Does Extending Unemployment Benefits Improve Job Quality?

Contrary to standard search model predictions, prior studies failed to estimate a positive effect of unemployment insurance (UI) on reemployment wages. This paper estimates a positive UI wage effect exploiting an age-based regression discontinuity in Austrian administrative data. A search model incorporating duration dependence determines the UI wage effect as the balance between two offsetting forces: UI causes agents to seek higher-wage jobs, but also reduces wages by lengthening unemployment. This implies a negative relationship between the UI unemployment duration and wage effects, which holds empirically both in our sample and across studies, reconciling disparate wage-effect estimates. Empirically, UI raises wages by improving reemployment firms' quality and attenuating wage drops

Does Extending Unemployment Benefits Improve Job Quality?

Contrary to standard search model predictions, prior studies failed to estimate a positive effect of unemployment insurance (UI) on reemployment wages. This paper estimates a positive UI wage effect exploiting an age-based regression discontinuity in Austrian administrative data. A search model incorporating duration dependence determines the UI wage effect as the balance between two offsetting forces: UI causes agents to seek higher-wage jobs, but also reduces wages by lengthening unemployment. This implies a negative relationship between the UI unemployment-duration and wage effects, which holds empirically both in our sample and across studies, reconciling disparate wage-effect estimates. Empirically, UI raises wages by improving reemployment firms’ quality and attenuating wage drops

Leadership effectiveness and its relationship with emotional stability among nurse managers in educational hospitals related to Isfahan University of Medical Science in 2007

زمینه و هدف: تغییرات گسترده ای که امروزه در مؤسسات مراقبت بهداشتی به وقوع پیوسته است ضرورت ترکیب مهارت های رهبری و مدیریت را به وجود آورده است. به علاوه، موفقیت در مراقبت از بیمار و جلب اعتبار بیمارستان تا حد زیادی به شایستگی و علاقه کارکنان پرستاری بستگی دارد. هدف این پژوهش تعیین اثربخشی رهبری و ارتباط آن با ثبات عاطفی در مدیران پرستاری می باشد. روش بررسی: این پژوهش از نوع توصیفی همبستگی بود که در سال 1386 بر روی 128 نفر از مدیران پرستاری بیمارستان های آموزشی وابسته به دانشگاه علوم پزشکی شهر اصفهان با روش نمونه گیری سرشماری انجام شد. ابزار جمع آوری اطلاعات پرسشنامه اصول رهبری اثربخش و پرسشنامه شخصیت کنتل (Kentel) بود. در این پژوهش اثربخشی رهبری و ثبات عاطفی مورد سنجش قرار گرفت. یافته ها: یافته های پژوهش نشان داد که 8/77 مدیران خدمات پرستاری، 7/66 سوپروایزران و 1/72 سرپرستاران مورد پژوهش اثربخشی رهبری قوی داشته اند. همچنین بین اثربخشی رهبری و ثبات عاطفی در رده سوپروایزران همبستگی مستقیم و معنی دار وجود داشت (02/0 = Pو 39/0r=-) اما در سایر رده ها بین اثربخشی رهبری و ثبات عاطفی همبستگی معنی داری مشاهده نشد. نتیجه گیری: نتایج نشان داد بین اثربخشی رهبری و ثبات عاطفی در رده سوپروایزران، ارتباط مستقیمی وجود دارد لذا توجه به ویژگی های شخصیتی در انتخاب این مدیران باید مورد توجه قرار گیرد

Obstacles to the Implementation of Intersectoral Planning in the Healthcare System

Health master plan is a tool designed to promote health and it is essential to define strategies, policies, and directions to health system programs. Therefore, the present study aimed to find obstacles to intersectoral planning and help it to implement. Methods: This was a qualitative and content analysis study (Mixed method). The study participants consisted of 12 managers and experts in planning involved in the development of the health master plan of Kerman and were selected through purposive and snowball sampling. Data collection was conducted through semistructured and were analyzed using framework analysis method. The four criteria of “Credibility, Dependability, Confirmability, and transferability” were considered to increase the validity and reliability of the research. Results: Two themes are internal and external factors in the form of 8 sub-themes, including Stewardship, management commitment, Financing, training, information, policymaking, laws and regulations and intersectoral collaboration identified as obstacles to the implementation of intersectoral planning in health master plan of Kerman. Conclusion: Each plan, especially long-term and intersectoral planning, needs a set of collaboration, resources, legal requirements and most importantly, commitment to achieve its ultimate goal that, if this goal is related to the health of the community, the importance of the program’s implementation is doubled

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

Deep model-based Reinforcement Learning (RL) has the potential to substantially improve the sample-efficiency of deep RL. While various challenges have long held it back, a number of papers have recently come out reporting success with deep model-based methods. This is a great development, but the lack of a consistent metric to evaluate such methods makes it difficult to compare various approaches. For example, the common single-task sample-efficiency metric conflates improvements due to model-based learning with various other aspects, such as representation learning, making it difficult to assess true progress on model-based RL. To address this, we introduce an experimental setup to evaluate model-based behavior of RL methods, inspired by work from neuroscience on detecting model-based behavior in humans and animals. Our metric based on this setup, the Local Change Adaptation (LoCA) regret, measures how quickly an RL method adapts to a local change in the environment. Our metric can identify model-based behavior, even if the method uses a poor representation and provides insight in how close a method's behavior is from optimal model-based behavior. We use our setup to evaluate the model-based behavior of MuZero on a variation of the classic Mountain Car task.Comment: NeurIPS 2020, code: https://github.com/chandar-lab/LoC

Continuous Coordination As a Realistic Scenario for Lifelong Learning

Current deep reinforcement learning (RL) algorithms are still highly task-specific and lack the ability to generalize to new environments. Lifelong learning (LLL), however, aims at solving multiple tasks sequentially by efficiently transferring and using knowledge between tasks. Despite a surge of interest in lifelong RL in recent years, the lack of a realistic testbed makes robust evaluation of LLL algorithms difficult. Multi-agent RL (MARL), on the other hand, can be seen as a natural scenario for lifelong RL due to its inherent non-stationarity, since the agents' policies change over time. In this work, we introduce a multi-agent lifelong learning testbed that supports both zero-shot and few-shot settings. Our setup is based on Hanabi -- a partially-observable, fully cooperative multi-agent game that has been shown to be challenging for zero-shot coordination. Its large strategy space makes it a desirable environment for lifelong RL tasks. We evaluate several recent MARL methods, and benchmark state-of-the-art LLL algorithms in limited memory and computation regimes to shed light on their strengths and weaknesses. This continual learning paradigm also provides us with a pragmatic way of going beyond centralized training which is the most commonly used training protocol in MARL. We empirically show that the agents trained in our setup are able to coordinate well with unseen agents, without any additional assumptions made by previous works. The code and all pre-trained models are available at https://github.com/chandar-lab/Lifelong-Hanabi.Comment: 19 pages with supplementary materials. Added results for Lifelong RL methods and some future work. Accepted to ICML 202

Chemical composition of the essential oils of Citrus sinensis cv. valencia and a quantitative structure-retention relationship study for the prediction of retention indices by multiple linear regression

The chemical composition of the volatile fraction obtained by head-space solid phase microextraction (HS-SPME), single drop microextraction (SDME) and the essential oil obtained by cold-press from the peels of C. sinensis cv. valencia were analyzed employing gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC-MS). The main components were limonene (61.34 %, 68.27 %, 90.50 %), myrcene (17.55 %, 12.35 %, 2.50 %), sabinene (6.50 %, 7.62 %, 0.5 %) and α-pinene (0 %, 6.65 %, 1.4 %) respectively obtained by HS-SPME, SDME and cold-press. Then a quantitative structure-retention relationship (QSRR) study for the prediction of retention indices (RI) of the compounds was developed by application of structural descriptors and the multiple linear regression (MLR) method. Principal components analysis was used to select the training set. A simple model with low standard errors and high correlation coefficients was obtained. The results illustrated that linear techniques such as MLR combined with a successful variable selection procedure are capable of generating an efficient QSRR model for prediction of the retention indices of different compounds. This model, with high statistical significance (R2 train = 0.983, R2 test = 0.970, Q2 LOO = 0.962, Q2 LGO = 0.936, REP(%) = 3.00), could be used adequately for the prediction and description of the retention indices of the volatile compounds

Risk-based selection in unemployment insurance: evidence and implications

This paper studies whether adverse selection can rationalize a universal mandate for unemployment insurance (UI). Building on a unique feature of the unemployment policy in Sweden, where workers can opt for supplemental UI coverage above a minimum mandate, we provide the first direct evidence for adverse selection in UI and derive its implications for UI design. We find that the unemployment risk is more than twice as high for workers who buy supplemental coverage, even when controlling for a rich set of observables. Exploiting variation in risk and prices to control for moral hazard, we show how this correlation is driven by substantial risk-based selection. Despite the severe adverse selection, we find that mandating the supplemental coverage is dominated by a design leaving the choice to workers. In this design, a large subsidy for supplemental coverage is optimal and complementary to the use of a minimum mandate. Our findings raise questions about the desirability of the universal mandate of generous UI in other countries, which has not been tested befor

Risk-based selection in unemployment insurance: evidence and Implications

This paper studies whether adverse selection can rationalize a universal mandate for unemployment insurance (UI). Building on a unique feature of the unemployment policy in Sweden, where workers can opt for supplemental UI coverage above a minimum mandate, we provide the first direct evidence for adverse selection in UI and derive its implications for UI design. We find that the unemployment risk is more than twice as high for workers who buy supplemental coverage. Exploiting variation in risk and prices, we show how 25- 30 percent of this correlation is driven by risk- based selection, with the remainder driven by moral hazard. Due to the moral hazard and despite the adverse selection we find that mandating the supplemental coverage to individuals with low willingness- to-pay would be suboptimal. We show under which conditions a design leaving choice to workers would dominate a UI system with a single mandate. In this design, using a subsidy for supplemental coverage is optimal and complementary to the use of a minimum mandate

Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi

Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in recent years. ZSC refers to the ability of agents to coordinate zero-shot (without additional interaction experience) with independently trained agents. While ZSC is crucial for cooperative MARL agents, it might not be possible for complex tasks and changing environments. Agents also need to adapt and improve their performance with minimal interaction with other agents. In this work, we show empirically that state-of-the-art ZSC algorithms have poor performance when paired with agents trained with different learning methods, and they require millions of interaction samples to adapt to these new partners. To investigate this issue, we formally defined a framework based on a popular cooperative multi-agent game called Hanabi to evaluate the adaptability of MARL methods. In particular, we created a diverse set of pre-trained agents and defined a new metric called adaptation regret that measures the agent's ability to efficiently adapt and improve its coordination performance when paired with some held-out pool of partners on top of its ZSC performance. After evaluating several SOTA algorithms using our framework, our experiments reveal that naive Independent Q-Learning (IQL) agents in most cases adapt as quickly as the SOTA ZSC algorithm Off-Belief Learning (OBL). This finding raises an interesting research question: How to design MARL algorithms with high ZSC performance and capability of fast adaptation to unseen partners. As a first step, we studied the role of different hyper-parameters and design choices on the adaptability of current MARL algorithms. Our experiments show that two categories of hyper-parameters controlling the training data diversity and optimization process have a significant impact on the adaptability of Hanabi agents