The Role of Social Comparison in Reducing Residential Water Consumption: Evidence from a Randomized Controlled Trial

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1 The Role of Social Comparison in Reducing Residential Water Consumption: Evidence from a Randomized Controlled Trial Salvador Lurbé, Jesse Burkhardt, Dale T. Manning, Chris Goemans May 23, 2018 Abstract Water and Electricity utilities typically use a portfolio approach to manage demand, increasingly relying on non-pecuniary interventions to influence behavior. Despite demonstrated effectiveness, it remains unclear whether observed treatment effects in Home Water and Energy Report (HWER) studies are due to increased awareness of water use (e.g., simply receiving a letter in the mail) or due to social comparison (e.g., being categorized as "Great", "Good", or "Take Action" relative to one s peers). In light of this uncertainty, we investigate if household responses depend on the social comparison they receive. In this paper, we evaluate a randomized controlled trial (RCT) of a home water report (HWR) conducted by a city in the Western US. We estimate the average and heterogeneous treatment effect of the HWR on household water consumption after two years of treatment and, to assess whether responses depend on social comparison, we evaluate if being classified in different normative categories has an effect on the responses to the treatment. We find an average treatment effect (ATE) of 2.4% and, based on the quantiles of the consumption net of pre-treatment household fixed effects, we find that treatment effects are negative for all quantiles and significant across the whole distribution except for those consumers on the very top or the very bottom, with point estimates ranging from 0.0% for those consumers in the lower quantiles (more technically efficient) to 3.0% for those in the upper quantiles of the consumption distribution (less technically efficient). With a regression discontinuity analysis and with a triple difference-in-difference, we find that the categorization effect is not statistically significant. Department of Agricultural and Resource Economics, Colorado State University, 1200 Center Ave. Mall, Fort Collins, CO 80523, slurbe@colostate.edu. Department of Agricultural and Resource Economics, Colorado State University, 1200 Center Ave. Mall, Fort Collins, CO 80523, jesse.burkhardt@colostate.edu. Department of Agricultural and Resource Economics, Colorado State University, 1200 Center Ave. Mall, Fort Collins, CO 80523, dale.manning@colostate.edu. Department of Agricultural and Resource Economics, Colorado State University, 1200 Center Ave. Mall, Fort Collins, CO 80523, cgoemans@rams.colostate.edu. 1

2 1 Introduction As providers of natural resources and energy face increasing scarcity, interest in incentivizing consumer conservation has surged. Because of political and legal constraints, such as cost-recovery pricing and equity concerns, many firms and utilities find it difficult to use pricing policies as the sole mechanism for promoting conservation. Instead, utilities typically use a portfolio approach to manage demand, increasingly relying on non-pecuniary interventions to influence behavior. In this context, Home Water and Energy Reports (HWERs) have been used as a means to reduce consumption. However, previous studies suggest that households respond to the treatment based on their efficiency of pre-treatment water use, but it is not clear if the signal about their efficiency comes from the social comparison, since a significant "categorization effect" could not be proven (Allcott (2011)). In other words, it remains unclear whether observed treatment effects in HWER studies are due to increased awareness of water use (e.g., simply receiving a letter in the mail) or due to social comparison (e.g., being categorized as "Great", "Good", or "Take Action" relative to one s peers). Understanding the mechanism through which HWERs are a successful means for reducing consumption would inform policy makers and utility managers on how to better craft non-price interventions to maximize conservation. If categories do play a role on how households react to the HWER, they can be designed to induce larger reductions. If categories do not play a role and the response is to information provision, HWERs should be redesigned according to the underlying mechanism. In this paper, we further investigate the apparent contradiction between the treatment effect observed on HWERs and the lack of a statistically significant categorization effect. To do so, we evaluate the first two years of a Randomized Controlled Trial (RCT) of a Home Water Report (HWR) administered by a city in the Western US. We first estimate the Average Treatment Effect (ATE) using a difference-in-difference method, assessing the overall success of the non- 2

3 pecuniary intervention. We then use quantile regression to examine the relationship between technical efficiency and the treatment effect by defining quantiles of of consumption net of time-invariant household characteristics. Finally, we evaluate whether being classified in different normative categories has an effect on the responses to the treatment, using a regression discontinuity approach Allcott (2011) as well as a triple difference to evaluate the presence of the "categorization effect". Consistent with previous literature (Allcott (2011); Delmas et al. (2013); Brent et al. (2015); Ferraro and Price (2013)), we find, after two years, an ATE of 2.4%. Based on the quantiles of the consumption net of pre-treatment household fixed effects, we find that treatment effects are negative for all quantiles and significant across the whole distribution except for those consumers on the very top or on the very bottom, with point estimates ranging from 0.0% for those consumers in the lower quantiles (more technically efficient) to 3.0% for those in the upper quantiles (less technically efficient) of the consumption distribution. Thus, we do not observe what previous literature has called the "boomerang effect", stating that those households that receive the message that their consumption is low relative to their neighbors could feel entitled to increase their consumption. We could not, nonetheless, reject the hypothesis that the coefficients are not statistically different from each other, indicating that even though we observe a trend in the point estimates, the relationship between technical efficiency and the treatment effect could not be proven. Using a regression discontinuity analysis and a triple difference, we test for the existence of the categorization effect on a bandwidth in which we can assume that households on one side or the other of the category cutoff are statistically identical. Consistent with Allcott (2011), we find that the categorization effect is not significant suggesting that other mechanisms drive the observed average treatment effects. Our results confirm the success of HWERs on, not only decreasing average consumption, but doing so across users across the spectrum of technical effi- 3

4 ciency. However, our results strengthen the argument against assuming that the mechanism behind HWER s success is social comparison, though we do not yet provide an alternative explanation. Further research should attempt to test alternative mechanisms to fill this gap, informing policy makers and utilities on how to better craft non-price interventions. This paper builds on a growing literature on the experimental effects of behavioral interventions. Previous studies have shown that Home Energy Reports (HERs) decrease mean electricity use (Allcott (2011)) and HWRs decrease water consumption (Ferraro and Price (2013); Brent et al. (2015)). Delmas et al. (2013) reviews the literature on the effect of information strategies on energy conservation, including social comparisons. The authors conclude that strategies based on social comparisons reduced energy use by 11.5% and are, on average, more effective than the rest of the information provision strategies aiming to lower consumption. In some studies, effects appear to persist over time, with no reduction in effect after two years of continual treatment (Allcott (2011); Brent et al. (2015)), while other studies show declining effects over time (Ferraro and Price (2013)).Allcott and Rogers (2014) expand on this idea and find that effects become more persistent as the intervention continues over time. Finally, Jessoe et al. (2017) demonstrate that the treatment effects can go beyond the targeted sector. The remainder of this paper is as follows. In Section 2 we outline the characteristics of the experiment and the data. In Section 3 we study the ATE, outlining the estimation strategy and results. In Section 4 we study the Heterogeneous Treatment Effects (HTEs). In Section 5 we study the Categorization Effect and in Section 6 we conclude and discuss the results. 4

5 2 Experiment Overview 2.1 Experiment The RCT used for this analysis started in September 2014 and is still ongoing. For the experiment, 7000 households were randomly chosen for the treatment group and 4000 households for the Control group. Households in the treatment group receive a HWR every two months, so for the first two years of treatment, households in the treatment group have received 12 HWRs. On the other hand, households in the Control group do not see their HWRs. HWRs include the water score and comparison to other households as well as conservation tips. Figure 1 is an example of the Water Score received by a household while Figure 2 is an example of the conservation tips. Figure 1: Example of a water score. Figure 2: Example of conservation tips. According to household s number of occupants, the size of the irrigable area, and the type of residence, every household is assigned to a cohort of similar houses. For each cohort, the 52nd and the 20th percentiles are calculated and, according to each household s consumption, a water score is assigned to them. If 5

6 Table 1: Summary Statistics (1) (2) (3) (4) (5) N mean sd min max gpd 321, ,180 Number of groups 10,065 10,065 10,065 10,065 10,065 the household s consumption is less than the 20th percentile, the utility assigns a water score equal to 1 and the message of "Great". If consumption is greater than the 20th percentile but less than the 52th percentile, the water score is 2 and the message received is "Good". Finally, if consumption is greater than the 52th percentile, and the household s per capita water consumption is larger than a certain value, the assigned water score is 3 and the message is "Take Action". This last feature of the experiment, which implies that the cutoff between water scores 2 and 3 is not clear, imposes to the analysis limitations that prevent us from studying the categorization effect on that cutoff. Conservation tips are specially crafted for households with specific characteristics and they consist of possible actions to reduce water consumption Data For each household participating in the experiment, either as treatment or as control, we observe monthly consumption, in gallons per day, their cohort s 20th percentile and median consumption, and the designated water score. The variable we are seeking to explain is consumption (gpd). The summary statistics, pooling the treatment and control groups, and pooling 8 months before treatment and 23 months after treatment, are as shown in table 1. An RCT is built on the assumption that, in the absence of a treatment, both treatment and control groups would have followed the same trend (Angrist and Pischke (2008); Deaton and Cartwright (2017); Ferraro and Hanauer (2014)). This identification assumption is then based on the treatment and the control groups being random samples from the same population (in this case, the universe of residential households in the municipality for which water is provided). To test 6

7 Table 2: Two sample t-test and F-test (1) (2) (3) (4) GROUPS Obs. Mean Std. Error Std. Dev Control 29, Treatment 51, for the validity of this assumption we compare sample means and standard deviations across the Treatment and Control groups. The results of these tests are shown in table 2. For the two sided t-test for means, the calculated p-value is Thus, we fail to reject the null hypothesis that the mean pre-treatment consumption of the treatment and control groups are equal. For the two sided F-test for standard deviations, the p-value is 0.85, so we failed to reject the null hypothesis that the ratio of standard deviations for treatment and control is different than 1. Having shown that means and standard deviations for the logarithm of consumption cannot be proven to be statistically different for treatment and control groups, we have no evidence to suggest that the groups were not the result of random sampling. However, Deaton and Cartwright (2017) make an interesting argument when they claim that if the sampling is indeed random, any significant difference in means or standard deviation would be in fact a false positive by construction, indicating that rejecting the null hypothesis does not necessarily mean that the samples were not randomly drawn from the same population. In this case, as we found that the difference in means and standard deviations is not statistically significant, we can use these findings as evidence to assume orthogonality of the treatment to other causes affecting the dependent variable. Therefore the expected value of the differences between the means of all other causes affecting both groups is zero. 7

8 3 Average Treatment Effect 3.1 Estimation To estimate the average treatment effect, we use a difference-in-difference model, where the specification is as follows: log(gpd) it = β 1 P t + β 2 T i + τ T i P t + µ my + υ i + ɛ it (1) The logarithm of household i s water consumption in month t (log(gpd) it ) depends on the treatment indicator T i, post-treatment indicator P t, and household and month-by-year fixed effects (υ i and µ my ). Treatment is defined as receiving a letter with the HWR on household water consumption in month t. To explain the mechanism behind estimating ATE on a difference-in-difference setting, we use the expected value operator on equation 1, conditional on being part of treatment or control groups and before and after treatment, as follows: E[log(gpd) it T = 1, P = 1] = β 1 + β 2 + τ + µ my + υ i (2) E[log(gpd) it T = 1, P = 0] = β 2 + µ my + υ i (3) E[log(gpd) it T = 0, P = 1] = β 1 + µ my + υ i (4) E[log(gpd) it T = 0, P = 0] = µ my + υ i (5) The difference-in-difference setting is then based on the difference between before and after for treatment and control groups: E[log(gpd) it T = 1, P = 1] E[log(gpd) it T = 1, P = 0] = β 1 + τ (6) 8

9 E[log(gpd) it T = 0, P = 1] E[log(gpd) it T = 0, P = 0] = β 1 (7) Finally, the difference-in-difference ATE is τ and results from the difference between equations 6 and 7. The parameters are estimated using OLS and standard errors are clustered at the household level to allow for dependence across months. Based on the randomized nature of the experiment, which we discussed in the previous section, we can expect the estimator to be unbiased (Deaton and Cartwright (2017)) only if there are not post-randomization confounding and selection biases. In medical sciences, this feature if often achieved by "policing" the experiment, requiring blindness of the participants and researchers. In our case, we can dismiss this source of bias under the assumption that there was no contamination between treatment and control group. Known as the "Stable unit treatment value assumption" (SUTVA), Cox (1958) states causal inference requires that the outcome of one unit should not be affected by the assignment of treatment to other units Results Table 3 presents the results of estimating ATE for the first 23 months in which households received 12 HWRs. T and P were omitted due to multicollinearity, with DD as the difference-in-difference variable T P. We find a statistically significant ATE of 2.4% for the first two years of treatment, as shown in table 3. Having the dependent variable in logarithm form allows us to interpret the estimated parameter as a proportional change. We can, then, contextualize this decrease in consumption by comparing it to the change in price needed to achieve the same reduction. According to Arbués et al. (2003), the elasticity of residential water consumption ranges in the literature from 0.1 to 1.8. Based on this estimation, the observed reduction in quantity is equivalent to an increase in prices between 0.2% and 4.3%. 9

10 Table 3: ATE (1) ATE DD *** ( ) Household FE Y Month by year FE Y Observations 321,448 R-squared Notes: Standard errors clustered by household. Star values: * 0.10 ** 0.05 *** However, as Deaton and Cartwright (2017) stated, it is important to discuss the external validity of the results. As the city used for the experiment is not the result of random selection, we cannot directly extrapolate our results to other regions, but we can infer what would be the result of scaling up the treatment to the whole population from which the samples were drawn. 4 Heterogeneous Treatment Effect 4.1 Estimation In this section we use quantile regression to study the heterogeneity in treatment effects at different points in the conditional distribution of the outcome variable net of pre-treatment household fixed effects. The goal is to make sure that we are capturing how the responses to the treatment are different based on pretreatment technical efficiency. In other words, being at the higher percentiles of the consumption distribution does not indicate per-se that a household is not being efficient, but it can also indicate that the household has certain characteristics that favor water use (number of occupants, size of yard, etc.). By subtracting the pre-treatment household s fixed effects from consumption, consumption associated with household s time invariant characteristics is left out and quantiles of this distribution are a more accurate representation of a household s technical 10

11 efficiency. We then implement a conditional quantile estimator for panel data, developed by Powell (2016), using robust standard errors, on the difference-indifference specification described in the previous section Results Figure 3 presents the results of estimating HTE for the first two years of treatment as a function of the quantiles of consumption net of pre-treatment household fixed effects. Figure 3: Quantile Regression. As shown in figure 3, we find that quantile treatment effects point estimates are negative across quantiles, but not statistically different than zero at a 90% level for those at the very bottom or very top of the distribution, where the confidence interval increases its width. Point estimates range from 0.0% for those consumers in the lower quantiles of the consumption net of pre-treatment fixed effects distribution (more technical efficient households) to 3.0% for those in the larger quantiles (less technical efficient households), with a quite stable treatment effect of around 2.4% across quantiles between 0.1 and 0.9. However, we could not reject the hypothesis that the coefficients are statistically different from each other, indicating that even though we observe a slight downward slope on the 11

12 point estimates, the relationship between technical efficiency and treatment effect could not be proven. Consistent with Allcott (2011), we did not find evidence of a "Boomerang Effect", that would imply that the most efficient users increase their consumption as a response to the HWRs. 5 Categorization Effect In this section, we test if the treatment effect is a function of the categorization that a household in the Treatment group receives. The existence of a "categorization effect" would imply that the mechanism behind the success of the HWR is social comparison. Having a clear cutoff between categories allows us to assume that, within a certain bandwidth, households on one side of the cutoff are statistically identical to those on the other side of the cutoff, and thus their treatment effects based on technical efficiency are also identical. The only difference is then on the water score they have received. We test for the categorization effect on the cutoff from "Great" to "Good". While this cutoff is based on a sharp fixed value (the 20th percentile of the household s cohort s consumption), the cutoff between "Good" and "Take Action" is less clear since it based on the 52th percentile of the household s cohort but adjusted based on the number of occupants in the house. This feature is imposing us limitations that we could not overcome with the available data. We use two alternative methods to test for the categorization effect. First, we use a Regression Discontinuity (RD) design, where we want to test if there is a change in the parameters of the functional form of the mean from one side of the cutoff to the other. Finally, we use a triple difference estimator to test if, within the same bandwidth, being categorized as "Great" or "Good" statistically alters the observed response. 12

13 5.1 Regression Discontinuity Estimation Knowing that there is a clear cutoff separating those who got one category or another, we can use an RD setting to study if there is a statistically significant categorization effect. Instead of comparing the trend on a control group with the trend on a treatment group, this identification exploits the arbitrary nature of the treatment when assigning water scores. For a given cohort, the assigned water score is a deterministic and discontinuous function of the distance to the cutoff. As we previously mentioned, we then rely on the assumption than for those treated households in the neighborhood of the discontinuity, we can extrapolate across covariate values as a control strategy (Angrist and Pischke (2008)). The model used for the estimation is as follows: log(gpd) it = β 0 + ρ I(D20 i t > 0) + β 1 D20 i t I(D20 i t < 0) +β 2 D20 i t I(D20 i t > 0) + µ my + υ i + ɛ it (8) : T i = 1, P it = 1, D20 i t < h, D20 i t 0 Where D20 i t is the distance to the 20th percentile cutoff in the previous month t, affecting the consumption of the current month t. I(D20 i t > 0) is an indicator function that is 1 when D20 i t > 0 and 0 otherwise, while I(D20 i t < 0) is an indicator function that is 1 when D20 i t < 0 and 0 otherwise. If D20 i t > 0, then the assigned water score is 2, if D20 i t < 0, then the assigned water score is 1. h is half the bandwidth in which we are assuming that households are statistically identical. Both D20 i t and h are measured in gallons per day. To explain the mechanism behind the RD setting, we use the expected value operator on equation 8, conditional on observing a positive or a negative distance to the cutoff calculated from the previous month s consumption: 13

14 E[log(gpd) it (D20 i t < 0)] = β 0 + β 1 D20 i t + µ my + υ i (9) E[log(gpd) it (D20 i t > 0)] = β 0 + ρ + β 2 D20 i t + µ my + υ i (10) We can then see that, to the left of the cutoff, the intercept is β 0 and the slope is β 1 while, to the right of the cutoff, the intercept is β 0 + ρ and the slope is β 2. The parameters of interest are then ρ and the difference between β 1 and β 2. The parameter ρ can be seen as the discontinuity in the intercept while, if there is a significant difference between β 1 and β 2, the regression discontinuity is manifested as a change in the slope. Finally, to better capture a change in the intercept, we estimate a RD specification but without allowing for different slopes, as follows: log(gpd) it = β 0 + ρ I(D20 i t > 0) + β 1 D20 i t + µ my + υ i + ɛ it (11) : T i = 1, P it = 1, D20 i t < h, D20 i t 0 If we use the expected value operator on equation 11, conditional on observing a positive or a negative distance to the cutoff calculated from the previous month s consumption: E[log(gpd) it (D20 i t < 0)] = β 0 + β 1 D20 i t + µ my + υ i (12) E[log(gpd) it (D20 i t > 0)] = β 0 + ρ + β 1 D20 i t + µ my + υ i (13) We can then see that, with this specification, to the left of the cutoff the intercept is β 0 while, to the right of the cutoff, the intercept is β 0 + ρ. The slope is, on both sides, β 1. The parameter of interest is then ρ, representing a location shift in the underlying populations on one side or the other of the cut-off. 14

15 5.1.2 Results Table 4 presents the results of estimating equation 8 (allowing for different slopes) for various values of the bandwidth h. Table 4: Regression Discontinuity (different slopes) (1) (2) (3) (4) (5) h=15 h=20 h=25 h=30 h=35 D20 i t I(D20 i t < 0) ** *** *** *** *** ( ) ( ) ( ) ( ) ( ) D20 i t I(D20 i t > 0) *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) I(D20 i t > 0) (0.0170) (0.0139) (0.0124) (0.0111) (0.0103) Household FE Y Y Y Y Y Month by year FE Y Y Y Y Y Observations 9,756 13,260 16,637 19,735 22,654 R-squared Notes: Standard errors clustered by household. Star values: * 0.10 ** 0.05 *** Table 5 presents the results of estimating equation 11 (without allowing for different slopes) for various values of the bandwidth h. Table 5: Regression Discontinuity (same slope) (1) (2) (3) (4) (5) h=15 h=20 h=25 h=30 h=35 D20 i t *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) I(D20 i t > 0) (0.0170) (0.0139) (0.0124) (0.0111) (0.0103) Household FE Y Y Y Y Y Month by year FE Y Y Y Y Y Observations 9,756 13,260 16,637 19,735 22,654 R-squared Notes: Standard errors clustered by household. Star values: * 0.10 ** 0.05 *** By looking at tables 4 and 5, we see that ρ is, for both specifications and for every considered bandwidth h, not statistically different than zero, indicating that there is no regression discontinuity manifested as a change in intercept. Slopes are, in both cases, positive. This result is as expected since it is capturing serial au- 15

16 tocorrelation. We could expect that the distance to the cutoff in month t would be correlated to the the distance to the cutoff in month t. Finally, when we allow for different slopes, the difference between the slope on the negative side and on the positive side (β 1 and β 2 ), is not statistically significant. In sum, we could not find evidence of a regression discontinuity indicating a categorization effect, not in the shape of a "bump" in the linear fit (ρ) nor in a change in slope. 5.2 Triple Difference Estimation As we previously mentioned, with the RD approach we rely on the assumption than for those treated households in the neighborhood of the discontinuity, we can extrapolate across covariate values as a control strategy. On the other hand, with the difference-in-difference approach we use for ATE and HTE, we assume orthogonality of the treatment to other causes affecting the dependent variable, thus the expected value of the differences between the means of all other causes affecting both groups is zero. Our last identification strategy for the categorization effect is a combination of both strategies, using a triple difference estimator for those observations within the specified bandwidth. The three differences are then between before and after, treated and untreated and negative and positive distance to the 20th percentile cutoff (water score equal to 1 or 2). This strategy can strengthen the results from the RD by incorporating an extra way to control for other causes affecting the dependent variable. The triple difference specification is as follows: log(gpd) it = β 0 + β 1 P t + β 2 T i + β 3 I(W S i t = 2) +β 4 T i I(W S i t = 2) + β 5 P t I(W S i t = 2) + β 6 T i P it (14) 16

17 +β 7 T i P it I(W S i t = 2) + µ my + υ i + ɛ it : D20 i t < h, D20 i t 0 As in the difference-in-difference model we use to calculate ATE, we use the expectation operator to explain the intuition. For those who got a water score equal to 2 and were treated: E[log(gpd) it T i = 1, P t = 0, W S i t = 2] = β 0 + β 2 + β 3 + β 4 + υ i (15) E[log(gpd) it T i = 1, P t = 1, W S i t = 2] = β 0 + β 1 + β 2 + β 3 + β 4 +β 5 + β 6 + β 7 + υ i (16) The difference between these two groups is β 1 + β 5 + β 6 + β 7. For those who got a water score equal to 2 and were untreated: E[log(gpd) it T i = 0, P t = 0, W S i t = 2] = β 0 + β 3 + υ i (17) E[log(gpd) it T i = 0, P t = 1, W S i t = 2] = β 0 + β 1 + β 3 + β 5 + υ i (18) The difference between these two groups is β 1 +β 5, and the overall differencein-difference for those who got a water score equal to 2 is β 6 + β 7. Finally, for those who got a water score equal to 1, using the expectation operator and the differences as we just explained, the overall difference-in-difference is β 6. The triple difference parameter is then β 7, which represents the extra treatment effect given by getting a "Great" instead of a "Good". 17

18 5.2.2 Results For different bandwidths h, the results of equation 14 are presented in table 6. The variable DDD is the difference-in-difference-in-difference variable T i P it I(W S i t = 2). Table 6: Difference in Difference in Difference (1) (2) (3) (4) (5) h=15 h=20 h=25 h=30 h=35 I(W S i t = 2) * (0.0489) (0.0406) (0.0360) (0.0327) (0.0302) T i P t (0.0477) (0.0404) (0.0357) (0.0325) (0.0304) T i I(W S i t = 2) (0.0643) (0.0526) (0.0461) (0.0417) (0.0385) P t I(W S i t = 2) (0.0497) (0.0413) (0.0366) (0.0333) (0.0308) DDD (0.0653) (0.0536) (0.0470) (0.0425) (0.0393) Household FE Y Y Y Y Y Month by year FE Y Y Y Y Y Observations 21,579 29,100 36,233 42,913 49,122 R-squared Notes: Standard errors clustered by household. Star values: * 0.10 ** 0.05 *** With the triple difference, the parameter of interest, β 7, associated with the DDD variable, is not statistically different from zero for any h considered. Therefore, these results do not support the existence of a categorization effect. 6 Conclusions and Discussions On average, HWRs work as a means to incentivize conservation, as shown by the ATE estimate here. Even though we have shown a trend between the point estimates of the treatment effect and the quantiles based on the household s technical efficiency, with our results could not prove that those estimates are different from each other. We found no evidence of a "boomerang effect", consistent with Allcott (2011) findings on HERs. Finally, we could not find evidence indicating 18

19 the existence of a "categorization effect" between categories "Great" and "Good". This result holds with both a RD approach and a triple difference estimation. The inexistence of a categorization effect would imply that extending the range of the Good or "Take action categories would not result in a greater treatment effect. Furthermore, the mechanism behind the effect might not be social comparison, but a response to information provision or simply to the knowledge that consumption is being monitored closely. Our work validates previous findings (Allcott (2011)), with a similar methodology on a different dataset and on HWRs instead of HERs. Finally our analysis includes a methodological extension based on the use of the triple difference estimation. With a deeper understanding behind the mechanisms through which HWRs incentivize conservation, we expect to inform policy makers and utilities on how to better craft non-pecuniary interventions. For future research, we expect to test for the existence of alternative mechanisms that could explain the observed reduction in consumption that occurs as a result of information provision. 19

20 References Allcott, H. (2011). Social norms and energy conservation. Journal of Public Economics, 95(9-10): Allcott, H. and Rogers, T. (2014). The short-run and long-run effects of behavioral interventions: Experimental evidence from energy conservation. American Economic Review, 104(10): Angrist, J. D. and Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist s companion. Princeton university press. Arbués, F., Garcıa-Valiñas, M. Á., and Martınez-Espiñeira, R. (2003). Estimation of residential water demand: a state-of-the-art review. The Journal of Socio- Economics, 32(1): Brent, D. A., Cook, J. H., and Olsen, S. (2015). Social comparisons, household water use, and participation in utility conservation programs: Evidence from three randomized trials. Journal of the Association of Environmental and Resource Economists, 2(4): Cox, D. R. (1958). Planning of experiments. Deaton, A. and Cartwright, N. (2017). Understanding and misunderstanding randomized controlled trials. Social Science & Medicine. Delmas, M. A., Fischlein, M., and Asensio, O. I. (2013). Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to Energy Policy, 61: Ferraro, P. J. and Hanauer, M. M. (2014). Advances in measuring the environmental and social impacts of environmental programs. Annual review of environment and resources, 39:

21 Ferraro, P. J. and Price, M. K. (2013). Using nonpecuniary strategies to influence behavior: evidence from a large-scale field experiment. Review of Economics and Statistics, 95(1): Jessoe, K., Lade, G. E., Loge, F., and Spang, E. (2017). Spillovers from behavioral interventions: Experimental evidence from water and energy use. Working Paper. Powell, D. (2016). Quantile regression with nonadditive fixed effects. RAND Corporation. 21