However, it is likely that the amount of such transfers will increase following the realization of shocks. For instance, in the Philippines, households with overseas migrants received higher remittances in the aftermath of floods (Yang and Choi, 2007). In this study, we improve on previous welfare impact studies by excluding food aid or in-kind and cash transfers received by households from the consumption per capita.

Various definitions and theoretical frameworks of vulnerability to poverty have been suggested in the literature (Calvo and Dercon, 2013; Hoddinott and Quisumbing, 2003; Pritchett, Suryahadi, and Sumarto, 2000). Even though there is no consensus as to which one is a superior approach, they all rely on the estimation of expected consumption per capita and variance of consumption per capita. Some vulnerability studies use panel data to assess insurance of consumption in the aftermath of shocks (Townsend 1994; Udry 1995). The main challenge with adopting this approach is the lack of nationally representative panel data, which is rare in developing countries where vulnerability and resilience matter the most. Indeed, most nationally representative data sets are cross-sectional, and some vulnerability studies utilize these rich data sets. For example, Chaudhuri (2003) interprets the residuals from a regression of consumption per capita on household characteristics as the variance of welfare due to shocks. The distribution of consumption is then assumed to be lognormal with variance being the one that was imputed to the shocks (Chaudhuri, 2003). An alternative to rigorously used cross-sectional data is to build a pseudo-panel before conducting the vulnerability estimation (Christiaensen and Subbarao, 2004). Other authors have designed methodologies bridging the gap between these two approaches. On the one hand, Gunther and Harttgen (2009) improve on Chaudhuri’s method by using a multilevel model that decomposes the error term into idiosyncratic and community shocks. This method relies on interpreting the residuals and may, therefore, be particularly sensitive to measurement errors (Gunther and Harttgen, 2009). On the other, Hill and Porter (2017) include both covariate and idiosyncratic shocks in the estimates of vulnerability to poverty. In this study, we improve on Hill and Porter’s paper by excluding transfers from the welfare measures. Similar to Hill and Porter (2017), using nationally representative data allows us to calculate estimates of vulnerability to poverty along different dimensions (such as rurality and regions).

Malawi suffers from recurrent weather shocks that are detrimental to poverty reduction effort. The latest shock is tropical cyclone _Idai_ that hit Malawi and neighboring countries in March 2019 and led to devastating floods. The post-disaster needs assessment (PDNA) estimated that 500,000 farmers or small businesses had their income affected by this event (Malawi Government, 2019). Three years earlier, in 2016, a severe drought resulted in poor harvests and prompted humanitarian emergency. This historical drought followed the 1 in 500 years flood of January 2015. The country was also affected by floods in January 2016. On 4