metadata: authors: 0: author: Headey, Derek D.
count: World,India
docna: 0: docna: The impact of the global food crisis on
            self-assessed food security
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Industrial Economics,Nutrition,Food Security,Economic Growth,Inflation,Inequality
teratopic: Agriculture,Industry,Health, Nutrition and Population,Poverty Reduction
abstracts: cdata!: The paper provides the first large-scale
            survey-based evidence on the impact of the global food
            crisis of 2007-08 using an indicator of self-assessed food
            security from the Gallup World Poll. For the sampled
            countries as a whole, this subjective indicator of food
            security remained the same or even improved, seemingly owing
            to a combination of strong economic growth and limited food
            inflation in some of the most populous countries,
            particularly India. However, these favorable global trends
            mask divergent trends at the national and regional levels,
            with a number of countries reporting substantial
            deterioration in food security. The impacts of the global
            crisis therefore appear to be highly context specific.
geo_reg_key: 80475,80451,80470
repnme: repnme: The impact of the global food crisis on
            self-assessed food security
display_title: The impact of the global food crisis
            on self-assessed food security
listing_relative_url: /research/2013/01/17195240/impact-global-food-crisis-self-assessed-food-security
topicv3: Economic Growth m1326241 471,Food Security m1326439 199,Poverty m1326687 695,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Malnutrition m1627088 4737
publishtoextweb_dt: 2013-01-01T00:00:00Z
historic_topic: Agriculture,Industry,Health, Nutrition and Population,Poverty Reduction
no_of_pages: 71
model_extractions: WPS6329 Policy Research Working Paper 6329 The Impact of the Global Food Crisis on Self-Assessed Food Security _Derek D. Headey_ The World Bank Development Economics Vice Presidency Partnerships, Capacity Building Unit January 2013
Policy Research Working Paper 6329

# **Abstract**

The paper provides the first large-scale survey-based evidence on the impact of the global food crisis of 2007–08 using an indicator of self-assessed food security from the Gallup World Poll. For the sampled countries as a whole, this subjective indicator of food security remained the same or even improved, seemingly owing to a combination of strong economic growth and limited food inflation in some of the most populous countries, particularly India. However, these favorable global trends mask divergent trends at the national and regional levels, with a number of countries reporting substantial deterioration in food security. The impacts of the global crisis therefore appear to be highly context specific.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at D.Headey@cgiar.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Impact of the Global Food Crisis on Self-Assessed Food Security**

_By_ DEREK D. HEADEY<sup>1</sup>

**JEL Codes:** I32; O11.

**Keywords:** Food crisis, food security, poverty, subjective indicators.

**Sector Board:** Poverty Reduction (POV)

1 Derek Headey, Research Fellow, International Food Policy Research Institute, PO Box 5689, Addis Ababa, Ethiopia. <u>D.Headey@cgiar.org. The author particularly wishes to thank Angus Deaton for the introduction to the GWP data as well as very</u> detailed comments on an early draft. Thanks also to Gallup staff for answering a number of questions and to Shahla Shapouri of the USDA for providing comments and answering questions regarding the USDA model. John Hoddinott, Olivier Ecker, Paul Dorosh, Bart Minten, Maggie McMillan, Maximo Torero, and Shenggen Fan contributed useful comments and suggestions. Participants at various seminars at the FAO and IFPRI provided insightful comments. The author also thanks USAID for financial support and Yetnayet Begashaw, Teferi Mequaninte, and Sangeetha Malaiyandi for excellent research assistance. Any errors are the author’s own.
The global food crisis of 2007–08 involved approximately a doubling of international wheat and maize prices in the space of two years and a tripling of international rice prices in the space of just a few months. Understandably, such rapid increases in the international prices of staple foods have raised concerns about the impact on the world’s poor. Household surveys suggest that most poor people earn significant shares of their incomes from agriculture but are nevertheless often net food consumers (World Bank 2008b). Consistent with this stylized fact, several multicountry World Bank simulation studies find that poverty typically increases when food prices increase (holding all else equal), with much of the increase in poverty taking place in poorer rural areas (Ivanic and Martin 2008; de Hoyos and Medvedev 2009; Ivanic, Martin and Zaman 2011). Likewise, the U.S. Department of Agriculture’s (USDA 2009) simulation found that approximately 75–80 million people went hungry during the 2008 food crisis, a number that the Food and Agriculture Organization (FOA) of the UN (FAO 2009) applied to its precrisis baseline numbers in the absence of an FAO model that could simulate a food price shock.<sup>2</sup> Subsequent USDA simulations were used by the FAO to estimate that over one billion people went hungry in 2009, up from 873 million in 2005–06.<sup>3</sup> 2 Some basic problems with the FAO model are reviewed in Headey (2011a) and FAO (2002). In the 2008 crisis, the FAO had an underlying model that only incorporated quantities, not prices, so the FAO’s capacity to simulate the effects of food price increases was very limited. Therefore, the FAO relied on a USDA trade model (USDA 2009). A major shortcoming of the USDA model was that it did not include middle-income countries, including large ones such as China, Mexico, and Brazil. Headey (2011a) also shows that the USDA (2009) estimates are contradicted by the USDA’s own historical production and import estimates for 2007–08 (USDA 2011).

3 In addition to the two basic approaches described above (the World Bank poverty simulations and the FAO/USDA hunger simulations), several authors have taken mixed approaches to estimate calorie availability trends, including Anriquez et al. (2010) and Tiwari and Zaman (2010). Dessus et al. (2008) adopt the net benefit ratio approach, but only for urban areas. There are also

> many country-specific simulation exercises; a particularly good one is Arndt et al. (2008). See Headey (2011a) for a more

> extensive overview and critique.

2
These studies have led some observers to conclude that global poverty or hunger increased during the 2008 food crisis. Fundamentally, however, most of the simulation studies cited above aim to predict and understand the impacts of higher relative food prices, holding all else equal. The use of this kind of partial simulation approach is justifiable on several grounds. First, partial simulations have an advantage in being able to produce very timely ex ante estimates of what might happen if food prices increase. Second, more sophisticated approaches (Ivanic and Martin 2008; de Hoyos and Medvedev 2009; Ivanic, Martin, and Zaman 2011) are useful for identifying the mechanisms by which higher food prices could influence poverty and the distributional consequences of food price changes. In that sense, they are certainly policy relevant. Third, these approaches provide the scope to explore the sensitivity of results to alternative assumptions.

However, the use of partial approaches to infer actual changes in global poverty is inappropriate because there are many ways their predictions might not eventuate. For example, several simulation studies assumed rates of international price transmission to domestic markets rather than using observed price changes (e.g., Ivanic and Martin 2008). There is also the poorly informed question of whether wages (rural and urban) might adjust to higher food prices, with some evidence suggesting that agricultural wages might adjust even in the short run (Lasco et al. 2008). More generally, strong income or wage growth (even without “adjustment”) may have buffered any negative impacts of higher prices in the 2000s, as Mason et al. (2011) observed in urban Kenya and Zambia. More ambiguously, households could mitigate the worst forms of hunger or poverty through any number of coping mechanisms, such as reducing dietary quality, selling assets, working longer hours, or reducing nonfood expenditures.<sup>4</sup>

> 4 Inevitably, measurement and estimation issues constrain these studies. Headey and Fan (2010) and Headey (2011a) provide an overview of some measurement and estimation issues (see also footnote 2). Of course, measurement issues also apply to the data used in this study (see section 2).

3
Because of these complexities, this article takes a different route by providing the first ex post analysis of survey data collected before, during and shortly after the 2008 food crisis across a large number of countries. Specifically, we examine the results from an indicator of self-assessed problems affording sufficient amounts of food, which was collected as part of the Gallup World Poll (GWP). Although subjective data certainly have shortcomings (an issue we discuss in detail below), their advantage in this context is that they are substantially cheaper to collect relative to the more objective monetary or anthropometric indicators found in standard household welfare surveys. Hence, the country and time coverage of the GWP surveys is their primary advantage. Specifically, the GWP surveys allow us to examine self-assessed food insecurity trends in 69 low- and middle-income countries, of which China is the most prominent exclusion. This substantial cross-country coverage also allows us to test whether changes in this indicator are explained by variations in food inflation and economic growth.

The basic conclusion from the Gallup data is that at the peak of the crisis (2008), global food insecurity was either not higher or even substantially lower than it was before the crisis. The raw results for the 69 countries for which we have precrisis (2005–06) and mid-crisis (2008) data suggest that 132 million people became more food secure. If 2007 is used as the “precrisis” benchmark, the picture is more neutral because self-assessed food insecurity was essentially unchanged between 2007 and 2008. However, these surprisingly optimistic global trends mask large regional variations. Global trends are clearly driven by declining food insecurity in India and several other large developing countries. However, on average, self-assessed food insecurity increased in many African countries and most Latin American countries. It decreased somewhat in Eastern Europe and Central Asia, but it probably rose in the Middle East (for which the GWP 4
sample is very small). In the average Asian country, there was basically no change, although we again observe variations around the mean.

Because this article introduces a new method for gauging trends in global food security, it is especially important to investigate the reliability of the Gallup indicator and to understand the factors that might explain these somewhat surprising results. In the analysis below, we note some of the general shortcomings of subjective indicators, which are now widely used in the contexts of general well-being (e.g., Headey et al. 2010; Kahneman and Deaton 2010; Deaton 2010; 2011), poverty (Ravallion 2012), and food security (Deitchler et al. 2011), as well as some specific problems with the Gallup indicator. We also conduct econometric tests to determine whether the observed trends in self-assessed food security are plausibly explained by changes in per capita GDP and various food price indices. As expected, we find that real economic growth improves self-assessed food security. Real GDP growth already controls for aggregate price changes. We also find some additional effects of aggregate inflation, but we find no significant additional effect of relative food price changes (i.e., changes in the food terms of trade). We also show that in many of the largest developing economies (i.e., those with the largest poor populations), nominal economic growth generally outpaced food inflation, even in 2008. Hence, it appears that strong real income growth has largely offset the adverse impacts of food inflation in many developing countries, including those with the largest poor populations.

# **<<A>>II. AN OVERVIEW OF THE GALLUP WORLD POLL FOOD INSECURITY INDICATOR**

In this section, we provide an overview of the GWP and the specific food security indicator used in this study. Our goal is limited to answering three questions. First, what is the general quality of the GWP surveys? Second, what limitations might the GWP indicator of self-assessed 5
food insecurity have? Third, do basic cross-country patterns in this indicator align with expectations? Because the GWP is conducted by a private organization and its collaborators, much of the description of the formal survey characteristics relies on Gallup materials. We explore correlations between the GWP indicator and non-GWP welfare indicators by conducting a correlation analysis of a cross-section of countries and, in the next section, a multivariate analysis of the full panel dataset.<sup>5</sup>

# **<<B>>** _General characteristics of the Gallup World Poll_

Since 2005–06, the GWP has interviewed households in approximately 150 countries, although not always annually. Most questions are constructed to have yes or no answers to minimize translation errors. In developing countries, all but one of the GWP surveys are conducted face to face (China 2009 is the exception), and most take approximately one hour to complete. The surveys follow a complex design and employ probability-based samples intended to be nationally representative of the entire resident civilian noninstitutionalized population aged 15 years and older. In the first stage of sampling, primary sampling units consisting of clusters of households are stratified by population size, geography, or both, with clustering achieved through one or more stages of sampling. When population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Gallup typically surveys 1,000 individuals in each country, except in larger countries such as India (roughly 6,000), China (4,000), and Russia (3,000). In the second stage, random route procedures are used to select sampled households within a primary sampling unit, and Kish grids are used to select respondents within households. Finally, the data are internally assessed for consistency and validity and then centrally aggregated and cleaned. Data weighting

> 5 Much of what follows is drawn directly from the Gallup _Worldwide Research Methodology_ (Gallup 2010a). The present author purchased country-level data directly from Gallup and corresponded with senior Gallup staff about specific questions.

6
is used to ensure a nationally representative sample for each country, with oversampling corrected accordingly.<sup>6</sup> This approach generates margins of error that are generally in the 3–4 percent range at the 95 percent confidence level, with a mean error margin of 3.3 percent.<sup>7</sup> Note, however, that because these surveys have a clustered sample design, the margin of error varies by question. It is therefore possible that the margin of error is greater for certain questions. We also note that the margins of error in China and India tend to be lower than the average (by 1.6 to 2.6 percentage points). However, in China in 2005–06, the food insecurity question followed some fairly detailed questions on income and welfare, which may have primed respondents to be more likely to answer “yes” to the food insecurity question. Although we were aware of this problem in China, there may be similar problems in other countries. It is certainly possible that the first wave of the GWP (2005–06) contains greater measurement error than subsequent waves because Gallup faced a steep learning curve in conducting such an ambitious global survey (we address this issue below in a sensitivity analysis).

# **<<B>>** _The Gallup World Poll question on food security_

Although these general characteristics of the GWP surveys are pertinent, we now turn to the specific question of interest, which is phrased as follows:

> 6 In a handful of cases, certain sections of the population are oversampled (see appendix S3). For example, urban areas were oversampled in Pakistan, Russia, and Ukraine in at least one year, and in the August–September 2009 survey in China, the provinces of Beijing, Shanghai, and Guangzhou were oversampled, possibly because of the unusual switch to telephone surveying. In other contexts, it appears that Gallup oversampled more educated groups (Senegal, Zambia), and in some developing countries, certain parts of the country were not sampled at all because of ongoing political instability or other accessibility problems.

> 7 Thus, if the survey were conducted 100 times using the same procedures, the “true value” around an assessed percentage of

> 50 would fall within the range of 46.7 percent to 53.3 percent in 95 out of 100 cases.

7
_“Have there been times in the past 12 months when you did not have enough money to buy the food that you or your family needed?”_ A simple yes or no answer is recorded. For simplification, we refer to this as the “food insecurity” indicator rather than a more cumbersome term such as “unaffordability of food.”<sup>8</sup> What are some of the strengths and weaknesses of this question? The strengths include a focus on access rather than availability, a recall period (12 months) capable of capturing seasonality and other short-run food price movements, and large cross-country and multiyear coverage. This last strength is a significant advantage in the absence of more regular economic or nutritional surveys, but there are also limitations with subjective data. Unlike simulation approaches, for example, subjective data do not provide much information about the mechanisms or magnitudes of welfare impacts. However, there are some indications that the simple yes/no indicator used here may not lead to much loss of information in practice. The GWP has data for Africa in which a similar question is asked that allows for five different answers based on the frequency of deprivation. Those data show a similar trend to the dichotomous indicator (see fig. S.1 in the supplemental online appendix, available at http://wber.oxfordjournals.org/).

A more significant problem is that the definition of food needs is not universal. For a well-off or well-educated family accustomed to a high-quality diet, “food” may mean a food bundle of

> 8 We note that there are other welfare indicators measured by Gallup, including a question pertaining to hunger rather than food affordability as well as a general life satisfaction question (scaled from one to ten). In earlier versions of this paper, we considered the hunger variable, but the sample size for that indicator was much smaller, and trends in that variable could not be significantly explained by economic growth or food inflation. The life satisfaction question was not explored because it is not obvious that changes in this indicator over 2006–08 would be substantially related to food inflation. Even so, that indicator generally suggests sizeable improvements in well-being in developing countries, with only a handful of exceptions (Pakistan, Sierra Leone, Egypt, and Afghanistan). Hence, we concentrate on the more relevant food insecurity question.

8
sufficiently high quality (e.g., meat, eggs, dairy). For a very poor family, however, “food” may just mean enough cereals or other staple foods. Hence, it is possible that the food insecurity measure is biased upward by education or income or downward by overly low standards of food intake. There is some indication of such biases in the data, although formal tests of the presence of biases proved to be inconclusive (Headey 2011a). For example, there is surprisingly high selfassessed food insecurity in developing countries with relatively high levels of education/literacy, such as the former Soviet Bloc countries and Sri Lanka (see the online supplemental appendix S2 for individual country-year observations). At the other extreme, food insecurity appears too low in several countries where we know that undernutrition is quite prevalent. In Ethiopia, for example, where diets are very monotonous and undernutrition is very high, self-assessed food insecurity was just 14 percent in 2006 (although it subsequently rose rapidly). However, in crosscountry regressions, we did not find an impact of education on food insecurity after controlling for income (see Headey 2011a). There are no indications that large numbers of poor countries systematically underreport food insecurity.

To illustrate this issue, table 1 reports regional means (the full Gallup data are presented in appendix S2). At the bottom of table 1, we observe that the mean “global” prevalence of households reporting problems with affording food is almost 32 percent. As expected, however, there are large variations around the world, with some countries reporting almost no food insecurity and others reporting that 80 percent of households had problems affording food. For the most part, the pattern across continents is plausible. Food insecurity is highest in sub-Saharan Africa, which is by far the poorest region in the world in monetary terms. Food insecurity in South Asia is higher than in East Asia, as expected, but only when two large outliers, Nepal and 9
Cambodia, are excluded.<sup>9</sup> In Latin America, food insecurity is surprisingly high (34 percent).

This may relate to the greater prevalence of urban poverty and of relatively poor net food consumers, although this is only a speculation.

# **Table 1. Regional Unweighted Means for the Two GWP Measures, Circa 2005, for Developing Countries Only (Percent)**

||**Food i**<br>|**nsecurity**<br>|
|---|---|---|
||**Mean**|**No. of obs.**|
|sub-Saharan Africa|58.3|27|
|South Asia*|31.2|5|
|East Asia*|24.0|6|
|Middle East & North Africa|26.5|2|
|Central America & Caribbean|34.7|9|
|South America|36.0|10|
|Transition<sup>a</sup>countries|29.1|23|
|OECD<sup>b</sup>|8.3|22|
|Low income<sup>c</sup>|48.6|49|
|Middle income<sup>c</sup>|29.6|28|
|Upper income<sup>c</sup>|11.0|34|
|Mean, total sample|31.7|433|

_Source_ : Data are from the GWP (Gallup 2011).

_Note_ : *Note that two outliers are excluded. Nepal is excluded from the South Asia results, and Cambodia is excluded from the East Asia results. In the case of Nepal, its food insecurity score is much lower than that of the other South Asian countries, whereas Cambodia’s is much higher. With the inclusion of these two outliers, the food insecurity scores for South Asia and East Asia are roughly equal at 31 percent.<sup>a</sup> _Transition_ refers to former Communist countries.<sup>b</sup> Members of Organization for Economic Co-operation and Development.<sup>c</sup> . _Low income_ is defined as a 2005 GDP per capita of less than USD 5,000 purchasing power parity; _middle income_ , as USD 5,000–13,000; and _upper income_ , as greater than USD 13,000.

The data also suggest a strong income gradient for food insecurity. Low-income countries have food insecurity rates that are 17 percentage points higher than middle-income countries, and the same difference is observed between middle- and upper-income countries. In terms of correlations with other welfare indicators (table 2), there is some support that cross-country patterns impart meaningful information. Of course, extremely high correlations are not necessarily expected given the well-known problems associated with measuring hunger and

> 9 Self-assessed food insecurity in Cambodia is unusually high (67 percent), but in Nepal, it is extremely low (9 percent).

> Including these two countries leaves the South and East Asian means roughly equal, at 31 percent.

10
poverty<sup>10</sup> and the fact that anthropometric indicators are heavily influenced by nonfood factors, such as health, education, family planning, and cultural norms. Bearing this in mind, we find that GDP per capita, mean household income, poverty rates, hunger rates, and anthropometric indicators are significantly correlated with the two GWP indicators, almost invariably at the onepercent level (table 2). The correlations are particularly strong for the (logarithmic) income and poverty indicators. In a very small sample—which excludes six important outliers—the correlation between the GWP indicators and the body mass index (BMI) of adult women is also very high (0.68). Table S1.1 in the appendix presents the full correlation matrix among the variables. It shows that the correlations between the GWP measure and the various benchmarks are at least as strong as the benchmark correlations for the FAO hunger measure and the World Bank poverty measure, if not stronger.

> 10 Indeed, in the context of critiquing standard poverty measures, Deaton (2010) suggested that the Gallup indicators used in this study might be more reliable than the World Bank poverty estimates. As a rough demonstration of their suitability, Deaton showed that the food security variable is highly correlated with GDP per capita.

11
**Table 2. Correlations between the Self-Reported Food Security Indicator and Other Indicators of Income, Poverty, Hunger, and Malnutrition, Circa 2005**

|**Alternative poverty/hunger indicator**||**Self-reported**|
|---|---|---|
|**(source)**||**hunger**|
|GDP per capita, purchasing power parity, log|Correlation|−0.71***|
|(World Bank)|No. of obs.|44|
|Household income per capita, USD, log|Correlation|−0.68***|
|(World Bank Povcal)|No. of obs.|59|
|Prevalence of hunger|Correlation|0.58***|
|(FAO)|No. of obs.|62|
|Prevalence of poverty, USD 1/day|Correlation|0.77***|
|(World Bank Povcal)|No. of obs.|58|
|Prevalence of poverty, USD 2/day|Correlation|0.67***|
|(World Bank Povcal)|No. of obs.|49|
|Prevalence of low-BMI women, excluding outliers|Correlation|0.73***|
|(DHS & WHO)|No. of obs.|17|
|Prevalence of underweight preschoolers, log|Correlation|0.55***|
|(DHS & WHO)|No. of obs.|45|
|Prevalence of stunted preschoolers, log|Correlation|0.48***|
|(DHS & WHO)|No. of obs.|45|

_Source_ : Dependent variable is from the GWP (Gallup 2011) . The sources of the independent variables are as follows: _World Bank_ , World Bank (2010b) _WDI_ ; _World Bank Povcal_ , World Bank (2010a); _FAO_ ; Food and Agriculture Organization (2011); _DHS_ ; Demographic Health Surveys (2010); _WHO_ , World Health Organization (2010).

_Note_ : *, **, and *** indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively. All variables are measured in 2005 or the nearest available year. _Log_ indicates that variable is expressed in logarithms to account for a nonlinear relationship. _Excluding outliers_ refers to the exclusion of six countries with the highest prevalence of low-BMI women in the sample, all above 20 percent: India, Bangladesh, Ethiopia, Cambodia, Nepal, and Madagascar. Without this exclusion, the correlation is statistically insignificant. Samples vary in size because of the paucity of some of the poverty and malnutrition indicators.

In table 3, we also show that the GWP food insecurity indicator is significantly explained by “relative food prices,” which is measured as the ratio of the purchasing power parity for food items to the exchange rate (both measured in 2005). This index can be interpreted as the extent to which a country’s food basket is expensive or cheap relative to the costs of importing food 12
(values of more than 100 imply that food is relatively expensive, whereas values of less than 100 imply that food is relatively cheap). However, because of Balassa-Samuelson effects, this indicator is likely to be higher in richer countries than in poorer countries. Hence, we use multivariate regressions to control for GDP per capita. However, even after controlling for GDP per capita, there are still substantial variations in food prices across developing countries (as the continent dummies in regression 1 suggest), which could be explained by transport costs, variations in agricultural productivity, the limited tradability of food (partly due to tastes), or even exchange rate distortions. Indeed, regression 2 suggests that variation in “relative food prices” across countries significantly explains variations in self-assessed food security after controlling for GDP per capita. However, the relationship is nonlinear: at low levels of food prices, the marginal effects of higher prices are quite large, but at the highest observed levels of relative food prices, the marginal effects are insignificantly different from zero. A caveat is that the result of regression 2 in table 3 is not very robust to the inclusion of continental dummies (introduced in regression 3), particularly the dummy for sub-Saharan Africa. This lack of robustness appears to be because relative food prices and self-assessed food insecurity are both very high in Africa.<sup>11</sup> Specifically, the inclusion of continent dummies results in the food price coefficients no longer being significant at the 10 percent level, although this insignificance also applies to the continent dummy coefficients, suggesting that multicollinearity is an issue.

> 11 An issue here is that food prices may be higher in Africa because of the way in which the 2005 round of the International Comparison Program was conducted on a continental basis. Specifically, it is possible that food prices in Africa are biased upward by methodological issues, although it is difficult to substantiate such a claim. A more general problem with purchasing power parities is the challenge of finding common items to compare across countries. Exchange rate distortions may be

> problematic for this index, although data on black market premia on exchange rates suggest that most exchange rate distortions have declined markedly over time.

13
**<u>Table 3. Whether Self-Assessed is Food Security Explained by Relative Food Prices</u>**

|Regression No.|1|2|3|
|---|---|---|---|
||Food price|Food|Food|
|Dependent variable|level|insecurity|insecurity|
|No. ofobservations|99|91|91|
|Constant|61.74***|17.0**|31.1**|
|GDP per capita ($1,000s)|2.80***|−3.1***|−2.3***|
|GDP per capita, squared||0.04***|0.03***|
|Food price ratio||63.8***|48.7|
|Food price ratio, squared||−19.4***|−9.2|
|Africa dummy|30.4||18.6|
|Latin America dummy|−12.3||10.5|
|Asia dummy|5.0||4.6|
|Europe-plus dummy|−12.5||5.9|
|R-squared|0.65|0.73|0.76|
|Adjusted R-squared|0.63|0.72|0.75|

_Source_ : “Food insecurity” is from the GWP (Gallup 2011) and is described in the text. GDP per capita is from the World Bank (2010) and is measured in constant purchasing power parity dollars. “Relative food prices” are measured as the purchasing power parity of food and nonalcoholic beverages relative to the nominal exchange rate for the year 2005. Information is from the World Bank (2008b). _Note_ : *, **, and *** indicate significant at the 10 percent, 5 percent, and 1 percent levels, respectively. “Europe-plus” includes Eastern European countries plus North America and Australasia. Note that self-reported food insecurity data are measured in 2005 or 2006, whereas the food price ratio is measured in 2005.

Overall, the results reported above present a mixed picture of the validity of cross-country patterns in the Gallup data. On the one hand, there are certainly some worrying outliers in the GWP indicator (particularly in the 2005–06 round). On the other hand, the data as a whole are plausibly patterned across countries and strongly correlated with other welfare indicators and relative food prices. However, we acknowledge that many social scientists are wary of subjective 14
indicators of welfare, even if this skepticism has been moderated in recent decades. There is, of course, an immense body of economic literature that uses indicators of self-assessed well-being and health (e.g., Headey et al. 2010), including indicators collected by Gallup (Kahneman and Deaton 2010; Deaton 2010; 2011). On the positive front, comparisons of self-assessed poverty and objectively measured indicators of poverty have uncovered close relationships between the two (Ravallion 2012). A recent assessment of food insecurity questions in six developing countries also found that questions pertaining to more severe forms of deprivation were highly comparable across countries, although concepts related to anxiety and dietary quality were not (Deitchler et al. 2010). In addition, there are longstanding concerns that such measures are sensitive to framing, question ordering, and other response biases. In terms of the third item, there is an extensive body of literature that examines biases in self-reported indicators (see, e.g., Benitez-Silva et al. 2004; Krueger and Schkade 2008; Ravallion 2012). A specific concern in the context of food security is that respondents may believe that more negative answers increase their chances of accessing food or cash transfers. Many such biases may only exist at certain levels but disappear when trends in the data are observed. However, any changes in question ordering could bias results, as a recent paper by Deaton (2011) shows. Substantial measurement errors could also mean that subjective indicators perform adequately in the cross-section but poorly in first differences (Bertrand and Mullainathan 2001). Clearly, there are important reasons to explore the validity of trends in the Gallup indicator, not just at certain levels.

# **<<A>>III. EXPLAINING CHANGES IN SELF-ASSESSED FOOD INSECURITY**

In this section, we explore the validity of changes in self-assessed food insecurity at the national level by gauging whether trends in the GWP indicators are explained by changes in 15
disposable income. The underlying model for these regressions is that the prevalence of food insecurity (F) at time _t_ in country _i_ is a function of disposable income per capita, or nominal income per capita (Y), deflated by a relevant set of prices (P): Although intuitive in principle, in practice, disposable income at the national level is measured with considerable error for several reasons. First, income inequality means that GDP per capita may be a flawed indicator of the purchasing power of a poor or vulnerable household in a country (the same is true of GDP growth as an indicator of changes in welfare). Second, the price index (P) used to deflate GDP per capita (the GDP deflator) may not represent the consumption patterns of the food-insecure population because the budget share they allocate to food expenditures will typically be higher than the share employed in calculating the (consumer price index) CPI.

Because of these complications, it is not obvious that changes in real GDP per capita adequately capture trends in the purchasing power of the poor. Hence, in the regressions below, we estimate several different specifications. First, we vary the choice of price index used to deflate growth in per capita GDP (the GDP deflator, the total CPI, and the food CPI). Second, we test whether changes in the total CPI or changes in relative food prices (i.e., the food CPI over the nonfood CPI) provide some additional explanatory power. Third, we test whether these relationships vary over income levels, in accordance with Engel effects and the fact that welfare programs may play a larger role in determining food security in wealthier countries than economic growth. Finally, we add fixed effects to the specifications to partially control for unobservable factors, such as income inequality and social safety net.<sup>12</sup>

> 12 Although adding fixed effects would seem desirable in principle, the valid addition of fixed effects rests on the

16
In addition to these issues of specification, there are some measurement considerations. First, in our preferred regression models, we specify the dependent variables as the change in the prevalence of food insecurity across two successive periods. This approach is in contrast to most of the analogous growth-poverty literature, in which it is common to measure the dependent variable as a percentage change. However, taking the percentage changes of a prevalence rate can cause scaling problems and create outliers (Deaton 2006; Headey 2011c **[[There is no “Heady 2011c” listed in the references section. Do you mean 2011a or 2011b?]]** ).<sup>13</sup> The only significant advantage of using a percentage change is that it allows for the derivation of elasticities that can be directly compared to the literature that examines the impact of economic growth on poverty. Therefore, in some of our results, we also report these elasticities, although our preferred estimates focus on first differences.

A second issue pertains to measurement error in the Gallup data. Some apparent outliers are indicative of this measurement error. In figure 1, we consider potential outliers more systematically with scatter plots between changes in food security and various indicators of assumption that both right-hand side variables are strictly exogenous at all leads and lags, which is unlikely. Hence, we do not solely rely on the fixed effects estimator.

13 The problem with taking percent changes in prevalence rates can be illustrated with an example of a country with high food insecurity and a country with low food insecurity. In the food-insecure country, suppose that food insecurity decreases from 42 percent at time _t_ − 1 to 40 percent at time _t_ . This yields a first difference of two percentage points and a percent change of approximately −4.7 percent (that is, 2/40 × 100). However, an equally large reduction in malnutrition prevalence in the foodsecure country from 4 to 2 percent yields a percent change of 50 percent. Not only is a 50 percent change likely to be an outlier, but it is also 10 times the value of the equally large reduction in malnutrition in the high-malnutrition country. Of course, one could argue that this may not matter if percent differences are applied to the right-hand-side variables. In the case of per capita income, however, this is not true because the denominator (initial income) is invariably large enough to produce more meaningful estimates of percent change. Moreover, percent changes in income make sense if there is a diminishing marginal impact of income on food insecurity.

17
economic growth and price changes.<sup>14</sup> In all of the scatter plots, there are some potentially influential outliers, including Azerbaijan, Angola, and Venezuela, which are three oil producers, several Eastern European countries (Armenia, Latvia, Estonia, and Ukraine) and several African countries (Tanzania, Mali, and Malawi). Note that these outliers are sometimes driven by large changes in the dependent variable as well as by unusual economic growth or inflation rates. Measurement error is therefore a problem in both the left- and right-hand side variables.

To gauge the influence of outlying observations, we calculated _dfbetas_ (an indicator of the influence of outliers) and earmarked observations with _dfbetas_ greater than 0.2.<sup>15</sup> One option is to run regressions that exclude outliers, which we do in the case of fixed effects regressions. Another option is to use a robust regressor that downweights outlying observations without completely discounting them. Hence, we use both robust regressors and fixed effects estimates that exclude these outlying observations. Furthermore, we report ordinary least squares regressions in appendix S1, in which all outliers are included.

14 Note that in all our regressions, we exclude observations for Zimbabwe because of its hyperinflationary episode, which leaves the country as an enormous outlier on the food inflation-food insecurity relationship. 15 This cut-off is fairly conservative. The usual cut-off for this sample size, 2/sqrt(N), is equal to 0.12. We calculate these _dfbetas_ for various models and exclude a common set of outlying observations: Algeria, 2009; Angola, 2008; Armenia, 2007 and 2009; Azerbaijan, 2007; Botswana, 2008; China, 2008; Denmark 2007 and 2008, and 2009; Djibouti, 2009; Iraq, 2008 and 2009; Kenya, 2007; Kuwait, 2009 and 2010; Romania, 2007; Rwanda, 2009; Tanzania, 2008; Trinidad and Tobago, 2008; Vietnam,

> 2009; and Zimbabwe, all observations. A good explanation of _dfbetas_ can be found in _Stata Web Books: Regressions with Stata,_

> _Chapter 2 – Regression Diagnostics_ : http://128.97.141.26/stat/stata/webbooks/reg/chapter2/statareg2.htm

18
**Figure 1. Scatter plots of self-reported food insecurity, economic growth, and various inflation indicators.**

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Turning to some results, we begin with descriptive statistics and correlations for our dependent and independent variables (tables 4 and 5). Over the entire period, the mean change in the first difference of the food insecurity measure was close to zero (0.2), although the standard deviation and range of this variable is quite large. The statistics for the percentage change in food insecurity show a similar pattern and indicate the presence of some of the previously mentioned problems with the use of percentages of a prevalence variable. There is a tendency to inflate 19
small changes at lower levels of food insecurity due to the small base. Next, the three economic growth indicators show similar variation around the mean, but the relatively rapid rate of food inflation over this period means that the GDP growth deflated by the food CPI has a mean of only 0.4, whereas deflating by the GDP or CPI deflators results in means of 2.7 percent and 2.9 percent, respectively. Thus, food inflation typically exceeded nonfood inflation. Turning to table 5, it is noteworthy that the correlations among different price indices are quite large, as high as 0.82 in the case of the relationship between food inflation and total inflation. Table 5 also presents some bivariate evidence that changes in food security are significantly related to both economic growth and overall inflation but not to our estimates of relative food inflation.

**Table 4. Descriptive Statistics for Dependent and Independent Variables**

||**Count**|**Mean**|**Std. De.**|**Min.**|**Max.**|
|---|---|---|---|---|---|
|Change in food insecurity|296|0.2|7.2|−31.0|24.0|
|Percent change in food insecurity|290|4.9|31.6|−83.0|200.0|
|Economic growth (GDP deflator)|291|2.9|5.7|−17.6|32.1|
|Economic growth (CPI deflator)|276|2.7|8.4|−27.1|41.1|
|Economic growth (food CPI deflator)|271|0.4|8.4|−34.2|34.0|
|Total CPI inflation|276|8.6|7.5|−8.9|51.8|
|Food CPI inflation|276|10.9|9.6|−11.5|67.6|
|Nonfood CPI inflation|276|6.4|6.2|−9.5|34.4|
|Relative food inflation|276|4.4|7.7|−20.8|41.8|

_Source_ : Food insecurity is from the GWP (Gallup 2011 **)** . Economic growth data are from the IMF (2010), and all inflation data are from the ILO (ILO 2011).

_Note_ : All data are in percent or percentage points. Economic growth is reported with three different means of deflation: the GDP deflator, the CPI deflator, and the food CPI deflator. Relative food inflation is the change in the ratio of the food CPI to the nonfood CPI.

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**Table 5. Correlations between Changes in Food Insecurity and Various Explanatory Variables**

||Change in|Economic|Total|Food inflation|Nonfood|
|---|---|---|---|---|---|
||food insecurity|growth<sup>a</sup>|inflation||inflation|
|Economic growth<sup>a</sup>|−0.10**|||||
|Total inflation|0.18***|0.20***||||
|Food inflation|0.15***|0.19***|0.82***|||
|Nonfood inflation|0.19***|0.19***|0.71***|0.51***||
|Relative food inflation<sup>b</sup>|0.04|0.06|0.41***|0.76***|−0.17***|

_Note_ : *, **, and *** indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively.<sup>~~a~~</sup> Growth in GDP per capita deflated by the GDP deflator.<sup>b</sup> Changes in the ratio of the food CPI to the nonfood CPI.

Table 6 reports the results for the full sample of countries with first differences in food insecurity as the dependent variable and various indicators of economic growth as the sole explanatory variable. Regressions 1 through 3 report results from the analysis using the robust regressor, and regressions 4 through 6 report results from using the fixed effects estimator. The main finding from table 6 is that the economic growth coefficient is always highly negative, significant, and quite large in magnitude. In terms of the size of the coefficients, the point estimates suggest that doubling the GDP per capita would reduce the rate of food insecurity by 12 to 24 percentage points, depending on the estimator and the indicator of economic growth. In general, the fixed effects estimators produce larger estimates. When fixed effects are used and outliers are removed, the choice of deflator makes virtually no difference. In table 6, we report elasticities in addition to the first difference coefficients. The elasticities are quite large, varying from 0.47 to 1.25, and are commensurate in size to growth-poverty elasticities (for example, those reported in Christiaensen et al. 2011).

21
**Table 6. Regressions of Changes in Self-Reported Food Insecurity against Economic Growth**

|Regression No.|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Means of deflating|GDP|Total|Food|GDP|Total|Food|
|economic growth|deflator|CPI|CPI|deflator|CPI|CPI|
|Outliers removed?|No|No|No|Yes|Yes|Yes|
||Robust|Robust|Robust|Fixed|Fixed|Fixed|
|Regressor|regressor|regressor|regressor|effects|effects|effects|
|Economic growth Coefficients|−0.24***|−0.14***|−0.12***|−0.21***|−0.22***|−0.23***|
||(0.06)|(0.04)|(0.04)|(0.08)|(0.06)|(0.07)|
|Elasticities|−0.56**|−0.55***|−0.47**|−1.25**|−0.93***|−0.82***|
||(0.27)|(0.20)|(0.19)|(0.48)|(0.29)|(0.30)|
|No. of observations|291|275|271|271|256|252|
|No. of countries|120|112|111|113|106|105|
|R-squared|0.05|0.04|0.03|0.06|0.05|0.05|

_Source_ : Dependent variables are from the GWP (Gallup 2011 **)** . Economic growth data are from the IMF (2010), and food and total CPI data are from the ILO (ILO 2011). _Note_ : *, **, and *** indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively. Standard errors are reported in parentheses. The robust regressions are estimated using the _rreg_ command in stata, with default settings. For fixed effects regressions, standard errors are adjusted for country clusters. Outliers are identified based on _dfbetas_ greater than 0.20. _Economic growth_ is the percent change in GDP per capita between the two years in which the GWP surveys were conducted. Note that the robust regressor does calculate a pseudo R-squared, but it is generally regarded as inappropriate to report this value. Hence, the R-squared reported in this table is derived from an ordinary least squares regression that excludes outlying values.

In table 7, we run the same regressions with the addition of separate price change indicators to determine whether certain types of inflation have additional explanatory power over real economic growth rates. Specifically, we add inflation in the total CPI and food CPI relative to the nonfood CPI. The first represents an aggregate price effect, and the second represents a relative food price effect. Table 7 shows that overall inflation has a significant positive effect on 22
the prevalence of food insecurity. Again, the coefficient point estimates are larger in the fixed effects regressions (0.22 versus 0.11), but these marginal effects are relatively large for both estimators. Doubling the CPI, for example, is expected to increase the prevalence of food insecurity by 11 to 22 percentage points, holding real economic growth constant.

Somewhat surprisingly, the relative food inflation coefficients in table 7 are insignificant at the 10 percent level, but they are still positive (in one regression, the relative food inflation coefficient is significant at the 13 percent level). One explanation may be greater measurement error in relative food inflation because we were required to estimate nonfood inflation rates for approximately half of our sample.<sup>16</sup> Nevertheless, the fact that food inflation was the main driver of overall inflation over the period in question (food inflation explained almost 80 percent of variation in total inflation from 2006 to 2008 in developing countries) indirectly points to the generally adverse role of higher food prices on self-assessed food insecurity. Moreover, a significant additional effect of overall price inflation on food insecurity could be consistent with microeconomic theories of labor markets. Specifically, most poor people engaged in wage labor (i.e., those who are not self-employed, such as farmers) tend to work in labor markets that are characterized by substantial slack (unemployment or underemployment). If various food and nonfood prices increase, then the nominal wages of workers in such markets would not be expected to increase commensurately, leading to a fall in real incomes (Headey et al. 2012).

16 The reason for the larger error in the relative food inflation measure is that the ILO only reports the total CPI and the food CPI. Because relative food inflation is measured as changes in the ratio of the food CPI to the nonfood CPI, we had to derive the nonfood CPI from the total CPI, the food CPI, and the share of food in the total CPI. However, only approximately 50 percent of countries reported the food weight to the ILO, so we were required to estimate food CPI weights for the remaining countries using regressions against GDP per capita (i.e., Engel effects). This interpolation is the best we could do, but it may mean that relative food inflation is measured with sizeable error. That said, alternative indicators of relative food prices, such as the change in the food CPI minus the change in the total CPI, essentially yield the same insignificant results.

23
Hence, it is possible for nominal price increases to induce real wage declines, and there is significant evidence pointing to the adverse impact of inflation on poverty reduction (see Ferreira, Prennushi, and Ravallion 2000).<sup>17</sup> 17 Ferreira et al. (2000) write, “While changes in the relative short-term returns to holding bonds versus stocks may redistribute income only among the non-poor, there is one major asset-type impact which affects the poor: inflation. The rate of inflation is a tax on money holdings. Because there are barriers to entry in most markets for non-money financial assets, the poor are constrained in their ability to adjust their portfolio to rises in inflation. Typically, they will hold a greater proportion of their wealth in cash during inflationary episodes than do the non-poor. The non-poor are generally better able to protect their living standards from inflationary shocks than the poor.” They go on to cite evidence from India, Brazil, the Philippines, and a larger cross-country review.

24
**Table 7. Augmenting the Regressions with Measures of Inflation**

|Regression No.|1|2|3|4|
|---|---|---|---|---|
|Means of deflating|Total|Total|Total|Total|
|economic growth|CPI|CPI|CPI|CPI|
||Robust|Robust|Fixed|Fixed|
|Regressor|regressor|regressor|effects|effects|
|Economic growth (CPI)|−0.14***|−0.13***|−0.22***|−0.19***|
||(0.04)|(0.04)|(0.06)|(0.07)|
|Relative food inflation|0.04||0.12||
||(0.05)||(0.08)||
|Total inflation||0.11***||0.22**|
|||(0.05)||(0.11)|
|Number of countries|105|105|105|105|
|Number of observations|252|252|252|252|
|R-squared: overall|0.05|0.04|0.05|0.06|

_Source_ : Dependent variables are from the GWP (Gallup 2011 **)** . Economic growth data are from the IMF (2010), and food and total CPI data are from the ILO (ILO 2011).

_Note_ : *, **, and *** indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively. Standard errors are reported in parentheses. Note that outliers are removed for all regressions. Outliers are identified based on _dfbetas_ greater than 0.20. The robust regressions are estimated using the _rreg_ command in stata with default settings. For fixed effects regressions, standard errors are adjusted for country clusters. _Economic growth_ is the percent change in GDP per capita between the two years in which the GWP surveys were conducted deflated by the total CPI. _Total inflation_ is the percent change in the food CPI between the month of the GWP survey and the month of the previous GWP survey, where the food CPI in any given month is actually the average food CPI in the previous 12 months. _Relative food inflation_ is the percentage change in the ratio of the food CPI to the nonfood CPI, where the both CPIs in any given month are actually the average CPIs in the previous 12 months. Note that the robust regressor does calculate a pseudo R-squared, but it is generally regarded as inappropriate to report this value. Hence, the R-squared reported in this table is derived from an ordinary least squares regression that excludes outlying values.

25
Finally, we ran a number of additional specification tests related to income-level effects and alternative inflation effects. Specifically, we ran interaction terms with GDP per capita (in linear and log form) and with income dummy variables (low, middle, upper). Although we strongly expected that changes in food insecurity would be more sensitive to changes in disposable income at lower levels of income, there were no significant interaction terms (results available upon request). We suspect that this result may be driven by the fact that growth rates, inflation rates, and changes in food insecurity were all much lower in upper-income countries, which would have the effect of making the relationships approximately linear.

From the perspective of providing validation that changes in self-assessed food insecurity impart useful information, the results in tables 6 and 7 are encouraging. It is particularly encouraging that changes in real GDP per capita significantly explain changes in self-assessed food insecurity, suggesting that the latter is sensitive to changes in disposable income.

Despite significant and robust marginal effects, there are some caveats to these results. First, there is the influence of outliers. In the online appendix (table S1.2), we report the results of reestimating the regressions in table 6 and including outliers. Although all of the economic growth coefficients are still significant at the 10 percent level or higher, the standard errors are significantly larger, and the point estimates are sometimes larger and sometimes smaller in magnitude than those in table 6. Our treatment of outliers therefore does not lead to qualitatively different results.

Nevertheless, the presence of outliers and the low explanatory power of the regressions reemphasize our concerns about measurement error. These concerns must be tempered, however, because the analogous literature on the impact of economic growth on poverty reduction reports regression models with similarly low explanatory power (see Christiaensen et al. 2011, for 26
example), suggesting that these types of short-run poverty/food insecurity episodes suffer from the measurement errors and misspecification problems noted above. Although the presence of large marginal effects of economic growth and inflation rates on self-assessed food insecurity are encouraging, we must interpret trends in the latter quite cautiously.

# **<<A>>IV. MEASURING AND INTERPRETING KEY TRENDS IN THE GALLUP DATA**

In the introduction to this paper, we noted our basic result at the global level: 132 million fewer people were food insecure in 2008 relative to 2005–06. In this section, we examine Gallup trends in more detail by observing regional variations within this global trend, considering important exclusions from the sample, engaging in an important sensitivity analysis, and exploring the factors that might explain the surprisingly positive global trend.

In table 8, we report simple averages of the GWP food insecurity indicator by various regions of the developing world for 2005–06, 2008, and 2009. These years quite neatly correspond to a precrisis survey round, a food crisis round, and an early financial crisis round. Starting at the top of table 8, we observe what superficially explains the very positive global trend: in the eight most populous developing countries (excluding China), food insecurity decreased by 4.7 percentage points between 2005–06 and 2008. However, in many other regions of the world, food insecurity increased, including coastal West Africa (but not the Sahel), Eastern and Southern Africa, and Latin America. In other developing regions, there was either no change or some improvement. We also note that the deterioration of food insecurity in much of Africa and Latin America is consistent with a number of simulation studies (see Headey and Fan 2010 for a review).

27
**Table 8. Regional Trends in Self-Reported Food Insecurity (Percent Prevalence)**

|**Developing region**|**No. of**<br>**obs.**|**2005–06 surveys**<br>**(precrisis)**|**2008 surveys**<br>**(food crisis)**|**2009 surveys**<br>**(financial crisis)**|
|---|---|---|---|---|
|Eight most populous developing<br>countries*|8|32.7|28.0|30.6|
|sub-Saharan Africa|14|55.8|54.6|57.2|
|West Africa, coastal|4|48.5|51.3|58.0|
|West Africa, Sahel|5|59.6|49.2|55.2|
|Eastern & Southern Africa|5|57.8|62.8|58.6|
|Latin America & Caribbean|15|33.2|36.4|35.7|
|Central America, Caribbean|7|38.4|41.4|40.3|
|South America|8|28.6|32.0|31.6|
|Middle East (including Turkey)|3|19.7|26.0|21.3|
|Transition countries|13|31.9|30.2|34.6|
|Eastern Europe|6|21.8|19.7|25.8|
|Central Asia|7|40.6|39.1|42.1|
|Asia|12|28.8|29.0|30.8|
|East Asia|7|30.1|30.6|32.7|
|South Asia|5|26.8|26.8|28.6|

_Source_ : Author’s calculations from GWP (Gallup 2011) self-reported food insecurity prevalence rates. _Note_ : * “Large and fast growing” includes India, Indonesia, Brazil, Pakistan, Bangladesh, Nigeria, Mexico, and Vietnam but excludes China.

Although the results in table 8 cover the majority of the developing world’s population, there are still sizeable omissions. Although the GWP surveys cover China, we excluded the 2005–06 rounds due to specific concerns about biases in the responses to the food insecurity question. However, a number of other countries are lacking the requisite data for 2005–06 or 2008. China, of course, has a population of over a billion people, but 16 other omitted developing countries 28
represent close to 600 million people. Hence, one way to explore the sensitivity of our “global” estimate to the omission of these countries is to posit some plausible trends for these omitted countries and then recalculate the global figures.

With regard to China, the assessed GWP observations for 2006 and 2008 suggest an unrealistically large drop in food insecurity over that time (20 percentage points), which is probably related to the aforementioned problems with the ordering of questions in the 2006 round. It is therefore pertinent to consider a more plausible scenario for China and what this scenario would suggest about global trends in food insecurity. Given China’s phenomenal economic growth and rather limited level of food inflation (nominal mean incomes increased by 65 percent over 2006–08, whereas the food CPI increased by approximately 30 percent), it is plausible that food insecurity fell several percentage points in China. We thus consider a 3- percentage-point reduction from 2006 to 2008 to be relatively conservative. However, the countries omitted from one of more rounds of the GWP include many that could be suspected to have experienced rapid food inflation, including the Philippines (the largest rice importer in the world), a number of Middle Eastern and North African countries (some of the largest wheat importers in the world), and Ethiopia (the second largest country in Africa, one of the poorest countries in the world, and a country that experienced one of the fastest inflation rates in the world over 2007–08). In table S1.3 in the appendix, we make rather pessimistic assumptions about trends in food insecurity in these 16 countries (based largely on observed food inflation data) and adjust the raw GWP estimates by adding the assumed changes in food insecurity from the omitted countries. The results of this exercise are assessed in table 9. The inclusion of assumed changes for these 16 countries adds 62 million people falling into food insecurity rather than coming out it, but the assumed trend in China would result in close to 40 million people 29
coming out of poverty. In short, the core results reported in the introduction are not highly sensitive to the omission of these admittedly important countries.

# **Table 9. Alternative Estimates of Global Self-Reported Food Insecurity Trends after Allowing for Omitted Countries (Millions of People)**

||**Estimated change in global food insecurity,**|
|---|---|
|**Estimation scenarios**|**2005–06 to 2007–08**|
|Raw results, 69 countries (excluding China),_covering 57%_<br>_of developing world population_|−132|
|As above, plus pessimistic assumptions for 16 omissions,||
|_covering 67% of developing_|−60|
|_world population_||
|As above, plus a 3-percentage-point reduction in China,<br>_covering 87% of world population_|−100|

_Source_ : Author’s calculations from GWP data (Gallup 2011), FAO Global Information and Early Warning System data (2010), and ILO food inflation data (2011). _Note_ : See text in this section for more details regarding the assumptions and data as well as table A3.

Another objection may be that the 2005–06 GWP results are less reliable than subsequent rounds because the first round of the GWP may be regarded as a trial run for Gallup. We have the option of using the second round of the GWP (2007) as a base year instead of the 2005–06 round, but the 2007 round contains fewer countries and does not include China. Nevertheless, the 2007 GWP round includes India and other large countries and therefore covers approximately 43 percent of the population in the developing world. A second potential problem with using the 2007 round as a base year is that maize and wheat prices were already increasing in 2007, so it is difficult to regard 2007 as a pure precrisis period. Thus, we might underestimate the food insecurity impacts of the crisis if the 2005–06 round is shown to be unreliable. However, we note that there is no analogous problem with the 2008 data. The vast majority of the GWP surveys in 30
2008 were conducted in the last three quarters of the year after international food prices peaked. Therefore, they cover the period of peak international prices. Some lag in domestic food inflation may still be a problem, although we have already assessed results for surveys conducted in 2009, which may capture the twin effects of slower growth (due to the financial crisis) and higher food prices.

Bearing these caveats in mind, table 10 reports the results of calculating the populationweighted averages of food insecurity prevalence and population numbers for 2007 and 2008. The results thus suggest that there was basically no change in the “global” prevalence of food insecurity between 2007 and 2008. However, table 8 also shows that this result is heavily driven by trends in India, where food insecurity fell 4 percentage points from 2007 to 2008. The bottom half of table 8 calculates trends excluding India (which, admittedly, represents approximately one-quarter of the developing world’s population) and finds that population-weighted food insecurity in the rest of the sample went up by 2.53 percentage points, representing approximately 43 million people. Therefore, using the 2007 round as a base suggests that many developing countries were somewhat worse off in the peak food crisis year relative to the previous year. The largest increases in self-assessed food insecurity occur in Tanzania (23 points), Turkey (21 points), Burkina Faso (14 points), Uganda (14 points), Mozambique (12 points), Kenya (11 points), Ecuador (10 points), Cameroon (9 points), Sri Lanka (9 points), Armenia (7 points), and Honduras (7 points). Although we cannot ignore measurement error and the role of other factors in explaining these trends (the result in Turkey stands out as somewhat implausible), it is notable that many of the countries listed above did experience quite rapid food inflation. Indeed, the average rate of food inflation in these countries was approximately 4 points higher than the rest of the sample.

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**Table 10** . **Changes in Self-Reported Food Insecurity from 2007 to 2008**

||**Prevalence of food insecurity (%)**<br>**Population of food insecure (millions)**|
|---|---|
||_48 developing countries (43.3% of developing world population)_|
|2007|29.33%<br>821.4|
|2008|29.28%<br>820.1|
||−0.05 percentage points<br>−1.3 million|
||_47 developing countries excluding India (23.3% of developing world population)_|
|2007|31.51%<br>532.8|
|2008|34.04%<br>575.9|
||2.53 percentage points<br>43.1 million|

_Source_ : Author’s calculations from GWP data (Gallup 2011) .

Although we have explored validity issues in previous sections, another relevant question is whether the GWP results are supported by any other survey evidence. One other reasonably large survey of developing countries that was conducted before and during the crisis is the Afrobarometer survey. A recent working paper by Verpoorten, Arora and Swinnen (2011) explores trends in an Afrobarometer indicator that pertains to a very similar question to the one asked in the GWP and finds a 3-percentage-point increase in food insecurity in urban Africa from 2005 to 2008 and a 2-percentage-point increase in rural Africa. Thus, the overall picture of some deterioration in food insecurity in Africa is common across both the GWP and Afrobarometer surveys. Second, and perhaps most important, the most recent World Bank estimates of poverty trends also suggest that global poverty fell between 2005 and 2008 on every continent (World Bank 2012). Third, the FAO (2012) has revised its estimates of large increases in global hunger in 2009. The most recent estimates show a relatively steady decline in global undernourishment, consistent with both the Gallup and World Bank poverty estimates.

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Finally, it is worth exploring why the GWP results (and the new World Bank and FAO numbers) tell a positive story at the global level. One clear pattern is that events in the largest developing countries heavily influence any appraisal of global trends, not only because of the obvious influence of their sheer size on global trends, but also because many large countries are characterized by limited food inflation, rapid economic growth, or both. The first of these is not surprising. Large countries are very reluctant to rely heavily on significant food imports and are more likely to impose export restrictions and set aside significant food reserves. For example, China, India, Indonesia, and Vietnam all imposed some restrictions on grain exports in 2007 or 2008, and Nigeria abolished a 100 percent tariff on rice imports (Headey and Fan 2010). Of course, the effect of these attempts to insulate domestic markets on global poverty is ambiguous given that effective trade restrictions by large countries may protect their own poor but may hurt the rest of the world’s poor by spurring further international food inflation. Another domestic policy factor that may explain the apparent reduction of food insecurity in some of the larger developing countries is the spread of major social safety net programs in these countries, particularly India’s National Rural Employment Guarantee Scheme. However, in addition to these factors, strong economic growth in most of the world’s largest developing countries clearly provides a plausible explanation for the largely favorable trends in self-assessed data in these countries.

To examine disposable income issues more explicitly, we deflate nominal economic growth in recent years by changes in the food CPI rather than by an overall price index (as we did in some of the regressions in tables 6 and 7). This indicator is clearly an imperfect indicator of food security because poor people also spend money on nonfood items (but not much on fuel, which is the major source of nonfood inflation) and because mean GDP growth is often not 33
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Figure 2 plots this indicator for the nine largest developing countries from 2005 to 2008, with Brazilian and Mexican incomes measured on a separate axis because of scaling issues. The results are quite striking. “Mean food-disposable income” rose by over USD 700 per capita in China, over USD 1,800 in Brazil, and over USD 400 in Indonesia. In India, the increase was surprisingly modest (USD 80), but the increase was notably large in Nigeria (USD 240). In Mexico, however, the results are completely reversed, with food-disposable incomes declining sharply in 2007 and especially in 2008, during the so-called “tortilla crisis.” In the other countries, the data show much more modest trends and some general declines in 2008 as food prices rose substantially in Bangladesh, Vietnam, and Pakistan. Although there are variations among these nine countries, it is clear that nominal income growth generally outpaced food inflation in most country-year observations by a large margin in the four most populous countries (China, India, Indonesia, and Brazil).

These results apply to the largest countries, but we can also experiment with predicting changes in self-assessed food security for the entire sample of developing countries based on our regression results. Specifically, we use the growth and inflation coefficients derived in regressions 2 and 4 in table 7 in conjunction with actual growth and inflation rates to predict changes in self-assessed food insecurity over 2006–08. From these regressions, it is clear that economic growth reduces self-assessed food insecurity, whereas the total inflation rate increases it. For the most part, the results in table 7 suggest that these two effects cancel each other out over 2006–08. In one estimate, global food insecurity decreases by 25 million people, and in the other, it increases by 6 million. In other words, the econometric predictions lead to a conclusion that is qualitatively similar to the raw descriptive statistics: a decrease in global self-assessed food insecurity or little or no change overall.

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# **Table 11. Econometrically Predicted Changes in Self-Assessed Food Insecurity over 2006–** 2 **008 (Millions of People)**

||Predicted change in food insecurity|Predicted change in food insecurity|
|---|---|---|
||using regression 2 in table 7|using regression 4 in table 7|
|Total predicted change in|||
|self-reported food insecurity|−104.5|−152.8|
|Change due to|||
|economic growth|79.4|158.8|
|Change due to total|||
|inflation|−25.1|6.0|

_Source_ : Author’s calculations based on the GWP (Gallup 2011) _Note_ : Changes in self-reported food insecurity are estimated by multiplying the coefficients from table 7 by changes in GDP per capita and changes in the total CPI from 2006 to 2008.

## **<<A>>V. CONCLUSIONS**

The innovation of this study is its use of survey-based evidence, rather than simulations, to assess the impact of the food crisis on global and regional food insecurity. We find no evidence that global food insecurity was higher in 2008 than it was in previous years, although it affected many regions, particularly a number of countries in Africa and Latin America. Though qualified by measurement issues, these results are broadly corroborated by recent World Bank estimates of a declining global poverty trend over 2005–08. Our results also cast doubt on the usefulness of simulation approaches in predicting global poverty trends, although the more sophisticated of these approaches are still useful for exploring the mechanisms and distributional impacts of food price impacts in an experimental setting.

Finally, our results raise the question of whether self-assessed indicators might be a useful addition to existing food security metrics. Further research is needed in this regard. As we noted in section 2, self-assessed indicators are susceptible to a number of biases. Nevertheless, these 36
weaknesses must be traded off against the fact that such indicators are easily, quickly, and cheaply measured relative to household expenditure or consumption data. There are also some potentially significant ways to improve self-reported data. For example, a more disaggregated ordering of self-assessed food security (such as using scales of 0 to 5) might reduce measurement errors. King et al. (2004) also argue for anchoring vignettes to make measurements more comparable across different socioeconomic groups. Another approach might be to ask households to report the frequency of consumption across different groups rather than asking about more subjective feelings of deprivation. Such dietary diversity or food consumption scores have been shown to be strong predictors of household calorie consumption and individual anthropometric outcomes (Wiesmann et al. 2009; Arimond and Ruel 2006) and might capture the fact that reducing dietary diversity is a common means of coping with higher staple food prices (Block et al. 2004). Others have argued for the use of sentinel sites to collect higher frequency measurements of food security and nutrition outcomes (Barrett 2010).

Regardless of the path that is pursued, there are strong grounds for making a large push to improve the measurement of food security. The global food crisis of 2007–08 revealed some significant deficiencies in our capacity to monitor coping strategies and welfare impacts in an acceptable timeframe. Moreover, if strong economic growth had not been prevalent in substantial parts of the developing world prior to and during the food crisis, the impacts of higher food prices might have been far more disastrous. Indeed, predictions of higher food prices and continued price volatility in the next decade or beyond (Headey and Fan 2010) would seem to justify greater investment and experimentation in food security measurement in the near future.

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