THE CRISIS OF LOCAL JOURNALISM: DEATH BY A THOUSAND PAPER CUTS

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[Bloomington, Ind.] : Indiana University

Abstract

The decline of local journalism in the United States represents a critical threat to democratic governance. Nearly one-fifth of Americans live in news deserts – which are communities with limited or no access to reliable local news. Predicated on this threat, we draw upon the political economy of media, public goods theory, and media pluralism frameworks to establish journalism as a public good characterized by positive externalities. However, the market mechanisms in place today have either consistently failed to adequately provide these externalities or been superseded by neoliberal capitalism's thirst for pillaging profit at any cost. To that end, this dissertation’s empirical analysis proceeds through two major studies. First, the theoretical analysis demonstrates how the confluence of technological disruption from digital platforms and these neoliberal deregulatory policies created conditions enabling aggressive financialized ownership – which we call greedy money models – to extract value from struggling newspapers while undermining their democratic functions. Using a stacked difference-in-differences design, we examine how private-equity-and-hedge-fund acquisitions of local newspapers affect electoral competitiveness in affected communities. Contrary to expectations of simple democratic decline, the findings reveal a surprising pattern: elections become substantially less competitive while simultaneously experiencing increases in voter turnout, declines in incumbent win rates, and increased candidate entry. These effects peak one year after acquisition before partially dissipating, suggesting immediate disruption to local political equilibrium followed by a degree of gradual adjustment. Second, we incorporate machine learning algorithms to identify counties that share similar socioeconomic profiles to news deserts. Addressing significant class imbalance in the data through various resampling techniques, the analysis finds that Random Forest combined with SMOTEENN is the strongest performer. The most significant predictor is the interaction between population density and GDP, highlighting how economic vulnerability intersects with demographic factors. Crucially, we incorporated neighboring counties' socioeconomic characteristics, which can help tease out how the formation of news deserts operates as a regional contagion rather than isolated local failures. These findings challenge conventional narratives about media ownership and democratic health. Rather than straightforward erosion, private equity acquisition triggers complex political reorganization in local communities. The temporal dynamics and spatial dependencies suggest specific intervention windows and the need for regional rather than purely local policy responses. Ultimately, this work demonstrates that the crisis of local journalism operates through patterns that, once understood, offer opportunities for intervention.

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Thesis (Ph.D.) - Indiana University, Media School, 2025

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Journalism, Newspapers, News desert, Data, Machine learning, Private equity

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This work is licensed under CC BY-NC: You are free to copy and redistribute the material in any format, as well as remix, transform, and build upon the material as long as you give appropriate credit to the original creator, provide a link to the license, and indicate any changes made. You may not use this work for commercial purpose.