Pattayat Shiba Shankar, Parida Jajati Keshari, Awasthi I C
Department of Economic Studies, Central University of Punjab, Bathinda, Punjab 151001 India.
Institute for Human Development, New Delhi, India.
Indian J Labour Econ. 2022;65(1):137-160. doi: 10.1007/s41027-022-00359-9. Epub 2022 Mar 15.
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.
基于二手数据，本文根据个人的部门就业状况估算贫困发生率，并探讨决定个人陷入贫困以及从事非农部门工作（微观层面）的联合概率的因素。研究还发现了农村非农部门就业对农村贫困发生率的影响（宏观层面），并确定了有助于降低印度农村贫困发生率的非农部门子部门。通过使用双变量概率模型、递归双变量概率回归模型，研究发现，由于教育和培训水平提高以及家庭户主职业地位较高，个人的人力能力显著决定了他们受雇于非农部门的概率，这反过来又有助于降低他们陷入贫困的几率。面板系统广义矩估计结果表明，印度各邦通过改善基础设施（道路、铁路、银行和工业）基础实现了更高水平的非农部门国内生产总值增长并创造了就业机会，成功地将农村贫困发生率降至相当低的水平。基于这些发现，本文认为，通过发展农村制造业、促进教育、卫生、通信、房地产以及金融和保险等现代服务业的增长以及基础设施建设，可以可持续地降低农村贫困发生率。
