Presentation on theme: "Seminar presentation IDPM, University of Manchester, 09/10/07"— Presentation transcript:
1Seminar 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, 09/10/07Richard Palmer-Jones, School of Development Studies, University of East Anglia, Norwich, NR4 7TJWith 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
3Outline Poverty is an important policy variable India and Bangladesh are significant case studiesbut there is controversy over trends (and patterns)Indian Planning Commission claims poverty come downcritics suggest hunger and poverty have increasedApparent modest improvements in child undernutrition but lacking decentralised recent dataIn 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 basesMethodsDCI, FEI, CBN, CPIPractice & PreceptTheory revisitedWhat does it mean and what to do?Recent controversy in Afghanistan – World Bank bullying
7Why be concerned about poverty? A personal history of “trickle down”Irrigation, agricultural growth, wage rates of agricultural labourers and poverty in Bangladesh and IndiaMDG Goal No 1 (and “headline” value)PRSPs & assessments of progress(south Asia – including Afghanistan)Manuals from World Bank and UNSTATSIncluding Sourcebook for PRSPWhy PovertyOutragepolicy analysis? – poverty profilesPoverty comparisonsCommon yardstick – the same thing
9Methods Minimum socially acceptable standard of living Comparable across domains space, social group and time)Set a poverty line(s) and aggregateIdentity, Incidence, Intensity & InequalityPoverty LinesCalorie basedRowntree – cost of nutrition & allowance for non-food expenditureDirect Calorie and Food Energy Intake (FEI & DCI)Cost of Basic Needs (CBN)Cost of Living Index methods (CPI)CoGIs or CoLIs?
10Aggregation is (arguably) less important than incidence Robustness - stochastic dominance does not address the key problem of comparabilityCompare aggregates for different relative poverty linesStochastic dominance is not that common, and calcualtions have to be “arranged” by cutting off low values.
11FEI & DCIThis 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
13CBN Method – recommended by World Bank (and UNSTATS) Food component – zfoodNon-food component - two levels (znfu & znfl)Upper and lower PLs (zu & zl)Food Component - recommendedBehavioural food bundle (households around poverty line)Scaled to normative caloriesPriced at local prices gives zf – the cost of food bundleTarp et al., 2002, variant - different food bundles in different domainsNon-food componentInverse Engel share of households around poverty lineEstimate the following regressionWhere zf is the food poverty line, yi is total expenditure, and d are demographic variablesAnd f(yi) is food expenditure
15CBN Poverty in Bangladesh R&S, 1996, for 1983/4 – 1991/21normative food bundle (from Alamgir, 1974)Not typical of consumption of poorMore 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 1983/4Updated using national Rural and Urban non-food CPIsWodon & World Bank, 1998; 1983/4 – 1995/6Same normative food bundleUVs estimated by “regression” to be poor relevantNon-food share from inverse Engel Curve for each HIESWorld Bank 2002Use Wodon 1991 CBN PLs and update using “synthetic” CPIs“Better”World Bank 2005Re-estimate CBN using same food bundle, 2005 prices & inverse Engel shares1: updated by Sen and Mujeri; based on critique of FEI & DCI for 1995/6 & 2000/12. median “unit values” for rural and urban sectors for 11 “composite” groups of items
17Is CBN so different from FEI? Calorie base to food componentEstimate non-food share by Engel regressionDifference is constraint on cost per calorieBoth give rising povertyBoth are inconsistent with elementary demand theory
20with 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 caloriesHicksian demand curves (utility compensated) show fall in demand for calorieswith fall in relative price of non-calories
21Now adding the FEI constant calorie intake demand we see even higher poverty lines CBN will suffer similar problems, more or less extremeFEI poverty line expenditure is higher than utility compensated expenditure
23Hicksian demand curves disappear with zero utility compensated substitution.
24CPI 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
26Suppose 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 equivalentIn real terms to the the food expenditure of rural households?
27What is to be done?Teach economists ethics – no code of practice! – and get them to practice themHonesty, transparency, humility?Improve capacity for diverse groups to practice evidence based policyReduce dependence on powerful donors and their agendasUse money-metric poverty for policy analysis more carefullyConstrain domains of comparisonEncourage 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 locationRecord 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 resourcesfield survey officials feel undervalued – “kill for a data set”Improve Consumer Price IndexesDon’t askAlternative methods of assessing differences and progress in well-beingLongitudinal studiesEnsure 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)