Wealth Disparities, Inequality, and Macroeconomic Policy
Paper Session
Sunday, Jan. 4, 2026 2:30 PM - 4:30 PM (EST)
- Chair: Salvador Contreras, University of Texas Rio Grande Valley
Retirement Outreach to Low- and Middle-Income Workers
Abstract
While the racial and ethnic gap in retirement preparedness is an established fact in the United States, how to address it remains an open question. This paper presents preliminary findings from the MyRetirement/MiRetiro intervention, a digital education program tailored to meet the retirement-preparedness needs of low-to-middle-income, especially Black and Hispanic, workers. The program was implemented as a randomized controlled trial through the Understanding American Study internet panel. Participants were randomized into three groups: a control (no intervention), an information treatment, and an information plus monetary treatment. The information treatment comprised a four-week educational program while the monetary treatment entailed a lottery to win $100-$500. We find that (1) the information treatment had a positive impact on retirement knowledge measured using the Retirement Knowledge Scale, (2) using a measure of where individuals are among five stages of retirement saving, the information plus monetary treatment increases the likelihood of moving to the next stage, (3) minority participants are more responsive than white participants to the information treatment while all participants respond similarly to the information plus monetary treatment, and (4) neither treatment seems to impact the probability of opening a retirement account.Measuring Income Inequality in the United States Using the Gini Index and Information Gain in Decision Trees (2003 & 2023)
Abstract
The Gini Index Gain (GIG), typically used in decision tree algorithms to measure the reduction in data impurity after a split, assesses how dividing populations (skilled vs unskilled groups) impacts income inequality. In the US, economic inequality varies significantly across racial/ ethnic groups; Whites generally hold more wealth, while Blacks and Hispanics face housing instability and lower equity. Among Asians, some groups (Indian, Taiwanese) excel due to education and immigration advantages, while others (Hmong, Cambodian) struggle with poverty and limited resources. We use GIG to analyze income inequality using US Census and American Community Survey (ACS) data from 2003 and 2023. The population is split by attributes like race, ethnicity, citizenship, and education, with GIG quantifying the reduction in inequality. Rather than optimizing for the best split, the study tests various splits and subsequent education-based divisions to explore their effects on inequality. Splitting by Hispanic vs. non-Hispanic consistently yields the highest GIG in both 2003 and 2023, indicating Hispanics contribute significantly to inequality among the studied groups, with disparities worsening by 2023. The Black vs. non-Black split remains significant but shows improvement (lower) over time. White vs. non-White splits declined in significance by 2023, while Asian vs. non-Asian splits grew more impactful. Citizenship status (citizen vs. non-citizen) consistently has the least influence. Education level strongly affects inequality: skilled (educated) groups exhibit lower GI than unskilled groups across all ethnicities in both years, underscoring its role in driving disparities.Measuring Real Output and Inflation: Official Statistics vs Economics Transactions Data
Abstract
Businesses, individuals, and government policymakers rely on accurate and timely measurement of nominal sales, inflation, and real output, but current official statistics face challenges on a number of dimensions. First, these key indicators are derived from surveys conducted by multiple agencies with different time frames, yielding a complex integration process. Second, some of the source data needed for the statistics (e.g., expenditure weights) are only available with a considerable lag. Third, response rates are declining, especially for high-frequency surveys. Focusing on retail trade statistics, we document important discrepancies between official statistics and measures computed directly from item-level transactions data. The long lags in key components of the source data delay recognition of economic turning points and lead to out-of-date information on the composition of output. We provide external data sources to validate the transactions data when their nominal sales trends differ importantly from official statistics. We then conduct counterfactual exercises that replicate the methodology that official statistical agencies use with the transactions data in the construction of nominal sales indices. These counterfactual exercises produce similar results to the official statistics even when the official nominal sales and item-level transactions data exhibit different trends.Asian "Chilling Effect" During the Pandemic: What Can We Learn from Reported Health Status?
Abstract
The COVID-19 pandemic created significant labor market and socioeconomic impacts. Recent literature suggests heterogeneity in these impacts across race and immigration status. There is also some growing evidence that Asian Americans were disproportionately affected during this time due to the prevailing backlash against Asians. In this research, we investigate this claim by focusing on reported health status. While a growing body of literature has examined the impacts of the pandemic on health-related outcomes across different racial and ethnic groups in the U.S, there has been limited exploration of disproportionate changes in self-reported health status across races and ethnicity in a large national survey. Our paper seeks to address this gap using data from the Annual Social and Economic Supplement (ASEC) of the Current Population Survey (CPS).Discussant(s)
Raffi Garcia Rensselaer
,
Rensselaer Polytechnic Institute
Ejindu Ume
,
Miami University
Jose Bucheli
,
University of Texas at El Paso
Joaquin Rubalcaba
,
University of North Carolina-Chapel Hill
Mary Lopez
,
Occidental College
JEL Classifications
- D3 - Distribution
- J1 - Demographic Economics