1. Isidoro DAVID - Chapter 3 - United Nations Statistics Division

Report
Chapter 3
Country Practices in Compiling
Poverty Statistics
I.P. David
New York, 28-30 June 2005
Contents
3.1. Introduction [still to be drafted]
3.2. The Demand for Poverty Statistics [ AFRISTAT ]
3.2.1. The Demand for Poverty Statistics
3.2.2. The Widening of the Scope of Poverty
3.3. Income or Expenditure Based Measurement Methods
3.4. Direct Measures of Food Poverty
3.5. Non-Income Measurement Methods
3.6. Harmonizing Poverty Statistics Production in
Developing Countries
References [still to be finalized]
3.3. Income or Expenditure Based Methods
Cost of Basic Needs (CBN) is Method Most Used.
Split basic needs into food and non-food; estimate costs separately;
3 broad steps involved:
1.
Specify dietary (energy) threshold (T); determine food basket
satisfying threshold; fpl = cost of food basket.
2.
Choose operational definition. of basic non-food needs; cost
is non-food poverty line (nfpl).
fpl + nfpl = tpl (total poverty line)
3.
Compare PLs against metric: income or expenditure
Remarks: Unit of analysis/observation is household; statistics are in
per capita and national currency.
3.3.1. Specify a Food Poverty Threshold

Dietary energy consumption used as proxy, based on simplifying
assumption that if one gets enough energy, he/she gets enough
of the other necessary nutrients.

Nutrition/Research Institutes in Health/Science Ministries get
into the act, guided by FAO-WHO recommendations or
practice. Outputs include RDAs/RENIs (Table 1), energy
threshold [T] (Table 2), and Food Composition or Conversion
tables.

Poverty statistics may be very sensitive to changes in T;
Bangladesh, Philippines, Vietnam
Table 3. Bangladesh Food Poverty Incidences
from DCI Method and Two Energy Thresholds
2120kcal
1805kcal
Difference
1983-84
62.6
36.8
25.8
1985-86
55.7
26.9
28.8
1988-89
47.8
28.4
19.4
.
1991-92
47.5
28.0
19.5
1995-96
47.5
25.1
22.4
Average
-
-
23.2
Year
Table 1. Dietary energy RDAs, Philippines and Sri Lanka, in kilocalories
Age groups
Under 1 year
1-3
4-6
7-9
10-12
13-15
16-19
20-39
40-49
50-59
60-69
70 & over
Philippines
----------------Male
Female
700
700
1350
1350
1600
1600
1725
1725
2090
1930
2390
2010
2580
2020
2570
1900
2440
1800
2320
1710
2090
1540
1880
1390
Sri Lanka
----------------Male
Female
818
818
1212
1212
1656
1656
1841
1841
2414
2238
2337
2300
2500
2200
2530
1900
2404
1805
2277
1710
2024
1520
1771
1330
Table 2. Dietary energy thresholds used by a sample of countries, 2000-2004
Threshold
2000 kcal
2030
2100
2124
2133
2138
2207
2238
2282
2250
2283
2288
2300
2309
2300
2436
2400
2470
2700
3000
Country
Maldives, Philippines (but also specifies 80% of protein
RDA which is equivalent of 50 milligrams per day).
Sri Lanka
Cambodia, China, Indonesia, Laos, Mongolia, Thailand,
Vietnam, Fiji, Turkey, Armenia
Nepal
Madagascar
Malawi
Paraguay (all country)
Oman
Moldova
Kenya
Burkina Faso
Albania
Cameroon
Jordan
Iran
Iraq
Senegal, St, Kitt & Nevis, Morocco, Bahamas
Belarus (all country)
Sierra Leone
Uganda
3.3.2 Construct Food Basket that Satisfies T
Rank
food items from consumption survey (based on
value, quantity, or frequency of households reporting).
Ranking is made from reference population, e.g. lowest
quartile of hholds per capita income distn.
Stopping rule: Food basket is the top items that provide
T’≈ T kilocalories. Items range from 7 to 205 with a
median 40.
Multiply all items’ contributions by (T/T’)
How many baskets?
3.3.3 Compute food poverty line (fpl).

Let q1, q2, …, qf be the quantities of the f items in the food
basket that supply e1 + e2+ … + ef = T’ kilocalories. Let p1, p2,
… , pf be the unit prices of the f food items.
fpl = (T/T’) ∑ qi pi
in national currency.

How many fpls? How to define reference population and what
prices to use to ensure consistent welfare level in each domain?
3.3.4 Alternative Approaches


Compute total expenditure and total kcalories consumed by the
reference population. The ratio, price per kcalorie, can be
multiplied by any choice of T to get as many fpls as there are
choices. Eschews food basket, but requires complete array of
expenditure and food composition (conversion) table for all food
items consumed.
(∑RDA) x price per kcal = household level fpl, where sum runs
through the age by sex energy RDAs of household. This can be
compared with total income or expenditure of household. This
is traced to Prof. Kakwani, and tried in Laos, Thailand, Jordan.
Avoids computing per capita values, but still in national currency.
3.3.5. Compute total poverty line (tpl)
Define essential non-food basic needs, estimate cost (nfpl), and add
to (fpl). Countries use one of three methods:
 List essential non-food needs, price each, and total cost is nfpl;
tpl = fpl + nfpl. Example, Indonesia.


Regression (World Bank). tpl = (2-a)fpl, where a is intercept
of OLS reg of S = fe/te) on log (te/fpl) in reference
population. Used in WB assisted countries.
Engel’s coefficient. Compute (fe/te) from hholds within a
narrow band around fpl; tpl = {2 – (fe/te)}fpl. Used by many
other countries not dependent on WB-LSS.
Comparisons of Three Methods

List tends toward smaller tpl. Highly subjective, decisions on
what to include/exclude subject to criticism or pressure.
Different bundles for different groups, e.g. bus for urban, bicycle
or motorized bike for rural, leads to different welfare levels?

Regression and Engel’s coefficient more likely lead to
comparable results. What to do when regression is not a good
fit? What band around fpl, and how many regressions or
coefficients? (See next slide).
Figure 1. Ratio of Food Expenditures to Total Expenditures,
1994, Philippines
fe/te
0.71
0.70
0.69
0.68
0.67
0.66
URBAN
0.65
RURAL
TOTAL
0.64
0.63
0.62
+/- 2
+/- 5
+/- 10
Band
+/- 15
+/- 20
Fourth Method of Incorporating nfpl
Instead of adding {1-fe/te)}fpl to fpl and arrive at
tpl = {2 - (fe/te)}fpl,
a few developing countries (Philippines, some in ECLAC) use
tpl = fpl/(fe/te) . This gives higher tpls:
fe/te
----½
2/3
¾
1
2 – fe/te
--------1.50
1.33
1.20
1
te/fe
----2
1.5
1.25
1
3.3.6. Compute Incidence and Related Statistics



Household and all M members with per capita income
(expenditure) < fpl are food-poor. Replace fpl with tpl and you
get absolutely poor. Design-based estimates of totals follow (e.g.
y=1 if household is poor, 0 otherwise; and y = M if household is
poor, 0 otherwise).
Poverty incidence is not straightforward. Some countries use
population projections as divisors (but these may not be available
for certain domains of interest). Design-based estimates may be
suggested, but these give different results in general.
Very few countries, if any, have projections of the number of
households. Problem of finding denominator not trivial;
complicated by need to reconcile with implications on
population projections. Philippines case.
3.3.6. Continued

Household poverty incidence < population poverty incidence.
Important to specify which.

Serious questions about quality of basic data on food
consumption (expenditure, quantity, unit prices), income and
expenditure from traditional HIES. Limited empirical evidence
point to different values obtained from different data capture
methods and recall periods. Need additional studies.

Results very sensitive to choice of divisor for per capita
calculations. Countries expressed need for guidance in using
adult equivalents (e.g. for food) and scale economy models (for
income or expenditure). Majority still use unadjusted M.
3.3.7 Updating Poverty Measures.

Food baskets, energy thresholds and reference populations
seldom changed.

fpl and tpl with list method can be updated anytime new prices
become available, e.g. annually. The same regression intercept or
Engel’s coefficient used to update tpl until the next HIES.

Poverty incidences and counts can be updated only when a new
HIES round is run because per capita income/expenditure is
needed. (see next slide) This is sometimes confused with
updating poverty lines, hence unduly heavy demand by users.
HIES are very costly and complicated undertakings.
3.3.8. Estimating Trends or Changes
For
ratio (Y/X), V(Y/X) = V(Y) + V(X) – 2 Cov(Y,X)
For
change in ratio,
.
V(Yt2 – Yt1 ) = V(Yt2) + V(Yt1) – 2 Cov(Yt2,Yt1
)
where the y’s are ratios themselves.
For inferences, Yt2 – Yt1 ± Z se(Yt2 – Yt1 ) may guard against
hasty declaration that the war against poverty is being won, or else
of search for kinks in the methodology when the observed change
is small or negative.
3.3.9 Relative and Subjective Income Based
Poverty Lines.
Examples of Relative PLs in Developing Countries:
50% of the median per capita income (ECLAC)
40%
50%
of the median per capita income (Oman)
of both the mean and median per capita
incomes (Iran).
Relative PLs are more popular in the developed
countries. Easier to measure, hence used more in poverty
intervention than in monitoring.
3.3.9 Continued
Examples of Subjective Poverty Lines

‘Self-assessed poverty’ approach , such as Philippines Social
Weather Station asking heads of households their income,
whether they consider themselves poor, and if so, how much
more income they need so they will no longer think of
themselves as poor. Egypt tried a similar approach but found
that the method overestimates the extent of poverty because
people’s expectations, especially the educated in the urban areas,
exceed their current levels of living by a large margin.

Philippines based on a small sample (1200-1500 households) and
repeated quarterly; hence 12 time series points in the 3-year
interval that official poverty statistics are produced.
3.4. Direct Measures of Food Poverty
3.4.1 Estimate empirical CDF of per capita energy consumption
Let
(ai)
= 1 if ai ≥ 0
= 0 if ai < 0
F (t) = Σ πi-1
(t – xi) / Σ πi-1
3.4.1. Continued
Example: Vietnam National Nutrition Survey, 2000
Energy cut-off
% of population
below cut-off
< 1500 kcal
4.1%
< 1800 kcal
<2100 kcal
17.9%
45.1%
Note: The official food poverty incidence from GSO was 12-13% in 2000
3.4.2. Household Size for Per Capita Calculations
Example: Philippines. From Food Consumption Survey of the Food
and Nutrition Research Institute.
Table 5. Per Capita Energy Consumption Distributions (% of Population) Using
M and M0.7 as Divisors, Metropolitan Manila - Philippines, 2003
Divisor/Cut-Off (kcal)
<1500
<1800
<2000
<2100
Family Size, M
48.0
74.0
83.0
88.0
M* = M0.7
7.9
16.0
22.5
26.3
3.4.3. Eschewing per capita calculations

∑kcal < ∑RDA can be used directly to classify hholds and
persons therein as either food poor or not.
Energy gap = ∑w{∑RDA - ∑kcal} if {∑RDA - ∑kcal} > 0
= 0 otherwise
The RDAs may be changed proportionately by ± 15% and ± 30% and end up
with five points that give a picture of how food poverty behaves with RDA
specifications. If countries have these, then comparable food poverty
estimates can be easily interpolated for any choice of common energy
threshold.
No per capita calculations, no currencies, no prices, no reference populations.
If desired, energy gap x price per calorie will provide energy gap in money
terms.
3.5 Non-Income Measurement Methods



Minimum Basic Needs (MBN) or Unmet Basic Needs (UBN)
most popular among developing countries. The other
approaches have not graduated beyond the small scale
experimental or analytical phase.
UBN indicators that are non-income and measure longer term
outcomes or outputs serve as complement to CBN indicators
that are income-based and measured from short-term inputs.
Examples of UBN indicators are the MDG indicators minus the
income indicators.
3.5.Non-Income Indicators, Continued


Nearly all countries in ECLAC have UBN poverty monitoring
systems in place . The number of dimensions and indicators
depend on data availability, e.g. from censuses, surveys and
administrative records. It is seldom that a new data collection
system is initiated mainly for compiling UBN indicators. Broad
categories are dwelling characteristics, access to safe water, and
access to sanitation facilities and basic education.
UBN systems also in place in many ESCAP countries. Bangladesh, for
example, uses infant mortality as proxy indicator for the primary
health care system, primary school enrollment rate for basic
education, and housing characteristics (access to tap water, toilet
facilities, electricity, and type of building material used) for living
conditions.
3.5 Non-Income Indicators, Continued

UBN approach is far from widespread in Africa. Only three of
the 10 members of the Economic Community of Western
African States (ECOWAS) acknowledged having a UBN system
in place. The main poverty dimensions considered are basic
education, primary health, and housing characteristics such as
access to safe water, toilet facilities and type of building materials
used.

UBN methods at times brought down to sub-national levels.
China monitors community level indicators, such as percent of
villages accessible by road, percent with land line phone
connection, and percent with electricity, illiteracy rate, child
enrollment rate, and labor migration rate.
3.5. Non-Income Indicators, Continued


Producing composite indexes from indicators expressed in
different units of measure is a perpetually subjective and difficult
task. This has not stopped some international agencies from
compiling them; e.g. HDI and other indexes in UNDP-HDR.
These indexes, however perhaps have more value as advocacy
tools and less as monitoring tools especially at the national and
sub-national levels.
Few developing countries, if any, compile composite UBN
indexes, preferring to use the indicators individually and
collectively in much the same way that they are used to monitor
progress in the MDGs.
3.6. Harmonizing Poverty Statistics Production

Harmony = Synchronized timing; comparability; balance
between supply and demand. Internationally, it also means
improving capacities in the statistics-deficient countries.

National statistical information systems have evolved to a point
that countries follow similar updating cycle and sequencing for
certain parts of their socioeconomic databases; e.g. censuses
every ten years, demographic surveys 3-5 years, agri surveys
every year or season, etc. This evolution has enabled IMF to
formalize the periodicities of statistical series in its General Data
Dissemination System (GDDS) and Special Data Dissemination
System (SDDS). Poverty statistics, however, are not covered in
these systems.
3.6. Harmonizing , Continued



Poverty reduction implementing agencies want statistics at
smaller domains and updated more frequently (usually yearly),
than what NSOs can provide (excepting censuses).
One strategy is NSO continues doing poverty monitoring
surveys say every 3 to 5 years which are the sources of official
statistics; NSO helps agencies plan and implement their poverty
information gathering program, so that longer- term,
comparability is improved; however, the agencies’ data should
not be used to produce aggregates for domains where NSO
official statistics exist.
International agencies generally want annual national data, and
will project, intrapolate or extrapolate otherwise. This is ok, as
long as these are for global comparison/analysis only..
3.6.3. Main Sources of Non-Comparability;
Possibilities for Improvement.

Different dietary energy thresholds (Table 2). True within country also, e.g.
India. Possible improvement: estimate per capita energy consumption CDF.
For food poverty, consider Kakwani’s approach or ∑kcal < ∑RDA
criterion.

Food baskets vary. Very difficult and not practical to recommend one food
basket. Possible solution: per capita energy consumption CDF, combined
with use of adult equivalents based on age by sex RDAs; or Kakwani’s
approach or ∑kcal < ∑RDA criterion for food poverty.

Definition and measurement of non-food basic needs vary. Suggestion: Use
either regression of Engel’s coefficient, combined with use of adult
equivalents possibly based on a scale economies of need model.
3.6. Main Sources of … Continued


Countries split between income and expenditure.
Recommendation: Each country sticks to one, but do some
empirical research to find out likely difference in poverty levels
between income and expenditure metrics. Use scale economies
of need for per capita calculations. For food poverty, consider
∑kcal < ∑RDA for determining the food poor.
Method of data capture varies. Very difficult to get agreement.
Sustainability a very important factor (e.g. Vietnam going back to
old method). Try combination of objective and recall methods;
e.g. combine food weighing (subsample) with face-to-face
interview (main sample). More evidence from empirical research
needed to guide on data capture decision.
Thank you!

similar documents