Ski-Lift Pricing, with an Application to the Labor Market

The market for ski runs or amusement rides often features lump-sum admission tickets with no explicit price per ride. Therefore, the equation of the demand for rides to the supply involves queues, which are systematically longer during peak periods, such as weekends. Moreover, the prices of admission tickets are much less responsive than the length of queues to variations in demand, even when these variations are predictable. We show that this method of pricing generates nearly efficient outcomes under plausible conditions. In particular, the existence of queues and the "stickiness" of prices do not necessarily mean that rides are allocated improperly or that firms choose inefficient levels of investment. We then draw an analogy between "ski-lift pricing" and the use of profit-sharing schemes in the labor market. Although firms face explicit marginal costs of labor that are sticky and less than workers' reservation wages, and although the pool of profits seems to create a common-property problem for workers, this method of pricing can approximate the competitive outcomes for employment and total labor compensation.

A Case Where Barro Expectations Are Not Rational

This note generalizes Feldstein’s (1976) criticism of Barro’s(1974) analysis for the case that the interest rate exceeds the growth rate. This is done by considering an economy in steady state where all agents hold “Barro expectations”: they believe that government debt must necessarily be repaid and therefore leave the present value of their income streams unchanged. In this scenario, a change in the mode of taxation affects the present value of disposable income in the private sector. This violates their Barro expectations

Inspiratory muscle training enhances pulmonary O2 uptake kinetics and high-intensity exercise tolerance in humans

Fatigue of the respiratory muscles during intense exercise might compromise leg blood flow, thereby constraining oxygen uptake (VO2) and limiting exercise tolerance. We tested the hypothesis that inspiratory muscle training (IMT) would reduce inspiratory muscle fatigue, speed VO2 kinetics and enhance exercise tolerance. Sixteen recreationally active subjects (mean ± SD, age 22 ± 4 yr) were randomly assigned to receive 4 wk of either pressure threshold IMT [30 breaths twice daily at ~50% of maximum inspiratory pressure (MIP)] or sham treatment (60 breaths once daily at ~15% of MIP). The subjects completed moderate-, severe- and maximal-intensity "step" exercise transitions on a cycle ergometer before (Pre) and after (Post) the 4-wk intervention period for determination of VO2 kinetics and exercise tolerance. There were no significant changes in the physiological variables of interest after Sham. After IMT, baseline MIP was significantly increased (Pre vs. Post: 155 ± 22 vs. 181 ± 21 cmH2O; P < 0.001), and the degree of inspiratory muscle fatigue was reduced after severe- and maximal-intensity exercise. During severe exercise, the VO2 slow component was reduced (Pre vs. Post: 0.60 ± 0.20 vs. 0.53 ± 0.24 l/min; P < 0.05) and exercise tolerance was enhanced (Pre vs. Post: 765 ± 249 vs. 1,061 ± 304 s; P < 0.01). Similarly, during maximal exercise, the VO2 slow component was reduced (Pre vs. Post: 0.28 ± 0.14 vs. 0.18 ± 0.07 l/min; P < 0.05) and exercise tolerance was enhanced (Pre vs. Post: 177 ± 24 vs. 208 ± 37 s; P < 0.01). Four weeks of IMT, which reduced inspiratory muscle fatigue, resulted in a reduced VO2 slow-component amplitude and an improved exercise tolerance during severe- and maximal-intensity exercise. The results indicate that the enhanced exercise tolerance observed after IMT might be related, at least in part, to improved VO2 dynamics, presumably as a consequence of increased blood flow to the exercising limbs

Recruitment Market Trend Analysis with Sequential Latent Variable Models

Recruitment market analysis provides valuable understanding of industry-specific economic growth and plays an important role for both employers and job seekers. With the rapid development of online recruitment services, massive recruitment data have been accumulated and enable a new paradigm for recruitment market analysis. However, traditional methods for recruitment market analysis largely rely on the knowledge of domain experts and classic statistical models, which are usually too general to model large-scale dynamic recruitment data, and have difficulties to capture the fine-grained market trends. To this end, in this paper, we propose a new research paradigm for recruitment market analysis by leveraging unsupervised learning techniques for automatically discovering recruitment market trends based on large-scale recruitment data. Specifically, we develop a novel sequential latent variable model, named MTLVM, which is designed for capturing the sequential dependencies of corporate recruitment states and is able to automatically learn the latent recruitment topics within a Bayesian generative framework. In particular, to capture the variability of recruitment topics over time, we design hierarchical dirichlet processes for MTLVM. These processes allow to dynamically generate the evolving recruitment topics. Finally, we implement a prototype system to empirically evaluate our approach based on real-world recruitment data in China. Indeed, by visualizing the results from MTLVM, we can successfully reveal many interesting findings, such as the popularity of LBS related jobs reached the peak in the 2nd half of 2014, and decreased in 2015.Comment: 11 pages, 30 figure, SIGKDD 201

The utilization of selection after 100-days lactation in forming a high productive Red and White herd

International audienc