While food security measurement has been substantially expanded in recent decades, there persists significant dissatisfaction with existing measurement systems, especially in the wake of the ongoing food and financial crises. In this paper we first set out a list of criteria that an ideal food security measurement system should satisfy. In addition to standard issues of cross-sectional validity, our criteria include inter-temporal validity (the ability to gauge trends and shocks), and nutritional relevance. Using a mixture of literature review and fresh empirical analysis, we then benchmark four types of indicators (calories, poverty, dietary diversity and subjective indicators) against these criteria as a means of systematically identifying their relative strengths and weaknesses, and comparing overall performance. We conclude that, overall, dietary diversity indicators are the best performing class of indicators: they are powerful predictors of economic status and malnutrition (both stunting and wasting), sensitive to shocks, and relatively cheap to measure. Our concluding section therefore also outlines possible steps for scaling up the measurement of dietary diversity (and other indicators) through a mixture of increased funding, greater inter-agency coordination and technological (ICT) innovations that will reduce the cost of high frequency food security measurement.
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We adopt the concept of food security, as defined at the 1996 World Food Summit (FAO 1996), a definition which is given below. Unlike for food security, there is no generally accepted definition of nutrition security. A key difference between the two concepts is that nutrition outcomes are significantly influenced by health factors and child care practices. Moreover, nutrition security tends to focus more on the welfare of young children and women in reproductive age, given the well-established hypothesis that growth faltering typically occurs in the first “thousand days” of life (Victora et al. 2010).
Indeed, somewhat in contradiction to our statement above, one anonymous reviewer suggested there had in fact been some convergence in recent years around these four classes of indicators. At the same time, we believe there is still a large variety of specific indicators within each class of indicator. For example, there still persists various different types of dietary diversity scores.
Conceivably, there are other indicators that could be considered here. For example, asset indices are sometimes referred to as food security indicators, although we view them as conceptually too remote to the latent concept of food security, even if they bear a strong correlation with food security outcomes in practice. There are also composite indexes incorporating different food and nutrition security indicators such as the Global Hunger Index of the International Food Policy Research Institute. We do not explicitly discuss such composite indexes since their strengths and weaknesses emerge from the individual indicators used.
Indeed, such assessments should very much be a research priority in the future. Too often, research in this area tries to prove that an indicator works well, rather than to prove that it doesn’t.
Though not shown, we found the same results for Yemen using data from the Household Budget Survey 2005/06.
Ideally, one should engage in multivariate tests. However, it is unlikely that these would substantially improve significance levels, and it is also striking that bivariate correlations are still significant for non-calorie indicators.
There is also some suggestion that rural households were better able to cope with the crisis than urban households thanks to a higher share of food from own production.
There are several explanations for this discrepancy. First, a bias may occur from the difficulties of pricing subsistence consumption. Second, unobserved seasonal shortfalls are a major issue in rural areas but much less so in urban areas. Third, monetary poverty indicators perform poorly in capturing access to and quality of essential services that are important for nutrition such as health, education, and family planning services.
Nationally representative household surveys collect data usually over 1 year (including all seasons). The sampling is often designed to account for seasonality through repeated visits of the same enumeration area so that seasonality effects should average out at the aggregate level. Nonetheless, the surveys cannot be used easily to examine seasonal dynamics because each household is interviewed normally only once during the survey year so that cross-sectional and inter-temporal effects cannot be separated.
Why is this dietary diversity indicator more strongly correlated with stunting, wasting and low BMI outcomes? There could be several possible reasons. First, although the dietary diversity and undernourishment estimates are calculated from the same data source (the FAO Food Balance sheets), dietary diversity is probably subject to less measurement error, because no assumptions are made about the distribution of diets, whereas the FAO undernourishment approach must estimate this distribution with infrequent data. Moreover, if the calories from staples and calories from non-staples are measured with similarly sized and similarly signed errors, then taking the ratio of the two will roughly cancel out the errors. In contrast, a simple measure of calories will still contain this error. Second, dietary diversity is presumably a better proxy for micronutrient deficiencies, which are indeed important for young children, particularly resisting diseases that cause short term wasting, which in turn contributes to stunting (a cumulative nutrition indicator). Third, since diets only diversify when calorie needs are satisfied, dietary diversity could in fact be thought of as an index of both quality and quantity of diets.
Analyzing the 2012 SOFI data (see http://www.fao.org/publications/sofi/food-security-indicators/en/) we again found correlation patterns similar to those in Table 3. In fact, the share of animal sourced proteins proved to have the strongest bivariate correlation with stunting (−0.79) and its correlation with wasting as strong as the correlation between wasting and the share of calories derived from non-starch foods (in both cases, −0.50).
We should add a caveat to that result. Brinkman et al. (2010) do not use a preferred regression framework in our view, since they use logs of levels rather than first differences. A differenced model would remove fixed effects, which could be causing simultaneity biases in the regressions. Moreover the sample sizes for two of the countries (Haiti and Nepal) are small enough (500–600 households) to suggest that they are not nationally representative. And, perhaps most critically, there is no means of distinguishing between net food producing and net food consuming households. Clearly net food producing households might benefit from higher food prices.
Unpublished work from two recent IFPRI studies tends to find a strong correlation between household and individual.
In several cases the indicator was only significant when interacted with other terms, although such interactions are often intuitive (such as interactions with the age of the child).
We thank Chris Barrett for this point. Barrett (2010) makes a compelling case for sentinel surveys, and argues that they should include subjective “early warning” questions.
These three questions on hunger were subsequently developed into the Household Hunger Scale. Also, while this paper does not consider the Reduced Coping Strategies Index as an indicator of food insecurity per se, a subset of the coping strategies was also validated for cross-cultural use (see Maxwell et al. 2008).
Similarly, Studdert et al.’s (2001) survey of Jakartan mothers found that some terms—such as the term “balanced diet”—translated very poorly to that context.
We should add a caution regarding over-interpretation of the correlation coefficients between the food security indicators, considering that estimates of (food) expenditure, calorie consumption, and dietary diversity are derived from the same module in the surveys that likely contributes to strong correlations.
One proposed means of improving subjective data is to use “anchoring vignettes” to elicit common reference frames (King et al. 2004). Other response biases could also be reduced through assurances of confidentiality, and informing respondents that their answers to these question have no bearing on their qualification for transfer schemes.
For example, how does one compare price levels between the Cameroon, where cassava is a key staple, and India, where it is not? These are fundamental issues that poverty measurement literature has been struggling with - yet persisting with - for decades.
This recommendation is essentially along the sentinel systems advocated by Barrett (2010).
While there is a large number of developing countries in the world, a subset of developing countries is much more exposed to natural disasters and economic volatility than others. This is evident from data on food aid receipts from the FAO (2012) and estimates of the number of people affected by disasters, produced by EM-DAT (2012).
In the 2011 drought in the Horn of Africa, it was estimated by major humanitarian agencies (such as UN-OCHA, WFP and USAID) that around 14 million people in four countries were in need of humanitarian assistance. In fact, it is quite difficult to ascertain how these specific numbers were obtained. However, the WFP was extremely helpful in sharing details of their methods. Specifically, they typically use Emergency Food Security Assessments (EFSA) to crisis situations, and their handbook on conducting EFSAs is available on the web (www.wfp.org/content/emergency-food-security-assessment-handbook). We were informed that EFSAs vary a lot by context in terms of being rapid or in-depth, quantitative or qualitative data, and in terms of what information is collected, and what existing data are available. For example, an EFSA in Bangladesh primarily relied on focus group interviews and some collected market price data. Interestingly, that EFSA also used some baseline data from the Bangladesh NSS. EFSAs in South Sudan and Senegal had more quantitative data, but the nature of this data varied substantially across the two countries. In South Sudan the WFP were utilizing sentinel sites, as well as a range of other household survey and census data. In Senegal a relatively small survey of 552 household was conducted in affected areas. Our perception is that this flexible approach is sensible given existing data constraints and limited resources, but our argument is that more high-frequency data could and should be collected in all but the most volatile environments. Indeed, high-frequency surveys should also be thought of as a capacity building instrument for the countries involved.
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We are very grateful to the Food Security Analysis Unit of the World Food Programme (WFP) and particularly Claudia Ahpoe, Siemon Hollema, Joyce Luma, Kathryn Ogden, and Astrid Mathiassen for their insights into WFP’s food and nutrition security assessment. Jean-Francois Trinh Tan of the International Food Policy Research Institute (IFPRI) provided excellent research assistance. We also thank our IFPRI colleagues Disha Ali and Michael Tedla for letting us report some of their results. And we are indebted to participants at the "International Scientific Symposium on Food and Nutrition Security Information: From Valid Measurement to Effective Decision Making" organized by the Food and Agriculture Organization of the United Nations (FAO) and the European Union (EU) (in Rome, January 2012) and at the International Association of Agricultural Economists (IAAE) triennial meeting (in Foz do Iguaçu, August 2012). All errors are our own.
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Headey, D., Ecker, O. Rethinking the measurement of food security: from first principles to best practice. Food Sec. 5, 327–343 (2013). https://doi.org/10.1007/s12571-013-0253-0