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The Future of Jobs Report 2023

research paper on labour market

1. Introduction: the global labour market landscape in 2023

The past three years have been shaped by a challenging combination of health, economic and geopolitical volatility combined with growing social and environmental pressures. These accelerating transformations have and continue to reconfigure the world’s labour markets and shape the demand for jobs and skills of tomorrow, driving divergent economic trajectories within and across countries, in developing and developed economies alike. The Fourth Industrial Revolution, changing worker and consumer expectations, and the urgent need for a green and energy transition are also reconfiguring the sectoral composition of the workforce and stimulating demand for new occupations and skills. Global supply chains must also quickly adapt to the challenges of increasing geopolitical volatility, economic uncertainty, rising inflation and increasing commodity prices.

Like previous editions, The Future of Jobs Report 2023 offers insights into these transformations and unpacks how businesses are expecting to navigate these labour-market changes from 2023 to 2027, leveraging a unique cross-sectoral and global survey of Chief Human Resources, Chief Learning Officers and Chief Executive Officers of leading global employers and their peers.

This report is structured as follows: Chapter 1 reviews the global labour-market landscape at the beginning of 2023. Chapter 2 explores how key macrotrends are expected to transform this landscape over the 2023–2027 period. Chapters 3 and 4 then discuss the resulting global outlooks for jobs and skills over the 2023–2027 period. Chapter 5 reviews emerging workforce and talent strategies in response to these trends. The report’s appendices provide an overview of the report’s survey methodology and detailed sectoral breakdowns of the five-year outlook for macrotrends, technology adoption and skills.

In addition, The Future of Jobs Report 2023 features a comprehensive set of Economy, Industry, and – for the first time – Skill Profiles. User Guides are provided for each of these profiles, to support their use as practical, standalone tools.

As a foundation for analysing respondents’ expectations of the future of jobs and skills in the next five years, this chapter now assesses the current state of the global labour-market at the beginning of 2023.

Diverging labour-market outcomes between low-, middle- and high-income countries

The intertwined economic and geopolitical crises of the past three years created an uncertain and divergent outlook for labour markets, widening disparities between developed and emerging economies and among workers. Even as a growing number of economies have begun to recover from the COVID-19 pandemic and its associated lockdowns, low- and lower-middle-income countries continue to face elevated unemployment, while high-income countries are generally experiencing tight labour markets.

At the time of publication, the latest unemployment rates stand below pre-pandemic rates in three quarters of OECD countries, 1 and across a majority of G20 economies (Figure 1.1). At 4.9%, the 2022 unemployment rate across the OECD area is at its lowest level since 2001. 2

By contrast, many developing economies have experienced a comparatively slow labour-market recovery from the disruptions induced by the COVID-19 pandemic. In South Africa, for example, the formal unemployment rate has climbed to 30%, five percentage points higher than it was pre-pandemic (Figure 1.1). Developing economies, especially those reliant on the sectors hardest hit by recurring lockdowns, such as hospitality and tourism, still exhibit slow labour-market recoveries.

research paper on labour market

The asymmetry of the recovery is exacerbated by countries’ varying capacities to maintain policy measures to protect the most vulnerable and maintain employment levels. While advanced economies were able to adopt far-reaching measures, emerging economies have provided less support to the most vulnerable firms and workers due to their limited fiscal space. 3,4

In 2022, various employment indicators pointed towards a strong labour-market recovery for high-income countries, with many sectors experiencing labour shortages. In Europe, for example, almost three in 10 manufacturing and service firms reported production constraints in the second quarter of 2022 due to a lack of workers. 5 Nursing professionals, plumbers and pipefitters, software developers, systems analysts, welders and flame cutters, bricklayers and related workers, and heavy truck and lorry drivers were among the most needed professions (Figure 1.2).

research paper on labour market

In the United States, businesses in Retail and Wholesale of Consumer Goods reported close to 70% of job openings remaining unfilled, with close to 55% of roles unfilled in manufacturing and 45% in leisure and hospitality. 6 Businesses also reported difficulties in retaining workers. According to a global survey conducted in late 2022 across 44 countries, one in five employees reported they intend to switch employers in the coming year. 7

Diverging employment levels by gender, age and education level

Women experienced greater employment loss than men during the pandemic 8 , and according to the World Economic Forum’s Global Gender Gap Report 2022 9 , gender parity in the labour force stands at 62.9% – the lowest level registered since the index was first compiled. The global pandemic also disproportionately impacted young workers, with less than half of the global youth employment deficit projected to have recovered by the end of 2022. 10 As highlighted in Figure 1.3, the youth employment deficit relative to 2019 is largest in Southern Asia, Latin America, Northern Africa and Eastern Europe, with only Europe and North America likely to have fully recovered at the time of publication.

research paper on labour market

Workers with a basic education were also hardest hit in 2020, and slower to recover their prior participation in the labour market. In many countries the increase in unemployment from 2019 to 2021 of workers with a basic education level was more than twice as large as the impact on workers with advanced education (Figure 1.4).

research paper on labour market

Access to social protection

From January 2020 to January 2022, almost 3,900 social-protection measures were implemented across 223 economies to support the labour force impacted by COVID-19. 11 These measures are estimated to have reached close to 1.2 billion people globally. Wage subsidies, cash transfers, training measures and extending unemployment-benefit coverage have all been crucial tools to protect the most vulnerable during the pandemic. Most such short-term support measures are now being phased out, 12 and targeted medium to long-term investments will be needed to alleviate the long-term effects of recurring economic shocks on firms and workers.

Yet, there remains an urgent need to provide adequate social protection to those not covered by full-time employment contracts (Figure 1.5). Nearly 2 billion workers globally are in informal employment, representing close to 70% of workers in developing and low-income countries, as well as 18% in high income ones. 13 Given their susceptibility to economic shocks and working poverty, informal workers represent a crucial labour-market cohort and need better representation in data, broad-based income support in the short term and a longer term shift towards formalization.

research paper on labour market

Real wages and cost of living

According to the International Labour Organization (ILO), labour income in many developing countries remains below pre-pandemic levels. 14 In 2020, the global economy started experiencing inflation levels not seen in almost 40 years. 15 With high inflation, the global cost-of-living crisis has hit the most vulnerable hardest. 16 According to the ILO, for the first time over the last 15 years, workers’ real wages have declined – by 0.9% in the first half of 2022. 17

Across regions, real wage growth was most affected in Northern, Southern and Western Europe; Latin America; Asia Pacific; and North America. 18 In Africa, real wages saw a 10.5% drop in 2020 due to the global pandemic. 19 However, real wages have continued to increase in 2022 across Asia Pacific, Central and Western Asia and Arab states. 20

In line with rising inflation, purchasing power has declined for the most vulnerable, given the higher weight of energy and food in expenditures of the lowest-income households. 21 According to recent research by the United Nations Development Programme (UNDP), rising food and energy prices could push up to 71 million people into poverty, with hot spots in Sub-Saharan Africa, the Balkans and the Caspian Basin. 22 This cost-of-living crisis highlights the importance of designing permanent models of social protection for non-standard employment and the informal economy that provide security and support resilience. 23

Worker preferences

In this context of diverging labour-market outcomes, issues around the quality of work have come to the fore. This section reviews some of the latest worker preference research to analyse which job attributes are of most importance to workers currently. As a starting point, data shows workers, openness to changing employer. Data on worker preferences from CultureAmp 24 and Adecco 25 find that more than a quarter (33% and 27% of workers, respectively) do not see themselves at their current company of employment in two years’ time. In line with this, a little under half of workers (42% and 45%, according to CultureAmp and Adecco, respectively) actively explore opportunities at different companies.

Worker surveys at both CultureAmp 26 and Randstad 27 suggest that salary levels are the main reason workers decide to change their job. 52% of Randstad respondents say they worry about the impact of economic uncertainty on their employment and 61% of respondents to Adecco’s worker-preference survey worry that their salary is not high enough to keep pace with the cost of living given rising rates of inflation. 28

Additional data explores the protection and flexibility of employment: 92% of respondents to Randstad’s employee survey 29 say job security is important and more than half of these respondents wouldn’t accept a job that didn’t give assurances regarding job security. 83% prioritize flexible hours and 71% prioritize flexible locations.

A fourth theme identified by workers is work-life balance and burnout: 35% of CultureAmp respondents indicate that work-life balance and burnout would be the primary reason to leave their employer. Workers responding to Randstad’s employee survey 30 value salary and work-life balance equally, with a 94% share identifying both aspects of employment as important to choosing to work in a particular role.

Data also suggests that diversity, equity and inclusion (DEI) at work is particularly important to young workers. According to Manpower, 31 68% of Gen Z workers are not satisfied with their organization’s progress in creating a diverse and inclusive work environment, and 56% of Gen Z workers would not accept a role without diverse leadership. Meanwhile, data suggests that fewer women than men are trained.

Lastly, workers across age ranges indicate dissatisfaction about training opportunities. Manpower data 32 show that 57% of surveyed employees are pursuing training outside of work, because company training programmes do not teach them relevant skills, advance their career development or help them stay competitive in the labour market. Respondents to Adecco’s survey criticize companies for focusing their efforts too much on managers’ development, skills and rewards. Only 36% of non-managers who responded to Adecco’s survey said that their company is investing effectively in developing their skills, compared to 64% of managers.

Employment shifts across sectors

The past two years have witnessed a volatility in the demand and supply of goods and services resulting from lockdowns and supply-chain disruptions. The global economic rebound has reconfigured the sectoral distribution of employment across industries. Figure 6 presents OECD data demonstrating that, while Information Technology and Digital Communications experienced a strong rebound in most countries, the Accommodation, Food and Leisure; Manufacturing and Consumer; and Wholesale and Consumer Goods sectors are experiencing a slower rate of recovery. Since the first quarter of 2019, a majority of countries have experienced employment growth in Professional Services, Education and Training, Health and Healthcare, and Government and Public Sector, but employment in the Supply Chain and Transportation and Media, Entertainment and Sports sectors lags behind 2019 levels.

In addition to the pandemic-induced employment shifts we have seen across sectors during the last few years, generative AI models are likely to continue shaping sectoral shifts in employment. While AI applications are shown to be effective general-purpose technologies, 33 the development of general-purpose technologies have previously been hard to predict, which is why regulation needs to be both prompt and adaptable as institutions learn how these technologies can be used.

research paper on labour market

Through research conducted for the Future of Jobs Report, LinkedIn has identified the fastest growing roles globally over the past four years, shedding further light on the types of jobs employers have been seeking (Box 1.1).

The transformations that labour markets are experiencing have also increased the need for swifter and more efficient job reallocation mechanisms within and across different firms and sectors. The coming years represent a generational opportunity for businesses and policy-makers to embrace a future of work which fosters economic inclusion and opportunity, sets in place policies which will influence not only the rate of growth but its direction, and contribute to shaping more inclusive, sustainable and resilient economies and societies.

The green transition, technological change, supply-chain transformations and changing consumer expectations are all generating demand for new jobs across industries and regions. However, these positive drivers are offset by growing geoeconomic tensions and a cost-of-living crisis. 34

The Future of Jobs Survey was conducted in late 2022 and early 2023 bringing together the perspective of 803 companies – collectively employing more than 11.3 million workers – across 27 industry clusters and 45 economies from all world regions. The Survey covers questions of macrotrends and technology trends, their impact on jobs, their impact on skills, and the workforce transformation strategies businesses plan to use.

This chapter analyses findings from the World Economic Forum’s Future of Jobs Survey to explore how businesses expect macrotrends and technology adoption to drive industry transformation and employment.

BOX 1.1 The fastest-growing jobs support sales growth and customer engagement, the search for talent, and technology/IT

In collaboration with LinkedIn

Research conducted by LinkedIn for the Future of Jobs Report 2023 describes the 100 roles that have grown fastest, consistently and globally, over the last four years – known as the “Jobs on the Rise”. While ILO and OECD data show which sectors are employing more people, Jobs on the Rise data identifies the specific job types that have experienced significant growth. Figure B.1 organizes the 100 Jobs on the Rise into broad types.

In line with ILO and OECD data on the growth of roles in the Information Technology and Digital Communication sector, Technology and IT related roles make up 16 of the top 100 Jobs on the Rise, the third-highest of all job groupings. Jobs related to Sales Growth and Customer Engagement top the list, with 22 of the 100 roles. With roles such as Sales Development Representatives, Director of Growth, and Customer Success Engineer featuring in this group, this may suggest an increasing focus on broadening customer groups and growth models in a world with increasing digital access and rapid technological advancement (more detail on how increasing digital access and adoption of frontier technologies could transform demand for specific job types is available in Chapter 3). Human Resources and Talent Acquisition roles are the second-most popular roles, and most of these relate to Talent Acquisition and Recruitment, including a specific role for Information Technology Recruitment – perhaps illustrating the increasing difficulty and importance of accessing talent in a generally strong labour market.

Of the groups further down the list, Sustainability and Environment related roles are notable for all being in the top 40, including three of the top 10 roles (Figure B.2). This might suggest the green transition is both a significant and developing labour-market trend, where roles have titles such as “Sustainability Analyst”. Chapter 3 further examines the outlook for roles related to a green transition.

research paper on labour market

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  • Published: 18 January 2024

The impact of artificial intelligence on employment: the role of virtual agglomeration

  • Yang Shen   ORCID: orcid.org/0000-0002-6781-6915 1 &
  • Xiuwu Zhang 1  

Humanities and Social Sciences Communications volume  11 , Article number:  122 ( 2024 ) Cite this article

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Sustainable Development Goal 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand. In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment. The findings of this study contribute to the understanding of the impact of modern digital technologies on the well-being of people in developing countries. To give full play to the positive role of artificial intelligence technology in employment, we should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.

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Introduction

Ensuring people’s livelihood requires diligence, but diligence is not scarce. Diversification, technological upgrading, and innovation all contribute to achieving the Sustainable Development Goal of full and productive employment for all (SDGs 8). Since the outbreak of the industrial revolution, human society has undergone four rounds of technological revolution, and each technological change can be regarded as the deepening of automation technology. The conflict and subsequent rebalancing of efficiency and employment are constantly being repeated in the process of replacing people with machines (Liu 2018 ; Morgan 2019 ). When people realize the new wave of human economic and social development that is created by advanced technological innovation, they must also accept the “creative destruction” brought by the iterative renewal of new technologies (Michau 2013 ; Josifidis and Supic 2018 ; Forsythe et al. 2022 ). The questions of where technology will eventually lead humanity, to what extent artificial intelligence will change the relationship between humans and work, and whether advanced productivity will lead to large-scale structural unemployment have been hotly debated. China has entered a new stage of deep integration and development of the “new technology cluster” that is represented by the internet and the real economy. Physical space, cyberspace, and biological space have become fully integrated, and new industries, new models, and new forms of business continue to emerge. In the process of the vigorous development of digital technology, its characteristics in terms of employment, such as strong absorption capacity, flexible form, and diversified job demands are more prominent, and many new occupations have emerged. The new practice of digital survival that is represented by the platform economy, sharing economy, full-time economy, and gig economy, while adapting to, leading to, and innovating the transformation and development of the economy, has also led to significant changes in employment carriers, employment forms, and occupational skill requirements (Dunn 2020 ; Wong et al. 2020 ; Li et al. 2022 ).

Artificial intelligence (AI) is one of the core areas of the fourth industrial revolution, along with the transformation of the mechanical technology, electric power technology, and information technology, and it serves to promote the transformation and upgrading of the digital economy industry. Indeed, the rapid iteration and cross-border integration of general information technology in the era of the digital economy has made a significant contribution to the stabilization of employment and the promotion of growth, but this is due only to the “employment effect” caused by the ongoing development of the times and technological progress in the field of social production. Digital technology will inevitably replace some of the tasks that were once performed by human labour. In recent years, due to the influence of China’s labour market and employment structure, some enterprises have needed help in recruiting workers. Driven by the rapid development of artificial intelligence technology, some enterprises have accelerated the pace of “machine replacement,” resulting in repetitive and standardized jobs being performed by robots. Deep learning and AI enable machines and operating systems to perform more complex tasks, and the employment prospects of enterprise employees face new challenges in the digital age. According to the Future of Jobs 2020 report released by the World Economic Forum, the recession caused by the COVID-19 pandemic and the rapid development of automation technology are changing the job market much faster than expected, and automation and the new division of labour between humans and machines will disrupt 85 million jobs in 15 industries worldwide over the next five years. The demand for skilled jobs, such as data entry, accounting, and administrative services, has been hard hit. Thanks to the wave of industrial upgrading and the vigorous development of digitalization, the recruitment demand for AI, big data, and manufacturing industries in China has maintained high growth year-on-year under the premise of macroenvironmental uncertainty during the period ranging from 2019 to 2022, and the average annual growth rate of new jobs was close to 30%. However, this growth has also aggravated the sense of occupational crisis among white-collar workers. The research shows that the agriculture, forestry, animal husbandry, fishery, mining, manufacturing, and construction industries, which are expected to adopt a high level of intelligence, face a high risk of occupational substitution, and older and less educated workers are faced with a very high risk of substitution (Wang et al. 2022 ). Whether AI, big data, and intelligent manufacturing technology, as brand-new forms of digital productivity, will lead to significant changes in the organic composition of capital and effectively decrease labour employment has yet to reach consensus. As the “pearl at the top of the manufacturing crown,” a robot is an essential carrier of intelligent manufacturing and AI technology as materialized in machinery and equipment, and it is also an important indicator for measuring a country’s high-end manufacturing industry. Due to the large number of manufacturing employees in China, the challenge of “machine substitution” to the labour market is more severe than that in other countries, and the use of AI through robots is poised to exert a substantial impact on the job market (Xie et al. 2022 ). In essence, the primary purpose of the digital transformation of industrial enterprises is to improve quality and efficiency, but the relationship between machines and workers has been distorted in the actual application of digital technology. Industrial companies use robots as an entry point, and the study delves into the impact of AI on the labour market to provide experience and policy suggestions on the best ways of coordinating the relationship between enterprise intelligent transformation and labour participation and to help realize Chinese-style modernization.

As a new general technology, AI technology represents remarkable progress in productivity. Objectively analysing the dual effects of substitution and employment creation in the era of artificial intelligence to actively integrate change and adapt to development is essential to enhancing comprehensive competitiveness and better qualifying workers for current and future work. This research is organized according to a research framework from the published literature (Luo et al. 2023 ). In this study, we used data published by the International Federation of Robotics (IFR) and take the installed density of industrial robots in China as the main indicator of AI. Based on panel data from 30 provinces in China covering the period from 2006–2020, the impact of AI technology on employment in a developing country with a large population size is empirically examined. The issues that need to be solved in this study include the following: The first goal is to examine the impact of AI on China’s labour market from the perspective of the economic behaviour of those enterprises that have adopted the use of industrial robots in production. The realistic question we expect to answer is whether the automated processing of daily tasks has led to unemployment in China during the past fifteen years. The second goal is to answer the question of how AI will continue to affect the employment market by increasing labour productivity, changing the technical composition of capital, and deepening the division of labour. The third goal is to examine how the transformation of industrial organization types in the digital economy era affects employment through digital industrial clusters or virtual clusters. The fourth goal is to test the role of AI in eliminating gender discrimination, especially in regard to whether it can improve the employment opportunities of female employees. Then, whether workers face different employment difficulties in different industry attributes is considered. The final goal is to provide some policy insights into how a developing country can achieve full employment in the face a new technological revolution in the context of a large population and many low-skilled workers.

The remainder of the paper is organized as follows. In Section Literature Review, we summarize the literature on the impact of AI on the labour market and employment and classify it from three perspectives: pessimistic, negative, and neutral. Based on a literature review, we then summarize the marginal contribution of this study. In Section Theoretical mechanism and research hypothesis, we provide a theoretical analysis of AI’s promotion of employment and present the research hypotheses to be tested. In Section Study design and data sources, we describe the data source, variable setting and econometric model. In Section Empirical analysis, we test Hypothesis 1 and conduct a robustness test and the causal identification of the conclusion. In Section Extensibility analysis, we test Hypothesis 2 and Hypothesis 3, as well as testing the heterogeneity of the baseline regression results. The heterogeneity test employee gender and industry attributes increase the relevance of the conclusions. Finally, Section Conclusions and policy implications concludes.

Literature review

The social effect of technological progress has the unique characteristics of the times and progresses through various stages, and there is variation in our understanding of its development and internal mechanism. A classic argument of labour sociology and labour economics is that technological upgrading objectively causes workers to lose their jobs, but the actual historical experience since the industrial revolution tells us that it does not cause large-scale structural unemployment (Zhang 2023a ). While neoclassical liberals such as Adam Smith claimed that technological progress would not lead to unemployment, other scholars such as Sismondi were adamant that it would. David Ricardo endorsed the “Luddite fear” in his book On Machinery, and Marx argued that technological progress can increase labour productivity while also excluding labour participation, thus leaving workers in poverty. The worker being turned ‘into a crippled monstrosity’ by modern machinery. Technology is not used to reduce working hours and improve the quality of work, rather, it is used to extend working hours and speed up work (Spencer 2023 ). According to Schumpeter’s innovation theory, within a unified complex system, the essence of technological innovation forms from the unity of positive and negative feedback and the oneness of opposites such as “revolutionary” and “destructive.” Even a tiny technological impact can cause drastic consequences. The impact of AI on employment is different from the that of previous industrial revolutions, and it is exceptional in that “machines” are no longer straightforward mechanical tools but have assumed more of a “worker” role, just as people who can learn and think tend to do (Boyd and Holton 2018 ). AI-related technologies continue to advance, the industrialization and commercialization process continues to accelerate, and the industry continues to explore the application of AI across multiple fields. Since AI was first proposed at the Dartmouth Conference in 1956, discussions about “AI replacing human labor” and “AI defeating humans” have endlessly emerged. This dynamic has increased in intensity since the emergence of ChatGPT, which has aroused people’s concerns about technology replacing the workforce. Summarizing the literature, we can find three main arguments concerning the relationship between AI and employment:

First, AI has the effect of creating and filling jobs. The intelligent manufacturing industry paradigm characterized by AI technology will assist in forming a high-quality “human‒machine cooperation” employment mode. In an enlightened society, the social state of shared prosperity benefits the lowest class of people precisely because of the advanced productive forces and higher labour efficiency created through the refinement of the division of labour. By improving production efficiency, reducing the sales price of final products, and stimulating social consumption, technological progress exerts both price effects and income effects, which in turn drive related enterprises to expand their production scale, which, in turn, increases the demand for labour (Li et al. 2021 ; Ndubuisi et al. 2021 ; Yang 2022 ; Sharma and Mishra 2023 ; Li et al. 2022 ). People habitually regard robots as competitors for human beings, but this view only represents the materialistic view of traditional machinery. The coexistence of man and machine is not a zero-sum game. When the task evolves from “cooperation for all” to “cooperation between man and machine,” it results in fewer production constraints and maximizes total factor productivity, thus creating more jobs and generating novel collaborative tasks (Balsmeier and Woerter 2019 ; Duan et al. 2023 ). At the same time, materialized AI technology can improve the total factor production efficiency in ways that are suitable for its factor endowment structure and improve the production efficiency between upstream and downstream enterprises in the industrial chain and the value chain. This increase in the efficiency of the entire market will subsequently drive the expansion of the production scale of enterprises and promote reproduction, and its synergy will promote the synchronous growth of the labour demand involving various skills, thus resulting in a creative effect (Liu et al. 2022 ). As an essential force in the fourth industrial revolution, AI inevitably affects the social status of humans and changes the structure of the labour force (Chen 2023 ). AI and machines increase labour productivity by automating routine tasks while expanding employee skills and increasing the value of work. As a result, in a machine-for-machine employment model, low-skilled jobs will disappear, while new and currently unrealized job roles will emerge (Polak 2021 ). We can even argue that digital technology, artificial intelligence, and robot encounters are helping to train skilled robots and raise their relative wages (Yoon 2023 ).

Second, AI has both a destructive effect and a substitution effect on employment. As soon as machines emerged as the means of labour, they immediately began to compete with the workers themselves. As a modern new technology, artificial intelligence is essentially humanly intelligent labour that condenses complex labour. Like the disruptive general-purpose technologies of early industrialization, automation technologies such as AI offer both promise and fear in regard to “machine replacement.” Technological progress leads to an increase in the organic composition of capital and the relative surplus population. The additional capital formed in capital accumulation comes to absorb fewer and fewer workers compared to its quantity. At the same time, old capital, which is periodically reproduced according to the new composition, will begin to increasingly exclude the workers it previously employed, resulting in severe “technological unemployment.” The development of productivity creates more free time, especially in industries such as health care, transportation, and production environment control, which have seen significant benefits from AI. In recent years, however, some industrialized countries have faced the dilemma of declining income from labour and the slow growth of total labour productivity while applying AI on a large scale (Autor 2019 ). Low-skilled and incapacitated workers enjoy a high probability of being replaced by automation (Ramos et al. 2022 ; Jetha et al. 2023 ). It is worth noting that with the in-depth development of digital technologies, such as deep learning and big data analysis, some complex, cognitive, and creative jobs that are currently considered irreplaceable in the traditional view will also be replaced by AI, which indicates that automation technology is not only a substitute for low-skilled labour (Zhao and Zhao 2017 ; Dixon et al. 2021 ; Novella et al. 2023 ; Nikitas et al. 2021 ). Among factors, AI and robotics exert a particularly significant impact on the manufacturing job market, and industry-related jobs will face a severe unemployment problem due to the disruptive effect of AI and robotics (Zhou and Chen 2022 ; Sun and Liu 2023 ). At this stage, most of the world’s economies are facing the deep integration of the digital wave in their national economy, and any work, including high-level tasks, is being affected by digitalization and AI (Gardberg et al. 2020 ). The power of AI models is growing exponentially rather than linearly, and the rapid development and rapid diffusion of technology will undoubtedly have a devastating effect on knowledge workers, as did the industrial revolution (Liu and Peng 2023 ). In particular, the development and improvement of AI-generated content in recent years poses a more significant threat to higher-level workers, such as researchers, data analysts, and product managers, than to physical labourers. White collar workers are facing unprecedented anxiety and unease (Nam 2019 ; Fossen and Sorgner 2022 ; Wang et al. 2023 ). A classic study suggests that AI could replace 47% of the 702 job types in the United States within 20 years (Frey and Osborne 2017 ). Since the 2020 epidemic, digitization has accelerated, and online and digital resources have become a must for enterprises. Many occupations are gradually moving away from humans (Wu and Yang 2022 ; Männasoo et al. 2023 ). It is obvious that the intelligent robot arm on the factory assembly line is poised to allow factory assembly line workers to exit the stage and move into history. Career guides are being replaced by mobile phone navigation software.

Third, the effect of AI on employment is uncertain, and its impact on human work does not fall into a simple “utopian” or “dystopian” scene, but rather leads to a combination of “utopia” and “dystopia” (Kolade and Owoseni 2022 ). The job-creation effects of robotics and the emergence of new jobs that result from technological change coexist at the enterprise level (Ni and Obashi 2021 ). Adopting a suitable AI operation mode can adjust for the misallocation of resources by the market, enterprises, and individuals to labour-intensive tasks, reverse the nondirectional allocation of robots in the labour sector, and promote their reallocation in the manufacturing and service industries. The size of the impact on employment through the whole society is uncertain (Fabo et al. 2017 ; Huang and Rust 2018 ; Berkers et al. 2020 ; Tschang and Almirall 2021 ; Reljic et al. 2021 ). For example, Oschinski and Wyonch ( 2017 ) claimed that those jobs that are easily replaced by AI technology in Canada account for only 1.7% of the total labour market, and they have yet to find evidence that automation technology will cause mass unemployment in the short term. Wang et al. ( 2022 ) posited that the impact of industrial robots on labour demand in the short term is mainly negative, but in the long run, its impact on employment is mainly that of job creation. Kirov and Malamin ( 2022 ) claimed that the pessimism underlying the idea that AI will destroy the jobs and quality of language workers on a large scale is unjustified. Although some jobs will be eliminated as such technology evolves, many more will be created in the long run.

In the view that modern information technology and digital technology increase employment, the literature holds that foreign direct investment (Fokam et al. 2023 ), economic systems (Bouattour et al. 2023 ), labour skills and structure (Yang 2022 ), industrial technological intensity (Graf and Mohamed 2024 ), and the easing of information friction (Jin et al. 2023 ) are important mechanisms. The research on whether AI technology crowds out jobs is voluminous, but the conclusions are inconsistent (Filippi et al. 2023 ). This paper is focused on the influence of AI on the employment scale of the manufacturing industry, examines the job creation effect of technological progress from the perspectives of capital deepening, labour refinement, and labour productivity, and systematically examines the heterogeneous impact of the adoption of industrial robots on employment demand, structure, and different industries. The marginal contributions of this paper are as follows: first, the installation density of industrial robots is used as an indicator to measure AI, and the question of whether AI has had negative effects on employment in the manufacturing sector from the perspective of machine replacement is examined. The second contribution is the analysis of the heterogeneity of AI’s employment creation effect from the perspective of gender and industry attributes and the claim that women and the employees of labour-intensive enterprises are more able to obtain additional work benefits in the digital era. Most importantly, in contrast to the literature, this paper innovatively introduces virtual agglomeration into the path mechanism of the effect of robots on employment and holds that information technologies such as the internet, big data, and the industrial Internet of Things, which rely upon AI, have reshaped the management mode and organizational structure of enterprises. Online and offline integration work together, and information, knowledge, and technology are interconnected. In the past, the job matching mode of one person, one post, and specific individuals has changed into a multiple faceted set of tasks involving one person, many posts, and many types of people. The internet platform spawned by digital technology frees the employment mode of enterprises from being limited to single enterprises and specific gathering areas. Traditional industrial geographical agglomeration has gradually evolved into virtual agglomeration, which geometrically enlarges the agglomeration effect and mechanism and enhances the spillover effect. In the online world, individual practitioners and entrepreneurs can obtain orders, receive training, connect resources and employment needs more widely and efficiently, and they can achieve higher-quality self-employment. Virtual agglomeration has become a new path by which AI affects employment. Another literature contribution is that this study used the linear regression model of the machine learning model in the robustness test part, which verified the employment creation effect of AI from the perspective of positive contribution proportion. In causal identification, this study innovatively uses the industrial feed-in price as a tool variable to analyse the causal path of AI promoting employment.

Theoretical mechanism and research hypothesis

The direct influence of ai on employment.

With advances in machine learning, big data, artificial intelligence, and other technologies, a new generation of intelligent robots that can perform routine, repetitive, and regular production tasks requiring human judgement, problem-solving, and analytical skills has emerged. Robotic process automation technology can learn and imitate the way that workers perform repeated new tasks regarding the collecting of data, running of reports, copying of data, checking of data integrity, reading, processing, and the sending of emails, and it can play an essential role in processing large amounts of data (Alan 2023 ). In the context of an informatics- and technology-oriented economy, companies are asking employees to transition into creative jobs. According to the theory of the combined task framework, the most significant advantage of the productivity effect produced by intelligent technology is creation of new demands, that is, the creation of new tasks (Acemoglu and Restrepo 2018 ). These new task packages update the existing tasks and create new task combinations with more complex technical difficulties. Although intelligent technology is widely used in various industries, it may have a substitution effect on workers and lead to technical unemployment. However, with the rise of a new round of technological innovation and revolution, high efficiency leads to the development and growth of a series of emerging industries and exerts job creation effects. Technological progress has the effect of creating new jobs. That is, such progress creates new jobs that are more in line with the needs of social development and thus increases the demand for labour (Borland and Coelli 2017 ). Therefore, the intelligent development of enterprises will come to replace their initial programmed tasks and produce more complex new tasks, and human workers in nonprogrammed positions, such as technology and knowledge, will have more comparative advantages.

Generally, the “new technology-economy” paradigm that is derived from automation machine and AI technology is affecting the breadth and depth of employment, which is manifested as follows:

It reduces the demand for coded jobs in enterprises while increasing the demand for nonprogrammed complex labour.

The development of digital technology has deepened and refined the division of labour, accelerated the service trend of the manufacturing industry, increased the employment share of the modern service industry and created many emerging jobs.

Advanced productive forces give workers higher autonomy and increased efficiency in their work, improving their job satisfaction and employment quality. As described in Das Kapital, “Although machines actually crowd out and potentially replace a large number of workers, with the development of machines themselves (which is manifested by the increase in the number of the same kind of factories or the expansion of the scale of existing factories), the number of factory workers may eventually be more than the number of handicraft workers in the workshops or handicrafts that they crowd out… It can be seen that the relative reduction and absolute increase of employed workers go hand in hand” (Li and Zhang 2022 ).

Internet information technology reduces the distance between countries in both time and space, promotes the transnational flow of production factors, and deepens the international division of labour. The emergence of AI technology leads to the decline of a country’s traditional industries and departments. Under the new changes to the division of labour, these industries and departments may develop in late-developing countries and serve to increase their employment through international labour export.

From a long-term perspective, AI will create more jobs through the continuous expansion of the social production scale, the continuous improvement of production efficiency, and the more detailed industrial categories that it engenders. With the accumulation of human capital under the internet era, practitioners are gradually becoming liberated from heavy and dangerous work, and workers’ skills and job adaptability will undergo continuous improvement. The employment creation and compensation effects caused by technological and industrial changes are more significant than the substitution effects (Han et al. 2022 ). Accordingly, the article proposes the following two research hypotheses:

Hypothesis 1 (H1): AI increases employment .

Hypothesis 2 (H2): AI promotes employment by improving labour productivity, deepening capital, and refining the division of labour .

Role of virtual agglomeration

The research on economic geography and “new” economic geography agglomeration theory focuses on industrial agglomeration in the traditional sense. This model is a geographical agglomeration model that depends on spatial proximity from a geographical perspective. Assessing the role of externalities requires a particular geographical scope, as it has both physical and scope limitations. Virtual agglomeration transcends Marshall’s theory of economies of scale, which is not limited to geographical agglomeration from the perspective of natural territory but rather takes on more complex and multidimensional forms (such as virtual clusters, high-tech industrial clusters, and virtual business circles). Under the influence of a new generation of digital technology that is characterized by big data, the Internet of Things, and the industrial internet, the digital, intelligent, and platform transformation trend is prominent in some industries and enterprises, and industrial digitalization and digital industrialization jointly promote industrial upgrading. The innovation of information technology leads to “distance death” (Schultz 1998 ). With the further materialization of digital and networked services of enterprises, the trading mode of digital knowledge and services, such as professional knowledge, information combination, cultural products, and consulting services, has transitioned from offline to digital trade, and the original geographical space gathering mode between enterprises has gradually evolved into a virtual network gathering that places the real-time exchange of data and information as its core (Wang et al. 2018 ). Tan and Xia ( 2022 ) stated that virtual agglomeration geometrically magnifies the social impact of industrial agglomeration mechanisms and agglomeration effects, and enterprises in the same industry and their upstream and downstream affiliated enterprises can realize low-cost long-distance transactions, services, and collaborative production through digital trade, resulting in large-scale zero-distance agglomeration along with neighbourhood-style production, service, circulation, and consumption. First, the knowledge and information underlying the production, design, research and development, organization, and trading of all kinds of enterprises are increasingly being completed by digital technology. The tacit knowledge that used to require face-to-face communication has become codable, transmissible, and reproducible under digital technology. Tacit knowledge has gradually become explicit, and knowledge spillover and technology diffusion have become more pronounced, which further leads to an increase in the demand for unconventional task labour (Zhang and Li 2022 ). Second, the cloud platform causes the labour pool effect of traditional geographical agglomeration to evolve into the labour “conservation land” of virtual agglomeration, and employment is no longer limited to the internal organization or constrained within a particular regional scope. Digital technology allows enterprises to hire “ghost workers” for lower wages to compensate for the possibility of AI’s “last mile.” Information technology and network platforms seek connections with all social nodes, promoting the time and space for work in a way that transcends standardized fixed frameworks. At the same time, joining or quitting work tasks, indirectly increasing the temporary and transitional nature of work and forming a decentralized management organization model of supplementary cooperation, social networks, industry experts, and skilled labour all become more convenient for workers (Wen and Liu 2021 ). With a mobile phone and a computer, labourers worldwide can create value for enterprises or customers, and the forms of labour are becoming more flexible and diverse. Workers can provide digital real-time services to employers far away from their residence, and they can also obtain flexible employment information and improve their digital skills through the leveraging of digital resources, resulting in the odd-job economy, crowdsourcing economy, sharing economy, and other economic forms. Finally, the network virtual space can accommodate almost unlimited enterprises simultaneously. In the commercial background of digital trade, while any enterprise can obtain any intermediate supply in the online market, its final product output can instantly become the intermediate input of other enterprises. Therefore, enterprises’ raw material supply and product sales rely on the whole market. At this time, the market scale effect of intermediate inputs can be infinitely amplified, as it is no longer confined to the limited space of geographical agglomeration (Duan and Zhang 2023 ). Accordingly, the following research hypothesis is proposed:

Hypothesis 3 (H3): AI promotes employment by improving the VA of enterprises .

Study design and data sources

Variable setting, explained variable.

Employment scale (ES). Compared with the agriculture and service industry, the industrial sector accommodates more labour, and robot technology is mainly applied in the industrial sector, which has the greatest demand shock effect on manufacturing jobs. In this paper, we select the number of employees in manufacturing cities and towns as the proxy variable for employment scale.

Core explanatory variable

Artificial intelligence (AI). Emerging technologies endow industrial robots with more complete technical attributes, which increases their ability to act as human beings in many work projects, enabling them to either independently complete production tasks or to assist humans in completing such tasks. This represents an important form of AI technology embedded into machinery and equipment. In this paper, the installation density of industrial robots is selected as the proxy variable for AI. Robot data mainly come from the number of robots installed in various industries at various national levels as published by the International Federation of Robotics (IFR). Because the dataset published by the IFR provides the dataset at the national-industry level and its industry classification standards are significantly different from those in China, the first lessons for this paper are drawn from the practices of Yan et al. ( 2020 ), who matches the 14 manufacturing categories published by the IFR with the subsectors in China’s manufacturing sector, and then uses the mobile share method to merge and sort out the employment numbers of various industries in various provinces. First, the national subsector data provided by the IFR are matched with the second National Economic Census data. Next, the share of employment in different industries to the total employment in the province is used to develop weights and decompose the industry-level robot data into the local “provincial-level industry” level. Finally, the application of robots in various industries at the provincial level is summarized. The Bartik shift-share instrumental variable is now widely used to measure robot installation density at the city (province) level (Wu 2023 ; Yang and Shen, 2023 ; Shen and Yang 2023 ). The calculation process is as follows:

In Eq. ( 1 ), N is a collection of manufacturing industries, Robot it is the robot installation density of province i in year t, \({{{\mathrm{employ}}}}_{{{{\mathrm{ij}}}},{{{\mathrm{t}}}} = 2006}\) is the number of employees in industry j of province i in 2006, \({{{\mathrm{employ}}}}_{{{{\mathrm{i}}}},{{{\mathrm{t}}}} = 2006}\) is the total number of employees in province i in 2006, and \({{{\mathrm{Robot}}}}_{{{{\mathrm{jt}}}}}{{{\mathrm{/employ}}}}_{{{{\mathrm{i}}}},{{{\mathrm{t}}}} = 2006}\) represents the robot installation density of each year and industry level.

Mediating variables

Labour productivity (LP). According to the definition and measurement method proposed by Marx’s labour theory of value, labour productivity is measured by the balance of the total social product minus the intermediate goods and the amount of labour consumed by the pure production sector. The specific calculation process is \(AL = Y - k/l\) , where Y represents GDP, l represents employment, k represents capital depreciation, and AL represents labour productivity. Capital deepening (CD). The per capita fixed capital stock of industrial enterprises above a designated size is used in this study as a proxy variable for capital deepening. The division of labour refinement (DLR) is refined and measured by the number of employees in producer services. Virtual agglomeration (VA) is mainly a continuation of the location entropy method in the traditional industrial agglomeration measurement idea, and weights are assigned according to the proportion of the number of internet access ports in the country. Because of the dependence of virtual agglomeration on digital technology and network information platforms, the industrial agglomeration degree of each region is first calculated in this paper by using the number of information transmissions, computer services, and software practitioners and then multiplying that number by the internet port weight. The specific expression is \(Agg_{it} = \left( {M_{it}/M_t} \right)/\left( {E_{it}/E_t} \right) \times \left( {Net_{it}/Net_t} \right)\) , where \(M_{it}\) represents the number of information transmissions, computer services and software practitioners in region i in year t, \(M_t\) represents the total number of national employees in this industry, \(E_{it}\) represents the total number of employees in region i, \(E_t\) represents the total number of national employees, \(Net_{it}\) represents the number of internet broadband access ports in region i, and \(Net_t\) represents the total number of internet broadband access ports in the country. VA represents the degree of virtual agglomeration.

Control variables

To avoid endogeneity problems caused by unobserved variables and to obtain more accurate estimation results, seven control variables were also selected. Road accessibility (RA) is measured by the actual road area at the end of the year. Industrial structure (IS) is measured by the proportion of the tertiary industry’s added value and the secondary industry’s added value. The full-time equivalent of R&D personnel is used to measure R&D investment (RD). Wage cost (WC) is calculated using city average salary as a proxy variable; Marketization (MK) is determined using Fan Gang marketization index as a proxy variable; Urbanization (UR) is measured by the proportion of the urban population to the total population at the end of the year; and the proportion of general budget expenditure to GDP is used to measure Macrocontrol (MC).

Econometric model

To investigate the impact of AI on employment, based on the selection and definition of the variables detailed above and by mapping the research ideas to an empirical model, the following linear regression model is constructed:

In Eq. ( 2 ), ES represents the scale of manufacturing employment, AI represents artificial intelligence, and subscripts t, i and m represent time t, individual i and the m th control variable, respectively. \(\mu _i\) , \(\nu _t\) and \(\varepsilon _{it}\) represent the individual effect, time effect and random disturbance terms, respectively. \(\delta _0\) is the constant term, a is the parameter to be fitted, and Control represents a series of control variables. To further test whether there is a mediating effect of mechanism variables in the process of AI affecting employment, only the influence of AI on mechanism variables is tested in the empirical part according to the modelling process and operational suggestions of the intermediary effects as proposed by Jiang ( 2022 ) to overcome the inherent defects of the intermediary effects. On the basis of Eq. ( 2 ), the following econometric model is constructed:

In Eq. ( 3 ), Media represents the mechanism variable. β 1 represents the degree of influence of AI on mechanism variables, and its significance and symbolic direction still need to be emphasized. The meanings of the remaining symbols are consistent with those of Eq. ( 2 ).

Data sources

Following the principle of data availability, the panel data of 30 provinces (municipalities and autonomous regions) in China from 2006 to 2020 (samples from Tibet and Hong Kong, Macao, and Taiwan were excluded due to data availability) were used as statistical investigation samples. The raw data on the installed density of industrial robots and the number of workers in the manufacturing industry come from the International Federation of Robotics and the China Labour Statistics Yearbook. The original data for the remaining indicators came from the China Statistical Yearbook, China Population and Employment Statistical Yearbook, China’s Marketization Index Report by Province (2021), the provincial and municipal Bureau of Statistics, and the global statistical data analysis platform of the Economy Prediction System (EPS). The few missing values are supplemented through linear interpolation. It should be noted that although the IFR has yet to release the number of robots installed at the country-industry level in 2020, it has published the overall growth rate of new robot installations, which is used to calculate the robot stock in 2020 for this study. The descriptive statistical analysis of relevant variables is shown in Table 1 .

Empirical analysis

To reduce the volatility of the data and address the possible heteroscedasticity problem, all the variables are located. The results of the Hausmann test and F test both reject the null hypothesis at the 1% level, indicating that the fixed effect model is the best-fitting model. Table 2 reports the fitting results of the baseline regression.

As shown in Table 2 , the results of the two-way fixed-effect (TWFE) model displayed in Column (5) show that the fitting coefficient of AI on employment is 0.989 and is significant at the 1% level. At the same time, the fitting results of other models show that the impact of AI on employment is significantly positive. The results confirm that the effect of AI on employment is positive and the effect of job creation is greater than the effect of destruction, and these conclusions are robust, thus verifying the employment creation mechanism of technological progress. Research Hypothesis 1 (H1) is supported. The new round of scientific and technological revolution represented by artificial intelligence involves the upgrading of traditional industries, the promotion of major changes in the economy and society, the driving of rapid development of the “unmanned economy,” the spawning a large number of new products, new technologies, new formats, and new models, and the provision of more possibilities for promoting greater and higher quality employment. Classical and neoclassical economics view the market mechanism as a process of automatic correction that can offset the job losses caused by labour-saving technological innovation. Under the premise of the “employment compensation” theory, the new products, new models, and new industrial sectors created by the progress of AI technology can directly promote employment. At the same time, the scale effect caused by advanced productivity results in lower product prices and higher worker incomes, which drives increased demand and economic growth, increasing output growth and employment (Ge and Zhao 2023 ). In conjunction with the empirical results of this paper, we have reason to believe that enterprises adopt the strategy of “machine replacement” to replace procedural and repetitive labour positions in the pursuit of high efficiency and high profits. However, AI improves not only enterprises’ production efficiency but also their production capacity and scale economy. To occupy a favourable share of market competition, enterprises expand the scale of reproduction. At this point, new and more complex tasks continue to emerge, eventually leading companies to hire more labour. At this stage, robot technology and application in developing countries are still in their infancy. Whether regarding the application scenario or the application scope of robots, the automation technology represented by industrial robots has not yet been widely promoted, which increases the time required for the automation technology to completely replace manual tasks, so the destruction effect of automation technology on jobs is not apparent. The fundamental market situation of the low cost of China’s labour market drives enterprises to pay more attention to technology upgrading and efficiency improvement when introducing industrial robots. The implementation of the machine replacement strategy is mainly caused by the labour shortage driven by high work intensity, high risk, simple process repetition, and poor working conditions. The intelligent transformation of enterprises points to more than the simple saving of labour costs (Dixon et al. 2021 ).

Robustness test

The above results show that the effect of AI on job creation is greater than the effect of substitution and the overall promotion of enterprises for the enhancement of employment demand. To verify the robustness of the benchmark results, the following three means of verifying the results are adopted in this study. First, we replace the explained variables. In addition to industrial manufacturing, robots are widely used in service industries, such as medical care, finance, catering, and education. To reflect the dynamic change relationship between the employment share of the manufacturing sector and the employment number of all sectors, the absolute number of manufacturing employees is replaced by the ratio of the manufacturing industry to all employment numbers. The second means is increasing the missing variables. Since many factors affect employment, this paper considers the living cots, human capital, population density, and union power in the basic regression model. The impact of these variables on employment is noticeable; for example, the existence of trade unions improves employee welfare and the working environment but raises the entry barrier for workers in the external market. The new missing variables are the average selling price of commercial and residential buildings, urban population density (person/square kilometre), nominal human capital stock, and the number of grassroots trade union organizations in the China Human Capital Report 2021 issued by Central University of Finance and Economics, which are used as proxy variables. The third means involves the use of linear regression (the gradient descent method) in machine learning regression to calculate the importance of AI to the increase in employment size. The machine learning model has a higher goodness of fit and fitting effect on the predicted data, and its mean square error and mean absolute error are more minor (Wang Y et al. 2022 ).

As seen from the robustness part of Table 3 , the results of Method 1 show that AI exerts a positive impact on the employment share in the manufacturing industry; that is, AI can increase the proportion of employment in the manufacturing industry, the use of AI creates more derivative jobs for the manufacturing industry, and the demand for the labour force of enterprises further increases. The results of method 2 show that after increasing the number of control variables, the influence of robots on employment remains significantly positive, indicating no social phenomenon of “machine replacement.” The results of method 3 show that the weight of AI is 84.3%, indicating that AI can explain most of the increase in the manufacturing employment scale and has a positive promoting effect. The above three methods confirm the robustness of the baseline regression results.

Endogenous problem

Although further control variables are used to alleviate the endogeneity problem caused by missing variables to the greatest extent possible, the bidirectional causal relationship between labour demand and robot installation (for example, enterprises tend to passively adopt the machine replacement strategy in the case of labour shortages and recruitment difficulties) still threatens the accuracy of the statistical inference results in this paper. To eliminate the potential endogeneity problem of the model, the two-stage least squares method (2SLS) was applied. In general, the cost factor that enterprises need to consider when introducing industrial robots is not only the comparative advantage between the efficiency cost of machinery and the costs of equipment and labour wages but also the cost of electricity to maintain the efficient operation of machinery and equipment. Changes in industrial electricity prices indicate that the dynamic conditions between installing robots and hiring workers have changed, and decision-makers need to reweigh the costs and profits of intelligent transformation. Changes in industrial electricity prices can impact the demand for labour by enterprises; this path does not directly affect the labour market but is rather based on the power consumption, work efficiency, and equipment prices of robots. Therefore, industrial electricity prices are exogenous relative to employment, and the demand for robots is correlated.

Electricity production and operation can be divided into power generation, transmission, distribution, and sales. China has realized the integration of exports and distribution, so there are two critical prices in practice: on-grid and sales tariffs (Yu and Liu 2017 ). The government determines the on-grid tariff according to different cost-plus models, and its regulatory policy has roughly proceeded from that of principal and interest repayment, through operating period pricing, to benchmark pricing. The sales price (also known as the catalogue price) is the price of electric energy sold by power grid operators to end users, and its price structure is formed based on the “electric heating price” that was implemented in 1976. There is differentiated pricing between industrial and agricultural electricity. Generally, government departments formulate on-grid tariffs, integrating the interests of power plants, grid enterprises, and end users. As China’s thermal power installed capacity accounts for more than 70% of the installed capacity of generators, the price of coal becomes an essential factor affecting the price of industrial internet access. The pricing strategy for electricity sales is not determined by market-oriented transmission and distribution electricity price, on-grid electricity price, or tax but rather by the goal of “stable growth and ensuring people’s livelihood” (Tang and Yang 2014 ). The externality of the feed-in price is more robust, so the paper chooses the feed-in price as an instrumental variable.

It can be seen from Table 3 that the instrumental variables in the first stage positively affect the robot installation density at the level of 1%. Meanwhile, the results of the validity test of the instrumental variables show that there are no weak instrumental variables or unidentifiable problems with this variable, thus satisfying the principle of correlation and exclusivity. The second-stage results show that robots still positively affect the demand for labour at the 1% level, but the fitting coefficient is smaller than that of the benchmark regression model. In summary, the results of fitting the calculation with the causal inference paradigm still support the conclusion that robots create more jobs and increase the labour demand of enterprises.

Extensibility analysis

Robot adoption and gender bias.

The quantity and quality of labour needed by various industries in the manufacturing sector vary greatly, and labour-intensive and capital-intensive industries have different labour needs. Over the past few decades, the demand for female employees has grown. Female employees obtain more job opportunities and better salaries today (Zhang et al. 2023 ). Female employees may benefit from reducing the content of manual labour jobs, meaning that further study of AI heterogeneity from the perspective of gender bias may be needed. As seen from Table 4 , AI has a significant positive impact on the employment of both male and female practitioners, indicating that AI technology does not have a heterogeneous effect on the dynamic gender structure. By comparing the coefficients of the two (the estimated results for men and those for women), it can be found that robots have a more significant promotion effect on female employees. AI has significantly improved the working environment of front-line workers, reduced the level of labour intensity, enabled people to free themselves of dirty and heavy work tasks, and indirectly improved the job adaptability of female workers. Intellectualization increases the flexibility of the time, place, and manner of work for workers, correspondingly improves the working freedom of female workers, and alleviates the imbalance in the choice between family and career for women to a certain extent (Lu et al. 2023 ). At the same time, women are born with the comparative advantage of cognitive skills that allow them to pay more nuanced attention to work details. By introducing automated technology, companies are increasing the demand for cognitive skills such as mental labour and sentiment analysis, thus increasing the benefits for female workers (Wang and Zhang 2022 ). Flexible employment forms, such as online car hailing, community e-commerce, and online live broadcasting, provide a broader stage for women’s entrepreneurship and employment. According to the “Didi Digital Platform and Female Ecology Research Report”, the number of newly registered female online taxi drivers in China has exceeded 265,000 since 2020, and approximately 60 percent of the heads of the e-commerce platform, Orange Heart, are women.

Industry heterogeneity

Given the significant differences in the combination of factors across the different industries in China’s manufacturing sector, there is also a significant gap in the installation density of robots; even compared to AI density, in industries with different production characteristics, indicating that there may be an opposite employment phenomenon at play. According to the number of employees and their salary level, capital stock, R&D investment, and patent technology, the manufacturing industry is divided into labour-intensive (LI), capital-intensive (CI), and technology-intensive (TI) industries.

As seen from the industry-specific test results displayed in Table 4 , the impact of AI on employment in the three attribute industries is significantly positive, which is consistent with the results of Beier et al. ( 2022 ). In contrast, labour-intensive industries can absorb more workers, and industry practitioners are better able to share digital dividends from these new workers, which is generally in line with expectations (in the labour-intensive case, the regression coefficient of AI on employment is 0.054, which is significantly larger than the regression coefficient of the other two industries). This conclusion shows that enterprises use AI to replace the labour force of procedural and process-based positions in pursuit of cost-effective performance. However, the scale effect generated by improving enterprise production efficiency leads to increased labour demand, namely, productivity and compensation effects. For example, AGV-handling robots are used to replace porters in monotonous and repetitive high-intensity work, thus realizing the uncrewed operation of storage links and the automatic handling of goods, semifinished products, and raw materials in the production process. This reduces the cost of goods storage while improving the efficiency of logistics handling, increasing the capital investment of enterprises in the expansion of market share and extension of the industrial chain.

Mechanism test

To reveal the path mechanism through which AI affects employment, in combination with H2 and H3 and the intermediary effect model constructed with Eq. ( 3 ), the TWFE model was used to fit the results shown in Table 5 .

It can be seen from Table 5 that the fitting coefficients of AI for capital deepening, labour productivity, and division of labour are 0.052, 0.071, and 0.302, respectively, and are all significant at the 1% level, indicating that AI can promote employment through the above three mechanisms, and thus research Hypothesis 2 (H2) is supported. Compared with the workshop and handicraft industry, machine production has driven incomparably broad development in the social division of labour. Intelligent transformation helps to open up the internal and external data chain, improve the combination of production factors, reduce costs and increase efficiency to enable the high-quality development of enterprises. At the macro level, the impact of robotics on social productivity, industrial structure, and product prices affects the labour demand of enterprises. At the micro level, robot technology changes the employment carrier, skill requirements, and employment form of labour and impacts the matching of labour supply and demand. The combination of the price and income effects can drive the impact of technological progress on employment creation. While improving labour productivity, AI technology reduces product production costs. In the case of constant nominal income, the market increases the demand for the product, which in turn drives the expansion of the industrial scale and increases output, resulting in an increase in the demand for labour. At the same time, the emergence of robotics has refined the division of labour. Most importantly, the development of AI technology results in productivity improvements that cannot be matched by pure labour input, which not only enables 24 h automation but also reduces error rates, improves precision, and accelerates production speeds.

Table 5 also shows that the fitting coefficient of AI to virtual agglomeration is 0.141 and significant at the 5% level, indicating that AI and digital technology can promote employment by promoting the agglomeration degree of enterprises in the cloud and network. Research Hypothesis 3 is thus supported. Industrial internet, AI, collaborative robots, and optical fidelity information transmission technology are necessary for the future of the manufacturing industry, and smart factories will become the ultimate direction of manufacturing. Under the intelligent manufacturing model, by leveraging cloud links, industrial robots, and the technological depth needed to achieve autonomous management, the proximity advantage of geographic spatial agglomeration gradually begins to fade. The panconnective features of digital technology break through the situational constraints of work, reshaping the static, linear, and demarcated organizational structure and management modes of the industrial era and increasingly facilitates dynamic, network-based, borderless organizational forms, despite the fact that traditional work tasks can be carried out on a broader network platform employing online office platforms and online meetings. While promoting cost reduction and efficiency increase, such connectivity also creates new occupations that rely on this network to achieve efficient virtual agglomeration. On the other hand, robot technology has also broken the fixed connection between people and jobs, and the previous post matching mode of one person and one specific individual has gradually evolved into an organizational structure involving multiple posts and multiple people, thus providing more diverse and inclusive jobs for different groups.

Conclusions and policy implications

Research conclusion.

The decisive impact of digitization and automation on the functioning of all society’s social subsystems is indisputable. Technological progress alone does not impart any purpose to technology, and its value (consciousness) can only be defined by its application in the social context in which it emerges (Rakowski et al. 2021 ). The recent launch of the intelligent chatbot ChatGPT by the US artificial intelligence company OpenAI, with its powerful word processing capabilities and human-computer interaction, has once again sparked global concerns about its potential impact on employment in related industries. Automation technology represented by intelligent manufacturing profoundly affects the labour supply and demand map and significantly impacts economic and social development. The application of industrial robots is a concrete reflection of the integration of AI technology and industry, and its widespread promotion and popularization in the manufacturing field have resulted in changes in production methods and exerted impacts on the labour market. In this paper, the internal mechanism of AI’s impact on employment is first delineated and then empirical tests based on panel data from 30 provinces (municipalities and autonomous regions, excluding Hong Kong, Macao, Taiwan, and Xizang) in China from 2006 to 2020 are subsequently conducted. As mentioned in relation to the theory of “employment compensation,” the research described in this paper shows that the overall impact of AI on employment is positive, revealing a pronounced job creation effect, and the impact of automation technology on the labour market is mainly positively manifested as “icing on the cake.” Our conclusion is consistent with the literature (Sharma and Mishra 2023 ; Feng et al. 2024 ). This conclusion remains after replacing variables, adding missing variables, and controlling for endogeneity problems. The positive role of AI in promoting employment does not have exert opposite effects resulting from gender and industry differences. However, it brings greater digital welfare to female practitioners and workers in labour-intensive industries while relatively reducing the overall proportion of male practitioners in the manufacturing industry. Mechanism analysis shows that AI drives employment through mechanisms that promote capital deepening, the division of labour, and increased labour productivity. The digital trade derived from digital technology and internet platforms has promoted the transformation of traditional industrial agglomeration into virtual agglomeration, the constructed network flow space system is more prone to the free spillover of knowledge, technology, and creativity, and the agglomeration effect and agglomeration mechanism are amplified by geometric multiples. Industrial virtual agglomeration has become a new mechanism and an essential channel through which AI promotes employment, which helps to enhance labour autonomy, improve job suitability and encourage enterprises to share the welfare of labour among “cultivation areas.”

Policy implications

Technology is neutral, and its key lies in its use. Artificial intelligence technology, as an open new general technology, represents significant progress in productivity and is an essential driving force with the potential to boost economic development. However, it also inevitably poses many potential risks and social problems. This study helps to clarify the argument that technology replaces jobs by revealing the impact of automation technology on China’s labour market at the present stage, and its findings alleviate the social anxiety caused by the fear of machine replacement. According to the above research conclusions, the following valuable implications can be obtained.

Investment in AI research and development should be increased, and the high-end development of domestic robots should be accelerated. The development of AI has not only resulted in the improvement of production efficiency but has also triggered a change in industrial structure and labour structure, and it has also generated new jobs as it has replaced human labour. Currently, the impact of AI on employment in China is positive and helps to stabilize employment. Speeding up the development of the information infrastructure, accelerating the intelligent upgrade of the traditional physical infrastructure, and realizing the inclusive promotion of intelligent infrastructure are necessary to ensure efficient development. 5G technology and the development dividend of the digital economy can be used to increase the level of investment in new infrastructure such as cloud computing, the Internet of Things, blockchain, and the industrial internet and to improve the level of intelligent application across the industry. We need to implement the intelligent transformation of old infrastructure, upgrade traditional old infrastructure to smart new infrastructure, and digitally transform traditional forms of infrastructure such as power, reservoirs, rivers, and urban sewer pipes through the employment of sensors and access algorithms to solve infrastructure problems more intelligently. Second, the diversification and agglomeration of industrial lines are facilitated through the transformation of industrial intelligence and automation. At the same time, it is necessary to speed up the process of industrial intelligence and cultivate the prospects of emerging industries and employment carriers, particularly in regard to the development and growth of emerging producer services. The development of domestic robots should be task-oriented and application-oriented, should adhere to the effective transformation of scientific and technological achievements under the guidance of the development of the service economy. A “1 + 2 + N” collaborative innovation ecosystem should be constructed with a focus on cultivating, incubating, and supporting critical technological innovation in each subindustry of the manufacturing industry, optimizing the layout, and forming a matrix multilevel achievement transformation service. We need to improve the mechanisms used for complementing research and production, such as technology investment and authorization. To move beyond standard robot system development technology, the research and development of bionic perception and knowledge, as well as other cutting-edge technologies need to be developed to overcome the core technology “bottleneck” problem.

It is suggested that government departments improve the social security system and stabilize employment through multiple channels. The first channel is the evaluation and monitoring of the potential destruction of the low-end labour force by AI, enabled through the cooperation of the government and enterprises, to build relevant information platforms, improve the transparency of the labour market information, and reasonably anticipate structural unemployment. Big data should be fully leveraged, a sound national employment information monitoring platform should be built, real-time monitoring of the dynamic changes in employment in critical regions, fundamental groups, and key positions should be implemented, employment status information should be released, and employment early warning, forecasting, and prediction should be provided. Second, the backstop role of public service, including human resources departments and social security departments at all levels, should improve the relevant social security system in a timely manner. A mixed-guarantee model can be adopted for the potential unemployed and laws and regulations to protect the legitimate rights and interests of entrepreneurs and temporary employees should be improved. We can gradually expand the coverage of unemployment insurance and basic living allowances. For the extremely poor, unemployed or extreme labour shortage groups, public welfare jobs or special subsidies can be used to stabilize their basic lifestyles. The second is to understand the working conditions of the bottom workers at the grassroots level in greater depth, strengthen the statistical investigation and professional evaluation of AI technology and related jobs, provide skills training, employment assistance, and unemployment subsidies for workers who are unemployed due to the use of AI, and encourage unemployed groups to participate in vocational skills training to improve their applicable skillsets. Workers should be encouraged to use their fragmented time to participate in the gig and sharing economies and achieve flexible employment according to dominant conditions. Finally, a focus should be established on the impact of AI on the changing demand for jobs in specific industries, especially transportation equipment manufacturing and communications equipment, computers, and other electronic equipment manufacturing.

It is suggested that education departments promote the reform of the education and training system and deepen the coordinated development of industry-university research. Big data, the Internet of Things, and AI, as new digital production factors, have penetrated daily economic activities, driving industrial changes and changes in the supply and demand dynamics of the job market. Heterogeneity analysis results confirmed that AI imparts a high level of digital welfare for women and workers in labour-intensive industrial enterprises, but to stimulate the spillover of technology dividends in the whole society, it is necessary to dynamically optimize human capital and improve the adaptability of man-machine collaborative work; otherwise, the disruptive effect of intelligent technology on low-end, routine and programmable work will be obscured. AI has a creativity promoting effect on irregular, creative, and stylized technical positions. Hence, the contradiction between supply and demand in the labour market and the slow transformation of the labour skill structure requires attention. The relevant administrative departments of the state should take the lead in increasing investment in basic research and forming a scientific research division system in which enterprises increase their levels of investment in experimental development and multiple subjects participate in R&D. Relevant departments should clarify the urgent need for talent in the digital economy era, deepen the reform of the education system as a guide, encourage all kinds of colleges and universities to add related majors around AI and big data analysis, accelerate the research on the skill needs of new careers and jobs, and establish a lifelong learning and employment training system that meets the needs of the innovative economy and intelligent society. We need to strengthen the training of innovative, technical, and professional technical personnel, focus on cultivating interdisciplinary talent and AI-related professionals to improve worker adaptability to new industries and technologies, deepen the adjustment of the educational structure, increase the skills and knowledge of perceptual, creative, and social abilities of the workforce, and cultivate the skills needed to perform complex jobs in the future that are difficult to replace by AI. The lifelong education and training system should be improved, and enterprise employees should be encouraged to participate in vocational skills training and cultural knowledge learning through activities such as vocational and technical schools, enterprise universities, and personnel exchanges.

Research limitations

The study used panel data from 30 provinces in China from 2006 to 2020 to examine the impact of AI on employment using econometric models. Therefore, the conclusions obtained in this study are only applicable to the economic reality in China during the sample period. There are three shortcomings in this study. First, only the effect and mechanism of AI in promoting employment from a macro level are investigated in this study, which is limited by the large data particles and small sample data that are factors that reduce the reliability and validity of statistical inference. The digital economy has grown rapidly in the wake of the COVID-19 pandemic, and the related industrial structures and job types have been affected by sudden public events. An examination of the impact of AI on employment based on nearly three years of micro-data (particularly the data obtained from field research) is urgent. When conducting empirical analysis, combining case studies of enterprises that are undergoing digital transformation is very helpful. Second, although the two-way fixed effect model and instrumental variable method can reveal conclusions regarding causality to a certain extent, these conclusions are not causal inference in the strict sense. Due to the lack of good policy pilots regarding industrial robots and digital parks, the topic cannot be thoroughly evaluated for determining policy and calculating resident welfare. In future research, researchers can look for policies and systems such as big data pilot zones, intelligent industrial parks, and digital economy demonstration zones to perform policy evaluations through quasinatural experiments. The use of difference in differences (DID), regression discontinuity (RD), and synthetic control method (SCM) to perform regression is beneficial. In addition, the diffusion effect caused by introducing and installing industrial robots leads to the flow of labour between regions, resulting in a potential spatial spillover effect. Although the spatial econometric model is used above, it is mainly used as a robustness test, and the direct effect is considered. This paper has yet to discuss the spatial effect from the perspective of the spatial spillover effect. Last, it is important to note that the digital infrastructure, workforce, and industrial structure differ from country to country. The study focused on a sample of data from China, making the findings only partially applicable to other countries. Therefore, the sample size of countries should be expanded in future studies, and the possible heterogeneity of AI should be explored and compared by classifying different countries according to their stage of development.

Data availability

The data generated during and/or analyzed during the current study are provided in Supplementary File “database”.

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Acknowledgements

This work was financially supported by the Natural Science Foundation of Fujian Province (Grant No. 2022J01320).

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Shen, Y., Zhang, X. The impact of artificial intelligence on employment: the role of virtual agglomeration. Humanit Soc Sci Commun 11 , 122 (2024). https://doi.org/10.1057/s41599-024-02647-9

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Assessing the current state of the global labour market: Implications for achieving the Global Goals

  • COVID-19 , informal economy , SDG labour market indicators , wages , women
  • March 13, 2023

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In this blog, we review the state of the global labour market in light of the SDGs, exploring the challenges we face as the global economic recovery from the COVID-19 pandemic is being hampered by rising inflation, supply chain disruptions, and the war in Ukraine. 

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Unemployment remains above pre-pandemic level

The global unemployment rate declined significantly in 2022, falling to 5.8 per cent from a peak of 6.9 per cent in 2020 as economies began recovering from the shock of the COVID-19 pandemic. Despite an uncertain global economic outlook, unemployment is projected to increase only moderately in 2023, as a large part of the shock is being absorbed by falling real wages in an environment of accelerating inflation. Global unemployment is projected to edge up slightly in both 2023 and 2024, reaching 211 million, although the rate will remain at 5.8 per cent.

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More workers pushed into informal employment

Globally, 58.0 per cent of those employed were in informal employment in 2022, amounting to around 2 billion workers in precarious jobs, most lacking any form of social protection. Before the onset of the pandemic, the incidence of informal employment had been slowly declining and stood at 57.8 per cent in 2019. The initial waves of the pandemic resulted in disproportionate job losses for informal workers, particularly for women, during 2020. The subsequent recovery has been driven by informal employment, which has caused a slight increase in the incidence of informality worldwide. Informal employment often acts as a “last-resort” option for earning a living, pushing more workers into jobs of worse quality and depriving others of adequate social protection.

Slowdown in productivity growth not just in developed economies

After a sharp decline in 2020 due to the COVID-19 pandemic, labour productivity rebounded in 2021, rising by 2.4 per cent. Productivity growth slowed in 2022, increasing by only 0.5 per cent. However, even before the onset of the COVID-19 pandemic, productivity growth had been slowing around the world. The latest estimates extend the downward growth trend, from an average annual rate of 1.8 per cent between 2000-14 to 1.4 per cent between 2015-22. This is a matter of much concern since productivity growth is key to addressing today’s multiple crises in purchasing power, well-being, and ecological sustainability .

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Dismal labour market prospects for youth

Almost a quarter (23.5 per cent) of the world’s youth were not in education, employment, or training (NEET) in 2022. Although this is a slight decrease since 2020, when the NEET rate was at an all-time high, it remains higher than pre-pandemic levels and above the 2015 baseline of 22.2 per cent. In other words, the COVID-19 pandemic exacerbated a trend already on the rise, as youth suffered higher employment losses than older workers and quit their studies due to the massive disruptions in education and on-the-job training. There has been minimal recovery.

Efforts to reduce youth NEET rates need to be intensified as the world recovers from the COVID-19 crisis. Too many young people – some 289 million – are neither gaining professional experience through a job nor developing their skills through participation in an educational or vocational programme. This is not only a waste of economic potential, it is also likely to have a lasting impact on affected youth, making it harder for them to transition into the labour market in the coming years.

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Gender parity in managerial positions will take generations

For decades, women have been facing persistent barriers to accessing decision-making positions such as legislators, senior officials, CEOs, and other managerial occupations. Globally, they held only 28.2 per cent of management positions in 2021, although they accounted for almost 40 per cent of total employment. While the share of women in management has been on the rise worldwide and is slightly higher than in pre-pandemic times, progress has been slow, with an increase of only 0.9 percentage points since 2015. At the current rate of progress, more than 140 years would pass before gender parity in managerial positions would be achieved.

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Gender pay gap much wider than previously thought

Equal treatment in employment, including fair and equitable earnings, is fundamental for achieving decent work for all. The median gender wage gap across 102 countries with recent and comparable data (based on hourly earnings of employees) is approximately 14 per cent.

While hourly earnings (and the derived gender pay gap) is the official SDG indicator, a new ILOSTAT indicator on gender labour income gaps points to much wider imbalances between women and men. Since almost half of the world’s workers are self-employed, labour income – encompassing earnings of all workers, not just employees – provides a more comprehensive picture of pay gaps. In 2020, for each dollar men earned in labour income, women earned only 52 cents .  

In low and lower-middle income countries, the gender disparity in labour income is much worse, with women earning 33 cents and 29 cents on the dollar respectively. This striking disparity in earnings is driven by both women’s lower employment level, as well as their lower average earnings when they are employed.

Concluding remarks

The COVID-19 pandemic has had a major impact on the global labour market in recent years. On top of this, new challenges have emerged that are also having detrimental effects on the world of work, including sharp rises in inflation, supply chain disruptions and the war in Ukraine. These multiple challenges are affecting prospects for achieving the SDGs. The trends highlighted in this blog underscore the urgent need for action to promote social justice, by addressing the issues of job creation, informality, productivity, youth employment, and gender parity. Policymakers, employers, workers, and civil society must work together to ensure a sustainable and inclusive recovery that leaves no one behind. This includes investing in education and training, strengthening social protection systems, promoting decent work, and advancing gender equality.

About the SDGs

The 2030 Agenda and the SDGs were adopted in 2015 by the United Nations General Assembly. The 17 SDGs are a universal call to action to end poverty, protect the planet, and ensure that all people enjoy peace and prosperity. They cover a broad range of social and economic development issues, including poverty, hunger, health, education, climate change, gender equality, water, sanitation, energy, the environment and social justice, with a focus on the most vulnerable and a commitment that “no one will be left behind.”

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Marie-Claire Sodergren

Marie-Claire is a Senior Economist in the Data Production and Analysis Unit of the ILO Department of Statistics.

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The impact of artificial intelligence on labor markets in developing countries: a new method with an illustration for Lao PDR and urban Viet Nam

Francesco carbonero.

1 University of Turin, Turin, Italy

Jeremy Davies

2 East Village Software Consultants, London, UK

Ekkehard Ernst

3 ILO Research Department, Geneva, Switzerland

Frank M. Fossen

4 University of Nevada, Reno, NV USA

5 IZA, Bonn, Germany

Daniel Samaan

Alina sorgner.

6 John Cabot University, Rome, Italy

7 IfW Kiel, Kiel, Germany

Associated Data

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

AI is transforming labor markets around the world. Existing research has focused on advanced economies but has neglected developing economies. Different impacts of AI on labor markets in different countries arise not only from heterogeneous occupational structures, but also from the fact that occupations vary across countries in their composition of tasks. We propose a new methodology to translate existing measures of AI impacts that were developed for the US to countries at various levels of economic development. Our method assesses semantic similarities between textual descriptions of work activities in the US and workers’ skills elicited in surveys for other countries. We implement the approach using the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. (Am Econ Assoc Pap Proc 108:43-47, 2018) for the US and the World Bank’s STEP survey for Lao PDR and Viet Nam. Our approach allows characterizing the extent to which workers and occupations in a given country are subject to destructive digitalization, which puts workers at risk of being displaced, in contrast to transformative digitalization, which tends to benefit workers. We find that workers in urban Viet Nam, in comparison to Lao PDR, are more concentrated in occupations affected by AI, which requires them to adapt or puts them at risk of being partially displaced. Our method based on semantic textual similarities using SBERT is advantageous compared to approaches transferring AI impact scores across countries using crosswalks of occupational codes.

Introduction

The impacts of digitalization and artificial intelligence (AI) technologies on labor markets are multifaceted. Workers performing predominantly work activities that can be automated are at risk of being displaced by digital machines. However, occupations combining activities that cannot be automated with those that can are likely to be transformed. Workers in these occupations may benefit from working closely with new digital technologies rather than being displaced by machines (Acemoglu and Restrepo 2018 ; Lane and Saint-Martin 2021 ).

Prior research has investigated the impact of new digital technologies on occupations primarily in the United States (Frey and Osborne 2017 ; Brynjolfsson et al. 2018 ; Felten et al. 2019 ; Acemoglu et al. 2020 ; Fossen and Sorgner 2021 , 2022 ) and in some cases in other developed countries (Arntz et al. 2016 , 2017 ; Georgieff and Hyee 2021 ). These papers develop measures of the impact of digitalization on occupations in these countries and proceed by testing effects on wages and unemployment (Felten et al. 2019 ; Fossen and Sorgner 2022 ). Few papers in the literature investigate the impacts of digitalization in developing countries. Carbonero et al. ( 2020 ) evaluate the impacts of robotization on employment in supply chains in developing countries. Aly ( 2022 ) looks at various digitalization indices in developing countries and their associations with macroeconomic variables including employment. Although many developing countries, including some of the world’s poorest, are already using basic AI technologies, for instance, in smart farming, credit scoring and targeted advertising, advanced AI technologies are not yet widely adopted there. Yet, there exists a substantial potential for adoption of such technologies to leapfrog traditional development models (IFC 2020 ; Soto 2020 ). The use of digital technologies has accelerated in developing and even the poorest countries, not least due to lockdown measures that governments implemented during the COVID-19 crisis. In the service sector in Lao PDR, for example, the lockdowns have led many enterprises to switch to digital processes (Homsombath 2020 ). Similar efforts were made in the education sectors in which many activities were held online. These developments may have been a trigger for further digitalization efforts in the near future. Research applying occupation-level data for the United States to other countries typically points to a substantial risk of job destruction and an imminent job crisis, especially when analyzing developing countries (Balliester and Elsheiki 2018 ).

There are several issues that need to be considered when analyzing the impacts of digitalization in the context of developing countries. Applying the occupational digitalization scores computed for the United States in the context of developing countries might lead to significantly biased results, since the occupational tasks in developing countries might differ considerably from the occupational tasks of a similarly coded occupation in the United States (Arntz et al. 2017 ). 1 Alternatively, reproducing approaches that assign AI impact scores to occupations based on extensive surveys of AI experts (Frey and Osborne 2017 ; Brynjolfsson et al. 2018 ) in developing countries would be very costly. Several studies have therefore relied on correction procedures. In particular, Arntz et al. ( 2016 , 2017 ) adjust occupation-level computerization risk calculated for the US occupations (Frey and Osborne 2017 ) by regressing them on individual- and job-specific characteristics from the OECD’s Survey of Adult Skills (PIAAC) or other national surveys available in the US and the country of interest. Then they use the estimated coefficients to make predictions of computerization risk for individual jobs and occupations in other countries. While the correction procedure partly accounts for peculiarities of national labor markets, it has several drawbacks. First, before the regression can be estimated, the occupational codes used in O*NET (6-digit level of SOC) must be translated to the occupational codes in PIAAC (ISCO) using a crosswalk, and the latter codes are only available at the imprecise 2-digit level. Arntz et al. ( 2016 , 2017 ) use a multiple imputation method to deal with this issue. Second, the approach starts with digitalization scores at the occupation level, whereas we suggest starting with scores directly attributed to the much finer level of detailed work activities to enhance accuracy and precision. Third, predictions from a regression have a lower variance than the original data, which is likely to be reflected in the results.

In this paper, we rely on the main advantage of previous cross-country adjustment methods, namely the use of individual-level survey data, but aim to overcome the drawbacks of prior approaches mentioned above. We develop a methodology that allows translating existing scores of AI impacts, most of which were developed using data for the U.S., to the contexts of other countries at the level of work activities. Our method allows comparing AI impacts on workers in countries at vastly different levels of development, including low-income and least-developed economies.

In a nutshell, we propose to use individual-level surveys of workers’ skills, such as the World Bank's Skills Measurement Program (STEP) for developing countries or PIAAC (for OECD countries). We use the state-of-the-art method SBERT to assess semantic similarities between textual descriptions of detailed work activities (DWA) from the O*NET occupational database for the US, 2 for which AI impact scores are available, and the textual descriptions of workers’ skills elicited in surveys available for developing countries, in particular the World Bank’s STEP Skills Measurement Program. We then use the matrix of relatedness to translate the AI impact scores to the level of individual workers’ skills in a given country. In this way, an additional advantage of our method is that it supports different levels of analysis of AI impact on labor markets: at the individual level distinguishing by workers’ characteristics such as age or gender, at the skill level, or at the occupation level.

We illustrate the method using the cases of two neighboring Asian countries: Lao PDR, a least developed country according to the United Nations classification, 3 and urban areas in Viet Nam, a developing country that has transformed from one of the poorest countries in the 1980s into a lower middle-income country today. Among the digitalization measures available, we choose the suitability of work activities for machine learning as reported by Brynjolfsson et al. ( 2018 ).

The picture that emerges from our approach is insightful and shows that the impact of AI on individual workers is more heterogeneous in urban Viet Nam than in Lao PDR. While most respondents in urban Viet Nam are moderately affected, a significant number of workers are at high risk of being displaced by digital technologies; in Lao PDR, the impact is more evenly distributed. The most common occupation reported by STEP respondents in Lao PDR, subsistence crop farming, has a comparably low suitability for machine learning, presumably due to the importance of non-routine manual tasks in this occupation. The most common occupations in urban Viet Nam are more suitable for machine learning, in particular the occupations of shop salespersons and textile machine operators, but also of crop growers (according to the tasks they perform in Viet Nam). At the same time, workers in these occupations perform a relatively large variety of tasks in Viet Nam, some of which cannot be automated; this makes it likely that these occupations will be transformed rather than completely automated.

It should be noted that these results only make an assessment regarding the impact of machine learning on jobs, not about the overall risk of automation due to other types of technologies, such as non-AI software and robots. Non-digital mechanization, for instance, might affect occupations such as subsistence crop farming in Lao PDR more immediately than digitalization and AI.

We also compare results obtained with the proposed method to the results from a naïve approach when the AI impact scores are transferred from the United States to Lao PDR and Viet Nam at the level of occupations. In comparison to our proposed method based on semantic textual similarity matching, the naïve approach seems to produce too much noise to derive meaningful insights.

Data and methodology

Ai impact measures.

Several measures of AI impacts on occupations in the United States have been suggested by recent literature. To illustrate our method, among the available digitalization measures that we will briefly discuss below, we choose the suitability of work activities for machine learning (ML) provided by Brynjolfsson et al. ( 2018 ). The main reason for this choice is that this measure is available at the very detailed level of work activities, while other measures are usually available at the less disaggregated level of workers’ abilities, work tasks or occupations.

Brynjolfsson and Mitchell ( 2017 ) identify eight key criteria that specify conditions under which ML techniques can be employed as substitutes or complements to human labor. 4 The authors emphasize that these criteria are developed solely on the basis of technical feasibility, and that other factors, such as the elasticity of labor supply, price and income elasticities, determine the economic feasibility of implementation of ML applications. Brynjolfsson et al. ( 2018 ) create a rubric of 23 questions that aim at estimating the degree to which a detailed work activity (DWA) as defined in the O*NET database (compiled by the US Department of Labor) falls under the eight above criteria, and hence, is “suitable for machine learning” (SML). Corresponding to the eight criteria, this rubric also only concentrates on technical feasibility, not on the economic, organizational, legal, cultural, and societal factors influencing ML adoption. Based on a survey, the authors evaluate the potential for applying machine learning to the 2,069 DWAs, 18,156 tasks, and 964 occupations in the O*NET database. The authors use Crowdflower, a Human Intelligence Task (HIT) crowdsourcing platform, where each DWA is scored by 7 to 10 respondents with knowledge in the area. Through the 23 questions respondents are asked to evaluate each DWA based on the eight criteria. Brynjolfsson et al. ( 2018 ) then aggregate their scores from the DWA level to the task level and further to the occupation level in the United States weighted by importance as recorded in O*NET. The result is an average SML score for each US occupation.

Since the SML scores reported by these authors focus on the possibility of automation of activities currently performed by human workers, the average SML of the work activities performed in an occupation can be interpreted as destructive digitalization in the sense of putting workers at risk of being displaced (see also Fossen and Sorgner 2022 ). In contrast, the standard deviation of SML scores across work activities performed within an occupation reflects transformative digitalization, because occupations combining activities that can be automated with activities that cannot be automated are likely to be reorganized (Brynjolfsson et al. 2018 ) and transformed rather than to displace workers. Workers in these occupations are more likely to benefit from their close interaction with new digital technologies than to lose their jobs. The SML scores have the advantage that they are first generated at the level of DWAs in O*NET. These DWAs resemble the skills and work activities elicited in surveys like STEP or PIAAC, which facilitates the translation of these scores to other countries. We elaborate further on the conceptual differences and similarities between the DWAs from O*NET and the skills questions from STEP in Section 2. 3.

Alternative currently available AI impact measures could also be applied within our methodological framework, but some adaption would be necessary. A second option are the AI Occupational Impact (AIOI) scores provided by Felten et al. ( 2018 ), potentially as a measure of transformative digitalization (as argued by Fossen and Sorgner 2021 , 2022 ). These scores are constructed at the ability level in O*NET. Although our approach could be suitable to use the AIOI scores in combination with individual-level surveys measuring workers’ abilities, there are only 52 abilities in O*NET, much less than DWAs. Moreover, the textual descriptions of abilities in O*NET seem to be quite dissimilar to the textual descriptions of skills provided in STEP, reflecting different concepts underlying these measures and, therefore, making the AIOI scores less suitable for applying our approach in combination with the STEP surveys. 5

A third option are the computerization probability scores provided by Frey and Osborne ( 2017 ) as a measure of destructive digitalization. However, these probability scores are only available at the occupation level, so one would have to break these down to the level of work activities, implying imprecision. One way to do so could be to regress the computerization probabilities at the occupation level on the nine bottleneck skills from O*NET identified by Frey and Osborne ( 2017 ). This would allow the prediction of computerization risk at the occupation level in countries where data on occupations linked to the bottleneck skills are available. Arntz et al. ( 2016 , 2017 ) pursue a similar approach by regressing the automation probability as provided by Frey and Osborne ( 2017 ) on a set of individual job-related characteristics (including tasks and skills) from the PIAAC survey. Yet, the assessment of which tasks are automatable is ultimately derived from the expert opinions assembled by Frey and Osborne ( 2017 ) on the occupational level. Alternatively, one would have to resort to the simple approach of transferring the measure to other countries at the occupation level, which does not seem to be accurate, as argued above.

A fourth option is provided by Webb ( 2020 ). He develops a measure of exposure of occupations to AI technology by matching descriptions of work tasks in O*NET to the text of patents using text similarity measures. This procedure generates AI exposure scores at the O*NET task level; however, the author currently only provides the data aggregated to the occupation level.

It should be noted that the different measures capture different technologies within digitalization and AI; Fossen and Sorgner ( 2022 ) provide a detailed discussion. In particular, machine learning is a subfield of AI from a technological perspective. Therefore, the rankings and relative positions of occupationsare not necessarily expected to be similar when using the different scores. Table ​ Table3 3 in the Appendix shows the mean SML score and its within-occupation standard deviation provided by Brynjolfsson et al. ( 2018 ), the computerization probability provided by Frey and Osborne ( 2017 ), and the AIOI scores provided by Felten et al. ( 2018 ), which were all developed for the United States, for the 10 largest occupations in the United States in terms of employment. Cashiers have the highest SML score among these occupations, and also the highest computerization probability, but a moderate AIOI score. Laborers and freight, stock and material movers (by hand) have the lowest SML score and AIOI score, but a high computerization probability. Therefore, analyses using different scores would be interesting as they would answer different research questions, but they are not suitable as robustness checks.

Measures of AI Impact on the Largest Occupations in the United States

Notes: The table lists the 10 occupations that have the largest employment numbers in the United States (Bureau of Labor Statistics 2018 ). The mean suitability for machine learning of tasks in an occupation (SML) and its within-occupation standard deviation (sdSML) are adopted from Brynjolfsson et al. ( 2018 ), the AI Occupational Impact scores from Felten et al. ( 2019 ), and the computerization probabilities from Frey and Osborne ( 2017 ). Fossen and Sorgner ( 2021 ) provide a similar table and discuss the differences and similarities between the scores in detail

Individual-level data on skills in developing countries: STEP survey

The STEP skills measurement program is provided by the World Bank. The goal of the survey is to provide representative individual-level data on the skills of the workforce and the usage of these skills in the individuals’ jobs that can be compared across countries. STEP is based on the adult population aged between 15 and 64 residing in urban municipalities 6 in developing countries and is comparable to the PIAAC survey by the OECD. While the focus of PIAAC is primarily on high-income developed countries, the STEP survey focuses on developing and transition economies. So far, STEP has been administered in two waves, in 2012 and 2013, in 13 countries, including Lao PDR and Viet Nam (surveys in these two countries were conducted in 2012). STEP surveys provide detailed information on individuals’ socio-demographic characteristics (e.g., age, gender, formal education level) and job characteristics.

The STEP survey specifically targets the measurement of skills of the workforce, broadly defined as “abilities to do certain things”. STEP distinguishes three types of skills: cognitive skills (e.g., reading and writing proficiency), socio-emotional skills (referring to social and emotional behaviors, personality, and attitudes), and job-relevant (technical) skills (see Pierre et al. 2014 , for more details). For the purpose of our study, we use a subsection of STEP questions that attempt to measure cognitive skills and job-relevant skills through self-reported information on respondents’ use of these skills in work-related activities (see Table ​ Table4 4 in the Appendix). These questions therefore link the relevant skills to typical work activities. These activities in the STEP questions resemble direct work activities (DWA) as defined in O*NET. We call these 44 activities “STEP skills” throughout the paper, even though, strictly speaking, these questions mostly relate to certain activities that are supposed to reveal information about underlying skills of the respondents in the three categories mentioned above (cognitive skills, socio-emotional skills, and job-relevant skills). We exclude respondents from the sample who did not work during the last 12 months before the interview because they are not asked about their work-related skills.

STEP Questions to Measure Workers’ Skills and SML Scores

Note: Questions to measure skills in 2012 STEP questionnaire. The SML (suitability for machine learning) scores are standardized at the level of these STEP skills

Matching O*NET work activities to skills in STEP

A major challenge regards matching the descriptions of work activities in O*NET, for which we have AI impact scores such as the SML scores, to skills in STEP. Even at the level of abilities, which is more aggregated than the level of DWAs, a manual approach seems infeasible. For example, there are 52 O*NET abilities and 44 skills in STEP, so a translation matrix would require determining 2,288 weighting scores. Furthermore, this approach would be entirely subjective.

Alternatively, one could conduct a new expert survey specific to a country of interest, similar to the approach of Brynjolfsson et al. ( 2018 ) or Frey and Osborne ( 2017 ), to produce new digitalization scores instead of using the existing scores developed for the United States. Although we consider this approach as a possible avenue for further research, a disadvantage is that it requires substantial resources (e.g., conducting a survey and collecting expert judgments), and it would be limited to a single country or region.

In this paper, we suggest and illustrate a third approach. We directly match 2,069 detailed work activities (DWAs) in O*NET to the 44 STEP skills creating a matrix of relatedness. The PIAAC survey could also be used instead of the STEP to target a different set of countries. O*NET uses its “Content Model” as its conceptual foundation and provides clear definitions for abilities (“enduring attributes of the individual that influence performance”), for skills (“developed or acquired attributes of an individual that may be related to work performance”), and for detailed work activities (“specific work activities that are performed across a small to moderate number of occupations within a job family”). The O*NET model defines a set of generic skills, for example, basic skills like “active listening”, “mathematics”, or cross-functional skills like “social skills” or “technical skills”, which can be further broken down into a total of 35 more detailed skills. Workers then need some of these 35 skills to successfully carry out tasks or activities in their occupations. These activities are described in detail as 2069 DWAs, which are then linked to the 1014 U.S. occupations. As we explained more elaborately in the previous section, the STEP survey collects a wide range of variables including questions about performed activities at work. It does not provide a detailed typology and rather asks the interviewee about actual activities he or she has performed recently (which may allow to draw conclusions on the skills of the surveyed person). The 44 “STEP skills” from the utilized questions resemble more the DWAs than the generic skills in O*NET. Thus, our approach works better at the DWA level than the abilities or skills level. This has affected the choice of the AI impact measure that we use to illustrate our method: since the SML scores of Brynjolfsson et al. ( 2018 ) are available at the work activities level, the application of our approach at the work activities level using the SML scores is straightforward.

To find semantic similarities between the textual descriptions of O*NET work activities and the STEP skill measures, we apply automated semantic textual similarity matching techniques (SBERT). By using SBERT, we avoid a manual assignment of similarity as discussed above. The main advantages of this approach are the following: it is systematic rather than subjective; it is automated; there is no need to conduct new surveys; and the same method can be used with different data sources such as STEP and PIAAC for many countries. As our method is based on activities performed within the occupation, it has the additional advantage that occupations not included in the original set of occupations with AI impact scores can be examined as well, including new or reorganized occupations.

A new method based on semantic textual similarity matching using SBERT

In this section, we describe our method in detailed steps. The first step involves processing the textual descriptions of the DWAs in O*NET and the descriptions of the skills used by employed STEP respondents in their main job. The latter are the questions from the STEP questionnaire that aim at assessing the skills of employed respondents (see Table ​ Table4 4 in the Appendix). We combine the textual descriptions to a single string vector. Then we preprocess the string data stored in this vector. This includes removal of accents, consecutive whitespaces, substitutions of various text characters (e.g., “- “, “,” and “.”), and text conversion to lowercase. In the next step, word (semantic) embeddings are created for both DWAs and STEP questions using the Sentence-BERT (SBERT) method (Reimers & Gurevych 2019 ). 7 The model we apply is provided by MS Marco, which is pre-trained with real user search queries from the Bing search engine, a corpus that consists of 8.8 million passages.

In the second step, we create a similarity matrix that contains cosine measures of similarity 8 between all documents in the sample using the semantic word embeddings created in the previous step. These similarity measures account for semantic similarity between the textual descriptions of 2069 DWAs from O*NET and 44 STEP questions. 9 O*NET also provides broader, less occupation-specific activity descriptions in a hierarchy. General work activities (GWAs) are the broadest category, followed by intermediate work activities (IWAs), and DWAs are the finest categories. In addition to the first cosine similarity matrix using the DWAs, we create a second cosine similarity matrix using the GWAs to add more information on the nature of each work activity. For example, consider the DWA “Prepare forms or applications.” We improve similarity matching results by adding information that this DWA belongs to the broader GWA category “Documenting/Recording Information”. This way we distinguish this DWA clearly from the DWA “Position construction forms or molds”, which also contains the word “form”, but belongs to the different GWA category “Handling and moving objects”. Our final similarity measure is built as the average between the two similarity measures: between STEP skills and DWAs on the one hand and STEP skills and GWAs on the other hand. The overall patterns of results are not very sensitive to the choice of whether the similarity scores of the DWAs are averaged with any similarity scores of higher-level categories: with the GWAs as done here, with the IWAs, with both the GWAs and IWAs, or with none of these higher-level categories. After this second step, we have for each of the 44 STEP skills 2069 similarity scores that link the particular STEP skill to the DWAs.

In the third step, we use these final similarity measures as weights to create SML scores at the level of STEP skills. We do so by calculating for each of the 44 STEP skills a weighted average of the SML scores at the O*NET DWA activity level:

While larger SML scores signify better suitability of the skills for machine learning, the units of the original SML scores provided by Brynjolfsson et al. ( 2018 ) do not have a direct interpretation. Therefore, we standardize the SML scores at this level of STEP skills, with each skill receiving the same weight. Table ​ Table4 4 in the Appendix shows the standardized SML score for each of the 44 skills in STEP. For example, ‘using databases’ and ‘searching for information on the internet’ are the skills most suitable for machine learning, as indicated by the largest SML scores, which seems very plausible. In contrast, ‘physically demanding work’ has the lowest SML score. An example for physically demanding work from the STEP questionnaire is ‘construction’ and one for physically not demanding work is ‘sitting at a desk answering a phone’; it is plausible that the latter task is much more suitable for machine learning (an example would be automated call centers using AI) than the former.

Fourth, we merge the SML scores calculated at the level of STEP skills with the individual-level STEP surveys for Lao PDR and Viet Nam conducted in 2012, the latest available year for both countries. There are three types of questions in STEP that are used to measure the skills respondents use in their jobs: yes/no questions about whether a certain skill is relevant in one’s job (e.g., if a job requires reading books); cards questions that measure on a 10-point Likert scale the extent to which a particular job characteristic is relevant for one’s main job (e.g., the extent to which a job is physically demanding); and frequency questions that measure (on a 4- or 5-point Likert scale) the time that a person dedicates to a particular skill or task in his or her main job. In order to make the responses to the different types of questions comparable, we normalize them such that the responses can take values within an interval between 0 and 1. Now we use the normalized individual responses to create a score capturing the SML of the skills each individual uses in his or her job. More precisely, we create an SML score for each individual i averaged over the skills and weighted by the normalized individual responses to the questions on the usage of these skills. This is our measure of labor-displacing (destructive) AI technology at the level of the individuals’ jobs:

Fifth, we create mean SML scores at the occupation level and the within-occupation standard deviation of the SML scores. We follow the method by Brynjolfsson et al. ( 2018 ) as closely as possible. These authors start with SML scores for each DWA in O*NET, then they aggregate them to a broader level of tasks and then to the level of occupations by building weighted averages (they call this mSML ). In addition, they calculate the standard deviation of SML across tasks within each occupation ( sdmSML ). Both mSML and sdmSML are weighted by the importance of the tasks in the occupation as provided in O*NET. Since detailed occupation databases like O*NET are unavailable for most countries, including Lao PDR and Viet Nam, we use the STEP survey to derive the task composition of occupations in these countries. To do so, we calculate the average of the usage of each skill, obtained from questions in STEP, over individuals i in each occupation occ in a country:

Then we create an SML score for each occupation as the average SML score over the skills, weighted by the average usage of the skills in the occupation. This is our measure of labor-displacing (destructive) AI technology at the level of occupations:

Finally, we calculate the standard deviation of the SML scores across the skills in each occupation, weighted by the average usage of the skills in the occupation in the country ( usage o c c , s k i l l ):

A large standard deviation of the SML scores within an occupations indicates that an occupation combines work activities that are suitable for machine learning with work activities that are not suitable for machine learning. This suggests that human workers will still be needed in the occupation but could closely collaborate with AI technologies in reorganized occupations (Brynjolfsson et al. 2018 ). Therefore, we interpret this measure as transformative AI technology at the level of occupations.

Results for Lao PDR and Viet Nam

To better understand the occupational structure of the labor markets in Lao PDR and Viet Nam, we first provide descriptive statistics based on data from the Labour Force Surveys (LFS) provided by the International Labour Organization (Table ​ (Table1). 1 ). Since the STEP data is available for both urban and rural areas in Lao PDR, but only for urban areas in Viet Nam, we show corresponding statistics in this table; Table ​ Table5 5 in the Appendix shows the occupational structure for both urban and rural areas in Viet Nam. According to the LFS, 44% of the workers in Lao PDR and 32% in Viet Nam reside in urban areas. Referring to Table ​ Table1, 1 , 40% of the workers in Lao PDR work in agricultural occupations. In Viet Nam, the largest occupations in urban areas are service and sales workers (28%) followed by elementary occupations and professionals (15% each). Elementary occupations comprise many different simple tasks, like door-to-door sale, cleaning and home care activities, simple farming tasks and steering animal-drawn vehicles. According to the information provided by the LFS, we do not observe a significant gender employment gap at the aggregate level, although large differences occur at the occupational level. In particular, women are under-represented among plant and machine operators and assemblers in both countries, among technicians and elementary occupations in Lao PDR and among managers and craft workers in Viet Nam.

Occupational Structure in Lao PDR and Viet Nam

Source: Labour Force Survey 2017 for Lao PDR, Labour Force Survey 2016 for Viet Nam (urban areas)

Occupational Structure in Viet Nam (Urban and Rural Areas)

Source: Labour Force Survey 2016 for Viet Nam (both urban and rural areas)

Next, we present the measures of destructive and transformative digitalization estimated for Lao PDR and urban Viet Nam following our proposed methodology. Figure  1 shows the kernel density of the SML of the skills reported by individuals in the STEP survey ( SML i ) in both countries. For Viet Nam, there is a bimodal distribution: Most respondents have a mix of skills that is moderately suitable for machine learning, which shows that these individuals are at moderate risk of being displaced by digital machines. However, a significant number of individuals also exhibit skills that are highly suitable for machine learning. This points toward the fact that the labor market in urban Viet Nam is more heterogeneous than in Lao PDR in terms of susceptibility of individual workers’ jobs to labor-displacing machine learning technologies.

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Suitability for Machine Learning of Individual Jobs in Lao PDR (Left) and Viet Nam (Right)

Overall, the mean SML score across workers is -0.619 in Lao PDR and -0.398 in Viet Nam, indicating that workers in urban Viet Nam are more affected by machine learning on average (see Table ​ Table2); 2 ); the difference is significant at the 1% level. The scores are comparable across countries because they were standardized at the level of skills in STEP, which are the same in both countries. The fact that the scores are negative in both countries indicates that workers in both countries use skills that are less suitable for machine learning than the average across the skills elicited in the STEP survey; in Lao PDR, the average SML score is 62% of a standard deviation away from the average across STEP skills.

Individual-level SML Scores by Country, Gender and Age

The SML scores were standardized at the level of STEP skills. We excluded individuals from the sample who did not work during the last 12 months before the interview

The comparison between urban Viet Nam and Lao PDR shows that the level of development is not necessarily an indicator for the suitability of jobs for machine learning. Lao PDR is less developed than Viet Nam with the largest share of workers in Lao PDR engaged in the agricultural sector as crop growers, subsistence crop farmers or animal producers. These are all occupations that have relatively low standardized SML scores, ranging from -0.147 to -0.570, and may not be easily replaced by digital machines. In urban Viet Nam, the largest share of workers are service workers, for example, street and market salespersons (-0.048), shop salespersons (0.075), or finance professionals (0.201), who all have considerably higher standardized SML scores. Informality also tends to be higher in the previous job categories in Lao PDR, which might correlate negatively with SML scores.

Our method also allows us to disaggregate by demographic characteristics such as gender or age. Figure  2 suggests that in both Lao PDR and urban Viet Nam, women use skills in their jobs that are somewhat more suitable for machine learning than men. However, the mean difference between genders is insignificant in both countries (Table ​ (Table2). 2 ). An interesting observation from the figure is that heterogeneity of AI impacts on occupations in urban Viet Nam is not specific to male or female workers.

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SML of Individual Jobs in Lao PDR (Left) and Viet Nam (Right) by Gender

Several results emerge when the data are disaggregated by workers in different age cohorts (Fig.  3 ). In Lao PDR, workers in the youngest age cohort (less than 25 years old) use skills in their jobs that are less suitable for machine learning than older cohorts. This is different in urban Viet Nam, where high SML scores are most concentrated among individuals between the ages of 25 and 35. 10 The differences in SML scores between age cohorts are significant at the 1% level in both countries (Table ​ (Table2 2 ).

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SML of Individual Jobs in Lao PDR (Left) and Viet Nam (Right) by Age

Next, we aggregate the SML scores at the occupation level ( mSML ) in Lao PDR and Viet Nam. This tilts the distribution more to higher SML scores in urban Viet Nam (Fig.  4 ). The mass of individuals with moderate SML scores we saw in Fig.  1 seems to be concentrated in a few occupations, such that more of the mass of occupations is concentrated at higher SML scores.

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SML of Occupations in Lao PDR (Left) and Viet Nam (Right)

Aggregation at the occupation level enables us to not only estimate the mean SML score ( mSML ) of the skills used in an occupation, but also the standard deviation of the SML scores ( sdmSML ) of the skills used within an occupation. As argued above, if the skills used in an occupation can be automated on average, workers are at risk of displacement, so mSML is a measure of destructive digitalization. However, if some skills used in an occupation can be automated whereas others cannot, resulting in a high sdmSML score, the occupation will be likely transformed (Brynjolfsson et al. 2018 ) and workers may benefit from increased productivity. Thus, sdmSML is a measure of transformative digitalization. Transformative digitalization may also be an indicator for required training or re-training on the job or within an occupation, which may have to be supported or enabled by policy makers and employers. Destructive digitalization or displacement risk of workers may require different policy responses such as re-training to different occupations, measures to support job creation in different sectors, or income support to allow workers to make transitions to other jobs. Figure  5 shows that the distribution of sdmSML is shifted toward higher scores in Viet Nam in comparison to Lao PDR, which reveals that more occupations in urban Viet Nam are likely to be transformed or reorganized due to AI than occupations in Lao PDR.

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Within-occ. Standard Deviation of SML in Lao PDR (Left) and Viet Nam (Right)

To visualize the effects of machine learning technologies on occupations in both countries, we map how much occupations in Lao PDR and Viet Nam are affected by destructive ( mSML ) and transformative ( sdmSML ) digitalization. We depict each occupation in Lao PDR (Fig.  6 ) and in urban Viet Nam (Fig.  7 ) as a bubble on a two-dimensional pane. Each bubble represents one occupation, and the size of the bubbles reflects the relative number of workers in the occupation in Lao PDR and urban Viet Nam, respectively, based on the LFS for both countries. 11 In Figure 11 in the Appendix, the size of the bubbles reflects the relative number of workers in the occupation based on the LFS for both urban and rural areas in Viet Nam. This makes the bubble sizes more directly comparable to those for Lao PDR; however, the underlying STEP data for Viet Nam only covers urban areas.

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Destructive and Transformative Digitalization in Lao PDR. Notes: Each bubble represents an occupation in Lao PDR. mSML denotes the mean suitability for machine learning of skills used in an occupation (standardized at the level of STEP skills) and is a measure for destructive digitalization. sdmSML denotes the standard deviation of the SML of skills used within each occupation and is a measure of transformative digitalization. The size of the bubbles represents employment in the occupations based on the 2017 Labour Force Survey for Lao PDR. The largest occupations are labeled

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Destructive and Transformative Digitalization in Viet Nam. Notes: Each bubble represents an occupation in Viet Nam. mSML denotes the mean suitability for machine learning of skills used in an occupation (standardized at the level of STEP skills) and is a measure for destructive digitalization. sdmSML denotes the standard deviation of the SML of skills used within each occupation and is a measure of transformative digitalization. The size of the bubbles represents employment in urban areas in the occupations based on the 2016 Labour Force Survey for Viet Nam. The largest occupations are labeled

We observe a tendency for significant labor market transformation in both countries: Occupations in the northeast corner are characterized by high transformative and destructive digitalization technologies, which puts pressure on workers to adapt, combined with a risk of partial displacement (‘machine terrain’). On the contrary, occupations close to the southwest corner show low SML scores and a low standard deviation in SML scores. These occupations can be considered to be in ‘human terrain’, with little expected impact from AI. Few occupations are present in the northwest corner, which represent ‘rising stars’ occupations, with limited risk of destruction and high potential for transformation. Similarly, very few occupations are placed in the southeast corner of ‘collapsing occupations’ with high risk of destruction and little potential for transformation involving human workers (see Fossen and Sorgner 2019 , for the characterization of the four sectors in the US context).

By comparing the two countries, we note that the same occupation can have very different mSML and sdmSML scores in different countries because of different work activities workers perform. This indicates that our method has a valuable discriminating power among different pools of workers in different country contexts.

Many occupations in Viet Nam that are important in terms of employment are more suitable for machine learning than many important occupations in Lao PDR and, therefore, they are potentially subject to destructive digitalization. At the same time, many of these occupations in Viet Nam are subject to transformative digitalization, thus, characterizing these occupations as within ‘machine terrain’ for the near future with high levels of both transformative and destructive digitalization (they are in the upper right corner of the chart). The most common occupations in urban areas in Viet Nam, represented by the largest bubbles in Fig.  7 , have relatively high SML scores in Viet Nam due to the activities performed in these occupations there. Among these occupations, the activities of textile machine operators are most suitable for machine learning on average. At the same time, the activities performed within this occupation have the highest standard deviation of SML, which suggests that the occupation will be reorganized, and human workers will still be needed in this occupation in the future to perform some of the activities.

In contrast, in Lao PDR, by far the largest share of STEP respondents work as subsistence crop farmers (large bubble in Fig.  6 ). The suitability for machine learning is lower in comparison to the above-mentioned occupations due to the manual non-routine tasks performed. In Viet Nam, some occupations are also located in the lower left quadrant, for example building frame workers, characterizing them as within ‘human terrain’ for the near future in this country with low levels of both destructive and transformative digitalization. The results suggest that Viet Nam is currently undergoing a significant shift from traditional occupations to those affected by industrialization and digitalization. In contrast, employment in Lao PDR is still dominated to a large extent by agricultural occupations that lie somewhere in the middle on the scales of both transformative and destructive digitalization. Therefore, workers in Lao PDR are currently less affected by AI, as the labor market there has not yet fully absorbed previous waves of automation.

In a nutshell, the gap between ‘machine terrain’ and ‘human terrain’ occupations is clearly more pronounced in Viet Nam than in Lao PDR. At the same time, none of the two countries have many occupations that must be characterized as ‘collapsing’ occupations, which are strongly affected by labor-displacing AI with little prospect of transformation involving human workers, or that fall into the category of ‘rising stars’ occupations, which have low displacement risk but at the same time a high potential for occupational transformation.

How do the measures of destructive and transformative digitalization for Lao PDR and Viet Nam compare to those for a developed economy? Fig.  8 shows the SML scores and their within-occupation standard deviation across tasks for the United States. These scores are directly provided by Brynjolfsson et al. ( 2018 ) for the US, so in contrast to Lao PDR and Viet Nam, no translation was necessary. Brynjolfsson et al. ( 2018 ) aggregate the SML scores from tasks to occupations weighted by importance in the O*NET database, which is analogous to our procedure. We standardized the scores for Lao PDR and Viet Nam at the level of skills in the STEP survey, but we cannot do this for the US because the STEP survey is not conducted in the US. Therefore, we show the original scores here. Also note that the figure for the US is based on the SOC classification of occupations (6 digits) used by Brynjolfsson et al. ( 2018 ), whereas the figures for Lao PDR and Viet Nam are based on the ISCO-08 classification (3 digits) used in the STEP surveys with less detailed occupations. While the scores cannot be directly compared between the US and the other two countries, the patterns can be compared. Retail or shop salespersons have a relatively high mSML score both in the US and in urban Viet Nam, indicating that these occupations are suitable for machine learning. Laborers doing physically demanding manual work (freight, construction) have relatively low mSML scores in these two countries, indicating that this work is not very suitable for machine learning. The patterns in Lao PDR are more different from those in the US. This may reflect that Viet Nam is closer to the US in terms of economic development, so tasks performed within occupations are more similar to the US in urban Viet Nam than in Lao PDR.

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Destructive and Transformative Digitalization in the United States. Notes: Each bubble represents an occupation in the United States. mSML and sdmSML are provided by Brynjolfsson et al. ( 2018 ). mSML denotes the mean suitability for machine learning of skills used in an occupation (not standardized), which we interpret as a measure for destructive digitalization. sdmSML denotes the standard deviation of the SML over tasks within each occupation, which we interpret as a measure of transformative digitalization. The size of the bubbles represents employment in the occupations as provided by the Bureau of Labor Statistics ( 2018 ) for the US. The largest occupations are labeled

Finally, we compare the SML scores translated from the US to Lao PDR and Viet Nam at the work activities and skills level following our approach to the SML scores simply transferred at the occupation level (naïve approach). The naïve approach requires applying a crosswalk between the SOC occupation codes provided for the SML scores by Brynjolfsson et al. ( 2018 ) and the ISCO-08 occupation codes available in the STEP survey. When we use this naïve approach and transfer the SML scores (not standardized) from the US to Lao PDR and Viet Nam at the occupation level (Figs. ​ (Figs.9 and 9  and 10 ), the maps show no clear patterns or different patterns between the two countries, despite heterogeneous economic conditions and different organization of occupations.

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Transferring SML Scores from the US to Lao PDR at the Occupation Level. Notes: Each dot represents an occupation in Lao PDR. The SML scores were translated from the United States to Lao PDR at the occupation level (naïve approach). The size of the bubbles represents employment in the occupations based on the 2017 Labour Force Survey for Lao PDR

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Transferring SML Scores from the US to Viet Nam at the Occupation Level. Notes: Each dot represents an occupation in Viet Nam. The SML scores were translated from the United States to Viet Nam at the occupation level (naïve approach). The size of the bubbles represents employment in urban areas in the occupations based on the 2016 Labour Force Survey for Viet Nam

We proposed a methodology that allows meaningfully assessing AI impacts on individuals, jobs, and occupations in different countries. So far, the analysis of AI impacts on labor markets in countries other than the United States has been rather limited, particularly so in developing countries. While the implementation of AI technologies is still rather low in developing countries, basic AI technologies are already in use in these countries, and substantial potential for adoption of more advanced AI technologies has been identified (IFC 2020 ). Pronounced interest in enhancing the implementation rate of AI technologies in developing countries is further driven by the promise of these technologies to help leapfrog development. 12 Hence, understanding the impacts of AI on labor markets in developing countries, including in least developed countries, is crucial, but is dependent on the availability of appropriate methods. Previous methods that we discussed in this paper do not sufficiently account for the fact that occupations are organized in different ways and comprise different work activities across countries. This has been the main challenge to the study of impacts of digitalization on occupations in various countries.

The novel method we propose in this paper relies on the assessment of the suitability for machine learning of 2,069 detailed work activities that constitute occupations. These detailed work activities are reasonably universal activities that can be considered relevant in all labor markets including those in developing and least developed countries. This highly disaggregated level of analysis allows us to overcome the main challenge described above. In a nutshell, our method is based on the SBERT assessment of semantic similarities between textual descriptions of detailed work activities in the occupational database O*NET in the United States, for which digitalization measures are available, and skills elicited in household surveys available in a wide range of countries, such as STEP or PIAAC. This makes it possible to translate measures of digitalization to other countries at the level of work activities and to compare the impact of digitalization across countries and for various groups of individual workers within countries. This method builds on and advances prior approaches such as that suggested by Arntz et al. ( 2016 , 2017 ), which starts from occupation-level digitalization scores instead of detailed work activities and relies on a crosswalk to 2-digit-level occupational scores.

We illustrate our approach using the suitability of work activities for machine learning (SML) provided by Brynjolfsson et al. ( 2018 ) as the AI impact measure, STEP as the survey of individual skills used at work, and the country cases of Lao PDR, a least developed country, and its neighbor Viet Nam, a developing country. Our methodology allows calculating AI impact scores at the level of individuals rather than at the level of occupations, and it provides less noisy and more insightful results than the naïve approach when digitalization measures are translated to other countries at the occupation level. While the mean of the suitability of work activities for machine learning in an occupation reflects destructive (potentially labor-displacing) AI technology, we also calculate the within-occupational variation of this measure to account for transformative effects of AI technology or the extent to which an occupation can be reorganized rather than replaced by technology.

The main insights from our analysis for Lao PDR and Viet Nam can be summarized as follows. First, we find that a larger share of individuals and occupations in urban areas in Viet Nam are exposed to labor-displacing machine learning technologies than in Lao PDR (where the data covers both urban and rural areas). This observation might reflect the differences in skill use between the two countries but also the fact that Viet Nam has already seen a larger transformation of its labor market through previous waves of mechanization, thus, making implementation of machine learning technologies easier. A significant share of workers in Lao PDR are employed in subsistence crop farming where the immediate implementation of AI technologies is challenging given the current state of technology and human capital in the country. This reduces the threat of rising unemployment due to this specific type of technology, but at the same time casts doubt on the feasibility of leapfrogging the current development path by means of AI technologies in Lao PDR. In Viet Nam, where the potential for labor-displacing automation is greater, policy responses could consist, for instance, in implementing measures to support job creation in less affected sectors or supporting workers in obtaining skills that will allow them to make transitions to jobs in these sectors.

Second, the urban labor market in Viet Nam is pronouncedly more heterogeneous with respect to the impacts of AI on individual workers, as compared to the labor market in Lao PDR. Both countries have a rather high share of workers in occupations that are characterized by high suitability of work activities for machine learning technologies, and, at the same time, have a high potential for re-organization of tasks within occupations. However, in Viet Nam there are some relatively highly populated occupations, such as building frame workers, that mainly consist of work activities that are not very suitable for machine learning technologies. While these occupations can be considered as safe in terms of labor-displacing effects of AI on them, there are not many opportunities for workers employed in these occupations to improve their productivity by means of AI. Thus, policy makers should monitor the aspects of inequality that may be due to unequally distributed opportunities for productive work using AI technologies across occupations.

Third, the results of gender-disaggregated analysis indicate that in both countries female workers are slightly more affected by labor-displacing AI technologies than their male counterparts. This is in line with previous research on the impacts of digital technologies on women in the context of developing countries (e.g., Sorgner 2019 ). We further show that heterogeneity of AI impact on occupations in urban Viet Nam does not seem to be driven by male or female workers, but that it is a rather general phenomenon in this country. Given that the digital gender gap is particularly pronounced in developing countries (Mariscal et al. 2019 ), policy makers should design and promote educational programs designed for girls and women, to increase their participation in STEM fields and prevent the aggravation of the digital gender gap.

Fourth, several insights emerge from our analysis disaggregated by workers in different age cohorts. We find substantial differences in both countries regarding the impact of AI technologies on younger workers. In Lao PDR, younger workers appear to be least affected by suitability of their work activities for machine learning technologies, while in urban Viet Nam younger workers seem to be among the most affected by this type of AI technology. This suggests that there are large differences in skill use among young workers in both countries, which deserves a more in-depth analysis given that particularly in Lao PDR the share of young individuals in the population is substantial.

Our analysis is not without limitations. Some limitations can be attributed to the methodology, while others are due to the data used in our analysis. In terms of methodology, we were able to improve earlier methods by significantly disaggregating the level of analysis and breaking it down to the level of detailed work activities. Still, one may wonder in how far the detailed work activities are comparable across countries, given different stages of economic development. We argue that using more than 2,000 detailed work activities is currently the most disaggregated level of analysis used in the literature, which represents an important advantage of our method. The highly disaggregated level of work activities makes them rather universal and applicable in various contexts. Moreover, our methodology is based on the application of semantic similarity matching techniques with textual data. We rely on the state-of-the-art Natural Language Processing technique, namely SBERT, to create semantic word embeddings to be used later for finding similar textual descriptions of work activities and skills. Should more advanced methods become available in the future, the method can be adjusted accordingly.

There are several limitations in terms of data used in the analysis. First, surveys like STEP and PIAAC elicit a rather restricted number of skills, which might lead to imprecise results of similarity matching with work activities, as some of the latter might be relevant for one’s job but corresponding information is missing in the survey. Therefore, household survey programs should ensure to include comprehensive information about skills and tasks that do not miss important areas.

Second, for illustration purposes we used the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. ( 2018 ). If other measures, for instance, of other types of AI technologies will be developed in the future that are available at this narrow level of analysis, they can be adopted with our methodology in a straightforward way. In addition, future surveys should also attempt to distinguish between work activities, tasks, and abilities in a more systematic way, because some existing AI measures are available at the level of abilities (e.g., Felten et al. 2018 ), which were not measured in the STEP survey and therefore could not be analyzed with our method. Moreover, considering the speed at which new AI technologies are being developed to automate tasks hitherto not feasible, a more forward-looking approach could be to translate patent data on AI to identify tasks and skills susceptible to be replaced in the future, similar to the approach undertaken by Webb ( 2020 ). In addition, it would be desirable to have measures of technology adoption in addition to the task suitability measures to assess the actual impact of digital technologies on job tasks. The actual impact of machine learning technologies on jobs in developing countries could be diminished by many barriers to automation, such as the availability of a young and relatively cheap labor force, the presence of tariffs on digital goods, a lack of high-quality human capital that is needed to adopt new digital technologies, and a relatively high cost of technology adoption given a high share of SMEs and informal businesses, among others (World Bank 2016 ).

Third, the STEP surveys for Lao PDR and Viet Nam are only available for the year 2012. It would be very useful to have similar surveys of adult’s skills in developing countries that are more recent, representative and include a sufficient number of respondents to allow for a meaningful analysis of different categories of workers. In addition, the measure of suitability of job tasks to machine learning technologies (Brynjolfsson et al. 2018 ) that we use in our analysis is slightly more recent than the STEP data. Thus, our results show how the occupations of individuals, captured in the structure that existed in Lao PDR and Viet Nam in 2012, were expected to become suitable for machine learning in subsequent years. If in the relatively short period between the collection of the STEP data and the construction of the SML measure the adoption of machine learning technologies in developing countries already affected the composition of job tasks individuals performed, our estimation would still be relevant because it demonstrates the potential impact of machine learning technologies on the structures that existed in 2012. Availability of more recent data on job tasks in developing countries would allow to estimate the extent to which job tasks have changed over the last decade and to relate these changes to the availability of machine learning technologies. In addition, the STEP survey was mainly conducted in urban areas of developing countries but given a strong urban–rural regional divide in these countries, it would be desirable to have data that also includes respondents residing in rural areas. In this paper, only data for Lao PDR covered population residing both in urban and rural areas.

Our proposed methodology opens avenues for future research by allowing the estimation of digitalization impact measures of choice for a wide range of different countries, both developing and developed countries. While our illustrative example focuses on SML scores, the STEP survey and the cases of Lao PDR and Viet Nam, other digitalization measures, other surveys such as PIAAC, and other countries should be investigated in the future. The full value of our approach will become visible when applying it to various countries, because the methodology allows using the same digitalization measures across countries, which makes the results comparable. This research will inform policymakers about challenges and opportunities that new digital technologies deliver to different labor markets outside of the United States in a more targeted and precise way than current approaches do. Comparing the impact of digitalization between developed and developing countries will allow adjusting economic development strategies in a timely manner. Future research will also be able to apply our methodology to regions within countries as far as representative surveys with sufficient sample sizes are available. This research will reveal regional digital divides due to digitalization and AI and allow policymakers to develop mitigating and enabling labor market policies such as targeted training programs.

Table ​ Table3Table 3

Table ​ Table4Table 4

Table ​ Table5Table 5

Figure  11

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Destructive and Transformative Digitalization in Viet Nam (Urban and Rural Areas). Notes: Each bubble represents an occupation in Viet Nam. mSML denotes the mean suitability for machine learning of skills used in an occupation (standardized at the level of STEP skills) and is a measure for destructive digitalization. sdmSML denotes the standard deviation of the SML of skills used within each occupation and is a measure of transformative digitalization. The size of the bubbles represents employment in the occupations based on the 2016 Labour Force Survey for Viet Nam (both urban and rural areas). The largest occupations are labeled

Acknowledgements

We thank Fabrizio Colella, Rafael Lalive, and participants at the 2021 International Joseph A. Schumpeter Society Conference in Rome, the 2021 United Nations “Least Developed Countries Future Forum” in Helsinki, the 2022 International Centre for Economic Analysis virtual “Future of Work” conference, the 2022 “AI in Strategic Management” workshop at the Center for the Future of Management at the NYU Stern School of Business, and seminar participants at the International Labor Organization and ESAI Business School, for valuable comments.

Data Availability

Declarations.

The authors have no relevant or material financial or non-financial interests that relate to the research described in this paper.

The authors have no competing interests to declare that are relevant to the content of this article. Any view expressed or conclusions drawn represent the views of the authors and do not necessarily represent ILO views or ILO policy. The views expressed herein should be attributed to the authors and not to the ILO, its management or its constituents.

1 Consider the following examples for differences between countries: Teaching is an important part of the occupation of craftspeople in Germany because they teach apprentices, whereas teaching crafts is performed by teachers in schools in other countries. Another example is farmers: A large share of a farmer’s work in a developing country may be manual field labor, whereas a farmer’s workday in the United States is filled to a larger extent with accounting work. Therefore, the impact of AI on farmers may be different across countries.

2 O*NET is a database of quantitative indicators about a variety of attributes for 1016 occupations in the United States. Based on expert opinions or worker surveys, these indicators cover various job-oriented attributes (occupational requirements, workforce characteristics, occupation-specific information) and worker-oriented attributes (worker characteristics, worker requirements and experience requirements).

3 https://www.un.org/development/desa/dpad/least-developed-country-category.html .

4 The following eight criteria are mentioned by the authors: (i) Learning a function that maps well-defined inputs to well-defined outputs, (ii) large (digital) data sets exist or can be created containing input–output pairs, (iii) the task provides clear feedback with clearly definable goals and metrics, (iv) no long chains of logic or reasoning that depend on diverse background knowledge or common sense, (v) no need for detailed explanation of how the decision was made, (vi) a tolerance for error and no need for provably correct or optimal solutions, (vii) the phenomenon or function being learned should not change rapidly over time, (viii) no specialized dexterity, physical skills, or mobility required.

5 In a related study, Tolan et al. ( 2021 ) map 59 generic tasks from worker surveys, such as PIAAC, to 14 cognitive abilities, and then to 328 AI evaluation tasks that they identify from the literature. They also rely on experts’ judgements to relate tasks to abilities and abilities to AI evaluation tasks.

6 For Lao PDR, the survey covered also rural areas.

7 SBERT is a state-of-the-art method in Natural Language Processing (NLP). It performs significantly better than alternative methods, such as averaging over a sentence’s individual word embeddings and BERT (Reimers and Gurevych 2019). The method has been applied, for instance, in the context of patent applications (Jansson and Navrozidis 2020 ) and gender differences in Covid-19 discourse on online discussion platforms (Aggarwal et al. 2020 ).

vectors are perpendicular. We normalize the cosine similarity measures to take values between 0 and 1, which allows us to use them as weights later when translating the SML scores from the level of DWAs to the level of STEP skills.

9 Consider the example of the final similarity scores for the STEP question “Do you (did you) read anything at this work, including very short notes or instructions that are only a few sentences long?” The highest similarity score (0.749) is obtained for the DWA “Receive information or instructions for performing work assignments” and the lowest similarity score (0.181) is obtained for the DWA “Drive passenger vehicles.”

10 Workers in this age cohort in Lao PDR are more likely to be in physically demanding jobs that are less suitable for machine learning than workers in this age cohort in urban Viet Nam. Young workers in urban Viet Nam are more likely to be in service occupations where they perform tasks that are more suitable for machine learning, for example involving various mathematical calculations.

11 Merging the LFS to the STEP is unproblematic because both datasets use the ISCO occupational codes. We make one manual adjustment: In Lao PDR, we merge the occupation “market gardeners and crop farmers” in the LFS to the occupation “subsistence crop farmers” in STEP.

12 See Ernst et al. ( 2019 ) for a discussion of the potential for AI technologies to support developing countries in their quest to catch up.

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Labor Market Research Paper

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View sample economics research paper on labor market. Browse economics research paper topics for more inspiration. If you need a thorough research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our writing service for professional assistance. We offer high-quality assignments for reasonable rates.

Work is a part of life for almost all people around the world. Though the types of work people do and the conditions under which that work is done vary endlessly, people get up each morning and choose to use their human capital in ways that generate some sort of productive good or service or that help prepare them to be productive economic citizens in the future. Some of this work is done in the privacy of the home, where beds are made, children are raised, and lawns are mowed. While this unpaid productive activity is essential to a well-functioning economy, this research paper addresses work time and skills that are sold in markets in exchange for wages and other compensation.

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The purpose of this research paper is to explore the unique nature of labor markets and to consider how these markets will evolve in response to changes in the nature of the work people do over time. Use of labor, like any economic resource, has to be considered carefully in light of productivity and opportunity costs. Though many factors affect this decision-making process, in most cases labor is allocated by market forces that determine wages and employment.

That said, several characteristics of labor as a resource create complexities. First, the demand for any type of labor services is derived from, or dependent on, the demand for the final product that it is used to produce. This means that highly trained and productive workers are only as important in production in an economy as there is demand for the product they produce. Second, because labor cannot be separated from the particular persons who deliver the resource, the scope of responses to labor market decisions is broad and affects outcomes in significant ways. The sale of labor services generates the majority of household income around the world—income that is used to sustain workers and their families. This means that labor markets determine, to a large extent, what resources a household has available and thus the quality of life for the members of the household. Decisions become very complex when workers and their families begin considering not only job market choices but also premarket education and training that might be required to prepare individuals for particular occupations.

Theory of Labor Market Allocation

As with all markets, buyers (employers or contractors) and sellers (employees) communicate their needs and offers with one another and exchange labor services for wages and other compensation. Some important assumptions underlie this model. First, assume that the wage and other monetary compensation is the most important determinant of behavior. This allows the construction of a model that has wages on the y-axis and quantity exchanged on the x-axis. Everything else held fixed, buyers and sellers will respond most directly to price signals exchanged between the two groups. Second, assume that workers are somewhat homogeneous—that is, that workers can be easily substituted for one another in any particular market. (Differences across workers will be explored later in this research paper.) Third, assume that workers are mobile and can move to places where there is excess demand for labor with their particular skills and away from places where there is excess supply of labor with their particular skills. Finally, assume that wages are flexible; they can move up or down in response to market signals.

Given these assumptions, the next step is to consider the behavior of buyers and sellers in what can be referred to as perfectly competitive markets for labor. In such markets, many relatively small employers hire relatively small amounts of labor; neither employers nor employees acting alone are a significantly large enough share of the market to be able to affect market wages. An example of this might be a college in New York City that hires administrative assistants. There are many such assistants in the market looking for jobs and many alternative employers, so both buyers and sellers have to accept the going wage for such work.

Labor Demand

Employers seek workers based on workplace needs and based on the demand for the good or service being produced. In addition to the market wage, employers consider the productivity of labor, the ability to substitute across other inputs in the production process, and the prices of other inputs when making hiring decisions.

Labor productivity is determined by a variety of factors, including human capital investments made by the worker himself or herself or the employer, skills and talents, and the quantity of capital and technology that the worker has at his or her disposal. As might be assumed, the greater the investment made in an individual worker, the greater his or her productivity. Like with any other investment opportunity, the investor spends money now (e.g., gets a master’s degree in social work or an MBA), hoping to eventually reap the benefits, in terms of greater income earning potential, in the future.

Substitution of inputs can be easy or difficult, depending on the production process being considered. For example, in an accounting firm, junior and senior partners might be equally productive (though not, perhaps, in the minds of important clients). Junior and senior partners can be relatively easily substituted for one another as tax forms are prepared. However, in a telemarketing firm, each caller needs to have his or her own phone. If an employer hires more callers but buys no more phones, no additional calls will be made. It might be easy to substitute junior for senior account managers, but it is impossible to substitute callers for telephones.

Understanding these sorts of trade-offs is very important for a firm; as the prices of inputs (junior and senior account managers or callers and phones) change, the employer will want to shift the input mix so that output is produced as efficiently and cheaply as possible. However, depending on the degree of substitutability, the changing input prices will create incentives to use more of the input that is becoming relatively cheaper and less of the input that is becoming relatively more expensive.

In spite of these other factors and their importance, firms tend to hire a greater number of hours or workers in the market, and more firms become buyers in a given market, as wages and compensation fall—everything else held constant. This empirically based conclusion is consistent with the law of demand. There is an inverse relationship between the quantity of labor demanded in a market and the price of labor in that market.

Labor Supply

Workers seek jobs based on their own skills and talents and based on the variety of factors that help to determine their income needs. Preferences over alternative jobs are important—some workers seek jobs that provide them with a sense of pride, accomplishment, and satisfaction. In other cases, workers have a preference for maximizing their own personal income, and so they search for jobs that are high paying, regardless of their other characteristics. Happily, the diversity of jobs combined with the heterogeneity of workers and their preferences generates labor markets that provide incentives for employers and for workers. Jobs that are distasteful to many workers for one reason or another (washing windows on skyscrapers or cleaning out pig sties) attract workers who are less averse to the negative aspects (heights or smells), and/or the work commands wage premiums that accrue from smaller pools of available workers. Thus, workers sort toward jobs that best meet their particular preferences and monetary needs.

Most often, workers consider the expected wage to be a key factor, if not the key factor, in determining what jobs to seek and accept. For some jobs where compensation is based on productivity (sales commissions, as an example, for real estate agents), there can be significant wage uncertainty. This uncertainty means that a job with a high wage might not be as appealing as a job with a lower wage that offers more security. For example, a worker might put her desire to be a professional tennis player aside in favor of the more stable employment position of a bookkeeper if she has elderly parents who need her care. High wages alone are not enough to attract workers to jobs; it is the entire employment package, and the level of employment and compensation risk, that must be considered when choosing a labor market to enter.

Given these other factors and their importance, workers tend to offer more hours in the market, and more workers enter a given market, as wages and compensation rise— everything else held constant. This empirically based conclusion is consistent with the law of supply that governs most market settings. There is a direct relationship between the quantity of labor supplied to a market and the price of labor in that market.

Market Equilibrium

When economists speak of equilibrium, they are using the term as any other scientist would—as a state of the world that, once reached, will not change unless a significant force acts upon it. Equilibrium price and quantity in a labor market is reached when there is no further tendency for wages or quantity of labor bought and sold to change, unless there is a change in the market that affects the demand curve or the supply curve.

Consider Figure 14.1. At W2, Q3 workers are demanded, and Q3 workers are supplied. Note that if the wage is W1, the quantity of workers demanded is greater than the quantity of workers supplied. There is a shortage of labor; the quantity of labor employers are willing and able to hire is greater than the quantity of labor people are offering for sale. Wages will rise as firms outbid one another to attract the workers they need. On the other hand, at W3, the quantity of workers demanded is less than the quantity of workers supplied. There is a surplus of labor; the quantity of labor employers are willing and able to hire is less than the quantity of labor people are offering for sale. Wages will fall as workers have to accept lower wages if they hope to gain employment. Thus, the only point at which wages and employment will not be under pressure to rise or fall is at W2, and it is defined as the current equilibrium in this market.

Note several things about this equilibrium price and quantity. First, economists often refer to this point as market clearing; this means that everyone who wants to buy labor at this wage rate can and everyone who wants to sell labor at this wage rate can. There is no excess supply or excess demand at the equilibrium wage. Second, although the market clears at this point, it is not necessarily a “good” wage and quantity combination. There are people in the market who would like to purchase labor at wages below W2 who cannot do so (Q3-Q6), and there are people in the market who would like to supply labor at wages above W2 who cannot find jobs (Q3 and above). They are left out of the market under these supply and demand conditions, and that might not be good for the workers or their families if they are unemployed or for the firms if they need workers to produce output to sell in the marketplace. Finally, note that some markets are very volatile, and so even though there are equilibrium wages and quantities exchanged, supply and/or demand are shifting significantly and often. For example, consider dockworkers. When a ship arrives at a port, the workload is heavy and demand for workers is high. However, when there are no ships in port, either because demand for the goods coming in has fallen or because supply has been affected by weather or other factors, demand for workers is low or nonexistent. These sorts of big swings in demand for workers can lead to significant wage volatility. It will become clear that unique sorts of contractual arrangements will be required to be sure that workers will be available for these types of tasks.

Imperfect Competition

The perfectly competitive market model applies when there are many, many workers in a market that also supports many, many employers. However, what if this is not the case? What if there is a single large employer that dominates the market for a particular type of labor? For example, major league baseball employs almost all of the professional baseball players in the United States. How does this situation impact the distribution of resources in a labor market? How will the equilibrium wage and quantity of labor exchanged be different under these circumstances?

labor-markets-research-paper-1

Figure 14.1   Perfectly Competitive Market for Labor and Firm Level Employment

Monopsony power refers to the ability of a single employer to control the terms of employment for all of, or a portion of, its workforce. The first significant examples of monopsonies emerged during the industrial revolution when factories (coal mines, steel mills, etc.) opened in small towns; the mine or the factory became the most significant employer in the town or local area, and workers had few other job opportunities. Because workers had limited alternative employment options, the firms were able to pay lower wages and exploit the workforce in a variety of other ways. In some cases, firms also established company stores that became monopolies in the provision of goods and services. Thus, the workers were subject to monopsony power in the terms of their employment and monopoly power in the markets in which they spent their income.

In response to this exploitation of workers by monopsonistic employers, workers formed unions—collective organizations of sellers that formed to gain some bargaining power in negotiations with their employers over the terms of their contracts. These unions gained legal status in most countries around the world in the late nineteenth and early twentieth centuries. However, in some countries that are at the earlier stages of industrialization, unionization and other types of worker movements may still be illegal or nonexistent. Consider the model in Figure 14.2. The labor demand curve, described as for the perfectly competitive market in Figure 14.1, depends on the output market in which the firm sells its final product and the productivity of its workers. However, now rather than paying a wage imposed by the free marketplace, firms have the ability to set wages at the lowest level possible. Firms are not assumed to be wage discriminators; all workers are paid the same amount per quantity supplied. (Wage discrimination is possible but is not considered here.) The key is that firms pay the lowest price they possibly can, given by the market supply curve at the optimal level of employment, and then increase the wage for all by the smallest amount possible, moving up the supply curve, when employment is increased by a small amount.

labor-markets-research-paper-2

Using Figure 14.2, suppose the current level of employment is Q0 and the wage is W0. If the labor monopsonist chooses to increase employment by one worker, labor costs increase by the amount of the wage payment to the new worker but also by the increased wage that has to be paid to all of the existing workers. Firms cannot discriminate and so must pay the same higher wage to all workers of the same type if a marginal worker is to be hired. Thus, the increase in the costs of employment to the firm in this market when a marginal worker is hired is greater than just the wage paid to the new worker; the marginal labor cost (MLC) of hiring one additional unit of labor is greater than the wage paid to that one marginal unit. The MLC curve lies everywhere above the supply curve because at any quantity of labor, MLC is greater than the wage.

In these imperfectly competitive markets, firms choose to hire where MLC intersects the demand curve, at MLC = D and Q*. This means the amount of money that comes into the firm when a marginal unit of labor is hired is just equal to the amount of money that goes out when the marginal unit of labor is hired. However, firms are able to pay a wage that is lower than the value of the marginal worker to the firm (W*). This wedge between the value of the worker to the firm and the wage paid has ignited anger among philosophers and workers around the world for hundreds of years. They read Karl Marx’s Communist Manifesto and believe in the labor theory of value, that the entire value of a final good or service should accrue to the labor inputs that were used to produce it. This alternative to a market model of wage determination had tremendous impacts on economies around the world during the twentieth century and will continue to affect decisions regarding trade-offs between the efficiency and equity of economic outcomes for years to come.

Applications and Empirical Evidence

There are endless applications of labor market theory to be found in economies around the world. Three of the most significant will be considered here, but the ideas presented can be adapted to fit a variety of other circumstances.

Economics of the Household or the New Home Economics

Increased labor force participation rates for women around the world, particularly in industrialized countries, were a major feature of the twentieth century. In addition to changing the landscape of labor markets, this phenomenon has had tremendous impacts on households, on families, and on the ways that cultures proscribe the management of unpaid work within the household. According to the U.S. Census Bureau, in 1900, 20% of women in the United States participated in the labor force (U.S. Department of Commerce, 1975). By 2000, that percentage had increased to 59.9% (International Labour Office [ILO], 2003). By contrast, in 2000 the female labor force participation rate was only 16.3% in Pakistan, 39.6% in Mexico, and 49.2% in Japan. These differences are significant and reflect alternative cultural norms and government support for medical care, child care, and other such programs that make it easier for women to enter the workforce.

In the United States, women began entering the labor market in significantly greater numbers during World War II to replace male employees who were fighting overseas. However, at the end of the war, women were forced out of employment in many cases by men, who were assumed to be heads of households, returning from active duty. It was not until the 1960s that women began to make their way back to work. Many factors can account for this, including the development of safe and reliable birth control, increased access to college and university education for women, declines in birthrates, and increases in divorce rates.

Economists like Gary Becker and Jacob Mincer, who first began investigating family decision making in the early 1960s, used models of international trade to explain how the law of comparative advantage applied equally well to household specialization and trade and to international specialization and trade. These models showed that, when the opportunity costs differed for family members producing the same goods, household production would be more efficient if each member produced those goods and services that they produced with lowest opportunity costs. Trade within the household ensured that each member would be able to consume a mix of goods and services.

This model helped provide some insights into the nature of household production, but it immediately led to alternative explanations and theories. Economists like Barbara Bergman and Julie Nelson have cited alternative explanations for the household structure that we most commonly see around the world—the male head of householder working in the labor market and supporting the efforts of other family members who are engaged in production within the household or human capital formation. One explanation might be found in bargaining models, which describe women and children who are less powerful in the household due to the absence of monetary income or having to depend on an altruistic head of household for economic resources. This means that the typical household structure is the result of differences in access to resources and bargaining power rather than any sort of efficiency in allocation processes or gains from trade explanation. In other cases, the household resource distribution system could be modeled as a Marxist process of exploitation; the “haves” (aka workers in the labor market) exploit the “have nots” (aka nonmarket workers in the household) to pursue their own self-interest and personal resource accumulation. These alternative models of resource allocation within the household have become increasingly important as economists have tried to better understand the nature of social problems like discrimination and domestic abuse.

Differences in Wages Across Occupations

In most economies, on average, doctors make more money than nurses, highway construction workers make more money than assembly line workers, plumbers make more money than administrative assistants, and stockbrokers make more money than teachers. Why is there so much wage dispersion in labor markets? What causes wages to vary so significantly across different types of workers and occupations?

There are a variety of answers to this question, but the key is that wages are often used to compensate for other aspects of a job that make it more or less attractive. For example, consider two jobs that are very similar in many ways: doctor and nurse. They both work in the same offices or hospitals, they both care for patients who have some form of health issue, and they both report to the same board of trustees or corporate owners of a health care facility. Because of these similarities, one might make an argument that the pay for these two occupations should be equal—what accounts for the differential in wages?

The key here lies in the significant difference in education and training required by doctors compared to nurses, in most cases. According to the Bureau of Labor Statistics, it takes 11 years of higher education, on average, to become a doctor, while the average registered nurse has a 4-year bachelor’s degree (U.S. Department of Labor, 2008-2009a, 2008-2009b). If people are to find the additional time spent in education and training, not to mention the added responsibility of being a doctor rather than a nurse, worthwhile, there must be some hope of a future payoff. It is true that many doctors find great satisfaction in helping patients, but additional pay into the future must be expected in order to repay student loans and to cover the opportunity cost of lost wages during long years in school. This sort of return to education and training compensates for an aspect of becoming a doctor that is negative and that would discourage entrants from pursuing the occupation.

In another example, highway construction workers versus assembly line workers, compensation is provided for risk of injury on the job. Though both these occupations typically require dexterity, strength, and concentration, the highway construction worker faces a much greater risk of injury while at work than does the assembly line worker. According to a brief prepared by Timothy Webster (1999) for the Bureau of Labor Statistics, the number of injuries on the job for workers in construction and manufacturing jobs were almost equal; however, construction workers were almost four times as likely to die on the job than manufacturing workers. In this case, the higher wages paid to construction workers, who have similar levels of education and training as manufacturing workers, compensates for the much higher risk of death while on the job.

 In other cases, higher wages compensate for unpleasantness on the job. Some of the most unpleasant working conditions can be found in meatpacking plants or in other food processing plants. Steel mills are notoriously hot and loud. Offshore oil drilling requires long periods away from home. Work in chemical plants may increase the risk of illness or cancer. All of these negative aspects of jobs lead to lower supplies of labor at any wage rate and hence higher wages.

One last aspect of an occupation to be considered is wage risk. Though two jobs, stockbroker and college professor, might have the same expected wage, the level of risk and uncertainty can be very different. For example, according to the 2005-2006 annual survey by the American Association of University Professors (2006), the average salary for a full professor was $94,738; typically a full professor has job and income security guaranteed by tenure but little hope of additional compensation. The Bureau of Labor Statistics cites the average salary for financial services sales agents in 2006 to be $111, 338; the bonuses and extra compensation available to such workers runs into the millions of dollars in some cases, but as we saw during 2008, job security in such positions is nonexistent when financial services industries feel the sting of recession (U.S. Department of Labor, 2007). Wage and employment risk must be compensated for by the prospect of big wins and large bonus plans.

Given the model of labor markets presented earlier, it is clear that interaction between the demand for labor and the supply of labor determines the equilibrium wage to be paid to workers. The sorts of job characteristics described above all affect the size of the applicant pool available to take on certain types of jobs and hence the position of the labor supply curve.

Unemployment

Though labor markets clear when quantity demanded is equal to quantity supplied, as described above, there are workers who are still seeking to work once the market clears, just at wages that are above market equilibrium. When people are actively seeking work but cannot find it, they are officially counted among the unemployed. Though in most all markets for goods and services that achieve equilibrium there are buyers and sellers who cannot participate because prices are too high or too low, when this happens in labor markets, households are left without income and other compensation, like health insurance and retirement plans. Hence, unemployment of labor resources has direct and immediate impacts on the lives of everyone who depends on the productivity of the unemployed worker.

To be unemployed according to the measures used in most industrialized countries, a worker has to be actively seeking work. The unemployment rate measures the percentage of the labor force, which includes those employed plus the unemployed who are actively seeking work but cannot find it. Sometimes the labor force participation rate is a better measure of how intensively productive resources are used in an economy; this measures the percentage of a total population (civilians, noninstitutionalized, over the age of 16) that is either employed or unemployed but actively seeking work. These measures can be applied to subpopulations so that economists can also track unemployment and labor force participation rates by gender, location, racial and ethnic origin, age cohort, and so forth. Given the significance of employment to both individual workers and to national productivity, it is important to watch for trends and understand patterns that might be occurring and their impacts on public policy.

Note that not every member of a population is included in the labor force, and not every person who is pursuing productive activities in an economy is included in the labor force. For example, discouraged workers, defined as people who are not working and have stopped seeking work, are not part of the labor force. People who volunteer in a variety of unpaid capacities, who are working in unpaid internships, or who are engaged in unpaid household or family production, are not included as part of the labor force. Employment data provide a proxy for the extent to which an economy is using its labor resources but come nowhere near truly measuring the output of labor resources in an economy over time.

Unemployment takes a variety of forms and can be considered more or less significant depending on its type. Frictional unemployment tends to be short in duration. When workers lose jobs, it naturally takes some time and effort to seek out and select new job opportunities. Contrast this with structural unemployment, which tends to be long term and occurs because a worker’s skills no longer fit the mix of jobs available in the economy in which he or she lives or because a worker lacks the skills needed to find a job that can use them. Seasonal unemployment occurs when the demand for workers of a particular type just does not exist at particular times of the year (there are no blueberries to pick in Maine in January), while cyclical unemployment occurs because demand for labor of many types decreases when the level of economic activity declines (fewer boat salespersons are needed during a recession.)

Figure 14.3 describes the path of unemployment in the United States for the 1999 to 2009 period. It is clear that recessionary pressures in late 2007 and 2008 had a tremendous impact on labor markets and on the number of jobs available to job seekers. Unemployment is referred to as a lagging indicator of the level of economic activity, which in this case means that recessionary pressures on other aspects of the economy, like demand for final goods and services or prices of other significant inputs like oil, were impacted months before firms began to lay off workers or decrease hiring. Toward the end of the recession, although other aspects of an economy might be showing marked signs of improvement, the unemployment rates might still be rising. Thus, it is very important that economists and policy makers use movements in unemployment rates with extreme care when making predictions about the health of the overall economy.

labor-markets-research-paper-3

Table 14.1 describes unemployment rates for a variety of Organisation for Economic Co-operation and Development (OECD) countries. It is clear that industrialized countries have different experiences in their own labor markets. This is caused by a variety of factors, some political and cultural in nature and others having to do with the product mix that the country produces or with historical factors that influence production and consumption. For example, Germany’s relatively higher unemployment rates may be due to its recent incorporation of East Germany as part of its economic base or to its generous social safety net that allows the unemployed to receive a greater package of benefits. It could be due to the changes in migration regulations and expectations that have come along with expanding membership in the European Union. It could also be due to the strong manufacturing tradition in Germany that may be increasingly moving to lower-wage countries. All of these sorts of factors must be weighed when comparing unemployment rates across time and across economies.

Though unemployment is always painful for the individual experiencing it, structural unemployment is the type that causes most distress for economists. It leads to an extended lack of income for individuals and their families and can lead to serious psychological problems, including depression, that can lead to other social ills, including domestic violence and substance abuse. The severity of these problems often depends on the social safety provided by the government for the unemployed, which differs widely across countries around the world. Though most industrialized countries have some level of support for unemployed workers, the level of support, the nature of the support (pure monetary support vs. access to job training and/or education), as well as the stigma attached to accessing this support, affect the willingness and ability of workers to remain unemployed.

Policy Implications

There are many, many public policy implications of labor market decisions and outcomes, some affecting the demand side of the marketplace and some affecting the supply side. Labor markets play fundamental roles in an economy, providing inputs for production, giving people a sense of purpose and well-being, and providing income and economic resources for household consumption. Unemployed workers tend to be angry voters, so most governments around the world embrace full employment, variously defined, as an important component of political and economic stability.

Employment Policy

Should the goal of an economy be to eliminate all unemployment? Should governments and policy makers establish programs that lead to an unemployment rate equal to zero? Two questions arise here: (1) Is it possible to have no unemployment, and (2) is it desirable to have no unemployment?

labor-markets-research-paper-4table

Economists sometimes use the term natural rate of unemployment, defined as the level of unemployment that will exist in an economy at full employment. This seeming oxymoron is actually a way of describing an economy that has reached a rate of productivity that can be maintained without placing inflationary pressure on resource prices or undue stress on productive resources of all sorts. The natural rate of unemployment in the U.S. economy seemed to be around 5% through the 1990s, but then as the combination of low interest rates and war in Iraq stimulated production, it seemed as though the natural rate fell to around 4.5%. In the United States, because government policy makers and the Federal Reserve Board can both impact the level of overall economic activity, the goal is to achieve a stable level of output and employment in the long run that guides decision making and policy action.

To steer the economy toward full employment, the government can use fiscal policies that affect aggregate demand. In some cases, this means changing the level of spending on some combination of entitlement programs and discretionary spending projects. For example, in 2008, when the U.S. economy seemed headed for a deep recession, the government increased the duration of unemployment benefits, shifted resources into “shovel ready” spending projects like roads and bridges, and promoted the Cash for Clunkers program to increase household spending on new automobiles.

Other government policies directly subsidize job training for workers whose skills do not match current job offerings. This might involve grants to subsidize college students (Pell grants, for example) or more targeted initiatives designed to train workers for occupations that are expected to expand in the near future. For example, the Employment and Training Administration (ETA) administers federal government job training and worker dislocation programs, federal grants to states for public employment service programs, and unemployment insurance benefits. These services are primarily provided through state and local workforce development systems.

In response to the significant challenges presented to American workers by the recession, the American Recovery and Reinvestment Act of 2009 (Recovery Act) was signed into law by President Obama on February 17, 2009. As part of this plan, the ETA will be a key resource for the administration’s “Green Jobs” initiative. As described on the ETA Web site (www. doleta.gov),

——-The Green Jobs Act would support on-the-ground apprenticeship and job training programs to meet growing demand for green construction professionals skilled in energy efficiency and renewable energy installations. The Act envisions sound and practical energy investments for 3 million new jobs by helping companies retool and retrain workers to produce clean energy and energy efficient components or end products that will result in residential and commercial energy savings, industry revenue, and new green jobs throughout the country.

This type of public policy, directed at workforce development and training, is designed to move workers into productive sectors of the American economy, making important human capital investments that lead to viable employment as well as to a more stable economy.

Employment Taxes

Income and other employment taxes play an important role in providing incentives for workers to participate in labor markets. One of the primary methods of taxation used by governments at many levels is to directly tax income. Typically, a percentage of each dollar earned is paid as tax, and in many cases the percentage increases as total income increases, making income taxes progressive in structure. This means that workers pay increasing percentages of marginal income as tax as income increases.

Income taxes serve a variety of purposes. First, they provide money to finance government expenditures. Local, state, and federal levels of government all need revenues in order to provide services; income taxes provide that revenue in many cases. Second, income taxes can affect the behavior of workers. If income taxes are increased, workers might increase their participation in labor markets in order to make up for household income lost to tax payments. However, workers might decrease their participation in labor markets in order to take advantage of the now lower opportunity costs of spending time out of the labor markets. That is, because effective wage rates fall as income tax percentages increase, it is less expensive to spend time out of the labor market.

If income taxes are decreased, workers might increase their participation in labor markets because the opportunity cost of spending time out of the labor market has increased. Higher effective wage rates mean that it is more expensive to spend time out of the labor market. However, workers might decrease their participation in labor markets because they can earn the same level of income now with less time spent on the job.

These opposing responses to changes in income tax rates make it very difficult to determine the right mix of policy to achieve government objectives. If the goal is to encourage workers to provide more work hours, should taxes be increased or decreased? If the goal is to encourage workers to provide fewer work hours, should taxes be increased or decreased? Policy has to be very carefully determined, based on the average levels of income earned in a particular market or the historical response of workers to tax changes in the past. For example, typically low-wage workers respond to increases in tax rates by working more hours. If the goal of policy is to encourage workers with jobs to provide a greater number of hours, it is smart to increase income tax rates. On the other hand, if the goal is to encourage labor force participation among discouraged workers, the appropriate policy is to increase the effective wage by lowering tax rates. This means that the opportunity costs of remaining unemployed have increased and it is now more expensive to stay out of the labor force.

Minimum Wages

In some instances, government policy makers intervene in markets for a variety of goods and services by setting legal minimum prices. These price floors, set to protect sellers of a good or service, provide sellers with higher levels of income than they would receive if the market equilibrium were allowed to allocate resources. Price floors are used in a variety of markets for goods and services in the United States, particularly in markets for agricultural products like cheese, milk, and sugar.

Minimum wages are used in labor markets for the same reasons. Legal minimum wages provide low-wage workers with protection from wages that may be low because of large supplies of workers who enter these markets. Particularly in urban areas or areas near borders, where large numbers of immigrant workers tend to settle, minimum wages help to alleviate poverty among the nation’s most vulnerable populations.

In the United States, the federal government established mandatory minimum wages through the Fair Labor Standards Act in 1938. In the midst of the Great Depression, this policy was designed to provide minimal levels of income for workers who were trying to make their way back into labor markets after extended spells of unemployment. In 2009, the federal minimum wage was increased from $6.55 per hour to $7.25 per hour. Some states, particularly those with very high costs of living, like Connecticut, set their minimum wages higher than what is federally required. Though federal lawmakers have increased the wage several times in recent years, the federal minimum is not indexed to inflation or to increases in labor productivity, and so workers have no guarantee that they will maintain purchasing power over time or that their compensation will rise as their own productivity increases.

Increases in the minimum wage have become quite controversial in most of the countries or markets in which they are imposed. Some argue that establishing wage floors causes higher levels of unemployment in affected markets. For example, some people argue that in the market for people who wash dishes in New York City, if firms are required to pay higher wages, they will move up their demand curves and hire fewer worker hours. Others argue that though this might be true, the demand for dishwashers in New York City is highly inelastic; this means that when wages rise, quantity demanded falls, but by a relatively small amount. So the question becomes an empirical one: When wages rise in low-wage labor markets, how significant is the decrease in quantity of labor demanded? If this decrease is small, then the income gains to workers who remain employed create greater benefits, even though some workers are forced out of the labor market. Many studies have been conducted to measure this impact, particularly on teenage workers, who are the most common recipients of the minimum wage. Though results are mixed, most conclude that the negative impact of increases in the minimum wage on employment in affected markets is small or nonexistent.

A more extreme form of the minimum wage that is gaining momentum is the living wage. This is defined as a minimum amount of money required by a worker to maintain his or her own living within a particular market. The Universal Living Wage Campaign is based on the premise that anyone working 40 hours per week should be able to afford basic housing in the market in which that labor is exchanged; this obviously requires higher hourly wage rates far in excess of the federal minimum wage. For example, the living wage in 2002 was $10.86 in New Haven, Connecticut, and $10.25 in Boston, Massachusetts. Even at these higher levels, the price elasticity of demand for labor in low-wage markets is quite inelastic, indicating that increasing wages to even this level will not result in significant increases in unemployment.

Future Directions

Labor markets are complex and dynamic, and so the possibilities for future directions are endless. The Bureau of Labor Statistics projects significant changes in the labor force in the coming years, as follows. Fewer younger workers will be available to enter the labor force, and most of the growth in the supply of labor will come from new immigrants to the American economy. On the demand side, manufacturing jobs will continue to move overseas, while job growth in the green economy and service sector will continue to increase (Toossi, 2007). Combined with these demographic and sector shifts, several other factors will help to determine the nature of labor markets in the coming decade.

As technology provides greater opportunities to enhance worker productivity, it also provides applications that replace worker effort with machines, computers, and robots. This tension between labor and capital as substitutes in production (capital equipment and technology replacing humans in production processes) and as complements in production (capital equipment and technology increasing the productivity of humans in production processes) will not be resolved easily and will need to be considered on a case by case basis across the economy as new technology changes the nature of the work that people do.

Immigration Policy

As noted above, immigrants are almost certain to account for a significant portion of the growth in the labor force in the next decade. Immigrants enter the United States seeking higher income and living standards than they have experienced in their home countries. Given the inequality in the distribution of income and wealth around the globe, as long as immigrants can gain access to the American marketplace, the investment in migration is more than worth the costs in terms of personal and family dislocation and downward job mobility.

The key to predicting the impact of this immigration is the degree to which U.S. citizens are able to provide welcoming communities for workers from other countries. Though a variety of laws and regulations limit the ability of firms and communities to discriminate against workers they do not want to accept, equal treatment and opportunity is not the norm in many regions of the country. This means that social networks among immigrant populations become more and more important, and the ability of immigrants to gain access to skills, attitudes, and workplace norms will be crucial if labor productivity is to continue growing as new migrants are absorbed into American life.

The burden of accommodating large immigrant populations can be quite overwhelming to a community. Particularly if border communities like Miami, Los Angeles, and Houston are considered, increases in immigration (both legal and illegal) strain public services like hospitals and schools. Even when people in a community want to welcome productive workers into their midst, they may find it difficult to provide for them in ways that are equitable.

Labor Unions

The union movement in the United States has declined steadily during the past five decades, with membership down to around 12% of the labor force from around 30% in the late 1950s. There are many reasons for this decline, some economic and some political. The primary sectors of the economy that led the union movement have declined in importance in recent years, led by autoworkers, mine workers, and garment workers. All of these industries have seen increasing competition from international producers and have subsequently been unable to compete with firms in the global economy that have gained a variety of efficiencies in production and employment.

Further, changes in the political climate have made it increasingly more difficult for unions to organize workers. On August 3, 1981, more than 12,000 members of the Professional Air Traffic Controllers Organization (PATCO) went on strike and were subsequently fired by President Ronald Reagan, who determined that the strike was illegal. This decision, in one instant, shifted the balance of power between firms and unions significantly in the direction of employers. From that time, policy and decisions by the National Labor Relations Board (NLRB) began to work in favor of the employer and more often against the ability of workers to form collective bargaining arrangements with their employers. Note that the labor movement in other countries, particularly in Europe, remains strong, with around half of all workers unionized in Great Britain, Germany, Austria, and Italy.

Finally, as the manufacturing sector of the economy has shrunk, the service sector has grown tremendously, representing nearly 80% of business activity in the United States today. Service employees have historically been difficult to organize because they are often female dominated (women are more difficult to organize) and often dispersed across a wide variety of work settings (small offices, working from remote locations, etc.). Unless employees in service industries find ways to unionize more effectively, the union movement will continue to lose relevance in the American economy.

In a perfect world, labor markets allocate human effort in production toward its most productive use. This research paper has endeavored to explain that allocation process by exploring the behavior of buyers and sellers in markets for human resources and then introduced the role of government policy makers in altering these market-determined outcomes.

The key to understanding the nuances of labor market behavior is in remembering that work is a fundamental source of human dignity. Though economists often focus on labor as a productive resource, which it of course is, there are aspects of the relationship between employer and employee that are clearly emotional, value laden, and culturally determined. This leads to a level of complexity in resource allocation that we do not see with other productive inputs like computers, robotics, or acres of land. However, it is this complexity that provides us as economists with rich avenues for intellectual investigation and policy analysis.

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research paper on labour market

Volume 50 Supplement 1

Retirement ages reform (pp. 1 – 28)

  • Open access
  • Published: 09 March 2017

The impact of temporary employment on productivity

The importance of sectors’ skill intensity

Auswirkungen befristeter Beschäftigung auf die Produktivität

Die Bedeutung der Qualifikationsintensität von Branchen

  • Domenico Lisi 1 &
  • Miguel A. Malo 2  

Journal for Labour Market Research volume  50 ,  pages 91–112 ( 2017 ) Cite this article

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Recent papers in the economic literature emphasise that the use of temporary contracts (TE) could have a detrimental effect on productivity. However, there are different reasons to believe that the impact of TE might not be homogeneous across sectors. In this article, we study the impact of TE on productivity growth and, in particular, we wonder if it differs according to sectors’ skill intensity. Our data set is an industry-level panel of European countries that allows to divide sectors according to the skill intensity. Our main result is that TE has a negative impact on productivity growth, but it is more damaging in skilled sectors. While an increase of 10 percentage points of the share of TE in skilled sectors would decrease labour productivity growth of about 1–1.5%, in unskilled sectors the decrease would be of 0.5–0.8%. This result is robust to different skill intensity indexes and productivity measures, as well as to the sample composition. We also discuss policy implications of this result for labour market regulation.

Zusammenfassung

Jüngste wirtschaftswissenschaftliche Abhandlungen betonen, dass die Nutzung befristeter Arbeitsverträge einen negativen Einfluss auf die Produktivität haben könnte. Es sprechen jedoch verschiedene Gründe dafür, dass die Auswirkungen befristeter Arbeitsverträge nicht in allen Branchen gleich sind. In diesem Artikel untersuchen wir den Einfluss von befristeten Arbeitsverträgen auf das Produktivitätswachstum und fragen insbesondere, ob es je nach der Qualifikationsintensität der Branchen Unterschiede gibt. Unser Datensatz ist ein Panel europäischer Länder auf Wirtschaftszweigebene, das es uns gestattet, die Branchen nach Qualifikationsintensität zu unterscheiden. Unser wichtigstes Ergebnis ist, dass befristete Beschäftigung einen negativen Einfluss auf das Produktivitätswachstum hat, dies aber in Branchen mit hoher Qualifikationsintensität stärkere negative Auswirkungen hat. Während ein Anstieg des Anteils an befristeter Beschäftigung in qualifikationsintensiven Branchen um 10 Prozentpunkte das Produktivitätswachstum um rund 1–1,5 % senken würde, betrüge dieser Wert in weniger qualifikationsintensiven Branchen nur rund 0,5–0,8 %. Dieses Ergebnis ist stabil für verschiedene Intensitätsindices und Produktivitätsmaßnahmen sowie für die Stichprobenzusammensetzung. Des Weiteren behandeln wir politische Auswirkungen dieses Ergebnisses für die Arbeitsmarktregulierung.

1 Introduction

Following the widespread diffusion of temporary employment (TE) in European countries, a large concern has been growing about direct and side negative effects of increasing flexibility of labour markets. The recent flexibility reforms had been introduced with the aim of removing labour market rigidities, which in the supply-side thinking (see e. g., OECD 1999 , 2003 ; IMF 2007 ) were seen as the main cause of stagnant labour markets, under the implicit assumption that more flexible conditions for workers would not affect innovative capacity and productivity growth (Vergeer and Kleinknecht 2014 ). In this regard, however, most recent papers studying the role of TE in different European countries, and employing different empirical approach, find a negative and significant impact of TE on innovation and productivity (Ortega and Marchante 2010 ; Cappellari et al. 2012 ; Lisi 2013 ; Kleinknecht et al. 2014 ). Furthermore, the recent macro stylized-facts and, principally, the growthless job creation condition have drawn even more attention on the impact of flexibility reforms on productivity (Boeri and Garibaldi 2007 ; OECD 2007 ).

The main objective of this paper consists in studying the impact of the share of TE on productivity, explicitly considering the differential effect in skilled and unskilled economic sectors. From this perspective, we go beyond to the current literature arguing that there are good reasons to suspect that the impact of TE could differ significantly according to sectors’ skill intensity. For this purpose, we build an industry-level panel of European countries that allows to divide sectors according to the skill intensity.

This research connects with the wide spreading concern about cost-saving flexibility reforms in general and, in particular, temporary contracts. In fact, the debate both in the literature and in public institutions has been moving from promoting all types of flexibilization of labour markets to fight ‘Euroesclerosis’ (OECD 1994 ), toward criticizing TE as a form of flexibility at the margin (Boeri and Garibaldi 2009 ), especially because temporary contracts are found to damage the career prospects for young people (Cazes and Tonin 2010 ; OECD 2015 ), decrease the provision of on-the-job training by firms (Albert et al. 2005 , 2010 ), reduce workers’ earnings (Booth et al. 2002 ; Garz 2013 ) and, as a side effect, negatively affect aggregate labour productivity (Kleinknecht 1998 ; Vergeer and Kleinknecht 2011 , 2014 ). In this perspective, it seems highly relevant to study the role of sectors’ skill intensity behind the impact of TE, as the different channels of transmission through which TE affects productivity, which we discuss below, could have a different role according to skills’ intensity by industry.

To the best of our knowledge, this is the first study that attempts to investigate the impact of TE differentiated according to sectors’ skill intensity. Our main result is that TE is even more damaging in skilled sectors, with a negative effect significantly heavier than in unskilled sectors, and this would seem robust to little changes in the skill intensity index and in the sample used, as well as to different productivity measures. In particular, an increase of 10 percentage points of the share of TE in skilled sectors would lead to a decrease of about 1–1.5% in labour productivity growth, whereas in unskilled ones the reduction would be of 0.5–0.8%. To some extent, this result might support the idea that TE is currently used more as a cheaper form of job, instead of as a least-cost way of screening new workers (Booth et al. 2002 ; Güell and Petrongolo 2007 ; Autor and Housman 2010 ; Garz 2013 ).

The paper proceeds as follows. Sect. 2 provides the background for the empirical analysis, with the previous literature on TE, flexibilization and productivity. In Sect. 3 we present the empirical analysis, focusing on the strategy to disentangle the impact of TE across sectors. Then, Sect. 4 describes the characteristics of our dataset and main variables. In Sect. 5 we present our estimates and provide different robustness checks. Finally, Sect. 6 collects the main conclusions of this study.

2 Background

2.1 theoretical arguments.

The labour market reforms of the last decades have been introduced with the objective of relaxing the rigidities and, thus, making labour markets more flexible. In particular, European flexibility reforms concentrated in the so-called “numerical” (external) flexibility that allows firms to adjust their workforce by flexible firing and hiring, and “wage” flexibility concerning the wage-setting institutions (Boeri and Garibaldi 2009 ; Vergeer and Kleinknecht 2014 ). Footnote 1 As a result, this process of flexibilization has led to labour markets increasingly characterized by lower employment protection (EPL) for regular workers and larger use of atypical contracts for hiring new workers (Lucidi and Kleinknecht 2010 ; Walwei 2014 ; Eichhorst and Tobsch 2015 ; ILO 2016 ). Footnote 2

The theoretical support for flexibilization policies, coming especially from the supply-side thinking (OECD 2003 ; IMF 2007), was usually grounded under the implicit assumption that more flexible labour markets would not affect innovative capacity and productivity growth, but only firms’ willingness to hire new workers and, thus, the level of unemployment. In addition, temporary work might lead to other advantages, as they might allow firms to decrease costs (Houseman 2001 ), a more efficient screening to select better workers (Wang and Weiss 1998 ; Autor 2001 ) or represent a buffer to take advantage of short-term positive shocks (Bentolila and Saint-Paul 1992 ; Goux et al. 2001 ). As we will see later, there is some empirical evidence supporting these theoretical predictions, especially for the case of temporary agency workers (TAW). Nevertheless, there is an increasing body of literature reasoning that TE might potentially affect labour productivity. Hereunder, we briefly discuss a few major channels between TE and productivity Footnote 3 , following the recent literature on labour market flexibilization (e. g., Lucidi and Kleinknecht 2010 ; Kleinknecht et al. 2014 ).

Firstly, temporary workers might be less willing to cooperate with their employers in developing innovations, as they presumably will not enjoy in any way the expected benefit from them (Kleinknecht 1998 ). Indeed, this argument might be especially relevant for TAW, who are not formally workers of the user firm. Furthermore, temporary workers might be more inclined to develop general skills, which increase their future employability in the labour market, than firm-specific skills if there is no long-term commitment with employers (MacLeod and Navakachara 2007 ). Moreover, the option of hiring new temporary workers implies that firms have no incentive to invest in “functional” (internal) flexibility, which instead might be favourable to develop innovation and enhance productivity growth (Michie and Sheehan 2003 ; Zhou et al. 2011 ). Therefore, a significant presence of TE might potentially frustrate firms’ innovative capacity.

Another channel of transmission through which TE might negatively affect productivity is the presumable negative effect on workforce training. In fact, temporary contracts are of too short duration, thereby reducing the firms’ incentive to invest in training, given the short payback period of the investment for fixed-term workers and, even more so, for TAW. However, if firms confine temporary workers to jobs requiring low qualification and/or low experience, the impact on productivity of their lower training may be negligible. In this respect, several empirical studies tend to confirm that temporary workers in European countries have less access to on-the-job training provided by firms (Alba-Ramirez 1994 ; Booth et al. 2002 ; OECD 2007 ; Albert et al. 2005 , 2010 ). Nonetheless, the cross-country evidence provided by Bassanini et al. ( 2005 ) shows that the negative correlation between temporary contracts and lower training is strongly due to the inclusion of Spain in the sample, while excluding this country the correlation does not longer hold. Footnote 4

Previous literature on personnel economics suggests that “high-trust” human resource management practices can generate favourable productivity effects (Lorenz 1999 ; Buchele and Christiansen 1999 ; Naastepad and Storm 2006 ). According to these theories, long-lasting working relations are an investment in trust and commitment between employees and employers, which might boost productivity growth. On the opposite, temporary contracts might be interpreted as a firms’ choice to not commit in long-lasting relations and, thus, TE might dampen productivity. However, because temporary employment helps the adjustment of workforce to address demand shocks, temporary work might have a positive effect on labour productivity (Hagen 2003 ).

Finally, looking at the workers’ effort as a relevant component of total factor productivity (TFP), in countries where “temp-to-perm” conversion rates are low and, thus, employers appear to use TE more as a cheaper form of job (Houseman 2001 ; Booth et al. 2002 ; Güell and Petrongolo 2007 ; Garz 2013 ), it might be rationale for temporary workers to exert a lower effort respect to permanent workers (Lisi 2012 ; Dolado et al. 2016 ), eventually reducing productivity. This might be especially true for TAW, for which the evidence in the literature does not lend empirical support to the stepping stone hypothesis (Kvasnicka 2009 ; Autor and Housman 2010 ).

In analysing the impact of temporary work on absenteeism, however, Jimeno and Toharia ( 1996 ) and Ichino and Riphahn ( 2005 ) find that the threat of losing their jobs decreases absenteeism in temporary workers respect to those with open-ended contracts. In a similar vein, Malo and Sánchez-Sánchez ( 2014 ) reason that, as temporary workers have a higher probability of losing their jobs than permanent workers, they have a lower probability of being involved in labour conflicts with a positive effect on labour productivity.

Therefore, the impact of temporary employment on labour productivity remains ambiguous at theoretical level and might depend on their use and amount in the specific firms and sectors, which calls for the empirical evidence.

2.2 Previous evidence

The empirical literature on the impact of temporary employment on productivity is rather large, and different (and not converging) empirical findings have emerged, depending also on the type of temporary employment. A few previous studies as Arvanitis ( 2005 ) and Nielen and Schiersch ( 2016 ) find no effects of temporary contracts using panel data at the firm level. On the other hand, Damiani and Pompei ( 2010 ) analyze multi-factor productivity across European countries and, as for the effect of temporary contracts, they find mixed results. In particular, though they find a negative impact of fixed-term arrangements, they also underline that labour provisions for the protection of fixed-term contracts may offset the negative effects deriving from a pure increase in temporary workers. The evidence seems more convergent on the positive impact of temporary agency work (TAW) on productivity (Bryson, 2013 ). Specifically, Hirsch and Mueller ( 2012 ) and Nielen and Schiersch ( 2014 ) find an inversely u‑shaped relationship between the share of TAW and firms’ performance. This interesting finding appears well-grounded in the argument that a low share of TE within a firm could be a means of enhancing numerical flexibility (Vidal and Tigges 2009 ) and screening new workers (Autor 2001 ), whereas a high share of TE could be a signal of a broader substitution between perms to temps, which is likely to lower the motivation and commitment of the workforce. In this respect, however, Nielen and Schiersch ( 2016 ) show that the same inversely u‑shaped effect does not emerge for temporary contracts. Therefore, productivity effects of temporary employment may depend on their use and type in the economy or in the specific sector.

Nevertheless, in the last years many empirical studies find evidence supporting the arguments on negative effects of flexible jobs on productivity. For instance, Auer et al. ( 2005 ) find a positive relationship between job tenure and labour productivity. Similarly, in a panel of EU countries Lisi ( 2013 ) finds that an increase of the share of flexible jobs would lead to a decrease in labour productivity growth, even after controlling for the potential endogeneity of TE in the productivity equation.

Among country-specific studies, Kleinknecht et al. ( 2006 ) find a not significant impact of temporary contracts in Dutch manufacturing firms as a whole, but they also find different results when they distinguish between innovative and non-innovative firms. Among innovators, temporary employment seems to have no effect on labour productivity growth; on the other hand, among non-innovating firms a higher rate of people on temporary contracts, as well as more workers hired from private manpower agencies, decreases labour productivity growth. However, these estimated effects are hardly significant at conventional levels, with the exception of the negative effect of temporary contracts among non-innovators. Still in the Netherlands, Kleinknecht et al. ( 2014 ) find that high shares of temporary workers have a negative impact on the firms’ investment in R&D, especially in those sectors with a “routinised” innovation regime. Looking at the Italian context, Boeri and Garibaldi ( 2007 ) find a negative effect of the share of fixed-term contracts on labour productivity growth in a sample of manufacturing firms. Similarly, Lucidi and Kleinknecht ( 2010 ) show that high shares of flexible workers, high labour turnover and lower costs of labour are significantly related to lower labour productivity growth. Cappellari et al. ( 2012 ) look specifically at the two Italian reforms concerning the “external” flexibility, concluding that while the reform of apprenticeship had a positive impact on productivity, the reform of fixed-term contract generated a negative effect on productivity. Finally, Ortega and Marchante ( 2010 ) analyse the impact of the increase in the use of TE in Spain, finding that productivity growth has been slowed down by the extensive use of temporary contracts as a regular form of jobs.

Overall, all these studies estimate an average effect of TE on productivity. Footnote 5 However, there are good reasons to suspect that the impact of TE could differ significantly according to sectors’ skill intensity, as the different channels of transmission through which TE affects productivity could have a different role according to skills’ intensity by industry.

For instance, we argued that temporary workers might be more inclined to develop general skills than firm-specific skills (MacLeod and Navakachara 2007 ), and this might affect labour productivity differently among industries according to the relative importance of specific human capital respect to general human capital in each industry. Furthermore, a lack of on-the-job training for temporary workers implies low levels of skill acquisition (Ortega and Marchante 2010 ), and this potential cost could be higher in those sectors in which work-related training has more importance. Similarly, as mentioned before, Bassanini et al. ( 2005 ) find a significant correlation between temporary contracts and training only when including Spain in their sample; therefore, how the lower access to training of temporary workers affects productivity remains an empirical issue. Finally, temporary contracts provide different incentives respect to permanent ones and, thus, temporary workers might exert a different effort in their jobs (Dolado et al. 2016 ), which might affect labour productivity differently according to sectors’ skill intensity.

Interestingly, the different arguments on the differential effect of TE between skilled and unskilled sectors would seem to depend on the way in which temporary contracts are used in the labour market. In particular, in skilled sectors the use of TE might potentially be more oriented towards screening new workers respect to unskilled ones, and this perspective could induce in temporary workers a higher motivation to develop firm-specific skills and exert higher effort (Engellandt and Riphahn 2005 ), as well as a higher firms’ willingness to provide on-the-job training. On the other hand, if TE is used in the market as a structural cheaper form of job, in skilled sectors the cost in terms of lack of workforce training and lower workers’ effort could be heavier, leading to an even greater effect in labour productivity growth (Dolado et al. 2016 ).

The opposite arguments concerning the differential effect of TE on productivity call for the empirical analysis. In the following, we aim to contribute to this literature by explicitly considering the differential effect of TE in skilled and unskilled sectors. Footnote 6

3 Empirical analysis

In this section, we show the empirical strategy employed in the study to disentangle the impact of TE across sectors and, in particular, we describe the method used to divide industries between skilled and unskilled sectors. Then, we discuss the main advantages, but also the potential drawbacks, of our empirical specification.

In our empirical analysis, we modify earlier productivity equations in the literature (Bassanini and Venn 2008 ; Bassanini et al. 2009 ; Lucidi and Kleinknecht 2010 ; Lisi 2013 ), adapting the model to estimate the differential impact of TE across sectors. Our starting point is that the impact of TE on productivity might not be homogenous across sectors and, in particular, we wonder if the effect differs according to sectors’ skill intensity. Therefore, dividing industries between skilled sectors  (S) and unskilled sectors  (US), we specify the following assumption (1) according to which the difference between the conditional expected productivity growth in the S group and in the US group is some function of the share of TE:

where the first element indicates the conditional expected productivity growth in the S group in country  i at time  t , the second one the same for the US group and TE is the share of TE in country  i in sector  j at time  t . In particular, the productivity growth in (1) are conditional in the sense that our assumption is valid after that all the other explanatory variables affecting productivity growth have been netted out; on the other hand, productivity growth are expected in the sense that in (1) they are the average across all sectors within the two groups. Finally, notice that with respect to the standard diff-in-diff assumption where only observations in the treatment group are treated, in our case we assume that is the impact of the treatment to be different between the two groups. To this extent, our assumption is very close to the spirit of the method introduced by Rajan and Zingales ( 1998 ) to evaluate the impact of market regulations.

From the empirical perspective, to divide industries between skilled sectors  (S) and unskilled sectors  (US) we compute the ratio between skilled and unskilled workers in each sector for different years and, then, we consider the mean across time as a general index of sector’s skill intensity (see e. g., Haskel and Slaughter 2002 ). Finally, we take the mean of these indexes across sectors and consider (un)skilled those sectors with a skill intensity (lower) higher than the average. This procedure leads us to the binary indicator SSII j , which is equal to 1 if  j is a skilled sector and equal to 0 if  j is an unskilled one, that is:

This indicator SSII j will be used in the productivity equation to disentangle the effect of the share of TE between skilled and unskilled sectors.

To make our results easily comparable with previous studies, we estimate also the impact of labour market regulation for regular workers, using the EPL index for PE as explanatory variable. As standard in this literature, to estimate the impact of EPL for PE we follow the method introduced in the finance literature by Rajan and Zingales ( 1998 ), then extended in labour (Bassanini and Venn 2007 , 2008 ). The main assumption of this approach is that, while the degree of labour market regulation is equal for all industries in a country, the impact of it could be different among industries, according to some “physiological” characteristics of each sector. In particular, we expect that EPL is more binding in those industries characterized by a higher need to reallocate resources Footnote 7 and, accordingly, in the productivity equation we interact the EPL for PE with the frictionless job reallocation rate FJR j for each sector, depurated from labour market frictions and aggregate shocks. Footnote 8 More specifically, the underlying assumption usually specified in the literature is (Bassanini et al. 2009 ; Cingano et al. 2010 ; Lisi 2013 ):

which states that the difference between the conditional expected productivity growth in two sectors  j and  k , in country  i at time  t , is a function of the degree of regulation weighted by the natural need of job reallocation in these sectors.

Finally, as suggested by the Schumpeterian growth literature (Griffith et al. 2004 , 2009 ; Aghion and Howitt 2006 ), in our productivity equation at the industry level we include the lagged productivity gap between each observation and the industry leader, to control for possible catching-up. Indeed, the inclusion of the productivity gap should control also for exceptional fluctuations in capacity utilisation, which might be important to capture strongly misleading productivity increases (Lucidi and Kleinknecht 2010 ). Similarly, a few papers in the literature (Griffith et al. 2004 , 2009 ; Bassanini and Venn 2007 ; Bassanini et al. 2009 ) suggest that the model of productivity growth for nonfrontier industries (i. e. for those observations at the industry level that are not on the frontier) should include as explanatory variable the contemporaneous productivity growth of the industry leader; specifically, this would require to include the contemporaneous productivity growth of the industry leader in the complete model (i. e. including nonfrontier and frontier industries) imposing the frontier growth term equal to 0 for the industry leaders. Footnote 9

Therefore, if we assume a linear functional form  f in (1) and (3), the main specification of our empirical model is (Bassanini and Venn 2007 , 2008 ; Lisi 2013 ):

where the dependent variable is the productivity growth (either labour productivity or TFP) in country  i in sector  j in year  t (measured as logarithmic difference). Then, the explanatory variables include:

the productivity growth of the industry productivity leader \(\Updelta {\ln}y_{jt}^{L}\) ,

the lagged productivity gap with respect to the industry leader \(\ln\left({y_{ijt-1}}/{y_{jt-1}^{L}}\right )\) ,

the share of temporary employment \(\text{TE}_{ijt}\) ,

the EPL index for regular worker \(\text{EPL}_{it}\) ,

other control variables affecting productivity growth \(X_{ijt}\) , such as trade union density ( TUD ) and product market regulation ( PMR ) Footnote 10 , including also the capital-to-labour growth \(\Updelta {\ln}k_{ijt}\) when the empirical model (4) is for labour productivity Footnote 11 ,

vectors of country \(\mu _{i}\) and time-specific \(\varphi _{t}\) fixed effects.

In our model, \(\gamma\) is the main coefficient of interest, the differential impact of TE on productivity growth in skilled sectors compared to unskilled ones. On the other hand, \(\delta\) represents the impact of TE in unskilled sectors, and its inclusion is important since it allows the differential impact \(\gamma\) to adjust upon a non-zero impact in unskilled sectors. Looking at the impact of the EPL index for PE, \(\theta\) is the marginal impact of EPL in a sector with a relative high FJR compared to a sector with a relatively low FJR; this implies that, if the estimated coefficient is negative, productivity growth in high FJR sectors decreases with respect to that in low FJR ones, meaning that EPL for PE have a negative impact on productivity growth (Cingano et al. 2010 ).

A potential drawback of specification (4) is that it produces consistent estimates under the strictly exogeneity of all covariates, which might not be the case in our empirical analysis. In particular, to the extent that hiring a temporary worker is a firm’s decision, the share of TE might be endogenous in the productivity equation. Therefore, following Lisi ( 2013 ) we perform also an IV-strategy, using the EPL index for TE as an instrumental variable for the share of TE. Footnote 12 In particular, the main idea in our IV-strategy is that the country legislation concerning the use of temporary contracts certainly affect the share of TE, like so the variation of the legislation affects the share over time. In this regard, the EPL index for TE turns out to be significantly correlated ( p -value = 0.000) with the share of TE in our sample. Differently, the legislation about TE should not have any impact on productivity except for the actual use of temporary contracts; in fact, as long as temporary contracts are not used in the labour market, a change in the legislation would be expected to have no impact on productivity. Footnote 13

We estimate several versions of our model, considering both labour productivity and TFP as dependent variable. Following the previous literature (Bassanini et al. 2009 ; Lucidi and Kleinknecht 2010 ), we have also repeated all our estimates including a vector of industry-specific fixed effects instead of the contemporaneous productivity growth of the industry leader, without changing at all our results. Moreover, as a few previous studies find an inversely u‑shaped productivity effect of TAW (Hirsch and Mueller 2012 ; Nielen and Schiersch 2014 ), we also estimate model (4) adding the quadratic term of the share of TE, to test whether the interaction term TE*SSII picks up the effect of non-linearity of TE in the productivity equation and, thus, leads to a wrong inference on the differential effect of TE among sectors. Finally, we provide different sensitivity checks, concerning the sectors’ skill intensity index and the sample used in our estimates, to test the robustness of our findings.

Overall, our empirical specifications follow the previous literature on the topic. However, instead of estimating an average impact of TE across sectors, in our paper we estimate the differential impact of TE according to sectors’ skill intensity. On the one hand, this should provide a more accurate description of the impact of TE; on the other hand, the investigation of this differential impact might offer some insight on how temporary contracts are currently used in the labour market (that is, least-cost way of screening new workers or cheaper form of job).

A troubling point in our empirical analysis regards the specifications using labour productivity as dependent variable. Indeed, while the model for TFP follows the intent to explain what factors affect its law of motion, the empirical model (4) for labour productivity is derived directly from an industry-level Cobb-Douglas production function, which clearly includes also the capital-labour ratio. Footnote 14 In this respect, however, previous studies in this literature (OECD 2007 ; Bassanini and Venn 2008 ; Cingano et al. 2010 ; Cappellari et al. 2012 ) have estimated reduced-form models as (4) for labour productivity omitting the capital-labour ratio, in order to capture the overall effect of the policy of interest on labour productivity. On the other hand, different papers focusing on firm-level production function have shown that omitting capital-labour ratio as regressor in the empirical model for labour productivity could bias the estimates (e. g., Levinson and Petrin 2003 ). Therefore, in order to provide a robust evidence on the differential effect of TE, in our empirical analysis we provide estimates both with and without the capital-labour ratio in the model for labour productivity.

A potential drawback of our empirical specification is related to our assumption concerning the differential effect of TE across sectors. In particular, if the use of TE changes extensively the skill composition of our sectors (i. e. the so-called “composition effect”, see Sect. 4) and, in turn, the selection of them in S‑ and US-sectors, then our assumption would not be useful anymore. In fact, in that case we are not exploiting the exogenous variation on the impact of TE between S‑ and US-sectors, because groups themselves are endogenously determined by the share of TE. Differently, if sectors’ skill composition and, in turn, S‑ and US-groups are exogenously set by sectors’ production functions, then our specification should allow us to estimate consistently the differential impact of TE across sectors.

Indeed, in line with the previous literature (e. g., OECD 2007 ; Cappellari et al. 2012 ) the clear picture emerging from our data is that the correlation between the share of TE and sectors’ skill composition is almost null. In particular, in Fig.  1 we report the scatter plot between TE and SSI and, as we can see, the cloud would suggest that there is no correlation. Furthermore, the small and statistically insignificant correlation coefficient ( \(\rho\) = −0.037) also indicates that there is no correlation between TE and SSI. Therefore, the different skill composition across sectors would seem more driven by the technology underpinning the production function in each sector, which leads us to pursue our empirical strategy to estimate the differential impact of TE on productivity.

Correlation between TE and SSI

Finally, a potential problem in our IV strategy concerns the source of variation which is exploited in the productivity equation through the use of our instrument (i. e. the EPL index for TE). Specifically, if the legislation on the use of TE is strongly correlated with the social and cultural traits in a country, then the variation in the share of TE induced by a difference in the EPL index for TE among countries may retrace differences in the social and cultural aspects that may, in turn, affect productivity. If this is the case, indeed, the source of variation exploited in our IV-strategy might not be useful anymore, as it might be already included in our productivity equation (4) through the inclusion of country fixed effects \(\mu _{i}\) , potentially capturing also social and cultural aspects of a country. On the contrary, if the EPL index for TE is not (or only slightly) correlated with the social and cultural traits of countries in our sample, then the variation we exploit in the IV-strategy would not be included in the productivity equation (4) and, thus, it has the potential to offer an exogenous source of variation in the share of TE allowing us to estimate consistently the impact of TE on productivity.

In this regard, following the previous literature on this topic (e. g., Tabellini 2008 , 2010 ; Aghion et al. 2010 ; OECD 2011 ; Charron et al. 2014 , 2015 ), we found six indicators concerning social and cultural traits under different perspectives, for all European countries in our sample. Footnote 15 Overall, the EPL index for TE turns out to be not (or very slightly) correlated with the abovementioned indicators of social and cultural traits Footnote 16 , suggesting that the variation exploited in our IV-strategy should represent a valuable source of variation to estimate the impact of TE in the productivity equation (4). Moreover, in Sect. 5 we provide the results of different statistical tests supporting further the use of our instrument.

4 Database and main variables

In our empirical analysis, we use an industry-level panel of EU countries. As emphasized by the previous literature, the advantage of using a panel of industry-level data is fourfold. First, not only the cross-country variation is still exploited, but also the variation on the impact of policies in different industries. Second, in contrast to the cross-country analysis, it potentially allows to control for unobserved fixed effects. Third, as the previous literature emphasised (e. g., OECD 2007 ) and as confirmed by our preliminary evidence (see Sect. 3), the within-industry “composition effect” appears to be negligible, allowing us to capture the “independent effect” of TE. Footnote 17 Fourth, to the extent that events in a single industry are not so relevant alone to affect the policy in a country, the specification is less subject to the simultaneity problem between the variable of interest and policy.

In particular, the dataset covers 10 sectors in 13 countries over the years 1992–2007, for a balanced panel of 2080 observations. Countries included are Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Portugal, Spain, Sweden and the United Kingdom. Since we make use of different data sources, we did some aggregation and the final sectors classification is based on the standard EUROSTAT classification (see Appendix B for details). The sectors are the following: “Agriculture, hunting and forestry”, “Manufacturing”, “Electricity, gas and water supply”, “Construction”, “Wholesale and retail trade”, “Hotels and restaurants”, “Transport, storage and communication”, “Financial intermediation”, “Real estate, renting and business activities”, “Other community, social, personal service activities”. With this sectors classification, we will define two aggregate groups of skill intensity consisting of five sectors with enough variability for our empirical analysis.

To collect our dataset we made use of different sources. The data on labour productivity, total factor productivity and employment level at the industry-level were collected from EU KLEMS dataset ( www.euklems.net ). This comprehensive database contains data on economic growth, productivity, employment and other variables at the industry-level for all EU countries, providing an important source for policy evaluation.

The labour productivity measure used is the “ gross value added per hour worked, volume indices, 1995 = 100 ”, defined in the following way:

where VA is the gross value added in volumes and L is the total amount of hours worked. Respect to other measures, the index measure with value added in volumes has different advantages and, in fact, it is the productivity measure largely most used in the literature (OECD 2007 ; Bassanini and Venn 2008 ; Cingano et al. 2010 ; Cappellari et al. 2012 ). Looking at the behaviour over time, the mean of labour productivity in the entire sample is 110.94, whereas the mean from 1995 (base year = 100) is 114.31, telling us that labour productivity grew in EU countries, even if not so significantly.

The other indicator we use for productivity is total factor productivity (TFP) or multi-factor productivity. In the economic literature, this variable is obtained as a residual, as it is defined as the effects on total output not caused by the inputs considered in the aggregate production function (typically, labour and capital). TFP is often seen as the main driver of economic growth, mirroring the long-term technological change. The TFP measure used is the “ TFP, 1995 = 100 ”. Footnote 18 Unfortunately, no data on TFP at the industry-level are available for Portugal; therefore, in the estimates considering TFP as dependent variable data for Portugal are not included. Looking at the behaviour over time, the mean of TFP in the entire sample is 103.74, whereas the mean from 1995 (base year = 100) is 104.96, telling us that TFP growth in EU countries in the last years has been very scarce.

The data on capital stock were collected from OECD STAN database, a comprehensive tool for analyzing industrial performance across countries. In particular, the capital stock measure used is the “ CPGK – gross capital stock in volume terms ”. Unfortunately, no data on capital stock at the industry-level are available for Ireland, Portugal and Sweden; therefore, in the estimates including the capital-labour ratio in the productivity equation these countries are dropped.

The shares of TE at the industry-level were constructed from EU Labour Force Survey launched by the EUROSTAT (see Appendix A for details) Footnote 19 and, as far as we know, it is the only industry-level measure of TE available for such large sample of countries. A potential limitation of this measure, however, is that it does not allow to distinguish between fixed-term contracts and temporary agency workers. Indeed, as discussed in Sect. 2, these two types of temporary employment might generate different effects on labour productivity and, thus, distinguishing between them might be relevant in our study. Moreover, though temporary agency employment levels grew strongly in all European countries over the period (CIETT 2009 ), still the use of TAW is rather heterogeneous among them (see Appendix B, Table B.4), which implies that also the composition of TE is somewhat mixed in our sample. In fact, while in a few countries TAW represent a significant part of temporary employment as in the United Kingdom (64%) and France (16%), in other countries as in Spain (2%), Portugal (4%) and Italy (6%) the use of temporary work agency appears to be less relevant, despite they exhibit among the highest share of TE in Europe. Therefore, a bit of caution in the interpretation of our results and in the subsequent policy implications seems appropriate.

As for the behaviour of TE in our sample, the mean and standard deviation of the share of TE are respectively 0.12 and 0.10, confirming that TE is an important feature of the labour market landscape in Europe by this time, but its importance differs significantly across countries. For instance, while in countries as Spain (0.32) and Portugal (0.16) the share of TE is far away from the mean, in the UK the mean is no more than 0.06 (see Appendix B, Table B.3). Interestingly, the share of TE turns out to be negatively correlated with labour productivity and total factor productivity, both cross-country ( \(\rho _{\textit{LP}_{i}}= -0.2972\) , \(\rho _{\text{TFP}_{i}}= -0.3224\) ) and cross-industry ( \(\rho _{\textit{LP}_{j}}= -0.4836\) , \(\rho _{\text{TFP}_{j}}= -0.2481\) ).

To construct our sectors’ skill intensity index, we divide workers between skilled and unskilled using two main indicators. Indeed, the idea initially was to use more than two indicators, to test as much as possible our results. However, all other plausible indicators led us to the same dichotomy among sectors of those two. For both indicators the data are collected from Science, technology and innovation database (EUROSTAT), which collects data from many different publications on these themes as R&D expenditure, workers knowledge, HRST, innovations.

The first indicator concerns the level of education and we consider skilled those workers with a tertiary education (level 5–6 ISCED 1997). Differently, the second indicator concerns the kind of task workers make in their job. In particular, the database gives us these values as a share of total employment, for each sector from 2001 to 2007. Notice that, actually, the time period of our analysis is wider than 2001–2007; unfortunately, data availability on the above mentioned indicators prevents us to construct our SSII using the same time period. Therefore, in our empirical analysis we are forced to rely on the assumption that 2001–2007 represents a consistent time period to split the industries, which could potentially be reasonable but still we need to rely on this assumption in our empirical analysis. In this regard, the idea to employ more than one indicator has been also driven by the intent to test our results by an alternative subdivision of sectors. Overall, these two indicators lead us to a similar, but still slightly different, subdivision of sectors (see Appendix B).

As measure of EPL for PE we made use of the cardinal index constructed by OECD ( 2004 ). In our sample from 1992 to 2007 the EPL index for PE ranges from 4.33 in Portugal (1992–2003) to 0.95 in the UK (1992–1999). The mean of the index follows a slightly decreasing trend, going from 2.47 to 2.33 at the end of the sample. However, the decreasing trend in the stringency of regulation of PE is far from being common to all countries, rather it seems to be driven by Spain and Portugal. On the other hand, the EPL index for TE ranges from 5.38 in Italy (1992–1996) to 0.25 in the UK (1992–2001). Similarly to PE, the mean of the index for TE follows a decreasing trend, going from 2.92 to 1.86. But differently to PE, this decreasing trend seems to be a common feature in fairly all EU countries.

Data on trade union density were collected from ICTWSS database, providing information on institutional characteristics of trade unions in 34 countries between 1960 and 2007. In particular, the variable used is “ the ratio of wage and salary earners that are trade union members, divided by the total number of wage and salary earners ”. The mean in the sample is 0.40, telling us how trade union are still an important subject in Europe. However, the standard deviation of 0.23 suggests how different is its importance across EU countries. Finally, product market regulation indicators used are the OECD Indicators of PMR, a comprehensive set of indicators measuring the degree to which policies promote or inhibit competition. In our sample PMR exhibits much variation, revealing that these policies are not homogenous in Europe.

A full description of variables and sources can be found in Appendix A, whereas different descriptive statistics, penetration rates of TAW among countries and the subdivisions of sectors between skilled and unskilled are reported in Appendix B.

5 Discussion of results

In this section, we present and discuss the main results of the empirical analysis. Table  1 shows the estimates from different specifications of model (4). In particular, columns (1) to (3) use labour productivity as dependent variable, whereas (4) to (6) use TFP. Then, in Table  2 we provide the estimates of the model for labour productivity including the capital-labour ratio.

More specifically, in column (1) we report the coefficients of the baseline model, a POLS regression without any fixed effects. Both point estimates of TE and TE*SSII are negative and significant, suggesting that TE is even more damaging in skilled sectors, with a negative effect significantly heavier than in unskilled sectors. Similarly, the point estimate of EPL*FJR is negative and significant, confirming the previous evidence on the negative effect of EPL for PE (Bassanini et al. 2009 ; Cingano et al. 2010 ). Both the growth of productivity frontier and relative productivity gap appear to be significantly associated with productivity growth with the expected sign; moreover, estimated coefficients seem to be in line with estimates found in the previous literature (Griffith et al. 2004 , 2009 ; Bassanini et al. 2009 ). In particular, the negative sign of the relative productivity gap suggests the presence of catching-up effects in productivity growth. Finally, the coefficient of PMR also appears to be significant, suggesting that product market regulations have a negative effect on productivity growth (Nicoletti and Scarpetta 2003 ). While these estimates are useful to get an insight on the direction of the effect, they cannot be interpreted as consistent, given the omitted variable bias and the potential endogeneity of TE in the productivity equation.

Therefore, in column (2) we introduce a large set of country and time-specific fixed effects, controlling for institutional and time differentials in productivity growth, allowed to be correlated with other explanatory variables. Still, the coefficients of TE and TE*SSII are negative and significant. Similarly, all other estimated coefficients maintain the same sign, but now also the coefficient of TUD appear significant, suggesting that a higher share of unionized employees has a negative effect on productivity growth. Finally, in column (3) we report the estimates of our IV model, where we use the EPL index for TE as an instrument for the share of TE (and EPL*SSII for TE*SSII), along with the results of different tests. As we can see, the endogeneity test in Table  1 tends to confirm that the share of TE is, indeed, endogenous in the productivity equation, implying that we need to implement the IV-strategy to estimate consistently the impact of TE (Lisi 2013 ). Likewise, the Kleibergen-Paap weak identification test reports a significantly high value of the F‑statistic Footnote 20 , saying that the EPL index for TE and its interaction with SSII are not weak instruments in our IV estimate. Nonetheless, even if different in magnitude respect to (2), both the estimated coefficients of TE and TE*SSII are still negative and significant. Similarly, all other estimated coefficients maintain the same sign and, furthermore, in (3) both the coefficient of TUD and PMR appear to be significant.

Since we are able to control for several unobserved factors, as well as for the endogeneity of the share of TE, we interpret the estimated effects as consistent and, in particular, the coefficient of TE*SSII as the differential effect of temporary employment on productivity growth between skilled and unskilled sectors. Our central result is that TE is even more damaging in skilled sectors, with a negative effect significantly heavier than in unskilled sectors. Specifically, an increase of 10 percentage points of the share of TE in skilled sectors would lead to a decrease of about 1–1.5% in labour productivity growth, whereas in unskilled ones the reduction would be only of 0.5–0.8%.

Notice that, in Lisi ( 2013 ) it is found a higher average effect of TE respect to our results, that is a decrease of about 2–3% in labour productivity. Indeed, this might appear a remarkable difference considering that, apart from the estimation of the differential effect of TE across sectors, we employ a similar dataset and empirical method. More specifically, the few differences respect to Lisi ( 2013 ) are given by the inclusion of Schumpeterian variables and product market regulation in the productivity equation, along with a slightly (i. e. two years) longer time period of our sample. In this regard, running the same IV estimate in Lisi ( 2013 ) with our longer sample, we find fairly similar results but a slightly lower average effect of TE (i. e. −0.233); on the other hand, including the Schumpeterian variables and product market regulation in the IV specification even in the shorter sample (i. e. 1992–2005) leads to a significantly lower effect of TE (i. e. −0.145). Overall, from our investigation we can conclude that the lower estimated effect of TE in our paper seems to be due especially to the inclusion of Schumpeterian variables and product market regulation in the productivity equation, while the different sample appears to explain only slightly the lower estimate of TE. Footnote 21 Therefore, this seems to suggest that the estimated effects of TE in this paper are a little more cautious, as they control also for different factors affecting productivity.

Then, we wonder whether the estimated effects and, in particular, the differential effect of TE hold also using TFP as dependent variable. From the empirical perspective, the question is legitimate because there are indeed theoretical reasons for which labour market regulations might also affect capital accumulation, even if potentially in both positive and negative directions (Bertola 1994 ; Saint-Paul 2002 ; Samaniego 2006 ). Therefore, in columns (4) to (6) we report the same estimates using TFP as productivity variable. Overall, we can see that fairly all the coefficients of interest are significant with the expected sign. In particular, the point estimates of TE and TE*SSII are negative and significant, confirming our result that TE is even more damaging in skilled sectors, with a negative effect on productivity growth significantly heavier than in unskilled sectors. Similarly, the estimates for EPL*FJR, as well as for the Schumpeterian growth variables, are also in line with our previous estimates.

Finally, as discussed in Sect. 3, while the reduced-form model (i. e. without capital-labour ratio) in Table  1 may allow to capture the overall effect of TE on labour productivity (OECD 2007 ; Bassanini and Venn 2008 ; Cappellari et al. 2012 ; Lisi 2013 ), omitting the capital-to-labour growth in the model for labour productivity could produce biased estimates (e. g., Levinson and Petrin 2003 ). Therefore, in order to provide a robust evidence on the differential effect of TE, in Table  2 we show the estimates of the model for labour productivity including the capital-to-labour growth as regressor. As we can see from Table  2 , as expected the coefficient of the capital-to-labour growth is always significant in the reasonable range of 0.2–0.3; however, even if the inclusion of the capital-labour ratio slightly changes the estimates, both TE and TE*SSII are still negative and significant with a magnitude close to those in Table  1 . Overall, the inclusion of the capital-labour ratio in the empirical model for labour productivity, while it affects the estimated coefficients of some explanatory variable, it does not seem to change significantly our conclusion on the differential effect of TE on productivity.

6 Robustness checks

As discussed in Sect. 2, a few previous studies in this literature (Hirsch and Mueller 2012 ; Nielen and Schiersch 2014 ) find that TAW – i. e. a component of TE provided by EU Labour Force Survey – generates an inversely u‑shaped effect in the user firms’ productivity. This piece of evidence appears well-grounded in the argument that a low share of TAW within a firm could be a means of enhancing numerical flexibility (Vidal and Tigges 2009 ) and screening new workers (Autor 2001 ), whereas a high share of TAW could be a signal of a broader substitution between perms to temps, which is likely to lower the motivation and commitment of the workforce (Hirsch and Mueller 2012 ). Although this argument in an industry-level dataset is less clear, it is still important in our analysis to control for the presence of a non-linear effect of TE in the productivity equation, because the interaction term TE*SSII in our model (4) might pick up the effect of non-linearity of TE, thus leading to a wrong inference on the differential effect of TE among sectors. Therefore, in Table  3 and Table  4 , respectively for POLS and FE models, we provide the estimates with the quadratic term of TE in the productivity equation, using both labour productivity and TFP as dependent variable.

In particular, in columns (1) and (3) of both tables, respectively for labour productivity and TFP, we estimate our productivity equation with the quadratic term of TE without the interaction term, to check for the presence of a non-linear effect of TE. As we can see in Table  3 and Table  4 , in all estimates the coefficient of the quadratic term results statistically insignificant; thus, they do not provide evidence of a non-linear effect of TE, at least in our industry-level panel. Then, in columns (2) and (4), respectively for labour productivity and TFP, we add also the interaction term TE*SSII, along with the quadratic term of TE. Again, both in Table  3 and Table  4 , the coefficients of the quadratic term are statistically insignificant, while those of TE*SSII are still negative and significant, confirming the higher negative effect of TE in skilled sectors. Overall, our findings would suggest that, at least in our industry-level dataset, there is no evidence of a non-linear productivity effect of TE, regardless of the productivity measure employed as dependent variable as well as the estimated model. Footnote 22 More importantly, the evidence in Table  3 and Table  4 provides further support to our inference on the interaction term TE*SSII as the differential effect of TE among sectors.

As for the sectors’ skill intensity index, to the extent that a subdivision between skilled and unskilled sectors has to be necessarily based on discretional criteria, in Table  5 we repeat the same estimates using a second sectors’ skill intensity index, concerning the kind of task workers make in their job (see Appendix A). This second index leads to a similar, but slightly different, subdivision of sectors and, therefore, represents a perfect candidate to test the stability of our findings. As can be clearly seen from Table  5 , this change in the SSII used in the estimation does not change our conclusions. Still, the coefficients of TE and TE*SSII2 are negative and significant, even with a magnitude very close to those in Table  1 . Looking at the other explanatory variables, we can also see from Table  5 that the use of SSII2 does not change markedly the estimated effects. Footnote 23

Finally, to check whether our results depend crucially on the inclusion of some countries in the sample, we re-estimate the model excluding all countries one-by-one. Therefore, we run many FE and IV regressions, using labour productivity as productivity variable Footnote 24 , where in each regression we exclude one different country. Indeed, this further robustness check should be especially relevant for the issue of temporary contracts, since we have already seen in Sect. 4 that the extent of TE is not homogeneous across EU countries (see also, e. g., Boeri and Garibaldi 2007 ). In particular, the inclusion of Spain and Portugal in the sample might potentially be important in driving our results, as both countries not only have had the highest share of temporary contracts for many years, but also they have implemented reforms reducing considerably the protection of permanent workers. In Fig.  2 are the coefficients of TE and TE*SSII, arranged from the greatest to the smallest, for both FE and IV. Footnote 25

Coefficients of TE and TE*SSII from the Reduced Sample

As Fig.  2 clearly shows, however, our results do not depend on the sample of countries included in the estimation. Indeed, both the coefficients of TE and TE*SSII are fairly always negative and significant, even omitting Spain and Portugal. Furthermore, the magnitude of the coefficients would seem to validate sufficiently our result that TE is even more damaging in skilled sectors, with a negative effect significantly heavier than in unskilled sectors.

7 Conclusions

In this paper we have studied the effect of the share of TE on productivity, explicitly considering the differential effect in skilled and unskilled economic sectors. Our industry-level panel of EU countries allowed to disentangle the effect of the share of TE between skilled and unskilled sectors, controlling also for different unobserved confounding factors and the potential endogeneity of the share of TE in the productivity equation. As discussed in the paper, the empirical analysis on this question appears to be important, given that from a theoretical point of view is ambiguous what sectors might be more affected by the use of temporary employment.

The main finding of the paper is that TE is even more damaging in skilled sectors, with a negative effect significantly heavier than in unskilled sectors, robust to little changes in the skill intensity index and in the sample used, as well as to the inclusion of non-linearity in the effect of TE. In particular, we find that an increase of 10 percentage points of the share of TE in skilled sectors would lead to a decrease of about 1–1.5% in labour productivity growth, whereas in unskilled ones the reduction would be only of 0.5–0.8%. Finally, we find similar results in the productivity equations with TFP growth as dependent variable.

Apart from offering a more accurate description of the impact of TE, these results have important policy implications and, certainly, lead us to question if the actual European regulation corresponds exactly to the lines of the best practice. In particular, this evidence might support the growing feeling that TE is currently used in fairly all industries more as a regular form of job to save on firms’ wage bill, much beyond the role of screening device (Booth et al. 2002 ; Güell and Petrongolo 2007 ; Garz 2013 ). Consequently, temporary employment seem to be related with permanently high levels of workers’ rotation, damaging productivity in all sectors but especially in skilled sectors, where production uses skills more intensively. Therefore, in line with recent literature on flexibilization and productivity (e. g., Lisi 2013 ; Kleinknecht et al. 2014 ; Vergeer and Kleinknecht 2014 ), we also conclude that the extensive use of flexible labour is not a free lunch not only for firms, but also for the society as a whole.

The main regulatory implication raising from this picture is that the real challenge for labour regulation is to find a design to address the use of temporary employment as a flexible way to enter in the market allowing firms to screen new workers towards more stable form of jobs, instead of as a structural cheaper form of work. Probably, only in those conditions labour market outcomes could be able to benefit from all the advantages in terms of flexibility induced by TE, without suffering the secondary consequences on labour productivity. Hence, the future agenda of labour market research should certainly include the identification of such kind of regulation.

As discussed in the paper, the potential limitation of our analysis is that, indeed, the employed measure of TE does not allow us to distinguish between fixed-term contracts and temporary agency workers, which instead might be relevant as the two types of TE might (and often are found to) have different effects in the user firm’s productivity. Under this perspective, disentangling the differential effect of these two types of TE would represent the next step on our understanding of the productivity effect of TE and, thus, a bit of caution in the interpretation of our results is needed. Due to data limitation, however, this is left for future research.

In our study, we focus more on the effect of “numerical” flexibility on productivity growth, to emphasize the role of sectors’ skill intensity. For a recent study concerning the effect of weak wage growth on productivity, see Vergeer and Kleinknecht ( 2014 ).

In principle, one might expect that temporary workers would receive a risk premium above the normal wage, for the higher risk of becoming unemployed. In practice, however, temporary workers appear to earn less on average respect to regular workers, as showed by large literature (Booth et al. 2002 ; McGinnity and Mertens 2004 ). Therefore, as emphasized by Vergeer and Kleinknecht ( 2014 ), labour market reforms concerning “numerical” and “wage” flexibility seem to work in the same direction of lowering the firms’ wage bill.

For a discussion on the major channels through which “wage” flexibility reforms could potentially affect productivity growth, see Vergeer and Kleinknecht ( 2014 ).

In this regard, Albert et al. ( 2005 ) find that temporary workers in Spain are less likely to be employed by firms providing training and, furthermore, they have a lower probability of being trained when hired in firms providing training activities.

The only exception appears to be Ortega and Marchante ( 2010 ), which state that the effect of TE on productivity growth “… has only been detected in the manufacturing and energy sector, in contrast to low-technology low-human capital sectors …”, in line with the following results in our paper. Respect to Ortega and Marchante ( 2010 ), however, our following estimates do not refer to a specific country, but to a large panel of EU countries.

The reader will note that we do not have data to disentangle the separate effect of each abovementioned channel through which TE might affect labour productivity. However, as highlighted by Lucidi and Kleinknecht ( 2010 ), even if such data were available, one would encounter substantial multi-collinearity problems, given that all channels between TE and productivity appear to work in the same direction.

For instance, as argued in Lisi ( 2013 ) “… if firms in a sector need to lay off workers in response to changes in technologies or product demand, a stricter EPL could slow the pace of reallocation. By contrast, in industries where changes are less frequent or where firms can reallocate labour through internal adjustments, EPL could be expected to have little impact on reallocation and, in turn, on productivity …”.

To obtain the frictionless job reallocation rate FJR j for each sector, we follow the method developed by Ciccone and Papaioannou ( 2006 ) and, then, employed by different previous studies in this literature (e. g., Cingano et al. 2010 ; Lisi 2013 ). In particular, we regress the actual job reallocation rates at industry level on industry dummies \(\pi _{j}\) , industry dummies interacted with the EPL index for PE \(\tau _{j}\times \text{EPL}_{it}\) and country-time dummies \(\vartheta _{it}\) : \(\textit{JR}_{ijt}=\pi_{j}+\tau_{j}\times \text{EPL}_{it}+\vartheta _{it}+v_{ijt}\) . The presence of country-time dummies \(\vartheta _{it}\) should control for any time-varying differences across countries, whereas the interaction term \(\tau _{j}\times\text{EPL}_{it}\) should absorb the effect of market regulation on job reallocation rate, allowing us to obtain an appropriate estimate \(\textit{FJR}_{j}=\hat{\pi}_{j}\) of natural rate of job reallocation in each industry.

As suggested by Griffith et al. ( 2004 ) “… Augmenting the specification for nonfrontier countries with an additional term in contemporaneous frontier TFP growth allows for a more flexible relationship between nonfrontier and frontier TFP …”; thus, for the sake of completeness, we decided to include also this term in our productivity equation. However, all estimates with the inclusion of industry-specific fixed effects instead of the productivity growth of the industry leader produce a very similar pattern of results. Indeed, the limited influence of the contemporaneous productivity growth of the industry leader in the estimates is fully in line with the previous evidence in the literature (see e. g., Griffith et al. 2004 , 2009 ; Bassanini et al. 2009 ).

We also tried to include some other explanatory variable potentially relevant in explaining productivity growth, such as tax wedge and unemployment benefits. Overall, they appear not significant, even if in some specifications without the EPL index and TUD they are significant. Indeed, this is not surprising given the high cross-country correlations among labour market institutions in OECD countries (OECD 2007 ; Bassanini et al. 2009 ). We therefore suspect that this result emerges because of multicollinearity and, for this reason, we preferred to keep them out from our estimates.

Notice that, as shown in the previous literature on industry-level productivity equation (see e. g., Bassanini and Venn 2007 , 2008 ; Lisi 2013 ), when we consider the labour productivity growth as dependent variable in (4), the empirical model (4) is derived directly from an industry-level Cobb-Douglas production function and, thus, it includes the capital-labour ratio as regressor.

As a standard practice with an interaction term with endogenous regressors (i. e. TE*SSII), in our IV estimates we use the interaction term EPL index for TE*SSII as an instrument for the differential effect TE*SSII.

As standard in the IV-procedure, while we can easily test for the correlation between instrument and instrumented variable, we cannot test for the exogeneity condition of our instrument. Nonetheless, in the literature this kind of instrument (index measuring the strictness of a national legislation) tend to be considered strictly exogenous in the productivity equation, because their effect should be only indirect and, in particular, generated only by the change induced in the specific object of the regulation (in our case, the EPL index for TE should induce an effect in productivity growth only by the change induced in the share of TE). For example, OECD ( 2004 ) and Bassanini et al. ( 2009 ) use the EPL index for TE following the same argument, whereas Amable and Ledezma ( 2013 ) use the product market regulation index as an exogenous instrumental variable. Moreover, in a previous general article on temporary work and productivity, the EPL index for TE has already been used in IV estimations (Lisi 2013 ).

For a formal derivation of the empirical model (4) from an industry-level Cobb-Douglas production function, see e. g. Bassanini and Venn ( 2007 ) and Lisi ( 2013 ).

The issue of the indicators of social and cultural traits in a country has been always a big challenge in the literature (for a discussion on this issue, see e. g., Tabellini 2008 , 2010 ) and, indeed, it is difficult to find indicators available for all European countries in our sample. In particular, the abovementioned six indicators of social and cultural traits, looking at the cultural aspects of a country under different perspectives, are mainly based on the previous literature (e. g., Tabellini 2008 , 2010 ; Aghion et al. 2010 ; OECD 2011 , Charron et al. 2014 , 2015 ): trust , the fraction of survey respondents believing that “most people can be trusted” (Source: European Social Survey); respect , the fraction of survey respondents who claim to consider “tolerance and respect for others” as an important quality (Source: European Social Survey); pro - social behavior , averages responses to three questions about whether the respondent has volunteered time, donated money to a charity and helped a stranger in the last month (Source: Gallup World Poll); average years of schooling (Source: UNESCO Institute for Statistics); European quality of governance indicators 2010 and 2013 (Source: Charron et al. 2014 , 2015 ).

More specific results on the correlation between the EPL index for TE and the abovementioned indicators of social and cultural traits are available upon request form the authors.

In the literature the impact of a labour market policy on productivity is usually divided into “composition effect” and “independent effect”. The first is the effect on productivity associated with the change in the composition of employment due to the policy variation (for instance, an increase in the share of unskilled workers). The second is the pure average effect of the policy on productivity (that is, ceteris paribus ) and, thus, it is often the effect of interest. In this regard, different previous studies emphasize that “composition effects” are somewhat relevant in the aggregate analysis and, indeed, they cannot be easily dismissed. Therefore, any aggregate analysis of the impact of some labour market policies on productivity hardly will be able to isolate the “independent effect” of the policy and, in turn, to produce a useful contribution for policy guidance. Differently, industry-level analyses suggest that the within-industry “composition effects” are fairly negligible (OECD 2007 ) and, therefore, the use of industry-level panel data should succeed in capturing the “independent effect” of the policy.

The two productivity measures correspond respectively to LP_I and TFPva_I in EU KLEMS database.

The EUROSTAT definition of temporary contracts is the following: “Employees with temporary contracts are those who declare themselves as having a fixed term employment contract or a job which will terminate if certain objective criteria are met, such as completion of an assignment or return of the employee who was temporarily replaced”.

As for the Kleibergen-Paap rk Wald statistic, Baum et al. ( 2007 ) suggest to apply the critical values for the F‑statistic reported in Stata provided by Stock and Yogo ( 2005 ). In particular, if we are willing to accept an actual rejection rate of 10% (the lowest tabulated in Stata), the critical value for the F‑statistic is 16.38. Therefore, the Kleibergen-Paap statistic of 59.971 in (3) indicates that the EPL index for TE and its interaction with SSII are not weak instruments in our IV estimate.

Full regressions on this specific point are available upon requests from the authors.

Even if we do not find evidence of a non-linear effect of TE, our results should not be interpreted as in contrast with the previous studies finding an inversely u‑shaped effect of TE in the user firms’ productivity (Hirsch and Mueller 2012 ; Nielen and Schiersch 2014 ). Notice that, in fact, while the above-mentioned argument on the inversely u‑shaped effect of TAW is certainly reasonable at the firm level, it is much less clear at the industry level. Therefore, it is not surprising that in our industry-level panel we do not find evidence of a non-linear effect of TE, but clearly this does not mean that at the firm level the inversely u‑shaped argument is not valid. Moreover, while the evidence on the inversely u‑shaped productivity effect is strong for TAW, Nielen and Schiersch ( 2016 ) find that the same inversely u‑shaped effect does not seem to emerge for fixed-term contracts, even at the firm level.

We have also estimated the specifications with SSII2 (available upon request) with the capital-to-labour growth as regressor for labour productivity, with results fully in line with those presented in Table  2 .

We have also run the same reduced sample regressions (available upon request) using TFP as productivity variable, with results fully in line with those presented in Fig.  2 .

Full regressions are available upon request from the authors.

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Acknowledgements

The authors would like to thank Stephen Machin, Roberto Cellini, two anonymous referees and participants in various conferences and seminars for helpful comments and suggestions. Miguel A. Malo acknowledges funding from the Spanish Ministry of Economy and Competitiveness (research project CSO2014-59927-R). The usual disclaimer applies.

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Appendix A: Data description

The majority of variables employed in the paper (e. g., labour productivity, share of TE, EPL index for PE and TE) are drawn from Lisi ( 2013 ), please refer to Lisi ( 2013 ) for a full description of the dataset and the source of variables. Hereunder, we provide the description only of the new variables in our empirical analysis.

1.1 Total Factor Productivity

Definition..

Total factor productivity (base 1995 = 100) (variable TFPva_I ).

EU KLEMS database.

1.2 Capital Stock

Gross capital stock in volume terms (variable CPGK ).

OECD STAN database.

1.3 Share of skilled workers in SSII

Share of workers with a tertiary education (level 5–6 ISCED 1997).

EUROSTAT Science, technology and innovation database.

1.4 Share of skilled workers in SSII2

Share of workers occupied in science and technology tasks (HRST).

1.5 Product Market Regulation

OECD Indicators of Product Market Regulation, a comprehensive set of indicators measuring the degree to which policies promote or inhibit competition in areas of the product market where competition is viable. The indicators cover formal regulations in the following areas: state control of business enterprises; legal and administrative barriers to entrepreneurship; barriers to international trade and investment.

OECD database.

Appendix B: Descriptive statistics

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Lisi, D., Malo, M.A. The impact of temporary employment on productivity. J Labour Market Res 50 , 91–112 (2017). https://doi.org/10.1007/s12651-017-0222-8

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