Impact of Risk and Time Preferences on Responses to Forest Tenure Land Reform: Empirical Evidence From Fujian, China

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1 Impact of Risk and Time Preferences on Responses to Forest Tenure Land Reform: Empirical Evidence From Fujian, China Karen A. Sullivan 1, Emi Uchida 2 and Jintao Xu 3 1 Contact Author, Department of Environmental and Resource Economics, University of Rhode Island, Kingston Coastal Institute, 1 Greenhouse Road, Kingston, RI Phone: (401) , karenannesullivan@gmail.com 2 Department of Environmental and Resource Economics, University of Rhode Island 3 Environmental Economic Program in China, College of Environmental Science and Engineering, Peking University Poster prepared for presentation at the Agricultural & Applied Economics Association 2010 AAEA, CAES, & WAEA Joint Annual Meeting, Denver, Colorado, July 25-27, 2010 Copyright 2010 by Karen A. Sullivan, Emi Uchida and Jintao Xu. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by an means, provided that this copyright notice appears on all such copies. 1

2 Introduction In an effort to stem ongoing degradation of the world s forests, many developing countries have implemented property right reforms that transfer responsibility for these resources from the state to communities and individuals, who can then make their own decisions in managing and harvesting the forests. The goal of such reforms is to encourage sustainable resource use; however, too often reform efforts have failed to achieve this goal (Bromley 1989; Ostrom 1990; Alston et al. 1999; Bohn and Deacon 2000). Contrary to the goal of the reform, individuals with a strong preference for immediate benefits or who are unwilling to take future risks (both of which are common among the poor) may invest less in their forest resources or use the forest resources faster even when given stronger property rights. Objective This research examines how individual preferences over time (present vs. future) and risk can affect people s forest management responses to strengthened forest property rights in the context of rural Fujian, China, where a large-scale reform of forest property rights has been implemented in areas where the poverty rate is still high. Research Methods This research uses a large-scale property rights reform to examine the heterogeneity of its impact on household forest management due to risk and time preferences. Since there has been a real change in forest property rights in China, a discrete measure of changes in property rights and responses of individuals to those changes can be made using pre- and post-reform village and household survey data. Step 1: To measure time and risk preferences, we use an economic experiment with real monetary rewards, which reduces hypothetical biases that exist in previous studies (Godoy et al. 1998; Godoy et al. 2001; Hagos and Holden 2006). Step 2: We combine the risk and time preference experiment data with household survey data in a difference-in-differences framework to identify how people respond differently to property rights reforms depending on their risk and time preferences. Data We use a household data set with pre- and post-reform data for 103 households spanning two counties, Sanming City and Datian County, and 10 villages in Fujian Province for three years: 2000 (before the reform), 2005 and 2008 (after the reform). The 2000 and 2005 data is part of a larger survey data set collected by a research team from Peking University, Gothenberg University and Forest Trends in In 2009, 120 households who had previously been surveyed were targeted for a follow-up household survey and to participate in the risk and time preference experiments. Of the 120 households targeted, 103 were found and choose to participate in both the household survey and the experiments. 2

3 Identification Strategy We exploit plot-level variation in the year that the household received a forest certificate for a plot from the recent forest tenure reform in a difference-in-differences framework. The variation in the year that the household received a forest certificate is the result of exogenous variation across villages of the starting year of the reform. We construct a balanced panel data set by using only those forest plots that were managed by the household in 2000, 2005 and 2008, so that we have pre- and post- reform data for every plot in the analysis. This results in a sample size of 228 plots, owned by 72 households. This allows us to compare the before-after changes in forest management on those plots that households received forest certificates to the before-after changes in forest management on those plots that households did not receive forest certificates, differencing out any common trends between the two groups. We estimate the model below, for three forest management related dependent variables including: labor allocation for applying inputs (laborinput), expenditure on forest inputs including chemical fertilizer, pesticide and seeds (inputs) and labor allocation for harvesting (laborharvest) used by household i on forest plot j at time t. For a better fit, we estimate a log transformation of the model. laborinput ijt = β 0 + β 1 (fcert ijt ) + β 2 (year2005 ijt ) + β 3 (year2008 ijt ) + β 4 (afteryear ijt ) + β 5 (risk ijt ) + β 6 (risk*afteryear ijt ) + β 7 (loss ijt ) + β 8 (loss*afteryear ijt ) + β 9 (probweight ijt ) + β 10 (probweight ijt *afteryear ijt ) + β 11 (r_hyp ijt ) + β 12 (r_hyp ijt *afteryear ijt ) + β 13 (num5to59 ijt ) + β 14 (num5to59 ijt *afteryear ijt ) + β 15 (percmale ijt ) + β 16 (percmale ijt *afteryear ijt ) + β 17 (assets ijt ) + β 18 (assets ijt *afteryear ijt ) + X ijt + ΩP ijt + ΨV ijt + e ijt where X ijt is a vector of demographic controls, P ijt is a vector of plot characteristic controls, and V ijt is village fixed effects. 3

4 Table 1. Variable Definitions Dependent Variables laborinput laborharvest Inputs Independent Variables fcert year2005 Dummy for year 2005 year2008 Dummy for year 2008 Descriptions Labor allocated to application of forest inputs (Yuan/ha) Labor allocated to harvesting (Yuan/ha) Expenditure on forest inputs, including chemical fertilizer, pesticide and seeds (Yuan/ha) Dummy for plot has had a forest certificate (in any year) afteryear Dummy for plot has a forest certificate in data year after the reform (1=yes, 0=no) risk Risk aversion parameter risk*afteryear risk and afteryear interaction variable loss Loss aversion parameter loss*afteryear loss and afteryear interaction variable probweight Nonlinear probability weighting measure probweight*afteryear probweight and afteryear interaction variable r_hyp Hyperbolic time discounting parameter r_hyp*afteryear r_hyp and afteryear interaction variable agehead Age of head of household yreduhead Years of education of head of household num5and59age Number of household members between age 5 and 59 percmale Percent of household members who are male hhtotarea Household's total forest plot area hhnewplot If hh has received an additional forest plot as a result of forest tenure reforms (1=yes, 0=no) assets Household's total assets (productive, nonproductive, savings, loans) area Forest plot area (ha) disthome Distance of plot from home distroad Distance of plot from road slope25over Dummy for gradient of plot is greater than 25 bamboo Dummy for bamboo plot (1=bamboo, 0=other) 4

5 Risk Preference Experiment Participants were asked a series of questions in which they had to choose between a pair of monetary rewards (or losses) with different probabilities of winning and different monetary values for the rewards (or losses). We followed a new method that expands the classic lottery experiment of Holt and Laury (2002) to allow for the estimation of a more flexible and richer description of a person s risk preference as described under prospect theory (Kahneman and Tversky 1979; Tanaka et al. 2007; Liu 2008). Figure 1. Example of risk preference experiment choice Tokens in the bag you will draw from if you choose A: Option A Tokens in the bag you will draw from if you choose B: Option B N o. Option Description Option Description 1 A If If, then receive 20 Yuan, then receive 5 Yuan B If If, then receive 34 Yuan, then receive 2.5 Yuan 5

6 Figure 2. Distribution of risk preference parameters Panel A. Distribution of σ (curvature of the value function) Panel B. Distribution of λ (loss aversion) Panel C. Distribution of α (probability weighting parameter) Note: An individuals risk preferences are described as risk averse when σ >0, risk neutral when σ =0, and risk seeking when σ <0. The higher the value of λ, the more loss averse an individual is. When α<1, an individual tends to overweight low probabilities and underweight high probabilities. 6

7 Time Preference Experiment Participants were asked to make a decision in a series of scenarios involving various amounts of money and periods of time, and received real monetary rewards on the date chosen for one of the scenarios. Originally introduced by Coller and Williams (1999) and Harrison and Lau (2002), this experimental methodology generates data from which we can estimate how much the participants value more immediate income over future income. We use a general time discounting model proposed by Benhabib et al. (2007) which allows us to test exponential, hyperbolic, quasi-hyperbolic, and a more general form. Figure 3. Example of time preference experiment question set Plan A Plan B 6-1 Receive 150 Yuan in 6 months Receive 25 Yuan today 6-2 Receive 150 Yuan in 6 months Receive 50 Yuan today 6-3 Receive 150 Yuan in 6 months Receive 75 Yuan today 6-4 Receive 150 Yuan in 6 months Receive 100 Yuan today 6-5 Receive 150 Yuan in 6 months Receive 125 Yuan today I choose A for questions 26 to.. I choose B for questions to 30. Table 2. Comparison of exponential, hyperbolic, quasi-hyperbolic and full discounting models Exponential Hyperbolic Quasi-hyperbolic Equation(1) µ *** *** *** *** (0.001) (0.001) (0.001) (0.001) r *** *** *** (0.001) (0.002) (0.000) (0.005) β *** *** β=1 β=1 (0.032) (0.039) θ θ=1 θ=2 θ=1 (3.514) Observations Adjusted R Note: ***Significant at the 1% level. Robust standard errors are in parentheses. 7

8 Figure 4. Distribution of r_hyp (hyperbolic discounting parameter) 8

9 Results Table 3. The impact of risk and time preferences on labor allocation for application of inputs in response to forest certification Independent variables ln(laborinput) year (0.66) (0.64) (0.84) (0.79) (0.86) year (1.055)*** (1.055)*** (1.258)*** (1.224)*** (1.216)*** afteryear (1.68) (4.290)** (7.809)*** (7.910)*** (8.117)*** ln(risk) (0.23) (0.22) (0.24) (0.24) ln(risk)*afteryear (0.25) (0.30) (0.34) (0.34) ln(loss) (0.34) (0.32) (0.30) (0.30) ln(loss)*afteryear (0.69) (0.69) (0.67) (0.69) ln(probweight) (0.66) (0.69) (0.63) (0.58) ln(probweight)*afteryear (1.19) (1.06) (1.06) (1.14) ln(r_hyp) (0.12) (0.14) (0.14) (0.18) ln(r_hyp)*afteryear (0.441)*** (0.507)*** (0.509)** (0.574)* ln(num5to59) (0.21) (0.17) (0.16) ln(num5to59)*afteryear (0.24) (0.21) (0.21) ln(percmale) (1.804)* (1.640)** (1.545)** ln(percmale)*afteryear (5.51) (5.18) (5.38) ln(assets) (0.30) (0.28) (0.32) ln(assets)*afteryear (0.554)** (0.567)** (0.612)*** _cons (0.657)*** (2.102)*** (8.81) (8.12) (9.50) No. Obs R-squared Household characteristics X X X Forest characteristics X X Village fixed effects X Notes: Absolute value of clustered standard errors reported in parentheses. Significance at the 10% level denoted by*. Household characteristic control variables include: fcert, ln(agehead), ln(hhtotarea) and hhnewplot. Forest characteristic control variables include: ln(area), ln(disthome), ln(distroad), slope25over, and bamboo. 9

10 Table 4. The impact of risk and time preference on investment in forest inputs in response to forest certification Independent Variable ln(inputs) Fcert (1.77) (1.94) (2.00) (2.05) (2.02) year (0.903)** (0.871)*** (1.301)** (1.307)* (1.295)** year (1.142)*** (1.147)*** (1.632)*** (1.615)*** (1.583)*** Afteryear (2.47) (4.454)*** (10.182)** (9.910)** (9.051)*** ln(risk) (0.087)*** (0.100)*** (0.115)*** (0.251)* ln(risk)*afteryear (0.24) (0.34) (0.37) (0.41) ln(loss) (0.56) (0.48) (0.45) (0.39) ln(loss)*afteryear (0.89) (1.01) (0.97) (0.90) ln(probweight) (1.19) (1.12) (1.05) (0.67) ln(probweight)*afteryear (1.58) (1.46) (1.33) (1.32) ln(r_hyp) (0.19) (0.18) (0.17) (0.22) ln(r_hyp)*afteryear (0.566)** (0.578)** (0.592)* ln(num5to59) (3.09) (2.94) (3.22) ln(num5to59)*afteryear (0.11) (0.10) (0.12) ln(percmale) (0.145)* (0.138)* ln(percmale)*afteryear (0.19) (0.22) (0.21) ln(assets) (3.34) (3.23) (2.72) ln(assets)*afteryear _cons (1.094)*** (3.100)*** (12.660)** (12.425)** (13.13) No. Obs R-squared Household characteristics X X X Forest characteristics X X Village fixed effects X Notes: Same as Table 4. Forest inputs include expenditure on chemical fertilizer, pesticide and seed. 10

11 Table 5. The impact of risk and time preferences on labor allocation for harvesting in response to forest certification Independent Variables ln(laborharvest) Fcert (1.48) (1.38) (1.46) (1.20) (1.08) year (0.873)*** (0.866)*** (1.044)*** (0.923)** (0.922)** year (1.36) (1.37) (1.61) (1.53) (1.55) Afteryear (4.355)** (7.692)** (6.796)** (6.427)** ln(risk) (0.054)*** (0.065)*** (0.057)*** (0.110)*** ln(risk)*afteryear (0.324)*** (0.166)*** (0.105)*** (0.157)*** ln(loss) (0.38) (0.37) (0.29) (0.21) ln(loss)*afteryear (0.51) (0.58) (0.40) (0.418)* ln(probweight) (0.79) (0.87) (0.75) (0.633)*** ln(probweight)*afteryear (1.16) (1.14) (0.96) (1.19) ln(r_hyp) (0.17) (0.19) (0.13) (0.09) ln(r_hyp)*afteryear (0.39) (0.36) (0.37) (0.37) ln(num5to59) (0.18) (0.13) (0.12) ln(num5to59)*afteryear (0.18) (0.18) (0.20) ln(percmale) (2.50) (2.16) (2.08) ln(percmale)*afteryear (3.67) (3.20) (3.14) ln(assets) (0.44) (0.32) (0.31) ln(assets)*afteryear (0.68) (0.561)* (0.57) _cons (0.754)*** (1.712)*** (12.97) (10.29) (8.26) No. Obs R-squared Household characteristics X X X Forest characteristics X X Village fixed effects X Notes: Same as Table 4. 11

12 Discussion of Results Forest certification Households that received a forest certificate for a forest plot allocated less labor for input application and had lower expenditure on forest inputs (chemical fertilizer, pesticide and seed) on that plot than households who did not receive a forest certificate for a plot. This result does not support the hypothesis that a household who received a forest certificate would increase investment in their forest plot (applying fertilizer, pesticides, afforestation, etc.), as a result increased tenure security. However, this result may support an alternative hypothesis that households without secure tenure invest in their forest plots in order to increase tenure security. Therefore, forest certification of a plot allows the household to decrease their investment on that plot, as we see in the results, because they no longer need to invest to increase their tenure security. The results suggest that this alternative hypothesis deserves further investigation. Households that received a forest certificate for their forest plot allocated less labor for harvesting than those household who did not receive a forest certificate for a plot. This result supports the hypothesis that increased tenure security, as a result of forest certification, allows the household to have greater confidence that if they delay harvesting their forest stock (allow it to grow longer), then they will be able to receive the future benefits. Risk preference and forest certification Households that were more risk averse had lower expenditures on forest inputs for their plot than households that were risk neutral or risk seeking. This results supports the hypothesis that a more risk averse household is less willing to assume the risk of investing in their forest plots (such as loss of forest stock due to pests, disease, illegal logging, natural disaster, re-distribution, etc.). Households that were more risk averse allocated more labor to harvesting from their plots than risk neutral or risk seeking households. However, in response to receiving a forest certificate, more risk averse households allocated less labor to harvesting labor from their plot that received a forest certificate than risk neutral or risk seeking households who also received a forest certificate for a plot. These results support the hypothesis that a more risk averse household is less willing to assume the risk of delaying harvest, and therefore harvest more than the risk neutral or risk seeking household. Furthermore, results indicate that a more risk averse household has a stronger response (larger reduction in harvesting) when their tenure security is increased than a risk neutral or risk seeking household. Time preference and forest certification Households with a stronger preference for income today who received a forest certificate for a plot allocated less labor for application of forest inputs than households with weaker preferences for income today who also received a forest certificate. This result supports the hypothesis that households with a stronger preference for income today will prefer not to invest in forests, which offer a relatively slow return on investment. 12

13 Liquidity constraint and forest certification Less liquidity constrained households who received a forest certificate for a plot allocated more labor for application of forest inputs than more liquidity constrained households who also received a forest certificate for a plot. This supports the hypothesis that a more liquidity constrained household has a weaker response (less increase in investment) to forest certification than a less liquidity constrained household. Labor constraint and forest certification There is no evidence that a households labor constraint has impacted their forest management responses to receiving a forest certificate. However, the results indicate that households with a higher percentage of male household members invested more labor for application of forest inputs than households with a lower percentage of male household members. Conclusion The results of this paper have wide implications to policymakers in China and elsewhere by informing when property right reforms may not work as intended. The results indicate that instruments to deal with risks and poverty need to be coupled with such reforms in order to increase investment in long-term, productivity enhancing forest activities. Although this research is conducted in the context of forests, the general finding may also apply to other natural resources where lack of property rights have been recognized as a key barrier to sustainable management (fisheries, aquifer, etc.). 13

14 References Alston, L. J., G. D. Libecap, et al. (1999). Titles, Conflict, and Land Use: The Development of Property Rights and Land Reform on the Brazilian Amazon Frontier. Ann Arbor, The University of Michigan Press. Bohn, H. and R. T. Deacon (2000). "Ownership Risk, Investment, and the Use of Natural Resources." American Economic Review 90(3): Bromley, D. W. (1989). "Property Relations and Economic Development: The Other Land Reform." World Development 17: Coller, M. and M. B. Williams (1999). "Eliciting Individual Discount Rates." Experimental Economics 2(2): Godoy, R., M. Jacobson, et al. (1998). "The Role of Tenure Security and Private Time Preference in Neotropical Deforestation " Land Economics 74(2): Godoy, R., K. Kirby, et al. (2001). "Tenure security, private time preference, and use of natural resources among lowland Bolivian Amerindians." Ecological Economics 38(1): Hagos, F. and S. Holden (2006). "Tenure security, resource poverty, public programs, and household plot-level conservation investments in the highlands of northern Ethiopia." Agricultural Economics: Harrison, G. W., M. I. Lau, et al. (2002). "Estimating Individual Discount Rates in Denmark: A Field Experiment." The American Economic Review 92(5): Holt, C. A. and S. K. Laury (2002). "Risk Aversion and Incentive Effects." The American Economic Review 92(5): Kahneman, D. and A. Tversky (1979). "Prospect Theory: An Analysis of Choice Under Risk." Econometrica 98(6): Liu, E. M. (2008). Time to Change What to Sow: Risk Preferences and Technology Adoption Decisions of Cotton Farmers in China. Working Paper #526, Princeton University, Industrial Relations Section. Ostrom, E. (1990). Governing the commons: The evolution of institutions for collective action. New York, Cambridge University Press. Tanaka, T., C. F. Camerer, et al. (2007). Risk and Time Preferences: Experimental and Household Survey Data from Vietnam. Working Paper, California Institute of Technology: