In spite of green revolution and rapid economic growth, India’s vast population still suffers from hunger and poverty, especially in the rural areas. Moreover, drought adversely affects India’s economy by declining agricultural production and purchasing power. It also escalates rural unemployment which ultimately affects household food security. Our study investigated the food security of drought prone rural households in a broader context by linking the dimensions of food security with dimensions of climate change vulnerability. We used the primary data of 157 drought prone rural households of Odisha state in India for analysis. This study employed polychoric principal component analysis to construct an aggregate food security index. An ordered probit model was used to estimate the determinants of food security. The FSI showed that three-fourth of the respondents were facing food security issues with varying degrees. The estimates of ordered probit model indicated that joint family, education, migration and health insurance are key variables that determine food security, whereas drought adversely affected food security of rural households. Overarching strategies are required to effectively address food security issues in the wake of increased drought risk. This study provides an insight for policy makers in India and in similar south Asian countries who must consider food security in the light of drought.
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Engel’s ratio for rural and urban India is 48.32 and 37.26 respectively; whereas Engel’s ratios for rural and urban Odisha are 51.98 and 39.26 respectively.
In our study we used ordered probit model and had interpreted the signs of the coefficients to discuss about the direction of movement. We were more interested in showing the casual relationship rather than using it for prediction. Regression models with categorical dependent variable (logit, probit and multinomial logit/probit) computes marginal effects. Marginal effects show the difference between predicted probabilities of one category against the reference category. But in our case estimating marginal effects would undo the very advantage of using ordered regression. The reason for using ordered regression is that we can use the ordered nature of the dependent variable to get the effect on each other. To capture magnitude, we can use odds ratio. Odds ratio gives as the relative odds of the occurrence of a category. But there is no odds ratio estimation available for ordered probit. So we had also calculated ordered logit and odds ratio. The signs of the co-efficient don’t vary in both the models.
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We would like to thank the German Academic Exchange Service (DAAD) for financial support for this research. We would also like to thank Stiftung Fiat Panis for their financial support during data collection. We are also grateful to Nagesh Barik of CIFA, Odisha for the help and support provided during data collection. We honour the contribution of people in the research site for their responses and support during the data collection. We are also very grateful to the reviewers who provided invaluable comments and suggestions.
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Sam, A.S., Abbas, A., Surendran Padmaja, S. et al. Linking Food Security with Household’s Adaptive Capacity and Drought Risk: Implications for Sustainable Rural Development. Soc Indic Res 142, 363–385 (2019). https://doi.org/10.1007/s11205-018-1925-0