Suchergebnisse

Multilevel models are widely used in education and social science research. However, the effects of omitting levels of the hierarchy on the variance decomposition and the clustering effects have not been well documented. This paper discusses how omitting one level in three-level models affects the variance decomposition and clustering in the resulting two-level models. Specifically, I used the ANOVA framework and provided results for simple models that do not include predictors and assumed balanced nested data (or designs). The results are useful for teacher and school effects research as well as for power analysis during the designing stage of a study. The usefulness of the methods is demonstrated using data from Project STAR. -- Variance decomposition ; nested designs ; clustering

This paper uses data from the German Socio-Economic Panel for the years 2000 to 2005 to study the earnings differential between self- and dependent employed German men. Constructing a counterfactual earnings distribution for the self-employed in dependent employment and using quantile regression decompositions we find that the earnings differential over the distribution cannot be explained by differences in endowments. Furthermore, low-earning self-employed could earn more in dependent employment. Finally, the observed earnings advantage for the self-employed at the top of the earnings distribution is not associated with higher returns to observable variables.

The Cartesian Integration Method in Stochastic Linear Programming -- Anwendungen der Dualität der Optimierungstheorie auf Nichtlineare Approximationsaufgaben -- Iterative Lösung Linearer Ungleichungssysteme -- Eine Primale Version des Benders'schen Dekompositionsverfahrens und Seine Anwendung in der Gemischt-Ganzzahligen Optimierung -- Schwache Stetigkeit bei Nichtlinearen Kontrollproblemen -- Die Berechnung von Verallgemeinerten Quadraturformeln vom Gausschen Typus, eine Optimierungsaufgabe -- Stetigkeitsfragen bei der Diskretisierung Konvexer Optimierungsprobleme -- Optimale Linienführung Innerhalb eines Korridors — Ein Nichtlineares Optimierungsproblem -- Dualität und Optimale Steuerungen -- Optimale Definite Polynome und Quadraturformeln -- Some Numerical Techniques for Optimal Control Governed by Partial Differential Equation.

New Keynesian Phillips Curves (NKPC) have been extensively used in the analysis of monetary policy, but yet there are a number of issues of concern about how they are estimated and then related to the underlying macroeconomic theory. The first is whether such equations are identified. To check identification requires specifying the process for the forcing variables (typically the output gap) and solving the model for inflation in terms of the observables. In practice, the equation is estimated by GMM, relying on statistical criteria to choose instruments. This may result in failure of identification or weak instruments. Secondly, the NKPC is usually derived as a part of a DSGE model, solved by log-linearising around a steady state and the variables are then measured in terms of deviations from the steady state. In practice the steady states, e.g. for output, are usually estimated by some statistical procedure such as the Hodrick-Prescott (HP) filter that might not be appropriate. Thirdly, there are arguments that other variables, e.g. interest rates, foreign inflation and foreign output gaps should enter the Phillips curve. This paper examines these three issues and argues that all three benefit from a global perspective. The global perspective provides additional instruments to alleviate the weak instrument problem, yields a theoretically consistent measure of the steady state and provides a natural route for foreign inflation or output gap to enter the NKPC. -- Global VAR (GVAR) ; identification ; New Keynesian Phillips Curve ; trend-cycle decomposition

This study aimed to identify the major factors underlying the large discrepancy in poverty levels between two Brazilian racial groups: whites and Afro-Brazilians. We performed an Oaxaca-Blinder-type decomposition for nonlinear regressions in order to quantify the extent to which differences in observed geographic, sociodemographic, and labor characteristics (characteristics effect) account for this difference. The remaining unexplained part (coefficients effect) provides evidence on how these characteristics differentially impact on the risk of poverty in each group. A detailed decomposition of both effects allows the individual contribution of each characteristic to be determined. Our results show that the characteristics effect explains a large part of the discrepancy in poverty levels, with education and labor variables of household members explaining at least one half of the effect, and geographic and demographic variables accounting for the remainder. However, the unexplained part that remains significant has increased in importance in recent last years, and probably results from unequal access to high-quality education and the persistence of discrimination against colored workers in the labor market.

Income-expenditure surveys typically provide incomes on the household level. As households can differ in size and needs, a reliable assessment of inequality in living standards, therefore, necessitates the conversion of the original heterogeneous into an artificial quasi-homogeneous population. Ebert and Moyes (2003) and Shorrocks (2004) theoretically explore the properties of two alternative conversion strategies: a weighting of household equivalent incomes by size and by needs. We use data from the Luxembourg Income Study for examining the sensitivity of the Gini and the Theil index to the chosen conversion strategy, and explain our results by means of an inequality decomposition by population subgroups. -- income distribution ; inequality ; inequality decomposition ; equivalence scale

This paper introduces a new and simple decomposition method for a binary choice model that is equivalent to the Blinder-Oaxaca decomposition analysis for wage differentials. The decomposition method is first developed for a single probit model and later generalized to a simultaneous equations model. Using Taylor expansion, we approximate the differences in the probabilities of choosing option 1 over option 0 between two groups in order to find the effects of the differences in "each" individual characteristic and the differences in "each" coefficient. We implement this decomposition analysis studying the racial gap in female labor market participation rates. The racial gap of participation rates among women can be almost exclusively explained by the differences in the coefficients.

The major contribution of this paper is ending a new and flexible way to measure the effects of selection on log-wages. In this context, we offer a general approach to performing decomposition analysis when selection effects are present. We call the difference between unconditional and conditional expectations of the log-wages a generalized selection bias (GSB) when the two expectations are measured using the estimates from the joint estimation of the whole model (log-wages and selection equations) by the MLE method. The unconditional and conditional expectations are, respectively, the deterministic component of log-wages, and the deterministic component plus the conditional expectation of the stochastic component of log-wages, where the deterministic component is computed using the estimates from the joint estimation. That is, the GSB is the expectation of the residuals estimated from the joint estimation. It is appropriate to apply the Blinder-Oaxaca decomposition method to the wage differentials adjusted for the GSB. The GSB approach to decomposition analysis is not only easy to implement and flexible enough to apply to virtually any kind of selection issue, but also efficient because it uses full information. We illustrate the GSB approach by applying it to the racial wage differentials among women using data from the Current Population Survey. We discuss the possibility of using semiparametric or Bayesian sampling method for the joint estimation and related modifications of decomposition analysis.