精准扶贫政策对国家级贫困县城乡收入差距的影响

doi:10.18402/resci.2021.08.04

摘要/Abstract

摘要：

精准扶贫是近年来中国典型的农村偏向性政策,对城乡收入差距的影响效应有待进一步检验。本文基于2010—2019年县域面板数据,以中国典型贫困地区——云南省为研究案例,利用倾向得分匹配-双重差分法（PSM-DID）研究了精准扶贫政策实施对国家级贫困县城乡收入差距的影响。结果表明：①精准扶贫政策有利于国家级贫困县农民增收,进而显著缩小了国家级贫困县城乡相对收入差距,但对城乡绝对收入差距作用不明显。其他条件不变的情况下,受到精准扶贫政策冲击后,国家级贫困县农民人均收入增长率比非贫困县平均上升0.1512,而城乡相对收入差距平均降低0.3668,但这种政策效应存在一定的波动性,政策累积效应不明显。②精准扶贫政策主要通过促进城镇化、增加资本投入、提高农业产出水平、以及产业结构非农化调整4个方面促进了国家级贫困县城乡收入差距的缩小。长远来看,在提高农业产出水平的同时,逐步推动产业非农化调整将给贫困地区农民增收带来持续累积效应,对改善贫困地区城乡收入分配产生积极影响。针对以上结论,进一步提出了相应的政策建议。

关键词: 精准扶贫, 城乡收入差距, 倾向得分匹配-双重差分法（PSM-DID）, 贫困县, 准自然实验, 云南省

Abstract:

Targeted poverty alleviation is the typical rural oriented policy in China in recent years, and its impact on urban-rural income gap needs to be further tested. Based on the county panel data from 2010 to 2019, a case study was conducted in Yunnan Province, a typical poverty-stricken area in China, to examine the impact of targeted poverty alleviation policy on urban-rural income gap in national poverty-stricken counties by means of propensity score matching-difference-in-differences method (PSM-DID). The results show that: (1) The targeted poverty alleviation policy is beneficial to the increase of income of the farmers in the national poverty-stricken counties, and can reduce the relative income gap between urban and rural areas. But it has no obvious impact on the absolute income gap between urban and rural areas. Other things being equal, the per capita income growth rate of farmers in poor counties at the national level rose by 0.1512 on average, while the relative income gap between urban and rural areas decreased by 0.3668 on average, but this kind of policy effect exhibits certain volatility, and the cumulative policy effect is not clear. (2) The targeted poverty alleviation policy has promoted the reduction of the urban-rural income gap in the poor counties by promoting urbanization, increasing capital input, raising the level of agricultural output, and adjusting the non-agricultural industrial structure. In the long run, while increasing the level of agricultural output, the gradual promotion of non-agricultural industrial adjustment will bring about a continuous cumulative effect on the income increase of farmers in poor areas and a positive impact on the improvement of income distribution between urban and rural areas. In view of the above conclusion, corresponding policy recommendations were further made.

Key words: targeted poverty alleviation, urban-rural income gap, PSM-DID, poverty-stricken counties, quasi-natural experiment, Yunnan Province

张博胜, 曹筱杨. 精准扶贫政策对国家级贫困县城乡收入差距的影响[J]. 资源科学, 2021, 43(8): 1549-1561.

ZHANG Bosheng, CAO Xiaoyang. Impact of targeted poverty alleviation policy on the urban-rural income gap in national poverty-stricken counties[J]. Resources Science, 2021, 43(8): 1549-1561.

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	政策实施前	政策实施后	差分
贫困县	β₀+β₁	β₀+β₁+β₂+β₃	β₂+β₃
非贫困县	β₀	β₀+β₂	β₂
poor×time	-	-	β₃

变量类型	变量名称	PSM匹配前样本均值			PSM匹配后样本均值
变量类型	变量名称	贫困县	非贫困县	标准偏差	贫困县	非贫困县	标准偏差
被解释变量	城乡相对收入差距	3.44	2.83	0.43	3.15	2.85	0.21
被解释变量	城乡绝对收入差距	1.53	1.71	0.12	1.71	1.66	0.04
控制变量	城镇化	0.11	0.25	0.10	0.14	0.22	0.06
	资本投入	1.01	0.93	0.06	1.13	0.91	0.15
	产业非农化	1.56	3.47	1.36	2.06	3.02	0.68
	农业产出/（万元/人）	0.80	0.90	0.07	1.02	0.90	0.09
	地方财政政策	0.46	0.22	0.17	0.39	0.23	0.11
	农村就业	0.59	0.61	0.02	0.60	0.61	0.01
样本数		860	330		355	271

变量	农民人均可支配收入	相对收入差距	绝对收入差距	农民人均可支配收入	相对收入差距	绝对收入差距
变量	(1)	(2)	(3)	(4)	(5)	(6)
poor×time	0.1516***	-0.3668***	-0.0114	0.3284***	-0.7669***	0.0418*
	(4.131)	(-6.804)	(-0.244)	(11.658)	(-11.274)	(1.928)
城镇化	0.4256	-0.6196*	0.0077	0.0963	0.2001	0.1551
	(1.590)	(-1.752)	(0.025)	(0.432)	(0.472)	(0.797)
资本投入	0.1723***	-0.1009*	0.1841***	0.1246***	-0.0552	0.1478***
	(3.978)	(-1.729)	(3.986)	(4.629)	(-0.972)	(5.244)
产业非农化	0.1115***	-0.0604**	0.2035***	0.1061***	-0.0577***	0.1663***
	(4.944)	(-2.612)	(7.304)	(6.351)	(-3.163)	(7.416)
农业产出	1.0024***	-0.5181***	1.1302***	1.0265***	-0.6408***	1.1043***
	(12.687)	(-5.574)	(11.103)	(16.895)	(-6.309)	(13.661)
地方财政政策	0.3804**	-0.5395***	0.2637	0.2470**	-0.4932**	0.0566
	(2.613)	(-3.290)	(1.407)	(2.611)	(-2.615)	(0.629)
农村就业	-0.0066	-0.4198	-0.0210	0.0125	-1.4750*	-0.6042**
	(-0.017)	(-0.731)	(-0.047)	(0.034)	(-1.900)	(-2.007)
常数项	-1.8649***	4.4737***	-0.1676	-1.8094***	5.3556***	0.4666**
	(-8.331)	(13.204)	(-0.626)	(-8.958)	(11.711)	(2.595)
PSM	YES	YES	YES	NO	NO	NO
样本量	626	626	626	1190	1190	1190
R²	0.887	0.694	0.844	0.896	0.731	0.848

变量	相对收入差距	绝对收入差距	相对收入差距	绝对收入差距
变量	(7)	(8)	(9)	(10)
poor×time（2014）	-0.3668***	-0.0632	-0.7590***	-0.0359*
poor×time₁（2015）	-0.3292***	-0.1011**	-0.7198***	-0.0385
poor×time₂（2016）	-0.3822***	-0.0044	-0.7882***	0.0576**
poor×time₃（2017）	-0.3844***	0.0353	-0.7890***	0.1205***
poor×time₄（2018）	-0.3706***	0.1050*	-0.7876***	0.2006***
poor×time₅（2019）	-0.3569***	0.0342	-0.7486***	0.1599***
控制变量	YES	YES	YES	YES
PSM	YES	YES	NO	NO
常数项	4.5086***	-0.1349	5.3980***	0.4608***
样本数	626	626	1190	1190
R²	0.696	0.856	0.732	0.870

变量	相对收入差距	绝对收入差距	相对收入差距	绝对收入差距
变量	(11)	(12)	(13)	(14)
pre_3（2011）	-0.0245	-0.0778	0.0634	-0.0225
pre_2（2012）	-0.0268	-0.0475	-0.0103	-0.0426**
pre_1（2013）	-0.0701	-0.0273	-0.0634	-0.0457**
Current（2014）	-0.3629***	-0.0727	-0.5322***	-0.1491***
post_1（2015）	-0.3935***	-0.0973	-0.5894***	-0.1582***
post_2（2016）	-0.3921***	0.0035	-0.5520***	-0.1681***
post_3（2017）	-0.3932***	0.0518	-0.5753***	-0.1889***
post_4（2018）	-0.3943***	0.1354*	-0.5873***	-0.2012***
post_5（2019）	-0.3560***	0.0932	-0.5688***	-0.1747***
控制变量	YES	YES	YES	YES
PSM	YES	YES	NO	NO
常数项	3.7725***	0.9986***	4.1566***	1.0381***
样本数	626	626	1190	1190
R²	0.683	0.848	0.802	0.942