Section A of Table 5 describes consumption shortfall for households who are vulnerable with a 50% threshold, Section B describes the additional households that are vulnerable when we lower the threshold to 40%, and Section C describes consumption shortfall for all households that are vulnerable with a 40% threshold. Using the 50% threshold, we by definition restrict to households whose expected level of consumption is below the poverty line. Thus, all households included in Section A experience a consumption shortfall, and the levels of shortfall are relatively large. The households in Section B all have expected consumption above the poverty line, and thus do not experience ‘shortfall.’ However, the percent above the poverty line in Section B is modest compared to the percent below the poverty line in Section A, except for households in Central where the difference between Section A and Section B is relatively small. When we combine the households from Sections A and B to create Section C, we see that the average shortfall is much smaller under the 40% threshold, and that the average gap is even positive.

# **6. Conclusions**

Considerations of risk and vulnerability are critical to understanding the dynamics of poverty in Malawi, where the majority of rural households are poor rain‐fed farmers. This study assessed the vulnerability to consumption shortfalls of rural households in Malawi using a two‐period panel, drawn from the 2010 IHS3 and 2013 IHPS.

The 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. In terms of empirical evidence, most studies find that poverty is the most important component of vulnerability. Nonetheless, risks – particularly rainfall and loss of off‐farm 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 calculation of expected shocks matters quite a bit in the assessment of vulnerability. Setting all shocks equal to zero results in much lower vulnerability rates. But this is unrealistic. In any given year, across the country or each region, the expected value of a shock is not zero, since some households will indeed face a shock while others do not. Thus, we constructed expected welfare outcomes by setting the shocks equal to the regional annual average rainfall and price shocks observed from historical rainfall data, and to regional level average malaria and employment shocks. While fewer households expect to be vulnerable next period than were poor in 2013, overall the government should expect more households to be vulnerable than observed poor in 2013. The empirical analysis confirmed that a sizable number of rural households were vulnerable to poverty in Malawi in 2010 due to the potential realization of risks (the occurrence of shocks) between 2012 and 2013. The latter results primarily because both rainfall and price shocks were relatively low in 2013 versus their long‐term historical average, as well as the importance of off‐farm employment shocks on consumption per capita.

Of the variables included in the welfare outcome and variance equations, 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. However, in country contexts similar to Malawi's where risk mitigation mechanisms are limited, shocks can force many households into poverty. In short, we found broad similarities between vulnerable and poor households in terms of explanatory characteristics. In both cases, vulnerable households are characterized by large families with high dependency ratios, and limited household wealth and agricultural assets, and located in more isolated communities with poorly developed socioeconomic infrastructure. While household wealth can be used 22