Online Labor Market Research Articles

Asymmetric labor-supply responses to wage changes: Experimental evidence from an online labor market

We test whether labor supply responds symmetrically to wage increases and decreases using a randomized real effort online experiment. The results show that wage increases have smaller effects on labor supply than wage decreases of equal magnitude, especially on the extensive margin where the response to a wage decrease is twice that to a wage increase. This finding suggests that labor-supply responses to wage changes are asymmetric. We discuss the potential mechanisms behind our results including standard models of labor supply, reference dependence in consumption and reciprocity.

Effects of Conversation Politeness on Hiring Decision in Online Labor Markets: An Inverted U-Shaped Relationship Exploration

This study examined the effect of politeness, as a key reflection of linguistic features of conversation in the online labor marketplace, on hiring behavior. Drawing on the politeness theory, a non-linear relationship was theorized. A hypothesis was put forward and examined against a large-scale archival dataset from a Chinese online labor market. Using an econometric model, the results demonstrated that there was an inverted U-shaped relationship between politeness and hiring decisions. The study offers theoretical implications to the online labor market literature and politeness theory by providing empirical insights on the role of politeness in hiring decision. In addition, our findings offer beneficial and practical contributions for vendors and platform operators.

Adjusting Skillset Cohesion in Online Labor Markets: Reputation Gains and Opportunity Losses

In online labor markets, contractors' ability to charge for their services largely depends on their skills. To keep up with shifting labor market needs, contractors often expand their skills with new skills. When the new skills are similar to the contractors' current skills, they often increase skillset cohesion (i.e., the average similarity of skills in a skillset). However, when the new skills have little similarity with contractors' current skills, skillset cohesion decreases. How do such adjustments of skillset cohesion affect contractor value in digital workplaces for short-term work? We argue that skillset adjustments affect market value through changes in the contractor's perceived reputation on the new skills and the additional job opportunities that new skills create. We hypothesize that compared with skills that decrease cohesion, skills that increase cohesion result in reputation gains and opportunity losses. Analysis of a panel data set of 47,638 tasks illustrates that for hourly wages, reputation gains are smaller than opportunity losses; hence, all else being equal, increasing skillset cohesion has a relatively negative effect on wages. However, for hiring rates, the opposite is true: Increasing skillset cohesion increases contractor hireability.

Hourly Wages in Crowdworking: A Meta-Analysis

In the past decade, crowdworking on online labor market platforms has become an important source of income for a growing number of people worldwide. This development has led to increasing political and scholarly interest in the wages people can earn on such platforms. This study extends the literature, which is often based on a single platform, region, or category of crowdworking, through a meta-analysis of prevalent hourly wages. After a systematic literature search, the paper considers 22 primary empirical studies, including 105 wages and 76,765 data points from 22 platforms, eight different countries, and 10 years. It is found that, on average, microtasks results in an hourly wage of less than $6. This wage is significantly lower than the mean wage of online freelancers, which is roughly three times higher when not factoring in unpaid work. Hourly wages accounting for unpaid work, such as searching for tasks and communicating with requesters, tend to be significantly lower than wages not considering unpaid work. Legislators and researchers evaluating wages in crowdworking need to be aware of this bias when assessing hourly wages, given that the majority of literature does not account for the effect of unpaid work time on crowdworking wages. To foster the comparability of different research results, the article suggests that scholars consider a wage correction factor to account for unpaid work. Finally, researchers should be aware that remuneration and work processes on crowdworking platforms can systematically affect the data collection method and inclusion of unpaid work.

Learning to Successfully Hire in Online Labor Markets

Hiring in online labor markets involves considerable uncertainty: which hiring choices are more likely to yield successful outcomes and how do employers adjust their hiring behaviors to make such choices? We argue that employers will initially explore the value of available information. When employers observe successful outcomes, they will keep reinforcing their hiring strategies; but when the outcomes are unsuccessful, employers will adjust their hiring behaviors. To investigate these dynamics, we propose a two-component framework that links hiring choices with task outcomes. The framework’s first component, a Hidden Markov Model, captures how employers transition from unsuccessful to successful hiring decisions. The framework’s second component, a conditional logit model, estimates employer hiring choices. Analysis of 238,364 hiring decisions from a large online labor market shows that, often, employers initially explore cheaper contractors with a lower reputation. When these options result in unsuccessful outcomes, employers learn and adjust their hiring behaviors to rely more on reputable contractors who are not as cheap. Such hiring tends to be successful, guiding employers to reinforce their hiring processes. As a result, the market observes employers transition from cheaper, lower-reputation options with poorer performance to more expensive reputable options with better performance. We attribute part of this behavior to employer confidence and risk attitude, which can change over time. This work is the first to investigate how employers learn to make successful hiring choices in online labor markets. As a result, it provides platform managers with new knowledge and analytics tools to target employer interventions. This paper was accepted by Anandhi Bharadwaj, information systems. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mnsc.2022.4426 .

Contract Choice, Moral Hazard and Performance Evaluation Evidence from Online Labor Markets

Due to the spatial and temporal separations between clients and freelancers, online labor markets (OLMs) are particularly susceptible to issues related to information asymmetry. Based on the economics of information, we hypothesize that contract format choice—i.e., between the fixed-priced (FP) contract and the time-and-materials (TM) contract—has important implications for preventing moral hazard during contract execution, and therefore will influence the client’s perceived contractual performance upon project completion. We test the predictions by assembling a dataset of analytics projects completed by freelancers on Upwork, the largest online freelancing platform. We find that consistent with our hypothesis, freelancers under a TM contract on average receive significantly lower performance ratings by their clients compared to those under a FP contract. Interestingly, we also find that the expertise requirement of a project moderates the effect of contract choice on client satisfaction; specifically, the negative impact of TM contract is smaller (less negative) when a project requires expert-level skills. Our study offers useful insights into an important institutional determinant of contractual performance evaluation, which has profound implications for freelancers’ reputation in OLMs.

“I’m sorry.”—“No, you’re not!” Experimental Evidence of the Effect of Hypocritical CSR Communication

The amount of micro-level corporate social responsibility (CSR) research focusing on employee behavior has been continually growing over the past two decades. While most of the research has been focusing on positive behavioral and attitudinal outcomes of CSR, adverse effects have been largely neglected. Applying social and moral identification theory, this research project explores how hypocritical communication, i.e., the misalignment between CSR and a company’s crisis response strategies in the aftermath of a corporate scandal, affects worker misbehavior. Based on field experimental data from an online labor market (n = 478), I find evidence that perceived hypocrisy increases undesirable worker behavior, such as shirking and withholding job effort. Given the immense financial cost caused by employee misbehavior managers and company leaders should make managing CSR perceptions a key priority. My contribution to our understanding of microlevel CSR theory, in this case the link between hypocritical CSR and employee misbehavior, thus, can provide a basis for cost savings and risk mitigation.

A Prediction Model of Human Resources Recruitment Demand Based on Convolutional Collaborative BP Neural Network.

This paper presents an in-depth study and analysis of the prediction model of force resource recruitment demand using a convolutional neural network combined with a BP neural network algorithm. BP neural network technology is introduced to be applied to enterprise management talent assessment activities. Using BP neural network has strong parallel processing characteristics, as well as unique adaptive learning and feedback adjustment capabilities while combining the traditional enterprise talent assessment system, to build a business management talent assessment model based on BP neural network technology, to circumvent the possible influence of subjective factors in talent assessment, reduce assessment errors, and improve the accuracy and validity of the assessment. The first layer of convolutional layers may only extract some low-level features such as edges, lines, and corners, and more layers of the network can iteratively extract more complex features from low-level features. The constructed applicant reputation evaluation model based on multiplicative long- and short-term recurrent neural network and the hybrid project recommendation model based on conditional variational self-encoder were experimented on Freelancer's dataset for effectiveness, respectively, and the experimental results showed that the proposed employer hiring decision model, reputation analysis model, and applicant project recommendation model have more reliable performance compared with the existing models. The research results achieve more efficient matching of labor supply and demand in the online labor market and provide technical support for the online labor market platform to realize personalized, intelligent, and accurate services for both employers and applicants.

Dynamics of Gig Work Economy: An Investigation of Worker Perspectives on Gig Work

Online technology has developed to an extent where traditional labor markets have transformed, creating opportunities for people and businesses to participate in the global marketplace by employing contract labor. Application-based transportation including ridesharing apps, food and commodity delivery platform apps, and other consumer-facing services brought in the concepts of flexible work reshaping gig work into the mainstream creating millions of jobs in the areas of operation. Even though the gig economy provides flexibility in work, the real situation of the gig workforce seems very complex. The article discusses the impacts of the gig economy on labor markets by identifying the challenges faced by gig workers in the online labor market and the impact of gig economy on their health and well-being. A detailed field research conducted among the gig workers in the state of Kerala aided examining the labor market trends, motives, and effect beyond the gig economy. It further supplemented developing specific actions to accelerate the development of the gig economy to ensure worker protection and safety needs in creating an inclusive economy.

Implementation of Online Labor Market Information System Innovation (Sipaja-Online) on Community Satisfaction Index at the Manpower and Transmigration Office of Banjar District

LILY AGUSTRIANA (1720422320005), 2021 "Implementation of Online Job Market Information System Innovation (Sipaja-Online) on the Community Satisfaction Index at the Manpower and Transmigration Office of Banjar Regency". Under the guidance of Prof. Udiansyah and Muhammad Ansar Nur. The online Job Market Information System (SIPAJA) service innovation is the creation of an online-based Job Seeker Card (AK1) and e-register, an inclusive excellent service system by facilitating and providing online Job Seeker Card (AK1) services. Job seekers can make Job Seeker Cards (AK1) wherever they are, in addition to services in the form of Job Market Information, Placement by company (report) and Notifications/whatsapp for reporting the status of job seekers who are already working. Broadly speaking 4 (four) types of services The services provided include online-based Job Seeker Card Services, Job Market Information Services which are directly administrated by the company, Placements by Companies (report) and Notifications/whatsapp for reporting on the status of job seekers who are already working (Report Placements). The research method used is a qualitative research approach and descriptive type. The research informants in this study were the head of the expansion of job opportunity development, placement and transmigration, as well as the head of labor protection, and the Sipaja Online management staff at the Manpower and Transmigration Service. The data collection techniques are observation, interviews and documentation taking into account the assessment of the Community Satisfaction Index in October 2021. The focus of the research uses George C. Edward III's theory of policy implementation. Data analysis used is data reduction, data presentation and verification. The results of the study show that there are 4 factors in the implementation of public policies, namely communication factors, resources, disposition (executive attitudes), and bureaucratic structure factors. All of these factors have been successfully implemented in the field, such as the disposition factor, namely the indicators for appointing bureaucrats and indicators for incentives, which have been successfully implemented. However, the communication factor is still not implemented optimally in the field because in the three indicators contained in the communication factor only the consistency indicator has been implemented optimally. Meanwhile, the other two indicators, namely the transmission and clarity indicators, have not been implemented optimally. Due to the lack of socialization given to the public regarding Sipaja Online and the Job Market.

Skill Spanning in the Online Labor Market: A Double-Edged Sword?

Freelancers in online labor markets often display many skills in their profiles to increase their chances of being hired. However, such behavior may lead to the skills they display straddling multiple categories, that is, “skill spanning.” In this paper, we extend the concept of category spanning into online labor markets in the form of skill spanning and empirically examine (1) how freelancers’ skill spanning affects employers’ hiring decisions for two different types of jobs (low- and high-skill jobs, respectively), and (2) how freelancers’ skill matching moderates the effects of skill spanning on employers’ hiring decisions. Based on a unique dataset of 12,428 high-skill jobs and 19,875 low-skill jobs on a leading online labor platform, we find that freelancers’ skill spanning has different impacts on employers’ hiring decisions for these two job types. Specifically, for high-skill jobs, freelancers’ skill spanning reduces their likelihood of winning contracts; however, for low-skill jobs, freelancers’ skill spanning and their probabilities of winning contracts demonstrate an inverse U-shape relationship. Furthermore, freelancers’ skill matching can moderate the negative effects of skill spanning for high-skill jobs but not for low-skill jobs. Our findings provide guidelines for different stakeholders in online labor markets, including freelancers and platform owners.

Contract Choice, Moral Hazard, and Performance Evaluation: Evidence from Online Labor Markets

Contract Choice, Moral Hazard, and Performance Evaluation: Evidence from Online Labor Markets

Multidimensional Procurement Auction with Loss-Averse Workers in Online Labor Markets

Multidimensional Procurement Auction with Loss-Averse Workers in Online Labor Markets

ВИРТУАЛЬНАЯ ТРУДОВАЯ МИГРАЦИЯ ВЫСОКОКВАЛИФИЦИРОВАННЫХ СПЕЦИАЛИСТОВ И ОНЛАЙН-РЫНОК ТРУДА

The article examines a new form of labor migration of highly qualified specialists’ virtual migration. The relevance of the issues of migration of highly qualified specialists in the conditions of digitalization is substantiated. It is noted that digitalization has contributed to the emergence of an online labor market, where new labor and migration relations are being formed, a new type of employee appears – a digital worker, digital labor platforms are being created. The positive and negative consequences of virtual migration are determined. There are new migration opportunities for highly qualified specialists within the country. In conclusion, Russia’s growing need for highly qualified specialists from the IT sector is noted. Government measures are required to ensure balance in the Russian labor market in the conditions of digitalization of the Russian economy

Role of Causality Orientations in Predicting Alcohol Use and Abstinence Self-Efficacy

ABSTRACT In the present study, we examined the ability of Self-Determination Theory’s causality orientations to predict alcohol use and abstinence self-efficacy. We also provided suggestions for counselors supporting client and student autonomy in clinical practice. Objectives: This study sought to answer the following questions: (a) Does a person’s causality orientation (autonomy, control, and impersonal) predict their alcohol use? (2) Does a person’s causality orientation (autonomy, control, and impersonal) predict their temptation to use drugs and alcohol? (3) Does a person’s causality orientation (autonomy, control, and impersonal) predict their confidence to use drugs and alcohol? Method: We utilized Amazon’s Mechanical Turk (MTurk), a crowdsourced online labor market approach to collect data from a community sample. Results: The results suggest heightened impersonal orientation was predictive of increased alcohol use and increased temptation to use while control orientation was also predictive of increased temptation. Higher autonomous orientation was predictive of increased confidence to not use while impersonal and controlled were not. Conclusion: This study’s findings underline the importance of SDT in substance use prevention, initiation, and treatment, and open the door to more empowering interventions. Through the intentional use of SDT, individuals may feel more empowered to set and achieve goals, feel a greater sense of control in their lives, strengthening their overall autonomy.

Problematic Smartphone Use Influences the Relationship Between Experiential Avoidance and Anxiety.

Experiential avoidance (EA), an unwillingness to stay in contact with unwanted inner experiences (e.g., emotions, thoughts, and bodily sensations), has been implicated in the development and maintenance of anxiety. Individuals with high levels of EA are more likely to employ maladaptive coping strategies (i.e., avoidance behaviors), which exacerbates emotional distress and anxious arousal. As smartphone ownership has become increasingly common in recent years, problematic smartphone use (PSU) has been suggested to serve as a "safety behavior" in situations in which individuals believe that they might experience emotional discomfort. That is, individuals experiencing emotional distress and/or anxious arousal may overengage in the use of technology to relieve emotional distress. As such, the purpose of this study was to examine PSU as a moderator of the relationship between EA and anxiety. Adult participants (N = 294) recruited through Amazon's Mechanical Turk (MTurk), an online labor market, completed an online battery of self-reported measures. Results indicated that the relationship between EA and anxiety became significantly stronger as PSU increased, thereby suggesting that PSU may exacerbate the effect of EA on anxiety. These findings are a first step toward the development of risk profiles that incorporate EA and PSU. Such risk profiles may be beneficial for early identification and intervention for individuals at high risk for the development of anxiety.

Examining associations between concussion history, subjectively experienced memory problems, and general health factors in older men

Objective Examine associations between subjective memory complaints (SMCs), concussion history, and cognitive, psychological, and physical health in older men from the general population. Method Participants were 504 men aged 50–79 who were recruited using an online labor market. Participants completed a survey assessing demographics, medication history, concussion history, recent memory problems, and additional aspects of recent cognitive, mental, and physical health. Results Men with a lifetime history of ≥3 concussions also reported a lifetime history of being prescribed medication for anxiety (46.8%), depression (43.5%), chronic pain (79.0%), and high blood pressure (66.1%). When asked about symptoms experienced over the past year and the past week, they endorsed higher rates of cognitive and mental health problems, migraines, and difficulties with sleep and fatigue. The multivariable logistic regression model for predicting mild or greater memory problems was significant, χ2(8) = 168.97, p < .001. In unadjusted analyses, significant predictors, in order of magnitude (strongest to weakest), were fatigue (odds ratio [OR] = 3.21), back or neck pain (OR = 2.28), migraines (OR = 2.11), anxiety (OR = 2.07), depression (OR = 2.04), difficulty sleeping (OR = 1.98), and concussion history (OR = 1.49). In the multivariable model, only back or neck pain (OR = 1.51, p = .004) and fatigue (OR = 1.99, p = .004) were significant predictors. Conclusions A personal history of multiple concussions was associated with perceived memory problems, but to a lesser degree than fatigue, back or neck pain, and migraines.

Adaptive Polling for Information Aggregation

The flourishing of online labor markets such as Amazon Mechanical Turk (MTurk) makes it easy to recruit many workers for solving small tasks. We study whether information elicitation and aggregation over a combinatorial space can be achieved by integrating small pieces of potentially imprecise information, gathered from a large number of workers through simple, one-shot interactions in an online labor market. We consider the setting of predicting the ranking of $n$ competing candidates, each having a hidden underlying strength parameter. At each step, our method estimates the strength parameters from the collected pairwise comparison data and adaptively chooses another pairwise comparison question for the next recruited worker. Through an MTurk experiment, we show that the adaptive method effectively elicits and aggregates information, outperforming a naive method using a random pairwise comparison question at each step.

Peanuts minus Schulz: Distributed Labor as a Compositional Practice/Le travail distribué comme pratique organisationnelle

Peanuts minus Schulz: Distributed Labor as a Compositional Practice/Le travail distribué comme pratique organisationnelle

Complexity, Competition, and Ambiguity in OLMs - Experimental Evidence on the Role of Gender

Online labor markets (OLMs) such as upwork.com and freelancer.com are increasingly common channels for engaging with trained individuals for technical and analytical work. Yet, like much of the tech industry, OLMs suffer from under-representation of women. In this paper, we address the broad question of why this may be the case by focusing on project factors that deter women from participating. We study how task complexity and competition increase the risk profile of projects, and disproportionately affect the odds of women choosing to bid for projects, as well as their bid amount, relative to men. Furthermore, we examine the role of environmental ambiguity, induced by Covid-19, on the propensity of women to bid for such projects. We conducted two large, randomized experiments conducted on Amazon Mechanical Turk (AMT) before and during the pandemic. We vary task complexity and competition as experimental treatments within the experiments, while we treat the pandemic as an external shock that raises environmental ambiguity between the two experiments. We find that women are indeed deterred by project complexity in their bidding behavior but are more likely to bid when faced with competition pre-pandemic. However, during the pandemic, women forbear from competition, suggesting the presence of significant opportunity and transaction costs for women as they deal with online work during the pandemic. We contribute to the literature on STEM and gender diversity by establishing the specific factors affecting women’s participation and wages in OLMs and suggest several actionable managerial insights to make OLMs more inclusive and attractive to women.