What active labor market policy works in a recession?

This paper discusses the case for expanding active labor market policy in recession. We find that there is reasonable case for relying more heavily on certain kinds of programs. The argument is tied to the varying size of the lock-in effect in boom and recession. If programs with relatively large lock-in effects should ever be used, they should be used in a downturn. The reason is simply that the cost of forgoing search time is lower in recession. We also provide new evidence on the relative effectiveness of different kinds of programs over the business cycle. In particular we compare an on-the-job training scheme with (traditional) labor market training. We find that labor market training is relatively more effective in recession. This result is consistent with our priors since labor market training features relative large lock-in effects.Active labor market policy; business cycle; unemployment

Internet-based training of coronary artery patients: the Heart Cycle Trial

© 2016, Springer Japan. Low adherence to cardiac rehabilitation (CR) might be improved by remote monitoring systems that can be used to motivate and supervise patients and tailor CR safely and effectively to their needs. The main objective of this study was to evaluate the feasibility of a smartphone-guided training system (GEX) and whether it could improve exercise capacity compared to CR delivered by conventional methods for patients with coronary artery disease (CAD). A prospective, randomized, international, multi-center study comparing CR delivered by conventional means (CG) or by remote monitoring (IG) using a new training steering/feedback tool (GEx System). This consisted of a sensor monitoring breathing rate and the electrocardiogram that transmitted information on training intensity, arrhythmias and adherence to training prescriptions, wirelessly via the internet, to a medical team that provided feedback and adjusted training prescriptions. Exercise capacity was evaluated prior to and 6 months after intervention. 118 patients (58 ± 10 years, 105 men) with CAD referred for CR were randomized (IG: n = 55, CG: n = 63). However, 15 patients (27 %) in the IG and 18 (29 %) in the CG withdrew participation and technical problems prevented a further 21 patients (38 %) in the IG from participating. No training-related complications occurred. For those who completed the study, peak VO 2 improved more (p = 0.005) in the IG (1.76 ± 4.1 ml/min/kg) compared to CG (−0.4 ± 2.7 ml/min/kg). A newly designed system for home-based CR appears feasible, safe and improves exercise capacity compared to national CR. Technical problems reflected the complexity of applying remote monitoring solutions at an international level

What active labor market policy works in a recession?

This paper discusses the case for expanding active labor market policy in recession. We find that there is reasonable case for relying more heavily on certain kinds of programs. The argument is tied to the varying size of the lock-in effect in boom and recession. If programs with relatively large lock-in effects should ever be used, they should be used in a downturn. The reason is simply that the cost of forgoing search time is lower in recession. We also provide new evidence on the relative effectiveness of different kinds of programs over the business cycle. In particular we compare an on-the-job training scheme with (traditional) labor market training. We find that labor market training is relatively more effective in recession. This result is consistent with our priors since labor market training features relative large lock-in effects.Active labor market policy; business cycle; unemployment

Career progression and formal versus on-the-job training

We model the choice of individuals to follow or not apprenticeship training and their subsequent career. We use German administrative data, which records education, labour market transitions and wages to estimate a dynamic discrete choice model of training choice, employment and wage growth. The model allows for returns to experience and tenure, match specific effects, job mobility and search frictions. We show how apprenticeship training affects labour market careers and we quantify its benefits, relative to the overall costs. We then use our model to show how two welfare reforms change life-cycle decisions and human capital accumulation: One is the introduction of an Earned Income Tax Credit in Germany, and the other is a reform to Unemployment Insurance. In both reforms we find very significant impacts of the policy on training choices and on the value of realized matches, demonstrating the importance of considering such longer term implications

Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval

In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to lean a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further proposed upon the adversarial training to strengthen the correlations between inputs and corresponding outputs. Our approach is generative to learn hash functions such that the learned hash codes can maximally correlate each input-output correspondence, meanwhile can also regenerate the inputs so as to minimize the information loss. The learning to hash embedding is thus performed to jointly optimize the parameters of the hash functions across modalities as well as the associated generative models. Extensive experiments on a variety of large-scale cross-modal data sets demonstrate that our proposed method achieves better retrieval results than the state-of-the-arts.Comment: To appeared on IEEE Trans. Image Processing. arXiv admin note: text overlap with arXiv:1703.10593 by other author

Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training

Apprenticeship Training and the Business Cycle

Dual apprenticeship training is a market-driven form of education at the upper secondary level, taking place in firms as well as in vocational schools. So far, little is known about the impact of the business cycle on the number of apprenticeship programs offered by firms. Using panel-data of Swiss cantons from 1988-2004, we find that the influence of the business cycle is statistically significant, but small in size. Instead, supply of apprenticeship programs is driven to a much greater extent by demographic change. Conversely, the number of first-year high school students is not affected by the business cycle. We find, however, that enrollment increases if the population at age 16 grows, but access to high schools does not become more restricted in times of negative growth.apprenticeship training, business cycle, high school enrollment