Preliminary Draft. Please do not quote or cite without permission.

Who Creates Jobs? Small vs. Large vs. Young*

By
John Haltiwanger
University of Maryland
Ron S Jarmin
U.S. Census Bureau
Javier Miranda
U.S. Census Bureau

July 2009

Abstract: There’s been a long, sometimes heated, debate on the role of firm size in employment
growth. Despite skepticism in the academic community, the notion that growth is negatively
related to firm size remains appealing to policymakers and small business advocates. Basically,
they argue that small firms grow faster than larger firms and are more important as a source ofjob creation. In this paper, we provide a more nuanced perspective on this debate. Using datafrom the Census Bureau Business Dynamics Statistics and Longitudinal Business Database, we
explore the many issues regarding the role of firm size and growth that have been at the core ofthis ongoing debate (such as the role of regression to the mean). We find that the relationshipbetween 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 sizeand growth. Indeed, once we control for firm age, we find that firms with between 5 and 499
employees have lower net growth rates than the largest firms (10,000 or more workers) in theeconomy. Our findings highlight the important role of business startups and young businesses in
U.S. job creation. Business startups contribute substantially to both gross and net job creation.
In addition, we find an “up or out” dynamic of young (and small businesses). These findings
imply that it is critical to control for and understand the role of firm age in explaining U.S. job
creation.

* We thank the Kauffman Foundation for financial support. Any opinions and conclusions expressed herein arethose of the author(s) and do not necessarily represent the views of the U.S. Census Bureau. All results have beenreviewed to ensure that no confidential information is disclosed.

1. Introduction
One of the common cited beliefs in public discourse about the U.S. economy is that small
businesses create the most jobs. Often the statement is something along the lines that “small
businesses create X percent of net new jobs” in the U.S. where X is usually a large percentage.
These statements allude to empirical studies, including the early work by Birch (1979, 1981,1987), that find that growth is inversely related to firm size. However, it is also already wellknown that there are many statistical pitfalls underlying this characterization as described indetail in Davis, Haltiwanger and Schuh (1996) (hereafter DHS). DHS highlight issues associatedwith regression to the mean effects as well as the importance of distinguishing between gross vs.
net job flows.1 DHS document the importance of these issues using data on U.S. manufacturing
establishments. A recent study by Neumark, Wall and Zhang (2008) (hereafter NWZ) uses the
NETS database covering the entire U.S. economy. In this carefully done study, the authors avoid
the misleading interpretations of the data highlighted by DHS. They find an inverse relationshipbetween net growth rates and firm size in the U.S. private sector using data from 1992 to 2004.
Our primary contribution is to emphasize the role of firm age and especially firm births inthis debate.2 We use comprehensive data tracking all U.S. firms and establishments in the U.S.
non-farm business sector from 1976 to 2005 from the Census Bureau’s Longitudinal Business
Database (LBD). Our main findings are summarized as follows. First, consistent with NWZ,
when we only control for industry and year effects, we find an inverse relationship between net
growth rates and firm size, although we find this relationship is quite sensitive to regression to
the mean effects. Second, once we add controls for firm age, we find no systematic relationshipbetween net growth rates and firm size. The key role for firm age is associated with firm births.
We find that firm births contribute substantially to gross and net job creation. New firms tend tobe small and thus the finding of a systematic inverse relationship between firm size and net
growth rates is entirely attributable to most new firms being classified in small size classes.
Our findings highlight the importance of business startups for understanding firm andestablishment employment growth dynamics in the U.S. Related to this finding, we find a rich
“up or out” dynamic of young firms in the U.S. That is, conditional on survival, young firms
grow more rapidly than their more mature counterparts. However, young firms have a muchhigher likelihood of exit so that the job destruction from exit is disproportionately high among
young firms. More generally, young firms exhibit much more churning of jobs as evidenced byhigh rates of gross job creation and destruction.
These findings highlight the importance of theoretical models and empirical analysis that
focus on the startup process – both the process of entry itself but also post-entry dynamics
1

There are numerous other studies that have also raised concerns about the common claims. Brown, Hamilton and
Medoff (1990) raise many of the same statistical issues as DHS but also explore the employer size wage differential.
2
An important early study that also emphasized the role of firm age for growth dynamics is Evans (1987). The
latter paper found an inverse relationship between firm growth and size and age using firm level data for U.S.
manufacturing firms. As Evans points out, the work is based on data with substantial limitations but interestingly
some aspects of his findings hold for our data that does not suffer from the same limitations. Specifically, thedepartures 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, Roberts andSamuelson (1989), Haltiwanger and Krizan (1999), Acs, Armington and Robb (1999). These latter studies focusedon establishment-age.

1especially in the first ten years or so of a firm’s existence. This is not to deny the importance ofunderstanding and quantifying the ongoing dynamics of more mature firms but to highlight thatbusiness startups and young firms are inherently different
Our findings contribute to the large literature on the connection between firm size andgrowth. One leading hypothesis is Gibrat’s law that states that growth should be independent offirm size.3 Our results show that there are substantial departures from Gibrat’s law for young
and small firms. In our preferred specification, for firms that are more than 10 years old andhave more than 20 employees there are not large systematic differences in net growth rate
patterns.
The paper proceeds as follows. In section 2, we provide further background on the literature.
Section 3 describes the data. Section 4 presents the main empirical results. Section 5 provides
concluding remarks.
2. Background
Investigating the relationship between employer size and net growth, while seemingly astraightforward exercise, poses numerous measurement and methodological challenges. First, toexplore these issues properly a longitudinal business database is required tracking bothestablishments and firms. For purposes of this discussion as well as the subsequent empiricalanalysis, 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. To help
understand these concepts, a useful example is to think about a large, national chain in retailtrade – an individual store is the establishment and the sum of the activity of all theestablishments of the large, national chain is the firm.
For purposes of studying the relationship between employer size and growth, it is critical tohave accurate information tracking the establishments over time as well as the parent firms that
own the establishments. If the only data available are at the firm level, longitudinal tracking iscomplicated by the many changes in ownership as well as mergers, acquisitions and divestituresthat are a ubiquitous feature of firm dynamics. Establishment-level job growth is a well-definedconcept and has the virtue that when we observe an establishment grow we know there are net
new jobs at that establishment. In contrast, firm growth may represent acquisitions oralternatively firm contraction may represent divestitures. It is also clear that having only
establishment-level data is inadequate as well as we now discuss.
If the only data available are at the establishment level, the relationship between growth andthe size and age of the establishment may be not provide much information about the relevant
firm size and firm age. Again, a large, national retail chain is a useful example. In retail trade,firms' primary margin of expansion is by opening up new stores rather than the expansion ofexisting stores (see Foster, Haltiwanger and Krizan (2006) and Jarmin, Klimek and Miranda(2009)). This implies there are many new establishments of existing firms – for the core issues
in this paper, the growth from such new establishments should be classified based upon the size3

See Sutton (1997).

2and age of the parent firm, not the size and age of the establishment.4 Much of the literature on
employer size and net growth has primarily been based on establishment-level or firm-level data
but not both which is a clear limitation.5 A strength of the data we use is that both establishment
and firm level information is available for the entire U.S. nonfarm private sector for a significant
period of time. Our approach is to link the establishment-level growth to the firm characteristics
and to do so on an employment-weighted basis so that our estimated effects reflect the aggregate
contribution of a given firm size or firm age.
A related set of challenges is high quality longitudinal linkages as well as accurate detectionof establishment and firm births. Given the ubiquitous changes in ownership among U.S. firms,
it is essential to build longitudinal links based on high quality establishment linkages. Detecting
when a new establishment first starts economic activity is critical as well and determining
whether that new establishment is part of an existing firm or is a new firm is critical. Early
versions of the D&B data used by Birch were plagued with problems along these lines withestablishments and firms entering the database not so much when economic activity began but
when the business applied for credit in a manner that showed up in the D&B data (see, Birley(1984) and Alrdrich et. al. (1988) for detailed discussion). The NETS data used by NZW isbased on a much improved version of the D&B data although there are some open questions
about the nature of the coverage in NETS.6 A strength of our study is that the LBD has very highquality longitudinal linkages at the establishment-level as well as high quality linkages between
establishments and firms. The latter, as described below, permits us to measure firm age forevery establishment.
Another set of challenges emphasized by DHS is to recognize the statistical pitfalls in
relating employer size and growth. One issue they highlight is the potential role of regression to
the mean effects. Businesses that have recently had a transitory negative shock (or even atransitory negative measurement error) are more likely to grow while businesses that haverecently had a positive transitory shock are more likely to shrink. This effect alone will yield an
inverse relationship between size and growth. As Friedman (1992) states, this type of regression
fallacy “is the most common fallacy in the statistical analysis of economic data”. Practically thisissue manifests itself in the method used to classify businesses into size classes. The early workby Birch and others has classified businesses into size classes using base year employment. Thatis, in quantifying the relationship between employer size and growth between t-1 and t, theemployer size used is the size in t-1. Businesses that have had a negative transitory shock in t-1
4

Although as discussed below, there is independent interest in the role of establishment size and age.
DHS analysis is restricted to U.S. manufacturing establishments although they were able to construct a measure offirm size at the manufacturing level. Dunne, Roberts and Samuelson (1989) examine the role of establishment size
and age for the growth and failure of U.S. manufacturing plants. Evans (1987) used firm-level continuers for U.S.
manufacturing firms over the 1976-1980 period. Birch (1979, 1981, 1997) uses the D&B data that has both firm andestablishment-level information although subject to the limitations of the D&B data. NZW use the NETS data thathas both firm and establishment-level information.
6
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 thathave 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 discussionof 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 startups especially for small
businesses.
53are more likely to be small so are more likely to be classified in smaller size classes in the base(t-1) year. Businesses that have had a positive shock in t-1 are more likely to be large and so are
more likely to be classified in large size classes in the base year. The implication is that
regression to the mean effects will yield an inverse relationship between growth and size usingbase year size classifications.
DHS propose using an alternative classification method to mitigate the role of regression to
the mean effects. They note that, while base year size classification yields a negative bias, using
end year size classification yields a positive bias. That is, if businesses are classified into sizeclasses based upon year t size, then transitory shocks would yield a positive bias to therelationship between net growth from t-1 to t and size. To avoid the negative or positive bias,
DHS propose using a current size classification where current size is based on the average of
employment in year t-1 and t. Using average size is a compromise between using year t-1 (base)or (end) year t size to classify firms.
Even though current-year (average) size is a compromise, it has limitations as well.
Establishments that belong to firms with large permanent firm level shocks that induce the firmcrossing over size class boundaries between t-1 and t will be classified into a size class that is in
between the starting and ending size class. Recognizing this potential limitation, the Bureau ofLabor Statistics has developed a dynamic size classification methodology (see Butani et. al.
(2006)). 7 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, the findings that compare
results across size methods (see Butani et. al. (2006)) show that the patterns using the current
size method for classification are quite similar to those that emerge using their dynamic
methodology.8
In what follows, we present results using the base year and current size class methodology.
This discussion as well as the findings by DHS and Butani et. al. (2006) make the current sizeclass methodology our preferred methodology as it is inherently more robust to regression to themean effects. However, we include the base year methodology for purposes of reference andalso to explore the sensitivity of the results to this methodological issue.
DHS also emphasize avoiding inferences that arise from the distinction between net andgross job creation. Policy analysts are inherently tempted to want to make statements along thelines that “Small businesses account for X percent of net job creation”. The problem with thisapproach is that since gross job flows dwarf net job flows, many different groupings ofestablishments can account for a large share the net job creation. That is, in the U.S., the averageannual net employment growth rate has been about 2.2 percent from 1976 to 2005. Underlying
this net employment growth rate has been an average annual gross job creation rate of 17.6
percent and a 15.4 percent average gross job destruction rate (statistics from the Business
Dynamic Statistics (BDS) which is described below). Decomposing growth into components is
problematic (at least in terms of interpretation) when some shares are negative. One way to see
7

Related evaluation work on alternative methodologies by BLS is found in Odelia (2004). We also note that the
BLS BED series releases net and gross job quarterly flows by a firm size measure. The firm size measure they use
is based on a taxpayer ID definition of the firm so that for multi-unit establishment firms that have multiple taxpayer
Ids their firm definition is somewhere between the establishment and overall firm.
8Acs, Armington and Robb (1999) in using a precursor to the LBD (the LEEM) at Census found evidence ofregression to the mean using base-year size and current-year size measures similar to those used here.

4this is that new firms account for about 3 percent of employment while net employment growthis around 2.2 percent. Using the share approach yields that new firms account for 135 percent of
all net new jobs. While it is interesting that the net growth from new firms alone exceeds theaverage, it is misleading to say they account for 135 percent of net new jobs since many otherfirms are also creating jobs. We show a specific example of this issue using the BDS below. A
more sensible approach that has been adopted in the literature is to look at the relationshipbetween net growth rates and size. It is this latter approach that we primarily use in this analysis.
Before proceeding to the more formal analysis, it is instructive to examine the tabular output
from the Business Dynamic Statistics on net job creation by firm size and firm age. The precise
definitions of firm size and firm age are discussed below (and are the same used by the BDS anddescribed on the BDS website). 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 andthe lower panel the current year size method. A quick perusal of the table yields a number ofinteresting observations. About 2.5 million net new jobs were created in U.S. private sector in2005. Strikingly, firm startups (firms with age 0) created about 3.5 million net new jobs.
Interestingly, every other firm age class except for the oldest firms exhibited net declines in
employment.9 However, it would be misleading to say that it is only firm startups and the mostmature firms that contributed to job gains. In both panels there are large positive numbers inmany cells but also large negative numbers in other cells. It is also clear that there aresubstantial differences in these patterns depending on using the base year or current year size
method although some common patterns emerge. For example, excluding startups, firms thathave employment between 5 and 99 workers consistently exhibit declines in net jobs. We limit
our discussion of the details of the firm size and age patterns here since the analysis that follows
explores these issues at length.
As will become clear, the patterns reflect two basic ingredients. Obviously, whether thesize/age class contributes positively or negatively depends on whether that size/age class has apositive or negative net growth rate. In addition, the magnitude of the positive or negative
contribution depends not surprisingly on how much employment is accounted for by that cell.
That is, a size/age class may have a large positive number not so much because it has anespecially high growth rate but because it accounts for a large fraction of employment (e.g., a 1
percent growth rate on a large base yields many net new jobs).
3. The Longitudinal Business Database
For this study we use the Longitudinal Business Database. The LBD has been recently
used to generate the public use statistics Business Dynamics Statistics (BDS).10 The BDS
includes tabulations of net and gross job flows at the national level but also by broad sector,
state, firm size and firm age. While we use the micro data in the LBD for our analysis, many ofthe patterns we discuss can be readily seen in the public domain BDS.11 We use the LBD rather
than the BDS to be able to control for detailed industry effects in our analysis.
9

A review of the BDS statistics for other years shows that the net job creation contribution of the oldest businesses
is highly procyclical.
10Indeed, some of the basic patterns from the BDS are seen in Table 1.
11
We use classifications not currently available in the BDS. For example, we control for detailed industry (4-digit
SIC or 6-digit NAICS as appropriate) in our analysis. Currently, the BDS only releases tabulations at the broad

5

The Longitudinal Business Database (LBD) covers all business establishments in the
U.S. private non-farm economy that file payroll taxes with the IRS. As such, it covers all
establishments in the U.S. nonfarm business sector with at least one paid employee.12 The file
begins in 1976 and currently runs through 2005. The LBD includes information on detailed
industry for every establishment. In the current paper, we use 4-digit SIC codes through 2001
and 6-digit NAICS codes after that. We note that the LBD (and in turn the BDS) employment
and job creation numbers track closely those of the County Business Patterns program and theStatistics of U.S. Business program of the U.S. Census Bureau (see Haltiwanger, Jarmin andMiranda, 2009).
The unit of observation in the LBD is the establishment defined as the single physicallocation where business is conducted. 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 offirms can be very dynamic and on occasion complex. Each establishment 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. We use these firm level identifiers to construct firmlevel characteristics for each establishment in the LBD. Further details about the LBD and its
construction can be found in Jarmin and Miranda (2002).
3.1 Measuring Firm Age and Firm Size
The construction of firm size measures is relatively straightforward. Firm size isconstructed by aggregating employment across all establishments that belong to the firm.
Employment for each establishment is obtained from administrative sources or Census
collections. Employment represents the number of employees on payroll during the pay period
including March 12. In this sense it is not unlike employment measures in other databases suchas the County Business Patterns (CBP, Current Establishment Survey (CES) and the Quarterly
Census of Employment and Wages (QCEW). There are some details in computing base year
size and current size that are worth noting. For base year firm size, we use the firm size for year
t-1 for all businesses except for new firms. For new firms, we follow the approach used by Birchand often by others and allocate establishments belonging to firm startups to the firm size class inyear t. For current year size, we use the average of firm size in year t-1 and year t.
The construction of firm age presents more difficult conceptual and measurement
challenges. We use the approach developed for the BDS and has been used in the recent
literature (see, e.g., Davis et. al. (2006)). When a new firm ID arises for whatever reason, we
assign the firm an age based upon the age of the oldest establishment that the firm owns in thefirst year of the new firm ID. The firm is then allowed to age naturally after that regardless ofmergers or acquisitions and as long as the firm ownership and control does not change. An
advantage of this approach is that firm births as well as firm deaths are readily and consistently
sector level. We have replicated our main findings using an extended version of the BDS with cell based
regressions at the detailed industry, age and size level of aggregation. We have also found that the basic patterns wereport also hold using the public domain BDS controlling only for broad sector. More generally, we have foundthat the patterns we find in our regression analysis can be replicated using a cell-based regression approach with net
employment growth rates defined at the industry by firm, size by firm and age by year level of aggregation.
12
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).

6defined. That is, a firm birth is defined as a new firm ID where all the establishments at the firm
are true births. Similarly, a firm death is defined as when a firm ID disappears and all of theestablishments associated with that firm ID exit.13
A strength of our firm size and age measures is that they are robust to ownership changes.
For a pure ownership change with no change in activity, there will be no spurious changes infirm size or firm age. When there are mergers, acquisitions, or divestitures, firm age will reflect
the age of the appropriate components of the firm that has changed activity. Firm size willchange but in a manner also consistent with the change in the scope of activity. In section 3.3
below, we provide further discussion on how our measurement methodology yields patterns of
the relationship between net growth, firm size and firm age that are robust to ownership changes
and M&A activity.
Before proceeding, we note that in the current paper we focus on growth dynamics ofestablishments and firms over the 1992 to 2005 period. The sample period is so that we candefine firm age consistently for all establishments for firm age groups 1 through 15 with anadditional right-censored group that are establishments that belong to firms that are 16 years orolder (in 1992 these are firms that have an establishment that existed in 1976). As is clear from
Table 1, the BDS has a larger number of age groups for the later years but also a smaller numberof age groups for the earlier years. We use the 1992 to 2005 period and the 1-15, 16+ categories
as a reasonable compromise.
3.2

Firm Size, Firm Age and Employment

This section describes firm size and firm age distributions. Figure 1 shows the share of
total employment in each firm size class between 1992 and 2005 and Table 2 shows both theshares and average employment in each size class. For these basic facts, we report distributions
for both base size and current size methodology. Interestingly, in terms of the cross sectional
distribution there is essentially no difference between these two methods. Employment is highly
skewed towards large employers. Small firms, those with fewer than 20 employees, employ
about 20 percent of all workers in the non-farm private economy. By contrast large firms, thosewith 500 or more employees employ roughly 48 percent of all workers in the non farm economy.
The largest firms, those with 10,000 or more employees employ close to one out of four workers.
Figure 2 shows shares of employment by firm age. About 3 percent of employment isaccounted for by firm births and about 15 percent of employment is accounted for by firms lessthan 6 years old. However, more than half of employment is accounted for by firms more than
16 years old. Taken together, Figures 1 and 2 highlight that employment is concentrated in bothlarger and mature firms.
Size and age are positively correlated, large firms tend to be older and young firms tend
to be small. Figure 3 shows the joint distribution of employment by firm size and firm age. It isapparent that most large businesses are mature businesses. Indeed, about 23 percent of
employment is accounted for by firms that have more than 10,000 employees and are more than13

The unit of analysis in the BDS is the establishment. The BDS then identifies job creation from firm births but
currently it does not provide information on the number of firm entries and exits.

7

16 years old. It is also apparent that most young businesses are small. The converse is less true.
Conditional on being small, there is roughly a u-shaped pattern with respect to age.
The relationship between business startups and firm size is of particular importance in the
results that follow. To highlight this relationship, Figure 4 shows the share of employment fromstartups in each size class using both methods. Table 3 provides the underlying statistics andemployment counts. Using either classification, we find that startups account for a largeshare of
employment in the smaller size classes, those with fewer than 20 employees. The contribution ofstartups declines with firm size and is negligible for the largest firm categories. While thequalitative patterns are similar, there is a notable difference in the share of employment in thesmallest size class across the two methods. We find a general pattern that startups tend to beclassified in smaller size classes using the current year size classification method.
It is also useful to abstract from the role of startups in examining the relationship between
firm size and firm age. Figure 5 describes the age of the firm for the average worker employed
at a continuing firm of a given size class. We exclude startups from the computation since thishas a disproportional effect on small size classes.14 The average employee of the largest US firms
works for a company that is 28 years old. By contrast the average employee of the smallest US
firms works for a company that is 11 years old.
3.3

The Establishment-Level and Aggregate Growth Rate Concepts

This section describes the establishment-level growth rate measures we use in the paper.
Some basic notation is as follows. Let Eit be employment in year t for establishment i. In
practice, this is a point-in-time measure reflecting the number of workers on the payroll for the
payroll period that includes 12 March. We measure establishment-level employment growth asfollows:
g it = ( Eit − Eit −1 ) / X it ,where
X it = .5 * ( Eit + Eit −1 ) .
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 Davis et al 1996, and Tornqvist, Vartia, and Vartia 1985).15 In what follows, we refer tothis as the DHS growth rate measure. Note that the DHS growth rate measure can be defined at
any level of aggregation (establishment, local area, industry, etc.)
Measures of job creation and destruction at the establishment level are given by:
14

Including startups reduces average age by 2 years in the smallest size class and has virtually no effect in thelargest size class.
15
The DHS growth rate like the log first difference is a symmetric growth rate measure but has the added advantagethat it accommodates entry and exit. It is a second order approximation of the log difference for growth rates around
zero.

8

JCit = max( g it ,0)JDit = max(− g it ,0)Job creation from entry at the establishment level is given by:
JCit = max( g it ,0) * I {g it = 2}
where I is an indicator variable equal to one if expression in brackets hold, zero otherwise, and git= 2 denotes an entrant.
Similarly job destruction from exit at the establishment level is given by:
JDit = max(− g it ,0) * I {− g it = 2}
where git = –2 denotes an exit.
Using these measures it is straightforward to generate aggregate measures of job creation anddestruction as well as job creation and destruction from entry and exit, respectively (at any levelof aggregation) given by:
JCt = ∑ ( X it / X t ) max{git , 0}i

JDt = ∑ ( X it / X t ) max{− git , 0}i

JC _ Entryt = ∑ ( X it / X t ) I {git = 2}max( git , 0) .
i

JD _ Exitt = ∑ ( X it / X t ) I {git = −2}max(− git , 0)iGiven these definitions, the following simple relationships hold:g t = JCt − JDt , JCt = JC _ Cont t + JC _ Entry t and JDt = JD _ Contt + JD _ Exitt
where JC_Cont and JD_Cont are job creation and job destruction for continuing establishments
respectively.
In the analysis that follows we consider employment weighted regressions ofestablishment-level data. The employment-weighted regressions yield that the mean of thedependent variable is equal to the appropriate employment weighted mean. For example,
consider a specification relating establishment-level net growth to the firm size categories in
Figure 1. The employment-weighted regression on firm size dummies yields the aggregate net
employment growth rate by firm size category. As we show in the appendix, employmentweighted establishment level growth rates for a given classification of establishments is
9equivalent to employment weighted firm level growth rates for the same classification of firms ina manner that is robust to abstracting from firm ownership changes and M&A activity.
While the details are in the appendix, a few remarks help illustrate this critical point.
First, by using establishment-level employment-weighted growth rates, we avoid spurious firm
births, deaths or growth from ownership changes or M&A activity. For example, consider a1000 person establishment that exhibits a net job gain of 100 jobs between year t-1 and t that alsochanges ownership between t-1 and t (i.e., is a divestiture from the originating firm and an
acquisition for the destination firm). A naïve treatment of firm level data would treat the loss of1000 for the divesting firm and the gain of 1100 for the acquiring firm. Our treatment is to countonly the 100 net new jobs. Moreover, as discussed in the prior subsection, our firm size and firm
age classification methods make clear how we are using the parent firm information in years t-1
and t to define the size and age characteristics.
While the employment-weighted establishment level net growth rates and employment
weighted firm level net growth rate patterns are equivalent for any common classification, it isimportant to emphasize that further decompositions of net growth rate patterns into job creation,
job destruction, establishment entry and establishment exit components are not the same at thefirm and establishment-level. The reason is straightforward since within the same firm there
may be some establishments expanding and opening while others are contracting and shutting
down. In what follows, we explore such decompositions of net growth rate patterns into thesecomponents.16

3.4

The Establishment-Level Growth Rate Distributions

Before proceeding to the relationship between firm size, firm age and net growth, it isinstructive to characterize the underlying distributions of establishment-level growth. Figure 6
shows the distribution of employment growth rates for all establishments, Panel A, as well as forcontinuing establishments, Panel B. Figure 6 reports both employment weighted and unweighted
results. Growth rate distributions are annual net growth rates using data for all establishments
between 1992 and 2005. Several patterns stand out. First, the U.S. economy is extremely
dynamic with large numbers of establishments opening and closing at any given time. The upper
panel of Panel A shows that approximately 20 percent of all establishments are new or die on
average during this period in any given year. The lower panel of Panel A shows that in terms of
employment establishment births and deaths account for a relatively smaller share of jobs,
approximately 5.7 percent, an indication that many of these establishments are small.
Second, high churning in the economy is combined with substantial inertia. Figure 6
shows that approximately 30 percent of establishment-year records in the LBD exhibit no change
in net employment from one year to the next. The share of jobs at establishments characterized16

Our analysis of continuing, entering and exiting establishments would also be enhanced if we more completely
linked these to continuing, entering and exiting firms. In this respect, our analysis of entering establishments andentering firms is already satisfactory as we clearly distinguish between entering establishments of new firms (firmage 0 firms) and existing firms. However, in our analysis of exiting establishments we do not currently identify
whether the establishment exit is associated with an exiting or continuing firm. We plan to explore these issues
further in the next draft.

10

by zero employment growth account for approximately 13 percent of all jobs an indication thatthese establishments tend to be small. It is striking there is this much inertia at an annual
frequency. Davis, Faberman and Haltiwanger (2006) report that about 80 percent of
establishments have zero net employment change at a monthly frequency and about 30 percent of
employment is at establishments that have zero employment change at the monthly frequency.
Third, it is clear there is considerable dispersion in establishment-growth. Within any
given year, there are a substantial number of establishments as well as a substantial amount of
employment at establishments with growth in excess of 10 percent and a substantial amount withgrowth less 10 percent. This dispersion highlights the dynamism of U.S. establishments but also
highlights that it is important to be careful about net vs. gross job creation and destruction in thecontext of attributing growth by firm characteristics.
It is also instructive to explore the serial correlation properties of net growth rates at theestablishment-level. Figure 7 shows the estimated autocorrelation patterns for all establishments
using both the base size and current size classification methods. Interestingly, there is a negative
correlation in all size categories. This reflects the presence and importance of transitory shocks.
That is, growth one year tends to be at least partially reversed the following year. In addition,with the base size category the negative correlation is larger in magnitude for the smaller sizeclasses. This suggests that the regression to the mean issues that make inferences about net
growth and firm size are more of an issue for the very small businesses when using the base size
category.
4. The Relationship Between Net Growth, Firm Size and Firm AgeThis section presents the main empirical results of the paper. Our objective is tounderstand the relationship between net employment growth, firm size and firm age. We use a
non-parametric regression approach to quantify these relationships. In our main specification,
we regress net employment growth at the establishment-level on firm size classes by themselves,on firm age classes by themselves and by firm size and age together. We focus on employmentweighted specifications since this enables the coefficients to be interpreted in terms of theaggregate net employment growth rate at the aggregate level for the specified category. Since
firm size and firm age distributions vary by industry and net growth rate patterns vary byindustry, we control for detailed industry fixed effects. In addition, to abstract from cyclical or
secular aggregate considerations we control for year effects. Given our non-parametric approachwith industry and year fixed effects, our results are readily interpretable as employmentweighted conditional means.17
In what follows, section 4.1 presents the results where the dependent variable is net
employment growth. Given the interesting patterns we find, section 4.2 presents results where
we decompose the net employment growth into intensive and extensive margins by examining
continuing, entering and exiting establishments. Finally, in section 4.3 we explore how thepatterns look if we use establishment size and age categories. As discussed in the introduction
17

We obtain virtually the same results using a cell-based regression approach described as follows. First, computethe net employment growth rate using the DHS methodology at the following level of aggregation: detailed
industry, size, age, industry and year. Second, estimate same non-parametric regression specification with size, age,industry and year effects.

11and section 2, the appropriate categories are firm size and firm age for the questions of interest in
this paper. However, there is independent interest in the role of establishment size and age. In
addition, many papers in the literature have been restricted to using establishment size and age
categories so this analysis serves as a check to help understand how the results are sensitive tothis potentially important discrepancy.
4.1. Net Employment Growth
Table 4 presents results on the role of firm size and firm age for net job creation. We
report results using both base size and current size measures of firm size. Data for the
regressions are available starting in 1976, the first birth cohort observed in the LBD. To ensure
we have sufficient observations across the firm age distribution, we restrict the analysis to theperiod between 1992 and 2005 to allow firm age for the first birth cohort to accumulate to aminimum of 15 years with a common right censored category of 16+ years18.
We start by reporting net employment growth rate patterns for firms of different size
ignoring the impact of age. Column 1 shows the results using base-year size. In interpreting theresults from these regressions, it is important to emphasize that the reported coefficients
represent differences relative to the omitted groups. We observe high net employment growth
rates for the smallest size firms (those with between 1 and 4 employees) relative to the largest
size class. The average annual rate of net employment growth in this size class is 18.9 percent
higher than that for the largest size firms. The effect declines monotonically as the size of thefirm increases. The relative net employment growth premium for being small decreases to 6.1
percent, 3.3 percent and 1.7 percent for size classes 5-9, 10-19 and 20-49 respectively. It stays atless than 1 percent for the larger size classes. These patterns support the hypothesis of an inverse
relationship between net growth and firm size when using size classes defined using the baseyear method and are similar in character to those found recently by NZW.
Turning to the current-year (average) size measures in the second column, we see asignificant drop in the magnitude of the estimated size effects relative to base year specifications.
They are still significantly higher for the smallest size class at 4.2 percent (compared to 18.9
percent) but much reduced. We also observe that the highest net growth rates tend to be for sizeclasses less than 500 but it’s also worth noting this is not a strictly monotonic relationship. For
example, we find reasonably high positive relative net growth rates for firms that are as large as2500 to 4999 workers. Comparing the first two columns suggest the effects of regression to themean are quite strong in the smallest size classes. This is not surprising given findings reported
in Figure 7 showing that transitory shocks play a more important role for smaller businesses.
Our primary contribution is to explore what happens to these patterns when we control
for firm age. Estimates from models that include only size categories are likely influenced by
the omission of startups and age controls on the specification. From the results in section 3, weknow many small firms are startups. These startups are likely to have a significant influence on
the estimated size coefficient. We also know there is a positive correlation between size and age.
This relationship is complex. There are many small firms that are old but relatively few make it
18

Annual records for the LBD contain codes that describe how they relate to a record for the preceding year. The
first year of source data for the LBD is 1975. Age can’t be determined for these cases and they are in the 16+
category in the first year used in our analysis, 1992.

12

past the first few years so large firms are older on average. Many of these small firms shut down
contributing significantly to job loss in the economy. There are also young firms that grow to belarge quickly. Ideally we want to isolate these types of effects.
Column 3 reports net employment growth rate patterns for firms of different age but
ignoring the role of firm size. Firm age is also treated in a non-parametric fashion.
Establishments of new firms (true startups) are classified in the age zero class. Firms up to age
15 are classified each in their own category. Older firms are grouped together as a group defined
16 and older. There is a very large coefficient for firm startups reflecting the high net growthrate for business startups.19 One can think about this effect as putting in a dummy variableindicating that the establishment is part of a startup so that then the remainder of the coefficientscan be interpreted as reflecting the behavior of establishments abstracting from startups. In
column 3, we find relatively high growth rates for the one year old firms relative to the mostmature firms. The premium for being one year old is 3.4 percent. Interestingly, all other firmcategories up to age 12 display negative net job creation relative to the oldest group with thelargest negative effects for firms age 2 at -3.2 percent. It increases monotonically after that to -.5
percent for firms age 12. These results suggest that on average young firms shed jobs rapidly
after age 1 contributing significantly to the overall job loss relative to old firms. However, as
before these estimates are likely influenced by the omission of firm size controls.
Columns 4 and 5 report the results for firms of different size while simultaneously
controlling for firm age. Column 4 has the results using base-year size while column 5 has theresults using current-year size. The estimates for the size coefficients in columns 4 and 5 differ
substantially from the analogous results in columns 1 and 2. Having controlled for firm age
effects, column 4 shows no systematic relationship between net growth and base firm size. The
smallest size class has the largest positive coefficient but the size classes in the range from 5 to499 have the most negative coefficients. 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 weuse current size (column 5), we also don’t observe a monotonic relationship, but we do find apositive relationship between net growth and firm size for all the size classes up through 5000
workers. Using this size class methodology, small businesses with employment between 1 and499 workers have substantially lower net growth rates than the largest businesses. While thedetails differ non-trivially 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.20
The firm age patterns in columns 4 and 5 are of considerable interest in their own right.
We focus on column 5 since this is the specification that includes the measure of firm size that isless subject to regression to the mean effects. We find that once we control for firm size there issubstantially higher relative net growth in age 1 firms and a more general pattern of higher net
growth for younger firms relative to the more mature firms. Of course, what stands out in both

19

Recall at the establishment-level, the net growth rate for an establishment at a firm startup is equal to 2 using DHS
methodology. The estimated coefficient in Table 3 for all firm age 0 establishments is close to 2 but not identical to
2 since this is a relative coefficient to the most mature firms.
20
The patterns in Table 4 are, not surprisingly, roughly consistent with the simple tabulations from the BDS in Table
1 where we observed many negative net job cells for smaller businesses abstracting from startups.

13

columns 4 and 5 is the role of firm startups. Firm startups contribute substantially to net growth
regardless of whether we control for firm size or how we control for firm size.
4.2. Different Margins of Adjustment
As illustrated in Figure 6, the economy is a complex aggregation of expanding, entering,
contracting and exiting establishments and firms. Thus, the patterns we have reported in Table 4
may mask quite different patterns by size and age on different margins of adjustment. We havealready seen the dominant role of firm startups in the patterns of net growth. In this section, we
focus on the different margins of adjustment.
Our approach here is to decompose the net growth rate patterns into continuingestablishments, entering establishments and exiting establishments. To accomplish this, weestimate a series of employment-weighted regressions with the same RHS variables as Table 4
but using a series of different LHS variables. In one specification, we restrict the sample tocontinuing establishments where the dependent variable is the net employment growth rate forsuch establishments. In alternative specifications, we use all establishments with the LHS
variable equal to the job creation from establishment entry or alternatively the job destruction
from establishment exit as defined in section 3. Since all specifications are employmentweighted the mean of the LHS variable is the aggregate component as defined (e.g., theaggregate net employment growth rate of continuing establishments). It is also important to notethat all specifications include the full set of controls for year and detailed industry effects.
To facilitate the exposition of these regression results, we report the estimatedcoefficients from these alternative specifications in figures 8 and 9. Figure 8 shows theestimated coefficients from firm size effects while Figure 9 shows the estimated coefficientsfrom firm age effects.21 Each figure has four panels. The upper left panel shows the patterns wehave already discussed from Table 4. The upper right panel shows net growth patterns forcontinuing establishment. The lower left panel shows the patterns for job creation fromestablishment entry and the lower right panel shows the patterns for job destruction fromestablishment exit. In interpreting these different components, it is important to emphasize that
establishment entry and exit is a common phenomenon at continuing firms. Put differently,
continuing establishments should not be interpreted as equivalent to continuing firms.
Continuing establishment results are a component of what happens at continuing firms but only acomponent. Our remarks will help draw out these distinctions.
The upper right panel for continuing establishments shows stark differences in thepatterns for small firms depending on the size class methodology. Using the base year
methodology, the continuing establishments of the smallest firms have substantially higher net
growth rates than the continuing establishments of the largest firms. Controlling for firm age has
little impact on these patterns. In contrast, when using current year size, continuingestablishments of small firms have about the same growth rate as continuing establishments oflarger firms when we don’t control for firm age. However, once we control for firm age,21

We also note that given the very large sample, the standard errors (not reported) are all very small (less than
0.0005 in all cases).

14

continuing establishments of small firms have lower growth rates than continuing establishmentsof larger firms. The stark differences here between base size and current size results for thecontinuing establishments of the smallest firms are consistent with strong regression to the meaneffects since these are primarily relevant for continuing establishments. Moreover, as we havealready highlighted, Figure 7 suggests that the role of transitory shocks is most important for theestablishments of the smallest firms.
The lower left panel shows that controlling for firm age has a substantial impact on therelationship between job creation from establishment entry and firm size. Without firm age
controls, smaller firms have higher job creation rates from establishment entry than larger firms.
However, once we control for firm age, we find that for both the base year size and current year
size methods that smaller firms have lower job creation rates from establishment entry thanlarger firms. In interpreting these findings, it is important to note that controlling for firm age
abstracts from the contribution of firm startups. As such, the results in the lower left panel
controlling for firm age provide insights into the relative job creation from establishment entry
for continuing firms. The implication is that large continuing firms are more likely to have job
creation from establishment entry than small continuing firms.
Turning to the patterns for job destruction from establishment exit, the lower right panel
shows that there is a general pattern of greater job destruction from establishment exit for smallerfirms than larger firms regardless of size class methodology or controlling for firm age. Whilethe qualitative patterns are similar, job destruction from establishment exit is higher for smaller
businesses using the current size relative to the base size methodology. This pattern is not due toregression to the mean effects since exit tends to be permanent.22 Instead, it reflects the fact thatthe current size methodology more closely mimics the dynamic sizing method of the BLS – thatis, small businesses are more likely to “pass through” the smallest size classes as they exit.
Note that controlling for firm age in the lower right panel of Figure 8 lowers job
destruction although this effect is relatively small. When using the base year size method,
controlling for firm age lowers the job destruction from exit for small firms. This makes sense as
many of the young firms are small and as we will soon see younger firms have higher rates ofjob destruction from exit. There is a similar pattern but even a smaller impact of firm age whenusing the current year size method.
Many of the patterns in Figure 8 are easier to understand by comparing and contrastingwith the patterns in Figure 9 that shows the analogous patterns for firm age. In Figure 9, the firm
startup estimated coefficient is not reported in the upper left hand panel or the lower left handpanel since it is much higher and close to 2. The upper left panel shows the results from Table 4.
The remaining panels show that the relatively weak relationship between firm age and net
growth in the upper left panel masks richer dynamics for surviving and exiting establishments.
In the upper right hand panel, we find that conditional on the establishment surviving (i.e., being
a continuing establishment), young firms exhibit substantially higher growth than more maturefirms. In contrast, the lower right hand panel shows that the job destruction due to establishment
exit is higher for young firms than more mature firms. In combination, these two panels show an

22

In a very small number of cases, an establishment shuts down temporarily.

15

“up or out” pattern for the establishments of young firms that is robust to controlling for firm
size (and robust to whichever size class method is used).23
The lower left panel shows that there is not much of a relationship between job creationfrom establishment entry and firm age for firms with age greater than or equal to one. It is notcorrect to interpret this finding as suggesting establishment entry rates are small for firms one
and older. The reported coefficients are relative effects compared to the omitted group so thecorrect interpretation for this (and other panels) is that there is not much difference in thecreation rates from establishment entry across age classes. Moreover, it is critical to emphasizethat that the firm age 0 coefficient (startups) is omitted from the chart since this coefficient isliterally off the chart (close to 2). Still, it is interesting to compare these patterns with the lower
left panel of Figure 8 where we observed that when we controlled for firm age, that largest
businesses have higher creation rates from establishment entry than smaller businesses.
The results depicted in Figures 8 and 9 show that the margins of adjustment differ
substantially across firm size and firm age classes. Figure 8 shows that the role of controllingfor firm age on firm size effects is mostly associated with the impact of firm age on job creationfrom establishment entry and job destruction from establishment exit. Figure 8 also shows thatthe large differences associated with the size class methodology are especially present forcontinuing establishments – the establishments for which regression to the mean effects are
relevant. Figure 9 shows that the overall firm age net growth relationship masks a rich “up orout” pattern for the establishments of young firms.
Figures 8 and 9 help highlight the volatility of young and small businesses are volatile
along a number of dimensions. Figures 8 and 9 show that young and small businesses havehigher rates of job destruction from exit. Moreover, Figure 9 shows that conditional on survival,
the establishments of young firms have higher net growth rates. In unreported results (available
upon request), we have also found that the continuing establishments of young firms exhibit
higher rates of job creation and destruction – so the higher net growth rate for the continuingestablishments of young firms in Figure 9 has high accompanying underlying volatility. Eventhe sensitivity in Figure 8 to the size class methodology for continuing establishments of small
firms can be interpreted as reflecting high volatility. That is, high transitory shocks are a form ofhigh volatility and it appears small firms are especially subject to a high variance of transitory
shocks.24
4.3. Results using Establishment Age
In this section, we explore the role of establishment size and age. Since some datasets arelimited in only having establishment-level information, it is useful to check how different thepatterns are on this dimension. There is also independent interest in the role of establishment
size and age – these characteristics reflect different characteristics that may be quite relevant for
employment growth rate dynamics.
23

In interpreting Figures 8 and 9 and for the results on job destruction from establishment exit in particular it isuseful to note that most of the young and small businesses are single unit establishment firms. As such,
establishment exit typically implies firm exit. Still, it would be of interest to explore the distinction between
establishment and firm exit more completely which we plan to do in the next draft.
24
Some caution in interpretation is required here since we don’t observe shocks. It could be for example that small
businesses are more responsive to transitory shocks given differences in adjustment costs.

16

Table 5 lists the results regressions highlighting the role of establishment size and age on
net job creation. It is the equivalent to Table 4 with the difference that the size categories aretruncated at 1000+. At the firm level, there is much activity in firms larger than 10,000
employees but for establishments there are relatively few establishments that large. The firstcolumn shows a strong inverse relationship between establishment size and net growth whenusing a base year size (now applied at the establishment level). However, the second column
shows that this relationship is not robust to using current size. It appears as if those patterns are
entirely due to regression to the mean effects.
The more interesting results are columns 4 and 5. In column 4, we find that even when
we control for establishment age, there remains a strong inverse relationship between net growthand establishment size. This pattern is not robust to using current size. For establishment age,
we find that establishment births obviously contribute substantially to net growth. Moreover, wefind that age 1 establishments have higher net growth than more mature counterparts and for our
preferred specification that virtually all ages have slightly higher growth than the oldest
establishments.
Overall, the patterns for establishment size and age are similar to those for firm size andage with one notable exception. Controlling for firm age in Table 4 eliminates any systematic
relationship between firm size and net growth. Controlling for establishment age in Table 5 hasmuch less of an effect on the base year size approach. Two likely effects are at work here. First,
regression to the mean effects are likely more important at the establishment than the firm level.
That is, for multi-unit establishment firms the role of transitory shocks is less likely to shift firms
across size classes. Second, firm age is a much more powerful control than establishment age.
5. Concluding Remarks
We use a recently developed longitudinal business database that tracks all establishments
and firms in the U.S. nonfarm business sector over the last few decades to explore the role offirm size and firm age in employment growth. There is a widespread popular perception that
small businesses create more or even most jobs in the U.S. We argue, as has the earlier literature,that the question of “what fraction of net new jobs are created by small businesses?” is not a well
posed question. Still, it is certainly of interest to explore the relationship between firm size andnet employment growth. We find some evidence in support of the popular perception along thefollowing 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. We caution, however, that even this simple relationship is complicated by regression
to the mean effects. Using the method that we argue is more robust to such concerns, the inverse
relationship between net growth rates and size remains but is not overwhelming.
A more important and robust finding is the role of firm age. We find that once wecontrol for firm age, the negative relationship between firm size and net growth disappears andmay even reverse sign as a result of relatively high rates of shutdowns amongst the smallest
firms. Our findings suggest that it is particularly important to account for business startups.
Business startups account for roughly 3 percent 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 percent per year. Startups tend to be small so most of the truth to the
popular perception is driven by the contribution of startups to net growth.
17We also find rich dynamics among the young firms in the U.S. Young firms exhibit highrates of gross job creation and destruction. Consistent with this pattern, we find that young firms
have very high job destruction rates from exit. However, we also find that, conditional on
survival, young firms grow more rapidly than their more mature counterparts. In combination,
our findings suggest a rich “up or out” dynamic of young firms.
Most of our focus in the analysis is on the net growth rate patterns by firm size and firmage (along with the underlying different margins of adjustment). However, our basic facts also
show that large, mature businesses account for a large fraction of jobs. Firms over 16 years oldthat have more than 10,000 workers account for about 1 in 4 jobs in the U.S. private sector. In
contrast, businesses that are young and small account for a relatively small share of the stock ofjobs. It is important to keep this skewed distribution of employment in mind when interpretingthe net growth rate patterns since the overall contribution to job growth depends both on thestock and the net growth rate patterns.
In closing, 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 ofbusiness formation, experimentation, selection and learning as important features of the U.S.
economy.25 Viewed from this perspective, the role of business startups and young firms is partof 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 startups and young businesses,
the frictions they face, their role in innovation and productivity growth, how they fare ineconomic downturns and credit crunches all are clearly interesting areas of inquiry given ourfindings of the important contribution of startups and young businesses. In a related manner, it isimportant to not focus only on jobs per se but also on the role of these dynamics in the patterns
of productivity and earnings behavior in the U.S. We think 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 need to be added to these databases
and related analyses.

25

There has, of course, been substantial theoretical development along these lines (e.g., Jovanovic (1982),
Hopenhayn (1992) and Ericson and Pakes (1995)) and our findings, like others in the literature (e.g., Dunne, Roberts
and Samuelson (1989) and DHS) provide broad empirical support for this class of models and ideas.

18

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20Appendix: Establishment, Firm and Aggregate Net Growth Relationships:
In this appendix, we explore the relationships between employment-weighted establishment levelnet growth rates and employment-weighted firm level net growth rates. In considering these
relationships, it is helpful to first abstract from ownership changes and M&A activity. In
particular, consider initially an environment where there are no acquisitions or divestitures andwhere any whole firm ownership changes don’t yield changes in firm ids (that is, when “Joe’s
Diner” becomes “Pete’s Diner” the firm ID is longitudinally linked so there are no spurious firm
births or deaths). Then the following identities hold:g ft = ∑ ( X it / X ft ) g iti∈ f

g st = ∑ ( X ft / X st ) g ft = ∑ ( X it / X st ) g it
f ∈s

i∈s

X ft = ∑ X iti∈ f

X st = ∑ X ft = ∑ X it
f ∈s

i∈s

Where i indexes establishments, f indexes firms, s indexes classifications of firms andestablishments into groups defined by s, X is the DHS denominator, and g is the DHS growthrate. That is, the employment-weighted net establishment employment growth aggregating all
establishments in the same firm yields the firm net growth rate and the aggregate net growth rate
can be generated either by first aggregating the establishment net growth to the firm level or byaggregating directly from the establishment-level net growth. This aggregate can be defined forany level of aggregation “s” where “s” can refer to economy-wide, industry, firm size, or firm
age classifications. This implies that as long as the establishments are classified into groups “s”
in a manner consistent with how firms are assigned to groups “s” that employment-weighted
establishment-level growth patterns by classification “s” yield the employment-weighted firmlevel growth patterns by classification “s”. For our purposes, this implies under the assumptions
stated that the employment-weighted net establishment level growth rate patterns by firm sizeclass yield the relevant employment-weighted net firm level growth rate patterns by firm sizeclass.
With M&A or ownership change activity, further considerations must be taken into account.
Note first that whole firm ownership changes are in principle straightforward. That is, if allownership changes are of the form where all establishments in a firm (including entering andexiting establishments) are subject to the same change in ownership then as long as the firm IDs
have been properly linked then the above equivalency holds. The more complicated cases arethose where there is M&A activity involving acquisitions and divestitures. The discussion that
follows shows that our current methodology offers a reasonable way to treat and classify the net
growth that is also associated with M&A activity.
An example is helpful to illustrate these issues and how our methodology offers a reasonable
way to treat M&A activity in a consistent manner at the establishment and the firm level.
Consider an establishment that has been involved in a divestiture and acquisition from t-1 to t.
21

The establishment-level classification used in the analysis under the current size classmethodology, assigns this establishment to the size class based upon the average of the firm thatthe establishment belonged to in t-1 and t. Suppose this establishment created 100 net new jobs.
The method in the text would assign this net 100 new jobs to the average of the size classes ofthe divesting and acquiring firm.
If instead we first aggregated the data to the firm level we would need to make a number ofassumptions. First, we would have to determine which firm gets the “credit” for the net new jobs
(the divesting or acquiring firm or both). That is, we would want to start by defining the net
employment growth for the firm as based on the continuing establishments of the firm, the true
entering establishments, and the true exiting establishments. We would then want to only
include any additional net growth from acquisitions or divestitures that represented true growthand not just changes in ownership. So if the establishment in question went from 1000 to 1100
workers between t-1 and t and we decided to allocate the net new growth to the acquiring firm,then we would only want to count the 100 new jobs (and obviously not the 1100 overall jobs) incomputing net growth. But it is not clear why the acquiring firm (or for that matter the divestingfirm) should be allocated the entire net new jobs. An alternative that would mimic what we do at
the establishment-level would be a method that credits both acquiring and divesting firms anddescribed as follows. First, define firm-level net growth for all firms in the absence of any
acquisitions or divestitures (and avoiding any spurious firm births and deaths due to whole firmownership changes). Then for any establishments involved in acquisitions or divestitures, treat
the net growth like we currently do in the text – i.e., allocating to the average of the size of theacquiring (t) and divesting firm (t-1). Viewed from this perspective, our current method can beseen as a classification method that robust to M&A activity as well as ownership changes with a
specific allocation rule that gives “credit” to both the acquiring and divesting firm for any net
growth that occurs.
Note that for the base year size methodology, care must still be given to treating M&A activity
but the decision on which firm to give credit to any net growth from establishments impacted by
M&A activity is straightforward. That is, under the base year size methodology, it is thedivesting firm that gets the “credit”. For the establishment-level approach used in the text, it isalso the divesting firm that gets the credit as the establishment is assigned the firm size classbased on the t-1 firm.
Finally, it is also important to note that the above identities hold for net growth rates but not forthe components of net growth such as gross job creation and destruction. That is, employmentweighted establishment-level gross job creation is not equal to employment-weighted firm-levelgross job creation and so on. The reason is obvious since there can be within firm establishmentlevel creation and destruction.

22

0.05

h) 500 to
999g) 250 to499f) 100 to249e) 50 to 99d) 20 to 49c) 10 to 19b) 5 to 90a) 1 to 4i) 1000 to
2499h) 500 to
999g) 250 to499f) 100 to249e) 50 to 99d) 20 to 49c) 10 to 19b) 5 to 9a) 1 to 40i) 1000 to
2499l) 10000+

0.1l) 10000+

0.2k) 5000 to
9999

0.25

k) 5000 to
9999

0.3

j) 2500 to
4999

Employment Firm Size Distribution:
Current Firm Size, Average 1992-2005j) 2500 to
4999

0.15
Share

0.15
Share

Figure 1

Employment Firm Size Distribution:
Base Firm Size, Average 1992-2005
0.3

0.25

0.2

0.1

0.05

23

Figure 2
Employment Share: by Firm Age Class
Average 1992-2005
0.7
0.6

Share

0.5
0.4
0.3
0.2
0.1
u. 15+

p. 15

o. 14

n. 13

m. 12

l. 11

k. 10

j. 9

i. 8

h. 7

g. 6

f. 5

e. 4

d. 3

c. 2

b. 1

a. 0

0

24

Figure 3
Average Annual Employment by Firm Age and Size, 1992-2005

25,000,000

20,000,000

15,000,000
Em ploym ent
10,000,000

5,000,000

0
0

1

2
3

4
5

Firm age610 1115 16+l) 10000+k) 5000 to 9999j) 2500 to 4999
i) 1000 to 2499h) 500 to 999g) 250 to 499f) 100 to 249e) 50 to 99d) 20 to 49
Firm sizec) 10 to 19b) 5 to 9a) 1 to 4

25

Figure 4

Share of Startups within Firm Size Class
Base Firm Size, Average 1992-2005
0.2
0.18
0.16

Share

0.14
0.12
0.1
0.08
0.06
0.04
0.02
to
49
h)
9
50
0
to
99
i)
9
10
00
to
24
j)
25
00
to
49
k)
50
00
to
99
l)
10
00
0+

24
9

g)
25
0

f)

10
0

to

to

99

49
e)
50

c)

10

d)
20

to

to

19

9
to
b)
5

a)
1

to

4

0

to
49
h)
9
50
0
to
99
i)
9
10
00
to
24
j)
25
00
to
k)
49
50
00
to
99
l)
10
00
0+

24
9

g)
25
0

to

99
f)
10
0

e)
50

to

49

d)
20

to

19
to

to

c)
10

b)
5

to

4

9

0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
a)
1

Share

Share of Startups within Firm Size Class
Current Firm Size, Average 1992-2005

26

10

00

50
to

to

99

49

19

9

4

49
9

24
9

to

to

to

to

to

to
99
i)
9
10
00
to
24
j)
25
00
to
49
k)
50
00
to
99
l)
10
00
0+

h)
5

g)
2

f)
10
0

e)
50

d)
20

c)

b)
5

a)
1

Share

Figure 5

Average Firm Age by Firm Size,
Continuer firms in 2005, Employment Weighted

30

25

20

15

10

5

0

27

Figure 6
Panel A

Panel B

28

-0.1

to
19

to
49
e)
50
to
f)
99
10
0
to
g)
24
25
9
0
to
h)
49
50
9
0
to
99
i)
10
9
00
to
j)
24
25
00
to
k)
49
50
00
to
99
l)
10
00
0+

d)
20

9

b)

1
to
4

5
to
c)
9
10
to
19
d)
20
to
49
e)
50
to
f)
99
10
0
to
g)
24
25
9
0
to
h)
49
50
9
0
to
i)
99
10
9
00
to
j)
24
25
00
to
k)
49
50
00
to
99
l)
10
00
0+

-0.1

10

to

5

b)

4

a)

-0.05

c)

1
to

-0.05

a)

Figure 7

Employment Growth Autocorrelations by Current Size

0

-0.15

-0.2

-0.25

-0.3

Employment Growth Autocorrelations by Base Size

0

-0.15

-0.2

-0.25

-0.3

29

Figure 8: Alternative Estimates of the Firm Size Growth RelationshipPanel A

Panel BNet Employment Growth Rates by Firm Size

0.2

0.25

0.1

0.15

0

0.05

-0.1

-0.05

-0.2

Net Employment Growth Rate for Continuing
Establishments By Firm Size

-0.15

1
a)

4
to

5
b)

9
9
9
+
99
9
99
99
99
49
19
49
99
24
49
00
to
to
to
to
o9
o4
o2
o9
to
to
0, 0
0t
0t
50
20
0t
10
0t
00
00
50
25
l) 1
e)
d)
c)
00
00
25
f) 1
h)
g)
i) 1
j) 5
k)

1
a)

Firm Size Class

4
to

5
b)

Firm Size Class

Base Year Size
Current (Avg) Size
Base Year Size with Age Controls
Current (Avg) Size with Age Controls

Base Year Size
Current (Avg) Size
Base Year Size with Age Controls
Current (Avg) Size with Age Controls

Job Creation from Establishment Entry by Firm Size

Job Destruction from Establishment Exit by Firm Size

0.25

0.25

0.15

0.15

0.05

0.05

-0.05

-0.05

-0.15

9
9
9
+
99
9
99
99
99
49
19
99
49
24
49
00
to
to
to
to
o9
o4
o2
o9
to
to
0, 0
0t
0t
50
20
0t
0t
10
00
00
50
25
l) 1
e)
d)
c)
00
00
25
f) 1
h)
g)
i) 1
j) 5
k)

-0.15

1
a)

4
to

5
b)

9
9
9
+
99
9
99
99
99
49
19
99
49
24
49
00
to
to
to
to
o9
o4
o2
o9
to
to
0, 0
0t
0t
50
20
0t
0t
10
00
00
50
25
l) 1
e)
d)
c)
00
00
25
f) 1
h)
g)
i) 1
j) 5
k)

Firm Size Class

Base Year Size
Current (Avg) Size
Base Year Size with Age Controls
Current (Avg) Size with Age Controls

1
a)

4
to

5
b)

9
9
9
+
99
9
99
99
99
49
19
99
49
24
49
00
to
to
to
to
o9
o4
o2
o9
to
to
0, 0
0t
0t
50
20
0t
0t
10
00
00
50
25
l) 1
e)
d)
c)
00
00
25
f) 1
h)
g)
i) 1
j) 5
k)

Firm Size Class

Base Year Size
Current (Avg) Size
Base Year Size with Age Controls
Current (Avg) Size with Age Controls

Note: Each reported point reflects the estimate for the size class in question relative to the omitted firm size class (10,000+)30

Figure 9: Alternative Estimates of the Firm Age Growth RelationshipPanel A

Panel BNet Employment Growth By Firm Age

0.15

0.2

0.1

0.15

0.05

0.1

0

0.05

-0.05

0

-0.1

Net Employment Growth for Continuing Establishments by
Firm Age

-0.05
1
a)

2
b)

c)

3

4
d)

5
e)

f) 6

7
g)

8
h)

i) 9

Firm Age ClassAge Only

k)

10

2
1
13
14
l) 1 m) 1
n)
o)

+
15
16
p)
q)

1
a)

2
b)

c)

3

4
d)

5
e)

f) 6

7
g)

8
h)

i) 9

Firm Age Class

With Base Year Size Controls

Age Only

With Current Year Size Controls

k)

10

2
1
14
13
l) 1 m) 1
o)
n)

+
15
16
p)
q)

With Base Year Size Controls

With Current Year Size Controls

Job Destruction from Establishment Exit by Firm Age

Job Creation from Establishment Entry by Firm Age
0.15

0.15

0.1

0.1

0.05

0.05

0

0

-0.05

-0.05

-0.1

-0.1
1
a)

2
b)

c)

3

4
d)

5
e)

Firm Age ClassAge Only
With Current Year Size Controls

f) 6

7
g)

8
h)

i) 9

k)

10

2
1
13
l) 1 m) 1n)With Base Year Size Controls

14
o)

+
15
16
p)
q)

1
a)

2
b)

c)

3

4
d)

5
e)

Firm Age ClassAge Only

f) 6

7
g)

8
h)

i) 9

k)

10

2
3
1
l) 1 m) 1 n) 1

14
o)

+
15
16
p)
q)

With Base Year Size Controls

With Current Year Size Controls

Note: Each reported point reflects the estimate for the age class in question relative to the omitted firm age class (16+)

31

Table 1 Net Job Creation by Firm Size and Firm Age, U.S. Private Sector, 2005
Firm Size (Base Year)
Firm Age
a) 0
b) 1
c) 2
d) 3
e) 4
f) 5
g) 6 to 10h) 11 to 15i) 16 to 20j) 21 to 25k) 26+m) ALLa) 1 to 4b) 5 to 9

731,515
79,75926,506
7,535
20,456
4,808
14,577
15,663
5,673
2,923
1,016
910,431503,644-12,547-24,840-22,650-18,442-19,792-71,332-47,730-36,856-28,173-38,599
182,683c) 10 to 19 d) 20 to 49 e) 50 to 99
498,317-20,836-31,883-26,855-23,212-24,392-99,235-67,923-58,236-42,609-71,235
31,901

553,181-47,837-44,488-37,824-29,616-29,425-110,111-81,876-71,299-51,490-107,390-58,175

313,511-41,006-26,738-15,918641-14,870-40,652-40,432-35,979-22,246-48,873
27,438f) 100 to
249g) 250 to499h) 500 to
999i) 1000 to
2499j) 2500 to
4999k) 5000 to9999l) 10000+All

292,348-57,188-18,026-14,813-9,816-6,222-1,324-27,666
9,780-13,346
10,309164,036

157,120-48,830-9,049-8,981-4,301-2,449-9,452-9,530-5,725
3,901
19,924
82,628151,518-5,476-13,579-7,548-5,436-6,8495,437
2,179
10,20010,26985,473
226,188

186,087-14,532-23,615-11,581-298-293-20,693-2,0283,204
36,48456,436
209,171

131,178-20,131-12,782-12,114-4,011-3,418-13,945
22,441
12,615
10,075
143,701
253,609

D
211
D
D
D
D
-9,9036,140
10,491
9,889
58,24590,973

D
-408
D
D
D
D
17,92869,4092,158-56,563
307,517360,2143,518,419-188,821-178,494-150,749-74,035-102,902-338,705-161,353-153,974-140,886
416,524
2,481,097

f) 100 to
249g) 250 to499h) 500 to
999i) 1000 to
2499j) 2500 to
4999k) 5000 to9999l) 10000+All

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
27,33423,43863,939349,945

128,772
14,346-9,743888
6,655
11,703
19,344
1,617
6,559
22,29869,597
272,036

188,493-6,509-13,282-10,155
7,375-4,8505,364
34,591
4,413
30,12093,401
328,961

D
7,898
8,392
D
-10,228
3,017
26,494
18,886
16,96934,280110,311
253,373

D
-42
D
3,699
D
D
23,546
20,201
14,550-46,129
38,147
71,269

-138
D
D
D
D
43,006
65,69932,733-9,197433,751
581,191

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

Firm Size (Current)
Firm Age
a) 0
b) 1
c) 2
d) 3
e) 4
f) 5
g) 6 to 10h) 11 to 15i) 16 to 20j) 21 to 25k) 26+m) ALLa) 1 to 4b) 5 to 9

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

541,230-1,242-34,487-30,935-27,141-28,196-112,735-74,905-59,389-43,548-83,682
44,970c) 10 to 19 d) 20 to 49 e) 50 to 99
453,073
10,705-24,132-25,119-19,487-23,791-99,872-75,477-61,306-47,143-107,356-19,905

445,091
3,028-15,745-12,259-7,210-16,205-76,025-60,259-60,496-42,924-114,182
42,814

236,121-20,046-5,3801,824
1,630-2,595-17,730-17,677-13,235-16,172-40,005
106,735

Source: U.S. Census Bureau, Business Dynamics Statistics at http://www.ces.census.gov/index.php/bds/bds_home32

Table 2
Employment Firm Size Distributions and Employment
Average, 1992-2005

Firm Size Class

Share of
Employment(Base Size)a) 1 to 4b) 5 to 9c) 10 to 19d) 20 to 49e) 50 to 99f) 100 to 249g) 250 to 499h) 500 to 999i) 1000 to 2499j) 2500 to 4999k) 5000 to 9999l) 10000+

0.061
0.064
0.076
0.105
0.074
0.087
0.056
0.051
0.070
0.053
0.054
0.249

Avg
Share of Average
Employment Employment Employment(Base Size) (Current Size) (Current Size)
6,406,352
6,743,142
8,007,175
11,089,682
7,869,962
9,172,856
5,930,095
5,433,570
7,407,325
5,654,126
5,722,462
26,396,542

0.055
0.064
0.077
0.105
0.074
0.087
0.056
0.051
0.070
0.054
0.055
0.252

5,831,743
6,754,350
8,081,992
11,134,655
7,880,766
9,187,202
5,881,189
5,398,075
7,401,133
5,703,652
5,855,149
26,723,384

33

Table 3
Employment Startup Share and Employment: By Firm Size Class
Average, 1992-2005

Firm Size Class

Share of Startup
Share of Startup
Employment Avg Employment
Employment(Base Size)(Base Size)(Current Size)

Average
Employment(Current Size)a) 1 to 4

0.105

675,474

0.189

1,098,360b) 5 to 9

0.074

497,4900.076

510,605c) 10 to 19

0.059

467,880

0.052

422,806d) 20 to 49

0.047

518,751

0.038

418,983e) 50 to 99

0.037

294,703

0.027

217,213f) 100 to 249

0.029

270,4420.022

200,146g) 250 to 499

0.024

144,073

0.014

81,552h) 500 to 999

0.015

81,4500.010

56,338i) 1000 to 2499

0.010

72,6430.007

56,507j) 2500 to 4999

0.007

40,7680.003

14,984k) 5000 to 9999

0.003

14,8380.002

13,383l) 10000+

0.001

24,7690.001

26,56834

Table 4: The Relationship Between Net Growth, Firm Size and Firm AgeParameter

(1)
(2)
(3)
Base Size Current Size Age Only

(4)
Base Size +Age(5)Current Size +AgeSize a) 1 to 4

0.189

0.042

0.083

-0.190Size b) 5 to 9

0.061

0.009

-0.011-0.102Size c) 10 to 19

0.033

0.006

-0.022-0.075Size d) 20 to 49

0.017

0.007

-0.024-0.051Size e) 50 to 99

0.009

0.011

-0.024-0.034Size f) 100 to 249

0.005

0.017

-0.021-0.018Size g) 250 to 499

0.002

0.016

-0.019-0.007Size h) 500 to 999

0.007

0.015

-0.006-0.002Size i) 1000 to 24

0.006

0.015

-0.0020.003

Size j) 2500 to 49

0.008

0.015

0.001

0.009

Size k) 5000 to 99

0.006

0.009

0.003

0.005

Size l) 10000+
Age

a. 0

1.995

1.985

2.074

Age

b. 1

0.031

0.019

0.085

Age

c. 2

-0.035-0.041

0.012

Age

d. 3

-0.025-0.029

0.019

Age

e. 4

-0.020-0.023

0.020

Age

f. 5

-0.017-0.018

0.021

Age

g. 6

-0.020-0.021

0.015

Age

h. 7

-0.014-0.014

0.020

Age

i. 8

-0.011-0.011

0.021

Age

j. 9

-0.013-0.012

0.017

Age

k. 10

-0.012-0.011

0.016

Age

l. 11

-0.005-0.004

0.020

Age

m. 12

-0.008-0.006

0.016

Age

n. 13

-0.002

0.000

0.021

Age

o. 14

-0.003-0.001

0.018

Age

p. 15

-0.005-0.0030.014

Age

u. 16+
0.184
92,974,732

0.185
92,974,732

0.188
92,974,732

R2
Obs

0.026
92,974,732

0.022
92,974,732

Notes: Standard Errors for all estimates are below 0.0005

35

Table 5: The Relationship Between Net Growth, Establishment Size and Establishment
Age
Parameter

(1)
(2)
(3)
Base Size Current Size Age Only

(5)
(4)
Base Size + Current Size +Age
AgeSize a) 1 to 4

0.304

0.034

0.236

-0.197Size b) 5 to 9

0.170

0.006

0.137

-0.100Size c) 10 to 19

0.134

0.003

0.115

-0.074Size d) 20 to 49

0.107

0.000

0.096

-0.055Size e) 50 to 99

0.083

0.001

0.075

-0.040Size f) 100 to 249

0.064-0.001

0.055-0.027Size g) 250 to 499

0.041

0.001

0.033

-0.013Size h) 500 to 999

0.025

0.001

0.026

-0.007Size l) 1000+
Age

a. 0

2.028

2.005

2.069

Age

b. 1

0.063

0.035

0.083

Age

c. 2

-0.007-0.029

0.009

Age

d. 3

0.001

-0.018

0.016

Age

e. 4

-0.002-0.020

0.011

Age

f. 5

-0.001-0.017

0.012

Age

g. 6

-0.003-0.018

0.008

Age

h. 7

0.003

-0.011

0.014

Age

i. 8

0.001

-0.012

0.011

Age

j. 9

0.002

-0.011

0.011

Age

k. 10

-0.002-0.014

0.006

Age

l. 11

0.002

-0.009

0.010

Age

m. 12

0.003

-0.007

0.010

Age

n. 13

0.003

-0.006

0.010

Age

o. 14

0.003

-0.006

0.009

Age

p. 15

0.001

-0.0070.007

Age

u. 16+
0.338
92,974,732

0.343
92,974,732

0.342
92,974,732

R2
Obs

0.032
92,974,732

0.022
92,974,732

Notes: Standard Errors for all estimates are below 0.0004

36