Simon C. Essig Aberg
Simon C. Essig Aberg
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I am a Business Economics Ph.D. student at Harvard University.
My fields are public economics and econometrics. My research focuses on philanthropy & the charitable sector and empirical methodology.
Working Papers
Working Papers
Reject and resubmit, American Economic Journal: Economic Policy.Charitable giving in the United States is increasingly concentrated among a small number of top donors, many of whom give through a family foundation. Using a novel database of foundation tax filings, I document facts about top donors, estimate how they respond to tax incentives over time, and evaluate the welfare effects of policy changes. I focus on two sets of tax incentives: the tax subsidy for a donor’s contributions to their foundation, and the tax penalty imposed if a foundation’s charitable disbursements average less than 5% of assets. I employ a new approach to identify short-run responses to these tax incentives. I then translate those estimates into dynamic elasticities using a new model-based method. I find that a 1% increase in the tax subsidy for contributions would increase contributions to foundations by 0.67% in present value terms, but would only raise charitable disbursements by 0.37%. Additionally, I find that a one percentage point increase in the 5% minimum payout rate would increase charitable disbursements by 0.33% in present value terms, primarily financed by foundation assets. I discuss how these findings relate to recent legislative proposals regarding the regulation of foundations and related charitable vehicles.
(with Isaiah Andrews and Jesse M. Shapiro). NBER Working Paper 35259.
Publications
Publications
(with Olivier Darmouni and Juha Tolvanen). Accepted, RAND Journal of Economics.
(with Brian Baisa). Journal of Economic Theory: Volume 230, 106094.
Resources
Resources
(with Isaiah Andrews and Jesse M. Shapiro). Github repository accessible at https://github.com/JMSLab/Young2019.
(with Isaiah Andrews and Jesse M. Shapiro). Github repository accessible at https://github.com/JMSLab/Young2019.
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