Introduction

To say that wages are important may be an obvious statement from the perspective of the individual. After all, wages tend to be a major source of income for individuals of working age. Wages allow individuals to purchase the things they need to live, including housing, food, and utilities, to name a few. What may be less obvious is why wages, and specifically if and how they grow, are important to the overall functioning of the economy. Wage growth is often used as a proxy for economic conditions and standard of living. If, over time, wages rise (in inflation-adjusted terms), then workers can afford more or better goods and services, thereby increasing their standard of living. In addition, if wages are growing, aggregate demand (total spending) would generally be strong and economic conditions therefore robust. As a result, if and by how much wages grow can have important policy implications for a wide array of issues.¹

How to actually define and measure wages is complicated. Colloquially, people often use terms such as wages, earnings, compensation, pay, or income to mean the money they receive from their jobs. However, statistical agencies such as the Bureau of Labor Statistics (BLS), Census Bureau, and Bureau of Economic Analysis (BEA) make distinctions among such terms. For simplification, this report generally uses the term wages to refer to money received from working a job, though terms specific to particular agencies or surveys will be used when series definitions and data are described.

In part because of this definitional issue, there are numerous government measurements of wages. This stands in contrast to many other economic concepts, for which there is only one main source of data. For example, while the federal government produces one statistic for gross domestic product, it produces numerous different measures of wages, compensation, and earnings at the aggregate level. Individual measures of wages discussed in this report include different categories of compensation, such as benefits or bonuses, and cover different groups of workers. Therefore, this report specifically defines each measure and its methodology. Further, there is not one specific measure preferred over others by all policymakers, economists, academics, or journalists. Each measure is different from the next and may cover different groups of wage earners. To this end, which measure of wages a policymaker chooses to consider can have major implications for policy.

Wages are not the same thing as income. In general economic theory, personal income is equal to the maximum amount an individual can consume without affecting his or her wealth. Wages, therefore, are a part of income but do not necessarily constitute all of it. Income may additionally include transfer payments from the government (e.g., Social Security and unemployment insurance payments) and capital income from investments (e.g., dividends, capital gains).² Another way to measure income is to distinguish its source as coming from one of the two main factors of production: labor and capital. Labor income is the amount of gross domestic product that is paid out in the form of wages, salaries, and benefits.³

This report covers selected official measures that measure wages from the perspective of workers and illustrate a range of methodologies.⁴ The following sections discuss these measures, their uses and methodologies, and considerations for when to use different types of measures. A companion report, CRS Report R47380, Average Wage Growth and Related Economic Trends in 2022, by Lida R. Weinstock, discusses recent trends in wages and their interactions with the broader economy.

Selected Wage Series

There are many different sources of data and methods for aggregation and calculation of wages and wage growth in the United States. Each source and methodology produces a unique measure that is not necessarily comparable to any other measure. Importantly, many measures cover different groups of individuals, which may be important in policy decisions that target specific groups of individuals, or otherwise benchmark against a specific wage measure. These differences among measures can result in data that show different levels and trends. To parse any differences in trends, it can be helpful to understand the differences in the measures. This section first provides an overview of a selection of common official wage measures that provide information on compensation from the perspective of the worker at the individual and aggregate level. A detailed methodology discussion follows the overview.

Overview of Selected Series

For ease of comparison and methodology discussion, the series in this section are organized by the surveys their data are sourced from. The series come from three different surveys: the Current Population Survey (CPS), the Current Employment Statistics Survey (CES), and the Quarterly Census of Employment and Wages (QCEW). The CPS is a household survey, while the CES and QCEW are establishment surveys. These surveys are discussed in more detail in the following methodology section. Basic information on each survey, its periodicity, and available data breakdowns is provided here.

Series from the Current Population Survey (CPS)

• BLS Usual Weekly Earnings: This series is a quarterly measure of wages based on what individuals estimate they "usually" earn in a week. Data are provided by quartile (buckets that divide the distribution into four parts), with the second quartile representing the median wage. This series can be broken down by sex, race, ethnicity, and age. This series may be less accurate than other sources as it is based on individuals' estimates. However, this series provides more information on the overall distribution of wages than other series and may therefore be useful to compare the wages of lower and higher wage workers.
• BLS Median Hourly Earnings: This series is an annual measure of median wages for workers paid hourly rates. This series can be broken down by sex, race, ethnicity, and age. Median hourly earnings is a specific measure that is not representative of the total labor force and is not provided frequently. Nonetheless, this series may be useful in the context of tracking and understanding the wages and characteristics of hourly workers.
• Federal Reserve Bank of Atlanta Wage Tracker: This tracker measures annual median hourly wage growth and provides data on a monthly basis. Data can be broken down by several demographic and job characteristics such as educational attainment, gender, and job stayers versus switchers. Unlike other series, the tracker only provides nominal (i.e., not inflation-adjusted) data, which is less useful in tracking purchasing power over time. The tracker does calculate median wage growth by tracking wage growth for individuals and then selecting the median growth rate. The growth in other series would be a change over time in the average or median wage at one point in time compared to another. For this reason, this series may provide more information on how individuals' wages are changing over time.

Series from the Current Employment Statistics Survey (CES)

• BLS Average Hourly and Weekly Earnings: BLS provides these series on average wages in dollar terms at both the hourly and weekly level. These series are provided on a monthly basis and can be broken down by industry. Average hourly earnings is one of the most commonly cited measures of wages and is part of the monthly BLS Employment Situation data release. These series can be a useful source of high frequency data.

Series from the Quarterly Census of Employment and Wages (QCEW)

• BLS Average Weekly Wage: This quarterly series provides average weekly wage data by locality, industry, ownership (private or government), and establishment size. This series is published with a lag and, therefore, is often less current than other quarterly or monthly series. The detailed locality breakdowns make this series useful in studying wage trends down to the county level and comparing wages and wage trends across localities at a level not always possible with other series.
• BLS Total Wages: This series is similar to the average weekly wage series except that wages are provided as a summed total instead of an average. BLS additionally provides annual aggregates for this series. Measures of total as opposed to individual wages can be useful in analyzing the health of the economy overall, as opposed to just workers.
• BEA Wages and Salaries by Industry: This series provides wage data at the monthly, quarterly, and annual levels for total wages with breakdowns by industry. The advantage of using this series over the total wages BLS series is that it is higher frequency and BEA includes estimates of total military wages. Military personnel are typically not included in wage data, making this series one of the most inclusive sources from the perspective of which workers are covered. The monthly series is imputed from quarterly data and, therefore, may not be as accurate as other monthly wage data.

Detailed Methodology of Selected Series

As with the above overview, this section is organized by data source and it includes basic information on each survey with relevant definitions. Tables for each survey are provided and include details on the methodology, advantages, and limitations of each series using data from a particular survey.

CPS

The CPS is administered by the Census Bureau and collects labor force and demographic information on a monthly basis from surveys of a nationally representative sample of households.⁶ Results are used to compile data published by BLS—a unit within the Labor Department—and the Federal Reserve Bank of Atlanta. For series published by BLS, data are characterized as earnings and specifically measure wage and salary earnings before taxes and other deductions from the main job of an individual. They do not include any benefits such as employer-provided health insurance. These earnings data are only for wage and salary workers, which does not include any kind of self-employed worker. In this case, wage and salary workers includes workers ages 16 and over who receive wages, salaries, commissions, tips, payments in kind, or piece rates.⁷ Of the series below, the two BLS series track median earnings, while the Federal Reserve Bank of Atlanta series tracks median earnings growth.

CES

The CES is a monthly survey of businesses and government agencies that gathers establishment employment information, including wages. It is administered and used to compile published data by BLS. CES data are characterized as earnings or payrolls. In this context, earnings is defined as the gross actual returns to employees excluding benefits, irregular bonuses, retroactive items, and payroll taxes. CES is an establishment survey, meaning that workers who do not work for establishments are not included.⁸ Excluded workers include proprietors, the unincorporated self-employed, unpaid volunteer or family employees, domestic employees, government employees, and military personnel.⁹

In addition to the nominal wage data, BLS also provides a dedicated news release for real wage data based on CES.¹⁰

QCEW

The QCEW is a quarterly aggregation of employment information reported by employers. Information is collected by BLS and used to compile data released by BLS and BEA. The QCEW uses state unemployment insurance administrative data, Annual Refiling Survey¹¹ data, and Multiple Worksite Report¹² data. QCEW data are characterized as pay, defined as "the total compensation paid, including bonuses, stock options, severance pay, profit distributions, the cash value of meals and lodging, tips and other gratuities, and, in some states, employer contributions to certain deferred compensation plans (such as 401(k) plans), during the calendar year, regardless of when the services were performed."¹³

Comparing Wage Data

With so many series to choose from, how can it be determined which is the best wage measure to consider? The answer may sometimes be obvious depending on the use of the data. For example, one might choose CES average wage data or Atlanta Fed wage growth data because they are available monthly. Median data might be preferred to average data or vice versa depending on whether there is concern about outliers in the data.²¹ Certain sources offer useful disaggregations by individual characteristic, locality, or industry. Different measures cover different groups of workers, from narrow to broad. Additionally, the definitional differences in wages, earnings, and compensation might make one series more attractive than another. For example, a compensation measure such as the ECI may provide a more accurate snapshot of the cost of employing workers to companies than an average earnings measure of pay received by employees (such as from the CES). In short, which data may be most useful depends on context, and there is no "best" measure for all situations. This final section discusses a few general things to consider when deciding on a wage measure to use in any given situation.

How Are the Data Measured?

One of the main differences in the series discussed in this report is whether data are provided as averages or medians. Average data are more vulnerable to changes in the composition of the workforce than are median or total data. For example, average data may increase more significantly than median data if low-wage workers drop out of the labor force. In this case, the data may not provide a full picture of workers' well-being. Median data represent the middle or typical value of a distribution and, therefore, may be more useful than average data in analyzing point estimates or short-term trends. Average and median data are usually both reasonable choices for longer-term trend analysis, and the decision between the two is more about preference in this situation.

Another key difference in how data are measured and presented is whether the data represent total wages or individual wages. Individual wages give a view of what a median or average worker makes. This is a generally intuitive measure because it is easily comparable to a minimum wage or the personal wage of the interested party. However, total wages can be useful in analyzing aggregate trends and can give a better look at concepts like the current size and state of the labor market.

Which Workers Are Included?

The types of workers included in the wage series discussed in this report range from narrow to broad. More narrow measures, such as wages for hourly workers, may be useful in analyzing questions about such a group, while broader measures may be more useful in providing a snapshot of the labor force as a whole. While the broadest measures of wages may seem the most useful or comprehensive at first glance, there are reasons why economists or policymakers may want to use a less inclusive measure. For example, in the context of unemployment insurance policy, it may be more useful to look at wages that include only workers covered by unemployment insurance. It can also simplify analysis to use a measure that does not include agricultural workers owing to the seasonality of this work, the complications of self-employment, unpaid family employment, hobby farmers, and the unknown number of undocumented workers in the agriculture industry.²²

Which Types of Compensation Are Included?

Different measures include different types of compensation given that definitions of wages change from survey to survey. For example, compensation that is not part of base salaries is not always included in measures. Benefits, taxes, and bonuses are not included in all measures. Series based on QCEW data include total compensation, whereas series based on CPS and CES data do not include benefits or irregular compensation. A total compensation measure is useful for understanding all forms of payment workers receive, whereas some of the more narrowly defined measures may give a better understanding of the typical amount of money a worker takes home in a specified period and budgets for living expenses.

What Breakdowns Are Available?

One of the most obvious ways to decide on a wage series is by how that series can be broken down to measure certain subgroups. Industry, locality, and demographic breakdowns may all be useful under certain circumstances. It can be particularly useful to choose series in this way when comparisons are being made between some total and a subcategory. Where possible, using the same type of series within one analysis is advantageous for isolating differences solely based on factors such as race, age, or state, for example.