Evaluating the Evaluated Socioeconomic Impacts of China's Sloping Land Conversion Program

被引:22
|
作者
Lu, Gang [1 ]
Yin, Runsheng [1 ,2 ]
机构
[1] Michigan State Univ, Dept Forestry, E Lansing, MI 48824 USA
[2] Beijing Forestry Univ, Sch Econ & Management, 35 Tsinghua East Rd, Beijing 100083, Peoples R China
关键词
Impact Evaluation; Treatment; Counterfactuals; Confounding Factors; Survey Data; Variable Enumeration; PRIORITY FORESTRY PROGRAMS; RURAL HOUSEHOLD INCOME; ECOSYSTEM SERVICES; PROPENSITY SCORE; IMPLEMENTATION; PAYMENTS; SUSTAINABILITY; CONSTRAINTS; INEQUALITY; LIVELIHOOD;
D O I
10.1016/j.ecolecon.2020.106785
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Many studies have evaluated the social-ecological impacts of China's Sloping Land Conversion Program (SLCP). And reviews of the literature have appeared with most of them summarizing the reported findings. The primary goal of this paper is to assess the strengths and weaknesses of the socioeconomic studies in terms of the quality of data used, the appropriateness of approaches, and the adequacy of variable selection, based on a collection of 33 peer-reviewed articles. It is found that about half of them use cross-sectional or repeated cross-sectional data of household surveys, which cannot fully capture the spatiotemporal variations in farmers' labor and land allocations. Moreover, a dummy variable is inadequate to distinguish the impacts of participation with different amounts of land enrolled. Aggregate covariates are rarely included to reflect the external change and regional heterogeneity. In addition to the often-used difference in differences and propensity score matching models, other evaluation methods like regression discontinuity and synthetic control can be adopted. While endogeneity is a common concern with program participation, few studies have confronted it directly. We hope that our review will contribute to setting up the counterfactuals and controlling for confounding factors more adequately, in future studies in and outside of China.
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页数:11
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