Abstract How does technology adoption in developing countries influence firms' use of flexible, low‐protection labour when local demand fluctuates? This paper studies how computer capital intensity moderates firms' use of contract workers in response to exogenous, weather‐driven demand shocks. Using panel data on Indian manufacturing firms from 1999–2000 to 2009–2010, we combine firm‐level information on computer capital stocks with district‐level rainfall shocks that shift agricultural incomes and, in turn, local demand for manufacturing goods. We find that in response to positive transitory demand shocks, firms with above‐average computer capital shares hire 2.32 fewer contract workers than those with below‐average shares, representing an 11.33% difference relative to mean contract employment. This effect primarily impacts workers in manufacturing activities rather than peripheral services. Our results remain robust across various alternative measures of computer capital intensity, including exogenous measures derived from the industry‐level US computer capital expenditure distribution, as well as the inclusion of additional controls for firm‐specific time‐varying characteristics. The results show that computer‐capital‐intensive firms adjust contract employment less in response to transitory demand shocks, highlighting how early‐stage computerization, flexible labour arrangements, and weather‐induced demand fluctuations jointly shape labour demand in developing economies.
In the present research, leisure sports habits and inactivity are analyzed, with special attention given to the factors hindering physical activity and the reasons for dropout. During the study, the past and current frequency of physical activity, as well as the perception of circumstances hindering sports, were assessed using a questionnaire method. The study demonstrates that although regular physical activity is essential for maintaining physical and mental health, 59% of the population in Hungary is considered physically inactive. The results highlight that there is a remarkably strong correlation between past and current sporting activity, and this active lifestyle typically discontinues when individuals' living conditions change, or their work and family obligations increase. The research concludes that although the commitment of the examined sample is high, the lack of work-life balance and the resulting lack of time pose a severe barrier to everyday activity. To resolve this, priority support should be given to school and workplace recreation in the future, which would not only improve individual health but also enhance economic performance and GDP growth at the societal level.
Abstract The peer‐review process in economics faces two well‐documented challenges: a shortage of willing referees, and prolonged turnaround times. We examine whether large language models (LLMs) can help to address these inefficiencies without introducing systematic bias, using two pre‐registered experiments across four LLMs, and over 29,000 evaluations of 1220 anonymized papers. LLMs reliably differentiate research quality by journal rank, and ratings are positively correlated with realized citation impact. However, model performance varies substantially: GPT and Gemma show the strongest evaluative gradients, while LLaMA assigns the maximum score to 81% of papers, rendering it uninformative for quality discrimination. GPT excels at detecting AI‐generated content; Claude‐generated papers receive the highest quality ratings and are the hardest to detect. Varying author identity in the second experiment reveals that three of the four LLMs assign significantly higher ratings to papers attributed to prominent economists and elite Western institutions, and lower ratings to papers with non‐Western affiliations, mirroring the prestige gradients documented in the human peer‐review literature. These biases persist when paper quality is held constant, highlighting the necessity for author‐blind inputs for any editorial deployment of LLM‐assisted screening.
Abstract We study the role of job‐to‐job transitions in shaping wage dynamics in the UK. We show that job moves have continued to generate substantial wage gains for movers even during a period of real wage stagnation and declining labour market mobility. Using an econometric model that accounts for worker, firm and match heterogeneity, we decompose the job mobility premium, and find a limited role for worker and firm effects, with most of the return explained by match‐specific factors. We also examine the determinants of job mobility, and show that much of its recent decline reflects compositional changes in the workforce, particularly ageing and increasing occupational professionalization. We conclude by discussing the policy challenges of promoting job mobility as a driver of wage growth and more efficient labour market reallocation.
We study productivity disparities across metropolitan and non‐metropolitan areas in Great Britain. Spatial disparities in productivity are large and persistent. Using a development accounting framework, we show that differences in area size and in the spatial distribution of human and business capital are key explanatory factors. A combination of area size and human capital explains 30% of the productivity variance, increasing to 43–57% once we add measures of business capital stocks. Applying our framework to a case study of Greater Manchester, we show that large increases in both types of capital are needed to narrow productivity disparities with London, illustrating the scale of the challenge for policies aimed at reducing spatial inequality.
