Reducing Rural Poverty Through Non-farm Job Creation in India.

IF 1 Q3 ECONOMICS
Indian Journal of Labour Economics Pub Date : 2022-01-01 Epub Date: 2022-03-15 DOI:10.1007/s41027-022-00359-9
Shiba Shankar Pattayat, Jajati Keshari Parida, I C Awasthi
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引用次数: 5

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.

Abstract Image

Abstract Image

通过创造非农业就业机会减少印度农村贫困。
本文基于二手数据,根据个体的部门就业状况估算了个体的贫困发生率,并探讨了决定个体贫困和从事非农部门工作(微观层面)联合概率的因素。它还发现了农村非农业部门就业对农村贫困发生率的影响(在宏观层面上),并确定了有助于减少印度农村贫困发生率的非农业部门的子部门。使用双变量probit,递归双变量probit回归模型,它发现由于更好的教育和培训以及其家庭户主的更高职业,个人的人类能力显着决定了他们在非农业部门就业的概率,这反过来有助于减少他们贫穷的机会。面板系统广义时间结果方法表明,印度的省级邦通过改善基础设施(公路、铁路、银行和工业)基础,实现了更高水平的非农业部门NSDP增长,并创造了就业机会,成功地将农村贫困发生率降低到相当低的水平。在此基础上,本文认为,通过发展农村制造业,促进教育、卫生、通信、房地产、金融和保险等现代服务业的增长,以及基础设施的发展,可以在可持续的基础上减少农村贫困的发生率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Indian Journal of Labour Economics
Indian Journal of Labour Economics Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
3.20
自引率
6.70%
发文量
48
期刊介绍: Indian Journal of Labour Economics (IJLE) is one of the few prominent Journals of its kind from South Asia. It provides eminent economists and academicians an exclusive forum for an analysis and understanding of issues pertaining to labour economics, industrial relations including supply and demand of labour services, personnel economics, distribution of income, unions and collective bargaining, applied and policy issues in labour economics, and labour markets and demographics. The journal includes peer reviewed articles, research notes, sections on promising new theoretical developments, comparative labour market policies or subjects that have the attention of labour economists and labour market students in general, particularly in the context of India and other developing countries.
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