A Model of Occupational Licensing and Statistical Discrimination

We develop a model of statistical discrimination in occupational licensing. In the model, there is endogenous occupation selection and wage determination that depends on how costly it is to obtain the license and the productivity of the human capital that is bundled with the license. Under these assumptions, we find a unique equilibrium with sharp comparative statics for the licensing premiums. The key theoretical result in this paper is that the licensing premium is higher for workers who are members of demographic groups that face a higher cost of licensing. The intuition for this result is that the higher cost of licensing makes the license a more informative labor market signal. (This is a similar insight to Spence 1973). The predictions of the model can explain, for example, the empirical finding in the literature that occupational licenses that preclude felons close the racial wage gap among men by conferring a higher premium to black men than to white men (Blair and Chung 2018). Moreover, we show that in general the optimal cost of licensing is nonzero: an infinitely costly licenses screens out all workers, while a costless license is no screen at all

Two-stage Kondo effect in side-coupled quantum dots: Renormalized perturbative scaling theory and Numerical Renormalization Group analysis

We study numerically and analytically the dynamical (AC) conductance through a two-dot system, where only one of the dots is coupled to the leads but it is also side-coupled to the other dot through an antiferromagnetic exchange (RKKY) interaction. In this case the RKKY interaction gives rise to a ``two-stage Kondo effect'' where the two spins are screened by two consecutive Kondo effects. We formulate a renormalized scaling theory that captures remarkably well the cross-over from the strongly conductive correlated regime to the low temperature low conductance state. Our analytical formulas agree well with our numerical renormalization group results. The frequency dependent current noise spectrum is also discussed.Comment: 6 pages, 7 figure

Analysis of margin classification systems for assessing the risk of local recurrence after soft tissue sarcoma resection

Purpose: To compare the ability of margin classification systems to determine local recurrence (LR) risk after soft tissue sarcoma (STS) resection. Methods: Two thousand two hundred seventeen patients with nonmetastatic extremity and truncal STS treated with surgical resection and multidisciplinary consideration of perioperative radiotherapy were retrospectively reviewed. Margins were coded by residual tumor (R) classification (in which microscopic tumor at inked margin defines R1), the R+1mm classification (in which microscopic tumor within 1 mm of ink defines R1), and the Toronto Margin Context Classification (TMCC; in which positive margins are separated into planned close but positive at critical structures, positive after whoops re-excision, and inadvertent positive margins). Multivariate competing risk regression models were created. Results: By R classification, LR rates at 10-year follow-up were 8%, 21%, and 44% in R0, R1, and R2, respectively. R+1mm classification resulted in increased R1 margins (726 v 278, P < .001), but led to decreased LR for R1 margins without changing R0 LR; for R0, the 10-year LR rate was 8% (range, 7% to 10%); for R1, the 10-year LR rate was 12% (10% to 15%) . The TMCC also showed various LR rates among its tiers (P < .001). LR rates for positive margins on critical structures were not different from R0 at 10 years (11% v 8%, P = .18), whereas inadvertent positive margins had high LR (5-year, 28% [95% CI, 19% to 37%]; 10-year, 35% [95% CI, 25% to 46%]; P < .001). Conclusion: The R classification identified three distinct risk levels for LR in STS. An R+1mm classification reduced LR differences between R1 and R0, suggesting that a negative but < 1-mm margin may be adequate with multidisciplinary treatment. The TMCC provides additional stratification of positive margins that may aid in surgical planning and patient education

Transfer of Training on the Vertical Motion Simulator

This paper describes a transfer-of-training study in the NASA Ames Vertical Motion Simulator (VMS). Sixty-one general aviation pilots, divided in four groups, trained on four challenging commercial transport tasks with four motion conditions: no motion, small hexapod, large hexapod, and VMS motion. Then, every pilot repeated the tasks with VMS motion to determine if training with different motion conditions had an effect. New objective motion criteria guided the selection of the motion parameters for the small and large hexapod conditions. Considering results that were statistically significant, or marginally, the motion condition used in training affected 1) longitudinal and lateral touchdown location; 2) the number of secondary stall warnings in a stall recovery; 3) pilot ratings of motion utility and maximum load factor obtained in an overbanked upset recovery; and 4) pilot ratings of motion utility and pedal input reaction time in the engine-out-on-takeoff task. Since the motion condition revealed statistical differences on objective measures in all the tasks, even with some in the direction not predicted, trainers should be cautious not to oversimplify the effects of platform motion. Evidence suggests that the new objective motion criteria may offer valid standardization benefits, as instances arose when the higher-fidelity hexapod motion, as predicted by the criteria, provided better cues in training than the lower-fidelity hexapod motion