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Poverty-Growth Links Applied Inclusive Growth Analytics Kenneth Simler and Roy Katayama (PRMPR) June 30, 2009.

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Presentation on theme: "Poverty-Growth Links Applied Inclusive Growth Analytics Kenneth Simler and Roy Katayama (PRMPR) June 30, 2009."— Presentation transcript:

1 Poverty-Growth Links Applied Inclusive Growth Analytics Kenneth Simler and Roy Katayama (PRMPR) June 30, 2009

2 Outline 1) Why look at poverty with growth? 2) Website: Measuring growth-poverty links 3) Five tools for measuring poverty-growth relationships 4) Summary

3 Why look at poverty? General consensus that: Poverty reduction is meaningful goal of development Growth is necessary for sustainable poverty reduction However, the extent to which growth translates into poverty reduction varies across countries. Benefits of growth may not be reaching the poor Distributional changes can offset growth effects

4 Bangladesh Bolivia Brazil Burkina Faso El Salvador Ghana India Indonesia Romania Senegal Tunisia Uganda Vietnam Zambia -10 10 -36 Annual GDP per capita growth, 1990s (%) Source: Pro Poor Growth in the 1990s. Country Case studies Annual change in poverty headcount (%) Growth and poverty reduction

5 Growth spells and poverty reduction Source: Bourguignon (2002)

6 Poverty-growth-inequality triangle Poverty reduction= f (growth, Δdistribution) What are effects of growth on distribution? What are effects of inequality on rate and pattern of growth? Source: Bourguignon (2004)

7 Poverty-growth-inequality triangle Ex-post analysis of this relationship can: Inform ex-ante analysis of poverty and distributional impacts of policies Help policymakers in evaluating policy options Source: Bourguignon (2004)

8 Looking beyond averages Inclusive growth analysis requires: Good understanding of growth at the mean, …but also the incidence of growth across the distribution,... and changes to the distribution and poverty. Review of ESW indicated: Many could have been strengthened by utilizing existing tools on growth-poverty links.

9 Overview of website and contents WEBSITE: MEASURING THE GROWTH-POVERTY LINK

10 Useful growth-poverty tools Website: Measuring the Growth-Poverty Link ( the Growth-Poverty Link Purpose: Make tools that explore growth-poverty links more accessible and results easier to understand 5 existing tools to explore growth, distribution, and poverty Growth elasticity of poverty Growth incidence curve Rate of pro-poor growth Growth-Inequality decomposition of poverty Sectoral decomposition of poverty

11 Overview of each tool on website Definitions and Concepts Limitations and Extensions Quick Results Data requirements Stata/ ADePT options Helpful tips Annotated examples Stata commands Interpretation of results References / Related Papers

12 With examples from Uganda case FIVE TOOLS

13 1. Growth elasticity of poverty Indicates how effectively growth has translated into poverty reduction. Misnomer: Should be GDP elasticity of poverty Initial conditions matter: Location of poverty line (initial poverty levels) Shape of the distribution (initial inequality)

14 Uganda: Growth elasticity of poverty 199320032006 Poverty headcount0.560.390.31 Per capita GDP (constant LCU)270,267375,829399,978 Gini0.370.430.41 Percent change1993-20032003-2006 in poverty headcount-31.2%-19.8% in per capita GDP39.1%6.4% Growth elasticity of poverty-0.8-3.1 Percentage point change in poverty headcount-0.18-0.08 Growth semi-elasticity of poverty-0.5-1.2

15 2. Growth incidence curves Illustrates growth rate of income (expenditure) for each percentile of a distribution. Gives equal weight to people…rather than to dollars Refers to anonymous percentiles Individual at 10 th percentile at t 0 is not necessarily same individual at 10 th percentile at t 1

16 Uganda: GICs 1992-2002 2002-2005 Growth rate in mean=4.09 Mean percentile growth rate=3.26 Headcount poverty (1992)=56.43 Rate of pro-poor growth=2.90 Growth rate in mean=3.61 Mean percentile growth rate=4.73 Headcount poverty(2002)=38.82 Rate of pro-poor growth=4.44

17 Growth incidence curves -- example

18 3. Rate of pro-poor growth Represents the mean growth rate of the poor Not to be confused with growth rate in the mean of the poor Related to GIC: Area under GIC up to poverty line (also equals the change in the Watts index) General definition: < <

19 = + + 4.Growth-inequality decomposition Quantifies the relative contribution of economic growth and redistribution to changes in poverty. Change in poverty Growth component Redistribution component Residual

20 Uganda: Growth-inequality decomp. a)Base year 1Base year 2g) Average effect ----------------------------------------------------------------------------- b) Poverty rate (P0) 56.427 38.819 ----------------------------------------------------------------------------- c) Change in P0 -17.608 ----------------------------------------------------------------------------- d) Growth component -25.134-26.211-25.672 ----------------------------------------------------------------------------- e) Redistributio n component 8.6027.5268.064 ----------------------------------------------------------------------------- f) Interaction component -1.076 0.000 ----------------------------------------------------------------------------------------------------------------------------------- 1992 as reference (base year 1) Uganda: 1992-2002

21 5. Sectoral decomposition of poverty Quantifies relative contributions to changes in aggregate poverty of: changes in poverty within sectors and inter-sectoral population shifts = + + Typical sectors for decomposition: Urban/rural Regions Economic sectors Change in poverty Intra-sectoral component Inter-sectoral component Interaction component

22 Urban-Rural Sectoral Decomposition (Uganda 19922002) Pop share (1)Pop share (2)Poverty (1)Poverty (2) Rural0.87580.862460.3542.72 Urban0.12420.137628.7714.35 Total1.0 56.4338.82 Rural-15.440487.7% Urban-1.7909610.2% TOTAL intra-sectoral-17.231397.8% population shift-0.423172.4% interaction0.043014-0.2% Total change (Headcount)-17.6115100.0%

23 Uganda: Rural / urban decomposition (1992 – 2002) a) Poverty in 1992 (headcount)56.4 b) Poverty in 2002 (headcount)38.8 Sector Popn share (period 1) Absolute change Contribution (%) c) Rural87.6-15.587.7 d) Urban12.4-1.810.2 e) Total intra-sectoral-17.297.8 f) Population shift effect-0.42.4 g) Interaction effect0.04-0.2 h) Change in poverty (HC)-17.6100.0

24 ZAMBIA Application to Zambia Household Survey Data (1996, 1998, 2004)

25 Zambia: Growth Elasticity of Poverty Ln(p0) = 10.07 – 0.47 (Ln(GDP/capita)) (3.70) (2.14) Adj R 2 = 0.642 199619982004 GDP (bn constant LCU)2,328.12,360.22,999.2 GDP / capita (constant LCU)245,107236,347266,128 Poverty headcount69.272.267.9

26 Zambia: Growth Incidence Curve 1996–1998 Growth rate in mean: – 3.6 Mean percentile growth rate:– 6.0 Headcount poverty (1996):69.2 Rate of pro-poor growth:– 7.6 Growth rate at median:– 3.8

27 Zambia: Growth Incidence Curve 1998–2004 Growth rate in mean: 2.4 Mean percentile growth rate:2.1 Headcount poverty (1998):72.1 Rate of pro-poor growth:2.1 Growth rate at median:1.5

28 Zambia: Growth Incidence Curve 1996–2004 Growth rate in mean: 0.8 Mean percentile growth rate: – 0.03 Headcount poverty (1996): 69.2 Rate of pro-poor growth: – 0.4 Growth rate at median: 0.1

29 Zambia: Growth – Inequality Decomposition 1996 – 19981998 – 20041996 – 2004 GDP growth rate (annual) GDP / capita growth rate (annual)– Poverty headcount 69.2 72.172.1 67.969.2 67.9 Change in poverty headcount+2.9– 4.2– 1.3 Growth component+2.8– 5.4– 2.6 Redistribution component+0.1+1.2+1.3

30 Zambia: Sectoral Growth-Poverty Decomposition Population SharesAbsolute Poverty Change 1996199820041996-981998-041996-04 Agriculture58.959.860.3-1.5-2.5 Industry12.610.910. Services28.529. Intra-sectoral effect1.8-2.5-0.6 Population shift0.30.10.5 Interaction effect-0.10.0-0.2 Change in poverty2.1-2.3-0.3

31 Summary Website: Measuring the Growth-Poverty Link ( Measuring the Growth-Poverty Link These tools provide an initial look beyond averages at the poverty and distributional impacts of growth. However, integration with growth story is necessary to get a fuller economic picture.

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