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The interaction of descriptive and injunctive social norms in promoting energy conservation

Abstract

Behavioural interventions that leverage social norms are widely used to foster energy conservation. For instance, home energy reports combine information on others’ behaviour (descriptive feedback) and approval for norm compliant behaviour (injunctive feedback). In a randomized controlled trial, we investigated how descriptive and injunctive feedbacks interact to affect electricity use, and evaluate the effects of additional normative feedback presented in the form of descriptive or injunctive energy conservation norm primes. We found that consistent descriptive and injunctive feedback boosts the effectiveness of social information in inducing energy conservation. When descriptive and injunctive feedback are in conflict, conservation behaviour is a function of the relative strength of the two types of feedback. Additional normative feedback produces smaller gains when it reinforces existing information of the same type. These results suggest complementarities between different types of normative messages rather than superiority of any one kind of feedback.

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Fig. 1: Hypothesized impact of injunctive and descriptive feedbacks in social information messages.
Fig. 2: Home Energy Report.
Fig. 3: Impact of the injunctive feedback on electricity usage.
Fig. 4: Heterogeneous impact of the normative primes at different injunctive feedback cutoffs.
Fig. 5: Prime impact by up- and downgrades in the injunctive feedback category.

Data availability

The data that support the findings of this study are proprietary data of the energy company and cannot be shared publicly. To inquire about access to the proprietary data, please contact M.T.

Code availability

The replication code is available on Open Science Framework at https://osf.io/wz8gb/.

References

  1. 1.

    Frey, B. S. & Meier, S. Social comparisons and pro-social behavior: testing ‘conditional coop-eration’ in a field experiment. Am. Econ. Rev. 94, 1717–1722 (2004).

    Article  Google Scholar 

  2. 2.

    Shang, J. & Croson, R. A field experiment in charitable contribution: the impact of social information on the voluntary provision of public goods. Econ. J. 119, 1422–1439 (2009).

    Article  Google Scholar 

  3. 3.

    Gerber, A. S. & Rogers, T. Descriptive social norms and motivation to vote: everybody’s voting and so should you. J. Polit. 71, 178–191 (2009).

    Article  Google Scholar 

  4. 4.

    Beshears, J., Choi, J. J., Laibson, D., Madrian, B. C. & Milkman, K. L. The effect of providing peer information on retirement savings decisions. J. Finance 70, 1161–1201 (2015).

    Article  Google Scholar 

  5. 5.

    Allcott, H., Mullainathan, S. & Taubinsky, D. Externalizing the Internality (New York Univ., 2011).

  6. 6.

    Allcott, H. & Rogers, T. The short-run and long-run effects of behavioral interventions: experimental evidence from energy conservation. Am. Econ. Rev. 104, 3003–3037 (2014).

    Article  Google Scholar 

  7. 7.

    Ayres, I., Raseman, S. & Shih, A. Evidence from two large field experiments that peer comparison feedback can reduce residential energy usage. J. Law Econ. Organ. 29, 992–1022 (2013).

    Article  Google Scholar 

  8. 8.

    Brent, D. A., Cook, J. H. & Olsen, S. Social comparisons, household water use, and participation in utility conservation programs: evidence from three randomized trials. J. Assoc. Environ. Resour. Econ. 2, 597–627 (2015).

    Google Scholar 

  9. 9.

    Ferraro, P. J. & Price, M. K. Using nonpecuniary strategies to influence behavior: evidence from a large-scale field experiment. Rev. Econ. Stat. 95, 64–73 (2013).

    Article  Google Scholar 

  10. 10.

    Ferraro, P. J., Miranda, J. J. & Price, M. K. The persistence of treatment effects with norm-based policy instruments: evidence from a randomized environmental policy experiment. Am. Econ. Rev. 101, 318–322 (2011).

    Article  Google Scholar 

  11. 11.

    Ferraro, P. J. & Miranda, J. J. Heterogeneous treatment effects and mechanisms in information-based environmental policies: evidence from a large-scale field experiment. Resour. Energy Econ. 35, 356–379 (2013).

    Article  Google Scholar 

  12. 12.

    Jaime Torres, M. M. & Carlsson, F. Direct and spillover effects of a social information campaign on residential water-savings. J. Environ. Econ. Manag. 92, 222–243 (2018).

    Article  Google Scholar 

  13. 13.

    Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J. & Griskevicius, V. The constructive, destructive, and reconstructive power of social norms. Psychol. Sci. 18, 429–434 (2007).

    Article  Google Scholar 

  14. 14.

    Bicchieri, C. & Xiao, E. Do the right thing: but only if others do so. J. Behav. Decis. Mak. 22, 191–208 (2009).

    Article  Google Scholar 

  15. 15.

    Meisel, M. K. & Goodie, A. S. Descriptive and injunctive social norms’ interactive role in gambling behavior. Psychol. Addict. Behav. 28, 592–598 (2014).

    Article  Google Scholar 

  16. 16.

    Biccheri, C. & Dimant, E. Nudging with care: the risks and benefits of social information. Public Choice https://doi.org/10.1007/s11127-019-00684-6 (2019).

  17. 17.

    Allcott, H. Social norms and energy conservation. J. Public Econ. 95, 1082–1095 (2011).

    Article  Google Scholar 

  18. 18.

    Jachimowicz, J. M., Hauser, O. P., O’Brien, J. D., Sherman, E. & Galinsky, A. D. The critical role of second-order normative beliefs in predicting energy conservation. Nat. Hum. Behav. 2, 757–764 (2018).

    Article  Google Scholar 

  19. 19.

    Andor, M. A., Gerster, A., Peters, J. & Schmidt, C. M. Social norms and energy conservation beyond the US. J. Environ. Econ. Manag. 103, 102351 (2010).

    Article  Google Scholar 

  20. 20.

    Byrne, D. P., Nauze, A. L. & Martin, L. A. Tell me something I don’t already know: informedness and the impact of information programs. Rev. Econ. Stat. 100, 510–527 (2018).

    Article  Google Scholar 

  21. 21.

    Costa, D. L. & Kahn, M. E. Energy conservation ‘nudges’ and environmentalist ideology: evidence from a randomized residential electricity field experiment. J. Eur. Econ. Assoc. 11, 680–702 (2013).

    Article  Google Scholar 

  22. 22.

    Bonan, J., Cattaneo, C., D’Adda, G. & Tavoni, M. Can We Make Social Information Programs More Effective? The Role of Identity and Values Working Paper 19-21 (Resources for the Future, 2019).

  23. 23.

    Bursztyn, L., González, A. L. & Yanagizawa-Drott, D. Misperceived Social Norms: Female Labor Force Participation in Saudi Arabia Working Paper 24736 (National Bureau of Economic Research, 2018); https://doi.org/10.3386/w24736

  24. 24.

    D’Adda, G., Dufwenberg, M., Passarelli, F. & Tabellini, G. Partial Norms SSRN Scholarly Paper ID 3362021 (Social Science Research Network, 2019).

  25. 25.

    List, J. A., Metcalfe, R. D., Price, M. K. & Rundhammer, F. Harnessing Policy Complementarities to Conserve Energy: Evidence from a Natural Field Experiment Working Paper 23355 (National Bureau of Economic Research, 2017); https://doi.org/10.3386/w23355.00000

  26. 26.

    Hallsworth, M., List, J. A., Metcalfe, R. D. & Vlaev, I. The behavioralist as tax collector: using natural field experiments to enhance tax compliance. J. Public Econ. 148, 14–31 (2017).

    Article  Google Scholar 

  27. 27.

    Bobek, D. D., Hageman, A. M. & Kelliher, C. F. Analyzing the role of social norms in tax compliance behavior. J. Bus. Ethics 115, 451–468 (2013).

    Article  Google Scholar 

  28. 28.

    Krupka, E. L. & Croson, R. T. A. The differential impact of social norms cues on charitable contributions. J. Econ. Behav. Organ. 128, 149–158 (2016).

    Article  Google Scholar 

  29. 29.

    Bahnot, S. Isolating the effect of injunctive norms on conservation behavior: new evidence from a field experiment in California. Organ. Behav. Hum. Decis. Process https://doi.org/10.1016/j.obhdp.2018.11.002 (2018).

  30. 30.

    Allcott, H. & Taubinsky, D. Evaluating behaviorally motivated policy: experimental evidence from the lightbulb market. Am. Econ. Rev. 105, 2501–2538 (2015).

    Article  Google Scholar 

  31. 31.

    Allcott, H. Site selection bias in program evaluation. Q. J. Econ. 130, 1117–1165 (2015).

    Article  Google Scholar 

  32. 32.

    Bruhn, M. & McKenzie, D. In pursuit of balance: randomization in practice in development field experiments. Am. Econ. J. Appl. Econ. 1, 200–232 (2009).

    Article  Google Scholar 

  33. 33.

    Andor, M. A. & Fels, K. M. Behavioral economics and energy conservation—a systematic review of non-price interventions and their causal effects. Ecol. Econ. 148, 178–210 (2018).

    Article  Google Scholar 

  34. 34.

    Farrow, K., Grolleau, G. & Ibanez, L. Social norms and pro-environmental behavior: a review of the evidence. Ecol. Econ. 140, 1–13 (2017).

    Article  Google Scholar 

  35. 35.

    Delmas, M. A., Fischlein, M. & Asensio, O. I. Information strategies and energy conservation behavior: a meta-analysis of experimental studies from 1975 to 2012. Energy Policy 61, 729–739 (2013).

    Article  Google Scholar 

  36. 36.

    Guiso, L., Sapienza, P. & Zingales, L. Long-term persistence. J. Eur. Econ. Assoc. 14, 1401–1436 (2016).

    Article  Google Scholar 

  37. 37.

    Steg, L., Perlaviciute, G., van der Werff, E. & Lurvink, J. The significance of hedonic values for environmentally relevant attitudes, preferences, and actions. Environ. Behav. 46, 163–192 (2014).

    Article  Google Scholar 

  38. 38.

    van der Werff, E., Steg, L. & Keizer, K. The value of environmental self-identity: the relationship between biospheric values, environmental self-identity and environmental preferences, intentions and behaviour. J. Environ. Psychol. 34, 55–63 (2013).

    Article  Google Scholar 

  39. 39.

    Bertrand, M., Duflo, E. & Mullainathan, S. How much should we trust differences-in-differences estimates? Q. J. Econ. 119, 249–275 (2004).

    Article  Google Scholar 

  40. 40.

    Benjamini, Y., Krieger, A. M. & Yekutieli, D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93, 491–507 (2006).

    MathSciNet  Article  Google Scholar 

  41. 41.

    Haushofer, J. & Shapiro, J. The short-term impact of unconditional cash transfers to the poor: experimental evidence from Kenya. Q. J. Econ. 131, 1973–2042 (2016).

    Article  Google Scholar 

  42. 42.

    Imbens, G. W. & Lemieux, T. Regression discontinuity designs: a guide to practice. J. Econom. 142, 615–635 (2008).

    MathSciNet  Article  Google Scholar 

  43. 43.

    Calonico, S., Cattaneo, M. D. & Titiunik, R. Robust nonparametric confidence intervals for regression- discontinuity designs. Econometrica 82, 2295–2326 (2014).

    MathSciNet  Article  Google Scholar 

  44. 44.

    Cattaneo, M. D., Idrobo, N. & Titiunik, R. A Practical Introduction to Regression Discontinuity Designs: Foundations (Cambridge University Press, 2019).

  45. 45.

    Cattaneo, M. D., Jansson, M. & Ma, X. Simple local polynomial density estimators. J. Am. Stat. Assoc. 0, 1–7 (2019).

    MATH  Google Scholar 

  46. 46.

    Canay, I. A. & Kamat, V. Approximate permutation tests and induced order statistics in the regression discontinuity design. Rev. Econ. Stud. 85, 1577–1608 (2018).

    MathSciNet  Article  Google Scholar 

  47. 47.

    Banerjee, A. et al. In Praise of Moderation: Suggestions for the Scope and Use of Pre-analysis Plans for RCTs in Economics Working Paper w26993 (National Bureau of Economic Research, 2020).

  48. 48.

    Calonico, S., Cattaneo, M. D. & Titiunik, R. Robust nonparametric confidence intervals for regression–discontinuity designs. Econometrica 82, 2295–2326 (2014).

    MathSciNet  Article  Google Scholar 

  49. 49.

    Calonico, S., Cattaneo, M. D., Farrell, M. H. & Titiunik, R. Regression discontinuity designs using covariates. Rev. Econ. Stat. 101, 442–451 (2019).

    Article  Google Scholar 

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Acknowledgements

The research leading to these results received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013)/ERC grant agreement no. 336155—project COBHAM, ‘The role of consumer behaviour and heterogeneity in the integrated assessment of energy and climate policies’.

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J.B., C.C., G.D. and M.T. conceived and designed the experiments. J.B. analysed the data. J.B. and G.D. contributed the analysis tools. J.B., C.C., G.D. and M.T. wrote the paper.

Corresponding author

Correspondence to Giovanna d’Adda.

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Supplementary Information

Supplementary Methods, Notes 1–6, Figs. 1 and 2 and Tables 1–18.

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Bonan, J., Cattaneo, C., d’Adda, G. et al. The interaction of descriptive and injunctive social norms in promoting energy conservation. Nat Energy 5, 900–909 (2020). https://doi.org/10.1038/s41560-020-00719-z

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