The Economics of Scale-Up

40 Pages Posted: 16 Oct 2017 Last revised: 15 Nov 2021

See all articles by Jonathan Davis

Jonathan Davis

University of Chicago - Harris School of Public Policy; University of Chicago - Harris School of Public Policy

Jonathan Guryan

Northwestern University - Human Development and Social Policy (HDSP) Program; National Bureau of Economic Research (NBER)

Kelly Hallberg

University of Chicago

Jens Ludwig

Georgetown University - Public Policy Institute (GPPI); National Bureau of Economic Research (NBER); IZA Institute of Labor Economics

Date Written: October 2017

Abstract

Most randomized controlled trials (RCT) of social programs test interventions at modest scale. While the hope is that promising programs will be scaled up, we have few successful examples of this scale-up process in practice. Ideally we would like to know which programs will work at large scale before we invest 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. Our goal in this paper is to propose a way out of this Catch-22. We first develop a simple model that helps clarify the type of scale-up challenge for which our method is most relevant. Most social programs rely on labor as a key input (teachers, nurses, social workers, etc.). We know 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. Our 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. We 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). We 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 we propose are likely to be particularly valuable. We 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.

Suggested Citation

Davis, Jonathan and Guryan, Jonathan and Hallberg, Kelly and Ludwig, Jens, The Economics of Scale-Up (October 2017). NBER Working Paper No. w23925, Available at SSRN: https://ssrn.com/abstract=3053675

Jonathan Davis (Contact Author)

University of Chicago - Harris School of Public Policy ( email )

1155 E 60th St
Chicago, IL 60637
United States

University of Chicago - Harris School of Public Policy ( email )

1155 E 60th St
Chicago, IL 60637
United States

Jonathan Guryan

Northwestern University - Human Development and Social Policy (HDSP) Program ( email )

2046 Sheridan Road
Evanston, IL 60208
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Kelly Hallberg

University of Chicago

Jens Ludwig

Georgetown University - Public Policy Institute (GPPI) ( email )

3600 N Street, NW Suite 200
Washington, DC 20057
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

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