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Articles 1 - 5 of 5
Full-Text Articles in Social and Behavioral Sciences
Hospital Treatment Rates And Spill-Over Effects: Does Ownership Matter?, Badi H. Baltagi, Yin -Fang Yen
Hospital Treatment Rates And Spill-Over Effects: Does Ownership Matter?, Badi H. Baltagi, Yin -Fang Yen
Center for Policy Research
This paper studies the effect of hospital ownership on treatment rates allowing for spatial correlation among hospitals. Competition among hospitals and knowledge spillovers generate significant externalities which we try to capture using the spatial Durbin model. Using a panel of 2342 hospitals in the 48 continental states observed over the period 2005 to 2008, we find significant spatial correlation of medical service treatment rates among hospitals. We also get mixed results on the effect of hospital ownership on treatment rates that depends upon the market structure where the hospital is located and which varies by treatment type.
A Robust Hausman-Taylor Estimator, Badi Baltagi, Georges Bresson
A Robust Hausman-Taylor Estimator, Badi Baltagi, Georges Bresson
Center for Policy Research
This paper suggests a robust Hausman and Taylor (1981) estimator, here-after HT that deals with the possible presence of outliers. This entails two modifications of the classical HT estimator. The first modification uses the Bramati and Croux (2007) robust Within MS estimator instead of the Within estimator in the first stage of the HT estimator. The second modification uses the robust Wagenvoort and Waldmann (2002) two stage generalized MS estimator instead of the 2SLS estimator in the second step of the HT estimator. Monte Carlo simulations show that, in the presence of vertical outliers or bad leverage points, the robust …
The Value Of A Statistical Life: Evidence From Panel Data, Thomas J. Kniesner, W. Kip Viscusi, Christopher Woock
The Value Of A Statistical Life: Evidence From Panel Data, Thomas J. Kniesner, W. Kip Viscusi, Christopher Woock
Center for Policy Research
Our research addresses fundamental long-standing concerns in the compensating wage differentials literature and its public policy implications: the econometric properties of estimates of the value of statistical life (VSL) and the wide range of such estimates from about $0 to almost $30 million. Here we address most of the prominent econometric issues by applying panel data, a new and more accurate fatality risk measure, and systematic application of panel data estimators. Controlling for measurement error, endogeneity, latent individual heterogeneity that may be correlated with the regressors, state dependence, and sample composition yields an estimated value of a statistical life of …
Panel Data Inference Under Spatial Dependence, Badi H. Baltagi, Alain Pirotte
Panel Data Inference Under Spatial Dependence, Badi H. Baltagi, Alain Pirotte
Center for Policy Research
This paper focuses on inference based on the usual panel data estimators of a one-way error component regression model when the true specification is a spatial error component model. Among the estimators considered, are pooled OLS, random and fixed effects, maximum likelihood under normality, etc. The spatial effects capture the cross-section dependence, and the usual panel data estimators ignore this dependence. Two popular forms of spatial autocorrelation are considered, namely, spatial auto-regressive random effects (SAR-RE) and spatial moving average random effects (SMA-RE). We show that when the spatial coefficients are large, test of hypothesis based on the usual panel data …
Fixed-Effect Estimation Of Highly-Mobile Production Technologies, William C. Horrace, Kurt E. Schnier
Fixed-Effect Estimation Of Highly-Mobile Production Technologies, William C. Horrace, Kurt E. Schnier
Center for Policy Research
We consider fixed-effect estimation of a production function where inputs and outputs vary over time, space, and cross-sectional unit. Variability in the spatial dimension allows for time-varying individual effects, without parametric assumptions on the effects. Asymptotics along the spatial dimension provide consistency and normality of the marginal products. A finite-sample example is provided: a production function for bottom-trawler fishing vessels in the flatfish fisheries of the Bering Sea. We find significant spatial variability of output (catch) which we exploit in estimation of a harvesting function.