The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Matthew A Kraft - One of the best experts on this subject based on the ideXlab platform.
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teacher Layoffs teacher quality and student achievement evidence from a discretionary Layoff policy
Education Finance and Policy, 2015Co-Authors: Matthew A KraftAbstract:AbstractMost teacher Layoffs during the Great Recession were implemented following inverse-seniority policies. In this paper, I examine the implementation of a discretionary Layoff policy in Charlotte Mecklenburg Schools. Administrators did not uniformly lay off the most or least senior teachers but instead selected teachers who were previously retired, late-hired, unlicensed, low-performing, or nontenured. Using quasi-experimental variation within schools across grades, I then estimate the differential effects of teacher Layoffs on student achievement based on teacher seniority and effectiveness. Mathematics achievement in grades that lost an effective teacher, as measured by principal evaluations or value-added scores, decreased 0.05 to 0.11 standard deviations more than in grades that lost an ineffective teacher. In contrast, teacher seniority has limited predictive power on the effects of Layoffs. Simulation analyses show that the district selected teachers who were, on average, less effective than th...
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Teacher Layoffs, Teacher Quality, and Student Achievement: Evidence from a Discretionary Layoff Policy.
Education Finance and Policy, 2015Co-Authors: Matthew A KraftAbstract:Most teacher Layoffs during the Great Recession were implemented following inverse-seniority policies. In this paper, I examine the implementation of a discretionary Layoff policy in Charlotte Mecklenburg Schools. Administrators did not uniformly lay off the most or least senior teachers but instead selected teachers who were previously retired, late-hired, unlicensed, low-performing, or nontenured. Using quasi-experimental variation within schools across grades, I then estimate the differential effects of teacher Layoffs on student achievement based on teacher seniority and effectiveness. Mathematics achievement in grades that lost an effective teacher, as measured by principal evaluations or value-added scores, decreased 0.05 to 0.11 standard deviations more than in grades that lost an ineffective teacher. In contrast, teacher seniority has limited predictive power on the effects of Layoffs. Simulation analyses show that the district selected teachers who were, on average, less effective than those teachers identified under an inverse-seniority policy, and also reduced job losses.
Edward S. Greenberg - One of the best experts on this subject based on the ideXlab platform.
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repeated downsizing contact the effects of similar and dissimilar Layoff experiences on work and well being outcomes
Journal of Occupational Health Psychology, 2004Co-Authors: Sarah Moore, Leon Grunberg, Edward S. GreenbergAbstract:In this longitudinal study, the authors compared 1,244 white- and blue-collar workers who reported 0, 1, or 2 contacts with Layoffs; all were employees of a large manufacturing company that had engaged in several mass waves of downsizing. Consistent with a stress-vulnerability model, workers with a greater number of exposures to both direct and indirect downsizing reported significantly lower levels of job security and higher levels of role ambiguity, intent to quit, depression, and health problems. Findings did not support the idea that workers became more resilient as they encountered more Layoff events. The authors found only partial evidence that the similarity or dissimilarity of the type of repeated downsizing exposure played a role in how workers reported changes in these outcome variables.
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Differences in psychological and physical health among Layoff survivors: the effect of Layoff contact.
Journal of occupational health psychology, 2001Co-Authors: Leon Grunberg, Sarah Moore, Edward S. GreenbergAbstract:This study examined health and well-being among workers who have experienced varying types of contact with Layoffs in an organization undergoing downsizing. Using survey data from a large organization employing both white- and blue-collar workers (N = 2,279), the authors argued that there are important differences among surviving workers as a function of their Layoff experiences. Having any kind of personal contact with Layoffs is found to be associated with less job security, more symptoms of poor health, depression, and eating changes as compared with having no Layoff contact. Being laid off and rehired is associated with more work-related injuries and illnesses and missed work days due to such events than is receiving a "warn" notice, indirect contact (i.e., friends or coworkers laid off), or no contact with Layoffs. Job security partially mediates the relationship between type of Layoff contact experiences and health.
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Surviving Layoffs: The Effects on Organizational Commitment and Job Performance
Work and Occupations, 2000Co-Authors: Leon Grunberg, Richard Anderson-connolly, Edward S. GreenbergAbstract:This article tests the hypotheses that the effects of Layoffs on surviving employees' level of organizational commitment and job performance will vary according to (a) how close employees are to the Layoffs, (b) their perceptions of the fairness of the Layoffs, and (c) their position in the organizational hierarchy. Analyses were conducted on 1,900 respondents employed by a large U.S. company. Results indicated that although perceptions of Layoff unfairness were associated with lower commitment regardless of employee position, close contact with Layoffs was associated with the greater use of sick hours by surviving managers and professionals, but with lower use of sick hours and higher work effort by employees in lower positions.
I-hsien Ting - One of the best experts on this subject based on the ideXlab platform.
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mining organizational networks for Layoff prediction model construction
Advances in Social Networks Analysis and Mining, 2009Co-Authors: Huo Tsan Chang, I-hsien TingAbstract:Global economic recession has been causing the unpaid leave and massive Layoffs in major high-tech firms of Taiwan, both factors present great potential hardship to many employees according to the reports from industry. Therefore, Layoff prediction and management have become great concerns of employees and managers. Employees wish to retain their jobs and keep their work for a long time. Hence, they need to predict the possible Layoff and then utilize their resources to retain their job. In response to the difficulty of Layoff prediction, this study applies social networks and data mining techniques to build a model for Layoff prediction. This study compares various techniques to propose a better approach to generate a possible Layoff list for employees. Through an empirical study, the results indicate that the proposed approach has pretty good prediction accuracy by using organizational networks, employee databases and Layoff records to build the Layoff prediction model.
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ASONAM - Mining Organizational Networks for Layoff Prediction Model Construction
2009 International Conference on Advances in Social Network Analysis and Mining, 2009Co-Authors: Huo Tsan Chang, I-hsien TingAbstract:Global economic recession has been causing the unpaid leave and massive Layoffs in major high-tech firms of Taiwan, both factors present great potential hardship to many employees according to the reports from industry. Therefore, Layoff prediction and management have become great concerns of employees and managers. Employees wish to retain their jobs and keep their work for a long time. Hence, they need to predict the possible Layoff and then utilize their resources to retain their job. In response to the difficulty of Layoff prediction, this study applies social networks and data mining techniques to build a model for Layoff prediction. This study compares various techniques to propose a better approach to generate a possible Layoff list for employees. Through an empirical study, the results indicate that the proposed approach has pretty good prediction accuracy by using organizational networks, employee databases and Layoff records to build the Layoff prediction model.
Mario G. Reyes - One of the best experts on this subject based on the ideXlab platform.
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On the estimation of stock-market reaction to corporate Layoff announcements
Review of Financial Economics, 2004Co-Authors: Tewhan Hahn, Mario G. ReyesAbstract:Abstract This study investigates the stock-market reaction to Layoff announcements where more than 1000 workers are affected. We employ a dummy variable regression (DVR) version of the market model and compare the results obtained using ordinary least squares (OLS) versus exponential GARCH (EGARCH), and value-weighted (VW) versus equally weighted (EW) market index. We find that the stock market responds negatively to Layoffs attributed to low demand. We also find that contrary to prior research, the market reacts positively to restructuring-related Layoffs on the announcement date. This pattern of market reaction is observed regardless of the market index used or the parameter estimation methods employed, although the empirical results indicate that using EGARCH/VW market index tends to generate fewer statistically significant test results and smaller (in the absolute size of the cumulative) abnormal returns (ARs). Taken together, our study provides additional support for the claim that studies of stock-market reaction to corporate events must account for the time variation in return volatility. Ignoring these could result in erroneous inferences.
Alex P Tang - One of the best experts on this subject based on the ideXlab platform.
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Layoff announcements stock market impact and financial performance
Financial Management, 1997Co-Authors: Oded Palmon, Hueylian Sun, Alex P TangAbstract:Announcing a Layoff decision could trigger either an increase or decrease in firm value, depending upon whether adverse market conditions or efficiency improvement are motivating it. Layoff announcements often contain information that indicates the motivation. This article finds that the Layoff announcement is a useful signal for investors. There are significantly positive (negative) abnormal stock returns around the announcement date for firms that cite efficiency improvements (demand declines) as the reason for the Layoffs.