Research, part of a Special Feature on Education and Differential Vulnerability to Natural Disasters 
Community Vulnerability to Floods and Landslides in Nepal 
Samir K.C.
Natural disasters occur and affect people’s lives and livelihoods in almost all parts of the world. Some populations are more vulnerable than others and disparity exists between nations and communities within a country. Furthermore, within communities different households may be affected differently and even within households the vulnerability of individual household members may vary. In this study, we empirically assessed the relative importance of socioeconomic factors associated with differential community vulnerability to floods and landslides in Nepal. In this context, our specific research question was to assess the relative importance of different dimensions of socioeconomic status and in particular to try to differentiate between the effects of education and income/wealth. The reason for this effort in the unpacking of socioeconomic status (SES) was that these two dimensions of SES imply quite different policy priorities for reducing household vulnerability: either investing more in education or in strengthening the economic aspects of livelihood. 
Empirical analysis of vulnerability to natural disasters’ drivers have been conducted at national and subnational levels (Phifer et al. 1988, Yohe and Tol 2002, UNDP 2004, Brooks et al. 2005, Pradhan et al. 2007, Toya and Skidmore 2007, Deressa et al. 2008, Makoka 2008, Shewmake 2008). Brooks et al. (2005), in their macrolevel study, found that at the national level governance, health, and education were the three main determinants of vulnerability. In a multicountry study, Toya and Skidmore (2007) found that both higher income and educational attainment were important measures of development in reducing vulnerability to disasters. 
At the microlevel, many studies applied regression analysis to find income as one predictor of vulnerability to natural disaster (Phifer et al. 1988, Pradhan et al. 2007). For example, Pradhan et al. (2007) have shown that the flood related fatality rate for children was very high among families with low socioeconomic status, measured by income-generating land ownership and the type of roof. Most of these studies used community characteristics that could be used to categorize community vulnerability as listed by King (2000): these included demographic indicators, such as size of the population, population aged 0-4, 65+, living arrangements, etc.; household types and structures; and economic indicators such as, unemployed and income level. Few studies considered education as a possible predictor of vulnerability (Phifer et al. 1988, Shewmake 2008). Phifer et al. (1988) chose education as a “rough” indicator of socioeconomic status instead of income, because of the high rate of nonresponse. Shewmake (2008) showed that “years of schooling” of the best-educated person in the household was one of the highest significant explanatory variables in explaining the variation in vulnerability of South African farmers to climate change (Shewmake 2008). 
There is a huge body of literature studying the positive impacts of education on a wide spectrum of desirable outcomes. A review of this literature goes far beyond the scope of this paper. It should just be mentioned that recent reviews exist on the strong impact of female education on lowering fertility and population growth (Lutz and K. C. 2011), on assessing the effects of education on economic growth (Lutz et al. 2008) and health (Baker et al. 2011), and even on its effects on the quality of institutions and democracy (Lutz et al. 2010). However, was it meaningful to assume that education also mattered directly for reducing the vulnerability to natural disasters? Deressa et al. (2008) have shown that at the household level, farmers with higher incomes were less 


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