6/6/2018: Monopsony Power in US labour market

I have recently written about rising firm power in labour markets, driven by monopsonisation of the markets thanks to the continued development of the contingent workforce: http://trueeconomics.blogspot.com/2018/05/23518-contingent-workforce-online.html. In this, I reference a new paper "Concentration in US labour markets: Evidence from online vacancy data" by  Azar, J A, I Marinescu, M I Steinbaum and B Taska. The authors have just published a VOX blog post on their research, worth reading: https://voxeu.org/article/concentration-us-labour-markets.

23/5/18: Contingent Workforce, Online Labour Markets and Monopsony Power

The promise of the contingent workforce and technological enablement of ‘shared economy’ is that today’s contingent workers and workers using own capital to supply services are free agents, at liberty to demand their own pay, work time, working conditions and employment terms in an open marketplace that creates no asymmetries between their employers and themselves. In economics terms, thus, the future of technologically-enabled contingent workforce is that of reduced monopsonisation.

Reminding the reader: monopsony, as defined in labour economics, is the market power of the employer over the employees. In the past, monopsonies primarily were associated with 'company towns' - highly concentrated labour markets dominated by a single employer. This notion seems to have gone away as transportation links between towns improved. In this context, increasing technological platforms penetration into the contingent / shared economies (e.g. creation of shared platforms like Uber and Lyft) should contribute to a reduction in monopsony power and the increase in the employee power.

Two recent papers: Azar, J A, I Marinescu, M I Steinbaum and B Taska (2018), “Concentration in US labor markets: Evidence from online vacancy data”, NBER Working paper w24395, and Dube, A, J Jacobs, S Naidu and S Suri (2018), “Monopsony in online labor markets”, NBER, Working paper 24416, dispute this proposition by finding empirical evidence to support the thesis that monopsony powers are actually increasing thanks to the technologically enabled contingent employment platforms.

Online labour markets are a natural testing ground for the proposition that technological transformation is capable of reducing monopsony power of employers, because they, in theory, offer a nearly-frictionless information and jobs flows between contractors and contractees, transparent information about pay and employment terms, and low cost of switching from one job to another.

The latter study mentioned above attempts to "rigorously estimate the degree of requester market power in a widely used online labour market – Amazon Mechanical Turk, or MTurk... the most popular online micro-task platform, allowing requesters (employers) to post jobs which workers can complete for."

The authors "provide evidence on labour market power by measuring how sensitive workers’ willingness to work is to the reward offered", by using the labour supply elasticity facing a firm (a standard measure of wage-setting (monopsony) power). "For example, if lowering wages by 10% leads to a 1% reduction in the workforce, this represents an elasticity of 0.1." To make their findings more robust, the authors use two methodologies for estimating labour supply elasticities:
1) Observational approach, which involves "data from a near-universe of tasks scraped from MTurk" to establish "how the offered reward affected the time it took to fill a particular task", and
2) Randomised experiments approach, uses "experimental variation, and analyse data from five previous experiments that randomised the wages of MTurk subjects. This randomised reward-setting provides ‘gold-standard’ evidence on market power, as we can see how MTurk workers responded to different wages."

The authors "empirically estimate both a ‘recruitment’ elasticity (comparable to what is recovered from the observational data) where workers see a reward and associated task as part of their normal browsing for jobs, and a ‘retention’ elasticity where workers, having already accepted a task, are given an opportunity to perform additional work for a randomised bonus payment."

The findings from both approaches are strikingly similar. Both "provide a remarkably consistent estimate of the labour supply elasticity facing MTurk requesters. As shown in Figure 2, the precision-weighted average experimental requester’s labour supply elasticity is 0.13 – this means that if a requester paid a 10% lower reward, they’d only lose around 1% of workers willing to perform the task. This suggests a very high degree of market power. The experimental estimates are quite close to those produced using the machine-learning based approach using observational data, which also suggest around 1% reduction in the willing workforce from a 10% lower wage."

To put these findings into perspective, "if requesters are fully exploiting their market power, our evidence implies that they are paying workers less than 20% of the value added. This suggests that much of the surplus created by this online labour market platform is captured by employers... [the authors] find a highly robust and surprisingly high degree of market power even in this large and diverse spot labour market."

In evolutionary terms, "MTurk workers and their advocates have long noted the asymmetry in market structure among themselves. Both efficiency and equality concerns have led to the rise of competing, ‘worker-friendly’ platforms..., and mechanisms for sharing information about good and bad requesters... Scientific funders such as Russell Sage have instituted minimum wages for crowd-sourced work. Our results suggest that these sentiments and policies may have an economic justification. ...Moreover, the hope that information technology will necessarily reduce search frictions and monopsony power in the labour market may be misplaced."

My take: the evidence on monopsony power in web-based contingent workforce platforms dovetails naturally into the evidence of monopolisation of the modern economies. Technological progress, that held the promise of freeing human capital from strict contractual limits on its returns, while delivering greater scope for technology-aided entrepreneurship and innovation, as well as the promise of the contingent workforce environment empowering greater returns to skills and labour are proving to be the exact opposites of what is being delivered by the new technologies which appear to be aiding greater transfer of power to technological, financial and even physical capital.

The 'free to work' nirvana ain't coming folks.

10/1/17: For Love or Money: Gender Gap in Online Labor Markets

The issue of a gender gap in the workplace, relating to gender differences in terms of occupations, is a highly contentious, politically charged and, despite a wealth of research on the subject, not fully explained to-date. One thing that economists generally agree on is that it is not one caused by a single factor or even a confluence of factors stemming from a single origin (e.g. access to education, time taken for maternity leave or concerted discrimination against women in the workplace, or any other set of closely linked factors). Instead, a range of exogenous, endogenous, personal, institutional, social etc factors determine the size of the gap, its existence and its evolution over time.

Hence, any new research identifying new factors is both - confusing (especially to those of us, who would stress the social equality dimension of the labour market outcomes) and important (especially to those of us, who prefer evidence-based policy and institutional responses to the issue). Note: the two sets of ‘us’ identified above are not mutually exclusive. In fact, I would suggest that majority of us - researchers, policymakers, analysts, and generally-speaking people, belong to both groupings, being concerned simultaneously with the social justice dimension of the labor market gender gaps and the need for well-designed policy responses to the problem.

With this preamble, here is a new piece of research on the subject. In their paper, titled “For Love or Money? Gender Differences in How One Approaches Getting a Job”, UC Berkeley researchers, Ng, Weiyi and Leung, Ming D (March 22, 2015: https://ssrn.com/abstract=2583592)note that current theories of the labor markets “conclude that women and men apply to different jobs”. However, these theories fail “to explain differences in how [men and women] may behave when applying to the same job.”

The authors “correct this discrepancy by considering gendered approaches to the hiring process. We propose that applicants can emphasize either the relational or the transactional aspects of the job and that this affects getting hired.”

What do these two approaches mean?

• “Relational job seekers focus on developing a social connection with their employer.”
• “Transactional job seekers focus on quantitative and pecuniary aspects of the job.”

The authors “hypothesize that the approach women take in applying for a job will differ from men. In particular, we believe that women, enacting their gender will focus on the relational aspects of the exchange: they emphasize the social, emotional aspects of the employment relationship and focus on mutually beneficial interests. On the other hand, men will be more transactional in nature: they focus more on the task at hand, their own qualifications and achievements, and highlight the quantifiable, observable and tangible aspects of the job.”

The evidence in support of these hypotheses is presented in the paper (for example, see Chart below).

Crucially, the authors note that “while both these approaches have their merits, this difference should result in variation in a person’s likelihood of being hired.”

The study then applies this theoretical hypothesis to see if it can account for “the hiring gap
between male and female job applicants we observe [in the actual data], net of controls for underlying ability, in an online market for contract labor, Elance.com.”

The reason the authors chose the online labor market data is that

1. “The online setting provides a richness and granularity of data which allows us to further unpack the nuances in the strategies employed by job seekers. The transparency of the setting provides a glimpse into the black box of the hiring process. For example, the data provides insight and access to the details of every job posting, the applicant pool, background work histories of each applicant, their photographs, how much they were willing to work for, the text of their job proposal, and the eventual winner of the job.”
2. Secondly, there is an “increasing trend towards self-employment whereby labor market participants eschew the long-term role as a corporate employee and instead participate on a contract basis, moving from job to job and working for different employers” which further validates the use of online labor market data. 

Based on the data and a barrage of econometric tests, the authors concluded that “women are more likely to be hired than men by about 5.2% [in the Elance.com type of the labor market]. Quantitative linguistic analysis on the unstructured text of job proposals reveals that women (men) adopt more relational (transactional) language in their applications. These different approaches affect a job seeker’s likelihood of being hired and attenuate the gender gap we identified.”

Besides own interesting insights and conclusions, the paper is well-worth reading for the quality of discussion it presents relating to existent social and economic literature on the subject of gender gaps. If anything, this discussion itself is worth paying close attention to, for it highlights the wealth of our knowledge on the subject as well as posits some serious questions about the future of gender gap research.

17/4/16: Human Capital, Management & Value-Added

The value of management to a given firm rests not only in more efficient use of physical resources and financial capital, as well as corporate / business strategy, but also in the ability of the firm to identify, hire, retain and enable high quality human capital. This is a rather common sense conclusion that might be drawn by any analyst of management systems and any business student.

However, the question always remains as to how much of the firm value-added arises from managerial inputs, as opposed to actual human capital inputs.

Stefan Bender, Nicholas Bloom, David Card, John Van Reenen, and Stephanie Wolter decided to attempt to quantify these differences. In their paper “Management Practices, Workforce Selection and Productivity” (March 2016, NBER Working Paper No. w22101: http://ssrn.com/abstract=2752306) they note that “recent research suggests that much of the cross-firm variation in measured productivity is due to differences in use of advanced management practices.”

“Many of these practices – including monitoring, goal setting, and the use of incentives – are mediated through employee decision-making and effort. To the extent that these practices are complementary with workers’ skills, better-managed firms will tend to recruit higher-ability workers and adopt pay practices to retain these employees.”

The authors then use a survey data on the management practices of German manufacturing firms, as well as data on earnings records for their employees “to study the relationship between productivity, management, worker ability, and pay”.

Per authors: “As documented by Bloom and Van Reenen (2007) there is a strong partial correlation between management practice scores and firm-level productivity in Germany. In our preferred TFP [total factor productivity] estimates only a small fraction of this correlation is explained by the higher human capital of the average employee at better-managed firms. A larger share (about 13%) is attributable to the human capital of the highest-paid workers, a group we interpret as representing the managers of the firm. And a similar amount is mediated through the pay premiums offered by better-managed firms.”

Human capital value-added is neither uniform across types of employees, nor is it independent of the management systems, which means that increasing the value of human capital in the economy requires more emphasis on the structure of the overall utilisation of talent, not just acquisition of talent. This is exactly consistent with the C.A.R.E. framework for human capital-centric economy that I outlined some years ago, here http://trueeconomics.blogspot.com/2013/11/14112013-human-capital-age-of-change.html, the framework of Creating, Attracting, Retaining and Enabling human capital.

The study also confirms that “looking at employee inflows and outflows, … better-managed firms systematically recruit and retain workers with higher average human capital.”

Overall, the authors concluded that “workforce selection and positive pay premiums explain just under 30% of the measured impact of management practices on productivity in German manufacturing.”

These results should add to questions about the ability of the Gig-economy firms, e.g. online platforms providers for labour utilisation, such as Uber, to significantly improve productivity in the economy. The reason for this is simple: contingent workforce talent pool is at least one step further removed from management than in the case of traditional employees. As the result, it is quite possible that contingent workforce productivity does not benefit directly from management quality. If so, that sizeable, ‘just under 30% of the measured impact’ in terms of improved productivity, arising from better management practices, workforce selection and pay premiums can be out of reach for Gig-economy firms and their contingent workers.

Again, as I noted repeatedly, including in my recent presentation at the CXC Global “Future of Work” Summit (see here: http://trueeconomics.blogspot.com/2016/04/7416-globalization-and-future-of-work.html), the key to developing a productive and sustainable Gig-economy will be in our ability to develop institutional, regulatory and strategic frameworks for improving management of human capital held by contingent workforce.

7/4/16: Globalization and the Future of Work

As promised, my slides deck from Tuesday's CXC Global event on the Future of Work and Contingent Workforce

6/4/16: Apps, Contingent Workforce and U.S. Employment Trends

Here is an interesting chart from the WSJ on the scale of the apps platforms-related Gig-economy employment and the underlying trends in growth on other contingent workforce:

Yes, overall app platform employment is low, as I mentioned in my presentation at the CXC forum on the future of workforce in San Francisco yesterday, but...

The big 'but' here is that overall app platforms-related employment growth is most likely contributing to the weakening of the quality of contingent workforce (in terms of skills, value added and sustainability), not strengthening it, and thus requires more systemic supports and changes in this workforce management and enablement.

More on this later, so stay tuned.

24/1/16: Unobserved Ability and Entrepreneurship

Yesterday, I posted some links relating to non-Cognitive Skills, contextualising these into the Gig-Economy related issues. Here is another interesting study relating to human capital and linking unobserved (and hard to measure) ability to entrepreneurship.

From the policymakers' and indeed investors and other market participants perspective, the question of why do some individuals become entrepreneurs is a salient one.

Identifying the causal relationships between external conditions, systems and policy environments, as well as behavioural and other drivers of entrepreneurship is of great value for setting policies and systems for enhancing the rate of entrepreneurship creation in the economy. A recent paper, titled "Unobserved Ability and Entrepreneurship" by Deepak Hegde and Justin Tumlinson (Ifo Institute at the University of Munich, April 20, 2015) attempts to answer to key questions surrounding the formation of entrepreneurship, namely:

1. Why do individuals become entrepreneurs? and
2. When do they succeed? 

The authors "develop a model in which individuals use pedigree (e.g., educational qualifications) as a signal to convince employers of their unobserved ability. However, this signal is imperfect…" So far - logical: upon attaining a level of education, and controlling for quality of that education (complexity of degree programme, subject matter, quality of awarding institution, duration of studies, attainment of grades etc), a graduate acquire more than a sum of knowledge and skills attached to the degree. They also acquire a signal that can be communicated to their potential employer that conveys they lateen (hidden) abilities; attitude toward work, aptitude, ability to work in teams, ability to work on complex systems of tasks etc.

Problem is - the signal is noisy. For example, a graduate with 4.0 GPA from a second tier university can have better potential abilities than a graduate with 3.7 GPA from a first tier ranked university. But that information may not be clearly evident to the potential employer. As the result, there can be a large mismatch between what an applicant thinks their ability is and what their CV signals to the potential employer.

In the paper, theoretical model delivers a clear cut outcome (emphasis mine): "…individuals who correctly believe their ability is greater than their pedigree conveys to employers, choose entrepreneurship. Since ability, not pedigree, matters for productivity, entrepreneurs earn more than employees of the same pedigree."

The authors use US and UK data to test their model prediction (again, emphasis is mine): "Our empirical analysis of two separate nationally representative longitudinal samples of individuals residing in the US and the UK supports the model’s predictions that

• (A) Entrepreneurs have higher ability than employees of the same pedigree, 
• (B) Employees have better pedigree than entrepreneurs of the same ability, and 
• (C) Entrepreneurs earn more, on average, than employees of the same pedigree, and their earnings display higher variance."

Point C clearly indicates that entrepreneurs earn positive risk premium for effectively (correctly, on average) betting on their ability over their pedigree. In other words, the take chance in themselves and, on average, win. The real question, however, is why exactly do their earnings exhibit higher variance - is it due to distributional effects across the entrepreneurs by their ability, or is it due to risk-adjusted returns being similar, or is it due to exogenous shocks to entrepreneurs incomes (e.g. tax system-induced or contractually-structured)?

These are key questions we do not yet address in research sufficiently enough to allow us to understand better what the Gig-Economy and entrepreneurship in modern day setting imply in terms of aggregate consumption, investment, household investment and decision making by entire household in terms of labour supply, educational choices (for parents and children), etc.

As some might say... it's complicated...

23/1/16: Non-Cognitive Human Capital

In my 2011 paper on the role of Human Capital in the emerging post-ICT Revolution economy, human capital will simultaneously:

1. Play increasingly more important role in determining returns to technical and processes innovation;
2. Become more diverse in its nature - or more diversified - spanning measurable and unmeasurable skills, traits, knowledge, attitudes to risk and innovation, capabilities etc.; and
3. Form the critical foundation of entrepreneurship and core employment base in the so-called Type 1 Gig-Economy - economy based on contingent workforce compered of highly skilled, highly value-additive professionals.

An interesting paper relating to the matter, especially to the last point, is a recent IZA Working paper (October 2015) titled “Non-Cognitive Skills as Human Capital” by Shelly Lundberg.

Per Lundberg: “In recent years, a large number of studies have shown strong positive associations between so-called “non-cognitive skills” — a broad and ill-defined category of metrics encompassing personality, socio-emotional skills, and behaviors — and economic success and wellbeing. These skills appear to be malleable early in life, raising the possibility of interventions that can decrease inequality and enhance economic productivity.”

Lundberg discusses “the extensive practical and conceptual barriers to using non-cognitive skill measures in studies of economic growth, as well as to developing or evaluating relevant policies. …There is a lack of general agreement on what non-cognitive skills are and how to measure them across developmental stages, and the reliance on behavioral measures of skills ensures that both skill indicators themselves, and their payoffs, will be context-dependent. The empirical examples show that indicators of adolescent skills have strong associations with educational attainment, but not subsequent labor market outcomes, and illustrate some problems in interpreting apparent skill gaps across demographic groups.”

From the Gig-Economy point of view, development of all (cognitive and non-cognitive) skills requires time and resources. In traditional workplace setting - of old variety - some of these resources and time allocations are supported / subsidised by employers (e.g. gym memberships, formal paid time off, formal paid career breaks, formal 'team building' activities, actual employer-paid training and education, employer-supported psychological wellness programmes for employees, and so on). In a Gig-Economy setting, these are not available, generally, to contingent workers.

Aside from having impact on contingent workforce skills and human capital, there are more 'trivial' considerations that should be put to analysis. Take, for example, health and psychological well-being. If a contingent workforce using company fails to assure the latter for its contingent workers, who is liable for any damages caused by over-worked, over-stressed, psychologically unwell contingent worker to the company clients?

Again, setting aside humanitarian, social and personal considerations, this question has implications for businesses using contingent workers:

• Insurance costs and coverage for businesses;
• Legal costs and coverage for business;
• Reputational risks for businesses;
• Counter-party risks for businesses; and so on

In a world where there is no such thing as a free lunch, Gig-Economy based companies should seriously consider how they are going to deal with potential costs of disruption from the Gig-Economy type of employment to life-cycle work practices and financial wellbeing of their contingent workers.


Note: More on the subject of non-cognitive skills and human capital:

• Early childhood: "Non Cognitive Skills and Personality Traits: Labour Market Relevance and their Development in Education & Training Systems" by Giorgio Brunello and Martin Schlotter, IZA DP No. 5743
• Early childhood and education: "Formulating, Identifying and Estimating the Technology of Cognitive and Noncognitive Skill Formation", by Flavio Cunha and James Heckman, University of Michigan Working Paper.
• Adult development: “Do Sporty People Have Access to Higher Job Quality?” by Charlotte Cabane, DIW working paper, 2010. 
• Related to sports and extracurricular activities: “Sports and Child Development” by Christina Felfe, Michael Lechner and Andreas Steinmayr, IZA Working Paper, 2011. And “Unemployment Duration and Sport Participation” by Charlotte Cabane,International Journal of Sport Finance, un-gated version ftp://mse.univ-paris1.fr/pub/mse/CES2011/11049R.pdf from 2011.
• Income effects: “Income Comparisons and Non-cognitive Skills” by Santi Budria and Ada Ferrer-i-Carbonell, DIW Working Paper, 2012.