基于匹配倍差法的乡村旅游扶贫农户增收效应

doi:10.18402/resci.2019.05.12

摘要/Abstract

摘要：

乡村旅游扶贫是中国脱贫攻坚战略的重要手段之一,其农户增收效应备受关注。本文利用湖北省恩施市龙凤镇青堡村和白杨坪乡麂子渡村农户的问卷调查数据,采用匹配倍差法研究了实验组和参照组2类农户在乡村旅游扶贫政策实施前后的家庭总收入、农业收入、工资性收入、经营性收入及其他收入的变化,以科学评估乡村旅游扶贫的增收效应。结果表明,乡村旅游扶贫对农户的家庭总收入、工资性收入及经营性收入的增加有显著作用。用倍差法估计乡村旅游扶贫对家庭总收入的效应时,其结果“虚高”13.1%,即716元;而用倾向得分匹配法则高出6.6%,即359元。使用匹配倍差法计算后得出：乡村旅游扶贫使得样本区实验组农户平均家庭总收入提高了5467元,其中,户均农业收入仅提高172元,不显著;而工资性收入和经营性收入分别显著提高了2132元和3128元;增收仍有较大潜力。本文据此提出：政府应采取各种措施引导当地依托特色资源,加强全域旅游建设,以获得稳定增长的各类旅游收入;扶持当地农户全员参与乡村旅游,确保相对贫困农户也有机会、有能力分享旅游业发展带来的各种增收机会;结合旅游扶贫后备箱工程实施,引导农民成立合作组织,将当地富硒有机农副产品深度加工成特色旅游产品等,以提高乡村旅游扶贫的增收实效。

关键词: 乡村旅游扶贫, 农户, 增收效应, 匹配倍差法, 湖北省恩施市

Abstract:

Rural pro-poor tourism is one of the important means of poverty alleviation in China, and its effects on farming households’ income is of great concern. Based on the questionnaire survey data from Qingbao Village of Longfeng Town and Jizhidu Village of Yangping Town in Enshi City, this study compared the changes of family income, agricultural income, wage income, operating income, and other incomes of farming households between the experimental group and the controlled group before and after the implementation of the rural pro-poor tourism policy, by adopting the propensity score matching and difference-in-differences (PSM-DID) method for analysis. The results show that rural pro-poor tourism has significant effects on family income, wage income, and operating income. By using the DID method to estimate the effect of rural pro-poor tourism on family income, the result is “artificially higher” by 13.1% (716 yuan), while the estimation from the PSM method is 6.6% (361 yuan) higher. In order to overcome the biases of the DID and PSM models, PSM-DID was used, and it produced more accurate results. The result indicates that the rural pro-poor tourism policy has increased average farming households’ income of the experimental group in the sample area by 5467 yuan. Of this, agricultural income only insignificantly increased by 172 yuan, while wage income and operating income increased significantly by 2132 yuan and 3128 yuan respectively, but there are still significant growth potentials. Based on these results, some corresponding policy recommendations are put forward to improve the efficiency of rural pro-poor tourism in increasing farming households’ income: the government should take various measures to guide the local communities to rely on the overall tourism development based on local resources in order to obtain sustained growth of all kinds of tourism income; support the participation of all farmers in rural tourism and ensure that relatively poor farmers also have the ability to benefit from income-generating opportunities brought about by tourism development; guide farmers to set up cooperative organizations and further process local Se-rich organic agricultural products into characteristic tourism products in the light of implementing the “tourism poverty alleviation reserve box” project, and so on.

Key words: rural pro-poor tourism, farming household, effects on income, propensity score matching and difference-in-differences (PSM-DID) method, Enshi City of Hubei Province

余利红. 基于匹配倍差法的乡村旅游扶贫农户增收效应[J]. 资源科学, 2019, 41(5): 955-966.

Lihong YU. Effects of rural pro-poor tourism on farming households’ income: A study based on the PSM-DID method[J]. Resources Science, 2019, 41(5): 955-966.

图/表 6

表1

表2

表3

表4

表5

表6

参考文献 18

[19]	Netherlands Development Organization and World Tourism Organization. Manual on Tourism and Poverty Alleviation: Practical Steps for Destinations [EB/OL]. (2010-12-16)[2018-12-07]. .
[20]	Ashley C, Boyd C, Goodwin H.Pro-poor Tourism: Putting Poverty at the Heart of the Tourism Agenda[R]. London: Overseas Development Institute(ODI), 2000.
[21]	恩施土家族苗族自治区人民政府. 恩施州人民政府关于发展乡村旅游促进旅游扶贫工作的意见[EB/OL]. (2016-09-22)[2019-01-30]. .
	[The people’s Government of Enshi Tujia & Miao Autonomous Prefecture. Instructions of the People’s Government of Enshi Prefecture on Developing Rural Tourism and Promoting Poverty Alleviation through Tourism [EB/OL]. (2016-09-22) [2019-01-30]. .]
[1]	国务院. 国务院印发《中国农村扶贫开发纲要(2011-2010年)》[EB/OL]. (2011-05-27)[2019-01-30].
	[The State Council. The State Council issued “The Outline for Development-Oriented Poverty Alleviation for China’s Rural Areas” [EB/OL]. (2011-05-27)[2019-01-30]. .]
[2]	国家旅游局. 国家旅游局发布《全国乡村旅游扶贫观测报告》[EB/OL]. (2016-08-20)[2019-01-30].
	[The State Tourism Administration. The State Tourism Administration issued the “National Observation Report on Poverty Alleviation in Rural Tourism”[EB/OL]. (2016-08-20)[2019-01-30]. .]
[3]	胡柳. 乡村旅游精准扶贫研究[D]. 武汉: 武汉大学, 2016.
	[Hu L.The Research on Rural Tourism Accurate Poverty Alleviation[D].Wuhan: Wuhan University, 2016.]
[4]	Blake A, Arbache J S, Sinclair M T, et al.Tourism and poverty relief[J]. Annals of Tourism Research, 2008, 35(1): 107-126.
[5]	黄渊基, 匡立波. 旅游扶贫的作用机理及减贫效应探析[J]. 南华大学学报(社会科学版), 2018, 19(1): 75-81.
	[Huang Y J, Kuang L B.Study on the mechanism of pro-poor tourism and the effects of poverty reduction[J]. Journal of University of South China(Social Science Edition), 2018, 19(1): 75-81.]
[6]	罗章, 王烁. 精准扶贫视阈下乡村旅游内生脱贫机制: 以重庆市“木根模式”为例[J]. 农村经济, 2018, (1): 51-55.
	[Luo Z, Wang S.The endogenous poverty alleviation mechanism of rural tourism from the perspective of accurate poverty alleviation: A case study of Chongqing ‘Mugen Model’[J]. Rural Economy, 2018, (1): 51-55.]
[7]	Vanegas M, Gartner W S, Senauer B.Tourism and poverty reduction: An economic sector analysis for Costa Rica and Nicaragua[J]. Tourism Economics, 2015, 21(1): 159-182.
[8]	Croes R, Rivera M.Tourism’s potential to benefit the poor: A social accounting matrix model applied to Ecuador[J]. Tourism Economics, 2017, 23(1): 29-48.
[9]	Mahadevan R, Amir H, NugrohoA. Regional impacts of tourism-led growth on poverty and income inequality: A dynamic general equilibrium analysis for Indonesia[J]. Tourism Economic, 2017, 23(3): 614-631.
[10]	Njoya E T, Seetaram N.Tourism contribution to poverty alleviation in Kenya: A dynamic computable general equilibrium analysis[J]. Journal of Travel Research, 2018, 57(4): 513-524.
[11]	杨霞, 刘晓鹰. 旅游流量、旅游构成与西部地区贫困减缓[J]. 旅游学刊, 2013, 28(6): 47-55.
	[Yang X, Liu X Y.The relationship between tourist flow, composition and poverty alleviation in the west of China[J]. Tourism Tribune, 2013, 28(6): 47-55.]
[12]	张遵东, 章立峰. 贵州民族地区乡村旅游扶贫对农民收入的影响研究: 以雷山县西江苗寨为例[J]. 贵州民族研究, 2011, 32(6): 66-71.
	[Zhang Z D, Zhang L F.Effects of farmer’s income by rural tourism poverty alleviation in Guizhou national areas: Taking Xijiang Miao Village in Leishan Town as an example[J]. Guizhou Ethnic Studies, 2011, 32(6): 66-71.]
[13]	郭舒. 基于产业链视角的旅游扶贫效应研究方法[J]. 旅游学刊, 2015, 30(11): 31-39.
	[Guo S.Industrial chain tracing method for assessing pro-poor tourism impact[J]. Tourism Tribune, 2015, 30(11): 31-39. ]
[14]	杨柳. 海南省乡村旅游扶贫绩效及实证研究[J]. 中国农业资源与区划, 2017, 38(5): 217-221.
	[Yang L.Poverty alleviation by rural tourism in Hainan[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2017, 38(5): 217-221.]
[15]	吴海民, 吴淑娟, 陈辉. 城市文明、交易成本与企业“第四利润源”: 基于全国文明城市与民营上市公司核匹配倍差法的证据[J]. 中国工业经济, 2015, (7): 114-129.
	[Wu H M, Wu S J, Chen H.Urban civilization, transaction costs and firm’s fourth profit source: Evidences from the private listed companies and national civilized cities based on KPSM-DID method[J]. China Industrial Economics, 2015, (7): 114-129.]
[16]	Mitchell J, Ashley C.Tourism and Poverty Reduction: Pathways to Prosperity[M]. London: Routledge, 2009.
[17]	Gunduz L, Hatemi J A.Is the tourism-led growth hypothesis valid for Turkey?[J]. Applied Economics Letters, 2005, 12(8): 499-504.
[18]	Zeng B, Carter R W, Lacy T D, et al.Effects of tourism development on the local poor people: Case study in Taibai region China[J]. Journal of Services Research, 2005, 5(5): 131-148.

样本农户特征		频数/户	频率/%	样本农户特征		频数/户	频率/%
是否参与乡村旅游	实验组农户	151	43.64	户主性别	男	267	77.18
是否参与乡村旅游	对照组农户	195	56.36	户主性别	女	79	22.82
户主年龄/岁	≤ 35	31	8.96	户主受教育程度	文盲	49	14.16
	36~45	68	19.65		小学	197	56.94
	46~55	84	24.28		初中	76	21.96
	56~65	91	26.30		高中及以上	24	6.94
	≥ 66	72	20.81

样本均值	旅游扶贫之前（2013年）	旅游扶贫之后（2017年）	变化幅度
总体样本	11 597	14 601	3 004
参与旅游扶贫的农户	11 935	18 430	6 495
未参与旅游扶贫的农户	11 273	11 586	313

样本均值	农业收入			工资性收入
样本均值	2013年	2017年	变化幅度	2013年	2017年	变化幅度
总体样本	3 107	3 295	188	6 514	7 418	904
参与旅游扶贫的农户	2 961	3 402	441	6 827	9 195	2 368
未参与旅游扶贫的农户	3 189	3 231	42	6 331	6 437	106
样本均值	经营性收入			其他收入
样本均值	2013年	2017年	变化幅度	2013年	2017年	变化幅度
总体样本	1 590	3 295	1 705	299	315	16
参与旅游扶贫的农户	1 856	5 492	3 636	291	341	50
未参与旅游扶贫的农户	1 449	1 606	157	304	312	8

变量	系数	标准差	边际影响	变量	系数	标准差	边际影响
户主年龄（X₁）	0.008	0.006	0.002	劳动人口数（X₇）	0.137**	0.085	0.025
户主性别（X₂）	-0.319*	0.172	-0.041	外出打工人数（X₈）	-0.124	0.069	-0.012
户主民族（X₃）	0.007	0.260	0.001	承包耕地面积（X₉）	-0.027*	0.019	-0.002
户主受教育程度（X₄）	0.091**	0.020	0.024	承包林地面积（X₁₀）	0.001	0.002	0.001
是否为村干部（X₅）	0.305***	0.197	0.059	交通设施条件（X₁₁）	0.006**	0.003	0.013
总人口数（X₆）	0.106	0.061	0.006	离镇中心距离（X₁₂）	-0.004	0.007	-0.001
常数项	-3.195	0.529
Log likelihood	-308.137
Prob>chi2	0.000
LR chi2(13)	59.201
Pseudo R²	0.059

变量名	变量代码	时点	平均值		标准偏差/%	偏差变动率/%	T检验
变量名	变量代码	时点	实验组	参照组	标准偏差/%	偏差变动率/%	T值	p>\|t\|
户主年龄	X₁	匹配前	47.916	51.720	-17.2	59.9	-2.73	0.002***
户主年龄	X₁	匹配后	47.902	48.016	-6.9	59.9	-0.59	0.204
户主性别	X₂	匹配前	0.870	0.973	-16.1	67.1	-3.08	0.027**
户主性别	X₂	匹配后	0.873	0.892	-5.3	67.1	-0.47	0.173
户主民族	X₃	匹配前	0.246	0.290	-15.2	81.5	-1.83	0.162
户主民族	X₃	匹配后	0.241	0.248	-2.8	81.5	-0.39	1.05
户主受教育程度	X₄	匹配前	7.691	6.385	30.6	78.7	4.21	0.008***
户主受教育程度	X₄	匹配后	7.649	7.386	6.5	78.7	0.94	0.373
是否为村干部	X₅	匹配前	0.116	0.051	42.6	87.4	2.94	0.024**
是否为村干部	X₅	匹配后	0.104	0.098	5.4	87.4	0.68	0.308
总人口数	X₆	匹配前	4.705	4.682	7.9	39.2	1.84	0.064*
总人口数	X₆	匹配后	4.697	4.679	4.8	39.2	0.92	0.165
劳动人口数	X₇	匹配前	2.816	2.401	21.4	82.7	3.05	0.018**
劳动人口数	X₇	匹配后	2.801	2.780	3.7	82.7	1.25	0.409
外出打工人数	X₈	匹配前	0.619	1.050	-51.3	86.1	-4.06	0.046**
外出打工人数	X₈	匹配后	0.632	0.694	-7.1	86.1	-1.93	0.423
承包耕地面积	X₉	匹配前	3.891	4.125	-21.5	85.1	-2.37	0.075*
承包耕地面积	X₉	匹配后	3.902	3.927	-3.2	85.1	-0.89	0.209
承包林地面积	X₁₀	匹配前	10.152	9.390	16.8	51.2	1.67	0.072*
承包林地面积	X₁₀	匹配后	10.028	9.796	8.2	51.2	0.62	0.204
交通设施条件	X₁₁	匹配前	2.501	2.170	27.4	68.6	1.48	0.020**
交通设施条件	X₁₁	匹配后	2.496	2.401	8.6	68.6	0.94	0.159
离镇中心距离	X₁₂	匹配前	26.390	32.810	-29.7	76.8	-3.56	0.037**
离镇中心距离	X₁₂	匹配后	26.485	27.579	-6.9	76.8	-0.85	0.208