the nutritional surveillance system surveys focus heavily on nutrition indicators, they also look at a wide range of household characteristics and coping behaviors (Box 3.4). Beyond the need to use higher-frequency surveys, resilience
measurement faces additional challenges in terms of the breadth of the resilience concept. Resilience is a highly multidimensional concept with numerous causes and manifestations. Moreover, some factors may be considered not only causes or sources of resilience, but also indi- cators of resilience. For example, a non-exhaustive list of factors that are simultaneously considered as “contributors” to and “results” of resilience includes: technological capacity, appropriate skills and edu- cation, gender empowerment, sustainable natural resource manage- ment, adequate livelihood assets, good governance, and access to infra- structure (Alinovi et al. 2010; USAID 2012; Tulane and UEH 2012; Vaitla et al. 2012). This clouding of the distinction between cause and effect limits our ability to compare or refute specific hypotheses (Fran- kenberger and Nelson 2013). In addition, this diverse and extensive list of factors poses
some serious challenges to both measurement and scientific analy- sis. Some of these factors are inherently difficult to measure, such as governance, natural resource management, and gender empower- ment. Many must be measured qualitatively rather than quantitative- ly. Some indicators must be measured at the individual or household level, but others need to be measured at the community level or even higher. Finally, some factors—as well as the definition of resilience itself—are likely to be context- and shock-specific, thereby limiting comparability across survey sites. Some factors fall under one disci- pline, such as economics, while others fall under very different dis- ciplines (ecology, political science, sociology). As already emphasized, most—if not all—of these factors ought to be measured in high-fre- quency surveys. Thus the practical challenges to effectively monitor- ing and measuring resilience are considerable. Yet collecting such an extensive set of data to measure resilience could help shape more informed responses to a wide range of crises.
Looking Back The complexity of the concept of resilience and the challenges of measuring and promoting it may paint a somewhat daunting picture for policymakers and development practitioners. Indeed, some vul- nerable countries and regions have found themselves mired for decades in poverty and food and nutrition insecurity in the face of shocks. Other highly vulnerable countries, though, have seemingly become more resilient. Much can be learned from the varied experi- ences of these groups of countries.
BOX 3.4 HELEN KELLER INTERNATIONAL’S NUTRITIONAL SURVEIL- LANCE PROJECTS IN BANGLADESH AND INDONESIA
Helen Keller International (HKI) set up nutritional surveillance systems in Bangladesh and Indonesia to document the effects of crises on the well-being of the poor. In Bangladesh, the system mon- itored the effect of disasters such as floods. In Indonesia, it was designed to monitor the effect of the Asian economic crisis of the late 1990s on nutrition and health. Over the years, these nutrition- al surveillance systems evolved into comprehensive, yet flexible, information systems providing timely, accurate, and important data for policy and program planning, nationally and internationally. The indicators in HKI’s surveillance systems are based on UNICEF’s conceptual framework of the causes of malnutrition and cover areas such as the nutrition and health status of mothers and children, socioeconomic status, food production and con- sumption, and health service use. In Bangladesh, the nutritional surveillance project originally collected data in disaster-prone sub- districts, but in 1998 the sampling procedure was revised to be nationally and divisionally representative. Data collection takes place every two months to capture seasonal changes in nutrition and health, which allows the impact of disasters to be distin- guished from seasonal effects. For example, as the top chart shows, the share of households that borrowed to cope with the 1998 floods in Bangladesh spiked to more than 50 percent from less than 10 percent over a 5-month period. In 1998, Bangladesh experienced one of the worst episodes of flooding on record. The nutritional surveillance project was instrumental in drawing attention to the plight of flood-affected areas and in helping target public responses to populations in need. The surveillance data also showed that child wasting more than doubled from the surplus season to the lean season. Reduc- ing such harmful effects of seasonality is an important part of building resilience.
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Understanding Resilience for Food and Nutrition Security | Chapter 03 | 2013 Global Hunger Index
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