Deeply Embedded Wages: Navigating Digital Payments in Data Work
<|reference_start|>Deeply Embedded Wages: Navigating Digital Payments in Data Work: Many of the world's workers 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 a flexible income in US dollars. Platform workers are deeply interconnected within a vast network of firms and entities that act as intermediaries for wage payments in digital currencies and its subsequent conversion to the national currency, the bolivar. 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 furnishes qualitative evidence regarding how this deep embeddedness impacts platform workers in Venezuela. Given the backdrop of a national crisis and rampant hyperinflation, the perks of receiving wages through various financial platforms include access to a more stable currency and the ability to save and invest outside the national financial system. However, relying on numerous digital and local 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. 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 unstable situations, who are saddled with escalating financial risks.<|reference_end|>
@article{posada2024deeply,
    title={Deeply Embedded Wages: Navigating Digital Payments in Data Work},
    author={Julian Posada},
    journal={arXiv preprint arXiv:2403.01572},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01572},
    primaryClass={cs.CY}
}