The U.S. Bureau of Labor Statistics (BLS) began collecting data on veteran status in the Consumer Expenditure Surveys (CE) in 2019. In this article, the expenditure patterns between veterans and nonveterans are considered to understand how veterans’ benefits relate to spending. This article shows that veterans are more likely than nonveterans to own their homes at younger ages and to have lower mortgage costs. Also, veterans spend less than nonveterans on total healthcare and on almost every major healthcare component.
The U.S. Bureau of Labor Statistics (BLS) regularly explores ways to integrate alternative data sources and evaluate the fitness for use of these data for BLS data collection programs. In this article, we evaluate transactional data in the context of how it could be used to complement the Consumer Expenditure Surveys (CE). We first discuss the differences between transactional data and survey data and describe the challenges in developing a concordance to compare trends. Next, we compare spending changes in high-level industries by using the two matched sources over the period of January 2019 to December 2021. We find that in some industries the data sources show very similar trends (like food, accommodation, and entertainment), but data sources have a lower correlation in trends for other industries (like information and telecommunication). Ultimately, while we find that transactional data may provide insight into specific data trends, transactional data would be difficult to integrate with CE data because transactional data are organized by merchant type instead of by the type of good or service provided, and transactional data lack demographic details.
Total employment is projected to grow 3.1 percent between 2024 and 2034, increasing from 170.0 million to 175.2 million, for a total of 5.2 million additional new jobs. Most of these projected job gains are in the healthcare and social assistance sector and the professional, scientific, and technical services sector. Four sectors are expected to experience job losses over the decade, the bulk of which are concentrated in the retail trade sector.
Official U.S. nonfarm business labor productivity growth estimates are based on the product-side output measure of gross domestic product (GDP). Labor productivity can also be estimated based on the income-side output measure of gross domestic income (GDI) and on the average of GDP and GDI, also known as gross domestic output (GDO). This article compares the relative attributes of these three output measures with respect to their usage for labor productivity analysis and provides a methodology for how alternative measures of labor productivity can be computed by using GDI and GDO.
This article examines three patterns of output growth—productivity-driven, hours-driven, and balanced growth in productivity and hours worked—in a sample of fast-growing industries. While growth in annual hours worked has slowed at the national level since 2000, many industries have sustained high rates of output growth through rapid gains in productivity. Less often, some industries have expanded by accelerating or maintaining growth in hours worked. Major developments in technologies and business practices are common themes in industries with high productivity growth rates. This article provides an industry-based framework for understanding aggregate output growth in an era of relative labor scarcity.
In this article, we focus on the childcare sector from the worker’s perspective, analyzing prepandemic (2014–18) Current Population Survey data to compare privately employed early educators and public K–8 teachers with bachelor’s degrees along several dimensions of job quality. Consistent with other research, we show that, compared with public K–8 teachers with bachelor’s degrees, private early educators with similar work patterns and educational attainment earned less and lacked critical employer-sponsored benefits such as health insurance and retirement accounts.
Wage gaps between male and female workers remain a persistent challenge in labor markets worldwide, often stemming from structural inequalities and unmeasured factors beyond productivity differences. This article introduces a novel decomposition methodology to analyze wage differentials between men and women in Peru, offering an alternative to traditional approaches such as the Kitagawa-Oaxaca-Blinder model. By simulating a counterfactual scenario in which male and female workers possess identical productivity characteristics, this method isolates the unexplained component of the wage gap while accounting for regional labor dynamics. The analysis reveals that substantial wage disparities persist even when observable factors are controlled.
In 2024, the U.S. unemployment rate ended the year at 4.2 percent in the fourth quarter, up from 3.8 percent a year earlier. The employment–population ratio edged down to 59.9 percent, while the labor force participation rate, at 62.5 percent, was little changed over the year. The telework rate, at 23.4 percent, continued to trend up in 2024.
In March 2025, the Local Area Unemployment Statistics (LAUS) program introduced novel intercensal estimates of the civilian noninstitutional population (CNP), which are used to produce statewide estimates of employment and unemployment. These intercensal estimates reconcile decadal differences between population projections from one census count to the next. These differences between census counts, known as the error of closure, introduce breaks in the LAUS CNP time series each decade. In this article, we note the usage of population data in LAUS estimation, describe the demographic methods used to produce the new intercensal population series, and show the impact of the new data on current LAUS time series.
We use data from the Current Population Survey and the American Time Use Survey to analyze trends in employment, real hourly earnings, and time use among married women with children in the United States. We find that college-educated married mothers were an anomaly in that both their employment–population ratios and their hours worked increased between 2000 and 2019. These increases contrast with declining employment–population ratios and hours worked for high school-educated married mothers, married women without children in all education groups, and for most groups of men during the same period. In addition, we document that real hourly earnings of college-educated mothers steadily increased between 2000 and 2019, unlike other demographic groups, who experienced smaller increases or stagnation in real hourly earnings. Lastly, increases in time doing paid work among college-educated married mothers coincided with declines in time spent on childcare and unpaid domestic work and increases in time spent on childcare among college-educated married fathers.
Using predicted shaking intensity data from the U.S. Geological Survey and establishment location data from the Quarterly Census of Employment and Wages (QCEW), this article matches establishment locations with shaking intensities and attempts to quantify the businesses, employees, and wages that would be affected by a 7.2 magnitude earthquake. This article updates several previous Bureau of Labor Statistics (BLS) articles, which provided economic damage estimates for businesses and employees in the San Francisco Bay area based on scenarios with lower magnitudes.
This article serves as a primer for researchers interested in estimating the size, characteristics, dynamics, and distribution of the oil and gas extraction (OGE) workforce using public data. The OGE workforce changes with shifts in energy markets and technology, and accurate OGE workforce estimates are important for assessing workforce needs, understanding injury and illness patterns, and measuring impacts of energy production on local economies. This article provides contextual information about the industry's structure, establishment types, and corresponding federal classification codes and presents descriptive workforce statistics from several sources, with emphasis on the differences between lead employers, who coordinate production, and specialized employers, who support and contract with the lead employers. Finally, this article also discusses the relationships between industry employer and workforce characteristics and observed variation in injury and illness rates.
The COVID-19 pandemic altered consumer spending on public transportation, including both intracity (mass transit, taxi, and limousine) and intercity (air, ship, bus, and train). According to the Consumer Expenditure Surveys, spending on public and other transportation fell 66.3 percent in 2020. With shifts toward virtual work and school attendance, many commuters’ 2 hours to the office became 2 minutes to the kitchen table. This article examines changes in dollars spent for public transportation, mainly intracity mass-transit spending from January 2018 to December 2021, among various unique demographic groups: urban and rural residency, occupation type, educational attainment, and selected metropolitan statistical areas. Essential workers generally were unable to telework. Simultaneously, private-transportation costs declined in 2020 for all, while affordability rose for many. Gasoline prices fell 46 cents (17 percent) per gallon in 2020, according to the Consumer Price Index. Furthermore, average annual income fell for certain occupational groups and rose for others (income rose 2 percent for all consumer units). A model of indifference curves and budget constraints is used to show which members of specific education groups are likely to substitute private for public transportation. Results in 2020 show that income growth and lower private-transportation costs compared with those of public transportation resulted in increased private-transportation spending of up to 5 percent, with accompanying public-transportation spending reductions of 40 to 50 percent. Although intracity mass-transit spending rebounded in 2021, it did not reach prepandemic levels.
Total employment is projected to grow by 4.0 percent and add 6.7 million jobs from 2023 to 2033, increasing from 167.8 million to 174.6 million. Around half of projected job gains are expected to be in the healthcare and social assistance and professional, scientific, and technical services sectors, driving demand for related healthcare and computer and mathematical occupations. Retail trade is the only sector projected to lose jobs over the period.
