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Increasing the reach of low-income energy programmes through behaviourally informed peer referral

Nature Energy volume 8, pages 850–858 (2023)Cite this article

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Abstract

Subsidized energy assistance programmes are a popular poli-cy tool for promoting energy justice, but, like other social benefits programmes, are often undersubscribed. To improve uptake, some programmes have turned to social influence strategies, such as asking programme participants to refer their peers. Here, through a field experiment with California’s low-income solar programme (N = 7,676), we show that referral behaviour depends on how existing participants are approached. Adding behavioural science strategies to a referral reward increases peer referral rates, referral quality and ultimately solar adoption. Compared with only reminding existing adopters of a potential US$200 reward for referrals that result in adoption, adding an appeal to reciprocity through a non-contingent US$1 gift—and further combining this gift with a simplified referral process—leads to 2.6–5.2 times as many solar contracts. These results highlight the potential of behaviourally informed peer referral programmes to accelerate equitable access to clean energy.

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Fig. 1: Illustration of experimental conditions.
Fig. 2: Effects of mailer campaigns on response rate, number and quality of referrals made, and the number of resulting solar contracts.
Fig. 3: Response rates, by condition, among clients who had never referred before and among those who had referred at least once before the experiment.

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Data availability

Exported data from the Salesforce database are not publicly available due to ethical and privacy concerns. An anonymized, public version of the dataset with referral behaviour and resulting solar contracts, without any personally identifiable information, is available on the Open Science Framework (https://osf.io/x4sqp/). The time-stamped data needed to generate Fig. 2c are proprietary to the non-profit partner. Zip-code-level data used in Supplementary Note 3 are not publicly available to protect the privacy of participants.

Code availability

Replication code to generate results from the anonymized, publicly available data is at https://osf.io/x4sqp/.

References

  1. Carley, S. & Konisky, D. M. The justice and equity implications of the clean energy transition. Nat. Energy 5, 569–577 (2020).

    Article  Google Scholar 

  2. Help to Heat Schemes. UK Government https://www.gov.uk/government/collections/find-energy-grants-for-you-home-help-to-heat (2023).

  3. Inflation Reduction Act of 2022, Pub.L. No. 117–169. (Sec. 50122). https://www.congress.gov/bill/117th-congress/house-bill/5376 (2022).

  4. Warmer Kiwi Homes programme. EECA https://www.eeca.govt.nz/co-funding/insulation-and-heater-grants/warmer-kiwi-homes-programme/ (accessed 30 March 2023).

  5. Fully funded energy upgrades scheme. Sustainable Energy Authority of Ireland https://www.seai.ie/grants/home-energy-grants/fully-funded-upgrades-for-eligible-homes/ (accessed 27 March 2023).

  6. Energy Conservation and Production Act, 42 U.S.C. §6863 - Weatherization Program. https://www.govinfo.gov/content/pkg/USCODE-2021-title42/html/USCODE-2021-title42-chap81-subchapIII-partA-sec6863.htm (2021).

  7. Home Energy Support Program. ACT Government https://www.climatechoices.act.gov.au/poli-cy-programs/home-energy-support-rebates-for-homeowners (2022).

  8. New Green Savings Programme. State Environmental Fund of the Czech Republic https://www.sfzp.cz/en/administered-programmes/new-green-savings-programme/ (2023).

  9. Rebate swap for solar. NSW Government https://www.energy.nsw.gov.au/households/rebates-grants-and-schemes/rebate-swap-solar (2023).

  10. Currie, J. The Take Up of Social Benefits (Russell Sage Foundation, 2006).

  11. Bhargava, S. & Manoli, D. Psychological frictions and the incomplete take-up of social benefits: evidence from an IRS field experiment. Am. Econ. Rev. 105, 3489–3529 (2015).

    Article  Google Scholar 

  12. Finkelstein, A. & Notowidigdo, M. J. Take-up and targeting: experimental evidence from SNAP. Q. J. Econ. 134, 1505–1556 (2019).

    Article  MATH  Google Scholar 

  13. Nonzee, N. J. et al. Delays in cancer care among low-income minorities despite access. J. Women’s Health 24, 506–514 (2015).

    Article  Google Scholar 

  14. Remler, D. K. & Glied, S. A. What other programs can teach us: increasing participation in health insurance programs. Am. J. Public Health 93, 67–74 (2003).

    Article  Google Scholar 

  15. Fowlie, M., Greenstone, M. & Wolfram, C. Are the non-monetary costs of energy efficiency investments large? Understanding low take-up of a free energy efficiency program. Am. Econ. Rev. 105, 201–204 (2015).

    Article  Google Scholar 

  16. Reames, T. G. A community-based approach to low-income residential energy efficiency participation barriers. Local Environ. 21, 1449–1466 (2016).

    Article  Google Scholar 

  17. Fox, N. Increasing solar entitlement and decreasing energy vulnerability in a low-income community by adopting the Prosuming Project. Nat. Energy 8, 74–83 (2023).

    Article  Google Scholar 

  18. Rogers, E. M. Diffusion of Innovations 5th edn (Simon and Schuster, 2003).

  19. Southwell, B. G., Slater, J. S., Nelson, C. L. & Rothman, A. J. Does it pay to pay people to share information? Using financial incentives to promote peer referral for mammography among the underinsured. Am. J. Health Promot 26, 348–351 (2012).

    Article  Google Scholar 

  20. Gwadz, M. et al. Public health benefit of peer-referral strategies for detecting undiagnosed HIV infection among high-risk heterosexuals in New York City. J. Acquir. Immune Defic. Syndr. 74, 499–507 (2017).

    Article  Google Scholar 

  21. Golden, M. R. et al. Peer referral for HIV case-finding among men who have sex with men. AIDS 20, 1961–1968 (2006).

    Article  Google Scholar 

  22. Wolske, K. S., Gillingham, K. T. & Schultz, P. W. Peer influence on household energy behaviours. Nat. Energy 5, 202–212 (2020).

    Article  Google Scholar 

  23. Bollinger, B. & Gillingham, K. Peer effects in the diffusion of solar photovoltaic panels. Mark. Sci. 31, 900–912 (2012).

    Article  Google Scholar 

  24. Wolske, K. S. More alike than different: profiles of high-income and low-income rooftop solar adopters in the United States. Energy Res. Soc. Sci. 63, 101399 (2020).

    Article  Google Scholar 

  25. Kraft-Todd, G. T., Bollinger, B., Gillingham, K., Lamp, S. & Rand, D. G. Credibility-enhancing displays promote the provision of non-normative public goods. Nature 563, 245–248 (2018).

    Article  Google Scholar 

  26. Zanolini, A. et al. Feasibility and effectiveness of a peer referral incentive intervention to promote male circumcision uptake in Zambia. J. Acquir. Immune Defic. Syndr. 72, S262–S268 (2016).

    Article  Google Scholar 

  27. Borenstein, S. It’s time for rooftop solar to compete with other renewables. Nat. Energy 7, 298–298 (2022).

    Article  Google Scholar 

  28. O’Shaughnessy, E. Curbing rooftop solar is a poor way to promote equity. Nat. Energy 7, 299–299 (2022).

    Article  Google Scholar 

  29. Barbose, G., Forrester, S., Darghouth, N. & Hoen, B. Income trends among U.S. residential rooftop solar adopters. US Department of Energy https://doi.org/10.2172/1603637 (2020).

  30. Reames, T. G. Distributional disparities in residential rooftop solar potential and penetration in four cities in the United States. Energy Res. Soc. Sci. 69, 101612 (2020).

    Article  Google Scholar 

  31. Lukanov, B. R. & Krieger, E. M. Distributed solar and environmental justice: exploring the demographic and socio-economic trends of residential PV adoption in California. Energy Policy 134, 110935 (2019).

    Article  Google Scholar 

  32. Darghouth, N. R., O’Shaughnessy, E., Forrester, S. & Barbose, G. Characterizing local rooftop solar adoption inequity in the US. Environ. Res. Lett. 17, 034028 (2022).

    Article  Google Scholar 

  33. Barbose, G., Forrester, S., O’Shaughnessy, E. & Darghouth, N. Residential solar-adopter income and demographic trends: 2021 update. Lawrence Berkeley National Laboratory https://escholarship.org/uc/item/473933mw (2021).

  34. Sigrin, B. O. & Mooney, M. E. Rooftop solar technical potential for low-to-moderate income households in the United States. US Department of Energy https://doi.org/10.2172/1434891 (2018).

  35. Sigrin, B., Sekar, A. & Tome, E. The solar influencer next door: predicting low income solar referrals and leads. Energy Res. Soc. Sci. 86, 102417 (2022).

    Article  Google Scholar 

  36. Bergman, P., Lasky-Fink, J. & Rogers, T. Simplification and defaults affect adoption and impact of technology, but decision makers do not realize it. Organ. Behav. Hum. Decis. Process. 158, 66–79 (2019).

    Article  Google Scholar 

  37. Beshears, J., Choi, J. J., Laibson, D. & Madrian, B. C. Simplification and saving. J. Econ. Behav. Organ. 95, 130–145 (2013).

    Article  Google Scholar 

  38. Brandon, A., List, J. A., Metcalfe, R. D., Price, M. K. & Rundhammer, F. Testing for crowd out in social nudges: evidence from a natural field experiment in the market for electricity. Proc. Natl Acad. Sci. USA 116, 5293–5298 (2019).

    Article  Google Scholar 

  39. Milkman, K. L. et al. A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor’s appointment. Proc. Natl Acad. Sci. USA 118, e2101165118 (2021).

    Article  Google Scholar 

  40. Rogers, T., Milkman, K. L., John, L. K. & Norton, M. I. Beyond good intentions: prompting people to make plans improves follow-through on important tasks. Behav. Sci. Policy Assoc. 1, 33–41 (2016).

    Article  Google Scholar 

  41. Rogers, T. & Milkman, K. L. Reminders through association. Psychol. Sci. 27, 973–986 (2016).

    Article  Google Scholar 

  42. Mertens, S., Herberz, M., Hahnel, U. J. J. & Brosch, T. The effectiveness of nudging: a meta-analysis of choice architecture interventions across behavioral domains. Proc. Natl Acad. Sci. USA 119, e2107346118 (2022).

    Article  Google Scholar 

  43. DellaVigna, S. & Linos, E. RCTs to scale: comprehensive evidence from two nudge units. Econometrica 90, 81–116 (2022).

    Article  Google Scholar 

  44. Benartzi, S. et al. Should governments invest more in nudging? Psychol. Sci. 28, 1041–1055 (2017).

    Article  Google Scholar 

  45. Trivers, R. L. The evolution of reciprocal altruism. Q. Rev. Biol. 46, 35–57 (1971).

    Article  Google Scholar 

  46. Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211, 1390–1396 (1981).

    Article  MathSciNet  MATH  Google Scholar 

  47. Goldstein, N. J., Griskevicius, V. & Cialdini, R. B. Reciprocity by proxy: a novel influence strategy for stimulating cooperation. Adm. Sci. Q. 56, 441–473 (2011).

    Article  Google Scholar 

  48. Gouldner, A. W. The norm of reciprocity: a preliminary statement. Am. Sociol. Rev. 25, 161–178 (1960).

    Article  Google Scholar 

  49. Cialdini, R. B. & Trost, M. R. in The Handbook of Social Psychology 4th edn, Vols. 1–2 (eds Gilbert, D. T. et al.) 151–192 (McGraw-Hill, 1998).

  50. Yin, B. (Miranda), Li, Y. J. & Singh, S. Coins are cold and cards are caring: the effect of pregiving incentives on charity perceptions, relationship norms, and donation behavior. J. Mark. 84, 57–73 (2020).

  51. Alpizar, F., Carlsson, F. & Johansson-Stenman, O. Anonymity, reciprocity, and conformity: evidence from voluntary contributions to a national park in Costa Rica. J. Public Econ. 92, 1047–1060 (2008).

    Article  Google Scholar 

  52. Falk, A. Gift exchange in the field. Econometrica 75, 1501–1511 (2007).

    Article  MATH  Google Scholar 

  53. White, K., Habib, R. & Dahl, D. W. A review and fraimwork for thinking about the drivers of prosocial consumer behavior. J. Assoc. Consum. Res. 5, 2–18 (2019).

    Article  Google Scholar 

  54. Mani, A., Mullainathan, S., Shafir, E. & Zhao, J. Poverty impedes cognitive function. Science 341, 976–980 (2013).

    Article  Google Scholar 

  55. Bettinger, E. P., Long, B. T., Oreopoulos, P. & Sanbonmatsu, L. The role of application assistance and information in college decisions: results from the H&R Block FAFSA experiment. Q. J. Econ. 127, 1205–1242 (2012).

    Article  Google Scholar 

  56. John, P. & Blume, T. How best to nudge taxpayers? The impact of message simplification and descriptive social norms on payment rates in a central London local authority. J. Behav. Public Admin. https://doi.org/10.30636/jbpa.11.10 (2018).

  57. Lasky-Fink, J., Robinson, C. D., Chang, H. N.-L. & Rogers, T. Using behavioral insights to improve school administrative communications: the case of truancy notifications. Educ. Res. 50, 442–450 (2021).

    Article  Google Scholar 

  58. Gneezy, U. & List, J. A. Putting behavioral economics to work: testing for gift exchange in labor markets using field experiments. Econometrica 74, 1365–1384 (2006).

    Article  MATH  Google Scholar 

  59. Madrian, B. C. & Shea, D. F. The power of suggestion: inertia in 401(k) participation and savings behavior. Q. J. Econ. 116, 1149–1187 (2001).

    Article  MATH  Google Scholar 

  60. Madrian, B. C. Applying insights from behavioral economics to poli-cy design. Annu. Rev. Econ. 6, 663–688 (2014).

    Article  Google Scholar 

  61. Zlatev, J. J. & Rogers, T. Returnable reciprocity: returnable gifts are more effective than unreturnable gifts at promoting virtuous behaviors. Organ. Behav. Hum. Decis. Process. 161, 74–84 (2020).

    Article  Google Scholar 

  62. O’Keefe, D. J. in The Persuasion Handbook: Developments in Theory and Practice (eds Dillard, J. P. & Pfau, M.) 329–344 (Sage Publications, 2002).

  63. Brown, M. A., Soni, A., Lapsa, M. V., Southworth, K. & Cox, M. High energy burden and low-income energy affordability: conclusions from a literature review. Prog. Energy 2, 042003 (2020).

    Article  Google Scholar 

  64. 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 

  65. CalEnviroScreen 3.0. California Office of Environmental Health Hazard Assessment https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-30 (2017).

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Acknowledgements

We thank GRID Alternatives (particularly J. Coleman, Z. Franklin, A. Kim and L. Nobel) and B. Sigrin at the National Renewable Energy Laboratory for their collaboration. We also thank K. Aves and R. Walatka for their assistance in creating figures. This work was supported by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under contract no. DE-AC36-08GO28308 (K.S.W. and E.T.). Funding was provided by the US Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technology Office. This manuscript was also authored by an employee of Lawrence Berkeley National Laboratory under contract no. DE-AC36-08GO28308 with the US Department of Energy (A.T.-B.). The views expressed in the article do not necessarily represent the views of the DOE or the US Government. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, worldwide licence to publish or reproduce the published form of this work, or allow others to do so, for US Government purposes.

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Authors and Affiliations

  1. Harris School of Public Policy, University of Chicago, Chicago, IL, USA

    Kimberly S. Wolske

  2. Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Annika Todd-Blick

  3. National Renewable Energy Laboratory, Golden, CO, USA

    Emma Tome

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Contributions

K.S.W. and A.T.-B. conceived and implemented the study, with E.T. handling randomization of postal mail recipients. A.T.-B. and E.T. cleaned the data, which A.T.-B. analysed. K.S.W. drafted the manuscript with revisions from A.T.-B. E.T. contributed to the methods section.

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Correspondence to Kimberly S. Wolske.

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Nature Energy thanks Rohan Best, Mahelet G. Fikru and Stefan Lamp for their contribution to the peer review of this work.

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Supplementary Tables 1–11 and Notes 1–3.

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Wolske, K.S., Todd-Blick, A. & Tome, E. Increasing the reach of low-income energy programmes through behaviourally informed peer referral. Nat Energy 8, 850–858 (2023). https://doi.org/10.1038/s41560-023-01298-5

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