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

Relativistic diffusion processes and random walk models

The nonrelativistic standard model for a continuous, one-parameter diffusion process in position space is the Wiener process. As well-known, the Gaussian transition probability density function (PDF) of this process is in conflict with special relativity, as it permits particles to propagate faster than the speed of light. A frequently considered alternative is provided by the telegraph equation, whose solutions avoid superluminal propagation speeds but suffer from singular (non-continuous) diffusion fronts on the light cone, which are unlikely to exist for massive particles. It is therefore advisable to explore other alternatives as well. In this paper, a generalized Wiener process is proposed that is continuous, avoids superluminal propagation, and reduces to the standard Wiener process in the non-relativistic limit. The corresponding relativistic diffusion propagator is obtained directly from the nonrelativistic Wiener propagator, by rewriting the latter in terms of an integral over actions. The resulting relativistic process is non-Markovian, in accordance with the known fact that nontrivial continuous, relativistic Markov processes in position space cannot exist. Hence, the proposed process defines a consistent relativistic diffusion model for massive particles and provides a viable alternative to the solutions of the telegraph equation.Comment: v3: final, shortened version to appear in Phys. Rev.