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Counting Women in Poverty: Potential Pitfalls in Conventional Poverty Analysis

Counting Women in Poverty: Potential Pitfalls in Conventional Poverty Analysis. Peter Lanjouw, DECPI PREM Knowledge and Learning Weeks “Exploring the Intersections between Poverty and Gender” World Bank, May 8, 2012. Outline. The principal problem Scouring the data for insights

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Counting Women in Poverty: Potential Pitfalls in Conventional Poverty Analysis

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  1. Counting Women in Poverty: Potential Pitfalls in Conventional Poverty Analysis Peter Lanjouw, DECPI PREM Knowledge and Learning Weeks “Exploring the Intersections between Poverty and Gender” World Bank, May 8, 2012

  2. Outline • The principal problem • Scouring the data for insights • A second problem • The sensitivity of conclusions • Can we estimate θ? • Engel-estimates • Estimates from subjective welfare models • Remaining caveats

  3. The principal problem • Conventional poverty analysis is based on a measure of household per capita consumption (or income) • Household consumption aggregate is built up from multiple components • Food • Basic Non-food items • Education (and health) expenditures • Consumer durables • Housing • Household consumption is divided through by household size to yield per capita consumption • Our best estimate of individual welfare • This approach side-steps whole issue of intra-household distribution • Huge and growing literature to study within-household allocation and distribution, but as yet no established procedure for capturing differential welfare levels at the individual level.

  4. Scouring the data for insights • What if we focus on poverty of female headed households? Of widows? • Rural India, 1986/7 NSS data (Dreze and Srinivasan, 1997) Family Type Incidence of Poverty All Households 63.4% Male-Headed 63.8% Female-Headed 57.7% Widow-Headed 58.3% Extended;male-headed 68.2%

  5. A Second Problem:Economies of Scale in Consumption • The use of a per capita measure of consumption imposes an assumption of no economies of scale in consumption. • Where might such economies come from? • Consumption of public goods within the household (radio, water pump) • Bulk purchase discounts on perishable food items • Economies in food preparation (fuel, time)

  6. Economies of Scale in Consumption • Suppose money metric of consumer’s welfare has an elasticity of θ with respect to household size. Then welfare measure of a typical member of any household is measured in monetary terms by:

  7. Economies of Scale in Consumption • Suppose that ρ is the proportion of household expenditure on purely private goods, and 1- ρ is allocated to public goods. • Then the correct monetary measure of per-capita welfare is: • Solving for θ yields:

  8. Economies of Scale in Consumption • In India (in 1986/7) average household size is 5.35. • If ρ =0.9 then θ=0.79 • If ρ =0.7 then θ=0.50 • If ρ =0.5 then θ=0.31 • Are conclusions sensitive?

  9. Household Type Mean size Economies of scale parameter θ 1 0.8 0.6 0.4 All households 5.35 63.4 59.6 54.5 49.5 Male-headed 5.56 63.8 59.4 53.9 48.6 Female-headed 3.60 57.7 61.6 62.0 62.6 Widow-headed 3.32 58.3 63.8 65.1 66.2 Extended; male-headed 6.78 68.2 60.3 51.0 43.5 The head-count ratio and economies of scale Source: Drèze and Srinivasan (1997), Table 4.

  10. The picture in Vietnam is more nuanced (Lanjouw and Marra, VHLSS 2010)

  11. Economies of Scale in Consumption • The per capita assumption is not innocuous. • Conclusions as to the relative poverty of widows versus others, or large households (many children) versus small (elderly), are usually quite sensitive. • Big issue in regions (ECA) where there are big debates regarding public spending priorities (pensions versus child benefits) • Note, over time, economies of scale parameters could evolve (Lanjouw, et al, 2004)

  12. Can we estimate θ? • Econometric analysis of Engel curves with cross section data offers one entry point. • Regress food share on the log of expenditure per person, including household composition as well as household size in the specification (Lanjouw and Ravallion, 1995) • Lanjouw and Ravallion (1995) estimate a value for θ of around 0.6 for rural Pakistan. • Lanjouw and Marra (2012) obtain an estimate of 0.68-0.69 for rural and urban Vietnam. • Lokshin and Ravallion (2002) obtain an estimate of around 0.4 in Russia. • Subjective welfare data provide an alternative entry point to gauge presence of economies of scale (Ravallion and Lokshin, 2002, Pradhan and Ravallion, 2000, Ravallion, 2012) • Lanjouw and Marra (2012) obtain an estimate for θ of around 0.53 for Vietnam.

  13. Remaining Caveats • Engel-curve analysis is prey to a fundamental identification problem • Problem first pointed out by Pollack and Wales (1979) • Deaton and Paxson (1998) find further puzzles with this line of enquiry. • Holding per capita income constant larger households report spending a smaller share of their budget on food. • Ravallion (2012) points to concerns with the interpretation of the subjective welfare-based estimates of θ • It is not clear that persons with different personalities respond in the same way to subjective welfare questions. • Controlling for latent personality effects with panel data results in non-robust estimates of θ (Lokshin and Ravallion, 2001).

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