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This chapter uses a college-by-graduate degree fixed effects estimator to evaluate the returns to 19 different graduate degrees for men and women. We find substantial variation across degrees, and evidence that OLS overestimates the returns to degrees with the highest average earnings and underestimates the returns to degrees with the lowest average earnings. Second, we decompose the impacts on earnings into effects on wage rates and effects on hours. For most degrees, the earnings gains come from increased wage rates, though hours play an important role in some degrees, such as medicine, especially for women. Third, we estimate the net present value and internal rate of return for each degree, which account for the time and monetary costs of degrees. Finally, we provide descriptive evidence that satisfaction gains are large for some degrees with smaller economic returns, such as education and humanities degrees, especially for men.

We highlight two mechanisms of limited attention for expert information intermediaries, i.e., analysts, and the effects of such limited attention on the market price discovery process. We approach analysts' limited attention from the perspective of day-to-day arrival of information and processing of tasks. We examine the attention-limiting role of competing tasks (number of earnings announcements and forecasts for portfolio firms) and distracting events (number of earnings announcements for non-portfolio firms) in analysts' forecast accuracy and the effects of such, on the subsequent price discovery process. Our results show that competing tasks worsen analysts' forecast accuracy, and competing task induced limited attention delays the market price adjustment process. On the other hand, distracting events can improve analysts' forecast accuracy and accelerate market price adjustments when such events relate to analysts' portfolio firms through industry memberships.

This chapter examines the sensitivity of executive incentive compensation to market-adjusted returns and changes in earnings for high-tech (HT) firms vis-à-vis firms (NHT) in other industries. Consistent with the hypotheses, this chapter uncovers the following evidence: First, the sensitivity of executive bonus compensation to market-adjusted returns is weaker and more symmetric for HT firms than for NHT firms (a control group), which implies that the problem of ex post settling up, documented in Leone et al. (2006), may be far less serious in HT firms than in NHT firms. Second, the sensitivity of executive incentive compensation to earnings changes is generally more symmetric for HT firms than for NHT firms, which is consistent with the view that HT firms engage in more conservative financial reporting than NHT firms. Third, the sensitivity of executive equity-based compensation to market-adjusted returns is significantly negative for HT firms compared to NHT firms when bad earnings news is announced. The results imply that HT firms, with a strong motive to attract and retain their highly talented executives, judiciously use both short-term and long-term incentive compensation schemes by compensating for a reduction of short-term incentive pay with an increase in long-term incentive pay. The issue of executive compensation has been a longstanding one in the United States and Canada, and the issue of executive compensation-performance sensitivity for HT firms is also relevant in this era of the information technology (IT) revolution, especially when prior research has shown that HT firms differ from NHT firms in their market-valuation process.

We examine the earnings management implications of using nonfinancial performance measures (NFPM) in executive compensation contracts. We argue and test that when a manager's compensation is based on financial and NFPM, he/she has less incentive to manipulate earnings to maximize compensation. Using panel data covering the period 1992–2005, we compare earnings management behavior for a sample of firms that used both financial and nonfinancial measures to a matched sample of firms that based their performance measurement solely on financial measures. The results are mainly consistent with a reduction in earnings management behavior for those firms that rely on NFPM in their compensation contracts.

This paper asks whether market fundamentals can explain the run-up and collapse of Enron’s stock price and price-earnings ratio. We use a variant of the discounted cash flow model proposed by Miller and Modigliani (1961) to show that the growth rates implied by the stock’s valuation have rarely been achieved in recorded business history. We also provide evidence of earnings management by the company that may have contributed to extravagant investor expectations of earnings growth. Between 1990 and 2000 the firm’s reported earnings met or exceeded analysts’ earnings forecasts 77% of the time. Furthermore, beginning in 1997 Enron used asset sales (often to related parties) to generate as much as 83% of its annual earnings.

Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method for this purpose in our discipline. We address this by offering a guide to the application of ML in strategy research, with a particular emphasis on data handling practices that should improve our ability to accurately measure our constructs of interest using ML techniques. We offer a brief overview of ML methodologies that can be used for measurement before describing key challenges that exist when applying those methods for this purpose in strategy research (i.e., sample sizes, data noise, and construct complexity). We then outline a theory-driven approach to help scholars overcome these challenges and improve data handling and the subsequent application of ML techniques in strategy research. We demonstrate the efficacy of our approach by applying it to create a linguistic measure of CEOs' motivational needs in a sample of S&P 500 firms. We conclude by describing steps scholars can take after creating ML-based measures to continue to improve the application of ML in strategy research.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

This paper reports a new experimental test of the notion that behavior switches from risk averse to risk seeking when gains are “reflected” into the loss domain. We conduct a sequence of experiments that allows us to directly compare choices under reflected gains and losses where real and hypothetical payoffs range from several dollars to over $100. Lotteries with positive payoffs are transformed into lotteries over losses by multiplying all payoffs by –1, that is, by reflecting payoffs around zero. When we use hypothetical payments, more than half of the subjects who are risk averse for gains turn out to be risk seeking for losses. This reflection effect is diminished considerably with cash payoffs, where the modal choice pattern is to exhibit risk aversion for both gains and losses. However, we do observe a significant difference in risk attitudes between losses (where most subjects are approximately risk neutral) and gains (where most subjects are risk averse). Reflection rates are further reduced when payoffs are scaled up by a factor of 15 (for both real and hypothetical payoffs).

Financial analysts' forecasts serve as a proxy for market earnings expectations, and research provides mixed evidence of the relation between financial analysts' expertise and forecast accuracy. The judgment and decision-making (J/DM) literature suggests that those with more expertise will not perform better when tasks exhibit either extremely high or extremely low complexity. Expertise is expected to contribute to superior performance for tasks between these two extremes. Using archival data, this research examines the effect of analysts' expertise on forecasting performance by taking into consideration the forecasting task's complexity. Results indicate that expertise is not an explanatory factor for forecast accuracy when the forecasting task's complexity is extremely high or low. However, when task complexity falls between these two extremes, expertise is a significant explanatory variable of forecast accuracy. Both results are consistent with our expectations.

We survey the literature on the Risk Augmented Mincer equation that seeks to estimate the compensation for uncertainty in the future wage to be earned after completing an education. There is wide empirical support for the predicted positive effect of wage variance and the negative effect of wage skew. We discuss robustness of the findings across specifications, potential bias from unobserved heterogeneity and selectivity and consider the core issue of students' information on benefits from education.