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Article
Janua r y 2 0 2 4

A consumption measure for automobiles
In this article, we estimate consumption of automobiles by using a user-cost approach and data from the Consumer Expenditure Surveys Interview Survey (CE). The user-cost
approach is a method for valuing the flow of services from long-lived goods. A key input into user cost is the depreciation of the good, which we estimate by using purchase
data from the CE.

Consumption measures of long-lived items are an important part of measuring the economic well-being of households. These long-lived items include owned housing and
durable goods. Measures of expenditure sometimes are used to approximate consumption, but the expenditure on goods and the consumption of goods differ for items that
are purchased infrequently and used for several years. Other than housing, automobiles are the most expensive durable good that households purchase. The economic
literature assumes that automobiles depreciate slowly over a long period; this assumption implies that the consumption of automobiles is spread over multiple years. Thus,
consumption and expenditures on automobiles take place at different times and may exhibit different patterns over time. We calculate the consumption of automobiles by
measuring their depreciation, opportunity cost, and the recurring costs of ownership. We follow a user-cost approach to sum these three amounts. This sum is the
consumption value for the flow of services from an automobile. Data from the Consumer Expenditure Surveys Interview Survey (CE) are used in our analysis.
The three amounts that produce a consumption value for automobiles are individually derived. First, we estimate depreciation by comparing the purchase price of similar
automobiles of different ages across time periods with CE data.1 Our preferred depreciation specification is an estimate of nonparametric depreciation; that is, the
depreciation rate can vary by automobile age. We also estimate constant geometric depreciation as a comparison with nonparametric depreciation. The nonparametric
approach is preferred because automobiles of different ages depreciate at different rates, as documented in the literature. Second, the opportunity cost is derived from
estimated current market values and interest rates. Third, we use CE data on vehicle maintenance and related costs to produce ownership-cost values. Thus, the two
estimated values and the ownership-cost values are summed to create a consumption value for automobiles.
Expenditures on automobiles fluctuate much more than the consumption of automobiles. Our research suggests that consumers buy automobiles when the economy is
growing, and expenditures on automobiles decline during recessions as consumers postpone purchases. Consumption follows a similar pattern because new automobiles
depreciate more quickly than older automobiles, but the decline in the consumption of automobiles is substantially smaller than the decline in expenditures during
recessions.
Our approach allows us to measure consumption for individual consumer units (CUs).2 We combine information on automobile ownership with estimates of depreciation
rates for automobiles (both datasets are from the CE). Real (inflation adjusted) 10-year treasury yields are used to calculate opportunity costs. Other components of user cost
(the recurring costs, which include maintenance and repairs, insurance, licensing, etc.) cannot be uniquely matched to CE data. However, we accommodate this matching
problem by adjusting the recurring costs for other vehicles by the number of automobiles and other vehicles owned by CUs. For example, if a CU owns a car, a truck, and a
boat with a motor, and if the average recurring costs for CUs with two automobiles is only 80 percent of the average recurring costs for CUs with two automobiles and one
other vehicle in the same year, then we assume that the recurring costs for the car and truck equal 80 percent of total recurring costs for the CU. Using these data, we
estimate automobile consumption individually for all CUs. These consumption estimates are positive in each period for all CUs that own automobiles. In contrast,
expenditure estimates for the purchase of automobiles are $0 for most CUs in each period.3
Our work is similar to approaches used to measure the consumption of owned housing and other durable goods. There are two main approaches to valuing the consumption
of durable goods. One is the acquisition approach, which assigns the full value of the good to the period it which the good was acquired. A limitation of the acquisition
approach is that it ignores that durable goods provide value over an extended period. An alternative to the acquisition approach is to value the flow of services received over
the lifetime of the durable good. The flow-of-services approach is generally recommended for measuring the consumption value of durable goods.4 There are two options to
value the flow of services from durables. The first is called rental equivalence; it uses the rental price for a similar good to value the flow of services. The other is to estimate
the user cost. While rental equivalence is the most common method used to value the flow of services from owned housing, it is not feasible for valuing motor vehicles
because vehicles are rarely leased past the first few years of their life. Thus, the user-cost approach is the only approach used to value the flow of services from vehicles.
There has been substantial prior work estimating consumption from owned vehicles that uses a user-cost approach.5 Also, research has been conducted on estimating the
depreciation rate of automobiles. Bruce Meyer and James Sullivan use a constant depreciation rate estimated by comparing the purchase prices of similar vehicles, and
Jonathan D. Fisher and David S. Johnson use an estimated constant depreciation rate of 10 percent for all automobiles.6 In this paper, we use the basic methods developed
by Meyer and Sullivan, but we allow the depreciation rate of automobiles to vary by age.
The issue of how to value durable goods also arises in the context of price indexes. Currently, the U.S. Bureau of Labor Statistics (BLS) uses different methods to value
different types of goods and services in the construction of price indexes. The acquisition approach is used for most goods and services, including automobiles and other
durable goods. However, rental equivalence is used for owner-occupied housing. Introducing the user-cost approach to value the flow of services from automobiles is in line
with the Committee on National Statistics (CNSTAT) recommendation for measuring the prices of durable goods: “The prices of durable goods should be converted to user
cost before being aggregated into a price index, whether a basket price index or a COLI [cost-of-living index].”7 Additionally, CNSTAT has recommended that the number
of owned automobiles be used as the quantity weight associated with automobiles in a cost-of-living-based price index. The value of the stock of owned automobiles is an
immediate outcome of our approach and can be used to construct the quantity weight. However, a major limitation for implementing a user-cost approach in the production
of price indexes is the lack of user-cost data in real time. Our estimates of depreciation rates are average values over a long period of time, but current values would be
needed for a timely price index.

In this article, we review how automobile consumption and expenditure can be estimated. To show this, we first discuss how the value of automobile consumption can be
measured with a flow-of-services approach. Second, we describe the data available on automobiles in the CE and our methods. Third, we estimate the depreciation rates of
automobiles listed in the CE. Fourth, we show how the current market value of automobiles can be imputed with CE data. Fifth, we present a national series of average
automobile consumption based on the two depreciation methods and compare them with expenditure on automobiles. Sixth, we estimate the consumption value of
automobiles at the level of the CU. Finally, after we summarize our findings, we discuss data sources and the calculation steps for the consumption value of automobiles in a
data appendix.

Measuring automobile consumption
We measure the value of automobile consumption by using a flow-of-services approach that combines automobile depreciation with the other costs of owning an
automobile. We estimate depreciation by using the difference in the purchase prices (as reported in the CE data) of similar automobiles of different ages. Purchasing an
automobile is similar to an investment in which an opportunity cost is incurred as a forgone return to investment. We estimate the opportunity cost of owning an automobile
by using an inflation-adjusted market yield on 10-year U.S. Treasuries as a proxy for the real interest rate. As noted earlier, we also use CE data on recurring costs, such as
automobile repairs and maintenance, insurance, and registration costs. The sum of depreciation, opportunity cost, and recurring costs equals the user cost, which
approximates the flow of services from owned automobiles.
We restrict our analysis to cars, sport utility vehicles (SUVs), and trucks because of the lack of detailed information about other types of vehicles. Throughout the remainder
of this article, we refer to cars, SUVs, and trucks as automobiles, and we do not include in our analysis other motorized vehicles, such as aircraft, boats, and motorcycles.
We estimate two measures of depreciation. One measure assumes a fixed depreciation rate over the life of each automobile, and the other measure allows the depreciation
rate to vary nonparametrically. A nonparametric measure estimates separate depreciation rates for each age of an automobile. Varying depreciation rates are calculated and
used because several sources suggest that automobiles depreciate at different rates throughout their life cycle.8 As we noted earlier, Meyer and Sullivan use a constant
depreciation rate estimated by comparing the purchase prices of similar vehicles, while Fisher and Johnson assume a constant depreciation rate of 10 percent.9 In this paper,
we essentially use Meyer and Sullivan’s method, but we allow the depreciation rate of automobiles to vary by age.
We consider three sets of recurring costs. Maintenance and repair costs include spending on service and parts, oil and other fluids, and auto service policies. Insurance costs
include all spending on insurance payments. Registration costs include all expenditures needed to obtain and maintain license plates on the automobile. The recurring costs
are adjusted for business use because our primary focus is the consumption of households. We do not include fuel purchases in this measure because they are not considered
part of user cost in the literature.
We produce a service-flow value of an automobile by combining the estimated depreciation rate of an automobile, the estimated current market value of the automobile, an
interest rate representing the opportunity cost of capital, and the recurring costs of automobile ownership. The service flow of an automobile i of age a at time t (SFi,t) is
calculated with the following formula:
(1)

,

where rt refers to the interest rate at time t, δa refers to the estimated depreciation rate for an automobile of age a, Pi,0 refers to the purchase price of automobile i at 0 years
of age, and RCi,t is the incurred recurring costs of the automobile i at time t. We use the all-item series of the Consumer Price Index for All Urban Consumers (CPI-U) to
adjust the purchase prices of automobiles into constant dollars (1982–84 dollars) in all intermediate steps. Next, we adjust the purchase prices back to the reference-perioddollar level for final measures. Total depreciation over the life of the automobile is equal to its purchase price in constant dollars. In our model, if the interest rate is 0
percent, then the automobile service flow without recurring costs is equal to the original purchase price or acquisition cost. The only difference between the acquisition
approach and the sum of the user cost over the vehicle lifetime would be the timing of when costs are recognized. That is, current total expenditure should equal the sum of
the depreciation over the life of the vehicle.
Because our primary goal is to measure the automobile consumption of households, we also adjust all three components of depreciation, opportunity cost, and recurring
costs for business use. In addition, not all automobile purchases are made through dealers. Nondealer purchase prices may not accurately represent current market prices
because of personal relationships, such as those of family members or friends. If the constant dollar purchase price of an automobile is less than $2,000, then we disregard it
as an invalid price; instead, we use an imputed price based on the characteristics of a similar automobile.10

Data description and method
Our primary data sources are information on household ownership of automobiles from the CE. In our study, we use data collected from the second quarter of 1996 (Q2
1996) to the first quarter of 2022 (Q1 2022). The availability of relevant data is limited for the years prior to 1996 (in particular, maintenance and repair variables are absent
from the series). We observe the makes and models of owned automobiles through vehicle descriptions or vehicle codes. These data are internal to BLS. While Public Use
Microdata (PUMD) are available for the stock of automobiles owned, details on automobile make and model needed for our estimations are not available for all years of
PUMD. As noted earlier, we restrict our analysis to cars, SUVs, and trucks; thus, we do not include observations of other types of vehicles, such as recreational vehicles
(RVs), boats, and aircraft. Also, based on purchase information reported in CE vehicle data, we exclude automobiles purchased as a gift for someone outside the CU. We
exclude these gift purchases because these automobiles do not generate a service flow for the purchaser. About 0.7 percent of all owned automobiles in the weighted sample
are gifts for someone outside the CU. These criteria leave us with more than 1 million automobiles reported in CE interviews from all quarters and all years. If we assume
that quarterly data are independent, then the total number of vehicles is a simple sum of automobiles reported in CE data across quarterly surveys. For example, an
automobile surveyed in Q1 2021 is treated as a different automobile in Q2 2021. This assumption of the independence of data is necessary because our goal is to produce a
value for the flow of services from automobiles for each quarter.
To estimate automobile depreciation, we need information about the original purchase price paid by the consumer of the automobile, whether the automobile was purchased
new or used, and the automobile’s characteristics (make and model). Among all automobiles that are listed in the stock of owned automobiles, only 24 percent include a
valid purchase price in the CE.11 This means that we can only produce depreciation rates for this subset of all automobiles. For those automobiles with no original purchase
prices reported, we impute their current market values based on the average values of automobiles with reported purchase prices and similar characteristics (such as make,
model, year, age, and condition). Using CE reporting, we find that approximately 39 percent of all owned automobiles were purchased new and 61 percent were purchased
used. The reported purchase prices are converted into constant dollars by using the CPI-U for all items. We estimate depreciation in constant-dollar terms to remove the
effect of varying inflation rates on changes in the nominal value of the vehicle. After estimating depreciation and current market values, we convert all consumption
measures in constant-dollar terms back to reference-period dollars by using the CPI-U for all items. Also, we convert all consumption measures into prices from 2 months
before the interview month to represent consumption for the current period (this is defined as the quarterly reference period). For example, if the interview took place in
February 2021 (Q1 2021 in the CE), then the flow of services was assigned to the period Q4 2020. Next, we use the CPI-U for all items from the middle of the quarter as a
deflator for the quarterly consumption measure.

We estimate constant-geometric-depreciation rates separately for new and used automobiles. Additionally, we also allow for nonconstant depreciation of automobiles 0 to 10
years of age. A new automobile depreciates substantially in its first years of life before the estimated depreciation rate stabilizes.
To estimate the age-specific depreciation rates for automobiles, we regress the log of the real purchase price of an automobile on an age indicator. Also, we control for make,
model, and model year in our regression. The regression equation for automobiles of age a is as follows:
(2)

,

where the coefficient βα can be interpreted as an annual average geometric-depreciation rate for automobiles of age a. To be exact, we calculate the depreciation rate as δa =
1 – exp(βa), where βa is the coefficient of age in the regression for the subgroup of automobiles of age a and age a + 1. We estimate the constant-geometric-depreciation rate
for all automobiles at different ages by using this equation. Also, we estimate age-specific depreciation rates by using this equation 11 times for automobiles 0 to 10 years of
age. These estimates include only automobiles of age a and age a + 1 in each regression for the nonparametric estimates. There are few transactions of older automobiles.
Accordingly, the sample size of older automobiles is not large enough to reliably estimate age-specific depreciation rates for these older automobiles. So, we estimate a
constant-geometric-depreciation rate for automobiles 11 years of age and over.
Next, we calculate the current market values of automobiles in the CE with two different treatments that are described below. One treatment has a purchase price, and the
other treatment does not have a purchase price. For automobiles with a reported purchase price, we calculate the current market value of an automobile at time t as follows:
(3)

,

where P0 denotes the real purchase price (in constant 1982–84 dollars) of an automobile at the time of acquisition, and a is the age of the automobile. About 24 percent of all
owned automobiles with a valid purchase price reported in the CE from 1996 to 2021 have a current market value derived from the depreciation approach, and the remaining
76 percent of the automobiles (which have no reported purchase price) are mapped to an imputed market value based on the similar characteristics of the automobiles.
These estimated current market values are adjusted for survival rate and business use. Our depreciation-rate estimates from equation (3) represent the expected loss in
market value of an automobile of a given age conditional on the continuing operation of the automobile. Automobiles may cease to be operable for a number of reasons,
including damage and mechanical failures. A portion of the yearly service-flow value of an automobile is the expected depreciation. The calculation of expected depreciation
is conditional on the probability that the automobile survives the year. This probability that an automobile survives in a specific year is called the survival rate. We estimate
the survival rate by using the age distribution of automobiles in the CE. For example, the ratio of the weighted total number of automobiles of age a to the number of
automobiles of age a + 1 in the CE data is the survival rate for automobiles of age a.
The estimated current market values and recurring operational costs are also adjusted for business use to generate the depreciation and opportunity-cost components for
CUs. Next, they are combined with the recurring-costs component to construct a complete series of user cost. Rewriting equation (1), we have the following equation:
(4)

,

where SFt refers to the service flow at time t, rt refers to the interest rate at time t, δa refers to the estimated depreciation rate for an automobile of age a, adj_CMVt refers to
the estimated current market value adjusted for business use and the survival rate of an automobile, and adj_RCt refers to the combined recurring costs of the automobile
adjusted for business use at time t.

Depreciation-rate estimates
Chart 1 shows estimated depreciation rates as annual percentages by automobile age from the owned-automobiles data series of the CE. We present rates based on the age of
the automobile and constant geometric depreciation. Automobiles purchased as new depreciate faster on average than automobiles purchased used. The depreciation rate of
new automobiles is 12.1 percent, and the depreciation rate of used automobiles is 10.3 percent. Not surprisingly, automobiles depreciate much faster in the earliest years of
their useful life. If they are purchased as new, automobiles lose almost 40 percent of their original value over the first 3 years. After 5 years, the depreciation rate declines to
5 percent by 10 years of age. This finding is consistent with estimates from Edmunds and Hardesty.12 These sources state that a new automobile can lose more than 20
percent of its initial value in its first year and may lose almost one-third of its initial value within 3 years of purchase. The estimated depreciation rate for used automobiles
is on average close to the fixed depreciation rate (10 percent) used by Fisher and Johnson.13
Chart 1. Annual depreciation rates by automobile age
Depreciation rate for new automobiles
New automobiles, average depreciation
Used automobiles, average deprecation

Percent
30.0

20.0

10.0

0.0
0

2

4

6

8

10

Years
Click legend items to change data display. Hover over chart to view data.
Note: The date range for this dataset is from the second quarter of 1996 to the first quarter of 2022.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Imputed current market values of automobiles
Similar to Meyer and Sullivan, we impute the current market values of automobiles in the CE either by applying the depreciation rates demonstrated above to the reported
purchase price of an automobile or by mapping those automobiles with no reported purchase price to similar automobiles with reported prices in the CE.14 As noted earlier,

we find that about one-quarter of automobiles in the CE data have reported purchase prices. We calculate the current market values in real terms by directly applying annual
depreciation rates to the reported real purchase prices. Those current market values are converted back to the current-period-dollar level by using the all-item CPI-U. For
example, if an automobile was reported as purchased in January 2010 during a CE interview in the second quarter of May 2020, then the original purchase price is first
converted into a price of constant dollars. Next, the current market price in constant-dollar terms is calculated after applying the annual depreciation rate by age (as
explained in the previous section). Finally, we use the all-item CPI-U to convert the current market price of the automobile in 1982–84 dollars into March 2020 dollars to
reflect the first quarter of 2020 consumption measure.
For the three-quarters of the automobiles in the CE with no reported purchase price, we first impute their current market values in constant dollars by averaging similar
automobiles in the pooled data by either full or partial match. The summary statistics of these imputation results are reported in table 1.

Table 1. Imputation results of the current market values of automobiles in the Consumer Expenditure Surveys, 1996–2022
Data condition

Imputation method

Purchase price is

Depreciation adjusted purchase

reported.

price

Purchase price is not
reported.

Average market value of same

Purchase price is not
reported.

Average market value of similar

Total

[1]

Number of

Percent share of

automobiles

automobiles

256,741

24

280,420

26

550,062

50

1,087,223

100

automobiles

automobiles

Comments
Nonconstant depreciation is used for new automobiles; constant depreciation
is used for used automobiles.
All five variables (make, model, year, age, and condition of new or used
automobile) are matched.
Partial matches are made based on available characteristics.
[1]

[1] Not applicable.
Note: Calculations use data from the Consumer Expenditure Surveys.
Source: U.S. Bureau of Labor Statistics.

To validate our imputation procedure, we compare data from the 2018 National Automobile Dealers Association (NADA) used-car guides (the only data available for
research purposes) with the estimated current market values of the stock of owned automobiles in the 2018 CE.15 We were able to map a total of 8,596 CE automobiles with
the same make, model, and year as a NADA automobile. The correlation is 0.74 between NADA prices and the estimated current market values from the CE for both
methods of depreciation. Also, we ran a simple regression (without an intercept) of NADA prices on the estimated current market values from CE data. The coefficients of
the CE price from this regression are 0.91 for nonparametric depreciation and 0.85 for constant geometric depreciation. Discrepancies between the two measures are at least
partially attributable to differences in body type (this information is not available in the CE) and the condition of the automobile (NADA retail prices are adjusted for
mileage). Overall, the NADA prices validate our estimates of CE current market values.
Example: expenditure versus consumption for households

We estimate the consumption of automobiles by spreading expenditures on them (a sum of estimated depreciation and the additional costs of ownership) over many years. If
a household buys a new automobile, then the net present value of consumption (excluding additional costs of ownership) is equal to the expenditure on that purchase. We
show this result with an example of a new automobile purchased for $30,000 in table 2. We compare consumption values based on constant geometric depreciation
(DEP_cg), nonparametric depreciation (DEP_np), and expenditure measures over the operating life of this automobile. We calculate both current market values (CMV_cg,
CMV_np) and depreciation by using our estimation of δ from CE data. In table 2, we show that the total values of the three measures differ, but not by substantial margins,
from one another because of the age restriction on owned automobiles in the CE. We estimate that automobiles that are over 25 years of age in the CE would have the same
current market value as those that are 25 years of age. Automobiles are assumed to generate a flow of service until they are completely totaled. A totaled automobile is one
for which the cost to repair exceeds the current market value of the automobile. Thus, the combined consumption value over the entire lifespan of the automobile would be
closer to $30,000 as the automobile operates beyond 25 years of age.

Table 2. Simulation of current market value and depreciation over the life of an automobile
Current market values
Age, in

Depreciation rate

years

(percent)

Consumption values

Constant geometric

Nonparametric

Constant geometric

Nonparametric

depreciation

depreciation

depreciation

depreciation

Expenditure

0

23.9

$30,000

$30,000

$3,633

$7,178

$30,000

1

11.3

26,367

22,822

3,193

2,590

0

2

10.8

23,174

20,232

2,807

2,194

0

3

14.0

20,367

18,038

2,467

2,517

0

4

13.7

17,900

15,520

2,168

2,130

0

5

13.1

15,732

13,391

1,905

1,754

0

6

11.4

13,827

11,637

1,675

1,325

0

7

8.7

12,153

10,312

1,472

897

0

8

9.9

10,681

9,415

1,294

930

0

9

6.9

9,387

8,484

1,137

583

0

10

4.9

8,250

7,901

999

390

0

11

10.3

7,251

7,511

878

775

0

12

10.3

6,373

6,736

772

695

0

13

10.3

5,601

6,041

678

623

0

14

10.3

4,923

5,418

596

559

0

15

10.3

4,327

4,859

524

501

0

16

10.3

3,803

4,358

461

450

0

17

10.3

3,342

3,908

405

403

0

18

10.3

2,937

3,505

356

362

0

19

10.3

2,582

3,143

313

324

0

20

10.3

2,269

2,819

275

291

0

21

10.3

1,994

2,528

242

261

0

22

10.3

1,753

2,267

212

234

0

23

10.3

1,540

2,033

187

210

0

24

10.3

1,354

1,824

164

188

0

25

10.3

1,190

1,636

144

169

0

[1]

[1]

[1]

28,954

28,533

30,000

Total

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

Average automobile consumption at the national level
In this section, we present a national series of quarterly average automobile consumption based on the two depreciation methods and compare them with automobile
expenditures. Averages are based on population-weighted quarterly aggregate consumption, population-weighted quarterly aggregate expenditure, and the total number of
CUs owning or purchasing automobiles during the reference period. Results are presented by adjusting consumption or expenditure for business use. This is in accordance
with the BLS publication of average automobile expenditures that are adjusted to reflect only nonbusiness use.16 One difference between the consumption-based averages
and BLS-published average automobile expenditures is that consumption accounts for not just the previous purchase of automobiles but also the receipt of these as gifts or
other transfer and without payment. In contrast, CE-published average expenditures reflect what is purchased in the reference period; this is the case whether or not the
purchase is for the consumption of the CU or for someone living outside the CU. The latter are referred to as “gifts given” in CE data files. A discrepancy can arise in the
two measures as the total number of vehicles purchased as gifts may not equal the total number of vehicles received as gifts in the sample in a given period.
To compute averages, we first estimate the value of consumption for each CU with an automobile. Next, we average those values across all CUs (including those CUs that
do not own automobiles) to obtain a national average series. For comparison, we construct a series with actual net purchase expenditures (this is the amount paid for a
vehicle after subtracting any trade-in allowance and any costs paid by an employer) in the CE interview reference period of the previous 3 months. We also construct a time
series based on the average expenditures for all vehicles purchased within the interview reference period. The total expenditure series includes the purchase price and the
recurring costs, but the opportunity cost (interest component) is not included (the opportunity cost is included in the flow-of-services approaches).
Chart 2 compares consumption estimates of the three methods. Both the nonparametric-depreciation series and the constant-geometric-depreciation series include
depreciation, interest (opportunity cost), and recurring expenditures. The two series that use depreciation to measure consumption have relatively smooth quarter-to-quarter
changes, while the expenditure series is more variable. Consumption estimated with nonparametric depreciation tends to track consumption estimated with constant
depreciation closely, but the two series diverged during the pandemic. It is likely that this divergence between the two series is due to the decline in new automobile
production. This production decline led to an increase in the average age of automobiles and an increase in the purchase of used automobiles. Also, the results of the
nonparametric-depreciation approach show higher consumption of automobiles in the first years of their lives.

Chart 2. Household consumption and expenditure on automobile purchases,
quarterly averages, 1996–2022
Nonparametric depreciation
Constant geometric depreciation
Expenditure on automobiles

User cost (dollars)
1,800
1,600
1,400
1,200
1,000
800
Q1 1996

Q1 2001

Q1 2006

Q1 2011

Q1 2016

Q1 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: All national average series
are based on authors' calculations using data from the Consumer Expenditure Surveys. Both the
nonparametric-depreciation series (nonlinear) and the constant-geometric-depreciation series (linear)
include depreciation, interest components (opportunity cost), and recurring expenditures, such as
maintenance and repair, vehicle insurance, and other operating costs spent on inspection, registration,
and property tax. The expenditure series includes automobile net purchase prices and recurring costs,
but the interest component is not included for comparison purposes. These data sets are seasonally
adjusted and adjusted for business use.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Consumer-unit consumption
Despite the similarities between average automobile consumption and expenditure, the picture at the CU level is quite different. Most CUs that own automobiles have $0 in
expenditures on automobile purchases in any quarter, although expenditures for recurring costs would be positive. Chart 3 plots the estimated parts of consumption,
depreciation and opportunity cost, and expenditures that represent the purchase of automobiles for all CUs participating in a CE interview in October, November, or
December 2021 (this is referred to as collection period Q4). Because both the consumption and expenditure series include the same recurring costs at the CU level, these are
excluded from chart 3. The depreciation and opportunity cost of consumption are calculated on the basis of the stock of vehicles reported as owned during the interview
reference period. Automobile purchase expenditures refer to those made in the same reference period (the 3 months prior to the interview month). Adjustments to reflect
nonbusiness use are only made in the calculations of consumption and expenditure. Unlike in our automobile consumption measure, here we include purchases of
automobiles given to someone outside the CU in the expenditures measure.
Chart 3. Consumption versus expenditure without recurring costs, fourth
quarter 2021
Quarterly consumption (dollars)
7,000

6,000

5,000
Consumption = 730 + 0.02 × Expenditure

4,000

3,000

2,000

1,000

0
0

25,000

50,000

75,000

100,000

125,000

150,000

Consumption expenditure (dollars)
Note: Consumption is the sum of depreciation and opportunity cost without recurring costs added
at the consumer unit level. Expenditure is the net purchase price after subtracting the trade-in
allowance of new and used automobiles reported in data from the Consumer Expenditure Surveys
at the consumer unit level.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Some CUs have purchase expenditures over $50,000, and no CU has estimated automobile consumption above $10,000 that excludes the recurring costs. The positive
correlation between expenditure and consumption is limited to the sum of depreciation and opportunity-cost components because automobiles depreciate the most in the
period in which they are purchased.
Approximately 4 percent of automobiles are either purchased as a gift to someone outside the CU (0.7 percent) or received as a gift (3.2 percent). When an automobile is
given to someone outside of the CU, the consumption value for the purchaser of the automobile is $0. However, expenditures are positive if a gift is purchased. Automobiles
purchased for someone outside the CU are excluded in the consumption series, but these purchases are included in the expenditure series that uses purchase information
reported in CE vehicle data.

Summary
In this article, we use a user-cost approach to compare estimates of the consumption of owned automobiles at the national level and the CU level, but each comparison uses
different depreciation assumptions. Over time, consumption in the average series is less volatile than in the expenditure series, although the levels of consumption and
expenditure are similar. But there are a few exceptions between the series. For example, at the national level, average expenditure was relatively lower than consumption

during the recessionary period just before and after 2008 and relatively higher during the COVID-19 pandemic period. At the CU level, consumption was positive in each
period for all households that owned automobiles, although in each period there were CUs with no expenditures.
Currently, economists at BLS are using automobile consumption values to construct a comprehensive consumption measure. Although additional research is needed, a flowof-services estimate of automotive consumption could replace the acquisition approach for the pricing of vehicles in the CPI.

Appendix
In this appendix, we discuss data sources and the steps to calculate the consumption value of automobiles.
Data sources

We use CE data from Q2 1996 to Q1 2022 to estimate consumption values from 1996 to 2021. Unlike other consumer products, automobiles are not expected to be
consumed immediately. The stock of automobiles is measured by asking a CU if they have any vehicle during the interview time period. Because there is no clear
determination of the month in the last 3 months the CU owned the vehicle, we use CE data in the following quarter to measure the consumption value of the stock of
automobiles for any reference quarter. For example, consumption values for the first quarter of 1996 are calculated from Q2 1996, for the second quarter from Q3 1996, for
the third quarter from Q4 1996, and for the last quarter from Q1 1997.
The quarterly estimated-average consumption series for CUs is again seasonally adjusted by taking a rolling average of measures from the two previous quarters, the current
quarter, and the following quarter. The rolling-average equation is as follows:

Consumptiont = (Consumptiont–2 + Consumptiont–1 + Consumptiont + Consumptiont+1)/4 ,
where t denotes the reference quarter of a year. Seasonal adjustments are made to smooth out large fluctuations across quarters.
It would have been ideal to use PUMD so outside researchers could reproduce our results, but not all relevant characteristics of an automobile, such as vehicle make, model,
and fuel type, are available in PUMD. Instead, we use CE data that are internal to BLS. We assume that each quarterly survey is independent and that CUs are unique across
surveys. We count a total of 1,281,071 motor vehicles from 689,811 CUs that are surveyed in the data. In any period, a CU owns an average of 1.86 vehicles, which includes
1.58 cars, SUVs, or trucks. We only include cars, SUVs, and trucks in our current calculation, and we exclude other types of vehicles, such as aircraft, boats, RVs, trailers,
and kayaks, because not enough information about these other types of vehicles is available for depreciation-rate estimation. Many other types of vehicles, such as aircraft
and boats, have no purchase prices reported, and it is difficult to impute a current market value from limited information. For example, a CU reported that it has a boat
without other specific details. A boat can have a market value ranging from $1,000 to over $1 million. Although we can apply some fixed depreciation rate, such as 5 or 10
percent, imputing a current market price is challenging in the first place.
The following tables provide more detail on the exact number of vehicles and CUs by type. We also list all variables used in this article, along with a brief explanation of
each. Table A-1 shows the total number of vehicles surveyed in the CE data; we assume that quarterly data are independent of one another. About 85 percent of all types of
vehicles are automobiles (cars account for 47 percent, and SUVs and trucks account for 38 percent).
Table A-1. Surveyed automobiles in the Consumer Expenditure Surveys, assuming quarterly reports are independent, second quarter 1996 to first quarter 2022
Vehicle category

Total number of vehicles in the

Percent share

Consumer Expenditure Surveys

All vehicles

1,281,071

100

Cars and trucks

1,087,223

85

Cars

605,906

47

Trucks

481,317

38

Source: U.S. Bureau of Labor Statistics.

Table A-2 presents vehicle ownership at the CU level. About 86 percent of CUs have any type of vehicle (including boats, aircraft, and RVs), and 83 percent of all CUs have
automobiles. Thus, 3 percent of CUs have no cars or trucks but only other types of vehicles. CUs without any vehicles account for 14 percent of the unweighted pooled
sample.
Table A-2. Surveyed consumer units in the Consumer Expenditure Surveys, assuming quarterly reports are independent, second quarter 1996 to first quarter
2022
Consumer unit (CU) category

Total number in the survey data

Percent share

All CUs

689,811

100

Total CUs with any vehicle

593,339

86

CUs with automobiles

575,641

83

CUs with cars

431,670

63

CUs with trucks

339,973

49

CUs with other vehicles but no automobiles

17,698

3

CUs with no vehicle at all

96,472

14

Source: U.S. Bureau of Labor Statistics.

Table A-3 lists all the variables we use in our computation of consumption, expenditure, and recurring costs. Some variables are directly extracted from the CE data, and
others are intentionally constructed during the estimation process.

Table A-3. Variables included in the internal data file from the Consumer Expenditure Surveys and used in calculations
Source

Category

Variable

Description

name

Comment

Variables from CE
CE interview data

CU characteristics

FAMID

CU identifier up to Q1 2015

[1]

CE interview data

CU characteristics

NEWID

CU identifier since Q2 2015

[1]

CE interview data

CU characteristics

SEQNO

Sequence number for vehicles owned by a CU

[1]

CE interview data

CU characteristics

QINTRVYR

Interview year

[1]

CE interview data

CU characteristics

QINTRVMO

Interview month

[1]

CE interview data

CU characteristics

FINLWT21

Calibration final weight for CU

[1]

CE interview data

Vehicle characteristics

VEHBSNZ

Percentage of the vehicle used for business

To be used for nonbusiness-use adjustment

CE interview data

Vehicle characteristics

VEHICYB

Vehicle type: "100" for cars and "110" for trucks

[1]

CE interview data

Vehicle characteristics

VEHICYR

Vehicle model year

[1]

CE interview data

Vehicle characteristics

VEHPURYR Vehicle purchase year

[1]

CE interview data

Vehicle characteristics

VEHPURMO Vehicle purchase month

[1]

CE interview data

Vehicle characteristics

NETPURX

CE interview data

Vehicle characteristics

QTRADEX

CE interview data

Vehicle characteristics

CE interview data

Net purchase price after discount, trade-in, or rebate including

[1]

destination fee
Amount paid for vehicle after trade-in allowance (NETPURX)

To be used to construct acquisition series based on

minus amount of cost paid by employer

expenditure in comparison with the user-cost series

TRADEX

Amount of trade-in allowance

[1]

Vehicle characteristics

MKMDLY

Vehicle make and model code (four-digit number)

[1]

CE interview data

Vehicle characteristics

MKMODEL

Vehicle make and model

[1]

CE interview data

Vehicle characteristics

MKMDESC

Vehicle make and model description

[1]

CE interview data

Vehicle characteristics

VEHBSNS

Where vehicle is used for business

[1]

CE interview data

Vehicle characteristics

VEHBSNZ

Percentage of vehicle milage for business use

CE interview data

Vehicle characteristics

VEHGFTC

Vehicle gift code

CE interview data

Vehicle characteristics

VEHNEWU

Was it new or used when acquired?

CE interview data

Maintenance and repair

QVOPEQPX

Quarterly amount paid for vehicle service and parts less

To be used to adjust current market value of automobiles
for business use
Automobiles purchased as a gift for someone outside the
CU are excluded from consumption
[1]

[1]

reimbursements
Quarterly amount spent on the following: motor coolant, antifreeze,

CE interview data

Maintenance and repair

VOPFLUDX

brake fluid, transmission fluid, gasoline additives, oil additives,

[1]

radiator cooling system protectors (excluding tuneup), excluding
current month

CE interview data

Maintenance and repair

VOPPLCYX

CE interview data

Insurance

QADINS3X

Property tax, licensing,
CE interview data

registration, and inspection VOPREGX
of vehicles

Quarterly expense for auto service policies, excluding current

[1]

month
Quarterly amount paid for vehicle insurance (adjusted for

[1]

business), reference period
Quarterly expenditure on vehicle licensing, registration, and

[1]

inspection

Variables from other sources
CPI for all urban
consumers (CPI-U)

Price level of all items

INDEX

The national level price index for all urban consumers
Market yield on U.S. Treasury securities at 10-year constant

Federal Reserve
Bank of St. Louis

Opportunity cost

INTEREST

maturity, inflation-indexed, percent, quarterly, not seasonally
adjusted

To be used for converting nominal values in real terms
and vice versa
To be used to calculate opportunity cost of owning
vehicles

Constructed variables
Constructed as
intermediate

Vehicle characteristics

VEHAGE

Vehicle's age when surveyed

= QINTRVYR – VEHICYR; replaced by 25 if over 25 years

Vehicle characteristics

PURAGE

Vehicle's age when purchased

Vehicle characteristics

VINTAGE

Years owned since purchased

intermediate
variable

Vehicle characteristics

PRICE

Vehicle's purchase price

= NETPURX + TRADEX

Constructed as
intermediate

Vehicle characteristics

RPRICE

Vehicle's purchase price in 1982–84 dollars

[1]

variable
Constructed as
intermediate
variable
Constructed as
intermediate
variable

= VEHPURYR – VEHICYR; replaced by 25 if over 25
years
= QINTRVYR – VEHPURYR; replaced by 25 if over 25
years

Constructed as

[1] No comment. Note: CE = Consumer Expenditure Surveys; CU = consumer unit.
Source: U.S. Bureau of Labor Statistics.

Source

Category

Variable

Description

name

Comment

variable
Constructed as
intermediate

Vehicle characteristics

MAKE

Vehicle make

Extracted from MKMODEL/MKMDESC

Vehicle characteristics

MODEL

Vehicle model without body type

Extracted from MKMODEL/MKMDESC

Vehicle characteristics

YEAR

Vehicle model year

= VEHICYR

variable
Constructed as
intermediate
variable
Constructed as
intermediate
variable

0: not imputed; 1: Imputed using purchase price; 2:

Constructed as
intermediate
variable

Identification

IMPUTE

Current market value imputation identifier

imputed using same characteristics of automobile; 3:
imputed using similar characteristics of automobile

Constructed as

Identification

VEH

Vehicle identifier

0: no vehicle; 1: has a vehicle

Identification

AUTO

Automobile identifier

0: other vehicle; 1: automobile

Vehicle characteristics

ACQ

Acquisition identifier

Depreciation

DEP_RATE

Annual depreciation rate of automobile at specific ages

Depreciation

RMV

Current market value of automobile in 1982–84 dollars

= RPRICE × (1 – DEP_RATE)VINTAGE

Depreciation

SURRATE

Survival rate at a specific age

[1]

Depreciation

CMV

Current market value of automobile

= RMV × INDEX/100

Depreciation

ADJ_CMV

intermediate
variable

Recurring cost

MREXP

Quarterly expense on vehicle service and parts for owning vehicle QVOPEQPX adjusted for owning purpose

Constructed as
final variable

CU identifier in the pooled

COMID

CU identifier in the pool data

Either FAMID or NEWID

intermediate
variable
Constructed as
intermediate
variable
Constructed as
intermediate
variable
Constructed as
intermediate
variable

0: not acquired during the last 3 months; 1: acquired
during the last 3 months
Estimated from regressions of automobile purchase prices
on automobile ages

Constructed as
intermediate
variable
Constructed as
intermediate
variable
Constructed as
intermediate
variable
Constructed as
intermediate
variable

Current market value of automobile adjusted for business-use,

[1]

survival rate and gift code

Constructed as

data

Constructed as
final variable

CU characteristics

CUWT

Quarterly CU final weight

= FINLWT21/4

Constructed as
final variable

Reference period

QTR

Calendar quarter

= interview quarter – 1

Constructed as
final variable

Reference period

CYEAR

Calendar year

= QINTRVYR if QTR > 1; = (QINTRVYR – 1) otherwise

Constructed as
final variable

Identification

N_VEH

Number of all types of vehicles owned by CU

[1]

Constructed as
final variable

Identification

N_AUTO

Number of automobiles owned by CU

= N_VEH – number of other vehicles

Constructed as
final variable

Expenditure

EXPQ

Quarterly expenditure on automobile purchase

= QTRADEX if purchased in the last 3 months

Constructed as
final variable

Expenditure

EXPY

Annual expenditure on automobile purchase

= 4 × EXPQ

Constructed as
final variable

Depreciation

DEP

Quarterly depreciation value of all cars and trucks owned by CU

= adj_CMV × DEP_RATE/4

Constructed as
final variable

Opportunity cost

INT

Constructed as
final variable

Depreciation

YDEP

Opportunity cost

YINT

Maintenance & repair

MR

Constructed as
final variable
Constructed as
final variable

Quarterly opportunity cost (forgone interest revenue in flow of
service value) of all cars and trucks
Annual depreciation value of all cars and trucks owned by CU
Annual opportunity cost (forgone interest revenue in flow of service
value) of all cars

= adj_CMV × interest/4

= adj_CMV × DEP_RATE

= adj_CMV × interest

Quarterly maintenance and repair expense

= MREXP + VOPFLUDX + VOPPLCYX

Quarterly operating expense

= VOPREGX

Property tax, licensing,
Constructed as
final variable

registration, and inspection REG
of automobiles

Constructed as
final variable
Constructed as
final variable
Constructed as

Insurance

INS

Quarterly automobile insurance payments

= QADINS3X

Recurring cost

RC

Quarterly combined recurring costs of owning automobile

= MR + REG + INS

Maintenance & repair

YMR

Annual maintenance and repair expense

= 4 × (QVOPEQPX + VOPFLUDX + VOPPLCYX)

[1] No comment. Note: CE = Consumer Expenditure Surveys; CU = consumer unit.
Source: U.S. Bureau of Labor Statistics.

Source

Variable

Category

Description

name

Comment

final variable

Property tax, licensing,
Constructed as
final variable

registration, and inspection YREG

Annual operating expense

= 4 × VOPREGX

of automobiles
Constructed as
final variable

Insurance

YINS

Annual automobile insurance payments

= 4 × QADINS3X

Constructed as
final variable

Recurring cost

YRC

Annual combined recurring costs of owning automobile

= 4 × (MR + REG + INS)

[1] No comment. Note: CE = Consumer Expenditure Surveys; CU = consumer unit.
Source: U.S. Bureau of Labor Statistics.

Calculation steps for the consumption value of automobiles

The constant-geometric-depreciation rate (δa) is estimated by comparing the purchase prices of the same model automobiles at different ages in the pooled sample data. The
regression model for constant geometric depreciation is as follows:
,
where age denotes an automobile’s purchase age. The variable age can take a value from 0 to 25 years. We set a limit for automobile age at 25 years because in many states
(including the District of Columbia, Virginia, and Maryland) automobiles over 25 years of age are considered antique or vintage, and these older automobiles may not have a
specific pattern of depreciation. The coefficient βa indicates how much an automobile’s value depreciates each year on average as a constant share of its current value. To be
precise, we calculate δa as 1 – exp(βa).
Similarly, the nonparametric-depreciation rate (δv) is estimated by comparing an automobile’s purchase price with the purchase price of the same model automobile of age v
and age v + 1. We assume that an automobile follows age-specific depreciation up to 10 years. After 10 years of age, we assume the automobile follows constant geometric
depreciation because of the limited sample size of old automobiles. We estimate δv by using age-specific depreciation rates for automobiles 0 to 10 years of age. This is done
with a subsample of automobiles of age v and age v + 1, in which v takes a value between 0 and 10 years. A total of 11 regressions are run separately on the 11 age-group
subsamples. In our estimations, we hold δa as constant no matter the age of the automobile (v), and we vary δv depending on the age of the automobile (v). The variable v
can take a value between 0 and 10 years.
We then calculate the current market values of automobiles after depreciation by separately using the estimated δa and δv. We calculate the current market value (CMVt) of
an automobile at time t with the constant-geometric-depreciation method as follows:
,
where x denotes the current age of the automobile, and y denotes the age of the automobile when it was purchased.
On the other hand, we calculate the CMVt of the same automobile with the nonparametric-depreciation method as follows:
,
where δv denotes the marginal depreciation rate of an automobile of age v.
Because not all automobiles, old automobiles in particular, are expected to survive to generate a flow of services until the next reference period, CMVt is age adjusted for
survival probability (SURRATE). We aggregate the total number of automobiles of age v and age v + 1 by using pooled CE data, and then we divide the two numbers to
calculate the survival rate for an automobile of age v. The calculated survival rates are shown in chart A-1.
Chart A-1. Survival rate of automobiles
Survival rate (percent)
1.10

1.00

0.90

0.80

0.70
0

5

10

15

20

25

Age (years)
Hover over chart to view data.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Our goal is to estimate the consumption value of owning an automobile for nonbusiness use. Therefore, we adjust the estimated current market value of automobiles for
business use by using the percentage of the automobiles used for business use as reported in the CE (VEHBSNZ). The formula for survival and business-use adjustment is as
follows:

Adj_ CMVt = (SURRATE × CMVt + (1 – SURRATE) × (CMVt /2)) × (1 – VEHBSNZ) .
In the equation above, we assume that even if an automobile did not survive until the end of the next period, then the automobile is on average expected to survive halfway
through the reference period.
Not all CUs report a purchase price for each owned automobile in the survey. We use the average current market value of a similar automobile in the imputation of the
current market value and depreciation of an automobile without relevant information, such as purchase price, automobile purchase age, and model year. When the parties of
a vehicle purchase transaction are known or related to each other, such as friends or family members, it is possible that a reported purchase price might not accurately reflect
the fair market value. Several sources suggest that there is a minimum price that a consumer has to pay a dealer for a used automobile in working condition.17 If the
purchase price of an automobile is less than $2,000 in 1982–84 dollars, then we assume the transaction was between related parties and use an imputed price based on
similar characteristics. The imputation results are shown in table 1.
Next, we impute depreciation (DEPt) and opportunity cost (INTt) (forgone investment revenue) of an automobile of age a at time t as follows:
,
and
,
where rt denotes the real interest rate at time t. We approximate the real interest rate by using the inflation-adjusted market yield on U.S. Treasury securities at 10-year
constant maturity.
Finally, we add together the other recurring costs (RC) associated with operating an automobile to calculate the full user cost of owning an automobile, such as maintenance
and repairs (MR); automobile insurance (INS); and registration, property tax, licensing, and inspection (REG). Thereby, the equation for the consumption value of a CU (u)
who owns k automobiles at time t (Consu,t) is as follows:

and

where RCj,t denotes the combined recurring costs, MR denotes maintenance and repair cost, INS denotes automobile insurance cost, and REG denotes other registration and
licensing expenses for automobile j at time t.

References
Bureau of Economic Analysis. “Personal consumption expenditures.” In NIPA Handbook: Concepts and Methods of the U.S. National Income and Product Accounts, 2022.
https://www.bea.gov/resources/methodologies/nipa-handbook/pdf/chapter-05.pdf.
Garner, Thesia I., George Janini, Willam Passero, Laura Paszkiewicz, and Mark Vendemia. “The CE and the PCE: a comparison.” Monthly Labor Review (September 2006),
pp. 20–46. https://www.bls.gov/cex/research_papers/pdf/garner-the-ce-and-the-pce-a-comparison-2006.pdf.
Garner, Thesia I. and Randal Verbrugge. “Reconciling user costs and rental equivalence: evidence from the U.S. Consumer Expenditure Survey.” Journal of Housing
Economics, vol. 18, no. 3 (September 2009), pp. 172–192. https://doi.org/10.1016/j.jhe.2009.07.001.
Diewert, W. Erwin, John Greenlees, and Charles Hulten. “Introduction: what are the issues?” In Price Index Concepts and Measurement. Studies in Income and Wealth, vol.
70. Edited by W. Erwin Diewert, John Greenlees, and Charles Hulten, pp. 1–16. Chicago, IL: University of Chicago Press and National Bureau of Economic Research,
2009. https://www.nber.org/books-and-chapters/price-index-concepts-and-measurement/introduction-what-are-issues.
Fisher, Jonathan D., David S Johnson, and Timothy M. Smeeding. “Measuring the trends in inequality of individuals and families: income and consumption.” American
Economic Review, vol. 103, no. 3 (May 2013), pp. 184–188. http://dx.doi.org/10.1257/aer.103.3.184.
Hamilton, James. “Dates of U.S. recessions as inferred by GDP-based recession indicator [JHDUSRGDPBR].” Federal Reserve Bank of St. Louis, December 7, 2023.
https://fred.stlouisfed.org/series/JHDUSRGDPBR.
International Monetary Fund, International Labour Organization, Statistical Office of the European Union (Eurostat), United Nations Economic Commission for Europe,
Organisation for Economic Co-operation and Development, and The World Bank. Consumer Price Index Manual: Concepts and Methods. Washington, DC: International
Monetary Fund, 2020. https://www.imf.org/~/media/Files/Data/CPI/cpi-manual-concepts-and-methods.ashx.
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SUGGESTED CITATION:

Caleb Cho, Brett Matsumoto, and Dominic Smith, "A consumption measure for automobiles," Monthly Labor Review, U.S. Bureau of Labor Statistics, January 2024,

https://doi.org/10.21916/mlr.2024.3

Notes

1 For a recent reference on the estimation of such depreciation methods in the production of consumption measures, see Giulia Mancini and Giovanni Vecchi, On the Construction of a
Consumption Aggregate for Inequality and Poverty Analysis (Washington, DC: World Bank Group,

2022), https://documents1.worldbank.org/curated/en/099225003092220001/pdf/P1694340e80f9a00a09b20042de5a9cd47e.pdf.

2 The U.S. Bureau of Labor Statistics defines a consumer unit as follows: “A consumer unit is defined as either (1) all members of a particular household who are related by blood, marriage,
adoption, or other legal arrangements; (2) a person living alone or sharing a household with others or living as a roomer in a private home or lodging house or in permanent living quarters in a
hotel or motel, but who is financially independent; or (3) two or more persons living together who pool their income to make joint expenditure decisions. Financial independence is determined
by the three major expense categories: housing, food, and other living expenses. To be considered financially independent, a respondent must provide at least two of the three major expense
categories.” See Glossary (U.S. Bureau of Labor Statistics), https://www.bls.gov/bls/glossary.htm.

3 Our estimates for automobile consumption are used in the construction of a comprehensive consumption measure. See Thesia I. Garner, Brett Matsumoto, Jake Schild, Scott Curtin, and
Adam Safir, “Developing a consumption measure, with examples of use for poverty and inequality analysis: a new research product from BLS,” Monthly Labor Review, April 2023,

https://doi.org/10.21916/mlr.2023.8.
4 “Report II: household income and expenditure statistics,” Seventeenth International Conference of Labour Statisticians (Geneva: International Labour Organization, 2003).

https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/meetingdocument/wcms_087588.pdf; and
OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth (Paris: Organization for Economic Co-operation and Development,
2013), https://doi.org/10.1787/9789264194830-en.

5 For more information, see David M. Cutler and Lawrence F. Katz, “Macroeconomic performance and the disadvantaged,” Brookings Papers on Economic Activity, vol. 1991, no. 2, 1991, pp.
1–74, https://doi.org/10.2307/2534589; Daniel T. Slesnick, “The standard of living in the United States,” Review of Income and Wealth, vol. 37, no. 4, 1991, pp. 363–386,

https://doi.org/10.1111/j.1475-4991.1991.tb00379.x;
Bruce Meyer and James Sullivan, “The effects of welfare and tax reform: the material well-being of single mothers in the 1980s and 1990s,” Working Paper 8298 (Cambridge, MA: National
Bureau of Economic Research, 2001), https://www.nber.org/papers/w8298; and Jonathan Fisher, David S. Johnson, and Timothy M. Smeeding, “Inequality of income and consumption in
the U.S.: measuring the trends in inequality from 1984 to 2011 for the same individuals,” Review of Income and Wealth, vol. 61, no. 4, 2015, pp. 630–650,

https://doi.org/10.1111/roiw.12129.
6 Bruce Meyer and James Sullivan, “Winning the war: poverty from the Great Society to the Great Recession,” Brookings Papers on Economic Activity, vol. 45, no. 2 (The Brookings Institution,
2012), pp. 133–200; and Jonathan D. Fisher and David S. Johnson, “Consumption mobility in the United States: evidence from two panel data sets,” The B.E. Journal of Economic Analysis &
Policy, vol. 6, no.1, 2006, pp. 1–38.

7

At What Price?: Conceptualizing and Measuring Cost-of-Living and Price Indexes (Washington, DC: The National Academies Press, 2002), p. 72, https://doi.org/10.17226/10131.

8 Chris Hardesty, “How to beat car depreciation,” Kelley Blue Book, September 11, 2023, https://www.kbb.com/car-advice/how-to-beat-car-depreciation/; and “Depreciation infographic:
how fast does my new car lose value?,” Edmunds, September 24, 2010, https://www.edmunds.com/car-buying/how-fast-does-my-new-car-lose-value-infographic.html.

9

Meyer and Sullivan, “Winning the war: poverty from the Great Society to the Great Recession;” and Fisher and Johnson, “Consumption mobility in the United States: evidence from two panel

data sets.”

10 This lower bound shows cases in which vehicles are purchased for low prices because of related party transactions.
11 The stock of automobiles owned during a reference period may not necessarily be purchased during the previous 3 months but may also include those purchased prior to the reference
period.

12 Hardesty, “How to beat car depreciation;” and “Depreciation infographic: how fast does my new car lose value?,” Edmunds.
13 Fisher and Johnson, “Consumption mobility in the United States.”
14 Meyer and Sullivan, “The effects of welfare and tax reform;” and Meyer and Sullivan, "Winning the war: poverty from the Great Society to the Great Recession.”
15 For more information on National Automobile Dealers Association used-car guides, see “Consumer vehicle values” (National Automobile Dealers Association),

https://www.nada.org/nada/consumer-vehicle-values.
16 For more information on published average annual expenditures from the Consumer Expenditure Surveys, see “Table R-1. All consumer units: annual detailed expenditure means, standard
errors, coefficients of variation, and weekly (D) or quarterly (I) percents reporting,” Consumer Expenditure Surveys (U.S. Bureau of Labor Statistics, 2021),

https://www.bls.gov/cex/tables/calendar-year/mean/cu-all-detail-2021.pdf.
17 Phillip Reed, “Tips on how to find a cheap, reliable used car to buy,” USA Today, November 12, 2016,

https://www.usatoday.com/story/money/personalfinance/2016/11/12/nerdwallet-buyin-a-reliable-used-car/93667546/; and Cara Smith, “How to buy a cheap, drivable used car,”
Nerd Wallet, July 18, 2022, https://www.nerdwallet.com/article/loans/auto-loans/cheap-drivable-used-car.

ABOUT THE AUTHOR

Caleb Cho
Cho.Caleb.S@dol.gov
Caleb Cho is an economist in the Office of Federal Contract Compliance Programs, U.S. Department of Labor.
Brett Matsumoto
Matsumoto.Brett@bls.gov
Brett Matsumoto is a research economist in the Office of Prices and Living Conditions, U.S. Bureau of Labor Statistics.
Dominic Smith
Smith.Dominic@bls.gov
Dominic Smith is a research economist in the Office of Prices and Living Conditions, U.S. Bureau of Labor Statistics.

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Nonprofits: a look at national trends in establishment size and employment
This article explores trends and highlights of 501(c)(3) organizations at the national level. An examination of total private employment and industry-level employment reveals
that nonprofit organizations tend to employ more workers at an establishment than do their for-profit peers. These data also show that nonprofit employment grew at a steady
rate from 2007 to 2017, including during the 2007–09 recession.
Nonprofit organizations are an integral part of the U.S. economy. These organizations—from museums and social advocacy groups to healthcare providers and universities—
can be found across all segments of the economy. To gauge the impact of this sector, the U.S. Bureau of Labor Statistics (BLS) uses its business register, which relies on data
from the Quarterly Census of Employment and Wages (QCEW) program, to produce employment, wage, and establishment estimates for nonprofit organizations on a 5-year
cycle.1
Although the BLS Business Register contains considerable information about business establishments, it cannot be used to comprehensively identify nonprofit organizations.
Thus, to create its nonprofit tabulations, BLS used publicly available data from the Internal Revenue Service (IRS) to identify private-sector nonprofit establishments on the
BLS Business Register. Although the IRS lists 29 classifications of tax-exempt entities, BLS restricted its nonprofit tables to the 501(c)(3) category, which identifies charitable
and religious organizations.2
For the purposes of this article, the phrase “501(c)(3) establishments” is used interchangeably with the phrase “nonprofit organizations.” Establishments not identified by this
approach are labeled as for-profit establishments. However, these for-profit establishments include a small number of tax-exempt organizations that are classified as 501(c)
organizations but not as 501(c)(3)s. Tax-exempt businesses that are not 501(c)(3)s include some types of co-ops, civic leagues and social welfare organizations, and domestic
fraternal societies.3 Annual estimates are available as a spreadsheet for 2007 to 2017 and can be downloaded from the BLS website.4 Total private and industry data are
available at the national, state, metropolitan statistical area, and county levels. Nonprofit statistics for 2018 to 2022 are scheduled to be released in 2024.
This article focuses on employment and establishment tabulations at the national level. This examination of nonprofit data begins by reviewing total private estimates. In
2017, the most recent year of published estimates, 501(c)(3) organizations made up 10 percent of employment and 3 percent of business establishments. An implication of
these two statistics is that a nonprofit establishment employs more workers, on average, than does a for-profit establishment.5
Both nonprofit employment and the number of nonprofit establishments increased steadily for all 11 years of data. These steady increases occurred despite the 2007–09
recession.6 By contrast, for-profit companies saw declines in employment and the number of business establishments during this recession.
To understand why this is the case, this article delves beyond the total private level and examines the industry composition of 501(c)(3) organizations. Although nonprofit
organizations can be found across all segments of the economy, they tend to be concentrated in four industry sectors: healthcare and social assistance; educational services;
other services; and arts, entertainment, and recreation.
After reviewing the industry composition of 501(c)(3) organizations, this article examines the distribution of employment between nonprofits and their for-profit counterparts
within each industry. Perhaps not surprisingly, the industries in which nonprofits are most prevalent also possess the highest ratios of nonprofit to for-profit employment. In
some industries, for example, the proportion of nonprofit jobs relative to for-profit jobs can be as high as 10 to 1.
These industry features provide insight into why nonprofits tend to be large in terms of employment size. Additionally, these same industry features also explain why 501(c)
(3) organizations had a steady expansion in employment when for-profit businesses had a decline in employment during the 2007–09 recession.

Highlights at the total private level
Employment and establishment count tabulations for 501(c)(3) organizations are available from 2007 to 2017. These annual estimates depict important differences between
nonprofit organizations and their for-profit counterparts.
In 2017, 501(c)(3) organizations made up 10.2 percent of the nation’s private-sector employment (12,488,563 jobs). (See table 1.) In terms of the number of business
establishments, however, nonprofits made up only 3.1 percent of the private-sector figure.

Table 1. Annual total employment and average number and size of establishments for nonprofits and for-profits, 2007 and 2017
Nonprofit establishments

For-profit establishments

Measure
Employment
Average number of establishments
Average establishment size

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

2007

2017

Percent change

2007

2017

10,534,183

12,488,563

232,396
47

Percent change

18.6

103,478,038

109,898,002

6.2

299,457

28.9

8,448,605

9,237,374

9.3

42

[1]

12

12

[1]

Annual changes in employment showed lower variability for nonprofit organizations compared with that of the for-profit segment of the economy. Nonprofit employment
grew at a steady pace every year for which there are published estimates, including during the 2007–09 recession. For nonprofits, the largest over-the-year change in total
private employment occurred in 2008, when employment increased by 2.9 percent. (See appendix table A-1.) The smallest increase was in 2010, when the total number of
nonprofit jobs rose by 1.0 percent. From 2007 to 2010 employment among nonprofits increased by 5.5 percent. Employment at nonprofit organizations was 18.6 percent
higher in 2017 than in 2007. (See table 1 and chart 1).
Chart 1. Total private employment for nonprofit and for-profit establishments,
2007–17
Nonprofit

For-profit

For-profit employment

Nonprofit employment

125,000,000

12,500,000

120,000,000

12,000,000

115,000,000

11,500,000

110,000,000

11,000,000

105,000,000

10,500,000

100,000,000

10,000,000

95,000,000

9,500,000
2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

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

View Chart Data

In contrast to the stable employment growth shown by nonprofit organizations, employment growth in the for-profit portion of the economy proved to be more sensitive to
economic fluctuations. The effects of the 2007–09 recession can be seen in the employment figures. From 2007 to 2010, total private employment for for-profit businesses fell
8.1 percent, with the greatest annual drop occurring in 2009, when employment fell 6.3 percent. (See appendix table A-1.) As the economy expanded coming out of the
recession, from 2010 until 2017, employment in for-profit companies grew 15.6 percent. The largest annual increase occurred in 2015, when employment grew by 2.4 percent.
For the full 11-year timeframe (from 2007 to 2017), total private employment increased 6.2 percent. (See table 1 and chart 1).
Comparisons between nonprofits and for-profit firms in terms of the number of business establishments are similar to the comparisons involving employment. The number of
501(c)(3) establishments increased every year from 2007 to 2017, with no slowdown during the 2007–09 recession. From 2007 to 2010, the number of nonprofit
establishments grew by 9.4 percent. Coming out of the recession, from 2010 to 2017, the number of 501(c)(3) establishments increased by 17.8 percent. The most rapid annual
increase occurred in 2008, when this figure rose by 3.4 percent. (See appendix table A-2.) The slowest growth occurred in 2017, when the number of nonprofit establishments
increased by 1.8 percent. From 2007 to 2017, the number of establishments expanded by 28.9 percent. (See table 1 and appendix table A-2.)
The steady increase in the number of nonprofit establishments stands in contrast to the greater variability of for-profit companies. As with employment, the number of forprofit business establishments fell during the recession. From 2007 to 2010, the number of for-profit establishments fell by 0.1 percent. Following the recession, the number of
for-profit business establishments increased by 9.4 percent from 2010 to 2017. The largest annual increase took place in 2016, when the number of establishments increased
by 2.1 percent. (See table A-2.) From 2007 to 2017, the number of for-profit establishments expanded by 9.3 percent. (See table 1.) For every year of available data, the rate of
growth of nonprofit establishments exceeded that of for-profit establishments.
On average, nonprofit establishments tend to have more workers than their for-profit counterparts do. In 2017, nonprofit organizations had, on average, 42 employees per
worksite. In contrast, the average number of employees at a for-profit establishment in 2017 was 12, a more than threefold difference in establishment size. (See table 1.)
Understanding why nonprofit establishments have more jobs, on average, than do for-profit establishments requires looking beyond estimates at the total private level and
taking into consideration an organization’s economic activity. The next section explores the industry profile of 501(c)(3) organizations and provides insight into this
employment size disparity. This industry examination also provides an explanation as to why nonprofits experienced steady employment growth.

Industry nonprofit employment levels
Nonprofit businesses can be classified into a variety of economic activities. One of the many strengths of the nonprofit data is that users can group them by industry. The
remainder of this article focuses on the characteristics of 501(c)(3) organizations and for-profit businesses by industry.
Among the 20 industry sectors specified by the North American Industry Classification System (NAICS), BLS did not produce nonprofit estimates for three of these sectors.
One of the 20 sectors included in NAICS, public administration, is outside the scope of this project.7 Additionally, BLS did not publish data for utilities or for mining,
quarrying, and oil and gas extraction because the number of 503(c)(3) establishments in these two sectors was too low. BLS is required by law to safeguard the confidentiality
of its respondents and publishing estimates related to these industries could lead to the release of employer identifiable information.8
Although 501(c)(3) establishments can be found across all segments of the economy, four industry sectors account for about 92 percent of nonprofit jobs. In 2017, the
healthcare and social assistance industry (8,306,650 jobs) represented 67 percent of all nonprofit employment. (See chart 2 and appendix table A-3.) The next two largest
industries were educational services (2,003,634 jobs) and other services (837,662 jobs), with employment shares of 16 percent and 7 percent, respectively. After these three
sectors, the next largest sector, the arts, entertainment, and recreation industry (355,965 jobs) accounted for 3 percent of nonprofit employment. (See chart 2).

Chart 2. Industry distribution of nonprofit employment, 2017

Healthcare and social assistance

Educational services

Other services (except public administration)

Arts, entertainment, and recreation

All remaining industries

0

25

50

75

100

Percent
Hover over chart to view data.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

For all 11 years of published data, nonprofit employment is concentrated in the same four industry sectors. Additionally, the employment share for each of these four sectors
shows little change during this period. For example, the healthcare and social assistance’s share of total nonprofit employment ranged between a low of 67 percent to a high of
68 percent, while educational services varied from 15 percent to 16 percent. The other two major industry sectors showed similar degrees of stability.
Examining the components of these four major industry sectors provides a more detailed picture of this concentration in nonprofit employment. Subsector data, which are at
the NAICS three-digit level, are available from 2007 to 2017. With grant funding from the Charles Stewart Mott Foundation and with support from the Center for Civil
Society Studies at Johns Hopkins University, BLS published data by industry group, which are at the NAICS four-digit level.9 Industry group tabulations are available for
2013 to 2017.
The healthcare and social assistance sector, the largest nonprofit sector, is made up of four major components. In 2017, the hospital subsector (4,206,764 jobs) accounted for
51 percent of the employment in the healthcare and social assistance sector and for 34 percent of overall nonprofit employment.10 (See table 2 and appendix table A-3.) Within
hospitals, most employment was concentrated in the general medical and surgical hospitals industry group (4,031,773 jobs), which includes children’s hospitals, osteopathic
hospitals, and general pediatric facilities. The two remaining industry groups are specialty hospitals (129,038 jobs) and psychiatric and substance abuse hospitals (45,943
jobs).

Table 2. Employment and employment shares for nonprofit and for-profit establishments, select industries, 2017 annual estimates
Industry title

Nonprofit

For-profit

Percent nonprofit

12,488,563

109,898,002

10

8,306,650

11,015,841

43

Hospitals

4,206,754

811,728

84

Social assistance

1,528,920

2,190,857

41

Ambulatory healthcare services

1,402,007

5,856,868

19

Nursing and residential care facilities

1,168,968

2,156,390

35

2,003,634

820,787

71

1,137,681

104,447

92

684,170

127,242

84

837,662

3,597,016

19

818,562

550,983

60

355,965

1,937,907

16

Museums, historical sites, and similar institutions

141,289

21,972

87

Amusement, gambling, and recreation industries

110,595

1,536,818

7

Performing arts, spectator sports, and related industries

104,081

379,118

22

Total private
Healthcare and social assistance

Educational services
Colleges, universities, and professional schools
Elementary and secondary schools
Other services (except public administration)
Religious, grantmaking, civic, professional, and similar organizations
Arts, entertainment, and recreation

Source: U.S. Bureau of Labor Statistics.

The social assistance industry (1,528,920 million jobs) is the next largest subsector of healthcare and social assistance in terms of nonprofit employment. (See table 2.) This
diverse industry consists of organizations that focus on local and community involvement. Sixty-six percent of the employment in social assistance was in individual and
family services (829,592 jobs). (See appendix table A-3.) Establishments in this industry include drug and alcohol addiction self-help organizations as well as adoption
agencies and disability support groups. The remaining 44 percent of social assistance employment is distributed among vocational rehabilitation services (288,693 jobs), child
day care services (256,140 jobs), and community food and housing and emergency and other relief services (154,495 jobs).
The second-largest sector in terms of nonprofit employment is educational services. Within this industry, nonprofit jobs were concentrated in two subsectors in 2017—
colleges, universities, and professional schools (1,137,681 million jobs) and elementary and secondary schools (684,170 jobs). (See table 2.) Taken together, these two
industries were 91 percent of all nonprofit employment in the educational services sector. (See appendix table A-3.) The remaining 9 percent of employment in educational
services was distributed across the remaining five industry subsectors—other schools and instruction (82,918 jobs), educational support services (60,153), technical and trade
schools (15,493 jobs), business schools and computer management (12,642 jobs), and junior colleges (10,578 jobs).
The other services sector is the third-largest industry in terms of nonprofit employment. In this sector, 98 percent of nonprofit employment is concentrated in the religious,
grantmaking, civic, professional, and similar organizations (818,562 jobs) subsector. (See appendix table A-3.) This subsector comprises organizations that promote religious
activities, support and advance a variety of causes through grantmaking, and advocate for social and political goals. Some of the other industries found in the other services

sector, such as automotive repair and maintenance, deathcare services, and dry cleaning and laundry services, did not have sufficient nonprofit employment to allow
publication.
The smallest of the four sectors included in chart 2 is arts, entertainment, and recreation. Within this sector, two industry groups account for 70 percent of all nonprofit
employment—museums, historical sites, and similar institutions (141,289 jobs) and other amusement and recreation industries (107,724 jobs). (See table 2.) The other
amusement and recreation industries include a diverse range of business ranging from bowling centers to golf clubs and from archery ranges to sailing clubs with marinas.
Within the arts, entertainment, and recreation sector, 501(c)(3) organizations can be found among what initially might seem to be unlikely industries. For example, the
gambling industries category, which includes casinos and bingo halls, accounted for 1,991 nonprofit jobs in 2017.11 (See appendix table A-3.)
Outside of these four sectors, all remaining industries account for only 8 percent of nonprofit employment. In particular, goods-producing industries, a large segment of total
employment in the economy, have relatively few nonprofits. This collection of industries includes construction, manufacturing, and agriculture. Although the goods-producing
sector amounts to less than 0.2 percent of all nonprofit employment in 2017, 501(c)(3) establishments can be found throughout a wide variety of subindustries within these
sectors. Some of these organizations may be engaged in the construction of low-income housing, the manufacture of Christmas ornaments, or the operation of livestock shows
and contests.

Employment distribution within industries
Not only do table 2 and appendix table A-3 show the industries with high levels of nonprofit employment, but they also detail the proportion of employment in nonprofit
organizations by industry.
In 2017, 71 percent of educational services jobs were in nonprofits. (See table 2.) Within colleges, universities, and professional schools, 92 percent of jobs were in 501(c)(3)
organizations, the highest share within the sector. In elementary and secondary schools, 84 percent of employment was in nonprofit organizations. The remaining five
industries within educational services all had nonprofit employment shares less than 50 percent. (See appendix table 3.) The lowest share was in technical and trade schools, in
which nonprofits accounted for 14 percent of employment.
In the healthcare and social assistance sector, 43 percent of jobs were in nonprofit organizations. However, this sector-wide figure hides large variability in 501(c)(3)
involvement in the subindustries that make up the sector. (See table 2.) For example, 91 percent of jobs in community food and housing and emergency and other relief
services were in nonprofit organizations. (See appendix table 3.) By contrast, none of the components of ambulatory healthcare services had a 501(c)(3) share of employment
greater than 50 percent. Companies in this subsector typically provide outpatient care across a broad range of health-related services. Offices of dentists showed the lowest
proportion of nonprofit employment, not just within this subsector but also in the entire healthcare and social assistance sector. The 9,825 jobs at 501(c)(3) dental offices
represented 1 percent of the overall employment in that industry.
Notably, general medical and surgical hospitals, which accounted for nearly half of all 501(c)(3) jobs in the healthcare and social assistance sector, had a large share of
nonprofit employment. Just over 86 percent of employment (4,031,773 jobs) was in nonprofit establishments, with the remaining share at for-profit establishments (638,476
jobs).
Within the other services sector, there was a high share of nonprofit employment in religious, grantmaking, civic, professional, and similar organizations. Employment within
the five industry groups that compose this subsector were distributed mostly toward nonprofit organizations. The industry with the largest share of nonprofit employment was
grantmaking and giving services, in which 92 percent of employment was in 501(c)(3) establishments. (See appendix table A-3.) Included in this industry are charitable trusts,
community foundations and health research funding organizations. Some of these organizations may fund a single institution—such as a university, museum, or hospital—or
they may opt to support smaller entities or a range of entities. Other industry groups within this subsector with large nonprofit employment shares were social advocacy
organizations (88 percent), religious organizations (85 percent), and civic and social organizations (85 percent).
The only industry group in the religious, grantmaking, civic, professional, and similar organizations subsector that did not have most of its employment in nonprofits was
business, professional, labor, political, and similar organizations. In this industry group, 14 percent of employment was in 501(c)(3) organizations. (See appendix table A-3.)
This group includes establishments involved in promoting the business interests of their members, such as real estate boards, trade associations, and farmers’ unions.
Additionally, sectors with relatively low levels of nonprofit employment also display low shares of nonprofit employment. Outside of the four largest sectors for 501(c)(3)
employment, the sector with the highest share of nonprofit employment is management of companies and enterprises. In this sector, nonprofits, which oversee and administer
other establishments within their company, accounted for 12 percent of all employment (281,078 jobs) in 2017. (See appendix table A-3.)
Nonprofit employment among goods-producing industries was particularly low. There were no sectors or subsectors with a nonprofit share of employment that exceeded 1
percent. (See appendix table A-3.) Among these industries, the highest share of 501(c)(3) employment was in construction of buildings, an industry in which 0.4 percent of
jobs were in nonprofit companies.

Number of establishments and employment size
One of the questions posed at the beginning of this article is why nonprofit organizations tend to have more jobs per establishment on average compared with for-profit
companies.12 An examination of the number of business establishments within each industry, combined with the previous discussion on industry employment and the
nonprofit share of this employment, provides some insights.
Table 3 specifies the distribution of establishments among both nonprofits and for-profit companies for the major industries discussed in this article. In addition to these
establishment figures, the table includes the average number of jobs per establishment.

Table 3. Establishment counts and average establishment size in select industries for nonprofit and for-profit establishments, 2017 annual estimates
Number of establishments

Average establishment size

Industry
Nonprofit

For-profit

Nonprofit

For-profit

299,457

9,237,374

42

12

138,319

1,393,815

60

8

5,171

4,853

814

167

Social assistance

67,884

784,418

23

3

Ambulatory health care services

36,058

554,580

39

11

Nursing and residential care facilities

29,205

49,965

40

43

32,832

84,647

61

10

12,879

5,090

53

25

5,331

3,603

213

29

Other services (except public administration)

64,730

81,534

13

5

Arts, entertainment, and recreation

15,261

126,241

23

15

Total private
Health care and social assistance
Hospitals

Educational services
Elementary and secondary schools
Colleges, universities, and professional schools

Source: U.S. Bureau of Labor Statistics.

Two themes emerge from this table: First, industries in which nonprofits are prevalent have an average establishment size greater than the national average of 12 jobs. For
both the healthcare and social assistance sector and the educational services sector, the average number of jobs per establishment is more than five times greater than the
average for their for-profit counterparts. Second, when comparisons are restricted to establishments within the same industry, nonprofit establishments often have more jobs
on average than their for-profit counterparts. All the industries shown in table 3, except for nursing and residential care facilities, feature an average employment size for a
nonprofit establishment higher than that of a for-profit. In many instances, this size differential is considerable, especially in industries with high levels of nonprofit
employment.
The subsectors that comprise the healthcare and social assistance sector illustrate these differences. In the hospital subsector, the largest in terms of nonprofit employment, the
average number of jobs at nonprofits was nearly five times higher than at for-profit hospitals. An even greater disparity exists among establishments in social assistance: the
average employment size of nonprofit establishments was more than seven times larger than their for-profit peers.
As another example, consider the size difference between the two largest industry groups in the educational services sector. In 2017, a for-profit college or university had an
average of 29 jobs. In contrast, the average for nonprofit establishments was over seven times larger, at 213 jobs. (See table 3.) Similarly, employment in an average for-profit
elementary or secondary school averaged 25 jobs, while their nonprofit peers averaged 53 jobs.
Most industries with high proportions of nonprofit organizations show similar tendencies. Appendix table A-3 lists all published sectors and the subsectors and industry groups
for the four sectors that account for most nonprofit employment. Estimates in this table show that within nearly all listed industries the average employment size for a
nonprofit establishment exceeded that of its for-profit counterpart.
In addition, as illustrated in chart 2 and table 3, most nonprofit employment is concentrated in industries characterized by large employers. Two-thirds of nonprofit
employment was in the healthcare and social assistance sector, in which the average nonprofit establishment had 60 jobs in 2017. Another 16 percent of 501(c)(3) employment
occurred in the educational services sector, in which nonprofit establishments averaged 61 jobs.
In contrast, employment among for-profit companies is more dispersed. Consider the two largest sectors in terms of for-profit employment. The retail trade sector constituted
14 percent of total private employment, and the accommodation and food services sector contributed a 12-percent share. (See appendix table A-3.) For-profit establishments in
these two sectors averaged 15 jobs and 20 jobs, respectively.
Industries in which the average employment size is generally large, such as hospitals, make up a considerably smaller portion of for-profit employment than is the case for
nonprofits. Specifically, the hospitals subsector made up less than 1 percent of for-profit employment. (See appendix table A-3). By contrast, the ambulatory healthcare
services subsector was 53 percent of the employment among for-profit establishments in the healthcare and social assistance sector. This industry group, which includes
offices of chiropractors, optometrists, and dentists, tends to be characterized by small-scale employers. The average establishment size among these for-profit businesses was
11.
Similarly, universities and elementary schools—which have large average establishment sizes—contributed much less to overall for-profit employment. The colleges,
universities, and professional schools industry group and the elementary and secondary schools industry group each constituted less than 1 percent of total private employment
among for-profit businesses.
These industry tabulations reveal why nonprofit establishments are large in terms of employment. Within most industries, nonprofit organizations are larger than their forprofit counterparts. These size differences are amplified by the industry concentration found among nonprofits: most 501(c)(3) organizations are in industries where the
average employment size of an establishment is large. Conversely, for-profit businesses are more broadly dispersed throughout the economy, including in many industries in
which the employment size is quite small. Taken together, these features provide an explanation as to why nonprofit establishments are considerably larger on average than
their for-profit peers.
Looking at employment by industry allows us to explain why nonprofit establishments have more jobs on average than their for-profit counterparts. These same industry
estimates can also be used to explain the stable employment growth experienced by nonprofit entities both during and after the 2007–09 recession.

Industry employment growth rates
Employment among nonprofit organizations grew steadily from 2007 to 2017, including during the 2007–09 recession. (See appendix table A-1.) Conversely, employment
among for-profit businesses declined during the 2007–09 recession, but then grew faster than did nonprofit employment after the recession.
The noticeable difference in employment growth for nonprofits relative to their for-profit peers reflects their differing industry compositions. Although the nonprofit segment
of most sectors had positive employment growth from 2007 to 2017, employment growth was driven by the two largest nonprofit industries.
Nonprofit organizations in the healthcare and social assistance sector experienced employment growth for all 11 years for which data are available.13 The largest over-the-year
increase occurred from 2007 to 2008, when the number of jobs in this industry increased by nearly 3 percent. Over the course of the 11 years, nonprofit employment expanded
by 16 percent. Like their nonprofit counterparts, for-profit businesses in this sector also experienced employment growth each year from 2007 to 2017, expanding more than
their nonprofit counterparts.14
Establishments in the educational services sector also experienced steady employment growth from 2007 to 2017. Both nonprofits and their for-profit peers had positive job
growth nearly every year. Among nonprofits in this sector, the number of jobs increased by 21 percent from 2007 to 2017. At the same time, however, employment among
non-501(c)(3) companies expanded at a faster rate, 29 percent.

The employment growth in these two sectors explains the steady growth in overall 501(c)(3) employment. As noted in the previous section, 67 percent of nonprofit
employment is in healthcare and social assistance and 16 percent is in educational services. Growth in these two sectors from 2007 to 2017, combined with the fact that more
than 80 percent of nonprofit jobs are found in these two industries, led to the steady growth in total nonprofit employment over the period.
Although employment in for-profit companies grew in these two sectors, this growth did not lead to steady increases in total private employment. Rather, as shown in chart 1,
the 2007–09 recession saw a clear contraction in overall employment among for-profit companies. Whereas the healthcare and social assistance sector and the educational
services sector made up 83 percent of nonprofit jobs, these same two sectors constituted only 8 percent of total for-profit employment in 2007. This much smaller share of
total private employment diluted the effect of these two industries on overall employment growth.
Also noteworthy is that industries that were more sensitive to the economic downturn of 2007 to 2009 played a small role in nonprofit employment but contributed greatly to
for-profit employment. Jobs in construction, manufacturing, and retail trade constitute only a small fraction of the employment at 501(c)(3) organizations yet comprise a
significant proportion of for-profit jobs. Among for-profit establishments, all three of these industry sectors experienced large job losses during the 2007–09 recession.
In 2007, the construction, manufacturing, and retail trade sectors were nearly 36 percent of employment for for-profit businesses but less than 1 percent of nonprofit
employment. (See appendix table A-3.) As table 4 shows, employment among for-profit companies declined by 27 percent in construction, by 17 percent in manufacturing,
and by 7 percent in retail trade.

Table 4. Employment change in select industry sectors for nonprofit and for-profit establishments, 2007–10
Nonprofit establishments

For-profit establishments

Sector
2007

2010

Percent change

2007

2010

Percent change

Construction

7,621

8,493

11

7,555,111

5,481,006

-27

Manufacturing

6,656

6,732

1

13,826,366

11,480,764

-17

51,926

58,161

12

15,457,091

14,423,163

-7

Retail trade

Source: U.S. Bureau of Labor Statistics.

A listing of employment changes in all industry sectors during the recessionary period from 2007 to 2010 is in appendix table A-4. As the table shows, 15 of the 17 listed
sectors had declines in employment for the for-profit segment of each industry. The only two sectors to have positive for-profit employment growth were the healthcare and
social assistance industry and the educational services industry.
Appendix table A-4 also shows that the nonprofit component of most sectors had positive employment growth from 2007 to 2010; 13 of the 17 published sectors showed an
increase in the number of jobs, while 4 had a decrease. These declining industries were information, finance and insurance, wholesale trade, and real estate and rental and
leasing. In 2007, these four sectors accounted for about 1 percent of all nonprofit employment and thus had little effect on the overall nonprofit employment trend.
After the recession, most nonprofit sectors experienced positive job growth. As appendix table A-4 shows, from 2010 to 2017, 501(c)(3) organizations experienced positive
employment growth in 16 of the 17 published industry sectors. The only sector to see a decline in employment was the real estate and rental and leasing sector. Between 2010
and 2017, total private nonprofit employment increased by 12 percent. Over the same period, for-profit employment grew at a faster rate (16 percent). All industry sectors saw
positive employment growth among for-profit businesses, and seven of these sectors grew at a faster rate than their nonprofit counterparts.

Conclusion
The 501(c)(3) nonprofit tabulations created by BLS provide valuable insight into this diverse segment of the economy. Nonprofit organizations display characteristics that are
not present among for-profit firms. One notable finding is that nonprofits are concentrated in only a few industries. The healthcare and social assistance sector and the
educational services sector account for 83 percent of nonprofit jobs. Among for-profit firms, however, the healthcare and social assistance sector and the educational services
sector make up just 11 percent of jobs. Other industries, such as retail trade and construction, are a small portion of employment among nonprofit organizations yet constitute
a large portion of employment among for-profit entities.
This nonprofit concentration has important implications. One notable consequence is that nonprofit organizations tend to be large in terms of employment size. Hospitals and
universities—which together account for about 4 of every 10 jobs in nonprofit employment—have a higher number of jobs per establishment, on average, than do retail outlets
or construction sites.
Another consequence of this concentration can be found in the changes in employment from 2007 to 2017. The 2007–09 recession led to large employment losses. However,
the effects of this economic downturn were not evenly spread throughout the economy. While for-profit firms experienced large job losses, nonprofit organizations—which
were concentrated in sectors of the economy that fared relatively well during the recession—exhibited steady employment growth during the downturn.
This article focuses on nonprofit estimates at the national level and highlights only some of the characteristics that can be found in the BLS 501(c)(3) data. These data
characteristics also include information at the state and county level as well as information on wages at nonprofit organizations. Future analyses of these data could lead to
insights about 501(c)(3) organizations at a more granular level.

Appendix

Table A-1. Total private employment for nonprofit and for-profit establishments, 2007–17
Nonprofit

For-profit

Year
Employment

Percent change

Employment

Percent change

2007

10,534,183

[1]

103,478,038

[1]

2008

10,837,928

2.9

102,350,715

-1.1

2009

10,997,668

1.5

95,949,436

-6.3

2010

11,111,096

1.0

95,090,136

-0.9

2011

11,265,233

1.4

96,919,562

1.9

2012

11,426,870

1.4

99,218,999

2.4

2013

11,599,269

1.5

101,359,065

2.2

2014

11,746,589

1.3

103,822,097

2.4

2015

11,945,181

1.7

106,362,536

2.4

2016

12,245,030

2.5

108,259,592

1.8

2017

12,488,563

2.0

109,898,002

1.5

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

Table A-2. Total private annual average number of establishments for nonprofit and for-profit establishments, 2007–17
Nonprofit

For-profit

Year
Number of establishments

Percent change

Number of establishments

Percent change

2007

232,396

[1]

8,448,605

[1]

2008

240,272

3.4

8,549,088

1.2

2009

247,026

2.8

8,462,089

-1.0

2010

254,236

2.9

8,441,362

-0.2

2011

261,673

2.9

8,513,984

0.9

2012

267,855

2.4

8,558,161

0.5

2013

273,333

2.0

8,638,841

0.9

2014

279,261

2.2

8,787,948

1.7

2015

286,427

2.6

8,937,909

1.7

2016

294,064

2.7

9,123,612

2.1

2017

299,457

1.8

9,237,374

1.2

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

Table A-3. Employment levels and average establishment employment size for nonprofits and for-profit establishments, selected industries, 2017 annual
estimates
Nonprofit

Industry

Percent of

Total private
employment

For-profit
Percent of

employment

employment

Average

Employment

establishment size

Total
private

Average

Employment

establishment size

Total

Sector

private

Sector

122,386,565

12,488,563

100.0

[1]

42

109,898,002

100.0

[1]

12

Health care and social assistance

19,322,491

8,306,650

66.5

[1]

60

11,015,841

10.0

[1]

8

Ambulatory healthcare services

7,258,875

1,402,007

11.2

16.9

39

5,856,868

5.3

53.2

11

2,572,041

653,456

5.2

7.9

39

1,918,585

1.7

17.4

10

Offices of dentists

933,738

9,825

0.1

0.1

18

923,913

0.8

8.4

7

Offices of other health practitioners

886,112

43,264

0.3

0.5

20

842,848

0.8

7.7

6

Outpatient care centers

889,352

396,544

3.2

4.8

37

492,808

0.4

4.5

20

Medical and diagnostic laboratories

269,282

22,752

0.2

0.3

50

246,530

0.2

2.2

12

1,407,028

190,154

1.5

2.3

73

1,216,874

1.1

11.0

38

301,323

86,012

0.7

1.0

30

215,311

0.2

2.0

21

5,018,482

4,206,754

33.7

50.6

814

811,728

0.7

7.4

167

4,670,249

4,031,773

32.3

48.5

892

638,476

0.6

5.8

241

118,991

45,943

0.4

0.6

167

73,048

0.1

0.7

84

229,242

129,038

1.0

1.6

341

100,204

0.1

0.9

75

3,325,358

1,168,968

9.4

14.1

40

2,156,390

2.0

19.6

43

1,617,065

361,827

2.9

4.4

130

1,255,238

1.1

11.4

84

624,987

415,031

3.3

5.0

22

209,956

0.2

1.9

22

918,205

280,092

2.2

3.4

81

638,113

0.6

5.8

28

165,101

112,019

0.9

1.3

30

53,082

0.0

0.5

18

3,719,777

1,528,920

12.2

18.4

23

2,190,857

2.0

19.9

3

2,341,858

829,592

6.6

10.0

24

1,512,266

1.4

13.7

2

Community food and housing, and
emergency and other relief services

169,361

154,495

1.2

1.9

16

14,866

0.0

0.1

10

Vocational rehabilitation services

335,404

288,693

2.3

3.5

42

46,711

0.0

0.4

18

Child day care services

873,154

256,140

2.1

3.1

15

617,014

0.6

5.6

11

2,824,421

2,003,634

16.0

[1]

61

820,787

0.7

[1]

10

811,412

684,170

5.5

34.1

53

127,242

0.1

15.5

25

27,952

10,578

0.1

0.5

52

17,374

0.0

2.1

26

1,242,128

1,137,681

9.1

56.8

213

104,447

0.1

12.7

29

67,466

12,642

0.1

0.6

11

54,824

0.0

6.7

6

Technical and trade schools

110,321

15,493

0.1

0.8

13

94,828

0.1

11.6

11

Other schools and instruction

419,343

82,918

0.7

4.1

12

336,425

0.3

41.0

7

Educational support services

145,799

60,153

0.5

3.0

12

85,646

0.1

10.4

7

4,434,678

837,662

6.7

[1]

13

3,597,016

3.3

[1]

5

Repair and maintenance

1,305,012

1,126

0.0

0.1

6

1,303,886

1.2

36.2

6

Personal and laundry services

1,471,650

17,462

0.1

2.1

11

1,454,188

1.3

40.4

7

Religious, grantmaking, civic, professional,
and similar organizations

1,369,545

818,562

6.6

97.7

13

550,983

0.5

15.3

7

Religious organizations

191,664

162,772

1.3

19.4

9

28,892

0.0

0.8

6

Grantmaking and giving services

143,416

131,273

1.1

15.7

9

12,143

0.0

0.3

6

Social advocacy organizations

211,659

185,370

1.5

22.1

10

26,289

0.0

0.7

6

Civic and social organizations

392,218

279,738

2.2

33.4

31

112,480

0.1

3.1

7

430,587

59,409

0.5

7.1

11

371,178

0.3

10.3

7

288471

511

0.0

0.1

2

287960

0.3

8.0

1

2,293,872

355,965

2.9

[1]

23

1,937,907

1.8

[1]

15

483,199

104,081

0.8

29.2

18

379,118

0.3

19.6

8

122,891

70,103

0.6

19.7

20

52,788

0.0

2.7

8

Total private

Offices of physicians

Home healthcare services
Other ambulatory healthcare services
Hospitals
General medical and surgical hospitals
Psychiatric and substance abuse
hospitals
Specialty (except psychiatric and
substance abuse) hospitals
Nursing and residential care facilities
Nursing care facilities (skilled nursing
facilities)
Residential intellectual and
developmental disability, mental health
Continuing care retirement communities
and assisted living facilities
Other residential care facilities
Social assistance
Individual and family services

Educational services
Elementary and secondary schools
Junior colleges
Colleges, universities, and professional
schools
Business schools and computer and
management training

Other services (except public administration)

Business, professional, labor, political,
and similar organizations
Private households
Arts, entertainment, and recreation
Performing arts, spectator sports, and
related industries
Performing arts companies

[1] Not applicable.
Note: Percentages may not sum to totals because of rounding.
Source: U.S. Bureau of Labor Statistics.

Nonprofit

Industry

Percent of

Total private
employment

For-profit
Percent of

employment

employment

Average

Employment

establishment size

Total
private

Average

Employment

establishment size

Total

Sector

private

Sector

141,877

3,509

0.0

1.0

12

138,368

0.1

7.1

23

141,120

28,692

0.2

8.1

17

112,428

0.1

5.8

18

26699

229

0.0

0.1

5

26470

0.0

1.4

6

50,611

1,548

0.0

0.4

4

49,063

0.0

2.5

2

163,261

141,289

1.1

39.7

28

21,972

0.0

1.1

15

163,261

141,289

1.1

39.7

28

21,972

0.0

1.1

15

1,647,413

110,595

0.9

31.1

25

1,536,818

1.4

79.3

20

211474

879

0.0

0.2

31

210595

0.2

10.9

68

122,414

1,991

0.0

0.6

11

120,423

0.1

6.2

39

1,313,524

107,724

0.9

30.3

26

1,205,800

1.1

62.2

17

Management of companies and enterprises

2,278,042

281,078

2.3

[1]

61

1,996,964

1.8

[1]

33

Professional, scientific, and technical
services

8,996,430

270,133

2.2

[1]

23

8,726,297

7.9

[1]

7

Administrative and support and waste
management and remediation services

9,064,811

100,918

0.8

[1]

20

8,963,893

8.2

[1]

17

Retail trade

15,854,454

93,484

0.7

[1]

16

15,760,970

14.3

[1]

15

Information

2,793,429

68,057

0.5

[1]

14

2,725,372

2.5

[1]

17

Finance and insurance

5,908,709

43,734

0.4

[1]

34

5,864,975

5.3

[1]

12

13,606,761

38,065

0.3

[1]

20

13,568,696

12.3

[1]

20

Real estate and rental and leasing

2,179,696

36,433

0.3

[1]

9

2,143,263

2.0

[1]

6

Transportation and warehousing

4,947,369

21,229

0.2

[1]

24

4,926,140

4.5

[1]

20

Construction

6,919,107

9,270

0.1

[1]

10

6,909,837

6.3

[1]

9

12,406,757

7,464

0.1

[1]

28

12,399,293

11.3

[1]

36

Wholesale trade

5,898,637

3,985

0.0

[1]

5

5,894,652

5.4

[1]

10

Agriculture, forestry, fishing, and hunting

1,261,312

2,976

0.0

[1]

9

1,258,336

1.1

[1]

12

Spectator sports
Promoters of performing arts, sports, and
similar events
Agents and managers for artists, athletes,
entertainers, and public figures
Independent artists, writers, and
performers
Museums, historical sites, and similar
institutions
Museums, historical sites, and similar
institutions
Amusement, gambling, and recreation
industries
Amusement parks and arcades
Gambling industries
Other amusement and recreation
industries

Accommodation and food services

Manufacturing

[1] Not applicable.
Note: Percentages may not sum to totals because of rounding.
Source: U.S. Bureau of Labor Statistics.

Table A-4. Change in industry sector employment for nonprofit and for-profit establishments, 2007–17 (in percent)
2007–2010

2010–17

2007–17

Industry
Nonprofit

For-profit

Nonprofit

For-profit

Nonprofit

For-profit

5

-8

12

16

19

6

6

8

10

28

16

37

Hospitals

5

0

6

24

10

24

Social assistance

7

10

10

103

18

124

Nursing and residential care facilities

3

8

-1

11

3

20

15

8

37

18

58

28

Educational services

4

17

17

10

21

29

Other services (except public administration)

1

-3

9

0

11

-2

1

-5

9

-2

10

-6

8

-4

29

19

39

14

1

0

27

34

28

33

Performing arts, spectator sports, and related industries

-1

-3

18

24

16

19

Amusement, gambling, and recreation industries

35

-4

45

18

96

13

7

-3

7

21

15

18

16

-1

44

20

67

19

3

-12

32

22

36

8

Information

-2

-11

7

3

5

-8

Retail trade

12

-7

61

9

80

2

-35

-8

72

7

12

-1

Real estate and rental and leasing

-3

-11

-2

14

-5

1

Accommodation and food Services

10

-2

20

23

32

20

Transportation and warehousing

16

-8

24

25

44

15

Construction

11

-27

9

26

22

-9

1

-17

11

8

12

-10

Wholesale trade

-3

-9

0

8

-3

-1

Agriculture, forestry, fishing and hunting

19

-2

71

10

104

8

Total private
Healthcare and social assistance

Ambulatory healthcare services

Religious, grantmaking, civic, professional, and similar organizations
Arts, entertainment, and recreation
Museums, historical sites, and similar institutions

Professional, scientific, and technical services
Management of companies and enterprises
Administrative and support and waste management and remediation services

Finance and insurance

Manufacturing

Source: U.S. Bureau of Labor Statistics.
SUGGESTED CITATION:

Erik Friesenhahn, "Nonprofits: a look at national trends in establishment size and employment," Monthly Labor Review, U.S. Bureau of Labor Statistics, January 2024,

https://doi.org/10.21916/mlr.2024.2

Notes

1

Many nonprofit organizations rely heavily or entirely on volunteer workers. However, only paid employment is included in the tabulations discussed in this article. The U.S. Bureau of Labor

Statistics (BLS) Business Register is based on unemployment insurance reports filed by employers, and only paid employees are included in these reports.

2 For more information about publicly available Internal Revenue Service (IRS) data, see “Exempt Organizations Business Master File extract” (Internal Revenue Service, last modified October
15, 2023), https://www.irs.gov/charities-non-profits/exempt-organizations-business-master-file-extract-eo-bmf. For a detailed discussion of the methodology and data sources used
to create these estimates, see Erik Friesenhahn, “Nonprofit organizations using the BLS Business Register to measure employment, wages, and establishment size,” Monthly Labor Review,
November 2023, https://doi.org/10.21916/mlr.2023.27.

3 For more information, see “Other Tax-Exempt Organizations” (Internal Revenue Service, last modified March 7, 2023), https://www.irs.gov/charities-non-profits/other-tax-exempt-

organizations.
4

For more information on nonprofits and how BLS classifies them, see “Research Data on the Nonprofit Sector,” Business Employment Dynamics (U.S. Bureau of Labor Statistics, last

modified May 14, 2020), https://www.bls.gov/bdm/nonprofits/nonprofits.htm.

5 There are administrative reasons that cause BLS tabulations for nonprofit organizations to have a larger employment size than for-profit establishments. Some states do not require nonprofit
organizations with fewer than 5employees to file unemployment insurance reports. Because the BLS Business Register is based on unemployment insurance reports, BLS estimates of
nonprofit establishment size are overstated in states that do not require small nonprofits to file. However, BLS analyses indicate that the effect is small. The estimated undercount of the total
number of nonprofit establishments is somewhat larger and leads to an increased average establishment size for nonprofits. An examination of these issues is presented in Friesenhahn,
“Nonprofit organizations.”

6

For more information, see “U.S. business cycle expansions and contractions” (National Bureau of Economic Research, last updated March 14, 2023),

https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions.
7 BLS, like other federal statistical agencies, uses the North American Industrial Classification System (NAICS) to categorize business establishments into industry groupings. In doing so this
classification method provides a uniform process by which to identify and tabulate business activity across the economy. NAICS is a hierarchical system in which each successive layer of
detail provides greater insight into an establishment’s business activity. A benefit of this approach is that it allows data users to partition each industry into its component parts and analyze data
at a more granular level. In NAICS codes, two digits denotes sector, three digits subsector, four digits industry group, five digits industry, and six digits denotes country of origin. Data users
interested in learning more about this classification system can find in-depth information at “North American Industry Classification System” (U.S. Census Bureau, last modified October 24,
2023), https://www.census.gov/naics/.

8 For more information about the confidentiality requirements of BLS, see “Confidentiality pledge and laws” (U.S. Bureau of Labor Statistics, last modified April 3, 2020),

https://www.bls.gov/bls/confidentiality.htm.
9 For more information on these data, see “Research Data on the Nonprofit Sector,” Business Employment Dynamics (U.S. Bureau of Labor Statistics, last modified May 14, 2020),

https://www.bls.gov/bdm/nonprofits/nonprofits.htm.

10 Note that the nonprofit data include only private-sector hospitals.
11

The IRS permits gambling and other gaming activities by nonprofits under certain circumstances. For a detailed discussion of permissible gaming and gambling activities that a 501(c)(3)

nonprofit may engage in, see “Gaming and Gambling Rules for 501(c)(3) Nonprofit Fundraising” Form1023 (website), last updated November 6, 2023, https://form1023.org/gaming-

gambling-rules-for-501c3-nonprofit-fundraising.
12

Differing reporting requirements among states lead to an undercount of small nonprofit establishments. Because some small nonprofit establishments are excluded from the BLS Business

Register, the average establishment size is somewhat higher than if these establishments had been included. This effect is most pronounced in industries in which establishment sizes tend to
be small, most notably the religious, grantmaking, civic, professional, and similar organizations subsector. However, even when taking the reporting difference into account, the average
establishment size for nonprofits is generally considerably higher than for for-profit organizations. For more information, see Friesenhahn, “Nonprofit organizations.”

13 For a thorough discussion of employment growth in the healthcare industry , see Catherine A. Wood, “Employment in health care: a crutch for the ailing economy during the 2007–09
recession,” Monthly Labor Review, April 2011, pp. 13–18, https://www.bls.gov/opub/mlr/2011/04/art2full.pdf.

14

From 2007 to 2017, measured employment among for-profit companies in the healthcare and social assistance industry expanded by 37 percent. However, the large increase in 2013 can

mostly be attributed to an administrative change in the way certain records were processed. This change affected data tabulations for business establishments classified in the services for the
elderly and persons with disabilities industry (NAICS 624120) and the private households industry (NAICS 814110). A review of administrative records revealed that certain establishments that
provide nonmedical, home-based services for the elderly and persons with disabilities had been misclassified into the private households industry. To correct this matter, these records were
reclassified into the services for the elderly and persons with disabilities industry. This correction led to a large employment increase in the 2013 estimates for services for the elderly and
persons with disabilities and a decrease in employment for private households. This administrative change does not account for all the employment growth in the healthcare and social
assistance sector in 2013 and does not change the overall strong upward trend in employment for this sector from 2007 to 2017. Furthermore, the over-the-year growth rate of employment for
for-profit establishments was greater than that of their nonprofit counterparts for all years of data.

ABOUT THE AUTHOR

Erik Friesenhahn
friesenhahn.erik@bls.gov
Erik Friesenhahn is an economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics.

RELATED CONTENT

Related Articles
Nonprofit organizations: using the BLS Business Register to measure employment, wages, and establishment size, Monthly Labor Review November 2023.
Nonprofits in America: new research data on employment, wages, and establishments, Monthly Labor Review, February 2016.
Nonprofit pay and benefits: estimates from the National Compensation Survey, Monthly Labor Review, January 2016.
Related Subjects
Unemployment Insurance
Firm size
industry
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Economic and Social Statistics

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Childcare employment—before, during, and after the COVID-19 pandemic
The employment rate in the United States fell dramatically in many industries during the COVID-19 pandemic—the childcare industry, in particular, was hit hard. In this
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the inner workings of the critical child daycare services industry before and during the pandemic and most importantly how the industry has managed since. Although the
childcare industry’s wages are low and it has high labor turnover, our findings show that it is critical in supporting workers across all industries.
Because of the COVID-19 pandemic, childcare centers were subject to mandated closures; increased safety requirements, including limits on capacity; and other disruptions.1
These disruptions in the childcare services industry rippled across the U.S. economy because workers were unable to access reliable childcare for their families.
The childcare industry fulfills multiple roles in the labor market. First, the childcare industry employs several thousands of workers who earn their living caring for, protecting,
and educating large portions of the nation’s youth. Second, the childcare industry facilitates efficiencies and equity in the labor market by enabling parents to work outside the
home. During the pandemic, mothers of younger children, those most often needing childcare, experienced the steepest declines in employment rates, specifically illustrating
the importance of the childcare industry for working mothers.2 Third, the childcare industry is crucial in educating and socializing the next generation of employees and
entrepreneurs. This article covers the first of these facets—employment.
In addition to the functional importance to the labor force, the childcare industry itself comprises hundreds of thousands of employees across tens of thousands of
establishments. In the most recent release of data for the first quarter of 2023, the U.S. Bureau of Labor Statistics (BLS) Quarterly Census of Employment and Wages
(QCEW) program reported that the 77,000 childcare services establishments in the United States employ over 942,000 workers and account for $7.2 billion in total quarterly
wages.3 The childcare industry is characterized by small establishments: 58 percent of childcare establishments employed fewer than 10 employees. Furthermore, according to
the BLS Business Response Survey (BRS) to the COVID-19 pandemic, establishments in the childcare industry experienced certain effects of the pandemic at a higher level
(in some cases much higher) than the national average.
In what follows, we leverage new data from the BLS BRSs and other BLS sources to assess employment, wages, telework, benefits, and dynamics of the critical child daycare
services industry before the pandemic, in the early days of the outbreak, and how the industry has coped since.4

Data sources and definitions
In 2020, BLS developed a new survey on how U.S. businesses changed their operations since the onset of the COVID-19 pandemic. Data from the first BRS to the COVID-19
pandemic help all data users understand how businesses responded during the pandemic through September 2020.5 Data were collected from July through September 2020. In
2021, BLS similarly developed a second survey with different questions about how businesses had changed operations through that stage of the pandemic.6 The 2021 BRS
was collected from July through September 2021. Most recently, in 2022, BLS developed a third BRS with questions about telework, hiring, and vacancies.7 The 2022 BRS
was collected from August through September 2022.
In this article, we complement the findings of the BRS with data from the QCEW. The QCEW is a quarterly count of employment and wages that covers about 95 percent of
U.S. jobs, made available at the county, metropolitan statistical area, and state and national levels by industry.8 QCEW includes employment and wages from establishments
subject to unemployment insurance (UI). This coverage represents a near census that, in the first quarter of 2023, included information on 11.6 million establishments. Note
that some establishments not covered by UI laws are excluded from the QCEW. These noncovered establishments include sole proprietorships, unincorporated self-employed,
and unpaid family members.9 Noncovered establishments are somewhat more prevalent in the childcare industry than most other industries.
For this analysis, the childcare industry is identified in the North American Industry Classification System (NAICS) as NAICS 6244, child daycare services, in which
establishments are subject to UI. According to the NAICS manual, “this industry comprises establishments primarily engaged in providing daycare of infants or children.
These establishments generally care for preschool children, but may care for older children when they are not in school and may also offer pre-kindergarten and/or
kindergarten educational programs.”10
The child daycare services industry falls within NAICS sector 62, healthcare and social assistance. As described in the NAICS manual, “the Health Care and Social Assistance
sector comprises establishments providing healthcare and social assistance for individuals. The sector includes both healthcare and social assistance because it is sometimes
difficult to distinguish between the boundaries of these two activities. The industries in this sector are arranged on a continuum starting with establishments providing medical
care exclusively, continuing with those providing healthcare and social assistance, and finally finishing with those providing only social assistance. Establishments in this
sector deliver services by trained professionals. All industries in the sector share this commonality of process, namely, labor inputs of health practitioners or social workers
with the requisite expertise.”11 In this article, we present national data for comparison from the BRSs for 2020, 2021, and 2022 and from the QCEW that are specifically for
healthcare and social assistance (NAICS 62) and for child daycare services (NAICS 6244).12

Before the COVID-19 pandemic (2015–19)
According to the QCEW, child daycare services employed 929,000 workers in April 2019.13 In the 5 years before the onset of the COVID-19 pandemic, the industry added
116,000 jobs and 4,235 establishments. (See chart 1.) The deepest employment loss due to the COVID-19 pandemic was in April 2020. Because QCEW data are not
seasonally adjusted, we chose April 2019 as a prepandemic baseline. In terms of wages, child daycare services had one of the lowest levels of average weekly wages
nationally. In the second quarter of 2019, child daycare services had an average weekly wage of $461, much lower than the national average of $1,085. Research consistently
finds that child daycare workers were among the lowest paid workers before, during, and after the pandemic.14

Chart 1. Number of child daycare center establishments and quarterly
employment, 2015–23
Quarterly employment
Number of establishments

Establishments

Quarterly employment

78,000

1,000,000

77,000

900,000

76,000

800,000

75,000

700,000

74,000
73,000

600,000

72,000

500,000

71,000

400,000

70,000

300,000

69,000

200,000

68,000

100,000

67,000
66,000

0
2015/Q1 2016/Q1 2017/Q1 2018/Q1 2019/Q1 2020/Q1 2021/Q1 2022/Q1 2023/Q1

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

In the 5 years before the onset of the COVID-19 pandemic, approximately 2,700 child daycare services closed, on average, per quarter. Those closings were counterbalanced
with approximately the same number of openings every quarter. On average, child daycare center closings affected around 13,600 employees each quarter. In second-quarter
2019, child daycare services industry was reduced by approximately six jobs per establishment, on average.

The start of the COVID-19 pandemic
As businesses and the public experienced the fullest economic impact of the COVID-19 pandemic, in April 2020, child daycare services experienced a year-over-year
employment decrease that was more than twice the national rate. According to QCEW data, employment at child daycare services decreased 33.9 percent from 928,929 in
April 2019 to 613,632 in April 2020.15 During this same time, employment fell nationally by 15.7 percent and by 9.1 percent in healthcare and social assistance.
The quarterly series on Business Employment Dynamics show that between the first and second quarters of 2020, nearly 13,000 child daycare services establishments
closed.16 Furthermore, 18,000 child daycare services reduced employment in first-quarter 2020. In the following quarter, 35,000 child daycare services reduced employment
but remained open.
Looking at 2020 BRS data reveals that 35.2 percent of child daycare services establishments experienced government-mandated closures, compared with 18.7 percent of
businesses across all industries.17 (See chart 2.) In addition to closures, an uncertainty about the safety of group settings swayed some parents to voluntarily hold their children
out of schools and child daycare services establishments.18 The 2020 BRS data show that child daycare services establishments experienced a slightly larger decrease in
demand for products or services compared with establishments across all industries—63.1 percent versus 55.6 percent. Child daycare services establishments were also much
more likely to tell employees not to work, with or without pay. Almost 83.0 percent of child daycare services establishments told employees not to work, with or without pay,
compared with 51.9 percent of all establishments. In addition, 43.3 percent of child daycare services establishments reduced hours for employees who worked at least some
hours. This figure was considerably higher than the national average of 29.9 percent.
Chart 2. U.S. business response to the COVID-19 pandemic, in percent, 2020
Total
Child daycare centers (NAICS 6244)

Percent
90
80
70
60
50
40
30
20
10
0
Government-mandated
​closure

Decrease in demand for Employees told not to
Reduction in employee
​products or services work, with or without pay
​work hours

Click legend items to change data display. Hover over chart to view data.
Note: Employees who worked reduced hours worked at least a few hours. NAICS = North American
Industry Classification System.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Given that workers in low-wage jobs are much less likely to have emergency savings available to them,19 these disproportionate disruptions in work hours would have been a
substantial hardship on child daycare workers during the already uncertain early stages of the pandemic. Research clearly shows that child daycare workers are made up of a
relatively larger share of employees who are women, Black, and Hispanic.20 Therefore, many hardships faced by child daycare workers during this period were endured
disproportionally by those who were women, Black, and Hispanic.21

During the COVID-19 pandemic
According to the 2020 BRS, 82.3 percent of businesses changed operations in some way because of the pandemic.22 As the pandemic continued through 2021, businesses
continued to change various operations. Workplace safety changes included requiring facial coverings to work onsite, temperature screenings before working onsite, and

COVID-19 vaccinations. For the child daycare industry, these operational changes occurred at higher rates than the national average and, at times, slightly higher than the
healthcare and social assistance industry. (See chart 3.)
Chart 3. U.S. business response to the COVID-19 pandemic, in percent, 2021
Total
Healthcare and social assistance (NAICS 62)
Child daycare centers (NAICS 6244)

Required some or all employees to
​get a COVID-19 vaccination before
​coming to work onsite

Required employees working onsite
​to have a temperature screening
​before entering their workplace
Required some or all employees to
​routinely wear facial covering or any
​protective gear while they were
​onsite
0

10

20

30

40

50

60

70

80

90

100

Click legend items to change data display. Hover over chart to view data.
Note: NAICS = North American Industry Classification System.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Results from the 2021 BRS show that child daycare services required employees to wear masks (facial coverings), get temperature screenings, and get a COVID-19 vaccine at
higher rates than the national average. In fact, child daycare services establishments had similar levels of masking and temperature screenings as did their counterparts across
the healthcare and social assistance industry, which were much higher than the national average. Of child daycare services establishments, 42.0 percent required vaccinations
for some or all employees. This percentage was higher than the 33.8 percent of establishments in healthcare and social assistance and much higher than the 17.5 percent of
establishments with vaccination requirements across all industries.

Telework during the COVID-19 pandemic
The 2020 and 2021 BRS data show that many establishments coped with the COVID-19 pandemic by offering employees telework or work-from-home arrangements.23 The
reduction or elimination of in-person operations reduced the risk of COVID-19 transmission while it also reduced costs and burdens associated with implementing in-person
precautions, such as masking, social distancing, or vaccination requirements. The nature of work associated with caring for young children requires that workers be present in
person and onsite. While some workers may have been able to increase telework, frontline childcare workers could not generally perform their work remotely. According to
the 2020 BRS, 62.6 percent of child daycare services establishments had no telework before or after the pandemic, compared with 52.3 percent of all establishments
nationally.24
Looking at the 2021 BRS reveals that 34.5 percent of all establishments increased telework for some or all employees because of the pandemic.25 (See chart 4.) Child daycare
services establishments were similar, with 32.0 percent of establishments increasing telework for some or all employees. Note that by the parameters the telework question
presented in the 2021 BRS, establishments would report that they had some employees with increased telework even if only one or two employees were permitted to telework
while others remained in person.
Chart 4. U.S. business response to the COVID-19 pandemic increase, in
telework, in percent, 2021
Percent

Total
Healthcare and social assistance (NAICS 62)
Child daycare centers (NAICS 6244)

70
60
50
40
30
20
10
0
Increased telework for some or all employees

Increased telework and expect the increase to
c​ ontinue when the COVID-19 pandemic is over

Click legend items to change data display. Hover over chart to view data.
Note: NAICS = North American Industry Classification System.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Although child daycare services increased telework for some or all employees during the pandemic, they did not expect these telework increases to continue once the
pandemic was over. Only 10.0 percent of child daycare services establishments expected increased telework to continue in their industry after the pandemic, compared with
60.1 percent of establishments across all industries. Whatever shifts to telework were made during the pandemic were largely expected to revert to onsite work in the future.
Indeed, in July 2022, the availability of telework was much lower for child daycare services than for establishments overall: 5.0 percent of child daycare services
establishments had employees who teleworked some or all the time, compared with 27.5 percent nationally.

Child daycare center wages

Research clearly shows that child daycare center workers are among the lowest paid workers in the United States.26 According to the 2020 BRS, 8.8 percent of child daycare
services establishments increased salaries and wages for some workers, compared with only 5.6 percent nationally.27 And 59.6 percent of child daycare services
establishments continued paying employees who were told not to work, compared with 51.3 percent nationally. QCEW data show that average weekly wages for child daycare
services were about 60 percent less than the national average from 2018 to 2022. From the BRS data, child daycare services and social assistance establishments saw slightly
higher rates of increases in base pay attributed to the COVID-19 pandemic when compared with other establishments. In the first quarter of 2020, average wages for child
daycare services were 63 percent below the national average. This gap closed slightly to 58 percent below the national average in the second quarter of 2020 but returned to 60
percent below the national average in the first quarter of 2023. (See chart 5.)
Chart 5. Average weekly wages at U.S. child daycare centers, in dollars, 2015–23
Average weekly wage (in dollars)

Childcare services (NAICS 6244)
Healthcare and social assistance (NAICS 62)
Total

1,600
1,400
1,200
1,000
800
600
400
200
2015/Q1 2015/Q4 2016/Q3 2017/Q2 2018/Q1 2018/Q4 2019/Q3 2020/Q2 2021/Q1 2021/Q4 2022/Q3

Year/quarter
Click legend items to change data display. Hover over chart to view data.
Note: NAICS = North American Industry Classification System. Q1 = first quarter, Q2 = second quarter, Q3 =
third quarter, and Q4 = fourth quarter.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

During the COVID-19 pandemic, employers made various changes in the pay of employees. These changes included not only increases in base wages but also one-time
monetary awards and other monetary or nonmonetary incentives for working under the extraordinary circumstances of the pandemic. We know that establishments in
industries classified as essential by the Centers for Disease Control and Prevention had different experiences from those in industries that were not deemed essential.28 And in
terms of pay, essential workers were often singled out for bonuses or other pay incentives based on the critical functions that they performed during the pandemic.
According to the 2021 BRS data, 19.4 percent of child daycare services establishments increased base wages or salary, which was similar to the rate for the healthcare and
social assistance industry.29 Both were higher than the national average of 14.5 percent of establishments that increased base wages or salary. Child daycare services and the
healthcare and social assistance industry also showed higher levels of paying one-time bonuses for work during the pandemic, at 17.6 percent and 17.0 percent, respectively.
These rates are much higher than the national average of 9.4 percent. (See chart 6.)
Chart 6. U.S. business response to the COVID-19 pandemic changes, in pay,
in percent, 2021
Percent

Total
Healthcare and social assistance (NAICS 62)
Child daycare centers (NAICS 6244)

25
20
15
10
5
0
Increased base wages (straight-time wages or
​salary)

Paid one-time special monetary awards or
​appreciation bonuses for working during the
​pandemic

Click legend items to change data display. Hover over chart to view data.
Note: NAICS = North American Industry Classification System.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Beyond changes in pay, another option that employers implemented during the pandemic was to offer increased flexibilities for hours of work. Rather than continue to require
the more ridged prepandemic schedules, in 2021, 34.5 percent of employers started offering at least one of the following flexibilities to their employees: flexible or staggered
work hours, alternative work schedules, voluntary reductions in hours (such as a move from a full- to part-time schedule), job sharing, and/or additional paid leave. Although
telework was less of an option for child daycare services to cope with the pandemic, increasing flexibility for hours worked was one strategy that the child daycare industry
could implement.
From the 2021 BRS, 32.1 percent of child daycare services started flexible or staggered work hours and 30.1 percent started compressed or alternative work schedules.30 Both
figures were higher than the national and healthcare and social assistance averages. (See chart 7.)

Chart 7. U.S. business response to the COVID-19 pandemic work-hours
flexibilities, in percent, 2021
Total
Healthcare and social assistance (NAICS 62)
Child daycare centers (NAICS 6244)

Percent
35
30
25
20
15
10
5
0
Started flexible or staggered work hours

Started compressed or alternative work schedules

Click legend items to change data display. Hover over chart to view data.
Note: NAICS = North American Industry Classification System.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Later stages of the COVID-19 pandemic
Data from the Job Openings and Labor Turnover Survey show that that the number of unemployed people per job opening nationally was at a series low in December 2021
and remained at historically low levels through 2022.31 The 2022 BRS continued to include questions about telework, similar to the prior two versions. But in response to the
tight labor market conditions, the survey added new topics to gain additional insight into businesses’ hiring practices, how long vacancies were open, and recruitment
practices.32
The data introduced in the remaining paragraphs of this section are from the 2022 BRS.33 In July 2022, 39.7 percent of child daycare services establishments hired employees,
compared with 22.4 percent of establishments nationally. Of child daycare services establishments that hired at least one new employee, 72.9 percent increased starting pay to
attract more applicants, while nationally, 32.6 percent of establishments did so. Furthermore, 20.1 percent of child daycare services filled positions that were open for more
than 30 days, nearly triple the national average of 7.0 percent. To attract more applicants, employers increased compensation, offered hiring bonuses, expanded benefits, and
offered more hours (e.g., changed positions from part time to full time). Employers also turned to noncompensation means of attracting applicants, such as advertising, using
recruiters, reducing qualifications (e.g., education or experience), and increasing telework or remote work.
To attract additional applicants, in July 2022, child daycare services establishments increased starting pay and offered hiring bonuses at much higher rates than those offered
by establishments nationally. However, unlike in 2021, when child daycare services establishments increased flexibilities for hours worked, in July 2022, child daycare
services were just slightly more likely (6.9 percent) to offer more hours than establishments in healthcare and social assistance (5.9 percent) and the nation overall (4.5
percent). Remember that childcare workers are and continue to be among the lowest paid workers (see chart 5), so these increases in pay were not substantial enough to
elevate wages in this industry to within even half the national average. (See chart 8.)
Chart 8. Compensation methods for attracting new hires by U.S. establishments
that hired at least one new employee in July 2022, in percent
Total
Healthcare and social assistance (NAICS 62)
Child daycare centers (NAICS 6244)
Offered more hours

Expanded benefits

Offered hiring
​bonuses

Increased starting pay

0

10

20

30

40

50

60

70

80

Click legend items to change data display. Hover over chart to view data.
Note: NAICS = North American Industry Classification System.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

In July 2022, in establishments that hired at least one new employee, 11.3 percent of child daycare services reduced qualifications to attract additional applicants, compared
with 5.8 percent nationally and 4.8 percent in healthcare and social assistance. (See chart 9.) Child daycare services were also more likely to expand advertising and start using
a recruiter. In keeping with all previous findings on telework, child daycare services were less likely to expand telework, at less than 1.0 percent, compared with 3.1 percent
nationally across all industries.

Chart 9. Noncompensation methods for attracting new hires by establishments
that hired at least one new employee in July 2022, in percent
Total
Healthcare and social assistance (NAICS 62)
Child daycare centers (NAICS 6244)
Reduced job qualifications
​(e.g., education or
​experience)

Started using recruiters or
​talent agencies

Expanded advertising

0

5

10

15

20

25

30

35

40

Click legend items to change data display. Hover over chart to view data.
Note: NAICS = North American Industry Classification System.
Source: U.S. Bureau of Labor Statistics.

View Chart Data

Child daycare services were more likely than establishments overall to have vacancies both in August and September 2022 and in the 12 months following. From August to
September 2022, 38.3 percent of child daycare services had at least one vacancy, compared with 20.9 percent nationally. Furthermore, 65.0 percent of child daycare services
had at least one vacancy between August 2021 and September 2022, compared with 40.5 percent nationally.

Conclusion and a look ahead
Most child daycare services employ fewer than 10 employees, have wages below the national average, and are made up of a relatively larger share of employees who are
women, Black, and Hispanic. The childcare industry was hit particularly hard by the COVID-19 pandemic. Data gaps limit the ability of data users to fully explain the impacts
to this crucial industry. The BLS BRSs for 2020, 2021, and 2022 served as nimble tools that allowed data users to study the impact of the coronavirus pandemic on the labor
market and broader economy. Using these data, we filled in a few of the known data gaps to more completely explain the impact of COVID-19 on the childcare industry.
Our results from the 2020 and 2021 BRS data show that some workplace flexibilities, such as telework, were offered at lower rates at child daycare services, while others,
such as alternative work schedules, were offered at higher rates.34 Required safety protocols for COVID-19 were implemented at higher rates at child daycare services.
The COVID-19 pandemic necessitated new, possibly permanent, requirements on child daycare services so that they can operate safely. These requirements included
vaccinations, COVID-19 testing, masking, screening tests, cleaning and disinfecting, handwashing and respiratory hygiene, and ventilation improvements.35 These
requirements have associated costs that affect operational costs of child daycare services.36
Results from the 2022 BRS data show that child daycare services establishments were more likely to hire and have vacancies than were establishments in the nation overall.
This finding is important because, in the childcare industry, continuity of individual workers is necessary to build bonds with young children and families.37 Costs are also
associated with hiring and filling vacancies, such as posting jobs, onboarding, and training new employees.
Looking at March 2023 QCEW data, the most recent datapoint available, we find that child daycare services employed 942,000 workers, surpassing its prepandemic level of
929,000 workers in April 2019.38 Average weekly wages in first-quarter 2023 were $592, compared with $1,487 nationally. Wages in childcare continue to be among the
lowest nationally.
The childcare industry is marked by low wages and high labor turnover, yet it is critical in supporting workers across all industries.39 Future research on the child daycare
services industry and the supporting labor market may be fertile ground to isolate the impact of COVID-19-related requirements on establishments with respect to
employment, wages, and labor turnover.
Also worth noting is that in March 2021, the American Rescue Plan (ARP) included $24 billion for the Child Care Stabilization Program.40 According to the Health and
Human Services Office of Child Care, as of December 31, 2022, the ARP Child Care Stabilization Program had served more than 220,000 childcare providers, affecting as
many as 9.6 million children. Providers used such awards to help with operational costs such as wages and benefits, rent and utilities, program materials and supplies, and
cleaning and sanitation.41 These federal funds needed to be fully used by states, territories, and tribes as of September 30, 2023.42 New research points to considerable
difficulty across the childcare industry as it continues to operate absent these stabilization funds.43
SUGGESTED CITATION:

Kevin Cooksey and Emily Thomas, "Childcare employment—before, during, and after the COVID-19 pandemic," Monthly Labor Review, U.S. Bureau of Labor Statistics, January 2024,

https://doi.org/10.21916/mlr.2024.1

Notes

1 Lauren Russell and Chuxuan Sun, “The effect of mandatory child care center closures on women’s labor market outcomes during the COVID-19 pandemic,” Covid Economics, no. 62,
December 2020, https://bpb-us-w2.wpmucdn.com/web.sas.upenn.edu/dist/0/610/files/2020/12/RussellChuxuan_2020_CovidEconomicsIssue62.pdf.

2 Liana Christin Landivar and Mark deWolf, “Mothers’ employment two years later: an assessment of employment loss and recovery during the COVID-19 pandemic” (U.S. Department of
Labor, Women’s Bureau, May 2022), https://www.dol.gov/sites/dolgov/files/WB/media/Mothers-employment-2%20-years-later-may2022.pdf.

3 “Quarterly census of employment and wages: overview,” BLS Handbook of Methods (U.S. Bureau of Labor Statistics, last modified February 17, 2023),

https://www.bls.gov/opub/hom/cew/.
4 The “child day care services” industry and the terms “child care” and “health care” are referred to as such in other official documents, websites, and databases, but they are referred to as
“child daycare services,” “childcare,” and “healthcare,” respectively, in this article to conform to the Government Publishing Office Style Manual publication standards. According to the Centers
for Disease Control and Prevention (CDC) COVID-19 timeline, on March 11, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. Around mid-March 2020, schools,
childcare centers, restaurants, and other businesses began shutting down in-person operations. By April 13, 2020, most states in the United States reported widespread cases of COVID-19. In
December 2020, Americans started to become eligible for the COVID-19 vaccine, and by April 2021, the United States surpassed 200 million vaccinations administered. In June 2021, the
COVID-19 Delta variant became the dominate variant in the United States, kicking off a third wave of infections during the summer 2021. In November 2021, the WHO identified Omicron as a

variant of concern, leading to another spike around and after the winter holidays. For more information, see “COVID-19 timeline,” David J. Sencer CDC Museum: in association with the
Smithsonian Institution (U.S. Department of Health and Human Services, CDC, no date), https://www.cdc.gov/museum/timeline/covid19.html.

5 “2020 results of the Business Response Survey,” Business Response Survey (U.S. Bureau of Labor Statistics), https://www.bls.gov/brs/2020-results.htm.
6 “2021 results of the Business Response Survey,” Business Response Survey (U.S. Bureau of Labor Statistics), https://www.bls.gov/brs/2021-results.htm.
7 “2022 results of the Business Response Survey,” Business Response Survey (U.S. Bureau of Labor Statistics), https://www.bls.gov/brs/2022-results.htm. For more information about the
Business Response Survey (BRS), see https://www.bls.gov/brs/.

8 For more information about the QCEW program, see https://www.bls.gov/opub/hom/cew/.
9 For more information about QCEW coverage and employment exclusions, see https://www.bls.gov/cew/overview.htm#coverage.
10 North American Classification System (U.S. Census Bureau), https://www.census.gov/naics/.
11 North American Classification System (U.S. Census Bureau, 2017), p. 523, https://www.census.gov/naics/reference_files_tools/2017_NAICS_Manual.pdf.
12

Data from the 2020 BRS for healthcare were published with a modification in North American Industry Classification System (NAICS) sector 62 to break out subsectors 621 to 623,

healthcare, from NAICS 624, social assistance. Healthcare is used as a comparison point in 2020, by using NAICS 621 to 623 compared with NAICS 624. The full sector 62 is used as a
comparison for 2020 and 2021 data in alignment with BRS publications.

13 “Quarterly census of employment and wages.”
14 Elise Gould, “Childcare workers aren’t paid enough to make ends meet,” Issue Brief 405 (Economic Policy Institute, November 5, 2015), https://www.epi.org/publication/child-care-

workers-arent-paid-enough-to-make-ends-meet/#:~:text=Child%20care%20workers%20are%20largely,in%20other%20occupations%20are%20women; “Labor force statistics
from the Current Population Survey,” Current Population Survey (U.S. Bureau of Labor Statistics), https://www.bls.gov/cps/; Asha Banerjee, Elise Gould, and Marokey Sawo, “Setting higher
wages for child care and home health care workers is long overdue,” report (Economic Policy Institute, November 18, 2021), https://www.epi.org/publication/higher-wages-for-child-care-

and-home-health-care-workers/; and “Bearing the cost report: how overrepresentation in undervalued jobs disadvantaged women during the pandemic,” report (U.S. Department of Labor,
March 15, 2022), https://www.dol.gov/sites/dolgov/files/WB/media/BearingTheCostReport.pdf.
15 “Quarterly census of employment and wages.”
16

“Business Employment Dynamics: overview,” BLS Handbook of Methods (U.S. Bureau of Labor Statistics, last modified December 24, 2015), https://www.bls.gov/opub/hom/bdm/.

17 “2020 results of the Business Response Survey.”
18 Caitlin Gibson, “‘The most crushing, anxious parenting choice’: to return to day care or not?,” The Washington Post, September 3, 2020, https://www.washingtonpost.com/lifestyle/on-

parenting/covid-day-care-decisions/2020/09/02/03dde7ea-ea1b-11ea-970a-64c73a1c2392_story.html. In this Post article, parents weigh daycare return amid continuing the COVID-19
pandemic.

19 “Financial health of workers in low-wage jobs,” Workplace, research paper (Financial Health Network, July 28, 2022), https://finhealthnetwork.org/research/financial-health-of-

workers-in-low-wage-jobs/.
20 “Household data annual averages—employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity,” Labor force statistics from the Current Population Survey (U.S.
Bureau of Labor Statistics), https://www.bls.gov/cps/cpsaat11.htm.

21 “Bearing the cost report.”
22 “2020 results of the Business Response Survey.”
23 Michael Dalton and Jeffrey A. Groen, “Telework during the COVID-19 pandemic: estimates using the 2021 Business Response Survey,” Monthly Labor Review, March 2022,

https://doi.org/10.21916/mlr.2022.8.
24 “2020 results of the Business Response Survey.”
25 “2021 results of the Business Response Survey.”
26 Gould, “Childcare workers aren’t paid enough to make ends meet.”
27 “2020 results of the Business Response Survey.”
28

“Comparing the experiences of essential and nonessential businesses during COVID-19,” The Economics Daily (U.S. Bureau of Labor Statistics, March 3, 2021),

https://www.bls.gov/opub/ted/2021/comparing-the-experiences-of-essential-and-nonessential-businesses-during-covid-19.htm.
29 “2021 results of the Business Response Survey.”
30 “2021 results of the Business Response Survey.”
31 “Number of unemployed persons per job opening, seasonally adjusted,” Graphics for Economic News Releases (U.S. Bureau of Labor Statistics), https://www.bls.gov/charts/job-

openings-and-labor-turnover/unemp-per-job-opening.htm.
32 “2022 results of the Business Response Survey.”
33 “2022 results of the Business Response Survey.”
34

See the 2020 to 2022 Business Response Surveys at https://www.bls.gov/brs/2020-results.htm, https://www.bls.gov/brs/2021-results.htm, and https://www.bls.gov/brs/2022-

results.htm, respectively.
35 “Guidance for child care programs that remain open” (U.S. Department of Health and Human Services, CDC, no date), https://archive.cdc.gov/#/details?

url=https://www.cdc.gov/coronavirus/2019-ncov/community/pdf/Reopening_America_Guidance.pdf. Note that the CDC have archived the source.
36 Simon Workman and Steven Jessen-Howard, “The true cost of providing safe child care during the coronavirus pandemic” (Center for American Progress, September 3, 2020),

https://www.americanprogress.org/article/true-cost-providing-safe-child-care-coronavirus-pandemic/.
37

Rob Grunewald, Ryan Nunn, and Vanessa Palmer, “Examining teacher turnover in early care and education” (Federal Reserve Bank of Minneapolis, April 29, 2022),

https://www.minneapolisfed.org/article/2022/examining-teacher-turnover-in-early-care-and-education.
38 QCEW program, https://www.bls.gov/opub/hom/cew/.

39 “Committee on Early Childhood Care and Education Workforce: A Workshop,” Institute of Medicine, National Research Council, The Early Childhood Care and Education Workforce:
Challenges and Opportunities: A Workshop Report (Washington, DC: National Academies Press (US); November 15, 2011, https://doi.org/10.17226/13238; and Meg Caven, Noman
Khanani, Xinxin Zhang, and Caroline E. Parker, “Center- and program-level factors associated with turnover in the early childhood education workforce,” REL 2021–069 (Department of
Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Northeast & Islands, March 2021),

https://ies.ed.gov/ncee/rel/regions/northeast/pdf/REL_2021069.pdf.
40 “ARP Act child care stabilization grants,” CCDF-ACF-IM-2021-02, Office of Child Care, Administration for Children and Families (Washington, DC: Department of Health and Human
Services, June 12, 2023), https://www.acf.hhs.gov/occ/policy-guidance/ccdf-acf-im-2021-02.

41 “ARP Child care stabilization funding state and territory fact sheets,” Office of Child Care, Administration for Children and Families (Washington, DC: Department of Health and Human
Services, June 2022), https://www.acf.hhs.gov/occ/map/arp-act-stabilization-funding-state-territory-fact-sheets.

42 “Overview of ARP Act child care stabilization guidance,” Office of Child Care, Administration for Children and Families (Washington, DC: Department of Health and Human Services, June
2021), https://www.acf.hhs.gov/sites/default/files/documents/occ/Overview_of_ARP_Act_Child_Care_Stabilization_Guidance.pdf.

43 Julie Kashen, Laura Valle Gutierrez, Lea Woods and Jessica Milli, “Child care cliff: 3.2 million children likely to lose spots with end of federal funds,” Care Economy (NY: The Century
Foundation, June 21, 2023), https://tcf.org/content/report/child-care-cliff/.

ABOUT THE AUTHOR

Kevin Cooksey
cooksey.kevin@bls.gov
Kevin Cooksey is a supervisory economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics.
Emily Thomas
thomas.emily.2@dol.gov
Emily Thomas, formerly a supervisory economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics, is the Director of Data Analytics and
Strategic Planning in the Office of Policy, Development, and Research, U.S. Employment and Training Administration.

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