El presente artículo quiere describir la relación entre división del trabajo y ética en las teorías de Adam Smith, Karl Marx y Karol Wojtyla. De este modo se intenta no sólo mostrar los elementos sustanciales del concepto de trabajo en estos tres autores, sino también cómo hay una relación conceptual entre ellos, en tanto que Wojtyla reacciona a la antropología marxista y el mismo Marx lee y hace una interpretación original de Smith.

The aim of this chapter is to introduce a theory of the firm based on the concept of the common good consistent with the moral and political philosophy of John Finnis. Using Finnis’ philosophy, it will provide a description of the nature of the firm first as a community of cooperative work geared toward the satisfaction of needs. However, such an account of the firm can be further developed. In fact, using Finnis’ interpretation of Aquinas’ moral philosophy, one can see human work as a virtue. In addition, the development of productive and cooperative organizations must be oriented toward the satisfaction of needs and the promotion of friendly human relations. In this sense, following Finnis’ definition, the firm’s common good could be defined as a community of cooperation, of virtuous work, and of friendship.

This article presents an approach to measure the effect of promotions on customer traffic, a measure of effectiveness of retail promotions important both to managers and scholars. This effect is unobservable because one lacks baseline measures of traffic: one cannot measure traffic simultaneously with and without a promotion. Hence, the assessment of this effect remains a challenge. However, adoption of imaging and other forms of electronic monitoring allows retailers to collect traffic data, until recently unavailable, on the behavior of both actual and potential customers. Making these data useful for business decisions requires new analytical methods. The approach of this research is novel in two ways: First, a counterfactual argument is the foundation to predict the baseline series. Second, the approach defines predicted residuals to estimate hourly-specific effects on traffic, which one computes after the promotion. Computing the baseline predictions uses a Poisson model with effect-parameters such as time of the day, day of the week, week of the month, secular trends and others, to capture sources of systematic variability. The article illustrates the use of simple plots to visualize and communicate the evolution of the promotion effects. An illustration uses data from Skillup-Chile, an imaging and analytics company.

Technology drives both health care spending and health improvement. Yet policy makers rarely see measures of cost growth that account for both effects. To fill this gap, we present the quality-adjusted cost of care, which illustrates cost growth net of growth in the value of health improvements, measured as survival gains multiplied by the value of survival. We applied the quality-adjusted cost of care to two cases. For colorectal cancer, drug cost per patient increased by $34,493 between 1998 and 2005 as a result of new drug launches, but value from offsetting health improvements netted a modest $1,377 increase in quality-adjusted cost of care. For multiple myeloma, new therapies increased treatment cost by $72,937 between 2004 and 2009, but offsetting health benefits lowered overall quality-adjusted cost of care by $67,863. However, patients with multiple myeloma on established first-line therapies saw costs rise without corresponding benefits. All three examples document rapid cost growth, but they provide starkly different answers to the question of whether society got what it paid for.

Do firms increase the riskiness of their cash flows in the presence of high levels of debt? So far, the evidence is mixed and difficult to interpret, because risk taking is hard to measure and because some assumptions from the theory do not map well into broad empirical settings. We overcome these problems by using a controlled experiment, with high-level managers as subjects. Our findings shed light on the environments in which risk-shifting is likely to happen (and could be detected using observational data): when managers’ reputation concerns are relatively unimportant, and when firms’ going concern values are low.