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Many large and small companies in the tech and startup sector have been laying off an unusually high number of workers in 2022 and 2023. We are interested in predicting when this period of layoffs might end, without resorting to economic forecasts. We observe that a sample of layoffs up to March 31, 2023 follow the pattern of noisy observations from an SIR (Susceptible-Infectious-Removed) model. A model is fitted to the data using an analytical solution to the SIR model obtained by Kröger and Schlickeiser. From the fitted model we estimate that the number of weekly layoffs will return to normal levels around the end of 2023.
In Australia and beyond, journalism is reportedly an industry in crisis, a crisis exacerbated by COVID-19. However, the evidence revealing the crisis is often anecdotal or limited in scope. In this unprecedented longitudinal research, we draw on data from the Australian journalism jobs market from January 2012 until March 2020. Using Data Science and Machine Learning techniques, we analyse two distinct data sets: job advertisements (ads) data comprising 3,698 journalist job ads from a corpus of over 8 million Australian job ads; and official employment data from the Australian Bureau of Statistics. Having matched and analysed both sources, we address both the demand for and supply of journalists in Australia over this critical period. The data show that the crisis is real, but there are also surprises. Counter-intuitively, the number of journalism job ads in Australia rose from 2012 until 2016, before falling into decline. Less surprisingly, for the entire period studied the figures reveal extreme volatility, characterised by large and erratic fluctuations. The data also clearly show that COVID-19 has significantly worsened the crisis. We then tease out more granular findings, incl
If AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on. We show that knowing this is not enough for firms to stop it. In a competitive task-based model of a transitioning economy, each firm captures the full cost saving from automation but bears only a fraction of the demand loss it creates in the product market; the rest falls on rivals. This demand externality traps rational firms in an automation arms race, displacing workers well beyond what is collectively optimal. The resulting loss harms both workers and firm owners. More competition and ``better'' AI amplify the excess; wage adjustments and free entry cannot eliminate it. Neither can capital income taxes, worker equity, universal basic income, upskilling, or Coasean bargaining. A Pigouvian automation tax can. The results suggest that policy should address not only the aftermath of AI labor displacement but also the competitive incentives that drive it.
After a disruptive event or shock, such as the Department of Government Efficiency (DOGE) federal layoffs of 2025, expert judgments are colored by knowledge of the outcome. This can make it difficult or impossible to reconstruct the pre-event perceptions needed to study the factors associated with the event. This position paper argues that large language models (LLMs), trained on vast amounts of digital media data, can be a viable substitute for expert political surveys when a shock disrupts traditional measurement. We analyze the DOGE layoffs as a specific case study for this position. We use pairwise comparison prompts with LLMs and derive ideology scores for federal executive agencies. These scores replicate pre-layoff expert measures and predict which agencies were targeted by DOGE. We also use this same approach and find that the perceptions of certain federal agencies as knowledge institutions predict which agencies were targeted by DOGE, even when controlling for ideology. This case study demonstrates that using LLMs allows us to rapidly and easily test the associated factors hypothesized behind the shock. More broadly, our case study of this recent event exemplifies how LLM
In this work we analyze the problem of, given the probability distribution of a population, questioning an unknown individual that is representative of the distribution so that our uncertainty about certain characteristics is significantly reduced -but the uncertainty about others, deemed private or sensitive, is not. Thus, the goal of the problem is extracting information being relevant to a legitimate purpose while preserving the privacy of individuals, which is crucial to enable non-intrusive selection processes in several areas. For instance, it is essential in the design of non-discriminatory personnel selection, promotion, and layoff processes in companies and institutions; in the retrieval of customer information being relevant to the service provided by a company (and no more); in certifications not revealing sensitive industrial information being irrelevant for the certification itself; etc. Interactive questioning processes are constructed for this purpose, which requires generalizing the notion of decision trees to account the amount of desired and undesired information retrieved for each branch of the plan. Our findings about this problem are both theoretical and practi
Several noteworthy scenarios emerged in the global textile and fashion supply chains during and after the COVID-19 pandemic. The destabilizing influences of a global pandemic and a geographically localized conflict are being acutely noticed in the worldwide fashion and textile supply chains. This work examines the impact of the COVID-19 pandemic, the Russo-Ukraine conflict, Israel-Palestine conflict, and Indo-Pak conflict on supply chains within the textile and fashion industry. This research employed a content analysis method to identify relevant articles and news from sources such as Google Scholar, the Summon database of North Carolina State University, and the scholarly news portal NexisUni. The selected papers, news articles, and reports provide a comprehensive overview of the fashion, textile, and apparel supply chain disruptions caused by the pandemic and the war in Ukraine, accompanied by discussions from common supply chain perspectives. Disruptions due to COVID-19 include international brands and retailers canceling orders, closures of stores and factories in developing countries, layoffs, and furloughs of workers in both retail stores and supplier factories, the increase
Computing Education faces significant challenges in equipping graduates with the resilience necessary to remain relevant amid rapid technological change. While existing curricula cultivate computing competencies, they often fail to integrate strategies for sustaining and adapting these skills, leading to reduced career resilience and recurrent industry layoffs. The lack of educational emphasis on sustainability and adaptability amid industry changes perpetuates a vicious cycle: As industries shift, skill fragmentation and decay lead to displacement, which in turn causes further skill degradation. The ongoing deficiency in adaptability and sustainability among learners is reflected in the frequent and intense shifts across the industry. This issue is particularly evident in domains marked by high technological volatility such as computer graphics and game development, where computing concepts, including computational thinking and performance optimization, are uniquely and continuously challenged. To foster sustainable and adaptive growth, this paper introduces, a new framework which addresses the question: How can computing education and professional development be connected to in t
When workers lose jobs to AI-driven restructuring, two very different conversations happen on X (formerly Twitter) at the same time. Tech executives and AI researchers talk about productivity, transformation, and opportunity. Laid-off workers and labour critics talk about job loss, uncertainty, and fear. This paper asks a simple question: which conversation gets more reach? We report three studies using two collection methods and 763 tweets from 20 named public accounts. Study 1 used keyword-based collection (n=392) and found no significant difference between corpora (p=0.891), revealing that keyword search is too noisy for this task. Study 2 used account-based collection (n=96) and found a 3.12x mean amplification advantage for capital discourse over labour discourse (p=0.000003, Cohen's d=0.555). Study 3 combined both methods (n=763) and confirmed the finding at 4.18x mean and 10.77x median amplification ratio (p<0.000001). Critically, after normalising for follower count, the asymmetry persists at 2.69x (p=0.000009, Cohen's d=0.491), demonstrating that the effect is not simply a consequence of capital accounts having larger audiences. The finding is robust across all tested a
Adverse economic shocks are known to reshape voter behavior -- the demand side of politics. Much less is known about their consequences for the supply side: how such shocks affect who becomes a politician. This paper examines how job losses influence individuals' decisions to enter politics and the implications for political selection. Using administrative data linking political participation records to matched employer-employee data covering all formal workers in Brazil, and exploiting mass layoffs for causal identification, we find that job loss significantly increases the likelihood of joining a political party and running for local office. Layoff-induced candidates are positively selected on various competence measures, indicating that economic shocks can improve the quality of political entrants. The increase in candidacies is strongest among laid-off individuals with greater financial incentives from holding office and higher predicted income losses. A regression discontinuity design further shows that eligibility for unemployment benefits increases political entry. These results are consistent with a reduction in individuals' opportunity costs -- both in terms of reduced pri
This paper presents a novel approach to distinguish the impact of duration-dependent forces and adverse selection on the exit rate from unemployment by leveraging variation in the length of layoff notices. I formulate a Mixed Hazard model in discrete time and specify the conditions under which variation in notice length enables the identification of structural duration dependence while allowing for arbitrary heterogeneity across workers. Utilizing data from the Displaced Worker Supplement (DWS), I employ the Generalized Method of Moments (GMM) to estimate the model. According to the estimates, the decline in the exit rate over the first 48 weeks of unemployment is largely due to the worsening composition of surviving jobseekers. Furthermore, I find that an individual's likelihood of exiting unemployment decreases initially, then increases until unemployment benefits run out, and remains steady thereafter. These findings are consistent with a standard search model where returns to search decline early in the spell.
The study investigates the impact of downsizing layoffs on the profitability of construction industries listed in BSE India. In India, construction industries have adopted downsizing long back in the organization to improve the firms performance. For the purpose of the study, Secondary data of 15 Construction companies listed in BSE India have been considered for a period of 10 years from FY.2010 to FY2019. Data has been taken from the companys official website. The variable considered for the analysis is Other Expenses, Returns on Net Worth, Employee Expenses, Number of Employees, and Profit Per Employee. The study has used the Co-integration test to see co-integration between the variables, Ordinary Least Square (OLS) and Vector Auto Regression (VAR) the model used for estimating the impact of downsizing on the profitability of construction companies. OLS and VAR model has been used to draw a conclusion based on the P values and R square. From the result, it can be concluded that, Expect Profit Per Employees are the downsizing variable that has no significant impact on the profitability of the firms performance. Whereas the other Downsizing variables Employee Expenses and the Num
We study the effect of Chile's Employment Protection Law (Ley de Protección del Empleo, EPL), a law which allowed temporal suspensions of job contracts in exceptional circumstances during the COVID-19 pandemic, on the fulfillment of firms' expectations regarding layoffs. We use monthly surveys directed at a representative group of firms in the national territory. This panel data allows to follow firms through time and analyze the match between their expectations and the actual realization to model their expectation fulfilment. We model the probability of expectation fulfilment through a logit model that allows for moderation effects. Results suggest that for those firms that expected to fire workers, for the firms that used the EPL, the odds they finally ended up with a job separation are 50% of the odds for those that did not used the EPL. Small firms increase their probability of expectation fulfilment in 11.9% when using the EPL compared to large firms if they declared they were expecting to fire workers.
To study the causes of the 2021 Great Resignation, we use text analysis to investigate the changes in work- and quit-related posts between 2018 and 2021 on Reddit. We find that the Reddit discourse evolution resembles the dynamics of the U.S. quit and layoff rates. Furthermore, when the COVID-19 pandemic started, conversations related to working from home, switching jobs, work-related distress, and mental health increased. We distinguish between general work-related and specific quit-related discourse changes using a difference-in-differences method. Our main finding is that mental health and work-related distress topics disproportionally increased among quit-related posts since the onset of the pandemic, likely contributing to the Great Resignation. Along with better labor market conditions, some relief came beginning-to-mid-2021 when these concerns decreased. Our study validates the use of forums such as Reddit for studying emerging economic phenomena in real time, complementing traditional labor market surveys and administrative data.
Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them, and predict changes in aggregate unemployment rates using call detail records (CDRs) from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe a structural break model to correctly detect the date of a mass layoff and estimate its size. We then use a Bayesian classification model to identify affected individuals by observing changes in calling behavior following the plant's closure. For these affected individuals, we observe significant declines in social behavior and mobility following job loss. Using the features identified at the micro level, we show that the same changes in these calling behaviors, aggregated at the regional level, can improve forecasts of macro unemployment rates. These methods and results highlight promise of new data resources to measure micro economic behavior and improve estimates of critical economic indicators.
Researchers found that twisting layered sheets of hexagonal boron nitride can dramatically change the light produced by quantum emitters embedded within the material。 The technique offers an unexpected new level of control over components that could power future quantum computers, communications systems, and sensors
Scientists have found that staple-shaped particles can tangle together to create a material that is both strong and flexible。 Unlike conventional materials, these particles can be locked into a sturdy structure or rapidly unraveled using vibrations。 The unusual behavior could open the door to recyclable buildings, reconfigurable structures, and eve
A new technique could solve one of the biggest challenges in making future computer chips from ultrathin materials。 Researchers found that coating molybdenum disulfide with oxygen or fluorine lets manufacturers remove just the top layer of atoms much more safely during plasma processing。 The result is a cleaner, more controlled path toward smaller
MIT researchers have shown that one fuel can power both chemical and electric spacecraft thrusters, potentially transforming what small satellites can do。 The approach combines quick bursts of speed with highly efficient long-range propulsion in a single compact system。 A NASA-supported CubeSat mission will soon test the technology in orbit