[PPT]Attacking Poverty in Papua New Guinea - unstats |...
Transcript of [PPT]Attacking Poverty in Papua New Guinea - unstats |...
Overview of Chapter IV:Statistical Tools and Estimation Methods for Poverty Measures
John GibsonDepartment of EconomicsUniversity of Canterbury
New Zealand
Overall Aim of the Chapter Attempt to describe tools that are simple
Extensions of methods that many statistics offices may already use
Interaction between data and method Highlight improvements in data collection that
may assist the further development of some of the estimation methods described
Possible additions/deletions to the chapter and recommendations in yellow
Structure4.0 Introduction4.1 Cross-cutting issues4.2 Types of surveys4.3 Assessing individual welfare
and poverty from household data4.4 Poverty dynamics from
longitudinal surveys
4.0Introduction Justify priority given to quantitative, monetary
indicators Generalisable Potentially consistent Able to be predicted/simulated Ease of budgeting interventions if poverty measured in a
money metric Note that poverty-focused surveys include both
quantitative and qualitative non-monetary indicators
Desirability of link between case study/qualitative evidence and quantitative survey evidence
Box 1: Poverty and Water in PNG
4.1Cross-cutting issuesCovers issues that a statistical agency may face
that are somewhat independent of the particular type of household survey used
1. Why consumption expenditure is the preferred welfare indicator
2. Need for consistency of survey methods3. Correction methods to restore consistency4. Variance estimators for complex samples
4.1.1 Reasons for favouring consumption as welfare indicator Most popular
52/88 countries in Ravallion (2001) Could drop this, given Chapter 2?
Reasons why consumption expenditure is increasing used
CONCEPTUAL Consumption is a better measure of both
current and long-term welfare PRACTICAL
It is more difficult for surveys to accurately measure income
Conceptual problems with current income as a welfare measure Current income has larger transitory
component than current consumption Consumption is a function of permanent
income rather than current income Households save and dis-save and use informal
support networks to smooth consumption over time Less inequality in current consumption than in
current income Profile of income-poor is less likely to identify
the characteristics of the long-term poor U.S. income-poor have home ownership rate of
30% versus only 15% for consumption-poor (60% for all HH)
Food budget share for income poor is 24% versus 32% for the consumption poor (NB: 19% for all households)
Expect different trends in income-poverty and consumption-poverty
Income-poor dis-save to maintain their consumption
With fixed poverty line and economic growth, get a rising consumption to income ratio for the poor
U.S. consumption poverty rate fell 2.5% per year (1961-89), income poverty rate fell by only 1.1% per year
0.5
1
1.5
2
1 2 3 4 5Income Quintile
Consumptionto incomeratio in across-section
Practical problems with current income as a welfare measure Requires longer reference period to capture
seasonal incomes Recall errors more likely
Seasonal variation in consumption less than in income More diverse income sources than types of
consumption Income surveys need a wider range of questions
Splitting household and business expenses for informal sector
assets data to get income flows, especially for livestock Income is more sensitive
Understated due to tax concerns and when some income is from illicit activities
4.1.2 Consistency of survey methods and poverty comparisons Highlight sensitivity of consumption
and poverty estimates to changes in survey methods
Selected experimental results Diary rather than recall raised reported
food expenditure by 46% in Latvia Detailed recall list (100 items) rather than
same items in broader categories (n=24) raised reported consumption by 31% in El Salvador
Reported spending fell by 2.9% for each day added to the recall period in Ghana
Recall error levels off at 20% after two weeks
4.1.2 Practical evidence on the effect of survey non-comparability
India’s NSS traditionally had 30-day recall for all items Switched to
7-day recall for food, 30-day for fuel and rent etc, 365 day recall for infrequent purchases
changes increase measured consumption of the poor Less forgetting of food in 7-days than 30 days Mean and variance of spending on infrequent items fell
Replaces zero monthly spending on infrequent items with low annual spending for the poor
Changes in survey method reduce measured poverty by 175 million!!
Scale attracted several experts who devised adjustment methods to restore comparability
But what about smaller, less significant countries…
Box 2: Incomparable Survey Designs and Poverty Monitoring in Cambodia
Non-comparable surveys in 1993 (detailed recall ≈ 450 items), 1997 (33 items) and 1999 (36 items)
1993: very detailed survey to calculate CPI weights but CPI price surveys only ever collected in capital city
Poverty line too detailed (155 items) for subsequent surveys to re-price
Short-recall surveys affected by other topics included in the rotating modules
1997: detailed health spending questions in social sector module gave higher expenditure than in the consumption module, consumption estimates were arbitrarily raised by up to 14%
Apparent fall in headcount from 39% to 36% reversed absent this 1999: attempt to reconcile consumption at household level with
detailed income module for a random half-sample Headcount poverty rate fell from 64% round 1 to 36% in round 2
No robust poverty trend for 1990s from these irreconcilable date
4.1.3 Correction methods for restoring comparability to poverty estimates
Change in commodity detail (Lanjouw/Lanjouw) Restrict food poverty line to items that are
consistently measured in the two surveys Estimate Engel curve to get non-food allowance
in each survey Normally only do it for baseline survey and inflate the
non-food allowance Potential contradiction between treatment in Ch. 3 and 4
Poverty comparisions are restricted to the headcount index at the upper poverty line
Distinction between the food share for lower (‘austere’) and upper poverty line is not clearly set out in any of the draft chapters – talk generically of Engel methods
4.1.3 Correction methods for restoring comparability to poverty estimates
Change in recall period (Deaton/Tarozzi)
From initial survey estimate:Pi = f(expenditure on items with unchanged recall
period) E.g. fuel and rent in India’s NSS Use regression or non-parametric estimation
Assuming that this relationship holds, use distribution of expenditures on the items with unchanged recall period in the new survey to predict poverty
4.1.4 Variance estimators for complex sample designs Most household surveys have samples that are
clustered, stratified and perhaps weighted Standard software gives incorrect inferences from
these samples Standard error of headcount poverty rate in Ghana 45%
higher once clustering and stratification taken account of, compared with wrongly assuming Simple Random Sampling
Variance Estimators Taylor series linearization
Variance estimator of a linear approximation Replication techniques
Repeated sub-samples from the data Estimates computed from each and variance calculated
from deviation of the replicate estimates from the whole sample estimate
List some software that has these estimators
4.2 Types of Surveys Discusses the types of surveys a statistical agency
can use to measure and analyse poverty Most surveys have multiple objectives and some
design features that reflect other purposes may not be desirable for poverty measurement
1. Income and expenditure (or budget) surveys
2. Correcting overstated annual poverty from short-reference HIES/HBS
3. LSMS surveys4. Core and module designs5. DHS (and MICS)
4.2.1 HIES and HBS Primary objective is to provide expenditure
weights for a CPI Appropriate design for a CPI objective is different
than for a poverty-focused survey Include few other topics because of burden on
respondents of recalling/reporting detailed consumption Many do not collect the local prices needed for CBN food
poverty line or spatial price index Short reference periods may not measure long-run
welfare Even for consumption, which is unlikely to be fully
smoothed
4.2.1 Problems with HIES/HBS: lack of local prices
Urban prices often collected for a CPI inapplicable in rural areas
Gap between IFLS and BPS estimates of poverty rise in Indonesia
Food expenditures (E) and quantities (Q) often available from HIES or HBS so unit values (E/Q) used as ‘prices’
Problems Reflect quality differences chosen by households Reporting errors in E and/or Q Only available for purchasing households
Deaton reports good performance of UVs in updating regional poverty lines in India but…
Capeau & Dercon (Ethiopia) and Gibson and Rozelle (PNG) find that UV’s overstate prices and cause rural poverty rates to be over-estimated by more than 20%
Recommend: more effort on collecting local prices
Aggregate food poverty rates from different food price data(PNG experiment – currently not in Ch. 4)
22
30
23.8
5.98.9
6.8
2.4 3.8 2.8
0
5
10
15
20
25
30
Headcount Poverty gap Poverty severity
Market pricesUnit valuesPrice opinions
Food poverty line calculated from:
4.2.1 Problems with HIES/HBS: short reference periods overstate annual poverty
Short reference periods because of difficulty of recalling or recording consumption
Includes many transitory shocks that are subsequently reversed
OK if just want mean budget shares or mean spending level
Causes higher poverty estimates if poverty line below the mode
Affects surveys that annualise from short reference periods and those that both collect and report on short periods
Weekly/monthly poverty rates less useful because dominated by transitory fluctuations
Welfare indicator
Density Poverty Line
Annual reference period
Monthly reference period
0 z
4.2.1 Problems with HIES/HBS: example of overstated poverty when annualizing from short periods
Respondents in HIES in urban China keep expenditure diary for full 12 month period
Benchmark to compare with extrapolation from short reference periods
1 month (x12 for each household) with sample spread evenly over the year
2 months (so x6 for each household) collected six months apart
6 months (collect every 2nd month of data on each household)
1 month
2 mths
6 mths
Mean annual expenditure
0.1% 0.1% 0.1%
Annual headcount poverty
53.1
%
32.2
%
15.0
%Annual poverty gap index
150% 77.8
%
19.4
%
Overstatement when extrapolate from
4.2.2 Correcting overstated annual poverty from short-reference periods True variance of households’ annual expenditures:
rt,t’ correlation between same households’ expenditures in t & t’
σt standard deviation across households in month t If dispersion across households does not vary from month
to month…
V(xm) is variance of monthly expenditures across all i households and t months in the year
r ̅ is the average correlation between the same household’s expenditures in all pairs of months in the year
May get reliable estimate of r̅ without 12 months of data
)(13212)( xVrxV ma
4.2.2 Correcting overstated annual poverty from short-reference periods Annual expenditures extrapolated from household
expenditures observed in one (staggered) month
Implicitly assumes r ̅= 1 (no instability in the monthly ranking of households) overstates the variance, inequality and poverty
Instead, scale each household’s deviation from monthly average, (xit-x ̅m) to annual value with factor based on empirical estimates of r ̅
E.g. if r ̅ = 0.5 scaling factor on deviations from monthly average is 8.8 (=78), rather than 12
Intuitively, many shocks causing (xit-x ̅m) are subsequently reversed so have less impact with this method
mama VVxx 14412
xxx mmitAi rx 1213212,
4.2.2 Correcting overstated poverty when annualizing from short periods: example
Correction method does good job of approximating the poverty estimates from 12 month diaries in HIES from urban China
Using just single revisit to estimate r ̅
Further economise by just revisiting sub-samples to get r ̅
Added 10% to cost of a cross-sectional survey in PNG
1 month
2 mths
Corrected
Mean annual expenditure
0.1% 0.1% 0.1%
Annual headcount poverty
53.1
%
32.2
%
0.1%
Annual poverty gap index
150% 77.8
%
5.0%
Overstatement when extrapolate from
4.2.3 LSMS Surveys Full coverage in Grosh and Glewwe and Deaton
and Grosh so only two aspects discussed Bounded recall to prevent telescoping
Consistent with the literature but unaware of any evaluation
Only used in some LSMS Annual recall of consumption, even for frequent
purchases Months purchased × times per month × usual purchase
per time If accurate overcomes problem of short reference periods
exaggerating annual poverty Limited evidence that estimates similar to previous month
recall but both collected in same interview so not independent More experiments needed on this
Box 3: modeling to help long-run poverty alleviation Better examples available?
4.2.4 Core-Module Surveys Simple core survey fielded frequently and
rotating modules tacked on Potentially get the high frequency and large sample for
monitoring and broad topic coverage for modelling Consumption and poverty from core
incompatiable with estimates from detailed module
SUSENAS core has mean-reverting error and no simple correction factor to give core-to-module consistency
Contents of rotating module can affect the core Interviewers, respondents and analysts may try to
reconcile or adjust core estimates based on what is reported in a detailed module
Lose core-to-core consistency
4.2.5 DHS (and MICS) Standardised questionnaires that aid cross-
country and temporal comparisons Available for almost all developing countries,
often for two points in time No income or consumption data Information on dwelling facilities and asset
ownership to form a “wealth index” that has been used for poverty and distributional analysis
Principal components or factor analysis used Some evidence this index is a reasonable proxy for
consumption no evidence on validity of “poverty” estimates
4.3 Assessing individual welfare and poverty from household data how should adjustments be made for
differences in household size and composition when inferring individual welfare and poverty status from household data?
are there reliable methods of observing whether some types of individuals within households, such as women or the elderly, are differentially poor?
4.3.1 Equivalence scales Convert households of different size and composition
into number of equivalent adults Ne = (A +φC )θ φ ≤ 1 θ ≤ 1
φ is adult equivalence of a child θ is elasticity of cost with respect to HH scale while φ = θ = 1 is most common choice in developing
countries, many use different values (chap 2?) Empirical data alone cannot identify φ and θ
Same demand function can be derived from two (or more) cost functions that embody different scale economies and costs of children
Two common identifying assumptions used: Engel: food share is a welfare measure across household types Rothbarth: expenditure on adult goods is a valid welfare measure
Varying φ and θ as sensitivity analysis may be best approach
4.3.2 Rothbarth method Valid method of estimating φ, the adult
equivalence of a child Cannot be used to estimate scale economies, θ Depends on a set of goods that children do not
consume Children only exert income effects on these goods Formal test for valid adult goods based on “outlay
equivalent ratios” Show effect of a demographic group on demand, from
budget share equation Also used in a method for detecting differential poverty
within the household (4.3.6)
4.3.2 Rothbarth method Require outlay x1 to restore adult goods spending to former
level (x1-x0) is cost of the child and (x1-x0)/(x0/2) is the adult
equivalence
xA0
x0 x1 Total expenditure
Reference household (2 adults)
Larger household (2-adult, 1-child)
Spending onadult goods
4.3.3/4 Engel method not recommended
No theoretical justification for using food share to measure either cost of children or economies of scale
If parents perfectly compensated for cost of a child, family food share would still rise
Food is larger share of child’s consumption than parent’s Rise in the food share indicates need for extra compensation
under logic of Engel method over-compensates Larger household with same per capita expenditure as a
smaller one Economies of scale make larger household better off Better off households have lower food shares according to
Engel method Per capita spending on food must fall (given constant PCX) When poor people become better off, dollar value of spending on
food is unlikely to fall, especially when under-nourished Sensitive to variation in survey design that affect
measured food shares (seems to give large scale economies with recall surveys)
4.3.5 Adjusting poverty statistics if adult equivalents are units Standard FGT formula uses N and Q
Total population and number of poor Overstates monetary value of
poverty gap if poverty defined in adult-equivalent terms Use adult equivalent numbers rather
than population Adjustment formula from Milanovic
4.3.6 Differential poverty within the household (intra-household allocation) Describe Deaton’s method of detecting
boy-girl bias Is reduction in spending on adult goods larger when
the child is a boy rather than a girl? Generally hasn’t worked as expected Finer disaggregation of adult goods when statistics
agencies form consumption recall lists may help Harder to study unequal allocations between
adults May reflect preferences, whereas children only had
income effects Emerging methods could be aided by surveys that use
diaries for each adult and also record if purchases are for own consumption or consumption of others
4.4 Poverty dynamics from longitudinal surveys Increased emphasis Very demanding surveys
Sampling frame of individuals or households rather than dwellings
Must be prepared to track split-offs and reformed households, plus movers
1. Methods of measuring chronic and transient poverty
2. Attrition bias in longitudinal survey data3. Reliability ratio approach to measurement
error in longitudinal survey data
Separating Poverty into Chronic and Transient Components Motivation
Transient poverty reduces sharpness of poverty profiles
Transient share likely to vary over time and space so distorts comparisons of long-run poverty
Different policies needed smoothing vs raising average consumption/income
Methods Spells Components
Don’t necessarily give same result
4.4.1 Spells vs Components decomposition
Spells HHs below poverty line each period Remaining poor are transient
Simple cross-tabulation with two-wave panel Weaknesses:
focuses attention on headcount ‘sometimes poor’ too broad if many vs few survey waves
Components chronic poor have mean welfare over time below the
poverty line Transient are residual component “always poor” are subset of chronically poor
Numerical example to show the two approaches may give different shares of chronic/transient poverty
T P C ( , , , )i i i iC P y y y
4.4.2 Attrition bias in longitudinal survey data Wide variation in attrition in LSMS
longitudinal surveys (from 16-69% attrite) Regression relationships seem unaffected
May be OK to just study stayers? Less evidence on effect on poverty
measurement UK evidence suggests a bias
Example and value of tracking out-of-village movers in IFLS
4.4.3 Measurement error in longitudinal survey data
Poverty dynamics overstated due to measurement error
Describe simple “reliability index” method for detecting measurement error
Some statistical agencies familiar with this for static variables, from test-retest or post-enumeration surveys
Correlation between two error-ridden reports on same variable can indicate data reliability, if measurement errors are uncorrelated
Tool does not work for dynamic variables because imperfect correlation expected because the variable ‘moves’
Requires extending panels from typical two waves to at least three waves
Reliability index for longitudinal data could be more widely calculated to temper conclusions about poverty dynamics
Is this redundant, given more sophisticated correction method described in Ch. 6?
Example of imperfect reliability: RLMS urban household income Measurement error
attentuates correlation coefficients
in proportion to squared reliability index
1-step correlation between expenditure in 1994 and 1996 is once-attentuated
2-step correlation from product of correlations between 1994-1995 and 1995-1996 is twice attentuated
If expenditure generated from a first order-autoregressive model, should be the same whether going directly or via 1995 expenditure
Y1994
Y1995
Y1996
r=0.42
r=0.51
r=0.29
2-step correlation: 0.42×0.51 = 0.221-step = 0.29Reliability index=(0.22/0.29)=0.86
Standard deviation of observed household expenditure in RLMS has true component of 86% and error component of 14%
Omissions How many food poverty line baskets?
Are regional taste and availability variations respected? Do different baskets mean different living standards?
Ravallion/Lokshin (Russia) and Simler et al (Mozambique) use WARP to test and adjust
Whose diet sets the CBN food basket and what if final poverty rate differs from the starting group?
Pritchett et al. have an iterative procedure Even if single basket, how many regions/sectors
should the basket be priced in? How to know if poverty line should vary by region, by
sector, or both Relationship between spatial price deflator and regional
values of poverty line