1. Evaluation of the impact of the Natural Forest Protection Programme on rural household incomes Katrina Mullan Department of Land Economy University of Cambridge

2. 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

3. 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

4. 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

5. 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)

6. 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

7. 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

8. 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

9. 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

10. 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

11. Descriptive statistics (1)

12. 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

13. 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)

14. 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