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Overview of Monetary Poverty Statistics Production Practices in the Region. Most countries use CBN Method . Food Poverty line ( fpl ) first, then inflate to Total Poverty line ( tpl ) Compare fpl and tpl against income or exp.
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Overview of Monetary Poverty Statistics Production Practices in the Region • Most countries use CBN Method. • Food Poverty line (fpl) first, then inflate to • Total Poverty line (tpl) • Compare fpl and tpl against income or exp. • Some use more than one, e.g. 3 in BAN, IRAN; however, CBN is usually included; hence comparability in one method may still be assumed.
1. Energy Consumption used as proxy to total food or dietary (in)adequacy Exception: PHI 100% RDAs for energy (2000 kcal/cap/day), 100% RDA for protein (50 grams), and 80% of the other nutrients and vitamins. Does this raise PLs compared to using energy only? By how much? No empirical study that we are aware of.
2. Specify energy threshold (T) Most use 2100 kcal/cap/day, except: • Philippines – 2000 (++) • India – 2100 for urban, 2400 for rural • Bangladesh – 2122; and a lower one for core poverty. • Iran – 2179 and 2300 Criteria used to set threshold? e.g. FAO recommendation, popn-composition-weighted RDAs, etc. Possible western bias (i.e. high threshold)? Sensitivity of results to changes in threshold? (see next slide for example; also Iran in notes).
Energy Consumption Distribution (% of Population) Using Three Different Divisors for Total Consumption, NCR-Philippines, 2003 Divisor/Cut-Off (kcal) <1500 <1800 <2000 <2100 Family Size, N 48.0 74.0 83.0 88.0 Consumption Units, TCU 29.0 53.0 69.0 74.0 Adjusted for Scale Economies, N* 7.9 16.0 22.5 26.3
3. Construct food bundle/basket from FCS • List food items in order of importance (w.r.t quantity consumed, value, or most frequently reported?) • Stopping rule: when ∑calories = T’ near T, then adjust each by T/T’ • Reference population? Lower 30%, middle, etc. Impact on PLs? • Any Economies or equivalence scales used? (see slide 4). Impact on PLs? • Composition table used: affects comparability?
3. Construct food basket, cont’d. • One national food basket (e.g. China)? Or different for regions? Specificity vs. consistency or comparability across space. • Reference population or reference family/hh? Impact on estimates? There seems to be a dearth of empirical studies. • Any adult equivalence scaling or economies of scale adjustment? Results may be sensitive to this.
3 Construct food bundle -- continued • Major exception – Philippines. One-day (poor) family’s menu constructed to provide 2000 kcal ++, and what is referred to as food bundle is list and amounts of ingredients in the menu.
4. Computing food poverty line (fpl) fpl = Σ(p×q) across food basket. • In national currencies, hence not comparable across countries. • Prices used relate to poor? Reference population from which prices were collected? • Annual updating of prices and fpl for most countries. (Not to be confused with pov. rates). • How frequently is food basket changed? Relevance vs. consistency across time.
5. Computing total poverty line (tpl). Two ways of inflating fpl: • Tpl = fpl/(fe/te) where fe = food exp. te = total exp. fe/te = Engel’s coeff of some segment of population. • Segment can be HH with te within ±10 % about fpl. (ex. Phil, Laos). • WB method is regress fe/te on log(te/fpl) on the segment of population (reference poor popn).Engel’s coeff is intercept (i.e. when te/fpl=1). • Both cases probably lead to close results. Basic idea is that inflation is for essential non-food items only.
5. Computing tpl, cont’d 2. Make a list or bundle of essential non-food items, estimate the cost and add to fpl. • The latter way will tend to lead to a smaller tpl than the former way. • Thus, ceteris paribus, country A using (1) will tend to have > tpl than B using (2). Ex. INO vs Phil in 1980s (next slide). • Country changing from (2) to (1) will see a jump in its tpl and poverty incidence. (INO, nxt2 slide) • Any change could trigger shift in tpl (nxt3 slide)
Indonesia up to the early 1990s used (2), and tpl = 1.10 fpl. Philippines used (1) and tpl = 1.7 fpl (Asra and Virola, 1993). The high inflation factor in PHI was due to use of Engel coeff computed from entire population of households. That has been corrected (which led to another interesting story).
Application of the World Bank regression approach in Indonesia led to Engel coeff = (0.70 – 0.75 range) for the reference poor population (Said and Widyanti, 2001). If adopted, this would lead to tpl = 1.4×fpl and much higher poverty incidences.
China before 1995 used tpl = 1.40×fpl. Application of the World Bank approach (1) in 1995 led to a change to tpl = 1.17×fpl. This big reduction in the adjustment factor from 40% to 17% has led to speculations that the pre-1995 estimates are not comparable to those from and including 1995, and that the latter may underestimate of the true magnitude of poverty (Park and Wang, 2000).
6. Estimating poverty Incidences Household per capita income (PHI, CHI) or expenditure (all others) distn. needed to compare against PLs. Food poor means fpl>per capita inc(exp). Absolutely poor means tpl> “ • Hard to generalize, but two metrics can lead to significantly different results. Example: PHI • Countries do not necessarily use int’ly recommended defns of income (exp). • Results very sensitive to equivalence scale or economies of scale used on income(expenditure)
7. Updating number and proportion of poor Inc (Exp) distn needs updating through household survey. Frequency: • Annual – China, Indonesia • Biennial - Thailand • Triennial – Philippines Sri Lanka • 5 years – India Thus, while PLs can be updated more frequently, not so with estimates of prevalence and number of poor.
Reference Population:Effect on Sub-national Comparability Suppose 30 percent per capita income cut-off point that translates into c national currency units; i.e. Fn(c) = 0.30, n for national. • Some countries implement by using Fr(x) = 0.30 and Fu(y) = 0.30 for rural and urban domains, respectively. • This corresponds to a 30 percent national cut-off iff distributions in the two domains are the same. • However, if there are proportionately more poor HH in the rural areas than in the urban areas, then getting a uniform proportion will result in under-representation of the rural poor on the one hand, and over-representation of the urban poor on the other hand, in the overall sample. • This will lead to non-comparability of the estimates. The welfare level-preserving procedure is to be guided by c in both domains, i.e. consider households that fall below Fr(c) and Fu(c), respectively.
Data Capture Methods • Example: Indian experiment Shortening the recall period for food expenses from 30 days to 7 days in the 1999-2000 consumer expenditure survey resulted in a decline in the estimated poverty incidence from 26.1% to 23.3%, respectively (Government of India Press Information Bureau, 2001). • Different methods/sources in same country. Twelve-monthly, diary, diary + visit, face-to-face interview quarterly, semi-annually, or annually. Recall periods form one day to one year depending on country, item. Concept range from actual to “usual” consumption of purchase in a period like one week.
