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Peer effects and inequalities in technology uptake. Evidence from a large-scale subsidy programme

Author

Listed:
  • Jakub SokoÅ‚owski
  • Karol MadoÅ„
  • Jan Frankowski

Abstract

The success of energy transition in addressing climate change depends on several factors, including the affordability of new technologies and the influence of peers within communities. However, concerns about affordability raise questions about how economic inequalities shape peer effects and whether they create barriers to equitable adoption. To this end, we explore how inequalities influence peer effects in the uptake of renewable heating sources. We leverage over 260,000 observations from unique and unpublished microdata from the Polish Clean Air Priority Programme – one of the largest retrofit schemes in Europe. Our results show that peer effects accelerate technology uptake, with each additional installation increasing the likelihood of subsequent adoption by 0.014 pp. Peer influence is affected by economic inequality. In more economically homogeneous regions, affluent individuals considerably impact their peers. In areas with higher economic disparities, this influence diminishes. Our findings highlight the role of heating technology type and adopter wealth in shaping peer effect magnitude. Less wealthy adopters of biomass stoves emerge as a significant driver of peer influence, especially in regions with lower income inequality. We advise direct transfers to address technology adoption inequalities, leveraging social capital in low-inequality regions and adopting individualised strategies in high-inequality areas.

Suggested Citation

  • Jakub SokoÅ‚owski & Karol MadoÅ„ & Jan Frankowski, 2025. "Peer effects and inequalities in technology uptake. Evidence from a large-scale subsidy programme," IBS Working Papers 03/2025, Instytut Badan Strukturalnych.
    • Handle: RePEc:ibt:wpaper:wp032025
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    1. Kristian S. Nielsen & Viktoria Cologna & Jan M. Bauer & Sebastian Berger & Cameron Brick & Thomas Dietz & Ulf J. J. Hahnel & Laura Henn & Florian Lange & Paul C. Stern & Kimberly S. Wolske, 2024. "Realizing the full potential of behavioural science for climate change mitigation," Nature Climate Change, Nature, vol. 14(4), pages 322-330, April.
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    3. Aditya Aladangady, 2017. "Housing Wealth and Consumption: Evidence from Geographically-Linked Microdata," American Economic Review, American Economic Association, vol. 107(11), pages 3415-3446, November.
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
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    Keywords

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    JEL classification:

    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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