The impact of capacity expansion on the market value of the firm

This paper empirically investigates the impact of capacity expansion decisions on the market value of the firm. Event study methodology is used to estimate the abnormal change in stock prices around capacity expansion decision announcements. On the day of the announcement, the magnitude of the price change is abnormally high, evidenced by a significantly positive mean standardized square of the abnormal change (Beaver's U-statistic). We also analyze factors that we could affect the direction and magnitude of the abnormal change in the stock prices. We find that the change in price on the day of the announcement is positively and significantly related to the real growth rate of the industry, and negatively and significantly related to the variability of demand. A negative relationship between the price change and industry capacity utilization is also found which can have important implications for companies which follow the wait-and-see approach to capacity expansion decisions. We also find management ownership to be a significant predictor in explaining stock price changes around these announcements.     

Measuring the Impact of Socio-Economic Factors on School Efficiency in Australia

This paper examines the productive efficiency of government schools in New South Wales (NSW) in Australia. The study uses a technical inefficiency effects model applied to a unique three-year panel dataset containing 1235 primary and 371 secondary schools in NSW. A stochastic frontier production function and an inefficiency effects function that control for school socioeconomic and environmental factors are estimated. The dataset contains information on various school inputs, school expenditures by major functional area, parental socioeconomic characteristics, student characteristics, and standardized test scores. We examine the degree to which school and non-school educational inputs influence student achievement scores and find that, overall, primary schools are 88.6% and secondary schools 96.4% efficient. The index describing community socio-educational advantage has the most significant influence on students’ achievement scores.     

Analysis of marginally specified semi-nonparametric models for clustered binary data

Generalized linear mixed models are widely used for analyzing clustered data. If the primary interest is in regression parameters, one can proceed alternatively, through the marginal mean model approach. In the present study, a joint model consisting of a marginal mean model and a cluster-specific conditional mean model is considered. This model is useful when both time-independent and time-dependent covariates are available. Furthermore our model is semi-parametric, as we assume a flexible, smooth semi-nonparametric density of the cluster-specific effects. This semi-nonparametric density-based approach outperforms the approach based on normality assumption with respect to some important features of 'between-cluster variation'. We employ a full likelihood-based approach and apply the Monte Carlo EM algorithm to analyze the model. A simulation study is carried out to demonstrate the consistency of the approach. Finally, we apply this to a study of long-term illness data.     

Selection of influential variables in ordinal data with preponderance of zeros

Presence of excess zero in ordinal data is pervasive in areas like medical and social sciences. Unfortunately, analysis of such kind of data has so far hardly been looked into, perhaps for the reason that the underlying model that fits such data, is not a generalized linear model. Obviously some methodological developments and intensive computations are required. The current investigation is concerned with the selection of variables in such models. In many occasions where the number of predictors is quite large and some of them are not useful, the maximum likelihood approach is not the automatic choice. As, apart from the messy calculations involved, this approach fails to provide efficient estimates of the underlying parameters. The proposed penalized approach includes ?₁ penalty (LASSO) and the mixture of ?₁ and ?₂ penalties (elastic net). We propose a coordinate descent algorithm to fit a wide class of ordinal regression models and select useful variables appearing in both the ordinal regression and the logistic regression based mixing component. A rigorous discussion on the selection of predictors has been made through a simulation study. The proposed method is illustrated by analyzing the severity of driver injury from Michigan upper peninsula road accidents.     

Developing Dividend and Financing Policies with a Computer Terminal

This study examines the value relevance of mandated disclosures by UK firms of the investor‐firm share of liabilities of equity‐accounted associate and joint venture investees. It does so for the six years following the introduction of FRS 9: Associates and Joint Ventures, which forced a substantial increase in such disclosures by UK firms. Since the increased disclosure requirements were partly motivated by concern that single‐line equity accounting concealed the level of group gearing, and in light of previous US results, it is predicted that the mandated investee‐liability disclosures have a negative coefficient in a value‐relevance regression. The study also examines whether value‐relevance regression coefficients on investee‐liability disclosures are more negative for joint ventures than for associates and whether they are more negative in the presence of investor‐firm guarantees of investee‐firm obligations than in the absence of such guarantees. The study reports that the coefficient on all investee‐liability disclosures taken together has the predicted negative sign, and is significantly different from zero. It finds little evidence that the negative valuation impact of liability disclosures is stronger for joint venture investees overall than for associate investees overall, or stronger for guarantee cases overall than for non‐guarantee cases overall. There is, however, some evidence that the impact for joint venture guarantee cases is stronger than that for joint venture non‐guarantee cases and stronger than that for associate guarantee cases.     

Bankruptcy risk,costs and corporate diversification

This paper studies the impact of diversification on firms that file for Chapter 11 bankruptcy. Prior research suggests that diversification affects both the probability and costs of distress. Treating bankruptcy as a special case of distress, we find that diversification reduces the likelihood of bankruptcy and liquidation in Chapter 11, which is consistent with the coinsurance hypothesis. However, we observe higher bankruptcy costs as measured by time spent in Chapter 11 and inefficient segment investment for diversified firms. Our evidence is consistent with the idea that diversification provides benefits to managers in terms of job security rather than to firms. Our findings may help firms to make diversification decisions and creditors determine lending policies toward different forms of organizations.     

A critical evaluation of alternative methods and paradigms for conducting mediation analysis in operations management research

Mediation as a theory testing approach has witnessed considerable adoption among Operations Management (OM) researchers. Although mediation-testing methods have evolved tremendously in the past decade, their dissemination in the OM field has not seen parallel growth. These advanced techniques facilitate the testing of existing and complex hypotheses in a more precise manner. With the intent of critically evaluating existing and alternative methods for conducting mediation analysis needed to support sophisticated empirical research, this paper first reviews OM studies that tested for mediation in the past eleven years (2002–2012) from top-tier OM journals. Four commonly used mediation approaches were identified. Based on principles of good theory building, type of mediation model, and properties of empirical data, we evaluate the existing methodologies and make recommendations on how to improve the rigor of OM mediation testing. Using published OM studies in top journals as examples, we then illustrate the relevance and advantages of these recommendations, as well as their ease of use. Furthermore, we empirically show that more robust and insightful results can be achieved by adopting these techniques, which in turn have the promise of leading to better theory building and testing in the field of operations management.     

Measurement of Technical Efficiency in Public Education: A Stochastic and Nonstochastic Production Function Approach

This paper uses both the stochastic and nonstochastic production function approach to measure technical efficiency in public education in Utah. The stochastic specification estimates technical efficiency assuming half normal and exponential distributions. The nonstochastic specification uses two-stage data envelopment analysis (DEA) to separate the effects of fixed inputs on the measure of technical efficiency. The empirical analysis shows substantial variation in efficiency among school districts. Although these measures are insensitive to the specific distributional assumptions about the one-sided component of the error term in the stochastic specification, they are sensitive to the treatment of fixed socioeconomic inputs in the two-stage DEA.     

On preferences with infinitely many subjective states

Models with subjective state spaces have been extremely useful in capturing novel psychological phenomena that consist of both a preference for flexibility and for commitment. Interpreting the utility representations of preferences as capturing these phenomena requires one to use the notion of a sign of a state. For linear preferences, we completely characterise the sign of a state in terms of its analytic representation as an integral with respect to a signed measure. In models with finitely many states, a state is either positive or negative, but never both. We show that in models with infinitely many states, a state can be both positive and negative. Thus, models with finitely many states may not capture all the behavioural features of an infinite model. Our methods are also useful in constructing utility functionals over menus with desired local properties.