Poverty-Growth Links Applied Inclusive Growth Analytics Kenneth Simler and Roy Katayama (PRMPR) March 24, 2009

Overview Importance of poverty-growth analytics Poverty-growth-inequality triangle Website: “Measuring growth-poverty links” 5 Tools Uganda example Application to Zambia case Summary

Importance of poverty-growth links 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 trickling down Distributional changes can offset growth effects Extent of poverty reduction depends on pattern of growth and redistribution policies.

Growth-poverty link Romania Zambia Indonesia Burkina Faso Bolivia Bangladesh Bolivia Brazil Burkina Faso El Salvador Ghana India Indonesia Romania Senegal Tunisia Uganda Vietnam Zambia -10 10 -3 6 Annual GDP per capita growth, 1990s (%) Source: Pro Poor Growth in the 1990s. Country Case studies Annual change in poverty headcount (%) Cross country: Shows trend that growth reduces poverty, but note variation (i.e. countries above and below fitted line). Senegal (1994-2001): 2.5 % GDP p.c. growth rate, -2.5% annual change in P0, 0.7% annual change in Gini, initial Gini of .33 El Salvador (1991-2000): 2.5 % GDP p.c. growth rate, -5.4% annual change in P0, 0.3% annual change in Gini, initial Gini of 0.51 Similar figures with more observations in Bourguignon (2002)

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) Poverty reduction in a country is determined by the rate of growth of the mean income (consumption) and the change in distribution of income (consumption). Effects of inequality on growth Some potential mechanisms by which inequality could effect growth Credit market imperfections may preclude the poor (who lack capital and collateral for loans) from making investments in productive activities that could benefit themselves as well as society. Or the poor may not be able to invest in their own or their children’s education and health… …or invest in good business opportunities due to lack of access to capital. Also, greater inequality may lead to greater social conflict and political instability, which in turn may lead to sub-optimal investment levels and undermine growth.

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)

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.

Website: “Measuring the Growth-Poverty Link” Overview of website and contents

Useful growth-poverty tools Website: Measuring the Growth-Poverty Link (http://go.worldbank.org/J70VTQSAK0) Purpose: Make tools that explore poverty-growth 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

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 Remember to point out “Installation of Stata ado files” “Sample Data for Uganda”

With examples from Uganda case 5 tools …of highly inclusive people

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) Variance of the distribution (initial inequality) Range of elasticities: -2.4 (India) -1.0 (Uganda, -0.7 (Bolivia)

Uganda: Growth elasticity of poverty   1993 2003 2006 Poverty headcount 0.56 0.39 0.31 Per capita GDP (constant LCU) 270,267 375,829 399,978 Gini 0.37 0.43 0.41 Percent change 1993-2003 2003-2006 in poverty headcount -31.2% -19.8% in per capita GDP 39.1% 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 Mention semi-elasticity of growth on poverty as an alternative measure average annual absolute change in poverty / annual % change in GDP p.c. Semi-elasticity of growth on poverty: This is an alternative measure of the sensitivity of poverty reduction to economic growth. Klasen and Misselhorn (2006) argue that percentage point changes in poverty reduction may be of more interest to policymakers and advocate for the use of the semi-elasticity measure. They point out that percent changes in poverty reduction (and the growth elasticity of poverty) can be easily misinterpreted. If the initial poverty headcount is relatively low, then a small absolute reduction in poverty can constitute a large percent change in poverty. For example, compare two countries with different initial poverty rates, one with an initial poverty rate of 6% and the other 60%. If they both reduce poverty by half, then one will have reduced poverty from 6% to 3% while the other from 60% to 30%. If they experienced the same GDP growth rates, the growth elasticity of poverty would be identical. They also point out that with growing countries, the growth elasticity of poverty tends to increase over time and may give the impression that growth is becoming more pro-poor.

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 10th percentile at t0 is not necessarily same individual at 10th percentile at t1 GIC unpacks weighted mean Because anonymous percentiles, GIC does not pick up movement of individuals Even with positive growth at each percentile, one cannot assume Pareto optimality …and it is wrong to say “everyone is better off”

Uganda: GICs 1992-2002 2002-2005 Growth rate in mean =4.09 1992-2002/03 > 0% for entire distribution Growth rate for the poor < growth rate in mean Inequality increasing (for Gini and most inequality measures) 2002/03-2005/06 95% are above “average” Rate of pro-poor growth > growth rate in mean Inequality decreasing Why? [NEED MORE HERE] Drop in ag prices in 2002 Drop in poverty rates 1992-1999 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

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 (w/ Watts index): Total growth rate of the poor divided by headcount poverty General definition: < Mean growth rate of poor  poor as defined at t0 Comparative Statics If growth rate increases, RPPG increases If ratio increases, RPPG increases If inequality decreasing, the ratio >1…Kakwani definition of pro-poor growth.

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 Decomposition is path dependent (i.e. choice of reference year, r, used to fix distribution and mean when calculating growth and redistribution components)

Uganda: Growth-inequality decomp. Base year 1 Base year 2 g) Average effect ----------------------------------------------------------------------------- b) Poverty rate (P0) 56.427  38.819   c) Change in P0 -17.608  -17.608 d) Growth component -25.134 -26.211 -25.672 e) Redistribution component 8.602 7.526 8.064 f) Interaction component -1.076 0.000 ----------------------------------------------------------------------------------------------------------------------------------- 1992 as reference (base year 1)

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 Economic sectors Regions Change in poverty Intra-sectoral component Inter-sectoral component Interaction component

Uganda: Sectoral decomposition ------------------------------------------------------------------------------------------------------------------------------------------- Sectoral Decomposition of a Change in Poverty: HeadCount ------------------------------------------------------------------------------------------------------------------------------------------- Uganda: Sectoral decomposition 1992-2002: sectoral decomposition of poverty by urban and rural areas a) Poverty in period 1 HeadCount 56.4274   b) Poverty in period 2  38.8189 ------------------------------------------------------------------------------------------------------------------------------------------ Sector Population share in period 1  Absolute change  Headcount c) Rural  87.58  -15.4373 87.67 d) Urban 12.42 -1.7910 10.17 e) Total Intra-sectoral effect -17.2283 97.84 f) Population-shift effect -0.4232 2.40 g) Interaction effect 0.0430 -0.24 h) Change in poverty (HC) -17.6085 100.00

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

Zambia: Growth Elasticity of Poverty 1996 1998 2004 GDP (bn constant LCU) 2,328.1 2,360.2 2,999.2 GDP / capita (constant LCU) 245,107 236,347 266,128 Poverty headcount 69.2 72.2 67.9 Ln(p0) = 10.07 – 0.47 (Ln(GDP/capita)) (3.70) (2.14) Adj R2 = 0.642 Interpretation: from 1996 to 2004, for every 1% increase in GDP per capita, the poverty headcount fell by 0.22%.

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 Negative per capita consumption growth for everyone except the top 5% or so. Contraction in mean consumption (-3.6%) is sharper than contraction in GDP/capita (-1.8%). Extremely negative consumption changes in the bottom decile – what happened?

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 Pro-poor growth by most definitions. Annual growth rate in consumption per capita (2.4%) a little higher than growth rate in GDP/capita (2.0%). Remarkably strong growth in the bottom decile. This and the 1996-98 data indicate the volatility at the tails, as is also shown by the size of the confidence bands.

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 Annual growth in mean consumption per capita (0.8%) is a little less than annual growth in GDP per capita (1.0%), but it’s close. However, the median household had almost no increase (0.1%), and consumption per capita contracted for almost the entire bottom half of the distribution. So the poverty reduction was almost entirely driven by those just below the poverty line scraping over it, while most of those below the poverty line fell further below. [Get P1 data??]

Zambia: Growth – Inequality Decomposition 1996 – 1998 1998 – 2004 1996 – 2004 GDP growth rate (annual) 0.7 4.1 3.2 GDP / capita growth rate (annual) – 1.8 2.0 1.0 Poverty headcount 69.2  72.1 72.1  67.9 69.2  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 Zambia growth rates and growth-inequality decomposition from 1996 to 2004 (inclusive) GDP growth in first period (1996-1998) was small, and was even negative in per capita terms. GDP growth in second period (1998-2004) was much better at 4.1% per year, or 2.0% per capita. Over the entire 8 year interval (1996-2004) annual GDP growth was 3.2%, or only 1.0% per capita. In this case, the direction of the change in poverty corresponds to the change in GDP per capita. When GDPpc fell, poverty went up, and when GDPpc rose, poverty fell. This is what we expect. In all of the periods the growth component accounts for most of the change in poverty. In fact, increasing inequality reduces the poverty-reducing impact of growth compared to distribution-neutral growth at the same rate.

Zambia: Sectoral Growth-Poverty Decomposition Population Shares Absolute Poverty Change 1996 1998 2004 1996-98 1998-04 1996-04 Agriculture 58.9 59.8 60.3 -1.0 -1.5 -2.5 Industry 12.6 10.9 10.7 0.03 1.0 Services 28.5 29.2 29.0 1.8 0.9 Intra-sectoral effect -0.6 Population shift 0.3 0.1 0.5 Interaction effect -0.1 0.0 -0.2 Change in poverty 2.1 -2.3 -0.3 NOTE: Poverty reduction in 1996-2004 is less here than shown earlier because of some data issues with sector of employment. Overall only small movements between sectors in the period we’re looking at. The population shift effect is pretty small, especially for the two sub-periods. For the overall period (1996-2004) the population shift is a sizeable share of a small change in poverty. Somewhat surprisingly, gains within the agriculture sector contributed more to poverty reduction than anything else.

Summary Website: Measuring the Growth-Poverty Link (http://go.worldbank.org/J70VTQSAK0) 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. These 5 tools provide a good look beyond the averages (statistical) and can reveal interesting questions for further investigation. However, they will not tell you what is driving these results Good ESW examples