Deeply embedded wages: Navigating digital payments in data work

Many workers worldwide rely on digital platforms for their income. In Venezuela, a nation grappling with extreme inflation and where most of the workforce is self-employed, data production platforms for machine learning have emerged as a viable opportunity for many to earn an income in US dollars. Data workers are deeply interconnected within a vast network of entities that act as intermediaries for wage payments in digital currencies. Past research on embeddedness has noted that being intertwined in multi-tiered socioeconomic networks of companies and individuals can offer significant rewards to social participants, while also connoting a particular set of limitations. This paper provides qualitative evidence regarding how this “deep embeddedness” impacts data workers in Venezuela. Given the backdrop of a national crisis and rampant hyperinflation, the perks of receiving wages through financial platforms include accessing more stable currencies and investment outside the national financial system. However, relying on numerous intermediaries often diminishes income due to transaction fees. Moreover, this introduces heightened financial risks, particularly due to the unpredictable nature of cryptocurrencies as an investment. This paper evaluates the effects of the platformization of wages and its effect on working conditions. The over-reliance on external financial platforms erodes worker autonomy through power dynamics that lean in favor of the platforms that set the transaction rules and prices. These findings present a multifaceted perspective on deep embeddedness in platform labor, highlighting how the rewards of financial intermediation often come at a substantial cost for the workers in precarious situations.