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What Causes Heterogeneous Responses to Social Comparison Messages for Water Conservation?

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Abstract

Social comparisons for water conservation are often implemented in conjunction with a broader set of drought management policies. We investigate the interaction of social comparisons with prior responses to voluntary appeals for water conservation using a large-scale field experiment in Reno, Nevada. We develop a new social comparison framed as performance toward a conservation goal in contrast to the traditional comparison made in gallons. Our new social comparison decouples the performance relative to the peer group from baseline water use, allowing us to investigate the role of the peer comparison independently from baseline water use. Using a traditional and our new social comparison, we investigate prior conservation and baseline water use as drivers of heterogeneous response to social comparisons. Baseline water drives treatment heterogeneity in the traditional social comparison, while prior conservation drives treatment heterogeneity the new social comparison. The results indicate that under-performance relative one’s peers is critical for generating water conservation. Simple targeting of both types of social comparisons can increase aggregate savings by 38% because our new social comparison generates conservation among a different set of households compared to the traditional social comparison.

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Notes

  1. See among others Allcott (2011), Allcott and Rogers (2014), Ayres et al. (2013), Costa and Kahn (2013) for energy and Ferraro et al. (2011), Ferraro and Price (2013), Brent et al. (2015) for water.

  2. Opower, which is now part of Oracle, and WaterSmart are two companies that used emoticons in SCMs for energy and water conservation, respectively.

  3. For example, WaterSmart software operates or operated multiple SCM campaigns in California utilities during the recent drought ending in 2015. In addition to the SCM campaigns, there was a statewide call for conservation that included voluntary conservation, mandatory outdoor watering restrictions, and water efficiency rebate programs such as converting grass to xeriscape.

  4. In related research Brent (2018) shows how converting to drought-resistant landscapes affects demand elasticity parameters.

  5. During this first year of drought the need for conservation was less urgent; TMWA did not make the request for voluntary conservation until late July.

  6. There is evidence of pervasive waste in outdoor irrigation, where households can achieve the same landscape using less water (Deoreo and Mayer 2012).

  7. Prominent examples of internalities in the water and energy sectors include: imperfect information about the costs of water/energy consumption (Allcott and Taubinsky 2015); dynamic inconsistencies in decision-making (Allcott et al. 2014); cognitive constraints (Brent and Ward Forthcoming); lack of salience of infrequent or automatic billing (Sexton 2015; Wichman 2017); and confusion about nonlinear price structures (Ito 2014; Wichman 2014; Lott 2017).

  8. Households were compared to other households in their same billing cycle, which the utility uses to divide customers into contiguous neighborhood units. Furthermore, customers within the same billing cycle were divided into above/below median yard size (square feet) and above/below median number of bedrooms. In a few cases in which comparison groups were sparse, customers were segmented based on above/below the median yard size only.

  9. Specifically, we included homes that (i) had metered water service; (ii) used enough water during at least one month of the 2013 irrigation season to exceed the tier 1 limit (6000 gals), indicating some outdoor water use; (iii) had lived at their current residence since April 2013, and therefore had summer 2013 bills for comparison; (iv) had a billing address that corresponded with the residential service address to eliminate rental occupants and other users who may not pay for water or have limited control over water use at the residence; (v) had a 2-inch service main or smaller, excluding unusually large water users; (vi) live within one of the targeted bill cycle regions (some regions were excluded because they had a low number of single-family households, see "Appendix"); and (vii) had nonzero water use during each month of the 2013 irrigation season (May–September) and pre-treatment months during the 2015 irrigation season (May–July) to exclude homes that were unoccupied for an extended period of time.

  10. Average pre-treatment consumption is equal to average water use during May, June, August, and September in 2013 and 2014, and May and June for 2014.

  11. Figure 2 uses data for households assigned to the relevant treatment. Panels (a) and (c) use households assigned to T1 and panels (b) and (d) use households assigned to T2. We have also generated the figures with all households in the sample (treatment and control) and the figures look almost identical. Using the full sample shows that patterns in Fig. 2 are not sensitive to the particular randomization assignment; the patterns hold more generally.

  12. The only reason why there are some low users who are above their peer group in the traditional SCM (T4) is that the norm is based on a peer group—defined by households in the same meter route who have similar number of bedrooms and yard size (above/below the median). By comparison, Ferraro and Price (2013) compare household consumption to the full sample median, producing a treatment where the strength of the descriptive norm is perfectly correlated with baseline consumption. Therefore, a household with a high-water-use peer group can be above the 75th percentile of the sample-wide distribution of baseline consumption, but still consume less than the peer group.

  13. The p-values of a t-tests for of prior conservation across the pooled treatment, T1, T2, and T1 versus T2, are 0.39, 0.85, 0.26, and 0.46 respectively. A visual depiction of the correlation between baseline consumption and prior conservation is shown in Fig. 10.

  14. For reference, in the full field experiment the generic tips treatment had no statistically-significant impact on conservation, and the ATE of the tips plus historical information treatment was slightly less than 1% and statistically significant.

  15. The creation of variables used in the results presented in Table 7 is documented in Table 16 in the "Appendix".

  16. The results in Table 7 show that the treatment effects quickly wane over time, which makes the months included in the sample important for the magnitude of the ATE. We selected our sample by including the peak summer demand months after the intervention (August-October) that TMWA targeted for their conservation efforts.

  17. The hypothetical targeted ATE is calculated by taking the weighted average of linear combinations of the treatment effect and relevant interaction for T1 and T2 presented in column (6) of Table 5 weighted by the proportion of the population in each subgroup.

  18. The formal tests rely on a Wald test for equality of the weighted average of the coefficients in column (6) of Table 5 using the weights as implemented versus the targeted weights. The difference in targeted versus actual ATEs is 120 gallons and the p-value for the Wald test for the difference being zero is 0.059.

  19. We are also considering the benefit of targeting an experiment with two SCM treatments, as opposed to the benefit of adding and targeting T2 to a an existing campaign that only uses the traditional SCM in gallons.

  20. We show average appraised values within quartiles of baseline consumption and prior conservation in Fig. 13 in the "Appendix".

  21. One exception is that households with irrigation systems who are more often high users have a greater response to mandatory restrictions.

  22. Due to the unequal size of the blocking groups, some timing treatments were oversampled, thereby creating some balance issues. We corrected these by identifying and dropping the oversampled observations after the conclusion of the field experiment (3677 households: 2025 control, 1652 treatment). All balance tables and regression results reflect the corrected sample.

  23. Allcott (2011) showed little impact of moving into one of the three distinct categories in the Home Energy Report (“Great”, “Good”, or “Below Average”) in a regression discontinuity design. In that study a household is assigned the category “Great” if they consume below the 20th percentile of peer consumption, “Good” if they consume below the average of peer consumption, or “Below Average” if they consume above the average of peer consumption.

  24. The graphs are generated with a data-driven approach using spacing estimators to generate the bin sizes in the plots (Calonico et al. 2015). The points on the graph are the average normalized residual consumption within each bin, and the lines are the fitted values of separate third-order polynomial regressions on either side of the distance threshold (zero).

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Acknowledgements

The authors are grateful to the Truckee Meadows Water Authority (TMWA) for financial support and all TMWA staff who contributed to the research through information about water demand issues, data management, and logistical support. We especially appreciate the help of Laine Christman. Brent acknowledges support from the Cooperative Research Centre for Water Sensitive Cities (CRC grant number 20110044).

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Correspondence to Daniel A. Brent.

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The authors are grateful to the Truckee Meadows Water Authority (TMWA) for financial support and all TMWA staff who contributed to the research through information about water demand issues, data management, and logistical support.

Appendices

Appendix

1.1 Randomization and Implementation

The randomization used a procedure of quasi-pairwise matching within blocking groups. This method first defines a set of blocks within which the randomization occurs. The blocking procedure ensures that assignment to a treatment is balanced within certain groups of interest. Blocks were defined by billing cycles, rate schedule and frequency of recorded meter data (i.e. monthly, daily, or hourly, though all customers only receive monthly usage totals). Within each block we ordered all observations on average water consumption in summer 2013 in sets of five households. We randomly assigned each household to one of five experimental samples that correspond to the five treatments (regardless of the ultimate assignment to treatment group vs. control group). This ensures a similar distribution of 2013 water consumption within each of the five experimental samples.

Next, within each of the five experimental samples we repeated the procedure to assign households to one of three possible timing treatments (single letter in July, single letter in August, or two letters repeated in July and August), or the control group. The same blocking structure was used within each experimental sample and then households were re-ordered based on summer 2013 water consumption and in sets of 12: two households are randomly assigned to each of the three timing treatments and six households are assigned to the control.Footnote 22

The process for generating and mailing letters was as follows:

  • 1–2 days after the most recent month’s consumption data is loaded into the billing system we pull this information into Stata and using a set of pre-programmed routines use it to generate the graphics and data for the mail merge.

  • A mail merge is performed in Microsoft Word using the generated data and graphics.

  • PDF’s of the letters are emailed to Digiprint

  • Digiprint prints and ships the letters within 1–3 days of receiving the electronic files.

The average time from the data upload to letters shipment was 2 days with a maximum of 8 days during this study. We had attrition during the study of about 1.5% of the treatment customers; 142 customers dropped out of the study in July and 211 customers dropped in August. This attrition was likely due to customers closing accounts or billing data (meter reading) errors. Furthermore, the mailers did not generate a very large increase in call center volume; out of the 23,213 customers we attempted to reach with this pilot we estimate that only 43 contacted the call center. Most of the customers who called the call center just wanted to ask clarification questions about the information in their letter; only 26 wanted further assistance beyond what the call center representatives could provide; and only five customers ended being truly upset by the pilot program.

1.2 Additional Treatments and Treatment Figures

Treatment A1 provided households with six tips that the TMWA media campaign publicized for how to reduce outdoor water consumption, similar to Ferraro et al. (2011). This letter was not customized to report on individual household water use. The six tips were printed on the reverse side of mailers for all other treatments including the two SCMs. The tips were the same for all household an example is provided in (Fig. 4),

Treatment A2 augmented the generic tips with personalized information about the customer’s water use, with a title introducing the letters that read: “Below is your customized water use report.” The A2 letter included a figure that displayed the customer’s water use in thousands of gallons (kgal) for May through September of 2013 and also their water use in 2015 for each month from May up to the last month billed before the letter was sent out (Fig. 5 shows the mailer). This figure and accompanying descriptive text was also included with A3, T1, and T2. Therefore, the SCM treatments also include water conservation tips and personalized historical water use information.

Treatment A3 contained the same components as A2, with the additional message “Saving water saves you money”, a figure displaying (i) the rate structure with tiers and price for each tier, (ii) the customer’s water use in kgal within TMWA’s increasing-block rate structure for the last month billed in 2015, and (iii) the upcoming month’s target of 10% less water than the same month in 2013 within the rate structure. The letter also provided the monetary savings that the customer could expect from meeting this goal (see Fig. 6).

Fig. 4
figure 4

Treatment A1—Tips

Fig. 5
figure 5

Treatment A2—Historical water use information

Fig. 6
figure 6

Treatment A3—Rate structure information

Fig. 7
figure 7

Treatment 1—Social comparison, reported in thousands of gallons

Fig. 8
figure 8

Treatment 2—Social comparison, reported as progress towards TMWA’s 10% conservation goal

Additional Balance Tests

See Figs. 7 and 8, Table 8.

Table 8 All treatments balance on observables
Table 9 Balance on observeables for pooled treatments (T1 and T2) by quartiles
Table 10 Balance on observeables for T1 by quartiles
Table 11 Balance on observeables for T2 by quartiles
Table 12 Balance on observeables for T1 versus T2 by quartiles
Fig. 9
figure 9

Distributions of Pre-treatment Consumption Across Treatment Status. Note: The lines are kernel density estimates of pre-treatment consumption for all treated households, all control households, and households within each treatment groups

Table 13 Kolmogorov–Smirnov tests

Discrete Effects Moving Above the Peer Group and 10% Goal

It is important to distinguish the continuous difference from peer group consumption from the discrete injunctive norm defining appropriate behavior (Schultz et al. 2007). We consider two separate effects. First, we test whether performing slightly worse than one’s peers has an effect on consumption, and second we test for the discrete effect of just barely missing the 10% conservation goal. In our setting the descriptive injunctive norm is based on whether a household met the 10% goal. Therefore if a household less than 10% it received the message, “Please do your part to help with the drought.”, while a household that saved more than 10% was told, “Keep up the good work!”. It is also possible that the household considered their performance relative to their peer group as an additional categorical norm. The results in Table ?? contain both the effect of the discrete injunctive norm and the continuous descriptive norm (Tables 9, 10, 11, 12 and 13).

To isolate the effect of the injunctive norm, we employ a regression discontinuity (RD) design (Imbens and Lemieux 2008; Lee and Lemieux 2010), analyzing behavior on either side of the injunctive category similar to Allcott (2011).Footnote 23 The running variable is the difference in performance between a household and the peer group in gallons for Treatment 1 and in percentage reduction for Treatment 2. The dependent variable in both is residual normalized consumption based on a regression of normalized consumption on weather, month fixed effects, and household fixed effects, following the approach of (Allcott 2011). The RD estimation assumes that factors varying with the difference from the peer group, such as pre-treatment water and the strength of the descriptive norm, are the same for households just above and below their peer group. Since some households were above their peer group but saved more than 10%. Similarly some households were below their peer and saved less than 10%. We repeat the analysis dropping these households and the results are very similar.

We begin with graphical evidence of differences in consumption near the peer group (Fig. 11), as is standard in RD approaches.Footnote 24 For both SCM the graphical evidence in Fig. 11 suggests that moving above the peer group does not affect consumption.

The graphical evidence is corroborated in the RD estimates for moving above the peer group. We use three different RD estimators developed by Calonico et al. (2014): the conventional, bias-corrected, and bias corrected with robust standard errors. In all specifications the impact for moving above the peer group is small and not statistically significant (Table 14). The RD estimates show that the effects in Table 5 are driven by the distance from the peer group as opposed to simply being above or below the peer group. There is either no effect associated with adding a negative injunctive norm.

Fig. 10
figure 10

Correlation of Baseline Water Use and Prior Conservation. Note: The graph displays the percentage of households in each quartile of prior conservation within quartiles of baseline water use. Prior conservation is defined as the percentage change in water from 2013 to 2014

Fig. 11
figure 11

Effect of Moving Above the Peer Group. Note: The dependent variable is residual normalized consumption and the units are percentage terms. The discontinuity is based on the moving above the peer group’s conservation rate

Table 14 Regression discontinuity estimates of the moving above the peer group

We repeat this exercise to see if moving slightly below above the 10% goal influences consumption. The analysis is the same as reported above except the running variable is the year-on-year percentage change in water consumption and the threshold is the \(-10\)%. To be consistent with the analysis in the main text we subtract 10% off the running variable and such that the threshold is at zero and year-on-year changes of less than 10% are positive and more than 10% are negative. Figure 12 graphs residual consumption on the y-axis with the year-on-year percentage change in water consumption (minus 10%) on the x-axis. There is no visual evidence of a change in consumption right at the threshold. This is corroborated with the RD estimates (Table 15) for each of the treatments (Figs. 9, 10, 11, 12 and 13).

Fig. 12
figure 12

Effect of the Failing to Meet the 10% Goal. The dependent variable is residual normalized consumption and the units are percentage terms. The discontinuity is based on the moving above the consumption threshold that constitutes the household’s 10% goal. Data from all treatments are included

Table 15 Regression discontinuity estimates of the failing to meet the 10% goal

1.1 Additional Tables and Figures

See Table 16.

Table 16 Creation of timing indicator variables
Fig. 13
figure 13

Average Appraised Value withing Quartiles of Pre-treatment Water Use and Prior Conservation. Note: The graph displays the averaged appraised value of properties based on quartiles of pre-treatment water use and prior conservation (\(\%\Delta W\))

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Brent, D.A., Lott, C., Taylor, M. et al. What Causes Heterogeneous Responses to Social Comparison Messages for Water Conservation?. Environ Resource Econ 77, 503–537 (2020). https://doi.org/10.1007/s10640-020-00506-0

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