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How farmers’non-agricultural employment affects rural land circulation in China? 被引量:15

农户的非农就业如何影响中国的土地流转?
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摘要 To clarify the impact of non-agricultural employment on rural land circulation in China,we built logit models using the Chinese Household Income Project 2013 dataset,which includes 18,948 household samples over 15 provinces,126 cities and 234 counties of China in 2013.We use the proportion of non-agricultural income,the proportion of non-agricultural laborers and non-agricultural fixed operating assets to reflect the degree of the households’dependence on agriculture,the degree of the households’laborers committed to non-agricultural employment and the stability of non-agricultural employment,respectively.The results show that the stability of non-agricultural employment is an important reason for farmers to transfer out their land,and an increase in non-agricultural income is the fundamental reason.The proportion of non-agricultural assets has the greatest impact on the decision to transfer land,followed by the proportion of non-agricultural income.Per unit increase in the non-agricultural income ratio has a stronger effect on the transfer-out decision than it does on the transfer-in decision,which is a 0.09 increase of the probability of transfer-out the land and a 0.07 decrease of the probability of transfer-in the land.In terms of regional differences,when considering the impact of non-agricultural employment on the land transfer-out decision,the impacts of non-agricultural income and labor force are the greatest in the Central region.The impact of non-agricultural assets is the greatest in the Eastern region.For the Eastern region,the decision to transfer out land is mainly affected by non-agricultural assets and the non-agricultural labor force,and the decision to transfer in land is mainly affected by non-agricultural assets.In the Central and Western regions,the decision to transfer out land is mainly affected by non-agricultural assets,non-agricultural income and the non-agricultural labor force,in that order.The decision to transfer in land in the Central region is not significantly affected by non-agricultural employment.The decision to transfer in land in the Western region is mainly affected by non-agricultural assets,non-agricultural labor force and non-agricultural income,in that order.We note that non-agricultural assets have a prominent impact on land transfer,which shows that the stability of non-agricultural employment has an important impact on land transfer decision-making.Vocational training for rural labor forces may be an effective means to promote stable non-agricultural employment and simultaneously facilitate rural land circulation,especially in Central and Western China. To clarify the impact of non-agricultural employment on rural land circulation in China,we built logit models using the Chinese Household Income Project 2013 dataset,which includes 18,948 household samples over 15 provinces,126 cities and 234 counties of China in 2013.We use the proportion of non-agricultural income,the proportion of non-agricultural laborers and non-agricultural fixed operating assets to reflect the degree of the households’dependence on agriculture,the degree of the households’laborers committed to non-agricultural employment and the stability of non-agricultural employment,respectively.The results show that the stability of non-agricultural employment is an important reason for farmers to transfer out their land,and an increase in non-agricultural income is the fundamental reason.The proportion of non-agricultural assets has the greatest impact on the decision to transfer land,followed by the proportion of non-agricultural income.Per unit increase in the non-agricultural income ratio has a stronger effect on the transfer-out decision than it does on the transfer-in decision,which is a 0.09 increase of the probability of transfer-out the land and a 0.07 decrease of the probability of transfer-in the land.In terms of regional differences,when considering the impact of non-agricultural employment on the land transfer-out decision,the impacts of non-agricultural income and labor force are the greatest in the Central region.The impact of non-agricultural assets is the greatest in the Eastern region.For the Eastern region,the decision to transfer out land is mainly affected by non-agricultural assets and the non-agricultural labor force,and the decision to transfer in land is mainly affected by non-agricultural assets.In the Central and Western regions,the decision to transfer out land is mainly affected by non-agricultural assets,non-agricultural income and the non-agricultural labor force,in that order.The decision to transfer in land in the Central region is not significantly affected by non-agricultural employment.The decision to transfer in land in the Western region is mainly affected by non-agricultural assets,non-agricultural labor force and non-agricultural income,in that order.We note that non-agricultural assets have a prominent impact on land transfer,which shows that the stability of non-agricultural employment has an important impact on land transfer decision-making.Vocational training for rural labor forces may be an effective means to promote stable non-agricultural employment and simultaneously facilitate rural land circulation,especially in Central and Western China.
作者 WANG Jiayue XIN Liangjie WANG Yahui 王佳月;辛良杰;王亚辉(Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographic Sciences and Natural Resources Research,CAS,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;School of Geographical Science,Southwest University,Chongqing 400715,China)
出处 《Journal of Geographical Sciences》 SCIE CSCD 2020年第3期378-400,共23页 地理学报(英文版)
基金 National Natural Science Foundation of China,No.41571095 National Key Basic Research Program of China,No.2015CB452702。
关键词 farmers’non-agricultural employment rural land circulation moderate scale management China CHIP2013 dataset farmers’non-agricultural employment rural land circulation moderate scale management China CHIP2013 dataset
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