Juan Land Conservation Policies


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Juan Land Conservation Policies

  1. 1. Impact of Land Conservation Policies on Deforestation Rates in Costa Rica Juan A. Robalino, CATIE and Alexander Pfaff, Duke Arturo Sanchez, University of Alberta Francisco Alpizar, CATIE Carlos Leon, Neotropica and Carlos Manuel Rodriguez Paul Ferraro GSU Kwaw Andam IFPRI
  2. 2. Question <ul><li>Did the implementation of Payments for Environmental Services Program (National Parks) reduce deforestation rates in Costa Rica between 2000 and 2005 (1986-1997)? </li></ul><ul><li>Answer: Yes but by a small amount </li></ul>
  3. 3. Why would the PES program or NP would not reduce deforestation rates? <ul><li>Illegal behavior </li></ul><ul><li>Payments (Parks) have been implemented in areas that were not going to be deforested anyway </li></ul><ul><li>Substitution Effect (Leakage Effect) </li></ul><ul><ul><li>Deforestation increases some where else </li></ul></ul>
  4. 4. Strategy <ul><li>Analysis: Empirical </li></ul><ul><li>Hypothesis: Payments decrease deforestation rates </li></ul><ul><li>Methodology: Matching </li></ul><ul><li>Location of the study: Costa Rica </li></ul><ul><ul><li>Forest maps for 87-97, and 2000-2005 </li></ul></ul><ul><ul><li>Maps: payments for environmental services, national parks, other protected areas, roads, schools, towns, cities, slope of the terrain, precipitation, elevation </li></ul></ul>
  5. 5. Literature <ul><li>93 Parks in different countries (Bruner et al. 01) </li></ul><ul><li>National Parks in Costa Rica ( Andam and Ferraro et al. 08 ) </li></ul><ul><li>Payments for Environmental Services in Costa Rica (Pfaff et al. 07, Sierra and Russman 06, Arriagada et al. 08) </li></ul>
  6. 6. Simple Model of PES Rents <ul><li>Payments increase the returns of forests </li></ul><ul><li>The reduction of forest will be in the segment between f y f’ </li></ul><ul><li>All landowners between f’ y L will try to enroll land in the program </li></ul>f f’ P Market L
  7. 7. Implications of the model <ul><li>The level of the payments (P) will affect the level of the impact </li></ul><ul><li>The distribution of land type will affect the level of the impact </li></ul><ul><li>However, we need to know what percentage of the land enrolled in the program is in the interval f-f’ and in the interval f-L </li></ul>
  8. 8. Measuring Treatment Effects <ul><li>Treatment effect in plot X = </li></ul>Parcel X enrolled in the program Parcel X not enrolled in the program Factual (Treatment) Counterfactual (Untreated) Factual Deforestation Rate - Counterfactual Deforestation Rate
  9. 9. How do we identify the impact? <ul><li>Ideally, experiment with random assignment </li></ul><ul><ul><li>Then, other deforestation drivers are canceled out in expectation </li></ul></ul><ul><ul><li>Policies are rarely applied randomly </li></ul></ul><ul><li>Alternatives: </li></ul><ul><ul><li>Instrumental Variable </li></ul></ul><ul><ul><li>Matching Strategies </li></ul></ul>
  10. 10. Matching Strategies Treated observations: Plots in PES or National Parks Untreated Observations: Plots away outside PES or National Parks
  11. 11. Advantages and Disadvantages <ul><li>Advantages </li></ul><ul><ul><li>Reduce the bias due to the lack of random assignment </li></ul></ul><ul><ul><li>Less dependent on the functional form assumed </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>Unobservables might bias the estimation of the effect </li></ul></ul><ul><ul><li>Loss of observations (degrees of freedom) </li></ul></ul><ul><ul><li>Standard Errors </li></ul></ul>
  12. 12. Matching techniques used <ul><li>Propensity Score Matching </li></ul><ul><ul><li>Determine for each observation, the prob. of being inside the treatment group (score) </li></ul></ul><ul><ul><li>Use the Score to find if a control observation is similar to a treated observation </li></ul></ul><ul><li>Covariate Matching </li></ul><ul><ul><li>Use the vector characteristics and find a distance between each vector </li></ul></ul><ul><ul><li>Standard Errors are shown to be correct (Abadie and Imbens 2005) </li></ul></ul>
  13. 13. Data <ul><li>We randomly draw locations in Costa Rica </li></ul><ul><ul><li>Locations covered by forest in 2000 </li></ul></ul><ul><ul><li>Locations enrolled in PES </li></ul></ul><ul><li>Probability of Deforestation </li></ul><ul><ul><li>Forest in 2000 and Cleared in 2005 (=1) </li></ul></ul><ul><ul><li>Forest in 2000 and Forest in 2005 (=0) </li></ul></ul><ul><li>Location Characteristics </li></ul><ul><ul><li>Distances: Cities, Roads, Rivers, Schools and Sawmills </li></ul></ul><ul><ul><li>Natural Characteristics: Life Zones, Rain, Elevation and Slopes </li></ul></ul>
  14. 14. Results: Testing similarity 3355 0.95 3504 3502 Precipitation 5.4 0.40 5.3 5.1 Distance Nat. Parks 3.3 0.71 1.5 1.5 Distance to Rivers 156 km 0.62 144 km 142 km Distance to Limon 66% 0.83 42% 49% Slopes 5.5 0.34 5.9 6.1 Distance Nat. Roads 3.3 km 0.02 3.2 km 3.5 km Distance Local Roads 122 km 0.32 119 km 121 km Distance to Caldera 114 km 0.48 101 km 102 km Distance to San Jose 43% 0.21 58% 61% Bad L. Zones for Ag. 32% 0.34 23% 22% Good L. Zones for Ag. All Controls Significance P-value Matched Controls Treated
  15. 15. Results: PES Program Effects: Pixel analysis and ** represent 5% and 1% significance level respectively Model 1 is controlling for: Life Zones, Distance to SJ and main ports and Slope Model 2 is controlling also for: Distance to Roads and Rivers -2.09* -2.11** CVM Program Effects on Deforestation (%) 2000-2005 N=4 -2.00** -1.40** -1.62** OLS -1.66* PSM No Bias adjustment 1.92** 1.89** CVM 2.32** 1.59** OLS 1.87** PSM With Bias adjustment Model 1 Model 2
  16. 17. Preliminary Results <ul><li>Between 2000 and 2005, only 2% of the land enrolled in the program would have been deforested </li></ul><ul><li>If we compare this result to the 1997-2000 period, when the impact was estimated in 0.6% (Pfaff et al. 2008), there was an increase in the impact </li></ul><ul><li>In terms of cost-efficiency, the program can improved significantly </li></ul>
  17. 18. Park Impacts within boundaries: PSM vs. CM Park Effects on 86-97 % Deforestation, N = 4 in each method -1.99* -9.39** Using All of the Untreated (Naive) -2.21** -1.72** Propensity Score Matching (PSM) -0.85 -2.19** Covariate Matching (CM) Adj. Diff. in Means Difference in Means Strategy
  18. 19. Implications <ul><li>For every 100 parcels protected in </li></ul><ul><ul><li>PES program, only 2 were saved during 2000-2005 </li></ul></ul><ul><ul><li>Parks, only 2 were saved during 1986-1997 </li></ul></ul><ul><li>PES seem to work better than parks </li></ul><ul><li>But PES are still the impact of the program seems very low </li></ul>