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Accepted Paper:
Falling through the cracks: when informal workers cannot be 'seen'
María Gabriela Palacio Ludena
(Leiden University)
Paper short abstract:
The paper invites a critical engagement with techno-fixes for poverty by looking at challenges to 'see' informal employment: while some marginalised populations are missing from data infrastructures and excluded from social protection, others are oversampled and kept under excessive surveillance.
Paper long abstract:
Following the onset of the COVID-19 pandemic, data have been at the centre of the social policy response and programmes delivery. Automated systems are increasingly making decisions on social and economic rights in a raft of data-driven policymaking. While data-driven solutions are valuable and have led to a partial inclusion of marginalised populations or as means to do away with what is considered a corrupt bureaucracy, they have shifted the focus away from power imbalances in the design of social interventions and the challenges of 'seeing' informality. This paper problematises how populations in informal employment are included in narrowly targeted social assistance interventions, which are heavily reliant on data infrastructures that fail to make informality legible to programme administrators, and, thus, the state. The focus lies in the politics of exclusion and inclusion that permeate data infrastructures, particularly social registries, the most popular statistical tool used to identify poor 'deserving' populations in the Global South. The study zooms into Ecuador's most prominent social assistance programme, Bono de Desarrollo Humano, and the COVID related programme Bono de Proteccion Familiar. Based on ethnographic work, interviews, and narrative analysis, it finds that social registries weaken the link between eligibility and informal employment, making it illegible to the state. More recent data innovations, such as machine learning, are insufficient to locate informal workers. They tend to replicate data ommissions and biases, e.g., assuming the poor are geographically concentrated in peripheral areas, unable to 'see' care circuits or keep track of demographic and occupational changes.
Click the star to add/remove an item to/from your individual schedule.
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Accepted Paper:
Paper short abstract:
Paper long abstract:
Following the onset of the COVID-19 pandemic, data have been at the centre of the social policy response and programmes delivery. Automated systems are increasingly making decisions on social and economic rights in a raft of data-driven policymaking. While data-driven solutions are valuable and have led to a partial inclusion of marginalised populations or as means to do away with what is considered a corrupt bureaucracy, they have shifted the focus away from power imbalances in the design of social interventions and the challenges of 'seeing' informality. This paper problematises how populations in informal employment are included in narrowly targeted social assistance interventions, which are heavily reliant on data infrastructures that fail to make informality legible to programme administrators, and, thus, the state. The focus lies in the politics of exclusion and inclusion that permeate data infrastructures, particularly social registries, the most popular statistical tool used to identify poor 'deserving' populations in the Global South. The study zooms into Ecuador's most prominent social assistance programme, Bono de Desarrollo Humano, and the COVID related programme Bono de Proteccion Familiar. Based on ethnographic work, interviews, and narrative analysis, it finds that social registries weaken the link between eligibility and informal employment, making it illegible to the state. More recent data innovations, such as machine learning, are insufficient to locate informal workers. They tend to replicate data ommissions and biases, e.g., assuming the poor are geographically concentrated in peripheral areas, unable to 'see' care circuits or keep track of demographic and occupational changes.
Informality: a way of surviving the post-pandemic city?
Session 1 Thursday 7 July, 2022, -