Routine-biased Technological Change Research Articles

R&D innovation, industrial evolution and the labor skill structure in China manufacturing

This paper utilizes the skill-biased technological change and routine-biased technological change hypotheses, along with the theory of firms' innovation behavior, to establish a comprehensive analytical framework. This framework aims to elucidate the mechanisms through which technological innovation, industrial evolution, and education affect the labor skill structure in China manufacturing. The empirical results that have been gathered can be succinctly described in the following manner: (1) Research and development innovation stimulates the demand for high-and low-skilled labor, and, the rising of total factor productivity reduces the demand for low-skilled labor, confirming the labor-friendly effect of product innovation and the labor-saving effect of process innovation. (2) The cyclical pattern of fluctuating demand for low-skilled labor can be attributed to the industrial concentration, driven by technological advancements. Nevertheless, the phenomenon of cyclicality has not been observed for high-skilled labor. (3) There exists a paradoxical relationship between higher education and industrial demand, wherein education and skill are mismatched. (4) There exists the SBTC in China manufacturing resulted from process innovation. However, the SBTC does not explain the cause or mechanism behind the relative productivity shift and the higher demand for educated workers. This paper examines several policy implications including R&D innovation, employment, and education.

The penetration of robotic technology, task content routinisation and job satisfaction: Evidence from China

Faced with a surge in labour costs, companies are strongly incentivised by local governments’ aggressive promotion to embrace robotic technology, triggering intense media debates. This, in turn, inevitably shapes workers’ attitudes towards their current jobs and brings uncertainty about their future workplace. This study investigates the connection between robotisation, routinisation and job satisfaction in China’s labour market. The results show that there is an observed positive relationship between an individual’s job satisfaction and exposure to robots in the local labour market, which may be related to the overwhelming benefits of these new technologies about certain work dimensions. However, fear of automation is not groundless. Our findings show that job satisfaction decreased with an increase in the routinisation of task content, particularly in areas that are increasingly exposed to robot adoption. This supports the routine-biased technological change hypothesis, which connects technology-induced unemployment with job routinisation. JEL Classification: J28, O33

Employment and wage dynamics in the electricity sector: Evidence from Portugal 2002–2020

Exploring a rich administrative matched employer-employee longitudinal dataset over the 2002–2020 period and a task-based approach, this study investigates to what extent the recent paradigm shift in the electricity sector has affected the structure of employment and wages in the Portuguese case. Our results show that the liberalization in the sector led to the entry of new players and firms' downsizing of the workforce, most notably in occupations involving routine cognitive tasks and non-routine manual tasks. In two decades, the employment share of occupations involving non-routine cognitive tasks (abstract or interactive) doubled, from 29.7% in 2002 to 58.1% in 2020. Regarding wage premiums, the results reveal a clear positive trend in real hourly wages for all types of occupations in the sector. However, we observe a lower wage growth acceleration for workers employed in routine (cognitive or manual) occupations, when compared with similar workers employed in non-routine occupations (cognitive or manual). Our findings are partly consistent with the skill-biased and routine-biased technological change hypotheses in the sense that we observe, respectively, a skill up-grading translated into an increase in employment share in non-routine cognitive occupations and a substantial decline in employment share in routine cognitive occupations.

Routine-biased technological change and employee outcomes after mass layoffs: evidence from Brazil

Abstract We investigate the impact of “routinization” on the labor outcomes of displaced workers. We use a rich Brazilian panel dataset and an occupation-task mapping to examine the effect of job displacement in different groups, classified according to their tasks. Our main result is that following a layoff, workers previously employed in routine-intensive occupations suffer a more significant decline in wages and more extended periods of unemployment. As expected, job displacement has a negative and lasting impact on wages. Still, workers in routine-intensive occupations are more impacted than those in non-routine occupations in terms of wages (an increase of one point in the routine-intensity index results in a further decline of 2% in workers’ relative wages) and employment. Furthermore, our results indicate that workers in routine-intensive occupations are more likely to change occupations after the shock, and those who do not switch occupational fields suffer a more significant decline in wages. Lastly, even though the loss of employer-specific wage premiums explains 13% of displaced workers’ drop in wages, it does not explain routine-intensive workers’ more substantial losses.

Investigating the effect of intelligent assistance systems on motivational work characteristics in assembly.

The online version contains supplementary material available at 10.1007/s10845-023-02086-4.

COVID-induced economic uncertainty, tasks and occupational demand.

Using monthly online job postings data at the occupation-US-state level in January–December 2020, we provide novel evidence on how COVID-induced economic uncertainty has impacted the occupational composition of US labour demand. The effects are identified by exploiting monthly variation in country-level uncertainty along with pre-COVID differences in shares of occupation-state pairs in occupational country-wide employment and occupational task content. Some of the effects are consistent with the secular and episodic aspects of routine-biased technological change (RBTC) leading to job polarisation and growing complementarity between cognitive analytical and interactive tasks. Interestingly, however, other effects are seemingly at odds with RBTC-induced job polarisation and rather rationalised by idiosyncratic features of the shock (e.g. health emergency) and responses to it (e.g. social distancing, lockdowns). Although additional evidence points to high persistence of most of these effects in 2020, further research in this direction would shed light on whether the effects will persist through the post-COVID era or phase out.

Routine-biased technological change and wage inequality: do workers’ perceptions matter?

The Routine-Biased Technological Change (RBTC) has been regarded as a relatively novel technology-based explanation of social changes affecting job and wage polarization. In this paper, we investigate wage inequality between routine and non-routine workers along the wage distribution in Italy. Thanks to unique survey data, we can estimate the wage differential using both the actual and the perceived level of routine intensity of jobs to classify workers. We adopt semi-parametric decomposition techniques to quantify the importance of worker characteristics in explaining the gaps. We also employ non-parametric techniques to account for self-selection bias. We find evidence of a significant U-shaped pattern in the wage gap, according to both definitions, with non-routine workers always earning significantly more than routine workers. Results show that worker characteristics fully explain the gap in the case of perceived routine, while they account for no more than 50% of the gap across the distribution in the case of actual routine. Thus, the results highlight the importance of taking into account workers’ perceptions to reduce the set of omitted vaiables when analyzing determinants of wage inequality.

Automation, AI & Work

Abstract We characterize artificial intelligence as “routine-biased technological change on steroids,” adding intelligence to automation tools that substitute for humans in physical tasks and substituting for humans in routine and increasingly nonroutine cognitive tasks. We predict how AI will displace humans from existing tasks while increasing demand for humans in new tasks in both manufacturing and services. We also examine the effects of AI-enabled digital platforms on labor. Our conjecture is that AI will continue, even intensify, automation's adverse effects on labor, including the polarization of employment, stagnant wage growth for middle- and low-skill workers, growing inequality, and a lack of good jobs. Though there likely will be enough jobs to keep pace with the slow growth of the labor supply in the advanced economies, we are skeptical that AI and ongoing automation will support the creation of enough good jobs. We doubt that the anticipated productivity and growth benefits of AI will be widely shared, predicting instead that they will fuel more inequality. Yet we are optimistic that interventions can mitigate or offset AI's adverse effects on labor. Ultimately, how the benefits of intelligent automation tools are realized and shared depends not simply on their technological design but on the design of intelligent policies.

Occupational change, computer use and the complementarity effect in the digital age: Evidence from Finland

This article examines the pattern of occupational change in Finland between 2013 and 2018 in light of hypotheses derived from skill- and routine-biased technological change arguments. As extensions to mainstream studies on occupational change, two alternative means of classifying occupations by skill level are used, and data from Statistics Finland’s Quality of Work Survey are combined with data from the Finnish Labour Force Survey. The analysis reveals a pattern of skill-biased change and indications of a digital divide between high-skilled and other occupations with no signs of job polarization. Reasons for country-wide differences in occupational change patterns and the implications of differences in the means of classifying occupations for the results are discussed with suggestions of topics for further studies.

Racing with or Against the Machine? Evidence from Europe

A fast-growing literature shows that technological change is biased towards routine tasks, changing the structures of employment and wages in developed economies. This paper is the first to estimate the absolute rather than relative employment effects of routine-biased technological change (RBTC) for Europe as a whole and at the level of 238 European regions. We develop and estimate a task model of regional labor demand in tradable and non-tradable industries, building on Goos et al. (2014), and distinguish the main channels through which technological change affects labor demand. These channels include the direct substitution of capital for labor in task production, but also the compensating effects operating through product demand and local demand spillovers. We empirically estimate the contributions of the channels in our model to analyze how RBTC affects both aggregate and regional employment. Our results indicate that RBTC has on net created more than 11 million jobs across 27 European countries over 1999-2010, comprising half of total employment growth, and can account for some 40 percent of the observed European regional variation in employment growth over this period.

Cities and tasks

This paper explores the relationship between routine-biased technological change and agglomeration economies. Using administrative data from the Netherlands, we first show that in dense areas, jobs are less routine-task intensive (i.e. less repetitive and automatable), meaning that jobs cover a larger spectrum of tasks. We then explore how the routine intensity of jobs affects the urban wage premium. We find that the urban wage premium is higher for workers performing non-routine tasks, particularly analytic tasks, while it is absent for workers in routine task intensive jobs. These findings also hold within skill groups and suggest that routinisation increases spatial wage inequality within urban areas. We further provide suggestive evidence that a better matching of skills to jobs and increased learning opportunities in cities can explain these findings.

Routine-biased technological change does not always lead to polarisation: Evidence from 10 OECD countries, 1995–2013

This article deals with a central paradox in the occupational polarisation literature: most scholars accept that technological change is biased against routine-intensive occupations, but in many countries, we do not see the pattern of occupational polarisation that the theory usually predicts. I argue and show empirically using a dataset of 10 OECD countries between 1995 and 2013 that technological change is both routine-biased and skill-biased, but that the result of routine-biased technological change may be occupational upgrading rather than polarisation. This is due to differences in occupational routine-wage hierarchies: only where routine occupations cluster around the middle of the wage distribution are we likely to see polarisation. Where routine occupations are concentrated near the bottom of the wage hierarchy, upgrading occupational change is the norm. Based on research on the US, the former has been widely assumed, but it does not hold true in all countries. Overall, this article shows that much previous work on routine-biased technological change and polarisation was built on premises that do not travel well. This underscores the importance of comparative research for building and testing robust general theories.

Routine-Biased Technological Change and Declining Employment Rate of Immigrants

We find that routine-biased technological change decreases the employment-to-population ratio of foreign-born population over the last three decades (1980–2010). This impact is greater for foreign-born population with lower English proficiency. As computerization and automation substitute for workers in routine occupations, switching from routine jobs to non-routine cognitive jobs may be more challenging for foreign-born workers than for native workers.

Transformation of the world job market in the conditions of the Fourth Industrial Revolution

The relevance of this research is substantiated by the fact that the world job market is currently under the influence of the Fourth Industrial Revolution (Industry 4.0). The goal lies in assessing the impact of cyclic and unexpected trends upon the job market, and thus, the forms of job management and employment functions. In this regard, analysis is conducted on the academic theories in the context of conceptualization of the impact of Industry 4.0 upon the job market and employment functions. The long-term trends in transformation of the job market are determined. The empirical basis of this research is comprised of the statistical data provided by Rosstat and the Ministry of Science and Higher Education of the Russian Federation, as well as the resources of analytical articles and scientific publications, including the materials of international organizations, leading consulting companies, global associations, leading educational institutions, other active participants of global educational environment, and job market experts. An overview is given to the trends that affect the companies, their strategies and business models; the impact of such trends upon the transformation of current professions and the emergence of new professions; skills required for these professions. The scientific novelty lies in substantiation of the concept of skill-biased technical change (SBTC) and the theory of routine-biased technological change (RBTC). The presented materials would allow the companies to combine business news with fundamental training of specialists for going along the new career trajectiory in light of the new technological challenges, which defines the practical value of this research.

Digitalization, routineness and employment: An exploration on Italian task-based data

This paper explores the relation between the digitalization of labour processes, the level of routineness of labour tasks and changes in employment in the case of Italy in the period 2011-16. The levels of digitalization and routineness of occupations in more than 500 4-digit ISCO professional groups are measured using data from a unique Italian profession-level survey on skill, tasks and work contents – the INAPP-ISTAT Survey on Italian Occupations (ICP), an O*NET-type dataset. Two digitalization indices are used: a digital use index, measuring the use of computers and e-mail in the workplace, and a digital tasks index, capturing the presence of a set of key digital tasks, such as those related to programming or activities concerning the use of specialized hardware. The same dataset is used to compute a composite routine task intensity index. The descriptive evidence presented in the paper shows strong differences across occupations in the level of digitalization and routineness, and the presence of a negative relation between the two in most professional groups. The econometric estimates show that digital-intensive occupations tend to grow more than the rest of the workforce, particularly when digitalization is measured relying on the digital use indicator. The level of routineness, in turn, is negatively or, in some specifications, not significantly associated to employment change. However, occupations that are both digital and routine-intensive turn out to be penalized in terms of employment growth, providing further support to (and further qualifying) the the routine biased technological change (RBTC) hypothesis. In other words, our results show that the impact of digitalization on employment is mediated by the level of routineness characterizing the tasks bundled in each occupation.

Educational mismatches, routine biased technological change and unemployment: evidence from Italy

this paper investigates the relation between educational mismatches and individual unemployment risk in italy with a special focus on the role of technological change and labour demand characteristics. a novel dataset obtained merging two surveys (icp and plus) is used to build different measures of educational mismatch and a measure of routine intensity on italy: the routine task index. the latter takes into account the effect of routine biased technical change (rbtc) in determining educational mismatches and unemployment risk. the results indicate that over−education is significantly associated with higher unemployment risk. educational mismatches, routine biased technological change and unemployment: evidence from italy piero esposito sergio scicchitano

Task content routinisation, technological change and labour turnover: Evidence from China

This article addresses the unresolved question of whether recent technological change causes job instability in a non-western context. China is now the world’s largest user of industrial robots. A Lewis turning point has been predicted, involving a transition from a plentiful supply of rural low-cost workers to a labour shortage economy in which rising labour costs drive labour-technology substitution. The routine-biased technological change hypothesis suggests that technology-induced routinisation in job task content has a profound impact on employment structure. This study captures the extent of routinisation of jobs in the transitional context of China and examines the incidence and impact of routinisation on labour turnover in the labour market. Using rotating panel data from the China Labour-force Dynamics Survey 2012, 2014 and 2016, this study, based on individual information with regard to flexibility in work schedules and degree of autonomy in workload and task content on the job, follows a recently developed measure to construct a routine intensity index and indicates a division into three routine intensity groups. The empirical findings show that the probability of job mobility is significantly increased with the magnitude of routine task intensity, suggesting that the process of technology-induced routinisation is strongly associated with labour turnover.JEL Codes: J63, J20

Jobs at risk? Task routineness, offshorability, and attitudes toward immigration

AbstractIn this paper, we study the relationship between occupational vulnerability and attitudes toward immigration in Western Europe. We measure occupational vulnerability as the risk of unemployment due to routine-biased technological change and offshoring of jobs to other countries. Previous empirical studies in political economy have shown that individuals’ policy preferences echo their economic risks and prospects. Workers in low routine occupations are most worried about their job market prospects, most likely to demand social protection and least likely to support free trade. We find that attitudes toward immigration become considerably more negative as occupational task routineness increases. We do not find a similar association between occupational offshorability and immigration attitudes. Direct exposure to global competition is not associated with increased worries about immigration. However, offshorability seems to be associated with the polarization of attitudes toward immigration between routine and nonroutine workers.

Innovation as a Firm-Level Factor of the Gender Wage Gap

Although much research has investigated how innovation affects wages and wage inequality in general, less is still known on how innovation in firms affects the gender wage gap. We show, using matched employer-employee data from Estonia, that technological (product and process) and non-technological (organizational and marketing) innovation, as well as the firm’s own RD therefore, both at the higher and lower end of the wage distribution, potentially indicating the importance of routine-biased technological change. We further show based on propensity score matching that men gain more from taking up a job at an innovative firm than women. The effect of innovation on men’s wages and on the gender wage gap is significantly larger among newly hired employees compared to incumbent employees. Among the newly hired employees at innovative firms, taking up a job at a more ‘open’ innovator appears to be associated with especially strong gains for newly hired women. However, even in this case the gains fall short of the gains for men.

Humans versus Machines: An Overview of Research on the Effects of Automation of Work

Digital automation has pervaded many areas of our daily activities, with serious repercussions for social, economic and political systems. Automation’s ever-enhancing capability to transform human lives has spawned a wide body of scholarly research, with inputs from social and economic sciences, engineering and technology. This paper1 provides a brief overview of the main arguments put forward by the researchers, particularly in labour economics, on the subject of digital automation, with a special focus on Germany. Such debates revolve around the impact of automation on the number of jobs performed by human labour and the restructuring of labour markets under the influence of automation. The overview starts with a short discussion about the meaning of digital automation. It then outlines the debates of how technology distributes work between humans and machines from the viewpoint of skill-biased technological change and routine-biased technological change research. This is followed by a summary of the way digital technologies have been restructuring the world of work. The overview concludes by pointing out research gaps that are particularly relevant in the German context. It emphasizes that a new research agenda should incorporate the role of existing education and training regimes (VET), in particular in light of employment polarisation and the shrinking employment segment of jobs with mid-level pay and skills. Moreover, there is a lack of research that considers the insights of industrial sociology with regard to the renegotiation of work organisation in the process of automation. In particular, the role of institutional factors, such as workers’ representatives, in the form of trade unions or works councils, has largely been neglected by studies on labour economics. Finally, there should be more attention paid to the differentiated effects of automation on specific socio-economic groups, such as women and men, but also between different generations.