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Decem b e r 2 0 2 3

An improved estimate of self-employment hours for quarterly labor productivity
In 2024, the U.S. Bureau of Labor Statistics will implement two adjustments to the self-employment hours series used for constructing productivity measures: (1) directly
compositing the self-employment hours estimates as is done for national employment and unemployment statistics and (2) removing part of the estimates’ irregular
component, calculated during the seasonal adjustment process, from the series. These adjustments will result in a substantially smoother self-employment hours series without
losing important information about changes in self-employment. In turn, this improvement will remove distortions in measured productivity.
Labor productivity is a Principal Federal Economic Indicator published by the U.S. Bureau of Labor Statistics (BLS), and its correct measurement is crucial for tracking the
sources of U.S. economic growth. Sometimes, however, quarterly labor productivity is unduly volatile because of large changes in measured self-employment hours. In this
article, we discuss this volatility and BLS efforts to improve labor productivity measures by changing the measurement of hours worked by unincorporated self-employed and
unpaid family workers.
Quarterly labor productivity is defined as the ratio of output to hours worked. Data on employee hours come from the BLS Current Employment Statistics (CES) survey,
which is an establishment survey.1 The CES sample is drawn from the longitudinal database of employer records for business establishments covered by the Unemployment
Insurance program. Therefore, the sample does not cover workers who work for themselves in their own unincorporated businesses or as independent contractors (referred to
as unincorporated self-employed workers or proprietors), nor does it cover those who work as unpaid family workers.2 To include hours of work for these classes of workers
in the total hours measure, the BLS productivity program supplements the CES hours with hours worked by the unincorporated self-employed and unpaid family workers
from the BLS Current Population Survey (CPS), which is a household survey.3 For simplicity, we henceforth use “self-employed” as shorthand for unincorporated selfemployed and unpaid family workers. Similarly, we use “self-employment” as shorthand for unincorporated self-employment and unpaid family work.
Hours worked by the self-employed can be volatile for two broad reasons. First, these hours can increase or decrease from one period to the next, as people start businesses
and shut them down or as they use self-employment as a bridge between wage-and-salary job spells.4 Second, because the self-employed constitute such a small share of the
labor force, it is harder to measure their hours by using the CPS.
Chart 1 shows the percent change in quarterly self-employment hours from 2000 to 2019.5 Self-employment was procyclical over this period, with substantial decreases in
self-employment hours during recessions.6 In this chart, and in most of the analyses that follow, we begin the series in 2000 because productivity measures are consistently
estimated back to 2000 and the current seasonal adjustment process uses data starting in 2000. We end the series in 2019 because the scale of changes in self-employment
during the COVID-19 pandemic dwarfed any earlier visible volatility.7 Although the series is considerably volatile, the source of that volatility is not obvious—no economic
theory beyond business cycle theories predicts sudden quarterly fluctuations in self-employment.
Chart 1. Annualized quarter-to-quarter percent change in hours for the selfemployed in the nonfarm business sector, seasonally adjusted, second quarter
2000 to fourth quarter 2019
Percent
30
20
10
0
-10
-20
-30
Q2 2000 Q2 2002 Q2 2004 Q2 2006 Q2 2008 Q2 2010 Q2 2012 Q2 2014 Q2 2016 Q2 2018
Hover over chart to view data.
Shaded areas represent recessions as determined by the National Bureau of Economic Research.
Note: Q1 = first quarter, Q2 = second quarter, Q3 = third quarter, and Q4 = fourth quarter.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

A concern with productivity measurement is that sometimes the volatility in self-employment hours is so outsized that it noticeably affects the measure of total hours, even
though self-employment hours make up only a small share of total hours (about 7.3 percent in the fourth quarter of 2022). Chart 2 shows hours growth for employees, the selfemployed, and all workers from the second quarter of 2000 through the fourth quarter of 2019. In several quarters, spikes in the self-employment hours series substantially
affect the hours for all workers, creating a divergence between the hours growth rate for employees and all workers. For example, in the third quarter of 2005, employee hours
grew by 2.5 percent, but hours for all workers grew by only 1.3 percent once a 10.0-percent fall in self-employment hours was factored in. Similarly, in the fourth quarter of
2014, employee hours grew by 3.4 percent, but hours for all workers grew by 4.6 percent because of a 19.3-percent increase in self-employment hours.
Chart 2. Annualized quarter-to-quarter percent change in hours, by class of
worker in the nonfarm business sector, seasonally adjusted, second quarter
2000 to fourth quarter 2019
Percent

Self-employed, published

Employees

All workers

30
20
10
0
-10
-20
-30
Q2 2000 Q2 2002 Q2 2004 Q2 2006 Q2 2008 Q2 2010 Q2 2012 Q2 2014 Q2 2016 Q2 2018
Click legend items to change data display. Hover over chart to view data.
Shaded areas represent recessions as determined by the National Bureau of Economic Research.
Note: Q1 = first quarter, Q2 = second quarter, Q3 = third quarter, and Q4 = fourth quarter.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Chart 3 illustrates another way of looking at the impact of growth in self-employment hours on growth in total hours, comparing the relative percentage-point contributions of
employees and the self-employed to the percent change in hours for all workers. Here, for illustrative purposes, we restrict the data to the first quarter of 2010 through the
fourth quarter of 2019. Because self-employment hours account for such a small share of total hours, we would expect the dots representing hours growth for all workers to be
close to the top of the bars measuring the contribution of employees, and the size of the bars measuring the contribution of the self-employed to be small relative to the bars
measuring the contribution of employees. For some data points, however, the dots are quite far from the end of the employee-contribution bars. For example, there are
substantial gaps in the first three quarters of 2019.
Chart 3. Contributions to annualized quarter-to-quarter percent change in hours,
by class of worker in the nonfarm business sector, first quarter 2010 to fourth
quarter 2019
Percent

Self-employed

Employees

All workers

5
4
3
2
1
0
-1
-2
Q1 2010 Q1 2011 Q1 2012 Q1 2013 Q1 2014 Q1 2015 Q1 2016 Q1 2017 Q1 2018 Q1 2019
Click legend items to change data display. Hover over chart to view data.
Note: Q1 = first quarter, Q2 = second quarter, Q3 = third quarter, and Q4 = fourth quarter.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Sources of volatility
In a companion working paper (forthcoming), Cindy Michelle Cunningham and Sabrina Wulff Pabilonia investigate the sources of volatility in self-employment, focusing on
sources relating to the CPS sample design that might be addressed to create a smoother series.8 The authors identify three sources of volatility in the CPS estimate of selfemployment, which is measured by a respondent’s reported class of worker for the job that he or she worked at last week, for which adjustments could feasibly be made to
reduce volatility. These sources of volatility include proxy responses, imputations, and sample rotation.
In the CPS, one household member age 15 years and older often provides labor force information on behalf of other household members. This member is a proxy reporter for
other household members. For example, a mother may answer for her college-age children, one spouse for another, and so on. Cunningham and Pabilonia look at how proxy
responses in the CPS might affect the volatility of self-employment hours—whether differences between how a worker describes their class of worker and the way another
member of the household responding on their behalf does might cause self-employment measures to change over time. In general, the authors find that while the selfemployment trends are similar whether measured by proxy reporters or self-reporters, in all periods proxy reporters report less self-employment (for the respondents on whose
behalf they are answering) than do self-reporters. In chart 4, we find somewhat more volatility in the annualized quarter-to-quarter growth of self-employment hours when that
information is collected by a proxy.9

Chart 4. Annualized quarter-to-quarter percent change in hours for the selfemployed in the nonfarm business sector, by type of reporter, not seasonally
adjusted, second quarter 2000 to fourth quarter 2019
Percent

Self

Proxy

80
60
40
20
0
-20
-40
-60
Q2 2000 Q2 2002 Q2 2004 Q2 2006 Q2 2008 Q2 2010 Q2 2012 Q2 2014 Q2 2016 Q2 2018
Click legend items to change data display. Hover over chart to view data.
Shaded areas represent recessions as determined by the National Bureau of Economic Research.
Note: Q1 = first quarter, Q2 = second quarter, Q3 = third quarter, and Q4 = fourth quarter.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

A second source of volatility examined by Cunningham and Pabilonia results from the imputation of class of worker when it is missing in the data because the respondent
answered “don’t know” or “refused” to the class-of-worker question (in other words, a case of item nonresponse as opposed to survey nonresponse). The number of imputed
instances of self-employment in the CPS has been rising slowly since 2000, although the weighted count of the imputed self-employed is still less than a tenth of the weighted
count of the nonimputed self-employed.10 Chart 5 compares the volatility of self-employment hours from imputed responses with that from nonimputed responses. The effect
of imputed responses is occasionally large enough to exacerbate an already large change in the nonimputed responses. For example, in the second quarter of 2010, hours grew
by 31.7 percent for the nonimputed self-employed and by 176.1 percent for the imputed self-employed, resulting in an overall growth in self-employment hours of 34.9
percent; in the second quarter of 2017, hours grew by 22.5 percent for the nonimputed self-employed and by 68.9 percent for the imputed self-employed, resulting in an
overall growth in self-employment hours of 30.0 percent. And in a few quarters, the growth rate of the imputed values moves in the opposite direction of the nonimputed
values and is large enough to slightly offset what would otherwise be a large increase in self-employment hours. For example, in the third quarter of 2006, hours grew by 9.5
percent for the nonimputed self-employed but fell by 33.8 percent for the imputed self-employed, resulting in an overall growth of 5.4 percent in self-employment hours.
Chart 5. Annualized quarter-to-quarter percent change in hours for the selfemployed in the nonfarm business sector, by imputation status, not seasonally
adjusted, second quarter 2000 to fourth quarter 2019
Percent

Imputed

Nonimputed

200
150
100
50
0
-50
-100
Q2 2000 Q2 2002 Q2 2004 Q2 2006 Q2 2008 Q2 2010 Q2 2012 Q2 2014 Q2 2016 Q2 2018
Click legend items to change data display. Hover over chart to view data.
Shaded areas represent recessions as determined by the National Bureau of Economic Research.
Note: Q1 = first quarter, Q2 = second quarter, Q3 = third quarter, and Q4 = fourth quarter.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Following CPS respondents across consecutive months, Cunningham and Pabilonia examine respondents’ transitions into and out of self-employment that coincide with
changes both between self- and proxy responses and between imputed and nonimputed responses. They find that these transitions from one month to the next occur at lower
rates when the reporter type stays the same (either a self-reporter in both months or a proxy reporter in both months) than when the reporter type changes from self to proxy, as
well as when the responses are nonimputed in both consecutive months compared with imputed in both months or changes in imputed status. In particular, the likelihood of
transitioning is largest between self- and proxy responses for those switching between unincorporated self-employed and not employed, suggesting that household respondents
do not always know about the self-employment work another household member does.
Cunningham and Pabilonia investigate methods to correct for this source of volatility by directly editing the underlying CPS data. For example, they replace one month’s
proxy response with an adjoining month’s self-response. This smoothing strategy requires subjective decision making regarding which response is more likely to be accurate,
how many months forward or backward to look for edits, and whether observed transitions might be genuine. Ultimately, the authors conclude that the edits to the underlying
CPS data have minimal impact on the volatility of self-employment hours. Given the subjective nature of these edits and the complexity of implementing them into official
statistics, we do not pursue this smoothing strategy. However, researchers interested in studying the dynamics of self-employment may choose to test whether making such
edits affects their results.
Finally, Cunningham and Pabilonia investigate how the CPS sample rotation design—respondents moving into and out of the sample—might lead to increased volatility in
measures of self-employment. In the CPS, respondents answer questions for 4 consecutive months (months in sample (MIS) 1–4), then are out of the sample for the next 8
consecutive months, and finally return to the sample for another 4 consecutive months (MIS 5–8). Thus, from one calendar month to the next, one-quarter of respondents exit

the sample and are replaced with entering (or, in the case of MIS 5, reentering) respondents. This sample rotation creates the potential for two sources of volatility. First, the
fraction of individuals who are self-employed may differ between entering and exiting rotations. This difference can arise by chance because of sampling error.11 The second
source, known as rotation-group bias, is due to systematic differences by MIS in how respondents report self-employment status, with workers in earlier MIS being more
likely to report self-employment.12 For the 2000–22 period, the average percentage of self-employed workers is about 7.0 percent in MIS 1, 6.8 percent in MIS 2–4, 6.7
percent in MIS 5, and 6.6 percent in MIS 6–8. These differences can arise for various reasons, including respondent fatigue, differences in unit nonresponse by class of
worker, and in-person versus telephone interviewing.
Chart 6 shows the hours growth in self-employment, in total and broken into two sources—the growth that comes from the difference between those who entered the sample
in a quarter and those who exited it in the previous quarter and the growth that comes from those who were surveyed in both quarters (continuers). We find evidence of both
sample rotation effects and rotation-group bias. Sample rotation effects are evident in the considerably greater volatility in the quarterly growth rates for respondents who were
not present in both quarters relative to continuers. The average growth rate is 8.8 percent for those who were not present in both quarters and −4.1 percent for continuers.13
The impact of rotation-group bias can be seen in chart 6 by looking at the self-employment growth rate for respondents present in both quarters. Because respondents are more
likely to report self-employment in MIS 1 than in subsequent months, the quarter-to-quarter changes in the number of self-employed workers in the sample tend to be
negative. These fluctuations, together with rotation-group sampling errors that affect the relative distribution of self-employment in incoming and outgoing rotation groups,
suggest that the sample rotation design plays an important role in the volatility of self-employment hours.
Chart 6. Annualized quarter-to-quarter percent change in hours for the selfemployed in the nonfarm business sector, by whether present in both quarters,
not seasonally adjusted, second quarter 2000 to fourth quarter 2019
Percent

Present in both quarters

Not present in both quarters

All self-employed

100
80
60
40
20
0
-20
-40
-60
Q2 2000 Q2 2002 Q2 2004 Q2 2006 Q2 2008 Q2 2010 Q2 2012 Q2 2014 Q2 2016 Q2 2018
Click legend items to change data display. Hover over chart to view data.
Shaded areas represent recessions as determined by the National Bureau of Economic Research.
Note: Q1 = first quarter, Q2 = second quarter, Q3 = third quarter, and Q4 = fourth quarter.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Given these findings, we describe below how we apply two adjustments to the self-employment hours series to reduce excess volatility. The first adjustment involves directly
compositing self-employment hours as is done for national employment and unemployment statistics also calculated from the CPS. The second adjustment involves removing
a component of seasonal adjustment—the final irregular component adjusted for extreme values—which we show is primarily picking up sampling error.

Directly compositing self-employment hours
In 1954, BLS began using a composite estimate for reporting employment and unemployment measures. Morris H. Hansen, William N. Hurwitz, Harold Nisselson, and
Joseph Steinberg describe the composite estimate as a weighted average of two different level estimates: one based solely on the current month and one that accounts for
information from the previous month for the three-quarters of the sample that is surveyed in both months.14 The composite estimates reduce the variance of estimates of levels
and changes, with the largest reductions occurring for estimates of changes.
Expanding on Hansen et al.’s description of the two weighted components of the composite estimate, Margaret Gurney and Joseph F. Daly, as well as Elizabeth T. Huang and
Lawrence R. Ernst, show that this estimate can be improved by adding a bias-correction term that gives slightly more weight to data from respondents in MIS 1 and 5—
months when unemployment responses have been found to be much higher.15 BLS uses these adjusted composite estimates, referred to as AK-composite estimates, for
reporting national employment, unemployment, and those not in labor force, but not for reporting self-employment hours. We use this method to directly composite selfemployment hours for the quarterly labor productivity series.
The composite estimate of self-employment hours in calendar month t, ŶtC, is measured by

where Ŷi,t is an estimate of self-employment hours for the population in month t using only responses in rotation group i, and A and K are parameters. The first bracketed term
is the direct estimate of self-employment, an average of the estimate over all eight rotation groups i, ignoring any possible differences in responses across rotation groups. The
second bracketed term adds to the previous month’s composite estimate, Ŷt−1C, the change in estimated self-employment among those continuing in the survey from the
previous month (i ∈ S (= 2, 3, 4, 6, 7, 8)). The final bracketed term is the bias correction that reweights the estimates from the incoming (i ∉ S) and continuing (i ∈ S) parts of
the current month’s sample.
In an AK-composite estimate, the parameters A and K are selected to minimize the variance of the AK estimator relative to the direct estimate. How closely a set of parameters
approximates the best linear unbiased estimate for a labor force characteristic depends both on the pattern of responses across rotation groups and on the correlation over time
in the labor force estimates. This means that the optimal values for estimating the level of employment may not be optimal for estimating the level of unemployment, or the
month-to-month change in unemployment. Janice Lent, Stephen Miller, and Patrick Cantwell find that A = 0.3 and K = 0.4 are optimal values for estimating unemployment
levels and close to optimal for month-to-month changes, whereas A = 0.4 and K = 0.7 perform the best for estimating employment levels and changes.16 For our composite
estimate of self-employment hours, we use A = 0.4 and K = 0.7, assuming that self-employment most closely resembles employment in terms of the correlation over time. We

directly composite only self-employment on main jobs because class-of-worker information for second jobs is only collected in MIS 4 and 8. To the directly composited
estimates, we add hours on secondary jobs, which have been weighted with CPS outgoing rotation weights.
Chart 7 compares the quarterly growth rate for the composited self-employment hours series with the growth rate for the published series from the second quarter of 2000 to
the fourth quarter of 2019. Compositing reduces the size of most spikes in the growth rate. For example, in the second quarter of 2014, the growth rate of self-employment
hours was −12.1 percent without compositing and only −6.0 percent with compositing. In the third quarter of 2019, the rate was 19.0 percent without compositing and 8.5
percent with compositing. Thus, it appears that compositing substantially reduces volatility in self-employment hours.
Chart 7. Annualized quarter-to-quarter percent change in hours for the selfemployed in the nonfarm business sector, published versus composited,
seasonally adjusted, second quarter 2000 to fourth quarter 2019
Percent

Self-employed, published

Self-employed, composited

View Chart Data

Seasonal adjustment and removing irregulars
To obtain quarterly self-employment hours for productivity measures, BLS seasonally adjusts the basic monthly CPS self-employment hours series and then averages these
monthly data to obtain a quarterly series. Although the primary purpose of seasonal adjustment is to provide a clearer picture of underlying trends and cyclical movements
distinct from regular seasonal movements, this adjustment can also be used to dampen unusual movements in the data. The X-13 ARIMA-SEATS program first models the
time-series properties of the data, then uses that model to adjust and extrapolate forward the series, and finally decomposes the adjusted series into a trend-cycle component, a
seasonal component, and an irregular component.17 Removing the seasonal component leaves the seasonally adjusted series, which consists of the trend-cycle component plus
the irregular component.
The irregular component consists of ordinary noise in the series that can be due to both sampling and nonsampling error, as well as extreme abnormal events such as
unseasonable weather, natural disasters, pandemics, or strikes. Outliers in the original data series are typically identified in the time-series model—either a priori or by the
program’s automatic outlier-detection feature. In this way, the seasonal component can be estimated without being distorted by the outliers, but the seasonally adjusted data
series will continue to include their impact.
We might expect greater sampling error in estimates of self-employment hours compared with estimates of hours for all workers, mainly because the sample sizes for the selfemployed are relatively smaller. Response error also can arise because differences between self-employment and contract work and between incorporated and unincorporated
self-employment are subtle and may be subject to greater differences in responses between the worker and a proxy respondent, and even from one interview to the next.
Because our objective is to reduce the volatility in our estimate of self-employment hours, we want to remove the part of the irregular component that may be due to sampling
error. The X-13 ARIMA-SEATS program used for seasonal adjustment produces a final irregular component series that excludes the impact of extreme values—which in most
cases are due to abnormal events. Thus, we can remove this irregular component (hereafter referred to as the extreme-value-adjusted (EVA) irregular), along with the seasonal
component, from the original series in order to obtain a smoother seasonally adjusted self-employment hours series.
To check whether the EVA irregular series is a reasonable estimate of the sampling error, we compare the size of the series’ irregulars with the standard errors of our estimates
of self-employment obtained from generalized variance functions (GVF).18 Because the CPS publishes GVF model parameters for employment only, we restrict our analysis
to the number of self-employed workers. We assume that the conclusions we draw from this analysis can be applied to self-employment hours.
Chart 8 shows the EVA irregular series obtained from a seasonal adjustment decomposition, standardized by dividing the EVA irregulars by the estimated standard errors of
our composited self-employment estimates.19 Nearly all the values of the irregular component fall within 2 standard errors of our self-employment estimate, suggesting that
the EVA irregular is due to sampling error and not economic outliers. This implies that removing this irregular component from the self-employment series can potentially
further smooth the series without removing real movements in self-employment hours.

Chart 8. Extreme-value-adjusted irregulars, standardized by dividing by the
estimated standard error (SE) of composited self-employment, January
2000–December 2022
Extreme-value-adjusted irregulars, standardized
+1 SE
−1 SE
+2 SE
−2 SE

Standard errors
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
Jan
2000

Jan
2002

Jan
2004

Jan
2006

Jan
2008

Jan
2010

Jan
2012

Jan
2014

Jan
2016

Jan
2018

Jan
2020

Jan
2022

Click legend items to change data display. Hover over chart to view data.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Charts 9a and 9b show the combined effects of compositing as well as removing the EVA irregular on the quarterly growth in self-employment hours. While chart 9a focuses
on the second quarter 2000 through the fourth quarter of 2019, chart 9b extends the series through the fourth quarter of 2022 to show that the new method of calculating selfemployment hours does not diminish the effects of the COVID-19 pandemic on the series. Removing the EVA irregular further reduces the volatility of the series beyond the
reductions obtained from compositing the series alone (chart 7), without oversmoothing and distorting important economic outliers. If we were to instead remove the entire
irregular series, we would remove most of the effects of the pandemic and potentially oversmooth the series during the 2000–01 recession and the 2007–09 Great Recession.
(See chart 10.)
Chart 9a. Annualized quarter-to-quarter percent change in hours for the selfemployed in the nonfarm business sector, published versus composited with
extreme-value-adjusted irregulars removed, seasonally adjusted, second
quarter 2000 to fourth quarter 2019
Percent

Self-employed, published
Self-employed, composited with extreme-value-adjusted irregulars removed

View Chart Data

Chart 9b. Annualized quarter-to-quarter percent change in hours for the selfemployed in the nonfarm business sector, published versus composited with
extreme-value-adjusted irregulars removed, seasonally adjusted, second
quarter 2000 to fourth quarter 2022
Percent

Self-employed, published
Self-employed, composited with extreme-value-adjusted irregulars removed

200
150
100
50
0
-50
-100
Q2 2000

Q2 2003

Q2 2006

Q2 2009

Q2 2012

Q2 2015

Q2 2018

Q2 2021

Click legend items to change data display. Hover over chart to view data.
Shaded areas represent recessions as determined by the National Bureau of Economic Research.
Note: Q1 = first quarter, Q2 = second quarter, Q3 = third quarter, and Q4 = fourth quarter.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Chart 10. Annualized quarter-to-quarter percent change in hours for the selfemployed in the nonfarm business sector, published, composited with extremevalue-adjusted irregulars removed, and composited with all irregulars removed,
seasonally adjusted, second quarter 2000 to fourth quarter 2022
Percent
200

Self-employed, published
Self-employed, composited with extreme-value-adjusted irregulars removed
Self-employed, composited with all irregulars removed

150
100
50
0
-50
-100
Q2 2000

Q2 2003

Q2 2006

Q2 2009

Q2 2012

Q2 2015

Q2 2018

Q2 2021

View Chart Data

Effects of adjustments to self-employment hours
The outsized effect of self-employment hours volatility on total hours growth is the primary motivation behind Cunningham and Pabilonia’s working paper and the new
method implemented here. An important check, then, is to see how this method of estimating self-employment hours affects the volatility in total hours growth. Chart 11
shows the published quarterly total hours growth and our estimate of quarterly total hours growth that uses the adjusted self-employment hours series. For most of the large
spikes in the series, the adjustment reduces the volatility of total hours growth, especially toward the end of the series. In two noticeable instances, however, the adjusted series
has larger spikes than the published series—in the second quarter of 2014 and in the third quarter of 2016. Looking back to those data points in chart 9a, we see that, in the
second quarter of 2014, self-employment hours growth was −12.1 percent before the adjustments and −2.7 percent after; in the third quarter of 2016, that growth was −10.5
percent before the adjustments and −0.5 percent after. These large negative spikes in the unadjusted self-employment hours series were distorting underlying positive spikes in
employee hours growth that now are evident after the self-employment hours series has been smoothed.

Chart 11. Annualized quarter-to-quarter percent change in hours for all workers
in the nonfarm business sector, published versus including hours for the selfemployed that are composited with extreme-value-adjusted irregulars removed,
seasonally adjusted, second quarter 2000 to fourth quarter 2019
Percent

All workers, published
Employees plus the self-employed, composited with extreme-value-adjusted irregulars removed

6
4
2
0
-2
-4
-6
-8
-10
-12
Q2 2000

Q2 2002

Q2 2004

Q2 2006

Q2 2008

Q2 2010

Q2 2012

Q2 2014

Q2 2016

Q2 2018

View Chart Data

To see the reduction in volatility better, in chart 12, we reexamine the contributions to total hours growth that we presented in chart 3. The bars showing the contribution of the
self-employed to hours growth are much smaller than they were in chart 3. For example, the contribution of the self-employed is substantially reduced in the third quarter of
2019. Under this new method of estimating self-employment hours, the self-employed contributed 0.2 percentage point to a 0.9-percent growth in total hours, whereas before
the adjustments they contributed 1.4 percentage points to a 1.9-percent growth in total hours. Similarly, in the second quarter of 2016, the self-employed contributed 0.8
percentage point to a 1.2-percent growth in total hours under the old method, but they contributed 0.5 percentage point to a 1.0-percent growth in total hours under the new
method.
Chart 12. Contributions to annualized quarter-to-quarter percent change in
hours, by class of worker in the nonfarm business sector, where the hours of
the self-employed are composited with extreme-value-adjusted irregulars
removed, seasonally adjusted, first quarter 2010 to fourth quarter 2019
Percent
5

Self-employed, composited with extreme-value-adjusted irregulars removed
Employees
All workers

4
3
2
1
0
-1
-2
Q1 2010 Q1 2011 Q1 2012 Q1 2013 Q1 2014 Q1 2015 Q1 2016 Q1 2017 Q1 2018 Q1 2019
Click legend items to change data display. Hover over chart to view data.
Note: Q1 = first quarter, Q2 = second quarter, Q3 = third quarter, and Q4 = fourth quarter.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Finally, in chart 13, we examine the effects of smoothing the self-employment hours series on annualized quarter-to-quarter growth in labor productivity. Over the 2000–22
period, the absolute change in productivity was greater than 0.5 percentage point in 21 of the period’s 91 quarters. In 8 of the 21 quarters, the productivity rate under the new
method was larger in magnitude than the rate under the old method. This difference occurred in quarters that, prior to adjustment, had unusually large spikes in selfemployment hours relative to employee hours. For example, in the third quarter of 2019, productivity grew at a 2.4-percent annual rate under the old method, but at a 3.4percent rate under the new method. In two instances (the fourth quarter of 2002 and the second quarter of 2011), productivity growth changed direction from a small decrease
to a small increase. Although we see changes in measured productivity using the new method, adopting the method does not affect long-run productivity growth, which
averaged 1.9 percent annually over the 2000–22 period.

Chart 13. Annualized quarter-to-quarter percent change in labor productivity in
the nonfarm business sector, old versus new method, seasonally adjusted,
second quarter 2000 to fourth quarter 2022
Percent

Old

New

Q2 2009

Q2 2012

20
15
10
5
0
-5
-10
Q2 2000

Q2 2003

Q2 2006

Q2 2015

Q2 2018

Q2 2021

View Chart Data

Conclusion
We find that self-employment hours are volatile because of survey-error-related volatility, which sometimes has outsized impacts on quarterly labor productivity. In 2024, to
improve its measure of productivity, BLS will implement two adjustments to self-employment hours estimates: (1) directly compositing the estimates and (2) removing the
EVA irregular component from the series. Because these adjustments will reduce survey-related volatility without oversmoothing the data, we will still be able to capture
cyclical changes in self-employment. Consequently, the adjustments will improve our measure of labor productivity.

ACKNOWLEDGMENT: We thank Lucy Eldridge, Marina Gindelsky, Nick Johnson, Justin McIllece, Brian Monsell, Drake Palmer, Matthew Russell, Jay Stewart, and
Zoltan Wolf for their helpful comments.

SUGGESTED CITATION:

Cindy Michelle Cunningham, Stephen M. Miller, Sabrina Wulff Pabilonia, and Michael Sverchkov, "An improved estimate of self-employment hours for quarterly labor productivity," Monthly Labor
Review, U.S. Bureau of Labor Statistics, December 2023, https://doi.org/10.21916/mlr.2023.26

Notes

For general information on the Current Employment Statistics (CES) survey, see “CES overview” (U.S. Bureau of Labor Statistics), https://www.bls.gov/web/empsit/cesprog.htm.

2 The concept of self-employment is often elusive. Audrey Light and Robert Munk find that 68 percent of jobs classified as self-employment are not also reported as self-owned businesses in
the 1979 National Longitudinal Survey of Youth. Instead, these jobs involve contract work or home-based, single-person pursuits, such as babysitting. See Light and Munk, “Business
ownership versus self-employment,” Industrial Relations, vol. 57, no. 3, 2018, https://doi.org/10.1111/irel.12213.

3 For general information on the Current Population Survey (CPS), see “Labor force statistics from the Current Population Survey overview” (U.S. Bureau of Labor Statistics),

https://www.bls.gov/cps/cps_over.htm.
4 See Kevin E. Cahill, Michael D. Giandrea, and Joseph F. Quinn, “New evidence on self-employment transitions among older Americans with career jobs,” Working Paper 463 (U.S. Bureau of
Labor Statistics, March 2013), https://www.bls.gov/osmr/research-papers/2013/pdf/ec130030.pdf; and Robert W. Fairlie and Frank M. Fossen, “Defining opportunity versus necessity
entrepreneurship: two components of business creation,” in Solomon W. Polachek and Konstantinos Tatsiramos, eds., Change at Home, in the Labor Market, and On the Job (Research in
Labor Economics, vol. 48) (Emerald Publishing Limited, Bingley, November 2020), pp. 253–289, https://doi.org/10.1108/S0147-912120200000048008.

5 Official data used in this article are based on data released in Productivity and Costs: Fourth Quarter and Annual Averages 2022, Preliminary, USDL 23-0150 (U.S. Department of Labor,
February 2, 2023), https://www.bls.gov/news.release/archives/prod2_02022023.htm.

6 Using the CPS panel, Fairlie and Fossen (“Defining opportunity versus necessity entrepreneurship”) find that the number of new business owners increases with the national unemployment
rate. They further examine opportunity versus necessity entrepreneurship over the business cycle and find that necessity entrepreneurship, defined as moving from unemployment to selfemployment between CPS matched months, is strongly countercyclical.

7 Charlene Marie Kalenkoski and Sabrina Wulff Pabilonia find that self-employment fell precipitously in the second quarter of 2020 relative to the decline in wage and salary employment. See
Kalenkoski and Pabilonia, “Impacts of COVID-19 on the self-employed,” Small Business Economics, vol. 58, 2022, pp. 741–768, https://doi.org/10.1007/s11187-021-00522-4.

8 Cindy Michelle Cunningham and Sabrina Wulff Pabilonia, “Why are measures of aggregate hours worked by the unincorporated self-employed so volatile?,” Working Paper 567 (U.S. Bureau
of Labor Statistics, forthcoming December 2023).

9 The charts in this section are replications based on Cunningham and Pabilonia (see ibid.), but here they measure hours instead of employment and include hours for unpaid family workers.
The measures are not seasonally adjusted.

10 Jonathan Eggleston, Mark A. Klee, and Robert Munk find that imputed self-employment rates are rising in other household surveys. In addition, using the Survey of Income and Program
Participation (SIPP), they find that the self-employed are not missing at random conditional on observables. This latter finding is likely also true for the CPS, which uses hot-deck imputation to
impute self-employment and incorporation status. Hot-deck imputation is limited in the number of respondent characteristics that are used to match nonrespondents to respondent donors.
Eggleston, Klee, and Munk use sequential regression multiple imputation and administrative data from the Social Security Administration Detailed Earnings Record and the Internal Revenue
Service Information Returns Master File to substantially improve imputation in the SIPP. See Eggleston, Klee, and Munk, “Self-employment status: imputations, implications, and

improvements,” SIPP Working Paper 303 (U.S. Census Bureau, March 2022), https://www.census.gov/content/dam/Census/library/working-papers/2022/demo/sehsd-wp2022-

06.pdf.
11

In quarter-to-quarter comparisons, of the respondents in 24 of the month-in-sample (MIS)–month pairs (3 months in a quarter times 8 MIS groups in each) in the second quarter, those in 12

of the pairs were also observed at least once in the previous quarter, and those in the other 12 pairs were entirely new to the CPS sample.

12 Rotation-group bias was first recognized by researchers attempting to construct estimates of gross flows between labor force states with the use of CPS data. These researchers found that
people responding to the CPS for the first time are more likely to report being unemployed than in the average month in sample, while those in the final rotation group are less likely to report
being unemployed. See Barbara A. Bailar, “The effects of rotation group bias on estimates from panel surveys,” Journal of the American Statistical Association, vol. 70, no. 349, 1975, pp. 23–
30, https://doi.org/10.2307/2285370; Hie Joo Ahn and James D. Hamilton, “Measuring labor-force participation and the incidence and duration of unemployment, “Review of Economic
Dynamics, vol. 44, April 2022, pp. 1–32, https://doi.org/10.1016/j.red.2021.04.005; and Andrew Halpern-Manners and John Robert Warren, “Panel conditioning longitudinal studies:
evidence from labor force items in the Current Population Survey,” Demography, vol. 49, no. 4, November 2012, pp. 1499–1519,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3648659/.
13 Given that the sample rotation effects are present every month, one would expect them to be even over time. However, in chart 6, the upward spikes are much larger than the downward
spikes. This is because quarterly growth rates are affected by the change in levels. For example, an increase in one quarter would require a smaller decrease in the subsequent quarter to
return to the initial level.

14 See Morris H. Hansen, William N. Hurwitz, Harold Nisselson, and Joseph Steinberg, “The redesign of the Census Current Population Survey,” Journal of the American Statistical
Association, vol. 50, no. 271, September 1955, pp. 701–719, https://www.jstor.org/stable/2281161?typeAccessWorkflow=login.

15 See Margaret Gurney and Joseph F. Daly, “A multivariate approach to estimation in periodic sample surveys,” Proceedings of the Social Statistics Section, vol. 242 (American Statistical
Association, 1965), pp. 242–257,

http://www.asasrms.org/Proceedings/y1965/A%20Multivariate%20Approach%20To%20Estimation%20In%20Periodic%20Sample%20Surveys.pdf; and Elizabeth T. Huang and
Lawrence R. Ernst, “Comparison of an alternate estimator to the current composite estimator in CPS,” Proceedings of the Survey Research Methods Section (American Statistical Association,
1980), pp. 303–308, http://www.asasrms.org/Proceedings/papers/1981_063.pdf.

16 Janice Lent, Stephen Miller, and Patrick Cantwell, “Composite weights for the Current Population Survey,” Proceedings of the Survey Research Methods Section (American Statistical
Association, 1994), https://www.bls.gov/osmr/research-papers/1994/pdf/cp940060.pdf.

17 The regARIMA model, which combines regression with autoregressive integrated moving average (ARIMA) modeling, includes controls for fixed seasonal effects, trading-day effects,
holidays, and various types of outliers that have been identified either a priori or by the program’s outlier-detection feature. The time-series model is also used to forecast data in order to
extend the series so that series endpoints are not estimated with asymmetrical information.

18 See “Calculating approximate standard errors and confidence intervals for Current Population Survey estimates,” technical documentation (U.S. Bureau of Labor Statistics, November
2018), https://www.bls.gov/cps/calculating-standard-errors-and-confidence-intervals.pdf.

19 Before dividing, we multiply the standard errors computed with generalized variance functions (GVF) by 0.8. This is necessary because the GVF was generated with nonseasonally adjusted
data, while our series is seasonally adjusted. It is well known that the standard errors of seasonally adjusted data are lower than those of nonseasonally adjusted data. See, for example,
Danny Pfeffermann and Michail Sverchkov, “Estimation of mean squared error of X-11-ARIMA and other estimators of time series components,” Journal of Official Statistics, vol. 30, no. 4,
2014, pp. 811–838, https://sciendo.com/article/10.2478/jos-2014-0049.

ABOUT THE AUTHOR

Cindy Michelle Cunningham
cunningham.cynthia@bls.gov
Cindy Michelle Cunningham is a research economist in the Office of Productivity and Technology, U.S. Bureau of Labor Statistics.
Stephen M. Miller
miller.steve@bls.gov
Stephen M. Miller is a research mathematical statistician in the Office of Survey Methods Research, U.S. Bureau of Labor Statistics.
Sabrina Wulff Pabilonia
pabilonia.sabrina@bls.gov
Sabrina Wulff Pabilonia is a research economist in the Office of Productivity and Technology, U.S. Bureau of Labor Statistics.
Michael Sverchkov
sverchkov.michael@bls.gov
Michael Sverchkov is a research mathematical statistician in the Office of Survey Methods Research, U.S. Bureau of Labor Statistics.

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Book Review
Decem b e r 2 0 2 3

Reflections on introductory economics education and its consequences
Economism: Bad Economics and the Rise of Inequality. By James Kwak. New York City, NY: Pantheon Books, 2017, 237 pp., $25.95 hardcover.
While economics courses can take many approaches to pedagogy, one guarantee is a graph—a graph with price on one axis, quantity on the other. Contained therein are two
lines, one indicating demand, and the other, supply. What some users of this ubiquitous model often forget is that this graph is meant to introduce ideas about the behavior of
perfectly competitive markets, and these ideas come with many assumptions that do not fully reflect reality. This overuse of the model is what leads James Kwak, the author
of Economism: Bad Economics and the Rise of Inequality, to assert that a blind adherence to the simplest of the laws of supply and demand has done a great deal of economic
harm, both in terms of allocation of resources and with respect to the welfare of many poor, working people.
Kwak defines the book’s titular term, economism, as “what you are left with if you learn the first-year models, forget that there are assumptions involved, and never get your
hands dirty with real-world data.” Throughout the nine chapters of the book, the author introduces readers to the problems plaguing the introductory model of supply and
demand, illustrating them with real-world accounts of market failure in fields such as healthcare, finance, and trade. He begins with a “crash course” in introductory economic
theory, providing readers with the information necessary for interpreting the perfectly competitive market model. He then dissects the effects of supply and demand shifts on
perfectly competitive markets and introduces the ideas of market equilibrium and dead-weight loss. This discussion is followed by a history of how such models gained
prevalence, as well as an account of the key actors involved in that process, including Friedrich Hayek, Milton Friedman, and Ludwig von Mises. Kwak is not only
foreshadowing the consequences of economism but also exposing how a full contextualization of the theory is what is sorely missing from introductory economics education.
Early in the book, Kwak uses history to begin to unwind the model of perfectly competitive markets, but it is in subsequent chapters that he solidifies his thesis. These
chapters begin by looking into the minimum wage, salaries and benefits given to corporate executives, and the disparity between wages and productivity. The discussion on
the minimum wage starts by introducing various views on the concept of a price floor, including those of economists such as Mises and Hayek. These views—portrayed as
representing economism—are then contrasted with economic research that studies how the minimum wage affects employment. Arguments of this variety are then assessed
with respect to chief executive officer compensation packages, as well as the incentives, or lack thereof, such packages create. The section on wages concludes with a
discussion of how worker wages have failed to keep pace with the accompanying steady rise in productivity over the previous few decades.
Economism is then discussed in the context of healthcare in the United States, with Kwak explaining how arguments based on the model of perfectly competitive markets fail
to accurately describe that domain. The author attributes this failure to two main causes: the existence of an insurance apparatus that ensures that medical services are seldom
paid for directly by those receiving healthcare and the fact that most people do not need healthcare on an ongoing basis. Distortions are then amplified, Kwak claims, by a
series of policy choices over the years that have failed to reflect best practices gleaned from either research or the experiences of other countries within the Organisation for
Economic Co-operation and Development. As always, the topic of healthcare is complex, but Kwak weaves through it with an appropriately critical lens.
Kwak’s best is revealed in a chapter concerning economism in the financial sector. The author’s expertise in banking and financial regulation helps inform his discussion of
the effects of markets in finance. Special attention is paid to developing arguments against economism and expanding upon the 2008 financial crisis. Although the crisis was a
complicated affair, Kwak dissects it efficiently, adding the history of how it came to pass. He spends considerable time describing how the logic of economism was used to
deregulate the banking sector in order to create new risky financial products and how these products then began to be traded among and within banks. He identifies the
products and describes how they worked, what led the banks to create them, and, ultimately, how they were the catalyst for the financial crisis itself. This much-needed history
could greatly benefit anyone seeking to know more about financial economics.
Moving on from the financial sector, Kwak then looks at trade, getting to the core of an issue that, while uniting nearly all economists, still calls into question the axioms of
economism. Although free trade does create wealth, it does not come without costs. Kwak begins this discussion with the simple two-country model of trade covered in
introductory economics classes, describing the value of trade when two entities each have a comparative advantage. However, as seen repeatedly in the book, the real-world
data do not necessarily reflect introductory theory. Using research on the results of the North American Free Trade Agreement, Kwak argues that while trade has contributed to
U.S. economic growth, that contribution has been very small and countered by the loss of many U.S. manufacturing jobs that have been allowed to flow out of the country.
Overall, Kwak makes valid points and backs them up with excellent data and evidence from existing research. Helped by his solid understanding of economics, he is able to
dismantle models demanding that everything be decided in a market. These models are useful for the introduction of core concepts, but they rarely hold when one is exposed
to further economics education. In introductory courses, buyers and sellers are price takers. In advanced courses, the attention shifts to how firms can decide both prices and
output. In introductory trade theory, the two-country model is excellent for illustrating trade’s benefits. However, advanced trade theory considers many countries, goods, and
distance costs simultaneously. Although introductory models are excellent for simplifying a dynamic, complex world, Kwak’s argument that dogmatic adherence to them has
dire consequences cannot be denied.

ABOUT THE REVIEWER

Hayden Grace
grace.hayden@bls.gov
Hayden Grace is an economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics.

U.S. BUREAU OF LABOR STATISTICS
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Article
Decem b e r 2 0 2 3

Effects of the PPI weight update on final-demand relative-importance values
In January 2023, the U.S. Bureau of Labor Statistics introduced its latest update of value weights used to calculate producer price indexes (PPIs). This update, based on 2017
value-of-shipments data, resulted in shifting PPI relative-importance values. This article analyzes the update’s effects on the relative-importance values of selected indexes for
goods, services, and construction products within PPI’s headline final-demand index.
The U.S. Bureau of Labor Statistics regularly updates the value weights used to calculate producer price indexes (PPIs). The purpose of these updates is to reflect recent
production and marketing patterns more accurately. The most recent weight update was introduced with the release of January 2023 PPI data and is based on 2017 value-ofshipments data collected from the census of manufactures, the census of mining, the census of services, and the census of agriculture. From January 2018 through December
2022, PPI weights were based on 2012 shipment values.
The most recent weight update affected all PPIs, including industry net-output indexes, traditional commodity-group indexes, Final Demand–Intermediate Demand (FD–ID)
indexes, special commodity-group indexes, and inputs-to-industry indexes. Although the update did not change the basic classification structures of the PPI commodity and
FD–ID indexes, it did result in shifting relative-importance values, which indicate the portion of an aggregate index that is accounted for by a component index at a given
point in time. This article analyzes shifts in relative-importance values within PPI’s headline final-demand index, focusing on shifts resulting from weight updates based on
2012 and 2017 value-of-shipments data.
The relative-importance tables available from the PPI program compare values from December 2021 and December 2022.1 These values changed because of updated valueof-shipments data and price movements from 2021 to 2022. To isolate the effects of the most recent weight update on PPI relative-importance values, this article compares
December 2022 relative-importance values based on 2012 value-of-shipments data with December 2022 relative-importance values based on 2017 value-of-shipments data.
By comparing relative-importance values for the same period, the analysis removes the effect of shifts in relative prices and isolates the effect of weight changes on relativeimportance values.

Final demand
The PPI for final demand measures price change for goods, services, and construction products sold for personal consumption, as capital investment, to government, and as
exports. As noted previously, the weight revision based on 2017 value-of-shipments data did not change the basic structure of the PPI for final demand, but it did shift the
relative-importance values of component indexes for various products within the overall index.
Within final demand, the update to 2017 weights resulted in positive relative-importance shifts for services and construction, and a negative shift for goods production. The
shift away from goods reflects the continuing trend of U.S. economic activity moving from manufacturing to services.2 With the shift from 2012 to 2017 weights, the relative
importance of the index for final-demand services increased from 64.5 to 66.7 percent, and the relative importance of the index for final-demand construction increased from
1.9 to 2.7 percent. In contrast, the relative importance of the index for final-demand goods fell from 33.6 to 30.7 percent.3 (See chart 1.)

Chart 1. Relative importance of major component indexes within the Producer
Price Index for final demand, calculated with 2012 and 2017 value weights,
December 2022
Percent

2012 relative importance

2017 relative importance

70
60
50
40
30
20
10
0
Final-demand goods

Final-demand services

Final-demand construction

Click legend items to change data display. Hover over chart to view data.
Note: Values may not add to 100 because of rounding.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Final-demand services
The index for final-demand services comprises three main component indexes for the following service categories: transportation and warehousing services; trade services;
and services less trade, transportation, and warehousing. Contributing most to the increase in the relative importance of the index for final-demand services, the relative
importance of the index for final-demand services less trade, transportation, and warehousing grew from 38.9 percent (based on 2012 value-of-shipments data) to 41.8 percent
(based on 2017 value-of-shipments data). In addition, with the shift from 2012 to 2017 value weights, the relative importance of the index for final-demand transportation and
warehousing services increased from 4.6 to 5.2 percent. Conversely, the relative importance of the index for final-demand trade services declined from 21.0 to 19.7 percent.
(See table 1.)
Table 1. Relative importance of selected component indexes within the Producer Price Index for final demand, calculated with 2012 and 2017 value weights,
December 2022
Index

2012 relative importance

2017 relative importance

Change

(percent)

(percentage points)

100.0

[1]

64.5

66.7

2.2

38.9

41.8

2.9

4.6

5.2

0.6

21.0

19.7

-1.3

1.9

2.7

0.8

33.6

30.7

-2.9

Final-demand foods

6.2

5.9

-0.3

Final-demand energy

6.1

5.9

-0.2

21.3

18.9

-2.4

Final demand
Final-demand services
Final-demand services less trade, transportation, and warehousing
Final-demand transportation and warehousing services
Final-demand trade services
Final-demand construction
Final-demand goods

Final-demand goods less foods and energy

[1] Not applicable.
Source: U.S. Bureau of Labor Statistics.

The remainder of this section discusses selected component indexes for detailed final-demand services with notable shifts in relative importance. The discussion examines the
underlying drivers of those shifts, highlighting the importance of technological advancements and monetary policy.
Automobile retailing

With the shift from 2012 to 2017 weights, the index for automobile retailing experienced one of the largest relative-importance changes—an increase from 0.8 to 1.5 percent
—among commodity indexes included in final demand.4 (See appendix table A-1.) A combination of factors, including low interest rates, greater availability of subprime
autoloans, pent-up demand, and declining fuel prices, helped boost automotive sales for 7 consecutive years after the end of the 2007–09 Great Recession. Consumers who
delayed car purchases because of the recession took advantage of the postrecessionary environment of low interest rates and replaced older vehicles with brand-new ones.5
Many of these consumers were approved for autoloans despite having low credit scores.6 Additionally, lower fuel prices further boosted demand, with gasoline prices falling
from $3.44 per gallon in January 2012 to $2.60 per gallon in December 2017.7
Brokerage fees and commissions from residential property sales and rentals

With the shift from 2012 to 2017 weights, the relative importance of the index for residential property sales and rental (brokerage fees and commissions) increased from 1.1 to
1.7 percent. (See appendix table A-1.) This increase was driven by consistent growth in housing sales. Between 2012 and 2017, the number of new homes sold grew from
368,000 to 613,000,8 and existing-home sales rose from 4.66 to 5.51 million units.9 More Americans also became renters, with the total number of renters jumping from 99.4
million in 2010 to 108.5 million in 2018.10 The rising demand for rentals is also evident in the number of rental units constructed between 2012 and 2017. In 2017, there were
331,800 rental units built, up from 152,100 in 2012.11 Home and rental prices moved in step with the rise in sales. Over the 2012–17 period, the average home price increased

from $244,400 to $322,425,12 and the median monthly rental price increased from $904 to $1,037.13 Although commission rates during this period declined, rising home and
rental prices helped offset potential losses in brokerage revenue. As homes became easier to sell during the recovery from the Great Recession, fewer agents were needed to
aid homeowners in selling their homes, which put downward pressure on commission rates.14 The boost in housing prices was facilitated by low interest rates and by demand
outpacing the available supply of housing.15
Residential internet access services

Since the weight revision based on 2012 value-of-shipments data, the internet has become accessible to more Americans. The percentage of U.S households with internet
access rose from 74.8 percent in 2012 to 78.0 percent in 2017.16 As a result, the relative importance of the index for residential internet access services increased from 0.1 to
0.3 percent. (See appendix table A-1.) Although the trend of growing internet access had existed since the 1990s, it was further bolstered in the 2010s, with the
telecommunications industry and the federal government increasing investment (a total investment of $795 billion between 2009 and 2017) for expanded access to broadband
internet in the United States.17 Between 2015 and 2017, the Federal Communications Commission expanded internet service to approximately 2.3 million residential and
small-business locations.18 This expansion was especially pronounced in rural areas, which typically face significant barriers to obtaining high-speed internet. Other potential
factors that may have contributed to the increase in internet access include a rise in online education, an expansion of telehealth services, and an increase in video on-demand
streaming.19
Home health and hospice care

Because of multiple factors, including a rise in the U.S. elderly population and an increase in demand for home-based healthcare services, the home health and hospice care
sector grew consistently between 2012 and 2017. With the shift from 2012 to 2017 weights, the relative importance of the index for home health and hospice care increased
from 0.7 to 0.9 percent. (See appendix table A-1.) Population growth among the elderly during the 2012–17 period was due mostly to the aging of the baby-boom generation,
which started reaching age 65 in 2011.20 Many providers of home health and hospice care services recognized this demographic shift, as well as the growing patients’
preference for receiving care at home instead of at long-term care facilities. As a result, the number of home health agencies rose from 7,528 in 2000 to 11,300 in 2019.21 A
similar rising trend occurred in total expenditures on home health and hospice care, with those expenditures increasing from roughly $70 billion in 2012 to just under $100
billion in 2017.22 The growing demand for home health and hospice care was also driven by cost avoidance on the part of patients, insurance companies, and the government,
because such care could be used as a more affordable substitute for services at expensive facilities and could prevent costly emergency-room visits by closely monitoring
chronic health conditions. The industry has provided relief for long-term care facilities, many of which are faced with limited capacity to house additional patients.23
Hospital care: outpatient and inpatient

The relative importance of the index for hospital outpatient care rose with the shift from 2012 to 2017 weights, increasing from 3.9 to 4.3 percent. (See appendix table A-1.)
Consumer demand and technological advances were among the main factors that drove the growth of outpatient care services in hospitals.24 Over the years, patients have been
increasingly drawn to the convenience and low cost of receiving outpatient care as an alternative to hospital admissions. Technological innovations have made clinical
procedures less invasive and thus more accessible to patients in outpatient facilities. Some surgical operations can now be performed more precisely with the aid of robotics,
and outpatient surgeons can use high-definition cameras for surgical procedures that require smaller incisions. As a result, patient recovery times have become shorter, limiting
the need for inpatient care and reducing costs for both patients and providers.25 In contrast, with the update to 2017 weights, the relative importance of the index for hospital
inpatient care decreased from 4.9 to 4.5 percent, providing further evidence of the shift from inpatient to outpatient services.
Furniture retailing

Margin revenue for furniture retailing rose as the U.S. housing market recovered during the 2012–17 period, boosting the relative importance of the index for furniture
retailing from 0.3 to 0.5 percent. (See appendix table A-1.) Furniture and bedding sales rose from $82.7 billion in 2012 to $105.2 billion in 2017, an increase of 27 percent.26
Besides the improving overall economy and housing market, other major sources of growth in furniture retailing were e-commerce and millennial consumers. From 2009 to
2017, e-commerce furniture sales grew at an annual rate of 22.2 percent, while sales at brick-and-mortar retail establishments grew at an annual rate of 3.0 percent.27 In 2014,
millennials became the largest consumer group in the U.S. furniture and bedding market, with a market share of 37 percent, compared with just 14 percent in 2012. This group
is also credited for the rise in e-commerce, especially purchases made via smartphones, as retailers experienced substantial growth in mobile traffic and mobile commerce
transactions.28
Cable and satellite subscriber services

With the shift from 2012 to 2017 weights, the relative importance of the index for cable and satellite subscriber services declined from 0.7 to 0.4 percent. (See appendix table
A-1.) Much of this decrease was due to the growing popularity of television streaming services. Increasingly expensive cable and satellite packages—as well as the
convenience of watching content on mobile devices such as laptops, cell phones, and tablets—drove the transition. Many consumers either opted for less expensive cable and
satellite packages with fewer channels or canceled their subscriptions entirely.29 The loss of subscriptions began in 2013 and intensified in subsequent years. In those years,
existing companies that offered internet-based programming grew in influence and competing companies entered the market, some providing exclusively on-demand content
and others also providing live television.30 This development, coupled with lower consumer demand, reduced advertising revenue for cable and satellite companies, further
contributing to the downward shift in the relative importance of the index for this category.31
Truck transportation of freight

The indexes for local and long-distance motor carrying both increased in relative importance with the shift from 2012 to 2017 weights. The relative importance of the index
for local motor carrying rose from 0.6 to 0.7 percent, and the relative importance of the index for long-distance motor carrying rose from 1.9 to 2.2 percent. (See appendix
table A-1.) These shifts reflect an upward trend in trucking revenues, which increased from just under $60 billion in the first quarter of 2013 to almost $70 billion in the fourth
quarter of 2017.32 The rise in e-commerce during this period can partly account for this growth, as consumers increasingly made purchases online. Online shoppers could also
return merchandise more freely, propping up the trucking sector. Additionally, to minimize the distance between product storage facilities and consumers’ homes, e-commerce
companies increased investments in urban warehousing, reducing delivery times.33 These investments likely contributed to the increase in the relative importance of the index
for local motor carrying. Other contributors to the rise in trucking revenue included an improving economy, greater operational efficiencies, and an aging workforce that
reduced the supply of truckers.34

Final-demand construction

With the shift from 2012 to 2017 weights, the relative importance of the index for final-demand construction increased from 1.9 to 2.7 percent. (See table 1.) This increase can
be traced primarily to an advance in new warehouse building construction.35
The relative importance of the index for new warehouse building construction rose from 0.2 to 0.5 percent. (See appendix table A-1.) This increase was driven mostly by
growth in online shopping, which forced e-commerce companies to acquire additional space to store inventory. Companies also sought out more warehousing facilities in
urban locations in order to reduce delivery times and cut transportation costs. The connection between e-commerce sales and demand for warehousing can be seen in the
progression of their growth rates, both of which accelerated between 2012 and 2017 relative to the prior 5-year period. E-commerce sales rose 11 percent annually from 2007
to 2012, and 14 percent annually from 2012 to 2017. Mirroring this trend, demand for warehouse space grew 0.7 percent annually from 2007 to 2012, and 1.1 percent annually
from 2012 to 2017.36 Additionally, available warehouse space became scarce, with vacancies reaching a 16-year low in 2016, intensifying the need for new warehouse
construction.37

Final-demand goods
The index for final-demand goods comprises three main component indexes for the following commodity categories: foods, energy, and goods less foods and energy. With the
update to 2017 weights, the decline in the relative importance of the index for final-demand goods was due mostly to a drop, from 21.3 to 18.9 percent, in the relative
importance of the index for final-demand goods less foods and energy. The relative-importance values of the indexes for final-demand foods and final-demand energy also
declined, with the former falling from 6.2 to 5.9 percent and the latter from 6.1 to 5.9 percent. (See table 1.)
The remainder of this section discusses selected component indexes for detailed final-demand goods with notable shifts in relative importance. Again, the discussion
highlights important economic trends that underpin those shifts.
Passenger cars and light motor trucks

Between 2012 and 2017, consumer preferences shifted from passenger cars to light trucks such as minivans, pickup trucks, and sport utility vehicles. Over this period, sales of
new light trucks increased more than 38 percent, from 6.2 to 8.6 million units, while sales of new passenger cars decreased 21 percent, from 5.7 to 4.5 million units.38 This
trend is reflected in changes in the relative importance of the indexes for both commodities. With the shift from 2012 to 2017 weights, the relative importance of the index for
light trucks rose from 1.3 to 1.6 percent, while the relative importance of the index for passenger cars fell from 0.9 to 0.4 percent. (See appendix table A-1.) In addition to low
interest rates and lax lending practices, low gas prices and greater consumer demand for more vehicle space contributed to the preference shift from cars to light trucks.39
Nonelectronic cigarettes

With the shift from 2012 to 2017 weights, the relative importance of the index for cigarettes (excluding electronic) declined from 0.5 to 0.4 percent. (See appendix table A-1.)
Cigarette consumption continued a decades-long decline beginning in 1965, when the Center for Disease Control National Health Interview Survey began tracking cigarette
usage among adults. The adult usage rate was 42.4 percent in 1965, 18.1 percent in 2012, and 13.9 percent in 2017.40 This downward trend can be attributed to rising cigarette
prices, antismoking campaigns, smoke-free laws, and smoking-cessation programs.41 Additionally, between 2010 and 2020, government policies were put in place to reduce
usage rates. The Children’s Health Insurance Program Reauthorization Act of 2009 increased federal taxes on cigarettes to $1.01 per pack, and the Patient Protection and
Affordable Care Act of 2010 mandated most health insurance plans to cover smoking-cessation programs. Cigarette consumption declined 16.7 percent between 2012 and
2017, as 14 million cigarette packs were sold in 2012, compared with 12 million in 2017.42 Although e-cigarettes likely contributed to lower usage rates, it remains unclear if
they played a significant role in the decline.43
Residential electric power

The use of electricity by U.S. households declined during the 2010s. As a result, the relative importance of the index for residential electric power decreased from 2.0 to 1.9
percent. (See appendix table A-1.) Advances in technology were the main drivers of this decline, with both electronic devices and appliances becoming smaller and more
energy efficient. The transition from traditional incandescent lightbulbs to bulbs with light-emitting diodes (LEDs) played the largest role in reducing electricity use.44
Consuming 85 percent less electricity than incandescent lightbulbs, LEDs became more popular as their prices declined. Between 2008 and 2016, LED prices fell 94 percent,
and the number of LED installations rose from less than 500,000 to over 450 million.45 Additional factors that reduced household electricity use were the increasing
prevalence of more-energy-efficient flat-screen television sets and the replacement of larger and less-energy-efficient desktop computers with laptops and tablets. Americans
also spent less time watching television and more time using the internet, further contributing to the decline.46
Pharmaceutical preparations

With the shift from 2012 to 2017 weights, the relative importance of the index for pharmaceutical preparations declined from 1.4 to 1.1 percent. (See appendix table A-1.)
Driving this trend was a consistent shift from domestic to foreign production of pharmaceuticals. This shift, which started in the mid-2000s and continued through the
2010s,47 resulted mostly from cost-cutting initiatives prompted by the rising trend of generic drugs hitting the market.48 Drug companies were drawn to lower labor and
production costs afforded by countries such as China and India, and they faced fewer environmental regulations in China around the buying, handling, and disposing of
chemicals used in the pharmaceutical manufacturing process.49

Conclusion
The result of the 2023 PPI weight update, which involved a transition from 2012 to 2017 value-of-shipments data, reveals an underlying theme in some of the largest shifts in
relative-importance values. Many of those shifts were driven by either technological advancements or monetary policy, which helped boost the economy after the 2007–09
Great Recession. Technological advancements, such as simplified medical procedures, television streaming services, and energy-efficient LEDs, played a major role in
reducing the relative importance of the indexes for hospital care, cable and satellite subscriber services, and residential electric power. Monetary policy affected the relative
importance of the indexes for real estate brokerages and automobile retailing, as both sectors realized growth in revenue, largely because of the Federal Reserve’s lowering of
interest rates.50 Additionally, lower borrowing costs incentivized discretionary spending, shifting consumer purchases from passenger cars to more expensive light trucks.
Consumers also shopped online more frequently, which in turn increased demand for trucking services and new warehouse building construction, boosting the relative
importance of the indexes for these categories.

Appendix

Table A-1. Relative importance of selected Producer Price Index detailed component indexes, calculated with 2012 and 2017 value weights, December 2022
Index

Commodity

2012 relative importance

2017 relative importance

Change (percentage

(percent)

points)

054121 Residential electric power

2.0

1.9

-0.1

057103 Unleaded premium gasoline

0.2

0.4

0.2

1.4

1.1

-0.3

0.4

0.2

-0.2

063802 Pharmaceuticals acting on the central nervous system and the sense organs

0.4

0.3

-0.1

063803 Pharmaceuticals acting on the cardiovascular system

0.1

0.0

063804 Pharmaceuticals acting on the respiratory system

0.2

0.1

-0.1

063805 Pharmaceuticals acting on the digestive or the genito-urinary systems

0.1

0.0

-0.1

063806 Pharmaceuticals acting on the skin

0.0

0.1

063807 Vitamin, nutrient, and hematinic preparations

0.1

0.0

063808 Pharmaceuticals affecting parasitic and infective diseases

0.1

0.0

141101 Passenger cars and chassis

0.9

0.4

-0.5

1.3

1.6

0.3

152101 Cigarettes, excluding electronic

0.5

0.4

-0.1

301201 Local motor carrying

0.6

0.7

0.1

301202 Long-distance motor carrying

1.9

2.2

0.3

341101 System software publishing

0.3

0.9

0.6

0.3

0.1

-0.2

372101 Cellular phone and other wireless telecommunication services

0.7

1.0

0.3

373101 Cable and satellite subscriber services

0.7

0.4

-0.3

374102 Residential internet access services

0.1

0.3

0.2

381101 Hosting, ASP, and other IT infrastructure provisioning

0.3

0.4

0.1

401101 Securities brokerage, dealing, and investment advice

0.6

1.0

0.4

402101 Portfolio management

2.0

1.6

-0.4

411101 Life insurance

0.4

0.9

0.5

412101 Annuities

0.5

0.2

-0.3

0.3

0.4

0.1

432101 Residential property sales and rental, brokerage fees and commissions

1.1

1.7

0.6

454101 Administrative and general management consulting services

0.3

0.2

-0.1

511101 Physician care

3.6

4.0

0.4

511103 Home health and hospice care

0.7

0.9

0.2

511104 Hospital outpatient care

3.9

4.3

0.4

512101 Hospital inpatient care

4.9

4.5

-0.4

552101 Motor vehicle repair and maintenance (partial)

0.5

0.4

-0.1

571102 Parts and supplies for machinery and equipment

1.3

0.7

-0.6

571104 Professional and commercial equipment wholesaling

0.9

0.7

-0.2

577101 Apparel wholesaling

0.5

0.3

-0.2

578101 Food wholesaling

0.9

1.1

0.2

579101 Other commodities wholesaling

1.9

2.4

0.5

581102 Food retailing

2.1

1.7

-0.4

583101 Apparel, footwear, and accessories retailing

1.2

1.0

-0.2

586101 Automobile retailing (partial)

0.8

1.5

0.7

58B101 Furniture retailing

0.3

0.5

0.2

58D101 Hardware and building materials and supplies retailing

0.3

0.2

-0.1

58F101 Automotive fuels and lubricants retailing

0.8

0.5

-0.3

601104 Support activities for oil and gas operations

0.7

0.5

-0.2

801101 New warehouse building construction

0.2

0.5

0.3

801102 New school building construction

0.4

0.6

0.2

801103 New office building construction

0.5

0.7

0.2

0638
063801

141105

351101

431201

Pharmaceutical preparations
Pharmaceuticals affecting neoplasms, the endocrine system, and metabolic
diseases

Trucks, truck tractors, and bus chassis 14,000 lb or less, including minivans and
SUVs

Affiliate agreements, programming sales, and retransmission fees for cable and
broadcast TV

Nonresidential property sales and leases including land, brokerage fees and
commissions

Note: ASP = active server pages; IT = information technology.
Source: U.S. Bureau of Labor Statistics.
SUGGESTED CITATION:

Arthur K. Edouard, "Effects of the PPI weight update on final-demand relative-importance values," Monthly Labor Review, U.S. Bureau of Labor Statistics, December 2023,

https://doi.org/10.21916/mlr.2023.29

Notes

“PPI relative importance tables” (U.S. Bureau of Labor Statistics), https://www.bls.gov/ppi/tables/.

2 Gretchen Frazee, “Why a slowdown in manufacturing matters for the U.S. economy,”
PBS News Hour, November 5, 2019, https://www.pbs.org/newshour/economy/making-sense/why-a-slowdown-in-manufacturing-matters-for-the-u-s-economy.

Relative-importance values may not add to 100 because of rounding.

4 In the Producer Price Index (PPI), retailing is measured by changes in margins, which represent the difference between selling and acquisition prices.
5 Natalie Sherman, “Why are U.S. car sales falling?,” BBC News, July 11, 2017, https://www.bbc.com/news/business-40523171.
6 Heather Long, “A record 107 million Americans have car loans,” CNN Business, May 19, 2017, https://money.cnn.com/2017/05/19/news/economy/us-auto-loans-soaring/index.html;
Jessie Romero, “Subprime securitization hits the car lot: are fears of a ‘bubble’ in auto lending overstated?,” Econ Focus (Federal Reserve Bank of Richmond, third quarter 2017),

https://www.richmondfed.org/publications/research/econ_focus/2017/q3/feature1; and Patrick Collinson, “Sub-prime cars: are car loans driving us towards the next financial crash?,”
The Guardian, February 10, 2017, https://www.theguardian.com/money/2017/feb/10/are-car-loans-driving-us-towards-the-next-financial-crash.

7 Neal E. Boudette, “Car sales end a 7-year upswing, with more challenges ahead,” The New York Times, January 3, 2018, https://www.nytimes.com/2018/01/03/business/auto-

sales.html; and “U.S. all grades all formulations retail gasoline prices” (U.S. Energy Information Administration), https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?
n=pet&s=emm_epm0_pte_nus_dpg&f=m.
8 “New houses sold” (U.S. Census Bureau), https://www.census.gov/construction/nrs/data/series.html.
9 “Demand data—home sales,” data series for existing-home sales (U.S. Department of Housing and Urban Development), https://www.huduser.gov/portal/ushmc/hd_home_sales.html.
10

Irina Lupa, “The decade in housing trends: high-earning renters, high-end apartments and thriving construction,” RentCafe, December 16, 2019, https://www.rentcafe.com/blog/rental-

market/market-snapshots/renting-america-housing-changed-past-decade/#apartmentconstruction.
11 Ibid.
12 “Median sales price of houses sold for the United States” (FRED, Federal Reserve Bank of St. Louis), https://fred.stlouisfed.org/series/MSPUS#0.
13 “Demand data—rental affordability,” data series for median rental price (U.S. Department of Housing and Urban Development), https://www.huduser.gov/portal/ushmc/hd_rai.html.
14

Jeff Ostrowski, “Real estate commissions fall to new lows as homes fly off the market,” Bankrate, March 2, 2021, https://www.bankrate.com/real-estate/real-estate-commissions-fall/.

15 Anna-Louise Jackson, “Quantitative easing explained,” Forbes Advisor, March 18, 2023, https://www.forbes.com/advisor/investing/quantitative-easing-

qe/#:~:text=When%20the%20fed%20funds%20rate,the%20economy%20from%20freezing%20up; John Weinberg, “The Great Recession and its aftermath,” Federal Reserve
History (Federal Reserve Bank of St. Louis, November 22, 2013), https://www.federalreservehistory.org/essays/great-recession-and-its-aftermath; and Michael Hyman, “Existing-home
sales trends, 2009–2019” (National Association of Realtors, February 21, 2020), https://www.nar.realtor/blogs/economists-outlook/existing-home-sales-trends-2009-2019.

16 “Internet access” (Organisation for Economic Co-operation and Development), https://data.oecd.org/ict/internet-access.htm.
17

Broadband: Observations on Past and Ongoing Efforts to Expand Access and Improve Mapping Data, GAO-20-535 (U.S. Government Accountability Office, June 2020),

https://www.gao.gov/assets/gao-20-535.pdf.
18 Ibid.
19

Nadine Diaz-Infante, Michael Lazar, Samvitha Ram, and Austin Ray, “Demand for online education is growing. Are providers ready?” (McKinsey & Company, July 20, 2022),

https://www.mckinsey.com/industries/education/our-insights/demand-for-online-education-is-growing-are-providers-ready; Michael L. Barnett, Kristin N. Ray, Jeff Souza, and
Ateev Mehrotra, “Trends in telemedicine use in a large commercially insured population, 2005–2017,” JAMA Network, November 27, 2018,

https://jamanetwork.com/journals/jama/fullarticle/2716547; and Kristina Zucchi, “5 reasons the cable TV industry is dying,” Investopedia, updated July 28, 2023,
https://www.investopedia.com/articles/personal-finance/062315/5-reasons-cable-tv-industrydying.asp#:~:text=Beginning%20in%202013%2C%20cable%20TV,the%20industry%20at%20a%20crossroads.
20 Adam Grundy, “Aging population linked to increased need for select health care and social assistance services” (U.S. Census Bureau, August 9, 2022),

https://www.census.gov/library/stories/2022/08/revenues-for-home-care-elderly-services-increase.html.
21 Daniel I. Levin, Bobby Van Dusen, and Nicholas J. Janiga, “2022 outlook: home health and hospice” (HealthCare Appraisers, February 11, 2022), https://healthcareappraisers.com/2022-

outlook-home-health-and-hospice/.
22 Ibid.
23 Susan Jaffe, “Home health care providers struggle with state laws and Medicare rules as demand rises,” Health Affairs, vol. 38, no. 6 (U.S. Department of Labor, June 2019),

https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2019.00529.
24 Ken Abrams, Andreea Balan-Cohen, and Priyanshi Durbha, “Growth in outpatient care: the role of quality and value incentives,” Deloitte Insights, August 15, 2018,

https://www2.deloitte.com/us/en/insights/industry/health-care/outpatient-hospital-services-medicare-incentives-value-quality.html/#endnote-27.
25 “3 factors influencing growth across the outpatient market,” blog post (Definitive Healthcare), https://www.definitivehc.com/blog/factors-influencing-outpatient-

care#:~:text=Why%20is%20this%20the%20case,be%20performed%20in%20outpatient%20facilities; and chapter 3, “Hospital inpatient and outpatient services,” in Report to the
Congress: Medicare Payment Policy (Washington, DC: Medicare Payment Advisory Commission, March 2019), https://www.medpac.gov/wpcontent/uploads/import_data/scrape_files/docs/default-source/reports/mar19_medpac_ch3_sec.pdf.
26 Laurie Northington, “Industry sales by quarter 2011 Q3 to 2018 Q3 furniture & bedding,” Factoids (Atlanta, GA: Home Furnishings Business, October 26, 2018),

http://hfbusiness.com/News/Factoids/ArticleId/18037/industry-sales-by-quarter-2011-q3-to-2018-q3-furniture-bedding.

27 Laurie Northington, “Furniture industry growth by outlet type,” Factoids (Atlanta, GA: Home Furnishings Business, October 19, 2018),

http://hfbusiness.com/News/Factoids/ArticleId/18005/furniture-industry-growth-by-outlet-type.
28 Deborah Weinswig, “A deep dive into the U.S. furniture market” (Fung Business Intelligence Centre, February 9, 2016), https://www.lifung.com/wp-content/uploads/2016/02/US-

Furniture-Market-Report-by-FBIC-Global-Retail-Tech-Feb-9-2016.pdf.
29

Neeraj Aggarwal, Frank Arthofer, John Rose, Jacob Rosenzweig, and Joachim Stephan, “The digital revolution is disrupting the TV industry” (Boston Consultant Group, March 21, 2016),

https://www.bcg.com/publications/2016/media-entertainment-digital-revolution-disrupting-tv-industry.
30 Zucchi, “5 reasons the cable TV industry is dying.”
31

Brad Adgate, “The rise and fall of cable television,” Forbes, November 2, 2020, https://www.forbes.com/sites/bradadgate/2020/11/02/the-rise-and-fall-of-cable-television/?

sh=145c95636b31.
32 Jennifer Cheeseman Day and Andrew W. Hait, “Number of truckers at all-time high” (U.S. Census Bureau, June 6, 2019), https://www.census.gov/library/stories/2019/06/america-

keeps-on-trucking.html.
33 Sal Arora and Scott McConnell, “Four forces to watch in trucking and rail freight” (McKinsey & Company, May 17, 2017), https://www.mckinsey.com/industries/travel-logistics-and-

infrastructure/our-insights/four-forces-to-watch-in-trucking-and-rail-freight.
34

Mary Ellen Biery, “Trucking companies hauling in higher sales,” Forbes, March 4, 2018, https://www.forbes.com/sites/sageworks/2018/03/04/trucking-companies-hauling-in-higher-

sales/?sh=7b8ef1df3f27.
35 U.S. Bureau of Labor Statistics coverage of the construction sector in the PPI is limited to selected areas of nonresidential construction, covering about 17 percent of total domestic
construction. For more information on this coverage, see “Producer Price Index data for the nonresidential building construction sector, NAICS 2362” (U.S. Bureau of Labor Statistics),

https://www.bls.gov/ppi/factsheets/producer-price-index-nonresidential-building-construction-initiative.htm.
36 Saurabh Mahajan, “The future of industrial real estate market: preparing for slower demand growth,” Deloitte Insights, 2019,

https://www2.deloitte.com/content/dam/Deloitte/ar/Documents/realestate/arg-2019-future-of-industrial-real-estate.pdf.
37

Craig Meyer, “Record demand brings new heights and challenges to industrial real estate market,”

Area Development, first quarter 2017, https://www.areadevelopment.com/economic-analysis/q1-2017/record-industrial-real-estate-market-demand.shtml; and “Amazon and the
state of industrial real estate” (Lee and Associates, August 2022), https://www.lee-associates.com/wp-content/uploads/2022/08/2022.08-Amazon-and-the-State-of-Industrial-Real-

Estate.pdf.
38 “New and used passenger car and light truck sales and leases” (U.S. Bureau of Transportation Statistics), https://www.bts.gov/content/new-and-used-passenger-car-sales-and-leases-

thousands-vehicles.
39 Neal E. Boudette, “Car sales end a 7-year upswing, with more challenges ahead,” The New York Times, January 3, 2018, https://www.nytimes.com/2018/01/03/business/auto-

sales.html.
40 “Percentage of adults who smoke,” American Heart Association News (Dallas, TX: American Heart Association, August 30, 2018), https://www.heart.org/-/media/Files/News/Text-

Only-PDFs/0830Smoking_textonly-infographic.pdf; and “Smoking in America: why more Americans are kicking the habit,” American Heart Association News (Dallas, TX: American Heart
Association, August 30, 2018), https://www.heart.org/en/news/2018/08/29/smoking-in-america-why-more-americans-are-kicking-thehabit#:~:text=But%20real%20strides%20in%20decreasing,in%201965%2C%20reiterated%20that%20message.
41 Ibid.
42 Lungile Nkosi, Satomi Odani, and Israel T. Agaku, “20-year trends in tobacco sales and self-reported tobacco use in the United States, 2000–2020” (Centers for Disease Control and
Prevention, July 28, 2022), https://www.cdc.gov/pcd/issues/2022/21_0435.htm#1; and Mila Kofman, Katie Dunton, and Mary Beth Senkewicz, “Implementation of tobacco cessation
coverage under the Affordable Care Act: understanding how private health insurance policies cover tobacco cessation treatments” (Georgetown University Health Policy Institute, November
26, 2012), https://www.kff.org/wp-content/uploads/sites/2/2012/11/coveragereport.pdf.

Angelica Peebles, “CDC says smoking rates fall to record low in U.S.,” CNBC, November 8, 2018, https://www.cnbc.com/2018/11/08/cdc-says-smoking-rates-fall-to-record-low-in-

us.html.
44 Justin Fox, “Americans keep using less electricity,” Bloomberg, March 1, 2018, https://www.bloomberg.com/opinion/articles/2018-03-01/americans-electricity-use-just-keeps-

falling#xj4y7vzkg.
45 Lucas Davis, “Evidence of a decline in electricity use by U.S. households,” Working Paper 279R (Energy Institute at Haas, May 2017), https://www.haas.berkeley.edu/wp-

content/uploads/WP279.pdf.
46 Rani Molla, “Even with all our gadgets, Americans are using less electricity than 10 years ago,” Vox, August 6, 2017, https://www.vox.com/2017/8/6/16103800/electronic-gadgets-

americans-using-less-electricity-energy-information-administration.
47 “The decline of U.S. pharmaceutical production,” The FRED Blog (Federal Reserve Bank of St. Louis, June 8, 2020), https://fredblog.stlouisfed.org/2020/06/the-decline-of-u-s-

pharmaceutical-production/.
48 Jacob Wiesenthal and Sarah Dolman, “Is the future of U.S. pharma manufacturing domestic?” (Recon Strategy, February 10, 2023), https://reconstrategy.com/2023/02/is-the-future-of-

us-pharma-manufacturing-domestic/.
49 “Safeguarding pharmaceutical supply chains in a global economy,” testimony before the House Committee on Energy and Commerce, Subcommittee on Health (U.S. Food and Drug
Administration, October 30, 2019), https://www.fda.gov/news-events/congressional-testimony/safeguarding-pharmaceutical-supply-chains-global-economy-10302019.

50 “The Federal Reserve’s response to the financial crisis and actions to foster maximum employment and price stability” (Board of Governors of the Federal Reserve System, May 10, 2021),

https://www.federalreserve.gov/monetarypolicy/bst_crisisresponse.htm; and Wayne Duggan, “A short history of the Great Recession,” Forbes Advisor, June 21, 2023,
https://www.forbes.com/advisor/investing/greatrecession/#:~:text=The%20End%20of%20the%20Great%20Recession&text=In%20March%202009%2C%20the%20Federal,advantage%20of%20lower%20interest%20rates.

ABOUT THE AUTHOR

Arthur K. Edouard
edouard.arthur@bls.gov
Arthur K. Edouard is an economist in the Office of Prices and Living Conditions, U.S. Bureau of Labor Statistics.

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The unequal responses to pandemic-induced schooling shocks
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Education is meant to benefit all students. The COVID-19 pandemic, however, changed this notion. The pandemic emphasized discrepancies in support and
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focused on computer access.
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respondents’ households” faced more class cancellations and less computer access when classes were switched online. These students’ long-term education was
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The COVID-19 pandemic profoundly affected the world economy. The U.S. economy lost 23 million jobs at the start of the pandemic, leading to a recession in
early 2020. The federal government responded with sharp increases in fiscal spending, and the Federal Reserve lowered interest rates to near zero and kept them
there for almost 2 years. The economy began to recover, but inflation rose to its highest level in decades. This led to debates among economists about what caused
the high inflation rates and how best to lower them. In a recent conference paper, “What caused the U.S pandemic-era inflation?” (presented at the Brookings
Institution, Washington, DC, May 23, 2023), Ben Bernanke and Olivier Blanchard analyze the complex set of factors that led to the highest inflation rates in more
than 40 years.
In their literature review, Bernanke and Blanchard divide policy analysts into two groups: the “inflation optimists” and “the critics” (counting themselves among
the latter). As the authors explain, standard economic theory suggests that easing fiscal and monetary policy can increase inflation if labor markets overheat and
output exceeds the economy’s potential. The inflation optimists argued that even if the new policies caused the unemployment rate to decline more than is optimal
(i.e., below 4.0 percent), any resulting rise in inflation would probably be minimal and temporary. By contrast, the critics argued that the large fiscal transfers and
easing of monetary policy risked increasing aggregate demand to the point of overheating the labor market and thus increasing inflation.
Bernanke and Blanchard introduce an analytical model that focuses on the behavior of wages, prices, and short- and long-term inflation expectations, with labor
market slack and shocks to prices taken as given. The authors use empirical data on wages and prices to test their “model of wage-price determination.” They
examine data from two periods: first quarter 1990 to fourth quarter 2019 (the pre-COVID period) and first quarter 2020 to first quarter 2023 (the COVID period).
The model allows the authors to “decompose” price and wage inflation and determine its sources at a more specific level. In particular, they examine demand
issues, such as food and energy prices, and supply issues, such as the shortages caused by production shutdowns and other supply-chain disruptions during the
pandemic. In addition, Bernanke and Blanchard use the ratio of job vacancies to unemployed workers in their analysis, arguing that it measures labor market
tightness more accurately. The ratio increased from less than 1.0 in April 2021 to 1.9 a year later, the highest level on record.
From their analysis, the authors present three main findings:
1. The shocks to food and energy prices contributed substantially to the sharp rise in inflation during the COVID-19 period. Energy price shocks were the primary
cause of the high inflation rates from late 2021 to the middle of 2022. Lower energy prices in the second half of 2022 contributed to the inflation decline during
that period.
2. The combined effects of increased demand for durables and shortages caused by supply-chain disruptions were the main source of inflation in the second
quarter of 2021. Both the direct and indirect effects of those supply-chain problems remained substantial through the end of 2022.
3. Tight labor-market conditions, one of the main concerns of the early critics of U.S fiscal and monetary policy, contributed only slightly to inflation. In fact, the
tight labor market affected the economy negatively in 2020 and early 2021. Since then, however, the traditional Phillips-curve effect has begun to reemerge, with
the high vacancy-to-unemployment ratio becoming an increasingly important factor in the high inflation rates.
Bernanke and Blanchard argue that the critics’ concerns of higher inflation were correct. But the sources of the high inflation differed from those the critics had
anticipated. The authors conclude that price shocks in product markets were the leading cause of the initial rise in inflation. However, as labor markets began to
overheat in 2022, with unsustainable employment increases, a high ratio of job openings to unemployed workers, and low levels of quits, labor market tightness
increasingly became the main cause of the persistently high inflation rates.

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Beyond BLS briefly summarizes articles, reports, working papers, and other
works published outside BLS on broad topics of interest to MLR readers.
De c e m b e r 2 0 2 3

What are the impacts of graduates receiving a college degree later in life?
Summary written by: Richard Hernandez
The path for most students who obtain a college education is thought to be straightforward. After students receive their high school diploma, they then enroll in a
postsecondary educational institution to earn their college degree in their 20s—they are known as early college graduates. However, what share of college
graduates finish their college education after turning 30? These college graduates are known as late bloomers. So, does the age of a person matter when obtaining
a college degree? In a recent working paper, “Late bloomers: the aggregate implications of getting education later in life” (National Bureau of Economic Research,
Working Paper 31874, November 2023), authors Zsófia L. Bárány, Moshe Buchinsky, and Pauline Corblet investigate the percentage of the population who earn
their degree after turning 30, the returns of a college education, and the resulting implications for the skilled workforce of obtaining a college degree later in life.
Using data from the U.S. Census Bureau and U.S. Bureau of Labor Statistics, Bárány and colleagues find that of people born between 1930 and 1970, around 20
percent have earned their college degree after turning 30. Furthermore, women tend to earn their degree later in life than men. The authors also discover that when
they further break down the data by race, Black and Hispanic individuals are more likely to be late bloomers compared with their White counterparts. Late
bloomers have contributed to shrinking the gap between the gender and racial college share. After graduating, late bloomers receive a college wage premium (the
difference in pay between a bachelor’s degree holder and a high school graduate). However, wages of late bloomers are less than those of early college graduates
who followed the traditional college path. But when comparing late bloomers with those without a college degree, the authors find that late bloomers substantially
outearn them.
In their working paper, the authors also explore how late bloomers affect the college share of overall workers. They find that since 1960, late bloomers have
increased the aggregate supply of the skilled workforce, and their share is increasing with each cohort group. The authors point out that to understand the varying
returns of earning a college degree at different ages, researchers should further investigate the reason people return to college at various ages later in life. They
suggest that learning and comparing the different forces that drive early college graduates and late bloomers would benefit policymakers who implement
educational public policies.

Full text of Monthly Labor Review : December 2023

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