Study on Effects of Land Transfer on Human Capital Accumulation of Rural Adolescents and Its Mechanisms

doi:10.11923/j.issn.2095-4050.cjas2023-0132

Abstract

Abstract:

Human capital accumulation is an inevitable requirement for the promotion of rural talent revitalization. Using the CFPS2018 database, in accordance with the logical framework of "land transfer → family education cognition→ intergenerational human capital investment → adolescent human capital accumulation", the paper uses the PSM model and the mediation effect model to explore the effects and mechanisms of land transfer policies on human capital accumulation of rural adolescents, and the differences in the effects are explored based on the gender of household decision makers. The results show that: (1) compared with rural families that did not participate in land transfer, rural families that participated in land transfer has a higher level of adolescent human capital accumulation and their children has a higher academic achievement;(2) rural families involved in land transfer have higher educational cognition, which in turn will increase the material and non-material human capital investments of their adolescent children and ultimately promote the accumulation of adolescent human capital investments; (3) in rural households involved in land transfer, female household decision-makers pay attention to education investment. So, the relevant policy design should focus on how to promote rural land transfer, make up for the shortcomings of human capital accumulation in rural areas, and carry out female poverty alleviation programs to further achieve optimal allocation of educational resources and effective reduction of the gap between urban and rural educational development.

Key words: land transfer, human capital accumulation, adolescents, gender differences, rural revitalization

HUA Jing, FAN Lujie, LI Ruining, ZHANG Jianxin. Study on Effects of Land Transfer on Human Capital Accumulation of Rural Adolescents and Its Mechanisms[J]. Journal of Agriculture, 2024, 14(5): 90-100.

Figures/Tables 10

References 39

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变量类型		变量	变量描述	均值	方差	最小值	最大值
处理变量		tdlz	是否将土地出租给别人：是=1，否=0	0.145	0.352	0	1
协变量	家庭特征变量	agep	家庭决策者的年龄	36.102	8.147	20	81
		agep^2	家庭决策者年龄的平方	1369.696	635.710	400	6561
		gender	家庭决策者的性别，女=0，男=1	0.490	0.500	0	1
		edumax	家庭的最高受教育水平，文盲/半文盲=1，小学=2，初中=3，高中=4，大专=5，大学本科=6，硕士=7，博士=8	2.950	1.099	1	8
		familysize	家庭成员数	5.799	2.171	2	21
		young15	10~15岁青少年子女个数	1.563	0.809	1	6
		old60	60岁及上家庭成员数量	0.883	0.920	0	2
		fs	家庭藏书量，没有=0，1~10本=1，11~20本=2，21~50本=3，51~100本=4，101~500本=5，501~1000本=6，≥1001本=7	1.377	1.597	0	7
协变量	家庭经济变量	fincome	家庭净收入总额（元），在原值上加1并取对数	10.885	0.876	0	13.815
		totalasset	家庭净资产（元），在原值上加1并取对数	12.282	1.125	0	17.727
		fm	是否有人从事个体私营，是=1，否=0	0.100	0.301	0	1
	宏观社区变量	cg	到达省城的时间（小时），样本村的人去省会最常用的交通方式，如汽车、火车、飞机等	5.606	6.343	0	60
		ch	外出打工的比例/%	37.200	22.111	0	90
		provcd	东部地区=1，中部地区=2，西部地区=3	2.092	0.830	1	3
结果变量	家庭教育认知	expect	希望孩子的教育程度：小学=3，初中=4，高中/技校/职校=5，大专=6，本科=7，硕士=8，博士=9	6.777	1.122	3	9
	家庭教育认知	saving	过去12个月为子女教育储蓄的钱（元），在原值上加1并取对数	8.477	1.347	0	12.206
	家庭代际人力资本投资	tv	放弃给孩子看电视的频率：从不=1，很少（每月一次）=2，偶尔（每周一次）=3，经常（每周2~4次）=4，经常（每周5~7次）=5	3.280	1.334	1	5
		talk	与孩子谈论学校的频率：从不=1，很少（每月一次）=2，偶尔（每周一次）=3，经常（每周2~4次）=4，经常（每周5~7次）=5	3.087	1.207	1	5
		block-tv	阻止孩子看电视的频率：从不=1，很少（每月一次）=2，偶尔（每周一次）=3，经常（每周2~4次）=4，经常（每周5~7次）=5	3.457	1.184	1	5
		tutor	过去12个月花费的课外辅导费（元），在原值上加1并取对数	5.794	2.953	0	10.413
	青少年人力资本积累	classrank	上次期中、期末考试班级排名(%)：前10%=1，前11%~25%=2，前26%~50%=3，前51%~75%=4，后24%=5	3.025	1.713	1	5
	青少年人力资本积累	graderank	上次期中和期末考试成绩排名(%)：前10%=1，前11%~25%=2，前26%~50%=3，前51%~75%=4，后24%=5	3.390	1.917	1	5

变量类型		变量	变量描述	均值	方差	最小值	最大值
处理变量		tdlz	是否将土地出租给别人：是=1，否=0	0.145	0.352	0	1
协变量	家庭特征变量	agep	家庭决策者的年龄	36.102	8.147	20	81
		agep^2	家庭决策者年龄的平方	1369.696	635.710	400	6561
		gender	家庭决策者的性别，女=0，男=1	0.490	0.500	0	1
		edumax	家庭的最高受教育水平，文盲/半文盲=1，小学=2，初中=3，高中=4，大专=5，大学本科=6，硕士=7，博士=8	2.950	1.099	1	8
		familysize	家庭成员数	5.799	2.171	2	21
		young15	10~15岁青少年子女个数	1.563	0.809	1	6
		old60	60岁及上家庭成员数量	0.883	0.920	0	2
		fs	家庭藏书量，没有=0，1~10本=1，11~20本=2，21~50本=3，51~100本=4，101~500本=5，501~1000本=6，≥1001本=7	1.377	1.597	0	7
协变量	家庭经济变量	fincome	家庭净收入总额（元），在原值上加1并取对数	10.885	0.876	0	13.815
		totalasset	家庭净资产（元），在原值上加1并取对数	12.282	1.125	0	17.727
		fm	是否有人从事个体私营，是=1，否=0	0.100	0.301	0	1
	宏观社区变量	cg	到达省城的时间（小时），样本村的人去省会最常用的交通方式，如汽车、火车、飞机等	5.606	6.343	0	60
		ch	外出打工的比例/%	37.200	22.111	0	90
		provcd	东部地区=1，中部地区=2，西部地区=3	2.092	0.830	1	3
结果变量	家庭教育认知	expect	希望孩子的教育程度：小学=3，初中=4，高中/技校/职校=5，大专=6，本科=7，硕士=8，博士=9	6.777	1.122	3	9
	家庭教育认知	saving	过去12个月为子女教育储蓄的钱（元），在原值上加1并取对数	8.477	1.347	0	12.206
	家庭代际人力资本投资	tv	放弃给孩子看电视的频率：从不=1，很少（每月一次）=2，偶尔（每周一次）=3，经常（每周2~4次）=4，经常（每周5~7次）=5	3.280	1.334	1	5
		talk	与孩子谈论学校的频率：从不=1，很少（每月一次）=2，偶尔（每周一次）=3，经常（每周2~4次）=4，经常（每周5~7次）=5	3.087	1.207	1	5
		block-tv	阻止孩子看电视的频率：从不=1，很少（每月一次）=2，偶尔（每周一次）=3，经常（每周2~4次）=4，经常（每周5~7次）=5	3.457	1.184	1	5
		tutor	过去12个月花费的课外辅导费（元），在原值上加1并取对数	5.794	2.953	0	10.413
	青少年人力资本积累	classrank	上次期中、期末考试班级排名(%)：前10%=1，前11%~25%=2，前26%~50%=3，前51%~75%=4，后24%=5	3.025	1.713	1	5
	青少年人力资本积累	graderank	上次期中和期末考试成绩排名(%)：前10%=1，前11%~25%=2，前26%~50%=3，前51%~75%=4，后24%=5	3.390	1.917	1	5

tdlz	系数	标准差	Z统计量
agep	0.002	0.0271	0.080
agep^2	0.00002	0.0003	0.070
gender	0.011	0.056	0.190
edumax	0.056**	0.025	2.220
young15	0.006	0.057	0.140
old60	-0.029	0.038	-0.770
familysize	-0.069***	0.017	-4.190
fs	0.0009	0.0176	0.050
fincome	0.159***	0.054	2.970
totalasset	0.063**	0.031	2.040
fm	0.513***	0.110	4.670
provcd	-0.212***	0.045	-4.710
cg	-0.015**	0.007	-2.050
ch	0.002	0.002	1.130
常数项	-1.418***	0.279	-5.090

tdlz	系数	标准差	Z统计量
agep	0.002	0.0271	0.080
agep^2	0.00002	0.0003	0.070
gender	0.011	0.056	0.190
edumax	0.056**	0.025	2.220
young15	0.006	0.057	0.140
old60	-0.029	0.038	-0.770
familysize	-0.069***	0.017	-4.190
fs	0.0009	0.0176	0.050
fincome	0.159***	0.054	2.970
totalasset	0.063**	0.031	2.040
fm	0.513***	0.110	4.670
provcd	-0.212***	0.045	-4.710
cg	-0.015**	0.007	-2.050
ch	0.002	0.002	1.130
常数项	-1.418***	0.279	-5.090

变量	匹配前(U)、后(M)	平均值		偏差/%	T统计量	P值
变量	匹配前(U)、后(M)	处理组	控制组	偏差/%	T统计量	P值
agep	U	36.169	36.228	-0.700	-0.140	0.891
agep	M	36.212	35.972	2.900	0.420	0.673
agep^2	U	1374.800	1378.400	-0.600	-0.110	0.915
agep^2	M	1378.400	1359.000	3.000	0.430	0.665
gender	U	0.496	0.495	0.200	0.050	0.964
gender	M	0.496	0.495	0.400	0.050	0.958
edumax	U	3.092	2.899	15.500	3.140	0.002
edumax	M	3.083	3.104	-1.700	-0.250	0.803
young15	U	1.499	1.588	-11.400	-2.060	0.039
young15	M	1.504	1.494	1.200	0.190	0.853
old60	U	0.872	0.909	-4.000	-0.760	0.447
old60	M	0.876	0.854	2.400	0.340	0.731
familysize	U	5.441	5.784	-16.800	-2.970	0.003
familysize	M	5.460	5.492	-1.600	-0.250	0.802
fs	U	1.508	1.412	6.000	1.140	0.255
fs	M	1.509	1.513	-0.300	-0.040	0.970
fincome	U	11.357	10.994	42.400	3.700	0.000
fincome	M	11.264	11.256	0.900	0.060	0.951
totalasset	U	12.564	12.250	27.400	5.280	0.000
totalasset	M	12.533	12.524	0.700	0.110	0.911
fm	U	0.142	0.093	15.300	3.110	0.002
fm	M	0.139	0.142	-0.900	-0.130	0.900
provcd	U	2.034	2.115	-10.200	-1.890	0.059
provcd	M	2.039	2.063	-3.000	-0.420	0.671
cg	U	4.175	5.066	-12.600	-2.410	0.016
cg	M	4.209	4.245	-0.500	-0.080	0.934
ch	U	36.977	35.872	4.900	0.940	0.350
ch	M	37.045	36.953	0.400	0.060	0.953
Pseudo R²	U	0.131
Pseudo R²	M	0.014
LR chi2	U	55.420
LR chi2	M	3.070
p>chi2	U	0.000
p>chi2	M	0.999
Mean Bias	U	22.700
Mean Bias	M	3.900