天气变化与临时劳动力迁移:印度部分半干旱村庄的面板数据分析

Kalandi Charan Pradhan, K. Narayanan
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引用次数: 6

摘要

摘要本研究的目的是了解天气变化与临时劳动力迁移之间的关系。在此过程中,我们研究了劳动力迁移如何被用作印度半干旱村庄气候变化的适应策略。我们使用作物产量偏差作为家庭层面天气变化的代理变量。为了调查研究的目的,我们使用了2005-2014年期间来自泰伦加纳邦和马哈拉施特拉邦六个村庄的210户家庭的面板数据,使用了村庄动态南亚(VDSA)数据集。我们使用村庄、州和总体水平的逻辑回归模型来展示这些水平上的因素如何影响临时劳动力迁移轨迹。研究发现,非迁入农户的作物产量偏差得分高于迁入农户,表明迁入农户对气候变化的适应能力高于迁入农户。此外,逻辑模型的经验证据表明,与人口和社会经济因素一起,天气变化在总体水平上影响临时迁移(全样本估计)。除此之外,我们还发现天气变化在统计上显著地决定了整个马哈拉施特拉邦在邦一级的临时迁移,而在村一级的估计中只有一个村庄(卡尔曼)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weather variation and temporary labor migration: a panel data analysis for select semi-arid villages in India
ABSTRACT The purpose of this study is to understand the relationship between weather variation and temporary labor migration. In doing so, we investigate how labor migration is used as an adaptation strategy to weather variation for the select Indian semi-arid villages. We use crop yield deviation as a proxy variable for the weather variation at the household level. In order to investigate the objective of the study, we employ panel data of 210 households using Village Dynamic South Asia (VDSA) data set of six villages from the state of Telangana and Maharashtra for the period 2005–2014. We have used village, state and aggregate level logistic regression models to demonstrate how factors at each of these levels can influence temporary labor migration trajectories. The study finds that the score of crop yield deviation for the non-migrant households is higher as compared to migrant households, which shows that migrant households have a higher adaptive capacity to weather variation as compared to their counterpart households. Further, the empirical evidence from the logistic model shows that along with demographic and socio-economic factors, weather variation influences the temporary migration at aggregate level (full sample estimation). In addition to this, we also find that weather variation is statistically significant in determining temporary migration for entire Maharashtra at state level estimation and only one village (Kalman) at village level estimation.
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