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Understanding Persistence
Author(s)
Date Issued
2020-09
Date Available
2020-09-08T10:33:50Z
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.
Type of Material
Working Paper
Publisher
University College Dublin. School of Economics
Start Page
1
End Page
50
Series
UCD Centre for Economic Research Working Paper Series
WP2020/23
Copyright (Published Version)
2020 the Author
Language
English
Status of Item
Not peer reviewed
This item is made available under a Creative Commons License
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