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 data are updated monthly after the underlying CPS data are released, usually two to three weeks after the release of the Employment Situation Report by the BLS. Stay informed of all Wage Growth Tracker updates by subscribing to our RSS feed or following the Atlanta Fed on Twitter.

The following interactive chart displays the Wage Growth Tracker along with versions of the tracker for select work and demographic characteristics (all shown as three-month moving averages). Note that "job stayers" are defined as people who are in the same occupation and industry as one year ago and who have had the same employer for the past three months. "Job switchers" are everyone else.

### Methodology

**Data source**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 an average of 2,400 individual wage growth observations per month.* We then compute the median of the distribution of individual 12-month wage changes for each month.

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. Chart 1 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 individual wage growth measures for several work and demographic characteristics for which we have sufficient observations for the median to be a reasonably reliable statistic at the monthly frequency. Specifically, we compute the median wage growth for individuals who usually work full-time hours (at least 35 hours a week), who work in service-producing industries, and who have completed a college degree (including an associate degree); for male and female workers separately; and for prime-age workers (ages 25 to 54 years).

* In the CPS data, there is no way to precisely identify individuals who are continuously employed over the whole 12-month period because they are unobserved for eight months. Daly et al. (2012) impose additional restrictions that increase the likelihood that an individual was employed in the same job over the whole 12-month period.

### Data Distribution

The Wage Growth Tracker is the time series of the

*median*wage*growth*of matched individuals. This is not the same as*growth in the median wage*. Growth in the median wage represents the experience of a worker whose wage is in the middle of the wage distribution in the current month, relative to a worker in the middle of the wage distribution 12 months earlier. These would almost certainly include different workers in each period.Chart 2 plots the time series of the median, along with the mean, and the 75th and 25th percentiles of the individual wage growth distribution (all shown as three-month moving averages). The mean wage growth measure displays more variability over time than does the median. The mean wage growth uniformly lies above the median because the distribution of individual wage growth is asymmetric. The asymmetry can be seen by noting that the gap between the 75th percentile wage growth and the median wage growth is about 10 percentage points, whereas the gap between the 25th percentile and the median is only about 5 percentage points. Also note that the 75th and 25th percentiles have generally moved in line with the median over time, so that the interquartile range (a measure of dispersion) has remained relatively stable.

One particularly interesting feature of the wage growth distribution is the proportion of individuals who experience no wage growth. Chart 3 shows the percentage of zero wage changes in our data (specifically the percent of individual wage growth falling in the range of +0.5 percent and -0.5 percent). For reference, also plotted is the median individual wage growth.

Notice that the proportion of zero wage changes increased during both of the last two recessions. During the Great Recession, wage freezes became especially prevalent and have persisted at a high rate through much of the recovery. Only in the last year have we seen any notable decline in the percent of individuals experiencing zero wage change. For more information on this and its relation to models of nominal wage rigidity, see the work by our colleagues at the Federal Reserve Bank of San Francisco (Daly, Hobijn, and Wiles 2012 and Daly and Hobijn 2014). The distribution of individual wage growth is broadly similar to that shown on the Federal Reserve Bank of San Francisco website, although the methodology underlying the construction of the individual wage growth distribution differs somewhat.

### Related Links

**Wage Growth Tracker on***macroblog***Research used to construct data:**- Dissecting Aggregate Real Wage Fluctuations: Individual Wage Growth and the Composition Effect, Daly, Hobijn and Wiles (2012)
- Downward Nominal Wage Rigidities Bend the Philips Curve (2014)
- Match Bias in Wage Gap Estimates Due to Earnings Imputation (2001)
- Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching (2006)
- Wage Rigidity Meter at the San Francisco Fed

**Data:**