country practices in compiling poverty statistics

country practices in compiling poverty statistics DRAFT VERSION presented in the Expert Group Meeting June 28-30, 2005 CHAPTER 3 COUNTRY PRACTICES IN COMPILING POVERTY STATISTICS Isidoro David INTRODUCTION ......................................................................................................................................................................... 2 3.1 THE DEMAND FOR POVERTY STATISTICS [INSERT AFRISTAT DRAFT HERE] ,................... 2 3.1.1 FROM DEVELOPMENT POLICIES TO POVERTY REDUCTION .................................................................................. 2 3.1.2 THE WIDENING OF THE SCOPE OF POVERTY ........................................................................................................... 2 3.2 INCOME OR EXPENDITURE BASED MEASUREMENT METHODS .................................................... 2 3.2.1 SPECIFY A FOOD POVERTY THRESHOLD.................................................................................................................. 3 3.2.2 CONSTRUCT A FOOD BASKET THAT SATISFIES THE ENERGY THRESHOLD......................................................... 6 3.2.3 COMPUTE FPL............................................................................................................................................................... 8 3.2.4 ALTERNATIVE APPROACHES: PRICE PER KCALORIE; HOUSEHOLD LEVEL FPL. .............................................. 11 3.2.5 COMPUTE TPL............................................................................................................................................................. 13 A. List of specified essential non-food needs................................................................................................................. 13 B. Regression ...................................................................................................................................................................... 14 C. Engel’s coefficient ........................................................................................................................................................ 15 D. Comparative performance of the three procedures ................................................................................................ 16 3.2.6 COMPUTE POVERTY INCIDENCE AND RELATED STATISTICS .............................................................................. 18 3.2.7 UPDATING POVERTY MEASURES ............................................................................................................................ 19 3.2.8 ESTIMATING TRENDS OR CHANGES; STANDARD ERRORS AND CONFIDENCE INTERVALS ............................. 22 3.2.9 RELATIVE AND SUBJECTIVE INCOME/EXPENDITURE BASED POVERTY LINES ................................................ 26 3.3 DIRECT MEASURES OF FOOD POVERTY ................................................................................................... 28 3.3.1 ESTIMATING THE EMPIRICAL CUMULATIVE DISTRIBUTION FUNCTION (CDF) OF PER CAPITA ENERGY CONSUMPTION............................................................................................................................................................................ 28 3.3.2 HOUSEHOLD SIZE FOR PER CAPITA CALCULATIONS ........................................................................................... 31 3.3.3 ESCHEWING PER CAPITA CALCULATIONS............................................................................................................... 34 3.5.1 INTRODUCTION .......................................................................................................................................................... 39 3.5.2 HARMONIZING NATIONAL AND SUB-NATIONAL POVERTY STATISTICS SUPPLY AND DEMAND................... 39 3.5.3 MAIN SOURCES OF NON-COMPARABILITY OF POVERTY STATISTICS AND POSSIBILITIES FOR IMPROVEMENT ........................................................................................................................................................................... 43 1 Introduction Basic needs – food and non-food. One group of methods involves costing the basic needs (CBN and variants). Second group is take a group of indicators of basic needs (UBN or MBN) Variations among and in-between. Developing indexes from basic needs. Scope of the chapter. 3.1 The Demand for Poverty Statistics [Insert AFRISTAT draft here] , 3.1.1 From Development Policies to Poverty Reduction 3.1.2 The Widening of the Scope of Poverty 3.2 Income or Expenditure Based Measurement Methods The four sub-regional workshops held in 2003-2004 confirmed that majority of the developing countries that compile poverty statistics follow a so-called cost of basic needs (CBN) approach. Everyone?s basic needs may be thought of as falling into two categories - food and non-food. Broadly, the CBN approach involves three steps: , Define the minimum nutritional requirements of a poor person and determine a food basket or bundle that can provide his or her minimum basic nutritional needs. The cost of buying the food bundle is a food poverty line (fpl). , Choose an operational definition of a poor person?s basic non-food needs that will allow estimating their cost directly or indirectly. Use this non-food basic needs cost to adjust fpl upward into a total poverty line (fpl). 2 , Compare fpl and tpl against some metric, e.g. distribution of income or expenditure per person. The proportion of persons whose incomes (expenditures) fall below fpl is an estimate of food poverty incidence. Some countries refer to this also as core poverty incidence or extreme poverty incidence. The proportion of persons whose incomes (expenditure) fall below tpl is an estimate of absolute poverty incidence. Some countries follow more than one approach and produce multiple sets of poverty statistics. However, if harmonization of methodologies and comparability of statistics are ultimate objectives, then it makes sense to promote the CBN approach because it is currently the most used and hence most understood among poverty measurement approaches. It or some modified version of it is also the approach most frequently supported by technical assistance from donor agencies. In the remainder of this section, the CBN approach as practiced in many developing countries will be discussed more thoroughly. The possibilities for harmonization as well as sources of non-comparability will be pointed out. Some avenues for improving comparability will also be mentioned. 3.2.1 Specify a Food Poverty Threshold National food poverty lines are based on nutritional thresholds. A person is counted as food poor if the nutritional content of the food (s)he consumed is less than the prescribed threshold. As a simplifying assumption, most countries use dietary energy as proxy for overall 3 nutritional status; i.e., if a person gets enough energy, then she also gets adequate levels of protein and the other essential nutrients. Countries are guided by FAO/WHO recommended daily allowance (RDA) for energy, defined as „the amount needed to maintain health, growth, and an 1“appropriate” level of physical activity? (WHO, 1985, p. 34). FAO uses 2100 kilocalories (kcal) consumption per person per day as threshold to estimate prevalence of undernourishment for ndividual countries (Naiken, 2003). The results form the basis of agency?s annual assessment of i the State of Food Insecurity (SOFI). FAO?s measure is also one of five indicators designated to monitor the first of the Millennium Development Goals – eradicate extreme poverty and hunger. Some countries have adopted the same 2100 kilocalories threshold. Many countries use FAO/WHO work in this area as initial guide to eventually develop their age by sex - specific RDAs. As examples, those for the Philippines and Sri Lanka are shown in Table 1. The weighted average of these RDAs, using the corresponding age by sex distribution of the population from a census, is one way to arrive at or justify using a particular energy threshold. Using 1990 census data in the Philippines, the weighted average was found to be 1,956 kcal per person per day, which rounds off to the 2000 kcal official threshold (David, 2002). The same calculation in Sri Lanka using age by sex population distribution elicited from the 2002 Household Income and Expenditure Survey led to the official 2030 kcal threshold (Widyaratne, 2004). Thus, different RDA specifications lead to divergent energy thresholds. Other countries use different thresholds for different population groups; e.g. 2100 and 2400 kcal per person per day for urban and rural areas respectively in India. Still others use more than one threshold to arrive at different food poverty lines; e.g. 1805 and 2122 kcal for so-called lower (or 1 RDA is the term used for any nutrient, e.g. energy, protein, vitamin A. For energy, the more specific term is recommended energy nutrient intake (RENI). For brevity, RDA is used generically in the chapter. 4 core) poverty and upper poverty lines respectively, in Bangladesh. The task of developing age by sex RDA tables and so-called food composition tables (i.e. the nutrient contents of individual food commodities consumed by the population) usually fall on research institutes under health or science ministries. The dietary energy thresholds used in most of the developing and transition countries are gathered in Table 2. The modal value is 2100. There is a second minor mode at 2400 made up of small island states in the Caribbean. The range is surprisingly wide, from 2000 to 3000 kcal per person per day. These differences in the energy thresholds represent the first major sources of non-comparability of (food) poverty measures among countries. The degree of non-comparability depends of course on the sensitivity of the results on incremental changes in the energy thresholds used, which could be considerable, as discussed in sub-section 2.3.3 below. Table 1. Dietary energy RDAs, Philippines and Sri Lanka, in kilocalories Age groups Philippines Sri Lanka ----------------- ----------------- Male Female Male Female Under 1 year 700 700 818 818 1-3 1350 1350 1212 1212 4-6 1600 1600 1656 1656 7-9 1725 1725 1841 1841 10-12 2090 1930 2414 2238 13-15 2390 2010 2337 2300 16-19 2580 2020 2500 2200 20-39 2570 1900 2530 1900 40-49 2440 1800 2404 1805 50-59 2320 1710 2277 1710 60-69 2090 1540 2024 1520 70 & over 1880 1390 1771 1330 Sources: Food and Nutrition Research Institute, Philippines The Medical Research Institute of Sri Lanka 5 Table 2. Dietary energy thresholds used by a sample of countries, 2000-2004 Threshold Country Single threshold 2000 kcal Maldives, Philippines (but also specifies 80% of protein RDA which is equivalent of 50 milligrams. 2030 Sri Lanka 2100 Cambodia, China, Indonesia, Laos, Mongolia, Thailand, Vietnam, Fiji, Turkey, Armenia 2124 Nepal 2133 Madagascar 2138 Malawi 2207 Paraguay (all country) 2238 Oman 2282 Moldova 2250 Kenya 2283 Burkina Faso 2288 Albania 2300 Cameroon 2309 Jordan 2300 Iran 2436 Iraq 2400 Senegal, St, Kitt & Nevis, Morocco, Bahamas 2470 Belarus (all country) 2700 Sierra Leone 3000 Uganda Multiple thresholds 1805 and 2120 Bangladesh, for lower and upper poverty lines respectively 2100 and 2400 India, for urban and rural areas respectively 2180 and 2220 Mexico, for urban and rural areas respectively 2730 and 2110 Russia, for able bodied men and women respectively Sources: Report of Four UNSD Sub-Regional Workshops (2004) and Survey of Poverty Measurement Practices (2005) 3.2.2 Construct a Food Basket that Satisfies the Energy Threshold The next step is to determine a bundle of food – by item and weight, e.g. rice, 0.25kg; sugar, 0.03 kg; etc. – which when converted into energy equivalents provide a total (T?) close to 6 the specified threshold (say T, in kcal per person per day). The conversion is made through a so-called food composition table from FAO/WHO that is adjusted or revised by individual countries to suit their individual situations. Basic data are obtained through a Household Food Consumption Survey (HFCS) or Household Income and Expenditure Survey (HIES). It is important that the surveys provide information for individual food items consumed, by quantity (weight) and value. The composition of the food basket depends on the choice of reference population. Since the object is to identify and count the poor, the reference population is usually some lower percentile of households according to their per capita income distribution; e.g. lowest 20 percentile, quartile or 2. The choice of the upper percentile cut-off is normally guided by the most recent 30 percentile poverty incidence estimate; that is, the reference population should be anticipated to roughly coincide with the poor population. The per capita food items consumed by this reference population are listed in order of importance, such as with respect to quantity, value, or in some cases frequency of reported consumption by the households. The food bundle is comprised of the top entries in this ordered list, stopping at the item where ? kcal = T? near T. (Since T? ? T in general, in practice the sum is forced to T by multiplying each food item?s weight consumed per capita by T/T?.) Based on the returns from the UNSD poverty questionnaire sent to countries in 2004, the 3number of items comprising the food baskets ranged from 7 to 205, with a median of 40 items. When different energy thresholds are used, it follows that the food baskets will be different as 2 A few countries use deciles around the median as reference population, others use a family, e.g. of 4 or 5 members specified by age and sex. However, at the four UNSD regional workshops a consensus started to emerge on the advantages and desirability for countries to use households occupying some lowest percentiles of per capita income distribution. 3 Twenty nine countries responded to the question on number of items in their respective food baskets. 7 well, e.g. urban and rural. There are countries that use only one threshold, but adopt multiple food baskets, such as one each for rural and urban areas or for each region. The basic considerations here are the relative importance that a country puts on constancy of a welfare level upon which the poverty statistics are based on the one hand and specificity of the statistics to sub-national differences in food availability, preferences and consumption on the other hand. 3.2.3 Compute fpl. Let q, q, …, q be the quantities of the f items in the food basket that supply e + e+ … 12f12 + e = T? kilocalories. Let p, p, … , p be the unit prices of the f food items. The food poverty f12f line is fpl = (T/T?) ? qpi i where the summation runs through f. Ideally, the prices should be period averages (usually one year) that the poor – or those in the reference population – paid for the commodities in the food basket. In practice, countries generally do not collect prices specifically for the purpose of compiling poverty statistics. The prices used may come from varied sources, such as HIES or HFCS. Quite often, however, what are collected in these surveys are quantity and expenditure for each food commodity consumed or bought; i.e. the unit prices are not collected directly but are derived as expenditure/quantity of each commodity. It is the opinion of some participants in the UNSD sub-regional workshops that expenditure can be more accurately collected from households, quantity less so especially when 8 the commodity is not traded in standard units of measure, and the unit price derived from the two 4is least accurate or least reliable. Price quotes used for consumer price index (CPI) compilation are reused routinely particularly, but not exclusively, for updating poverty lines. These have the advantage of providing average unit prices for the year that the poverty lines are updated, since majority of developing countries maintain monthly or quarterly CPI series.. One disadvantage, however, is that these quotes generally come from retail outlets. Also, the outlets in urban areas and provincial and town centers tend to be over-represented in CPI samples. Under these circumstances, it can be argued that the CPI sample prices could deviate from the actual prices paid by the final consuming poor households. On the one hand, a number of factors could make the prices paid by the rural poor households higher; e.g. transport and middlemen?s markup from retail outlets to small village stores, which is particularly true for processed commodities; no volume discount because sales are in small quantities; etc. On the other hand, it is possible that rural households pay less for own produced goods or goods produced within the locality, which is particularly true for basic staples like rice, fish and vegetables. However, these latter price advantages could be offset easily by government price controls and subsidies that in many developing countries tend to favor urban consumers. There is little empirical study on these issues and their effects on the magnitudes of the price deviations. Price data obtained directly from rural households would be more suited for rural poverty calculations. One source is a Survey of Prices Paid and Received by Farmers that is conducted 4 There was almost full unanimity in this opinion at the Sub-Regional Workshop of West African States wherein most of the participants were heads of national statistical offices. 9 regularly in many developing countries mainly for agricultural price policy setting and national accounts GVA coefficients updating. Although the coverage of such survey is limited, price quotes on farm products should be preferable to, say imputing prices of own-produced and bartered products. The choice of energy threshold T directly influences fpl (as well as tpl and other functionally related poverty measures). Exploratory studies in the Philippines showed that the per capita energy consumption cumulative distribution rose by three percentage points for every 5 100 kcal increase in the threshold in the 1500 to 2100 kcal range (David, David et. al. 2004). This implies that, other things remaining constant, changing the threshold from the country?s 2000 kcal official threshold to 2100 that is used by majority of the developing countries would result in a three percentage points increase in the estimate of food poverty incidence. Higher sensitivities are exhibited by results from Vietnam (Ministry of Health, 2003). The Bangladesh Bureau of Statistics previously used alongside the CBN method a variation called direct calorie intake (DCI) method. In the latter, households and members therein whose calculated per capita energy consumption fall below a predetermined threshold (2112 for urban and 2122 for rural) are considered (food) poor. The threshold is lowered to 1805 kcal to estimate what the country calls the hard core or extremely poor. Results from 1983-84 to 1995-96 are summarized in Table 3. The 23.2 percent average difference in poverty incidence between the 2120 kcalories and 1805 kcalories thresholds imply a more than 7 percent change per 100 kcal change in the assigned food poverty threshold. Thus, the findings from the three countries raise the possibility that differences in energy thresholds between countries (Table 2) could bring about significant non- 5 This is when the per capita energy consumption is computed using family sizes adjusted for economies of scale as divisors of the estimated total family consumption. Using unadjusted family sizes led to higher sensitivity of the per capita energy consumption distribution in the same energy range. 10 comparability in the national poverty statistics as well as between sub-national estimates (e.g. rural versus urban). If it turns out that further experiences from other countries support these findings, then the need for flexible or robust alternative methodologies take on added importance; (see, e.g. subsection 3.2.4 and section 3.3). Table 3. Bangladesh Food Poverty Incidences from DCI Method and Two Energy Thresholds (%) . Year 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 Note: 2120 kcal is average of urban and rural thresholds weighted by .20 and .80 population proportions respectively. Source: World Bank, From Counting the Poor to Making the Poor Count (1998). 3.2.4 Alternative Approaches: Price Per Kcalorie; Household Level fpl. Some countries avoid constructing a food basket, by calculating the total expenditure and total kcalories content of all the food consumed by the reference population; the ratio between the two totals is a price per kcal estimate which when multiplied by the energy threshold provides an estimate of fpl. Once a price per kcal estimate is calculated, fpls for as many choices of energy thresholds are easily computed. Bangladesh, which as noted above uses two energy thresholds, follows this approach. The approach also does not require unit prices which, as mentioned previously, are more problematic to obtain and may not even be collected in some 11 countries. However, the approach requires as many food expenditures and conversion into energy equivalents as there are food commodities consumed by the reference population. Some countries do not bother to report fpl (and related statistics, e.g. incidence and number of food poor), since they see it merely as a necessary input in calculating the total poverty line (tpl) and absolute poverty measures. This is unfortunate, since on their own food poverty statistics have important uses. They also offer possibilities for closer comparability of statistics at local and international levels than tpl and other more composite poverty statistics. Two such possibilities are discussed here. Another approach proposed by Kakwani (xxx) and implemented in a number of countries (Laos, Thailand, Jordan) involves taking the sum of the age x sex-specific RDAs of the members of the sample household (?RDA). A household level food poverty line, hfpl = (?RDA) x cost per kcal is computed and compared with the estimated total income or expenditure (Y) of the household. All the members of the household (say M) are considered food-poor if Y fpl is expected to hold for most sample households in the reference population ; otherwise log(te/fpl) < 0 and if this happens in a sizable subset of the sample the regression equation may not provide a good fit to the data. A more attractive alternative in this case is direct estimation of Engel?s coefficient. C. Engel’s coefficient Many countries use a more pragmatic approach by computing Engel?s coefficient fe/te directly from the sample households with expenditures within ?D percentage points of fpl. D = 10 per cent is a popular choice; e.g., Lao PDR, Philippines. Similarly as in the regression method, tpl may be computed as fpl + (1 – fe/te) fpl, or tpl = {2 - (fe/te)}fpl Why 10 per cent and not 5 per cent or some other per capita expenditure band around fpl? Countries often based their choice on neighboring country practice or on a consultant?s recommendation. It is preferable to base the choice on empirical evidence by calculating fe/te for several values of D. An example is shown in Figure 1, where fe/te was computed from the Philippines 1994 Family Income and Expenditure Survey data with D ranging from 2 to 20 per cent. The Engel coefficient seem to be robust for D in the 2 to 5 per cent range, but it begins to decline continuously as D approaches 10 percent. The coefficient behaves differently for rural 15 and urban areas, with the latter exhibiting markedly lower value, hence higher tpl. This is to be expected, as urban dwellers generally pay more than rural residents for housing, transport and other essential non-food goods and services. This raises an issue whether one national tpl is all that is needed or whether separate tpls should be computed for the urban and rural areas. 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 RURAL TOTAL 0.65 0.64 0.63 0.62 +/- 2 +/- 5 +/- 10 +/- 15 +/- 20 Band D. Comparative performance of the three procedures Aside from being highly judgmental and subjective, a fixed list of essential non-food goods and services is unaffected by both differences in purchasing power between households and between measurement periods. And since the total cost of the list is simply added to fpl, it is easy to see that change in tpl will be slow. A list could also be susceptible to criticism and pressures to add (increase poverty incidence) or drop items (decrease poverty incidence). Indonesia uses the list method. In the early 1990s, the country?s tpl = 1.10fpl, i.e. only 10% of 16 6 Later experiments with the regression fpl was allowed for essential non-food basic needs. method resulted in Engel coefficients in the .70 to .75 range or a 20 to 25 per cent adjustment, hence significantly much higher poverty incidence levels. These, however, have not been adopted and the current official methodology remains based on separate lists of essential non-food goods and services for the rural and urban areas (Said and Widyanti, 2001). Regression and direct use of Engel?s coefficient can be expected to lead to similar tpls particularly when the latter is computed from a sub-sample of households falling inside a narrow band, say those with per capita expenditures within ? (2 to 5) per cent of fpl. The sub-sample, however, gets smaller as the band is narrowed. Since a bigger sub-sample implies a more precise fe/te estimate, there are instances where a band as wide as 10 per cent is justified. Compared to running regressions, estimating fe/te directly may be less taxing to the national statistics office, especially if this has to be done for every HIES round. The inflation of fpl to tpl with the regression intercept a or Engel coefficient fe/te could be done in a non-linear fashion; i.e, tpl = fpl/a or tpl = fpl/(fe/te) . The Philippines? official poverty statistics, for example, are computed based on the latter equation. The results would be higher tpls, as seen from the values that {2-(fe/te)} and {1/ (fe/te)} take for different values of fe/te: 6 At about the same time, the Philippines tpl = 1.70fpl which was based on Engel?s coefficient. The difference in the methodology for computing tpl was found to be the main reason why Indonesia?s official poverty incidence was much lower than that of the Philippines. (Asra, et.al., 1993) 17 fe/te 2 – fe/te te/fe ----- --------- ----- ? 1.50 2 1.33 1.5 2/3 ? 1.20 1.25 1 1 1 3.2.6 Compute Poverty Incidence and Related Statistics The poverty lines fpl and tpl, which are in per capita terms and in the national currency of the country, need to be compared with a similarly expressed metric in order to be able to estimate poverty levels, such as incidence, count, depth and severity. Countries use either per capita income or per capita expenditure estimated from HIES or similar household based surveys. A household with per capita income (expenditure) falling below fpl. is considered food-poor. All the members of the household are also considered food-poor. Design-based estimates of the total ber of food-poor households and persons are normally calculated from the same HIES that num the income (expenditure) distribution is estimated. Calculating food poverty incidences require estimates of the total number of households and the total population count. In practice these are projections from the last census or, in fewer instances, these could come from current household 7surveys such as HIES and demographic and health surveys (DHS). Since food-poor households in general are bigger than non-food-poor households, the population food poverty incidence will be higher than the household incidence. The difference can be as high as three to four percentage points for countries with younger and faster growing populations. Hence, it is important to know which poverty incidence is being cited in any given situation. 7 Countries generally have inter-census projections of population size, but not of household counts. The latter therefore, is estimated from inter-censal surveys either directly or indirectly via an estimate of average household size. 18 The above paragraph holds true for computing absolute poverty statistics by simply replacing fpl with tpl. The estimated proportion of absolutely poor persons in the population, which is called headcount ratio, is by far the most popular and easily understood poverty statistic. When expressed in percentage it is sometimes called headcount index. It belongs to the so-called FGT class of poverty measures (named after the authors Foster, Greer and Thorbecke, 1984), α= (1/N) ? w{(tpl – y)/tpl} when (tpl-y) > 0, Pα i ii = 0 otherwise. The summation runs through the sample households in the domain of interest (e.g. region, urban area, or country), y is per capita expenditure or income, N is the population size of the domain i which in practice is replaced by a projection, and wis the household design-based weight i 0, ?wbecomes an estimate of the total number of poor defined in such a way that when α = i persons in the domain; i.e., P = headcount ratio. Developing countries also routinely compute 0 the poverty gap index, P, which is an indicator of the shortfall in the poor persons? expenditure 1 (income) from the poverty line expressed as an average in the domain. The poverty gap, which is simply the (weighted) sum of all the individual poverty gaps in the domain, can be used as an indicator of the minimum cost of eliminating poverty using perfectly targeted transfers. With α = 2, the resulting Pis called a poverty severity index which fewer countries compute compared to 2 P and P. 01 3.2.7 Updating Poverty Measures In the interest of continuity of the poverty statistics series, food baskets, energy thresholds and reference populations are seldom changed. This means that countries can and do update their food poverty lines (fpl) anytime that new unit prices of the commodities in the food 19 basket become available. When the method of estimating the total poverty line (tpl) is by direct addition of the cost of a bundle of essential non-food goods and services, then new prices of the latter are required also to update tpl. In countries where tpl is computed via regression or Engel?s coefficient, updating is sometimes done by using the same coefficient for the years that a HIES is not done; it is assumed implicitly that the coefficient does not change, or change very slowly, in the reference population and during a period of one to two years. The coefficients are recomputed only when there is a new HIES round. Sometimes the CPI is used, e.g. the food CPI and non-food CPI to update the food and non-food components respectively of the total poverty line. It has been noted, however, that the CPI as currently constructed in most countries, might not reflect the consumption pattern of the reference population used in determining the poverty lines; see e.g. discussion in subsection 3.2.3 above. Another key limitation is that the basket of good used for the CPI may significantly differ from the one used to construct poverty line. These limitations can be more pronounced when estimating sub-national poverty lines. Some countries attempted to address these issues by using sub-national CPIs constructed from household survey data (e.g. Sri Lanka, Vietnam and Thailand). Sticking to the same mode of updating is important for the country?s poverty lines to be comparable across time. Updating the statistics on the number and proportion of poor persons or households will require new estimates of per capita income (expenditure) distributions, which in turn require a 20 new HIES round. Very few users would be willing to assume that these distributions remain constant over a period of two or even one year, because doing so would nullify the need to update the poverty statistics in the first place. As obvious as this seems, its practical implications seem to be lost to some users at times. It is not unheard of that users want annual updates on the estimates of the number and proportion of poor persons (households), which means that a HIES is conducted yearly at great cost. And sample sizes, hence human and material requirements, rise 8 even more as users demand that the updates be done for progressively smaller sub-populations.Some of the countries that update poverty counts and incidences annually (e.g. China) simplify the methodology, such as having one national poverty line and releasing national level estimates only, and thereby keeping the survey sample size relatively small. Doing otherwise, such as updating annually at sub-national levels, could quickly lead to very large surveys (e.g. Indonesia?s annual socio-economic survey has a sample size of 200 thousand households) and to delayed release of results, which defeat the very purpose of updating yearly. If user demands are not aligned with the technical and material resources available to the national statistical system, a point is soon reached that the poverty monitoring system becomes unsustainable. The frequencies of updating of poverty incidences and counts in a sample of countries – which coincide with the frequencies of conducting household income and expenditure surveys - are shown in Table 4. The range is from yearly to every five years for those that have a poverty monitoring program in place. This is not to say that countries that follow the same updating frequency of more than one year do it in the same years. Many countries still have no regular schedule of updating, inasmuch as a HIES is conducted only when funds become available, 8 What matters is little n, not n/N , and n is the sample size in the smallest domain of interest -- is a truism that needs repeating every so often. 21 usually from an external donor. Of the 79 countries that responded to the UNSD Poverty Questionnaire, 16 have yet to measure poverty. Thus, the desirable goal of synchronized poverty measurement and monitoring requires agreement among countries on the frequency and timing for the supporting household income and expenditure surveys. Table 4. Updating frequency of poverty incidences and counts in selected countries. _____________________________________________________________________ Frequency Countries Yearly China, Indonesia, Every 2 years Thailand, Iran, Every 3 years Jordan, Mongolia, Philippines, Every 5 years India, Malaysia, Sri Lanka, Vietnam Irregular, depending Bangladesh, Cambodia, Laos, Fiji, the Central on funds availability Asian Republics (Azerbaijan, Kyrgystan, etc.), Not yet measuring 16 of 79 countries that responded to the Poverty UNSD Poverty Questionnaire 3.2.8 Estimating Trends or Changes; Standard Errors and Confidence Intervals The sampling errors of counts like the number of food-poor or absolutely poor can be = 1 if poor, 0 computed using design-based variance estimators. The variable is binary (y otherwise). Incidences like the proportion of absolutely poor persons are ratio estimates in general, but estimation of the sampling variance depends on the nature of or assumptions made on the denominator. Countries usually do not estimate the variance of population projections, as 22 9 In this case, the denominator is treated as a it could very well be a technically daunting problem. constant and the variance estimation reverts to that of a count as mentioned above. The result may be considered a conditional variance. However, when the denominator is a random variable, then the appropriate variance form is that of a ratio estimator. For example, household poverty incidence can be computed as the ratio of the design-based estimate of the number of poor households (Y say) and the design-based estimate of the total number of households (X) from the same survey. What is most often used in practice is the first order approximation from a Taylor series expansion (see e.g. Cochran, xxx, Kish xxx, or Sukhatme et. al. xxx); i.e. the variance of Y/X is V(Y/X) = V(Y) + V(X) – 2 Cov(Y,X) . place of the parameters on the right hand side. The Design-based estimates are substituted in -1/2approximation is of order n, hence except for domains with smallish samples, the bias in the estimate should be negligible. Countries that produce the sampling variances do so via general purpose statistical packages (e.g. SAS) or specialized survey data processing software (e.g. STATA, SPSS) which require the survey design weight of each sample household. In monitoring, the main interest is in the change in poverty levels - if any - between measurement periods, say t and t. If Y and Y are the poverty statistics, we would like to 12t1t2 “know” whether the observed difference Y – Y is indicative of a real change or of a status quo. t2t1 Objective, i.e. sound statistical techniques, are critically needed to guard against hasty 9 High, medium and low population projections are often computed based on varying assumptions concerning migration and birth, death and fertility rates. However, the differences in the projections cannot be taken as proxies for either sampling or mean squared error. Very few, if any, countries put out projections of household counts. 23 – Y > 0 on the one hand, and a frantic search for declaration of an improvement whenever Yt2 t1 chinks in the measurement methodology and survey design when Y– Y ? 0 on the other hand. At the very least, what is required is an estimate of the variance t2 t1 of the difference V(Y– Y) = V(Y) + V(Y) – 2 Cov(Y,Y) t2 t1 t2t1t2t1 The terms on the right hand side can be estimated in accordance with the procedures in the first two paragraphs of this sub-section; i.e., design-based variance estimates of counts or of ratio estimates. Let the square root of the resulting estimate be se(Y-Y); i.e. the standard error of the t2t1 difference. The interval Y– Y ? Z se(Y– Y) t2 t1 t2 t1 defines a confidence band about the true difference, with the level of confidence dependent on Z which is a positive number chosen by the user, or more often suggested by the agency in charge of producing the official poverty statistics. The more common choices are 1.96 (which rounds off to an easily remembered 2) and 1.64, which yield approximately 95 percent and 90 percent confidence bands about the difference in the poverty parameters between the two measurement periods. An interval that is to the left of zero is indicative of a worsened poverty situation, one that captures zero supports a no change hypothesis, while an interval to the right of zero provides empirical evidence for an improved poverty scenario. For a given a choice of Z, the width of the confidence interval depends on the difference between the poverty statistics and on the standard error. Under normal conditions wherein the poverty situation changes slowly the real difference in poverty incidence narrows as the interval between tand tis shortened. This means a commensurately very small standard error is 2 1 24 required to detect a small change in the poverty incidence. Thus, more frequent monitoring does not mean smaller sample size for each survey round. On the contrary, a more efficient sampling design and bigger sample are needed to reduce the noise (sampling error) to a level that would provide a good chance of detecting a weak signal (change in poverty incidence); otherwise, there would be no point in the monitoring exercise if it were known a priori that the computed confidence interval will most likely straddle zero. It is to be noted also that all these considerations, including sample size, pertain equally if not more to sub-national domains of interest, e.g. urban-rural and regions, than to the national level estimates. Frequent monitoring is justified when the poverty incidence is high and falling rapidly, or conversely, when it rises quickly. This former is exemplified by China during the last two decades of 2000. On the other hand, the Asian financial crisis that started in 1997 caused spikes in the poverty incidence among the severely affected countries such as Thailand and Indonesia. This had been described as transitory poverty brought about by stagflation – economic contraction and precipitous currency devaluation. The poverty monitoring frequency was increased briefly to twice a year and then to yearly in these countries. Now that the poverty incidence in Thailand has gone back to pre-crisis levels of about 10 per cent, the monitoring has been scaled back to once in two years. As mentioned previously, China and Indonesia continue to update their poverty incidence levels annually. With China?s official (rural) poverty estimated at fewer than 6 per cent, the amount of reduction that can be achieved in a year?s time is naturally very much constrained; hence the chance of detecting a change through statistical means will require a very efficient and large household income survey. (Although China uses both income and expenditure, the former is the basis for the officially released poverty statistics). 25 3.2.9 Relative and Subjective Income/Expenditure Based Poverty Lines The poverty lines discussed above are referred to as absolute poverty lines in the sense that these are meant to measure the same level of welfare across time and/or space. With the food poverty line (fpl), for example, this is enhanced by not changing the reference population, energy threshold, food bundle, survey methodology, and the estimation procedure. The current prices applied on the food bundle are deflated first to make them constant relative to base year prices. And to assess whether there has been a real change in the proportion or number of food poor persons, similar price deflation is applied to the metric used (per capita income or expenditure 10 Ensuring comparability across space (e.g. between distribution) to compare the fpl estimate. sub-national domains) is often more difficult to achieve than comparability across time because of issues of specificity versus constancy, such as: using the same food bundle ignores variations in food preferences and availability; on the other hand, different prices and deflators may have to be applied to the different domains (e.g. urban versus rural). It is to be noted that sub-national comparability is a prerequisite to a simple aggregation of poverty statistics to the next higher domains. Income based relative poverty lines often are simple functions of the median or mean of the per capita income distribution. They are more frequently used by developed than developing countries. The ECLAC countries have used 50% of the median per capita income (Rio Group Report, 2003). Oman defines as poor a person with income less than 40% of the population?s 10 Poverty lines and per capita income (expenditure) distributions based on current market prices have other uses and are therefore often computed also. However, these are not applicable when monitoring changes. 26 median per capita income (UNSD-ESCWA Sub-Regional Poverty Statistics Workshop Report, November 2004). Iran uses 50% of both the mean and median per capita incomes (UNSD-ESCAP Sub-Regional Poverty Statistics Workshop Report, October 2004). These relative poverty lines are much easier to establish and are suitable for quickly finding out who are poor and where they live. When applied to small areas, they could be used to classify individuals as well as rank communities, thereby enabling sharper allocation of poverty reduction resources in a relatively short time. However, estimates are influenced by shifts in the central values as well as shape of the per capita income distribution; hence, as already mentioned, they are not meant to be used to monitor the poverty situation from one period to another. n the Philippines, a private market research type organization asks heads of households I about their income, whether they consider themselves poor, and if so, how much more income do they need in order that they will no longer think of themselves as poor. This „self-assessed poverty? approach yields what are sometimes referred to as subjective poverty estimates. Like many opinion poll type investigations, the surveys are small, typically with around 1500 sample households, so that the results can be put out very quickly. Since the survey is repeated quarterly, the process is capable of generating 12 time series estimates in the three years that the national statistical system is able to update the official poverty statistics once. Egypt?s national statistical system has constructed a subjective poverty line based also on views of the heads of households regarding the minimum income required for an adequate standard of living. The experience of Egypt showed, however, that this methodology overestimates poverty especially in urban areas 27 where the expectations of people, and most specifically educated ones, tend to exceed their current levels of living by a large margin (UNSD-ESCWA, op. cit). 3.3 Direct Measures of Food Poverty 3.3.1 Estimating the Empirical Cumulative Distribution Function (CDF) of per capita energy consumption As implemented by countries, the cost of basic needs (CBN) method discussed in section 2.3. yields one estimate of food poverty for each specification of the energy threshold T. This means non-comparable statistics for countries and sub-national domains that adopt different Ts?(see Table 2). One way out of this real predicament is to estimate the entire per capita energy consumption CDF; that is, divide the calculated total energy consumption (?kcal) by some measure of the number of consuming members of the sample household. This is done in some countries, but not in the agencies charged with producing the official statistics. For example, Vietnam?s General Statistics office (GSO) uses the CBN method in compiling the official poverty statistics from its Multipurpose Household Survey and Vietnamese Living Standards Survey.. The official population food poverty incidence estimates for 1998 and 2002 were 15.0% and 10.9% respectively. (GSO, as cited in Vietnam Development Report 2004). The National Institute of Nutrition of the Ministry of Health conducts a General Nutrition Survey (GNS) in which household food consumption is obtained using 24-hour recall combined with weighing of some of the food items. From the 2000 GNS which had a national sample of 7,658 households, the institute obtained the following three points of the empirical per capita energy consumption CDF (General Nutrition Survey 2000 Report): 28 Energy cut-off < 1500 kcal < 1800 kcal <2100 kcal % of population below cut-off 4.1% 17.9% 45.1% Based on direct, unmonetized dietary energy consumption, it was estimated that 45.1% of the population were food-poor or had consumed less than the official 2100 kcal threshold. One significant advantage of having the empirical CDF is that the proportion of persons (or households) consuming less than any chosen energy threshold is readily available. This means that for any group of countries with empirical CDFs, a user or agency with his or her choice of energy threshold can easily pick off or interpolate comparable estimates of food poverty 11 incidence. Moreover, the method eschews prices, choosing a reference population, estimating a poverty line in money terms, and estimating an income or expenditure distribution. As a consequence, the only remaining main sources of non-comparability among countries? estimates would be food composition tables (see Table 1) and the method of data collection (i.e. survey design and methodology for obtaining household food consumption). Furthermore, from a primary data point of view, improving the accuracy of the food poverty estimate is reduced to improving survey design and the method of collecting the consumption quantities of food items. The 45.1% National Institute of Nutrition estimate is much higher than the 12-13% official estimate from GSO for 2000. There are two major sources of this difference: the methods of data capture and the choices of denominator for computing per capita kcal consumption. These are illustrated more clearly by a second example from the Philippines. 11 It may be worth noting that the method can be applied to other nutrients, e.g. protein. It also extends readily to joint CDFs, e.g. of energy and protein. 29 The Food and Nutrition Research Institute (FNRI) in the Department of Science and Technology, Philippines conducts a National Nutrition Survey (NNS) every five years. The survey has a food consumption module that uses a one-day actual weighing of all food items cooked by the sample household in the course of the day, with the sample households spread 12randomly over the seven days of the week. Left over portions, amount fed to pets, etc. was weighed also. Family members who ate outside were asked to recall their meals particulars, at the same time that the estimated consumption of guests who ate with the family during the day was netted out. The total energy consumption, (?kcal), of each sample family is derived from the net amounts of food commodities consumed converted into energy using FNRI?s own food composition table. Per capita values are generated by dividing (?kcal) by some measure of family size. As discussed in the next sub-section 3.3.2, the choice of divisor is not trivial, and various choices could lead to substantially different poverty incidence estimates. 13 CDFs are estimated parametrically through models, e.g. lognormal or gamma. Alternatively, empirical CDFs that are non-parametric or model-free estimates may be used. The latter are more common and almost routinely constructed by national statistical agencies during the processing and analysis of household sample surveys. This can be seen in the following representation. Let (a) = 1 if a ? 0 ii 12 The sampling unit is actually the family, which differs from the household by the exclusion of helpers like maids and drivers. In the reference (poor) population, there is little difference between family and household. 13 This is what is behind FAO?s methodology for estimating the proportion of the population consuming less than 2100 kcalories per capita per day, which is indicator number 5 of the Millennium Development Goals ; see e.g. Naiken, 2002. 30 = 0 if a< 0 i Let π; i = 1, 2, …, n be the inclusion probabilities of the sample units (households), which in i practice are adjusted for non-response, non-coverage and other perturbations in the implementation of the survey. Let xbe the per capita energy consumption estimate of the ith i sample unit for a particular choice of denominator. A design-based estimator exists, which is a Horvitz-Thompson estimator applied to estimating CDFs (Chambers and Dunstan,1986). ^ -1-1 F (t) = Σ π (t – x) / Σ π iiii ? n i ? n This is just a formal representation of the construction of a weighted cumulative distribution table. Cumulative frequencies and cumulative relative frequencies are computed for the upper lass boundaries, say t = 1200, 1500, 1800, 2100, 2400, … kilocalories. The points may be c connected to “draw” the empirical CDF in its entirety. 3.3.2 Household Size for Per Capita Calculations An obvious candidate for divisor of total household energy consumption is household size M. Since poor households tend to be bigger and with proportionately more children, the result will underestimate real per capita consumption and consequently lead to higher food 14poverty incidences. Another is an adult-equivalent adjusted M* = ?w , where { 0< w ? 1; i = ii 1, …, M}are age and sometimes sex- differentiated weights assigned to household members; e.g. maximum 1 for working age males and less for adolescents, children and females. Note that M* ? M. Using the RDAs for dietary energy as basis for defining M* is an intuitively appealing idea. 14 The same holds for estimates of the non-food poverty line, total poverty line and per capita income (expenditure), distributions, more so on account of economies of scale for both food and non-food needs. 31 For example, the RDAs for the Philippines in Table 1 may each be divided by 2570. Doing the same for Sri Lanka, with 2530 as common divisor, leads to a different M*, which points to the desirability of herding countries towards adopting more uniform RDAs. Other choices involve models of M* that adjust further for scale economies. For example, f, wPalestine (UNSD-ESCWA Poverty Workshop report) uses M* = (A + PC)here A = number of adults, C = number of children, P = child-to-adult proportionality factor (= 0.46), and f = scale economy factor (= 0.86). In the Philippines, the double-log function log F = α + β log Y + τ log M between family food expenditure (F), income (Y) and size (M) had been tried on an experimental basis but not adopted in the official methodology. F/Y connotes levels of welfare; it is Engel?s ratio when Y is replaced by expenditure. For F/Y constant, the family elasticity of income is ? = ( ?logY) / ( ?logN) = τ / (1 – β) Bantilan et. al. (1992) ran a regression of the model on the 1985 Family Income and Expenditure 0.7 Survey data, and obtained an elasticity estimate of 0.7. Thus, M* = Mmaybe used for per capita calculations. As illustration, the empirical CDF of family per capita energy consumption was estimated from the Metropolitan Manila sub-sample of the 2003 NNS of the Food and 0.7 Nutrition Research Institute described above, first using M and then M* = M; see Table 5. 32 Table 5. Per Capita Energy Consumption Distributions (% of Population) Using 0.7 M and Mas Divisors, Metropolitan Manila - Philippines, 2003 Divisor/Cut-Off (kcal) <1500 <1800 <2000 <2100 Family Size, M 48.0 74.0 83.0 88.0 0.7M* = M 7.9 16.0 22.5 26.3 Source: David et. al (2004) Predictably, the results with M lead to very high food poverty incidence rates. The food poverty ncidence at the official 2000 kcal threshold is an unrealistically high 83%. This phenomenon i had been observed before by other researchers and it shows also in the Vietnam case mentioned above. The empirical CDF with scale economy adjusted family size as divisor leads to much lower food poverty incidence rates. One advantage of a CDF estimate is seeing the effect that a change in energy threshold will have on the incidence. For instance, moving the Philippines? official 2000 kcal threshold to 2100 kcal that is used by the majority of Asian countries will mean about a four percentage points increase in the estimated food poverty incidence, from 22.5 percent to 26.3 percent. From the UNSD?s regional workshops and poverty questionnaire returns, it appears that many of the developing countries use household size (M) to compute per capita food and non-food consumption, as well as per income and expenditure. Others use some adjustment only a step or two removed from M; for example, Senegal uses only two weights, namely w = 0.5 for 1 household members below 15 years old and w= 1 for all others. Some use size adjusted for 2 composition, i.e. adult equivalents, but this is usually for calculating per capita food consumption only. Very few use any adjustment in estimating per capita income or expenditure. The likely effect could be overestimated poverty incidences and counts. Countries that attended UNSD sub- 33 regional poverty workshops expressed interests in technical information and assistance in implementing adult equivalent and/or economies of scale adjusted per capita methods. Alternatively, in so far as food poverty is concerned, per capita food consumption, thresholds, income and expenditure may be circumvented altogether. 3.3.3 Eschewing per capita calculations Most countries have developed their respective per capita recommended daily allowances (RDAs) for dietary energy, as well as for other nutrients, for different age by sex groupings of their populations (see e.g. Table 1). Those that have not either adopt their neighboring countries? or the latest recommendations of FAO or WHO. Instead of resorting to a per capita threshold, it is natural to regard the sum of the RDAs of a household, ?RDA, as the energy threshold for that particular household. Thus, if ?kcal represents the estimated daily total energy consumption of the same household, the inequality ?kcal < ?RDA can be used directly to classify households and persons therein as either food poor or non-food poor. More than one set of RDAs may be considered, giving rise to as many thresholds and food poverty estimates. For example, a 1971 FAO/WHO Expert Committee on Energy and Protein Requirements accepted a 15% coefficient of variation of energy requirement between individuals in a population or group with similar demographics (WHO, 1985, p.6). Hence, reducing the individual recommended daily RDAs proportionately by 15% and by 30% will yield ?RDAs that correspond approximately with minus one and minus two standard deviations from the original ?RDA respectively; and similarly, increasing by the same amounts will 34 correspond with plus one and plus two standard deviations from the original household energy threshold. Six point estimates together provide a fuller idea of the relationship between RDA specifications/thresholds and poverty measures. Comparable food poverty estimates across and within country can be interpolated easily for any given choice of household threshold within the (1 ? 0.3) ?RDA interval. The weighted sum of the energy shortfall among the food poor households is a direct estimate of an energy gap. That is, ?w{?RDA - Energy gap = ?kcal} if {?RDA - ?kcal} > 0 = 0 otherwise where the inner summations run through the members of the household, w denotes the design weight of the sample household, and the outer summation runs through all sample households. The energy gap estimates the amount of dietary kilocalories needed to bring all the food poor families up to their respective food poverty thresholds. If desired, this can be expressed in money terms by multiplying by the cost per kcalorie (obtained from the reference population). This simple and straightforward interpretation of this statistic can have mass appeal to policy makers and lay persons alike. 3.4 Non-Income Measurement Methods Poverty has many dimensions. Being poor may be viewed as a condition wherein one is deprived of some of life?s basic needs – food, shelter, clothing, basic education, primary health care, security, etc. The very definition of basic needs is an issue that leads to other dimensions 35 and variations in measurement methods. Consideration of access to the basic needs leads to empowerment, exclusion, and capability dimensions of poverty. Empowerment has legal and political dimensions, exclusion can be social, cultural or physical, and capability could mean financial, intellectual or even technical. Judging from current practices, the basic needs approach to poverty assessment has gained highest acceptance among the developing countries. Basic needs are grouped broadly into food and non-food and the income approach to measurement involves estimating the costs of the two groups, as seen in the discussion of the cost of basic needs (CBN) approach in section 3.2. It may be worth noting that what are being priced are short-term inputs; e.g. how much food is being consumed in a day and extrapolated to a year at most, as well as annual cost of clothing, housing rent, health care, transportation, etc. The other half of CBN that is also shared by the other income approaches is to determine whether the household or person has command over financial resources needed to avail of the basic needs; hence the need for per capita income or expenditure distribution estimates. A complement of the income-based basic needs approach is the so-called minimum basic needs (MBN) or unmet basic needs (UBN) approach. In the latter non-monetary indicators representing different dimensions of poverty are chosen, estimated and monitored. The subset of MDGs minus the income indicators provide an excellent example: proportion of underweight children to represent malnutrition; school enrollment, primary school completion and youth illiteracy rates representing basic education; infant and under five mortality rates, maternal mortality ratio, and births attended by skilled health staff representing primary health care; ratio 36 of girls to boys in primary and secondary school, ratio of literate females to males, proportion of seats held by women in parliament, and share of women employed in the non-agriculture sector representing the gender equality dimension or goal; etc. Many, though not all, of these indicators are long-term outcome or output indicators; e.g. a child being underweight is the result of years of chronic undernourishment. Also, these UBN indicators are expressed in different units of measure. This has made the task of producing a composite index a difficult and perpetually subjective task. However, this has not prevented agencies, particularly international bodies, from constructing such indexes. The Human Development Index (HDI) and other indices that UNDP puts out annually for each country in Human Development Report are examples of such composite indexes. These may have added value more as advocacy than as monitoring tools. Few developing countries compile composite indexes, preferring to use the indicators individually and collectively in much the same way that they will be used to monitor progress in the MDGs. It is worth noting also that national statistical systems have been compiling many of the UBN indicators long before the international development agencies declared poverty reduction their overriding strategic objective. Many are extracted from population and housing censuses, demographic and health surveys, civil registries and other administrative reporting systems. In fact, it is often the case that the choice of indicators in a country?s UBN information system is determined by already existing data collection systems; it is seldom the case that a new system is established just to meet the additional requirements of new indicators. This is the case in Bangladesh, for example, where infant mortality is used as proxy indicator for the primary health care system, primary school enrollment rate for basic education, and housing characteristics 37 (access to tap water, toilet facilities, electricity, and type of building material used) for living conditions. Nearly all countries in ECLAC have UBN poverty monitoring systems in place; (Rio Group Report, May 2004). However, unlike income poverty statistics compilations, there are differences in the selection of dimensions and indicators for the basic needs, partly owing to variations in data availability. The three broad categories of basic needs often considered are dwelling characteristics, access to safe water, and access to sanitation facilities. Basic education and economic capacity (e.g. GDP growth rate) are sometimes included in an expanded UBN set of indicators. In the ECLAC context, it is noted that the UBN approach has solid conceptual foundations as it intends to measure the actual satisfaction or dissatisfaction of needs rather than the capability of satisfying them. In this light it is complementary to the income poverty line approach. Dimensions of basic needs chosen are often those highly correlated with income, so much so that they have been used to identify households under extreme poverty. The assessment and monitoring of poverty through the 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 can and are at times brought down to sub-national levels. For example, China monitors community level indicators, such as percent of villages accessible by road, 38 percent with land line phone connection, and percent with electricity, illiteracy rate, child enrollment rate, and labor migration rate. 3.5 Harmonizing Poverty Statistics Production in Developing Countries 3.5.1 Introduction Improving harmony in the poverty statistics means working for advancement in at least three areas: timing of production/release, comparability, and strengthening statistical system apacity in the poverty statistics-deficient countries. The last area is particularly relevant to c countries falling in the last two rows of Table 4. A fourth area is improving the availability/sharing not only of the aggregate statistics, indicators and indices, but also of the primary data files to users outside of the main statistical agencies. This fourth area, however, aims to expand the analysis and use of poverty-relevant data; it does not impact directly on basic data production. This section discusses the first two areas only. The last two are addressed through technical assistance from multilateral donors and between countries. 3.5.2 Harmonizing National and Sub-National Poverty Statistics Supply and Demand National statistical information systems have evolved to a point that developing countries more or less follow similar updating frequencies for certain parts of their socioeconomic databases. Thus, censuses have a ten year cycle, demographic and health surveys five years, nutrition surveys three to five years, agricultural surveys one season or one year, etc. Being 39 relatively new, poverty statistics have not had time to be part of this evolution. The IMF has formalized the periodicities of statistical series in its General Data Dissemination System (GDDS) and Special Data Dissemination System (SDDS). For example, countries that subscribe to GDDS agree to update their price indexes monthly, and those that sign up on SDDS agree to compile national accounts quarterly. However, poverty statistics are not covered in these dissemination systems. As seen in Table 4, the frequency of updating of poverty incidence and related statistics is varied enough at the national level. Individual countries have their own reasons for choosing a particular updating frequency. The choice is often a compromise between the users? desire for more frequent updating at smaller domains on the one hand, and the reality of limited resources that can be counted on to be made available to repeatedly run the needed monitoring surveys on the other hand. In the end, the recurrent budget levels that the government is able to provide for the monitoring surveys and the desired accuracy and timeliness of the results become the deciding factors on the updating frequency. While external technical assistance is able at times to influence the frequency of the updating in some countries, this often proves to be co-terminus with the technical assistance. As mentioned previously, poverty lines can be updated more frequently using new price data. However, updating poverty incidences and counts require current estimates of per capita income or expenditure distributions, i.e. a new HIES round. The high cost of an HIES is what makes majority of developing countries decide on a three to five years poverty monitoring program. If more frequent updating is desired, one strategy that has some chance of being 40 sustained is where sub-national updates are made every three to five years based on a large HIES, and only national updates are made more frequently based on a much smaller HIES. The variations in updating frequency increase when poverty information compilation and monitoring at sub-national levels done by stakeholders other than the national statistical office (NSO) at sub-national levels are included. The demand for sub-national poverty statistics come from local officials; central government agencies; non-government organizations, including esident missions of international bodies that focus their interventions on specific disadvantaged r groups or areas; and analysts. Country level poverty statistics are required by central governments, analysts, and international agencies. Some of the latter need annual updates to feed into their global monitoring activities, e.g. in UNDP?s Human Development Report, World Bank?s World Development Report, FAO?s State of Food Insecurity, and the UN Secretary General?s annual progress on the MDGs report to the General Assembly. These agencies put up internal capacities for estimating, interpolating or projecting from past and current but partial information from countries. These are the main sources of poverty information at the regional or global levels which do not run into problems of inconsistency or non-comparability since countries are not involved in similar activities. It is when the international agencies also publish their own produced national estimates that non-comparability with the countries? statistics can and do arise. In general, national statistics offices are able to provide some of the needed statistics at the level of villages, districts, ethnicity and other demographic groupings during census years 41 only. Traditional inter-census national sample surveys can provide reasonably precise statistics for large domains only, such as regions, urban, rural and country. The NSOs will have to continue these surveys to monitor poverty at these macro-levels, as well as to provide input data for monitoring at the global level. The NSOs could not hope to have the time and muster the resources required to successfully launch sample surveys with adequate sample sizes for areas below regions and domains of special interest such as ethnic groups, the handicapped, and similar vulnerable segments of the population. However, poverty information is needed for these smaller domains for more efficient targeting of poverty alleviation interventions as well as for monitoring and evaluation of the impact of these same interventions. Strategies for filling these data gaps for small areas are critically needed. The specialized agencies and ministries responsible for planning and implementing poverty reduction programs at sub-national levels will necessarily be involved in the generation of the needed information at these levels. For example, Thailand?s Ministry of Interior conducts an annual Basic Minimum Needs Survey to identify the villages that are eligible for poverty alleviation assistance. There were attempts in Cambodia and Lao PDR to construct district vulnerability indexes from village level participatory poverty assessment censuses or surveys. A complete enumeration of households is carried out by Vietnam?s Ministry of Labor, Invalids and Social Affairs (MOLISA), to identify the poor households qualified to receive subsidies. Indonesia?s Central Bureau of Statistics and National Planning Board collaborate in an annual activity to measure the welfare level of each household in order to identify those eligible to receive poverty subsidies and determine the amount of assistance. Moreover, administrative records of the above ministries as well as those of education, health, and civil registrations are rich sources of poverty information for small domains. 42 It is important not to expect that results from these different sources and methods will be consistent or comparable or will follow the same updating frequencies. They serve different purposes. Monitoring and evaluation at aggregate levels should remain anchored in the more quantitative and replicable methods, and hence on the NSO national surveys. If these are broken down to say state or regional levels within the country, inconsistencies and inaccurate comparisons are avoided if the information from the other sources is not aggregated up to these levels. At the same time, some countries endeavor to improve comparability of data from different sources over the long term, by giving NSO and research institutes the additional responsibility to promote -- e.g. through training and provision of technical assistance/advice -- the use of similar concepts, methods and indicators to the extent possible. 3.5.3 Main Sources of Non-Comparability of Poverty Statistics and Possibilities for Improvement For income based poverty statistics, comparability of sub-national estimates is a prerequisite to simple and correct aggregation to the next higher domains. The same applies to aggregating country statistics into regional or global estimates, but with the added complications of having to deal with varying purchasing powers of national currencies. In general, countries following different methodologies will have non-comparable income based statistics. This is still true even for countries that follow the same methodology, such as CBN because sources of non-comparability arise in every step. 43 , Countries use different dietary energy thresholds; and the poverty statistics are sensitive to changes in the threshold. The consequence for global estimates can be significant; for instance, India, which harbors a large proportion of the world?s poor, uses 2400 kcal threshold for the rural sector. On the other hand, getting a country to move to a common threshold will result in non-comparability in the country?s time series. Possibilities include trying to convince countries to use multiple thresholds that include a common value, say 2100 kcal. Estimating the per capita energy cumulative distribution function is one way to provide this flexibility. , The food baskets vary accordingly with the energy threshold, both in composition and number of items. The energy threshold is influenced by the specifications of the recommended daily allowances (RDAs) for energy. The food composition or conversion tables vary across countries also, although their impact on non-comparability is minor relative to that of the energy threshold. Differences in eating habits, food preferences and availabilities will make it difficult and perhaps impractical to aim for a common food basket. , The definition and measurement of non-food basic needs and the manner of incorporating he result with the food poverty line to arrive at the total poverty line also varies across t countries. The use of an Engel?s ratio of food expenditure to total expenditure of households whose per capita expenditures closely approximate the estimated food poverty line (fpl) seems to be the preferred approach by the majority of developing countries. It may not make much difference whether the calculation of the ratio is made 44 via regression or from households with per capita expenditures falling within a narrow band around fpl. The choice of divisor for calculating per capita figures, however, can make a significant difference in the results. , To estimate the proportion and number of poor, some countries – including China and majority of the ECLAC countries for example - compare their poverty lines against per capita incomes. Others – including the majority in the ESCAP region – use per capita expenditures. Recognizing the difficulty of convincing all countries to switch to one metric, the alternative is to suggest that countries stick to one metric and not vacillate from one to another. , Per capita calculations are required for estimating poverty lines and income (or expenditure). Unfortunately, majority of countries still use household size as divisor, which leads to positively biased estimates of the proportion and number of poor households and persons. Further empirical work is needed that could lead to evidence based recommendations on the use of the age by sex RDA specifications as basis for setting adult equivalents to be used in estimating food poverty lines for example; and on the use of models to incorporate scale economies in deriving divisors for computing per capita non-food poverty lines and income (expenditures). Different approaches can potentially lead to significantly divergent results. Concerning food poverty, per capita calculations can be circumvented by comparing the estimated total energy consumption of the household members against the total of their recommended daily allowances for dietary energy. 45 , Last but not least, the method of data capture employed in the poverty surveys varies greatly among countries;. from one year recall of income and expenditures ( (e.g. Indonesia), to six months recall (Philippines), to monthly (India), and to daily diary method supplemented by weekly recall (China). Actual food weighing is also employed in some food consumption surveys. That the basic data obtained from these various data capture methods can be drastically different is brought into sharp focus by a study in India. By changing nothing but the recall period for food expenses from 30 days to 7 days in its 1999-2000 consumer expenditure survey, India found out that the estimated poverty head count ratio declined from 26.1% to 23.3% respectively (GOI Press Information Bureau, 2001). The difference is equivalent to 28 million out of India?s 1 billion populations at the end of 2000, which equals the total number of poor in Vietnam. 46 EFERENCES , R 47