Suchergebnisse

This article adopts the nonradial Russell measure in the context of data envelopment analysis to measure the relative efficiency of public education in the Upper Peninsula of Michigan, a geographically remote but homogenous region. The empirical analysis focuses on the effect of community characteristics on the schools efficiency by using school district–level data. Several different forms of model specifications in the stage of data envelopment analysis are executed to check the robustness of the findings by adopting extreme‐bound analysis and thick modeling approach. Interestingly, despite the homogeneity of the Upper Peninsula, wide differences in the efficiency of education are found. These differences are robust as to model specification, suggesting that efficiency studies might be a useful guide for policy makers. Community factors such as income and educational levels, obtained from the census data by school district, are introduced in the second stage because they will influence the efficiency of the schools and the technology by which schools help students learn. Median family income is the most important explanatory variable, whereas the median value of housing is insignificant. In addition, private school enrollments are unrelated to the efficiency of public education, contrary to what many advocates of private schools have contended. These findings help understand education efficiency, having policy implications for education‐oriented states such as Michigan. (JEL I2, N3, H52)

Phillips' (1958) original curve involves a nonlinear relationship between inflation and unemployment. We consider how his original results change due to updated theoretic and empirical studies, increased computer power, enlarged datasets, increases in data frequency and developed time series econometric models. In the linear models, there was weak causation from unemployment to inflation. Rather than using any of the many nonlinear models that are now available, we adopt a time‐varying parameter linear model as their convenient proxy, which empirically supports Phillips' use of nonlinear model form and causation, but the strength of this result is much weaker in recent periods.