Stephan Klasen and Mark Misselhorn

The Growth Semi-Elasticity of Poverty Reduction Explaining Heterogeneity

across Space and Time

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Background

• Many efforts to estimate a general growth elasticity of headcount poverty

• Chen and Ravallion (1997) estimated growth elasticity to be around 3;

• World Development Report 2000/2001: elasticity between closer to 2 (Bhalla: 5!)

• Cross-Country Heterogeneity, sample and time period differences

• Must be the case: Mathematical link between growth, inequality change, and (absolute) poverty reduction;

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Poverty Reduction and Growth

Source: Bourguignon 2003

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Decomposition

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Background

• Bourguignon (2003): Growth elasticity of poverty reduction depends on:

– Initial inequality

– Location of poverty line (relative to mean incomes)

• Under assumption of lognormal income distribution, can calculate these elasticities precisely. Works empirically quite well (for headcount, not so well for depth, severity).

• Allows quick prediction of poverty impacts of growth and distributional change.

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This Paper

• Argues: – Growth Semi-Elasticity (i.e. percentage point change

in poverty as a result of growth) more useful from a policy perspective;

– Avoids some distortions in growth elasticity;

• Derives:– Determinants of growth and distribution semi-elasticity

under the assumption of log-normal income distribution;

• Applies– Poverty spells database to show a better empirical fit

enabling a greater use of data, and better interpretation and prediction.

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Decomposition Identity

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Decomposition Identity

• Growth elasticity:– Higher, the lower initial inequality (at least as

long as poverty line smaller than mean income).

– Higher in richer countries (low ratio z/y).– By implication: increasing over time as

countries grow.

• Growth semi-elasticity:– Higher, the lower initial inequality (if z<y)– Generally higher in high poverty countries.

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Growth Elasticity of P0

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Growth Semi-Elasticity of P0

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Why Semi-Elasticity?

• Percentage point changes in poverty easier to understand and compare and more relevant for policy-makers;

• ‚Bias‘ in Poverty Elasticity:• Higher in richer countries;• Growing with development;• More affected by growth in richer countries;

• Empirical advantages:• No need for arbitrary ‚sample selection‘;• Better fit for all poverty measures (and thus better

predictive power).

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Empirical Application

• Poverty spells database;

• Empirical Strategy (following Bourguignon, 2003):• Dependent variable: Percent (percentage point) change in poverty;

• ‚Naive‘ poverty-growth model;

• Augmented by change in inequality;

• Augmented by location of poverty line and initial Gini (mathematical relation to log normal distribution parameter);

• Augmented by interactions with inequality change;

• Compared to calculated elsticities under lognormal assumption;

• Problem for Elasticity regressions:• Must exclude data where % changes are very large or undefined.

• Unable to explain poverty depth/severity measures.

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Relative Changes in P0

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Relatives Change in P1

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Relatives Change in P2

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Absolutes Changes in P0

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Absolutes Change in P1

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Absolutes Change in P2

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Application• To understand past poverty reduction

performance:– E.g. India‘s recent success versus lower success in

growing SSA economies (e.g. South Africa);

• To ‚predict‘ poverty-effect of policy based on assumption about their growth and distributional effects;

• To simulate growth and/or distributional change requirements to reach the MDGs; or alternatively: to project MDG success based on assumed growth/distributional change patterns;

• Note: Measurement not policy tool !

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‚Predicting‘ Headcount Poverty

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Conclusion

• Semi-elasticities more policy-relevant and not prone to ‚bias‘;

• Allow integration of more growth spells;

• Can explain changes in FGT-Measures better;

• Could also be used for simulations about growth and distributional change requirements to achieve MDGs;

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Distribution Semi-Elasticity