES summer School 2017, Prato

2. Introduction • 1. Growing focus on within-country inequality trends • 2. Many deteriorations (India-left), several improvements (LA-below) • 3. Situation in SSA remained unexplored - but data on low poverty alleviation elasticity of growth suggest inequality remained high or rose • 4. This is first systematic study documenting (i) recent Gini changes, (ii) their drivers and (iii) improved design of public policies& programs 46.5 (2015)

3. Literature findings so fa on SSA inequality • Pinkowsky & Sala I Martin (2010) argue that inequality fell since1990 but rely on very few data & assumption all distributions are log-normal. • Christiansen et al. (2003 - for 6 countries in 1990s) use micro surveys for 6 countries, find falling rural inequality (due to agric. market liberalization of 1980-90s) • Chotikapanich,et al. (2014) 10 countries: heterogeneous trends • Fosu (2014) on 23 countries for early 1990s-mid/late 2000s. (compounded annual rates of change of Ginis between first-last year) findsheterogeneous trends. • Anyanwu et al.AfDB (2016) found trends congruent with ours, for W. Africa

4. Documenting inequality changes in SSA: building IID-SSA • Databases of Gini: WIIDv3, SWIID, POVCAL, WYD, I2D2, (RIGA), national data Coverage & data quality vary. • We compiled IIS-SSA Gini database for countries with at least 4-5 well spaced observations between 1991-3 and 2011 • Select ‘best data’ (fully documented) from above sources (mainly WIIDv3) and eliminate 6-7 ‘obvious outliers’ • Retained 29 countries (out of 48) which represent > 90% SSA’s pop and GDP • About 220-240 observed and controlled data for 1990-2010 • Data show different country trends, due to SSA’s structural heterogeneity • Grouped the 29 countries by their Gini trends into 4 groups: rising, falling, U and ∩

5. Different trends emerge from our data analysis

6. If look only at 2000s, 17 falling ineq.& 12 (60%pop) rising ineq.

7. Estimated Gini are lower bound of real Gini • our Gini are lower bound estimates of real Gini. This may affect its level and/or its trend due : • Interpersonal distribution (gender bias) ignored • Differences in survey design over time (recall period & n. of items surveyed) • Differences in stat. assumptions for data handling across countries • Under-sampling of ‘top incomes’, and use of tax return data • Diverging trends btw HBS-based Gini and ‘labour share’ from National Accts • Ignoring incomes on assets held abroad by SSA nationals (over 200 bn$) • impact of differences in price dynamics btw food prices & average CPI

8. 2. What explains the Gini bifurcation observed over 1991/3-2011 ?

9. Explaining SSA’s inequality bifurcation To reply to this question we follow a two step methodology : • Immediate causes of inequality changes - based on micro- decompositions by household sectoral consumption/inequality or by income type – both such data are derived from household budget surveysbtw 2 points over time 2. Underlying causes (affecting immediate causes) of inequality changes based on economic theory, country panel regressions for region as a whole, sectoral studies

10. 1. Examples of micro-decomposition

11. Malawi 2004-11, decomposition by type of income

12. 2.Traditional causes of ineq. (incl. due to colonial legacy) • (i) A highly dualistic production structure • Subsistence agriculture - Commercial agriculture • Enclave/mining sector - Urban formal sector • Urban informal sector • Generally characterized by • Ycres encl > Ycurb form > Yccomm. agr > Ycurb inf > Ycsub agr • Gres encl > Gurb inf > Gurb form > Gcomm agr > Gsub agr • or - in countries with a high land concentration • Gres encl > Gcomm agr > Gurb inf > Gurb form > Gsub agr • Labour transfers from low to high Gini & income/c (as in Lewis-Kuznets model) raises income inequality • This was avoidable if Ranis Fei model(emphasizing agricultural productivity growth) was chosen

13. Continued • (ii) High asset concentration (mines, human capital and - in Eastern-Southern Africa - land ) • (iii) Dependence on exports of natural resources and commodity cycles (‘curse of natural resources’) • (iv) urban-rural bias and migration • (v) limited/regressive redistribution by the state • (vi) ethnic inequality & gender inequality

14. 3. Inequality changes over 1991-2011

15. 3a. GDP growth accelerates over time but no effect on Gini GDP growth (x axis) and Gini coeff (y axis) Why not like this ?

16. Source: Author’s calculation on official data. In another ten countries there was a rapid surge of the unequalizing mining sector; for instance, in Equatorial Guinea oil/mining absorbed in 2011 89.4 of value added, up from 4.2 per cent in 1990. In another nine, there was an ‘informal tertiarization’, with most of the value added and jobs being

17. 3b Inequality changes due to non virtuous struct. transition from low-to-high Gini sectors‘-->reprimarization’&’informal tertiarization’ VA share of manufacturing stagnated or declined, incl. due to trade liberalization

18. VA shares and Gini coefficients

19. 3c. Increase index agric output /capita drives growth in 14 countries likely with equalizing effects in many of them

20. In some countries increase in output driven by bumpy rise in yields

21. Land distribution &D agr output  Gini? • Equalizing effect of ↑ agric. output expected to be stronger where land distribution is egalitarian • What happened to land distribution ? • State and local level ‘land titling’ programs • Land redistribution (Ethiopia) • Endogenous pressures twd land concentration where land becomes scarce due to population growth (e.g. Niger) • Land grabs ? Unclear whether fully implemented

22. Are ‘land grabs’ equalizing or unequalizing ? - varies a lot, may increase productivity, in land abundant countries - concerned also countries with low man/land ratios - compensation for expropriated farmers ? Bi-variate relations btw arable land/man ratio(x) &% land deals/total arable land(y) 2000s expected ex-ante observed

23. 3d. Increase in production & exports of mining-oil resources: at least 18 countries depend on (un-equalizing)oil/min rents) Problems of oil-mining driven growth : - Unequal distribution of rewards - political capture of rents -Dutch Disease - fiscal lazyness -Regressive revenue system/no redistr.– poor governance/’greed wars’ -

24. New factors 4a. A favorable global economic environement) • Terms of trade rose for most of 2000s for both agric. & minerals • migrant remittances rose sharply (only half of them recorded offic.) • Increase in FDI (mostly in mining) – charts

25. TOT, remittances, FDI,Aid, Debt cancellation

26. Distributive impact of changes in global economy • Direct effect (partial equilibrium analysis) • Equalizing • Tot gains for agriculture (low Gini sector –limited role of enclaves ) • Remittances (, theory is mixed in this regard) • Debt cancellation (HIPC) • Indeterminate - controversial literature • Aid flows (but positive effect of HIPC debt cancellation) • Unequalizing • Oil and mineral exports • FDI (mostly in the mining sector) • Indirect effect (general equilibrium analysis): (i) ‘income effect’, (ii) +current account balance + growth + jobs?

27. 5.Demography &other exog. changes • Exogenous changes in Total Fertility Rates (TFR), birth rates, dependency rates • Shocks (spread of HIV, and cellphones) • Institutional changes ? Transition to democracy and governance

28. 5a.Demographic changes Trends in population growth rates by main sub-regions of SSA Similar results obtained for the dependency ratio

29. Demography ….continued • Slow decline in TFR, birth rates & dependency rates • When demographic transition will occur, will the current high birth rates be a source of ‘demographic dividend’? • Or an ‘inequality time bomb’? (TFR drops first among the ‘rich’-middle class when 2ary female education rises) • Huge increase in labor supply while demand stagnates • but positive alternatives: Bangladesh, Ethiopia, Rwanda • Worst cases: Nigeria, Niger • Only Afghanistan-Pakistan have similar TFR trends

30. Total fertility rates in SSA vs other regions

33. Figure. Projection of the increase in the population 15-24 years of age (mn) in SSA, China and India, 1950-2050. 100 million

34. 5b. Public policy

35. Economic policies and outcomes:a. macro: inflation control Inflation and Gini 1991-2001 Inflation and Gini 2001-2011

36. b. Macro: continued • Budget balances: low and little related to Gini • Real effective exchange rate (RER) is essential to shift resources from T to NT. But strong push to appreciation in resource countries and CFA countries • Trade liberalization? Impact is debatable, gains due to rise in commodity prices, but fall in manufacturing share in GDP. (figure) • Rising tax/GDP (see next slide)

37. 2.(i) Macro policy (CPI, deficits, debt) ok but ….trade liberalization Malawi: tariff rate (left scale) & manufact. v.a. share (right scale) WDI data

38. Unweighted Regional Tax/GDP ratios, early 1980 to 2008 Revenue collection data (% of GDP) by type of tax, 15 SSA countries

39. Social protection: two SSA models • Most of Africa: small pilot projects of C & NC cash transfers - but no aggregate impact yet. -Southern African model, with state (formerly white-only but now universal) institutions - Potential for expansion, with equalizing effects & fiscal sustainability (esp in countries with large resource rents …. but political economy?

40. Education and skill premium: Fast rise in 1ary, less for 2ary (technical knowledge). Likely disequalizing effects via rise in skill premium Relation btw average yrs of educ (x) & Gini educ (y) Educational Inequality of L.F. 6 to 9 Average n. of yrs of educ of l.f.

41. As a results: skills are poorly distributed: enrolm.rates of the poorest (blue) & richest quintile (green) of 15-19 yrs who completed grade 6, late 2000s Source: Ferreira (2014)

43. Education: Ratio of skilled workers (2-3ary/education) to unskilled workers (1ary or none), Skilled/unskilled vs rural population (%)

44. c. Provision of social services (educ., health, social provision) • With MDGs paradigm (and HIPC ) some drive to raise social expenditure

45. 6. Have ethnic problems lessened?

46. Trends in political regimes (right, centre, left), 1990-2009 But many of these indexes (Freedom House, Polity 2, etc.) are unable to capture changes in real governance, efficiency of state administration, & corruption

47. 7. Exogenous shocks (HIV AIDS and cells/internet)

48. Bivariate relation btw HIVrate (x-axis) & Gini (y-axis)

49. Trend in HIV prevalence in countries with rates greater than 5 % (should be equalizing where it falls

50. 7b. Mobile & internet: greater market integration? And Gini?