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Seminar presentation IDPM, University of Manchester, 09/10/07

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1 Seminar presentation IDPM, University of Manchester, 09/10/07
Poverty Measurement in India and Bangladesh: a Great Indian Rope Trick? Seminar presentation IDPM, University of Manchester, 09/10/07 Richard Palmer-Jones, School of Development Studies, University of East Anglia, Norwich, NR4 7TJ With acknowledgements but no inculpation of Amaresh Dubey, or Kunal Sen, sometime partners in this …. the Indian rope trick is “[S]ometimes described as "the world’s greatest illusion"”. Its origins are obscure but our use of it is to suggest that and claim that current methods provide a reliable basis for poverty lines and poverty aggregates that represent a comparable standard of welfare is an illusion


3 Outline Poverty is an important policy variable
India and Bangladesh are significant case studies but there is controversy over trends (and patterns) Indian Planning Commission claims poverty come down critics suggest hunger and poverty have increased Apparent modest improvements in child undernutrition but lacking decentralised recent data In Bangladesh World Bank (and BBS) claim poverty has come down but child undernutrition may not (by WHO method). Standard methods of poverty assessment have dubious theoretical bases Methods DCI, FEI, CBN, CPI Practice & Precept Theory revisited What does it mean and what to do? Recent controversy in Afghanistan – World Bank bullying

4 HCR Poverty decline in India


6 Child Anthropometry in India

7 Why be concerned about poverty?
A personal history of “trickle down” Irrigation, agricultural growth, wage rates of agricultural labourers and poverty in Bangladesh and India MDG Goal No 1 (and “headline” value) PRSPs & assessments of progress (south Asia – including Afghanistan) Manuals from World Bank and UNSTATS Including Sourcebook for PRSP Why Poverty Outrage policy analysis? – poverty profiles Poverty comparisons Common yardstick – the same thing

8 Also 2005

9 Methods Minimum socially acceptable standard of living
Comparable across domains space, social group and time) Set a poverty line(s) and aggregate Identity, Incidence, Intensity & Inequality Poverty Lines Calorie based Rowntree – cost of nutrition & allowance for non-food expenditure Direct Calorie and Food Energy Intake (FEI & DCI) Cost of Basic Needs (CBN) Cost of Living Index methods (CPI) CoGIs or CoLIs?

10 Aggregation is (arguably) less important than incidence
Robustness - stochastic dominance does not address the key problem of comparability Compare aggregates for different relative poverty lines Stochastic dominance is not that common, and calcualtions have to be “arranged” by cutting off low values.

11 FEI & DCI This is usually estimated from a regression of reported (constructed) expenditure per capita on reported (constructed) per capita calorie “consumption” DCI HCR poverty is the ratio of population with c < cnorm / total population

12 FEI Poverty Lines

13 CBN Method – recommended by World Bank (and UNSTATS)
Food component – zfood Non-food component - two levels (znfu & znfl) Upper and lower PLs (zu & zl) Food Component - recommended Behavioural food bundle (households around poverty line) Scaled to normative calories Priced at local prices gives zf – the cost of food bundle Tarp et al., 2002, variant - different food bundles in different domains Non-food component Inverse Engel share of households around poverty line Estimate the following regression Where zf is the food poverty line, yi is total expenditure, and d are demographic variables And f(yi) is food expenditure


15 CBN Poverty in Bangladesh
R&S, 1996, for 1983/4 – 1991/21 normative food bundle (from Alamgir, 1974) Not typical of consumption of poor More high calorie cost foods (pulses, milk, oils, meat, fish, sugars, fruits) (Unclear origin of food “unit values”2 – not poor relevant) Non-food share “guesstimated” at 35% of cost of food in 1983/4 Updated using national Rural and Urban non-food CPIs Wodon & World Bank, 1998; 1983/4 – 1995/6 Same normative food bundle UVs estimated by “regression” to be poor relevant Non-food share from inverse Engel Curve for each HIES World Bank 2002 Use Wodon 1991 CBN PLs and update using “synthetic” CPIs “Better” World Bank 2005 Re-estimate CBN using same food bundle, 2005 prices & inverse Engel shares 1: updated by Sen and Mujeri; based on critique of FEI & DCI for 1995/6 & 2000/1 2. median “unit values” for rural and urban sectors for 11 “composite” groups of items


17 Is CBN so different from FEI?
Calorie base to food component Estimate non-food share by Engel regression Difference is constraint on cost per calorie Both give rising poverty Both are inconsistent with elementary demand theory

18 CBN & FEI (cost per kcal unconstrained)

19 CBN & FEI (rps/kcal < 1.4)

20 with fall in relative price of non-calories
Compensated demand curves when relative price of calories rises (price of non-calories lower in urban areas) – leads to fall in calories consumed in both Hicksian and marshallian demand curves. Hence, maintaining constant expenditure on calories Hicksian demand curves (utility compensated) show fall in demand for calories with fall in relative price of non-calories

21 Now adding the FEI constant calorie intake demand we see even higher poverty lines
CBN will suffer similar problems, more or less extreme FEI poverty line expenditure is higher than utility compensated expenditure


23 Hicksian demand curves disappear with zero utility compensated substitution.

24 CPI Methods: CoLI Poverty Lines & Utility consistency
In principle the fact that urban consumers at the CoLI PL spend different shares on food and non-foods is not inconsistent with these PLs representing a common level of utility; the problem lies in whether indeed these PLs correspond to the same level of well-being

25 Deaton (and Tarrozi)’s method

26 Suppose we treat Deaton’s method as calculating the urban cost of the food expenditure
of rural households’ food expenditure, what should we add as an allowance for non-food? Would it be the non-food share of urban households whose food expenditure was equivalent In real terms to the the food expenditure of rural households?

27 What is to be done? Teach economists ethics – no code of practice! – and get them to practice them Honesty, transparency, humility? Improve capacity for diverse groups to practice evidence based policy Reduce dependence on powerful donors and their agendas Use money-metric poverty for policy analysis more carefully Constrain domains of comparison Encourage greater data availability and more critical use of official data (set our data free) Encourage evidence based policy analysis (and quality data production) Forget comparability with earlier series (all that intellectual capital!) Adjust for household type and location Record value of public goods and environment to comply with Canberra group concept of income (heavy!) Triangulate with other indicators (nutrition, health, educational attainments) Adopt more sophisticated procedures taking account of the value of services in kind, public goods, the environment, culture, etc. Improve survey concepts, methods and procedures, and resources field survey officials feel undervalued – “kill for a data set” Improve Consumer Price Indexes Don’t ask Alternative methods of assessing differences and progress in well-being Longitudinal studies Ensure good practice – can we rely on those who brought us money-metric poverty assesment to do a better job with longitudinal studies? Take deliberative and participatory democracy seriously (no media stunts please)

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