Title: “More than Mere Dead-Weight: The Variety of Ways that Regulators, Innovators, and Entrepreneurs Co-Create Disruptive Technological Innovation in Advanced Industrial Democracies”

Chair: Christopher Ansell

Committee: Steven K. Vogel, J. Nicholas Ziegler, Ann Keller

Method: Bayesian Type Verification (BayesTV) If you are a regulator, innovator, or entrepreneur and are interested in being interviewed for this project, please click here.

Primary Cases:


Disruptive technological innovation (DTI) is the contemporary face of innovation and a dominant force in society. Change is accelerating, upsetting existing scientific and technical policy systems. Entrepreneurs and innovators often believe that regulation cannot keep up with the pace of change and therefore policy makers should stay out of their way. Like many folk models, this folk perception of regulation-as-intrinsic-impediment-to-innovation may sometimes be true but is not always true. Worse yet, this folk perception leads entrepreneurs and innovators to ignore opportunities to co-create beneficial regulations and instead create their own bad outcomes by inciting pernicious regulations to counter their non-compliance.

Innovators thus oppose regulation not because they’ve had bad experiences but because they think they will in the future. The contemporary folk economic model brandishes the word “disrupt” while storming the halls of stodgy industries and agencies. Despite this disruptive innovation narrative, substantial technological change has happened before. The seductively simple rhetoric of the folk economic model has convinced disruptive entrepreneurs that regulation is synonymous with state inadequacy yet regulators have invariably (if imperfectly) adapted to technological change. This project explains how regulators have before and can again become allies of innovators when entrepreneurs look past their own myopic perceptions.

Regulatory scholars who study actually existing regulation know the folk economic model as “capture” within “command and control” regulation and repeatedly demonstrate the deceptive inadequacy of catch-all models of regulation. Nevertheless, scholars who do not study regulation still use this folk economic baseline to judge all work on regulation. With these scholarly limitations, lay entrepreneurs’ misperceptions are no surprise.

Failure is loud, success quiet. Regulatory failures like the Deepwater Horizon oil spill and 2008 Global Financial Crisis are loudly publicized. Quieter are non-failures like American recombinant DNA regulation following the 1975 Asilomar Conference and the 1956 Consent Decree which led to the creation of Silicon Valley. This mismatch reinforces a folk economic perception of regulators as merely deadweight destined to fail. Worse yet, loudly prognosticating regulators’ inevitable failure often fosters failure where alternative rhetoric could encourage success.

To overcome the limited folk economic perception of disruptive technological innovation (DTI), this project develops a deductive typology of regulatory responses using Bayesian Type Validation(BayesTV): a novel qualitative empirical method which combines deductive typological theory with Bayesian process tracing to deductively develop and inductively refine a seven-model typology of regulatory responses to DTIs. Deductive typological theory maps a typological property space of mutually exclusive and exhaustive (MEE) types based on combinations of constitutive variables. Bayesian process tracing then adjudicates between the MEE rival explanations to transparently validate deductive typologies with inductive empirical data.

The folk economic model arose when Christensen’s The Innovator’s Dilemma (1997) simplified Stigler’s “The Theory of Economic Regulation” (1971) into a blanket proclamation that innovation only happens when entrepreneurs evade regulators. This project reconceptualizes that fixed interpretation into variables in light of Streeck’s counterdemonstration in “Beneficial Constraints”(1997) that some constraints lead to economic benefits. The typology expands Stigler’s orthodoxy with Streeck’s heterodoxy by generalizing underlying concepts into variables to define an MEE set of regulatory responses.

The typology spans from Stiglerian impediment thru Steeckian beneficial constraints and discovers the further possibility of driver of adoption. Process tracing then inductively refines this deductive typology using archival material and interviews with entrepreneurs, innovators, and regulators. Through BayesTV, this project thus provides a conceptually complete and empirically validated map of the range of ways that regulators respond to DTIs in order to guide policy makers, innovators, and entrepreneurs towards more fruitful collaborations.

Of the seven imaginaries deductively defined, I draw on archival and interview evidence to inductively validate a characteristic empirical case for the two imaginaries most critical to studying the regulation of disruptive technological innovation: the beneficial constraints, and adoption catalyst. In the introduction, I explicate the pernicious folk economic model by showing how Uber’s entrepreneurial guerrilla warfare creates the regulatory dystopia they fear. Next, I explore the various definitions of safety at work in the regulation of a new technology within an established regulatory regime by examining the beneficial constraints of Connected and Autonomous Vehicle (CAV) regulation. Then, the processes and outcomes of beneficially constraining regulation of a newly emerging regulatory regime in the US and EU is explored through the recombinant pasts and CRISPR futures of gene editing demonstrating that the same method (constraint) can lead to different outcomes (pro/anti-GMO). Finally, the mandated adoption of electronic health records (EHR) in the US and EU shows how states catalyze the adoption of economically beneficial and socially responsible innovations beyond the imagination of the market. Together, these three cases will demonstrate how regulation can serve innovators’ material interests and need not always be merely deadweight.

Yet perceptions create preferences before outcomes breed interests. Innovators distrust regulation not because they’ve had bad experiences (though they might have) but because they think they will (again) in the future. The folk economic model is not always wrong; without some correlation, it wouldn’t work as a “statement of the common-sense understandings that people use in ordinary life.” Yet not always wrong is not the same as always correct and the folk economic model leads entrepreneurs and innovators to ignore opportunities to co-create beneficial regulations and instead create their own bad outcomes by forcing regulators to respond draconianly to malicious behavior.

To break this pernicious cycle, policy makers must work with entrepreneurs to coordinate on response models which foster more positive interaction. This project catalogs the full range of six alternatives to the folk economic model and empirically explicates the two most promising ones, beneficial constraints and driver of adoption, to guide policy makers in shaping better outcomes. Regulators were once and can again become allies of innovators if only entrepreneurs can be guided past the myopia of their own rhetoric. At the very least, we should recognize that they can be so much more than mere dead-weight.