Alexander Busch

I am a PhD candidate at the Institute for Work and Employment Research at MIT Sloan, advised by Nathan Wilmers and Anna Stansbury. My research assesses how organized labor shapes work — through strikes, (sectoral) collective bargaining, and shopfloor representation.

Previously, I worked as a pre-doctoral fellow for Simon Jäger and Benjamin Schoefer at MIT and UC Berkeley, and studied Economics and Sociology at the LSE, Heidelberg University, and Goethe University Frankfurt.

You can reach me at abusch@mit.edu and alexander1busch@gmail.com — please cc both addresses if you require a timely response, and note that I am on the US East Coast.

Portrait of Alexander Busch

CV (PDF) · Google Scholar · GitHub · LinkedIn · Bluesky · Twitter/X (inactive)

Education

Research

Work in progress

Union Responses to Revenue Shocks: Evidence from Right-to-Work Laws in the US

with Nidhaanjit Jain and Sebastian Puerta

Sectoral Bargaining and the Wage Structure

with Kilian Weil

Abstract

We use a newly digitized panel of collective bargaining agreements linked to quarterly labor market data to study how sectoral agreements shaped aggregate wage inequality in Germany between 1950 and 2025.

“Women’s Wages” in West Germany

with Kilian Weil

Abstract

We study the effect of explicitly and implicitly gendered pay scales in West German collective bargaining agreements on gender gaps in wages and employment.

Software

LPDID: Stata module implementing Local Projections Difference-in-Differences (LP-DiD)

with Daniele Girardi, in collaboration with Arindrajit Dube, Òscar Jordà, and Alan M. Taylor

Statistical Software Components S459273, Boston College Department of Economics, 2023

Package on SSC

Abstract

LPDID performs the Local Projections Difference-in-Differences estimator (LP-DiD) proposed by Dube, Girardi, Jordà and Taylor (2023). LP-DiD is a convenient and flexible regression-based framework for implementing Difference-in-Differences with multiple time periods. It can provide both dynamic event-study estimates and pooled estimates of the overall average effect in a post-treatment window. Treatment can be absorbing or non-absorbing. The estimation sample is restricted to units entering treatment and “clean” controls, thus avoiding the negative-weights bias of two-way fixed-effects estimators. The command allows reweighting to estimate an equally-weighted average effect, flexible choice of the pre-treatment base period, and inclusion of control variables.

BibTeX 
@misc{buschgirardi2023lpdid,
  author = {Busch, Alexander and Girardi, Daniele},
  title  = {LPDID: Stata module implementing Local
            Projections Difference-in-Differences (LP-DiD)},
  howpublished = {Statistical Software Components S459273,
            Boston College Department of Economics},
  year   = {2023}
}

Teaching

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