Can Developmental Interventions Reduce Households’ Vulnerability? Empirical Evidence from Rural India

Abstract

Vulnerability is a multidimensional concept incorporating notions of risk and poverty. While it has been established that higher incidence of poverty in developing countries exacerbates vulnerability, the role of risk requires closer inspection. Developmental interventions in these countries target poverty reduction, which in turn, could reduce vulnerability. However, a key question is whether developmental interventions reduce the vulnerability and risks faced by households. To answer this, the present study empirically examines the impact of developmental interventions on the vulnerability of households in a rural Indian setting. The major advantage hence is that it not only looks into the impact on aggregate vulnerability but also its different components such as poverty, covariate, idiosyncratic and unexplained risks. Empirical analysis is based on a survey of 800 households in the drought-prone villages of western Odisha, India, where a key developmental intervention, Western Orissa Rural Livelihood Project was implemented during the last decade. Adopting 'vulnerability as expected utility' approach, this study reveals three major findings. First, both aggregate risk and poverty are the dominant sources of vulnerability, with the former accounting for a sizable share. Second, the households that benefited from livelihood interventions are found less vulnerable. Third, the other major determinants of vulnerability are education, access to social network, family size and crop-diversification. From a policy perspective, results support continuation of these programmes, but realigning these also target risk reduction.

Synergy between adaptations and resilience of livelihood from climate change vulnerability: A group-wise comparison of adapters and non-adapters

“…Moreover, the findings of the study showed that an increase in age would increase the chances to adopt the CC adaptations. Inversely, the negative sign of the coefficient for wheat's yield specifies that with the increasing age, the capacity to work also goes down and these arguments are maintained by [51,55,68,78], conversely, outcomes of [79] opposed this. Results show that total landholding and time spent by the HHH on-farm could have a positive and significant impact on the yield of adapters, implying an increase in the values of these both parameters would increase farm productivity overtime.…”

“…ESR is a parametric approach, which addresses the problem of selective biased and missing unobserved characteristics, which influence the decision to adopt the adaptation to CC [66][67][68][69][70]. It follows the two-stage analysis.…”

Do risk management strategies prevent economic and non‐economic loss and damages? Empirical evidence from drought affected households in western India

“…There could be either positive or negative relationship for household size, and dependency ratio is the major factor for deciding the sign of the association. Several studies found a positive coefficient value, i.e., a directly proportional association between household size and impact (Bahinipati, 2016;Patnaik et al, 2016Patnaik et al, , 2017. Similar observation (positive/negative) is also anticipated for age of the HH.…”

“…Both idiosyncratic and covariate risks and shocks are a part and parcel of the farm households' livelihood in a developing nation like India, and these are negatively affecting their well-being, particularly those in the lower quintiles (Bahinipati, 2016;Patnaik et al, 2016Patnaik et al, , 2017.…”

“…In regard to the former, studies identify the major influential factors under socioeconomic characteristics, farm characteristics, agro-climatic and meteorological conditions, and risk attitude behavior of the households (see Bahinipati & Patnaik, 2021). Several studies, on the other hand, also look into the extent of loss and damage (L&D) or impact that could be mitigated by enhancing resilience capacity of society through various generic and disaster-specific adaptation measures (Bahinipati & Patnaik, 2015, 2020Patnaik et al, 2017Patnaik et al, , 2019. Under this backdrop, micro-level studies empirically proved the effectiveness of several adaptation measures, such as farm-level, drought tolerant rice, and insurance (Cariappa et al, 2020;Patnaik et al, 2019).…”