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cadolph at uw dot edu

Working Papers

Pandemic Politics: Timing State-Level Social Distancing Responses to COVID-19  

28 March 2020. Also available on MedRxiv

Christopher Adolph, Kenya Amano, Bree Bang-Jensen, Nancy Fullman, and John Wilkerson

Social distancing policies are critical but economically painful measures to flatten the curve against emergent infectious diseases. As the novel coronavirus that causes COVID-19 spread throughout the United States in early 2020, the federal government issued social distancing recommendations but left to the states the most difficult and consequential decisions restricting behavior, such as canceling events, closing schools and businesses, and issuing stay-at-home orders. We present an original dataset of state-level social distancing policy responses to the epidemic and explore how political partisanship, COVID-19 caseload, and policy diffusion explain the timing of governors’ decisions to mandate social distancing. An event history analysis of five social distancing policies across all fifty states reveals the most important predictors are political: all else equal, Republican governors and governors from states with more Trump supporters were slower to adopt social distancing policies. These delays are likely to produce significant, on-going harm to public health.


The Influence of Changing Marginals on Measures of Inequality in Scholarly Citations: Evidence of Bias and a Resampling Correction  

29 May 2020

Lanu Kim, Christopher Adolph, Jevin West, and Katherine Stovel

Scholars have debated whether changes in digital environments have led to greater concentration or dispersal of scientific citations, but this debate has paid little attention to how other changes in the publication environment may impact the commonly used measures of inequality. We demonstrate using Monte Carlo experiments that a variety of inequality measures – including the Gini coefficient, the Herfindahl-Hirschman index, and the percentage of papers ever cited – are substantially biased downwards by increases in the total number of papers and citations. We propose and validate a resampling-based correction for this ‘marginals bias,’ and apply this correction to empirical data on scholarly citation distributions using Web of Science data covering four broad scientific fields (Health; Humanities; Mathematics and Computer Sciences; and Social Sciences) during 1996–2014. We find that in each field the bulk of the apparent decline in citation inequality in recent years is an artifact of marginals bias, as are most apparent inter-field differences in citation inequality. Researchers using inequality measures to compare citation distributions and other distributions with many cases at or near the zero-bound should interpret these metrics carefully and account for the influence of changing marginals.


Reports and Unpublished Papers

Small Sample Properties of Partially-Observed Rank Data Estimators  

This Version: 6 October 2011

Christopher Adolph

Several estimators from the social science toolkit might be used to model the relationship between imprecisely-observed ranks in a hierarchy, and covariates explaining those ranks. But application of standard methods – such as linear regression, ordered probit, or censored regression – is complicated by the interdependence of rank observations. Monte Carlo evidence shows that estimators which either delete partially observed ranks and/or inappropriately assume ranks are iid perform poorly, yielding inefficient and sometimes biased estimates, and wildly inaccurate confidence intervals. In contrast, a Bayesian partial rank model – designed to impute missing ranks within known bounds, and account for interdependence across ranks – performs well, even when most or all ranks are observed imprecisely.


Report on the 2004 Washington Gubernatorial Election  

Submitted May 12, 2005 to the Superior Court of Chelan County, Washington

Christopher Adolph

Expert witness report on ecological inference and the statistical analysis of elections in Borders v. King County, which contested the 2004 Washington gubernatorial election. The final (unappealed) ruling in favor of Governor Christine Gregoire can be found here. Wikipedia's entry on the 2004 Washington election can be found here, though I cannot vouch for the validity of wiki content.


Evidence on Time Series Cross-Section Estimators and Specifications from Monte Carlo Experiments  

March 19, 2005

Christopher Adolph, David M. Butler, and Sven E. Wilson

Political scientists often and increasingly analyze time-series cross-sectional (tscs) data. These data come with significant problems, such as accounting for unobserved variation across sample units and appropriately specifying dynamics. Furthermore, even though fixed-effects (or least squares dummy variable (lsdv) models) can address unit heterogeneity, least squares (ls) estimation of modelds with fixed-effects and lagged dependent variables are known to be biased. Alternative estimators, mostly from economics, and generally designed for short panels, have been proposed to address this bias, but it is generally not well known how these estimators perform in comparison to simple methods like ls and lsdv on tscs data. The preliminary results we illustrate here suggest that lsdv is generally as good or better than instrumental variables (iv) approaches in terms of bias and efficiency. We examine estimator performance under conditions where the importance of the unit effects and the correlation of the unit effects with the independent variables are allowed to vary and find that lsdv performs well. Unfortunately none of the estimators, particularly ls, perform well when the dynamics of the model are mis-specified. The lesson is that new estimators do not, in general, solve the problem of mis-specifying the model’s dynamics.


Homeowner Association Foreclosures and Property Values in Harris County, 1985–2001  

October 12, 2002

Christopher Adolph

In recent years, homeowner associations (hoas) in Harris County, Texas have filed thousands of lawsuits threatening foreclosure against residents who owed dues, late fees, or fines. An event count analysis of hoa foreclosures by neighborhood from 1985—2001 shows the bulk of these filings occur in neighborhoods with low median home values. Overall, homeowners in the bottom quartile of home value face more than ten times the risk of hoa foreclosure proceedings as those in the top quartile. Legal changes in 1987 and 1995 also seem to have encouraged hoas to bring more foreclosures to court: across the spectrum of home values, the annual pace of filing after 1995 is roughly double the previous decade’s rate. Although hoa foreclosures are ostensibly motivated by efforts to improve property values, neither foreclosure activity nor hoas appear linked with above average home price growth.

Replication:  Data are drawn from HOAdata, which should be consulted for more recent data.


Playing Favorites:
How Parties Distribute School Finance by Income and Race
 

June 21, 2001

Christopher Adolph

A partisan legislative logic influences the distribution of school funding in the American states. Regression analysis and simulation using data on state aid to American school districts from 1992-1997 reveals three aspects of partisan education policy. First, partisan state governments allocate resources towards their core constituents – as grouped by income and race – while matching each other in funding the median voter. Second, students in low income or densely black districts benefit substantially from Democratic control of the state government, but receive a smaller share of the state education budget under Republican regimes. Finally, the party-mediated effect of race on educational resource distribution is even greater than the party-mediated income bias.




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