Regulation and Distrust
GAICIL Insights
People who distrust their government often demand more government.1 That paradox sits at the heart of Regulation and Distrust, by Philippe Aghion, Yann Algan, Pierre Cahuc and Andrei Shleifer. Across countries, government regulation is strongly negatively correlated with measures of trust. A simple model explains why: distrust creates public demand for regulation, and regulation in turn discourages the formation of trust, producing multiple equilibria.
The counterintuitive implication: individuals in low-trust countries want more government intervention even though they know the government is corrupt. When people distrust each other, they prefer regulation to unregulated interaction, even with untrustworthy regulators. The authors test this and other implications with country- and individual-level data on trust and beliefs about government's role, including changes in beliefs during the transition from socialism. A corrupt referee is preferred to no referee when nobody trusts the players.
Legal origins enter the story. Measures of regulation are predicted by legal origins, and French legal origin countries on average exhibit lower trust than common law and Scandinavian legal origin countries. Over time, the level of regulation can itself influence investment in social capital. More heavily regulated colonies may have developed lower trust because of a more controlling state, and lower trust may have sustained demand for regulation. History writes the rulebook, and the rulebook writes the culture.
The feedback loop is the core. Distrust leads to regulation, and regulation reduces incentives to build trust. Societies can stick in low trust with heavy regulation, or sustain high trust with light regulation. Both are self-reinforcing. Which equilibrium a society lands in can outlast the reasons it got there.
Artificial intelligence governance is shaped by exactly this loop. Public attitudes toward AI vary widely across countries, and demand for AI regulation partly reflects trust in technology firms, in institutions and in one another. Where trust is low, public demand for AI regulation will be high, even if people doubt regulators' competence or integrity. AI rulebooks may be thermometers of social trust as much as assessments of risk.
The loop carries a warning. Heavy AI regulation may reduce incentives to build the trust that would make lighter regulation sufficient. If firms and users rely on regulation to govern interactions, they may invest less in reputations, norms and voluntary standards. Over time, societies could lock into heavy AI regulation with costs for innovation. A rule that replaces trust may prevent trust from ever forming.
The other equilibrium shows the way out. Where trust is high, lighter regulation coexists with responsible behavior because parties invest in reputation and cooperation. For AI, building trust through transparency, accountability and reliable performance may reduce demand for heavy-handed regulation, letting innovation proceed with appropriate safeguards. The AI industry's best regulatory strategy may be to become trustworthy.
The corrupt-government finding is sobering. Regulation may be demanded as a response to distrust, regardless of its efficiency. Public demand for AI rules may reflect distrust, with little careful assessment of regulatory benefits. Rules designed also to build trust, for example through transparency requirements, may help break the cycle. Disclosure can be a rule that makes future rules less necessary.
Legal origins help explain international divergence. Countries' approaches to AI regulation may reflect long-standing patterns of trust and regulation, explaining differences in AI governance and the difficulty of coordination. Harmonizing AI rules across societies with different trust levels may be harder than harmonizing technical standards.
Taken together, regulation and trust are jointly determined. Distrust creates demand for regulation, which can undermine trust, producing self-reinforcing equilibria. As societies decide how to govern AI, the trust they have built will shape the regulations they demand. Building trust in AI and in institutions may be the most effective path to governance that protects the public while preserving room for innovation.
Something for AI policymakers to sit with: measure trust before writing rules, and design rules that rebuild it. A regulatory regime that leaves trust lower than it found it may guarantee that every future AI rule has to be heavier than the last.
Introduction
We document that, in a cross section of countries, government regulation is strongly negatively correlated with measures of trust. In a simple model explaining this correlation, distrust creates public demand for regulation, whereas regulation in turn discourages formation of trust, leading to multiple equilibria. A key implication of the model is that individuals in low-trust countries want more government intervention even though they know the government is corrupt. We test this and other implications of the model using country- and individual-level data on trust and beliefs about the role of government, as well as on changes in beliefs during the transition from socialism.
Takeaway
We note two aspects of the problem that were mentioned in the discussion, but not analyzed in any detail. The first is the relationship between our findings and research on legal origins. A number of papers summarized in La Porta et al. (2008) show that the very same measures of government regulation that we consider in this paper are predicted by legal origins. This raises the question of the relationship between legal origins and distrust, and their respective influences on regulation.1
It is easy to show that French legal origin countries, on average, exhibit lower levels of trust than common law and Scandinavian legal origin countries, but is there a deeper relationship here? Glaeser and Shleifer (2002) argue that France and England developed their legal systems many centuries ago in response to very different levels of disorder prevalent in the two countries, with England being much more peaceful and orderly than France. The two legal traditions were subsequently transplanted through conquest and colonization to many parts of the world, and there is no reason to think that the colonies of the two countries started with different levels of distrust (Nunn and Wantchekon, 2008). On the other hand, our paper suggests that, over time, the level of regulation can itself influence investment in social capital. It is possible, then, that compared to the English colonies, the more heavily regulated French colonies over the decades have developed lower levels of trust (because of a more controlling role of the state) and that this lower trust has generated continued demand for government regulation. If this hypothesis is correct, one reason that legal origins have had such a pervasive influence on outcomes over the years might be that their influence is mediated by trust in a self-fulfilling equilibrium. This might be a new explanation for the persistent effects of legal origins.
A second aspect of the problem that deserves some additional attention is our assumption that accumulation of social capital is largely decentralized because it takes place in families. In fact, in our model, if the community can agree on a program of public education that raises the level of social capital, and if this program is successfully implemented, the bad equilibrium is eliminated. It is unquestionably the case that, in some countries, an important goal of public education is to build social capital (Glaeser et al. 2007). But, evidently, this goal is not universal.
This observation is of great consequence to our discussion of transition economies, and in particular raises the question of whether, in light of our evidence, these economies are stuck to a future of low social capital, heavy regulation, and low output.
Notes
- Philippe Aghion et al., Regulation and Distrust, 125 Q.J. Econ. 1015 (2010), https://doi.org/10.1162/qjec.2010.125.3.1015. Takeaway source: Conclusion, reproduced from a free author or repository copy (working-paper version). ↩
Cite as: Philippe Aghion et al., Regulation and Distrust, 125 Q.J. Econ. 1015 (2010), https://doi.org/10.1162/qjec.2010.125.3.1015
Header image: Federal Building and US Courthouse, Roswell, NM-front.jpg by Myotus, CC BY 4.0, via Wikimedia Commons.
