3.2 Food Security and Money-Metric Welfare

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At the household level, two measures of multidimensional poverty have been estimated for most countries: the United Nations Development Programme and Oxford Poverty and Human Development Initiative’s Global Multidimensional Poverty Index (Global MPI) and the World Bank’s multidimensional poverty headcount rate.14 The key difference between them is that the Global MPI includes only nonmonetary dimensions, while the World Bank measure includes both monetary and nonmonetary dimensions.

Food security is another nonmonetary measure of welfare that is commonly used. Food security measures whether people can buy food (is it available and affordable?) and benefit nutritionally from eating it (does it contain the right nutrients, can they prepare it properly, and can they metabolize it?). Food security can be defined and measured in different ways and is commonly used by the United Nations and other humanitarian actors to target aid. It is often correlated with monetary poverty but can differ for important reasons (see box 3.2).

Box 3.2

Food Security and Money-Metric Welfare

Food (in)security is a metric of welfare used to prioritize assistance during times of stability and times of shock. Given its widespread use, it is useful to understand its relationship to money-metric measures.

Food security covers several dimensions: (1) availability (is food available in a certain location?), (2) access (can a household access adequate food, given prices, incomes, and its access to formal and informal social assistance?), and (3) utilization (can individuals and households make good use of the food to which they have access?). These dimensions matter in a cumulative manner: if food is not available in an area, a household or individual will not have an adequate diet. But even when food is available, households must be able to afford to buy sufficient food. Even then, do they choose a nutritious diet or not? Do they prepare it properly to deliver its full nutritional value? Are they healthy enough to metabolize and absorb the nutrients? See Barrett (2010) for further discussion.

How do food security measures relate to monetary poverty measures? At extreme levels of poverty, monetary poverty and food insecurity are likely to be highly correlated, as affording a minimum number of calories is the first objective households and individuals will satisfy.

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Box 3.2 (continued)

This correlation is not perfect, in that a very poor household may receive social or humanitarian assistance and thus be in monetary poverty but not food insecure. As incomes increase, there are competing uses for the new income. It could go to more calories, more nutritious calories (for example, protein and micronutrients), or tastier calories, but it could also go to rent, utilities, health care, education, clothing, and other essentials. The important difference is that food security is just one (albeit important) dimension of household welfare, whereas monetary welfare (even without examining nonmonetary poverty) reflects the (in)ability to afford multiple dimensions of well-being. In addition, an increase in food prices might not result in a reduction in calories if households spend more to eat, but this results in a drop in nonfood consumption (and therefore welfare); in this case, food security measures will not pick up but monetary poverty will (in theory).

Food security and monetary poverty measures may differ for other reasons. For example, as Headey and Ecker (2012) observe for Ethiopia, the urban-rural gap in monetary poverty can be much smaller than for nutrition-based measures (such as child stunting), reflecting the difficulties of pricing subsistence consumption across geographies, unobserved seasonal shortfalls, and access to the quality essential services needed for nutrition, all of which are not well-captured in monetary poverty measures. Thus, monetary poverty is not necessarily the best proxy for food security. Headey and Ecker (2012) conclude that dietary diversity measures are the best food security indicators, both in times of stability and in times of shock, with monetary poverty measures second, ahead of other food security measures. Consequently, food security measures are best used when food security is the program or policy objective, while monetary poverty measures are best used when a broader welfare definition is being considered. This conclusion is reinforced by evidence from Jensen and Miller (2011), who find that in-kind food assistance or food subsidies have limited impact on food consumption (and thus food security) as households substitute their own spending away from food to nonfood essentials, behavior that is captured under monetary poverty measures.

This book focuses on the monetary dimension of welfare because it has greater implications for targeting. To determine the type of program and targeting objectives needed for a given dimension of multidimensional poverty, such as a child not being in school, means assessing why the child is not in school. Is this for financial reasons (cost of tuition and books, opportunity cost of working, and so forth)—in which case, a monetary poverty–targeted program might make sense—or for availability reasons

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(there is no school nearby, the road to the school is impassable, or the teacher is often absent)—in which case a geographic or facility-targeted program might make sense. However, in this context, this book focuses more on methods to assess monetary poverty, since in many places nonmonetary dimensions of poverty are directly observable while the monetary dimension is less so (and measurable food security indicators are well-established). This is not to discount the importance of also measuring and targeting other dimensions.

Even a monetary measure of welfare is not a straightforward concept in practice. Basic welfare economics starts with the notion that consumption is what provides utility (happiness). Consumption equals income plus any change in assets (net wealth), and these are concepts used widely in both the analytics of poverty and eligibility determination in social assistance practice. Consumption is expected to be somewhat less variable than income, buffered precisely by a change in assets—the proverbial savings for a rainy day, although of course assets can take forms much more varied than changes in cash savings and include the value of insurance, whether publicly provided or market based.

Measuring income, consumption, or assets is a somewhat inexact art, with all sorts of data issues (see, for example, Mancini and Vecchi [2020] for a recent update on the classic paper by Deaton and Zaidi [2002]). Indeed, the practicalities of what is measurable often trump the conceptual discussions of what it is desirable to measure, a topic taken up in chapter 6 in much more detail. This chapter describes some of the challenges in measuring these three concepts.

The body of survey work and poverty diagnostics contains much discussion about which measure of welfare to use, for which purpose, and when and operates with some “stylized facts.” Each of these facts is intuitive and backed by survey evidence.15 Similar issues are pertinent for direct eligibility determination processes since they are often quite like abbreviated household surveys, whether done in the applicant’s dwelling or an office setting.

Income • Income may be easily measured for households with a regular, stable payment (whether in cash, check, or e-transfer) from a factory or firm.

This type of income is a number that people are likely to know and does not vary from payment to payment. It may be slightly more difficult to measure income for the increasing share of formal sector service workers working part time with variable hours per week, although the numbers will be salient enough that over short periods, a worker may recall them, and they may have to do the accounting to figure out an annual tax statement and thus be exposed in a salient way to that figure.

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• It can be difficult to measure income when it comes largely from smallholder agriculture or informal home enterprises. It is likely that business accounts are intertwined with household spending, there is no written accounting, and revenue and expense streams are highly variable from day to day or month to month. • Thus, income is easy to measure in the same places it may be easy to verify from paystubs or tax payments. In more informal settings, it is difficult for the household to put a number on income and for administrators to verify it.

Consumption • Consumption may be easier to measure where it is focused on core food goods and a few other essentials, like water, fuel, rent, and purchases that are salient due to the difficulty in making ends meet. Consumption can be difficult to measure where it is spread across a wide variety of goods and high enough that each individual purchase may not be very salient to the purchaser.

Assets • Assets may be hard to measure for several reasons as valuation of holdings may be difficult, especially when the asset portfolio of a household is diversified and/or when markets are thin. • Financial assets, like money in stocks or savings accounts, may only apply to better-off households and can be even harder to ask about than income or consumption because people do not wish to reveal their wealth. • Debt balances (credit card debt or debt to moneylenders) may also be hard to ask about, for reasons of stigma. • It may be easier to get people to report that they own land or buildings or large durable goods like cars or machinery that are sources of pride and already probably known to their neighbors, but those assets may be hard to value. • Livestock is an important asset for the poor in many countries, but it is changeable in number (through the births, slaughter, or other deaths of the animals) and subject to unit price fluctuations. • For poor people, it may be difficult to measure some important assets because their values are so small that they may be classified as consumption items in surveys. Examples include stocking up on foodstuffs or purchases of small implements or inputs for the household enterprise, such as a new pick for a vegetable garden or a supply of fabric for a dressmaker.

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Poverty Assessments and Eligibility Determination Mechanisms In poverty assessments, income and consumption are commonly employed welfare measures used to classify households from the poorest to the richest. The differences in economic patterns and ease of measurement of household welfare just discussed have meant that income has tended to be used more in household surveys and distributional studies in richer countries. Consumption tends to be the core welfare measure in poorer countries, although the pattern is not absolute.

For eligibility determination, there is some flexibility in measuring welfare. Means testing focuses on income or some combination of income and assets. Inferential methods like hybrid means testing, proxy means testing, or small area poverty mapping associate observables with income or consumption.

Assets play a role in several kinds of eligibility determination mechanisms but in different ways. In means testing, holding assets of certain types or above a threshold value is often used to exclude households from eligibility (“affluence testing”). In hybrid means testing, the flow of consumption from productive assets may be imputed and that value added to more directly measured flows of income from wages and transfers. In proxy means testing, assets are used to help predict consumption or income, but final eligibility is determined based on the prediction only. In programs geared to respond to natural disasters, change in assets may trigger eligibility.

Where Should Eligibility Thresholds Be Set?

It is well acknowledged in the poverty literature that there is not an absolutely clear or unique place to draw the poverty line. Welfare distributions are continuous and often relatively flat over several deciles and/or there may be a sizable share of households just above the official poverty line who are not much better off than those just below it. The case of Indonesia illustrates this phenomenon (figure 3.2). While 10 percent of Indonesians were poor in 2018 according to the local poverty line, a further 18 percent of the population lived just above the poverty line but by less than 50 percent more.16

In part, poverty analysis deals with the problem of where to draw the poverty line by thoughtful grounding for an initial choice of lines, often by using a variety of poverty lines and careful interpretation. Usually, the authorities take due care in setting the initial anchoring of the basic poverty line, often in the cost of a food basket calculated to represent the expense of a low-cost, culturally acceptable, and nutritionally adequate food basket. This may be used as an “extreme” poverty line or “food” poverty line. The main or national poverty line that is often drawn tops up the basic poverty

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Figure 3.2 Indonesia: Poverty and Near-Poverty

16,000,000

14,000,000

12,000,000

9.8%

below poverty line

Individuals

10,000,000

8,000,000

6,000,000

4,000,000

28%

below 1.5 x poverty line

74%

below 3.5 x poverty line

2,000,000

0

807,678

1,615,355

Monthly household per capita consumption (Rupiah)

Source: Indonesia, National Social Economic Survey (SUSENAS) 2018.

2,423,033

line with some sort of calculation or allowance for nonfood expenditures. Often this is done by examining the nonfood expenditures of a “reference group” or households living around the estimated line. While the procedure is, roughtly speaking, intuitive, it involves a host of details: Which foods in which combination? Which nutritional requirements to take into account? Only calories? Proteins? How many of a long list of micronutrients? Which vector of foods supplies those at low cost, which may differ in different agroclimatic zones? How much allowance should be made for flavor and culture over just nutrients? How to allow for nonfood expenditures—with a similar list of necessary goods and prices? If so, which goods? Or with a “share of expenditure” calculation? If so, which calculation? None of these questions has absolutely clear answers, much less answers that are precisely comparable across countries or time. Trying to answer them all results in widespread acceptance that there is a measure of arbitrariness in whatever poverty line is picked (see Haughton and Khandker 2009; Ravallion 1998; World Bank 2018a).17 However, despite the fact that the construction of a national poverty line is part science and part art, it nonetheless represents a country’s stated goal for the minimum standard of living to which it aspires for all its residents. As such, the national poverty line provides an anchor for determining the coverage of different social protection programs and against which eligibility can be assessed.

It is similarly common to use multiple thresholds for different programs in the social protection system. This reduces a bit the problem that there is no clear or unique place where people start/stop needing support.

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Through reducing benefits from individual programs as welfare rises or layering multiple programs with different thresholds, it is possible to minimize stark cliffs in benefits. The use of multiple programs that are thoughtfully layered or segued together is common. This approach is desirable as families and individuals in need must have access to a packet of services to help them prosper enough that they are not economically vulnerable. In most countries that have moderate or high coverage for the overall social protection system, such layering of different services and programs is available to different segments of the population. For example, the nonpoor may receive assistance with natural disasters, catastrophic health insurance, or unemployment insurance. Those vulnerable to poverty may benefit from those programs plus seasonal assistance for utilities in cold climates or rainfall insurance for farmers. The chronic poor may receive all these plus basic income support to help provide for basic needs. Use of benefits differentiated by welfare level is somewhat less common. Of course, they are inherent in guaranteed minimum income programs and found sometimes in block rather than continuous form in the degree of subsidy for health insurance or utility price subsidies; they are less frequent in other forms of simple cash transfers.

The operational thresholds for individual programs may be set higher than the conceptual target group to ensure that errors of exclusion in the conceptual target group are reduced, although of course with an increased budget requirement. Errors of exclusion are always of concern, and simulations and targeting assessments usually show that those related to mis-measurement are particularly dense closer to the eligibility threshold.18 Setting the threshold on the high side can help ameliorate the problem.

Figure 3.3 illustrates the results of simulations that were done to inform the government of an upper-middle-income country as it was considering compensation for a reduction in energy subsidies. The government’s goal was to protect the poorest 30 percent of the population from the average income loss resulting from higher energy bills. The government was considering a hybrid means test to determine eligibility for the program. Simulations of such a scenario showed that with a threshold set at the third decile, a program could cover three-quarters of the group, but a quarter would be excluded. Then the analysts ran a simulation using a threshold covering 50 percent of the population. In this variant, coverage among the bottom 30 percent increased to 88 percent of the group. Of course, the cost increased as well in proportion to the larger coverage, by 67 percent. Therefore, an intermediate variant was considered that drew the threshold at the fifth quintile but tapered benefits to those in the fourth and fifth quintiles. This scenario maintains the higher coverage of the population of interest but reduces the extra costs to about 25 percent. Chapter 5 presents in detail the steps in setting up and interpreting such simulations, which