BLS WORKING PAPERS 

 U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Employment and Unemployment Statistics 

What’s In a City?: 

Understanding the Micro-Level Employer Dynamics Underlying 

Urban Growth 

R. Jason Faberman, U.S. Bureau of Labor Statistics 

Working Paper 386

December 2005

All views expressed in this paper are those of the author and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
What’s In a City?: 

Understanding the Micro-Level Employer Dynamics Underlying Urban Growth 

R. Jason Faberman 

U.S. Bureau of Labor Statistics 

Suite 4945 

2 Massachusetts Ave NE, Washington D.C. 20212 

Faberman.Jason@bls.gov

(202) 691-5192 

October 2005 

Abstract: 

This paper synthesizes the literatures on labor dynamics and urban growth and 

agglomeration by presenting new evidence on the micro-level establishment dynamics of 

metropolitan areas. I explore how the patterns of job reallocation and entry and exit affect 

the growth and composition of these areas. I find that high-growth metropolitan areas 

have high rates of job and establishment turnover, primarily though higher rates of gross 

job creation and establishment entry, and have a relatively young distribution of 

establishments. Variations in the age distribution and differences in the entry and exit 

patterns of young establishments account for a sizeable portion of regional differences in 

labor dynamics and growth, even after controlling for regional differences in industry 

composition. These results suggest that variations in the age distribution and the 

dynamics that lead to such variations are important factors in understanding urban growth 

and agglomeration. 

JEL Codes: E24, J63, R11 

Keywords: job reallocation, urban growth and agglomeration, firm dynamics 

Acknowledgements 

I thank John Haltiwanger, Judy Hellerstein, Jim Spletzer, Ed Montgomery, and John 

Shea for helpful advice and guidance on this and earlier versions of this paper, including 

my dissertation. Randy Eberts, Schott Schuh, Stewart Rosenthal and seminar participants 

at the University of Maryland, College Park, the Bureau of Labor Statistics, the Census 

Bureau, and the Federal Reserve Bank of Cleveland also provided helpful comments. The 

views expressed are solely my own and do not necessarily reflect the official positions or 

policies of the U.S. Bureau of Labor Statistics or the views of other staff members. 
1. Introduction 

This paper aims to bridge the gap between the empirical literature on aggregate 

labor market dynamics and the literature on urban growth and agglomeration. For 

decades, economists have studied why cities exist, why some are larger than others, and 

why some grow faster than others. In recent years, researchers such as Glaeser et al. 

(1995) have explored how positive externalities, such as increasing returns and 

knowledge spillovers, may contribute to urban growth. The literature based on this 

approach is often motivated by endogenous growth models like those by Lucas (1988) 

and Romer (1986), since their implications are consistent with the factors economists 

believe underlie agglomeration. 

The models, however, usually assume a representative agent in production, much 

like the real business cycle models that preceded them. Work by Dunne, Roberts, and 

Samuelson (1989a, 1989b), Davis and Haltiwanger (1990, 1992), and others have since 

shown that the economy has a distribution of heterogeneous agents continuously 

interacting in a dynamic environment. This depiction of the aggregate economy lends 

itself to models where micro-level frictions, idiosyncratic shocks, and underlying 

heterogeneity play a critical role; models of creative destruction (Caballero and 

Hammour, 1994), firm learning and selection (Jovanovic, 1982), and labor market search 

(Mortensen and Pissarides, 1994) are examples of such models. 

In this paper, I use a relatively new source of establishment microdata to study the 

relationship between an employer’s behavior and the growth of its metropolitan area. In 

doing so, I highlight the importance of firm and labor dynamics in accounting for 

regional differences in growth and agglomeration, and show how related models help 

1
explain urban growth. Existing urban empirical work often uses aggregated data to study 

the potential benefits of agglomeration. Many results suggest that firms are more 

productive when clustered within in a particular location (or industry and location). These 

are cited as evidence of positive external economies, which may stem from local factors 

(e.g., local government policies, comparative advantages in resources, a transport costminimizing location) or the co-location itself (e.g., the spillover of local innovations, 

savings from shared inputs, the sharing of built-up knowledge or “trade secrets” within a 

local industry). Yet, one may find more productive firms in a particular location (and 

hence evidence of agglomeration economies) for other reasons. For example, firms that 

are ex ante more productive may choose to locate in one city over another. Competition 

among firms may vary across areas and, through a process of entry and exit, less efficient 

firms may be selected out of the market, leaving relatively more productive firms in some

areas through a greater lower-truncation of their firms’ productivity distribution. These 

latter phenomena lend themselves to models of agglomeration that incorporate 

heterogeneity and constant churning. In fact, several recent studies have moved toward 

studying the returns to agglomeration at the micro-level (Henderson, 2003), the role of 

entry and exit in either reinforcing or weakening industry agglomeration (Dumais, 

Ellison, and Glaeser, 2002; Rosenthal and Strange, 2003).1

This paper differs from the above studies in that the focus is on variations in 

urban growth rather than agglomeration per se. I do this for two reasons. First, while 

there is much research on the causes of urban growth using aggregate data, there is little 

empirical work on its microfoundations. I use a rich source of longitudinal microdata 

 1

 Hyclak (1996) also has an earlier study of structural change in local labor markets using Dun & Bradstreet 

microdata. 

2
from the Bureau of Labor Statistics (BLS) that allows a study of employment dynamics at 

a scope and level that was previously impossible.2 Second, previous work on this topic, 

perhaps driven by the available data, has often focused on differences in industry 

composition (for example, through industry “shift-share” analyses). My data allow me to 

study the composition of a city’s establishments on a variety of different margins. For 

example, I can quantify how much variations in urban growth are due to variations in the 

establishment age distribution across cities. 

I find that high-growth metropolitan areas have high rates of job reallocation—

growing cities are more dynamic. This higher turnover comes more from greater 

variations among job-creating establishments rather than job-destroying establishments, 

reinforcing earlier findings by Eberts and Montgomery (1995). I also find that highgrowth areas have higher establishment entry and exit rates, as well as a relatively small 

and young distribution of establishments. These results hold even after controlling for 

differences in industry, establishment size, and wages. In addition, regional variations in 

the age distribution preserve neither its mean nor its shape. Instead, high-growth areas 

have disproportionately higher shares of very young establishments and comparable 

shares of older establishments. These variations account for 44 percent of the differences 

in metropolitan growth (28 percent after controlling for industry). Nevertheless, there 

remain considerable variations in establishment growth and reallocation within age 

groups—high-growth areas have notably higher establishment growth and job 

reallocation among younger establishments. An analysis of entry and exit patterns shows 

 2

 Eberts and Montgomery (1995), Davis, Loungani, and Mahidhara (1997), and Schuh and Triest (2002) 

also document regional variations in labor dynamics using establishment microdata, though their studies 

are limited in varying degrees by data quality, sample scope (e.g., being limited to manufacturing), and the 

availability of various data items (e.g., measures of establishment age). 

3
that a greater share of their entrants exit within five years and, conditional on survival, 

entrants’ wage growth is higher within high-growth areas, suggesting that a “shakeout” 

process at young ages leaves these cities with a relatively smaller, but potentially more 

productive share of entrants within a given cohort. 

One can draw several conclusions from these findings. First, urban agglomeration 

and growth are truly dynamic processes. Others (Henderson, Kuncoro, and Turner, 1995; 

Glaeser et al., 1992) have drawn similar conclusions with aggregate data, but this study 

depicts much more complex dynamics that determine a heterogeneous distribution of

establishments within each city. Second, understanding regional variations in the 

establishment age distribution is an important part of understanding regional differences 

in growth, even more so than variations in industry mix. Finally, regional variations in 

establishment composition cannot fully explain the observed differences in growth across 

metropolitan areas, suggesting that location-specific characteristics, such as the 

concentration of human capital, remain as important factors as well. 

The following section describes the data and methodology used throughout the 

paper. Section 3 presents the evidence. Section 4 discusses the theoretical implications of

my results, and Section 5 concludes. 

2. Data and Concepts

Access to a robust source of longitudinal establishment microdata is essential to 

this study. I employ the data BLS uses for its Business Employment Dynamics (BED) 

program, a relatively new source of statistics that measure quarterly gross job flows for 

the U.S. private sector. The data come from administrative records from state 

unemployment insurance (UI) programs, compiled for the Quarterly Census of 

4
Employment and Wages (also known as the ES-202) program of the BLS, and are a 

virtual universe of all businesses.3 To generate the BED estimates, the BLS links these 

records longitudinally over time.

4

 The data are quarterly, and include an establishment’s 

employment for each month, payroll for the quarter, and a variety of characteristics, such 

as industry, location (to the county level), organization (i.e., public versus private 

ownership, whether it is part of a multi-unit firm), and initial UI liability date (a proxy for 

age). 

I use a sample of private sector establishments in 53 Metropolitan Statistical 

Areas (MSAs) and Primary Metropolitan Statistical Areas (PMSAs) across five U.S. 

states. I use only five states because of the attention to the data needed to identify true

entrants and exits from temporary openings and closings, mergers and acquisitions, and 

administrative changes.5

 I choose my five states to satisfy several conditions. A state has 

to be relatively large and it has to contain multiple MSAs and PMSAs—this allows me to 

condition out state fixed effects where needed while preserving across-MSA variation 

(e.g., for estimating establishment age, where differences in state UI laws may affect 

reported initial liability dates.) Collectively, the states must also be somewhat 

representative of the U.S. in terms of employment growth, with regard to both the 

average rate and the variation across metro areas. I choose Colorado, North Carolina, 

Michigan, Ohio, and Pennsylvania; first two states represent the relatively high-growth 

cities of the South and West, while the latter three represent the lower-growth cities of the 

 3

 The self-employed and the military being the primary exceptions. 4

 More details about the BED and the record-linkage process can be found in the appendix, as well as in 

Pivetz, Searson, and Spletzer (2001) and Spletzer et al. (2004). 5

 I discuss my methodologies for identifying entrants and exits briefly in the appendix, and in more detail in

my dissertation (Faberman, 2003). 

5
Northeast and Midwest.6

 The resulting sample represents approximately 15 percent of all 

private employment and establishments in the U.S. and contains quarterly data from

March 1992 through March 2000. The sample has 25.4 million observations of 1.43 

million distinct establishments, with the average quarter having approximately 796,000 

active establishments. Table 1 lists the sample’s summary statistics for all state 

observations, observations in metropolitan areas only (i.e., the sample for this study), and 

the public BED estimates. The employment growth estimates are comparable; sample job 

flow estimates are somewhat lower because of the extra attention I give to entry, exit and 

record linkage. Summary statistics for each MSA are in Appendix Table A.1. 

I measure quarterly employment at the third month of each quarter. Gross job 

flows are measures of net employment growth at the establishment level. Job creation is 

the sum of all gains at either expanding or opening establishments, while job destruction

is sum of all losses at either closing or contracting establishments. The aggregate net 

change is simply the difference between job creation and job destruction. Job 

reallocation, a measure of the overall churning in a labor market, is the sum of job 

creation and destruction. One critique of the job reallocation measure is that it can be 

high simply because a labor market has a very high (or very low) net growth rate. An 

alternative measure is excess reallocation, which is job reallocation less the absolute 

value of the net change. Following the methodology of Davis, Haltiwanger and Schuh 

(1996), I translate net growth and the job flow measures into rates by dividing them by 

the average of the current and previous quarters’ employment. 

 6

 To maintain continuity of all metro areas in the sample, I also append data from five other states (Indiana, 

Kentucky, New Jersey, South Carolina, and West Virginia) where MSA or PMSA definitions cross state 

borders.

6
Formally, let Njkt denote the net growth rate for establishment k in MSA j in quarter t. The 

growth rate for the MSA at t is simply ( ) jkt k

N jt =∑ e jkt e jt N , where ejkt is the average 

of previous and current employment for establishment k. Similarly, MSA job creation, 

job destruction, job reallocation, and excess reallocation rates are (respectively), 

(1) 

{ }

{ }

jt jt jt jt

jt jt jt

k jkt

jt

jkt

jt

k jkt

jt

jkt

jt

XR C D N

JR C D

N

e

e

D

N

e

e

C

= + −

= +

=

=

∑

∑

min ,0

max ,0

. 

 Average earnings, Wjkt, are payroll divided average employment and are deflated 

by the Consumer Price Index to express them in real (1992) dollars. Establishment size is 

simply ejkt. Establishment age is measured from the initial date of UI liability, which 

generally represents the start date of the establishment. Mean establishment age is about 

10 years and the oldest UI accounts date back to 1936, so upper truncation of the age 

measure is of little concern. Missing values and state differences in liability dates are a 

concern, however, and I describe my methodology for dealing with these issues in the 

appendix. 

3. Results 

I begin the presentation of my results by noting the tremendous churning that 

occurs in all metropolitan areas. Table 1 shows that, on average, 7.3 percent of jobs are 

created and 6.7 percent of jobs are destroyed each quarter within the private sector of

these areas, implying that the excess reallocation of these labor markets averages 13.4 

percent of employment. There is substantial entry and exit each quarter as well, with the 

7
quarterly rates of each averaging 2.3 percent of establishments. The average 

establishment pays $6,453 each quarter in real earnings, employs 17.8 workers, and is 

41.4 quarters (10.4 years) old. 

3.1. Basic Relations across Metropolitan Areas 

I now move on to the relationships between growth and other labor market 

variables. Figure 1 illustrates the unconditional relations of job flows, establishment size 

and establishment age with net growth across MSAs. For reference, each panel highlights 

the largest cities in the sample. Both job creation and job destruction vary positively with 

MSA growth. This is consistent with previous findings by Eberts and Montgomery 

(1995). In new evidence, average establishment size and age both vary negatively with 

growth. Variations in growth account for a sizeable portion (53 and 65 percent, 

respectively) of across-MSA variations in job creation and age. Together, the results 

imply that higher-growth MSAs have higher rates of job reallocation among smaller, 

younger establishments. 

Table 2 presents the across-MSA correlations of job flows and establishment 

characteristics with net growth, earnings growth, average earnings, and average age. 

Across MSAs, job creation, destruction, and reallocation are positively related to growth 

and negatively related to age. The job flows have positive relations to earnings growth 

and negative relations to the earnings level, though most of these correlations are 

insignificant. Average establishment size is positively related to both age and earnings, 

consistent with earlier findings for the aggregate economy.

7

 Entry and exit are both 

positively correlated with net growth, earnings growth, and the earnings level, and 

strongly negatively correlated with average age. The latter is also consistent with earlier 

8
findings. Across, MSAs, employment growth and earnings growth are positively 

correlated, while employment growth and the earnings level are uncorrelated. Job 

creation and job destruction highly correlated (with a coefficient of 0.94) across MSAs. 

Taken together, these correlations suggest several things: i) many of the patterns 

found in the labor and firm dynamics literature (i.e., wages and size increase with age, 

job reallocation decreases with age, job creation and job destruction are positively 

correlated in the cross-section) are consistent with the cross-sectional evidence across 

metropolitan areas; ii) high-growth metropolitan areas are dynamic, with relatively high 

rates of job reallocation and establishment turnover, on average; and iii) the average age 

of a MSAs establishments is strongly related to nearly all of the labor market variables of 

interest. 

3.2. Accounting for Establishment Characteristics 

The results in Table 2 are unconditional relations. It is well known in the urban 

economics literature that establishment characteristics, particularly the industry 

composition, can vary widely and non-randomly across metropolitan areas (e.g., Ellison 

and Glaeser, 1997). In addition, the literature on firm and labor dynamics has found 

tremendous variations in job flows and establishment characteristics across industries 

(see Anderson and Meyer, 1994; Davis, Haltiwanger, and Schuh, 1996; Foote, 1998; and 

Burgess, Lane, and Stevens, 2000). Consequently, it is quite possible that across-MSA 

variations in characteristics such as industry composition explain much of the relations 

reported in Table 2. 

To explore this hypothesis, I recalculate selected correlations controlling for a 

variety of characteristics (including industry). My controls condition out fixed effects for 

 7

 See, for example, Dunne, Roberts and Samuelson (1989a, b) and Davis, Haltiwanger, and Schuh (1996). 

9
each characteristic using regressions on pooled establishment-quarter observations. I then 

recalculate the correlations using the MSA means estimated from the residuals of these 

regressions.8

 In other words, for variable Yj or net growth rate Nj the correlations are 

(2) ( ) x x

j j

Y Nˆ , ρ ˆ , with 

(3) =∑ ( ) − and 

k kxj kxj x

x Yj θ Y β ˆ ˆ =∑ ( − ) k kxj kxj xx N j θ N γˆ ˆ , 

where and β x

ˆ x γˆ are the fixed effects coefficients for characteristic x, and θkxj is the 

appropriate weight. 

My results are in Table 3. The top row of the table reports the unconditional 

correlations (identical to Table 2). The second row reports correlations conditional on 

972 4-digit Standard Industrial Classification (SIC) industries. The across-MSA 

correlations of job creation and average age with net growth persist after controlling for 

industry; to a lesser extent, the correlation of size to growth persists as well. Correlations 

of job destruction and excess reallocation with net growth do not persist, however, 

suggesting that their unconditional positive correlations stem from high-growth MSAs 

having high-reallocation, high-turnover industries located there. In fact, the results 

suggest that, conditional on industry mix, high-growth cities actually have lower job 

destruction rates. The third row controls for the age distribution by conditioning out 

establishment age measured in quarters (giving 256 groups). The correlation between job 

creation and growth is again robust to the control, while the relations of job destruction, 

excess reallocation, and size become insignificant. Controlling for establishment size 

(using a quartic in employment levels) has little effect on the correlations. Controlling for 

 8

 Job flow and net growth rate regressions are weighted by average employment; establishment age and size 

10
earnings (using a quartic in average earnings) only affects the relation of job destruction 

to growth notably. As one might expect, controlling for all characteristics (industry, age, 

size, earnings) alters the relations considerably, with only the relations of establishment 

size and age remaining significant and of the original sign. The correlation between job

creation and growth remains positive, but is insignificant. 

Overall, the results suggest that variations in industry composition do play a role 

in explaining the across-MSA correlations (particularly for job destruction and excess 

reallocation), but that variations in the age distribution are at least as important, and that 

some relations, notably the relations of job creation and average age to growth, persist 

even after applying a variety of controls. 

3.3 The Importance of Entry, Exit, and the Age Distribution 

The age distribution of establishments can vary widely across MSAs, as depicted 

in Figure 2. The figure shows the frequency distribution of pooled establishment age 

observations in MSAs ranking in the upper and lower quintiles of mean net growth.9

 One 

can see that the high-growth MSAs have a younger distribution, on average. Further, the 

younger distribution is not a leftward shift of the density function, but instead stems from

a relatively high concentration of very young establishments (younger than 5 years) in 

these areas. 

Differences in the age distribution account for a large part of the above 

correlations, and the relation of age to MSA growth is robust to a variety of controls, 

 regressions are not. 9

 The quintiles are based on an establishment-weighted ranking; 11 MSAs are in the upper quintile and 16

MSAs are in the lower quintile, since the MSAs in the latter have fewer establishments. The specific MSAs

in each group are noted in Appendix Table A.1. 

11
leading one to ask, “Just how much do variations in the MSA age distribution account for 

variations in MSA labor dynamics?”

To answer this question, I perform a counterfactual exercise to obtain the values 

of net growth, job creation, job destruction and excess reallocation predicted from

differences in the MSA age distribution alone, holding their values within each age 

constant across metropolitan areas. The predicted estimates answer the question, “What 

would MSA growth rates and job flows be if the only thing that mattered was 

establishment age?” One can write the actual estimate of variable Yj, with Yj ∈ {Nj, Cj, Dj, 

XRj}, as 

(3) ∫ Yj = Yj (a) f j (a)da

where Yj(a) is the mean value of Y in MSA j for establishments aged a and fj(a) is the 

density of establishments aged a in MSA j. Using this notation, the predicted estimate of

Yj is then 

(4) ∫ Y = Y a f a da j j ( ) ( ) ˆ

where Y(a) is the mean value of Y for all establishments aged a in the sample, and all 

other notations follows from before. 

I obtain the predicted estimates for the variables noted above and then regress the 

actual values on the predicted estimates using OLS for the 53 MSA observations. The Rsquared values from these regressions give the percentage of MSA variations due to 

differences in the age distribution alone. My results are in Table 4. The upper panel gives 

the percentages of variation explained as well as the correlations with MSA net growth 

implied by the predicted estimates. The lower panel reports the results of the exercise

12
done with the residuals of the net growth and job flow rates after conditioning out 

industry fixed effects. The results suggest that MSA differences in the age distribution 

account for a large portion of the variations in labor dynamics, as well as the correlations 

of job flows to net employment growth. Differences in the age distribution alone account 

for 44 percent of the across-MSA variations in growth and 48 percent of the variations in 

job creation. They account for a smaller portion (12 percent) of the variations in job 

destruction and excess reallocation. Even after controlling for industry mix, differences in 

the age distribution still account for 28 percent of the variation in growth and 19 percent 

of the variation in job creation, but account for a negligible (2 percent) portion of the 

variations in job destruction and excess reallocation. 

Thus, variations in the age distribution play an important role in understanding 

urban growth, even after controlling for regional differences in industry mix, though there 

remains a sizeable portion of these variations unaccounted for. Differences in the MSA 

age distribution are an important and previously unexplored facet of urban growth, but 

they are not the whole story. Figure 3 illustrates this point. In panels (a) through (d), it 

shows the mean net growth rate, job creation rate, job destruction rate, and excess 

reallocation rate, respectively, as a function of age. It does so for the pooled observations 

for MSAs in the upper and lower quintiles of the growth rankings (as described 

previously). In each panel of the figure, the two groups have their greatest differences 

among their younger establishments (those aged 7 years or less). Among these 

establishments, high-growth MSAs have significantly higher growth and higher job 

flows. 

13
Consequently, understanding regional differences among these younger 

establishments is crucial to understanding the relationship between the age distribution 

and the growth rate of a city, which implies that one must know how patterns of entry and 

exit vary across metropolitan areas. I take a subsample of establishments that enter 

between June 1992 and June 1995 and follow them for 5 years. To illustrate variations

across MSAs, I compare entry and exit patterns between the MSAs in the upper and 

lower quintiles used above. 

Table 5 lists the summary statistics for the entrant subsample. The sample 

contains nearly 2.9 million observations on 208,000 entrants. Entry in high-growth MSAs 

is notably higher than in low-growth MSAs—2.8 percent of all establishments as 

opposed to 2.0 percent. Exit rates are high in all areas, but entrants in high-growth MSAs 

have relatively greater attrition—49.7 percent of them exit within five years, as opposed 

to 46.7 percent in low-growth MSAs. Within the first five years, entrants in high-growth 

MSAs also have higher growth, excess reallocation, and average earnings (conditional on 

survival to quarter t, that is). 

Figure 4 illustrates the differences in exit hazard rates for the two groups. 

Throughout the first five years after entry, establishments in high-growth MSAs have 

consistently higher hazard rates, though the difference with those in low-growth MSAs is 

only significant during the first 6 quarters of business. The lower panel of Figure 4 shows 

that the patterns are robust to controlling for industry differences. Figure 5 gives evidence 

that that the higher hazard rates in high-growth MSAs may translate into a greater 

selection of low-wage (and potentially low-productivity) establishments. Earnings begin 

somewhat higher in high-growth MSAs, and grow substantially faster in these areas—

14
32.1 percent versus 17.9 percent—than in the low-growth MSAs. Again, the results are 

robust to controlling for industry. 

Taken together, the results suggest that variations in the age distribution are an 

important part of the observed differences in MSA growth. Differences in the growth and 

concentration of young establishments, driven by variations in entry, exit, and a potential 

selection mechanism, help generate these variations in the age distribution and thus also 

play an important role in generating differences in urban growth. 

3.4 Employment Dynamics and Other Labor Market Characteristics 

Entry, exit, and the age distribution of establishments are important, but what 

local factors account for their regional variations? To get at this question, I present basic 

evidence on the relationship of some local characteristics and labor market dynamics. 

Table 6 reports the correlations of growth, job flows, and establishment age, entry, and 

exit with a variety of MSA characteristics. City size, measured by either employment or 

population, has little relation to the variables of interest. The only exception is 

establishment exit, which is more likely in larger cities. The correlations of the selected

variables with population growth mimic their relations to employment growth, as one 

might expect. A city’s average unemployment rate is negatively correlated with 

employment growth, but essentially uncorrelated with job creation, job destruction, or job 

reallocation. Cities with high unemployment, though, have lower rates of entry and exit 

among a relatively older set of establishments. Lastly, cities with a relatively young (age 

20-34) and educated (bachelors degree or more) population have higher growth and job 

reallocation among relatively young establishments with high rates of entry and exit, 

suggesting (as has been found before) that dynamic, high-growth cities are also high15
human capital cities. On the surface, these relations are consistent with the evidence on

growth dynamics from Blanchard and Katz (1992) and evidence on migration patterns 

from Topel (1986). The positive relation between the age and education of a city’s 

population and the observed churning and establishment composition, however, lend a 

new facet to this line of research. 

4. Theoretical Implications 

The results suggest that the relationship between labor market churning and 

metropolitan growth stems primarily from regional variations in entry, exit, and the age 

distribution of establishments. Many current theories of urban growth, such as Black and 

Henderson (1999), assume that growth and agglomeration occur through scale economies 

that are external to the firm and localized knowledge spillovers. Externalities stemming

from a greater concentration of human capital in an area lead all firms in that area to be

more productive. My findings suggest that such a characterization of agglomeration 

economies is over-simplified. They instead suggest that a high concentration of human 

capital may affect firm productivity and urban growth through a composition effect on 

the distribution of a city’s businesses. This composition effect not only drives entry into 

the local labor market, but also affects the process by which firms either grow or exit. My 

results are still consistent with localized knowledge spillovers as an underlying factor in 

urban growth and agglomeration. In fact, these spillovers may affect the dynamics and 

composition in important ways. Being able to adequately model the relationships of

human capital and knowledge spillovers to growth in an environment of heterogeneity 

and constant labor market churning is thus vital to understanding the underlying causes of 

urban growth and agglomeration. 

16
One approach in this spirit may be to incorporate a process of Schumpeterian 

“creative destruction” into a model of urban growth. Aghion and Howitt (1992) and 

Caballero and Hammour (1994) model such a process for the macroeconomy. In their 

models, exogenous technological innovation drives a process of vintage replacement, 

where new firms enter with the latest technology and are able to outcompete incumbents 

with older technologies. The steady state is characterized by a constant churning 

(stemming from the vintage replacement process) among a distribution of firms varying 

in their productivity based solely on age. In an urban setting, the pace of innovation may 

depend on knowledge spillovers stemming from the concentration of human capital.

Cities with more human capital would then have greater entry and greater churning 

among a relatively young firm distribution—in such a setting, the opportunity cost of 

keeping the oldest firms in production is relatively high, inducing greater exit among 

them and thus shifting the distribution towards the younger firms. 

Such a model of creative destruction would be consistent with much of the 

evidence in this paper, except that its exit occurs among the oldest firms, and in the data, 

it occurs mostly among younger firms. The “shakeout” observed in the data is instead 

consistent with a process of firm learning and selection depicted in models by Jovanovic 

(1982), Erickson and Pakes (1995) and others. In these models, firms vary in their ex ante

productive abilities, which are unknown at entry. Firms learn their productivity by 

updating expectations over time—as expectations increase, firms grow and as they 

decrease, firms contract. If expectations fall below a threshold, the firm exits. Thus, 

average productivity increases over time through the self-selection, of low-productivity 

firms out of the market. Given that these models primarily focus on industry life-cycle 

17
dynamics (and not labor market dynamics), it is difficult to speculate on how local factors 

might affect urban growth through such a process. Some fruitful paths to explore include 

a model where local factors affect the distribution of ex ante productivities, the learning 

process through a greater speed of convergence, or the expectations threshold at which 

firms choose to exit. Baldwin and Okubo (2005) have a recent model that accounts for 

firm heterogeneity and models the processes by which sorting and selection into a 

particular area can generate the empirical findings attributed to urban agglomeration. 

Finally, models of labor market search and matching may prove successful in 

explaining the empirical relations reported here. Such models assume that there are 

search frictions in the matching of workers to firms. Matches have an idiosyncratic 

component, which may be random or stem from a heterogeneous distribution of workers 

and firms. In the model of Mortensen and Pissarides (1994), workers meet firms through 

a matching function. Upon meeting, workers and firms enter a Nash bargaining process, 

and if the returns to each satisfy their reservation values, a match is made. Once matched, 

a worker and firm stay together unless an adverse shock lowers the match returns below 

some threshold. The heterogeneity and constant churning present in most search and 

matching models suggest that search frictions may account for some of my results, 

though, without more evidence on the relation between worker characteristics and 

establishment dynamics, it is difficult to speculate on how such a process would operate 

within a local labor market. Nevertheless, researchers have already made some headway 

on this approach, with Helsey and Strange (1990) and Wheeler (2001) each having 

models of labor market search within an urban framework. 

18
5. Conclusions 

This paper presents new evidence on the relations of labor market dynamics and 

establishment characteristics to urban growth using a rich new microdata source. I find 

that growing cities tend to have higher rates of both job creation and job destruction 

among a relatively younger distribution of establishments. This younger distribution 

occurs through more entry, and through a selection process that has greater exit, but also 

has survivors with relatively higher growth in their early years. Survivors in these cities 

also tend to have higher earnings growth than their counterparts. The regional variation in 

the age distribution that results accounts for a sizeable portion of the regional variation in 

employment growth. Much of this evidence is robust to regional variations in industry 

composition, implying that regional specializations in the production of a particular good 

cannot account for the observed patterns. Finally, I find that growing, dynamic cities tend 

to have a younger, more educated population. 

These results suggest that regional dynamics stem from complex interactions 

among heterogeneous agents. Thus, the benefits of local characteristics such as 

knowledge spillovers and the concentration of human capital may not be as simple as a 

positive externality that benefits all local firms. Instead, these characteristics may alter 

the composition and dynamics of a heterogeneous distribution of firms, which in turn

causes some cities to grow faster than others. Models of creative destruction, firm

learning and selection, or labor market search may do well in accounting for the observed 

behavior of these dynamics. Further research, empirical and theoretical, will shed light on 

how these processes operate within an urban setting, and how they may affect the 

patterns of agglomeration. 

19
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22
Appendix 

A. Data Description and Record Linkage 

This appendix describes the data and measurement in more detail. The UI 

administrative data used in this study cover nearly all private employment in the sample 

areas. The data have several advantages over other sources. First, they cover all 

industries; much of the previous research on firm and employment dynamics has focused 

solely on manufacturing, and used the Longitudinal Research Database of the Census 

Bureau. Second, they are a universe, and not a sample (covering 98 percent of 

employment) so save for the self-employed and military, there are no exclusions (thus 

avoiding potential selection bias) and there is a robust number of observations that allow 

analyses even within highly detailed categories. Finally, the BLS has an algorithm to link 

the data across time, providing a longitudinal history for each establishment. 

This linkage process is important, but also imperfect. The data are primarily used 

for UI tax collection, and there are many things firms can do (e.g., changes in corporate 

ownership, firm restructuring, and UI account restructuring) to complicate record linkage, 

causing missed links to occur. This falsely counts continuous records as openings and 

openings, thereby overstating entry, exit, and job flows. To ensure that my estimates of 

entry and exit are accurate as possible, I limit my sample to the five states noted in 

Section 2, and perform a manual review of all large employment changes (300 workers or 

more).10 I use this review on top of the BLS methodology because of the large impact a 

single missed link can have on a regional analysis. For example, a missed link of a 5,000-

employee establishment likely has a negligible effect on the national BED statistics, but 

 10 I summarize my methodology in more detail in my dissertation (Faberman, 2003). 

23
may have a tremendous effect on estimates for a small area like Greeley, CO (which is 

part of my sample). I also restrict my definition of entry and exit to those who enter the 

sample for the first time or leave permanently—in contrast, the BED data only estimate 

openings and closings, which include both temporary and permanent changes. 

B. Measuring Establishment Age 

The age variable, derived from an establishments initial date of UI liability must 

deal with two measurement concerns. First, nearly a third of observations at the 

beginning of the sample are missing their liability dates. Second, there are state 

differences in UI laws that appear to create state-specific differences in establishment age 

that persist even after a variety of controls. To deal with the first issue, I impute the 

missing ages using means calculated from state-industry-size class cells, which use 4-

digit SIC industries and six size classes. These means are highly detailed, with nearly 

20,000 cells estimated. Robustness checks of the data show that this imputation does not 

distort the establishment age distribution. For establishments that enter the sample with a 

missing age, I simply assign them an age of zero at entry. 

To deal with the second issue, I remove state fixed effects from the age variable 

(after imputations), controlling for a variety of other factors. To do so, I use the pooled 

establishment data to regress age on state fixed effects, with controls for quarter, industry, 

single versus multi-unit ownership, and a quartic each in employment level and average 

earnings. I then remove the state effects from establishments aged 3 years or more while 

preserving the sample mean—I choose this cutoff to avoid adjustments to a negative age 

and because the imputations already remove differences for many of the younger 

establishments. I use this adjusted age for all analyses throughout the paper. 

24
Figure 1. MSA Labor Market Characteristics vs. Net Growth 

(a) 

Job Creation Rate

4.0

5.0

6.0

7.0

8.0

9.0

10.0

-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Net Growth

R2 = 0.53

ρ = 0.74

Pittsburgh Charlotte

Philadelphia

Raleigh

Cincinnati

Cleveland

Denver

Detroit

(b) 

Job Destruction Rate

4.0

5.0

6.0

7.0

8.0

9.0

10.0

-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Net Growth

R2 = 0.20

ρ = 0.45

Charlotte

Pittsburgh

Philadelphia

Raleigh Cincinnati

Cleveland

Denver

Detroit

25
(c) 

Average Establishment Size

(number of employees)

10

12

14

16

18

20

22

24

26

-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Net Growth

R2 = 0.24

ρ = -0.50

Charlotte

Pittsburgh

Philadelphia

Raleigh

Cincinnati

Cleveland

Denver

Detroit

(d) 

Average Establishment Age

(in years)

7

8

9

10

11

12

13

14

-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Net Growth

R2 = 0.65

ρ = -0.80

Charlotte

Pittsburgh

Philadelphia

Raleigh

Cincinnati

Cleveland

Denver

Detroit

Note: Figures are scatter plots of mean MSA values of listed variables versus the 

mean MSA growth rate. Larger MSAs are highlighted and the solid line 

represents the OLS trend relation, with R-squared and correlation coefficient 

values listed.

26
Figure 2. Establishment Age Densities for High- and Low-Growth MSAs 

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 5 10 15 20 25 30 35 40

Age (years)

Density

(percent)

Upper Quintile of MSAs, by Growth Rate

Lower Quintile of MSAs, by Growth Rate

Note: Figure plots the frequency distributions of establishment age for the pooled observations of highand low-growth MSAs. “High-growth” MSAs are those whose average growth rates rank in the top 

quintile of the 53 MSAs in the sample; “low-growth” MSAs are those who rank in the bottom quintile. 

27
Figure 3. Employment Dynamics versus Age, High- and Low-Growth MSAs 

(a) 

Net Growth Rates

-0.02

-0.01

0.00

0.01

0.02

0 5 10 15 20 25 30 35 40

Age (years)

Upper Quintile of MSAs, by Growth

Rate

Lower Quintile of MSAs, by Growth

Rate

(b) 

Job Creation Rates

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.11

0 5 10 15 20 25 30 35 40

Age (years)

Upper Quintile of MSAs, by Growth

Rate

Lower Quintile of MSAs, by Growth

Rate

28
(c) 

Job Destruction Rates

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.11

0 5 10 15 20 25 30 35 40

Age (years)

Upper Quintile of MSAs, by Growth

Rate

Lower Quintile of MSAs, by Growth

Rate

(d) 

Excess Reallocation Rates

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0 5 10 15 20 25 30 35 40

Age (years)

Upper Quintile of MSAs, by Growth

Rate

Lower Quintile of MSAs, by Growth

Rate

Note: Figures plot the growth and job flow rates as a function of establishment age for the pooled

observations of high- and low-growth MSAs. “High-growth” MSAs are those whose average growth

rates rank in the top quintile of the 53 MSAs in the sample; “low-growth” MSAs are those who rank in 

the bottom quintile. Functions are smoothed for each series using a centered, 3-year (i.e., across 

establishment years, as opposed to across time) moving average. Thin dotted lines represent 95 percent 

confidence intervals.

29
Figure 4. Hazard Rates of Entrants for the First 5 Years of Existence, High- and 

Low-Growth MSAs 

(a) 

Exit (Hazard) Rates

0.02

0.03

0.04

0.05

0.06

0 1 2 3 4 5

Age (years)

Upper Quintile of MSAs, by Growth

Rate

Lower Quintile of MSAs, by Growth

Rate

(b) 

Exit (Hazard) Rates, Conditional on Industry

0.02

0.03

0.04

0.05

0.06

0 1 2 3 4 5

Age (years)

Upper Quintile of MSAs,

by Growth Rate

Lower Quintile of MSAs,

by Growth Rate

Note: Figures plot establishment exit (i.e., hazard) rates as a function of establishment age for the

pooled observations of high- and low-growth MSAs. “High-growth” MSAs are those whose average

growth rates rank in the top quintile of the 53 MSAs in the sample; “low-growth” MSAs are those who 

rank in the bottom quintile. Thin dotted lines represent 95 percent confidence intervals. The top panel 

depicts the unconditional functions, while the bottom panel depicts the functions conditional on 4-digit 

industry classification.

30
Figure 5. Earnings of Entrants for the First 5 Years of Existence, High- and LowGrowth MSAs 

(a) 

Earnings (1992 $)

4,000

4,500

5,000

5,500

6,000

6,500

7,000

0 1 2 3 4 5

Age (years)

Upper Quintile of MSAs, by Growth

Rate

Lower Quintile of MSAs, by Growth

Rate

(b) 

Earnings (1992 $), Conditional on Industry

4,000

4,500

5,000

5,500

6,000

6,500

7,000

0 1 2 3 4 5

Age (years)

Upper Quintile of MSAs, by

Growth Rate

Lower Quintile of MSAs, by

Growth Rate

Note: Figures plot wages (in 1992 dollars) as a function of establishment age for the pooled

observations of high- and low-growth MSAs. “High-growth” MSAs are those whose average growth

rates rank in the top quintile of the 53 MSAs in the sample; “low-growth” MSAs are those who rank in 

the bottom quintile. Thin dotted lines represent 95 percent confidence intervals. The top panel depicts 

the unconditional functions, while the bottom panel depicts the functions conditional on 4-digit 

industry classification.

31
Table 1. Sample Statistics, Quarterly Means and Variation, 1992:2 – 2000:1 

Sample (MSA

observations only) 

Sample (MSA and nonMSA observations) BED 

(public data)

Job Creation Rate 7.0 

[1.0]

7.3 

[1.2]

8.2 

[0.9]

Job Destruction Rate 6.5 

[1.0]

6.7 

[1.0]

7.5 

[1.1]

Net Growth Rate 0.6 

[1.9]

0.6 

[2.0]

0.7 

[1.9]

Average Earnings 

 (1992 Dollars) 

6,695 

[420] 

6,453 

[398] --- 

Average Establishment Size 

 (no. of employees) 

18.7 

[0.2]

17.8 

[0.2] --- 

Average Establishment Age 

 (no. of quarters) 

41.4 

[1.8]

41.4 

[1.7] --- 

Entry Rate 2.2 

[0.3]

2.3 

[0.3] --- 

Exit Rate 2.2 

[0.4]

2.3 

[0.4] --- 

Notes: Sample MSA statistics are for the 53 MSAs within CO, MI, NC, OH, and PA (with appended data 

from 5 other states, where required by MSA definition). Sample MSA and non-MSA data includes all 

observations within the 5 noted states, plus the appended observations. BED statistics come directly from

publicly available data. Standard deviations are in brackets. 

Table 2. Across-MSA Correlations, Selected Statistics 

Net

Growth 

Earnings 

Growth 

Average 

Earnings 

Average 

Age 

Job Creation Rate (Cj) 0.74 

[0.00]

0.31 

[0.03]

-0.18 

[0.21]

-0.66 

[0.00]

Job Destruction Rate (Dj) 0.45 

[0.00]

0.16 

[0.25]

-0.23 

[0.10]

-0.47 

[0.00]

Job Reallocation Rate (JRj) 0.62 

[0.00]

0.25 

[0.07]

-0.20 

[0.15]

-0.59 

[0.00]

Excess Reallocation Rate (XRj) 0.45 

[0.00]

0.16 

[0.25]

-0.23 

[0.10]

-0.47 

[0.00]

 

Average Establishment Size -0.50 

[0.00]

-0.11 

[0.45]

0.44 

[0.00]

0.44 

[0.00]

Average Establishment Age -0.79 

[0.00]

-0.66 

[0.00]

-0.08 

[0.59]

1.00 

[---]

Entry Rate 0.89 

[0.00]

0.52 

[0.00]

0.14 

[0.31]

-0.89 

[0.00]

Exit Rate 0.57 

[0.00]

0.55 

[0.00]

0.49 

[0.00]

-0.70 

[0.00]

ρ(Cj, Dj) = 0.94 

[0.00]

ρ(Net Growth, 

Earnings) =

0.00 

[0.99]

ρ(Net Growth, Earnings 

Growth) = 

0.47 

[0.00]

Notes: Statistics are Pearson correlations with the variable noted in each column. Correlations use the 

pooled mean statistics for 53 MSAs. p-values are reported in brackets. 

32
Table 3. Across-MSA Correlations, Conditional on Establishment Characteristics 

ρ(Cj, Nj) ρ(Dj, Nj) ρ(XRj, Nj) ρ(Sizej, Nj) ρ(Agej, Nj)

Unconditional 

Correlation 

0.74 

[0.00]

0.45 

[0.00]

0.45 

[0.00]

-0.50 

[0.00]

-0.79 

[0.00]

Controlling for 

Industry

0.41 

[0.00]

-0.22 

[0.11]

-0.01 

[0.95]

-0.30 

[0.03]

-0.70 

[0.00]

Controlling for Age 0.52 

[0.00]

0.14 

[0.31]

-0.08 

[0.56]

-0.17 

[0.23] --- 

Controlling for Size 0.65 

[0.00]

0.27 

[0.05]

0.45 

[0.00] --- -0.80 [0.00]

Controlling for 

Earnings 

0.65 

[0.00]

0.12 

[0.39]

0.29 

[0.03]

-0.66 

[0.00]

-0.53 

[0.00]

Controlling for All 0.19 

[0.18]

-0.64 

[0.00]

-0.53 

[0.00]

-0.54* 

[0.00]

-0.27* 

[0.06]

Note: Correlations are for the pooled MSA means of residual values of the listed variables after 

conditioning out the listed characteristic(s). Industry controls are 946 4-digit SIC industries, age controls

are yearly age categories from 0 to 64 years, size controls are a quartic of employment, and wage controls

are a quartic of wages. * The correlation with size excludes the quartic in size, and the correlation with age 

excludes the age effects. 

Table 4. Accounting for Variations in MSA Age Densities

Nj Cj Dj XRj

Pct. of across-MSA variation accounted for 

by differences in age densities 43.7 47.8 12.3 12.3

Across-MSA correlation with Net predicted 

by age density differences 1.00 0.87 

[0.00]

0.46 

[0.00]

0.46 

[0.00]

Pct. of across-MSA variation accounted for 

by differences in age densities, 

conditional on industry

28.4 19.3 1.7 1.9

Across-MSA correlation with Net predicted 

by age density differences, conditional 

on industry

1.00 0.94 

[0.00]

0.46 

[0.00]

0.87 

[0.00]

Note: Percentages are R-squared values from the OLS regression of the actual (upper panel) or conditional 

(lower panel) MSA pooled mean on the pooled mean predicted by the MSA age distribution, holding

values within each age constant. Correlations are across MSAs and use the predicted mean values. See text 

for further details. 

33
Table 5. Entrant Statistics, First 5 Years of Existence, High- vs. Low-Growth MSAs 

All Entrants

Entrants of HighGrowth MSAsEntrants of LowGrowth MSAs

Total Entrants 207,916 56,347 37,594

 Share of Establishments 2.3 2.8 2.0

Total Exits After 5 Years 99,837 28,021 17,563

 Share of Entrants 48.0 49.7 46.7

Average Net Growth Rate 4.97 5.15 4.86

Average Excess Reallocation 

Rate 19.6 20.9 19.8

Average Earnings(1992 $) 5,177 5,357 4,770

Total Observations 2,938,461 755,088 536,414

Note: Statistics are for a subsample of all establishments who entered between June 1992 and June 1995. 

“High-growth” MSAs are those whose average growth rates rank in the top quintile of the 53 MSAs in the 

sample; “low-growth” MSAs are those who rank in the bottom quintile. 

Table 6. Across-MSA Correlations of Labor Market Dynamics to Selected Labor 

Market Characteristics 

Nj Cj Dj XRj Agej Entryj Exitj

MSA Avg. Net Growth 

Rate 1.00 0.74 

[0.00]

0.45 

[0.00]

0.45 

[0.00]

-0.79 

[0.00]

0.89 

[0.00]

0.57 

[0.00]

MSA Size (Avg. 

Employment) 

0.01 

[0.96]

-0.06 

[0.68]

-0.08 

[0.57]

-0.08 

[0.57]

-0.09 

[0.53]

0.10 

[0.47]

0.44 

[0.00]

MSA 1990 Population 

-0.05 

[0.70]

-0.06 

[0.66]

-0.06 

[0.69]

-0.06 

[0.69]

-0.05 

[0.74]

0.05 

[0.75]

0.41 

[0.00]

Population Growth, 1990-

2000 

0.70 

[0.00]

0.49 

[0.00]

0.29 

[0.04]

0.29 

[0.04]

-0.64 

[0.00]

0.71 

[0.00]

0.42 

[0.00]

Average Unemployment 

Rate 

-0.56 

[0.00]

-0.15 

[0.28]

0.08 

[0.54]

0.08 

[0.55]

0.59 

[0.00]

-0.49 

[0.00]

-0.31 

[0.02]

Share of 1990 Populated 

Aged 20-34 

0.42 

[0.00]

0.45 

[0.00]

0.37 

[0.01]

0.37 

[0.01]

-0.58 

[0.00]

0.40 

[0.00]

0.30 

[0.02]

Share of 1990 Population 

25 or Older with at Least 

a College Degree 

0.63 

[0.00]

0.44 

[0.00]

0.25 

[0.07]

0.25 

[0.07]

-0.69 

[0.00]

0.68 

[0.00]

0.65 

[0.00]

Notes: Statistics are Pearson correlations with the variable noted in each column. Unless a specific year is 

noted, correlations use the mean statistics for 53 MSAs (March 1992 – March 2000). p-values are reported 

in brackets.

34
Appendix Table A.1 Quarterly Mean Statistics for Sample MSAs 

Metro Area Ej (000s) Cj Dj Nj Sizej Agej Wj Entryj Exitj

Akron, OH PMSA 264.0 6.9 6.5 0.5 18.0 11.2 6,376 2.1 2.1

Allentown-Bethlehem

MSA2 227.3 6.7 6.4 0.4 18.3 10.9 6,429 2.0 2.1

Altoona, PA MSA 47.2 6.5 6.1 0.5 17.3 11.2 4,907 1.8 1.9

Ann Arbor, MI PMSA 211.5 7.2 6.6 0.6 19.8 9.7 7,072 2.2 2.2

Asheville, NC MSA 85.6 7.2 6.5 0.7 16.4 9.7 5,240 2.5 2.2

Benton Harbor, MI

MSA2 59.1 7.8 7.4 0.4 17.8 11.3 5,850 1.9 2.1

Boulder, CO PMSA1 123.3 7.9 6.5 1.4 14.0 8.3 7,453 3.1 2.6

Canton, OH MSA 151.4 6.5 6.0 0.4 18.1 11.8 5,708 1.9 1.9

Charlotte-Gastonia, 

NC-SC MSA1 635.9 7.1 6.2 1.0 18.9 9.4 6,769 2.7 2.3

Cincinnati OH-KY-IN

PMSA 669.5 7.0 6.5 0.6 20.4 10.9 6,763 2.2 2.2

Cleveland-Lorain, OH 

PMSA 945.4 6.7 6.2 0.5 18.5 11.3 6,783 2.1 2.1

Colorado Springs, CO

MSA1 170.2 8.3 7.1 1.2 15.6 9.0 5,984 3.0 2.6

Columbus, OH MSA1 643.1 7.3 6.5 0.9 20.9 10.3 6,352 2.4 2.3

Dayton, OH MSA2 383.2 6.6 6.2 0.4 20.7 11.3 6,447 2.0 2.1

Denver, CO PMSA1 854.9 7.9 6.8 1.0 15.3 9.1 7,310 2.8 2.6

Detroit, MI PMSA 1,742.4 7.5 6.9 0.5 20.6 10.4 8,143 2.1 2.3

Erie, PA MSA2 110.0 6.5 6.1 0.4 19.2 11.4 5,690 1.8 2.0

Fayetteville, NC MSA 71.6 7.6 7.0 0.6 15.9 9.8 4,777 2.2 2.1

Flint, MI PMSA2 147.4 6.6 6.5 0.1 20.5 10.3 7,550 2.0 2.3

Ft. Collins, CO MSA1 81.0 8.8 7.5 1.3 12.9 8.8 5,820 2.8 2.3

Goldsboro, NC MSA 33.1 7.0 6.2 0.8 16.6 10.9 4,601 2.0 1.9

Grand Junction, CO 

MSA 35.6 8.7 7.6 1.1 12.3 9.5 4,952 2.6 2.1

Grand RapidsMuskegon, MI MSA 462.1 7.1 6.3 0.7 22.7 10.5 6,372 2.0 2.0

Greeley, CO PMSA1 49.1 8.5 7.4 1.1 14.9 10.0 5,462 2.4 2.1

Greensboro-Winston

Salem, NC MSA 543.0 6.2 5.6 0.6 20.2 10.1 5,956 2.2 2.1

Greenville, NC MSA1 42.7 8.6 7.8 0.9 16.8 9.1 4,922 2.3 2.0

Hamilton, OH MSA 97.1 7.1 6.4 0.7 18.5 10.5 6,302 2.2 2.1

Harrisburg, PA MSA 260.9 6.3 5.9 0.5 21.0 10.8 6,080 2.0 2.1

Hickory-Morganton, 

NC MSA 152.6 5.3 4.8 0.5 23.2 10.8 5,141 1.9 1.8

Jackson, MI MSA 46.6 6.9 6.5 0.4 17.4 11.7 6,130 1.8 1.9

Jacksonville, NC MSA 23.9 8.8 7.9 0.8 11.6 9.1 3,577 2.4 2.2

Johnstown, PA MSA2 69.0 6.8 6.6 0.2 14.7 11.8 4,734 1.8 1.9

Kalamazoo-Battle 

Creek, MI MSA2 173.7 7.2 6.8 0.4 21.1 11.1 6,295 1.8 2.0

Lancaster, PA MSA 184.8 6.1 5.6 0.5 20.3 10.6 5,907 2.0 2.0

Lansing, MI MSA 157.0 7.0 6.5 0.5 19.1 10.4 6,199 2.0 2.1

Lima, OH MSA 64.4 6.5 6.0 0.5 19.7 12.5 5,778 1.8 1.8

(continued on next page) 

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Appendix Table A.1 (continued) 

Metro Area Ej (000s) Cj Dj Nj Sizej Agej Wj Entryj Exitj

Mansfield, OH MSA2 67.1 6.6 6.3 0.3 19.1 12.5 5,483 1.8 2.0

Philadelphia, PA-NJ 

PMSA 1,877.4 7.0 6.6 0.4 18.0 10.3 7,402 2.1 2.5

Pittsburgh, PA MSA2 903.1 6.8 6.5 0.3 18.1 11.3 6,516 1.9 2.1

Pueblo, CO MSA 39.7 7.8 7.0 0.8 14.7 11.0 4,819 2.1 2.0

Raleigh-Durham, NC 

MSA1 465.5 7.4 6.3 1.1 18.1 8.5 6,673 2.8 2.3

Reading, PA MSA2 141.4 6.3 5.9 0.4 20.2 11.4 6,368 1.9 2.0

Rocky Mount, NC

MSA2 56.0 7.2 6.9 0.2 20.2 10.7 5,282 2.0 2.0

Saginaw-Bay City, MI

MSA2 145.9 6.4 6.1 0.3 19.3 10.9 6,957 1.8 1.9

Scranton-Wilkes-Barre, 

PA MSA2 231.1 6.8 6.5 0.3 18.0 10.9 5,228 1.9 2.1

Sharon, PA MSA 39.7 7.0 6.5 0.5 16.6 11.3 5,216 2.0 2.1

State College, PA MSA 39.8 7.4 7.0 0.4 15.8 10.2 4,947 2.0 2.0

Steubenville-Weirton, 

OH MSA2 42.2 6.3 6.3 0.0 16.5 12.1 5,921 1.8 2.0

Toledo, OH MSA 259.0 7.2 6.6 0.5 20.0 11.5 6,147 2.0 2.1

Williamsport, PA MSA2 44.7 6.0 5.8 0.2 18.1 11.6 5,114 1.8 2.0

Wilmington, NC MSA1 77.4 9.0 8.1 0.9 13.1 8.5 5,328 3.0 2.4

York, PA MSA 141.4 6.3 5.8 0.4 20.8 11.0 6,058 2.0 2.1

Youngstown, OH MSA2 206.5 6.9 6.6 0.3 17.3 11.7 5,900 1.9 2.0

Notes: Estimates are the pooled mean statistics for each MSA. Employment levels are in thousands. Job 

flow and net growth rates are percentages of employment. “Size” refers to the average establishment size, 

in employees. “Age” refers to average establishment age, in years. Earnings are for a quarter and expressed 

in 1992 dollars. Entry and exit rates are percentages of establishments. 

1. Ranked in the (establishment-weighted) upper quintile of MSA growth. 

2. Ranked in the (establishment-weighted) upper quintile of MSA growth. 

36