Inflation influences college students’ future perceptions and aspirations through changes in prices for necessities like housing, food, transportation, and education. Recent historical events, including the end of the COVID-19 pandemic, political changes, and ongoing conflicts, have created a unique economic environment for studying the effects of inflation. Following the end of the pandemic, inflation initially experienced an extreme increase before declining and eventually leveling off, while additional factors have contributed to rapid increases in housing costs. The current state of the economy after such effects is still being studied, with a particular interest in the effects of recent economic changes on individuals’ spending and saving patterns.
Research continues to examine how inflation affects the financial decisions of both individuals and households, including changes in spending and saving behavior. Although measures such as the Consumer Price Index (CPI), income change reports, and other economic measures provide a broad measure of changes in prices and economic conditions, there is not a direct measure that captures differences in specific student costs and economic conditions. These developments grew our interest in examining college students’ cost burden and its potential implications for student inflation perceptions and expectations. With a particular interest in understanding California’s student cost burden, we examined cost measures for high-performing universities across the state, including UCLA, UCSB, UC Berkeley, and Cal Poly Humboldt, as well as their surrounding areas.
We compiled economic, demographic, and contextual measures for each university and its surrounding geographic area to examine differences in students’ potential cost burdens and inflationary environments. These measures include:
[Income Bracket Data (City & County), Tuition (In-State & Out-of-State), Housing Costs (On/Off Campus), Meal Plan Costs, Grocery Costs (Includes Multiple Cuisine Costs), Transportation Costs (On & Off Campus), Gas Prices, Student Loan Debt, Political Lean (Democratic, Republican, Independent, Other, & No Party Preference), Political Voter Turnout, Home Values (City & County, 2020-2025), Demographics, Inflation Rate, and more].
Income, tuition, housing, meal plans, groceries, transportation, gas prices, and student loan debt were included to capture the different costs and financial pressures that may contribute to students’ overall cost burden. City and county home values from 2020 to 2025 were examined to understand changes in housing costs over time, as well as differences between the areas surrounding each institution.
Beyond economic measures, we examined political lean and voter turnout to capture whether differences in political environments or exposure to political and policy information may be associated with students’ inflation perceptions. Demographic measures and local inflation rates were also included to account for differences in the populations and economic environments surrounding each university. These contextual measures allow for a broader view of the factors that may contribute to differences in students’ economic experiences.
Through analysis of these measures and drawing on research on inflation perceptions, such as The Causal Effects of Inflation Uncertainty on Households’ Beliefs and Actions, our findings highlight the need for further research on how differences in student cost burden may influence students’ perceptions of inflation and their expectations for the future.
AI Governance and
Economic Strategy:
Lessons for
State Governments
Tucker Gauss, Jorgen Jore,
Ari Fomalont, Matthias
Osenberg,
Rheanna Mahboobani, Evan
Davis
As artificial intelligence (AI) adoption accelerates, state governments face critical decisions in navigating the evolving technological landscape. While early development favored open ecosystems, economic incentives, competitive pressures, and strategic considerations are driving major AI players toward closed models, raising concerns about access, competition, and economic equity.
This report, developed by the Behavioral Economics Association at UC Berkeley with mentorship and encouragement from IBM Research and Professor Emmanuel Saez, examines the economic and behavioral dimensions of open versus closed AI ecosystems, arguing that a predominantly closed AI future is the most probable outcome. Through game-theoretic analysis, expert interviews, explorations of competitive market dynamics, the strategic value of exclusive data, and the importance of cost-effective AI distribution, we identify key factors shaping the AI landscape.
We analyze regulatory approaches, comparing the US and EU, alongside macroeconomic impacts and behavioral economic insights to develop state-level policy recommendations. These focus on fostering innovation via strategic investments (e.g., competitive grants, appropriate subsidies), mitigating negative consequences for employment and inequality, and promoting broad economic stability. Case studies of California, Wyoming, and Ohio illustrate how states can tailor these strategies by leveraging unique advantages—such as regulatory leadership, infrastructure capacity, or key government partnerships—ultimately offering a framework for balancing innovation, accessibility, and stability
in the dynamic AI field.