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July 13, 2022
Rounded Wage Data and the Wage Growth Tracker: An Update
In an earlier Policy Hub: Macroblog post, I noted that the US Census Bureau had announced that it planned to make changes to the Current Population Survey Public Use File (CPS PUF). Those changes, part of the Enhanced Disclosure Protection program, included the rounding of the reported wage data in a way that would have a dramatic impact on the usefulness of the Atlanta Fed's Wage Growth Tracker.
The Census Bureau subsequently revised its plans and has proposed a different rounding method described here, to be introduced in February 2023 . This Macroblog post looks at the new method's potential impact on the Wage Growth Tracker. It also considers another of the Census Bureau's other proposed changes to the CPS PUF.
So, for example, $19.99 an hour would become $20, whereas $19.95 an hour would be unchanged. Also, $999 a week would be rounded to $1,000, while $995 would be unchanged.
How much impact would this revised scheme have had on the Wage Growth Tracker if it had been used in the past? The following chart plots three versions of the Wage Growth Tracker time series. The blue line is the published Wage Growth Tracker using unrounded data. The gray line is the Tracker based on the original proposal and described in the earlier Macroblog post. The orange line is the Tracker based on the revised rounding rules. The following table summarizes the current proposed rounding rules:
The chart makes clear that the impact on the Wage Growth Tracker under the current proposed method for rounding is much smaller than the original proposal. While the revised method holds some impact, the basic time series properties of the historical Wage Growth Tracker remain largely intact. The largest difference between the Wage Growth Tracker based on the current proposal and the Tracker computed using unrounded wage data is 0.13 percentage points, the average difference is −0.002 percentage points, and the mean absolute difference is 0.03 percentage points.
No approach is perfect, though, and one quibble I have with the current proposal is that the rounding schemes for reported hourly and weekly wages are not very consistent. For example, for someone who usually works 40 hours a week (the most commonly reported workweek), rounding an hourly wage less than $20 to the nearest $0.05 should be the same as rounding a weekly wage less than $800 to the nearest $2. But the current proposal rounds a weekly wage less than $800 to the nearest $5 instead. For someone reporting a wage of between $20 and $39.99 an hour, the proposed rounding to the nearest $0.25 equates to rounding a 40-hour weekly wage between $800 and $1,599 to the nearest $10. However, the current proposal rounds a weekly wage between $800 and $1,000 to the nearest $5, and a weekly wage above $1000 to the nearest $25. Finally, for someone reporting an hourly wage of $40 or more, the proposed rounding to the nearest $0.50 equates to a 40-hour weekly wage of $1,600 or more rounded to the nearest $20. But the proposal rounds a weekly wage of $1,600 or more to the nearest $25.
The preceding analysis suggests that a more consistent method would be to round a weekly wage less than $800 to the nearest $2, a weekly wage between $800 and $1,599 to the nearest $10, and a weekly wage above $1,600 to the nearest $20.
This alternative rounding method reduces the impact on the Wage Growth Tracker series relative to the current proposal by about one-third. Specifically, the mean absolute difference between the unrounded Tracker series and the series based on the currently proposed rounding scheme is 0.03 percentage points, versus 0.02 percentage points using my alternative. The largest difference is 0.09 percentage points, and the average difference is 0.002 percentage points.
For the CPS PUF, the current proposal has another aspect relevant to the Wage Growth Tracker: the future computation of topcoded earnings data. Currently, a threshold hourly wage that varies with hours worked is used to determine if an hourly wage is topcoded. For weekly earnings the threshold is $2884.61 ($150,000 a year). However, these threshold values have not changed since 1998, and because of generally rising nominal wages over time this has led to the topcoding of more wage observations each year (see here for more discussion of this issue). The Wage Growth Tracker's calculations exclude topcoded wage values because their inclusion would be computed as zero wage change—artificially pulling median wage growth lower.
The current proposal would instead compute a dynamic topcode value that varies in a way that would result in the top-coding of only the highest 3 percent of earnings each month. Although that change means more observations to use to compute the Tracker, those observations will come from a part of the wage distribution that might exhibit quite distinct wage growth properties. For example, wage growth tends to be lower for people at the end of their careers than at the start, and if the highest wages are mostly from people with relatively low wage growth, median wage growth could be pulled lower. Unfortunately, without access to the historical wage data that are not topcoded, constructing a counterfactual to explore the impact of this proposed change is simply not possible. Perhaps someone at the Census Bureau will explore the impact this change has on the properties of the wage growth distribution.
The Census Bureau is seeking comments on the Enhanced Disclosure Protection proposals through July 15, 2022. If you have any suggestions on any aspect of the proposal, send an email to ADDP.CPS.PUF.List@census.gov. I will be sending them a copy of this post for their consideration.
July 15, 2019
Making Analysis of the Current Population Survey Easier
Speaking from experience, research projects often require many grueling hours of deciphering obtuse data dictionaries, recoding variable definitions to be consistent, and checking for data errors. Inevitably, you miss something, and you can only hope that it does not change your results when it's time to publish the results. It would be far less difficult if data sets came prebuilt with time-consistent variable definitions and a guidebook that makes the data relatively easy to use. Not only would research projects be more efficient, but also the research would be easier to replicate and extend.
To this end, we have worked closely with our friends at the Kansas City Fed's Center for the Advancement of Data and Research in Economics (CADRE) to produce what we call a harmonized variable and longitudinally matched (HVLM) data set. This particular data set uses the basic monthly Current Population Survey (CPS) data published by the U.S. Census Bureau and the Bureau of Labor Statistics. The HVLM data set underlies products such as the Atlanta Fed's Wage Growth Tracker and the various tools on the Atlanta Fed's Labor Force Participation Dynamics web page.
You may be wondering how this data set is different from the basic monthly CPS data available at IPUMS. Like the IPUMS-CPS data, the HVLM-CPS data set uses consistent variable names and includes identifiers for longitudinally linking individuals and households over time. Unlike the IPUMS-CPS data, the HVLM-CPS also has time-consistent variable definitions. For example, the top-coded values for the age variable in the IPUMS-CPS is not the same in all years, whereas the HVLM-CPS age variable is consistently coded by using the most restrictive age top-code. As another example, the number of race categories is not the same in every year in the IPUMS-CPS (having increased from 3 to 26), while the race variable in the HLVM-CPS data set is consistently coded by using the original three race categories. Applying these types of restrictions means that the resulting data set can be more readily used to make comparisons over time.
The screenshot below shows how accessible the HVLM-CPS data are. For a visual of each variable over time, click on Charts at the top to see a PDF file of time-series charts. Code Book is an Excel file containing the details of how each variable has been coded. You can see in the screenshot how each variable ends with two numbers. These two numbers correspond to the first year that variable is available. For example, mlr76 is coded with consistent values (1 = employed, 2 = unemployed and 3 = not in labor force) from 1976 until today. The Data File is a Stata (.dta) format file with variable labels already attached. For users wishing to use the panel structure of the CPS survey, lags of many variables are provided on the data set already—for example, mlr76_tm12 is an individual's labor force status from 12 months ago).
Clicking on the c icon under Code Book opens a screen with the values of the corresponding variable. The screenshot shows lfdetail94 and nlfdetail94 as examples. The first variable, lfdetail94, contains a large amount of detail on those engaged in the labor market, while nlfdetail94 contains detailed categories for those not engaged in the labor market.
The HVLM-CPS data set is freely available to download and is updated within hours of when the CPS microdata are published, thanks to sophistical coding techniques and the fast processors at the Kansas City Fed. To access the data, go to the CADRE page (using Chrome or Firefox). At the top right, select Sign in, then Google Login. Then, under schema, select Harmonized Variable and Longitudinally Matched [Atlanta Federal Reserve] (1976–Present).
June 1, 2018
Part-Time Workers Are Less Likely to Get a Pay Raise
A recent FEDS Notes article summarized some interesting findings from the Board of Governors' 2017 Survey of Household Economics and Decisionmaking. One set of responses that caught my eye explored the connection between part-time employment and pay raises. The report estimates that about 70 percent of people working part-time did not get a pay increase over the past year (their pay stayed the same or went down). In contrast, only about 40 percent of full-time workers had no increase in pay.
This pattern is broadly consistent with what we see in the Atlanta Fed's Wage Growth Tracker data. As the following chart indicates, the population of part-time workers (who were also employed a year earlier) is generally less likely to get an increase in the hourly rate of pay than their full-time counterparts. Median wage growth for part-time workers has been lower than for full-time workers since 1998.
This wage growth premium for full-time work is partly accounted for by the fact that the typical part-time and full-time worker are different along several dimensions. For example, a part-time worker is more likely to have a relatively low-skilled job, and wage growth tends to be lower for workers in low-skilled jobs.
As the chart shows, the wage growth gap widened considerably in the wake of the Great Recession. The share of workers who are in part-time jobs because of slack business conditions increased across industries and occupation skill levels, and median part-time wage growth ground to a halt.
While part-time wage growth has improved since then, the wage growth gap is still larger than it used to be. This larger gap appears to be attributable to a rise in the share of part-time employment in low-skilled jobs since the recession. In particular, relative to 2007, the share of part-time workers in the Wage Growth Tracker data in low-skilled jobs has increased by about 3 percentage points, whereas the share of full-time workers in low-skilled jobs has remained essentially unchanged. Note that what is happening here is that more part-time jobs are low skilled than before, and not the other way around. Low-skilled jobs are about as likely to be part-time now as they were before the recession.
How does this shift affect an assessment of the overall tightness of today's labor market? Looking at the chart, the answer is probably “not much.” As measured by the Wage Growth Tracker, median wage growth for both full-time and part-time workers has not been accelerating recently. If the labor market were very tight, then this is not what we would expect to see. The modest rise in average hourly earnings in the June 1 labor report for May 2018 to 2.7 percent year over year, even as the unemployment rate declined to an 18-year low, seems consistent with that view. A reading on the Wage Growth Tracker for May should be available in about a week.
April 18, 2018
Hitting a Cyclical High: The Wage Growth Premium from Changing Jobs
The Atlanta Fed's Wage Growth Tracker rose 3.3 percent in March. While this increase is up from 2.9 percent in February, the 12-month average remained at 3.2 percent, a bit lower than the 3.5 percent average we observed a year earlier. The absence of upward momentum in the overall Tracker may be a signal that the labor market still has some head room, as suggested by participants at the last Federal Open market Committee (FOMC) meeting, who noted this in the meeting:
Regarding wage growth at the national level, several participants noted a modest increase, but most still described the pace of wage gains as moderate; a few participants cited this fact as suggesting that there was room for the labor market to strengthen somewhat further.
Although wages haven't been rising faster for the median individual, they have been for those who switch jobs. This distinction is important because the wage growth of job-switchers tends to be a better cyclical indicator than overall wage growth. In particular, the median wage growth of people who change industry or occupation tends to rise more rapidly as the labor market tightens. To illustrate, the orange line in the following chart shows the median 12-month wage growth for workers in the Wage Growth Tracker data who change industry (across manufacturing, construction, retail, etc.), and the green line depicts the wage growth of those who remained in the same industry.
As the chart indicates, changing industry when unemployment is high tends to result in a wage growth penalty relative to those who remain employed in the same industry. But when the unemployment rate is low, voluntary quits rise and workers who change industries tend to experience higher wage growth than those who stay.
Currently, the wage growth premium associated with switching employment to a different industry is around 1.5 percentage points and growing. For those who are tempted to infer that the softness in the Wage Growth Tracker might signal an impending labor market slowdown, the wage growth performance for those changing jobs suggests the opposite: the labor market is continuing to gradually tighten.
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