Stefano DellaVigna, Guido Imbens, Woojin Kim, David M. Ritzwoller
arXiv 17 Apr 2025 · Econometrics · 1 citations (OpenAlex)
arXiv:2504.13295 · PDF · DOI · OpenAlex · Extracted main text
Empirical research in economics often examines the behavior of agents located in a geographic space. In such cases, statistical inference is complicated by the interdependence of economic outcomes across locations. A common approach to account for this dependence is to cluster standard errors based on a predefined geographic partition. A second strategy is to model dependence in terms of the distance between units. Dependence, however, does not necessarily stop at borders and is typically not determined by distance alone. This paper introduces a method that leverages observations of multiple outcomes to adjust standard errors for cross-sectional dependence. Specifically, a researcher, while interested in a particular outcome variable, often observes dozens of other variables for the same units. We show that these outcomes can be used to estimate dependence under the assumption that the cross-sectional correlation structure is shared across outcomes. We develop a procedure, which we call Thresholding Multiple Outcomes (TMO), that uses this estimate to adjust standard errors in a given regression setting. We show that adjustments of this form can lead to sizable reductions in the bias of standard errors in calibrated U.S. county-level regressions. Re-analyzing nine recent papers, we find that the proposed correction can make a substantial difference in practice.
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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 | Conley, T. G (1999) GMM estimation with cross sectional dependence | 1.000 | 23 | 7 | 100% |
| 2 | Bernini, A., Facchini, G., and Testa, C (2023) Race, representation, and local governments in the us south: the effect of the voting rights act | 1.000 | 18 | 5 | 100% |
| 3 | Müller, U. K. and Watson, M. W (2022) Spatial correlation robust inference | 1.000 | 14 | 5 | 100% |
| 4 | Müller, U. K. and Watson, M. W (2023) Spatial correlation robust inference in linear regression and panel models | 1.000 | 14 | 5 | 100% |
| 5 | Efron, B (2007) Size, power and false discovery rates | 1.000 | 5 | 3 | 100% |
| 6 | Acemoglu, D., Naidu, S., Restrepo, P., and Robinson, J. A (2019) Democracy does cause growth | 0.874 | 9 | 2 | 100% |
| 7 | Calderon, A., Fouka, V., and Tabellini, M (2023) Racial diversity and racial policy preferences: the great migration and civil rights | 0.874 | 8 | 2 | 100% |
| 8 | Chetty, R., Hendren, N., Kline, P., and Saez, E (2014) Where is the land of opportunity? the geography of intergenerational mobility in the united states | 0.874 | 7 | 2 | 100% |
| 9 | Moulton, B. R (1986) Random group effects and the precision of regression estimates | 0.843 | 3 | 3 | 100% |
| 10 | Moulton, B. R (1990) An illustration of a pitfall in estimating the effects of aggregate variables on micro units | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 89 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Big Wins, Small Net Gains: Direct and Spillover Effects of First Industry Entries in Puerto Rico | 0.843 | 3 | 3 |
| 2 | Bandwidth Selection for Spatial HAC Standard Errors | 0.405 | 1 | 1 |