household size has a negative impact on per capita consumption or income (Christiaensen & Subbarao, 2005; Jha, Dang, & Tashrifov, 2010; Bronfman, 2014; Klasen, Lechtenfeld, & Povel, 2015; Imai, Gaiha, & Thapa, 2015). Studies that include measures of health shocks also generally find that these do increase vulnerability, as expected (Abimbola, Yusuf, Omonona, & Okunmadewa, 2011). Various measures of wealth and education, on the other hand, often reduce vulnerability, though this is generally through higher expected consumption or income per capita rather than reduced variance (Ligon & Schechter, 2003; Christiaensen & Subbarao, 2005; Makoka & Kaplan, 2005; Jadotte, 2010; Jha, Dang, & Tashrifov, 2010; Klasen, Lechtenfeld, & Povel, 2015). Interestingly, education increased vulnerability in a small‐ sample study undertaken in Ethiopia by Bogale (2012), and post‐secondary education also led to higher consumption variance in Vietnam (Imai, Gaiha, & Kang, 2011). Another interesting finding is that most studies that have included a dummy for gender of household head do not find that female‐headed households are more vulnerable (Christiaensen & Boisvert, 2000; Ribas & Machado, 2007), and in some cases, are less vulnerable (Bronfman, 2014; Imai, Gaiha, & Thapa, 2015). An exception is Klasen, Lechtenberg & Povel (2015), who exploit a two‐year panel data set from Thailand and Vietnam to show that _de facto_ female‐headed households are more vulnerable to consumption poverty than male‐headed households.

# **3. Data and Descriptive Statistics**

This paper exploits the rural sample of Malawi’s nationally representative panel data set, which became available in 2013 via the Integrated Household Panel Survey (IHPS). The Malawi National Statistical Office conducted the surveys on which our data set is based, with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS‐ISA) project. The baseline round is from a subsample of 3,247 households interviewed between March and November 2010 as part of the Third Integrated Household Survey (IHS3) 2010/11 survey, which is a nationally representative survey of 12,271 households fielded over 12 months (March 2010–March 2011). The follow‐up round is from the Integrated Household Panel Survey (IHPS) 2013 survey. The IHPS tracked households and individuals to new locations when necessary. Hence, the IHS3 panel sample grew from 3,247 households in 2010 to 4,007 households in 2013 mostly because households split and formed new households. The IHPS utilized a complex household sample design allowing for 6 key domains of inference: the combination of urban and rural areas with the 3 main regions in the country (North, Central, and South). The data set contains detailed information on a rich variety of topics including demographics, education, health, employment, housing, assets, and agricultural production, as well as self‐reported shocks, their impact and coping responses to them.

This paper uses an unbalanced panel on rural households only. This unbalanced panel consists of 2283 households in 2010, which had split into 2789 households in 2013. This characteristic of the data set is viewed as strengthening the results since it more accurately captures outcomes for all 2010 household members, as compared with simply choosing which 2013 household matches the 2010 household and ignoring members who have joined different households.

# **_Welfare Measures_**

This study employs household total consumption per capita, food consumption per capita, income per capita, and maize harvest per capita as the main outcome measures for detecting vulnerability. Consumption and income capture two different measurements of households’ economic security. Maize harvest is a particularly critical welfare measure in Malawi because of the importance of maize as a staple crop in Malawi (Smale, 1995; Ecker & Qaim, Analyzing Nutritional Impacts of Policies: An Empirical Study 7