Victor Chernozhukov, Iván Fernández-Val, Jonas Meier, Aico van Vuuren, Francis Vella
arXiv 18 Aug 2025 · Econometrics
arXiv:2508.12716 · PDF · DOI · OpenAlex · Extracted main text
We employ distribution regression (DR) to estimate the joint distribution of two outcome variables conditional on chosen covariates. While Bivariate Distribution Regression (BDR) is useful in a variety of settings, it is particularly valuable when some dependence between the outcomes persists after accounting for the impact of the covariates. Our analysis relies on a result from Chernozhukov et al. (2018) which shows that any conditional joint distribution has a local Gaussian representation. We describe how BDR can be implemented and present some associated functionals of interest. As modeling the unexplained dependence is a key feature of BDR, we focus on functionals related to this dependence. We decompose the difference between the joint distributions for different groups into composition, marginal and sorting effects. We provide a similar decomposition for the transition matrices which describe how location in the distribution in one of the outcomes is associated with location in the other. Our theoretical contributions are the derivation of the properties of these estimated functionals and appropriate procedures for inference. Our empirical illustration focuses on intergenerational mobility. Using the Panel Survey of Income Dynamics data, we model the joint distribution of parents' and children's earnings. By comparing the observed distribution with constructed counterfactuals, we isolate the impact of observable and unobservable factors on the observed joint distribution. We also evaluate the forces responsible for the difference between the transition matrices of sons' and daughters'.
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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 | Chernozhukov, Fernández-Val and Luo (2018) Distribution regression with sample selection, with an application to wage decompositions in the UK | 0.909 | 8 | 5 | 75% |
| 2 | Meier (2020) Multivariate distribution regression self | 0.874 | 7 | 2 | 100% |
| 3 | Wang, Oka and Zhu (2022) Bivariate distribution regression with application to insurance data | 0.874 | 6 | 2 | 100% |
| 4 | Chetty, Hendren, Kline and Saez (2014) Where is the land of opportunity? The geography of intergenerational mobility in the United States | 0.843 | 3 | 3 | 100% |
| 5 | Chernozhukov, Fernández-Val and Melly (2013) Inference on counterfactual distributions | 0.644 | 15 | 3 | 27% |
| 6 | Mogstad and Torsvik (2023) Chapter 6 - Family background, neighborhoods, and intergenerational mobility | 0.644 | 2 | 2 | 100% |
| 7 | Fernández-Val, Meier, van Vuuren and Vella (2024) Distribution regression difference-in-differences self | 0.511 | 2 | 1 | 100% |
| 8 | Landers and Heckman (2017) The Scandinavian fantasy: the sources of intergenerational mobility in Denmark and the US | 0.511 | 2 | 1 | 100% |
| 9 | van der Vaart and Wellner (1996) | 0.511 | 2 | 1 | 100% |
| 10 | Abramitzky, Boustan, Jácome and Pérez (2021) Intergenerational mobility of immigrants in the united states over two centuries | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 28 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nonparametric and Semiparametric Estimation of Upward Rank Mobility Curves | 0.405 | 1 | 1 |