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Date: 2024-05-20 Page is: DBtxt003.php txt00021218



Wage Growth Tracker

Original article:
During the Clinton administration wage growth was around 5% and could be considered quite strong. This changed after the bubble burst and the Bush administration had to face the aftermath of the 9/11 attacks and wage growth declined by around one (1) percentage point.

At the end of the Bush administration the financial crisis of 2008 caused a further drop in wage growth to under 2%.

The wage growth slowly improved during the Obama administration, from a very low point. Wage growth was constrained in large part by the austerity imposed by the GOP under the leadership of Senator McConnell, but nevertheless increased to around 3% by the end of the Obama administration.

During the Trump administration, this level of wage growth was sustained until the disruption of the Covid-19 pandemic. It is worth observing that the aggressive Trump tax reform initiative in 2017 has done very little to increase wage growth.

In the early Biden administration it appears that wage growth is on track to be around 4%. higher than it has been at any time since before the Great Recession (2008).

For the GOP the new crisis is emerging inflation, but inflation caused by improvement (increase) in American wages is going to be beneficial for the national economy is ways that are very different from the catastrophic inflaton of the 1970s.

In the 1970s the inflation was a cost-push inflation caused in the Arab oil embargo and the establishment of the PEC oil cartel which repriced crude oil from aroun $3.50 a bearrel to around $13.59 a barrel in 1973/4 and then to around $31.00 a barrel around 1979.

The price inflation that is happening in 2021 is being caused by 'demand pull' and while it is resulting in higher prices which upsets consumers it is praised by investors because it is enabling record levels of corporate profits and stock market prices.

There is a stronger labor market now in the USA in 2020 than at any time in the past 50 years, but this is seen as a problem by most GOP politicians, and especially GOP leadership.
Peter Burgess

Wage Growth Tracker

Updated on November 12, 2021

The Atlanta Fed's Wage Growth Tracker is a measure of the nominal wage growth of individuals. It is constructed using microdata from the Current Population Survey (CPS), and is the median percent change in the hourly wage of individuals observed 12 months apart. Our measure is based on methodology developed by colleagues at the San Francisco Fed.

The Wage Growth Tracker is updated once the Atlanta Fed's CPS dataset is constructed (see the 'Explore the Data' tab below). This is usually by the second Friday of the month. The exact timing depends on when the Bureau of the Census publishes the micro data from the CPS.

Stay informed of all Wage Growth Tracker updates by subscribing to our mailing list, subscribing to our RSS feed RSS feed icon, downloading our EconomyNow app, or following the Atlanta Fed on Twitter. Twitter icon

You can also build your own cuts of Wage Growth Tracker data using the CPS Data Application from CADREOff-site link or alternatively from here compressed zip file. Look for instructions and program files in the 'Explore the Data' tab below.

The data we use to compute the Atlanta Fed's Wage Growth Tracker are from the monthly Current Population Survey (CPS), administered by the U.S. Census Bureau for the Bureau of Labor Statistics. (You can find an overview of the CPS on the Census website.) The survey features a rotating panel of households. Surveyed households are in the CPS sample four consecutive months, not interviewed for next eight months, and then in the survey again four consecutive months. Each month, one-eighth of the households are in the sample for the first time, one-eighth for the second time, and so forth. Respondents answer questions about the wage and salary earnings of household members in the fourth and the last month they are surveyed. We use the information in these two interviews, spaced 12 months apart, to compute our wage growth statistic.

Calculating hourly earnings

The methodology is broadly similar to that used by Daly, Hobijn, and Wiles (2012). The earnings data are for wage and salary earners, and refer to an individual's main job (earnings data are not collected for self-employed people). Earnings are pretax and before other deductions. The Census Bureau reports earnings on either a per-hour or a per-week basis. We convert weekly earnings to hourly by dividing usual weekly earnings by usual weekly hours or actual hours if usual hours is missing.

We further restrict the sample by excluding the following:
  • Individuals whose earnings are top-coded. The top-code is such that the product of usual hours times usual hourly wage does not exceed an annualized wage of $100,000 before 2003 and $150,000 in the years 2003 forward. We exclude wages of top-coded individuals because top-coded earnings will show up as having zero wage growth, which is unlikely to be accurate.
  • Individuals with earnings information that has been imputed by the BLS because of missing earnings data. (See, for example, Hirsch and Schumacher 2001 and Bollinger and Hirsch 2006 for research showing that using imputed wage data can be problematic.)
  • Individuals whose hourly pay is below the current federal minimum wage for tip-based workers ($2.13).
  • Individuals employed in agricultural occupations (such as farm workers).
These restrictions yield an average of 9,300 earnings observations each month.

Constructing the wage growth tracker statistic

Once we have constructed the individual hourly earnings data, we match the hourly earnings of individuals observed in both the current month and 12 months earlier. The matching algorithm results in about 2,000 individual wage growth observations per month. We then compute the median of the distribution of individual 12-month wage changes for each month.

Explore the data

The final step is to smooth the data using a three-month moving average. That is, we average the current month median wage growth with the medians for the prior two months. The chart below shows the unsmoothed and three-month average versions of the median wage growth series.

Note that our matched dataset has a slightly greater share of older, more educated workers in professional jobs than does the sample of all wage and salary earners. This is primarily due to the requirement that the individual has earnings in both the current and prior year. Older, more educated workers are more likely to be continuously employed than other wage and salary earners.

Wage Growth Tracker by select employment and demographic characteristics

We also report Wage Growth Tracker measures for several job and demographic characteristics listed below (unless otherwise noted, the definitions refer to the individual’s status in the current month):

  • High-skill: Managers, Professionals, Technicians
  • Middle-skill: Office and Administration, Operators, Production, Sales
  • Low-skill: Food Preparation and Serving, Cleaning, individual Care Services, Protective Services
  • Construction and mining
  • Education and health
  • Finance and business services: Finance, Information, Professional and business services
  • Leisure and hospitality: Leisure, Hospitality, Other services
  • Manufacturing
  • Public Administration
  • Trade and transportation: Trade, Transportation, Warehousing, Utilities
  • Service Sector
  • In an industry other than construction, mining, or manufacturing
  • Usually works 35 hours per week or more
  • In a different occupation or industry than a year ago or has changed employers or job duties in the past three months.
Note: Because the Current Population Survey is a survey of addresses, if a person moves to a new address they will be missing from the data. Therefore, job switching is defined only in a geographically local sense.

Paid Hourly
  • Paid at an hourly rate in both the current month and a year ago
  • Not paid at an hourly rate in the current month and a year ago
Average Wage Level

Ranking based on the distribution of average hourly wage in the current month and a year ago. Those in the lowest 25 percent of average wages are in the 1st quartile and those in the highest 25 percent of average wages are in the 4th quartile.

  • 16-24
  • 25-54
  • 55+
  • White
  • Nonwhite
  • High school or less
  • Associates degree
  • Bachelor degree or higher
  • College Degree
  • Has an Associate degree or higher
  • Metropolitan Statistical Area (MSA) as defined by the U.S. Office of Management and Budget
  • Excludes those whose MSA status is not identified
Census Division
  • New England: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont
  • Mid-Atlantic: New Jersey, New York, Pennsylvania
  • East North Central: Illinois, Indiana, Michigan, Ohio, Wisconsin
  • West North Central: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota
  • South Atlantic: Delaware, Florida, Georgia, Maryland, North Carolina, South Carolina, Virginia, District of Columbia, West Virginia
  • East South Central: Alabama, Kentucky, Mississippi, Tennessee
  • West South Central: Arkansas, Louisiana, Oklahoma, Texas
  • Mountain: Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming
  • Pacific: Alaska, California, Hawaii, Oregon, Washington
  • Puerto Rico and other U.S. territories are not part of any census division
Weighting Series

Unless otherwise noted, all the series are based on an unweighted sample. The weighted series is constructed after weighting the sample to be representative of each month's population of wage and salary earners in terms of sex, age, education, industry, and occupation groups (irrespective of whether the person was also employed a year earlier). The weighted 1997 series is constructed after weighting the sample to be representative of the 1997 population of wage and salary earners in terms of sex, and age, education, industry, and occupation groups. These weighted series are described in two macroblog posts here and here.

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