Minimum wage effects by gender

Recent criticisms have led some to dismiss time-series analyses in the debate over the minimum wage. We investigate previous time-series studies showing that raising the minimum wage has a smaller impact on females than males. We reanalyze the data in light of recent developments in time-series methods and find that the minimum wage has a similar significant negative impact on both males and females. We conclude that, following a 10 percent increase in the minimum wage, both male and female employment drops from between 2 and 4 percent over a two-year period. This employment decrease slowly erodes as economic growth and inflation cause the minimum wage to fall below the market-clearing wage. We thank David Card and Alan Krueger for generously providing the data.     

Mixture structure analysis using the Akaike Information Criterion and the bootstrap

Given i.i.d. observations x1,x2,x3,...,xn drawn from a mixture of normal terms, one is often interested in determining the number of terms in the mixture and their defining parameters. Although the problem of determining the number of terms is intractable under the most general assumptions, there is hope of elucidating the mixture structure given appropriate caveats on the underlying mixture. This paper examines a new approach to this problem based on the use of Akaike Information Criterion (AIC) based pruning of data driven mixture models which are obtained from resampled data sets. Results of the application of this procedure to artificially generated data sets and a real world data set are provided.     

Attributes of Successful and Unsuccessful Acquisitions of US Firms

Acquisitive growth strategies continue to be popular, in spite of increasing evidence that they often do not enhance the financial performance of acquiring firms and may adversely affect innovation. However, some acquisitions are associated with both increases in financial performance and a strengthened commitment to R&D while others experience decreases in both. Multiple theories have been offered to explain acquisitions and their outcomes, but few have received strong empirical support. This paper describes a multiple rater, multiple-case study of acquisitions that had highly favourable outcomes and others that experienced highly unfavourable outcomes. All twelve of the high performing acquisitions studied were found to exhibit the dual characteristics of friendliness during acquisition negotiations and resource complementarities between the two firms. Additionally, debt played an important role in the success (low to moderate debt) or lack of success (high or extraordinary debt) in 21 of the 24 acquisitions studied. Inadequate target evaluation was a factor in 11 of the 12 acquisitions with low performance. Importantly, the results of both sets of acquisitions suggested that a configuration of attributes affected post-acquisition performance. Other findings both supported and contradicted commonly held beliefs about acquisitions, as well as highlighted variables not typically associated with acquisition strategies. The study provides directions for future theory development and empirical research on acquisitions.     

Governance in Public-Private Community Health Partnerships: A Survey of the Community Care Network SM Demonstration Sites

This review of the governance practices of twenty-five public-private partnerships involved in addressing a broad range of community health needs shows that governance in public-private community partnerships departs significantly from traditional notions of institutional governance. Governance structures and degrees of progress toward governance goals vary widely and appear to be systematically related to the organization, composition, location, and activity of each partnership.     

Acculturation and Risk Behavior among Pregnant Mexican American Females: A Structural Equation Model

The purpose of this study was to test a theoretical model designed to predict risk behavior (i.e., cigarette and/or alcohol use) in a sample of Mexican American females who were pregnant and ranged in age from adolescence to early adulthood. The model consisted of six hypothesized predictors–acculturation status, intergenerational family conflict, acculturative stress, educational attainment, household income, and age of respondent, which were proposed as antecedents of risk behavior. Structural equation modeling was used to test both the direct and indirect effects for a set of proposed antecedents to the tendency for Mexican American females to engage in risk behaviors. Several of the hypothesized relationships were supported, with the strongest predictors of risk behavior being acculturation status, intergenerational family conflict, and acculturative stress. An important conclusion was that risk behavior by Mexican American females increased as they became more acculturated to the social circumstances of the U.S. Another notable result was that risk behavior increased among Mexican American females as they experienced higher levels of intergenerational conflict within their families.     

Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion

Many different methods have been proposed to construct nonparametric estimates of a smooth regression function, including local polynomial, (convolution) kernel and smoothing spline estimators. Each of these estimators uses a smoothing parameter to control the amount of smoothing performed on a given data set. In this paper an improved version of a criterion based on the Akaike information criterion (AIC), termed AIC_C, is derived and examined as a way to choose the smoothing parameter. Unlike plug-in methods, AIC_C can be used to choose smoothing parameters for any linear smoother, including local quadratic and smoothing spline estimators. The use of AIC_C avoids the large variability and tendency to undersmooth (compared with the actual minimizer of average squared error) seen when other 'classical' approaches (such as generalized cross-validation (GCV) or the AIC) are used to choose the smoothing parameter. Monte Carlo simulations demonstrate that the AIC_C-based smoothing parameter is competitive with a plug-in method (assuming that one exists) when the plug-in method works well but also performs well when the plug-in approach fails or is unavailable.