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)