Job Recovery Research Articles

Impact of 2009 American Recovery and Reinvestment Act (ARRA) health center investments on disadvantaged neighborhoods after recession

BackgroundFederally qualified health centers (FQHCs) are integral to the U.S. healthcare safety net and uniquely situated in disadvantaged neighborhoods. The 2009 American Recovery and Reinvestment Act (ARRA) invested $2 billion in FQHC stimulus during the Great Recession; but it remains unknown whether this investment was associated with extended benefits for disadvantaged neighborhoods.MethodsWe used a propensity-score matched longitudinal design (2008–2012) to examine whether the 2009 ARRA FQHC investment was associated with local jobs and establishments recovery in FQHC neighborhoods. Job change data were obtained from the Longitudinal Employer-Household Dynamics (LEHD) survey and calculated as an annual rate per 1,000 population. Establishment change data were obtained from the National Neighborhood Data Archive (NaNDA) and calculated as an annual rate per 10,000 population. Establishment data included 4 establishment types: healthcare services, eating/drinking places, retail establishments, and grocery stores. Fixed effects were used to compare annual rates of jobs and establishments recovery between ARRA-funded FQHC census tracts and a matched control group.ResultsOf 50,381 tracts, 2,223 contained ≥ 1 FQHC that received ARRA funding. A higher proportion of FQHC tracts had an extreme poverty designation (11.6% vs. 5.4%), high unemployment rate (45.4% vs. 30.3%), and > 50% minority racial/ethnic composition (48.1% vs. 36.3%). On average, jobs grew at an annual rate of 3.84 jobs per 1,000 population (95% CI: 3.62,4.06). In propensity-score weighted models, jobs in ARRA-funded tracts grew at a higher annual rate of 4.34 per 1,000 (95% CI: 2.56,6.12) relative to those with similar social vulnerability. We observed persistent decline in non-healthcare establishments (-1.35 per 10,000; 95% CI: -1.68,-1.02); but did not observe decline in healthcare establishments.ConclusionsDirect funding to HCs may be an effective strategy to support healthcare establishments and some jobs recovery in disadvantaged neighborhoods during recession, reinforcing the important multidimensional roles HCs play in these communities. However, HCs may benefit from additional investments that target upstream determinants of health to mitigate uneven recovery and neighborhood decline.

미국 헤어디자이너의 워라벨과 직무회복탄력성이 직무만족도에 미치는 영향

This study was conducted on 100 hairdressers in the Washington D.C., Maryland, and Virginia area on the American East Coast, By studying the relationship between work-life balance, job resilience, and job satisfaction, the study sought to find ways to improve the job satisfaction of hairdressers. The purpose of the study was individually explained to the subjects, and the survey was conducted using Google Form for those who agreed to participate. The specific results of data analysis based on the responses are as follows.
 Most subjects worked 4-5 days a week, and the most common daily working hours were 8 hours, followed by over 9 hours, over 5 hours, less than 8 hours, and less than 4 hours, which showed that the working hours were relatively free compared to Korea. The salary level was 1.5 times higher than the Korean average.
 The subjects' work-life balance and job resilience showed a significant positive correlation, and work-life balance and job satisfaction also showed a positive correlation. Job resilience and job satisfaction also showed a statistically significant positive correlation, and it was found that the higher work-life balance and job recovery resilience, the higher job satisfaction (p<.001). Additionally, the impact on job satisfaction was related to work-life balance and job recovery resilience.
 Therefore, in order to fulfill the job satisfaction of hairdressers, priority must be given to improving the work environment or working conditions that can improve work-life balance. If work-life balance is met, mental and physical fatigue caused by work is reduced, and job satisfaction can be achieved. In the follow-up research, it is hoped that an in-depth qualitative study of hairdressers will be conducted based on this study results, and efforts to find the specific ways to improve work-life balance will be made.

P080 Using your Mobile/Tablet Device at Bedtime for Work: Implications for Adult Sleep and Job Performance

Abstract Introduction A substantial number of workers post-COVID-19 now work at home. Remote work allows for greater flexibility in work timing including the preference to work at bedtime. Thus, increasing the likelihood of digital device use at bedtime with possible negative consequences for sleep, job recovery and, in turn, job performance. This interplay remains to be tested. Methods 202 university workers (54M, 144F; mean age (SD) = 48.38 ± 10.23y) completed a 10 day diary over two working weeks (Oct/Nov 2021) assessing digital device use, sleep, workload, worksite location, job recovery in the evening and job performance. Results Linear mixed model analyses revealed when comparing between individuals that greater digital device at bedtime was associated with greater wakefulness after sleep onset and shorter sleep and, in particular, if they worked from home compared to the office. By contrast, when comparing within individuals it was not the case if an individual used their digital device more on one night than another that this was associated with worse sleep nor was sleep worse on those nights when an individual had worked at home rather than the office. Analyses examining job recovery and job performance are currently under review. Discussion The present findings suggest that individuals who habitually use their digital device at bedtime are at a greater risk for poor sleep and supports the need for workplace implementations to discourage workers taking work to bed.

Payroll Employment at the Weekly Frequency

Nontraditional data can provide critical economic insights in real time. We document the development of weekly employment indexes based on microdata from the payroll processor ADP. These data have provided insights into labor market developments in the fast-moving pandemic environment of recent years, as well as in more quiescent periods. We describe processes for weighting and benchmarking, seasonally adjusting weekly data, and ultimately combining the signals from ADP and official data. Finally, we show how the weekly employment indexes have fared during the pandemic jobs recovery, focusing on revisions to official data for the second half of 2021.

MINING AND PRODUCTIVE SPECIALIZATION: ECONOMIC IMPACTS OF THE COLLAPSE OF THE DAM OF FUNDÃO IN MARIANA/BRAZILzil

This paper analyzes the economic impacts in the municipality of Mariana from the Fundão Dam rupture occurred at the end of 2015. The central hypothesis is that economic dependence on the mineral extraction sector makes this municipality more vulnerable to adverse shocks affecting this productive sector, compromising its job recovery capacity. In order to calculate the impacts of this disaster, datas on formal employment estimated through the Synthetic Control Method were used. The results showed that, although Mariana and its synthetic version show similar formal employment trajectory in the pre-shock period, Mariana recovered employability in the second year after the dam rupture, unlike the control group. However, this result is not due to the recovery of formal employment based on sustainable economic diversification strategies, but essentially by dependence of the mining activity and the post-disaster remediation actions that has been gradually implemented in order to recover affected areas.

Tracking Turnover Among Health Care Workers During the COVID-19 Pandemic

The health care sector lost millions of workers during the COVID-19 pandemic and job recovery has been slow, particularly in long-term care. To identify which health care workers were at highest risk of exiting the health care workforce during the COVID-19 pandemic. This was an observational cross-sectional study conducted among individuals employed full-time in health care jobs from 2019 to 2021 in the US. Using the data from the Current Population Survey (CPS), we compared turnover rates before the pandemic (preperiod, January 2019-March 2020; 71 843 observations from CPS) with the first 9 months (postperiod 1, April 2020-December 2020; 38 556 observations) and latter 8 months of the pandemic (postperiod 2, January 2021-October 2021; 44 389 observations). Health care workforce exits (also referred to as turnover) defined as a health care worker's response to the CPS as being unemployed or out of the labor force in a month subsequent to a month when they reported being actively employed in the health care workforce. The probability of exiting the health care workforce was estimated using a logistic regression model controlling for health care occupation, health care setting, being female, having a child younger than 5 years old in the household, race and ethnicity, age and age squared, citizenship status, being married, having less than a bachelor's degree, living in a metropolitan area, identifier for those reporting employment status at the first peak of COVID-19, and select interaction terms with time periods (postperiods 1 and 2). Data analyses were conducted from March 1, 2021, to January 31, 2022. The study population comprised 125 717 unique health care workers with a mean (SD) age of 42.3 (12.1) years; 96 802 (77.0%) were women; 84 733 (67.4%) were White individuals. Estimated health care turnover rates peaked in postperiod 1, but largely recovered by postperiod 2, except for among long-term care workers and physicians. We found a 4-fold difference in turnover rates between physicians and health aides or assistants. Rates were also higher for health workers with young children (<5 years), for both sexes and highest among women. By race and ethnicity, persistently higher turnover rates were found among American Indian/Alaska Native/Pacific Islander workers; White workers had persistently lower rates; and Black and Latino workers experienced the slowest job recovery rates. The findings of this observational cross-sectional study suggest that although much of the health care workforce is on track to recover to prepandemic turnover rates, these rates have been persistently high and slow to recover among long-term care workers, health aides and assistants, workers of minoritized racial and ethnic groups, and women with young children. Given the high demand for long-term care workers, targeted attention is needed to recruit job-seeking health care workers and to retain those currently in these jobs to lessen turnover.

Malaysian Apprenticeship Implementation: Issues and Challenges Towards Effective Employers Engagement

While the jobs recovery is underway in many countries, persistently high rates of youth unemployment remain a significant labour market challenge. In response, there has been increasing interest in apprenticeships both as a route into employment and also in raising the skill levels of the workforce. Apprenticeship is one of the methods to promote young people's employment. It is historically prevalent and successful in Europe and is emulated in other countries around the world to promote employment especially amongst youth. Apprenticeships and other work-based training opportunities are valuable training pathways for improving the transition from school-to-work. At the local level, apprenticeship programmes can contribute to regional development objectives and provide local employers with the skilled workforce they require to remain competitive and create jobs. Therefore, this study was carried out to investigate the currents issues and challenges faced by Malaysian industrial employers in employer engagement of apprenticeship programmes through action research and descriptive studies. This action-based research as a methodology is to gather the responses of Malaysian local companies’ experiences in the manufacturing sector, across Multinational Companies (MNC), Government-Linked Corporations (GLC) and Small and Medium-scale Enterprises (SME) and identify the key issues and solutions that could create a beneficial prospect for TVET graduates and participating companies. Both descriptive quantitative and qualitative data from the survey questionnaires and interviews, respectively, were employed. 71 companies involved in the online survey and 6 manufacturing companies participated in the interviews and discussions as well as brainstorming. From the perspective ofindustries, this study found that the main key issues and challenges are: scarcity of competent industries instructors and coaches; provision of CPD; availability of apprenticeship bridging programs; teaching capacity; skills to deliver high quality employer focused apprenticeship; and industries and training providers collaboration. These six main issues are fundamentally systemic and highly intertwined in nature and will have to be resolved by all the stakeholders based on an agreeable working framework of apprenticeship in Malaysia. The findings of this study could provide policy makers and social partners with specific learnings to remove barriers to engaging employers in apprenticeship programmes, broaden access to training opportunities and improve the economic development and labour market performance of local areas.

Phoebe

Easy-to-use programming interfaces paired with cloud-scale processing engines have enabled big data system users to author arbitrarily complex analytical jobs over massive volumes of data. However, as the complexity and scale of analytical jobs increase, they encounter a number of unforeseen problems, hotspots with large intermediate data on temporary storage, longer job recovery time after failures, and worse query optimizer estimates being examples of issues that we are facing at Microsoft. To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer. Given a set of constraints and an objective function at compile-time, Phoebe is able to determine the decomposition of job plans, and the optimal set of checkpoints to preserve their outputs to durable global storage. Phoebe consists of three machine learning predictors and one optimization module. For each stage of a job, Phoebe makes accurate predictions for: (1) the execution time, (2) the output size, and (3) the start/end time taking into account the inter-stage dependencies. Using these predictions, we formulate checkpoint optimization as an integer programming problem and propose a scalable heuristic algorithm that meets the latency requirement of the production environment. We demonstrate the effectiveness of Phoebe in production workloads, and show that we can free the temporary storage on hotspots by more than 70% and restart failed jobs 68% faster on average with minimum performance impact. Phoebe also illustrates that adding multiple sets of checkpoints is not cost-efficient, which dramatically reduces the complexity of the optimization.

Informality, Gender Employment Gap, and COVID-19 in Mexico: Identifying Persistence and Dynamic Structural Effects

The objective is to analyze the impact of the COVID-19 pandemic on the dynamics of the Mexican labor market (formal-informal employment) by gender. It is built consistent micro-founded time-series from 1987:Q1 to 2019:Q4 using the Mexican urban employment surveys and estimate a VAR model linking aggregate production and each market segment. Our results suggest significant adverse effects on formal employment resulting from the COVID-19 pandemic, with lengthy job recovery for females and males. The informal sector in both genders presents a lower forecasted response to the initial production shock but substantial observed employment losses, potentially linked to structural changes in the market. In the COVID-19 crisis, the informal sector is not a substitute for formal employment losses. The complexity of this crisis suggests crafting policies to improve the easiness of the market to enhance formal job recovery while promoting gender equality. Our main contribution is to estimate the diverse employment losses by segments and a critical structural change in the labor market dynamics resulting from the COVID-19 pandemic focusing on urban employment.

From Job Vulnerability to Job Recovery: The Phenomenological Study on Overseas Filipino Workers Job Cessation in the State of Qatar

From Job Vulnerability to Job Recovery: The Phenomenological Study on Overseas Filipino Workers Job Cessation in the State of Qatar

Job Demands and Recovery Experience: The Mediation Role of Heavy Work Investment

The evolving labor market is increasingly competitive and more demanding for most occupations and especially pertinent to health professionals investing many resources to provide excellent care service. However, the nature of the external factors, such as organizational culture and workload, seemed to be related to heavy work investments (HWI) and health outcomes, such as recovery experiences. This study aims to explore among Colombian health professionals, the effect of job demands (JD) on recovery experience (RE), mediated by HWI. The proposed model was assessed through Partial Least Squares Structural Equation Modelling (PLS-SEM) and an importance-performance map analysis was performed to expand the initial results. Results support the stated hypotheses regarding direct and indirect effects among JD, RE and HWI. This study contributes to understanding job demands within the health sector. Moreover, it pinpoints opportunities to foster adequate management of work investment and recovery experiences that protect well-being while maintaining a quality care service.

COVID-19's impact on the U.S. labor market as of September 2020.

This paper summarizes the impact of COVID-19 (through mid-September 2020) on the U.S. labor market through the lens of measures found in monthly Bureau of Labor Statistics’ Employment Situation releases. It describes the pandemic’s impact thus far by looking at payroll jobs, the unemployment rate, a broader measure of job disruptions, and disparities by race and sex. The conclusion discusses forces that will drive outcomes in the coming months. The findings are as follows: (1) The COVID-19 shock was very abrupt and deep by historical standards, and headline numbers understate the magnitude of job disruptions. (2) The pace of the jobs recovery has slowed markedly since June. (3) The share of disrupted workers with ties to employers, which began very high, is falling rapidly, dimming prospects for further rapid recovery. (4) Hispanic, African American and women workers’ jobs were more disrupted than others’. (5) Prospects for a speedy jobs recovery depend strongly on the path of the pandemic and degree of fiscal stimulus, both aided by official statistics to guide decisions at all levels during this critical time.

Religiosity and Regional Resilience to Recession

Literature shows that religiosity can provide individual resilience to life shocks as well as regional resilience to disasters caused by natural hazards. Related work has examined the complicated links between religion and economic growth. Yet few, if any, studies examine the role of regional levels of religiosity on a region's resilience to recession—or how quickly the employment rate returns to pre‐recession levels (a common measure of resilience in the economics literature). As the recovery period of the Great Recession cools and economists warn of future economic downturns, all known variables that may be linked with regional resilience are worthy of exploration. Using survey results from the Gosling‐Potter Internet Project and General Social Surveys, we applied logarithmic functions to pre‐ and post‐Great Recession employment data for 2,836 U.S. counties. We found a modest and statistically significant association between religious belief and regional resilience to recession. Religiosity was the strongest of sixteen psychosocial variables that we examined in association with the speed of job recovery; despite having negative links with other economic variables. This has particular salience for more rural economies; policy implications are discussed.

Economic Recovery and Effectiveness of Active Labour Market Initiatives for the Unemployed in Spain: A Gender Perspective of the Valencian Region

Macroeconomic indicators regarding employment have been gradually improved by southern European countries during recent years. However, the labour market still seems to be highly polarized across regions and some groups are persistently excluded from jobs recovery. This paper analyses the effectiveness of active labour market initiatives in the Valencian region, one of the worst-affected areas regarding unemployment in Spain. By using a large official database from the Valencian government, results of the probit model show that participating on active labour market initiatives have a positive impact on the probability of exiting unemployment, even after controlling for age, level of education and gender of candidates. The research also reveals that people aged 55 and older and females constitute the most vulnerable groups. Regarding women, only those with higher education increase their probability of finding a job.

Towards fault tolerance optimization based on checkpoints of in-memory framework spark

Performance optimization especially for fault tolerance optimization has been a significant aspect for in-memory computing computing. When node failure occurs, in-memory computing may lose the data, which increases the execution time without checkpoint. In the traditional Spark strategy, the programmer chooses the checkpoint with the uncertainty and risk. Therefore, we aims at the checkpoint strategy of in memory computing framework Spark in this paper. After the theoretical analysis, the checkpoint selection algorithm which taking into account the length of the RDD lineage, the computational cost, the operation complexity and the size in setting the checkpoint is presented. The greater the weight of RDD, the higher priority it has. The RDD with higher cost will be set as the checkpoint first, which can reduce the recomputation cost of the task. When failure occurs, the recovery algorithm is executed, and the efficiency of the task recovery can be effectively improved. And the experimental results show that the strategy optimizes the fault tolerance mechanism for Spark and improves the efficiency of the job recovery.

The Weekend Activity and Employee Involvement in Creative Tasks : The Mediating Roles of Feeling of Energy and Job Recovery Experience

창의적 직무활동에 대한 기존연구 대부분은 조직차원에서의 기술개발 관련시스템 개선을 통한 업무환경의 변화나, 개인차원에서 개인의 역량과 직무특성, 성격특성을 고려하여 개선하려는데 집중하였다. 조직행동분야에서도 리더의 지도력을 통해 구성원의 창의성이나 혁신행동을 어떻게 이끌어 낼 것인가에 연구의 초점을 두고, 조직 즉 직장 내부 관점에서의 해결방안을 모색하였다. 그러나 오늘날 사회와 라이프스타일의 변화를 반영하듯, 퇴근 후의 개인적 활동, 업무 외 활동, 특히 주말활동은 월요일의 업무활동에 많은 영향을 끼칠 것으로 생각된다. 따라서 조직 외부, 즉 업무외부에서 발생 가능한 중요요인에 대해서도 많은 관심을 가져야 하겠으나 현재까지 이러한 연구는 부족하다. 따라서 본 연구는 조직 외부에서의 개인의 자율적 활동인 주말활동에 관심을 갖고 종업원의 창의적 직무활동에 어떠한 영향을 끼치는가에 대한 연구를 하고자 한다.BR 본 연구의 목적은 종업원의 주말활동과 창의적 직무몰입 사이의 관계를 규명하는 것이다. 이를 위해 본 연구는 종업원의 직무활력과 회복경험이 위의 인과 관계에 매개영향을 미치는지 실증분석 하였다. 본 연구의 가설을 검증하기 위해 비교적 창의적 직무활동의 요구가 높은 국내 23개 제조업체 제품설계 담당자 및 관리직을 대상으로 수집한 380개의 설문데이터를 사용하였다.BR 그 결과 종업원의 주말활동은 창의적 직무몰입과 직무활력, 회복경험에 긍정적인 관계가 있었으며, 주말활동과 창의적 직무몰입 간에 직무활력의 매개효과는 유의미한 긍정적인 효과를 보여주었으나 회복경험은 지지되지 않았다. 창의성향상과 관련된 기존 연구들은 종업원의 직무특성이나 조직 내에서의 기술적 특성들과 창의적 활동과의 관계분석에 초점을 두었으나, 본 연구는 종업원의 일과 후의 활동을 통해서도 창의적 직무 활동에 영향관계가 있음을 밝힘으로, 조직 내 창의적 활동과 직무몰입을 증가시키기 위하여 새로운 시각의 다양한 접근이 필요함을 시사하고 있다.

Multilingual Competences and Labour Market Access: The Point of View of Swiss Personal Advisers in the Regional Employment Centres

Since the creation of regional employment centres in Switzerland, personal advisers and consultants have been playing an important role in the assessment of the unemployed and the jobseekers’ language skills and their promotion. In this study, 646 of these personal advisers from the three main speaking areas of Switzerland (French, German and Italian) were interviewed via an online questionnaire on how they practically assess the language skills of the jobseekers they deal with; on the reasons they do or not recommand and provide language courses; and on how these courses should be financed. There were also more general questions about the link between language skills and the labour market. Statistically significant differences were observed, for instance regarding the quality of the language-competence evaluation tools; the evaluated importance of language skills for labour market integration; and the relevance of language courses for jobseekers. A correlation was observed between these significant differences and two factors: the linguistic area and the unemployment rate in a given area. Some problematic issues of political interest linked to language skills and job recovery were also highlighted and discussed in this paper.

The effect of job stressor on flourishing

This study was to investigate the relationship between job stress and flourishing, and to examine a moderated mediating effect of recovery through Emotional stability. For the purpose of the study, a total of 307 participants completed a survey including measures of job stress, flourishing, recovery and emotional stability. The results were as follows: first, the findings in a correlation analysis indicated that job stress with flourishing, recovery, and emotional stability were negatively correlated. Second, the relationship between job stress and flourishing was mediated by recovery. Third, emotional stability moderated the relationship between job stress and recovery. Fourth, mediating effect of recovery was moderated by emotional stability in the relationship between job stress and flourishing. Finally, implications for organization situation about limitations of the study and suggestions for future research were discussed.

Integrating Job Loss, Unemployment, and Reemployment With Social Cognitive Career Theory

Job loss and recovery remain critical challenges in the United States and Europe in the wake of the Great Recession. However, the experience of unemployment is poorly integrated in theories of vocational psychology. In this article, we explore how job loss and recovery can be understood through the lens of social cognitive career theory’s career self-management (SCCT-CSM) model. We apply the SCCT-CSM model to understand the critical importance of person-cognitive variables, individual differences, and contextual affordances to the experiences of job loss and job recovery. Implications for future research, including research with particular groups of unemployed persons, are discussed. Overall, our analysis indicates that the SCCT-CSM model is a fruitful perspective for organizing future scholarship related to job loss and recovery.

SC‐OCR: similarity‐based clustering and optimum cache replacement approach

SummaryBig data is a new term used to identify the large scale and complex datasets. Big data is now rapidly expanding in all science and engineering domains, owing to the fast development of networking, data storage, and data collection capacity. Big data mining is the capability of extracting useful information from these large datasets. Nowadays, the integration of cloud computing with the data mining for the big data mining process is a challenging task. In order to process the huge amount of data, it is necessary to concentrate the improvement on the big data computation. Most of the existing approaches use the MapReduce to compute the big data. The increase in the computational cost and memory consumption are the main drawbacks of the existing approaches. To overcome these limitations, this paper proposes a similarity‐based clustering and optimum cache replacement approach for big data computing applications. The job recovery process is initiated by copying the data in the cloud server and forwarding the data copy for further processing. Then, the job is divided into clusters based on the similarity‐based clustering approach. Finally, the cache concept is introduced with the optimum cache replacement algorithm to avoid repeated execution of the jobs by queue management. The proposed approach is compared with the existing Spark and Hadoop approaches. The proposed approach achieves better performance in terms of iteration time, query response time, job completion time, and clustering accuracy. Copyright © 2016 John Wiley &amp; Sons, Ltd.