Does Active Labour Market Policy Work? Lessons from the Swedish Experiences

The Swedish experiences of the 1990s provide a unique example of how large-scale active labour market programmes (ALMPs) have been used as a means to fight high unemployment. This paper surveys the empirical studies of the effects of ALMPs in Sweden. On the whole, ALMPs have probably reduced open unemployment, but also reduced regular employment. The overall policy conclusion is that ALMPs of the scale used in Sweden in the 1990s are not an efficient means of employment policy. To be effective, ALMPs should be used on a smaller scale.

Does Active Labour Market Policy Work? Lessons from the Swedish Experiences

The Swedish experiences of the 1990s provide a unique example of how large-scale active labour market programmes (ALMPs) have been used as a means to fight high unemployment. This paper discusses the mechanisms through which ALMPs affect (un)employment and surveys the empirical studies of the effects of ALMPs in Sweden. The main conclusions are: (i) there is hardly any evidence for a positive effect on matching efficiency; (ii) there are some indications of positive effects on labour force participation; (iii) subsidised employment seems to cause displacement of regular employment, whereas this appears not to be the case for labour market training; (iv) it is unclear whether or not ALMPs raise aggregate wage pressure in the economy; (v) in the 1990s, training programmes seem not to have enhanced the employment probabilities of participants, whereas some forms of subsidised employment seem to have had such effects; and (vi) youth programmes seem to have caused substantial displacement effects at the same time as the gains for participants appear uncertain. On the whole, ALMPs have probably reduced open unemployment, but also reduced regular employment. The overall policy conclusion is that ALMPs of the scale used in Sweden in the 1990s are not an efficient means of employment policy. To be effective, ALMPs should be used on a smaller scale. There should be a greater emphasis on holding down long-term unemployment in general and a smaller emphasis on youth programmes. ALMPs should not be used as a means to renew unemployment benefit eligibility.Active; Labour; Market; Policy

Remarks by David F. Cavers to Duke Students Converning the Origin of and Vision for Law and Contemporary Problems

Objectives To present a method for generating reference maps of typical brain characteristics of groups of subjects using a novel combination of rapid quantitative Magnetic Resonance Imaging (qMRI) and brain normalization. The reference maps can be used to detect significant tissue differences in patients, both locally and globally. Materials and Methods A rapid qMRI method was used to obtain the longitudinal relaxation rate (R1), the transverse relaxation rate (R2) and the proton density (PD). These three tissue properties were measured in the brains of 32 healthy subjects and in one patient diagnosed with Multiple Sclerosis (MS). The maps were normalized to a standard brain template using a linear affine registration. The differences of the mean value ofR1, R2 and PD of 31 healthy subjects in comparison to the oldest healthy subject and in comparison to an MS patient were calculated. Larger anatomical structures were characterized using a standard atlas. The vector sum of the normalized differences was used to show significant tissue differences. Results The coefficient of variation of the reference maps was high at the edges of the brain and the ventricles, moderate in the cortical grey matter and low in white matter and the deep grey matter structures. The elderly subject mainly showed significantly lower R1 and R2 and higher PD values along all sulci. The MS patient showed significantly lower R1 and R2 and higher PD values at the edges of the ventricular system as well as throughout the periventricular white matter, at the internal and external capsules and at each of the MS lesions. Conclusion Brain normalization of rapid qMRI is a promising new method to generate reference maps of typical brain characteristics and to automatically detect deviating tissue properties in the brain

Preferences for Short-Term Versus Long-Term Bonuses for Stock Investments

Performance-related bonuses in the finance sector are considered important tools to provide incentives. An example is that stock portfolio managers are awarded bonuses conditionally on their portfolios producing superior returns either relative to an index or equivalent funds. Concerns are however expressed that bonuses to portfolio managers are based on too short time intervals, which may impact negatively on the degree to which environmental and social factors are taken into account in investment decisions. The question addressed in this article is how bonus schemes can be designed so that delayed payouts will be equally motivating as short-term payouts. We have conducted two experiments to investigate preference for bonus payments that are paid out either frequently of infrequently. In Experiment 1 employing 27 undergraduates, preferences were measured for one certain long-term bonus versus four certain bonuses evenly distributed across time. A majority chose the short-term bonuses, and in order for a long-term bonus to be equally preferred the results showed that it needs to be approximately 40 percent higher than the four combined short-term bonuses. Experiment 2 employing another 36 undergraduates introduced uncertainty of outcomes which more accurately reflects the setting faced by stock investors. A four-year bonus is compared to four one-year bonuses. Uncertainty was the same, decreasing or increasing over the four years. The results showed that decreasing uncertainty made a majority prefer the four-year bonus to the added one-year bonuses. In conclusion, introducing uncertainty in choices concerning future outcomes is shown to reduce the extent to which future bonus outcomes are discounted relative to immediate bonus outcomes.Portfolio management; Performance-related bonus; Time discounting

Bayesian uncertainty quantification in linear models for diffusion MRI

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.Comment: Added results from a group analysis and a comparison with residual bootstra

Expression of mRNA for phospholipase A(2), cyclooxygenases, and lipoxygenases in cultured human umbilical vascular endothelial and smooth muscle cells and in biopsies from umbilical arteries and veins

Arachidonic acid (AA) is released by phospholipase A(2) (PLA(2)) and then converted into vasoactive and inflammatory eicosanoids by cyclooxygenases (COX) and lipoxygenases (LOX). These eicosanoids are important paracrine regulators of vascular permeability, blood flow, local pro- and anticoagulant activity and they play a major role in the local inflammatory response. We have investigated the presence of mRNAs for PLA(2) and for isoforms of COX and LOX in both human endothelial cells (EC) and in human smooth muscle cells (SMC) in culture and in vascular biopsies of human umbilical veins (HUVB) and arteries (HUAB) by using the reversed transcription-polymerase chain reaction (RT-PCR) technique. Results show detectable levels of PLA(2) type IV (cPLA(2)) in cultured EC and SMC and in vascular wall biopsies from HUAB and HUVB. The cultured EC and SMC demonstrate higher levels of both COX-1 and COX-2 with PCR analyses than do vascular wall biopsies from HUAB and HUVB. This indicates a difference in the native expression of COX-1 and COX-2 in cultures of EC and SMC compared to that in biopsies from intact vessel walls. The EC and SMC in culture do not express mRNA for 5-LOX, that was, however, expressed in the vascular wall biopsies. This speaks in favour of a constitutive, i.e, in vivo expression of 5-LOX in SMC in the vascular wall of both umbilical vein and arteries. Thus results from in vitro studies of constitutive COX and LOX expression in EC and vascular SMC in culture cannot simply be extrapolated to represent in vivo conditions

The Sensitivity of Language Models and Humans to Winograd Schema Perturbations

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues.Comment: ACL 202