Job mobility among young college graduates

This study focuses on the question of whether job mobility relates to improved labor market outcomes among young college-educated individuals in the United States. I analyze unemployment duration, overeducation, and wage earnings among college graduates. The analysis centers around three specific questions: (1) Are there differences in labor market outcomes for those who migrate ( movers ) and those who stay ( stayers )? (2) Did the recent economic crisis exacerbate the mover-stayer differences? (3) Do mover-stayer differences vary for individuals based on their demographic characteristics or where they live? I examine data on migrant status, location before and after a move, reasons for moving, wages, overeducation (by occupation), unemployment duration, and other related socioeconomic characteristics of college graduates aged 22 to 30 years. I use yearly data from the March Supplements of the Current Population Survey (CPS). The data are consistent over time, allowing for comparisons between the time periods before and after the 2008 economic crisis. ^ The results for the relationship between job mobility and labor market outcomes are mixed. Moving for job reasons correlates with shorter unemployment durations before and (seemingly more strongly) after the recession. For certain individuals, job mobility relates to lower overeducation propensities, but by and large overeducation and job migration do not seem to move together. Regarding wages, once again an overall correlation between moving and earnings is not found. Certain specific demographic groups experience positive (“boomerang” movers before the recession and immigrants after the recession) and negative (women before the recession) correlations between the two variables. Among groups of individuals for whom moving for job reasons counterintuitively correlates with worsened labor market performance, it is likely that some unmeasured confounding effect (perhaps amenity preference) is present. The research is of some interest to policy makers hoping to attract young highly educated individuals, but due to uncertainty regarding causality its applicability is limited

Classification Techniques for Predicting Graduate Employability

Unemployment is a current issue that happens globally and brings adverse impacts on worldwide. Thus, graduate employability is one of the significant elements to be highlighted in unemployment issue. There are several factors affecting graduate employability, traditionally, excellent academic performance (i.e., cumulative grade point average, CGPA) has been the most dominant element in determining an individual’s employment status. However, researches have shown that not only CGPA determines the graduate employability; in fact other factors may influence the graduate achievement in getting a job. In this work data mining techniques are used to determine what are the factors that affecting the graduates. Therefore, the objective of this study is to identify factors that influence graduates employability. Seven years of data (from 2011 to 2017) are collected through the Malaysia’s Ministry of Education tracer study. Total number of 43863 data instances involved in this employability class model development. Three classification algorithms, Decision Tree, Support Vector Machines and Artificial Neural Networks are used and being compared for the best models. The results show decision tree J48 produces higher accuracy compared to other techniques with classification accuracy of 66.0651% and it increased to 66.1824% after the parameter tuning. Besides, the algorithm is easily interpreted, and time to build the model is small which is 0.22 seconds. This paper identified seven factors affecting graduate employability, namely age, faculty, field of study, co-curriculum, marital status, industrial internship and English skill. Among these factors, attribute age, industrial internship and faculty contain the most information and affect the final class, i.e. employability status. Therefore, the results of this study will help higher education institutions in Malaysia to prepare their graduates with necessary skills before entering the job market

Using Machine Learning Software in the Human Resource Recruiting Process for Candidates from Dubai Police Academy

Since Machine learning software explored the first recruitment software and found that utilizing technology improves their efficiency at work, speed, and makes the process easier, the use of machine learning for recruitment has become one of the major themes in human resources. In a few years, hiring top talents may lean entirely on the ability of the recruiters to automate their workflows intelligently. Over time, the function of human resource management has indeed evolved in organizations, as technology has been marveled for its greater efficiency in almost every sector. The use of Machine learning for recruiting in organizations has not only saved recruiters’ time but has also enhanced the quality of hiring, as top talents are often in high demand. Furthermore, using machine learning has improved the functionalities of human resource management and made the process of recruiting of new staff and candidates easier. This paper aims to bring to light the importance of using Al in the recruitment process for the Dubai Police Academy and to develop and test a prototype of the system for the functionalities it is meant to perform. This paper has three objectives, which include assessing the need for Machine learning in the organization’s recruitment processes, assessing the levels of adopting this technology, and, finally, investigating the number of complaints during such crucial exercises in the organization. It also uses a survey research design and triangulates both qualitative and quantitative methods for improving the validity and credibility of the study outcomes

Do Dropouts Benefit from Training Programs? Korean Evidence Employing Methods for Continuous Treatments

Failure of participants to complete training programs is pervasive in existing active labor market programs both in developed and developing countries. The proportion of dropouts in prototypical programs ranges from 10 to 50 percent of all participants. From a policy perspective, it is of interest to know if dropouts benefit from the time they spend in training since these programs require considerable resources. We shed light on this issue by estimating the average employment effects of different lengths of exposure to a program by dropouts in a Korean job training program. To do this, we employ parametric and semiparametric methods to estimate effects from continuous treatments using the generalized propensity score, under the assumption that selection into different lengths of exposure is based on a rich set of observed covariates. We find that participants who drop out later – thereby having longer exposures – exhibit higher employment probabilities one year after receiving training, and that marginal effects of additional exposure to training are initially fairly small, but increase sharply past a certain threshold of exposure. One implication of these results is that this and similar programs could benefit from providing incentives for participants to stay longer in the program.training programs, dropouts, developing countries, continuous treatments, generalized propensity score, dose-response function

Human Capital Externalities and Employment Differences across Metropolitan Areas of the U.S.

It has been well documented that employment outcomes often differ considerably across areas. This paper examines the extent to which the local human capital level, measured as the share of adults with a college degree, has positive external effects on labor force participation and employment for U.S. metropolitan area residents. We find that the local human capital level has positive externalities on participation for women, but an inconsistent effect on participation for men. However, the local human capital level reduces unemployment for both men and women. We also find that less educated workers generally receive the largest external benefits.employment; unemployment; human capital externalities; agglomeration

A Regression Study of Salary Determinants in Indian Job Markets for Entry Level Engineering Graduates

The economic liberalisation of Indian markets in early 90s boosted the economic growth of the nation in various sectors over the next two decades. One such sector that has seen a massive growth in this time is Information Technology (IT). The IT industry has played a very crucial role in transforming India from a slow moving economy to one of the largest exporters of IT services. This growth created a huge demand in the labour markets for skilled labour, which in turn made engineering one of the top choices of study after high school over the years. In addition, the earning potential and an opportunity to contribute to technology advancements after engineering, makes it a popular choice of study. These growth dynamics along with the diversified education and labour markets demands gives insight into the factors affecting the employment outcomes of engineering students. This research study focuses on studying the key salary determinants for entry-level engineering graduates in India Labour Markets. The research examined the impact of demographics, academic performance, personality traits and standardised test scores on the starting salary. The research findings indicated that the academic performance in school and college, college reputation, school affiliation and engineering major are key predictors for starting salary. The findings also revealed that Cognitive skills English and Quantitative ability along with a desire to do a task well are significant contributors to the starting salary of engineering graduates in Indian Labour Markets

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review

In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s) of these emerging technologies, most of them are still in the early stages of adoption of these technologies. Thus, a systematic literature review (SLR) on the literature published over the last 5 years on potential applications of machine learning in higher education is necessary. Following the PRISMA guidelines, out of the 1887 initially identified SCOPUS-indexed publications on the topic, 171 articles were selected for review. To screen the abstracts and titles of each citation, Rayyan QCRI was used. VOSViewer, a software tool for constructing and visualizing bibliometric networks, and Microsoft Excel were used to generate charts and figures. The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students. The implications will be invaluable for researchers and practitioners to explore how ML and AI technologies ,in the era of ChatGPT, can be used in universities without jeopardizing academic integrity.info:eu-repo/semantics/publishedVersio

Job Search, Bargaining, and Wage Dynamics

What are the sources of rapid wage growth during a worker's early career? To address this question, I construct and estimate a model of strategic wage bargaining with on-the-job search to explore three different components of wages: general human capital, match-specific capital, and bargaining position. Workers search for alternative job opportunities on the job and accumulate human capital through learning-by-doing. As the workers find better job opportunities, the current employer has to compete with outside firms to retain them. This between-firm competition improves the outside option value of the worker, which results in wage growth on the job even when productivity remains the same. The model is estimated by a simulated minimum distance estimator and data from the NLSY 79. The parameter estimates are used to simulate counterfactuals. Through these simulations I find that only 60% of the observed wage growth reflects the accumulation of general human capital. The growth match-specific capital through job changes accounts for about 20%. The improved bargaining position explains the remaining 20% of the wage growth. The results suggest that labor market frictions explain a larger part of wage growth than previously considered in the literature.