Created in 1986, the Low Income Housing Tax Credit (LIHTC) serves as one of the main subsidized housing policy tools actively tackling housing-related barriers for low-income households via the provision of affordable units across the country. This study provides preliminary evidence on how proximity to LIHTC developments affects K-12 public school students’ educational outcomes. This paper adopts an Event Study Design and a staggered Difference-in-Differences (DiD) approach, utilizing publicly available datasets and student-level administrative data for Wisconsin public school students. Findings suggest that LIHTC exposure generates statistically significant, but modest effects: students in exposed schools experience a 0.49 percentage point decline in attendance rates and a 1.74 percentage point increase in chronic absenteeism. Achievement effects show that ELA scores improve by 0.042 standard deviations while math scores decline by 0.026 standard deviations. Event study analyses show no evidence of differential pre-trends, but reveal persistent negative attendance effects that accumulate over time.

To develop tools to nowcast food hardship, we use national and state-level data from the Household Pulse Survey (HPS) and USDA’s Weekly Retail Food Sales data to identify food insufficiency predictors with Least Absolute Shrinkage and Selection Operator (LASSO). Current and lagged food spending predict food insufficiency in an unexpected positive direction, but unit sales are predictive in the expected direction. Using food spending categories improves predictions. Within categories, Fats, Oils, and Sweeteners, as well as Vegetable sales, possess predictive power across models. Beverages and Grains predict food insufficiency using national data, while Fruit and Other predict using state data.

GLP-1 (Glucagon-Like Peptide-1) medications – such as Ozempic, Wegovy, and Mounjaro – have gained widespread popularity for their role in promoting weight loss and managing Type-2 diabetes. Yet, despite their potential, their high cost and limited insurance coverage of these medications remain significant barriers to access, particularly among low-income individuals. This research leverages the staggered adoption of Medicaid coverage for GLP-1 to examine whether the expansion of such coverage leads to an increase in GLP-1 medication take-up. We utilize publicly available quarterly data from the Centers for Medicare & Medicaid Services (CMS) Medicaid State Drug Utilization data from 2018 to 2024, to construct a panel dataset at the state-by-quarter level, and track changes in the volume and composition of Medicaid-reimbursed GLP-1 prescriptions over time. We adopt a simple Difference-in-Difference (DiD) design, comparing states that expanded the Medicaid coverage for weight management to those that did not, while controlling for state and time fixed effects. Preliminary descriptive statistics indicate significant increases in GLP-1 take-up across the U.S., with some states showing sharp quarter-to-quarter jumps exceeding 50% in prescription volume.