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The green transformation of the economy is expected to lead to a sharp reduction in employment in carbon-intensive industries. For designing policies to support displaced workers, it is crucial to better understand the cost of job loss, whether there are specific effects of being displaced from a carbon-intensive sector and which workers are most at risk. By using German administrative labour market data and focusing on mass layoff events, we estimate the cost of involuntary job displacement for workers in high carbon-intensity sectors and compare it with the displacement costs for workers in low carbon-intensity sectors. We find that displaced workers from high carbon-intensity sectors have, on average, higher earnings losses and face stronger difficulties in finding a new job and recovering their earnings. Our results indicate that this is mainly due to human capital specificity, the regional clustering of carbon-intensive activities and higher wage premia in carbon-intensive firms. Workers displaced in high carbon-intensity sectors are older, face higher local labour market concentration and have fewer outside options for finding jobs with similar skill requirements. They have a higher probability to switch occupations and sectors, move to occupations that are more different in terms of skill requirements compared to the pre-displacement job, and are more likely to change workplace districts after displacement. Women, older workers and those with vocational degrees as well as workers in East Germany, experience particularly high costs in case they are displaced from high carbon-intensity sectors.

This paper provides evidence on the effects of digitalisation on the labour market in Slovenia using a unique dataset of Slovenian workers and firms for the years 2016 to 2020. Results show that at the firm level, digitalisation – measured in terms of ICT investment, is associated with positive and statistically significant effects on employment. However, job growth is not evenly distributed: High-skilled workers and younger workers benefit the most from employment gains, whereas there is little to no employment increases for low- and medium-skilled workers and older workers aged 50 or more. Furthermore, employment effects from digitalisation are strongest for private manufacturing firms. In contrast, ICT investment by state-owned firms is not associated with employment gains.

Artificial intelligence (AI) is rapidly reshaping firms’ production and organisational processes, yet whether it can serve as a driving force for corporate green transformation remains an open question. Using a sample of Chinese listed manufacturing firms from 2012 to 2023, this study systematically examines the relationship between AI and firms’ green total factor productivity (GTFP), and explores potential underlying mechanisms. At the theoretical level, drawing on the task-driven nature of AI as a form of technological innovation, this study proposes that AI may enhance GTFP through two channels, namely the structural labour reallocation effect and the managerial dissipation reduction effect. The empirical results show the following: (1) Firms’ AI technical level is significantly associated with improvements in GTFP. (2) Mechanism tests indicate that AI is significantly related to an increasing share of creative task employees and a declining share of structural task employees, thereby providing empirical evidence for the structural labour reallocation effect. Moreover, from four dimensions, including information dissipation, resource allocation dissipation, process coordination dissipation, and incentive and learning dissipation, this study provides supportive evidence that AI is linked to reduced managerial dissipation. (3) Heterogeneity analysis suggests that this association is more pronounced among firms with greater scope for green improvement, such as non-heavily polluting firms and those characterised by managerial myopia. Overall, this study deepens the understanding of the relationship between AI and GTFP from the perspectives of labour structure and corporate organisation, and emphasises that AI’s contribution to firms’ GTFP is more likely to arise as a systemic facilitation embedded in production and organisational processes, rather than through the direct substitution of specialised green technologies.

I study regulation of noncompete employment contracts, assessing the trade-off between restricting worker mobility and encouraging firm investment. I develop an on-the-job search model in which firms and workers sign dynamic wage contracts with noncompete clauses and firms invest in their workers’ general human capital. Employers use noncompete clauses to enforce buyout payments when their workers depart, ultimately extracting rent from future employers. This rent extraction is socially excessive, and restrictions on these clauses can improve efficiency. The optimal regulation policy is characterized. In an application to the managerial labor market using a novel contract data set, I find the optimal policy to be quantitatively close to a ban.

This article examines the nexus between trade, structural change, and labour productivity growth in India’s organised manufacturing sector. The study aims to investigate the impact of trade on labour productivity and labour reallocation that has contributed to aggregate productivity growth during the 1991–2018 period. The descriptive statistical analysis reveals that both exports and imports have increased significantly, and the composition of exported products has shifted towards medium–high-technology-intensive products. We find that the major growth in value added and wages has been observed in the medium-to-high-technology industries, whereas the low-technology segment continues to dominate the employment generation. The panel econometric analysis suggests that productivity growth is primarily driven by technical change within industries, and trade mainly induces intra-sectoral productivity growth with limited impact on labour reallocation between sectors. The absence of structural change from trade openness can be attributed to the existence of market frictions and structural rigidities that prevent efficient resource allocation across the manufacturing sector in India.

Earnings growth dispersion contains information about trends in labor reallocation, unemployment change, and, ultimately, aggregate output. We find that initial macroeconomic estimates released by government statistical agencies do not fully incorporate this information. As a consequence, earnings growth dispersion predicts future restatements in nominal and real GDP growth (and unemployment change) both in the in-sample and out-of-sample tests. Further, when we adjust GDP estimates using the out-of-sample restatement predictions, we find statistically and economically significant effects for the monetary policy prescriptions (Taylor rule) and banking regulation (Basel III).

Declines in migration across labor markets have prompted concerns that the U.S economy is becoming less dynamic. In this study, we examine the relationship between residential migration and employer-to-employer transitions in the United States, using both survey and administrative records data. We first note strong disagreement between the Current Population Survey (CPS) and other migration statistics on the timing and severity of any decline in U.S. interstate migration. Despite these divergent patterns for overall residential migration, we find consistent evidence of a substantial decline in economic migration between 2000 and 2010. We find that composition and the returns to migration have limited ability to explain recent changes in interstate migration.

Purpose The purpose of this study is to analyze the predictability of firm level data for determining macroeconomic indicators such as unemployment. Design/methodology/approach This study uses quarterly GDP and unemployment data manually collected from the Statistical Center of Iran (SCI). Accounting numbers are also collected from the Tehran Stock Exchange library for the 2004-2015 period. Dispersion of earnings growth provides related data about labour reallocation, unemployment change and finally aggregate output. To summarize, this study attempts to examine the effect of these variables using classical and Bayesian approaches. Findings At a firm level, our results suggest that sectoral shift in previous years is likely to increase labour reallocation in subsequent years. At the macro level, the results reveal that dispersion of earnings growth and labour reallocation has a negative and positive impact on unemployment changes, respectively. However, the study suggests no significant relationship between stock return and unemployment changes. Consequently, we determine that the real estimates of macroeconomic indicators have predictive power because nominal estimates are not statistically associated with firm-level details. Finally, the results obtained from classical and Bayesian approaches suggest similar findings, thus confirming the robustness of our conclusions. Note that, based on Bayesian approach, the nominal reallocation has predictive power in unemployment rate. Originality/value The study is the first conducted in a developing country and the results provide important insight into current line of accounting literature.

In recent years, China’s economic growth rate has slowed down significantly, exceeding the normal range of cyclical fluctuations in terms of declining rate and period. However, the research on the structural problems of the economic slowdown from the sector level is still limited. This paper uses a novel index decomposition method to decompose the covariant effect according to the influence of factors. It separates the labor input effect (LIE), labor reallocation effect (LRE), and labor productivity effect (LPE) from China’s economic growth rate from 1989 to 2019. The evolving characteristics and influence of these effects are revealed. It also focuses on the structural problems of the economic slowdown caused by the LRE. The study found that: (i) the economic contribution rate of LIE declined during the study period and had recently shown a negative value; (ii) the economic contribution rate of LRE peaked in 2014 and then rapidly declined; (iii) LPE has always been an essential contributor to China’s economic growth, with an annual contribution rate of 80%. The key factors behind China’s downward economic growth are the decline of the new labor force input, the weakening of LRE, and the technological progress rate in some sectors that have declined. The analysis of the LRE found that 37% of the economic slowdown could be explained by it. The reason behind economic slowdown lies in how the labor force transfers: (i) from agriculture to non-modern services without manufacturing; and (ii) from high-productivity sectors (usually manufacturing) to low-productivity sectors (usually non-modern services). In order to reduce the downward pressure of economic growth, future development intervention measures should focus on improving the employment absorption capacity of manufacturing, enabling enterprise innovation, correcting distorted industrial development policies, and prudently treating environmental protection policies and industrial upgrading policies.

PurposeIn the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.Design/methodology/approachThis study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.Findings(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.Originality/valueBy applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.