Stephane Bonhomme, Angela Denis
arXiv 1 Apr 2024 · Econometrics · publishedLabour Economics (2024) · 13 citations (OpenAlex)
arXiv:2404.01495 · PDF · DOI · OpenAlex · Extracted main text
A growing number of applications involve settings where, in order to infer heterogeneous effects, a researcher compares various units. Examples of research designs include children moving between different neighborhoods, workers moving between firms, patients migrating from one city to another, and banks offering loans to different firms. We present a unified framework for these settings, based on a linear model with normal random coefficients and normal errors. Using the model, we discuss how to recover the mean and dispersion of effects, other features of their distribution, and to construct predictors of the effects. We provide moment conditions on the model's parameters, and outline various estimation strategies. A main objective of the paper is to clarify some of the underlying assumptions by highlighting their economic content, and to discuss and inform some of the key practical choices.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chetty and Hendren (2018) The impacts of neighborhoods on intergenerational mobility II: County-level estimates | 1.000 | 8 | 6 | 100% |
| 2 | Arellano and Bonhomme (2012) Identifying distributional characteristics in random coefficients panel data models | 1.000 | 5 | 4 | 100% |
| 3 | Kline, Rose, and Walters (2022) Systemic discrimination among large US employers | 1.000 | 5 | 4 | 100% |
| 4 | Jiang and Nguyen (2007) Linear and generalized linear mixed models and their applications | 0.928 | 4 | 4 | 100% |
| 5 | Abowd, Kramarz, and Margolis (1999) High wage workers and high wage firms | 0.928 | 4 | 3 | 100% |
| 6 | Andrews, Gill, Schank, and Upward (2008) High wage workers and low wage firms: negative assortative matching or limited mobility bias? | 0.928 | 4 | 3 | 100% |
| 7 | Bonhomme, Lamadon, and Manresa (2019) A distributional framework for matched employer employee data | 0.843 | 3 | 3 | 100% |
| 8 | Chetty and Hendren (2018) The impacts of neighborhoods on intergenerational mobility I: Childhood exposure effects | 0.843 | 3 | 3 | 100% |
| 9 | Bonhomme, Holzheu, Lamadon, Manresa, Mogstad, and Setzler (2023) How much should we trust estimates of firm effects and worker sorting? | 0.737 | 3 | 2 | 100% |
| 10 | Kline, Saggio, and Slvsten (2020) Leave-out estimation of variance components | 0.737 | 3 | 2 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.