Online Labor Platforms Research Articles

An investigation of factors affecting the visits of online crowdsourcing and labor platforms

Nowadays, the economic activities have become increasingly digital since hundreds of millions of Internet users are using crowdsourcing platforms either to work at an online job as workers, or as a model of problem-solving and production as requesters. This growing workforce makes it necessary from the perspective of the online platforms, to fully understand the business dimensions of this emerging and innovative “online labor” phenomenon, which can rapidly change the future of work and work organization in the online world. This paper aims to investigate and analyze the visits of online labor platforms that offer crowdsourcing and crowdfunding services. Using websites’ metrics data drawn from Alexa for the time period 2012-2016 the paper uses Ordinary Least Squares (OLS) and Fixed Effects (FE) regression analysis to examine correlations between visits and website characteristics. The research shows that the sessions of an online labor marketplace website from mobile devices have an increasing trend to be positively correlated to the quality mechanisms a website deploys as well as on location-dependent factors. The results are expected to provide insights on how the online labor website characteristics affect their traffic and thus inform about their evolution and improvement.

Pricing and Job Allocation in Online Labor Platforms

Problem Definition: We model, analyze, and optimize the operations of an online labor platform that matches jobs to workers. The arrival of jobs and workers to the platform is stochastic and the job processing time is random. The platform chooses the fees per job and assigns jobs to workers with the goal of (i) maximizing platform revenues, (ii) minimizing the unpredictability in workers' profits, and (iii) minimizing any delay in processing the incoming jobs. Workers are sensitive to the revenue they make in the platform and, therefore, the worker arrival rate depends on the platform's pricing and job allocation strategy. Academic/Practical Relevance: We contribute to the fields of online platform operations and stochastic systems. The asymptotically optimal policy that we introduce is simple and practical. Our analysis provides new insights into the management of uncertainty in online platforms and how uncertainty impacts objectives (i), (ii), and (iii) above. Methodology: We propose a continuous-time stochastic model that describes the mechanics of the platform. We introduce a policy that sequentially allocates jobs to workers in a rotating manner. The optimal parameters of this policy are corrected versions of the fluid solution. Then, through an asymptotic analysis, we prove that this policy is optimal as the platform scales. Finally, we examine the policy's performance through a discrete event simulation. Results: We introduce a policy class called Uniform Allocation (UA) and provide an analytical characterization of the platform's behavior and performance under this policy class. Then, we design a UA policy that simultaneously optimizes objectives (i), (ii), and (iii) as the system scales. We obtain insights into the UA policy's behavior through a discrete event simulation and find that it leads to similar profits but much lower worker income variability compared to other policies. Implications: Our proposed pricing and assignment policy align objectives (i), (ii), and (iii) above. From an academic standpoint, we demonstrate how the platform's revenue maximizing pricing and allocation policies can also act as a form of risk pooling. From a practical standpoint, we determine that platform revenue maximization is not incompatible with predictable profits and stable schedules for workers.

Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy.

This article evaluates the job quality of work in the remote gig economy. Such work consists of the remote provision of a wide variety of digital services mediated by online labour platforms. Focusing on workers in Southeast Asia and Sub-Saharan Africa, the article draws on semi-structured interviews in six countries (N = 107) and a cross-regional survey (N = 679) to detail the manner in which remote gig work is shaped by platform-based algorithmic control. Despite varying country contexts and types of work, we show that algorithmic control is central to the operation of online labour platforms. Algorithmic management techniques tend to offer workers high levels of flexibility, autonomy, task variety and complexity. However, these mechanisms of control can also result in low pay, social isolation, working unsocial and irregular hours, overwork, sleep deprivation and exhaustion.

Role of Monetary Incentives in the Digital and Physical Inter-Border Labor Flows

By allowing individuals to engage in remote relationships with foreign employers, online labor markets have the potential to mitigate the inefficiency costs due to the legal barriers and other frictions deterring international physical migration. This study investigates how the supply of foreign labor in digital and physical markets responds differently to monetary incentives. We use a unique data set containing information on digital labor flows from a major global online labor platform in conjunction with data on physical labor flows. We exploit short-term fluctuations in the exchange rate as a source of econometric identification: a depreciation of a country’s currency against the U.S. dollar increases the incentives of its workers to seek digital and physical employment from employers based in the United States. Using a panel count data model, we find that monetary incentives induced by depreciations of foreign currencies against the U.S. dollar are positively associated with the supply of foreign labor in digital markets, as expected from the frictionless nature of electronic markets. However, we fail to find a positive relationship between monetary incentives and the supply of foreign labor in physical markets, which might be expected due to the substantial bureaucratic restrictions and transaction costs associated with physical migration. We further examine how countries’ income and information and communications technologies development levels moderate the positive relationship between monetary incentives and digital labor flows. Our findings are useful for gauging the extent to which digital labor flows can alleviate the economic inefficiencies from the restrictions on physical migration.

Workers of the Internet unite? Online freelancer organisation among remote gig economy workers in six Asian and African countries

This article presents findings regarding collective organisation among online freelancers in middle‐income countries. Drawing on research in Southeast Asia and Sub‐Saharan Africa, we find that the specific nature of the online freelancing labour process gives rise to a distinctive form of organisation, in which social media groups play a central role in structuring communication and unions are absent. Previous research is limited to either conventional freelancers or ‘microworkers’ who do relatively low‐skilled tasks via online labour platforms. This study uses 107 interviews and a survey of 658 freelancers who obtain work via a variety of online platforms to highlight that Internet‐based communities play a vital role in their work experiences. Internet‐based communities enable workers to support each other and share information. This, in turn, increases their security and protection. However, these communities are fragmented by nationality, occupation and platform.

Unemployment and Online Labor

Online labor markets experienced a rapid growth in recent years. They allow for long-distance transactions and offer workers access to a potentially ‘global’ pool of labor demand. As such, they bear the potential to act as a substitute for shrinking local income opportunities. Using detailed U.S. data from a large online labor platform for microtasks, we study how local unemployment affects participation and work intensity online. We find that, at the extensive margin, an increase in commuting zone level unemployment is associated with more individuals joining the platform and becoming active in fulfilling tasks. At the intensive margin, our results show that with higher unemployment rates, online labor supply becomes more elastic. These results are driven by a decrease of the reservation wage during standard working hours. Finally, the effects are transient and do not translate to a permanent increase in platform participation by incumbent users. Our findings highlight that many workers consider online labor markets as a substitute to offline work for generating income, especially in periods of low local labor demand. However, the evidence also suggests that, despite their potential to attract workers, online markets for microtasks are currently not viable as a long run alternative for most workers.

Home Bias in Global Employment

We study the nature of home bias in online employment, wherein the employers prefer workers from their own home countries. Using a unique large-scale dataset from a major online labor platform, we identify employers’ home bias in their online employment decisions. Moreover, we find that employers from countries with high traditional values, lower diversity, and smaller (user) population size, tend to have a stronger home bias. Further, we investigate the nature of employers’ home bias using a quasi-natural experiment wherein the platform introduces a monitoring system to facilitate employers to keep track of workers’ progress in time-based projects. After matching comparable fixed-price projects as a control group using propensity score matching, our difference-in-difference estimations show that the home bias does exist in online employment, and at least 40.93% of home bias is driven by statistical discrimination.

A Nordic approach to regulating intermediary online labour platforms

A Nordic approach to regulating intermediary online labour platforms

Micro-entrepreneurs, Dependent Contractors, and Instaserfs: Understanding Online Labor Platform Workforces

The rapidly growing number of people who find work via online labor platforms are not employees, nor do they necessarily fit traditional conceptualizations of independent contractors, freelancers, or the self-employed. The ambiguous nature of their employment status and its implications for worker well-being have attracted substantial controversy, but to date most empirical research in this area has focused on the market efficiency of a single platform rather than on workers themselves and related human resource management issues. Research progress will require understanding how online labor platform work differs from other types of nonstandard employment arrangements, as well as critical differences across labor platform firms in how work and workers are managed. This paper proposes a conceptual classification framework to facilitate research on the attitudes, experiences, and outcomes of workers who use these platforms. We explore how labor platform firms’ operational choices shape how control is allocated across workers, clients, and the firm, and how they influence workers’ autonomy, incentives, and degree of economic dependence on the firm. Implications for theory development, research, and managing worker–firm relations are discussed.

Digital labour and development: impacts of global digital labour platforms and the gig economy on worker livelihoods.

As ever more policy-makers, governments and organisations turn to the gig economy and digital labour as an economic development strategy to bring jobs to places that need them, it becomes important to understand better how this might influence the livelihoods of workers. Drawing on a multi-year study with digital workers in Sub-Saharan Africa and South-east Asia, this article highlights four key concerns for workers: bargaining power, economic inclusion, intermediated value chains, and upgrading. The article shows that although there are important and tangible benefits for a range of workers, there are also a range of risks and costs that unduly affect the livelihoods of digital workers. Building on those concerns, it then concludes with a reflection on four broad strategies – certification schemes, organising digital workers, regulatory strategies and democratic control of online labour platforms – that could be employed to improve conditions and livelihoods for digital workers.

Geographical Discrimination in Digital Labor Platforms

This study seeks to empirically test the narrative of a frictionless global market for digital labor. The empirical strategy is based on the examination of internal data from Nubelo, one of the largest Spanish-language online labor platforms. The results suggest that information-related frictions long observed in traditional labor markets are exacerbated in online platforms, resulting in discrimination based on country of origin. We show that, after controlling for observable workers’ characteristics and their job bids, foreign job-seekers are 42 percent less likely to win contracts from Spanish employers, which represent about two-thirds of all employers in the platform. We attribute this result to the activation of social stereotypes that orient employers’ hiring decisions, in the absence of verifiable information about the quality of individual workers. We draw implications for platform design and the governance of online labor contracts.

Unemployment and Worker Participation in the Gig Economy: Evidence from an Online Labor Platform

The gig economy has low barriers to entry, enabling flexible work arrangements and allowing workers to engage in contingent employment, whenever, and in some cases, such as online labor markets, wherever workers desire. The growth of the gig economy has been partly attributed to technological advancements that enable flexible work environments. In this study, we consider the role of an alternative driver, economic downturns, and associated financial stressors in the offline economy—e.g., unemployment. As the exact nature of the relationship between online labor supply and offline unemployment is not immediately clear, in this work, we seek to quantify the relationship, exploring heterogeneity across a variety of county-specific characteristics. We study these relationships in the context of a leading online labor market, combining data on the participation of workers residing in counties across the United States with county-level data on unemployment from the Bureau of Labor Statistics. Our results demonstrate a positive and significant association between local (county) unemployment in the traditional offline labor market and the supply of online workers residing in the same county, as well as significantly larger volumes of online project bidding activity from workers in the same county. Specifically, we estimate that a 1% increase in county unemployment is associated with a 21.8% increase in the volume of county residents actively working online at the platform. Further, our results suggest significant heterogeneity in the relationship, such that a significantly larger supply of online labor manifests when unemployment occurs in counties characterized by better Internet access, younger and more educated populations, and populations whose social ties are dispersed over a wider geographic area. We discuss the theoretical and practical implications for workers, online labor markets, and policy makers.

Logged labour: a new paradigm of work organisation?

This essay argues that, during the period following the financial crisis of 2007–8, several different trends, already visible in earlier periods, have converged to create a new pattern of work organisation, a pattern which is now approaching critical mass. Across all sectors of the economy, primary, secondary or tertiary, whether previously classified as public or private, formal or informal, manual or white-collar, high-skilled or low-skilled, this emerging model of work introduces a range of common features whose combined impact is large enough to justify the proposition that a new paradigm of work is in creation, perhaps representing the final dissolution of the normative model that emerged in developed economies in the third quarter of the twentieth century. In this new model, workers are increasingly managed via online platforms, monitored indirectly and expected to produce measurable outcomes. Their work is ‘logged’ in three distinct senses: it is cut up into standard, quantifiable components; it is subjected to continuous surveillance and monitoring; and it requires the worker to be connected to an online platform in order to obtain work. In a curious paradox, work is increasingly formalised even while it becomes less predictable and more precarious, with workers having to resubmit themselves repeatedly for employment, funding, promotion or inclusion in a particular team, and required to respond at short notice to unpredictable demands for work. The ramifications of this development are huge, since it creates major mismatches between the realities of the labour market and other aspects of social and economic life, including welfare systems, labour and consumer regulation and time regimes. The article draws on a large body of past work by the author on work organisation restructuring as well as current research on online labour platforms.

Effects of IT-Enabled Monitoring on Labor Contracting in Online Platforms: Evidence from a Natural Experiment

Many online labor platforms nowadays employ reputation systems and monitoring systems to mitigate moral hazard. While reputation systems have the potential to reduce moral hazard, they suffer from the cold-start problem, that is, new entrants with no reputation face a high entry barrier. Unlike reputation systems that provide information on workers’ prior performance, monitoring systems operate by providing employers with direct observation of workers’ actions. Given monitoring systems can reduce information asymmetry by tracking workers’ hours and effort levels, employers face a lower moral hazard risk with new entrants. Therefore, monitoring systems can potentially alleviate the cold-start problem. In this paper, based on a unique dataset from Freelancer.com, we empirically investigate the effect of monitoring systems on the entry barrier by looking at both employers’ hiring preferences and workers’ entry behaviors. We leverage the differential availability of the monitoring system across two project types (i.e., it is only available for time-based projects but not for fixed-price ones) to implement a difference-in-differences (DID) estimation. Based on a sample including 1,635 fixed-price projects and 1,023 time-based projects matched on a comprehensive list of observable characteristics, we provide evidence that employers’ preference for experienced workers decreases after the introduction of the monitoring system. Consistently, we find that the introduction of the monitoring system increases the number of bids on time-based projects by 22.4% and the incremental bids come primarily from new entrants with no reputation (i.e., inexperienced workers) on the platform. Our additional analyses reveal that the monitoring system lowers the hiring price of labor and improves the on-time delivery performance of projects. Our results suggest that monitoring systems alleviate the cold-start problem on online platforms.

A system for scalable and reliable technical-skill testing in online labor markets

The emergence of online labor platforms, online crowdsourcing sites, and even Massive Open Online Courses (MOOCs), has created an increasing need for reliably evaluating the skills of the participating users (e.g., “does a candidate know Java”) in a scalable way. Many platforms already allow job candidates to take online tests to assess their competence in a variety of technical topics. However the existing approaches face many problems. First, cheating is very common in online testing without supervision, as the test questions often “leak” and become easily available online along with the answers. Second, technical-skills, such as programming, require the tests to be frequently updated in order to reflect the current state-of-the-art. Third, there is very limited evaluation of the tests themselves, and how effectively they measure the skill that the users are tested for.In this article we present a platform, which continuously generates test questions and evaluates their quality as predictors of the user skill level. Our platform leverages content that is already available on question answering sites such as Stack Overflow and re-purposes these questions to generate tests. This approach has some major benefits: we continuously generate new questions, decreasing the impact of cheating, and we also create questions that are closer to the real problems that the skill holder is expected to solve in real life. Our platform leverages the use of Item Response Theory to evaluate the quality of the questions. We also use external signals about the quality of the workers to examine the external validity of the generated test questions: questions that have external validity also have a strong predictive ability for identifying early the workers that have the potential to succeed in the online job marketplaces. Our experimental evaluation shows that our system generates questions of comparable or higher quality compared to existing tests, with a cost of approximately $3–$5 dollars per question, which is lower than the cost of licensing questions from existing test banks, and an order of magnitude lower than the cost of producing such questions from scratch using experts.

Hiring Preferences in Online Labor Markets: Evidence of a Female Hiring Bias

Online labor marketplaces facilitate the efficient matching of employers and workers across geographical boundaries. The exponential growth of this nascent online phenomenon holds important social and economic implications, as the hiring decisions made on these online platforms implicate the incomes of millions of workers worldwide. Despite this importance, limited effort has been devoted to understanding whether potential hiring biases exist in online labor platforms and how they may affect hiring outcomes. Using a novel proprietary data set from a leading online labor platform, we investigate the impact of gender-based stereotypes on hiring outcomes. After accounting for endogeneity via a holistic set of job and worker controls, a matched sample approach, and a quasi-experimental technique, we find evidence of a positive hiring bias in favor of female workers. An experiment was used to uncover the underlying gender-specific traits that could influence hiring outcomes. We find that the observed hiring bias diminishes as employers gain more hiring experience on the platform. In addition, the female hiring bias appears to stem solely from the consideration of applicants from developing countries, and not those from developed countries. Subanalyses show that women are preferred in feminine-typed occupations while men do not enjoy higher hiring likelihoods in masculine-typed occupations. We also find that female employers are more susceptible to the female hiring bias compared to male employers. Our findings provide key insights for several groups of stakeholders including policy makers, platform owners, hiring managers, and workers. Managerial and practical implications are discussed. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2756 . This paper was accepted by Chris Forman, information systems.

A Workforce of Micro-Entrepreneurs: Online Labor Platforms in the Sharing Economy

The rapid growth of online labor platforms such as Uber and TaskRabbit has been accompanied by substantial controversy. These firms view themselves as technology companies that provide opportunitie...

Hiring Preferences in Online Labor Markets: Evidence of a Female Hiring Bias

Online labor marketplaces facilitate the efficient matching of employers and workers across geographical boundaries. The exponential growth of this nascent online phenomenon holds important social and economic implications, as the hiring decisions made on these online platforms implicate the incomes of millions of workers worldwide. Despite this importance, limited effort has been devoted to understanding whether potential hiring biases exist in online labor platforms and how they affect hiring outcomes. Using a novel proprietary dataset from a leading online labor platform, we investigate the impact of gender-based stereotypes on hiring outcomes. After accounting for endogeneity via a holistic set of job and worker controls, a matched sample approach, and a quasi-experimental technique, we find evidence of a positive hiring bias in favor of female workers. We find that the observed hiring bias diminishes as employers gain more hiring experience on the platform. In addition, the female hiring bias appears to stem solely from the consideration of applicants from developing countries, and not those from developed countries. Sub-analyses show that women are preferred in feminine-typed occupations while men do not enjoy higher hiring likelihoods in masculine-typed occupations. We also find that female employers are more susceptible to the female hiring bias compared to male employers. We further run an experiment to uncover the underlying gender-specific traits that could influence hiring outcomes. Our findings provide key insights for several groups of stakeholders including policymakers, platform owners, hiring managers, and workers. Managerial and practical implications are discussed.

Incentives to Counter Bias in Human Computation

In online labor platforms such as Amazon Mechanical Turk, a good strategy to obtain quality answers is to take aggregate answers submitted by multiple workers, exploiting the wisdom of crowds. However, human computation issusceptible to systematic biases which cannot be corrected by using multiple workers.We investigate a game-theoretic bonus scheme, called peer truth serum (PTS), to overcome this problem. We report on the design and outcomes of a set of experiments to validate this scheme. Results show peer truth serum can indeed correct the biases and increase the answer accuracy by up to 80%.