Business Practices of Wal-Mart in Northwest Indiana

Wal-Mart symbolizes the strength of economic and commercial activity in any region. Wal-Mart has built a business empire on its low-cost model. Customers love Wal-Mart stores for its low prices. At the same time, Wal-Mart is under a barrage of criticism for labor practices and indirect burdens on our social and welfare programs. Some of the business practices of Wal-Mart like the employees’ wage-benefits package, and the underemployment of women and minorities are the subject of ongoing debate at the national level. Our main objective is to review this issue within a regional context. We examined whether what is being alleged about the business practices of Wal-Mart at the national level is mirrored at the regional level, like northwest Indiana. The findings presented are from a survey designed to analyze the impact of the business practices of Wal-Mart on customers and employees. Our results concur with earlier national studies that there are hidden costs for the community which shops and supports Wal-Mart and that a large number of employees are older, work part time, earn below the regional average income, and depend on state welfare programs. We found gender differences in employment, earnings, and career advancement opportunities. The price of low cost goods may be too high for the region economy and it will likely affect women more than men.discrimination, gender, cost, benefits

On the J-test for nonnested hypotheses and Bayesian extension

Abstract Davidson and MacKinnon’s J-test was developed to test non-nested model specification. In empirical applications, however, when the alternate specifications fit the data well the J test may fail to distinguish between the true and false models: the J test will either reject, or fail to reject both specifications. In such cases we show that it is possible to use the information generated in the process of applying the J-test to implement a Bayesian approach that provides an unequivocal and acceptable solution. Jeffreys’ Bayes factors offer ways of obtaining the posterior probabilities of the competing models and relative ranking of the competing hypotheses. We further show that by using approximations of Schwarz Information Criterion and Bayesian Information Criterion we can use the classical estimates of the log of the maximum likelihood which are available from the estimation procedures used to implement the J test to obtain Bayesian posterior odds and posterior probabilities of the competing nested and non- nested specifications without having to specify prior distributions and going through the rigorous Bayesian computations.specification testing, non-nested hypotheses, Bayes factor, Bayesian Information Criteria, Marginal likelihood

Introductory Note on the Ostrom Memorial Session and Panel Discussions

Introduction to a presentation by two scholars who collaborated, coauthored, and worked together at Indiana University Bloomington with Elinor Ostrom for more than two decades

International perspectives on Gender, science and Development

International perspective on Issues in Gender, Science and Economic Development Abstract The gender issues in science and economic development have two major dimensions: economic opportunities for women and abilities of women. The focus of this study is on economic opportunities for women from a global perspective. While there are significant increases in the female labor force participation rates in almost all countries, the proportion of female professional and technical workers remains much smaller. Using data from fifty countries with high human development index, we find that high index of achievement in education and high per capita incomes are important factors that contribute to the growth of professional and technical women workers. Gender empowerment index alone does not guarantee increased participation of women in science and technology.Gender empowerment; human development