A note on the behavioral political economy of innovation policy

Abstract

We propose that policy-making in the realm of innovation policy can be fruitfully analyzed from the perspective of Behavioral Political Economy. Citizens, policy-makers and also bureaucrats are prone to biases that have been empirically identified in behavioral economic and psychological research. When applied to innovation policy, it can be shown that under certain conditions, policy-makers are willing to support riskier innovative projects and that this tendency is amplified by public sector incentives, such as soft budget constraints. The same holds for a tendency to support ongoing innovative projects even if their profitability becomes increasingly doubtful. Finally, we also highlight how special-interest policies aimed at distorting risk perceptions can slow down the innovation process.

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Notes

  1. 1.

    On the difference between both strands of research, see the debate between Blankart and Koester (2006) and Alesina et al. (2006).

  2. 2.

    An application to the fashionable area of behavioral public policies (such as nudges) is suggested by Schubert (2017). For a constitutional economics framework of nudging see Schubert (2014).

  3. 3.

    To be sure, the term has been used before, but with wildly divergent meanings. For instance, Della Vigna (2009: 364) defines BPE as the study of “how politicians change their behavior to respond to voter biases”. Berggren (2012) defines the field, also rather narrowly, as the application of the analytical tools of behavioral economics to “political decision-makers”. See also Wallerstein (2004).

  4. 4.

    See Schnellenbach and Schubert (2015: 396) for details. See also Viscusi and Gayer (2015).

  5. 5.

    Note that that model’s predictions differ markedly from those of the standard median voter model: While in the standard model, moderate voters abstain because they are indifferent between the two dominant parties, in the expressive voting model it’s the extremists who abstain, for they feel alienated.

  6. 6.

    Relatedly, one may argue that rent-seeking lobbyists may successfully persuade naïve policy-makers or naïve voters that certain nudges are ‘harmless’, which leads to the same result: excessive nudging (ibid.)

  7. 7.

    Non-orthodox economics poses challenges for normative economics; see also Schubert (2015) and Dold and Schubert (2018).

  8. 8.

    The assumption of fixed preferences has long been standard in economics, but Bowles (1998) gives an early overview over theories of preference change. Welfare implications have been discussed among others by Weizsäcker (2005) and Schnellenbach (2019).

  9. 9.

    See http://www.deutschlandfunkkultur.de/voelklingen-millionen-bei-meeresfischzucht-versenkt.1001.de.html?dram:article_id=317695 (last opened on June 30, 2017).

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Correspondence to Christian Schubert.

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Schnellenbach, J., Schubert, C. A note on the behavioral political economy of innovation policy. J Evol Econ 29, 1399–1414 (2019). https://doi.org/10.1007/s00191-019-00625-y

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Keywords

  • Biases
  • Heuristics
  • Sunk cost fallacy
  • Availability bias
  • Overconfidence
  • Loss aversion

JEL classification

  • O38
  • D72
  • D78
  • H11