Education, inequality and use of digital collaborative platforms: The European case

1 Education, inequality and use of digital collaborative platforms: The European case Jesús M. Artero Cristina Borra ORCID https://orcid.org/0000-0003-2277-8342 Rosario Gómez-Alvarez Universidad de Sevilla, Spain Corresponding author: Cristina Borra, Facultad de Ciencias Económicas y Empresariales, Universidad de Sevilla, Ramon y Cajal 1, Sevilla 41018, Spain. Email: [email protected] Abstract This study examines the causal relationship between education and the use of digital collaborative platforms (DCPs) as a first step in exploring the potential impact of the new digital labour markets on inequality. From the viewpoint of transaction costs theory, the less educated could benefit significantly from the digital collaborative economy due to the reduction in information costs made possible by this new form of exchange. Conversely, a positive relationship between educational level and platform use may be expected following neoclassical and institutionalist economic theories. Using microdata from the 2016 Eurobarometer survey, together with an instrumental variables (IV) strategy and conventional ordinary least squares (OLS) models, hypothesis testing reveals that education has a clear positive effect on DCP use. As a result, the less educated are less likely to access the job opportunities offered by digital labour markets. Understanding the relationship 2 between education and access to DCPs and impacts on socio-economic inequality is crucial for designing future public policies that promote social justice and wellbeing in a disrupted landscape. JEL codes: I24, J47, O34 Keywords: Inequality, digital collaborative platforms, education, non standard employment, Fourth Industrial Revolution, 4IR Introduction Digital Collaborative Platforms (DCPs) are one of the most disruptive innovations taking place in the Fourth Industrial Revolution (De Ruyter et al., 2019; De Stefano, 2015). Collaboration via platforms is continually gaining ground on conventional business models, and the profits obtained via these platforms are expected to increase from 15 billion dollars in 2015 to 335 billion in 2025 (PwC, 2015). This phenomenon has given rise to numerous debates on the concept of DCPs itself and its possible economic, political, social and cultural consequences (Belk, 2010; 2017; European Commission, 2016; Flanagan, 2017; Arcidiacono et al., 2018; Pesole et al., 2018). Awareness of the impact of educational inequality as a possible barrier to accessing DCPs for both suppliers and users is a prominent feature in these debates. Therefore, an analysis of the relationship between education and access to these platforms can make an important contribution to the current discussion of the possible effects of the new digital labour markets (Acquier et al., 2017). As Martin (2016) argues, the sharing economy is perceived in contrasting and contradictory ways, whether as a pathway to sustainability or as a nightmarish form of neoliberal capitalism. Success stories such as Airbnb and Uber 3 have led to the media, ICT industries and the general public having a common view of the sharing economy as a disruptive agent that modifies established socio-economic structures. Hence, from a political perspective, it is important to understand the relationship between educational inequality and access to DCPs and to ascertain the extent to which educational differences act as a barrier to the economic and social benefits these platforms can provide. Similarly, it is important to know how the design of educational and regulatory policies on working and fiscal conditions and privacy may affect the evolution of the digital economy. A greater understanding of the relationship between educational level and use of collaborative platforms is crucial to predict the effects of participation in digital labour markets on inequality and to determine what type of regulatory policies may be required. From a theoretical perspective, the new opportunities created by DCPs could either increase or reduce social inequalities. DCPs drastically reduce transaction costs, especially those related to searching for information and negotiating contracts (Williamson, 1981). Nowadays, millions of transactions take place that did not in the past, opening up new possibilities for society as a whole and facilitating the reallocation of wealth towards small producers and consumers (Sundararajan, 2016). Nonetheless, the strong link between ICTs, platforms and new business models also entails greater demands for educational competencies (Schor and Attwood‐Charles, 2017; Cansoy and Schor, 2016; Smith, 2016; Goldin and Katz, 2008). Thus, inequality in participation in DCPs could arise from lack of adequate knowledge. Furthermore, in the context of asymmetric information, education credentials may also signal those better equipped to navigate the changed labour market (Thurow, 1975; Sobel, 1982). If use of DCPs requires high qualifications, better-qualified individuals will gradually take over employment opportunities, traditionally contracted offline and undertaken by persons with few qualifications, as an alternative or supplementary source of income, thereby creating a crowding-out effect that will also 4 increase inequality (Frenken and Schor, 2017; Andreotti et al., 2017; Minter, 2017; Stanford, 2017). The question is whether educational barriers are large enough to offset the potential equalising impact of reduced transaction costs. The limited empirical evidence available on education and use of digital platforms is confronted with two fundamental problems. First, lack of data is a widespread shortcoming, either because platforms do not provide data access or because the official statistics do not address the necessary issues (Abraham et al., 2018). Thus many studies are limited to simply documenting the educational group that individual participants come from, without estimating the probability of participation within each educational group. Moreover, most studies are qualitative analyses (see, e.g. Schor, 2017), and the few quantitative analyses use data from a specific platform (e.g. Cansoy and Schor (2016) and Quattrone et al. (2016) from Airbnb, and Fraiberger and Sundararajan (2015) from Getaround) and obtain contradictory results. The second shortcoming comes from the fact that, to the best of our knowledge, no study aims at obtaining an unbiased estimate of the impact of educational level on the use of collaborative platforms. Individuals who invest in a university education, for example, differ from those who do not in many unobserved aspects, such as subjective individual discount rates or cognitive abilities, which prevent a mere comparison of platform participation rates of both groups from being informative regarding the causal effect of education (Lemieux, 2006). Moreover, the relationship between educational level and platform use may present problems of reverse causality. The growing weight and relevance of the new digital business model in the economy may motivate the spread of new curricula directly related to the study of digital platforms so that use of platforms and educational level mutually affect each other (Newlands et al., 2018). 5 We use microdata from a 2016 Eurobarometer survey on the use of collaborative platforms, corresponding to the 28 member states of the European Union, to estimate the causal relationship between educational level and participation in DCPs. We use the number of universities existing in the respondent’s region of residence when they were aged 16 according to the European Tertiary Education Register (ETER, 2018) as an instrumental variable of educational level to estimate the unbiased effect of education on the probability of participation (Card 1995, 1999; Currie and Moretti, 2003; Hryshko et al., 2012). The justification of the instrument is based on the fact that the availability of universities plays a central role in the decision to enrol in higher education (Card 1995), while it does not seem to have a direct influence on the use of collaborative platforms. Our model incorporates region fixed effects to consider the permanent differences between regions, including different levels of income and wealth across regions, and cohort fixed effects to consider the common trends in platform use across individuals of the same age. Thus, our estimates are identified by differential changes in the availability of educational services between regions and cohorts. It should be noted that the regional distribution of higher education institutions is linked to regional income and wealth and consequently, the regional distribution of participation in higher education and access to digital platforms may reflect existing inequalities. However, our estimates are based on changes in college availability across regions over time and assume away permanent correlations of wealth and availability of higher education institutions within regions. The results of both the instrumental variables (IV) approach and the ordinary least squares (OLS) model suggest that education increases the probability of using digital platforms. Specifically, one more year of education increases the likelihood of using digital platforms by 5.03 percentage points. Furthermore, having university education or higher entails an increase of 75 percentage points in the probability of platform use, which 6 suggests that the profile of the typical user is a highly qualified individual. The heterogeneity analysis by area of residence shows that our results are mainly driven by countries in the EU-15, given that the instrumental approach is not valid for countries in the Eastern Bloc. The gender-based results do not show appreciable differences between men and women. Our results are highly relevant in the current context of the Fourth Industrial Revolution, greatly accelerated by the recent COVID-19 Crisis. When innovations extend to all economic spheres (Schwab, 2016), companies need highly adaptive, most likely educated, workers (Krueger and Kumar, 2004; Van Dijk, 2005). This study examines the causal impact of education on DCP use, but due to limitations in the data it does not address benefits potentially generated by digital platforms, such as improved convenience, cost savings, time savings, and increased income. We find that education increases access, but we cannot guarantee that access generates tangible benefits, given that increasing access does not necessarily mean increasing benefits. This study makes significant contributions to the growing literature on DCPs. To the best of our knowledge, our study is the first to analyse the causal effect of educational level on the use of digital collaborative platforms. Contrary to quantitative empirical studies, such as those of Cansoy and Schor (2016) and Quattrone et al. (2016), we use quasiexperimental techniques to control for the possible presence of endogeneity and unobserved heterogeneity in estimating the causal effect of educational level on the use of digital platforms. Furthermore, in contrast to most studies which analyse the educational profile of participants by focusing on specific platforms, we use a database that considers digital platforms in general and includes information on both participants and nonparticipants in DCPs. As a result, we are able to quantify the effect of educational level on the probability of participating in any digital platform in Europe. 7 Theoretical considerations: Collaborative economy, digital platforms and educational inequality Digital platforms are technological building blocks acting as a foundation upon which other firms develop complementary products (Gawer, 2009). Examples range from Microsoft Windows to Google. Digital platforms are usually subject to scale and scope economies in the sense that large fixed costs contrast with close to zero marginal costs and the value of the service increases with the number of users (OECD [Organisation for Economic Cooperation and Development], 2017). DCPs refer to digital platforms that function as intermediaries between providers and users (or workers and end-users) facilitating exchange by enabling information sharing and secure payment systems (Stewart and Stanford, 2017). According to the European Commission (2016), DCPs involve three categories of actors: i) service providers who share assets, resources, time and/or skills. These can be private individuals offering services on an occasional basis (‘peers’) or service providers acting in their professional capacity (‘professional service providers’). ii) Users of these services iii) Intermediaries that connect providers with users via an online platform and facilitate transactions between them (‘collaborative platforms’). As a consequence, not every digital platform is a DCP. DCPs’ main objective is to act as brokers between users and providers, facilitating exchange. To what extent does education constitute an entry barrier to DCPs? One can distinguish two sets of theoretical expectations for the relationship between educational inequality and use of DCPs. The first hypothesis stems from the reduction in transaction costs enabled by the collaborative economy (Williamson, 1981; Sundararajan, 2016). 1 Technological transformations enable handling and transport of information, that is, digital information, at a much lower cost. The exponential growth in hardware capacity has facilitated the proliferation of devices among the population that enable permanent connectivity and the 8 continuous emergence of new applications. These technological transformations have made it possible to create systems of reputation and trust among strangers. In summary, the collaborative economy may open new possibilities to all social classes (Andersson et al., 2013). As such, it represents an innovation that is capable of reallocating wealth across the ‘value chain’ towards small producers and consumers. This hypothesis has been supported by empirical studies that provide positive results for low socio-economic profiles via the use of platforms (see Table B.1). Fraiberger and Sundararajan (2015) simulate a dynamic model of a P2P (peer to peer) market for vehicles with data from the Getaround platform, which predicts an exponential profit for the group below the median income (lower educational profile). Quattrone et al. (2016) use data from Airbnb listings and the London census, showing the growing presence of accommodation in poorer districts and among owners of low educational level. The second hypothesis assumes that a minimum educational level is required to use ICTs. Therefore, use of DCPs to access goods and services and education level are positively related. Torero and von Braun (2006) show that access to ICTs depends on income, education and resources and that the so-called digital divide is part of a much broader development divide. They argue that socio-economic development contributes to a greater use of ICTs rather than the reverse. Regarding service suppliers, both neoclassical and institutional economists theorise that technological change will favour the labour market outcomes of the highly educated. For example, developments in the neoclassical human capital theory emphasise the higher-level labour-market skills required by technological change (Goldin and Katz, 2008) 2 . In addition, institutionalists’ job competition theories argue that educational qualifications signal more able individuals (Thurow, 1975). 3 In this scenario, the higher educated may have an advantage in securing employment in low-skilled jobs, crowding out the employment opportunities for those with 9 lower levels of education (Gautier et al., 2002; Di Stasio et al., 2015). This trait is also likely to be reproduced in the collaborative economy, especially in the so-called cloud work or crowd work of online labour markets (OLMs), which are potentially global, and not so much in the gig work or on-demand work of mobile labour markets (MLMs), which are geographically localised (Codagnone et al., 2016). 4 From an empirical perspective, many descriptive studies support the positive relationship between education and the use of DCPs (see Appendix Table A.1). For the United States, Smith (2016) reports that 89% of college graduates have used DCPs, and 39% have used four or more of these services compared with just 8% of those with a high school degree or less. Through 80 semi-structured interviews with active users of DCPs, Schor et al. (2016) qualitative analysis highlights the high educational profile of users of four specific platforms: Wintrepreneur, CraftWorks, Food Swap, and Time Bank. Similarly, DCP providers are also specially selected among the highly educated. Schor (2017) interviewed 43 suppliers of three platforms in the United States, Airbnb, RelayRides and TaskRabbit, between 2013 and 2015 and provides a profile of a platform participant who is highly educated and engaged in other high-skilled jobs. These people displace the workers of a low educational profile who have traditionally done much of the manual labour characterised in conventional business models, but which is progressively more concentrated on collaborative platforms. Ravenelle (2017) interviewed 78 suppliers of Airbnb, TaskRabbit, Kitchensurfing and Uber in New York in 2015, and found that more than 61% of those interviewed had higher education qualifications. Thebault-Spieker et al. (2015) use a fixed-effects logistic estimation model and a subsequent qualitative analysis of 40 suppliers of TaskRabbit interviewed in the Chicago area. The results revealed that those living in neighbourhoods of low socio-economic profile needed to pay an extra charge to contract service providers. Cansoy and Schor (2016) use a database with 125,000 listings of 16 from the Eastern Bloc, education and platform use were also positively related in the OLS specification: one more year of study was associated with an increase in the probability of using DCPs by approximately 0.7 pp (column 3). We cannot estimate the causal impact of one more year of study for this group, but this positive impact is consistent with the findings for the EU-15 group in Column 1. To the best of our knowledge, no previous studies have documented differences in participation in digital markets by European country group and education level. [Table 2 here] [Table 3 here] Discussion This study provides new evidence on the impact of educational inequality on access to DCPs, using data from the Eurobarometer 438, ‘The use of collaborative platforms’. By exploiting changes in accessibility to university education across regions and cohorts in an IV approach, we find that years of schooling impact positively on the use of DCPs, in line with the previously reported high-skill profile of digital platform users (Schor, 2017; Cansoy and Schor, 2016; Smith, 2016; Quattrone et al., 2016; Edelman, et al., 2017; Ravenelle, 2017)For both OLS and IV estimates, we found a positive relationship between educational level and DCP use, irrespective of gender and country of origin. Our results provide suggestive evidence that education constitutes an effective barrier to access to the digital economy, which the reduction in transaction costs enabled by DCPs is insufficient to overcome. Similarly, regarding the use of the Internet, DiMaggio et al. (2004) demonstrated that equality in access is not achieved through public investment in infrastructure alone: knowledge and education are also needed. The educational barriers to DCP use may have several implications for access inequality in the digital labour markets as it may impact both supply of and demand for 17 labour. First, from the perspective of labour supply, low-educated individuals may fail to gain access to the new jobs created in the digital economy (Schor and Attwood-Charles, 2017) owing to the complexity of new technologies, which constitutes a fundamental barrier differentially disadvantaging lower-educated people (Roger, 2005). Second, the more highly educated may replace the less educated in low-skilled jobs that are now performed online (Schor, 2017). Third, DCP use can provide privileged access to information on desirable jobs and highlight the positive qualities of potential workers, thus widening the inequality gap (DiMaggio and Bonikowski, 2008). Fourth, DCPs open new job and human capital investment opportunities that may be disproportionally monopolised by the higher educated (DiMaggio et al., 2004). With respect to labour demand, in the ever-changing environment characteristic of the Fourth Industrial Revolution, firms require more educated individuals who tend to possess more general skills and are better able to adapt to new production technologies (Krueger and Kumar, 2004; Van Dijk, 2005). Moreover, companies use education as a signal of workers’ ability to adapt continuously and learn new skills and approaches within a variety of contexts. Limitations While this study has made a significant contribution to identifying the relationship between educational inequality and use of digital platforms, its findings should be interpreted in the light of several limitations that future research may seek to overcome. First, additional data are needed to investigate the causal pathways linking education to DCP use. Education can neither increase productive knowledge, as implied in human capital theory (Lochner, 2011), nor can it solely affect the quality of the social network available to an individual (Calvó- Armengol and Jackson, 2004). In the first case, pursuing a university education would increase students’ chances of successfully engaging with DCPs, depending on the curricular 18 content of the specific studies in which they enrol; in the second case, their participation in the new digital forms of exchange would fundamentally depend on their ability and the technological curiosity of their classroom peers. Similarly, an implicit assumption in our analysis is that accessing DCPs benefits its users. Additional data are needed to investigate the types of benefits potentially generated by different types of users, such as consumers versus labour providers. Conducting further surveys detailing the benefits obtained by DCP participants and the curricular contents of their studies will likely tackle these issues. A third limitation arises from the use of one-year, cross-sectional data from the European Union. While the European Union is a diverse group of countries, the generalisability of our findings may be subject to differences in university funding, entry requirements, facilities and infrastructure. These differences may limit the validity of this study beyond the European Union. A fourth limitation may be that our instrument considers all university programmes, without making content-related distinctions. While we are able to discriminate between private and public universities, investigating the different impacts of university places on science versus social programmes may prove to be an interesting avenue for further research. Finally, we have not broached the question of inequality in digital labour markets. Whether there are differences in participation in these markets depending on educational status, whether there are similar education gaps in online and MLMs, and whether service providers use digital labour markets as a primary or supplementary source of income are all interesting issues that we would like to address in the future. Conclusions Notwithstanding these limitations, out study suggests that DCPs increase socio-economic inequality and underline the need to propose economic policies that reduce the gap in educational inequality, especially given the current growth in precariousness, both in 19 traditional and gig-economy labour markets (Flanagan, 2017; Stanford, 2019). If highly educated individuals benefit most from the digital economy, the obvious policy solution is to ease access to university education by increasing provision of university places and through awarding scholarships and reducing public tuition fees (Agrawal et al., 2019). Moreover, our first-stage model underlines how the availability of university centres in an individual’s region of residence affects the likelihood of pursuing higher education. Channelling resources towards regions with fewer university places could prove to be a compelling policy option for increasing educational attainment and reducing inequality in DCP use. Additionally, policies should identify demographic groups suffering the most from the digital divide (Wu et al., 2015; Goedhart et. al., 2019) and target specific resources towards them. Finally, public policies must advance digital preparedness by including digital skills into educational curricula and across educational levels. It is difficult to map the competencies and skills expected in industries not yet created (Schwab, 2016). Although Van Laar et al. (2017) argue that 21st-century skills are broader than digital skills, the technical, information management and communication skills characteristic of digital literacy are considered core skills, fundamental for performing tasks that are needed in a broad range of occupations. Other skills are also important, but digital skills are a prerequisite for many 21st century occupations. Our study suggests that higher-educated individuals will have easier access to the new jobs that will be demanded in the Fourth Industrial Revolution, accentuating socioeconomic inequality with respect to the less educated. Undoubtedly, the COVID-19 virus pandemic is a clear example of how companies and individuals who have the characteristics and knowledge to carry out activities in the digital field have not had to suspend productive activity and have continued obtaining income, compared to other 20 sectors that have had to cease operating completely. 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Transaction costs economics emphasizes that hierarchical institutions such as firms arise to minimize the transaction costs of operating through markets (Coase, 1960; Williamson, 1981; Lobel, 2019). 2 Human capital theory rests in the assumption that education increases the individual’s skills and productivity and ultimately her earnings (Becker, 1962). The skill-biased technological change hypothesis suggests that technological change shifts labour demand towards the highly skilled and consequently highly educated workers benefit from the implementation of new technologies. However, such wage premiums are temporary, and in the long run market forces ensure the balancing of skill supply and skill demand, causing returns to flatten or decline (Acemoglu 2002; DiMaggio and Bonidowski, 2008). 3 Institutionalist theories of wage bargaining emphasize that productivity resides in the job rather than in the individual hired to perform it. In the job competition model, there is a labour queue of individuals facing a distribution of jobs and earnings. An individual’s position in the labour queue is based on perceived training costs, which is inversely related to educational qualifications. Consequently, the best jobs in the labour queue are allocated to individuals with higher qualifications (Thurow, 1975; Sobel, 1982; Greenwood, 2016). 4 Examples of OLMs, digital labour markets that allow remote delivery of electronically transmittable services, include Freelancers, Amazon Mechanical Turk, and Upwork; examples of MLMs, digital labour markets that require direct interaction for the physical delivery of the services, include Uber, Deliveroo, and TaskRabbit. 5 IV methods can usually only identify the impact of the main regressor on the outcome for the subpopulation induced by the instrument to change the value of the endogenous regressor. In our case, using the number of universities in the region as an instrument for years of education can only identify the impact of an additional year of education for those individuals induced to increase their years of study by an additional university opening in the region. In this respect, this estimated impact is considered a local estimate, that is, a local average treatment effect or LATE (Imbens and Angrist, 1994). 6 We provide a discussion of the validity of the instrument in the Online Appendix C.