Determinants of Poverty, Self-Reported Shocks, and Coping Strategies: Evidence from Rural Nepal

Abstract

This paper assesses the interrelationship between poverty, climatic and non-climatic shocks, and shock coping strategies adopted by farm-based rural households in Nepal. An analysis is based on a comprehensive data set collected from 300 randomly selected households from three purposively chosen villages of Gandaki province. The study utilizes binary and ordered probit regression models to analyze the determinants of poverty, shocks, and coping strategies. Findings reveal that the Dalit (ethnic group), large-sized, and agricultural households are more likely to be characterized as poor. The study further shows that majority of the households are exposed to the severe shock of climatic types. Patterns of shock exposure vary with the household’s characteristics. In particular, poor households in the hills primarily dependent on forest for livelihood are more likely to experience severe shocks. Further analyses indicate that the households ex-post choose dissaving, borrowing, shifting occupation, and migration to cope with severe climatic shocks. The analyses also reveal that the nature of shocks, socio-economic, demographic, geographic contexts, and factor endowments effect adopting a particular coping strategy. Findings suggest household level-specific support should be provided to mitigate the effects of shock, and appropriate measures are taken to protect their means of living.

Determinants of poverty among Indigenous people in Mexico's Guerrero Mountain Region

“…Third, the variables related to public services ( internet access and landline telephone service ) have a positive impact on decreasing poverty. These findings are consistent with studies carried out around the world (Berger, 2019; Gautam et al., 2021; Liu et al., 2017; Olarinde et al., 2020; Xie & Xie, 2021). For education, evidence suggests that, in addition to being a social right, it allows households to make more informed decisions about the investment of their assets, which in turn enables them to obtain a higher income (Berger, 2019; Gautam et al., 2021; Liu et al., 2017; Olarinde et al., 2020).…”

“…However, Gautam et al. (2021) and Xie and Xie (2021) echo our results in finding that a larger household size is associated with lower well‐being and income, since poor households are located in ecologically degraded lands without access to technology. This means that the labour income of an additional member does not compensate for their expenses and, therefore, represents a burden for maintaining a stable level of well‐being.…”

“…This vast literature on the determinants of multidimensional poverty has been used with greater emphasis to examine poverty levels in L&MICs, such as Ethiopia (Bersisa & Heshmati, 2021), Nigeria (Olarinde et al., 2020), Cameroon (Gautam et al., 2021), Brazil (Medeiros et al., 2021), and Bangladesh (Lázár et al., 2020). For example, Bersisa and Heshmati (2021) showed that demographic and regional characteristics influence household poverty levels in Ethiopia.…”

“…Studies that address this perspective have become popular because they focus on more general aspects of well-being (consumption, food security, health, education, labour rights, and human dignity) (Alkire et al, 2021). This vast literature on the determinants of multidimensional poverty has been used with greater emphasis to examine poverty levels in L&MICs, such as Ethiopia (Bersisa & Heshmati, 2021), Nigeria (Olarinde et al, 2020), Cameroon (Gautam et al, 2021), Brazil (Medeiros et al, 2021), and Bangladesh (Lázár et al, 2020). For example, Bersisa and Heshmati (2021) showed that demographic and regional characteristics influence household poverty levels in Ethiopia.…”

“…On the other hand, education, health, and access to credit have a positive influence on escaping poverty (Mora‐Rivera et al., 2020; Olarinde et al., 2020). Conversely, adverse events, such as family illnesses and extreme climate events (floods and cyclones), positively affect transitory poverty (Gautam et al., 2021; Lázár et al., 2020; Liu et al., 2017). Variables such as the provision, quality, and use of infrastructure have also been included in the analysis of the factors that determine multidimensional poverty (Medeiros et al., 2021).…”