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Below results based on the criteria 'Omitted Variable Bias'
Total number of records returned: 3
Empirical Modeling Strategies for Spatial Interdependence: Omitted-Variable vs. Simultaneity Biases
Spatial Lag Models
Omitted Variable Bias
Scholars recognize that time-series-cross-section data typically correlate across time and space, yet they tend to model temporal dependence directly while addressing spatial interdependence solely as nuisance to be “corrected” (FGLS) or to which to be “robust” (PCSE). We demonstrate that directly modeling spatial interdependence is methodologically superior, offering efficiency gains and generally helping avoid biased estimates even of “non-spatial” effects. We first specify empirical models representing two modern approaches to comparative and international political economy: (context-conditional) open-economy comparative political-economy (i.e., common stimuli, varying responses) and international political-economy, which implies interdependence (plus closed-economy and orthogonal-open-economy predecessors). Then we evaluate four estimators—non-spatial OLS, spatial OLS, spatial 2SLS-IV, and spatial ML—for analyzing such models in spatially interdependent data. Non-spatial OLS suffers from potentially severe omitted-variable bias, tending to inflate estimates of common-stimuli effects especially. Spatial OLS, which specifies interdependence directly via spatial lags, dramatically improves estimates but suffers a simultaneity bias, which can be appreciable under strong interdependence. Spatial 2SLS-IV, which instruments for spatial lags of dependent variables with spatial lags of independent variables, yields unbiased and reasonably efficient estimates of both common-stimuli and diffusion effects, when its conditions hold: large samples and fully exogenous instruments. A tradeoff thus arises in practice between biased-but-efficient spatial OLS and consistent- (or, at least, less-biased-) but-inefficient spatial 2SLS-IV. Spatial ML produces good estimates of non-spatial effects under all conditions but is computationally demanding and tends to underestimate the strength of interdependence, appreciably so in small-N samples and when the true diffusion-strength is modest. We also explore the standard-error estimates from these four procedures, finding sizable inaccuracies by each estimator under differing conditions, and PCSE’s do not necessarily reduce these inaccuracies. By an accuracy-of-reported-standard-errors criterion, 2SLS-IV seems to dominate. Finally, we explore the spatial 2SLS-IV estimator under varying patterns of interdependence and endogeneity, finding that its estimates of diffusion strength suffer only when a condition we call cross-spatial endogeneity, wherein dependent variables (y’s) in some units cause explanatory variables (x’s) in others, prevails.
Does Private Money Buy Public Policy? Campaign Contributions and Regulatory Outcomes in Telecommunications
de Figueiredo, Rui
omitted variable bias
To what extent can market participants affect the outcomes of regulatory policy? In this paper, we study the effects of one potential source of influence – campaign contributions – from competing interests in the local telecommunications industry, on regulatory policy decisions of state public utility commissions. Using a unique new data set, we find, in contrast to much of the literature on campaign contributions, that there is a significant effect of private money on regulatory outcomes. This result is robust to numerous alternative model specifications. We also assess the extent of omitted variable bias that would have to exist to obviate the estimated result. We find that for our result to be spurious, omitted variables would have to explain more than five times the variation in the mix of private money as is explained by the variables included in our analysis. We consider this to be very unlikely.
Misspecification and the Propensity Score: When to Leave Out Relevant Pre-Treatment Variables
Clarke, Kevin A.
omitted variable bias
The popularity of propensity score matching has given rise to a robust, albeit informal, debate concerning the number of pre-treatment variables that should be included in the propensity score. The standard practice is to include all available pre-treatment variables in the propensity score. We demonstrate that this approach is not always optimal for the goal of reducing bias in the estimation of a treatment effect. We characterize conditions under which including an additional relevant variable in a propensity score increases the bias on the effect of interest across a variety of different implementations of the propensity score methodology. We find that matching within propensity score calipers is slightly more robust against such bias than other common methods.