Understanding Persistence

Morgan Kelly

No 202023, Working Papers from School of Economics, University College Dublin

Abstract: A large literature on persistence finds that many modern outcomes strongly reflect characteristics of the same places in the distant past. These studies typically combine unusually high t statistics with severe spatial autocorrelation in residuals, suggesting that some findings may be artefacts of underestimating standard errors or of fitting spatial trends. For 25 studies in leading journals, I apply three basic robustness checks against spatial trends and find that effect sizes typically fall by over half, leaving most well known results insignificant at conventional levels. Turning to standard errors, there is currently no data-driven method for selecting an appropriate HAC spatial kernel. The paper proposes a simple procedure where a kernel with a highly flexible functional form is estimated by maximum likelihood. After correction, standard errors tend to rise substantially for cross sectional studies but to fall for panels. Overall, credible identification strategies tend to perform no better than naive regressions. Although the focus here is on historical persistence, the methods apply to regressions using spatial data more generally.

Keywords: Deep origins; Robustness checks; Spatial noise; Explanatory variables; Standard errors (search for similar items in EconPapers)
Pages: 50 pages
Date: 2020-09
New Economics Papers: this item is included in nep-ecm and nep-geo
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://hdl.handle.net/10197/11538 First version, 2020 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ucn:wpaper:202023

Access Statistics for this paper

More papers in Working Papers from School of Economics, University College Dublin Contact information at EDIRC.
Bibliographic data for series maintained by Nicolas Clifton ().