Demand,Information, and Competition: Why Do Food Prices Fall at Seasonal Demand Peaks?

Prices for seasonal food products fall at demand peaks. Price declines are not driven by falling agricultural input prices; indeed, farm to retail margins narrow sharply. I use electronic scanner data from a sample of US supermarkets to show that seasonal price declines are closely linked to market concentration, and are much larger in markets with several rivals than where a single brand dominates. Seasonal demand increases reduce the effective costs of informative advertising, and increased informative advertising by retailers and manufacturers in turn may allow for increased market information and greater price sensitivity on the part of buyers.     

Expert opinion and cuisine reputation in the market for restaurant meals

As food is an experience good, the market for restaurant meals is a market where the cost of acquiring information regarding quality is relatively high. In such markets consumers often turn to reputation measures to guide purchase decisions. As Australia does not have a longstanding cuisine style of its own, and given Australia has been open to substantial immigration inflows since federation, it represents an especially appropriate market to study regarding the impact of individual restaurant reputation and collective cuisine reputation on meal prices. The following study uses the hedonic price approach to investigate the implicit price of individual reputation indicators, cuisine type reputation indicators and other objective indicators in the market for restaurant meals. The empirical findings presented suggest that both individual restaurant reputation and cuisine type reputation are important. Other important factors are shown to include the quality of the restaurant wine list, the availability of private dining rooms, and whether or not there is an outdoor dining option.     

Some economics of content protection

We are grateful to a perceptive referee for many constructive comments on an earlier version of this paper entitled The Economics of Content Protection: A Dual Approach. The usual disclaimer applies.     

Real exchange rate behavior

Using random simulations with artificial data with identical sample characteristics to the long-sample exchange rate data employed by Lothian and Taylor (Lothian, J.R. and Taylor, M.P. (1996). The recent float from the perspective of the past two centuries. Journal of Political Economy 104, 488–509.), we show that standard unit-root tests have extremely low power over sample sizes corresponding to the recent float. The probability of rejecting the null hypothesis when it is false is extremely low with 20 years or even 50 years of data and only reaches an acceptable level over much longer spans.     

Spatial econometric Monte Carlo studies: raising the bar

We discuss Monte Carlo methodology that can be used to explore alternative approaches to estimating spatial regression models. Our focus is on models that include spatial lags of the dependent variable, e.g., the SAR specification. A major point is that practitioners rely on scalar summary measures of direct and indirect effects estimates to interpret the impact of changes in explanatory variables on the dependent variable of interest. We argue that these should be the focus of Monte Carlo experiments. Since effects estimates reflect a nonlinear function of both \(\beta \) and \(\rho \), past studies’ focus exclusively on \(\beta \) and \(\rho \) parameter estimates may not provide useful information regarding statistical properties of effects estimates produced by alternative estimators. Since effects estimates have recently become the focus of inference regarding the significance of (scalar summary) direct and indirect impacts arising from changes in the explanatory variables, empirical measures of dispersion produced by simulating draws from the (estimated) variance–covariance matrix of the parameters \(\beta \) and \(\rho \) should be part of the Monte Carlo study. An implication is that differences in the quality of estimated variance–covariance matrices arising from alternative estimators also plays a role in determining the accuracy of inference. An applied illustration is used to demonstrate how these issues can impact conclusions regarding the performance of alternative estimators.     

General training by firms, apprentice contracts, and public policy

Workers will not pay for general on-the-job training if contracts are not enforceable. Firms may if there are mobility frictions. Private information about worker productivities, however, prevents workers who quit receiving their marginal products elsewhere. Their new employers then receive external benefits from their training. In this paper, training firms increase profits by offering apprenticeships which commit firms to high wages for those trainees retained on completion. At these high wages, only good workers are retained. This signals their productivity and reduces the external benefits if they subsequently quit. Regulation of apprenticeship length (a historically important feature) enhances efficiency. Appropriate subsidies enhance it further.     

Predicting credit card fraud with Sarbanes‐Oxley assessments and Fama‐French risk factors

This research developed and tested machine learning models to predict significant credit card fraud in corporate systems using Sarbanes‐Oxley (SOX) reports, news reports of breaches and Fama‐French risk factors (FF). Exploratory analysis found that SOX information predicted several types of security breaches, with the strongest performance in predicting credit card fraud. A systematic tuning of hyperparamters for a suite of machine learning models, starting with a random forest, an extremely‐randomized forest, a random grid of gradient boosting machines (GBMs), a random grid of deep neural nets, a fixed grid of general linear models where assembled into two trained stacked ensemble models optimized for F1 performance; an ensemble that contained all the models, and an ensemble containing just the best performing model from each algorithm class. Tuned GBMs performed best under all conditions. Without FF, models yielded an AUC of 99.3% and closeness of the training and validation matrices confirm that the model is robust. The most important predictors were firm specific, as would be expected, since control weaknesses vary at the firm level. Audit firm fees were the most important non‐firm‐specific predictors. Adding FF to the model rendered perfect prediction (100%) in the trained confusion matrix and AUC of 99.8%. The most important predictors of credit card fraud were the FF coefficient for the High book‐to‐market ratio Minus Low factor. The second most influential variable was the year of reporting, and third most important was the Fama‐French 3‐factor model R² – together these described most of the variance in credit card fraud occurrence. In all cases the four major SOX specific opinions rendered by auditors and the signed SOX report had little predictive influence.     

Executive succession: Organizational antecedents of ceo characteristics

Based on 195 succession events in Business Week 1000 firms, this study examines the organizational antecedents of CEO demographic characteristics. Study findings suggest that antecedent conditions of lower firm profits and firm growth are associated with the selection of outsider CEOs. Additionally, R&D intensity is associated with the selection of CEOs having technical functional backgrounds and higher levels of education.     

Discretionary and Non-Discretionary Revisions of Loss Reserves by Property-Casualty Insurers: Differential Implications for Future Profitability, Risk and Market Value

We develop and estimate a PC-industry specific model in which proxies for both discretion and non-discretion are used to partition loss reserve revisions into discretionary and non-discretionary components. The use of such proxies enables us to test directional hypotheses about the relations between the revision components and future profitability, risk and market value. We predict and find that discretionary revisions are negatively associated with future profitability, positively associated with firm risk, and negatively associated with market-to-book ratios. We predict and find that non-discretionary revisions are positively associated with future profitability and risk but are not associated with market-to-book ratios.