The Economics of Scale-Up (WP-17-22)


WP-17-22

Jonathan M. V. Davis, Jonathan Guryan, Kelly Hallberg, and Jens Ludwig

Most randomized controlled trials (RCT) of social programs test interventions at modest scale. While the hope is that promising programs will be scaled up, there are few successful examples of this scale-up process in practice. It would be ideal to know which programs will work at large scale before investing the resources to take them to scale. But it would seem that the only way to tell whether a program works at scale is to test it at scale. The goal of this paper is to propose a way out of this Catch-22. The researchers first develop a simple model that helps clarify the type of scale-up challenge for which their method is most relevant. Most social programs rely on labor as a key input (teachers, nurses, social workers, etc.). People vary greatly in their skill at these jobs. So social programs, like firms, confront a search problem in the labor market that can lead to inelastically-supplied human capital. The result is that as programs scale, either average costs must increase if program quality is to be held constant, or else program quality will decline if average costs are held fixed. The researchers' proposed method for reducing the costs of estimating program impacts at large scale combines the fact that hiring inherently involves ranking inputs with the most powerful element of the social science toolkit: randomization. They show that it is possible to operate a program at modest scale n but learn about the input supply curves facing the firm at much larger scale (S × n) by randomly sampling the inputs the provider would have hired if they operated at scale (S × n). They build a simple two-period model of social-program decision making and use a model of Bayesian learning to develop heuristics for when scale-up experiments of the sort they propose are likely to be particularly valuable. The researchers also present a series of results to illustrate the method, including one application to a real-world tutoring program that highlights an interesting observation: The noisier the program provider’s prediction of input quality, the less pronounced is the scale-up problem.

Jonathan M. V. Davis, Postdoctoral Fellow, University of Chicago
Jonathan Guryan, Professor of Human Development and Social Policy and IPR Fellow, Northwestern University
Kelly Hallberg, Scientific Director, Poverty Lab, University of Chicago
Jens Ludwig, McCormick Foundation Professor of Social Service Administration, Law, and Public Policy, University of Chicago

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