The Review of Economics and Statistics
VOL. XCV
MAY 2013
NUMBER 2
WHO CREATES JOBS? SMALL VERSUS LARGE VERSUS YOUNG
John Haltiwanger, Ron S. Jarmin, and Javier Miranda*
Abstract—The view that small businesses create the most jobs remains
appealing to policymakers and small business advocates. Using data from
the Census Bureau’s Business Dynamics Statistics and Longitudinal Business Database, we explore the many issues at the core of this ongoing
debate. We find that the relationship between firm size and employment
growth is sensitive to these issues. However, our main finding is that once
we control for firm age, there is no systematic relationship between firm
size and growth. Our findings highlight the important role of business
start-ups and young businesses in U.S. job creation.
I.
Introduction
A
common popular perception about the U.S. economy
is that small businesses create the most private sector
jobs. This perception is popular among politicians of different political persuasions, small business advocates, and the
business press.1 While early empirical studies (see Birch,
1979, 1981, 1987) provided support for this perception, a
variety of subsequent empirical studies have highlighted
(see, in particular, Davis, Haltiwanger, Schuh, 1996) statistical and measurement pitfalls underlying much of the evidence in support of this perception. These include the lack
of suitable data to study this issue, the failure to distinguish
between net and gross job creation, and statistical problems
associated with size classification methods and regression
to the mean.2 From a theoretical perspective, the notion of
an inverse relationship between firm size and growth runs
Received for publication August 25, 2010. Revision accepted for publication December 21, 2011.
* Haltiwanger: University of Maryland and NBER; Jarmin and Miranda:
U.S. Census Bureau.
We thank Philippe Aghion, Peter Huber, Harald Oberhofer, Michael
Pfaffermayr, an anonymous referee, and conference and seminar participants at the NBER 2009 Summer Institute Meeting of the Entrepreneurship
Working Group, CAED 2009, World Bank 2009 Conference on Small
Firms, NABE Economic Policy Conference 2010, OECD Conference on
Entrepreneurship 2010, Queens University and the 2010 WEA meetings
for helpful comments. We thank the Kauffman Foundation for financial
support. Any opinions and conclusions expressed here are our own and do
not necessarily represent the views of the U.S. Census Bureau. All results
have been reviewed to ensure that no confidential information is disclosed.
A supplemental appendix is available online at http://www.mitpress
journals.org/doi/suppl/10.1162/REST_a_00288.
1
Policymakers regularly state that small businesses create most net new
jobs. One of there common claims is that small businesses create twothirds or more of net new jobs. Every president since President Reagan
has included such statements in major addresses (often in the State of the
Union addresses to Congress), and many other leaders in the U.S. House
and Senate have made similar remarks. A list of selected quotes from
speeches is available on request.
2
Brown, Hamilton, and Medoff (1990) raise many related statistical
issues in considering statistics by firm size but focus more on the impact
of measurement issues for the employer size wage differential.
counter to that described by Gibrat’s law (see Sutton,
1997). But in spite of these questions from the academic literature, given the lack of definitive evidence to the contrary, the popular perception persists.
Neumark, Wall, and Zhang (2011; hereafter NWZ)
recently performed a careful analysis where they avoid the
misleading interpretations of the data highlighted by Davis,
Haltiwanger, and Schuh (1996; hereafter DHS). Using the
National Establishment Time Series (NETS) data including
coverage across the U.S. private sector from 1992 to 2004,
they find an inverse relationship between net growth rates
and firm size. Their analysis indicates that small firms contribute disproportionately to net job growth.
In this paper, we demonstrate that an additional critical
issue clouds the interpretation of previous analyses of the relationship between firm size and growth. Data sets traditionally
employed to examine this relationship contain limited or no
information about firm age. Our analysis emphasizes the role
of firm age and, especially, firm births in this debate using
comprehensive data tracking all firms and establishments in
the U.S. nonfarm business sector for the period 1976 to 2005
from the Census Bureau’s Longitudinal Business Database
(LBD).3 As will become clear, the LBD is uniquely well suited to study these issues on an economy-wide basis.
Our main findings are summarized as follows. First, consistent with NWZ, when we do not control for firm age, we
find an inverse relationship between net growth rates and
firm size, although this relationship is quite sensitive to
regression-to-the-mean effects. Second, once we add controls for firm age, we find no systematic inverse relationship
between net growth rates and firm size. A key role for firm
age is associated with firm births. We find that firm births
3
An important early study that also emphasized the role of firm age for
growth dynamics is Evans (1987), who found an inverse relationship
between firm growth and firm size (holding firm age constant) and
between firm growth and firm age (holding firm size constant) using firmlevel data for U.S. manufacturing firms. As Evans points out, the work is
based on data with substantial limitations for tracking start-ups and young
firms, but, interestingly, some aspects of his findings hold for our data
which do not suffer from the same limitations. Specifically, the departures
from Gibrat’s law are primarily for young and small firms. A variety of
other studies have also examined the role of employer age for employer
dynamics and employment growth, including Dunne et al. (1989), Haltiwanger and Krizan (1999), and Acs, Armington, and Robb (1999). These
studies focused on the establishment–age establishment-growth relation,
including patterns of growth and failure, as well as the volatility of new
establishments. All of these studies with the exclusion of Acs et al. (1999)
are limited to the manufacturing sector.
The Review of Economics and Statistics, May 2013, 95(2): 347–361
No rights reserved. This work was authored as part of the Contributor’s official duties as an Employee of the United States Government and is therefore a work of the United States
Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. law.
348
THE REVIEW OF ECONOMICS AND STATISTICS
contribute substantially to both gross and net job creation.
Importantly, because new firms tend to be small, the finding
of a systematic inverse relationship between firm size and
net growth rates in prior analyses is entirely attributable to
most new firms being classified in small size classes.
Our findings emphasize the critical role start-ups play in
U.S. employment growth dynamics. We document a rich upor-out dynamic of young firms in the United States. That is,
conditional on survival, young firms grow more rapidly than
their more mature counterparts. However, young firms have a
much higher likelihood of exit, so job destruction from exit is
also disproportionately high among them. More generally,
young firms are more volatile and exhibit higher rates of gross
job creation and destruction than their older counterparts.
These findings highlight the importance of theoretical
models and empirical analyses that focus on the start-up
process—both the entry process and the subsequent postentry dynamics, especially in the first ten years or so of a
firm’s existence. This is not to deny the importance of
understanding and quantifying the ongoing dynamics of
more mature firms but to highlight that business start-ups
and young firms are inherently different.
Using the rich data available from the LBD and its public
use version, the Business Dynamics Statistics (BDS), we
highlight how the complex dynamics underlying firm formation, growth, decline, and exit combine to determine net
job creation in the economy. The formation and execution
of effective policies intended to increase net job creation
require a rich and nuanced understanding of these processes. A natural conclusion from our findings on the role
of firm size and age is that policies that target businesses of
a certain size, while ignoring the role of age, will likely
have limited success in improving net job creation. Our
findings show that small, mature businesses have negative
net job creation, and economic theory suggests this is not
where job growth is likely to come from. Alternatively, our
findings show that start-ups and young firms are important
sources of job creation but that young firms are inherently
volatile, with a high exit rate. It may be that even if the latter patterns are qualitatively consistent with healthy business dynamics, the challenges that start-ups and young
firms face (such as regulatory challenges and market failures) warrant policy intervention. Exploring the latter is
beyond the scope of this paper, but our findings highlight
that effective policymaking in this area requires a rich
understanding of such business dynamics. We return to this
theme in our concluding remarks.
The rest of the paper proceeds as follows. In section II,
we provide further background on the literature. Section III
describes the data. Section IV presents the main empirical
results. Section V provides concluding remarks. In several
places, we point interested readers to an online appendix
containing several analyses not discussed in detail here.4
4
Available at http://www.mitpressjournals.org/doi/suppl/10.1162/REST
_a_00288.
II.
Background
Much of the support for the hypothesis of an inverse relationship between employer size and growth comes from
interpreting patterns observed in public use data products.
An example is the Census Bureau’s Statistics of U.S. Business (SUSB) that is released in partnership with the Small
Business Administration.5 However, as demonstrated by
NWZ and confirmed in this paper, this finding can also be
obtained from a careful analysis of business microdata. In
this section, we review the data and measurement issues in
prior studies of firm size and growth and describe the characteristics of data sets suited to such analyses. We then
briefly highlight findings from the Census Bureau’s Business Dynamics Statistics (BDS). This new public use product gives data users a much richer window on the interactions of size, age, and growth that was previously available
only to those with access to restricted-use data.
A. Review of Data and Measurement Issues
Analyses of the relationship between firm size and growth
have been hampered by data limitations and measurement
issues. As a consequence, these studies fail to emphasize a
much richer description of the firm dynamics associated with
the creative destruction process prevalent in market economies. Results from the new public use BDS, as well as from
its underlying source data, the LBD, reveal a more accurate
picture of firm dynamics with a more limited role for firm size.
This section describes the basic characteristics of these data
and how we address some of the limitations of prior analyses.
The analytical power of the LBD and data products constructed from it for understanding firm dynamics comes
from its ability to accurately track both establishments and
their parent firms over time.6 This is a critical feature of the
data since it is difficult to discern the relationships of interest using only firm- or establishment-level data. Measures
of job growth derived solely from establishment-level data
have the virtue that they are well defined; when we observe
an establishment grow, we know there are net new jobs at
that establishment. In contrast, job growth observed in firmlevel data may simply reflect changes in firm structure
brought about by mergers, acquisitions, and divestitures.
These activities clearly have an impact on observed employment at firms engaging in them and are ubiquitous features of market economies. For the purposes of allocating
employment growth across different classes of firms (for
example, by size, age, and industry), we clearly want to
abstract from changes that reflect only a reallocation of
employment across firms due to mergers and acquisitions.
5
SUSB data are available at http://www.census.gov/econ/susb
/index.html.
6
For purposes of this discussion as well as the subsequent empirical
analysis, we use the definitions of establishments and firms as defined by
the U.S. Census Bureau. Specifically, an establishment is a specific physical location where business activity occurs, while a firm reflects all the
establishments under common operational control.
WHO CREATES JOBS?
Having only establishment-level data is inadequate as well.
If the only data available are at the establishment level, the
relationship between growth and the size and age of the establishment may not provide much information about the relevant
firm size and firm age. A large, national retail chain is a useful
example. In retail trade, a firm’s primary margin of expansion
is opening new stores rather than the expanding existing stores
(see Doms, Jarmin, & Klimek, 2004; Foster, Haltiwanger, &
Krizan, 2006; Jarmin, Klimek, & Miranda, 2009). This implies that there are many new establishments of existing firms,
and for the core issues in this paper, the growth from such new
establishments should be classified based on the size and age
of the parent firm, not the size and age of the establishment.
Much of the literature on employer size and net growth has
been based primarily on establishment-level or firm-level data
but not both.7 Tracking the dynamics of both firms and their
constituent establishments permits clear and consistent measures of firm growth, as well as firm entry and exit.8
Even with rich source data, a key challenge in analyzing
establishment and firm dynamics is the construction and
maintenance of high-quality longitudinal linkages that allow
accurate measurement of establishment and firm births and
deaths. Given the ubiquitous changes in ownership among
U.S. firms, a common feature observed in business microdata is spurious firm entry and exit caused by purely legal
and administrative actions. Early versions of the D&B data
Birch used were plagued with these limitations, which hampered the ability of researchers to distinguish between real
business dynamics and events triggered by legal actions or
business transactions such as credit applications (see Birley,
1984, and Aldrich et al., 1988, for detailed discussion). The
NETS data used by NZW is based on a much improved version of the D&B data, although there are some open questions about the nature of the coverage in NETS.9 For our ana7
DHS analysis is restricted to U.S. manufacturing establishments,
although they were able to construct a measure of firm size at the manufacturing level. Dunne et al. (1989) examine the role of establishment size
and age for the growth and failure of U.S. manufacturing plants. Evans
(1987) used firm-level data for a sample of firms in the U.S. manufacturing
sector in continuous operation between 1976 and 1980. Birch (1979, 1981,
1997) uses the D&B data, which have both firm and establishment-level
information although subject to the limitations of the D&B data. NZW use
the NETS data with both firm- and establishment-level information.
8
In our analysis, firm entry is defined when all of the establishments at
that firm are de novo establishment entrants. Firm exit is defined when all
of the establishments at that firm cease operations.
9
NWZ report about 13.1 million firms and 14.7 million establishments
in a typical year. The LBD (and the closely related County Business Patterns) report about 6 million firms and 7 million establishments in a typical year that have at least one paid employee. The Census Bureau also
reports more than 15 million additional nonemployer businesses in a typical year. It appears that NETS is some combination of employer and
nonemployer businesses but does not reflect the universe of businesses.
For our purposes, we focus on employer businesses. For discussion of the
importance of nonemployer businesses and the relationship between
nonemployer and employer businesses, see Davis et al. (2009). There also
remain questions about how well NETS captures start-ups especially for
small businesses. These questions about coverage also raise questions
about whether the type of analysis we conduct here focusing on the role
of firm age would be feasible with NETS. We provide a table comparing
the major characteristics of the principal data sets available to study the
dynamics of U.S. businesses in our online appendix.
349
lysis, we minimize the impact of these data quality issues by
using the LBD’s high-quality longitudinal establishment linkages and its within-year linkages of establishments to their
parent firms.
DHS recognized the statistical pitfalls in relating
employer size and growth. One issue they highlight is the
role of regression-to-the-mean effects. Businesses that
recently experienced negative transitory shocks (or even
transitory measurement error) are more likely to grow,
while businesses recently experiencing positive transitory
shocks are more likely to shrink. This effect alone will yield
an inverse relationship between size and growth. Friedman
(1992) states that this type of regression fallacy ‘‘is the most
common fallacy in the statistical analysis of economic
data.’’ This issue is particularly relevant when studying the
business size–growth relationship and is manifest in the
method used to classify businesses into size classes in many
commonly used data sources. The early work by Birch and
others classified businesses into size classes using base year
employment, a method now known to yield results that suffer from regression to the mean.
DHS propose an alternative classification method to mitigate the effects of regression to the mean. They note that
while base-year-size classification yields a negative bias,
using end-year-size classification yields a positive bias. To
avoid the bias, negative or positive, DHS propose using a
classification based on current average size, where current
average size is based on the average of employment in
years t 1 and t. Using current average size is a compromise between using year t 1 (base) or year t (end) size to
classify firms. In what follows, we refer to current average
size as simply average size.
Although average size is a compromise, it has limitations
as well. Firms that are affected by permanent shocks that
move the firm across multiple size class boundaries
between t 1 and t will be classified into a size class that is
between the starting and ending size classes. Recognizing
this potential limitation, the Bureau of Labor Statistics has
developed a dynamic size classification methodology (see
Butani et al., 2006).10 Specifically, the methodology attributes job gains or losses to each of the size classes that the
firm passes through in its growth or contraction. Interestingly, comparisons across size-classification methods show
that the average (DHS) and dynamic (BLS) size classification methodologies yield very similar patterns. This is not
surprising since both are a form of averaging over time to
deal with transitory shocks.
We prefer the average size class methodology because it
is inherently more robust to regression-to-the-mean effects.
However, we also report results using the base-year metho10
Related evaluation work on alternative methodologies by BLS is
found in Okolie (2004). We also note that the BLS BED series releases
net and gross job quarterly flows by this firm size measure. The firm size
measure they use is based on a taxpayer ID definition of the firm, so for
multiunit establishment firms that have multiple taxpayer IDs, their firm
definition is somewhere between the establishment and overall firm.
350
THE REVIEW OF ECONOMICS AND STATISTICS
dology for our core results and also in order to explore the
sensitivity of the results to this methodological issue.11
DHS also emphasize avoiding inferences that arise from
the distinction between net and gross job creation. Policy
analysts are tempted to want to make statements along the
lines that ‘‘small businesses account for X% of net job creation.’’ The problem with statements like this is that many
different groupings of establishments can account for a large
share of the net job creation since gross job flows dwarf net
job flows. For example, the annual net employment growth
rate for U.S. nonfarm private sector business establishments
between 1975 and 2005 averaged 2.2%. Underlying this net
employment growth rate were establishment-level average
annual rates of gross job creation and destruction of 17.6%
and 15.4%, respectively (statistics from the BDS, which we
describe below). Decomposing net growth across groups of
establishment or firms is problematic (at least in terms of
interpretation) when some shares are negative. We elaborate
on these issues in section II.B by taking a closer look at the
Census Bureau’s new BDS data.
B. Overcoming Data and Measurement Issues with the BDS
To help illustrate these points before proceeding to the
more formal analysis, we examine some tabular output
from the BDS on net job creation by firm size and firm age.
The precise definitions of firm size and firm age are discussed below (and are described on the BDS website, http://
www.census.gov/ces/dataproducts/bds/index.html). Table 1
shows the number of net new jobs by firm size and firm age
class in 2005. The upper panel shows the tabulations using
the base-year-size method and the lower panel the averagesize method. The table yields a number of interesting observations. About 2.5 million net new jobs were created in the
U.S. private sector in 2005. Strikingly, firm start-ups (firms
with age 0) created about 3.5 million net new jobs. In contrast, every other firm age class except for the oldest firms
exhibited net declines in employment in 2005.12 However,
it would be misleading to say that it is only firm start-ups
and the most mature firms that contributed to job gains. In
both panels there are large positive numbers in many cells
but also large negative numbers in other cells. It is also
clear that there are substantial differences in these patterns
depending on using the base-year or average-size method,
although some common patterns emerge. For example,
excluding start-ups, firms that employ between 5 and 99
workers consistently exhibit declines in net jobs.
The patterns reflect two basic ingredients. Obviously,
whether the size or age class contributes positively or negatively depends on whether that class has a positive or negative
net growth rate. In addition, the magnitude of the positive or
negative contribution depends, not surprisingly, on how
11
The online appendix includes all results by base-year size methodology.
Note that LBD processing uses longitudinal edits that can alter values
in the BDS tables.
12
much employment is accounted for by that cell. That is, a size
or age class may have a large positive number not so much
because it has an especially high growth rate but because it
accounts for a large fraction of employment (for example, a
1% growth rate on a large base yields many net new jobs).
Figure 1 summarizes these patterns in the BDS over
1992 to 2005 by broad size and age classes.13 The figure
shows the fraction of job creation and job destruction
accounted for by small (fewer than 500 workers) and large
firms (500 workers and above) broken out by whether they
are firm births, young firms (less than 10 years old), or
mature firms (10 years and above). Also included is the
share of employment accounted for by each of these groups.
We focus on gross job creation and destruction at the establishment level but classified by the characteristics of the
firms that own them.
Several observations emerge. First, for the most part, the
fraction of job creation and destruction accounted for by the
various groups is roughly proportional to the share of
employment accounted for by each group. For example, it is
the mature and large firms that account for most employment
(about 45%) and most job creation and destruction. This
observation, while not surprising, is important in the debate
about what classes of businesses create jobs. The basic
insight is that the firms that have the most jobs create the
most jobs, so workers looking for the places where the most
jobs are being created should go where the jobs are: large
and mature firms. This is not the whole story, of course, as
what we are primarily interested in is whether any identifiable groups of firms disproportionately create or destroy
jobs. The rest of the paper is a rigorous examination of this
issue. However, figure 1 nicely previews some of our primary findings. Young firms disproportionately contribute to
both job creation and job destruction. Included among young
firms are firm births, which by definition contribute only to
job creation. Nearly all firm births are small.14 Before the
BDS, all publicly available data that could be used to look at
the role of firm size in job creation were silent on the age
dimension. As such, it is easy to see how analysts perceived
an inverse relationship between size and growth in the data.
Before proceeding, it is instructive to discuss briefly
the implications of focusing on March-to-March annual
changes of employment at the firm and establishment level
in our analysis of firm dynamics and job creation. One
implication is that we neglect high frequency within year
firm and establishment dynamics—for example, changes
that are transitory and reverse themselves within the year.
We think that for the most part, neglecting such highfrequency variation is not important for the issues of con13
We use the base-year size method in figure 1. The results in figure 1
are robust to using either of the size classification methods discussed in
the analysis that follows. Precise definitions of job creation and destruction are provided below.
14
These large firm births are often associated with the appearance of a
new U.S. affiliate of a foreign-owned firm or changes in employment
arrangements, for example, the use of employee leasing firms.
541,230
1,242
34,487
30,935
27,141
28,196
112,735
74,905
59,389
43,548
83,682
44,970
503,644
12,547
24,840
22,650
18,442
19,792
71,332
47,730
36,856
28,173
38,599
182,683
5–9
453,073
10,705
24,132
25,119
19,487
23,791
99,872
75,477
61,306
47,143
107,356
19,905
498,317
20,836
31,883
26,855
23,212
24,392
99,235
67,923
58,236
42,609
71,235
31,901
10–19
445,091
3,028
15,745
12,259
7,210
16,205
76,025
60,259
60,496
42,924
114,182
42,814
553,181
47,837
44,488
37,824
29,616
29,425
110,111
81,876
71,299
51,490
107,390
58,175
20–49
236,121
20,046
5,380
1,824
1,630
2,595
17,730
17,677
13,235
16,172
40,005
106,735
313,511
41,006
26,738
15,918
641
14,870
40,652
40,432
35,979
22,246
48,873
27,438
50–99
U.S. Census Bureau, Business Dynamics Statistics at http://www.ces.census.gov/index.php/bds/bds_home.
1,157,210
188,206
102,079
77,770
61,216
54,616
190,115
105,596
74,278
49,929
89,878
163,527
731,515
79,759
26,506
7,535
20,456
4,808
14,577
15,663
5,673
2,923
1,016
910,431
0
1
2
3
4
5
6–10
11–15
16–20
21–25
26þ
All
Firm age
0
1
2
3
4
5
6–10
11–15
16–20
21–25
26þ
All
1–4
Firm age
250–499
500–999
216,911
28,733
3,125
2,215
2,221
6,890
13,713
11,166
12,172
4,020
42,481
286,181
151,518
20,118
5,036
2,572
3,505
5,779
26,305
20,401
27,334
23,438
63,939
349,945
128,772
14,346
9,743
888
6,655
11,703
19,344
1,617
6,559
22,298
69,597
272,036
292,348
157,120
151,518
57,188
48,830
5,476
18,026
9,049
13,579
14,813
8,981
7,548
9,816
4,301
5,436
2,449
6,849
6,222
1,324
9,452
5,437
27,666
9,530
2,179
9,780
5,725
10,200
13,346
3,901
10,269
10,309
19,924
85,473
164,036
82,628
226,188
Firm Size (Current)
100–249
Firm Size (Base Year)
188,493
6,509
13,282
10,155
7,375
4,850
5,364
34,591
4,413
30,120
93,401
328,961
186,087
14,532
23,615
11,581
298
293
20,693
2,028
3,204
36,484
56,436
209,171
1,000–2,499
TABLE 1.—NET JOB CREATION BY FIRM SIZE AND FIRM AGE, U.S. PRIVATE SECTOR, 2005
D
7,898
8,392
D
10,228
3,017
26,494
18,886
16,969
34,280
110,311
253,373
131,178
20,131
12,782
12,114
4,011
3,418
13,945
22,441
12,615
10,075
143,701
253,609
2,500–4,999
D
42
D
3,699
D
D
23,546
20,201
14,550
46,129
38,147
71,269
D
211
D
D
D
D
9,903
6,140
10,491
9,889
58,245
90,973
5,000–9,999
138
D
D
D
D
43,006
65,699
32,733
9,197
433,751
581,191
D
408
D
D
D
D
17,928
69,409
2,158
56,563
307,517
360,214
10,000þ
3,518,419
188,821
188,295
145,040
103,896
102,864
338,705
161,353
153,974
140,886
416,524
2,481,097
3,518,419
188,821
178,494
150,749
74,035
102,902
338,705
161,353
153,974
140,886
416,524
2,481,097
All
WHO CREATES JOBS?
351
352
THE REVIEW OF ECONOMICS AND STATISTICS
FIGURE 1.—SHARES OF EMPLOYMENT, JOB CREATION, AND DESTRUCTION BY BROAD FIRM (AVERAGE), SIZE AND AGE CLASSES: ANNUAL AVERAGE RATES, 1992–2005
0.50
0.45
Small (1-500)
Large (500+)
0.40
Shares
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
Firm Births
Young (1-10)
Mature (10+)
Employment
Firm Births
Job Creation
Young (1-10)
Mature (10+)
Job Destruction
cern in this paper but would be of more relevance in exploring cyclical volatility by firm size and age.
However, a related implication of focusing on March-toMarch annual changes is that very short-lived firms that enter
and exit between March of one year and March of the subsequent year are not captured in our analysis. The neglect of
the latter might be important in the current context given our
findings of the important role of firm births for job creation,
as is evident in table 1 and figure 1. Fortunately, the LBD
includes information that suggests that such short-lived firm
births are not especially important. That is, the LBD also
includes annual payroll for all establishments and firms. The
payroll measure captures any positive activity of establishments and firms, including very short-lived firms, whereas
employment is measured only as of March 12. Using the
same longitudinal links as used in the BDS and LBD, we calculated the payroll-weighted firm entry rate as 1.72% of payroll. This compares to the employment-weighted firm entry
rate of 2.79% of employment in figure 1. It is not surprising
that the payroll-weighted entry rate is lower than the
employment-weighted entry rate given that entrants are
small and pay lower wages. Of more interest is how much of
the payroll-weighted entry rate is accounted for by very
short-lived entrants. Excluding the short-lived entrants
(defined as firm start-ups that do not survive until March),
the payroll-weighted entry rate is 1.64%. This negligible
decline in the payroll-weighted entry rate from short-lived
entrants implies that such entrants account for very little of
the activity even for start-ups. Abstracting from such shortlived firms should not have a quantitatively important impact
on our analysis. It does, however, remind us of the highly
volatile nature of start-ups, an issue that we discuss further.
BDS. However, we use the LBD microdata rather than the
BDS since in using the LBD microdata, we can identify
firms and abstract from firm growth due to ownership
changes in the manner we describe below.15
The LBD (Jarmin & Miranda, 2002) covers all business
establishments in the U.S. private nonfarm sector with at
least one paid employee.16 The LBD begins in 1976 and
currently covers over thirty years of data, including information on detailed industry and employment for every
establishment. We note that the LBD (and, in turn, the
BDS) employment and job creation numbers track closely
those of the County Business Patterns (CBP) and Statistics
of U.S. Business (SUSB) programs of the U.S. Census
Bureau (see Haltiwanger, Jarmin, & Miranda, 2009) as they
all share the Census Bureau’s Business Register (BR) as
their source data. However, due to design features and differences in processing, in particular the correction of longitudinal establishment and firm linkages, the statistics generated from the LBD diverge slightly from those in CBP and
SUSB.
The unit of observation in the LBD is the establishment
defined as a single physical location where business is conducted. Each establishment-year record in the LBD has a
firm identifier associated with it, so it is possible to track
the ownership structure of firms in any given year, as well
as changes over time. Firms can own a single establishment
or many establishments. In some cases, these firms span
multiple geographic areas and industries. Establishments
can be acquired, divested, or spun off into new firms, so the
ownership structure of firms can be dynamic and complex.
We use these firm-level identifiers to construct firm-level
characteristics for each establishment in the LBD.
C. Data and Measurement
15
Future versions of the BDS will include firm-level net employment
growth rates and components using the type of methodology we have
developed for this paper.
16
This is one clear distinction with the NETS database, which apparently includes both employer and nonemployer businesses (but also
apparently not the universe of both).
The LBD underlies the public use statistics in the BDS.
As suggested in section II.B, many of the patterns we discuss in this paper can be readily seen in the public domain
WHO CREATES JOBS?
A. Measuring Firm Age and Firm Size
The construction of firm size measures is relatively
straightforward. Firm size is constructed by aggregating
employment across all establishments that belong to the
firm. As discussed above, we measure firm size using both
the base-year and average-size methodologies. For baseyear firm size, we use the firm size for year t 1 for all
businesses except new firms. For new firms, we follow the
approach used by Birch and others and allocate establishments belonging to firm start-ups to the firm-size class in
year t. For average size, we use the average of firm size in
year t 1 and year t. We use the same approach for new,
existing, and exiting firms when using average size.
The construction of firm age presents more difficult conceptual and measurement challenges. We follow the
approach adopted for the BDS and based on our prior work
(see Becker et al., 2006, and Davis et al., 2007). The firm
identifiers in the LBD are not explicitly longitudinal. Nevertheless, they are useful for tracking firms and their changing
structure over time. A new firm identifier can appear in the
LBD either due to a de novo firm birth or changes in existing
firms. For example, a single-location firm opening additional
locations is the most common reason for a continuing firm in
the LBD to experience a change in firm ID. Other reasons
include ownership changes through mergers and acquisitions.
When a new firm identifier appears in the LBD for whatever
reason, we assign it an age based on the age of the oldest
establishment that the firm owns in the first year the new firm
ID is observed. The firm is then allowed to age naturally (by
one year for each additional year the firm ID is observed in
the data) regardless of mergers or acquisitions and as long as
the firm’s ownership and control do not change. An advantage of this approach is that firm births as well as firm deaths
are readily and consistently defined. That is, a firm birth is
defined as a new firm ID, where all the establishments at the
firm are new (entering) establishments. Similarly, a firm
death is defined as when a firm ID disappears and all of the
establishments associated with that firm ID cease operations
and exit. If a new firm identifier arises through a merger of
two preexisting firms, we do not treat it as a firm birth. Rather,
the new firm entity associated with the new identifier is given
a firm age equal to the age of the oldest continuing establishment of the newly combined entity.
Thus, our firm size and age measures are robust to ownership changes. For a pure ownership change with no change
in activity, there will be no spurious changes in firm size or
firm age. When there are mergers, acquisitions, or divestitures, firm age will reflect the age of the appropriate components of the firm. Firm size will change, but in a manner consistent with the change in the scope of activity.
Before proceeding, we note that we focus on growth
dynamics of establishments and firms over the 1992–2005
period. We limit our analysis to this period so that we can
define firm age consistently over the period for all establishments with firm age less than fifteen years. We also include
353
a category for establishments belonging to firms that are
sixteen years or older (in 1992, these are the firms with
establishments in operation in 1976 and for which we cannot give a precise measure of firm or establishment age).
B. The Establishment-Level and Aggregate Growth Rate
Concepts
This section describes the establishment- and firm-level
growth rate measures we use in the paper in more detail.
Let Eit be employment in year t for establishment i. In the
LBD, establishment employment is a point-in-time measure
reflecting the number of workers on the payroll for the payroll period that includes March 12. We measure the establishment-level employment growth rate as follows:
git ¼ ðEit Eit1 Þ=Xit ;
where
Xit ¼ :5 ðEit þ Eit1 Þ:
This growth rate measure has become standard in analysis of establishment and firm dynamics because it shares
some useful properties of log differences but also accommodates entry and exit. (See DHS and Tornqvist, Vartia, &
Vartia 1985.17)
Note that the DHS growth rate measure can be flexibly
defined for different aggregations of establishments. We
first discuss the measures of net growth used in the analysis.
In particular, consider the following relationships,
X
X
X
ðXst =Xt Þgst ¼
ððXst =Xt Þ
ðXit =Xst Þgit Þ;
gt ¼
s
s
i2s
where
Xt ¼
X
s
Xst ¼
XX
s
Xit ;
i2s
where gt is the aggregate DHS growth rate and s indexes
classifications of establishments into groups defined for any
level of aggregation s where s can refer to firm, industry,
firm size, or firm age classifications. Thus, the DHS net
growth rates for various aggregations of interest are just
properly weighted sums of establishment-level growth rates
where the establishment is the lowest level of aggregation
in the LBD. Important groupings for this paper include
firms and firm size and age categories.
Before discussing components of the DHS net growth
that we use in our analysis, it is important to discuss how
computing DHS net growth rates at different levels of
17
The DHS growth rate like the log first difference is a symmetric
growth rate measure but has the added advantage that it accommodates
entry and exit. It is a second-order approximation of the log difference for
growth rates around 0. Note that the use of a symmetric growth rate does
not obviate the need to be concerned about regression-to-the-mean
effects. Also, note that the DHS growth rate is not only symmetric but
bounded between 2 (exit) and 2 (entrant).
354
THE REVIEW OF ECONOMICS AND STATISTICS
aggregation can affect interpretation. We are interested in
computing net growth rates at both the establishment and
firm levels. In the LBD, we have access to both levels of
data where the establishment structure of the firms is well
specified. In other settings, however, the analyst may have
access to only establishment or only firm-level data. Thus,
it is critical to understand how using one or the other can
affect interpretation.
An important difference in computation and interpretation arises when establishments undergo changes in ownership due to mergers, divestitures, or acquisitions. In these
instances, net growth rates computed from firm-level data
alone will reflect changes in firm employment due to adding
or shedding continuing establishments. This occurs even if
the added or shed establishments experience no employment changes themselves.
To avoid this problem, we compute firm growth rates as
suggested in the expressions above. Namely, the period t 1 to period t net growth rate for a firm is the sum of the
appropriately weighted DHS net growth rate of all establishments owned by the firm in period t, including acquisitions,
plus the net growth attributed to establishments owned by
the firm in period t 1 that it has closed before period t. For
any continuing establishment that changes ownership, this
method attributes any net employment growth to the acquiring firm. Note, however, that if the acquired establishment
exhibits no change in employment, there will be no accompanying change in firm-level employment induced by this
ownership change. The general point is that this method for
computing firm-level growth captures only organic growth
at the establishment level and abstracts from changes in
firm-level employment due to mergers and acquisitions.18
We use the establishment- and firm-level growth rate measures to compute not only net growth but also job creation
and job destruction (and the related job creation from entry
and job destruction from exit). At the establishment level,
job creation is measured as the employment gains from all
new and expanding establishments and job destruction as the
employment losses from all contracting and closing establishments. At the firm level, job creation is measured as the
employment gains from all expanding and new firms and job
destruction as the employment losses from all contracting
and exiting firms. By construction, our methods of computing growth imply that firm-level measures of job creation
and destruction are lower than establishment-level measures
since the latter includes within-firm reallocation of jobs
across establishments. For these measures, we follow the
approach developed by DHS. Details of the measurement of
these concepts are provided in the online appendix.19 A key
18
In the online appendix, we provide a detailed hypothetical example
to clarify how in practice we handle mergers and acquisitions. This example is useful to understand the details, as well as for practitioners who
want to implement our methodology.
19
The online appendix also includes depictions of the distribution of
firm- and establishment-level net growth rates underlying the job creation
and destruction statistics.
identity that we exploit is that the overall net employment
growth rate at the cell and aggregate level can be decomposed into the appropriately weighted sum of net employment growth from continuing firms, job creation from entry,
and subtracting the job destruction from exit. As we show in
the online appendix, our overall net growth results can be
equivalently obtained from direct estimation of the effects
on overall net growth or by first estimating the effects on the
components and computing the employment-weighted sum
of the components.
IV.
The Relationship of Employment Growth, Firm Size,
and Firm Age
Our primary objective is to understand the relationship
between net employment growth, and its components, and
firm size and age. In this section, we use a nonparametric
regression approach to quantify these relationships. In our
main specification, we regress net employment growth and
its components at the firm level on firm size classes by themselves, on firm age classes by themselves, and by firm size
and age interacted together. The latter specification follows
naturally from the tabulations in table 1, which show net
growth patterns for firm size and firm age cells. All of the
empirical models we consider are fully saturated dummy
variable models. They are either one-way dummy variable
models in firm size or firm age or two-way models with a
complete set of interactions. As Angrist and Pischke (2009)
highlight, estimating fully saturated dummy variable models
with OLS is fully general regardless of the distribution of
the dependent variable. This is intuitive since by construction, the estimated coefficients will be the cell means for
each of the saturated cells. This property is important in our
case since the DHS net growth rate is bounded between 2
and 2. When we estimate specifications with the dependent
variables being the job creation from entry and job destruction from exit, the specifications are equivalent to linear
probability models (albeit weighted as noted below). But
again, as Angrist and Pischke (2009) discuss, the fully saturated dummy variable model avoids any econometric issues
with using a bounded or limited dependent variable.20
20
We discuss the desirable econometric properties of fully saturated
dummy variable models further in the online appendix in section VII.D.
We also note that earlier versions of this paper (Haltiwanger, Jarmin, &
Miranda, 2010) considered two-way models with firm size and firm age
dummies without interactions and were potentially subject to econometric
concerns with predicted values that may lie outside the range of the
dependent variable. We show in appendix VII.D that the partial effects of
firm size controlling for firm age (and vice versa) are quite similar for the
models with and without interactions. Thus, the results from earlier versions and the current version yield very similar results and inferences.
While the specifications without interactions have potential limitations,
such specifications are transparent and parsimonious in terms of generating partial effects. Moreover, the two-way model without interactions permits estimating more detailed firm size and firm age effects. We also note
that in earlier versions, we considered specifications controlling for industry and year effects. As we show in the online appendix, all of our results
are robust to controlling for industry and year effects. We thank Peter
Huber, Harald Oberhofer, and Michael Pfaffermayr for helpful discussions on these issues.
WHO CREATES JOBS?
We focus on employment-weighted specifications since
this enables the coefficients to be interpreted in terms of the
impact on net employment growth rates at the aggregate level
for the specified category. Given the discussion, our estimates
are equivalent to employment-weighted cell means. As such,
we can replicate all of the results in what follows using a cellbased regression approach where net growth rates and components are measured at firm size, firm age, and year level of
aggregation.
Our focus is on comparing the effects of firm size on net
growth (and components of net growth) with and without controls for firm age (and vice versa). To quantify the effects of
firm size using the two-way model with interactions, we compute partials of firm size from that model holding the age distribution of employment constant at the sample mean. The
partial effect of firm age analogously holds the size distribution of employment constant at the sample mean. Our
approach can be interpreted in two closely related ways. First,
it is equivalent to the standard approach in regression models
when one computes partials of a variable x when the model
includes interaction terms such as x z by evaluating the
interaction coefficients at the mean of z. Second, our approach
is equivalently interpretable as a form of a shift and share
decomposition holding the age (size) composition constant
when examining the firm size (age) effect. Specifically, our
firm size (age) estimates are age (size) composition constant
estimates based on computing the weighted average of the
firm size by firm age cell means using the overall employment
distribution by firm age (size) as weights. Finally, we note that
our findings controlling for firm age (size) are essentially the
same if we use simpler and more transparent two-way models
without interactions, as we discuss in the online appendix. We
focus on the results from fully saturated models in the text
since they use a more general and robust econometric specification (see the online appendix for further discussion).
In estimating our fully saturated dummy variable specifications, we need to take into account that some parts of the
joint firm size and firm age distribution get very sparse (for
example, young firms with more than 10,000 employees are
virtually nonexistent, as seen in table 1). So relative to the
firm size and firm age classes in table 1, we restrict ourselves to eight size classes (1–4, 5–9, 10–19, 20–49, 50–99,
100–249, 250–499 and 500 and up) and nine age classes (0,
1–2, 3–4, 5–6, 7–8, 9–10, 11–12, 13–15, and 16 and up).
The fully saturated two-way model generates a very large
number of estimated coefficients, especially as we estimate
specifications for both initial size and current (average)
size, as well as overall net growth and the components of
net growth. Given our focus on the partial effects of size
controlling for firm age and vice versa, we report our results
in the subsequent sections in figures.21 We note that the
21
In table W.2 we show the results for the net overall growth fully saturated one-way and two-way models. In table W.3 we show the results for
the net growth fully saturated models (one-way and two-way) for continuing firms. Estimates for other specifications are available on request.
355
underlying firm-level regressions include more than 70 million firm-year observations; consequently, the standard
errors for the estimates are very small.22
B. Net Employment Growth and Firm Size
We present all of our remaining results with the aid of figures. To facilitate comparisons between the one-way models
and the partials from the two-way models with interactions,
we focus on comparing the differences in effects relative to
a baseline or omitted group. In all of the subsequent figures,
this is the largest (500 and up) or oldest (16 and up) group.
To facilitate the interpretation of the magnitudes, we report
the baseline group at its unconditional mean from the oneway model. In turn, we simply rescale the other effects by
adding the value of the unconditional mean for the baseline
category (for example, the firm size class of 500 and up)
from the one-way model to each effect. Adding the unconditional mean to all categories does not distort the relative differences but provides perspective about the magnitude of
the effects.
Figure 2 shows the relationship between net employment
growth and firm size. The upper panel displays results from
the regressions for all firms. The lower panel displays the
size effects from the regressions where we limited the sample to continuing firms only. Beginning with the main
results in the upper panel, the plotted curve for the baseyear size specification without age controls shows a strong
inverse relationship between firm size and net employment
growth. The average annual rate of net employment growth
in the smallest size class is about 15.2 percentage points
higher than that for the largest firms (500 or more employees). The effect declines more or less monotonically as the
size of the firm increases. The relative net employment
growth premium for being small declines to 3.3% and 1.7%
for size classes 5–9 and 10–19 employees, respectively.
As argued above, however, the base-year measure of firm
size has several undesirable attributes for examining firm
size and growth. The curve plotting the estimated effects
from our preferred average size specification with no age
controls shows that the inverse relationship remains, but the
quantitative relationship is substantially muted. Comparing
the base and average size results suggests that the effects of
regression to the mean are quite strong in the smallest size
classes. In the online appendix, we show that consistent
with these patterns, the negative serial correlation of firmlevel net employment growth rates is especially large in
absolute value for small firms. But the more general point is
that in the absence of controls for firm age, we obtain similar qualitative results as those in NZW. That is, size classification methodology matters, but there still is a small inverse
22
The largest standard error in any of the fully saturated models for net
employment growth, net employment growth for continuers, job creation
from entry, or job destruction from exit is 0.003. Most standard errors are
below 0.001.
356
THE REVIEW OF ECONOMICS AND STATISTICS
FIGURE 2.—RELATIONSHIP BETWEEN NET GROWTH AND FIRM SIZE
FIGURE 3.—FIRM EXIT BY FIRM SIZE
0.25
A. All Firms
0.2
0.2
0.1
0.15
0
0.1
-0.1
0.05
-0.2
0
Firm Size Class
Firm Size Class
Base Year Size
Current (Avg) Size
Base Year Size
Current (Avg) Size
Base Year Size with Age Controls
Current (Avg) Size with Age Controls
Base Year Size with Age Controls
Current (Avg) Size with Age Controls
B. Continuing Firms only
0.2
0.1
0
-0.1
-0.2
Firm Size Class
Base Year Size
Current (Avg) Size
Base Year Size with Age Controls
Current (Avg) Size with Age Controls
relationship between net employment growth and firm size
when not controlling for firm age.
Controlling for firm age, however, has a dramatic impact
on these patterns. Regardless of the size classification methodology, once we control for firm age, we observe no systematic inverse relationship between net growth and firm
size. When we use base-year size, the smallest size class
has the largest positive coefficient, but the size classes in
the range from 5 to 499 have the most negative effects. This
implies that firms in the 5 to 499 range have lower net
growth rates on average than the largest businesses once we
control for firm age. When we use average size, we find a
positive relationship between net growth and firm size for
all the size classes up through 500 workers. While the
details differ nontrivially depending on which size class
method we use, the main point is that once we control for
firm age, there is no evidence that small firms systematically have higher net growth rates than larger businesses.
In the lower panel of figure 2, we show the results when
we restrict the analysis to continuing firms only. The first
thing to note is that there is a less dramatic impact of con-
trolling for firm age since there is, by construction, no role
for start-ups.23 Exploring this more deeply, we find a strong
inverse relationship between net growth and firm size for
continuing firms when we use the base-size methodology.
This is the case whether or not we control for firm age.
However, using average size, there is a positive relationship
between net firm growth and firm size regardless of whether
one controls for firm age. Hence, for continuing firms, it is
primarily the size class methodology that matters. The stark
differences for small continuing firms between the base-size
and average-size results are consistent with the strong
regression-to-the-mean effects for these firms.
Some of the differences between the patterns across the
two panels of figure 2 reflect the role of firm exits.24 We
explore this further in figure 3, which shows the patterns of
job destruction from firm exit by firm size with and without
age controls. Job destruction from firm exit is directly interpretable as an employment-weighted firm exit rate. The
firm exit rate falls monotonically with firm size regardless
of size class methodology and with or without firm age controls. Controlling for firm age yields somewhat higher exit
rates for small businesses, but this effect is quite modest
when using average-size class methodology. Thus, a robust
finding is that small firms are more likely to exit than larger
firms, even controlling for age.
Combining figure 3 with the lower panel of figure 2 helps
account for the patterns in the upper panel of figure 2, especially for the results controlling for firm age. The lower
panel of figure 2 shows that when controlling for firm age,
there is a modest but increasing relationship between net
growth and average size for continuing firms. Combining
23
NWZ briefly discuss a similar result they obtained using the NETS
data when they exclude start-ups.
24
Section VII.E of the online appendix shows that the overall net
effects we report in table 2 and figure 2 can be generated by using the estimates for the components of net growth (continuers, job creation from
entry and job destruction from exit). This property holds for all the overall
net effects we report in the paper.
WHO CREATES JOBS?
this effect with the patterns in figure 3 where small firms
(controlling for firm age) have much higher exit rates yields
that net growth rates are strongly increasing in average firm
size controlling for firm age.
Figures 2 and 3 also shed light on Gibrat’s law (the prediction that firm growth should be independent of size).
Figure 2 suggests that Gibrat’s law holds approximately if
we exclude the smallest firms, especially if we use the average size measure and do not control for firm age. That is,
departures arise for the smallest firms (where regression-tothe-mean effects are especially an issue) and for entering
and young firms, which, as we will see below, have their
own interesting dynamic not well captured by the models
underlying the predictions of Gibrat’s law (Sutton, 1997).
An appropriate measure of firm age is critical for obtaining the patterns in figures 2 and 3. As we noted above, these
results can also be obtained by estimating employmentweighted establishment-level regressions on firm size and
age characteristics. This implies that we can check the
robustness of controlling for establishment as opposed to
firm-level characteristics. For brevity, we summarize only
the results looking at establishment characteristics and point
interested readers to the online appendix. We find that controlling for establishment as opposed to firm age does not
yield the same stark patterns of figures 2 and 3. That is, when
controlling for only establishment age, the relationship
between firm size and net growth remains strongly negative
when using base size unlike the pattern in figure 2, which
shows a nonmonotonic relationship between firm size and
net growth when we control for firm age. Moreover, the positive relationship between average size and net growth in figure 2 when controlling for firm age becomes notably weaker
when controlling for establishment age. These findings highlight the important distinction between firm and establishment age that comes about because there are many young,
small establishments of large, mature firms.
B. Net Employment Growth and Firm Age
We now turn to exploring the patterns of net employment
growth by firm age. For ease of exposition, in what follows
we show in figures only the results for firm age by itself and
controlling for firm size using our preferred average-size
measure. (For completeness, we provide the results controlling for base-year size in the online appendix.)
The top panel of figure 4 shows the results for firm age.
In the figure, we omit the estimated coefficient for start-ups
since it is much higher (for the fully saturated models, the
predicted estimate is identically equal to 2).25 The panel
25
Recall that at the firm level, the net growth rate for a firm start-up is
equal to 2 using the DHS methodology. The fully saturated models yield
the predicted value as the cell means, so the predicted value is identically
equal to 2. Note that in the appendix, we consider models where we control for industry and year effects that break this identity. Even then, we
find that the predicted values for firm age ¼ 0 are close to 2 and very
similar across size classes. We also find that the results controlling for
industry and year effects are very similar to those we report in the text.
357
FIGURE 4.—RELATIONSHIP BETWEEN NET EMPLOYMENT GROWTH AND FIRM AGE
A. All Firms
0.15
0.1
0.05
0
-0.05
-0.1
Firm Age Class
Age Only
With Average Size Controls
B. Continuing Firms Only
0.2
0.15
0.1
0.05
0
Firm Age Class
Age Only
With Average Size Controls
reveals a relatively weak relationship between firm age and
net growth when we exclude start-ups (there is a mild positive relationship without firm size effects and a mild inverse
relationship with firm size effects). However, in the lower
panel of figure 4, we find that conditional on survival,
young firms exhibit substantially higher growth than more
mature firms. This pattern is robust to controlling for firm
size, and it clearly indicates that the fastest-growing continuing firms are young firms under the age of 5.
Reconciling the patterns of the upper and lower panel
requires investigating the relationship between firm age and
firm exit. That is, the firms not included in the lower panel
of figure 4 relative to the upper panel are firm exits. Note
that firm entrants are not driving the large difference in patterns across the upper and lower panel of figure 4 since they
are not included in either panel. The relationship between
firm age and job destruction from firm exit is reported in
figure 5, where it is apparent that young firms have much
higher firm exit rates than more mature firms do.
Taken together, figures 4 and 5 describe an up-or-out pattern for young firms that is robust to controlling for firm
size (and robust to whichever size class method is used).
This up-or-out pattern highlights that the net patterns by
firm age depicted in the top panel of figure 4 mask the rich
dynamics of young firms. This dynamic is an important feature of market-based economies and is consistent with predictions in models of market selection and learning (see
Jovanovic, 1982; Hopenhayn, 1992; Ericson & Pakes,
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THE REVIEW OF ECONOMICS AND STATISTICS
FIGURE 5.—FIRM EXIT BY FIRM AGE
FIGURE 7.—UP-OR-OUT PATTERNS BY INDUSTRY
A. Net Employment Growth for Continuing Firms by Firm Age, by Industry
0.2
0.2
0.15
0.15
0.1
0.1
0.05
0.05
0
0
-0.05
Firm Age Class
Age Only
With Average Size Controls
Firm Age Class
Services
Durables Mfg
Nondurables Mfg
Wholesale
& Retail
Economy
B. Job Destruction from Exit by Firm Age, by Industry
FIGURE 6.—ENTRY RATES BY SECTOR
Entry Rate, Selected Sectors,
Employment Weighted
0.04
0.03
Rate
0.03
0.02
0.02
0.01
0.01
0.00
Durables Mfg
Non-Durables
Mfg
Services
Wholesale &
Retail
Economy
1995). It is also consistent with models where it takes time
for firms to build up demand capital (Foster, Haltiwanger, &
Syverson, 2010) or firms to build up reputation in credit markets (Evans & Jovanovic, 1989).
The up-or-out pattern of young firms also helps put the
job creation from start-ups in perspective. Each wave of
firm start-ups creates a substantial number of jobs. In the
first years following entry, many start-ups fail (the cumulative employment weighted exit rate derived from figure 5
implies that about 47% of the jobs created by start-ups are
eliminated by firm exits in the first five years), but the surviving young businesses grow very fast.26 In this respect,
the start-ups are a critical component of the experimentation process that contributes to restructuring and growth in
the United States on an ongoing basis.
We check the robustness of the results in figure 4 by considering whether the patterns are potentially driven by large,
young businesses. Although our measurement methods avoid
creating new firms as the outcome of mergers and acquisitions, there are some large, young firms creating jobs, as seen
in table 1. As seen in figure 1, the latter do not account for
much of the contribution of firm births, but it is possible they
26
Without controlling for firm size effects, the growth from the survivors does not fully compensate for the exits. The cumulative net growth
rate implied by figure 4 is about 16% in the first five years after entry.
Note, however, that this still implies that five years after entry, a typical
cohort has contributed a substantial number of jobs. When we control for
firm-size effects, one can see that the cumulative net growth for firms less
than 5 years old will be positive.
are high-growth firms contributing to the patterns in figure 4.
To check on the contribution of such large firms to the analysis, we estimated the specifications underlying figures 2
through 5 restricting attention to firms that have fewer than
500 workers. We find the patterns in figures 2 through 5 are
robust to considering only such firms in the analysis.27
C. Firm Entry and Up-or-Out Dynamics by Sector
One question raised by the striking patterns in figures 4
and 5 is whether the up-or-out dynamics are driven by specific industries. It might be that the factors that yield such
young firm dynamics are more important in some sectors
than others. Moreover, firm entry rates vary across sectors,
and the pace of entry may influence the nature of young
firm dynamics.
Figure 6 shows employment-weighted firm entry rates by
selected broad sectors.28 Not surprisingly, sectors such as
Services and Wholesale and Retail Trade have much higher
entry rates than either Durable or Nondurable Goods Manufacturing. Firm entry rates are especially low in Durable
Goods Manufacturing.
Figure 7 shows the up-or-out patterns by industry. It is
striking that in spite of the large differences in entry rates,
the patterns are so similar across industries. In all of the
27
The results for this robustness check are in the online appendix.
A supplemental file available electronically includes all sectors, but
we focus on selected sectors in the main text for the sake of brevity.
28
WHO CREATES JOBS?
FIGURE 8.—ESTABLISHMENT ENTRY AND EXIT BY FIRM AGE
359
FIGURE 9.—ESTABLISHMENT ENTRY AND EXIT BY FIRM SIZE
A. Job Creation from Establishment Entry by Firm Age
A. Job Creation from Establishment Entry by Firm Size
0.2
0.25
0.15
0.2
0.15
0.1
0.1
0.05
0.05
0
0
Firm Age Class
Age Only
With Average Size Controls
Firm Size Class
B. Job Destruction from Establishment Exit by Firm Age
Current (Avg) Size
Current (Avg) Size with Age Controls
0.2
B. Job Destruction from Establishment Exit by Firm Size
0.25
0.15
0.2
0.1
0.15
0.05
0.1
0
0.05
Firm Age Class
Age Only
With Average Size Controls
0
Firm Size Class
Current (Avg) Size
broad sectors, young businesses either grow fast or they
exit. There are some interesting differences in the magnitudes of the patterns by sector. Young, continuing firms in
the Service sector have especially high growth relative to
firms in the Wholesale and Retail sector. In addition, there
is a more pronounced decline in the exit pattern for young
firms in Manufacturing Durable and Nondurable Goods. In
unreported results we have found that the peak exit rate is
not in the first year but in the second year after entry for
these sectors. The pattern suggests that the nature of the
learning and selection dynamics differs in the Manufacturing sector. Still, it is striking how similar the qualitative patterns are by sector. The implication of figures 6 and 7 is that
while entry rates vary substantially across sectors, conditional on entry, the same rich up-or-out dynamics are present in every sector.
D. The Entry and Exit Margins: Establishment versus Firm
The focus thus far has been on firm entry and exit. In this
section, we compare and contrast the patterns of firm entry
and exit with the patterns of establishment entry and exit.
Figure 8 shows establishment entry and exit by firm age.
The upper panel shows that establishment entry exhibits a
slight tendency to increase with firm age that is mitigated
after controlling for firm size. By contrast, the lower panel
shows job destruction from establishment exit falling
Current (Avg) Size with Age Controls
monotonically with firm age. Taking these patterns together
with those of figures 4 and 5 implies that young firms create
jobs by expanding existing establishments rather than opening new ones.29 Additionally, in comparing the establishment-level patterns in figure 8 to the firm-level patterns in
figure 5, we see that job destruction from establishment exit
declines less rapidly with firm age than does job destruction
from firm exit. Mature firms are much less likely to exit
than young firms, but establishments of mature firms have
relatively high exit rates compared to establishments of
young firms conditional on other observable factors (Dunne,
Roberts, & Samuelson, 1989, also found this result for U.S.
manufacturing).
Figure 9 shows relationships of establishment entry and
exit by firm size. Establishment entry tends to fall with firm
size when not controlling for firm age, and this reflects the
obvious relationship between firm entry and establishment
entry. However, after controlling for firm age, we observe
establishment entry rising with firm size. The lower panel
of figure 9 shows that job destruction from establishment
29
In the online appendix, we also compare job creation by firm age
with job creation by establishment entry by firm age, which makes this
point more transparent.
360
THE REVIEW OF ECONOMICS AND STATISTICS
exit tends to decline with firm size. These patterns are quite
similar to those for firm exit in figure 3.
To sum up, there are notable differences in the patterns of
firm versus establishment entry and exit by firm age and firm
size. First, looking at establishments, we find that young
firms are less likely to exhibit job creation from opening
new establishment than are mature firms. Young firms, however, disproportionally create jobs by expanding existing
establishments. Large firms are more likely to open new
establishments when we control for firm age. Second, firm
entry rates are much higher for the smallest size classes, but
this simply reflects the fact that new firms tend to be small.
Once we control for firm age, large firms are more likely to
open new establishments than small firms are. Establishment
exit is also more likely for smaller firms, and this holds even
after controlling for firm age.
V.
Conclusion
There is a widespread popular perception that small businesses create the most jobs in the United States. This perception has basis in empirical observation, but we demonstrate that the inverse relationship between net job growth
and firm size disappears after controlling for firm age. To
draw this conclusion, we take advantage of newly developed economy-wide longitudinal firm and establishment
data available at the U.S. Census Bureau that permits accurate tracking of business start-ups, business exits, and continuing firms.
Our analysis focuses on measurement, not policy. However, measurement issues clearly can influence policy discussions, and our findings give those charged with policies
aimed at the job creation much to consider. For example, to
the extent that policy interventions aimed at small businesses ignore the important role of firm age, we should not
expect much of an impact on the pace of job creation.
Effective policy design in this area requires a richer understanding of business dynamics, as well as any relevant market failures. Our analysis addresses only the first issue.
We find some evidence in support of the popular perception that small businesses create most jobs along the following lines. If one looks at the simple relationship between
firm size and net growth rates, there is evidence that net
growth rates tend to be higher for smaller as opposed to larger businesses. This is the case using widely available data
such as the Census Bureau’s SUSB, but can also be
obtained through careful analysis of microdata, as both
NWZ and we demonstrate. Of course, the caveats raised
over years, such as the role of regression to the mean, still
apply. Using our preferred firm size classification that we
argue is more robust to such concerns, the inverse relationship between net growth rates and size remains but is not
overwhelming.
Our results show that the more important and robust finding is the role of firm age and its relationship with firm
growth dynamics. We find that once we control for firm age,
the negative relationship between firm size and net growth
disappears and may even reverse sign as a result of relatively
high rates of exit among the smallest firms. Our findings
suggest that it is particularly important to account for business start-ups. Business start-ups account for roughly 3% of
U.S. total employment in any given year. While this is a reasonably small share of the stock, it is large relative to the net
flow, which averages around 2.2% per year. We also find
rich dynamics among young firms that help put the contribution and role of start-ups into perspective. Young firms exhibit high rates of gross job creation and destruction. Consistent with this pattern, we find that young firms have very
high job destruction rates from exit, so that after five years,
about 40% of the jobs initially created by start-ups have
been eliminated by exit. However, we also find that, conditional on survival, young firms grow more rapidly than their
more mature counterparts.
Most of our focus is on the net growth rate patterns by
firm size and firm age (along with the underlying different
margins of adjustment). However, we also show that large,
mature businesses account for a large fraction of jobs.
Firms that are over 10 years old and have more than 500
workers account for about 45% of all jobs in the U.S. private sector. In turn, we show that these large, mature firms
account for almost 40% of job creation and destruction.
The share of jobs created and destroyed by different groups
of firms is roughly their share of total employment. An
important exception in this context is the contribution of
firm start-ups: they account for only 3% of employment but
almost 20% of gross job creation.
We think our findings help interpret the popular perception of the role of small businesses as job creators in a manner that is consistent with theories that highlight the role of
business formation, experimentation, selection, and learning as important features of the U.S. economy. Viewed
from this perspective, the role of business start-ups and
young firms is part of an ongoing dynamic of U.S. businesses that needs to be accurately tracked and measured on
an ongoing basis. Measuring and understanding the activities of start-ups and young businesses, the frictions they
face, their role in innovation and productivity growth, and
how they fare in economic downturns and credit crunches
are clearly interesting areas of inquiry given our findings.
To the extent that market failures are found to underlie
these frictions, there might be a role for well-designed corrective policies that help entrepreneurs start and grow
dynamic young firms that boost overall net job creation.
More broadly, our findings suggest that the policy debate
about encouraging private sector job creation should be
refocused. The job-creating prowess of small businesses is
often used by policymakers to motivate and justify specific
policies. Our findings suggest that policies targeting firms
based on size without taking account of the role of firm age
are unlikely to have the desired impact on overall job creation. Taking the patterns of firm dynamics we show here
into account may help identify the specific market failures
WHO CREATES JOBS?
that prevent entrepreneurs from starting and growing new
businesses.
In a related manner, it is important to focus not only on
jobs per se but also on the role these dynamics play in productivity and earnings growth in the United States. Similarly
we need to develop the data and associated analyses that will
permit investigating the complex relationships between
young and mature businesses. It may be, for example, that
the volatility and apparent experimentation of young businesses that we have identified are critical for the development of new products and processes that are in turn used by
(and perhaps acquired by) the large and mature businesses
that account for most economic activity. Viewed from this
perspective, our findings show that the LBD and the BDS
are rich databases to track U.S. business dynamics, but it is
also clear that additional information about the productivity
and earnings dynamics, as well as business-to-business relationships, need to be added to these databases and related
analyses.
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