households’ incomes in rural Malawi are still dominated by agricultural income, shocks that affect crop production also impact on incomes, as does price volatility. And, consumption is highly correlated with agricultural income, though informal insurance mechanisms within a community or extended household may help to smooth consumption in the face of agricultural production and income shocks. In addition, given imperfect labor and credit markets, household labor shocks including illness or death, can affect production and off‐farm income earnings, and subsequently consumption. A priori, we expect harvested quantities to exhibit the greatest variability, followed by income and then consumption. In light of this consideration, this paper is motivated by two key questions: _(1) To what extent is the phenomenon of vulnerability to consumption and income poverty and low crop production per capita present across rural households in Malawi?_ and _(2) What are the determinants of this vulnerability, e.g. the risks they face, assets endowments, and household socio‐demographic characteristics, particularly those expected to proxy for risk management and risk coping capacity?_ To answer these questions, we construct measures of vulnerability to poor realizations of maize harvest per capita, income per capita, and consumption per capita, following the vulnerability measures developed in Christiaensen and Subbarao (2005) and Chauduri et al. (2002). This measure is categorized as a “vulnerability to expected poverty” (VEP) measure, which is comprised of the probability of falling below the poverty line multiplied by a Foster‐Greer‐Thorbecke (FGT) measure of the severity of the expected shortfall below the poverty line. We explicitly consider a number of different shocks that may affect rural households, including rainfall, maize price, health and off‐farm income employment shocks. We use the poverty line developed by the National Statistical Office (NSO, 2012), which is based on minimum subsistence requirements for consumption (see forthcoming Malawi Poverty Assessment), and household level panel data collected by the NSO, in conjunction with the World Bank, covering two time periods, 2010 and 2013. We augmented this data set with measures of current period rainfall shocks and measures of long‐term rainfall variability obtained from the National Oceanic and Atmospheric Administration (NOAA), and data on variance of maize prices obtained from Malawi Agriculture Statistics Bulletin, of the National Statistical Office. These additional data sources enable us to rely on objective measures of shocks, such as for rainfall, to capture the temporal variation in rainfall.

Results show that many households in Malawi are vulnerable to poverty, though as with many other studies of rural areas in other countries, much of vulnerability is due to chronic poverty. Nonetheless, risks – particularly rainfall and employment shocks – are also important in explaining why poor households remain poor, and why some non‐poor households are more likely to fall into poverty in the next period. The results also underscore the importance of having access to long‐term measures of variability, as opposed to relying on spatial variation as a proxy for temporal risks. In particular, rainfall patterns in 2013 were relatively better than generally observed over the period 1983‐2012. Using the information from the longer‐term rainfall data to generate expected shocks better captures the number of households vulnerable to falling into poverty. Of the additional explanatory variables included in the vulnerability analysis, both household wealth and agricultural asset indices are the most important in protecting households from falling into poverty and reducing the severity of the fall when shocks occur.

The paper contributes to the literature in two main ways. First, the data set includes explicit information on a number of shocks hypothesized to affect vulnerability, including longer‐term, objective measures of rainfall variability and current period rainfall shocks. Many previous analyses have limited information on actual shocks faced, and as we shall see below, the standard vulnerability analyses without such information makes it difficult to disentangle which shocks are more important for explaining vulnerability using various welfare measures. Second, the paper uses a two‐period data set covering households in rural Malawi; contributing to very sparse evidence on vulnerability in Southern Africa using this particular 3