Risk aversion, generalized correlation and investment in manufacturing capacities

This study analyzes the relationship between product demand correlation and risk on the investment in dedicated and flexible manufacturing capacities. It is proved that for a risk averse (or risk neutral) decision-maker, increased generalized correlation between output demands reduces the values of both dedicated and flexible manufacturing investments for all multivariate distributions. It is shown that no such monotonic relationship generally exists for the capacities themselves because of an economic tradeoff between risk and return. It is also proved that increased generalized correlation reduces the value of flexible capacity relative to dedicated capacity for the risk-neutral decision maker. However, contrary to the extant literature, flexible capacity is still shown to be potentially valuable even if product demands are perfectly positively correlated. Finally, it is shown that increases in the risk of product demand, holding correlation (of product demand) constant, reduces the value of the investment in manufacturing capacities when output demands are either jointly normal or independent.     

Modeling response to repetitive promotional stimuli

Marketers frequently include promotional stimuli which elicit some form of response from the recipient among the tactics used to market products or services. Print ads, including 800 numbers which allow consumers to respond, and direct mail campaigns are examples of such activities. Promotions of this nature are often repeated a number of times, thus providing several opportunities to respond. Understanding consumer response to such campaigns is critical for more efficient design and use of these activities. A conceptual framework of response to repetitive stimuli is proposed, and stochastic models of alternative response patterns are developed. Alternative contexts in which such models are useful are also noted. Variations of the models are provided for those situations when only a fraction of the target population will ever respond. Estimation of model parameters is discussed, and data from actual campaigns are used to demonstrate how to apply the models. He received his Ph.D. in mathematical statistics from Michigan State University. He joined the Terry College of Business after a 15-year career in industry, including 10 years in consumer research at Procter & Gamble. His research interests include discrete choice models, market response to promotional stimuli, forecasting marketing potential from initial sales results, and, in general, applications of quantitative methods to business problems. He has published articles in such journals as theAnnals of Mathematical Statistics, Annals of Statistics, Journal of the Academy of Marketing Science, Journal of Retailing, and theJournal of Advertising. He earned his Ph.D. at Columbia University and has served as assistant and associate professor of marketing at New York University, as visiting assistant professor at Columbia University, and as visiting associate professor at the University of California, Los Angeles. He is a member of the editorial review boards ofMultivariate Behavioral Research andJournal of Marketing Channels. He has published in theJournal of Marketing Research, Journal of Marketing, andManagement Science, among others. His research interests include the study of brand extensions, relationship marketing, strategic alliances, and television program scheduling. He received his Ph.D. in marketing from the University of Georgia. His current research interests include marketing strategy, strategic alliances, relationship marketing, and the implications of new technologies on business research and practice. He has published in various conference proceedings and theInternational Business Review.     

On using relative prices to measure capital-specific technological progress

Recently, Greenwood, Hercowitz and Krusell (GHK) have identified the relative price of (new) capital with capital-specific technological progress. In a two-sector growth model, however, the relative price of capital equals the ratio of the productivity processes in the two sectors. Restrictions from this model are used with data on wages and prices to construct measures of productivity growth and test the GHK identification, which is easily rejected by the data. This raises questions about various measures of the contribution that capital-specific technological progress might make to the economy. This identification also induces a negative correlation between the resulting measures of capital-specific and economy-wide technological change, which potentially explains why papers employing this identification find that capital-specific technological change accelerated in the mid-1970s. We impose structure on the productivity measures based on their long-run behavior and find evidence of a slowdown in productivity in the 1970s that is common to both sectors and an acceleration in the mid-1990s that is exclusive to the capital sector.     

MaxVaR for non-normal and heteroskedastic returns¶

In this work we propose Monte Carlo simulation models for dynamically computing MaxVaR for a financial return series. This dynamic MaxVaR takes into account the time-varying volatility as well as non-normality of returns or innovations. We apply this methodology to five stock market indices. To validate the proposed methods we compute the number of MaxVaR violations and compare them with the expected number. We also compute the MaxVaR-to-VaR ratio and find that, on average, dynamic MaxVaR exceeds dynamic VaR by 5–7% at the 1% significance level, and by 12–14% at the 5% significance level for the selected indices.     

TOURISM AND REGIONAL IMMISERIZATION

Abstract.  This paper analyzes the consequences of tourism in rural and urban areas on regional incomes, welfare and urban unemployment using a generalized Harris Todaro model. In this model two urban and two rural goods are produced. A distinguishing characteristic of this model is that the urban non-traded good is not consumed in the rural region and, similarly, the rural non-traded good is not consumed in the urban region. The most important result we obtain is that a tourist boom in the urban region may immiserize the rural area. Hence the welfare interests of rural and urban consumers may be in conflict as a result of tourist expansion in the urban region.     

R&D Reporting Biases and Their Consequences*

The immediate expensing of research and development (R&D) expenditures is often justified by the conservatism principle. However, no accounting procedure consistently applied can be conservative throughout the firm's life. We therefore ask the following questions: (1) When is the expensing of R&D conservative and when is it aggressive, relative to R&D capitaliza‐tion? (2) What are the capital‐market implications of these reporting biases? To address these questions we construct a model of profitability biases (differences between reported profitability under R&D expensing and capitalization) and show that the key drivers of the reporting biases are the differences between R&D growth and earnings growth (momentum), and between R&D growth and return on equity (ROE). Companies with a high R&D growth rate relative to their profitability (typically early life‐cycle companies) report conservatively, while firms with a low R&D growth rate (mature companies) tend to report aggressively under current generally accepted accounting principles. Our empirical analysis, covering the period 1972‐2003, generally supports the analytical predictions. In the valuation analysis we find evidence consistent with investor fixation on the reported profitability measures: we detect undervaluation of conservatively reporting firms and overvaluation of aggressively reporting firms. These misvaluations appear to be corrected when the reporting biases reverse from conservative to aggressive and vice versa. This evidence is consistent with behavioral finance arguments about investor cognitive biases.     

Targeted advertising as a signal

This study presents a signaling model of advertising for horizontally differentiated products. The central ingredients of the model are two important characteristics of advertising—targeting, and noisy information content. The theory yields interesting results about the informational role of targeted advertising, and its consequences. First, targeting can itself serve as a signal on product attributes. Second, the effectiveness of targeting depends not only on firms knowing consumer preferences, but on consumers knowing that firms know this. This creates a distinction between strategies of targeting and personalization. Third, the effectiveness of targeting in equilibrium may (far) exceed the information contained directly in the targeted message. Fourth, information content is not, however, superfluous. Specifically, when ads contain no information, a targeting equilibrium does not exist. Together, these results reveal how advertising conveys information both through the content of the message and the firm’s choice of advertising medium. Furthermore, the model is robust to the various critiques of prior work on ads-as-signals: namely, that ad content is irrelevant, ad exposure is unnecessary, and the choice of ads as signals is inherently arbitrary.