attempt to fulfill our secondary objective within an instrumental variable regression framework. The subsequent section describes our data, provides definitions for indices of community collective capacity and measures of diversity and similarity. This section concludes with descriptive statistics, and details the estimation strategy.

# **4. Empirical Approach**

# **4.1.   Data**

Our analysis is data-intensive, capitalizes on the multi-topic nature of the Third Integrated Household Survey (IHS3) 2010/11 survey instruments and takes advantage of administrative and geospatial data that could be linked with the communities of interest. The IHS3 was implemented from March 2010 to March 2011 by the Malawi National Statistical Office, with support from the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) project.<sup>6</sup> The IHS3 enumeration areas (EAs) and households were selected as part of a two-stage cluster sampling design with the EAs, in each survey stratum, selected in the first stage with probability proportional to the household count from the 2008 Population and Housing Census. Households were then selected at random in the second-stage in each sampled EA following a full household listing. The 31 IHS3 strata correspond to all 27 rural districts, with the exception of Likoma Island, and 4 urban cities, namely Lilongwe, Blantyre, Zomba, and Mzuzu. The total sample is 12,271 households in 768 EAs (approximately 16 households per EA) that is representative at the national-, urban/rural-, regional, and district levels. For the purposes of our analysis we focus on the rural sample, consisting of 628 EAs.

The IHS3 instruments included Household, Agriculture, Fishery, and Community Questionnaires. All sample households were geo-referenced and administered the multi-topic Household Questionnaire that collected individual-disaggregated information on demographics, education, health, wage employment, nonfarm enterprises, anthropometrics, and control of income from non-farm income sources, as well as data on housing, food consumption, food and non-food expenditures, food security, and durable and agricultural asset ownership, among other topics. The data allow for the computation of a comprehensive household consumption aggregate, which in turn enables us to calculate community level median estimate of household real annual consumption expenditures per capita as one of our measures of welfare.<sup>7</sup>

> 6 The authors include the point person for the World Bank technical assistance towards the design and implementation of the IHS3 under the LSMS-ISA initiative, which is a household survey program established by a grant from the Bill and Melinda Gates Foundation to provide financial and technical support to governments in subSaharan Africa in the design and implementation of nationally-representative multi-topic panel household surveys with a strong focus on agriculture (www.worldbank.org/lsms-isa). The IHS3 data and documentation are publicly available through the LSMS website (www.worldbank.org/lsms).

> 7 The analysis uses the official consumption aggregate that was jointly calculated by the NSO and the World Bank, as documented in Chapter 13 and Appendix B of the NSO IHS3 household socio-economic characteristics report.

13