ANNEX A

WHAT DO WE REALLY KNOW?

METRICS FOR FOOD INSECURITY AND MALNUTRITION

Hartwig de Haen, Stephan Klasen and Matin Qaim1

Paper presented at the workshop on Measuring Food Insecurity and Assessing the Sustainability of Global Food Systems, February 16-17, 2011, Keck Center of the National Academies, Washington, DC.

1. INTRODUCTION

Recently, the International Food Policy Research Institute (IFPRI) published an estimate of hunger in 12 sub-Saharan African countries. Based on an analysis of household surveys the authors found that in the late 1990s 59 percent of the population was food energy deficient (Smith et al., 2006). This result was in stark contrast to estimates by the Food and Agriculture Organization of the United Nations (FAO), based on food balance sheets for the same countries, the same period and using the same criterion of energy deficiency as an indicator of undernourishment. The FAO prevalence estimate was 39 percent (Smith et. al., 2006, p 45), hence significantly lower. Not only did the two methods differ with respect to the mean level of undernourishment, the ranking of the 12 countries differed as well. In other words, there is not even a close correlation between the two estimates. This example of divergent estimates of hunger, measured with the same criterion, namely food energy deficiency, suffices to raise interest in a thorough comparative assessment of the various methods used to estimate hunger.

Numerous statistics are published reporting on the food security and nutrition situation at global, country, household and individual levels. A comprehensive overview of available or conceivable indicators can be found under the FAO-led Food Insecurity and Vulnerability Information and Mapping Systems initiative, FIVIMS, (http://www.fivims.org). FIVIMS was established, initially as an inter-agency initiative following the World Food Summit (WFS) in

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1 The authors are Professors at the Georg-August-University of Göttingen. H. de Haen and M. Qaim are, respectively, Emeritus and Chair, International Food Economics and Rural Development, at the Department of Agricultural Economics and Rural Development. S. Klasen is Chair, Theoretical Economics and Development Economics at the Faculty of Economic Sciences. The authors would like to thank Mark Smulders, FAO, and participants at the workshop for helpful comments and suggestions.



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ANNEX A WHAT DO WE REALLY KNOW? METRICS FOR FOOD INSECURITY AND MALNUTRITION Hartwig de Haen, Stephan Klasen and Matin Qaim1 Paper presented at the workshop on Measuring Food Insecurity and Assessing the Sustainability of Global Food Systems, February 16-17, 2011, Keck Center of the National Academies, Washington, DC. 1. INTRODUCTION Recently, the International Food Policy Research Institute (IFPRI) published an estimate of hunger in 12 sub-Saharan African countries. Based on an analysis of household surveys the authors found that in the late 1990s 59 percent of the population was food energy deficient (Smith et al., 2006). This result was in stark contrast to estimates by the Food and Agriculture Organization of the United Nations (FAO), based on food balance sheets for the same countries, the same period and using the same criterion of energy deficiency as an indicator of undernourishment. The FAO prevalence estimate was 39 percent (Smith et. al., 2006, p 45), hence significantly lower. Not only did the two methods differ with respect to the mean level of undernourishment, the ranking of the 12 countries differed as well. In other words, there is not even a close correlation between the two estimates. This example of divergent estimates of hunger, measured with the same criterion, namely food energy deficiency, suffices to raise interest in a thorough comparative assessment of the various methods used to estimate hunger. Numerous statistics are published reporting on the food security and nutrition situation at global, country, household and individual levels. A comprehensive overview of available or conceivable indicators can be found under the FAO-led Food Insecurity and Vulnerability Information and Mapping Systems initiative, FIVIMS, (http://www.fivims.org). FIVIMS was established, initially as an inter-agency initiative following the World Food Summit (WFS) in 1 The authors are Professors at the Georg-August-University of Göttingen. H. de Haen and M. Qaim are, respectively, Emeritus and Chair, International Food Economics and Rural Development, at the Department of Agricultural Economics and Rural Development. S. Klasen is Chair, Theoretical Economics and Development Economics at the Faculty of Economic Sciences. The authors would like to thank Mark Smulders , FAO, and participants at the workshop for helpful comments and suggestions. 87

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88 A SUSTAINABILITY CHALLENGE: FOOD SECURITY FOR ALL 1996, which determined that food security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life (WFS Plan of Action, 1996). This widely accepted definition underlines the multidimensional nature of food security, comprising level and stability of food access and availability, adequacy of food use and food consumption and nutritional status. Conversely, it explains that food insecurity, i.e. the absence of food security, can be the result of very diverse factors. The equally broad and overlapping concept of nutrition security determined by dimensions of food, care and health, can be assessed through a number indicators, including those measuring undernutrition as well as overnutrition. Per Pinstrup-Andersen talks about the triple burden of malnutrition, differentiating between (i) food energy deficiency, (ii) deficiency in specific nutrients, especially micronutrients, which are also key for an active and healthy life, and (iii) excessive net energy intake leading to overweight and obesity (Pinstrup-Andersen, 2007). In view of this multi-facetted character of food insecurity and malnutrition, it is not surprising that—when indicators measure different dimensions—the conclusions may also be different from one indicator to another. However, where different methods are used to measure the same phenomenon, one would expect only little, if any differences. The comparative assessment provided in this paper intends to discuss the reasons for differences between methods and indicators in more detail. While we recognize that the obesity problem is increasing, including in developing countries, we concentrate primarily on measures of food deprivation and undernutrition. Obviously, before a specific food insecurity information and mapping system is set up, clarification is needed, as to which aspect of food insecurity is to be measured in each particular situation and by which indicator. Expressed in simple terms, people are deemed food insecure when their consumption of food is insufficient, insecure and/or unsustainable (Maxwell and Frankenberger, 1995). They live in hunger or fear of starvation. Although hunger is commonly understood as a sensation of not having enough to eat, its definition and measurement are not at all trivial. On the one hand, the extent of hunger can be measured as a lack of essential nutrients in the diet. A widely used indicator for this is food energy deficiency. On the other hand, hunger may also be the result of humans’ inability to absorb and use food energy and specific nutrients for body functions, implying that the overall nutritional status may also be affected by people’s health. Accordingly, the combined effects of access to food and of food absorption and use are best measured through outcome indicators that inform about people’s actual nutrition status such as undernutrition or overnutrition. Before proceeding to discuss the advantages and disadvantages of these various approaches to the topic, it is necessary to clarify the purpose of measurement. Two quite different purposes can be distinguished. One is to be informed about the extent and consequences of an actual food emergency caused by a sudden drop in supply or access to food. In such situations, indicators must provide information about people’s immediate needs of essential nutrients to ensure survival. Indicators must be easy and quick to measure and useful for the design of humanitarian aid action. The second purpose relates to chronic food insecurity, caused by long term food deprivation linked to structural poverty and poor nutrition. One such indicator is “undernourishment”, a measure of ‘chronic food insecurity, in which food intake is insufficient to meet basic energy requirements on a continuing basis’ (FAO, (SOFI, 1999), p. 11). Information about chronic food insecurity is needed for an assessment of level, geographical

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ANNEX A 89 distribution and trends of hunger and/or for the design and implementation of anti-hunger policies, strategies and investment that seek to reverse undesirable trends. This paper focuses on indicators of chronic food insecurity. To be useful for a comprehensive assessment, indicators of food insecurity should provide answers to at least three questions, namely: Who are the food-insecure? How many are they? And where do they live? If the purpose of the measurement goes beyond assessment and includes the design of policy responses, the indicators should also help answering the more ambitious question: Why are people food insecure, what are the underlying causes and hence, what should be done? Numerous methods are in use to measure certain aspects of food insecurity. They can be summarized as follows: 1. Indicators derived from food balance sheets 2. indicators based on household consumption surveys 3. indicators derived from anthropometric measurements 4. indicators derived from medical assessments 5. Composite indicators. Methods (1) to (3) currently represent the principal tool kit. Both the first and the second compare levels of nutrient consumption with levels of nutrient requirements. While both use science-based nutritional norms as requirement standards, they differ in the source of information about people’s food consumption. The first, used by FAO, calculates food available for human consumption from national food balance sheets (FBS) and uses different information sources for a statistical measure of dispersion to approximate the distribution of food consumption levels within countries. The second derives the estimates of mean as well as dispersion of food consumption from household surveys, asking respondents to recall food consumption during a reference period. The third method relies on physical measurements of people (principally weight and height measurement, often concentrating on children) as indications of their nutritional status. The fourth method provides additional data from medical analysis. This can include clinical assessments, such as the observation of physical signs on the body that are symptomatic of nutritional disorders (e.g., loss of skin pigment, edema) or biochemical assessment through the examination of blood or urine. At the population level, health indicators such as child mortality or low birth weight are also sometimes used as proxies for nutritional status. Finally, a number of efforts have been undertaken recently to combine specific indicators into composite indicators seeking to capture several critical dimensions of food insecurity and malnutrition at the same time. The Global Hunger Index published jointly by IFPRI and the German Welthungerhilfe is such an example designed for cross-country comparison. While they do not as such generate additional measurements, composite indicators aim to facilitate communication of the comprehensive nature of food insecurity and malnutrition. To date, a consensus among experts on the reasons for discrepancies between the results obtained from different methods is still elusive. The mentioned case of contradicting estimates of undernourishment in countries of Sub-Sahara-Africa is one such example. The apparent contradictions between only moderate estimates for the prevalence of undernourishment in the overall population of India versus the much higher rates of undernutrition among India’s children that result from anthropometric measurements are another example (see below). It is hoped that

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90 A SUSTAINABILITY CHALLENGE: FOOD SECURITY FOR ALL the following discussion, which particularly examines the first three of the mentioned methods in detail, will contribute to some progress towards a consensus regarding the most realistic measurements. We critically review the three principal methods with regard to their measurement approach, the accuracy of the underlying data, and their usefulness for policy decisions, including projections and the simulation of nutritional impacts of shocks. The intention is not to describe the real food security situation but rather to describe and compare methods and indicators and make some suggestions for improvement and future research. As part of a focus on sustainable global food security, the National Research Council’s Roundtable on Science and Technology for Sustainability is planning a workshop to examine these indicators, reviewing the approaches used in developing the indicators and assessing their strengths and weaknesses. This paper is a background paper for this workshop. 2. THE FAO INDICATOR OF UNDERNOURISHMENT Definitions, Assumptions and Main Sources of Empirical Evidence FAO estimates the prevalence of undernourishment, expressed as the share of people in a national population not meeting their minimum food energy requirements. It is assumed that food energy deficiency is the most critical indicator of hunger. The method is based on three key parameters: the mean quantity of calories available for human consumption per person, the rate of inequality in access to those calories within the population and the minimum amount of kilocalories required by that population on average, based on the gender and age structure. The graph in Figure I A-1 illustrates the methodological procedure for estimating the proportion of the population whose food energy availability is below requirement, i.e., who are undernourished. The function f(x) depicts the proportion of the population corresponding to different dietary energy consumption levels (x), µx the mean dietary energy intake per person and rl the minimum acceptable dietary energy requirement (MDER). The area under the curve left of rl represents the proportion of the population not reaching the minimum level of dietary energy requirement, i.e. the prevalence of undernourishment, pU. Multiplied with the size of the population for the respective period it gives the number of undernourished.

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ANNEX A 91 pU µx 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 5,500 6,000 rL Dietary energy consumption (Kcak/person/day) FIGURE I A-1: The FAO method of estimating undernourishment NOTE: Distribution of dietary energy consumption The amount of food available for human consumption is calculated from national FBS, compiled as the balancing item after considering production, trade, stock changes, non-food uses and extra-household waste. The per capita Dietary Energy Supply (DES) is obtained by aggregating all food items, converting the quantities into energy values and dividing the aggregate volume by the total population. It is noted that the indicator measures food available for human consumption at the household level, and not actual food intake. However, FAO considers it to be a close enough approximation of actual dietary energy consumption. The inequality of access to food is estimated assuming a log-normal distribution function (Figure I A-1). This function with its short lower and longer upper tail was chosen because it reflects ‘the fact that wastages, food fed to pets etc. are likely to be confined to the upper tail representing the richer and more affluent households’ (FAO, 2003, p.12). The function is defined by the mean level of dietary energy consumption per person and the Coefficient of Variation (CV). The mean is assumed to equal the DES from the FBS. The CV is derived from the sample distribution of kilocalorie consumption per person as measured from available household surveys. Where food consumption information is not directly available from household surveys, survey data on food expenditure or income are used to derive estimates of dispersion. Where no survey data are available at all, data from comparable neighboring countries are used. The third principal parameter needed for the FAO method is the aggregated Minimum Dietary Energy Requirement per person (MDER). This is the amount of food energy needed to balance energy expenditure in order to maintain an acceptable minimum body-weight, body composition and a level of minimum (‘sedentary’) physical activity, consistent with long-term good health. This includes the energy needs for optimal development of children, deposition of tissues during pregnancy and secretion of milk during lactation consistent with the good health of

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92 A SUSTAINABILITY CHALLENGE: FOOD SECURITY FOR ALL mother and child. The recommended level of dietary energy consumption for the average of a population is the mean energy requirement of the healthy, well-nourished individuals who constitute that population.2 Who Uses the FAO Indicator and for What? FAO has been publishing estimates of undernourishment in irregular intervals in its World Food Surveys since the 1960s. While scope and contents have been kept broadly similar, the country coverage as well as details of the methodology have been gradually adjusted over the years. Since 1999, the indicator is being published annually as a three-year average in the flagship publication ‘The State of Food Insecurity in the World’ (SOFI). Beginning with the 2009 edition, SOFI is published jointly by FAO and the World Food Program (WFP). According to the latest edition (SOFI, 2010), covering the period 2005-2007, the total number of undernourished worldwide was estimated at 847 million people, of whom 835 million were living in developing countries. The number has hardly changed since 1990-1992 (the base year for the WFS goal and of the hunger target of the first Millennium Development Goal (MDG-1) aimed at halving, respectively, the number and the percentage of undernourished by 2015. On the other hand, the prevalence of undernourishment declined from 16 to 13 percent worldwide and from 20 to 16 percent in the developing countries. Since 2008, FAO has also published a preliminary estimate of undernourishment for the respective current year, using a simplified ex-post projection (see below). According to this method, the number of undernourishment was estimated at 925 million in 2010, down from 1,02 billion that had been estimated for 2009 using the same ex-post projection method (SOFI, 2009). The main purpose of publishing the indicator regularly for a very large number of countries is to inform the global community about levels and trends of undernourishment (chronic hunger) in the world and thus facilitate global and regional governance of food security, while also advocating for stepped up efforts in hunger reduction. The indicator measures chronic food insecurity at national levels. It does not inform about the actual distribution of the number of hungry within countries nor is it the intention to provide actionable information for policy responses at sub-national levels. The estimates are therefore primarily of interest for international comparisons and for assessments of changes over time. The publication is receiving wide attention by the media and the wider public and clearly fulfills its purpose to advocate action against hunger. The indicator is also used by food security analysts. FAO and its governing bodies, in particular the Committee on World Food Security (CFS) as well as many other international and national organizations concerned with development cooperation, refer to the estimates regularly. Presumably, various donors use the indicator also as one key information source for the ranking of priorities for aid allocations. The FAO Undernourishment estimates also serve as one of the two official indicators of progress towards target 2 of Millennium Development Goal One (“Halve, between 1990 and 2015, the proportion of people who suffer from hunger”). The other indicator is ‘Prevalence of underweight children under five years of age’ using anthropometric assessments (see below). 2 The norms have been defined by the FAO/WHO/UNU Expert Consultation on Human Energy Requirements in 2001, which established energy standards, published in 2004, for different sex and age groups performing sedentary physical activity and with a minimum acceptable body-weight for attained heights.

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ANNEX A 93 Since a number of years, IFPRI has used the FAO estimates of the prevalence of undernourishment as one of three equally weighted indicators to construct its Global Hunger Index (GHI), with the other two being the prevalence of underweight in children under the age of five and the mortality rate of children under five years of age. Governments of developing countries do take note of FAO’s undernourishment estimates as an indicator of the extent of hunger and of progress or retreat over time. Severe levels of undernourishment in any one country provide justification for appropriate policy measures to be put in place to remedy the situation. However, the indicator does not, and is not meant to, provide directly actionable information for policy design at sub-national level, Sources of Funding – Past and Future As informing the world about the scale, geographical distribution and implications of food insecurity belongs to the core functions of FAO, work in basic statistics as well as the preparation of the undernourishment indicator and its publication are normally funded from FAO’s core budget. However, funding of FAO’s statistics program has been rather tight for a number of years. The problem was recognized and a reform project launched, but its implementation is still ongoing. This may have critical implications for the quality of the data base and for the expert capacity in FAO to conduct the compilation of the undernourishment indicator. Strengths and Weaknesses of the FAO Indicator Undoubtedly, the main strength of the FAO method is its world-wide coverage with estimates for more than 100 countries, which enables the monitoring of national trends and tracking of progress and setbacks using the same methodology and criteria for all. The main weakness is the fact that it relies on national statistics compiled in FBS for the estimation of the dietary energy supply, so that the accuracy of the method depends critically on the quality of the statistical data obtained from member states and stored in FAOSTAT following a quality check. One can therefore not rule out that both levels of undernourishment between countries as well as changes in the indicator from one year to another within a country are determined by erroneous data rather than a real change in the number of undernourished. The short (yearly) intervals between publications of the indicator make such ‘over-interpretation’ more likely. Various authors have also criticized methodological issues, including the focus on food energy, the compilation of the dispersion of the intra-national distribution of food consumption and the standards used for calculation of minimum dietary requirements. In the following, some of these points will be discussed in more detail. Mean Dietary Energy Supply per Person (DES) FAO compiles the DES from FBS and uses it as an indicator of food energy consumption. The quantities of food commodities available for human consumption are calculated after deducting the net exports, stock increase, non-food use and extra-household waste from domestic production. This raises several questions. The first is whether food energy

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94 A SUSTAINABILITY CHALLENGE: FOOD SECURITY FOR ALL deficiency is an adequate indicator of food insecurity; the second is whether dietary energy supply is a good approximation of food energy intake; the third concerns the accuracy of the FAOSTAT data base. (1) Is average food energy deficiency an adequate indicator of food insecurity? Obviously, an adequate, healthy diet must satisfy human needs for energy and all essential nutrients. In fact, according to the Report of a Joint FAO/WHO/UNU Expert Consultation on Human energy requirements (FAO, 2001) “dietary energy needs and recommendations cannot be considered in isolation of other nutrients in the diet, as the lack of one will influence the others.” Adequate intake of food energy is essential for the metabolic and physiological functions of humans, and in this sense FAO focuses on the key indicator. However, as very often other nutrients are lacking in the diet, in particular micronutrients such as iron, vitamin A and zinc, comprehensive assessments of people’s nutritional status should ideally not be limited to the food energy deficiency indicator. In principle, the food balance sheet data can also be used to assess the level of micronutrient consumption and adequacy (Wuehler et al., 2005), but the level of commodity group aggregation is relatively high, which is a drawback that weighs more heavily for micronutrients than for calories. The use of three year averages generates other uncertainties. In a country where the fluctuations within the three years are very large, food insecurity is arguably a much more serious problem than in a country where the three year average is the same but caloric availability is much more stable. (2)Is dietary energy supply a good approximation of dietary energy intake? As the dietary energy supply includes foods, which are subsequently lost or wasted at the retail and household levels, the method by definition overestimates the actual food energy intake. (3) How accurate are the FAO food balance sheet data? FAO has been criticized by various authors for a lack of accuracy of the data inputs used to calculate the mean per caput DES. Svedberg suggests that “food availability is underestimated (by FAO) in most parts of the developing world, although less so elsewhere than in Africa” (Svedberg, 2002). He suggests that often the substantial share of food produced for subsistence tends to be underestimated in official statistics, leading to an overestimation of undernourishment. Deviations from FAO’s estimates have also been found in the IFPRI study of 12 African countries mentioned in the Introduction, although in this case, the critique is that FAO’s measure underestimates hunger. Testing the validity of these claims is not easy. Conceptually, the FAO method does capture all components of supply and utilization, including subsistence production; however the estimates are obviously subject to possible errors. In particular, assumptions regarding post- harvest losses are often not transparent and there is very little hard data available on its level (let alone its country-by-country distribution and trends over time). Moreover, it must be noted that, although the FAO Statistics Division has to fill in missing data, in particular for stock changes, non-food use and wastage, or use data from other sources, e.g., on trade, a major part of the data input originates directly from countries. The case of India is worth mentioning here, as FAO’s estimates of rising numbers of undernourished in spite of the country’s strong economic growth are rather surprising. Whereas one would assume that economic growth in recent years should have increased per capita food consumption significantly, the statistics used to estimate undernourishment do not confirm this. FBS data show India’s per caput consumption stagnating around 2300 kcal/person/day between 1999/01 and 2005/07. India’s own surveys even show a

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ANNEX A 95 steady decline of per caput consumption during this period (Chattapadhyay and Chowdhury, 2010). A thorough analysis to explain this paradox is urgent, also because the development in India is of great significance for global trends (see also Deaton and Drèze, 2008; SOFI, 2008; and discussion below). Inequality of Food Energy Consumption at National Level FAO’s approach to compile the coefficient of variation (CV) of the intra-national distribution of dietary energy supply has been subject to intensive debate among experts. The debate has been centering around two questions, one regarding the realism of the CV estimate, the other regarding the assumptions for changes of the distribution over time. Is the CV parameter realistic? The critical arguments raised by experts are not all consistent and partly contradictory. Svedberg suggests, for example, that “FAO must have overestimated the variance in the calorie-availability distribution across households, because the ensuing habitual intakes in the lower tail are impossibly low for living households” (Svedberg, 2003, p. 25). The mentioned IFPRI study of 12 African countries comes to the opposite conclusion. Based on household expenditure surveys, Smith et al. (2006) estimate an average CV of energy availability of 0.62 for the 12 African countries, whereas the FAO estimate for the same countries is 0.3, hence much lower. Other household surveys result in similar high dispersion parameters for food intake (Ecker et al., 2010). FAO itself recognizes that the coefficient cannot be completely specified even without considering problems associated with survey practices, measurement errors and sample design (FAO, 2003, p. 23) The reason given relates to the (realistic) hypothesis that people’s food consumption is not only influenced by income, but also by their age/sex specific energy requirement. The following formula is used to calculate the CV: where CV(x) is the total CV of the household per capita dietary energy consumption, CV(x |v) is the component due to household per capita income (v), and CV(x |r) is the component due to other sources of variation, in particular energy requirement (r). CV(x |r) is considered to be a fixed component and is estimated to correspond to about 0.20. CV(x |v) is, however, estimated on the basis of household survey data (FAO, 2003, p. 38). According to FAO, the CV resulting from the analysis of survey data using the formula above is occasionally further corrected to remove components of variation that are considered not plausible. Moreover, as the log-normal distribution would not exclude energy intake levels below the absolute minimum for survival or above possible maximum food intake levels, lower

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96 A SUSTAINABILITY CHALLENGE: FOOD SECURITY FOR ALL and upper bounds for the range of the CV have been set at 0.2 and 0.35.3 The IFPRI study suggests that due to these adjustments the FAO CVs may be biased downward (Smith et al. 2006, p. 50). However the empirical evidence for such a conclusion is limited. Is it realistic to assume no change of the CV over time? FAO has so far kept most CVs constant over time. Adjustments of the CVs have been limited to a few cases. FAO justifies this by a lack of available survey data, but suggests also that “there has been little, if any, change in the inequality of income/expenditure in most countries” (FAO, 2003, p. 16). The implications of this procedure for the estimates of undernourishment could indeed be significant. Firstly, empirical evidence suggests that, especially since the 1990s when structural adjustment programs began to take effect in more developing countries, income distributions do change as economies grow. In fact, there is evidence that income and expenditure inequality in a majority of developing countries increased (at least slightly) between the early 1980s and the mid 1990s; since then trends are more heterogeneous.4 Secondly, even if the relative income distribution remains unchanged while average incomes grow, the food demand will grow faster in the lower income brackets due to their higher demand elasticity. This alone would make it likely that the CVs of food consumption would decline as average incomes and food consumption grow; similarly, one would presume that drastic rise in global food prices, as witnessed in 2007/08 and again 2010 would have a differential impact on food consumption patterns of different income groups, thereby affecting the CV. More generally, fixing the CV also means that changes in measured hunger across the world will be driven by changes in the DES. This gives the erroneous impression that changes in hunger over time are largely a problem of ‘food availability’, rather than changes in entitlements (Sen, 1984) of different groups in the population to access to food. Situations where hunger in a population goes up despite stable or rising DES (e.g., due to a regional national catastrophe, rising food prices, conflicts, etc.) are ruled out by definition this way; any change in entitlements across population groups would immediately imply a change the CV. Thus to study hunger, one needs to examine entitlements of groups which can be affected as much by food prices, employment, and wages as by food availability in the country; such assessments would lead to a changing CV. Lastly, a more technical issue is whether the CV is actually the best measure of dispersion to estimate and apply in this case. As is well-known, the CV is particularly sensitive to the distribution of calories in the upper parts of the caloric distribution. The use of the CV is consequently problematic for two reasons. First, it is not ideal to use a distributional indicator that will be heavily influenced by the distribution of calories among the ‘non-hungry’. Second, as a result of the sensitivity to high levels of caloric consumption, any measurement error among that group of ‘non-hungry’ will have an important impact on the resulting CV and the hungry. There are ready alternatives to the CV, including the Atkinson inequality measure (see Atkinson, 1970) or the Theil family of inequality measures which are both sensitive to the distribution of calories at the bottom end of the distribution, which is of interest here. 3 This ‘plausible range’ is based on the analysis of realistic variances of food intake levels within hypothetical populations with highest and lowest food energy supplies per person. 4 See, for example, Gruen and Klasen (2003) for an analysis of these trends.

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ANNEX A 97 Minimum Dietary Energy Requirement (MDER) The third key parameter of the FAO method, the minimum requirement of dietary energy, is defined as the consumption level that will balance energy expenditure. Questions raised regarding the approach concern the assumptions determining the dietary energy requirements of different age-sex groups and the rationale for a singular country-specific cutoff point. (1) Are the assumptions determining the dietary energy requirements of different age-sex groups correct? Components of energy expenditure comprise the basal metabolic rate (BMR), i.e. the energy expended for the functioning of an individual in a state of complete rest; the energy needed for digesting food, metabolizing food and storing an increased food intake; and the energy required for performing light physical activities, both work and non-work. The BMR ranges between 1300 and 1700 kcal/day for adults, depending on age, sex, height and body weight, to which 55 or 56 percent are added for light activity of male and female adults, respectively. For children, the energy required for growth is taken into account. An allowance is also for children below age two from developing countries for the energy needed to recover from frequent infections. For women during pregnancy and lactation, the energy required for the deposition of tissue and secretion of milk is considered. As FAO specifies these dietary energy requirements in accordance with the recommendations by the Joint FAO/WHO/UNU Expert Consultation on Human energy (FAO, 2001), it is assumed that the assumptions are realistic. However, further research is needed to examine the realism of the assumptions in the light of various critical reviews.5 (2) Is a singular cutoff point a good approximation of a population’s minimum dietary energy needs? The minimum per capita dietary energy requirement is derived by aggregating the estimated sex-age-specific minimum dietary energy requirements, using the relative proportion of the population in the corresponding sex-age groups as weights. As the sex-age distribution of the population changes over time, this so-called cutoff point is being regularly adjusted to demographic change (FAO, 2003). Svedberg suggested that this method has a “built-in flaw that leads to biased estimates” (Svedberg, 2002, p. 6) because it fails to consider that even after taking into account the effects of age, sex, activity and body weight, individuals differ in their energy requirements due to differences in the efficiency of energy use. He suggests therefore replacing the singular cutoff point by a bivariate distribution according to which the probability of an individual not meeting the food energy requirements is not only determined by the distribution of food intake but also by the covariance between food energy intake and requirements. According to Svedberg, following this approach would as such result in a notably higher incidence of undernourishment. Responding to this criticism, FAO experts showed that if 5 According to Svedberg, FAO uses a BMR that is unrealistically high for countries in the tropics, thus overestimating undernourishment (Svedberg 2002). In contrast, Smith et. al. (2006, p. 48) use energy requirements that are higher than those used by FAO, averaging 2050 kcal per day as compared to 1800 by FAO. This in itself could explain why Smith et. al arrive at higher estimates of undernourishment than FAO. While both approaches assume the same light activity level, they make different assumptions regarding the level of the requirements for given age-sex groups. FAO classifies a person as undernourished that consumes less than the minimum dietary energy requirement (MDER) for the respective age-sex group, whereas Smith et. al. classify all people as undernourished who consume less than the average requirement.

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114 A SUSTAINABILITY CHALLENGE: FOOD SECURITY FOR ALL As can be seen, none of the three approaches has a clear advantage in meeting all criteria. In particular, there is no conclusive evidence regarding the accuracy of the three approaches in assessing the prevalence of undernourishment or undernutrition. The consumption survey and anthropometric approaches have clear advantages over the FAO method in several criteria, especially in terms of measuring diversity and heterogeneity within countries. Although currently hardly done, they could potentially serve to generate even more information that is important for a complete assessment. For example, household consumption surveys are potentially very useful to assess dietary diversity and micronutrient status. While the analysis so far leads to the conclusion that there are several reasons to suspect that the FAO method generates biased estimates, the direction of the likely biases cannot currently be conclusively stated. On the other hand, the approach has the advantage of drawing a regular picture, consistent with national aggregate food production and trade statistics, of undernourishment for the population as a whole at national levels and above, which may be useful for global governance and discussions on hunger and ways to combat it. 6. OPTIONS FOR IMPROVING FOOD SECURITY AND NUTRITION INDICATORS In 2002, FAO organized an International Scientific Symposium on Measurement and Assessment of Food Deprivation and Malnutrition, which considered all major methods including qualitative methods for measuring people’s perception of hunger. There was wide consensus that “no single method can capture all aspects of hunger while at the same time providing policy-makers with relevant and timely information in a cost-effective manner” (FAO, 2003, p. XV). Accordingly, the Symposium concluded that a ‘suite’ of indicators was needed to cover the different dimensions of food security. We believe that this conclusion is still valid. One of our central suggestions for improving and broadening the empirical data base is running more surveys. In the longer term, a regular availability of results from representative surveys could greatly enhance the worldwide information on food insecurity and malnutrition and reduce the need for additional indicators derived from macro food balance sheets. However, even if this suggestion is followed, data availability would only improve gradually. Moreover, the possibility of inconsistent and non-representative household data could still not be ruled out. Therefore, our recommendations are twofold. First, the FAO method should be improved through better data and more transparent science-based assumptions. Available data from household consumption surveys can be used to improve the FAO parameter assumptions. In this connection, lessons about how to deal with missing data through interpolation can be learned from the World Bank, which uses living standard surveys to compile global poverty statistics and updates in certain intervals. Second, household consumption survey and anthropometric approaches should be further improved, both conceptually and through improved data bases. These recommendations are further elaborated in the following.

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ANNEX A 115 Improving the FAO Indicator At the outset it should be noted that the experts in the FAO Statistics and Agricultural Development Economics Divisions are currently fully aware of the weaknesses in the methodology for estimating undernourishment. Work is underway to improve both the data base and part of the methodology. Many critical points need to be addressed of which several are currently under consideration in FAO. Of importance are in particular the following: Food consumption: Continuing the overall reform of FAOSTAT with the view to improve the quality and consistency of all data inputs for the FBS. The aim should be to improve the estimates of daily dietary food energy supplies. Whereas consumption has so far mostly been compiled as the balance of supply-utilization accounts (SUA) for the various commodities and foods, efforts should be promoted, working with national authorities, to reconcile the estimates with survey data. Improved estimates will also be needed for other significant components of the FBS, namely food waste and losses inside households and commercial kitchens. Given the overwhelming importance of the DES estimate for the FAO hunger measure (and many other important uses of these data), improvements in the accuracy of these estimates is a top priority. Intra-national inequality of food consumption: Examining the validity and updating of the coefficients of variation (CV) of food consumption. Efforts currently underway in FAO to improve the measures of inequality using household surveys are to be supported. Close cooperation with national and international organizations conducting such surveys is recommended so as to ensure consistency of the foods included and possibility of regular updates to reflect change of CVs over time. Minimum dietary energy requirements. Continuing inter-agency cooperation to determine best science based estimates of MDERs. Regular updates are needed to reflect the effects of changing age structures (in particular rising shares of adults in populations) and changing heights and weight standards. Moving Beyond the FAO Indicator Even with the suggested improvements, the FAO method would not satisfy all information needs with regard to food insecurity, nor would it suffice to provide policy makers with actionable information needed to address the main obstacles to overcoming hunger and malnutrition through effective food security strategies at country level. For these purposes, consumption surveys and anthropometric measurements are much more useful, but there is also substantial scope for improvement of these approaches. In our view, the most important ones are the following: Data availability: Improvements are required, especially with respect to nationally representative household consumption surveys, which are usually integrated in more comprehensive living standard surveys. Such living standard surveys with sufficiently disaggregated food consumption modules should be carried out more frequently, and in a larger number of countries, to improve the micro level information base. This will require additional

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116 A SUSTAINABILITY CHALLENGE: FOOD SECURITY FOR ALL resources, but such data can be used for a variety of purposes. Beyond the food security context, nationally representative household surveys can be used for tracking all sorts of developments at the micro level and for planning and evaluating policy interventions. To the extent possible, the survey formats should be standardized internationally. Anthropometric measurements: Here the data base is much better, but anthropometric surveys contain little other socioeconomic variables and no information on food consumption. As nutrition, health, consumption, and income are so closely related, we propose linking anthropometric surveys with household living standard surveys. This will not only help improve the understanding of food security issues, but will also constitute a precious resource for broader micro level research related to food, nutrition, health, demography and overall welfare. Research: There are a variety of conceptual issues related to appropriate assumption for minimum energy requirements, anthropometric standards etc., which need further research. Integrated research that compares food intake and nutritional outcome indicators, controlling for other health-related aspects, would be particularly useful to better understand the existing contradictions and complementarities and improve the methodologies. This requires the proposed link between (or integration of) anthropometric and household living standard surveys. Dietary diversity: More research is also required beyond a calorie focus, to better understand the role, determinants, seasonality and appropriate measurement of dietary quality and diversity. A simple count of different food groups consumed by households (food variety score) has been proposed as a good indicator of nutritional status and even of food security more generally, but questions remain on advantages, drawbacks and limitations of such dietary diversity measures in particular situations (Ruel, 2003). Such research would also benefit a lot from a wider availability of nationally representative linked anthropometric and food consumption data. Depth of hunger: In SOFI (2000) FAO had published estimates of ‘depth of hunger’, defined as the extent to which consumption levels of the undernourished fall below requirements. For example, it was shown that, in 1996-1998, 46 percent of countries in Sub-Sahara Africa had an average depth of more than 300 kcal per person per day, whereas this depth was only found in 16 percent of countries of Asia. Publication of this measure was not continued in later issues of SOFI. Indeed, while information on the depth of hunger is of great interest for comprehensive assessments of the state of food insecurity, compiling such an indicator can only add value if the estimates are derived from empirical data with regard to people’s real consumption and not from assumptions about the intra-national inequality of food consumption. More research is needed in this field and a resumption of depth of hunger compilations could be considered once the compilation and regular updating of empirically reliable CVs of food consumption has been completed. Policy impact simulations: Concerning the simulation of nutritional impacts of policies and shocks at country level, household food consumption data currently seem to constitute the best starting point. Since these surveys also contain information on food prices and household incomes or total expenditures, calorie price and income elasticities can be estimated for the population as a whole as well as for population subgroups. These elasticities, together with the results on household food security, can then be used to predict changes in the prevalence of undernourishment due to price and income changes. Ecker and Qaim (2010) have recently developed such an approach, which beyond calories also captures micronutrient deficiencies and

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ANNEX A 117 related price and income elasticities. Anriquez et al (2010a and b) have also used household survey data to assess the possible effects of staple food price increases on households’ food consumption and undernourishment. Of course, there is scope for improvement, but in general the approach seems useful to simulate micro level nutrition effects of food price spikes or economic crises to identify short-term problems that cannot wait for the assessment based on a new household survey. If living standard surveys were linked with anthropometric surveys, as proposed, then such analyses could be extended to also simulate impacts of policies and shocks on the prevalence of child underweight, wasting, and stunting. 7. CONCLUSIONS Improvements in the metrics of food insecurity and malnutrition are not only urgently needed, but also possible. This assessment of available methods has shown various entry points for improvement. A consistent and fully transparent process is recommended, comprising additional research and stepwise updating of the various indicators. The aim would be to develop an “Integrated Suite of Indicators,” in line with the recommendations already made by the International Scientific Symposium on Measurement in 2002. Such a suite would eventually use an improved data base to calculate a reformed FAO hunger indicator, combined with survey and simulation-based estimates of hunger using household expenditure surveys, linked with anthropometric surveys. The proposed process could involve the following three steps: The agencies involved in the collection of relevant data and compilation of relevant indicators should collaborate through active networking. An example for an appropriate institutional framework for such networking could be the emerging global Food Security Information Network (FSIN), which is currently being developed by FAO, WFP and IFPRI. It links the past FAO FIVIMS work with the new FAO corporate strategy on Information Systems for Food and Nutrition Security (ISFNS). This is a promising initiative which could eventually be expanded to include agencies like WHO, UNICEF, World Bank and the EU Commission. A complete inventory of estimates of relevant national indicators of food insecurity and malnutrition should be established, published, and regularly updated. This could not only include the main indicators, the food balance sheet approach of FAO, household surveys and anthropometry, but also, as available, other useful indicators such as micronutrient deficiency and dietary diversity. The inventory would contribute to greater transparency and enable more comprehensive comparative assessments, identification of complementarities and contradictions between indicators and areas requiring further research and data improvement. An appropriate online portal would have to be found. One possible location of such a Portal for consideration by the relevant agencies could be FSIN mentioned above. A major and coordinated effort is also recommended towards enhancement of the empirical data base, comprising all data relevant for measuring food insecurity and malnutrition. More specifically, the following steps towards improving the quality and the accessibility of the database are recommended: • Continue the current review of the food balance sheet data base in FAO. This will play a major role in efforts to improve the accuracy and reliability of aggregate food consumption data. The FBS approach has the advantage of generating consumption data consistent with

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118 A SUSTAINABILITY CHALLENGE: FOOD SECURITY FOR ALL national agricultural statistics. However, currently this important variable is normally not derived from direct estimates, but calculated as a residual after accounting for all other components of the equation. As several of these, for example stock change and post-harvest losses, are subject to uncertain assumptions, the residual may actually accumulate a number of statistical errors. FAO’s Statistics Division is aware of the underlying problems and seeking to improve the approach. • Reconcile data on food consumption derived from FBS and from household surveys. In principle, mean levels of consumption per person derived, respectively, from national FBS on and from representative household surveys should be similar. It is hoped that where both types of estimates are available for the same countries, the proposed inventory of data may serve to identify major reasons for differences and entry points for resolution of those differences. Eventually, improved and representative household survey based estimates of food consumption could substitute for the use of FBS in more and more countries. • Enlarge the country coverage and frequency of household living standard and anthropometric surveys so as to broaden the empirical base, including assessments of changes over time and comparisons between countries. • Harmonize the formats used for household living standard and anthropometric surveys so as to facilitate comparisons over time and space. • Coordinate the sampling frames of household living standard and anthropometric survey, or even merge both survey types, so as to facilitate comparison of different food insecurity and malnutrition indicators and enable important research on economic-nutrition-health linkages. • Parallel to establishing an inventory and improving the data bases, a systematic improvement of methods and assumptions is recommended for all three principal indicator methods, making use of the latest findings in nutritional science and following up, inter alia, on the various suggestions made in this paper. Such an assessment should also include a careful assessment of the reliability and comparability of cut-offs currently used to determine inadequate access to calories, nutrients, as well as to determine nutritional status. Ongoing improvement efforts should be enhanced. • More efforts should be made to use existing intergovernmental platforms for advocacy and support of work with indicators of food insecurity and malnutrition. The most prominent examples are the Committee on World Food Security (CFS) and the United Nations System Standing Committee on Nutrition (SCN). Effective action towards such improvements requires political will of governments and governing bodies at regional and global levels, including the Committee on Word Food Security (CFS), which has just been reformed “with the aim to become the central United Nations political platform dealing with food security and nutrition”. Accurate knowledge about who is food insecure, where they live and why they are food insecure and malnourished is a central precondition for effective action at all levels. Improving metrics of food insecurity is also a contribution to the monitoring of the realization of human rights. As stated in the background document for the World Summit on Food Security in 2009, “where the existence of hunger and malnutrition results from negligence or ignorance by responsible policy-makers, it is also a violation of people’s basic human right to adequate food and to a life in good health and dignity” (FAO, 2009).

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ANNEX A 119 It is encouraging to note that the weaknesses of the current metrics of food insecurity and malnutrition have been recognized by the agencies concerned, in particular the FAO, which is currently undertaking a fundamental review of the data and methods with the aim to improve them. We recommend that FAO interact closely with other relevant agencies, in particular WHO, UNICEF and World Bank, as well as with national governments to ensure broad support, consistency and mutual complementarities of the improvements. With the current state of evidence it is safe to conclude that the available estimates of chronic hunger are inaccurate, but it is not possible to conclude whether the real number of undernourished is above or below the available estimates. It seems not even certain whether the direction of change has been correctly assessed for the different countries. Even with revised methods and more accurate data, estimates of food insecurity and malnutrition are bound to be subject to measurement errors and projections will remain uncertain. Thus the conclusion by the participants at the 2002 Symposium on Measurement and Assessment of Food Deprivation and Undernutrition remains valid that if the magnitudes of food insecurity are uncertain, at least the trends should be correctly captured. We believe that this is possible through improved data and methodological approaches. While in the short run, an improved FAO method may be used, we argue that, in the longer run, global measures of food insecurity and malnutrition should increasingly be based on household surveys that combine food consumption and anthropometric measurements. REFERENCES Adams, R. 2004. Economic growth, inequality and poverty: estimating the growth elasticity of poverty. World Development 32(12):1989-2014. Alexandratos, N. 2009. World Food and Aagriculture to 2030/50: Highlights and Views from Mid-2009. Expert Meeting on How to Feed the World in 2050. Rome, Italy: FAO. Alexandratos, N, ed. 1995. World Agriculture towards 2010. Chichester, UK: John Wiley and Sons, and Rome, Italy: FAO. Anand, S., and M. Ravallion. 1993. Human development in poor countries: on the role of private incomes and public services. Journal of Economic Perspectives 7:133-150. Anand, S., and Bärninghausen. 2004. Human resource and health outcomes: cross-country econometric study. The Lancet 364:1603-1609. Anriquez, G., S. Daidone, and E. Mane. 2010a. Rising food prices and undernourishment—a cross- country inquiry. FAO/ESA (Agricultural Development Economics Division) Working Paper No. 10- 01. Rome, Italy: FAO. Anriques, G., P. Karfakis, S. Daidone, and E. Mane. 2010b. Measuring dietary energy deficiency and the impact of food price variations at the household level. FAO/ESA Working Paper No. 10-02. Rome: FAO Behrman, J. R., and A. B. Deolalikar. 1987. Will developing country nutrition improve with income? A case study for rural South India. Journal of Political Economy 95:492-507. Bouis, H., L. Haddad, and E. Kennedy. 1992. Does it matter how we survey demand for food? Evidence from Kenya and the Philippines. Food Policy 17:349-360.

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