Hiring Process – Double Information Asymmetry

9

October

2021

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Hiring efforts can be considered futile when a freshly on-boarded employee decides to leave the company after barely a month of two of trial. However, recently companies have come to the conclusion that these mishaps might be due to a reason other than the fault of the specific individual that decided to leave the company.

Many of you probably know this already, hiring processes nowadays are extremely complex in their format. Usually composed of a multitude of stages from interviews to technical tests, the average hiring process takes about one month to be complete. The essential lesson here is that if a person decides to leave during their trial period, it is not due to a lack of motivation as they had the will to through the entirety of the hiring process in the first place.

The problem that was investigated was an instance of double information asymmetry.

On the job seeker perspective, the candidates have the advantage of knowing exactly what is sought after by the companies. By analyzing the job description, a person could precisely determine what experiences, traits of character or values that the company is looking for. Usually, by exploiting this information, the candidates will personalize and modify their profile in order to meet the employer’s wants. This is a case of Morale Hazard as the misinformation is conducted prior to the transaction, i.e. hiring that person, under the form of signaling the employer with non-representative characteristics.

On the other side, from the point of view of the employer within a company, the job description will also be an altered version of what the real job is. The Morale Hazard here is also done through signaling, by misrepresenting the core job functions in order to attract as much talent as possible. For example, stating that the job’s missions are usually 50% administrative tasks and 50% project management tasks while in reality the ratio might be closer to 80-20.


Interviews are used as screening tools to try to separate the most suited candidates from the least suited. However, these tools can prove to be less effective than expected as the interviewee can easily prepare for it in a non-truthful way.

Here we mention ‘non-truthful’ or mention how sneaky it might be to exaggerate about your profile, but the candidates cannot be blamed. The double information asymmetry happens because there is also an asymmetry of goals.

On one side, the goal of the job seekers is to get the job. It is not to ‘enhance the company’s growth’ or ‘help them in their mission’ as commonly stated during interviews, but often it is simply to get hired. And on the other side, of course the company’s goal is to find the best person for the job at a reasonable salary.

An interesting solution to put an end to this information asymmetry would be to use platforms that accept only one resume per candidate (instead of one per job application) and where details about the job description are revealed later, such as during the first interview.

What are your thoughts and ideas to tackle this information asymmetry?

Resources:
LinkedIn post that inspired me (in French):
https://www.linkedin.com/feed/update/urn:li:activity:6851817268762968064/

Article for further discussion:
https://www.business2community.com/human-resources/how-to-crush-hiring-by-recognizing-information-asymmetry-02251645

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The fear of being replaced by automation

8

October

2020

No ratings yet. The world is becoming more and more technology driven – IoT, big data analytics, AI and robots. We are living in an age in which automation technologies such as robots play an essential role. Automation has many benefits such as it increases productivity, optimises processes and saves costs (McKinsey & Company, 2017). However, the increase in automation also raises growing concerns of workers about being replaced by these technologies. Studies have shown that 1.5 million UK workers are at a high risk of being replaced by automation (White et al., 2019) and that automation will steal 20% of all UK jobs in 2030 (McKinsey & Company, 2017). Furthermore, research by the economists Frey and Osborne (2017) has shown that about 47 percent of total US employment is at risk of job loss due to automation. The question therefore arises, whether workers have to fear to be replaced by automation?

 

Some economists and researcher believe in the skilled-biased technological change theory. This theory suggests that the demand for low skilled workers decreases relative to the demand for high skilled workers due to technological changes (Bound & Johnson, 1995; Acemoglu & Autor, 2011). According to this approach, automation therefore decreases the demand for low-skilled workers. However, others such as the labour economists David Autor argue that automation does not decrease the overall level of employment, but rather substitute and complement jobs. Jobs that require repetitive tasks are likely to be replaces by automation whereas jobs that perform non routine cognitive tasks are complemented by automation (Autor et al., 2003).

 

What many people do not consider is that automation technologies also create new jobs. Automation changes the types of jobs available in the market by creating new job opportunities. Many new jobs have been created over the past years, due to automation technologies such as software developers, programmers and data analysists. Therefore, I do not believe that workers have to fear that they are replaced by automation. Rather than worrying about being replaced by automation, workers should invest in good education and acquire automation-proof skills such as communication, management and creative skills. Robots can replace humans in many tasks, however, some skills especially inter-personal skills, cannot be replaced by robots.

 

References:

Autor, D. H., & Acemoglu, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In Handbook of labor economics (Vol. 4, pp. 1043-1171). Elsevier.

Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly journal of economics, 118(4), 1279-1333.

Bound, J., & Johnson, G. (1995). What are the causes of rising wage inequality in the United States?. Economic Policy Review, 1(1).

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological forecasting and social change, 114, 254-280.

McKinsey & Company. (2017). Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages. Retrieved 25 March 2020, from https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages.

White, S., Lacey, A., & Ardanaz-Badia, A. (2019). The probability of automation in England: 2011 and 2017.

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Policy makers have to understand the forces of AI and technology better

12

October

2019

No ratings yet. Fast development and spread of technology, especially AI is reshaping our society and the way value is created. The economy is constantly requiring different skills from the workforce. Aligning human capabilities to the future of technological development is essential for the prosperity of our society. Governments need to be prepared for these changes and empower citizens to be prepared for the labour market of the next decades.

General purpose technologies that reshape several aspects of life such as the steam engine, electricity or the internet have all had significant effects on the way humans work (Jovanovic & Rousseau, 2005). The rise of AI and job automatization is probably not new for anyone reading this post. Predicting their effect on the job market is far from easy though.

Brynjolfsson and McAfee (2015) argue that humans will not be replaced by AI in the way internal combustion engine replaced horses. They believe that some services require interpersonal elements that machines will never be able to substitute. It is indeed hard to imagine seeing robots performing in a theatre or a good therapist without the ability of forming human bonds with the patient. Brynjolfsson and McAfee (2015) also say that the creative innovative capabilities of humans can not be replaced either. The ability of forming goals and hypothesises to reach them distinguishes us from artificial intelligence.

Governments have the most power and responsibility to act and improve the latter strengths of their people. Mitchell and Brynjolfsson (2017) published an article in Nature that examined the capabilities of governments to form policies that can effectively cut the mismatch of the workers’ skillset and the requirements of technological change.

The first problem is that we do not even understand the past changes precisely enough. Among many other researchers, Restrepo (2015) showed that the employment rate in clerical and sales jobs have been sharply decreasing from the first days of the internet until now. Professional jobs were the opposite while the share of service jobs has started to slowly decrease recently. This kind of macro level data is available for policy makers. However, it is not detailed enough to guide successful policy making.

Mitchell and Brynjolfsson (2017) draw the attention to several already existing labour force data sources government could leverage. Universities have detailed profile of their students and their careers. Job-seeker websites have extensive knowledge of the skills required by employers and their dynamics of change. The phenomena of freelancers and gig economy employees are also black spots for the government.

Governments will have to find means to access more job market data and learn to use it for data driven decision making. I believe that it is the common interest of private sector and governments to capture the societal value of sharing this data between organizations.

 

References

Brynjolfsson, E. & McAffe, A. (2015). Will Humans Go the Way of Horses? Labor in the Second Machine Age. Foreign Affairs https://www.foreignaffairs.com/articles/2015-06-16/will-humans-go-way-horses

Jovanovic, B. & Rousseau, P.,L. (2005) General Purpose Technologies. NBER Working Paper No. 11093. https://www.nber.org/papers/w11093

Mitchell, T. & Brynjolfsson, E. (2017) Track how technology is transforming work. Nature. https://www.nature.com/news/track-how-technology-is-transforming-work-1.21837

Restrepo, P. (2015). Skill Mismatch and Structural Unemployment, Massachusetts Institute of Technology, http://pascual.scripts.mit.edu/research/01/PR_jmp.pdf

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