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Evaluation of the impact of the Natural Forest Protection Programme on rural household incomes Katrina Mullan Department of Land Economy University of Cambridge
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Structure Introduction to case study: Natural Forest Protection Programme Description of programme Findings of previous studies Evaluation problem and methods used to address it Difference in differences Matched DID Weighted DID Empirical results – impacts of NFPP on household incomes Conclusions
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Map of Forest Cover Relatively low forest area across whole of China: 0.11ha forest land per capita compared with world average of 0.77ha Forests in remote mountainous areas – south, southwest and northeast
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Natural Forest Protection Programme Policy introduced in 2000 in 17 Provinces and Autonomous Regions Aims: restore natural forests; protect biodiversity; protect soil and water; increase timber production Programme: Ban on logging in natural forests Measures to encourage new plantations Compensation for unemployed state forest workers and pensions for retired workers
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Previous studies on NFPP Programme generally accepted to have reduced timber harvesting (Xu et al, 2002; Demurger and Fournier, 2003), which is likely to have reduced soil erosion and improved water conservation (Yang, 2001). But with some negative impacts: Loss of employment in state sector and loss of income for those providing services to state sector Loss of local government revenues Impacts on households in collective forest areas: Loss of income from timber harvesting Reduction in employment in forest enterprises; Loss of access to forest products; Infringement of property rights Existing studies state that income losses are significant. However, based on case studies of individual villages – not quantified (e.g. Xu et al, 2002; Shen, 2001)
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Household Survey Collaborative project involving Peking University, UCL, and Cambridge University Survey in Summer 2005 – carried out by Professor Zhang Shiqiu and students from the School of Environmental Sciences, Peking University Face to face survey of 285 households in Guizhou Province 40 villages in 3 counties of Qiandongnan District (south of Guizhou Province) – Jinping and Liping had NFPP; Congjiang did not have NFPP Questions about property rights; income from all sources in 1997 and 2004; views on logging ban; stated preference questions about welfare losses from ban
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Evaluation problem Identification problem: Impact of programme is =Y 1 - Y 0 but can never observe both outcomes because individual is either participating in the programme or not We focus on Average Treatment Effect on the Treated: ATT = E(Y 1 – Y 0 D=1) = E( | D=1) Requires estimation of Y 0 : generate counterfactual with which to compare outcomes of programme participants
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Evaluation methods (1) Difference in differences (Ashenfelter and Card, 1985): Y i,t = D i,t + i + t + it Removes individual specific ( i ) and time specific ( t ) unobservable variation Assumes that temporary individual specific effects ( it ) are uncorrelated with D Estimator based on difference between changes in Y for participants and non-participants: DID = E(Y i1 – Y i0 |D=1) - E(Y i1 – Y i0 |D=0) Estimate with and without controls for observable variation Requires assumptions about functional form
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Evaluation methods (2) Matched DID (Heckman et al, 1997): Matching method creates counterfactual control sample with same observable characteristics as participants: outcome of each participant compared with weighted outcome of non-participants with similar characteristics match = i {D=1} w N0, N1 (i) [ Q 1i - j {D=0} W N0, N1 (i, j) Q 0j ] Can match on X or on function of X: P(X) = Pr( D=1 | X) Matched DID uses same method as matching, but with Q i = Y i1 – Y i0 Controls for observable variation and time and individual specific unobservable variation
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Evaluation methods (3) Weighted DID (Abadie, 2005): Weights control observations on the basis of their similarity to participant observations => balanced sample Also uses propensity score Estimator: Controls for observable variation and time and individual specific unobservable variation
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Descriptive statistics (1)
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Descriptive statistics (2) Variable NFPP Non -NFPP Significant determinant of propensity score? Ethnic group - Miao 0.230.4 Yes Ethnic Group - Han 0.180.09 Yes Ethnic Group - Dong 0.570.38 Yes Adults with more than primary education 2.191.58 Yes Household size 5.125.25 Yes Value of house (RMB) 151958249 Yes Ability to borrow (1=yes) 0.650.6 No Rating of transport quality (1=poor to 5=good) 3.082.51 Yes Participation in SLCP (1=yes) 0.30.17 Yes Distance to county town (km) 30.6727.64 No Size of village 11841273 Yes Electricity in village (1=yes) 0.880.86 No
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Impacts on income DID no covariates DID with covariates PS matching, kernel regression PS matching, local linear regression PS weighting Total income per HH 2415 (1297)* 1227 (1441) 2142 (1390) 2911 (1872) 1825 (1988) Total income per capita 407.9 (238.8)* 165.4 (276.5) 337.78 (286.9) 365.3 (377.0) 155.6 (513.5) Income from timber -237.3 (166.1) -306.0 (185.5)* -215.6 (189.7) -237.9 (124.9)* -220.7 (131.9)* Income from NTFPs -233.7 (105.0)** -76.89 (117.4) -82.96 (160.7) -1.6956 (107.34) -41.18 (100.0) Income from employment 2348 (1235)* 971.4 (1359) 1628 (1436) 2113 (1574) 1470 (1903) Income from agriculture -106.0 (298.6) -447.3 (336.2) -457.1 (294.7) -687.6 (314.1)** -310.4 (436.5)
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Conclusions Small but significant reduction in income from timber – no impact on overall income or income from other sources Different to previous studies: Partly because non-quantitative => don’t account for alternative uses of labour May be greater impacts on total income in other areas May have been initial impacts, but not lasting impacts Reasons for small impacts: Labour could be easily re-employed Timber harvesting declining in importance in all areas Additional considerations: Impacts on different groups – especially if increases inequality Impacts on incentives for forest conservation
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