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Reducing Rural Poverty Through Non-farm Job Creation in India

Shiba Shankar Pattayat, Jajati Keshari Parida () and I. C. Awasthi
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Shiba Shankar Pattayat: Central University of Punjab
Jajati Keshari Parida: Central University of Punjab
I. C. Awasthi: Institute for Human Development

The Indian Journal of Labour Economics, 2022, vol. 65, issue 1, No 8, 137-160

Abstract: Abstract Based on secondary data, this paper estimates the incidence of poverty by sectoral employment status of individuals and it explores the factors determining individual’s joint probabilities of being poor and being engaged in the non-farm sector jobs (at micro-level). It also finds the impact (at macro-level) of rural non-farm sector employment on the incidence of rural poverty, and it identifies the subsectors of the non-farm sector, which help reduce the incidence of rural poverty in India. Using bivariate probit, recursive bivariate probit regression models, it finds that individual’s human capabilities owing to better education and training and higher occupations of their head of the family significantly determine their probability of being employed in the non-farm sectors, which in turn help reduce their chance of being poor. The panel system generalized methods of moment result suggest that the provincial states of India, which have achieved higher level of non-farm sector NSDP growth along with the creation of jobs through an improved level of infrastructure (roads, railways, banking, and industries) base, have succeeded to reduce the incidence of rural poverty to substantially low levels. Based on these findings, it is argued that the incidence of rural poverty can be reduced on a sustainable basis through the development of rural manufacturing, and by promoting growth of modern service sectors like education, health, communication, real estate, and finance and insurance, along with the infrastructural development.

Keywords: Non-farm employment; Income poverty; Bi-variate probit regression; India (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s41027-022-00359-9

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