Peter Lanjouw, DECPI PREM Knowledge and Learning Weeks “Exploring the Intersections between Poverty and Gender” World Bank, May 8, 2012

 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

 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.

 What if we focus on poverty of female headed households? Of widows?  Rural India, 1986/7 NSS data (Dreze and Srinivasan, 1997) Family TypeIncidence of Poverty All Households63.4% Male-Headed63.8% Female-Headed57.7% Widow-Headed58.3% Extended;male-headed68.2%

 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)

 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:

 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:

 In India (in 1986/7) average household size is ◦ 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?

The head-count ratio and economies of scale Household Type Mean size Economies of scale parameter θ All households Male- headed Female- headed Widow- headed Extended; male- headed Source: Dr è ze and Srinivasan (1997), Table 4.

 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)

 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 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.

 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).