Poverty Measurement and Analysis

Peter Lanjouw, DECPIMichael Lokshin, DEPIZurab Sajaia, DECPI

Roy vand der Weide, DECPI

WithProfessor James Foster (GWU)

Module 4, Feb 28-March 1, 2011

Monday Feb 28 Session 1 (Peter Lanjouw): 9:00-11:00

◦ Opening Remarks and Introductions◦ Concepts, Definitions and Data◦ Constructing a Consumption Indicator:◦ Adjustments for Family Composition and Size

Session 2 (Michael Lokshin): 11:15-12:30◦ Poverty Lines

Session 3 (Michael Lokshin and Zurab Sajaia): 2:00-3:30◦ Poverty Measures: Properties and Profiles◦ ADePT Software for Poverty Analysis

Outline

Tuesday, March 1 Session 4 (Roy van der Weide): 9:00-10:30

◦ Statistical Inference and Poverty Decomposition Session 5 (Professor James Foster): 10:30-

12:00◦ New Frontiers in Poverty Measurement I Multidimensional poverty measurement Chronic versus transient poverty

Session 6 (Peter Lanjouw): 2:00-3:30◦ New Frontiers in Poverty Measurement II Small area estimation of poverty Making non-comparable data comparable Developing pseudo-panel data for poverty

comparisons

Outline Cont.

What do we mean by “poverty”? Common definition:

◦ Lack of command over commodities; “..a severe constriction of the choice set [over commodities]” (Harold Watts)

More narrow definition: ◦ Lack of specific consumptions (e.g. too little

food energy intake; too little of specific nutrients; too little leisure.)

Less narrow definition: ◦ Poverty as lack of “welfare” e.g., lack of “capabilitiy”: inability to achieve certain “functionings” (“beings and doings”) (Amartya Sen)

Concepts, Definitions and Data

Other important definitions Consumption: destruction of goods and

services by use Expenditure: consumption valued at prices

paid (whether or not there was an actual transaction) 

Income: maximum possible expenditure on consumption without depleting assets

Often used interchangeably, but beware!

Concepts, Definitions and Data

Poverty data can inform, and misinform, anti-poverty policy.

1. The growth strategyHas poverty increased? Did growth help the poor? How did relative price changes affect the poor? What is the impact of food price fluctuations? Who were the losers and who were the gainers from economy-wide policy reforms? (ex-ante versus ex-post)

2. Social SpendingWho uses public services? At what cost? Who benefits from government subsidies? On average, versus at the margin? Who will be hurt by retrenchment?

3. Targeted InterventionsWho are the target groups? How should transfers be allocated? How much impact will they have on poverty?

Why Measure Poverty?

How do we measure “welfare”? When do we say someone is "poor"? How do we aggregate data on welfare into a

measure of “poverty”? How robust are the answers?

Questions for the analyst:

Common practice emphasizes economic welfare: ◦ add up expenditures on all commodities

consumed (with imputed values at local market prices) and

◦ deflate by a poverty line (depending on household size and composition and location/date)

“real expenditure” or “welfare ratio”

The welfare ratio

Survey data  Differ according to:

◦ - the unit of observation (household/individual), ◦ - no. observations over time (cross-section/panel);◦ - living standards indicator (consumption/income)

Sources of Information

Survey design Does the sample frame cover the entire population? Is there a response bias? What is the sample structure (clustering, stratification)?

Goods coverage and valuation Is the goods coverage comprehensive? Is the survey integrated (e.g. price analysis)? Are there valuation problems?

Four sets of questions to ask about your data:

Variability and the time period of survey Is there significant variability over time? Can this be encompassed within the recall period? What are the implications for the choice between consumption and income?

Inter-personal comparisons Is consumption a sufficient statistic? What other variables matter? (Prices, demographics, publicly provided goods)

Four sets of questions to ask about your data:

Income per capita◦ Poor indicator when incomes vary; hard to

measure Consumption per equivalent single adult

◦ Problems with forming scales; composition versus size economies; intra-household inequality.

Food-share ("Engel's Law")◦ Sources of noise: other parameters; problem of

income elasticity near unity. Identification problems.

Nutritional indicators◦ "Welfarist" critique (welfare and nutrition are

different things); nutritional requirements/ anthropometric standards.

Alternative measures in practice

Anthropological methods◦ Concerns about representativeness and

objectivity; lack of integration with other methods. We can learn a lot from case studies, but they don’t represent a whole country.

Subjective/qualitative methods◦ Can provide useful extra information – to help

solve the identification and referencing problems.

Alternative methods in practice

Use a comprehensive consumption measure, spanning consumption space

Choice between income and consumption is largely driven by the greater likelihood of accuracy of information on consumption.

Recognize the limitations of consumption based measures; look for supplementary measures, esp access to public services, subjective welfare, for complementary insights.

Recommendations

The consumption measure serves as the foundation upon which much of the subsequent poverty analysis rests.

Principles◦ Should be comprehensive◦ Retain transparency and credibility◦ Goal is to be able to rank individuals credibly in

terms of welfare

Constructing a Consumption Measure

Construct a food consumption measure Add basic non-food items (from

consumption module) Add other non-food items (other modules) Add housing expenditures Add use-value of consumer durables

Common Steps

Surveys typically ask about food consumption, item by item, over a specific reference period. ◦ In general, the longer (and more disaggregated) the list

of items, the higher is food consumption Sometimes information is asked about

consumption in a typical reference period (e.g. month). ◦ This has the attraction of helping to overcome

seasonality concerns. Surveys vary in terms of recall periods

◦ Some surveys use diary methods rather than recall periods.

We expect that the longer the reference period, the more expenditures are likely to be biased downwards (recollection difficulties)

Aggregating Food Expenditures

But, the shorter the reference period, the noisier are the expenditure data. ◦ This can hamper poverty comparisons.

Food expenditures should include not only purchased items but also consumption out of home production. ◦ Home production should be valued using local

market prices (preferably farm-gate) Most surveys record not only food expenditures,

but also quantities ◦ This information is of use for the construction of

poverty lines ◦ When dealing with separate price, quantity and

frequency information, on an item by item basis, one often needs to impute certain missing values.

Aggregating Food Expenditures

The typical consumption module includes a range of non-food items alongside food.

Other parts of the questionnaire may also include certain non-food expenditures (housing section, education and health sections) 

Key issue is to distinguish between investments and consumption (avoid double counting).

Health expenditures are usually excluded◦ Lumpy◦ If we include we should also have a good

measure of need.

Adding Non-Food Items

Two Additional omissions:◦ Leisure◦ Public goods

The problem is essentially one of pricing:◦ Should we value an unemployed person’s

leisure based on wages in a market to which the individual has no access?

◦ What is the value to individuals of publicly provided goods?

Imputations for public goods and leisure are likely to compromise credibility and usefulness of welfare measures.◦ We should still document who gets public

services, or is unemployed, and examine whether these are poor or rich.

Adding Non-Food Items

In most settings a minority of households rent their dwelling, and a majority reside in their own homes.

Home-owners consume a stream of services from their homes. This is precisely what rentors pay for with their rent.

The challenge is thus to impute for home-owners what they would be paying in rent if they were renting rather than owning their house.

Many surveys ask specifically what a household would pay in rent if it were renting. Where credible this number can be used for home owners.

Elsewhere one can try to predict rent paid based on regression models estimated on subset of renting households.

Imputing Housing Expenditures

Purchases of irregular, lumpy items such as consumer durables (tv, car, etc.) cannot be directly added to the consumption definition.◦ Many households are unlikely to make such

purchases within the reference period of the survey.

◦ But many will be consuming a stream of services from those items that they own.

Surveys often solicit information on the age of assets owned, original purchase price, and current replacement price.

Based on this information it is possible to calculate the consumption stream of services from the durable as:◦ Consumption Stream = Current value*(real

interest rate + depreciation rate).

Imputing Durables Consumption

The Headcount With Alternative Consumption Aggregations Consumption Aggregate Ecuador Nepal Brazil

Food Spending plusBasic Non-Food Spending 1.00 1.00 1.00

Food plus Basic Non-Food 0.85 0.91 0.89Spending Including Energyand Education Spending

Above With Actual or Imputed 0.81 n/a n/aWater Expenditures Above With Actual or Imputed 0.70 0.77 0.65Value of Housing Services Above With Imputed Value 0.68 0.76 n/aof Owned Consumer Durables 

Do not compare Apples with Oranges!!!

Poverty headcount by module type(Tanzania: study by Beegle et al, 2011)

24

1. R

ecal

l: Lo

ng, 1

4 da

y

2. R

ecal

l: Lo

ng, 7

day

3. R

ecal

l: Su

bset

, 7 d

ay

4. R

ecal

l: Col

laps

e, 7

day

5. R

ecal

l: Lo

ng U

sual

12

mon

th

6. D

iary

: HH, F

requ

ent

7. D

iary

: HH, I

nfre

quen

t

8. D

iary

: Per

sona

l

62.8

54.9 55.1

66.864.6

59.555.6

47.5

Figure 1a. Poverty headcount rate (%) at $1.25/person/day by module type

Ultimate goal is to arrive at a money metric of individual welfare.

Consumption (and income) aggregates are usually constructed at the level of the household.

Convention is to divide household consumption by the number of family members to arrive at a measure of per capita consumption.

This approach sidesteps two issues:◦ Different people may have different needs◦ The cost per person of reaching a certain

welfare level may be lower in large households than small ones.

Adjusting for Family Composition and Size

In principle equivalence scales can be used to adjust for differences in needs.◦ E.g. If a child needs half as much as an

adult, then a two adult - two child household will consist of three equivalent adults.

◦ If the total consumption of household is 120 then equivalent-consumption will equal 40. All four individuals will be allocated this equivalent-consumption.  

Where do equivalence scales come from?◦ Huge range of candidate scales

Differences in needs

Nutritional scales – derived from health studies. At best can be used to deflate food expenditures.

Behavioral scales – econometric estimates based on observed allocations. Major difficulties with identification. For example, if we observe that female children get less, do they need less? Or is it that they are systematically discriminated against?

Little guidance as to which scales are best. One option to conduct sensitivity analysis. (India example)

Differences in Needs

The head-count ratio and equivalence scales Household Type

Equivalence scales

  (1,1,1) (1,1,0.6) (1,0.8,0.6) (1,0.7,0.4)

All households 63.4 63.2 62.9 63.8

Male-headed 63.8 63.6 63.5 64.5

Female-headed

57.7 57.4 54.3 52.7

Widow-headed 58.3 61.9 58.2 58.6

Extended; male-headed

68.2 69.5 67.6 67.4

Source: Drèze and Srinivasan (1997), Table 3.Note: The equivalence scales are written as triplets indicating the weights for ‘adult male’, ‘adult female’ and ‘child’, in that order.

We often find that poverty profiles do not change much as a result of equivalence scale adjustments.   Use of per capita welfare measure may

not be too misleading  This is an empirical question that needs to

be checked on a case-by-case basis.

Differences in Needs

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)

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:

Economies of Scale in Consumption

n

xx *

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:  

Economies of Scale in Consumption

xn

x

n

x)1()(

)ln(

)1ln(

nn

In Ecuador, average household size is 4.76. ◦ If ρ =0.9 then θ=0.8◦ If ρ =0.7 then θ=0.51

If average size = 6◦ ρ =0.9 then θ=0.77◦ ρ =0.7 then θ=0.49 

Problem, as with equivalence scales, is that there isn’t a good way of estimating θ

Best bet is sensitivity analysis again. (India Example)

Economies of Scale in Consumption

 The head-count ratio and economies of scale 

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

Source: Drèze and Srinivasan (1997), Table 4.

Message now is that the per capita assumption is not innocuous.

Conclusions as to the relative poverty of 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)

Economies of Scale in Consumption

Martin Ravallion, Poverty Lines in Theory and Practice, Living Standards Measurement Study Working Paper 133, World Bank, Washington DC., 1998.

Martin Ravallion, "Issues in Measuring and Modeling Poverty", Economic Journal, Vol. 106, September 1996, pp. 1328-44.

Martin Ravallion, Poverty Comparisons, Fundamentals of Pure and Applied Economics Volume 56, Chur, Switzerland: Harwood Academic Publishers, 1994.

Further Reading (general)

Beegle, K., De Weerdt, J., Friedman, J., and Gibson, J. (2011) ‘Methods of Household Consumption Measurement Through Surveys: Experimental Results from Tanzania’ Policy Research Working Paper No. 5501, the World Bank.

Hentschel and Lanjouw (1995) ‘Aggregating Consumption Components and Setting Demographic Scales: Implications for Measured Poverty in Ecuador’, mimeo, DECRG.

Lanjouw, J. and Lanjouw, P. (2002) How to Compare Apples and Oranges: Poverty Measurement Based on Different Measures of Consumption, Review of Income and Wealth 47(1), 25-42.

 Deaton, A. and Zaidi, S. (1999) ‘Guidelines for Constructing Consumption Aggregates for Use as a Money-Metric Welfare Measure’, LSMS working paper No 135.

Further Reading (consumption)

Deaton, A. and Paxson, C. (1998) Economies of Scale, Household Size and the Demand for Food, Journal of Political Economy, 106(5): 897-930.

Lanjouw, P.F. and Ravallion, M. (1995): Poverty and Household Size, Economic Journal, Vol 105, No. 433.

Lanjouw, J., Lanjouw, P., Milanovic, B., and Paternostro, S. (2004) Economies of Scale and Poverty: the Impact of Relative Price Shifts During Economic Transition, Economics of Transition 12(3) 509-536.

Further reading (adjustments)