农户加入合作社的收入效应和资产效应——基于CHIP数据的实证研究

摘要/Abstract

摘要：

本文使用中国家庭收入调查(CHIP)数据,实证研究农户加入合作社对家庭可支配收入和农业经营性固定资产的影响。考虑可观测变量和不可观测变量的潜在自选择偏差问题,本文应用AI稳健标准误进行倾向得分匹配(PSM)估计,并用MLE方法估计处理效应模型(TEM)。实证结果表明,加入合作社的农户人均家庭可支配收入和农业经营性固定资产显著增加,但在不同市场化程度的地区,其影响显著不同:市场化程度较高地区的收入效应明显较大,而市场化程度较低地区的资产效应明显较大。样本总体的处理效应显示,与非农民合作社成员相比,加入农民合作社导致人均家庭可支配收入和农业经营性固定资产分别增加16%和45%。最后,本文还讨论了实证发现的潜在政策应用。

关键词: 农民合作组织, 收入效应, 资产效应, 倾向得分匹配, 处理效应模型

Abstract:

While Chinese farmer cooperatives have developed rapidly in the past two decades due to strong support of the government and other factors, most of previous studies have focused on their effects on farmers’ income and few studies have assessed their effects on farmers’ production assets. This study first analyzes the mechanisms of how joining farmer cooperatives may increase farmers’ household income and fixed agricultural production assets and then uses the Chinese Household Income Project (CHIP) survey data to assess the effects empirically.
There are four major mechanisms for the members of farmer cooperatives to earn higher income in China: by overcoming both technical and market limitations of China’s small-scale farm operations through collective actions and the economy of scale, by receiving higher prices through the cooperatives’ contract sales, product standardization and brand recognition, by sharing the profits of cooperatives according to the cooperative agreements, and by benefiting from government financial and technical supports offered through farmer cooperatives. Similarly, there are three major mechanisms for farmer cooperatives to help their members increase their fixed agricultural production assets: (1) The increased scale of operation under farmer cooperatives makes it feasible and profitable for farmers to purchase advanced and applicable agricultural machinery, (2) the Chinese government has provided fiscal subsidies for farmers to purchase agricultural machinery and many of such programs are provided via farmer cooperatives, and (3) the government subsidies have shown a leverage effect and stimulated more capital investment of cooperative members in fixed production assets.
In the empirical analyses, this study uses the Abadie-Imbens standard errors to address the potential self-selection bias problems for the observable variables. In addition, this study estimates the model using two sub-samples, one with the total marketization index less than the sample mean and the other with the index greater than the sample mean, respectively. The PSM estimation results show that farmer cooperative members earned significantly higher household income and had significantly more fixed agricultural production assets. Also, the positive impact on the household income was higher for the sub-sample with a higher level of marketization index, while the positive impact on fixed agricultural production assets was higher for the sub-sample with a lower level of marketization index. Considering the potential self-selection bias problems for the non-observable variables, this study uses the MLE method to estimate a treated effects model (TEM) with the number of years each province had been promoting farmer cooperatives as the instrument variable (IV). The TEM estimation results for the whole sample indicate that farmer cooperative membership increased their per capita disposable household income and per capita fixed agricultural production assets by 16% and 45%, respectively. This study suggests that the development of farmer cooperatives has not only increased farmers' household income and fixed agricultural production assets, but also shown the potential of increasing the productivity via mechanization and contributing to sustainable development of the agricultural sector in China.

Key words: Farmer Cooperatives, Income Effect, Asset Effect, Propensity Score Matching, Treated Effects Model

中图分类号:

邹洋, 王庆斌, 季荣妹. 农户加入合作社的收入效应和资产效应——基于CHIP数据的实证研究[J]. 财经论丛, 2021, 37(11): 13-24.

ZOU Yang, Wang Qingbin, Ji Rongmei. Effects of Joining Farmer Cooperatives on Family Income and Agricultural Fix Production Assets in China: A Study Based on CHIP Survey Data[J]. Collected Essays on Finance and Economics, 2021, 37(11): 13-24.

图/表 6

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变量	定义
lnperhincome2013	2013年末农户家庭人均可支配收入的对数
lnperassets2013	2013年末农户家庭人均拥有的农业经营性固定资产现价估计净值的对数
coop	是否加入合作社,加入=1,未加入=0
lnperland	2013年初家庭人均持有的土地亩数的对数
lnl	2013年农忙季节从事家庭农林牧渔业生产小时数的对数
lnsize	农户家庭规模的对数
lnedu	受正规教育年限的对数
marriage	是否已婚,是=1,否=0
lnage	年龄的对数
gender	是否男性,是=1,否=0
变量	定义
lnagegender	年龄对数与性别交叉项
health	健康状况,健康=1,较健康=2,一般健康=3,不健康=4,非常不健康=5
carder	是否为村干部,是=1,否=0
sala	是否在本地从事工资性工作,是=1,否=0
nonagr	是否在本地从事非农生产经营活动,是=1,否=0
outwork	是否外出打工,是=1,否=0
implementyears	各省实施促进合作社发展办法年份到2013年的年数:2013减去实施开始年份
market2012	2012年市场化总指数
east	是否属于东部地区,是=1,否=0;东部包括北京、辽宁、江苏、山东、广东
central	是否属于中部地区,是=1,否=0;中部包括山西、安徽、河南、湖北、湖南
west	是否属于西部地区,是=1,否=0;西部包括重庆、四川、云南和甘肃

变量	定义
lnperhincome2013	2013年末农户家庭人均可支配收入的对数
lnperassets2013	2013年末农户家庭人均拥有的农业经营性固定资产现价估计净值的对数
coop	是否加入合作社,加入=1,未加入=0
lnperland	2013年初家庭人均持有的土地亩数的对数
lnl	2013年农忙季节从事家庭农林牧渔业生产小时数的对数
lnsize	农户家庭规模的对数
lnedu	受正规教育年限的对数
marriage	是否已婚,是=1,否=0
lnage	年龄的对数
gender	是否男性,是=1,否=0
变量	定义
lnagegender	年龄对数与性别交叉项
health	健康状况,健康=1,较健康=2,一般健康=3,不健康=4,非常不健康=5
carder	是否为村干部,是=1,否=0
sala	是否在本地从事工资性工作,是=1,否=0
nonagr	是否在本地从事非农生产经营活动,是=1,否=0
outwork	是否外出打工,是=1,否=0
implementyears	各省实施促进合作社发展办法年份到2013年的年数:2013减去实施开始年份
market2012	2012年市场化总指数
east	是否属于东部地区,是=1,否=0;东部包括北京、辽宁、江苏、山东、广东
central	是否属于中部地区,是=1,否=0;中部包括山西、安徽、河南、湖北、湖南
west	是否属于西部地区,是=1,否=0;西部包括重庆、四川、云南和甘肃

变量	(1)几率 (稳健标准误)	(2)平均边际效应 (Delta法标准误)	(3)平均边际效应 (Delta法标准误)
lnperland	0.960(0.080)	-0.001(0.002)	-0.001(0.002)
lnl	1.066(0.091)	0.002(0.002)	0.002(0.002)
lnsize	0.921(0.163)	-0.002(0.005)	-0.002(0.005)
lnedu	1.248(0.224)	0.006(0.005)	0.006(0.005)
marriage	1.325(0.554)	0.008(0.011)	0.008(0.011)
lnage	1.764(0.673)	0.015(0.010)	0.015(0.010)
gender	5.959(9.917)	0.048(0.045)	0.048(0.045)
lnagegender	0.642(0.277)	-0.012(0.012)	-0.012(0.012)
health	0.769^***(0.067)	-0.007^***(0.002)	-0.007^***(0.002)
carder	1.403(0.434)	0.009(0.008)	0.009(0.008)
sala	0.924(0.158)	-0.002(0.005)	-0.002(0.005)
nonagr	0.615^*(0.173)	-0.013^*(0.008)	-0.013^*(0.008)
outwork	0.583^*(0.162)	-0.014^*(0.008)	-0.015^**(0.008)
implemenyears	0.908^***(0.035)	-0.003^***(0.001)	-0.003^***(0.001)
east	0.638^**(0.143)	-0.012^**(0.006)	-0.012^**(0.006)
west	1.510^***(0.244)	0.011^***(0.004)	0.011^***(0.004)
观测值	8125	8125	8125
Pseudo R²	0.023
Wald卡方统计值(概率)	66.200(0.000)

变量	(1)几率 (稳健标准误)	(2)平均边际效应 (Delta法标准误)	(3)平均边际效应 (Delta法标准误)
lnperland	0.960(0.080)	-0.001(0.002)	-0.001(0.002)
lnl	1.066(0.091)	0.002(0.002)	0.002(0.002)
lnsize	0.921(0.163)	-0.002(0.005)	-0.002(0.005)
lnedu	1.248(0.224)	0.006(0.005)	0.006(0.005)
marriage	1.325(0.554)	0.008(0.011)	0.008(0.011)
lnage	1.764(0.673)	0.015(0.010)	0.015(0.010)
gender	5.959(9.917)	0.048(0.045)	0.048(0.045)
lnagegender	0.642(0.277)	-0.012(0.012)	-0.012(0.012)
health	0.769^***(0.067)	-0.007^***(0.002)	-0.007^***(0.002)
carder	1.403(0.434)	0.009(0.008)	0.009(0.008)
sala	0.924(0.158)	-0.002(0.005)	-0.002(0.005)
nonagr	0.615^*(0.173)	-0.013^*(0.008)	-0.013^*(0.008)
outwork	0.583^*(0.162)	-0.014^*(0.008)	-0.015^**(0.008)
implemenyears	0.908^***(0.035)	-0.003^***(0.001)	-0.003^***(0.001)
east	0.638^**(0.143)	-0.012^**(0.006)	-0.012^**(0.006)
west	1.510^***(0.244)	0.011^***(0.004)	0.011^***(0.004)
观测值	8125	8125	8125
Pseudo R²	0.023
Wald卡方统计值(概率)	66.200(0.000)

变量	(1)	(2)
一对一(卡尺0.1内)最近匹配	0.123^**(0.064)	0.162^**(0.072)	0.245^***(0.054)
一对四(卡尺0.1内)最近匹配	0.153^***(0.053)	0.107^**(0.052)	0.317^***(0.036)
观测值	8125	3532	4593