Signalling Skills in an Online Labour Market
Signalling Skills in an Online Labour Market
12th June 2018
Extended abstract for Reshaping Work in the Platform Economy 2018
Various online labour markets such as Upwork, Freelancer, and Amazon MTurk have sprung up in recent decade.1 The introduction of these new types of intermediaries has enabled companies and individuals to contract directly with each other without having to resort to specialist outsourcing companies (Agrawal et al., 2015). This has a large potential for reducing transaction costs and increasing welfare (Autor, 2001).
Nonetheless, online freelancing labour markets are riddled with information frictions. Since attributes such as worker quality and motivation of the worker are difficult to observe without direct interactions such as face-to-face interviews, the employers are often forced to make their hiring decisions based on limited information (Autor, 2001; Malone and Laubacher, 1998). The global nature of freelancing labour markets might exacerbate the information frictions as freelancers’ work and education credentials are often earned from unfamiliar companies, schools and institutional contexts (Oreopoulos, 2011). Even if these types of information asymmetries are to an extent present in traditional labour markets, the decentralised nature of online freelancing can amplify them. Due to the nature of online marketplaces, screening and hiring freelancers is partially a public good Once a freelancer has been hired and evaluated, all other market participants have access to the information provided by the employer who made the first hire. In this sense, all employers have an incentive to free-ride on other employers’ screening efforts. In equilibrium, this mechanism may lead to an outcome where new freelancers face difficulties breaking into the market, whereas older, already screened freelancers have an upper hand due to the fact that there is less uncertainty on their quality (Pallais, 2014).
I demonstrate empirically that earning skill certificates operates as a type of a signalling device in the spirit of Spence (1973). They do not increase the productivity of freelancers but demonstrate their ability. In the text book version of a signaling model, agents’ decision to signal is determined by the cost of signaling, which depends on agents’ ability. In contrast, I argue that in this context the net benefit of signaling, and therefore the freelancers’ decision to signal is determined by both costs of signaling, and the benefits from signaling. The benefits, in turn, depend on the uncertainty that prospective employers have on freelancers’ ability. A recurring challenge in estimating returns to signalling is that returns to signalling by education are confounded with increases in human capital; for instance, if we observe that education increases wages, it is oftentimes difficult to tell whether the higher earnings are caused by increased information or by the increase in individuals’ productivity Chevalier et al. (2004). Transaction level data provided by online freelancing platforms has many appealing features for studying this phenomenon. First, the data contains a rich set of background characteristics of freelancers who can be included as control variables. In addition, the fact that these projects are relatively short and follow each other relatively frequently allows me to use the longitudinal dimension of the data to account for freelancer unobserved heterogeneity.
In an ideal research setting, a researcher would fully control freelancer ability and human capital when studying the effect of signalling on earnings. In this paper, I approximate the ideal setting by comparing freelancers’ earnings before and after acquiring a skill certificate. This allows me to gauge all time-invariant unobservable factors into freelancer fixed effects. In addition, I limit my attention to a 14-day time period around the awarding of the certificate. With this, I can confirm that the return estimates are not contaminated by individual learning or other time varying human capital effects. I find that the return to completing an extra certificate has a positive effect on both employment and earnings margins. The OLS estimates for return to completing skill certificates are smaller in absolute value in comparison to fixed effects estimates. Consequently, the positive return estimates are only found in models that control unobserved heterogeneity using freelancer fixed effects. This finding suggests that the freelancers who are worse off in the labour market tend to signal more to offset their disadvantage.
A signalling model has a set of clear-cut empirical predictions which will be used to validate the theory. In particular, the incentive of freelancers to signal is expected to be smaller if the employer uncertainty on freelancer quality is smaller. This prediction is supported by the observation that standardised information generated by completing projects on the platform decreases returns to signalling. This implies that signalling ability through skill certificates is to some extent a substitute for other types of standardised information on freelancer quality. In addition, the returns to signalling are found to be decreasing in line with the number of skill tests completed. This suggests that the marginal effect of completing an extra certificate is smaller for freelancers who have already earned certificates.
The results of this paper contribute to multiple strands of empirical literature. First, the research links to emerging literature on how various types of online labour market institutions affect employment outcomes on online platforms. Further, this paper touches on the literature using standardised tests as a method for revealing information on worker quality in traditional labour markets (Autor and Scarborough, 2008; Hoffman et al., 2015). A recurring theme in this literature is that standardised tests can benefit minorities and other statistically discriminated against groups in the labour market. More broadly, the results link to empirical studies on job market signalling (Tyler et al., 2000; Lang and Manove, 2011; Pinkston, 2003; Arcidiacono et al., 2010).
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