Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions vis-à-vis previous work. Instead of discouraging the use of fixed-effects models, we encourage more critical applications of this rigorous and promising methodology. The most important deficiencies—Type II errors, biased coefficients and imprecise standard errors, misleading p values, misguided causal claims, and various theoretical concerns—should be weighed against the likely presence of unobserved heterogeneity in other regression models. Ultimately, we must do a better job of communicating the pitfalls of fixed-effects models to our colleagues and students.
We consider the association between state political ideology and population mobility during the coronavirus (COVID-19) pandemic. We use first-party geo-behavioral data to estimate the average distance traveled by approximately 15,000,000 devices over 10 weeks (February 24, 2020 to April 27, 2020). Regression models with state clustered robust standard errors show lower shelter-in-place rates and higher mobility scores in states with larger percentages of voters who supported Trump in the 2016 presidential election. We also find that shelter-in-place rates increased and mobility scores declined at slower rates in states with greater Trump support. Shelter-in-place rates and average mobility scores were comparable in states governed by Republicans and Democrats. There was some evidence that shelter-in-place rates increased and average mobility scores declined at slower rates in states governed by Republicans. Overall, states with more Trump voters are more resistant to public health recommendations and state stay-at-home orders during the coronavirus pandemic.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.