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Measuring the Influence of Political Actors on the Federal Budget

Published online by Cambridge University Press:  27 January 2020

BEN HAMMOND*
Affiliation:
Princeton University
LEAH ROSENSTIEL*
Affiliation:
Princeton University
*
*Ben Hammond, Ph.D. Candidate, Department of Politics, Princeton University, blh2@princeton.edu.
Leah Rosenstiel, Ph.D. Candidate, Department of Politics, Princeton University, leahsr@princeton.edu.

Abstract

When estimating the political determinants of the federal budget, scholars face a choice between using measures of funding and measures of spending as their outcome of interest. We examine the consequences of this choice. In particular, we argue that spending outcomes may serve as a poor test of the research questions scholars seek to answer, since spending data conflate competing budgetary influences, are downstream measures of the appropriations that originated them, and induce measurement error. To test our claim, we compare the spending data used in a recent study (Berry and Fowler 2016: American Journal of Political Science 60 (3): 692–708) with an original data set of military construction appropriations. While an analysis of the spending data produces a null result, the same analysis using the appropriations data provides strong evidence that legislators use their committee positions to distribute pork. Our findings have broad implications for studies that use measures of spending in the congressional and presidency literatures.

Type
Letter
Copyright
Copyright © American Political Science Association 2020 

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Footnotes

We thank Brandice Canes-Wrone, Adam Dynes, Nathan Gibson, Patricia Kirkland, Asya Magazinnik, Nolan McCarty, Marc Ratkovic, the editor, and two anonymous referees for their invaluable feedback. June Hwang and Will Lowe provided guidance assigning military bases to congressional districts. We are especially grateful to Christopher Berry and Anthony Fowler for their helpful comments. All errors are our own. Replication files are available at the American Political Science Review Dataverse: https://doi.org/10.7910/DVN/UN3KML.

References

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