Job Flows Research Articles

The effect of regional import shocks on job flows in Japanese manufacturing establishments

Using a dataset of all Japanese manufacturing establishments in 2006 and 2016 and industry- and region-level import shocks, this study examined the impact of the China Shock on the job flows of establishments. Regression analyses found that the industry- level import shock increased the probability of exiting in only a few groups. Interestingly, small establishments in single-unit firms located in non-agglomerated regions adopted a hibernation strategy to cope with the region-level import shock. Furthermore, surviving establishments in this category accelerated both job creation and destruction in response to these two import shocks, thereby amplifying the overall magnitude of job flows. These results became observable owing to the use of establishment-level observations and a detailed classification of job flows.

The technological origins of the decline in labor market dynamism

In the last decades, there has been a marked decline in the job flows to and from unemployment and between employment. We ask whether and how technological change can account for his secular decline in labor market dynamism. We propose a theory that focuses on the determinants of technology broadly defined: 1. the complementarity between worker skill and firm productivity; and 2. the volatility in productivity shocks; and 3. search frictions. We derive job flows in a sorting model with search frictions and endogenous search effort both on and off the job, as well as shocks that lead to mismatch. We quantify our model using the US data and find an increase in the complementarity between labor and technology, a decline in the frequency and volatility of productivity shocks, and a decline in the match efficiency as well as an increase in the search costs. The changing nature of these features of the technology contributes to the secular decline in labor market dynamism.

Job Flow Patterns and Productivity Dynamics in Turkish Manufacturing

In this paper, we analyze the job creation and destruction process, and the productivity dynamics in Turkish manufacturing by size, export status, import status and ownership by using a comprehensive firm-level dataset for the period of 2010–2015. Our focus is on the effect of turnover, which is due to the entry and exit of firms, on both job flows and industrial productivity growth. Our results show that while small firms contribute most to job creation, it is the large firms that contribute most to productivity growth. Regarding ownership, domestic private firms perform better than foreign firms in both job creation and productivity growth. With respect to export status, even though non-exporters outperform exporters in job creation, exporters dominate the productivity growth. As for import status, in job creation, like in the case of export status, non-importers do better but in productivity growth, unlike in the export status, no group of firms dominate, more specifically importers’ and non-importers’ contributions are close to each other. Another interesting finding is that, turnover effect on industry productivity is positive but very low. The role of incumbent firms in generating productivity growth is much higher than that of entering and exiting firms.

Heterogeneous Effects of Monetary Policy on Job Flows across Income, Race, Gender, and Age

We study the heterogenous impact of monetary policy, through high-frequency identified shocks, across demographic and income groups. Our evidence shows significant heterogeneity in the monetary transmission across income groups, while significant differences across demographics emerge only for individuals whose income is below the median. We then build a model with idiosyncratic risk and occupational choices that provides a rationale for the channels we uncover: with uninsurable risk, low earners experience a larger decline in earnings, which reduces their likelihood of participating in the labor market and moving across occupations.

An efficient and adaptive design of reinforcement learning environment to solve job shop scheduling problem with soft actor-critic algorithm

Shop scheduling is deeply involved in manufacturing. In order to improve the efficiency of scheduling and fit dynamic scenarios, many Deep Reinforcement Learning (DRL) methods are studied to solve scheduling problems like job shop and flow shop. But most studies focus on using the latest algorithms while ignoring that the environment plays an important role in agent learning. In this paper, we design an effective, robust and size-agnostic environment for job shop scheduling. The proposed design of environment uses centralised training and decentralised execution (CTDE) to implement a multi-agent architecture. Together with the observation space we design, environmental information that is irrelevant to the current decision is eliminated as much as possible. The proposed action space enlarges the decision space of agents, which performs better than the traditional way. Finally, Soft Actor-Critic (SAC) algorithm is adapted to learning within this environment. By comparing with traditional scheduling rules, other reinforcement learning algorithms, and relevant literature, the superiority of the results obtained in this study is demonstrated.

Rising Markups and Declining Business Dynamism: Evidence From the Industry Cross Section

In recent decades, various measures of “business dynamism”—such as new business entry rates and gross job or worker flows—have seen significant declines in the U.S.. Over a similar time frame, there is evidence that an important measure of market power—the average markup—has risen significantly (figure 1, left panel; De Loecker, Eeckhout, and Unger 2020). A natural question is whether these patterns are related.

Improving batch schedulers with node stealing for failed jobs

SummaryAfter a machine failure, batch schedulers typically re‐schedule the job that failed with a high priority. This is fair for the failed job but still requires that job to re‐enter the submission queue and to wait for enough resources to become available. The waiting time can be very long when the job is large and the platform highly loaded, as is the case with typical HPC platforms. We propose another strategy: when a job fails, if no platform node is available, we steal one node from another job , and use it to continue the execution of despite the failure. In this work, we give a detailed assessment of this node stealing strategy using traces from the Mira supercomputer at Argonne National Laboratory. The main conclusion is that node stealing improves the utilization of the platform and dramatically reduces the flow of large jobs, at the price of slightly increasing the flow of small jobs.

Job Flows Into and Out of Health Care Before and After the COVID-19 Pandemic

Anecdotal evidence suggests that health care employers have faced increased difficulty recruiting and retaining staff in the wake of the COVID-19 pandemic. Empirical research is needed to understand the magnitude and persistence of these changes, and whether they have disproportionate implications for certain types of workers or regions of the country. To quantify the number of workers exiting from and entering into the health care workforce before and after the pandemic and to examine variations over time and across states and worker demographics. This cohort study used US Census Bureau state unemployment insurance data on job-to-job flows in the continental US to construct state-level quarterly exit and entry rates for the health care industry from January 2018 through December 2021 (Arkansas, Mississippi, and Tennessee were omitted due to missing data). An event study design was used to compute quarterly mean adjusted rates of job exit from and entry into the health care sector as defined by the North American Industry Classification System. Data were examined from January to June 2023. The COVID-19 pandemic. The main outcomes were the mean adjusted health care worker exit and entry rates in each quarter by state and by worker demographics (age, gender, race and ethnicity, and education level). In quarter 1 of 2020, there were approximately 18.8 million people (14.6 million females [77.6%]) working in the health care sector in our sample. The exit rate for health care workers increased at the onset of the pandemic, from a baseline quarterly mean of 5.9 percentage points in 2018 to 8.0 (95% CI, 7.7-8.3) percentage points in quarter 1 of 2020. Exit rates remained higher than baseline levels through quarter 4 of 2021, when the health care exit rate was 7.7 (95% CI, 7.4-7.9) percentage points higher than the 2018 baseline. In quarter 1 of 2020, the increase in health care worker exit rates was dominated by an increase in workers exiting to nonemployment (78% increase compared with baseline); in contrast, by quarter 4 of 2021, the exit rate was dominated by workers exiting to employment in non-health care sectors (38% increase compared with baseline). Entry rates into health care also increased in the postpandemic period, from 6.2 percentage points at baseline to 7.7 percentage points (95% CI, 7.4-7.9 percentage points) in the last quarter of 2021, suggesting increased turnover of health care staff. Compared with prepandemic job flows, the share of workers exiting health care after the pandemic who were female was disproportionately larger, and the shares of workers entering health care who were female or Black was disproportionately smaller. Results of this cohort study suggest a substantial and persistent increase in health care workforce turnover after the pandemic, which may have long-lasting implications for workers' willingness to remain in health care jobs. Policymakers and health care organizations may need to act to prevent further losses of experienced staff.

Valuation of Active Chilean Employment Support Policies Seeking Economic Sustainability through Market Flows

This paper proposes a market flow methodology to assess Chilean employment support policies between 1996 and 2019 based on the probability of finding or losing a job and market flow analysis. The proposed methodology is analyzed with data obtained from the National Institute of Statistics (INE), establishing flows and probabilities of entry and exit from employment and their contributions to the variability of unemployment to clarify the behavior of Chilean employment support and select the type of measures through which to provide improved recovery and job protection and the other possible improvements to be made. Employment is an essential issue of a country’s sustainability. The results show the efficiency of the proposed approach in evaluating employment support policies in different countries.

A heuristic approach to minimizing the waiting time of jobs in two-stage flow shop scheduling

The paper presents the influence of the waiting time of jobs in a 2 machine k- job Flow Shop Scheduling (FSS) problem. The main intention of the study is to find a sequence of jobs that delivers the least sum of the time of waiting for jobs. A heuristic approach has been adopted to achieve the desired objective. The experiments are conducted for more than 2000 problems of various sizes for the problems with special structures and problems with random times of processing. The weighted mean absolute error (WMAE) for the average of the sum of the waiting times of jobs is computed for both kind of problems after comparing with the optimal solutions. WMAE has been obtained less than 0.0075 for problems with special structures and less than 0.087 for problems with random times of processing. The WMAE is also reducing significantly with the increase in job size. The results demonstrate that the presented step-by-step procedure of the heuristic delivers significantly close to optimal solutions.

Material Hardship, Labor Market Characteristics and Substantiated Child Maltreatment: A Bayesian Spatiotemporal Analysis

Child maltreatment is a critical public health problem whose structural underpinnings underscore the need to move prevention efforts from individual-level risk factors to social policy. Despite previous studies exploring the evolution of child maltreatment risk in socially vulnerable contexts, little is known about how neighborhood level material deprivation and job market characteristics, beyond the employment context, impact substantiated maltreatment risk. The present analysis integrates multiple streams of data to explore the complexity of child maltreatment in the most populous county in New Mexico as a case-study. A geospatial model was used to produce posterior risk estimates and exceedance probabilities of substantiated child maltreatment derived from administrative records controlling for financial strength, economic inequality and hardship, educational attainment, housing and food insecurity and labor market characteristics. Findings showed that over the nine-year study period, the average relative risk of child maltreatment increased substantially, however, there was substantial regional and temporal heterogeneity. More specifically, substantiated child maltreatment risk became more highly concentrated into the most deprived 20% of neighborhoods over time. The results showed a very strong area deprivation effect such that: (1) the risk of maltreatment in the most deprived 20% of neighborhoods on financial strength was 130.78% higher compared to the least deprived 20% of neighborhoods; and (2) maltreatment rates in the bottom 20% of neighborhoods on economic inequality and hardship were 40.52% higher compared to the least deprived 20% of neighborhoods. Finally, substantiated child maltreatment was significantly associated with multiple labor market characteristics including commuting times to work, origin–destination job flows, and mode of transportation to work. From a policy perspective, the results of this study support structural interventions aimed at reducing neighborhood-level material hardship and labor market disadvantage as avenues to support parents so that children and families can thrive.

Design of experiment and simulation approach for analyzing automated guided vehicle performance indicators in a production line

Several manufacturing industries try to reduce transportation waste using automated material handling systems, which can enhance the transportation of raw materials from one location to another in the production line of a manufacturing area. The issue with transportation and job flow is a critical factor in a production line because some production stations need to wait for the work-in-progress to be delivered. Automated guided vehicle (AGV) transportation needs a setup of traffic control over a factory’s physical infrastructure and simulation. Doing so can help showcase and evaluate possible deficiencies that can be improved in the real job flow scenario of the production line. The design of experiment plays a huge role in finding and explaining variations of information under conditions that are regularly put as a hypothesis to reflect or describe the variation. A simulation model is implemented by adopting simplified AGV parameters. The model development follows the structure of system specification → machine specification → AGV specification → discrete-event simulation model → experimental design → analysis of performance indicators (PIs). To precisely reflect an alternative for evaluating aforementioned issues, this study proposes the model stated above and an analysis that is based on the PIs. Analysis of variance (ANOVA) results are chosen to analyze different factors affecting the PIs. Using the factorial ANOVA test results, this study uses one-way and two-way interactions to compare the relationship between job flow time, AGVs, AGV utilization, number of AGVs, and average waiting time.

코로나19 사태가 우리나라 기업의 직업재이동 현상에 미친 영향

Purpose: This paper aims to explore how job reallocation changes in response to the Covid-19 pandemic. More specifically, the paper calculates 5 job flows (job creation, destruction, reallocation, net employment growth and excess reallocation) and how they increase or decrease after the Covid-19 outbreak and examine whether the within-group reallocation is reinforced after the pandemic. Furthermore, the research estimates the key factors (variables) that bring a change in 5 job flows and what affects the most important job flow, namely excess job reallocation.
 Research design, data, and methodology: The research collects data necessary for empirical analysis from KISVALUE, the firm-level data source provided by NICE, a credit rating agency in Korea. And the paper adopts a standard methodology to generate 5 job flows, which was developed by Davis and Haltiwanger(1992). Also, i t also employs to Differences-in-Differences method to address what brings a change in 5 job flows after the outbreak of the pandemic.
 Results: The paper finds out evidence that there were a slight increase in job reallocation shortly after the pandemic unfolded and however job reallocation was alleviated in 2021 and 2022., which is inconsistent with previous studyies on financial crises (including 1997 financial crisis in Korea). Job reallocation between heterogeneous groups were intensified in the post-pandemic period. Furthermore, key performance indexes such as wage-to-sales ratio, leverage, average borrowing rate, ratio of financial expenditures to sales (or total costs) and net profit explain a increase or an decrease in 5 job flows. Notably, excess reallocation depends on leverage and financial expenditures, meaning that a change in the two variables have heterogeneous effects across industrial sectors.
 Implications: All in all, the main results indicate 3 important implications on job reallocation in the post Covid-19 period. First, Covid-19 is an economic shock that has a temporary effect on the magnitude of job reallocation. Second, although mild, it is a reallocative shock that brings reallocation of jobs across sectors or other groups of firms with different properties. Third, financial reasons such as leverage and financial expenditures are sources that results in job reallocation between 2-digit industrial sectors.

Pointless or Populism?

This article began as a response to Daniel Cook’s recent article on ‘Turning the Tap Off – Stopping the Flow of Pointless Jobs in the Royal Australian Air Force (RAAF)’ (Cook, 2023); as such, it will address some of the points raised in that article within the context a broader trend towards what I term ‘military populism’.

Digital Twinning and Optimization of Manufacturing Process Flows

Abstract The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amounts of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models—job flow graph and AGV traveling graph—to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories. The sequential design of experiments effectively reduces the computation overhead of expensive simulations while optimally scheduling the AGV to achieve production throughput cost-effectively. This research is strongly promised to help SMMs fully utilize big data and DT technology for gaining competitive advantages in the global marketplace.

Uncertainty shocks and employment fluctuations in Germany: the role of establishment size

Abstract Uncertainty shocks are found to affect labour market outcomes adversely. Most studies interrelate non-convex labour adjustment costs with the propagation of macroeconomic uncertainty to the labour market. I show that non-convex labour adjustment costs differ by establishment size in Germany. Hence, uncertainty shocks should affect large and small establishments differently. Therefore, this article studies the effects of uncertainty shocks on employment adjustments in large and small establishments employing four structural vector auto-regressive models for Germany from 1992 to 2014. These four models estimate the effects of uncertainty shocks on employment, worker flows, job flows, as well as worker churn in establishments with fewer than 100 and with at least 100 employees. The results suggest that uncertainty shocks trigger considerable employment fluctuations in large establishments while they barely affect small establishments. Furthermore, large establishments adjust their labour input by delaying the replacement of workers.

The China shock and job reallocation in Japan

This study investigates the characteristics of manufacturing job reallocation in Japan induced by import shocks from China during 1996–2016. Three types of import shocks are considered: direct, upstream, and downstream. Some salient features of job reallocation include decrease in total jobs from direct import, increase in small establishments’ jobs from downstream import, and job changes mainly induced establishments’ entry and exit. The sizeable difference of implied job changes in industry-level analysis and those in region-level analysis attributes to the local reallocation and aggregate demand effects determined by regional characteristics. The total job effect of three import shocks is negative in all cases examined. The method of decomposing job changes into detailed job flows and further into industry and regional factors, proposed in this study, enabled obtaining a clearer view of job reallocation and how import shocks travel through labor market.

Ailing neurosurgical services in rural Africa

Ailing neurosurgical services in rural Africa

Earnings growth, job flows and churn

How much do workers making job-to-job transitions benefit from moving away from a shrinking and towards a growing firm? Matched employer-employee data show that earnings growth in the transition increases with net employment growth at the destination firm and, to a lesser extent, decreases if the origin firm is shrinking. These results are not driven by composition, that different workers are going to growing or from shrinking firms, but rather implies that firm dynamics themselves are key to workers’ earnings growth during job-to-job transitions. Further, firms’ net employment growth rather than gross hires mostly drives the growth.

Turning the Tap Off - Stopping the Flow of Pointless Jobs in the RAAF

WGCDR Borg’s monograph Royal Australian Air Force 2025: A new organizational structure to optimise combat effectiveness in an era of great power competition (Borg, 2023) included a statement that is more telling than it appears at first glance: 'We are too busy mopping the floor, we don’t have time to turn off the tap.' - Anonymous The opening quote accurately depicts the current feeling across many squadrons, wings and groups across the RAAF today.” (Borg, 2023, p. 29)