A Sufficient Statistics Approach for Aggregating Firm-Level Experiments

38 Pages Posted: 17 Jan 2018

See all articles by David Alexandre Sraer

David Alexandre Sraer

University of California, Berkeley; Princeton University

David Thesmar

Massachusetts Institute of Technology (MIT) - Economics, Finance, Accounting (EFA)

Multiple version iconThere are 3 versions of this paper

Date Written: January 2018

Abstract

We consider a dynamic economy populated by heterogeneous firms subject to generic capital frictions: adjustment costs, taxes and financing constraints. A random subset of firms in this economy receives an empirical "treatment", which modifies the parameters governing these frictions. An econometrician observes the firm-level response to this treatment, and wishes to calculate how macroeconomic outcomes would change if all firms in the economy were treated. Our paper proposes a simple methodology to estimate this aggregate counterfactual using firm-level evidence only. Our approach takes general equilibrium effects into account, requires neither a structural estimation nor a precise knowledge on the exact nature of the experiment and can be implemented using simple moments of the distribution of revenue-to-capital ratios. We provide a set of sufficient conditions under which these formulas are valid and investigate the robustness of our approach to multiple variations in the aggregation framework.

Suggested Citation

Sraer, David Alexandre and Thesmar, David, A Sufficient Statistics Approach for Aggregating Firm-Level Experiments (January 2018). NBER Working Paper No. w24208. Available at SSRN: https://ssrn.com/abstract=3102026

David Alexandre Sraer (Contact Author)

University of California, Berkeley ( email )

310 Barrows Hall
Berkeley, CA 94720
United States

Princeton University ( email )

22 Chambers Street
Princeton, NJ 08544-0708
United States

David Thesmar

Massachusetts Institute of Technology (MIT) - Economics, Finance, Accounting (EFA) ( email )

77 Massachusetts Avenue
Cambridge, MA 02139-4307
United States
16172259767 (Phone)

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