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US job market may be near tipping point, research shows

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The job seeker’s guide to 2024’s job market.

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Woman in a job search in 2024

Despite a lot of fear and uncertainty, the U.S. job market enjoyed a surprisingly smooth 2023, even amidst a fair share of volatility and tailwinds from factors like inflation and high interest rates. It remained fairly strong even after many projections for a recession, a banking crisis, geopolitical crises, and a historically fast tightening of monetary policy by the Federal Reserve. While that's an impressive feat, events like a reduction in layoffs, increased hiring numbers, slow wage growth, and an influx of AI tools in the market had to happen to enable the economy’s soft landing.

Whether the 2024 labor market will continue to maintain strength remains to be seen. From every indication, things should look good this year, economy-wise. As reassuring as that can be to job seekers, there is a need to brace for the unexpected, as past performance is no guarantee for future results. For the 2024 labor market to progress smoothly, there are five economic trends that will need to maintain their momentum during this year in order to see a strong and fruitful job market for job seekers.

Economists Believe Job Seekers Should Look Out For These 5 Economic Trends in 2024

According to an Indeed research director , there’s a case for optimism in 2024, but it’s best not to oversell. Below are about five economic trends that will shape the 2024 labor market, including which industries are predicted to do well or struggle:

1. Moderate hiring demand:

The demand for workers has moderated in 2023, but further reductions could be upsetting. That’s why, in 2024, it needs to stay strong, either through increased job postings or employee retention.

Even though we saw a hike in the number of available jobs last year, they were still lower than recent job market peaks. For instance, job openings as of late November 2023 were down 62,000 to 8.79 million from their March 2021 peak, and part of the decline is likely because employers have been able to fill many positions.

Best High-Yield Savings Accounts Of 2024

Best 5% interest savings accounts of 2024.

With that said, the labor market outlook for 2024 depends not only on whether worker demand continues to fall or not but also on whether any future decline will come primarily from less hiring rather than more layoffs. When hiring continues to cool, it becomes easier for the Federal Reserve to cut interest rates and reduce inflation. However, it has to be done gradually, as a rapid descent may spike unemployment.

2. An influx of prime-age workers:

Over the past several years, the participation rate of prime-age workers from ages 25–54 in the labor market experienced one of the largest surges we’ve seen in over 20 years, bolstered by elevated immigration levels after the pandemic and the return of previously sidelined workers .

The prediction for 2024 is that an increase of younger workers will continue to join the workforce if the labor market remains tight and immigration flows stay high. Additionally, the share of the aging population aged 65 and above is expected to rise from 17.5% in 2023 to 20.9% in 2035, indicating that, overall, the labor force will grow this year.

However, even with the increased participation levels of the aging U.S. population, employers could still face a shrinking talent pool in the long-term future when these workers retire and the number of prime-age workers in the labor force decreases.

3. Stable quitting rate:

The quit rate among workers plateaued in 2023, coming in at 2.3% back in September, marking the end of the Great Resignation. However, it will need to hold firm at its current pace, a level consistent with the 2019-era rates but still elevated by historical standards.

A stable and fairly low quitting rate bodes well for 2024. This means that employers might have to hire more people who are currently unemployed , instead of those who already have jobs, to fill open positions. This will help reduce wage growth and inflation.

Although the days of high quits rates are behind us and a return to pre-pandemic levels within sight, that doesn’t mean workers are suddenly out of new job opportunities or that employers don’t need to focus on retaining current staff. Employees are still quitting and job-switching at near-historic rates. This simply means that we’re coming out of the unusual trends that the pandemic set for the job market. As such, businesses will still need to improve their talent retention measures, such as market appropriate salaries and benefits or changes to workplace flexibility, to earn employee engagement and loyalty.

4. A downward trend in wage growth:

One of the determinants for measuring the labor market cooldown is that nominal wage comes down from its all-time highs. With the predictions for low hiring demands, growth in the labor force, and a decline in the quitting rate considered, wage growth is expected to return to a pre-pandemic pace. All the same, proceed with cautious optimism, as nothing is guaranteed and we don’t know how long the return will take or whether inflation will follow a similar trajectory.

For now, indications point to slowing wage growth, which could hit the 3.5%-4% sweet spot before the middle of this year and well below the January 2022 peak of 9.3%, as per data from the Indeed Wage Tracker. While the gradual fall in wage growth may not spike unemployment, as seen in 2023, it may, however, affect workers' purchasing power. Therefore, wage growth cannot fall below the rate of inflation, and employees need to keep receiving those inflation-adjusted raises to keep up.

5. Increased usage of Generative AI

Ever since the emergence of artificial intelligence, especially Generative Artificial Intelligence, at the end of 2022, it has found adoption in various industries as companies recognized its potential to transform operations and increase efficiency.

For 2024 and beyond, GenAI may spread rapidly through the economy and boost productivity growth. In fact, it could reconfigure a wide variety of jobs and potentially create new ones.

Though we don’t see AI displacing workers in 2024, it will impact hiring in multiple ways. Jobs creating AI tools and ones leveraging them for other roles, such as marketing, will likely surge—reshaping the way people work.

How Certain Industries Will Fare in 2024

According to Indeed data, job seekers are increasingly shifting their interest in jobs outside their fields. As such, quitting levels have increased compared to 2019 in certain sectors, such as leisure and hospitality, versus rates for professional and business services.

At the same time, employers changed their hiring plans in light of the slowing U.S. economy, shifting consumer demand, and high-interest rates. Now, previously high-flying industries , including finance, software, IT, marketing, and media, saw an uptick in layoffs and a decline in job postings. In contrast, sectors related to companies that offer in-person services, including restaurants, hotels, and hospitals, enjoyed robust hiring demand—and that may continue in 2024.

Tips to Prepare for the 2024 Job Market

The job market in 2024 will likely be highly competitive. However, with careful preparation and an understanding of industry trends, job seekers can set themselves up for success. Here are some tips to get started:

Research your industry

One of the best ways to prepare for the job market is to research the industries and companies that you are interested in. This will help you identify the skills and qualifications that employers are looking for, and it will also give you a better understanding of the current job market trends.

Update your skills

The job market is constantly changing, and it is important to keep your skills up-to-date. This may include taking continuing education courses, attending workshops, or learning new software programs. By actively seeking ways to upskill and reskill, you stay ahead of the curve and become more marketable to potential employers. Additionally, think creatively about transferable skills that are industry agnostic and how you can leverage your unique experience, skills, and values.

Network with professionals

Networking is an essential part of any job search, and it is especially important in the 2024 job market. Attend industry events, connect with people on LinkedIn, and reach out to friends, family, and colleagues for referrals. The more people you know, the more likely you are to hear about job openings. These days, capitalizing on employee referrals to get in to companies you’re excited about working with will go much further than an application.

Prepare your resume and LinkedIn Profile

Your resume and LinkedIn are sometimes your first chance to make a good impression on potential employers. These are the physical representations of your personal and professional brand and your value proposition, so it’s important to make sure that they are well-written, error-free, and present you as the industry leader or expert you are. Highlight your skills, experience, and achievements, focusing on the results you’ve been able to accomplish throughout your career as well as what makes you unique as this will help you stand out among the crowd.

Practice your interview skills

Interviews can be nerve-wracking, but there are a few things you can do to prepare and make sure that you put your best foot forward. Review your resume, your accomplishments, and your results and ensure you know your best, most impressive stories backwards and forwards. Practice your elevator pitch so you can deliver it confidently and directly. Prepare for any objections you think may come up in the interview so you can overcome them confidently. Make sure to practice either with a coach, a friend, or even with yourself by recording your answers and watching them back to see how you can improve.

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SCE Labor Market Survey Shows Sharp Increase in Job Seekers, While Current Job Satisfaction Deteriorates

NEW YORK—The Federal Reserve Bank of New York’s Center for Microeconomic Data today released the July 2024 SCE Labor Market Survey , which shows a sharp increase in the proportion of job seekers compared to a year ago. Satisfaction with wage compensation as well as with nonwage benefits and promotion opportunities at respondents’ current jobs all deteriorated. The average expected likelihood of receiving an offer in the next four months increased compared to a year ago, while the average expected likelihood of becoming unemployed in the next four months reached a series high. The average expected wage offer (conditional on receiving one) declined year-over-year, while the average reservation wage (the lowest wage at which respondents would be willing to accept a new job) increased year-over-year but retreated slightly from a series high recorded in March 2024.

Experiences

• Among those who were employed four months ago, 88% were still with the same employer, a series low since the start of the survey and down from 91.4% in July 2023. The rate of transitioning to a different employer increased to 7.1%—the highest reading since the start of the survey—from 5.3% in July 2023. The increase compared to a year ago was primarily driven by women.
• The proportion of individuals who reported searching for a job in the past four weeks increased to 28.4%—the highest level since March 2014—from 19.4% in July 2023. The increase was most pronounced among respondents older than age 45, those without a college degree, and those with an annual household income less than $60,000.
• 19.4% of individuals reported receiving at least one job offer in the past four months, essentially unchanged from July 2023. The average full-time offer wage received in the past four months decreased slightly to $68,905 from $69,475 in July 2023.
• Satisfaction with wage compensation, nonwage benefits, and promotion opportunities at respondents’ current jobs all deteriorated relative to a year ago. Satisfaction with wage compensation at the current job fell to 56.7% from 59.9% in July 2023. Satisfaction with nonwage benefits fell to 56.3% from 64.9%. And satisfaction with promotion opportunities dropped to 44.2% from 53.5%. These declines were largest for women, respondents without a college degree and those with annual household incomes less than $60,000.

Expectations

• The expected likelihood of moving to a new employer increased to 11.6% from 10.6% in July 2023, while the average expected likelihood of becoming unemployed rose to 4.4% from 3.9% in July 2023. The current reading is the highest since the series started in July 2014.
• The average expected likelihood of receiving at least one job offer in the next four months increased to 22.2% from 18.7% in July 2023. The average expected likelihood of receiving multiple offers in the next four months rose to 25.4% from 20.6% in July 2023.
• Conditional on expecting an offer, the average expected annual salary of job offers in the next four months declined to $65,272 from $67,416 in July 2023, though it remains significantly higher than pre-pandemic levels. The decline was broad-based across age and education groups.
• The average reservation wage—the lowest wage respondents would be willing to accept for a new job—increased to $81,147 from $78,645 in July 2023, though it is down slightly from a series high of $81,822 in March 2024.
• The average expected likelihood of working beyond age 62 increased to 48.3% from 47.7% in July 2023, and versus a series low of 45.8% in March 2024. The average expected likelihood of working beyond age 67 increased to 34.2% from 32% in July 2023, partially reversing the steady declining trend observed in the series since the onset of the pandemic.

Detailed results are available here .

About the SCE Labor Market Survey

The SCE Labor Market Survey , fielded as part of the Survey of Consumer Expectations (SCE) since March 2014, provides information on consumers’ experiences and expectations regarding the labor market. Every four months, approximately 1,000 SCE panelists are asked details about their current (or most recent) job. Respondents are asked about job transitions, and about their job search effort and outcomes (number of job offers and offer wages), over the last four months. The currently employed are also asked about their level of satisfaction with wages, non-wage benefits, and their prospects for advancement at their current job. In addition, the survey elicits respondents’ expectations about job transitions over the next four months. Respondents are asked about the likelihood of receiving at least one job offer over the next four months, the expected number of offers, and the expected wages for these offers. The survey also elicits the respondents’ “reservation wage” and retirement expectations. 

More information about the SCE survey goals, design, and content can be found here .

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The job market might be more fragile than previously thought

Jonaki Mehta

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Katia Riddle

NPR’s Ailsa Chang talks to senior economist at Wells Fargo, Sarah House, about the revision in jobs numbers that show the U.S. economy employed 818,000 fewer people that originally reported in March.

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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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• Development studies

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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Economists forecast that a revision to payrolls data could undercut a robust picture of the labor market, further pressuring the Federal Reserve to cut rates.

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• Searching for Work in the Digital Era
• 1. The internet and job seeking

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• 2. Job seeking in the era of smartphones and social media
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The state of the job market consistently ranks among Americans’ top policy priorities , and access to online resources has long been viewed – by policymakers and the public alike – as an essential tool to help Americans find and apply for jobs. The FCC’s National Broadband Plan cited expanded access to jobs and training as a key benefit of increased broadband adoption, and a 2010 Pew Research Center survey found that two-thirds of Americans believe people without broadband are at a disadvantage when it comes to finding out about job opportunities or learning new career skills. Other studies have found that the internet is especially important to the job seeking habits of certain demographic groups, such as African Americans.

This report, based on a nationally representative survey of 2,001 U.S. adults ages 18 and older, documents the current state of digital job seeking in America. It first examines the basic contours of this issue – how many people have looked or applied for a job online, how the internet stacks up to other sources of job information in terms of overall importance, and how confident Americans feel in their own digital job-seeking skills. After that, the report examines the specific role smartphones and social media platforms are playing in Americans’ job seeking habits.

The internet is a near-universal resource among those who have looked for work recently

Digital resources are now more important than ever to Americans’ ability to research and apply for jobs. A majority of Americans (54%) have gone online to look for information about a job, and nearly as many (45%) have applied for a job online. The proportion of Americans who research jobs online has doubled in the last 10 years: In a Pew Research Center survey conducted in early 2005, 26% of Americans had used the internet to look for job information. 3

Notably, these figures are based on the entire public – many of whom are retired, not in the job market, or have simply not had a reason to look for a job recently. Narrowing the focus to the 34% of Americans who have actually looked for a new job in the last two years, fully 90% of these recent job seekers have ever used the internet to research jobs, and 84% have applied to a job online.

Not surprisingly, young adults are the demographic group most likely to engage in these online job seeking behaviors. Roughly eight-in-ten Americans ages 18 to 29 have researched (83%) as well as applied for a job (79%) online. However, a substantial majority of those ages 30 to 49 (and a sizeable minority of those ages 50 to 64) have engaged in these behaviors as well.

Along with these differences related to age, African Americans are more likely than whites to engage in online job-seeking behaviors; urban and suburban residents are more likely to do so than those living in rural areas; and Americans with higher levels of income and educational attainment are more likely to do so than those with lower income and education levels.

Online employment resources now rival personal and professional networks as a top source of job information

Clearly, the vast majority of American job seekers have utilized online resources at one time or another to look for and apply for jobs – but the internet is just one resource that job seekers might take advantage of when looking for work. How do online resources stack up to the many other ways of looking for and finding employment, whether online or offline?

To examine this question more deeply, the survey asked a series of questions about the resources recent job seekers took advantage of in their most recent search for employment. These findings illustrate that Americans utilize a wide range of resources when looking for work – but online resources, along with personal and professional networks, are especially important when it comes to finding employment in America today.

Roughly one-third of Americans (34%) indicate that they have looked for a new job at some point in the last two years, and 79% of these job seekers utilized resources or information they found online as part of their most recent employment search. By comparison, 66% of these recent job seekers turned to personal connections with close friends or family members, 63% turned to professional or work connections, and 55% sought assistance from acquaintances or friends-of-friends. Taken together, 80% of recent job seekers used professional contacts, close personal connections, and/or more distant personal connections in their most recent search for employment – nearly identical to the 79% who used online resources and information.

Several other resources are used by a substantial minority of recent job seekers: 32% utilized government or private employment agencies in their most recent job search, 32% utilized ads in print publications, and 28% utilized events such as conferences or job fairs.

Job seekers in a range of demographic groups rely heavily on the internet as an employment resource, 4  but Americans with high levels of educational attainment are especially likely to do so. Some 88% of college graduates utilized online resources and information as part of their most recent job search, compared with 77% of those who have attended but not graduated from college, and 69% of those who have not attended college at all.

Educational attainment has long been a strong predictor of whether or not Americans go online or not, but the differences noted here are not merely the result of higher rates of internet adoption by Americans with relatively high education levels. Even when non-internet users are excluded from this analysis, job seekers who have attended or graduated from college are substantially more likely to rely on online resources compared with job seekers with only a high school education.

These better-educated job seekers are also more likely than job seekers with lower education levels to have relied on professional connections (but not close personal connections or friends-of-friends) in their most recent job search. Nearly three-quarters (72%) of college graduates utilized professional connections in their most recent job search, compared with 59% of those who have attended but not graduated from college, and 57% of those who have not attended college at all.

34% of job seekers point to resources and information they found online as the most important source of information in their most recent job search

Americans today typically incorporate a number of different information sources into their hunt for employment: Fully 52% of recent job seekers indicate that they utilized four or more resources (out of a total of seven) in their most recent employment search, while just 11% indicate that they used only one resource. But although job seekers tend to leave few stones unturned when searching for employment, a small number of resources – including those found online – stand out as being especially important to a large number of Americans.

In addition to asking which resources they utilized in any way during their most recent job search, the survey also asked these job seekers to indicate the resource they consider to be the single most important in helping them look for work. Roughly one-third of job seekers (34%) say resources and information they found online were the most important resource they used in their last job search; 20% cite close personal connections, and 17% cite professional or work contacts as their most important resource.

Relatively modest numbers of job seekers point towards other types of resources as their most important source of assistance during their most recent job search: 7% cite connections with acquaintances or friends-of-friends, 5% cite employment agencies, 5% cite events such as job fairs, and 4% cite ads in print publications.

There are relatively few demographic differences when it comes to the resources job seekers rely on most heavily when looking for work. Younger job seekers and those who have not attended college were a bit more likely to say personal connections with friends or family members were most important when they were looking for work, while college graduates and older job seekers tend to indicate they relied more heavily on professional or work contacts.

Minority of Americans lack confidence in digital job-seeking skills

As job-related services and information increasingly move online, most Americans feel fairly confident in their ability to navigate various aspects of the digital job hunt. But at the same time, a minority lack confidence in their ability to perform even relatively basic tasks such as emailing potential employers or finding lists of available jobs online. This is especially true among those who have not attended college and those who are not currently employed for pay.

The survey asked Americans (not including those who indicate that they are either retired or disabled when asked for their employment status) how easy it would be for them to perform a number of tasks in the event they needed to look for a new job and found that:

• 87% say it would be easy to look up online services and programs available to job seekers , with 58% saying this would be “very easy.”
• 86% say it would be easy to contact and follow up with potential employers via email , with 70% saying this would be “very easy.”
• 86% say it would be easy to fill out a job application online , with 65% saying this would be “very easy.”
• 85% say it would be easy to go online to find lists of available jobs , with 63% saying this would be “very easy.”
• 80% say it would be easy to create a professional resume , with 54% saying this would be “very easy.”
• 74% say it would be easy to highlight their employment skills using a personal website or social media profile , with 45% saying this would be “very easy.”

Clearly, the ability to engage in these behaviors might be especially useful for people who are not currently employed – and yet, Americans who are not employed for pay are much more likely than those who are to indicate that they would have a difficult time performing these tasks. For instance, 28% of Americans who are currently not employed indicate that it would not be easy to create a professional resume if they needed to do so (compared with 14% of those who currently have a job); 22% would have a hard time filling out an online job application (compared with 10% of those who are currently employed); and 19% would have a hard time contacting employers via email, finding lists of jobs online, or looking up services available to job seekers. 5

Americans who have not attended college also indicate that they would have a particularly difficult time performing many of these tasks. Roughly one-in-five adults with a high school diploma or less indicate that it would not be easy to contact a potential employer via email, find programs online that help job seekers, fill out an online job application, or find lists online of available jobs in their local area. And nearly one-in-three who haven’t attended college indicate that it would be not be easy for them to create a professional resume or use social media to highlight their job skills. In each case, Americans who have attended and/or graduated from college are significantly more comfortable with these aspects of the modern job seeking process.

• This is the first time Pew Research Center has conducted a stand-alone measurement of how many Americans actually apply for jobs online. ↩
• Due to the relatively small number of African Americans (n=87) and Latinos (n=85) in this survey who indicated looking for a new job in the last two years, we are not able to include a stand-alone analysis of the specific resources that these groups utilize when looking for work. ↩
• Among Americans who are not currently employed for pay, 13% do not use the internet, 43% lack broadband service at home, and 58% have a high school diploma or have not completed high school. ↩

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Research the Job Market: A Step-by-Step Guide

Key Takeaways

• Gain a competitive edge in your job search by following a step-by-step guide to research the job market effectively.
• Understand the importance of defining your goals, analyzing job descriptions, and staying updated on industry trends to tailor your job search strategy.
• Utilize online job platforms, networking, and informational interviews to uncover hidden job opportunities and position yourself as a strong candidate.

Are you on the hunt for a new job?

Feeling overwhelmed by the seemingly endless sea of opportunities and unsure of where to begin?

Look no further.

Researching the job market is a crucial first step that can significantly enhance your chances of finding the perfect job that aligns with your skills, interests, and aspirations.

In this comprehensive and step-by-step guide, we will walk you through the process of researching the job market.

By following these tried-and-tested methods, you will gain valuable insights into industry trends, job demand, salary ranges, and much more.

Armed with this knowledge, you’ll be able to make informed decisions and tailor your job search strategy for success.

Before diving into job market research, it’s essential to start by assessing your own goals, skills, and preferences.

Understanding your strengths, interests, and values will help you narrow down your job search and focus on roles that truly resonate with you.

We’ll guide you through a series of exercises and self-reflection techniques to help you define your career goals and aspirations accurately.

Get a top CV and Resume using the world’s free and modern CV Builder, UrbanCV .

Once you have a clear understanding of your goals, it’s time to explore the job market landscape.

We’ll provide you with valuable tips and resources to gather relevant information about industry trends, growth opportunities, and job sectors in demand.

You’ll learn how to analyze salary ranges, compensation packages, geographical considerations, and market dynamics to make informed decisions about where to focus your job search efforts.

The internet is a treasure trove of job opportunities, but it can also be overwhelming.

We’ll navigate you through the vast array of online job platforms, offering insights into popular websites and strategies for optimizing your job search.

Discover how to use advanced search options, keyword filters, and job alerts effectively.

Additionally, we’ll explore professional networking platforms and highlight their significance in gaining valuable job market insights.

Networking is a powerful tool that can unlock hidden job opportunities and provide valuable industry insights.

We’ll guide you through building and expanding your professional network, leveraging existing connections, and reaching out to industry professionals for informational interviews.

Learn how to conduct effective informational interviews that will help you gain firsthand knowledge about the job market, industry trends, and potential career paths.

Job descriptions often contain valuable information about the skills, qualifications, and expectations employers seek in candidates.

We’ll teach you how to analyze job descriptions effectively, decode the key elements, and identify the common requirements across different roles.

Discover strategies for aligning your resume, cover letter, and interview preparation with the specific job requirements to stand out as a qualified candidate.

The job market is constantly evolving, influenced by technological advancements, economic changes, and industry shifts.

We’ll show you how to stay ahead of the curve by subscribing to industry newsletters, following thought leaders on social media, and attending conferences and webinars.

By staying informed about industry news and trends, you’ll gain a competitive edge and be better equipped to adapt your job search strategy accordingly.

As you progress through your job search journey, it’s crucial to evaluate and refine your strategy based on the insights gained from your research.

We’ll guide you through reflecting on your findings, reassessing your goals, and seeking feedback from mentors or career coaches.

Learn how to adapt your job search approach to changing market conditions, ensuring you’re always maximizing your chances of landing the ideal job.

By the end of this step-by-step guide, you’ll have developed a comprehensive understanding of how to research the job market effectively.

Armed with this knowledge, you’ll be able to navigate the job market landscape with confidence, make informed decisions, and tailor your job search strategy to secure your dream job.

Get ready to embark on a journey of self-discovery and exploration. Let’s dive into the world of job market research and set yourself up for career success.

Before we venture further into this article, we like to share who we are and what we do.

9cv9  is a business tech startup based in Singapore and Asia, with a strong presence all over the world.

With over six years of startup and business experience, and being highly involved in connecting with thousands of companies and startups, the 9cv9 team has listed some important learning points in this overview of the guide on how to Research the Job Market with A Step-by-Step Guide.

If you are looking for a job or an internship, click over to use the  9cv9 Job Portal to find your next top job and internship now.

• Defining Your Goals and Preferences
• Gathering Job Market Information
• Utilizing Online Job Platforms and Resources
• Networking and Informational Interviews
• Analyzing Job Descriptions and Requirements
• Staying Updated on Industry News and Trends
• Evaluating and Refining Your Job Search Strategy

1. Defining Your Goals and Preferences

Before diving into the vast job market, it’s crucial to start by understanding your own goals, skills, and preferences.

Taking the time to define your career aspirations and align them with your interests and values will significantly enhance your job search process.

Here’s a comprehensive breakdown of how you can effectively define your goals and preferences:

Assessing your Skills, Interests, and Values

Begin by evaluating your unique combination of skills, talents, and expertise.

Consider both hard skills (technical abilities) and soft skills (communication, problem-solving, etc.) that you possess.

Reflect on your previous work experiences, academic background, and extracurricular activities that have helped shape your skillset.

Additionally, identify your core interests and passions.

What subjects or areas genuinely excite you?

Aligning your career with your interests can lead to greater job satisfaction and long-term success.

Moreover, reflecting on your values is essential.

What principles and beliefs do you hold dear?

Consider aspects like work-life balance, ethical considerations, and company culture alignment when defining your preferences.

Example: Let’s say you have a background in data analysis and programming, and you enjoy problem-solving and working with numbers. You value work-life balance and have a passion for environmental sustainability. These insights will guide your job market research towards roles that involve data analytics, sustainability initiatives, and companies known for their inclusive work environment.

Determining your Career Goals and Aspirations

Once you have assessed your skills, interests, and values, it’s time to define your career goals and aspirations.

Ask yourself where you envision yourself in the next few years or what kind of impact you want to make through your work.

Set clear, achievable goals that can serve as guiding stars during your job search.

To learn more about Goal Setting, read our top articles:

• Why SMART Goals are Essential for Career Growth in the Workplace
• How to Set Goals and Achieve Success in the Workplace

Example: Suppose your career goal is to become a data scientist specializing in environmental sustainability. Your aspiration might be to work for a leading renewable energy company, leveraging data analytics to drive sustainable initiatives and make a positive impact on the environment.

Identifying your Preferred Job Characteristics

Understanding your preferences in terms of job characteristics is crucial in finding a role that aligns with your lifestyle and work style.

Consider factors such as industry, company size, work environment (remote or in-office), flexibility, and career advancement opportunities.

Example: You might prefer working in the technology sector, as it aligns with your interest in data analysis. However, you may also value the flexibility of remote work , allowing you to maintain a healthy work-life balance.

Read more on “ Remote Jobs. What are they and how to apply for them? ” to learn how to apply for remote jobs.

By taking the time to define your goals, skills, interests, values, and preferred job characteristics, you lay a strong foundation for your job market research.

Your defined parameters will serve as a compass, guiding you towards opportunities that truly resonate with your professional aspirations.

Remember, the more specific and targeted your goals are, the easier it becomes to identify relevant job opportunities and tailor your job search accordingly.

According to a survey conducted by LinkedIn, professionals who align their careers with their passions are more likely to report higher job satisfaction and happiness in their roles .

Furthermore, a study by Harvard Business Review revealed that employees who feel their values align with their organization’s values are more engaged, productive, and less likely to experience burnout .

Remember, this initial step of defining your goals and preferences is crucial for a successful job search.

Invest time and effort into self-reflection, and you’ll pave the way for a fulfilling and rewarding career path.

2. Gathering Job Market Information

To navigate the job market effectively, it’s essential to gather relevant and up-to-date information about industry trends, job sectors in demand, salary ranges, geographical considerations, and market dynamics.

Armed with this knowledge, you can make informed decisions and strategically target your job search efforts.

Here’s a comprehensive breakdown of how you can gather valuable job market information:

Exploring Industry Trends and Growth Opportunities

Stay informed about the latest industry trends and developments to understand the direction in which the job market is heading.

Industry trends can shed light on emerging job roles, in-demand skills, and sectors experiencing growth.

Keep an eye on market reports, industry publications, and reputable online resources to gather insights.

Example: Suppose you are interested in the healthcare sector. Research indicates that with the aging population and advancements in medical technology, there is a growing demand for healthcare professionals, especially in fields like telemedicine, health informatics, and geriatric care .

Researching Specific Job Sectors and their Demand

Dive deeper into specific job sectors or industries that align with your goals and preferences.

Identify sectors that are experiencing growth, have a high demand for talent, or offer ample career opportunities. Explore job boards, industry-specific websites, and professional associations to gather sector-specific information.

Example: If you are interested in the technology industry, research indicates that roles in artificial intelligence (AI), cybersecurity, and software engineering are in high demand due to increased digital transformation across various sectors .

Analyzing Salary Ranges and Compensation Packages

Understanding salary ranges and compensation packages for different roles is crucial to negotiate effectively and ensure fair remuneration.

Research salary data specific to your industry, job role, and location to gauge the market value of your skills.

Online salary calculators, industry surveys, and salary benchmarking websites can provide valuable insights.

Example: According to Glassdoor’s Salary Explorer, the average salary for a data scientist in the United States is $122,338 per year, with variations depending on factors such as experience, location, and industry.

Identifying Geographical Considerations and Job Market Dynamics

Take into account the geographical considerations when conducting job market research.

Job markets can vary significantly from one location to another, both in terms of demand and opportunities.

Research the job market dynamics of specific cities, regions, or countries to identify areas with a thriving job market in your chosen field.

Example: If you are interested in the finance sector, you might find that cities like New York, London, and Singapore have robust financial industries, offering a multitude of job opportunities in banking, investment, and fintech.

By gathering job market information, you gain insights into the current landscape, job demand, salary expectations, and industry trends.

Armed with this knowledge, you can tailor your job search strategy, focus your efforts on sectors with growth potential, and position yourself as a desirable candidate.

According to the U.S. Bureau of Labor Statistics, overall employment in healthcare occupations is projected to grow 13 percent from 2021 to 2031.

The World Economic Forum’s “ Future of Jobs Report ” predicts that by 2025, artificial intelligence, machine learning, and data analysis will be among the most in-demand job skills across industries.

Remember, regularly monitoring industry trends, job sector dynamics, salary ranges, and geographical considerations will help you stay ahead of the game and make informed decisions throughout your job search journey.

3. Utilizing Online Job Platforms and Resources

In today’s digital age, online job platforms such as 9cv9 Job Portal and resources play a pivotal role in connecting job seekers with potential employers.

These platforms offer a vast array of job opportunities across industries and locations.

By utilizing them effectively, you can streamline your job search process and access a wide range of relevant job openings.

Here’s a comprehensive breakdown of how you can make the most of online job platforms and resources:

Overview of Popular Job Search Websites and Platforms

Familiarize yourself with popular job search websites and platforms that cater to your industry or location. Some well-known platforms include 9cv9 Jobs , LinkedIn, Indeed, Glassdoor, and CareerBuilder.

Each platform has its own unique features and user base.

Explore these platforms to understand their functionalities, job listing quality, and user reviews.

Example: LinkedIn, with over 930 million members worldwide, is a powerful platform for professional networking and job searching. It offers a range of features, including job listings, company profiles, and networking opportunities.

Tips for Optimizing Your Job Search Using Keywords and Filters

Job search platforms provide search filters and keyword-based algorithms that can help you narrow down your job search and find relevant opportunities.

Optimize your job search by using industry-specific keywords, job titles, and location filters.

Experiment with different combinations of keywords to refine your search results and increase your chances of finding suitable positions.

Example: If you are searching for a marketing role, use keywords like “digital marketing,” “marketing coordinator,” or “social media specialist” in combination with the desired location to find specific job listings in that field.

Utilizing Advanced Search Options and Alerts for Relevant Opportunities

Take advantage of advanced search options available on job platforms to fine-tune your search criteria.

These options may include filtering by experience level, salary range, company size, or employment type (full-time, part-time, freelance).

Additionally, set up job alerts based on your preferences, so you receive email notifications whenever new job postings that match your criteria become available.

Example: Suppose you are interested in remote job opportunities. Utilize the advanced search options to filter for remote or work-from-home positions. Set up email alerts specifically for remote jobs to stay updated on the latest openings.

Leveraging Professional Networking Platforms for Job Market Insights

In addition to job search platforms, professional networking platforms like LinkedIn offer valuable insights and networking opportunities.

Join industry-specific groups and engage in discussions to connect with professionals in your field.

Leverage these platforms to expand your network, gain industry insights, and discover hidden job opportunities.

Example: Joining LinkedIn groups related to your industry, such as marketing or finance, allows you to connect with professionals who share similar interests and expertise. Engaging in group discussions can provide valuable insights into job market trends, industry challenges, and potential job openings.

Read more on how to use Social Media to boost your job search in our article “ Boost Your Job Search with Social Media: Best Practices and Tips “.

By effectively utilizing online job platforms and resources, you can maximize your job search efforts and access a wide range of job opportunities.

These platforms provide a convenient and efficient way to connect with potential employers and discover roles that align with your goals and preferences.

According to LinkedIn’s Talent Blog, 95% of recruiters actively use LinkedIn to find and source candidates .

A survey found that 41% of candidates rely on job boards to discover new jobs.

Remember, optimizing your job search using relevant keywords, filters, and advanced search options will enhance your chances of finding the right job.

Additionally, leveraging professional networking platforms can provide valuable industry insights and networking opportunities that can further support your job search efforts.

4. Networking and Informational Interviews

Networking plays a crucial role in the job search process, enabling you to expand your professional connections, gain valuable insights, and uncover hidden job opportunities.

Engaging in informational interviews is a powerful networking tool that allows you to connect with industry professionals, learn about their experiences, and gather valuable job market insights.

Here’s a comprehensive breakdown of how you can effectively network and conduct informational interviews:

Building a Professional Network and Leveraging Existing Connections

Start by building and expanding your professional network.

Attend industry events, join relevant professional associations, and engage in online communities.

Actively connect with colleagues, alumni, friends, and acquaintances who work in your desired industry or have connections in your target companies.

Leverage your existing connections to explore potential networking opportunities and gather referrals.

Example: Reach out to your university alumni network to connect with graduates who pursued careers in your desired field. Alumni networks often provide a valuable platform for networking and mentorship.

Reaching Out to Industry Professionals and Requesting Informational Interviews

Identify professionals in your industry of interest and reach out to them for informational interviews.

These interviews are not job interviews but rather an opportunity to gather insights about the industry, specific job roles, and company cultures.

Craft a personalized and concise message when reaching out, expressing your interest in their work and kindly requesting a brief conversation or meeting.

Example: Suppose you are interested in a career in marketing. Reach out to a marketing manager at a company you admire, expressing your interest in learning more about their marketing strategies and requesting a 20-minute phone call or coffee meeting.

Conducting Effective Informational Interviews

Prepare for informational interviews by researching the interviewee’s background, their company, and industry trends.

Develop a list of thoughtful questions to ask during the interview to gain valuable insights.

Listen attentively, take notes, and engage in a genuine conversation.

Remember, the goal is to learn, build connections, and gather information that will aid your job search.

Example: Ask questions like, “What skills and qualifications do you look for when hiring for marketing roles?” or “What trends do you see shaping the future of the marketing industry?” These questions can provide valuable insights into the job market and help you better position yourself as a candidate.

How to Utilize Networking Events and Career Fairs for Job Market Research

Attend networking events and career fairs specific to your industry to meet professionals and learn about job opportunities.

Prepare your elevator pitch, bring copies of your resume, and engage in meaningful conversations.

Networking events and career fairs offer a platform to showcase your skills, make connections, and gain industry insights directly from hiring managers and industry experts.

Example: If you are interested in the technology industry, attend technology-focused career fairs or networking events where you can interact with representatives from tech companies, learn about their hiring needs, and establish valuable connections.

According to a survey conducted by LinkedIn, 85% of all jobs are filled through networking.

Additionally, an article by Business Insider found that around 70% of jobs are never advertised publicly , emphasizing the importance of networking and accessing hidden job markets.

Remember, networking and informational interviews are powerful tools that can open doors to hidden job opportunities and provide valuable industry insights.

Building and nurturing your professional network can significantly enhance your job search success.

5. Analyzing Job Descriptions and Requirements

Analyzing job descriptions and requirements is a critical step in your job search process.

Also, read “ Mastering the Art of Writing Effective Job Descriptions: A Comprehensive Guide ” to learn more about Job Descriptions.

It allows you to understand the specific skills, qualifications, and expectations employers have for a particular role.

By carefully examining job descriptions, you can tailor your application materials, highlight your relevant experiences, and position yourself as a strong candidate.

Here’s a comprehensive breakdown of how you can effectively analyze job descriptions and requirements:

Understanding the Key Elements of Job Descriptions

Job descriptions typically consist of several key elements that provide essential information about the role.

Pay attention to sections such as the job title, company overview, job summary, responsibilities, qualifications, and application instructions. Each section provides valuable insights into the position and what employers are looking for in candidates.

Example: A marketing job description might include a job summary that highlights the primary responsibilities of the role, such as developing marketing strategies, executing digital campaigns, and analyzing marketing metrics.

Analyzing Common Job Requirements and Qualifications

Focus on the required qualifications and skills mentioned in the job description.

These requirements often include specific educational backgrounds, years of experience, technical proficiencies, certifications, and industry knowledge.

Analyze each requirement to determine how well your qualifications align with what the employer is seeking.

Example: If a job description states that a candidate should have a bachelor’s degree in marketing or a related field, possess 3+ years of experience in digital marketing, and be proficient in Google Analytics, you can evaluate whether you meet these qualifications.

Identifying Skills and Qualifications in High Demand

Take note of skills and qualifications that are frequently mentioned across multiple job descriptions within your target industry.

Identifying these in-demand skills can help you focus your professional development efforts and highlight your relevant expertise in your application materials.

Example: Data analysis, project management, and proficiency in specific software programs like Adobe Creative Suite or Salesforce might be in high demand for marketing roles in the digital era.

How to Tailor Your Resume and Cover Letter Based on Job Descriptions

Once you have analyzed the job requirements, customize your resume and cover letter to showcase your qualifications and experiences that directly align with the job description.

Highlight relevant skills, accomplishments, and projects that demonstrate your ability to fulfill the specific responsibilities mentioned.

Example: If a job description emphasizes the need for social media management skills, ensure that your resume includes specific examples of successful social media campaigns you’ve executed or the growth you achieved for previous employers.

According to a study conducted by TheLadders, recruiters spend an average of 7.4 seconds reviewing a resume before making an initial decision .

Remember, analyzing job descriptions and requirements is crucial for tailoring your application materials effectively.

By aligning your qualifications with the desired skills and qualifications outlined in the job description, you increase your chances of standing out as a qualified candidate.

6. Staying Updated on Industry News and Trends

Staying updated on industry news and trends is vital to keep pace with the ever-evolving job market.

By staying informed, you can gain a competitive edge, identify emerging opportunities, and adapt your job search strategy accordingly.

Here’s a comprehensive breakdown of how you can stay updated on industry news and trends:

Statistics: According to a survey by McKinsey Global Institute, up to 375 million workers (around 14% of the global workforce) may need to switch occupations or acquire new skills by 2030 due to automation and technological advancements (source: McKinsey Global Institute, “Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation”).

Subscribing to Relevant Industry Newsletters and Publications

Subscribe to newsletters and publications that cover your industry or field of interest.

These sources provide valuable insights, industry updates, thought leadership articles, and analysis of market trends.

They can help you stay informed about the latest developments, innovations, and challenges within your industry.

Example: If you’re interested in the technology sector, you could subscribe to newsletters from technology-focused publications like TechCrunch, Wired, or MIT Technology Review. These publications cover topics ranging from startups and emerging technologies to industry trends and innovations.

Following Industry Influencers and Thought Leaders on Social Media

Social media platforms provide an excellent opportunity to stay connected with industry influencers and thought leaders.

Follow industry experts, professionals, and relevant organizations on platforms like LinkedIn, Twitter, and industry-specific forums.

Their posts and insights can keep you informed about industry trends, best practices, and emerging opportunities.

Example: If you’re interested in the marketing field, follow marketing influencers on platforms like LinkedIn and Twitter. Engage with their content, participate in discussions, and gain insights into the latest marketing strategies, tools, and trends.

Attending Conferences, Webinars, and Seminars for Industry Insights

Industry conferences, webinars, and seminars offer valuable networking opportunities and insights into the latest trends, innovations, and challenges.

These events bring together industry experts and thought leaders who share their expertise and provide a platform for discussion and knowledge exchange.

Attend relevant events to stay informed and connect with professionals in your industry.

Example: If you’re in the healthcare industry, attending healthcare conferences like HIMSS (Healthcare Information and Management Systems Society) can provide valuable insights into digital healthcare transformation, healthcare IT trends, and emerging technologies.

Engaging in Professional Development Opportunities to Stay Ahead

Invest in continuous learning and professional development to stay ahead of industry changes.

Enroll in relevant online courses, certifications, or workshops that enhance your skills and knowledge.

Platforms like LinkedIn Learning, Udemy, and Coursera offer a wide range of industry-specific courses to help you stay updated and improve your marketability.

Example: If you’re in the finance industry, you might consider enrolling in courses or certifications that cover topics such as financial technology (fintech), blockchain, or data analytics to stay abreast of industry advancements.

Furthermore, a report by LinkedIn indicates that professionals who engage in online learning are more likely to be promoted within their organizations .

Remember, staying updated on industry news and trends demonstrates your commitment to professional growth and can give you a competitive advantage in the job market.

By being knowledgeable about the latest industry developments, you’ll be well-equipped to adapt your job search strategy, showcase your expertise, and align yourself with emerging opportunities.

7. Evaluating and Refining Your Job Search Strategy

Evaluating and refining your job search strategy is essential to ensure that you are making progress and maximizing your chances of success.

As the job market evolves, it’s crucial to adapt your approach, learn from your experiences, and make necessary adjustments to achieve your career goals.

Here’s a comprehensive breakdown of how you can evaluate and refine your job search strategy:

Reflecting on Your Research Findings and Adjusting Your Goals

Take time to reflect on the insights and findings from your job market research.

Evaluate the alignment between your initial goals and the information you’ve gathered.

Consider whether any adjustments or refinements to your career goals are necessary based on your new understanding of the job market dynamics and opportunities.

Example: If your initial goal was to work in a specific company but your research revealed limited job openings, consider expanding your target companies or focusing on similar organizations in the same industry.

Reassessing Your Job Search Approach Based on Market Insights

Analyze the job market insights you’ve gained and reassess your job search approach.

Identify the strategies that have yielded positive results and those that need improvement.

For example, if you find that networking has been more effective in uncovering job opportunities than online applications, allocate more time and effort to networking activities.

Example: If you’re in a highly competitive field like graphic design, your research might reveal that showcasing a diverse portfolio of creative projects and engaging in freelance work can help differentiate you from other candidates.

Seeking Feedback and Advice from Mentors or Career Coaches

Seek feedback and guidance from mentors, professionals in your industry, or career coaches.

They can provide valuable insights into your job search strategy, offer advice on improving your resume or interview skills, and suggest new approaches you may not have considered.

Their expertise and experience can help you refine your job search strategy.

Example: Connect with a mentor in your field who can review your resume, provide feedback on your job search approach, and offer insights on industry trends and hiring practices.

Adapting Your Job Search Strategy to Changing Market Conditions

Keep track of changing market conditions and adapt your job search strategy accordingly.

Industries and job markets are dynamic, influenced by factors such as technological advancements, economic shifts, and societal changes.

Stay informed about industry trends, emerging job roles, and new skill requirements to position yourself effectively.

Example: If you’re in the software development field, staying updated on emerging technologies and programming languages can help you adapt your skills and target job opportunities in high-demand areas like artificial intelligence, cloud computing, or cybersecurity.

According to a survey by TalentWorks, on average, it takes 100-200+ applications to receive one job offer .

Furthermore, a report by Jobvite indicates that employee referrals have the highest applicant-to-hire conversion rate at 40%, highlighting the importance of networking and leveraging connections in the job search process .

Remember, evaluating and refining your job search strategy is an ongoing process.

Continuously monitor your progress, adapt to changes in the job market, and learn from your experiences.

By implementing data-driven adjustments and seeking guidance from mentors or career professionals, you’ll be well-positioned to optimize your job search strategy and increase your chances of securing the ideal job.

In today’s competitive job market, conducting thorough research is crucial for job seekers to stand out and secure their desired positions.

The step-by-step guide provided in this article has equipped you with the necessary tools and strategies to effectively research the job market and optimize your job search.

By following these steps, you can make informed decisions, align your goals with market realities, and position yourself as a strong candidate.

Throughout this guide, we emphasized the importance of defining your goals, skills, interests, and preferences in Step 1.

This self-reflection helps you gain clarity about your career aspirations and ensures that your job search is targeted and purposeful. By understanding your unique combination of skills, values, and interests, you can identify job opportunities that align with your professional ambitions.

In Step 2, we explored the significance of gathering job market information.

By staying informed about industry trends, growth opportunities, salary ranges, and market dynamics, you can make well-informed decisions about the sectors and companies you want to target.

With access to valuable data and statistics, you can identify emerging job roles, high-demand skills, and geographical considerations that may influence your job search.

Step 3 highlighted the importance of utilizing online job platforms and resources.

By leveraging popular job search websites and platforms, optimizing your search using keywords and filters, and utilizing advanced search options, you can efficiently navigate the vast sea of job opportunities available online.

Moreover, tapping into professional networking platforms allows you to connect with industry professionals, gain valuable insights, and uncover hidden job opportunities.

Networking and conducting informational interviews took center stage in Step 4.

Building a strong professional network, reaching out to industry professionals, and engaging in informational interviews provide unique opportunities to learn directly from insiders.

By leveraging your connections and making meaningful connections, you can gain valuable industry insights, refine your understanding of job roles, and potentially unlock hidden job opportunities.

Step 5 focused on the importance of analyzing job descriptions and requirements. By carefully examining job descriptions, understanding key elements, and identifying common job requirements, you can tailor your application materials to showcase your qualifications effectively.

By aligning your skills and experiences with the desired qualifications, you increase your chances of standing out as a qualified candidate.

Staying updated on industry news and trends was emphasized in Step 6.

Subscribing to industry newsletters, following thought leaders on social media, attending conferences, and engaging in professional development opportunities ensure that you stay ahead of the curve.

By staying informed about industry developments, emerging trends, and evolving job market dynamics, you can position yourself as a knowledgeable and adaptable candidate.

Lastly, in Step 7, we discussed the importance of evaluating and refining your job search strategy.

Reflecting on your research findings, reassessing your approach, seeking feedback from mentors, and adapting to changing market conditions are crucial for optimizing your job search efforts.

By continuously evaluating and refining your strategy, you can maximize your chances of success in a dynamic job market.

In conclusion, researching the job market is a vital step in the job search process.

By following the step-by-step guide outlined in this article, you have gained valuable insights and actionable strategies to conduct effective job market research.

Remember, the job market is ever-evolving, so it’s essential to stay proactive, adapt to changes, and continuously enhance your skills and knowledge.

Armed with a deep understanding of your goals, preferences, and the job market landscape, you are well-positioned to embark on a successful job search journey.

Leverage your research findings, network strategically, and apply a targeted approach to secure your dream job.

Good luck in your job search, and may your research efforts lead you to a fulfilling and rewarding career.

If you find this article useful, why not share it with your friends and also leave a nice comment below?

We, at the 9cv9 Research Team, strive to bring the latest and most meaningful data, guides, and statistics to your doorstep.

To get access to top-quality guides, click over to  9cv9 Blog.

If you are keen to find a job or internship, then click on  9cv9 Job Portal now.

People Also Ask

How do you find the market rate of a job.

To find the market rate of a job, you can utilize various resources such as industry salary surveys, online job platforms, professional networking, and salary benchmarking websites. Additionally, consult with recruitment agencies, industry associations, and HR professionals to gather insights on prevailing compensation ranges.

What resources can help you with your job market research?

There are several resources available to assist with job market research, including online job platforms, industry-specific websites and publications, government labor market data, professional networking platforms, industry associations, career fairs, recruitment agencies, salary benchmarking websites, and informational interviews with industry professionals. These resources provide valuable insights into industry trends, job openings, salary ranges, and market dynamics to inform your job search strategy.

How do you research a potential employer?

To research a potential employer, explore their official website, social media presence, and online news articles. Review their mission, values, and company culture. Check employee reviews on platforms like Glassdoor. Network with current or former employees for insights. Researching their industry position and competitors can also provide valuable context.

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Research Culture: A survey-based analysis of the academic job market

• Jason D Fernandes
• Sarvenaz Sarabipour
• Christopher T Smith
• Natalie M Niemi
• Nafisa M Jadavji
• Ariangela J Kozik
• Alex S Holehouse
• Vikas Pejaver
• Orsolya Symmons
• Alexandre W Bisson Filho

• Department of Biomolecular Engineering, University of California, Santa Cruz, United States ;
• Institute for Computational Medicine, Johns Hopkins University, United States ;
• Department of Biomedical Engineering, Johns Hopkins University, United States ;
• Office of Postdoctoral Affairs, North Carolina State University Graduate School, United States ;
• Morgridge Institute for Research, United States ;
• Department of Biochemistry, University of Wisconsin-Madison, United States ;
• Department of Biomedical Sciences Midwestern University, United States ;
• Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, United States ;
• Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, United States ;
• Department of Biomedical Informatics and Medical Education, University of Washington, United States ;
• The eScience Institute, University of Washington, United States ;
• Department of Bioengineering, University of Pennsylvania, United States ;
• Department of Biology, Brandeis University, United States ;
• Rosenstiel Basic Medical Science Research Center, Brandeis University, United States ;
• Department of Biomedical Sciences, University of North Dakota, United States ;
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• Figures and data

Introduction

Materials and methods, data availability, decision letter, author response, article and author information.

Many postdoctoral researchers apply for faculty positions knowing relatively little about the hiring process or what is needed to secure a job offer. To address this lack of knowledge about the hiring process we conducted a survey of applicants for faculty positions: the survey ran between May 2018 and May 2019, and received 317 responses. We analyzed the responses to explore the interplay between various scholarly metrics and hiring outcomes. We concluded that, above a certain threshold, the benchmarks traditionally used to measure research success – including funding, number of publications or journals published in – were unable to completely differentiate applicants with and without job offers. Respondents also reported that the hiring process was unnecessarily stressful, time-consuming, and lacking in feedback, irrespective of outcome. Our findings suggest that there is considerable scope to improve the transparency of the hiring process.

The number of PhDs awarded in science, technology, engineering and mathematics (STEM) has increased dramatically over the past three decades ( Cyranoski et al., 2011 ; Ghaffarzadegan et al., 2015 ), but the number of faculty positions available has essentially remained constant ( Schillebeeckx et al., 2013 ). In the US, for instance, the situation has not changed significantly since 2003, when the National Institutes of Health (NIH) received a major budget increase ( Alberts et al., 2014 ). Given the low numbers of faculty positions compared to the numbers of PhDs produced ( Larson et al., 2014 ; Committee to Review the State of Postdoctoral Experience in Scientists and Engineers, 2014 ), trainees are limited in their job prospects. Many also emerge from academic training feeling underprepared and under-mentored for any other type of job search ( McDowell et al., 2015 ). This leads to a high number of applicants per academic position, many of whom are uncertain about their chances of obtaining a faculty job ( Grinstein and Treister, 2018 ; Sauermann and Roach, 2016 ).

Cohorts of new PhDs are also both more diverse than before and more diverse than many current hiring committees ( Alberts et al., 2014 ; White, 2019 ; Bhalla, 2019 ). Scientific publishing is also faster-paced than it used to be: for example, evolutionary biologists recruited as "junior researchers" in 2013 had published nearly twice as many articles (22 ± 3.4) as those hired in 2005 (12.5 ± 2.4); the same study also found that the length of time between first publication and recruitment as a faculty member had increased from 3.25 (±0.6) to 8.0 (±1.7) years ( Brischoux and Angelier, 2015 ). Longer training periods have been reported repeatedly in many STEM fields, and are perceived as detrimental to both the greater scientific community and individuals in temporary postdoctoral positions ( Committee to Review the State of Postdoctoral Experience in Scientists and Engineers, 2014 ;  Ahmed, 2019 ; Rockey, 2012 ; Acton et al., 2019 ).

Despite these changes, the academic job search has largely remained the same, resulting in academic hiring being perceived as an opaque process with no clear standards or guidelines. Beyond a requirement for a doctoral degree and possibly postdoctoral training, faculty job advertisements rarely contain specific preferred qualifications. Furthermore, the criteria used to evaluate applicants are typically determined by a small departmental or institutional committee and are neither transparent nor made public. The amount of materials required for faculty job applications is also highly variable among hiring institutions, and often places a heavy burden on both applicants and search committees ( Lee, 2014 ).

Previous studies agree on a need to increase transparency in career outcomes and hiring practices ( Golde, 2019 ; Polka et al., 2015 ; Wright and Vanderford, 2017 ). The annual pool of faculty job applicants is large and provides a unique opportunity for examining the application process. We performed an anonymous survey, asking applicants for both common components of research and scholarly activity found on an academic CV, as well as information on their success through the 2018–2019 job cycle. We further performed a small-scale, complementary survey of search committee members. Here we present qualitative and quantitative data on the academic job market, including information on the number of successful off-site and on-site interviews, offers, rejections, and the lack of feedback.

Job applicants start by searching for relevant job postings on a variety of platforms ( Supplementary file 1 ). The initial electronic application generally consists of a cover letter addressing the search committee, a teaching philosophy statement, CV, and a research plan ( Figure 1 ). The length and content of these materials can vary drastically based on the application cycle, region, institution, or particular search committee. In the current system, the expectation is that application materials be tailored for each specific institution and/or department to which the applicant is applying. This includes department-specific cover letters ( Fox, 2018a ), but may also involve a range of changes to the research, teaching, and diversity statements.

An overview of the academic job search process.

The first column defines common terms in the academic job search; while the second column outlines how the search for an academic job progresses, from a job being posted to an offer being accepted.

The search committee convenes for a few meetings to shortlist the applicants. Applicants are then contacted for interviews somewhere between one to six months after application materials are due. Searches may include an initial off-site (remote) interview, followed by an on-site interview at the hiring university. The on-site interview typically lasts one or two days and consists of a research seminar, possibly a teaching demonstration, and likely a chalk-talk ( Rowland, 2016 ). The on-site interview also usually consists of one-on-one meetings with other faculty members, including a meeting with the hiring department chair, trainees, and the administrative staff.

After the interviews, candidates may be contacted and offered a position, usually in writing. The offer package will include the proposed start date, salary and start-up funds ( Macdonald, 2019 ). The time to offer is also variable, but is usually shorter than the time between application and first contact (based on anecdotal information). Importantly, a single search can result in multiple offers (for instance the department may be able to fund multiple competitive candidates, or the first-choice candidate may decline and the second candidate is given an offer). Searches can also fail if the committee does not find a suitable candidate for their program/department or "go dry" if the applicant(s) deemed qualified by the search committee decline their offer.

We designed a survey for early-career researchers aimed at bringing transparency to the academic job market (see Materials and methods and Supplementary file 41 ). The survey was distributed via Twitter, the Future PI Slack group, and email listservs of multiple postdoctoral associations, resulting in 322 responses from self-identified early-career researchers who applied for academic positions in the 2018–2019 application cycle. Of these, data from 317 respondents passed simple quality filters and were used for analyses. As all questions were optional, these 317 responses represent the maximum number in our analyses; in cases where respondents chose not to answer the question, we analyzed only the applicant subset with responses and list the number of responses used for each analysis in the appropriate figures and supplementary files.

Demographics of respondents

Respondents reported a large range in the number of submitted applications from a minimum of one to a maximum of 250 (median: 15). The respondent pool was notably enriched in applicants who received at least one off-site interview (70%), at least one on-site interview (78%) and at least one offer (58%); this may represent a significant bias towards successful applicants in our study, as a recent study shows that less than 23% of PhDs eventually secure a tenure-track position ( Langin, 2019 ).

Respondents represented researchers in a wide variety of fields, with 85% from life sciences and related fields, with relatively equal numbers of applications from men and women across this group ( Figure 2A ). Our survey captured data from an international applicant pool, representing 13 countries ( Figure 2B ). However, 72% of our respondents reported currently working in the United States, which may reflect the larger circulation of our survey on social media platforms and postdoctoral associations there. Most candidates applied to jobs within the United States (82%), Canada (33%), and the United Kingdom (24%). 96% of respondents entered the job market as postdoctoral researchers ( Figure 2C ). The applicants spent 1 to 13 years (median: 4 years) in a postdoctoral position. These data are consistent with a recent report suggesting that postdocs in the United States across a variety of fields spend an average of 2.5–3.6 years in their positions ( Andalib et al., 2018 ).

Demographics of academic job applicants.

( A ) Distribution of survey respondents by self-identified gender and scientific field ( Supplementary file 2 ). Fields highlighted in green were grouped together as life-science related fields for subsequent analyses. ( B ) Distribution of countries where respondents were researching at the time of the survey (top, see Supplementary file 3 ) and the countries in which they applied to faculty jobs (green slices of pie charts, bottom; see Supplementary file 4 ). ( C ) Self-reported positions of applicants when applying for faculty jobs ( Supplementary file 5 ). ( D ) The number of years spent as a postdoctoral researcher ranges from 1 year or fewer (4% of applicants) to eight or more years (9% of applicants; maximum of 13 years, top). Life-science related postdoctoral training (n = 268 respondents) takes significantly longer than in other fields (n = 49 respondents; p=6.5×10 −6 , bottom; for data see Supplementary file 6 ; for statistical analysis see Supplementary file 7 ). ( E ) Number of postdoctoral positions held by survey applicants ( Supplementary file 8 ). ( F ) Median values for metrics of research productivity in the applicant pool ( Supplementary file 9 ).

Notably, in our survey population, postdocs in the life sciences spent a median of 5 years in a postdoctoral position, significantly longer than those in other fields, who reported a median postdoc length of 2.75 years prior to applying for a faculty position ( Figure 2D ), consistent with previous findings on increased training times in the life/biomedical sciences before junior faculty recruitment ( Committee to Review the State of Postdoctoral Experience in Scientists and Engineers, 2014 ; Brischoux and Angelier, 2015 ; Ahmed, 2019 ; Powell, 2017 ; Rockey, 2012 ). 68% of respondents went on the job market while in their first postdoctoral position ( Figure 2E ).

Applicants had a large range in their publication records, including number of papers co-authored, h-index, and total citation count. Respondents reported a median of 13 total publications (including co-authorships and lead authorships), with a median of 6 first author papers when entering the job market ( Figure 2F ).

Publishing metrics by gender

Gender bias in publishing and evaluation is well documented ( Aileen Day and Boyle, 2019 ; Centra and Gaubatz, 2000 ; Cameron et al., 2016 ; Witteman et al., 2019 ). The respondents to our survey were relatively evenly distributed across self-identified genders, with 51% identifying as male, 48% as female, and 1% preferring not to disclose this information (no applicants identified as non-binary; Figure 3A ). Men reported significantly more first-author publications, total publications, overall citations, and a higher h-index compared to women ( Figure 3B ); more men also reported being authors on papers in three journals with high impact factors (Cell, Nature and Science; Figure 3C ) than women. The gender differences we observe mirror those seen in other reports on differences in citation counts in STEM fields based on the corresponding author gender ( Schiermeier, 2019 ). Despite popular discussions on a need for papers in Cell, Nature, Science or other journals with a high impact factor ( Brock, 2019 ; McKiernan et al., 2019 ), 74% of respondents were not authors on a paper in Cell, Nature or Science (CNS), and a greater majority (~84%) did not have a first author publication in these journals ( Figure 3C ). Of the 51 respondents with papers in these journals, 49 (96%) were in a life science-related field, indicating that the valuation of these journals was highly field-specific ( Figure 3C ).

Applicant scholarly metrics by gender.

( A ) Distribution of gender (male, female, did not disclose) amongst survey respondents ( Supplementary file 2 , first row). ( B ) Publication metrics of survey respondents including number of first author papers (top), total publications (middle top), total citations (middle bottom), and h-index (bottom) for male and female respondents. Men in our survey reported more first-authored papers than women (medians of 7 and 5, respectively; p=1.4×10 −4 ), more total publications (medians of 16 and 11; p=3.0×10 −3 ), more overall citations (medians of 343 and 228; p=1.5×10 −2 ), and a statistically significant higher h-index (medians of 9.0 and 7.0; p=5.40×10 −3 ; see Supplementary files 7 and 9 ). ( C ) Although most applicants (83.6%) did not have first-author papers in CNS, those in the life sciences had more than applicants in other fields (p=0.012), and men had more than women (p=0.45; see Supplementary files 7 and 11 ). Note: CNS papers do not include papers in spin-off journals from Cell, Nature or Science. ( D ) Distribution of funding reported within training period (doctoral fellowship only in blue, postdoctoral fellowship only in red, fellowships during PhD and postdoc in purple, and no fellowship in gray). Females reported significantly more fellowship funding than males (42% of women vs 36% of men for predoctoral fellowships, and 72% of women, 58% of men for postdoctoral fellowships, p=2.40×10 −3 , χ 2  = 12.10, Chi-squared test, df = 2, see Supplementary files 7 and 13 ). ( E ) Preprints were posted by 148 of 270 (55%) individual candidates, with an average of 1.57 preprints reported per candidate (top). Number of preprints posted which were not yet accepted for journal publication (bottom) while applying for faculty jobs (see Supplementary file 14 ).

While 78% of respondents reported having obtained fellowships at some point in their career, this figure was 87% for women and 72% for men ( Figure 3D ). Women had better success at receiving both doctoral and postdoctoral fellowships. However, the questions in our survey did not distinguish between the types (e.g. government funded versus privately funded, full versus partial salary support) or number of fellowships applied to; many of these factors are likely critical in better understanding gender differences in fellowship support ( Figure 3D ).

Applications, interviews and offers

The 317 respondents submitted a total of 7644 job applications in the 2018–2019 application cycle, with a median of 15 applications per respondent ( Figure 4A ). Applicants were invited for a total of 805 off-site interviews (phone, Zoom or Skype; median: 1) and 832 onsite or campus interviews (median: 2), receiving 359 offers (median: 1; Figure 4A ). Although many hiring processes consist of an off-site (remote) interview, we found that this was not standard since the typical applicant received more on-site than off-site interviews. In our dataset, 42% of participants received no offers, 33% received one offer, 14% received two offers, 6% received three offers, and 6% received more than three offers. Candidates who received offers typically submitted more applications than those who received no offers, indicating that some candidates may not have submitted enough applications to have a reasonable chance of getting an offer ( Figure 4A,D ). According to a recent poll on Twitter (which received over 700 responses), most faculty received between one and three offers when they were applying for faculty positions ( Whitehead, 2019 ; Supplementary file 15 ).

Job application benchmarks and their impact on success.

( A ) Total and median numbers of applications, off-site interviews, on-site interviews and offers recorded in survey responses ( Supplementary file 19 ). ( B ) Correlations between the total number of applications submitted and off-site interviews (top; R 2  = 0.28), onsite interviews (middle) and offers (bottom; R 2  = 4.77×10 −2 ). ( C ) Correlations between the number of interviews completed and offers received (R 2  = 0.62). See Figure 4—figure supplement 1 for more details. ( D ) Total number of off-site interviews (top, p<4.10×10 −24 , on-site interviews (middle, p=1.20×10 −13 ) and offers (bottom, p=5.0×10 −5 ) for applicants who submitted at least 15 (the median) applications (in red) and less than 15 applications (in blue). ( E ) Fraction of applications that resulted in offers (offer percentages) for survey respondents who did not apply for jobs outside of faculty positions is significantly higher (p=2.0×10 −3 , Supplementary file 7 ) than for those who also applied for both academic and other types of jobs ( Supplementary file 14 ).

Despite the fact that successful candidates submitted more applications, the number of applications per candidate did not correlate with the number of offers, while being only weakly correlated with the number of off-site interviews ( Figure 4B ). Not surprisingly, the number of on-site interviews strongly correlated with the number of offers received ( Figure 4C , bottom). Population medians changed slightly by gender as men submitted slightly more applications, but received slightly fewer off-site interviews. These small differences by gender were not statistically significant ( Figure 4A ). The median number of offers also did not vary by gender.

We split our population into two groups by application number, one group either at or below the median ( < 15 applications, n = 162) and the other group above the median (>15 applications, n = 155). These groups had a significant difference in success rates: respondents who submitted more than 15 applications had a significantly higher average number of off-site interviews ( Figure 4D ). We also asked whether respondents applied for non-faculty positions during this cycle ( Supplementary file 16 ). 71% of applicants did not apply for other jobs and these applicants had a small, but significant increase in offer percentage ( Figure 4E ).

Taken together, these data seemingly indicate that increasing the number of applications submitted can lead to more interviews, as suggested by others ( Jay et al., 2019 ), with the typical candidate submitting at least 15 applications to achieve one offer. However, the lower correlation between application number and offers (compared to application number and interviews) suggests that while higher application numbers can generate more interview opportunities, other criteria (e.g. the strength of the interview) are important in turning an interview into an offer.

Publication related metrics

The number of papers published, and the impact factors of the journals these papers were published in, can influence the chances of an early-career researcher obtaining an independent position ( van Dijk et al., 2014 ; Powdthavee et al., 2018 ). As mentioned previously, it is widely believed that you need a paper in Cell, Nature or Science to secure a faculty position in the life sciences ( McKiernan et al., 2019 ; Sheltzer and Smith, 2014 ; Fox, 2018b ). Our data demonstrates that a CNS paper is not essential to an applicant receiving a faculty job offer.

The majority (74%) of our respondents were not an author on a CNS paper ( Figure 5A ), and yet most participants received at least one offer (58%). However, applicants with a CNS paper did have a higher number of onsite interviews and faculty job offer percentage. Of our respondents, 16% were first author on a CNS paper, and these applicants had a significantly higher percentage of offers per application (p=1.50×10 −4 , median offer percentages: 11% with a CNS paper and 2% without a CNS paper) and on-site interviews (p=2.70×10 −4 , median onsite interview percentages: 21% with a CNS paper, and 10% without a CNS paper; Figure 5A ).

Traditional research track record metrics slightly impact job search success.

( A ) Pie charts show the fraction of candidates with authorship of any kind on a CNS paper (purple) versus those without (gray), and fraction of candidates who were first author on a CNS paper (purple) versus those who were not (gray). Distributions of off-site interviews (top; p=0.33), onsite interviews (middle; p=2.70×10 −4 ) and offers (bottom; p=1.50×10 −4 ) for applicants without a first-author paper in CNS (gray), and those with one or more first-author papers in CNS (purple; Supplementary files 11 , 12 , 17 ). ( B ) Significant associations were found between offer percentage and the number of first-author papers in CNS (top panel, p=1.70×10 −3 ), career transition awards (second panel, p=2.50×10 −2 ), total citations (third panel, p=2.92×10 −2 ), and years on the job market (fourth panel, p=3.45×10 −2 ). No significant associations were found between offer percentage and having a postdoc fellowship (fifth panel), being above the median in the total number of publications (sixth panel), being an author in any position on a CNS paper (seventh panel), h-index (eighth panel), years as a postdoc (ninth panel), number of first-author papers (tenth panel), number of patents (eleventh panel), or graduate school fellowship status (twelfth panel; Supplementary files 6 , 7 , 9 , 10 , 11 , 12 , 13 and 21 ). ( C ) The plots show total citations for those without an offer (blue) and those with one or more offers (gold), for all applicants with one or more first-author papers in CNS (top left); for all applicants without a first-author paper on CNS (bottom left); for all applicants with independent funding (top right); and for all applicants without independent funding (bottom right). In two cases the p value is below 0.05. The bar charts show the offer percentages (gold) for the four possible combinations of career award (yes or no) and first-author paper in CNS (yes or no): for applicants with a first-author paper in CNS, p=0.56, χ 2  = 0.34; for applications without, p=0.17, χ 2  = 1.92). ( D ) Summary of significant results testing criteria associated with offer outcomes through Wilcoxon analyses ( Supplementary file 7 ) or logistic regression ( Supplementary file 24 ).

Since the number of on-site interviews and offers are highly correlated ( Figure 4C ), it is unclear if this increased success simply represents a higher chance at landing more onsite interviews. It is important to note that this effect is correlative and these candidates likely had other attributes that made them appealing to the search committee(s).

We examined several other publication metrics and found no correlation with the number of offers. Specifically, the total number of publications, the number of first author publications, the number of corresponding author publications, and h-index did not significantly correlate with offer percentage ( Figure 4—figure supplement 1 ). When we separated candidates who were above and below the medians for each of these metrics and compared the distribution of offer percentages, only the total number of citations significantly associated with a higher offer percentage ( Figure 5B ). Although the offer percentage was generally higher for applicants above the median for the other metrics, none of these differences were statistically significant ( Figure 5B ).

Preprints, or manuscripts submitted to an open-access server prior to peer-reviewed publication, are becoming increasingly popular among early-career researchers ( Sever et al., 2019 ), particularly in the life sciences, and can boost article citations and mentions ( Sarabipour et al., 2019 ; Fraser et al., 2019 ; Abdill and Blekhman, 2019 ; Conroy, 2019 ; Fu and Hughey, 2019 ).

We received 270 applicant responses on the use of preprints; 55% of respondents had posted at least one preprint, and 20% had posted between two and six preprints ( Figure 3E , top). At the time of faculty job application, 40% of these respondents had an active preprint that was not yet published in a journal ( Figure 3E , bottom), with an average of 0.69 active preprints per person. A number of candidates commented that preprinted research was enormously helpful and served to demonstrate productivity before their paper was published ( Supplementary files 17 and 18 ).

Fellowships and career transition awards

Respondents were highly successful in obtaining fellowship funding during their training (80% received a fellowship of any kind, Figure 3D ). Applicants with a postdoctoral fellowship had a greater offer percentage than those without, although the effect was not significant after correcting for multiple comparisons (p=0.17); doctoral fellowships did not appear to influence offer percentage ( Figure 5B ).

Receiving funding as an early-career researcher is part of a favorable research track record ( Eastlack, 2017 ). A recent study of publicly available data indicates that the proportion of faculty receiving their first large research program grant (an R01 through the NIH) with a history of funding as a trainee (F and K awards through the NIH) is significantly increasing, driven mostly by K awards. Pickett states: "While not a prerequisite, a clear shift is underway that favors biomedical faculty candidates with at least one prior training award" ( Pickett, 2019 ).

Our survey differentiated the types of funding a trainee can receive into predoctoral and postdoctoral fellowships (discussed above), and career transition awards, for which the trainee is listed as the PI and funds can often transition with the trainee to a hiring institute (e.g. the Burroughs Wellcome Fund Career Awards at the Scientific Interface or the NIH K99/R00 Pathway to Independence award). Career transition awards were less frequent, with 25% of respondents receiving awards on which they were PI/co-PI ( Supplementary file 20 ). Respondents with transition funding received a higher percentage of offers ( Figure 5B ).

Patents are considered positive metrics of research track record, although their importance and frequency can vary between fields. Only 19% of applicants reported having one or more patents on file from their work when entering the job market ( Supplementary file 21 ). The number of patents held by the applicant did not correlate with the number of offers received ( Figure 4—figure supplement 1 ) and the percentage of offers did not change between those with or without a patent ( Figure 5B ).

Years on the job market

We also asked how many application cycles they had been involved in. Approximately 55% of our respondents were applying for the first time, and these candidates fared significantly better in terms of offer percentages than those who were applying again ( Figure 5B ). Additionally, a number of applicants took advantage of resources that provided information about the job application process ( Supplementary file 22 ), and those that did found them helpful ( Supplementary file 23 ).

Analyses such as the work presented here may help applicants refine and present their materials and track record in a manner that might improve success and decrease repeated failed cycles for applicants.

Interplay between metrics

We next examined the relationship between each of the traditional criteria that were significantly associated with an increase in offer percentage. The criteria included being first author on a CNS paper, total citations, and career transition awards.

Overall, we had 241 applicants that fully responded to all of our questions about these metrics. Pairwise testing of each of these criteria found no statistically significant relationships between variables (p=0.45, career transition awards vs CNS; p=0.26 total citations vs CNS; p=0.29 career transition awards versus total citations). Regardless, we plotted subgroups based on offer status and each of these criteria to see if there was evidence for any general trends in our dataset ( Figure 5C ). Notably, respondents who were first author on a CNS paper and received at least one offer had a greater number of total citations than those who were first author on a CNS paper but did not receive any job offers. Applicants who were first author on a CNS paper or who had a career transition award had higher percentages of securing at least one offer, and those with both had an even greater percentage, although the differences between these groups was not statistically significant.

This analysis suggests that the combination of different criteria holistically influence the ability to obtain an offer. Therefore, we performed logistic regression to examine the relationship between multiple variables/metrics on the successful application outcome of receiving an offer on a subset of applicants (n = 105) who provided answers across all variables. We implemented a rigorous variable selection procedure to maximize accuracy and remove highly correlated variables. This resulted in a model that included only seven variables ( Supplementary file 24 ).

This regression model revealed that a higher number of applications, a higher citation count and obtaining a postdoctoral fellowship were significantly associated with receipt of an offer. When missing values were imputed and the full applicant pool (n = 317) was considered, all previous variables remained significant, and a significant positive coefficient was also observed for having a career transition award. In both versions of the model, the search for non-academic jobs was significantly negatively associated with offer status ( Figure 5D ). We note that the model with imputed data was more accurate than that with missing values excluded at distinguishing between applicants with and without offers in 10-fold cross-validation experiments. However this accuracy was found to only be 69.6%, which is insufficient to construct a usable classifier of offer status. Due to the predominance of applicants from the life sciences in our dataset, we also repeated these analyses on a subset containing only these applicants. While more variables were included in the model, the general trends remained the same, with the addition of the number of years spent on the job market as a significant negative factor in receiving an offer ( Figure 5—figure supplement 1 ; Supplementary file 25 ).

Finally, we extended this analysis to visualize the interplay between all variables in Figure 5B by learning a decision tree automatically from the collected data ( Figure 5—figure supplement 2 ). The algorithm tries to partition the applicants into groups such that each group is entirely composed of individuals with at least one offer or without. A variety of different classifier groups were identified, but no group contained more than ~19% (61 out of 317) of the dataset. In fact, the accuracy of the overall decision tree in distinguishing between candidates with offers and those without was only ~59% ( Figure 5—figure supplement 2 ).

Taken together, these results suggest that there are multiple paths to an offer and that the variables we collected do not sufficiently capture this variability.

Levels of teaching experience

Discussions surrounding the academic job market often center on publications and/or funding, while teaching experience generally receives much less attention. However, the level of teaching experience expected from the applicants can vary, but mostly depends on the type of hiring institution.

We asked applicants whether they focused their applications to a specific type of institution (R1, PUI, or both; see Box 1 for definitions), allowing us to examine teaching experience across R1 and/or PUI applicants. Most respondents applied to jobs at R1 institutions ( Figure 6A ), which may explain the focus on research-centric qualifications. It remains unclear what the emphasis on teaching experience is for search committees at R1 institutions, however the literature suggests that there seems to be a minimal focus ( Clement et al., 2019 ). Additionally, there might be differences in departmental or institutional requirements that are unknown to outsiders. What is commonly accepted is that many applications to an R1 institution require a teaching philosophy statement.

Summary of applicant teaching experience and impact on job search success.

( A ) Distribution of institution types targeted by survey applicants for faculty positions (PUI only in blue, R1 institutions only in green, or both in red, Supplementary file 26 ). ( B ) Distribution of teaching experience reported by applicants as having TA only experience (in purple), beyond TA experience (e.g. teaching certificate, undergraduate and/or graduate course instructorship, guest lectureship and college adjunct teaching, (in orange), or no teaching credentials (in green; Supplementary files 27 and 28 ). ( C ) Distribution of teaching experience (TA experience, right, vs. Beyond TA experience, left) for applicants who applied to R1 institutions only (in green), PU institutions only (blue), or both R1 and PUIs (in red), ( Supplementary file 27 ). The degree of teaching experience did not change based on the target institution of the applicant (p=0.56 (ns), χ 2  = 0.41; Chi-squared test). ( D ) Association between offer percentage and teaching experience is not significant (p=0.16; Supplementary files 7 , 27 and 28 ).

Definition of specific terms used in this study.

Early-career researcher (ECR): For the purpose of this study, we define an ECR to be anyone engaged in research who is not recognized as an independent leader/investigator of a research group. This includes graduate and postdoctoral researchers; junior research assistants, research associates, and staff scientists.

Principal Investigator (PI): A scholar recognized as an independent leader of a research group. This includes full professors, group leaders, and tenure-track, non-tenure-track or tenured faculty.

Faculty Job Applicant: An early-career researcher with a PhD (a recent graduate, postdoctoral fellow or research scientist) who seeks to apply for a PI position (see above), usually at the assistant professorship level.

STEM Fields: STEM is an acronym for degrees in fields related to science, technology, engineering, and mathematics. STEM graduates work in a wide variety of fields including the life sciences, the physical sciences, different areas of engineering, mathematics, statistics, psychology, and computer science.

Research Mentor: A research advisor, usually the PI of a lab who mentors graduate and postdoctoral researchers during their academic training in his/her lab.

Adjunct Lecturer: A teacher or post-PhD scholar who teaches on a limited-term contract, often for one semester at a time. This individual is ineligible for tenure.

Teaching Assistant (TA): An individual who assists a course instructor with teaching-related duties in a lecture-based and/or laboratory-based undergraduate or graduate level course.

Doctoral/Graduate and Postdoctoral Fellowships : Funding mechanisms to support the training of a graduate or postdoctoral researcher: the proposal for this is written by the trainee and contains a mentoring/training plan and request for funding to support the trainee salary and/or part of their research expenses such as equipment, lab supplies and travel expenses typically for 1–3 years.

Career Transition Awards: Funding mechanisms facilitating senior trainees towards independent research careers: Includes core/substantial funds to fully support 1–3 years of postdoctoral salary and additional 2–5 years of independent faculty research and staff salaries as well as support for research expenses such as equipment, lab supplies and travel expenses. As a result, some portion of these funds can transition from the training institute to the hiring institute.

R1 University: There are 131 institutions in the United States that are classified as "R1: Doctoral Universities – very high research activity" in the Carnegie Classification of Institutions of Higher Education (2019 update), can be private or public.

R2 University: There are 135 institutions in the United States that are classified as “R2: Doctoral Universities – high research activity" in the Carnegie Classification of Institutions of Higher Education (2019 update), can be private or public.

R3 University, PUI or Small Liberal Arts College (SLAC): Primarily undergraduate institutions (PUI) are often smaller than large research universities, can be private or public, and offer varying levels of resources for students and faculty. Many faculty at PUIs run a research lab while maintaining significant teaching loads and heavy contact hours with students.

Almost all respondents (99%) had teaching experience ( Figure 6B ): for roughly half this experience was limited to serving as a Teaching Assistant (TA; Box 1 ), with the rest reporting experience beyond a TA position, such as serving as an instructor of record ( Figure 6B ). The degree of teaching experience did not change based on the target institution of the applicant ( Figure 6C ), nor did the percentage of offers received significantly differ between groups based on teaching experience ( Figure 6D ).

Research versus teaching-intensive institutions

To our knowledge, there is a lack of systematic evidence describing the process or expected qualifications of a PUI-focused ( Box 1 ) job search ( Ramirez, 2016 ). A subgroup of 25 "PUI Focused" applicants responded to our survey, and, despite this small number, we aimed to describe this important sub-group relative to "R1 Focused" applicants as well as applicants who applied to both types of institutes. The PUI subgroup included a majority of female applicants (60%, Figure 7A ) while the R1 subgroup had a majority of male applicants (54%, Figure 7A ). Within the PUI subgroup, no differences were seen in the number of first author publications across genders ( Figure 7B ), although women had a better fellowship history ( Figure 7C ). The median number of remote interviews, onsite interviews, and offers was also similar to that for the R1 subgroup, although the PUI subgroup submitted fewer applications ( Figure 7E ). Although both subgroups reported teaching experience ( Figure 7D ), the PUI subgroup was enriched in adjunct, visiting professor, instructor of record, community college, or contract-based teaching experiences ( Figure 7F ). Having adjunct experience did not significantly increase the median number of offers received for applicants focused on PUIs, R1s, or both types of institutions ( Figure 7G ).

PUI focused applicants differ only in teaching experience from the rest of the application pool.

( A ) The gender distribution applicants who focused on applying to PUIs ( Supplementary file 26 ). ( B ) The gender distribution and number of first-author publications of the applicant who focused on applying to PUIs (p=0.88). ( C ) Summary of the fellowship history by gender for PUI focused applicants ( Supplementary file 13 ). ( D ) Distribution of teaching experience of PUI focused applicants ( Supplementary file 27 ). ( E ) The median number of applications, off-site interviews, on-site interviews and offers for PUI focused applicants. ( F ) Percentage of survey respondents who identified having "adjunct teaching" experience ( Figure 1 ) based on target institution (p=5.0×10 −4 ; χ 2  = 27.5, Chi-squared test). ( G ) The number of offers received segregated by "adjunct teaching" experience in either PUI focused applicants (p=0.55) or R1/both R1 and PUI focused applicants (p=0.98).

A time-consuming and opaque process with little feedback

We asked the applicants to comment on whether any aspect of their training or career was particularly helpful or harmful to their faculty applications ( Figure 8A–B ). We used word clouds ( Supplementary files 27 and 28 ) to analyze recurrent themes in these open-ended questions. The applicants identified funding as most helpful for their applications, and no-funding as subsequently harmful; this perception agrees with the data presented above ( Figure 8A , Figure 5C , Figure 4—figure supplement 1 ). Additionally, perceptions were also in line with the rest of the data, in that they were unable to largely agree on other measurable aspects of their career that were perceived as helpful. Qualitative aspects that were perceived as particularly helpful included networking and attending/presenting at conferences. Interestingly interdisciplinary-research, which is often highlighted as a strength and encouraged by institutions and funders, was perceived by candidates as a challenge to overcome. Indeed, interdisciplinary candidates may pose an evaluation challenge for committees, given the differences in valuation of research metrics across fields, the extended training time required to master techniques and concepts in multiple fields, as well as valuation of interdisciplinary teams of specialists over interdisciplinary individuals ( Eddy, 2005 ).

Perceptions of the job application process.

Three word clouds summarizing qualitative responses from the job applicant survey respondents to the following questions: A ) "What was helpful for your application? " (top; Supplementary file 17 ), ( B ) "What was an obstacle for your application? " (middle; Supplementary file 18 ), and C ) "What is your general perception of the entire application process?" (bottom; Supplementary file 31 ). The size of the word (or short phrase) reflects its frequency in responses (bigger word corresponds to more frequency). Survey respondents were able to provide longer answers to these questions, as shown in Supplementary files 17 , 18 and 31 . 'CNS-papers' refers to papers in Cell, Nature or Science; 'Pedigree' refers to the applicant’s postdoc lab pedigree or postdoc university pedigree; 'Grant-Writing' refers to the applicant’s grant writing experience with their PhD or postdoctoral mentor; 'Peer-reviewing' refers to the experience of performing peer-reviewing for journals; 'Interdisciplinary-research' refers to comments stating that Interdisciplinary research was underappreciated; 'two-body problem' refers to the challenges that life-partners face when seeking employment in the same vicinity; 'No-Feedback' refers to lack of any feedback from the search committees on the status, quality or outcome of applications.

Notably, many applicants found the amount of time spent on applications and the subsequent lack of feedback from searches frustrating ( Figure 8B–C ). Most applicants never received any communication regarding their various submissions. For instance, an applicant who applied for 250 positions only received 30 rejections. Overall, our respondents submitted 7644 applications ( Figure 4A ) and did not hear anything back in 4365 cases (57% of applications), receiving 2920 formal rejection messages (38% of applications; Supplementary file 19 ). Application rejection messages (if received at all) most often do not include any sort of feedback. Additionally, a considerable amount of time is spent on writing each application and extensive tailoring is expected for competitive materials. Combining these insights, it is therefore unsurprising that almost all applicants, including applicants that received at least one offer ( Supplementary file 29 ), found the process "time-consuming", a "burden on research", and "stressful" ( Figure 8B–C ).

44% of respondents had applied for faculty jobs for more than one cycle ( Supplementary file 30 ). Though applicants who applied for more than one cycle had significantly lower offer percentages (p=3.45×10 −2 ; Figure 5B ), many reported perceived benefits from significant feedback from their current PI through their previous application cycles. Though mentorship was not as often reported as specifically helpful ( Supplementary file 17 ), the lack of mentorship was a commonly cited harmful obstacle ( Figure 8B , Supplementary file 18 ). Lastly, multiple candidates felt that issues pertaining to family, the two-body problem (need for spousal/significant other hire), parental leave, or citizenship status significantly harmed their prospects.

The view from the search committees

To learn more about the characteristics search committees valued in applicants, we performed an exploratory survey of members of such committees. This anonymous survey was distributed in a limited fashion, taking advantage of the professional networks of the authors. Fifteen faculty members responded, with nine having been involved in search committees for over ten years ( Figure 9A ). As with our survey of applicants, we focused on faculty members at R1 academic centers working in life sciences (14/15 of those polled) and engineering (1/15) within the United States ( Figure 9A ).

Summary of metrics valued by search committees.

Search committee members were asked on how specific factors were weighted in the decision on which applicant to extend an offer to ( Supplementary files 33 – 38 ). All search committee members surveyed were based at R1 universities ( Box 1 ). ( A ) Distribution of the fields of study and years of experience for the search committee survey respondents. ( B ) The median number of faculty job openings, number of applicants per opening, applicants that make the first cut, applicants who are invited for phone/Skype interviews, and offers made. ( C ) The quantitative rating of search committee faculty on metrics: candidate/applicant research proposal, career transition awards, postdoctoral fellowships, graduate fellowships, PI/mentor reputation (lab pedigree), Cell/Nature/Science journal publications, Impact factor of other journal publications, Teaching experience and value of preprints based on a 5-level Likert scale where 1 = not at all and 5 = heavily. ( D ) Visual summary of the job applicant perception (from word cloud data) and the results of both surveys (statistical analyses of the applicant survey and criteria weighting from the search committee survey). A number of metrics mentioned in short answer responses were not measured/surveyed across all categories. These missing values are shown in gray.

Two-thirds of respondents replied that the search committees they sat on typically received over 200 applicants per job posting, with one-third receiving 100–199 applications per cycle. Between 5 and 8 applicants were typically invited to interview on-site; one-third of respondents replied that off-site interviews (e.g., via phone or Skype) were not performed ( Figure 9B ). These statistics help demonstrate the challenges that hiring committees face; the sheer volume of applicants is overwhelming, as mentioned explicitly by several search committee respondents ( Supplementary file 32 ).

We asked what factors search committee members found most important, what their perception of the market was, and how they felt it had changed since they first became involved in hiring. We also asked them to weigh specific application criteria in evaluating an application from 1 (not weighted at all) to 5 (heavily weighted; Figure 9C ). Criteria such as transition awards were consistently ranked highly, matching applicant perception; however, committee members also placed substantial emphasis on the research proposal. Two-thirds viewed preprints favorably, although their strength may not yet be equivalent to published peer-reviewed work ( Figure 9C ). In follow-up questions, a number of respondents emphasized that the future potential of the candidate both as a colleague and a scientist was important.

Since this last point was not prominent in our survey of job applicants, we looked for discrepancies in the two sets of responses ( Figure 9D ). In general, search committees placed greater emphasis on the future potential and scientific character (research proposal, research impact, collegiality), while applicants focused on publication metrics and funding. However, despite the search committees placing less emphasis on papers in CNS, candidates with papers in these journals were more successful.

We also asked if there were additional factors that search committees wished applicants knew when applying ( Figure 10 ). Several emphasized the quality of the research and papers was the most important factor for assessing prior achievement, but added that a compelling and coherent research proposal was also critical, and was sometimes underdeveloped in otherwise competitive candidates. The importance of departmental fit was also emphasized; interpersonal interactions with faculty members at the interview stage were also mentioned. This last sentiment is consistent with a recent Twitter poll which found that "overall attitude/vibe" was the single most important factor for selection at the interview stage ( Tye, 2019 ). Intriguingly, while one faculty respondent noted that they rarely interview any applicant without a career transition award, such as a K99/R00 Pathway to Independence Award from the NIH (a situation they noted as problematic), another lamented that applicants worried too much about metrics/benchmarks anecdotally perceived to be important, such as receiving these awards. Finally, a majority of respondents noted that it was easy to identify good candidates from their submitted application (11/15), that there were too many good applicants (10/15), and that candidates often underperformed at the interview stage (10/15) ( Figure 10 , Figure 10—figure supplement 1 , Supplementary File 35 ).

Search committee perception of the faculty job application process.

Two word clouds representing responses from members of search committees in response to the following questions: A) "What information do you wish more candidates knew when they submit their application?", and B) "Have you noticed any changes in the search process since the first search you were involved in?" The size of the word/phrase reflects its frequency in responses, with larger phrases corresponding to more frequent responses. Search committee faculty members were able to provide long answers to both questions ( Supplementary files 38 and 39 ).

Challenges in the academic job market

Currently, there is little systematic evidence for what makes a competitive faculty candidate. As with any opaque, high-pressure environment, an absence of clear guidelines and expectations coupled with anecdotal advice can lead individuals to focus on tangible goals and metrics that they feel will help them stand out in the system. Our findings were consistent with several commonly held notions: the number of applications submitted, career transition awards (e.g. a K99/R00 award), and total citation counts were significantly associated with obtaining offers in our Wilcoxon test and when jointly considering all variables in a logistic regression analysis. Joint academic/industry job searches were negatively associated with obtaining academic offers in both analyses, while the number of years an applicant was on the job market was negatively associated in our Wilcoxon analysis. Papers in CNS were only significantly associated with offers in the Wilcoxon analysis, while postdoc fellowships were only significant in the logistic regression.

Metrics such as career transition awards and postdoctoral fellowships can be broadly categorized as funding metrics and the positive association between these metrics and offer outcomes likely reflects the hiring institute being confident that the candidate will be competitive for future funding for their research program. Indeed, career transition awards essentially provide additional start up funds, while postdoc fellowships provide a track record of funding. Although postdoc fellowships were not significant in our Wilcoxon analyses, this metric was significant in our life science-specific Wilcoxon subgroup analysis ( Figure 5—figure supplement 1 .) as well as our logistic regression on the whole dataset ( Figure 5D ). The search committee respondents confirmed the benefit of career transition funding as major strengths for an application.

Association between offers and the number of applications, non-academic job searches, and years on the academic job market requires cautious interpretation. Given that receiving any single faculty offer is a low-probability event, there is value in submitting enough applications to increase the odds of receiving an offer. However, there is likely a balance in ensuring the quality of each application, which requires time and effort to individually tailor to each position. Searching for non-academic jobs might detract from the time available to tailor applications, although the negative association may also reflect other factors such as the typically swifter non-academic hiring timeline, which could cause applicants to remove themselves from a search prior to its conclusion. Likewise, the negative association between repeated years on the job market and offers might reflect fundamental problems with the quality of an application, or more complex factors such as geographical constraints. As we did not collect data that would allow us to determine the quality of application, or the fit of an application to a particular opening, we cannot evaluate these metrics beyond the broad associations found in our dataset. Additionally, other unmeasured factors (e.g. applicant pedigree) are likely important considerations, consistent with recent data implicating institutional prestige and non-meritocratic factors in faculty hiring ( Clauset et al., 2015 ). This should be a major consideration for future studies of the academic job market.

When examining publication-related metrics, we found that total citation counts were significantly associated with receiving a job offer in both the Wilcoxon and logistic regression analyses. There was also a significant positive association between being first author on a CNS paper and receiving a job offer in the Wilcoxon analysis, but not in our logistic regression models. Examination of our data also revealed a gender gap in publication metrics, with males reporting more CNS papers and more papers overall, indicating that opportunities for publication are not equally available ( Arvanitis and Cho, 2018 ; Gumpertz et al., 2017 ). Second, the results of our automated variable selection procedure suggest that being an author in any position on a paper in CNS is an advantage overall (though the result is not significant); however, within the life sciences, being the first author is more of an advantage (again, not significant). Finally, papers in CNS and other journals with high impact factors have been regarded as a major benchmark for trainees in the life sciences ( van Dijk et al., 2014 ), and qualitative comments from our applicant survey conveyed a perception that the absence of a CNS paper is deemed detrimental to offer prospects. Collectively, our data suggest that while being first author on a CNS paper increases the chances of receiving an offer (particularly in life sciences), papers in CNS were neither necessary nor sufficient for securing an offer, as the majority of our respondents received offers without having a paper in CNS.

Consistently, being the author of a CNS paper was not deemed highly important by the search committee members we surveyed. These data may reflect a discordance of priorities for individual faculty members compared to their peers and the system at-large, as recently reported ( Niles et al., 2019 ). This could lead to an unspoken expectation that faculty (especially pre-tenure faculty) see themselves as passive participants in the current academic system, instead of active participants with the authority to realign priorities through search committees ( Niles et al., 2019 ). Future studies with higher numbers of faculty respondents should endeavor to further explore this phenomenon.

Despite challenges in the job market ( Larson et al., 2014 ; Andalib et al., 2018 ; Kahn and Ginther, 2017 ), our survey revealed positive outcomes that suggest progress in select areas. Nearly half of the job applicants we surveyed reported posting at least one preprint. Several of the search committee members we surveyed confirmed that while published papers carry the most weight, preprints are generally viewed favorably. Further, despite the fact that women face numerous challenges in academia, including underrepresentation at the faculty level in most STEM departments ( Arvanitis and Cho, 2018 ; Gumpertz et al., 2017 ; Ceci and Williams, 2015 ; Leaper and Starr, 2019 ), and trail men in publication-related metrics ( Figure 3B ), our data suggest very few differences in outcomes in the May 2018–May 2019 job cycle. Both genders received similar numbers of interviews and offers, and gender-based differences in publication-related metrics persisted even when considering only the 185 individuals with offers, suggesting that committees are becoming increasingly aware of gender bias in publication-related metrics and are taking them into account when evaluating applicants ( Supplementary file 40 ).

Overall, the respondents were generally highly qualified according to the metrics we measured, and yet they reported high stress and frustration with their experiences of the faculty job search. In a large number of cases, applicants were not notified of a receipt of their application, nor were they updated on its status, given a final notice of rejection, or informed that the search may have failed. This uncertainty further complicates an already stressful process that can be mitigated by improving practices for a more streamlined application process. Applicants perceived poor mentorship as a major obstacle to their applications. Further, we found that most metrics were differentially valued by candidates and committees. Collectively, these differences in expectations between applicants and hiring institutions, coupled with the opaque requirements for obtaining a faculty position, likely drive the high stress reported by both candidates and committee members alike.

Limitations of this study and measuring outcomes in the academic job market

There are several limitations of this study imposed by both the original survey design and general concerns, such as the anonymity of respondents, and the measurability of various contributing factors. For future data collection we suggest keeping surveys focused on region-specific job markets. Our pool of applicants was largely those seeking a position in North America. We believe these results can be aggregated, but the survey questions may not all be applicable to other large markets (e.g. Europe, China, India). We did not receive a sizable response from applicants looking outside of North America and in fields outside of life sciences to make useful comparisons. A similar survey circulated in each market individually with a similar number of responses would have broader impact.

We purposely did not ask for race, ethnicity, or citizenship demographics, PhD or postdoc institution, and region or institution where offers were received. We believe the addition of these metrics could potentially jeopardize the anonymity of respondents. Despite this, these factors could be significant contributors to the receipt of an academic job offer. Racial inequalities in all STEM fields at all levels exist and need to be addressed ( Whittaker et al., 2015 ), specifically with how they intersect with gender ( Gumpertz et al., 2017 ). As indicated in our open question responses ( Figure 8B ), international postdocs may be specifically challenged in obtaining faculty job offers in the United States and Europe due to immigration policies as well as how mobility is interpreted by the job market ( Cantwell, 2011 ). The reputation of a training institution is questionably measurable, but is also often listed in anecdotal advice as important. Recently it was reported that a majority of new faculty are hired from a minority of institutions providing postdoc training ( Clauset et al., 2015 ; Miuccio et al., 2017 ). It is possible that adding institutional reputation to the other traditional metrics we measured could provide a more complete picture of the current path to a faculty position.

While we measured some of the attributes widely perceived as important in faculty hiring (e.g. funding track record), others are less easily quantified (e.g. the research proposal, lab pedigree, or letters of recommendation that comments from our search committee survey revealed to be important) and data collection on these items would be highly recommended in future surveys. Addressing the quality of application materials is highly context-specific (given the field, search committee, and institutional needs) and can improve ( Grinstein and Treister, 2018 ). Other aspects which are not directly measurable and are often cited as important for applicants in the academic job market are "fit" and "networking" ( Wright and Vanderford, 2017 ). Respondents agreed that networking, conferences, collaborations, and connections were helpful in their job search ( Figure 8A ). Conference organizers are also starting to offer badges that those searching for faculty jobs can wear at events; exploring the relationship between networking metrics (such as number of conferences and networking events attended) and success on the job market could be a topic for future research. Departmental or institutional "fit" is largely determined by the search committee on an individual basis, and it is likely that we will never be able to measure fit adequately ( Saxbe, 2019 ).

All questions in our survey were optional. We chose this survey design in order to make the survey easier for respondents to complete; however, missing answers represent a source of potential bias as unanswered questions may represent answers that could be negatively perceived and/or zero in value. For example, some individuals may not have felt comfortable indicating they had zero offers, which could lead to the offer percentages we report being inflated. Such bias could also affect the imputations in our logistic regression, and for these reasons we have attempted to provide multiple transparent and qualified analyses of the data. Future surveys may benefit from all questions requiring a response. It is also possible that participation in the survey from the outset suffers from survivorship bias, in that those applicants that had a positive experience are more likely to reflect upon it and complete a survey on the process. Our survey was also likely completed by a highly-engaged group of aspiring future faculty. The Future PI Slack group itself is a space for postdoctoral researchers most interested in obtaining a faculty career to engage with and learn from one another. Thus, the survey data likely reflects a highly motivated and accomplished group and not the full pool of applicants to faculty positions each year. Wider dissemination of future surveys will hopefully be aided by the publication of these results and increased awareness of the survey among trainees in various research communities.

Finally, the data from our survey of job applicants focused on candidates and not the search committees. It is unclear how many individual searches are represented in our dataset. It is likely that as many as ~200–500 committees were represented in our aggregated job applicant data, and different committees may adopt distinct assessment criteria. Our limited search committee survey responses show that the committees represented by our sample favor a holistic assessment of candidates and that decision by universal criteria (especially based solely on career transition awards or papers in CNS) is likely not unilateral, especially across disciplines. Future studies would benefit from surveying a larger pool of search committees to see what major trends and practices dominate, whether the majority of searches adopt a comprehensive evaluation approach, or if there is heterogeneity among committees in how tenure-track hiring assessments are conducted.

The search process for faculty jobs lacks transparency and data regarding what makes a successful applicant. Here, we began to address this deficiency through a survey targeted at the applicants themselves, and including their perceptions of the application process. Of over 300 responses by job applicants, we did not receive a single positive comment about the process, despite the fact that 58% of our participants received at least one job offer. Our data suggest that baseline thresholds exist for those more likely to receive a faculty job offer, but that many different paths can lead to a job offer. This variety of paths likely reflects both the preparation done by applicants and the different evaluation criteria used by individual search committees. For these reasons, we urge applicants not to conclude that lower than average metrics in any one area are automatically disqualifying. Indeed, we believe that increasing the transparency of the application process through systematic data collection will allow a more detailed study of the many paths to obtaining a faculty offer.

Our data also show the mental strain on applicants during the hiring process. We propose a number of potential solutions with the understanding that hiring faculty is a complex process involving multiple stakeholders. We believe the application process could be improved by simplifying the process, including standardizing application materials (e.g. requirements for research statements are similar for R1 institutions) and requesting references only after candidates are shortlisted, so that the burden of application preparation time can be reduced. Constructive feedback from mentors is vital for success during the application and interview preparation stages. Additionally, if possible, communication from search committees about unsuccessful applications would be helpful. We understand that these points may increase the workload of mentors and search committees but, if put into place, could alleviate some of the stress related to the academic job application process. In addition, applicants need to work to be sure their materials are strong and well-researched as the quality of these materials and demonstrating fit for a job posting are important to faculty on search committees ( Clement et al., 2019 ). Further work into the challenges search committees face is needed to improve their experience of the application process.

It is our hope that this and future work will not only allow all stakeholders to make informed decisions, but will also enable critical examination, discussion, and reassessment of the implicit and explicit values and biases being used to select the next generation of academic faculty. Such discussions are crucial in building an academic environment that values and supports all of its members.

Survey materials

We designed a survey (the "applicant survey") to collect demographics and metrics that were commonly discussed on Future PI Slack during the 2018–2019 academic job search cycle. The survey was designed to take less than 5 min in order to maximize response rates, and respondents were not required to answer all questions.

After collecting and performing initial analyses of this survey, we designed an additional survey for search committees (the "search committee survey"). The text of both surveys used in this work is included in the Supplementary files 41 and 42 . A Google form was used to conduct both surveys.

The applicant survey was distributed on various social media platforms including the Future PI Slack group, Twitter, and Facebook, and by several postdoctoral association mailing lists including in North America, Europe and Asia. The survey was open for approximately six weeks to collect responses.

The search committee survey was distributed to specific network contacts of the various authors. Though this distribution was more targeted, a Google form link was still used to maintain anonymity. The search committee survey was open for approximately three weeks to collect responses. In both cases, respondents to the surveys were asked to self-report, and the information collected was not independently verified. The surveys can be found in Supplementary files 41 and 42 .

Data analysis

Prior to analysis, we manually filtered out five responses in which answers were not interpretable or did not appear to answer the correct questions. Microsoft Excel and RStudio were used to graph the results of both surveys shown in Figures 1 – 6 and 8 . Specifically, data was filtered and subdivided using the 'tidyverse' collection of R packages, and figure plots were generated using the 'ggplot2' package. Whenever statistical analyses were used, the exact tests, p-values and χ 2 values are reported in the appropriate figure or figure legend or caption, results section and Supplementary file 7 , and represent the implementations in the basic R 'stats' package.

A p-value of less than 0.05 was considered significant. Where a number of demographics are combined in the reporting throughout this study, any analysis groups with less than five respondents were combined with other similar values instead of the raw n value in an effort to protect the anonymity of participants. Briefly, statistical methods are as follows: in general, the two-tailed Wilcoxon rank sum test (with Holm correction when applicable) or Chi-squared test was used to report p-values (see Supplementary file 7 for detailed breakdown).

The qualitative survey comments were categorized by theme (keywords/context) describing each comment and the frequency of comments pertaining to a particular theme and tabulated ( Supplementary files 17 , 18 , 38 and 39 ). Word clouds were generated using the WordItOut platform ( WordItOut, 2020 ; Figures 7 and 9 ). The visual summary heatmap of the job applicant perception and the survey results along with the search committee survey results ( Figure 9D ) was created by counting the frequency of comments for each metric (i.e. publications, fellowships, preprints) from the respondents to the qualitative (long answer) questions ( Supplementary files 17 , 18 , 38 and 39 ). The job applicant survey quantitative results were also used to rank metrics based on significance (as determined by Wilcoxon analysis or logistic regression analysis ( Supplementary file 7 )) and were also incorporated into the heatmap ( Figure 9D ). A number of metrics were not measured/surveyed as part of our study. These missing values are shown in gray.

Logistic regression analysis was performed in R using the 'glm' function with the 'family' parameter set to 'binomial'. All variables collected in the survey were included as independent variables, except those that were considered to be outcomes (numbers of remote interviews, onsite interviews and offers). The outcome variable was a binary 'Offer' or 'No offer' variable. All continuous variables were z-score normalized to ensure that they were centered and scaled consistently. To reduce collinearity between variables, a forward stepwise variable selection approach was adopted by starting with the variable that was most accurate in predicting offer status when included in a logistic regression model and then iteratively adding a variable to the model to maximize accuracy at every step. Furthermore, at every step, a variable would only be added if it was not correlated (Spearman correlation coefficient ≤0.5) with a variable already included in the model from a previous step. The model with the most accurate variable-combination was used to report coefficients. When multiple independent variables were considered together, missing values accounted for nearly two-thirds of the data, and were therefore imputed by fitting a bagged tree model for each variable (as a function of all the others; 63). Both variations of the analysis (missing data excluded and missing data imputed) were reported. In addition, this entire logistic regression analysis was repeated on a subset, solely comprising of applicants from the life sciences.

In order to visualize the potential paths to an offer, a decision tree was learned automatically from the data using the C5.0 algorithm ( Kuhn and Johnson, 2013 ). All possible combinations of the following parameter settings were evaluated: ( Cyranoski et al., 2011 ) either the tree-based variant or the rule-based variant of the algorithm was run, ( Ghaffarzadegan et al., 2015 ) winnowing of irrelevant variables was set to 'TRUE' or 'FALSE', and ( Schillebeeckx et al., 2013 ) the number of boosting 'trials' was set to 1, 4, 16, 32 or 64. The parameter combination with the best accuracy in predicting offer status in a 10-fold cross-validation experiment (as implemented in the 'caret' package in R) was chosen ( Kuhn, 2008 ). Since decision trees naturally handle missing values and differences in scales, no additional imputation or data normalization was performed before training and testing. The most accurate tree was found to be the one that used the rule-based variant, had no winnowing and no boosting (trials = 1) and was plotted using the 'plot' function in the 'partykit' R package ( Hothorn and Zeileis, 2015 ) and then manually grouped in Illustrator.

The authors confirm that, for approved reasons, access restrictions apply to the data underlying the findings. Raw data underlying this study cannot be made publicly available in order to safeguard participant anonymity and that of their organizations. Ethical approval for the project was granted on the basis that only aggregated data is provided (as has been provided in the supplementary tables) (with appropriate anonymization) as part of this publication.

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• Toseland CP
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• Helena Pérez Valle Reviewing Editor; eLife, United Kingdom
• Peter Rodgers Senior Editor; eLife, United Kingdom
• Adriana Bankston Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by two members of the eLife Features Team. The following individuals involved in review of your submission have agreed to reveal their identity: Adriana Bankston (Reviewer #2).

This article (which is based on a survey of more than 300 early-career researchers who are/were on the job market) has the potential to be important. However, the data require further analysis and the presentation and discussion need to be improved.

Essential revisions:

1. The authors should remove the Twitter poll and related analysis from this paper, leaving just the main survey and the faculty survey.

2. Throughout the manuscript, the authors seek to minimize the importance of papers published in Cell, Nature or Science (CNS). However, there are at least two instances in which having a CNS paper appears to be the strongest predictor of job market success (Table S7 and Figure 4B). Additionally, prior studies have also suggested the same (see Pinheiro et al., 2014; and links to multiple peer reviewed studies in Cuff, 2017). It is absolutely critical that the instances where having a CNS paper appears to be the strongest predictor of job market success be noted and described in the text. (See comments from reviewer #1 for a fuller discussion of this issue).

- Cuff, A. J. (2017). An Academic Lottery or a Meritocracy? Inside HigerEd. Retrieved from https://www.insidehighered.com/advice/2017/05/03/phds-need-real-data-how-potential-employers-make-hiring-decisions-essay.

- Pinheiro, D., Melkers, J., & Youtie, J. (2014). Learning to play the game: Student publishing as an indicator of future scholarly success. Technological Forecasting & Social Change, 81, 56-66. doi:10.1016/j.techfore.2012.09.008

3. Men have more first author publications than women particularly in CNS journals - the authors should comment on this.

4. In Figure 4D the authors should identify the factors that differentiate between candidates with one or more offers vs. those who received none.

5. Regarding the logistic regression: rather than conducting an analysis with 16 variables, many of which are collinear, the authors should conduct this analysis one variable at a time to identify the single features that predict job market success. The authors can then test these variables for collinearity and combine uncorrelated variables into models that include two, three, four, etc. independent predictors.

6. 96% of the respondents with CNS publications were in fields in "Biomedical or life sciences" or "Biology (other)" as described in Figure 1A. The authors should therefore repeat several of the key analyses on success predictors on the life science cohort alone, including making a new version of Figure 4 that includes data for the life science cohort alone. The authors should also update the tables S7, S9, S10 and S22 to include statistics about CNS publications done for the life scientists in the cohort alone (alongside the data already presented for the full cohort).

7. The authors conducted a "PUI-only" subgroup analysis (Figure 6), but it would also be informative to conduct an analysis among candidates who only applied to research-intensive positions. The differences between the positions may be noteworthy, and by combining too many unrelated job searches into one analysis, the authors may be missing out on important relationships.

8. The authors should provide some more background on the survey and how it was distributed at the start of the results section. What checks were in place to ensure that only early-career scientists answered it? Similarly, the authors should provide more information in the results section on how the search committee survey was distributed and who answered it.

9. This study really does not represent a cross section of different types of early career researchers from different fields nor "a wide variety of fields." Nor is it really an international applicant pool since data on race/ethnicity or nationality or citizenship status was not asked. It really represents a sample of postdocs (96%) from the biomedical and biological sciences with (72%) currently working within the U.S., Canada and U.K. who are on the job market. This is an important distinction to make because prior research Cantwell (2011), Cantwell & Taylor (2015), and Sauerman & Roach (2016) and others have done research in this area and shown how international postdocs working in the U.S. have had limited success in transitioning into tenure-track faculty positions and provide reasons to suggest why. The authors should discuss this at an appropriate place in the text and consider citing some or all of the following references:

- Cantwell, B., & Taylor, B. J. (2015). Rise of the science and engineering postdoctorate and the restructuring of academic research. Journal of Higher Education, 86(5), p 667-696.

- Cantwell, B. (2011). Transnational mobility and international academic employment: gatekeeping in an academic competition arena. Minerva: A Review of Science, Learning and Policy, 49(4), p 425-445.

- Sauermann, H. & Roach, M. (2016). Why pursue the postdoc path? Science 352:663-664. Doi: 10.1126/science.aaf2061

10. Another concern is an attempt to make correlations between number of applications with both number of interviews and offers, without taking into consideration the quality of the application, nor where the applicants received their training. There have been some large quantitative studies done by others (e.g. Clauset, Arbesman, & Larremore, 2015) that found faculty hiring follows a common and steeply hierarchical structure where doctoral prestige and where an applicant did both their Ph.D. and postdoc appointment better predicts hiring, especially in R1 institutions. The authors should discuss this concern at an appropriate place in the text and consider citing the following reference:

- Clauset, A., Arbesman, S., & Larremore, D. B. (2015). Systematic inequality and hierarchy in faculty hiring networks. Science Advances 1: e1400005. doi:10.1126/sciadv.1400005

11. Under "Statement of Ethics", the authors write that an IRB exemption was obtained on 08/29/2019. However, the survey itself was conducted in April 2019. I believe that retroactive IRB approval is generally prohibited by federal regulations. Was the IRB aware that the study had already been conducted when they granted the exemption? Can the authors comment on this serious discrepancy?

12. Teaching experience was not assessed either from the applicant or the institution perspective. Applicants self-disclosed their presumed teaching experience and then when a number of them did get job offers at R1s, the statement was made that applicants fulfill the teaching requirements for any university type. That is an oversimplification of findings. The authors' data indicates that the majority of applicants applied to R1s. This should be further discussed by the authors.

13. The authors could comment on whether the number of postdoc positions attained correlated with their ability to obtain faculty positions (in other words, are 3 postdoc appointments more desirable as compared to 1 when applying for a faculty position at a prestigious institution?).

14. The authors could address how the application process might be improved so it is a more positive experience.

[We repeat the reviewers’ points here in italic, and include our replies in Roman.]

Essential revisions: 1. The authors should remove the Twitter poll and related analysis from this paper, leaving just the main survey and the faculty survey.

We have now removed the section below from the “Applicants perceive the process to be time-consuming and opaque, with minimal to no feedback” results, including the corresponding tables:

“A separate Twitter poll indicated that applicants in general, not specifically for this cycle, typically spend more than 3 hours tailoring each application (49) (Table S26 Supplementary File 1). Our pooled applicants at minimum then spent a combined 22,932 hours (7,644 applications x 3 hours preparation each), or 2.62 years, on these applications. Individually, this number amounts to 72 hours for each applicant on average, but does not take into account how long the initial creation of “base” application materials takes, which is often a much longer process. In another follow-up Twitter poll, a majority of respondents felt that time spent on preparing faculty job applications impeded their ability to push other aspects of their career forward (Table S27 Supplementary File 1) (50).”

2. Throughout the manuscript, the authors seek to minimize the importance of papers published in Cell, Nature or Science (CNS). However, there are at least two instances in which having a CNS paper appears to be the strongest predictor of job market success (Table S7 and Figure 4B). Additionally, prior studies have also suggested the same (see Pinheiro et al., 2014; and links to multiple peer reviewed studies in Cuff, 2017). It is absolutely critical that the instances where having a CNS paper appears to be the strongest predictor of job market success be noted and described in the text. (See comments from reviewer #1 for a fuller discussion of this issue). - Cuff, A. J. (2017). An Academic Lottery or a Meritocracy? Inside HigerEd. Retrieved from https://www.insidehighered.com/advice/2017/05/03/phds-need-real-data-how-potential-employers-make-hiring-decisions-essay - Pinheiro, D., Melkers, J., & Youtie, J. (2014). Learning to play the game: Student publishing as an indicator of future scholarly success. Technological Forecasting & Social Change, 81, 56-66. doi:10.1016/j.techfore.2012.09.008

We thank the reviewers for this comment, and would like to clarify our position. We agree with the reviewers that the discussion of the importance of CNS publications is a sensitive and well-known issue. The relationship between job market success and CNS publications is complex. We have read the references suggested by the reviewers, and we agree that several studies have concluded that CNS publications are generally held in high regard by search committees. Moreover, we discuss in the text that there is a high perceived importance of CNS papers to applicants. However, our data shows 2 clear findings with regards to CNS: 1) Most applicants who get offers do *not* have a CNS paper and 2) Applicants with a CNS paper appear to have a higher chance of getting an offer than those who do not have a CNS paper. We believe that the demonstration of the first point (most candidates do not have a CNS paper) is an important point for postdocs to consider but does not detract from the 2nd point (CNS papers appear to confer an advantage to an applicant). We note that an analysis of the offer association with CNS publications is shown in Figure 4D. We observed a 7-15% increase in offer rate for candidates which had published in CNS journals. We also note that despite this observation, ~60% of our survey respondents still received at least one offer. Moreover, our data (Figure 4C) suggest that those individuals with CNS authorship who also received a job offer were more highly cited than those with CNS authorship without a job offer, suggesting that assessment of the impact of an applicant’s work is a complex blend of many factors.

Our small survey of faculty search committee members, revealed an attitude towards CNS publications that was discordant with that of the applicants and the conventional expectation. While we acknowledge that we do not have extensive information on the decision making process of every single search committee, the volume of current literature around this issue speaks to the culture shift that is occurring around academic hiring. Formal discussions of CNS as a metric and its impact on equity in the professoriate have resulted in the development of guidelines such as DORA, that reflect the burgeoning culture change. While an in-depth analysis of impact is outside the scope of this work, we look forward to future work on this topic.

We note that these results are presented in Figure 2B and 2C, as well as discussed in the “applicant scholarly metrics by gender” results subsection. How these results are in line with previous findings is presented within the introductory paragraph to that results section. We additionally revisit this point in the discussion stating “Further, despite the fact that women face numerous challenges in academia, including underrepresentation at the faculty level in most STEM departments (52,53,56,57), and trail men in publication-related metrics (Figure 2B), our data suggest very few differences in outcomes in the May 2018-May 2019 female applicant pool relative to their male counterparts. Both genders received similar numbers of interviews and offers, and gender-based differences in publication-related metrics persisted even when considering only the 185 individuals with offers, suggesting that committees are becoming increasingly aware of gender bias in publication-related metrics and are taking them into account when evaluating applicants (Table S40 Supplementary File 1). We have also now added a further point in the discussion pertaining specifically to the intersection of gender and CNS publications. “First, examination of our data revealed a gender gap in publication metrics, with males reporting more CNS authorship and publications overall, indicating that opportunities for publication are not equally available (52,53). ” Our survey has captured numerous aspects of the academic job market and the factors influencing the success of applicants. We recognize that gender (and other marginalized identities) play a large role. We believe the role of gender is thoroughly discussed in our manuscript while allowing in depth analysis of other factors.

We thank the reviewers for this comment, and for the opportunity to clarify this point within our manuscript. In our study, we performed two different analyses to understand which factors differentiate candidates based on their success in obtaining offers. We performed Wilcoxon tests (presented in Figure 4B and Table S7 Supplementary File 1) comparing the offer percentage (the number of offers/the number of applications) and also performed a logistic regression comparing candidates with offers vs those without (Table S22). The data from these two analyses are summarized in Figure 4D, as noted by the reviewer, but were confusing due to the wording in both the text and the figures. We have clarified this in the text by adding further details regarding the offer status and positive correlations in the following sentence: “When missing values were imputed, significant positive coefficients were observed for having a higher h-index, higher application numbers, career transition awards and identifying as female and obtaining an offer.” Further, we have clarified this in the figure legend for Figure 4D as follows: “Summary of significant results testing criteria associated with 1+ offer (positive) or no offers (negative) with offer outcomes either through Wilcoxon analyses (Table S7 Supplementary File 1) or logistic regression (Table S24 Supplementary File 1) ordered by decreasing effect size.”

We thank the reviewer for bringing this to our attention. As suggested by the reviewer, we have undertaken a greedy stepwise variable selection procedure in which we started with the variable that was the most predictive of offer status (1+ offer vs 0 offers) and then incrementally added one variable at a time, selecting for the best pair, triplet, and so on. While doing this we also ensured that no new variable was highly correlated or anticorrelated with a previously included variable (Absolute Spearman correlation coefficient cutoff of 0.5). This resulted in the exclusion of two variables that were highly correlated with citation count: h-index and total number of publications (see correlation plot below).

Among the remaining 14 variables, the combination of seven variables resulted in the highest accuracy in cross-validation experiments: (Applying to) Other jobs, Application number, Citations, Years on job market, Postdoc fellowships, (transition to independence) Funding and CNS (co-authorship) (see Author response table 1 below).Author response table 1.

We note that while this 7-variable model is more accurate in predicting offer status than the full model presented in the earlier version, there is a possibility of bias typically arising from the subjective decisions made during a variable selection exercise. For instance, the choice of a greedy stepwise approach, the use of Spearman correlation as a measure of collinearity, the correlation coefficient thresholds, the choice of accuracy as the quantity to maximize, among others, are all likely to influence which variables are selected and how many are deemed optimal. Nonetheless, the agreement between the single-variable analyses (Wilcoxon tests) and the more rigorous logistic regression analysis gives us confidence to update the logistic regression analysis in the manuscript to this 7-variables model (see updated Figures 4 and Table S24 Supplementary File 1). We have also modified the text summarizing these results in the “Interplay between metrics” section to read as follows:

"We implemented a rigorous variable selection procedure to maximize accuracy and remove highly correlated variables. This resulted in a model that included only seven variables (Table S24 Supplementary File 1) that was tested on a subset of applicants (n=105) who provided answers across all variables. This regression model revealed that a higher number of applications, a higher citation count and obtaining a postdoctoral fellowship were significantly associated with receipt of an offer. When missing values were imputed and the full applicant pool (n=317) was considered, all previous variables remained significant, and a significant positive coefficient was also observed for having a career transition award. In both versions of the model, the search for non-academic jobs was significantly negatively associated with offer status (Figure 4D). We note that the model with imputed data was more accurate than that with missing values excluded at distinguishing between applicants with and without offers in 10-fold cross-validation experiments. However this accuracy was found to only be 69.6%, which is insufficient to construct a usable classifier of offer status. Due to the predominance of applicants from the life sciences in our dataset, we also repeated these analyses on a subset containing only these applicants. While more variables were included in the model, the general trends remained the same, with the addition of the number of years spent on the job market as a significant negative factor in receiving an offer (Table S25 Supplementary File 1; Figure 4 – Supplement 1)."

We have updated Table S7 in Supplementary File 1 and added new tables S9 and S11 equivalents for Life/Biomedical Sciences applicants (respondents who indicated their field of research as Chemistry, Biology, Bioengineering or Biomedical or Life Sciences) scholarly metrics to the Supplementary file. The new added tables are numbered S10 and S12 in Supplementary File 1. Figure 4 – Supplement 1 shows the same analyses as in Figure 4, but restricted to the life sciences cohort. Table S25 in Supplementary File 1 reports the coefficients, p-values and other related information in the same manner as in Table S24 in Supplementary File 1.

We have updated Figure 6 to include comparisons of candidates who only applied to PUIs (PUI-focused), those who only applied to R1 institutions (R1 Focused) and candidates who applied to both. Our original observations still hold (candidates applying to PUI positions are more likely to have more extensive teaching experience). However the new analysis also reveals some interesting trends (the R1 Focused subgroup is majority male in contrast to the two other groups) that we now comment on in the “Research versus Teaching-intensive institutions” section.

We have altered the text in two separate places to provide clarity on this point. The first paragraph under “Academic Job Applicant Demographics” has been changed to include statements that participants in the survey self-identified as applicants in the academic job market in 2018-2019. The first paragraph under “Search Committees Value the Future” has been changed to state that the search committee survey was sent to a limited number of faculty, from within the authors’ professional networks, who were known to serve on search committees.

9. This study really does not represent a cross section of different types of early career researchers from different fields nor "a wide variety of fields." Nor is it really an international applicant pool since data on race/ethnicity or nationality or citizenship status was not asked. It really represents a sample of postdocs (96%) from the biomedical and biological sciences with (72%) currently working within the U.S., Canada and U.K. who are on the job market. This is an important distinction to make because prior research Cantwell (2011), Cantwell & Taylor (2015), and Sauerman & Roach (2016) and others have done research in this area and shown how international postdocs working in the U.S. have had limited success in transitioning into tenure-track faculty positions and provide reasons to suggest why. The authors should discuss this at an appropriate place in the text and consider citing some or all of the following references: - Cantwell, B., & Taylor, B. J. (2015). Rise of the science and engineering postdoctorate and the restructuring of academic research. Journal of Higher Education, 86(5), p 667-696. - Cantwell, B. (2011). Transnational mobility and international academic employment: gatekeeping in an academic competition arena. Minerva: A Review of Science, Learning and Policy, 49(4), p 425-445. - Sauermann, H. & Roach, M. (2016). Why pursue the postdoc path? Science 352:663-664. Doi: 10.1126/science.aaf2061

We thank the reviewer for this comment and note that the complete demographic data of survey participants' country of origin, field of scientific research, and current position is included in Figure 1 and detailed in Tables S2, S3 and S5 in Supplementary File 1. We acknowledge that our survey did not fully capture the difficulties faced by non-citizens in obtaining a US faculty position, though these difficulties have been demonstrated in previous literature. We have added that citizenship status was a common issue thought to hinder applicants’ progress in the last line of the last paragraph of the “Applicants perceive the process to be time-consuming and opaque, with minimal to no feedback” section and highlight its prominence in Figure 7B. We have also added the following text to the discussion of the limitations of our study: “As indicated in our open question responses (Figure 7B), international postdocs may be specifically challenged in obtaining faculty job offers in the United States and Europe due to immigration policies as well as how mobility is interpreted by the job market (59).” We note that we had previously referenced Sauermann, H. & Roach, M. (2016) in our introduction, it is reference number 9.

10. Another concern is an attempt to make correlations between the number of applications with both number of interviews and offers, without taking into consideration the quality of the application, nor where the applicants received their training. There have been some large quantitative studies done by others (e.g. Clauset, Arbesman, & Larremore, 2015) that found faculty hiring follows a common and steeply hierarchical structure where doctoral prestige and where an applicant did both their Ph.D. and postdoc appointment better predicts hiring, especially in R1 institutions. The authors should discuss this concern at an appropriate place in the text and consider citing the following reference: - Clauset, A., Arbesman, S., & Larremore, D. B. (2015). Systematic inequality and hierarchy in faculty hiring networks. Science Advances 1:e1400005. doi:10.1126/sciadv.1400005

While we acknowledge that variables such as training institution and prestige of the applicants’ graduate school and postdoctoral mentors undoubtedly influence who obtains faculty job offers, studying this relationship was beyond the scope of the current study. The current study was not designed to explore these variables because of privacy concerns for our respondents. We did not inquire about respondents’ training institutions, advisors’ prestige, or other metrics associated with their “networks” which may have influenced their job search success. Future work to explore these variables are certainly needed. We have cited Clauset et al in our discussion section when we discuss the potential impact of these training/mentoring/network variables we did not assess in the current study. From “Challenges in the Academic Job Market” in the Discussion:

“Additionally, other unmeasured factors (e.g. applicant pedigree) are likely important considerations, consistent with recent data implicating institutional prestige and non-meritocratic factors in faculty hiring (51). This should be a major consideration for future studies of the academic job market.”

We would like to add that our open-ended question regarding “What was helpful for your application” (Figure 7A) indicated applicants perceived networking methods and pedigree to be valuable in helping them land faculty jobs. So, there is clearly merit to study these metrics in future work.

We initially conducted the surveys for the Future PI Slack community only. When we decided on data analysis and wider dissemination of the results, we applied for IRB exemption for use of existing data. We have now corrected our statement of ethics to reflect the exemption criteria for publication of the survey data: “The surveys used in this manuscript were designed and implemented by the authors listed above on a voluntary basis outside of their post-doctoral positions at the time. The authors respect the confidentiality and anonymity of all respondents, and no identifiable private information was collected. Participation in both surveys was voluntary and the respondents could stop responding to the surveys at any time. The use of the data collected in these surveys was determined to meet the exemption criteria for secondary use of existing data [45 CRF 46.104 (d)(4)] by the University of North Dakota Institutional Review Board (IRB) on 08/29/2019. IRB project number: IRB-201908-045. Please contact Dr. Amanda Haage ([email protected]) for further inquiries.”

We have revised the teaching section within the results section to address the reviewers’ comments about oversimplification of the findings. The statement that applicants fulfill the teaching requirement for any university type has been removed, as requested by reviewers. Below is the revised text.

“ Levels of teaching experience varied among respondents

Discussion surrounding the academic job market is often centered on applicants' publications and/or funding record, while teaching experience generally receives much less attention. Accordingly, a candidate’s expected teaching credentials and experience vary, largely depending on the type of hiring institution. We asked applicants whether they focused their applications to a specific type of institution (R1, PUI, or both; see Box 1 for definitions), allowing us to examine teaching experience across R1 and/or PUI applicants. Most respondents applied to jobs at R1 institutions (Figure 5A), which may explain the focus on research-centric qualifications. It remains unclear what the emphasis on teaching experience is for search committees at R1 institutions, however the literature suggests that there seems to be a minimal focus (47). Additionally, there might be differences in departmental or institutional requirements that are unknown to outsiders. What is commonly accepted is that many applications to an R1 institution requires a teaching philosophy statement. The majority (99%) of our survey respondents have teaching experience (Figure 5B), with roughly half of applicants’ experience limited to serving as a Teaching Assistant (TA) (Box 1), and half reporting experience beyond a TA position, such as serving as an instructor of record (Figure 5B). The degree of teaching experience did not change based on the target institution of the applicant (Figure 5C), nor did the percentage of offers received significantly differ between groups based on teaching experience (Figure 5D).”

We did not look for a correlation between the number of postdoc positions and the ability to obtain faculty positions since the length of postdoctoral positions vary widely in our dataset (i.e. 1 postdoctoral position does not necessarily infer a shorter length of training compared to training spanning across more than one postdoctoral positions) and the majority of our respondents had a single postdoctoral appointment (see Figure 1E). In our survey, we did ask respondents for the total number of years (across all appointments) of postdoc training (Figure 1D) and this further reveals field-specific differences in expectations of postdoc appointments and length. Therefore we believe that the relationship between likelihood of receiving a faculty job offer and training extent is complex and likely requires additional considerations (e.g. quality of training over a training timespan, length of graduate training, etc) and is beyond the scope of the analysis in this paper.

We note the reviewer’s desire to provide recommendations for hiring institutions and applicants going on the job market. We have added a paragraph to the conclusion of the paper to address how the application process might be improved, so that it is a more positive experience. Additionally, we plan to share our perspective and recommendations for policy changes in a companion piece currently under preparation that provides opinion on data reported here.

The following text has been added to the conclusion of the manuscript:

“ Conclusions

The faculty job search process lacks transparency and data regarding what makes a successful applicant. Here, we began to address this need via a job market survey targeted towards the applicants themselves, including their perceptions of the application process. Of over 300 responses by job applicants, we did not receive a single positive comment on the process, despite the fact that 58% of our applicants received at least one job offer. Our data suggest that baseline thresholds exist for those more likely to receive a faculty job offer, but that many different paths can lead to a job offer. This variety of paths likely reflects both the applicant's preparation as well as different evaluation criteria used by individual search committees. For these reasons, we urge applicants not to conclude that lower than average metrics in any one area are automatically disqualifying. Indeed, we believe that increasing the transparency of the application process through systematic data collection will allow a more detailed study of the many paths to obtaining a faculty offer.

Our data show that there is a mental strain on applicants during this process, and we propose a number of potential solutions with the understanding that faculty hiring is a complex process involving multiple stakeholders. We believe the application process could be improved by simplifying the process, including standardizing application materials (e.g. requirements for research statements are similar for R1 institutions) and requesting references only after candidates are shortlisted, so that the burden of application preparation time can be reduced. Constructive feedback from mentors is vital for success during the application and interview preparation stages. Additionally, if possible, communication from search committees about unsuccessful applications would be helpful. We understand that these points may increase the workload of mentors and search committees but, if put into place, could alleviate some of the stress related to the job application process. In addition, applicants need to work to be sure their materials are strong and well-researched as the quality of these materials and demonstrating fit for a job posting are important to faculty on search committees (47). More work is needed to understand the challenges search committees face in order to improve their experience of the application process.

It is our hope that this and future work will not only allow all stakeholders to make informed decisions, but will also enable critical examination, discussion, and reassessment of the implicit and explicit values and biases being used to select the next generation of academic faculty. Such discussions are crucial in building an academic environment that values and supports all of its members.”

Author details

Jason D Fernandes is in the Department of Biomolecular Engineering, University of California, Santa Cruz, United States and is a member of the eLife Community Ambassadors programme

Contribution

Contributed equally with, competing interests.

Sarvenaz Sarabipour is in the Institute for Computational Medicine and the Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States and is a member of the eLife Early-Career Advisory Group

Christopher T Smith is in the Office of Postdoctoral Affairs, North Carolina State University Graduate School, Raleigh, United States

Natalie M Niemi is in the Morgridge Institute for Research, Madison, United States and in the Department of Biochemistry, University of Wisconsin-Madison, Madison, United States

Nafisa M Jadavji is in the Department of Biomedical Sciences Midwestern University, Glendale, United States and is a member of the eLife Community Ambassadors programme

Ariangela J Kozik is in the Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, United States

Alex S Holehouse is in the Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, United States

Vikas Pejaver is in the Department of Biomedical Informatics and Medical Education and the eScience Institute, University of Washington, Seattle, United States

Orsolya Symmons was at the Department of Bioengineering, University of Pennsylvania, Philadelphia, United States. Current address: Max Planck Institute for Biology of Ageing, Cologne, Germany

Present address

Alexandre W Bisson Filho is in the Department of Biology and the Rosenstiel Basic Medical Science Research Center, Brandeis University, Waltham, United States

Amanda Haage is in the Department of Biomedical Sciences, University of North Dakota, Grand Forks, United States

For correspondence

University of north dakota (start-up funds), national institute of general medical sciences (f32gm125388), national heart, lung, and blood institute (t32hl007749), midwestern university (start-up funds), washington research foundation (fund for innovation in data-intensive discovery), university of washington (moore-sloan data science environments project), washington university in st. louis (start-up funds).

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank Carol Greider, Feilim Mac Gabhann, Cori Bargmann, Mark Kunitomi, Needhi Bhalla, Lucia Peixoto, Sarah Stone and Dario Taraborelli for their valuable comments on an earlier version of this manuscript. The authors are members of the Future PI Slack community and would like to thank the entire Future PI Slack community and those who support them in this work.

Human subjects: This survey was created by researchers listed as authors on this publication, affiliated with universities in the United States in an effort to promote increased transparency on challenges early career researchers face during the academic job search process. The authors respect the confidentiality and anonymity of all respondents. No identifiable private information has been collected by the surveys presented in this publication. Participation in both surveys has been voluntary and the respondents could choose to stop responding to the surveys at any time. Both 'Job Applicant' and 'Search Committee' survey has been verified by the University of North Dakota Institutional Review Board (IRB) as Exempt according to 45CFR46.101(b)(2): Anonymous Surveys No Risk on 08/29/2019. IRB project number: IRB-201908-045. Please contact Dr. Amanda Haage ([email protected]) for further inquiries.

Publication history

• Received: December 3, 2019
• Accepted: June 3, 2020
• Accepted Manuscript published : June 12, 2020
• Version of Record published : July 14, 2020

© 2020, Fernandes et al.

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Understanding the job market

Most graduating PhDs seeking jobs in academia, government, or industry will participate in the job market for economists and may interview around the time of the ASSA Annual Meeting in early January.  

The job market process

The AEA provides a  guide to the job market process  created by John Cawley. It details the following:

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The American Economic Association provides the JOE Network (Job Openings for Economists Network) for employers and job-seekers who are participating in the annual economics job market cycle.

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Further reading

Auerbach, Alan J., Francine D. Blau, and John B. Shoven. 2004. "The Labor Market for New Ph.D. Economists: Panel Discussion." American Economic Review , 94 (2), pp. 286-290.

Coles, Peter, John Cawley, Phillip B. Levine, Muriel Niederle, Alvin E. Roth, and John J. Siegfried (2010) "The Job Market for New Economists: a Market Design Perspective" Journal of Economic Perspectives 24(4) (Fall): 187-206.

Ehrenberg, Ronald G. 2004. "Prospects in the Academic Labor Market for Economists." Journal of Economic Perspectives , 18 (2), pp. 227-238 .

Jihui, Chen, Qihong Liu, and Sherrilyn Billger. 2012. “Where Do New Ph.D. Economists Go? Evidence from Recent Initial Job Placements.” Journal of Labor Research , 34, pp. 312-338.

Krueger, Anne O. 1999. "Implications of the Labor Market for Graduate Education in Economics." Journal of Economic Perspectives , 13 (3), pp. 153-156 .

Smeets, Valerie, Frederic Warzynski, and Tom Coupe. 2006. "Does the Academic Labor Market Initially Allocate New Graduates Efficiently?" Journal of Economic Perspectives , 20(3), pp. 161-172.

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