Chapter 3Chapter 3

Country Practices in Country Practices in CompilingCompiling

Poverty StatisticsPoverty Statistics

I.P. DavidI.P. David

New York, 28-30 June 2005New York, 28-30 June 2005

ContentsContents

3.1. Introduction [still to be drafted]3.1. Introduction [still to be drafted]

3.2. The Demand for Poverty Statistics [ AFRISTAT ]3.2. The Demand for Poverty Statistics [ AFRISTAT ]

3.2.1. The Demand for Poverty Statistics3.2.1. The Demand for Poverty Statistics

3.2.2. The Widening of the Scope of Poverty3.2.2. The Widening of the Scope of Poverty

3.3. Income or Expenditure Based Measurement 3.3. Income or Expenditure Based Measurement MethodsMethods

3.4. Direct Measures of Food Poverty3.4. Direct Measures of Food Poverty

3.5. Non-Income Measurement Methods3.5. Non-Income Measurement Methods

3.6. Harmonizing Poverty Statistics Production in 3.6. Harmonizing Poverty Statistics Production in

Developing CountriesDeveloping Countries

References [still to be finalized]References [still to be finalized]

3.3. Income or Expenditure Based 3.3. Income or Expenditure Based MethodsMethods

Cost of Basic Needs (CBN) is Method Most Used.Cost of Basic Needs (CBN) is Method Most Used.Split basic needs into Split basic needs into foodfood and and non-foodnon-food; estimate ; estimate

costs separately; 3 broad steps involved:costs separately; 3 broad steps involved:1.1. Specify dietary (energy) threshold (T); determine Specify dietary (energy) threshold (T); determine

food basket satisfying threshold; fpl = cost of food food basket satisfying threshold; fpl = cost of food basket.basket.

2.2. Choose operational definition. of basic non-food Choose operational definition. of basic non-food needs; cost is non-food poverty line (nfpl).needs; cost is non-food poverty line (nfpl).fpl + nfpl = tpl (total poverty line)fpl + nfpl = tpl (total poverty line)

3.3. Compare PLs against metric: income or Compare PLs against metric: income or expenditureexpenditure

Remarks: Unit of analysis/observation is household; Remarks: Unit of analysis/observation is household; statistics are in per capita and national currency.statistics are in per capita and national currency.

3.3.1. Specify a Food Poverty Threshold3.3.1. Specify a Food Poverty Threshold

Dietary energy consumption used as proxy, based on Dietary energy consumption used as proxy, based on simplifying assumption that if one gets enough simplifying assumption that if one gets enough energy, he/she gets enough of the other necessary energy, he/she gets enough of the other necessary nutrients.nutrients.

Nutrition/Research Institutes in Health/Science Nutrition/Research Institutes in Health/Science Ministries get into the act, guided by FAO-WHO Ministries get into the act, guided by FAO-WHO recommendations or practice. Outputs include recommendations or practice. Outputs include RDAs/RENIs (Table 1), energy threshold [T] (Table RDAs/RENIs (Table 1), energy threshold [T] (Table 2), and Food Composition or Conversion tables.2), and Food Composition or Conversion tables.

Poverty statistics may be very sensitive to changes in Poverty statistics may be very sensitive to changes in T; Bangladesh, Philippines, VietnamT; Bangladesh, Philippines, Vietnam

Table 3. Bangladesh Food Poverty Incidences from DCI Method and Two Energy Thresholds

Year2120kcal 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

.

Table 1. Dietary energy RDAs, Philippines and Sri Lanka, in kilocalories

Age groups Philippines Sri Lanka ----------------- -----------------

Male Female Male FemaleUnder 1 year 700 700 818 8181-3 1350 1350 1212 12124-6 1600 1600 1656 16567-9 1725 1725 1841 184110-12 2090 1930 2414 223813-15 2390 2010 2337 230016-19 2580 2020 2500 220020-39 2570 1900 2530 190040-49 2440 1800 2404 180550-59 2320 1710 2277 171060-69 2090 1540 2024 152070 & over 1880 1390 1771 1330

Table 2. Dietary energy thresholds used by a sample of countries, 2000-2004

Threshold Country2000 kcal Maldives, Philippines (but also specifies 80% of protein

RDA which is equivalent of 50 milligrams per day).2030 Sri Lanka2100 Cambodia, China, Indonesia, Laos, Mongolia, Thailand,

Vietnam, Fiji, Turkey, Armenia2124 Nepal2133 Madagascar2138 Malawi2207 Paraguay (all country)2238 Oman2282 Moldova2250 Kenya2283 Burkina Faso2288 Albania2300 Cameroon2309 Jordan2300 Iran2436 Iraq2400 Senegal, St, Kitt & Nevis, Morocco, Bahamas2470 Belarus (all country)2700 Sierra Leone3000 Uganda

3.3.2 Construct Food Basket that Satisfies T

Rank food items from consumption survey Rank food items from consumption survey (based on value, quantity, or frequency of (based on value, quantity, or frequency of households reporting).households reporting).Ranking is made from Ranking is made from reference reference population, population, e.g. lowest quartile of hholds e.g. lowest quartile of hholds per capita income distn.per capita income distn.Stopping rule: Food basket is the top Stopping rule: Food basket is the top items that provide T’items that provide T’≈ T kilocalories. Items ≈ T kilocalories. Items range from 7 to 205 with a median 40.range from 7 to 205 with a median 40.Multiply all items’ contributions by (T/T’)Multiply all items’ contributions by (T/T’)

How many baskets? How many baskets?

3.3.3 Compute food poverty line (fpl).

Let qLet q11, q, q22, …, q, …, qff be the quantities of the f items in be the quantities of the f items in the food basket that supply ethe food basket that supply e11 + e + e22+ … + e+ … + eff = T’ = T’ kilocalories. Let pkilocalories. Let p11, p, p22, … , p, … , pff be the unit prices of be the unit prices of

the f food itemsthe f food items. .

fpl = (T/T’) ∑ qfpl = (T/T’) ∑ qii p pii

in national currency.in national currency.

How many fpls? How to define reference How many fpls? How to define reference population and what prices to use to ensure population and what prices to use to ensure consistent welfare level in each domain?consistent welfare level in each domain?

3.3.4 Alternative Approaches3.3.4 Alternative Approaches Compute total expenditure and total kcalories Compute total expenditure and total kcalories

consumed by the reference population. The ratio, consumed by the reference population. The ratio, price per kcalorieprice per kcalorie, can be multiplied by any choice of , can be multiplied by any choice of T to get as many fpls as there are choices. Eschews T to get as many fpls as there are choices. Eschews food basket, but requires complete array of food basket, but requires complete array of expenditure and food composition (conversion) table expenditure and food composition (conversion) table for all food items consumed.for all food items consumed.

(∑RDA) x price per kcal = household level fpl, where (∑RDA) x price per kcal = household level fpl, where sum runs through the age by sex energy RDAs of sum runs through the age by sex energy RDAs of household. This can be compared with total income household. This can be compared with total income or expenditure of household. This is traced to or expenditure of household. This is traced to Prof. Prof. KakwaniKakwani, and tried in Laos, Thailand, Jordan. Avoids , and tried in Laos, Thailand, Jordan. Avoids computing per capita values, but still in national computing per capita values, but still in national currency.currency.

3.3.5. Compute total poverty line (tpl)3.3.5. Compute total poverty line (tpl)

Define essential non-food basic needs, estimate cost Define essential non-food basic needs, estimate cost (nfpl), and add to (fpl). Countries use one of three (nfpl), and add to (fpl). Countries use one of three methods:methods:

ListList essential non-food needs, price each, and total essential non-food needs, price each, and total cost is nfpl; tpl = fpl + nfpl. Example, Indonesia.cost is nfpl; tpl = fpl + nfpl. Example, Indonesia.

RegressionRegression (World Bank). tpl = (2- (World Bank). tpl = (2-aa)fpl)fpl, , where where aa is is intercept of OLS reg of S = fe/te) on log (te/fpl) in intercept of OLS reg of S = fe/te) on log (te/fpl) in reference population. Used in WB assisted countries.reference population. Used in WB assisted countries.

Engel’s coefficientEngel’s coefficient. Compute (fe/te). Compute (fe/te) from hholds from hholds within a narrow band around fpl; tpl = {2 – within a narrow band around fpl; tpl = {2 – (fe/te)}fpl. Used by many other countries not (fe/te)}fpl. Used by many other countries not dependent on WB-LSS.dependent on WB-LSS.

Comparisons of Three MethodsComparisons of Three Methods

List List tends toward smaller tpl. Highly subjective, tends toward smaller tpl. Highly subjective, decisions on what to include/exclude subject to decisions on what to include/exclude subject to criticism or pressure. Different bundles for criticism or pressure. Different bundles for different groups, e.g. bus for urban, bicycle or different groups, e.g. bus for urban, bicycle or motorized bike for rural, leads to different motorized bike for rural, leads to different welfare levels?welfare levels?

RegressionRegression and and Engel’s coefficient Engel’s coefficient more likely more likely lead to comparable results. What to do when lead to comparable results. What to do when regression is not a good fit? What band around regression is not a good fit? What band around fpl, and how many regressions or coefficients? fpl, and how many regressions or coefficients? (See next slide).(See next slide).

Figure 1. Ratio of Food Expenditures to Total Figure 1. Ratio of Food Expenditures to Total Expenditures, 1994, PhilippinesExpenditures, 1994, Philippines

0.62

0.63

0.64

0.65

0.66

0.67

0.68

0.69

0.70

0.71

+/- 2 +/- 5 +/- 10 +/- 15 +/- 20

Band

fe/te

URBAN RURAL TOTAL

Fourth Method of Incorporating nfplFourth Method of Incorporating nfpl

Instead of adding {1-fe/te)}fpl to fpl and arrive atInstead of adding {1-fe/te)}fpl to fpl and arrive at

tpl = {2 - (fe/te)}fpl, tpl = {2 - (fe/te)}fpl, a few developing countries (Philippines, some in ECLAC) usea few developing countries (Philippines, some in ECLAC) use

tpl = fpl/(fe/te) . tpl = fpl/(fe/te) . This givesThis gives higher tpls: higher tpls:

fe/tefe/te 2 – fe/te2 – fe/te te/fete/fe

---------- ------------------ ----------

½½ 1.501.50 22

2/32/3 1.331.33 1.51.5

¾¾ 1.201.20 1.251.25

11 11 11

3.3.6. Compute Incidence and Related 3.3.6. Compute Incidence and Related

StatisticsStatistics Household and all M members with per capita income Household and all M members with per capita income

(expenditure) < fpl are (expenditure) < fpl are food-poorfood-poor. Replace fpl with tpl . Replace fpl with tpl and you get and you get absolutely poorabsolutely poor. Design-based estimates of . Design-based estimates of totals follow (e.g. y=1 if household is poor, 0 otherwise; totals follow (e.g. y=1 if household is poor, 0 otherwise; and y = M if household is poor, 0 otherwise).and y = M if household is poor, 0 otherwise).

Poverty incidence is not straightforward. Some Poverty incidence is not straightforward. Some countries use population projections as divisors (but countries use population projections as divisors (but these may not be available for certain domains of these may not be available for certain domains of interest). Design-based estimates may be suggested, interest). Design-based estimates may be suggested, but these give different results in general.but these give different results in general.

Very few countries, if any, have projections of the Very few countries, if any, have projections of the number of households. Problem of finding denominator number of households. Problem of finding denominator not trivial; complicated by need to reconcile with not trivial; complicated by need to reconcile with implications on population projections. Philippines case.implications on population projections. Philippines case.

3.3.6. Continued3.3.6. Continued

Household poverty incidence < population poverty Household poverty incidence < population poverty incidence. Important to specify which.incidence. Important to specify which.

Serious questions about quality of basic data on food Serious questions about quality of basic data on food consumption (expenditure, quantity, unit prices), consumption (expenditure, quantity, unit prices), income and expenditure from traditional HIES. income and expenditure from traditional HIES. Limited empirical evidence point to different values Limited empirical evidence point to different values obtained from different data capture methods and obtained from different data capture methods and recall periods. Need additional studies.recall periods. Need additional studies.

Results very sensitive to choice of divisor for per Results very sensitive to choice of divisor for per capita calculations. Countries expressed need for capita calculations. Countries expressed need for guidance in using adult equivalents (e.g. for food) and guidance in using adult equivalents (e.g. for food) and scale economy models (for income or expenditure). scale economy models (for income or expenditure). Majority still use unadjusted M.Majority still use unadjusted M.

3.3.7 Updating Poverty Measures.3.3.7 Updating Poverty Measures.

Food baskets, energy thresholds and reference Food baskets, energy thresholds and reference populations seldom changed.populations seldom changed.

fpl and tpl with list method can be updated anytime fpl and tpl with list method can be updated anytime new prices become available, e.g. annually. The same new prices become available, e.g. annually. The same regression intercept or Engel’s coefficient used to regression intercept or Engel’s coefficient used to update tpl until the next HIES.update tpl until the next HIES.

Poverty incidencesPoverty incidences and counts can be updated only and counts can be updated only when a new HIES round is run because per capita when a new HIES round is run because per capita income/expenditure is needed. (see next slide) This is income/expenditure is needed. (see next slide) This is sometimes confused with updating poverty lines, hence sometimes confused with updating poverty lines, hence unduly heavy demand by users. HIES are very costly unduly heavy demand by users. HIES are very costly and complicated undertakings.and complicated undertakings.

3.3.8. Estimating Trends or Changes3.3.8. Estimating Trends or Changes For ratio (Y/X), For ratio (Y/X), V(Y/X) = V(Y) + V(X) – 2 Cov(Y,XV(Y/X) = V(Y) + V(X) – 2 Cov(Y,X) )

For change in ratio,For change in ratio,

V(YV(Yt2t2 – Y – Yt1t1 ) = V(Y ) = V(Yt2t2) + V(Y) + V(Yt1t1) – 2 Cov(Y) – 2 Cov(Yt2t2,Y,Yt1t1 ) )

where the y’s are ratios themselves.where the y’s are ratios themselves.

For inferences, YFor inferences, Yt2t2 – Y – Yt1t1 ± Z se(Y ± Z se(Yt2t2 – Y – Yt1t1 ) may ) may guard against hasty declaration that the war guard against hasty declaration that the war against poverty is being won, or else of search for against poverty is being won, or else of search for kinks in the methodology when the observed kinks in the methodology when the observed change is small or negative. change is small or negative.

.

3.3.9 Relative and Subjective 3.3.9 Relative and Subjective Income Based Poverty Lines.Income Based Poverty Lines.

Examples of Relative PLs in Developing Examples of Relative PLs in Developing Countries:Countries:50% of the median per capita income 50% of the median per capita income (ECLAC)(ECLAC)40% of the median per capita income40% of the median per capita income (Oman)(Oman)50% of both the mean and median per 50% of both the mean and median per capita capita incomes (Iran).incomes (Iran).

Relative PLs are more popular in the Relative PLs are more popular in the developed countries. Easier to measure, developed countries. Easier to measure, hence used more in poverty intervention hence used more in poverty intervention than in monitoring.than in monitoring.

3.3.9 Continued3.3.9 Continued

Examples of Subjective Poverty LinesExamples of Subjective Poverty Lines ‘‘SSelf-assessed poverty’ approach , such as elf-assessed poverty’ approach , such as

Philippines Social Weather Station asking heads of Philippines Social Weather Station asking heads of households their income, whether they consider households their income, whether they consider themselves poor, and if so, how much more income themselves poor, and if so, how much more income they need so they will no longer think of themselves they need so they will no longer think of themselves as poor. Egypt tried a similar approach but found that as poor. Egypt tried a similar approach but found that the method overestimates the extent of poverty the method overestimates the extent of poverty because people’s expectations, especially the because people’s expectations, especially the educated in the urban areas, exceed their current educated in the urban areas, exceed their current levels of living by a large margin.levels of living by a large margin.

Philippines based on a small sample (1200-1500 Philippines based on a small sample (1200-1500 households) and repeated quarterly; hence 12 time households) and repeated quarterly; hence 12 time series points in the 3-year interval that official series points in the 3-year interval that official poverty statistics are produced. poverty statistics are produced.

3.4. Direct Measures of Food Poverty3.4. Direct Measures of Food Poverty

3.4.1 Estimate empirical CDF of per capita energy 3.4.1 Estimate empirical CDF of per capita energy consumptionconsumption

Let (ai) = 1 if ai ≥ 0

= 0 if ai < 0

F (t) = Σ πi-1 (t – xi) / Σ πi

-1

3.4.1. Continued3.4.1. Continued

Example: Vietnam National Nutrition Survey, 2000

Energy cut-off < 1500 kcal < 1800 kcal<2100 kcal

% of population below cut-off 4.1% 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 3.4.2. Household Size for Per Capita CalculationsCalculations

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 3.4.3. Eschewing per capita

calculationscalculations ∑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 otherwiseThe 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 Methods3.5 Non-Income Measurement Methods

Minimum Basic Needs (MBN) or Unmet Basic Minimum Basic Needs (MBN) or Unmet Basic Needs (UBN) most popular among developing Needs (UBN) most popular among developing countries. The other approaches have not countries. The other approaches have not graduated beyond the small scale experimental graduated beyond the small scale experimental or analytical phase.or analytical phase.

UBN indicators that are non-income and UBN indicators that are non-income and measure longer term outcomes or outputs serve measure longer term outcomes or outputs serve as complement to CBN indicators that are as complement to CBN indicators that are income-based and measured from short-term income-based and measured from short-term inputs.inputs.

Examples of UBN indicators are the MDG Examples of UBN indicators are the MDG indicators minus the income indicators. indicators minus the income indicators.

3.5.Non-Income Indicators, Continued3.5.Non-Income Indicators, Continued

Nearly all countries in ECLAC have UBN poverty Nearly all countries in ECLAC have UBN poverty monitoring systems in place . The number of dimensions monitoring systems in place . The number of dimensions and indicators depend on data availability, e.g. from and indicators depend on data availability, e.g. from censuses, surveys and administrative records. It is censuses, surveys and administrative records. It is seldom that a new data collection system is initiated seldom that a new data collection system is initiated mainly for compiling UBN indicators. Broad categories mainly for compiling UBN indicators. Broad categories are dwelling characteristics, access to safe water, and are dwelling characteristics, access to safe water, and access to sanitation facilities and basic education.access to sanitation facilities and basic education.

UBN systems also in place in many ESCAP countries. UBN systems also in place in many ESCAP countries. Bangladesh, for example, uses infant mortality as proxy Bangladesh, for example, uses infant mortality as proxy indicator for the primary health care system, primary indicator for the primary health care system, primary school enrollment rate for basic education, and housing school enrollment rate for basic education, and housing characteristics (access to tap water, toilet facilities, characteristics (access to tap water, toilet facilities, electricity, and type of building material used) for living electricity, and type of building material used) for living conditions.conditions.

3.5 Non-Income Indicators, Continued3.5 Non-Income Indicators, Continued

UBN approach is far from widespread in Africa. Only UBN approach is far from widespread in Africa. Only three of the 10 members of the Economic Community three of the 10 members of the Economic Community of Western African States (ECOWAS) acknowledged of Western African States (ECOWAS) acknowledged having a UBN system in placehaving a UBN system in place. . The main poverty The main poverty dimensions considered are basic education, primary dimensions considered are basic education, primary health, and housing characteristics such as access to health, and housing characteristics such as access to safe water, toilet facilities and type of building safe water, toilet facilities and type of building materials used.materials used.

UBN methods at times brought down to sub-national UBN methods at times brought down to sub-national levels. China monitors community level indicators, such levels. China monitors community level indicators, such as percent of villages accessible by road, percent with as percent of villages accessible by road, percent with land line phone connection, and percent with land line phone connection, and percent with electricity, illiteracy rate, child enrollment rate, and electricity, illiteracy rate, child enrollment rate, and labor migration rate. labor migration rate.

3.5. Non-Income Indicators, Continued3.5. Non-Income Indicators, Continued

Producing composite indexes from indicators Producing composite indexes from indicators expressed in different units of measure is a expressed in different units of measure is a perpetually subjective and difficult task. This has perpetually subjective and difficult task. This has not stopped some international agencies from not stopped some international agencies from compiling them; e.g. HDI and other indexes in compiling them; e.g. HDI and other indexes in UNDP-HDR. These indexes, however perhaps have UNDP-HDR. These indexes, however perhaps have more value as advocacy tools and less as monitoring more value as advocacy tools and less as monitoring tools especially at the national and sub-national tools especially at the national and sub-national levels.levels.

Few developing countries, if any, compile composite Few developing countries, if any, compile composite UBN indexes, preferring to use the indicators UBN indexes, preferring to use the indicators individually and collectively in much the same way individually and collectively in much the same way that they are used to monitor progress in the MDGs.that they are used to monitor progress in the MDGs.

3.6. 3.6. HarmonizingHarmonizing Poverty Statistics Poverty Statistics

ProductionProduction Harmony = Synchronized timing; comparability; Harmony = Synchronized timing; comparability; balance between supply and demand. balance between supply and demand. Internationally, it also means improving capacities in Internationally, it also means improving capacities in the statistics-deficient countries.the statistics-deficient countries.

National statistical information systems have National statistical information systems have evolved to a point that countries follow similar evolved to a point that countries follow similar updating cycle and sequencing for certain parts of updating cycle and sequencing for certain parts of their socioeconomic databases; e.g. censuses every their socioeconomic databases; e.g. censuses every ten years, demographic surveys 3-5 years, agri ten years, demographic surveys 3-5 years, agri surveys every year or season, etc. This evolution has surveys every year or season, etc. This evolution has enabled IMF to formalize the periodicities of enabled IMF to formalize the periodicities of statistical series in its General Data Dissemination statistical series in its General Data Dissemination System (GDDS) and Special Data Dissemination System (GDDS) and Special Data Dissemination System (SDDS).System (SDDS). Poverty statistics, however, are not Poverty statistics, however, are not

covered in these systems.covered in these systems.

3.6. Harmonizing , Continued3.6. Harmonizing , Continued

Poverty reduction implementing agencies want statistics Poverty reduction implementing agencies want statistics at smaller domains and updated more frequently (usually at smaller domains and updated more frequently (usually yearly), than what NSOs can provide (excepting yearly), than what NSOs can provide (excepting censuses).censuses).

One strategy is NSO continues doing poverty monitoring One strategy is NSO continues doing poverty monitoring surveys say every 3 to 5 years which are the sources of surveys say every 3 to 5 years which are the sources of official statistics; NSO helps agencies plan and implement official statistics; NSO helps agencies plan and implement their poverty information gathering program, so that their poverty information gathering program, so that longer- term, comparability is improved; however, the longer- term, comparability is improved; however, the agencies’ data should not be used to produce aggregates agencies’ data should not be used to produce aggregates for domains where NSO official statistics exist. for domains where NSO official statistics exist.

International agencies generally want annual national International agencies generally want annual national data, and will project, intrapolate or extrapolate data, and will project, intrapolate or extrapolate otherwise. This is ok, as long as these are for global otherwise. This is ok, as long as these are for global comparison/analysis only..comparison/analysis only..

3.6.3. Main Sources of Non-3.6.3. Main Sources of Non-Comparability; Possibilities for Comparability; Possibilities for

ImprovementImprovement.. Different dietary energy thresholds (Table 2). True within Different dietary energy thresholds (Table 2). True within

country also, e.g. India. Possible improvement: estimate country also, e.g. India. Possible improvement: estimate per capita energy consumption CDF. For food poverty, per capita energy consumption CDF. For food poverty,

consider Kakwani’s approach or ∑kcal < ∑RDAconsider Kakwani’s approach or ∑kcal < ∑RDA criterioncriterion.. Food baskets vary.Food baskets vary. Very difficult and not practical to Very difficult and not practical to

recommend one food basket. Possible solution: per capita recommend one food basket. Possible solution: per capita energy consumption CDF, combined with use of adult energy consumption CDF, combined with use of adult equivalents based on age by sex RDAs; or Kakwani’s equivalents based on age by sex RDAs; or Kakwani’s

approach or ∑kcal < ∑RDAapproach or ∑kcal < ∑RDA criterion for food poverty.criterion for food poverty. Definition and measurement of non-food basic needsDefinition and measurement of non-food basic needs vary. vary.

Suggestion: Use either regression of Engel’s coefficient, Suggestion: Use either regression of Engel’s coefficient, combined with use of adult equivalents possibly based on a combined with use of adult equivalents possibly based on a scale economies of need model.scale economies of need model.

3.6. Main Sources of … Continued3.6. Main Sources of … Continued

Countries split between income and expenditure. Countries split between income and expenditure. Recommendation: Each country sticks to one, but do Recommendation: Each country sticks to one, but do some empirical research to find out likely difference in some empirical research to find out likely difference in poverty levels between income and expenditure poverty levels between income and expenditure metrics. Use scale economies of need for per capita metrics. Use scale economies of need for per capita calculations. For food poverty, consider ∑kcal < ∑RDAcalculations. For food poverty, consider ∑kcal < ∑RDA for determining the food poor.for determining the food poor.

Method of data captureMethod of data capture variesvaries. . Very difficult to get Very difficult to get agreement. Sustainability a very important factor (e.g. agreement. Sustainability a very important factor (e.g. Vietnam going back to old method). Try combination Vietnam going back to old method). Try combination of objective and recall methods; e.g. combine food of objective and recall methods; e.g. combine food weighing (subsample) with face-to-face interview weighing (subsample) with face-to-face interview (main sample). More evidence from empirical research (main sample). More evidence from empirical research needed to guide on data capture decision.needed to guide on data capture decision.

Thank you!