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Thesis/Dissertation
Date
2025-05
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Political Science
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https://doi.org/10.34944/ynte-x035
Abstract
In the Global South, the expansion of non-discretionary benefits, such as cash transfers, has intensified the need for states to produce systematic knowledge about the poor. Whether for exclusion or inclusion in welfare protection, states collect, standardize, and codify data to exert greater control for poverty relief interventions. These efforts depend on a bureaucratic workforce tasked with implementing data gathering. However, in weak states, data-gathering bureaucracies often lack Weberian features such as professionalism, hierarchy, career stability, and clear incentives. Moreover, states with limited capacity to reach poor populations decentralize data collection to bureaucrats in the periphery, who operate under irregular and under-resourced local governments, exacerbating oversight problems and multiple principal challenges. The dissertation addresses how the state renders poverty legible under this context. It examines the local implementation of Peru’s social registry system. Since 2004, the Peruvian state has decentralized intake and outreach responsibilities for social programs to municipalities, assuming that local governments can more efficiently reach low-income populations. Using original administrative datasets covering nearly a decade and all municipalities in Peru, I analyze both the administrative capacity of local officials and their performance in ensuring data consistency—one of the central goals of local officials’ routines.
The dissertation presents three findings that contribute to our understanding of the bureaucratic politics of state knowledge in developing countries, in particular when the central state decentralizes responsibilities to subnational governments. First, the challenges of knowledge production reflect the broader administrative weaknesses of local governments (Chapter 2). Since local politicians have no direct control over benefit allocation, they exhibit political indifference toward outreach and intake processes. In contrast to perspectives that emphasize deliberate resistance to legibility by local elites, I argue that the lack of incentives for local politicians also undermines the state’s ability to collect data. This results in understaffed registry offices, high bureaucratic turnover, and poor data-making performance.
Second, bureaucratic capacity, already weak, is further undermined by specific local political dynamics (Chapters 3 and 4). When politicians win landslide victories, they consolidate control over administrative resources, leading to the de-professionalization of welfare officials. Building on literature that links declining political competition to patronage and resource exploitation, I extend this argument to contexts where politicians have short time horizons, such as Peru’s weakly institutionalized party system. I find that, in these settings, where exploitation is widely expected, its extent still varies across districts depending on how empowered politicians feel after electoral outcomes (Chapter 4). In contrast, I find that political continuity, measured by mayoral reelection, does not affect positively or negatively the quality of local officials (Chapter 3).
Third, despite the absence of Weberian institutions, local officials manage to collect and process data in ways that align with the state’s classification rules and intake protocols—reproducing the administrative grid. I argue that tacit and uncodified knowledge, acquired through experience, is essential for achieving this consistency in collected data (Chapter 5). Unlike approaches that emphasize the role of social ties or local embeddedness in shaping legibility performance, I show that bureaucratic stability fosters a learning curve that enables officials to improve data accuracy. This finding suggests that making poverty legible depends less on lowering the costs of outreach and data collection and more on providing stable conditions that help frontline bureaucrats improve their data-processing skills over time.
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