Leadership: A personnel economics approach

A theory of leadership is proposed and tested. Leaders perform many roles in a firm. They become leaders because they succeed at these tasks more than others and at least some of their successes are visible. The theory implies that leaders tend to be more able, place themselves in visible decision making situations more frequently and are generalists. Also, the most able leaders should be found in the highest variance industries, where decision making has the greatest payoff. The theory is tested using data on Stanford alumni and is confirmed. Leaders are generalists rather than specialists, both innately and in their pattern of skill acquisition.     

What is (or should be) the difference between competency modeling and traditional job analysis?

We argue that Competency Modeling (CM) has the potential to fill an important void in Traditional Job Analysis (TJA), specifically the infusion of strategic concerns in day-to-day employee behavior. Moreover TJA and CM pursue fundamentally different goals, which those who argue for and against either of these human resource methods at times may overlook. To buttress this point we compare TJA and CM along six dimensions: purpose (describe versus influence behavior), view of the job (an object to be described versus a role to be enacted), focus (job versus organization), time orientation (past versus future), performance level (typical versus maximum), and measurement approach (latent trait versus clinical judgment). We conclude with a series of recommendations regarding ways in which TJA can be joined with CM so that an organization may achieve, among other outcomes, the critical purpose of directing employee behavior toward the accomplishment of its strategic objectives.     

A specification test for the propensity score using its distribution conditional on participation

Propensity score matching has become a popular method for the estimation of average treatment effects. In empirical applications, researchers almost always impose a parametric model for the propensity score. This practice raises the possibility that the model for the propensity score is misspecified and therefore the propensity score matching estimator of the average treatment effect may be inconsistent. We show that the common practice of calculating estimates of the densities of the propensity score conditional on the participation decision provides a means for examining whether the propensity score is misspecified. In particular, we derive a restriction between the density of the propensity score among participants and the density among nonparticipants. We show that this restriction between the two conditional densities is equivalent to a particular orthogonality restriction and derive a formal test based upon it. The resulting test is shown via a simulation study to have dramatically greater power than competing tests for many alternatives. The principal disadvantage of this approach is loss of power against some alternatives.