Data streams merging to represent race, ethnicity, and economic statistics.

Decoding the Future: How Combined Race and Ethnicity Reporting Will Reshape Earnings Statistics

Lena Kashyap in Business & Economy December 2025 • 5 min read.

"A deep dive into the potential impacts of the U.S. Census Bureau's new single-question format for race and ethnicity reporting on long-term earnings data and what it means for understanding economic disparities."

For over two centuries, the United States has tracked race and ethnicity through its decennial census, a practice initially intertwined with the subjugation of minority and indigenous populations. However, since 1970, this data collection has evolved, serving a crucial role in enforcing civil rights laws by providing statistical evidence of disparities. The regulations governing these data collections have been underpinned by two key principles: self-identification, ensuring individuals define their own race and ethnicity, and the ability for respondents to select multiple racial identities.

A significant shift is on the horizon. In March 2024, a third principle was introduced: allowing respondents to choose from any of seven categories—the five from the Statistical Policy Directive (SPD) 15, plus Hispanic or Latino, and Middle Eastern or North African (MENA)—without needing to differentiate between racial and ethnic categories. This marks a move towards a "single-question" format, departing from the traditional two-question approach where ethnicity (Hispanic or Latino) was asked separately and before race.

This change, driven by research suggesting that a single, comprehensive question yields better quality data and reduces “Some Other Race” responses, has the potential to significantly impact how we understand and address economic disparities. Our analysis delves into these potential impacts, focusing on long-term earnings statistics and the visibility of specific racial and ethnic groups.

The Methodology Behind the Numbers: Understanding the Data

Data streams merging to represent race, ethnicity, and economic statistics.

To assess the implications of these changes, we leverage a robust dataset combining information from the Social Security Administration (SSA), the Longitudinal Employer-Household Dynamics (LEHD) Program, and the 2000 and 2010 Censuses. This allows us to examine long-term earnings differentials across various self-identified race and ethnicity categories, focusing particularly on foreign-born individuals from heavily Hispanic countries and those from the Middle East and North Africa (MENA).

Our methodology involves several key steps:

Defining Key Groups: We categorize individuals based on their self-identified race and ethnicity, as well as their place of birth. A place of birth is classified as “Hispanic” if at least 50% of individuals born there self-identify as Hispanic. For MENA countries, we rely on geographic location due to the lack of reliable MENA self-identification measures in the 1997 SPD 15 format.
Measuring Long-Term Earnings: Our primary outcome measure is average annual real labor market earnings over a six-year period (2010-2015). This provides a more stable picture of economic well-being compared to single-year snapshots.
Accounting for Socioeconomic Factors: We employ regression analysis to adjust for factors such as education, age, years living in the U.S., labor force attachment, and hours of work. This helps isolate the impact of race and ethnicity on earnings, independent of other contributing factors.
Analyzing Second-Generation Immigrants: We extend our analysis to include U.S.-born children of immigrants, examining how their earnings differ based on their parents’ place of birth and their own self-identified race and ethnicity.

By comparing outcomes based on self-identification with administrative data (place of birth recorded on Social Security applications), we aim to understand how the shift to a single-question format might alter the landscape of race and ethnicity reporting and, consequently, our understanding of economic disparities.

Navigating the Future of Data: A Call for Vigilance and Nuance

The shift to a single-question format for race and ethnicity reporting presents both opportunities and challenges. While it promises to improve data quality and reduce respondent burden, it also risks obscuring important nuances within racial and ethnic groups. As we move forward, it is crucial to maintain a commitment to data disaggregation and to explore alternative methods for capturing the complexity of identity. Only then can we ensure that our statistics accurately reflect the lived experiences of all Americans and inform effective policies to promote equity and opportunity.

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2407.12775,

Title: Estimating The Potential Impact Of Combined Race And Ethnicity Reporting On Long-Term Earnings Statistics

Subject: econ.gn q-fin.ec

Authors: Kevin L. Mckinney, John M. Abowd

Published: 17-07-2024

Everything You Need To Know

What is the key change in race and ethnicity reporting introduced in March 2024, and how does it differ from previous methods?

The key change introduced in March 2024 is the implementation of a single-question format for race and ethnicity reporting. This approach allows respondents to choose from seven categories: the five from the Statistical Policy Directive (SPD) 15, plus Hispanic or Latino, and Middle Eastern or North African (MENA). This marks a departure from the traditional two-question approach where ethnicity (Hispanic or Latino) was asked separately and before race, which had been in use since 1970 and based on self-identification and multiple racial identity options.

How might the new single-question format impact the visibility of specific racial and ethnic groups in earnings statistics?

The single-question format could potentially alter the visibility of specific racial and ethnic groups. The change might affect how groups are categorized and understood in the data, potentially leading to the obscuring of nuances within racial and ethnic groups. The shift could impact the analysis of earnings differentials, especially for groups like those from MENA countries, or those who identify as Hispanic or Latino, influencing the insights derived from the earnings statistics.

What data sources and methodology are being used to assess the implications of these changes on earnings statistics?

The assessment leverages a robust dataset combining information from the Social Security Administration (SSA), the Longitudinal Employer-Household Dynamics (LEHD) Program, and the 2000 and 2010 Censuses. The methodology involves defining key groups based on self-identified race and ethnicity, measuring long-term earnings (average annual real labor market earnings over a six-year period), accounting for socioeconomic factors through regression analysis, and analyzing second-generation immigrants.

What is the significance of the 'place of birth' classification, especially concerning Hispanic and MENA populations, in the study's methodology?

The place of birth is a crucial element in the methodology. It is used to categorize individuals, with 'Hispanic' defined as a place where at least 50% of the individuals born there self-identify as Hispanic. For MENA countries, geographic location serves as a proxy due to the absence of reliable MENA self-identification measures in the 1997 SPD 15 format. This is key to understanding how earnings vary based on self-identified race/ethnicity versus the place of birth, which can provide a nuanced perspective of economic disparities.

What are the potential benefits and challenges of shifting to a single-question format for race and ethnicity reporting?

The shift to a single-question format has both benefits and challenges. It promises to improve data quality and reduce respondent burden. However, it risks obscuring important nuances within racial and ethnic groups. The challenge lies in ensuring that the new format accurately reflects the lived experiences of all Americans and provides detailed enough data to inform effective policies to promote equity and opportunity. The commitment to data disaggregation and exploring alternative methods for capturing identity complexity is crucial to mitigate these risks.