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The Ecology of Entrepreneurship

Gorkem Aksaray*

Emory University

Peter Thompson**

Georgia Institute of Technology

May 2015

This paper reports the results of a new examination of the well-established negative

effect of localized density on survival in established industries. In the standard

theory, this effect is generically attributed to resource competition, where both

“resource” and “competition” are broadly defined. We posit and test the competi-

tion story against two alternative mechanisms – associations between local density

and (i) the opportunity cost of entrepreneurs’ time, and (ii) geographic variations

in minimum efficient scale. Using a sample from the confidential geocoded NLSY,

we find evidence that consistently points in favor of the opportunity cost mecha-

nism.

Keywords: Density dependence, organizational ecology, entrepreneurship, sunk

cost, opportunity cost, first-passage distributions, Brownian bridge.

* Goizueta Business School, Emory University, 1300 Clifton Road, Atlanta, GA 30322;

gaksara@emory.edu ** Scheller College of Business, Georgia Institute of Technology, 800

West Peachtree Street, NW, Atlanta, GA, 30308; peter.thompson@scheller.gatech.edu. We

are most grateful to Anand Swaminathan for frequent and helpful advice.

1

1. Introduction

There has been increasing recognition that while entrepreneurial activity tends to

concentrate in space, new and small entrepreneurial firms may be more vulnerable

to competitive forces as industries evolve. This explanations is in line with well-

established observation in ecological studies that concentration makes firm exit

more likely especially in mature industries. However, the causal mechanism con-

ventionally suggested has yet to be tested against alternatives.

One of the major ecological approaches to organizational mortality is density de-

pendence. According to density dependence theory (Carroll and Hannan, 1989;

Hannan and Carroll, 1992; Hannan and Freeman, 1987; 1988; 1989), population

density (the number of organizations in a defined population) controls the popula-

tion-level processes of legitimation and competition. In emerging populations with

low levels of density, increases in density enhance the institutional legitimacy of a

population. Population members become more able to acquire resources necessary

for greater performance and likelihood of survival. Improved prospects for success

in turn make further entry more attractive. However, as density continues to in-

crease and the population gets established, competition for common resources in-

tensifies as a result of crowding. More intense competition (i) leads to declining

performance among active firms and thus higher failure rates, and (ii) discourages

further entry. The combination of these two opposing forces suggests a non-mono-

tonic relationship between density and population-level parameters of interest.1

1 The notions of both legitimacy and competition are highly general (Singh, 1993). For example, legitimacy may induce such diverse events as access to financing, willingness of suppliers to invest in capacity, willingness of skilled labor to join firms in the indus-try, and expansion of demand through diffusion of taste or information, among many other specific mechanisms. (Some of these are, of courses, just instances of Alfred Mar-shall’s forces for agglomeration economies). Similarly competition may be driven by, inter alia, mechanisms such as competition for foot traffic, competition for skilled labor, the filling of product niches in a differentiated goods markets (Prescott and Visscher, 1977), process innovations that reduce costs and raise the minimum efficient scale of production (Jovanovic and McDonald, 1994) or dynamic economies of scale (Klepper, 1996).

2

The most common empirical implementation of the density dependence is regress-

ing firm entry and survival on linear and quadratic terms for density. The theory

predicts in both regressions a positive coefficient on the linear term and a negative

coefficient on the quadratic term. There is a large literature supporting this pre-

diction, much of which is reviewed in Baum and Shipilov (2006). Most studies have

used directory data to construct samples of entry and exit dates of firms within an

industry over a long period of time, ideally since the birth of the industry. The

long timespan covered by these datasets is attractive for revealing both the early

stage when legitimacy is held to dominate and the late stage when competition is

expected to dominate. However, by limiting the measure of performance to firm

survival, these data fails to explain explicitly whether mortality is mediated by low

performance (Gimeno et al., 1997). Only a handful of studies have investigated

alternative measures of performance. Stuart and Sorenson (2003) use the hazard of

going public as a measure of performance in explaining negative effects of spatial

concentration on new venture performance. Folta et al. (2006) use the hazard of

private equity offering, patent, and strategic alliance events as alternative measures

of performance to show diseconomies of agglomeration. Dahl and Sorenson (2012),

having access to comprehensive data on Danish start-ups, employed annual profit-

ability to explain the effects of regional tenure of entrepreneurs on the performance

of their businesses. Other studies that generally do not encompass the birth of the

industry usually found a negative effect of density on performance, albeit without

distinguishing between different casual mechanisms (e.g., Amburgey and Rao,

1996).

A well-received elaboration of the standard model holds that firm performance in

established industries depends negatively on the extent of localized competition

(Singh and Lumsden, 1990; Baum and Mezias, 1992). The notion of localized com-

petition asserts that more similar organizations, whether measured by size, organ-

izational structure, market niche, or geographic proximity, compete at a greater

level of intensity due to overlapping resource requirements (Baum, 1996). For ex-

amples: Baum and Mezias (1992) find that as the Euclidean distance of focal or-

ganizations to other organizations within a competitive window in the population

increases, the hazard of failure decreases; Baum and Haveman (1997) suggest that

new entrants try to avoid direct competition from competitors similar in size by

locating farther from them; and Lomi and Larsen (1996, 2001) argue that the rela-

tionship between density and failure rates is highly sensitive to spatial proximity

between individual organizations.

3

This paper reports on a new examination of the well-established negative effect of

localized density on survival in established industries.2 In the standard theory, this

effect is generically attributed to resource competition, where both “resource” and

“competition” are broadly defined. We posit and test the competition story against

two alternative mechanisms – associations between local density and (i) the oppor-

tunity cost of entrepreneurs’ time, and (ii) geographic variations in minimum effi-

cient scale (MES). Both mechanisms can induce the same empirical relationship,

but neither can be reasonably subsumed under the rubric of competition. By mod-

elling the stochastic process of population dynamics in relation to localized firm

density, and having access to individual-level entrepreneurial data on earnings and

entry-exit decisions, we are able to distinguish between the three mechanisms.

Density and opportunity costs. Founders of firms located in high-density areas

enjoy greater outside opportunities and, as a result, are more likely to close down

a business than is a founder operating a firm in a less dense area. Prior research

found that higher founding rates in more concentrated areas change the oppor-

tunity structure: new jobs and vacancies are created for employees in new and

failed organizations (Haveman and Cohen, 1994) allowing for greater job mobility

(Freedman, 2008). Newly founded small entrepreneurial firms lack concerted action

by dependent actors (i.e. workers and/or managers) to maintain the organization

when faced with low performance (Meyer and Zucker, 1989). The departure of the

founder to obtain alternative employment is very often synonymous with the clo-

sure of the business. The influence of outside opportunities on firm continuation is

likely to be especially high among such firms most responsible for the variation in

failure rates (Gimeno et al., 1997).

2 It is important to note that we focus on the established industries where the legiti-mation process is not likely to be the dominant force. As will be seen, we use the main industry (sector) codes to capture the positive density effects on mortality. Finer in-dustry definitions that could potentially reflect emerging populations of firms do not necessarily appear on data. Moreover, contrary to many previous studies on industry concentration and clusters, we do not limit our analysis to a single industry with spatial heterogeneity.

4

We do not need to argue that outside earnings for any given job are higher in more

dense areas, although this may be the case. To the contrary, standard search mod-

els predict that, even when the distribution of wage offers is held constant, expected

earnings are higher when the cost of job search is lower or when job offers arrive

more frequently.3

Density and MES. Some areas may be denser than others because they support

firms with a lower MES. It has also been suggested that clusters attract weaker

firms as a result of adverse selection (Akerlof, 1970) and thus suffer from survival

disadvantages (Shaver and Flyer, 2000). Lower MES increases density in two ways.

First, it increases the number of firms required to serve any given level of demand.

Second, by reducing the cost of entry, it eases the liquidity constraints that may

otherwise exclude some potential entrepreneurs. A smaller MES is associated with

higher failure rates because income shocks are more likely in smaller firms to drive

business earnings below the entrepreneur’s opportunity cost. This implies a differ-

ent causality for the negative relationship between density and survival than the

conventional explanation.

The remainder of the paper is structured as follows. In Section 2 we develop a

simple stochastic model of the evolution of profits and firm survival to guide the

empirical analysis. The model has six predictions - on firm exit, profit levels, and

profit growth – that together offer the potential to discriminate between the three

mechanisms for density effects. Section 3 describes our data, and Section 4 reports

the results of our tests of the predictions. The evidence consistently points in favor

of the opportunity cost mechanism.

2. The Model

Agents creating a business begin with profit 0

(0) ,π π= which is a draw from the

distribution 0

( ).F π Profits then evolve stochastically according to the diffusion

process

3 There are other consequences of higher opportunity costs that may serve to offset the direct effect of quicker exit. In particular, potential entrepreneurs with good options for local wage work will be less likely to establish businesses of poor quality. However, if opportunity costs are to explain the association between density and exit, this effect must be of secondary importance.

5

( ) ( )d t dt d tπ β σ ξ= + , (1)

where ( )tξ is a standard zero-drift Wiener process with boundary condition ξ(0)=0.

Exit occurs the first time that ( ) ,t wπ ≤ where wdt, the return from wage employ-

ment, is the opportunity cost of running the business.

Let γ denote the local density of competing businesses. We propose three mecha-

nisms by which density may influence firm survival:

A. Competition. Greater density tends to reduce the growth rate of profits:

( )β β γ= and ( ) 0β γ′ < .

B. Opportunity cost. Greater density increases the opportunity cost of entre-

preneurship because the thicker labor market provides improved outside op-

portunities: ( )w w γ= with ( ) 0.w γ′ >

C. Efficient Scale. Greater density exists in response to local conditions that

permit smaller firms to compete effectively: 0

( | ),F F π γ= which is increasing

in γ.

2.1 Firm exit

Because much of the extant empirical evidence has used registry data recording

firm survival, we begin with the unconditional probability of firm exit. To do so,

we require the distribution of the Markov time, τ, that satisfies

{ }min : ( )t

t t wτ π= ≤ , (2)

where ( )tπ is a random variable with normally distributed increments in each pe-

riod, having mean 0

( )t tπ π β= + and variance 2tσ . Define

0( )

( )t t

x tπ π β

σ σ

−= + , (3)

which is a standard Wiener process. The Markov time in (2) can now be written

as

{ }1 2min : ( )

tt x t tτ ζ ζ= ≥ + , (4)

6

where 1 0

( ) /wζ π σ= − and 2

/ .ζ β σ= Equation (4) defines the first passage of a

Wiener process to a single upper linear boundary, 1 2 ,tζ ζ+ that is positively [neg-

atively] sloped when [ ]0β > < (see Figure 1). The distribution of first passage

times, ( ; ),P τ i for this problem is given by the well-known Bachelier-Lévy formula

[e.g., Cox and Miller (1965:221)],

1 221 2 1 2

1 2( ; , )P e

ζ ζζ ζ τ ζ ζ ττ ζ ζ

τ τ

− + − = Φ − + Φ −

, (5)

where ( )Φ i is the distribution function of a standard normal random variable.

Taking the limit of (5) as τ →∞ yields the probability that the firm ever exits:

1 2

1 2 1 2 2

1, if 0( , ) lim ( ; , )

1, if 0P P

eζ ζτ

βζ ζ τ ζ ζ

β

∞−→∞

≤= = < >

. (6)

The hazard of firm exit conditional on 0π ,

0( | ) ( ) / (1 ( ))h t P t P tπ ′= − , is unimodal

and positively-skewed. For 0β > the hazard declines asymptotically to zero, while

for 0β < it declines to a strictly positive constant. It is a well-known property of

the first-passage problem that anything that moves the boundary, 1 2

,tζ ζ+ up-

wards [downwards] at time t reduces [increases] 0

( | )h t π . Thus, a reduction in β or

0tτ

1 2tζ ζ+( )x t

FIGURE 1. First passage problems for firm exit, β<0. Sam-

ple path is drawn excessively smooth for visual clarity.

7

0π induces an increase in h(t) as does an increase in w (see Figure 2). Hence, each

mechanism induces a positive association between the exit hazard and density.

PROPOSITION 1. Let ( )

0 0( ) ( | ) ( )Jh t h t dFπ π= ∫ denote the expected exit hazard un-

der mechanism J. ( )( )Jh t is increasing in γ for J = A, B, and C.

2.2 Exit Hazards Conditional on Current Profit

The stochastic process is Markovian, and so the exit hazard conditional on current

profits can be written simply by updating the time index in the Bachelier-Lévy

formula:

1 221 2 1 2

1 2

( ) ( )( ; , )

tt

t t tP t e

t t

ζ ζζ ζ τ ζ ζ ττ ζ ζ

τ τ

− + + − + + = Φ − + Φ − + +

(7)

where 1

( ) / .t

twζ π σ= − Initial profit does not appear in (7):

PROPOSITION 2. Let ( )( | ( ))Jh t tτ π+ denote the exit hazard under mechanism J

conditional on profits earned at time t. ( )( | ( ))Jh t tτ π+ is increasing in γ for J

= A and B, and is independent of γ for J = C.

2.3 Firm Profits (level effects)

As the unconditional distribution of ( )x t is normal with mean zero and variance t,

it follows that the distribution of all x(t) satisfying 1 2

( )x t tζ ζ≤ + is truncated

normal with support 1 2

( , )tζ ζ−∞ + . Choose any 1 2

( , )x tζ ζ∈ −∞ + . The probabil-

ity that ( )x t x= without having previously crossed the upper barrier is given by

the complement to the crossing probability of a Brownian bridge that begins at

FIGURE 2. Firm exit hazards. Baseline β=0, π0=10,

w=5, σ=10.

8

(0) 0,x = terminates at ( ) ,x t x= and has an absorbing boundary 1 2

tζ ζ+ . This is a

well-known distribution [e.g., Scheike (1992), Proposition 3], given by

{ }1 2( ) Pr ( ) [0, ] | ( )tx x s s s t x t xψ ζ ζ≡ < + ∀ ∈ =

{ }1 1 21 exp 2 ( ) /t x tζ ζ ζ= − − + − , (8)

It then follows that the density of x(t) among surviving firms is given by

1 2

1 2

1 2

( ) ( ),

( ) ( )if ( )

( ( ))

if ( )0,

t t

t

t

x x

x x dxx t t

g x t

x t t

ζ ζ

ψ φ

ψ φζ ζ

ζ ζ

+

−∞

≤ += > +

∫, (9)

where ( )txφ is the density of Normal random variable with mean zero and variance

t. Finally, using the method of transformations, the density of profits among sur-

viving firms is

( )1 1

0( ) , if ( )

( ( ))if ( )0,

g t t wf t

t w

σ π π β σ ππ

π

− − − + ≥= <

. (10)

It is, as before, simplest to document the behavior of (10) by means of numerical

plots. Figure 3 plots the distribution ( ( ))F tπ , and shows that a higher w is associ-

ated with stochastically greater profits in the sense of first-order dominance. Mech-

anism B therefore induces an unambiguous relationship between density and the

distribution of profits. This is not the case for A and C, however. To see why,

consider the following discrete-time, discrete-state example. Let 0

2.2π = and

1w = , and suppose that at t=1, 1π takes on one of the values

0 0 0{ 1, , 1}π π π− +

each with equal probability. There is consequently no firm exit at t=1, and the

average profit among surviving firms is 1

[ ] 2.2E π = . Suppose now that 0π declines

to 2.1. Then there continues to be no exit and 1

[ ]E π also declines to 2.1. In con-

trast, if 0π declines to 1.9, one third of the firms exit at t=1, and the average profit

among surviving firms increases to 2.4. Thus, when selection effects are weak

[strong] a decline in initial profits is associated with a decline [an increase] in future

average profits. A similar ambiguity exists for changes in β.

9

PROPOSITION 3. Let ( )( ( ))JF tπ denote the distribution of profits under mechanism

J. ( )( ( ))JF tπ is decreasing in γ for J = B, but may be either increasing or

decreasing in γ for J = A and C.

Proposition 3 provides a way to obtain evidence against mechanism B in favor of

mechanisms A or C, but it provides a way neither to reject A and C nor to dis-

criminate between them. Of course, we can eliminate the effect of survivor bias by

looking at initial earnings.

PROPOSITION 4. Let ( )

0( )JF π denote the distribution of initial profits under mecha-

nism J. ( )

0( )JF π is independent of γ for J = A and B, and is decreasing in γ

for J = C.

2.4 Firm Profits (growth effects)

Next, we turn to growth rates of surviving firms over the interval [ , ]t t τ+ condi-

tional on profit at time t. Mechanism C is the most straightforward: density has

no effect on conditional growth rates. Mechanism B has an unambiguous effect: an

increase in w increases the fraction of firms with negative growth rates that are

selected out, thereby increasing the average growth rate of surviving firms. How-

ever, this selection mechanism becomes weaker the further current profit is from

the exit threshold. Under mechanism A, a reduction in β brought about by an

increase in density reduces the conditional growth rate of all firms. However, among

FIGURE 3. Distribution of profit. Baseline β=0, π0=10,

w=5, σ=5, t=3.

10

firms close to the exit threshold, selection effects may offset this negative effect of

density. This offsetting effect of selection also becomes weaker the further current

profit is from the exit threshold, so the negative effect of density on profit growth

is stronger for firms further from the exit threshold.

PROPOSITION 5. In a regression of the form , 0 1 2 3t t t tτπ α α γ α π α γπ

+∆ = + + + , the

three posited mechanisms predict: A) 1 3

0, 0,α α< > B) 1 3

0, 0,α α> < and

C) 1 3

0, 0.α α= =

2.5 Founder’s post-dissolution earnings

The final prediction, concerning the earnings of entrepreneurs returning to wage

work, is straightforward:

PROPOSITION 6. Post-dissolution earnings, w, are increasing in γ for J = B, but

independent of γ for J = A and C.

2.6 Summary

Table 1 summarizes the predictions of our simple model. Clearly, there is no one

test that can discriminate between the three mechanisms. Moreover, it is likely

that each of them is present in the data. Hence, our empirical tests (the results of

which are, for convenience, given in the final column of Table 1) can at best tell

us which effect appears to dominate in the data.

Table 1. Testable Predictions: Effect of an Increase in Density

Dependent Variable Mechanism Results from

Section 4 A B C

P1. Exit probability + + + +

P2. Exit probability conditional on profit + + 0 +

P3. Firm profit level at time t ? + ? +

P4. Firm profit level at time 0 0 + - +

P5. Firm profit growth - + 0 +

Plus: Sign of coefficient on interaction between profit level and density

+ - 0 -

P6. Post-dissolution earnings 0 + 0 +

11

3. Data and measures

This study uses the National Longitudinal Survey of Youth 1979 (NLSY79) spon-

sored by the Bureau of Labor Statistics, U.S. Department of Labor. NLSY79 ob-

serves a nationally representative sample of American youth born between 1957

and 1964 over an extended period. It has certain advantages over other sources of

data such as the Current Population Survey and the Survey of Business Owners,

making it suitable for studying the dynamics of self-employment. First, it is a long

panel where data on individuals are collected annually until 1994 and biannually

from 1995 to 2012. This allows us to identify transitions into and from self-employ-

ment over a long period of time. Second, it provides rich data on demographics,

income, education, and family background, which contextualizes the labor market

experience of the self-employed. Third, it allows us to identify geographic location

of respondents over time at the state and county level, making it possible to match

individuals with local economic variables such as density.

Self-employment status. NLSY79 gathers self-reported current or most recent job

(CPS) information from each respondent at the time of each interview. Respond-

ents are specifically asked to report whether they were 1) employees of a private

company business or individual for wages, salary or commission; 2) a government

employee; 3) self-employed in own-business, professional practice, or farm; 4) work-

ing without pay in family business or farm; or 5) working for a non-profit organi-

zation4. They are also asked to report the industry code for the CPS jobs. Employer

information gathered from all respondents is then aggregated and corrected by

NLSY79 to create class-of-worker status and industry code variables for each indi-

vidual. Unpaid work in own family farm or business is not counted as self-employ-

ment. Following the transition from self-employment to other types of wage-em-

ployment, we collapse class-of-worker status into a binary indicator taking the

value 1 if self-employed in a particular year and 0 otherwise.

Self-employment earnings. Earnings of the self-employed are measured as the sum

of military income, wage and salary income, and business or farm income in the

previous calendar year. The reason why all three sources of income are combined

is that more than half of the self-employed respondents who earn positive earnings

do not report business income. According to Fairlie (2005), this might be due to

4 The fifth option is available only in the most recent surveys.

12

several reasons: 1) incorporated business owners reporting their income as wage

and salary income instead of business income, 2) the ordering of questions on the

questionnaire, or 3) the self-employed reporting only their labor income from the

business under wage and salary income. We also utilize supplementary questions

on the three income types asked of respondents who do not know, or refuse to

answer, the primary questions. For example, if a respondent does not know the

exact amount of money received from a business in the past calendar year, he or

she is then asked to give approximate upper and lower values as a range for that

amount. In this case, we take the mid-point of the reported range as the business

income. Similarly, if a respondent refuses to report this amount, he or she is then

asked multiple questions such as “would it amount to $40,000 or more?” to classify

respondents into income categories. In this case, we take the minimum or maximum

amount that would conform with their answers to all of these questions.5

Localized density. In order to capture localized density, we first need to know the

geographic location of respondents. Non-public NLSY79 Geocode data provides

information on current residence at the state and county level. As the NLSY79

Geocode data does not specifically report the geographic location of jobs, the

county of current residence is assumed to be the same as that of the CPS job.

NLSY79 data is then supplemented with the County Business Patterns (CBP) data

released by the US Census Bureau. CBP provides annual economic data by indus-

try at the county level and is therefore very useful for studying local economic

activity. CBP electronic data files are available from 1986 to present, limiting the

period of study to 1986-2012. NLSY79 respondents reported 3-digit 1980 Census

codes until 1996 and 2002 Census codes thereafter. CBP data, on the other hand,

is categorized by SIC codes until 1997 and NAICS codes thereafter. We therefore

follow industry concordances between 1980 Census and SIC for all years until 1996,

1980 Census and NAICS for 1998 and 2000; and 2002 Census and NAICS for the

remaining years. We then match each respondent-year observation with the corre-

sponding number of establishments reported in CBP using county of residence and

5 For example, if the respondent reports to have received more than $10,000 as well as more than $40,000 in the next question, business income is assumed to be $40,000. Otherwise, it is assumed to be $10,000. This is a more conservative approach than averaging $10,000 and $40,000 if the respondent is known to be in this category; how-ever, its effect on our estimations is negligible.

13

main (divisional) industry code for CPS job at the time of interview.6 This provides

us the longitudinal account of local density unique for each self-employment spell.

In other words, the density that a respondent faces during his or her self-employ-

ment spell is localized in terms of both the industry type and the geographic loca-

tion.

Controls. Besides the measures of transition to and from self-employment, earn-

ings, and local density, we employ NLSY79 to control for demographic (race and

sex), family (family size and marital status), and educational background (highest

grade completed as of the time of interview). In addition, whether the respondent

resided in a standard metropolitan area (SMSA) at the time of interview is recoded

as a binary variable. The effect of economic recessions is controlled for by creating

a binary indicator for recession years.7 Finally, to account for the change from

annual to biannual reporting in 1994, we include a dummy variable for post-1994

observations in our survival regressions.

Summary Statistics. Descriptive statistics for the variables used in the analysis are

shown in Tables 2 and 3. The columns of Table 2 show variable means and stand-

ard deviations for sample definitions used in different sets of regressions. Our sam-

ple of entrepreneurs is 59 percent married, 39 percent female, 20 percent Black,

and 17 percent Hispanic. These proportions do not vary much across our subsam-

ples, which reflects the fact that in the NLSY79 there are only modest differences

across groups in rates of transition into and out of self-employment. Mean incomes

vary much more across samples than do the demographic variables. On average,

earnings during the whole self-employment spell ($36,507) are considerably higher

than the initial self-employment earnings ($28,684), as well as earnings immedi-

ately before ($28,123) and after ($23,433) the self-employment spell.

6 For example, consider a respondent reporting industry code of his or her CPS job in 2000 as 732, which corresponds to “Business Management and Consulting Services” in 1980 Census coding. We recode it as 54 because NAICS classification of “Professional, Scientific, and Technical Services” sector include business consulting services coded as 5416. By doing so, we are able to match this respondent to 2000 CBP data for the corresponding sector. 7 Recessions are announced by NBER’s Business Cycle Committee: 1990, 1991, 2001, 2008, and 2009 are coded as recession years (See http://www.nber.org/cycles).

14

Table 2

Descriptive statistics for all variables

Condition for included observations: All self-employed at

time t Entry: Self-employed

at time t & not self-employed at time t-1

Exit: Self-employed at time t & not self-em-ployed at time t+1

Growth: Self-em-ployed both at time t and t-1

Variable M SD M SD M SD M SD

Density 4332.14 11114.31 4652.75 11529.18 4453.84 10972.35

Earning $36,507.70 $52,802.21 $28,684.85 $42,019.78

Pre self-employment wages $28,123.86 $38,532.72

Post self-employment wages $23,433.67 $32,476.86

Earning growth rate 0.52 2.16

Race = Black 0.20 0.22 0.21 0.18

Race = Hispanic 0.17 0.18 0.18 0.15

Sex; 1 if female 0.39 0.40 0.42 0.37

Family size 3.14 1.60 3.05 1.60 3.12 1.62 3.16 1.56

Marital status; 1 if married 0.59 0.55 0.54 0.62

Highest grade completed 13.00 2.59 13.01 2.61 12.98 2.56 13.11 2.59

SMSA; 1 if residence is in SMSA 0.81 0.81 0.81 0.81

Recess; 1 if in recession year 0.17

15

Table 3

Pairwise correlations

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9)

(1) Class of worker for CPS job; 0 if self-employed 1

(2) Total earnings in past calendar year -0.083 1

(3) Racial/ethnic cohort 0.041 -0.089 1

(4) Sex; 1 if female 0.057 -0.214 -0.003 1

(5) Family size -0.015 -0.036 0.113 0.103 1

(6) Marital status; 1 if married -0.037 0.156 -0.128 0.033 0.359 1

(7) Highest grade completed 0.013 0.322 -0.174 0.041 -0.128 0.097 1

(8) Current residence; 1 if SMSA 0.009 0.109 0.137 0.007 -0.044 -0.044 0.116 1

(9) Recession; 1 if recession year -0.009 0.007 0.006 0.001 -0.024 0.006 0.001 0.006 1

16

4. Results

We estimate random-effects models throughout. In our sample, there is often no

variation within subjects in the dependent variable as well as a number of controls.

Random effects estimators allow us to exploit both the cross-sectional and time

series components of the data (Kennedy, 2008), to estimate the effects of time-

invariant controls, and to avoid losing subjects for which the dependent variable

does not change over the observation period.

Propositions 1 and 2. The first column of Table 4 shows that, as predicted by

each of the three mechanisms under consideration, an increase in density has a

positive effect on business mortality in the next period.8 The second column of

Table 4 presents the same model with the log of self-employment earnings added

as a control. Consistent with mechanisms A and B in P2, the effect of density

remains positive and significant.

Proposition 3, 4 and 6. Table 5 summarizes models of earnings at different chap-

ters of a self-employment spell: at time t, at the time of entry, and post-exit. P3

concerns the relationship between density and time t profits, which, as the first

column of Table 5 shows, is positive, consistent with mechanism B. P4 concerns

initial profits, which we take to be earnings in the first year the business is estab-

lished.9 Two tests of P4 are reported, the second of which includes a control for

earnings in the year prior to business creation. In both cases, the relationship be-

tween density and initial earnings is positive, which result is again consistent only

with mechanism B. The last column of Table 5 regresses earnings in the year after

an entrepreneur has returned to the paid labor force on, inter alia, density (P6).

The coefficient on density is positive, providing support, again for mechanism B.

8 The relationship between the control variables and firm exit are familiar from much previous work. For example, self-employment earnings are associated with lower exit rates, as are being male, married, and living in SMSAs. 9 We add a time trend in order to control for changes in the price index.

17

Table 4

Random-effects logit model of exit from self-employment

DV = 1 if subject exits self-employment at end of

period, 0 otherwise.

Variables P1 P2

Log of density 0.0488** (0.0212)

0.0456** (0.0219)

Log of current earnings -0.211*** (0.028)

Race = 1 if Black 0.228** (0.099)

0.161 (0.103)

Race =1 if Hispanic 0.152 (0.109)

0.214* (0.112)

Sex = 1 if female 0.241*** (0.078)

-0.00267 (0.0858)

Family size 0.0297 (0.024)

0.0122 (0.0255)

Marital status = 1 if married -0.377*** (0.0804)

-0.311*** (0.0846)

Highest grade completed -0.0129 (0.0152)

0.0167 (0.0161)

Current residence = 1 if SMSA -0.222** (0.107)

-0.162 (0.111)

Recession Year = 1 -0.233*** (0.0761)

-0.198** (0.0806)

Post-1994 = 1 -0.554*** (0.0656)

-0.492*** (0.071)

Constant -0.491** (0.238)

1.183*** (0.328)

Observations Number of cases

8,619 2,581

7,715 2,418

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

18

Table 5 Random-effects linear model of self-employment earnings

Dependent Variable: Ln(current earnings)

Ln(initial self-employment earnings)

Ln(post-exit earnings)

P3 P4(i) P4(ii) P6

Log of density 0.0270*** 0.0400*** 0.0252* 0.0313**

(0.00905) (0.0144) (0.0134) (0.0155)

Log of pre-entry wages 0.513***

(0.0202)

Race = Black -0.435*** -0.421*** -0.250*** -0.321***

(0.0559) (0.0664) (0.0598) (0.0734)

Race = Hispanic -0.0369 -0.0903 -0.0552 -0.0724

(0.0623) (0.0727) (0.0653) (0.0786)

Sex; 1 if female -1.111*** -0.972*** -0.487*** -1.011***

(0.0446) (0.0529) (0.0503) (0.0571)

Family size -0.0382*** -0.0388** -0.0110 -0.0800***

(0.00976) (0.0161) (0.0154) (0.0177)

Marital status; 1 if married 0.185*** 0.253*** 0.110** 0.326***

(0.0334) (0.0534) (0.0511) (0.0581)

Highest grade completed 0.127*** 0.121*** 0.0549*** 0.125***

(0.00845) (0.0102) (0.00941) (0.0115)

Current residence; 1 if SMSA 0.0864** 0.200*** 0.119* 0.0279

(0.0436) (0.0726) (0.0671) (0.0762)

Year 0.0751*** 0.0695*** 0.0272*** 0.0790***

(0.00258) (0.00473) (0.00497) (0.00525)

Constant 7.438*** 7.381*** 3.673*** 7.351***

(0.127) (0.161) (0.207) (0.182)

Observations 8,650 2,551 2,217 2,095

Number of cases 2,677 1,977 1,764 1,689

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

19

Table 6 Random-effects linear model of earnings growth rate (P5)

Log of density 0.616*** (0.127)

Log of current earnings 1.0203*** (0.086)

Density × ln(Current earnings) -0.0699*** (0.123)

Race = Black 0.403*** (0.0883)

Race = Hispanic 0.0103 (0.0955)

Sex; 1 if female 0.667*** (0.0713)

Family size 0.0156 (0.0235)

Marital status; 1 if married -0.163** (0.0784)

Highest grade completed -0.0482*** (0.0128)

Current residence; 1 if SMSA -0.0257 (0.101)

Year -0.0463*** (0.00724)

Constant -8.318*** (0.886)

N=4,834; 1,463 cases. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

20

Proposition 5. Table 6 provides evidence that the effect of density on growth in

self-employment earnings is consistent with mechanism B. An increase in density

increases the growth rate, whereas the coefficient on the interaction term created

by multiplying density and self-employment earning is negative. These results in-

dicate that, while earnings growth is higher among survivors in denser competitive

environments, the effect is offset by the size of the business or practice in terms of

current earnings.10

5. Conclusions

In this paper, we examined evidence in the NLSY for several mechanisms that

might underlie the well-known negative relationship between firm performance and

local firm density in established industries. We proposed three alternative mecha-

nisms: (A) the conventional view that higher density increases resource competi-

tion; (B) opportunity costs of entrepreneurship are higher in more dense locations

because there are more salient job opportunities, and (C) locations may be more

dense because they support a smaller minimum efficient scale. We developed a

simple stochastic model of firm growth and exit, and derived six propositions that

collectively offer a test that can discriminate between the three mechanisms. Our

results consistently favored the opportunity cost story: the evidence was consistent

with all six propositions for mechanism B. Most noteworthy, perhaps, we observed

a positive association between profit growth and density, which constitutes strong

prima facie evidence against the traditional story that high density is a proxy for

greater resource competition.

The results presented in this paper suggest a complementary aspect of density

dependence in relation to entrepreneurial behavior: high-density areas provide

greater job mobility for the self-employed. This is especially important for the

founders of start-ups and small businesses. Most of the variation in survival rates

10 For robustness checks, we replicated the analysis for all propositions using the num-ber of establishments with 1 to 4 employees, and again with 1 to 9 employees. The self-employed may be mostly competing against smaller firms in their location and sector. The results, however, remain materially the same. We also added density squared term to test whether it has any significant effect. The results do not suggest a non-monotonic relationship between density and variables of interest, possibly due to our focus on established industry specifications.

21

arises due to relative instability of such firms (Freeman, Carroll, and Hannan, 1983;

Stinchcombe, 1965) Although aggregate data at the population level of analysis

suggest that firms may have difficulty surviving in concentrated areas due to in-

tense competition, these areas might at the same time be allowing more entrepre-

neurial activity by giving would-be entrepreneurs an outside option in case of fail-

ure.

References

Amburgey, Terry L., and Rao, Hayagreeva (1996): “Organizational Ecology: Past, Pre-sent, and Future Directions.” Academy of Management Journal, 39(5):1265-1286.

Akerlof, George A. (1970): “The market for lemons: Quality uncertainty and the mar-ket mechanism.” The Quarterly Journal of Economics, 84(3):488-500.

Baum, Joel A.C. (1996): “Organizational ecology.” In S. Clegg and C. Hardy (eds) Studying Organization: Theory and Method, London: Sage, pp. 71-108.

Baum, Joel A.C, and Heather A. Haveman (1997): “Love thy neighbor? Differentiation and agglomeration in the Manhattan hotel industry, 1898-1990.” Administra-tive Science Quarterly, 42(2):304-338.

Baum, Joel A.C., and Stephen J. Mezias (1992): “Localized competition and organiza-tional failure in the Manhattan hotel industry, 1898-1990.” Administrative Sci-ence Quarterly, 37(4):580-604.

Baum, Joel A.C. and Andrew V. Shipilov (2006): “Ecological Approaches to Organi-zations.” In S. Clegg, C. Hardy, T. Lawrence, and W. Nord (eds), Sage Hand-book for Organization Studies, London: Sage pp. 55-110.

Carroll, Glenn R., and Michael T. Hannan (1989): “Density dependence in the evolu-tion of populations of newspaper organizations.” American Sociological Review, 54(4):524-541.

Cox, David R., and H.D. Miller (1965): The Theory of Stochastic Processes. New York: Wiley.

Dahl, Michael S., and Olav Sorenson (2012): “Home sweet home: Entrepreneurs’ loca-tion choices and the performance of their ventures.” Management Science, 58(6):1059-1071

Fairlie, Robert W. (2005): “Self-Employment, Entrepreneurship, and the NLSY79.” Monthly Labor Review, 128(2):40–47.

Folta, Timothy B., Arnold C. Cooper, and Yoon Suk Baik (2006): “Geographic Cluster Size and Firm Performance.” Journal of Business Venturing, 21(2):217–42.

22

Freedman, Matthew L. (2008): “Job Hopping, Earnings Dynamics, and Industrial Ag-glomeration in the Software Publishing Industry.” Journal of Urban Econom-ics, 64(3):590–600.

Freeman, John, Glenn R. Carroll, and Michael T. Hannan. (1983): “The Liability of Newness: Age Dependence in Organizational Death Rates.” American Socio-logical Review, 48(5):692–710.

Gimeno, Javier, et al. (1997): “Survival of the fittest? Entrepreneurial human capital and the persistence of underperforming firms.” Administrative Science Quar-terly, 42(4):750- 783.

Hannan, Michael T., and Glenn Carroll (1992): Dynamics of Organizational Popula-tions: Density, Legitimation, and Competition. Oxford: Oxford University Press.

Hannan, Michael T., and John Freeman (1987): “The ecology of organizational found-ing: American labor unions, 1836-1985.” American Journal of Sociology, 92(4):910-943.

Hannan, Michael T., and John Freeman (1988): “The ecology of organizational mor-tality: American labor unions, 1836-1985.” American Journal of Sociology, 94(1):25-42.

Hannan, Michael T., and John Freeman (1989): Organizational Ecology. Cambridge, MA: Harvard University Press.

Haveman, Heather A. and Lisa E. Cohen (1994): “The Ecological Dynamics of Careers: The Impact of Organizational Founding, Dissolution, and Merger on Job Mo-bility.” American Journal of Sociology, 100(1):104-152.

Jovanovic, Boyan, and Glenn M. MacDonald (1994): “The life cycle of a competitive industry.” Journal of Political Economy, 102(2):322-47.

Klepper, Steven (1996): “Entry, exit, growth, and innovation over the product life cycle.” American Economic Review, 86(3):562-83

Lomi, Alessandro, and Erik R. Larsen (1996): “Interacting locally and evolving glob-ally: A computational approach to the dynamics of organizational popula-tions.” Academy of Management Journal, 39(5):1287-1321.

Lomi, Alessandro, and Erik R. Larsen (2001): Dynamics of Organizations: Computa-tional Modeling and Organization Theories. Cambridge, MA: MIT Press.

Meyer, M.W., and Lynne G. Zucker (1989): Permanently Failing Organizations. New-bury Park, CA: Sage.

Prescott, Edward C., and Michael Visscher (1977): “Sequential location among firms with foresight.” Bell Journal of Economics, 8(2):378-393.

Scheike, Thomas H. (1992): “A boundary-crossing result for Brownian motion.” Jour-nal of Applied Probability, 29(2):448-453.

23

Shaver, J. Myles, and Fredrick Flyer. (2000): “Agglomeration Economies, Firm Heter-ogeneity, and Foreign Direct Investment in the United States.” Strategic Man-agement Journal, 21(12):1175–93.

Singh, Jitendra V. (1993): “Density dependence theory—Current issues, future prom-ise.” American Journal of Sociology, 99(2):464–73.

Singh, Jitendra V., and Charles J. Lumsden (1990): “Theory and research in organiza-tional ecology.” Annual Review of Sociology, 16:161-195.

Stinchcombe, Arthur L. (1965): “Social Structure and Organizations.” In James G. March (eds) Handbook of Organizations, Chicago: Randy-McNally, pp. 142–93.

Stuart, Toby, and Olav Sorenson (2003): “The geography of opportunity: spatial het-erogeneity in founding rates and the performance of biotechnology firms.” Re-search Policy, 32(2):229-253.