|Title:||Understanding Persistence||Authors:||Kelly, Morgan||Permanent link:||http://hdl.handle.net/10197/11538||Date:||Sep-2020||Online since:||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/Report no.:||UCD Centre for Economic Research Working Paper Series; WP2020/23||Copyright (published version):||2020 the Author||Keywords:||Deep origins; Robustness checks; Spatial noise; Explanatory variables; Standard errors||Language:||en||Status of Item:||Not peer reviewed|
|Appears in Collections:||Economics Working Papers & Policy Papers|
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