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      Did private election administration funding advantage Democrats in 2020?

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          Significance

          Private donors contributed more than $350 million to local election officials to support the administration of the 2020 election. Supporters argue these grants were neutral and necessary to maintain normal election operations during the pandemic, while critics worry these grants mostly went to Democratic strongholds and tilted election outcomes. These concerns have led 24 states to restrict private election grants. We find that, while counties that favor Democrats were much more likely to apply for a grant, the grants did not have a noticeable effect on the 2020 presidential election.

          Abstract

          Private donors contributed more than $350 million to local election officials to support the administration of the 2020 election. Supporters argue these grants were neutral and necessary to maintain normal election operations during the pandemic, while critics worry these grants mostly went to Democratic strongholds and tilted election outcomes. How much did these grants shape the 2020 presidential election? To answer this question, we collect administrative data on private election administration grants and election outcomes. We then use advances in synthetic control methods to compare presidential election results and turnout in counties that received grants to counties with similar election results and turnout before 2020. While Democratic counties were more likely to apply for a grant, we find that the grants did not have a noticeable effect on the presidential election. Our estimates of the average effect on Democratic vote share range from 0.03 to 0.36 percentage points. Our estimates of the average effect of receiving a grant on turnout range from 0.03 to 0.14 percentage points. Across specifications, our 95% CIs typically include negative effects and all fail to include effects on Democratic vote share larger than 0.58 percentage points and effects on turnout larger than 0.40 percentage points. We characterize the magnitude of our effects by asking how large they are compared to the margin by which Biden won the 2020 election. In simple bench-marking exercises, we find that the effects of the grants were likely too small to have changed the outcome of the 2020 presidential election.

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          Most cited references29

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          Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program

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            Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies

            This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy reweighting scheme that calibrates unit weights so that the reweighted treatment and control group satisfy a potentially large set of prespecified balance conditions that incorporate information about known sample moments. Entropy balancing thereby exactly adjusts inequalities in representation with respect to the first, second, and possibly higher moments of the covariate distributions. These balance improvements can reduce model dependence for the subsequent estimation of treatment effects. The method assures that balance improves on all covariate moments included in the reweighting. It also obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments. We demonstrate the use of entropy balancing with Monte Carlo simulations and empirical applications.
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              Why is There so Little Money in U.S. Politics?

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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                21 May 2024
                28 May 2024
                21 May 2024
                : 121
                : 22
                : e2317563121
                Affiliations
                [1] aIndependent Researcher , Redwood City, CA 94063
                [2] bDepartment of Political Science , University of California Los Angeles , Los Angeles, CA 90095
                Author notes
                2To whom correspondence may be addressed. Email: dthompson@ 123456polisci.ucla.edu .

                Edited by Seth J. Hill, University of California, San Diego, La Jolla, CA; received October 10, 2023; accepted February 20, 2024, by Editorial Board Member Margaret Levi

                1A.L. and D.M.T. contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-3697-614X
                https://orcid.org/0000-0002-0890-7577
                Article
                202317563
                10.1073/pnas.2317563121
                11145248
                38771875
                0623b197-100a-45f2-8da1-5a539d4773b1
                Copyright © 2024 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                : 10 October 2023
                : 20 February 2024
                Page count
                Pages: 11, Words: 8379
                Funding
                Funded by: UCLA Academic Senate, FundRef ;
                Award ID: NA
                Award Recipient : Daniel M Thompson
                Categories
                research-article, Research Article
                pol-sci, Political Sciences
                429
                Social Sciences
                Political Sciences

                election administration,political economy,synthetic control

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