Adam N. Glynn

V-Dem Codebook V6

All variables that V-Dem is compiling are included in the Codebook.

Increasing Inferential Leverage in the Comparative Method Placebo Tests in Small-n Research

We delineate the underlying homogeneity assumption, procedural variants, and implications of the comparative method and distinguish this from Mill’s method of difference. We demonstrate that additional units can provide “placebo” tests for the comparative method even if the scope of inference is limited to the two units under comparison. Moreover, such tests may be available even when these units are the most similar pair of units on the control variables with differing values of the independent variable. Small-n analyses using this method should therefore, at a minimum, clearly define the dependent, independent, and control variables so they may be measured for additional units, and specify how the control variables are weighted in defining similarity between units. When these tasks are too difficult, process tracing of a single unit may be a more appropriate method. We illustrate these points with applications to two studies.

Front-door Versus Back-door Adjustment with Unmeasured Confounding: Bias Formulas for Front-door and Hybrid Adjustments with Application to a Job Training Program

ABSTRACT We demonstrate that the front-door adjustment can be a useful alternative to standard covariate adjustments (i.e., back-door adjustments), even when the assumptions required for the front-door approach do not hold. We do this by providing asymptotic bias formulas for the front-door approach that can be compared directly to bias formulas for the back-door approach. In some cases, this allows the tightening of bounds on treatment effects. We also show that under one-sided noncompliance, the front-door approach does not rely on the use of control units. This finding has implications for the design of studies when treatment cannot be withheld from individuals (perhaps for ethical reasons). We illustrate these points with an application to the National Job Training Partnership Act Study.

ALLEVIATING ECOLOGICAL BIAS IN POISSON MODELS USING OPTIMAL SUBSAMPLING: THE EFFECTS OF JIM CROW ON BLACK ILLITERACY IN THE ROBINSON DATA

In many situations, data are available at some aggregate level, but one wishes to estimate the individual-level association between a response and an explanatory variable (or variables). Unfortunately, this endeavor is fraught with difficulties because of the ecological level of the data. The only reliable approach for overcoming the inherent identifiability problem associated with the analysis of ecological data is to supplement the ecological data with individual-level data. In this article, the authors illustrate the benefits of gathering individual-level data in the context of a Poisson modeling framework. Additionally, they derive optimal designs that allow the individual samples to be chosen so that information with respect to a particular model is maximized. The methods are illustrated using Robinson’s classic data on illiteracy rates. The authors show that the optimal design, if used with an appropriate model, produces accurate inference with respect to estimation of relative risks, with ecological bias removed.

Resolving Contested Elections: The Limited Power of Post-Vote Vote-Choice Data

In close elections, the losing side has an incentive to obtain evidence that the election result is incorrect. Sometimes this evidence comes in the form of court testimony from a sample of invalid voters, and this testimony is used to adjust vote totals (Belcher v. Mayor of Ann Arbor 1978; Borders v. King County 2005). However, while courts may be reluctant to make explicit findings about out-of-sample data (e.g., invalid voters that do not testify), when samples are used to adjust vote totals, the court is making such findings implicitly. In this paper, we show that the practice of adjusting vote totals on the basis of potentially unrepresentative samples can lead to incorrectly voided election results. More generally, we demonstrate that even when frame error and measurement error are minimal, random samples of post-vote vote-choice data can have limited power to detect incorrect election results without high response rates, precinct level polarization, or the acceptance of large Type I error rates. Therefore, in U.S. election disputes, even high-quality post-vote vote-choice data may be insufficient to resolve contested elections without the use of modeling assumptions (whether or not these assumptions are acknowledged).

Learning About Causal Mechanisms: Bounding the Indirect Effect of Oil Through State Weakness on Civil War

Conflict scholars have devoted considerable attention to the natural resource curse, and specifically to connections between natural resources, state weakness, and civil war. Many have posited a state weakness mechanism - that significant oil production causes state weakness, and state weakness consequently increases the likelihood of civil war onset. In this paper, I develop nonparametric methods for bounding mechanism-specific causal effects to show, using standard measures, that the state weakness mechanism does not exist in the short or medium term. In only two cases is there the possibility of a medium term effect, and the state weakness mechanism is unlikely to be operative even in these two cases. Furthermore, the methods introduced in this paper do not rely on assumptions about unmeasured confounders, so this result is robust to the consideration of other risk factors for civil war onset. The state weakness mechanism may still exist in the form of long term effects or an effect that reinforces pre-existing war and/or state weakness. However, the null hypothesis of no long-term and/or reinforcing effect cannot be rejected without the use of additional assumptions.