Online Labor Market Research Articles

Just DM Me (Politely): Direct Messaging, Politeness, and Hiring Outcomes in Online Labor Markets

This study examines the role of text-based direct messaging systems in online labor markets, which provide a communication channel between workers and employers, adding a personal touch to the exchange of online labor. We propose the effect of workers’ use of the direct messaging system on employers’ hiring decisions and conceptualize the information role of direct messaging. To empirically evaluate the information role of the direct messaging system, we leverage data on the direct messaging activities between workers and employers across more than 470,000 job applications on a leading online labor market. We report evidence that direct messaging with a prospective employer increases a worker’s probability of being hired by 8.9%. However, the degree to which workers benefit from direct messaging is heterogeneous, and the effect amplifies for workers approaching employers from a position of disadvantage (lacking tenure or fit with the job) and attenuates as more workers attempt to message the same prospective employer. The effects also depend on message content. In particular, we find that the benefits of direct messaging for workers depend a great deal on the politeness of the workers, and this “politeness effect” depends on several contextual factors. The beneficial effects are amplified for lower-status workers (i.e., workers lacking tenure and job fit) and workers who share a common language with the employer. At the same time, the beneficial effects weaken in the presence of typographical errors. These findings provide important insights into when and what to message to achieve favorable hiring outcomes in online employment settings.

RETRACTED ARTICLE: Influence of the social and economic characteristics of Saudi women on their attitudes toward empowering them in online labor market

The study aims to investigate the attitudes of Saudi women toward empowering them in the online labor market and study the impact of the social and economic characteristics on their attitudes towar...

Deflated reputation using multiplicative long short-term memory neural networks

Current reputation systems are facing the inflation problem, which renders reputation systems to lose information and sometimes even cause misunderstandings. To address this problem, we propose a data-driven approach that combines natural language processing techniques with the conditional logit model for reputation deflation. We consider multiplicative long short-term memory neural networks (mLSTM) to predict sentiment scores from the feedback content. The mLSTM was pre-trained on 82.83 million unique reviews. We conduct experiments on one of the largest online labor marketplaces, Freelancer.com. We focus on comparing ratings and predicted sentiment scores in the online labor market. The results show that our proposed model can estimate deflated reputation information effectively. In addition, the estimated sentiment score is a quality disclosure signal, and has a better effect on the market outcome than the inflated reputation rating.

Lock-In Effects in Online Labor Markets

Lock-In Effects in Online Labor Markets

Agronomist vacancy in the online labor market: how employers want to see it

In the article, the author identifies the professional and critical competencies of the Russian agronomist. The purpose of the study is to analyze the requirements of employers for vacancies of agronomists. The main research method is the intellectual analysis of the data of the online labor exchange. The author emphasized that the majority of vacancies contain invitations for full-time employment of agronomists, have requirements for work experience from 1 year to 6 years, salary offers from 25,000 rubles. Employers have more diverse requirements for competencies that differ from the competencies set out in the professional standard.

Who Benefits from Online Gig Economy Platforms?

This paper estimates the magnitude and distribution of surplus from the knowledge worker gig economy using data from an online labor market. Labor demand elasticities determine workers’ wages, and buyers’ past market experience shapes both their job posting frequency and hiring rates. We find that workers on the supply side capture around 40% of the surplus from filled jobs. Under counterfactual policies that resemble traditional employment regulation, buyers post fewer online jobs and fill posted jobs less often, reducing expected surplus for all market participants. We find negligible substitution on the demand side between online and offline jobs by assessing how changes in local offline minimum wages affect online hiring. The results suggest that neither online or offline knowledge workers will benefit from applying traditional employment regulation to the online gig economy. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Cheap Talk Messages for Market Design: Theory and Evidence from a Labor Market with Directed Search

In a model with cheap talk, employers can send messages about their willingness to pay for higher ability workers, which job-seekers can use to direct their search and tailor their wage bid. Introducing such messages leads—under certain conditions—to an informative separating equilibrium which affects the number of applications, types of applications, and wage bids across firms. This model is used to interpret an experiment conducted in a large online labor market: employers were given the opportunity to state their relative willingness to pay for more experienced workers, and workers can easily condition their search on this information. Preferences were collected for all employers, but only treated employers had their signal revealed to job-seekers. In response to revelation of the cheap talk signal, job-seekers targeted their applications to employers of the right “type” and they tailored their wage bids, affecting who was matched to whom and at what wage. The treatment increased measures of match quality through better sorting, illustrating the power of cheap talk to improve market outcomes. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Designing Informative Rating Systems: Evidence from an Online Labor Market

Problem definition: Platforms critically rely on rating systems to learn the quality of market participants. In practice, however, ratings are often highly inflated and therefore, not very informative. In this paper, we first investigate whether the platform can obtain less inflated, more informative ratings by altering the meaning and relative importance of the levels in the rating system. Second, we seek a principled approach for the platform to make these choices in the design of the rating system. Academic/practical relevance: Platforms critically rely on rating systems to learn the quality of market participants, and so, ensuring these ratings are informative is of first-order importance. Methodology: We analyze the results of a randomized, controlled trial on an online labor market in which an additional question was added to the feedback form. Between treatment conditions, we vary the question phrasing and answer choices; in particular, the treatment conditions include several positive-skewed verbal rating scales with descriptive phrases or adjectives providing specific interpretation for each rating level. We then develop a model-based framework to compare and select among rating system designs and apply this framework to the data obtained from the online labor market test. Results: Our test reveals that current inflationary norms can be countered by reanchoring the meaning of the levels of the rating system. In particular, positive-skewed verbal rating scales yield substantially deflated rating distributions that are much more informative about seller quality. Further, we demonstrate that our model-based framework for scale design and optimization can identify the most informative rating system and substantially improve the quality of information obtained over baseline designs. Managerial implications: Our study illustrates that practical, informative rating systems can be designed and demonstrates how to compare and design them in a principled manner.

Incongruent skills and experiences in online labor market

Abstract In online labor markets (OLMs), employers evaluate job applicants based on the published information in their profiles. In this study, we empirically investigate how applicants’ skill information as well as their previous experiences affect employers’ hiring decisions when considering the heterogeneity of online labor markets. We find that (1) employers prefer job applicants with more incongruent skills in low-skill industries, but not in high-skill industries; (2) job applicants’ experiences attenuate employers’ price sensitivity on making hiring decisions in both high-skill industries and low-skill industries. Our findings provide a different perspective from the prior literature on OLMs by considering the labor market heterogeneity, i.e. high-skill industries and low-skill industries. We also provide new insights into employers’ assessment on job applicants’ skill and experience information.

Leveraging upfront payments to curb employee misbehavior: Evidence from a natural field experiment

Abstract We use a natural field experiment in which we hired over 2000 workers from an online labor market to explore how upfront payment affects worker motivation and misbehavior on the job. We start with a simple theory that shows paying upfront can increase misbehavior through reducing the perceived costs of cheating, but it can decrease misbehavior through generating a gift-exchange effect. Motivated by the theory, we designed a task that provided workers with opportunities to reciprocate or misbehave. A unique aspect of our design is that we are permitted an opportunity to measure the curvature of the gift-exchange value of the upfront payment. Our results suggest paying workers upfront induces a gift-exchange effect that is concave in the share of total wage paid upfront. Moreover, the impact is strong enough to suggest that small upfront payments are a cost-effective means for an employer to curb employee misbehavior.

Demand-Aware Career Path Recommendations: A Reinforcement Learning Approach

A skill’s value depends on dynamic market conditions. To remain marketable, contractors need to keep reskilling themselves continuously. But choosing new skills to learn is an inherently hard task: Contractors have very little information about current and future market conditions, which often results in poor learning choices. Recommendation frameworks could reduce uncertainty in learning choices. However, conventional approaches would likely be inefficient; they would model previous (often poor) observed contractor learning behaviors to provide future career path recommendations while ignoring current market trends. This work proposes a framework that combines reinforcement learning, Bayesian inference, and gradient boosting to provide recommendations on how contractors should behave when choosing new skills to learn. Compared with standard recommender systems, this framework does not learn from previous (often poor) behaviors to make future recommendations. Instead, it relies on a Markov decision process to operate on a graph of feasible actions and dynamically recommend profitable career paths. The framework uses market information to identify current trends and project future wages. Based on this information, it recommends feasible, relevant actions that a contractor can take to learn new, in-demand skills. Evaluation of the framework on 1.73 million job applications from an online labor market shows that its implementation could increase (1) the marketplace’s revenue by up to 6%, (2) contractors’ wages by 22%, and (3) the diversity of new skill acquisitions by 47%. A comparison with alternative recommender systems highlights the limitations of approaches that make recommendations based on previously observed learning behaviors. This paper was accepted by Chris Forman, information systems.

RELEVANCE OF THE BOLOGNA PROCESS ACHIEVEMENTS FOR THE CREATIVE CLASS DEVELOPMENT IN THE CONTEMPORARY ICT REVOLUTION OF POST- INDUSTRIAL SOCIETY

Article is devoted the processes of creative class development in pandemic crisis and contemporary ICT revolution of post-industrial society. Article is analysed the opportunities of European qualification system (EQF and OC) via the relevance of practical application in the new global off-line and on-line labor market.

Real-Time Labor Market Estimates During the 2020 Coronavirus Outbreak

This project aims to provide labor market data several weeks ahead of official statistics via a national online labor market survey of a sample representative of the US working age population. We refer to the survey as the Real-Time Population Survey (RPS). Core questions in the RPS closely follow the official US labor market survey (the CPS), which allows us to construct estimates consistent with theirs. The RPS was conducted twice monthly from March through October, and since then is conducted monthly.

Two to Tango? Psychological Contract Breach on Online Labor Markets

Despite some advantages over traditional (offline) labor markets – such as lower search costs, better matching and improved monitoring – online labor markets (OLMs) have not taken off as initially expected. In this paper, we study the factors that limit perceived project success on OLMs. Using psychological contract theory, we theorize how common OLM features including contracts with virtual monitoring, multi-freelancer projects, and simultaneous projects by a client trigger the perception of psychological contract breach among OLM participants and reduce perceived project success for both participants. We test these hypotheses using an extensive dataset with more than 143,000 transactions on the world’s largest freelancing platform, Upwork, and find that – contrary to predictions from agency theory – projects equipped with strict freelancer monitoring (hourly-pay contracts) and projects enabling peer comparison (multi-freelancer projects or multiple simultaneous projects), lead to lower perceived project success both from the freelancer’s and the client’s perspective. Our work implies that transactions on online labor markets should not be viewed solely as agency relations, and that some features that supposedly reduce agency costs and improve efficiency on OLMs come at the cost of triggering the perception of psychological contract breach.

Invisible Cages: How Opaque Control Tactics Influence Worker Behavior

Most studies of control focus on how organizations’ visible control tactics impact worker behavior, as exemplified by the metaphor of the “iron cage.” Drawing on interviews, archival data, and observations as a registered user on an online labor market, I studied how such a market’s opaque control tactics affected freelancers’ work practices. Specifically, my findings show that the market’s implementation of an opaque algorithmic evaluation led workers’ experiences to resemble less of an iron cage and more of an “invisible cage.” That is, freelancers were never able to understand whether and how their actions contributed to changes in their rating evaluation. The results suggest that by leveraging significant power and information asymmetries over workers, an invisible cage inverts how control operates because the organization can change the criteria for success and rate at which changes occur without notice or recourse, rendering the control tactics opaque to workers. I found that even highly rated workers, among the primary intended beneficiaries of the opaque algorithmic evaluation, exhibited paranoia and wariness, taking steps to withdraw from the platform. I discuss the implications and boundary conditions these findings have as related to the literature on control and evaluations in the emerging economy.

For Love or Money? Factors Associated With the Choice Between Couple-Based Versus Individual Financial Coaching

There has been much effort in recent years to address some of the damage of the recent global financial crisis with financial literacy education. Little research has been done, however, on the factors that might affect the decision to seek individual versus couples-based financial education. We used a survey instrument administered via the online labor market, Mechanical Turk, to examine factors associated with this outcome: whether members of a couple would choose individual or couples financial coaching. All participants were screened for current membership in a committed relationship for at least 6 months. Most participants reported a preference for couples versus individual financial counseling. Key factors that predicted a likelihood to opt for couples' counseling include gender, age, and satisfaction with one's relationship. Results from this study suggest that how and why consumers seek financial education may be affected by social, cultural, emotional, and relational factors as well as financial concerns. Such factors should be considered by practitioners in this field if program marketing, design, and delivery are to be relevant to participants and effective.

Amazon Mechanical Turk workers can provide consistent and economically meaningful data

AbstractAmazon Mechanical Turk (AMT) is an online labor market that is being used increasingly often in the social sciences. This occurs despite significant questions regarding efficacy of the platform. In this article, we attempt to address some of these questions by exploring the consistency of the characteristics of individuals who participate in studies posted on AMT. The primary individuals analyzed in this study are subjects who participated in at least two of eleven experiments that were run on AMT between September of 2012 and January of 2018. We demonstrate subjects consistently report their age, gender, subjective willingness to take risk, and impulsiveness. Further, subjective willingness to take risk is found to be significantly correlated with decisions made in a simple lottery experiment with real stakes—even when the subjective risk measure is reported months, sometimes years, in the past. This suggests the quality of data obtained via AMT is not terribly harmed by the lack of control and low stakes.

Who is agile? An investigation of the psychological antecedents of workforce agility

Understanding the precursors of workforce agility is crucial for organizations that wish to detect and foster agility in their employees. This research investigates the psychological antecedents of workforce agility, and develops a scale to measure them. First, in a qualitative study, we interviewed 11 professionals to identify the core processes of workforce agility, and the psychological resources needed to support them. Second, through a quantitative study of 808 members of the US‐based Microworkers online labor market, we developed and tested our agility index, a psychometric measure of the potential for agility, which was based on the resources identified in the first study. We believe our research offers managers valuable insights into the psychological antecedents of workforce agility, which will help them assess, train, and guide employees.

The Demotivating Effects of Communicating a Social-Political Stance: Field Experimental Evidence from an Online Labor Market Platform

Despite a recent surge in corporate activism, with firm leaders communicating about social-political issues unrelated to their core businesses, we know little about its strategic implications. This paper examines the effect of an employer communicating a stance about a social-political issue on employee motivation, using a two-phase, preregistered field experiment in an online labor market platform. Results demonstrate an asymmetric treatment effect of taking a stance depending on whether the employee agrees or disagrees with that stance. Namely, I observe a demotivating effect of taking a stance on a social-political issue with which employees disagree and no statistically significant motivating effect of taking a stance on a social-political issue with which employees agree. This study has important implications for the nascent scholarship on corporate activism, as well as the scholarship on strategic human capital management. This paper was accepted by Greta Hsu, organizations.

Who are you? Inconsistent identity reporting in online labor markets

Purpose Online labor pools continue to grow in popularity, serving as an inexpensive, readily available source of research data. Despite early skepticism, accounting research has generally found evidence that supports the use of these labor pools. However, one important distinction unique to online labor markets is the pre-screening process that qualifies participants for future studies. As the identity of online participants are generally unknown, researchers rely on participants’ self-reported identities to establish a pool of qualified respondents. This paper aims to provide evidence of the reliability of online participants’ answers to pre-screening questions. Design/methodology/approach Following the current literature’s recommendations on pre-screening candidates, I employ a multi-stage design using two similar surveys that are taken by each participant. I compare participants' answers on the first survey and the second survey to provide evidence on the consistency of their responses. Findings My results indicate that online participants are responding with substantial inconsistency to survey questions related to their social identity at a rate that may not be tolerable for many researchers. This has implications for researchers who use these online labor markets to represent a particular population of interest. Originality/value This study is the first to provide evidence on the consistency of online labor market participant responses. Additionally, it is the first to test the efficacy of current recommended guidelines for identifying populations of interest in the literature.