The Effectiveness of Gun Control Laws:

ABSTRACT In the July, 1998 issue of The American Journal of Economics and Sociology Professor Tomislav V. Kovandzic strongly criticized the 1997 work on gun control by Kwon, Scott, Safranski and Bae, which appears in the same journal. A rejoinder by Kwon et al. refuted Kovnanzic's criticisms and the authors stand by their published results. The purpose of this comment is to reconsider some of the points raised by Kovandzic and to consider other issues not raised in the original exchange. The bottom line is that several of the criticisms by Kovandzic are valid and, in fact, other serious econometric problems may plague the original paper by Kwon et al.     

Direct density estimation as an ill-posed inverse estimation problem

A possible definition of ill-posedness in statistical estimation is the lack of qualitative robustness. In this sense direct density estimation shares ill-posedness with the more obviously ill-posed indirect density estimation models, of which it is a special case. A general construction pattern for estimators is proposed, based on suitable preconditioning, that works for both direct and indirect density estimation. Special emphasis is on its application to the direct case, where in general it yields delta-sequence estimators. More specifically both kernel and series type estimators are included depending on the choice of preconditioning operator. In particular sinc and other flattop kernel estimators emerge in a natural way.     

Job Loss and Entrepreneurship

We examine the impact of job loss on entrepreneurship behaviour. Our identification strategy relies on the use of mass layoffs caused by bankruptcies as indicators of exogenous displacement. Building on Norwegian register data, we find that working in a company which is going to close down due to bankruptcy in the near future raises the subsequent entrepreneur propensity by 155% for men and 180% for women, compared to working in a stable firm. These estimates are much larger than previously reported in the literature. Taking into account that many workers lose their jobs in the comparison group of stable firms also, we suggest that the full effects of displacement are even larger.     

Factual and modal notions in social research

The article discusses a distinction between factual and modal notions, and corresponding generalizations, in social research. The discussion starts from the suggestion, made by Charles Ragin, that theoretical statements in social research most often can be formulated as statements about sets of cases and relations between such sets. In contrast to this view, it is argued that theoretical statements in social research often require modal notions referring to possibilities and probabilities which cannot be formulated as statements about sets of cases. In order to show this, the article reformulates Ragin’s set-theoretic approach in the conceptual framework of statistical variables. It is shown that this can be done for both crisp and fuzzy set versions of Ragin’s approach. The article then goes on to argue that social research is often interested in modal generalizations (probabilistic and deterministic rules) which require a fundamentally different conceptual framework. The article shows how such a framework can be defined, and finally indicates its usage for causal interpretations.     

Environmental Policy,Rankings and Stock Values

This paper examines the impact of Newsweek's ‘The Greenest Big Companies in America’ on stock values for large companies. Newsweek ranks the biggest Companies in America from one to 500. An event window of ten days following the rankings release to the public was examined to analyze the impact of relative rankings on stock values. Dummy variables were constructed denoting firms ranked in the highest and lowest quartiles. Control variables included return to the Dow Jones Industrial Average, the firm's beta coefficient and total revenue. The main finding of the study is that inclusion in the top quartile of the rankings has a positive and statistically significant impact on firms' stock values, while the coefficient for the lowest quartile was not statistically different from zero. The results provide evidence of a positive impact on stock values from favorable environmental recognition but no effect for low ranked firms. Copyright © 2011 John Wiley & Sons, Ltd and ERP Environment.     

Pre-positioning hurricane supplies in a commercial supply chain

Inventory control for retailers situated in the projected path of an observed hurricane or tropical storm can be challenging due to the inherent uncertainties associated with storm forecasts and demand requirements. In many cases, retailers react to pre- and post-storm demand surge by ordering emergency supplies from manufacturers posthumously. This wait-and-see approach often leads to stockout of the critical supplies and equipment used to support post-storm disaster relief operations, which compromises the performance of emergency response efforts and proliferates lost sales in the commercial supply chain. This paper proposes a proactive approach to managing disaster relief inventories from the perspective of a single manufacturing facility, where emergency supplies are pre-positioned throughout a network of geographically dispersed retailers in anticipation of an observed storm's landfall. Once the requirements of a specific disaster scenario are observed, supplies are then transshipped among retailers, with possible direct shipments from the manufacturer, to satisfy any unfulfilled demands. The manufacturer's pre-positioning problem is formulated as a two-stage stochastic programming model which is illustrated via a case study comprised of real-world hurricane scenarios. Our findings indicate that the expected performance of the proposed pre-positioning strategy over a variety of hurricane scenarios is more effective than the wait-and-see approach; currently used in practice.     

Zero-inflated count regression models with applications to some examples

In this paper, we employed SAS PROC NLMIXED (Nonlinear mixed model procedure) to analyze three example data having inflated zeros. Examples used are data having covariates and no covariates. The covariates utilized in this article have binary outcomes to simplify our analysis. Of course the analysis can readily be extended to situations with several covariates having multiple levels. Models fitted include the Poisson (P), the negative binomial (NB), the generalized Poisson (GP), and their zero-inflated variants, namely the ZIP, the ZINB and the ZIGP models respectively. Parameter estimates as well as the appropriate goodness-of-fit statistic (the deviance D) in this case are computed and in some cases, the Pearson’s X ² statistic, that is based on the variance of the relevant model distribution is also computed. Also obtained are the expected frequencies for the models and GOF tests are conducted based on the rule established by Lawal (Appl Stat 29:292–298, 1980). Our results extend previous results on the analysis of the chosen data in this example. Further, results obtained are very consistent with previous analyses on the data sets chosen for this article. We also present an hierarchical figure relating all the models employed in this paper. While we do not pretend that the results obtained are entirely new, however, the analyses give opportunities to researchers in the field the much needed means of implementing these models in SAS without having to resort to S-PLUS, R or Stata.