Xiaodong Chen Kennedy School of Government Harvard University Agent-based Modeling of the Effects of Social Norms on Enrollment in Payments for Ecosystem Services

Global Conservation Investments Conservation investments are far below the requirements for conserving ecosystems worldwide The efficiency of conservation investments may be improved through Payments for Ecosystem Services (PES) Factors considered for efficient conservation investment  Biological values, demographic conditions, economic conditions, political conditions Little is known about the effects of social norms

Social norms are shared understanding of how individual members should behave in a community Social norms can be represented as actions of members in a community Social norms may have substantial impacts on human decision-making Changes in human decision-making due to social norms may in turn change social norms Emergence and Evolution of Social Norms

Agent-based modeling is a bottom-up approach focusing on individual decision making Agents are capable of interacting with other agents to obtain ‘knowledge’ about others’ actions and perceive social norms Perceived social norms of agents may be imperfect and contain random noise Agents improve their ‘knowledge’ about social norms through ‘learning’ from interactions with others Agent-based Modeling of the Evolution of Social Norms

 Main objective: increase forests and grassland to prevent soil erosion  Secondary objective: restore ecosystems and provide wildlife habitat  Payment: 3450 yuan/ha in southwest 2400 yuan/ha in northwest (currently, 1 USD = 6.7 yuan) Payments for Ecosystem Services in China -- Grain-to-Green Program (GTGP) An example GTGP plot Farmland in cabbage

Wolong Nature Reserve, China Habitat to over 6000 plant and animal species Home to about 4500 people

Objectives Develop agent-based model to simulate effects of social norms on land enrollment in PES programs Assess effects of PES program design on patterns of social norms

Methods

Household Interviews Sample: 304 of 1197 households Response rate: 95% Questions: demographic, socioeconomic, land reconversion (22.6%) Policy scenario questions for people who plan to reconvert GTGP plots when current payments end

Policy Scenarios GTGP land that household planned to reconvert after the payment ends Re-enrollment plans if a new policy was in place Policy Attributes Payment: 1500, 3000, 4500 yuan/ha Neighbors’ behavior: 25%, 50%, 75% would reconvert Attribute combinations varied across respondents

Opportunity Cost Estimation is the probability the jth GTGP plot is re-enrolled is the probability the jth GTGP plot is reconverted to crop production after the payments end is the probability of re-enrolling the jth GTGP plot under a new payment program, for plots that will be reconverted to crop production after initial payments end

Opportunity Cost Estimation P(reenroll j ) is estimated at different payments The per hectare opportunity cost of a land plot is the payment level at which the land plot will be re- enrolled

Simulation Rules -- PES Program PES program contracts last for one unit of time All households make reenrollment decisions for all of their GTGP plots at each time point

Simulation Rules -- Agents Each household was modeled as an agent Agents were simulated for multiple units of time to allow for multiple opportunities for making reenrollment decisions Agents interact with each other to perceive social norms as measured by reenrollment decisions of their neighboring agents at previous times Agents would reenroll a GTGP plot if the payment is larger than the opportunity cost of the plot

Social norms in each community were measured as the proportion of households reconverting their GTGP plots Agents cannot obtain all information on social norms through one round of interaction Perceived social norms time = 1 = 0.5 Perceived social norms time = j = knowledge time = j * neighbors’ action time = j-1 + (1 – knowledge time = j ) * random norm Knowledge increases through agents’ ‘learning’ from additional interactions with other agents Simulation Rules – Emergence and Evolution of Social Norms

Simulation Experiments One-time reenrollment of GTGP land under different payments Dynamics in land reenrollment due to social norms under different levels of payments, initial_knowledge, and learn

Results

Independent variablesParameters Proportion of neighbors’ reconverting GTGP plots - Payment level (yuan) + Number of people in the household - Cropland of the household (ha) + Age of household head + Chi-square *** *** p < Pooled Logit Estimation of Re-enrollment under A New Payment Program

Amount of GTGP Land Reenrolled at Different Payments current payment

Dynamics in GTGP Land Reenrollment under Different Payments 6.4 ha 7.7 ha 6.2 ha

Dynamics in GTGP Land Reenrollment under Different Levels of Initial_knowledge about Social Norms

Dynamics in GTGP Land Reenrollment under Different Levels of Learn about Social Norms

Conclusions Over 15% more GTGP land can be reenrolled at the same payment if social norms are leveraged through multiple rounds of interactions. The effects of social norms were largest at intermediate payments. Land enrollment may converge to different levels at different times due to different levels of ‘initial_knowledge’ and ‘learn’ about social norms

Acknowledgments People F. Lupi, L. An, R. Sheely, A. Vina, J. Liu Financial Support National Science Foundation National Institutes of Health National Aeronautics and Space Administration Michigan Agricultural Experimental Station MSU Environmental Research Initiative

Thank You!