the same calorific contribution (del Ninno, Dorosh and Smith, 2003).<sup>6</sup> Hill and Fuje (2017) note that the impact of droughts on local grain prices has diminished over time, particularly in areas where market access has improved.

In summary, the empirical evidence suggests that households subject to extreme weather events often suffer large losses in agricultural income. The impact on consumption and calories tends to be lower than the impact on crop income but still significant, indicating that households are not able to perfectly smooth consumption. Nonetheless, households can mitigate negative impacts via household risk-coping strategies, such as re-allocating labor, selling assets and accessing transfers from friends and relatives. Additionally, greater access to a number of institutions also enables households to cope with the impacts of extreme weather, including access to credit, to well-functioning markets and to social safety net programs.

# **3. Data**

Our analysis uses data from the Malawi Flood Impact Assessment Survey (FIAS), which was conducted by the National Statistical Office (NSO) in November-December 2015.<sup>7</sup> FIAS attempted to track 590 rural households who had previously been surveyed by the Malawi Integrated Household Panel Survey (IHPS) in 2013, and before that by the Malawi Third Integrated Household Survey (IHS3) in 2010.<sup>8,9</sup> Since FIAS 6 Despite huge floods in September 1998 and the loss of 10 percent of annual food consumption, these safety nets combined with the longer-term changes in economic structure meant that recovery from the floods was relatively rapid. By April 1999, income for flood-affected households had increased 45 percent on that of the previous December following the floods, and a further 50 percent by the following November.

7 FIAS 2015 was implemented with technical support from the World Bank Living Standards Measurement Study (LSMS), the World Bank Poverty and Equity Global Practice, and LEAD Analytics, and with the World Bank funding from the Global Facility for Disaster Reduction and Recovery (GFDRR), the Disaster Risk Financing and Insurance team, the Finance and Markets Global Practice, and the Global Solutions Group on Managing Risks within the Poverty and Equity Global Practice.

> 8 IHPS and IHS3 were both implemented by the NSO, with financial and technical support from the <u>World Bank Living</u> – - <u>Standards Measurement Study Integrated Surveys on Agriculture (LSMS ISA) program. In 2013, the IHPS was</u> implemented from April to December 2013, with the objective of tracking and resurveying 3,246 households across 204 enumeration areas (EAs) that had been previously surveyed as part of the IHS3 2010/11. The IHPS sample had been designed in 2010 to be representative at the national-, urban/rural, regional levels, and for the six strata defined by the combinations of region and urban/rural domains. In 2013, IHPS targeted all individuals that were part of the IHS3, including those that moved away from the IHS3 dwelling locations between 2010 and 2013. Once a splitoff individual was located, the new household that he/she formed or joined since the IHS3 interview was brought into the IHPS sample. As a result, the overall IHPS database includes 4,000 households, which could be traced back to 3,104 IHS3 households. Attrition was limited to only 3.8 and 7.4 percent of households and individuals, respectively. The anonymized, unit-record data and documentation from the IHPS 2013 and the IHS3 2010/11 can be accessed through www.worldbank.org/lsms.

9 FIAS was implemented on a computer-assisted personal interviewing (CAPI) platform that was designed using the World Bank _Survey Solutions_ CAPI software (www.worldbank.org/capi). The FIAS CAPI experience was a key input into the design and implementation of the Fourth Integrated Household Survey (IHS4) and Panel Subcomponent later in 2016/17, also using a _Survey Solutions_ -powered CAPI platform.

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