Xiaodong ChenKennedy 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, ChinaWolong 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

),0|(*)(

)(1)(

reconvertpayreenrollPreconvertP

reconvertPreenrollP

jj

jj

)( jreenrollP is the probability the jth GTGP plot is re-enrolled

)( jreconvertP is the probability the jth GTGP plot is reconverted to crop production after the payments end

),0|( reconvertpayreenrollP j 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 variables Parameters

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 < 0.001

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!