1 Long-Term, Heterogeneous Treatment Effects from Non-Pecuniary Environmental Programs: A Large-Scale Field Experiment Paul J. Ferraro Department of Economics Georgia State University Juan Jose Miranda Department of Economics Georgia State University

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3 Information Campaigns (1) Info on behavioral and technological modifications (2) Request users to voluntarily change their behavior for the public good (3) Provide social comparisons to induce individuals to conform to a social norm.

4 Field Experiment Water system in Metro Atlanta: with Ferraro, implemented targeted, residential information campaign as randomized experiment. Three treatments sent via first class mail in May Ferraro and Price focus on short-term, mean treatment effects in summer 2007.

5 Ferraro and Miranda Longer-term impacts (2008, 2009). Heterogeneous Treatment Effects Relevant for policy and practice Relevant for understanding mechanisms

6 Treatment 1: Tip Sheet Information on behavioral changes and technologies that can reduce water consumption

7 Treatment 2: Weak Social Norms (standard message) Tip sheet+ Civic Duty Language

8 Treatment 3: Strong Social Norms Tip sheet+ Letter with “Civic Duty” Language + Social Comparison

9 Social Comparison Your own total consumption June to October 2006: 52,000 gallons Your neighbors’ average (median) consumption June to October 2006:35,000 gallons You consumed more water than 73% of your Cobb County neighbors.

10 Reduction in Summer ’07 Consumption {95% CI} Number of Homes Control -- 71,643 Treat 1 (Tip) - 0.7% {-1.7%, 0.4%} 11,675 Treat 2 (Weak Norms) - 2.7% {-3.7%, -1.8%} 11,675 Treat 3 (Strong Norms) - 4.8% {-5.7%, -3.9%} 11,676

11 Summer 2008 Summer 2009 Treat 1 (Tip) ~0% Treat 2 (Weak Norms) ~0% Treat 3 (Strong Norms) -2.5% (p<0.01) -1.3% (p<0.05) Persistence of Treatment Effects

12 Heterogeneous Treatment Effect Nonparametric Tests (Crump et al. 2008) Test Null of Zero Conditional Average Treatment Effects Cannot reject that Treatment 1 effect is zero for all subgroups.

13 Heterogeneous Treatment Effect Nonparametric Tests (Crump et al. 2008) Test Null of Constant Conditional Average Treatment Effects Reject null that Treat 2 (p<0.10) and Treat 3 (p<0.01) have constant treatment effects

14 Treatment 1 (tips) Treatment 2 (weak norm) Treatment 3 (strong norm)

15 Subgroup Analysis Household Wealth (market value of house), age of home, ownership Neighborhood characteristics Education, race, ownership

Treatment 3 (2007) by Subgroups More responsive: wealthy households, owners and households in neighborhoods with more white and educated householders and fewer renters. More responsive: wealthy households, owners and households in neighborhoods with more white and educated householders and fewer renters. No difference conditional on year in which home was built. No difference conditional on year in which home was built. 16

Nonpecuniary vs Pecuniary Strong social norm message: 2007 effect equivalent to average price increase of ~12-15% effect equivalent to average price increase of ~12-15%. Immediately detectable one month later. Immediately detectable one month later. High-income households High-income households Most responsive to norms Most responsive to norms Least responsive to price (Mansur & Olmstead 2007) Least responsive to price (Mansur & Olmstead 2007) Effect declines over time. Effect declines over time. 17

Mechanisms Evidence consistent with behavioral changes with recurring costs rather than one-time fixed-cost investments. Mostly from outdoor use. Evidence consistent with behavioral changes with recurring costs rather than one-time fixed-cost investments. Mostly from outdoor use. Evidence consistent with interpretation that social comparison operates through social norms rather than private efficiency signals to a boundedly rational agent. Evidence consistent with interpretation that social comparison operates through social norms rather than private efficiency signals to a boundedly rational agent. 18

19 Choosing Among Non- experimental Estimators “Design-replication study” Form a non-experimental comparison group from households in neighboring counties. Then use “best-practice” econometric methods (regression, matching, panel data, regression discontinuity) to estimate treatment effect. Contrast non-experimental estimates to experimental estimates.