Managing for Value 2.0

In Finance 101, future corporate managers are taught that the social mission of public companies is to maximize their own longrun (or “intrinsic”) value by investing in all positive net present value (NPV) projects—that is, projects that are expected to earn at least their opportunity cost of capital. In markets that are reasonably efficient, provided management does an effective job of communicating its business plan and its progress in meeting its strategic goals, companies that follow this “NPV rule” can expect to be rewarded with increases in their share prices, at least in the longer run. But in the real world, of course, the pursuit of earnings and other “key performance indicators” (KPIs) often leads to managerial shortsightedness and destruction of value. To explain why—and to help companies avoid this outcome—this article presents an approach that envisions the intrinsic value of the company as an invisible “blue line” that moves through time on a graph, while showing observable key performance indicators, including revenue and earnings (and even the current stock price), as “red lines” on the same graph. The root of the problem is the failure of many companies to distinguish between their KPIs and the underlying drivers of value. KPIs, to be sure, are reflections of important aspects of the business; but however important and useful for strategic planning, they should not be used in performance evaluation or compensation plans for top management as surrogates for the underlying value of the business. Genuine value creation requires systems and a corporate culture that compel managers to pursue all projects that promise to earn the opportunity cost of capital—while treating earnings and other KPIs as means to creating value rather than ends in themselves.     

New Technologies And The Demand For Heterogeneous Labor: Firm-Level Evidence For The German Business-Related Service Sector

This paper investigates the impact of modern information and communication technologies on the demand for heterogeneous labor. It starts with an interrelated factor demand system. The ‘desired’ level of employment which is needed in such models, is derived from a Generalized Leontief cost function with quasi-fixed factors. Firm-level, cross-sectional data taken from an innovation survey in the service sector are used in the empirical analysis. The model is estimated by a trivariate ordered probit model. Evidence in favor of skill-biased technological change in the fast-growing German business-related services sector is found. ‘Ibe paper suggests a new method of calculating skill-specific and firm-specific labor cost from information on total labor cost and the share of each skill group in total employment only.     

Interval Estimation of Actuarial Risk Measures

Abstract

This article investigates performance of interval estimators of various actuarial risk measures. We consider the following risk measures: proportional hazards transform (PHT), Wang transform (WT), value-at-risk (VaR), and conditional tail expectation (CTE). Confidence intervals for these measures are constructed by applying nonparametric approaches (empirical and bootstrap), the strict parametric approach (based on the maximum likelihood estimators), and robust parametric procedures (based on trimmed means).

Using Monte Carlo simulations, we compare the average lengths and proportions of coverage (of the true measure) of the intervals under two data-generating scenarios: “clean” data and “contaminated” data. In the “clean” case, data sets are generated by the following (similar shape) parametric families: exponential, Pareto, and lognormal. Parameters of these distributions are selected so that all three families are equally risky with respect to a fixed risk measure. In the “contaminated” case, the “clean” data sets from these distributions are mixed with a small fraction of unusual observations (outliers). It is found that approximate knowledge of the underlying distribution combined with a sufficiently robust estimator (designed for that distribution) yields intervals with satisfactory performance under both scenarios.     

Price Discrimination in the Subscription Market for Economics Journals

We examine what factors affect the degree of price discrimination for an academic journal by analyzing data on 190 of the 208 economics journals indexed in the 2008 edition of Journal Citation Reports. We find that (i) the library‐to‐individual price ratio of a for‐profit journal is 37% higher than that of a comparable nonprofit journal because the price premium of a for‐profit journal in the library market is disproportionately larger than that in the individual market, (ii) journals with higher citations per page or impact factor are more price discriminatory, and (iii) Elsevier and Wiley‐Blackwell practice the highest degree of price discrimination of all publishers.     

Forestalling a serious incident: A detective story

Two low-level dust fires and explosions occurred in rotary dryers in one of our east Texas plants. Although there was no significant damage, the potential for safety or loss incidents initiated a concerted effort to determine the cause. The analytical and explosive hazards efforts showed an iron-adipic compound most probably autooxidized and caused the problem.     

Do insurers adjust prices for the adoption of loss prevention technologies? Evidence from Danish municipal contracts

Using data from 40 Danish municipalities on building characteristics, loss prevention technologies, insurance claims, and insurance bids from 2008 to 2019, we investigate whether and how loss prevention technologies influenced contract prices. The insurance bids cover a variety of municipal buildings across a range of risks including fire, water leakage, and building security as well as structure detection and alarm systems. The study shows that the magnitude of historical claims may affect both pricing offers and interest in loss prevention technologies. We do not find that loss prevention technologies have any significant influence on contract price. This suggests inefficiencies in the market from imperfect information.