Petra Zaal (Utrecht University)
Andrea M. Herrmann (Utrecht University)*
Maryse Chappin (Utrecht University)
Brita Schemmann (Utrecht University)
Extended Abstract
To date, the opinion that higher levels of education lead to higher income levels in dependent employment is virtually uncontested (Day & Newburger, 2002; de Wolff & van Slijpe, 1973; Miller, 1960). Theoretically, this paradigm is founded on the asymmetric information and, thus, the adverse selection problem that employers face before hiring employees (see Jensen & Meckling, 1976). To address this problem, employees signal their qualities to potential employers through their educational certificates. This signalling mechanism does not only reassure future employers of the employees’ qualities, it also serves as a basis for employees to negotiate their future salaries. Accordingly, the labour economics literature demonstrates a systematic link between the educational attainment of employees and their pay levels: The higher the employees’ educational attainment, the higher their income levels (Card, 1999; de Wolff & van Slijpe, 1973; Miller, 1960).
Online labour markets, or the ‘gig economy’ – which allows organisations and individuals alike to hire workers though online platforms for a one-time service job – fundamentally challenge this paradigm: Gig workers do not need educational certificates to offer their services on online platforms, such as Upwork, Freelancer, PeoplePerHour or Twago. Rather than through educational certificates, adverse selection is prevented through the platforms’ review system, which invites gig requesters to evaluate the gig workers they hired. Given that diploma lose their signalling function towards gig requesters, they presumably also no longer serve as a basis for negotiating income levels. This raises the question whether educational attainment still is a driver of income levels in the gig economy: Do gig workers with higher levels of education obtain higher levels of income?
To answer this question, we analyse the profiles of 2327 gig workers in 14 Western economies, who are regularly active on one of the largest platforms for high-skilled online services, including in particular programming, design, translations, and writing tasks. Our OLS regression results of these gig workers indicate that education is indeed not correlated to their income levels. Instead, adverse selection is prevented through the platform’s review system as well as the gig workers’ level of previous job experience.
Our findings have several implications: At a theoretical level, they support the idea that signalling mechanisms, addressing adverse selection problems in work relationships, are core drivers of workers’ income levels. Importantly though, in the gig economy, these drivers do no longer consist in the educational degree of gig workers but rather in their previous work experience and the reviews they obtained. These findings contribute to the existing literatures at the intersection of labour economics, educational research and economic sociology investigating the link between education and income.
At a practical level, the insight that education does not matter for income levels of gig workers challenges the current education paradigm that higher qualifications are a route to economic wealth. This, in turn, also challenges the design of our current education systems: If the gig economy indeed develops into a major labour market of the future, Western education systems would benefit from reconsidering how to better prepare gig workers for their future jobs. Furthermore, our findings also point to the power of platforms’ review systems and the potential need to regulate the ways in which they operate: While national education systems are governed and supervised by the state through accreditation systems, review systems are exclusively designed by platforms, which thus have the power to influence the employability of gig workers with a simple change of the algorithm determining the workers’ evaluation.
* Corresponding author:
Dr. Andrea M. Herrmann; Associate Professor of Innovation Management
Department of Innovation and Environmental Studies; Utrecht University
Princetonlaan 8a; 3584 CB Utrecht; the Netherlands
