Dynamic Spatial Competition in Early Education: an Equilibrium Analysis of the Preschool Market in Pennsylvania (Job Market Paper)
High-quality preschool is one of the most cost-effective educational interventions, yet the United States invests little in early childhood education. Recent policy discussions call for increasing preschool enrollment and raising the quality provided, especially for disadvantaged children, but equilibrium responses of private providers which make up most of the market generate trade-offs between these objectives. Supply expansion may lower incentives to invest in quality, and price responses to demand subsidies can increase the costs faced by non-subsidized parents. This paper develops a dynamic model of the preschool market to evaluate the effectiveness of policies at achieving these objectives. The model nests a static equilibrium model of spatial competition and preschool choice within a dynamic model of providers’ entry, exit and quality investments. I estimate this model using data on the universe of child-care centers in Pennsylvania. I use the model to simulate the aggregate and distributional consequences of proposed approaches to early education expansion. I find that policies focused on expanding supply raise access but decrease the quality children attend due to parents’ value for proximity. Demand subsidies generate market expansion, but on their own do not create sufficient incentives for providers to invest in quality. Among the simulated policies, the most cost-effective at expanding high-quality enrollment combine demand subsidies targeted to low-income families with financial support to providers serving disadvantaged children. These policies increase access by reducing exit of providers, and expand high-quality enrollment for low-income children through subsidies. In addition, these targeted policies generate spillovers to the educational quality of non-targeted families by creating incentives for centers to invest in quality.
Wait Times for Surgery in the U.S.: Measurement and Allocative Efficiency in Private Insurance with Michael Dickstein and Guillaume Fréchette
In healthcare systems across the world, limited capacity implies that patients must wait to access surgical care. To evaluate the efficiency and equity consequences of rationing care via queues, however, requires comprehensive measurement of the length of these waits for multiple treatments, patient types, and insurance generosities. We employ machine learning models trained on a large claims dataset of U.S. patients with employer-sponsored insurance to measure wait times as the delay between (a) the moment our models can confidently classify a patient as in need of surgery and (b) the day of the surgery. We use this novel measure to study the distribution of wait times for roughly one million patients across many common surgeries. We find that men wait less than women, while older patients and patients with comorbidities wait longer, suggestive of potential medical inefficiencies. Similarly, we show that health insurance design affects surgical wait times in ways that may not coincide with the value of care. Using an instrument based on weekly congestion in patients insurance plan, we find that delays have adverse effects on recovery across a breadth of medical outcomes. Patients who wait a month more are 3.1% more likely to be readmitted in a hospital, spend 5.9% more, and are prescribed 6.6% more opioids in the six months following a surgery. Combining this empirical design with recent machine learning tools to recover heterogeneous effects, we quantify the medical allocative efficiency of surgical wait-lists. Applying our estimates to a subset of the surgeries that patients undergo, we find that reassigning patient priorities in the queue could substantially reduce hospital spending.
News Media Concentration and Content Diversity with Nicolas Longuet Marx and Marguerite Obolensky
The rise in political polarization over the recent years has fostered scrutiny of the structure of the news industry’ s influence on political outcomes. How should policymakers regulate news producers when they value news diversity and large publishers shape the ideological landscape? To answer this question, we develop an empirical model of competition for readership and advertisers between news producers. We recover the topic content and ideological positions of 200 major U.S. daily newspapers using recent advances in Natural Language Processing on millions of published articles. We find that over the period 2007-2017, the median newspaper in our sample got closer to the ideology of the Democratic party. Second, we embed these topics and ideal points in a demand model for differentiated products with heterogeneous readers. Our model shows that rich readers lean democrat and consume more news about social and political questions while the elderly are more conservative and care more about local news. Using the estimated demand model and data on advertising contracts and readership, we can recover the cost of producing each type of content. Given this model of news supply, we intend to use our framework to provide recommendations on antitrust rules weighing both consumer welfare and ideological diversity.