Online Labor Market Research Articles

The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market

Generative artificial intelligence (AI) holds the potential to either complement workers by enhancing their productivity or substitute them. We examine the short-term effects of the recently released generative AI models (ChatGPT, DALL-E 2, and Midjourney) on the employment outcomes of freelancers on a large online platform. We find that freelancers in highly affected occupations suffer from the introduction of generative AI, experiencing reductions in both employment and earnings. We find similar effects studying the release of other image-based generative AI models. Exploring the heterogeneity by freelancers’ employment history, we do not find evidence that high-quality service, measured by their past performance and employment, moderates the adverse effects on employment. In fact, we find suggestive evidence that top freelancers are disproportionately affected by AI. These results suggest that generative AI may transform the role of human capital in the organization and reduce overall demand for workers. Supplemental Material: The online appendices are available at https://doi.org/10.1287/orsc.2023.18441 .

Do entrepreneurial skills unlock opportunities for online freelancing? Experimental evidence from El Salvador

This paper reports on a randomized experiment in El Salvador that aimed to improve online labor market outcomes by teaching the entrepreneurial skills required to engage with online marketplaces. Despite low completion rates, we find that assignment to the training significantly increases online freelancing outcomes, such as the probability of having an online freelancing profile, the number of proposals sent, receiving at least one job offer, and securing at least one online freelancing contract. We also observe improved socioemotional skills. However, we find no significant effects of the program on the number of job offers, contracts, or any broader labor market outcomes. Further analysis suggests that poor initial job ratings may have hindered sustained success in online freelancing. Overall, despite some initial success, the program failed to have a lasting impact on the livelihoods of the participants.

Job training and search assistance for microwork: Evidence from Haiti

This paper presents the findings of a randomized experiment in Haiti aimed at improving online labor market outcomes through training individuals in microworking, a type of work involving small digital tasks completed via online platforms. Six months post-program, we observe significant positive impacts on platform-related microwork outcomes and some broader labor market outcomes. The treatment increases the likelihood of having a microwork profile, the number of tasks completed, hours worked, and microwork earnings, leading to a larger percentage of monthly income from microwork. The program also enhances socioemotional skills and English ability. However, we observe no significant effects on current employment and related outcomes, possibly as participants view microwork as a supplementary activity rather than employment.

Variations of Freelancers’ “Effort-Bargain” Experiences in Platform Work: The Role of Skills

This article delves into freelancers’“effort-bargain” experiences within platform work, where effort is exchanged for income. The study of experiences enables us to understand how freelancer–platform relationships evolve in light of the platforms’ lack of recognition of freelancers’ skills. Drawing on 63 interviews across four platforms within the online labor market (OLM), the authors present a theoretical framework explaining a “skill-driven continuum” in such relationships. At one extreme ( cooperation), freelancers experience the effort-bargain as recognition exchange, facilitated by non-competitive, regular transactions allowing them to monetize their skills. At the other extreme ( exploitation), freelancers experience their skills going unrecognized because of competitive bidding for casual gigs. While freelancers with specialized skills are sometimes able to disintermediate, thereby retaining recognition by claiming autonomy and monetary gains, those with generalized skills may resort to gaming, seeking to gain recognition by boosting their ratings. The authors provide insights into freelancers’ effort-bargain experiences by the meanings associated with skill-recognition and skill-exchange.

A study of the peculiarities of freelance activity of the Russian population on online labor market platforms

The relevance of the study is determined by the emergence and consolidation of new trends in the implementation of labor activity of the population in the context of digitalization and new challenges, as well as the need for their comprehensive study.The purpose of the study is to study the peculiarities of freelance activity of the Russian population on online labor market platforms, as well as to identify a set of measures necessary to obtain benefits from the implementation of this type of activity in order to stabilize the labor market during a period of change and reduce social tension.The objectives of the study are related to the analysis of supply and demand for the services of Russian freelancers on international online labor market platforms, including by comparing its positions with other countries; identifying measures to optimize the activities of online freelancers and stabilize the labor market during the period of change.Methodology. The research was implemented using the method of collecting information from open Internet sources, tabular and graphical methods, the method of economic analysis, analysis and synthesis, systematization, comparison, logical method and systematic approach.Results. It is determined that the freelance activity of the Russian population in the online format has great growth potential. The prerequisites for the development of online freelancing are highlighted, as well as the motives for Russian specialists entering the digital platforms of the global labor market. The analysis of the supply and demand of online freelancers on the largest online employment platforms in the period 2017-2022 was carried out. The specifics of Russia are revealed in comparison with the trends typical for China, India, Germany, Great Britain and the USA.Conclusion. The conclusion is made about the high activity of Russian freelancers on the online platforms of the global labor market, about the predominantly growing dynamics of this market segment, as well as about the existing restrictions in this area, which can be overcome by greater support from the state.

Does CSR Attract the Righteous? Experimental Evidence from an Online Labor Market

Does CSR Attract the Righteous? Experimental Evidence from an Online Labor Market

Gender differences in job satisfaction among gig workers in Bangladesh

The gig economy (temporary, contract, and freelance online jobs rather than permanent positions) is a component of the fourth industrial revolution and preview of future work. The rise of digital platforms has increased career opportunities and income potential, particularly for women. Yet, the sex-disaggregated evidence regarding platform usage, employment characteristics, and working motivations and satisfaction remains untapped. Using data from a quantitative survey of Bangladeshi gig workers (242 men and 201 women) conducted in 2022, this paper addresses these gaps between male and female workers. The gig economy appears to be gender-segregated across digital platform usages and working categories. Women tend to prioritize digital work options for managing multiple responsibilities, while men are often driven by the potential for higher income. This study conceptually utilized the two-factor theory and empirically ordered a probit model to identify gender differences in job satisfaction. Job satisfaction was significantly increased by work-life balance, monthly income, and social-media connection, while an increase in working hours, complexity in payment systems, and unstable networks all led to a decrease in job satisfaction. The findings have implications for the future growth of the gig economy, provide new insights into gender differences in job satisfaction, and underscore the need for gender-sensitive policies in the online labor market.

How has the COVID-19 pandemic shaped behavior in crowdsourcing? The role of online labor market training

AbstractThe outbreak of the COVID-19 pandemic and the associated lockdown measures have been a shock to market systems worldwide, affecting both the supply and demand of labor. Intensified by this pandemic-driven recession, online labor markets are in many ways at the core of the economic and policy debates about their technological innovation, which could be used as a way of economic reform and recovery. In this work, we focus on crowdsourcing, which is a specific type of online labor. We apply a unique dataset of labor data to investigate the effects of online training, a policy that was provided to requesters by the platform during the COVID-19 period. Our findings suggest that workers indirectly finance on-the-job online training by accepting lower wages during the pandemic. By utilizing a difference-in-difference research design, we also provide causal evidence that online training results in lower job completion time and the probability of being discontinued. Our findings show that both employers and employees in our online labor context reacted to the pandemic by participating in online labor procedures with different risk strategies and labor approaches. Our findings provide key insights for several groups of crowdsourcing stakeholders, including policy-makers, platform owners, hiring managers, and workers. Managerial and practical implications in relation to how online labor markets react to external shocks are discussed.

Informing or persuading? Unveiling the complex dynamics of live chat in online labor markets

This paper delves into the impact of live chat signals, manifested by persuasive and informative signals, on contract decision and the moderating role of business familiarity. Utilizing a dataset from a Chinese online labor market, our results confirm that politeness, response frequency, and reply speed positively influence contract decision. Regarding the moderating effects, business familiarity undermines the impact of politeness and enhances that of reply speed on contract decisions. The implications are as follows: first, this paper is among the early studies to empirically explore the live chat mechanism in online labor platforms. Second, this paper integrates the differential roles of live chat signals and business familiarity into a comprehensive model to specify the intricacies involved in the signal-behavior linkages. Third, our study illustrates how live chat works and prescribes how to effectively use this tool to bring benefits to vendors and platform managers, thus further extending the literature regarding live chat.

Shocking Offers: Gender, Wage Inequality, and Recessions in Online Labor Markets

Using data from the largest online job portal in Nigeria, we document (i) gender differences in salary offers for jobs and (ii) the response of (i) to recessions. Jobs in industries where the number of job applicants skews female offer lower starting salaries than jobs in industries where applicants skews male. During Nigeria's 2016 recession, applications to jobs in industries that skew female fell, and salary offers rose. In contrast, applications to jobs in industries that skew male rose, and salary offers fell. In accordance with this relative shift in applications, in 2016 the salary-offer gap almost disappears.

Cooperation in the Gig Economy: Insights from Upwork Freelancers

Existing literature on online labor markets predominantly focuses on how freelancers individually complete tasks and projects. Our study examines freelancers' willingness to work collaboratively. We report results from a survey of 122 freelancers on a leading online labor market platform (Upwork) that examine freelancers' preferences for collaborative work arrangements, and that explore several antecedents of cooperative behaviors. We then test if actual cooperative behavior matches with freelancers' stated preferences through an incentivized social dilemma experiment. We find that respondents cooperate at a higher rate (85%) than reported in previous comparable studies (between 50-75%). This high rate of cooperation may be explained by an ingroup bias. Using a sequential mediation model, we demonstrate the importance of a sense of shared expectations and accountability for cooperation. We contribute to a better understanding of the potential for collaborative work on online labor market platforms by assessing if and what social factors and collective culture exist among freelancers. We discuss the implications of our results for platform designers by highlighting the importance of platform features that promote shared expectations and improve accountability. Overall, contrary to existing literature and predictions, our results suggest that freelancers in our sample display traits that are more consistent with belonging to a coherent group with a shared collective culture, rather than being anonymous actors in a transaction-based market.

Platformization of Inequality: Gender and Race in Digital Labor Platforms

We contribute empirical and conceptual insights regarding the roles of digital labor platforms in online freelancing, focusing attention to social identities such as gender, race, ethnicity, and occupation. Findings highlight how digital labor platforms reinforce and exacerbate identity-based stereotypes, bias and expectations in online freelance work. We focus on online freelancing as this form of working arrangement is becoming more prevalent. Online freelancing also relies on the market-making power of digital platforms to create an online labor market. Many see this as one likely future of work with less bias. Others worry that labor platforms' market power allows them to embed known biases into new working arrangements: a platformization of inequality. Drawing on data from 108 online freelancers, we discuss six findings: 1) female freelance work is undervalued; 2) gendered occupational expectations; 3) gendered treatment; 4) shared expectations of differential values; 5) racial stereotypes and expectations; and 6) race and ethnicity as an asset. We discuss the role of design in the platformization and visibility of social identity dimensions, and the implications of the reinforced identity perceptions and marginalization in digital labor platforms.

Labor market peer firms: understanding firms’ labor market linkages through employees’ internet “also viewed” firms

AbstractThis paper studies the grouping of firms based on their labor-market connections, a significant departure from the traditional approach of grouping based on product-market connections. It also proposes a measure of labor market peers by analyzing the “also viewed” companies on two major online labor market platforms, LinkedIn and Glassdoor. Using the labor market peer measure, I examine whether firms that hire employees with similar skills and that are presumably exposed to the same labor-related risks and shocks exhibit a strong comovement of stock returns and accounting-based performance variables. I find that labor market peers overlap but differ from traditional product-market-based industry groupings, have significant incremental power to explain stock return and accounting-based performance measure comovements, and outperform traditional industry groupings in explaining return and wage comovements when a base firm shares more labor skills with its peers. Overall, the study highlights that labor market peers capture fundamental linkages between firms that are challenging to identify using traditional industry measures.

Monitoring and the Cold Start Problem in Digital Platforms: Theory and Evidence from Online Labor Markets

In the realm of online labor platforms, addressing moral hazard is crucial. Reputation systems have been the conventional solution, yet they pose a cold-start problem for newcomers. Alternatively, monitoring systems provide real-time oversight to employers, directly tackling moral hazard. This study combines theory and empirical analysis using data from a leading online labor platform. We find that monitoring systems effectively reduce the cold-start problem, leading to a 27.8% increase in bids on projects, primarily from inexperienced workers. We further find that following the introduction of the monitoring system, employers’ preference for experienced workers diminishes, accompanied by an average reduction of 19.5% in labor costs, whereas we observe no significant decrease in project completion and review rating. Our results collectively suggest that monitoring systems alleviate the cold-start problem in online platforms and contribute to fostering a more inclusive online labor market.

Real-Effort Incentives in Online Labor Markets: Punishments and Rewards for Individuals and Groups

Online labor markets and the humans that power them serve a critical role in the advancement of artificial intelligence and supervised machine learning via the creation of useful training datasets. The use of human effort in online labor markets is not enough, however, as a key factor is understanding the possible interventions that market operators can leverage to incentivize human effort among their labor force. We propose that platforms could implement mechanisms such as rewards or punishments at individual or group levels to incentivize real-effort and output. We apply our interventions using a collaborative image tagging experiment—a folksonomy—and the results provide interesting insights and nonobvious consequences. On average, interventions applied at the group level outperformed interventions applied at the individual level. Punishing the group provided the most controversial incentive strategy and provided a nonobvious significant improvement in effort. Rewarding or sanctioning an individual had similar effects on average, with both treatments leading to significant increases in effort post-intervention. In contrast to predictions, sanctioning appears to have significantly motivated those that were punished. Overall, the interventions applied in our real-effort collaborative image tagging experiment had a significant impact on behavior, which provides guidance for online labor market operators and the use of incentives in the creation of labeled machine learning training datasets.

You don’t need an invoice, do you? An online experiment on collaborative tax evasion

Collaborative evasion of taxes and social security fees is prevalent when two parties can coordinate to circumvent third-party reporting mechanisms. Prominent examples are household services when a household hires a service provider and no third party is involved; however, the evidence on the determinants of collaborative tax evasion is scarce. This study examines two coordination mechanisms of collaborative tax evasion: a partner’s signaled intention and information about the majority’s evasion behavior (empirical evasion expectation). We implement an interactive online tax evasion game with 560 participants from an online labor market. Our findings show that manipulating an empirical evasion expectation increases the fraction of evaded transactions by 19 percentage points. Our treatment manipulation of intention signals does not significantly affect evasion; however, when the willingness to evade is signaled first in the chat, the probability of evasion increases by 44 percentage points.

The Effects of Betrayal Aversion on Effort Provision When Incentives Are Fragile

This paper studies the impact of betrayal aversion on employees’ effort provision in exchange for a bonus payment that is at the employers’ expense and discretion. In our first study, using a laboratory experiment, we find that the performance of an employee with average betrayal aversion is lower by more than 15% relative to betrayal-neutral employees. Inspired by the findings, we theoretically identify a tradeoff for the effort provision of betrayal-averse employees. That is, higher effort not only increases the level of betrayal when the bonus is not paid, but it also increases the chances to receive a bonus avoiding betrayal. In a second study, we confirm our findings in an online labor market. The data also find support for the identified tradeoff. In a third study, we manipulate employees’ beliefs about the chances of receiving the bonus to provide causal evidence for the tradeoff. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by the Joachim Herz Foundation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02405 .

Shocking Offers: Gender, Wage Inequality, and Recessions in Online Labor Markets

Shocking Offers: Gender, Wage Inequality, and Recessions in Online Labor Markets

Is Ignorance Bliss? Sealed Versus Open Auctions in Online Labor Markets

Is Ignorance Bliss? Sealed Versus Open Auctions in Online Labor Markets

Do Recruiters Penalize Men Who Prefer Low Hours? Evidence from Online Labor Market Data

Do Recruiters Penalize Men Who Prefer Low Hours? Evidence from Online Labor Market Data