## Creative destruction in local markets.

Introduction and summary

Competition from entrepreneurs with innovative business strategies

continually forces established firms to either keep up with their

younger counterparts or exit. Many firms fail to adapt to new

competitive conditions. The consequent failure of unprofitable firms and

their replacement by new firms is a familiar aspect of competition.

Because a firm’s failure frees the labor and capital it employed

for use at a more profitable

entrant

*n.*

One that enters, especially one that enters a competition.

[French, from present participle of

`entrer`,

*to enter*, from Old French; see

**enter**.

, this process may be described as

creative destruction. Although there are costs associated with creative

destruction, such as the lost labor of temporarily unemployed workers,

it benefits an economy in the long run by moving productive resources

into more profitable uses.

If there are no potential rivals to challenge a few dominant

producers, then creative destruction must halt. Indeed, recent history

provides many prominent examples of large firms that dominate their

markets without substantial fear of new competition. Such

market

dominance

can arise by default from the absence of potential competitors

or from established firms’ efforts to discourage entry. Dominant

firms might discourage the entry of new rivals by building excess

capacity to commit to fierce price competition or by introducing

otherwise unprofitable brands to fill product niches. If dominant firms

routinely deter entry, then the economy loses the benefits of creative

destruction.

Although firms with market power might have the potential to reduce

creative destruction, there is little systematic evidence that they do

so. In this article, we examine empirically whether market power is

associated with reduced creative destruction, using sales data from

Texas bars’ and restaurants’ alcohol tax returns. Bars and

restaurants differ greatly from well-known dominant firms in other

sectors of the economy, but they may dominate their relatively small

geographic and product niche. Although there are many restaurants in

Houston, the market for a particular variety of food and drink in a

particular neighborhood may be small. An advantage of examining creative

destruction among bars and restaurants is that there are many

geographically segmented markets in our sample. Thus, we can move past

the compilation of anecdotes about a small number of very large firms

and establish a

statistical regularity

about a large number of smaller

firms.

We group producers into market areas on the basis of their

locations. These market definitions are undoubtedly too broad, because

they do not incorporate any information about the variety and

substitutability of producers’ products. Hence, we consider the

market areas in our analysis to be aggregates of markets that are

smaller but more economically meaningful. For example, there might be

separate markets for Chinese, French, and Italian restaurants in the

river-walk area of

San Antonio

, city (1990 pop. 935,933), seat of Bexar co., S central Tex., at the source of the San Antonio River; inc. 1837.

. We measure market power using the sum of

firms’ squared market shares, the Herfindahl-Hirschman Index of

sales concentration (

HHI

HHI Heinrich Hertz Institut

HHI Hilton Head Island

HHI Household Income

HHI Hyundai Heavy Industries Co, Ltd

). This has a desirable aggregation property–if

all economically meaningful markets within a market area have the same

sales, then the market area’s HHI equals the markets’ average

HHI divided by the number of markets. Thus, although the levels of

market areas’ HHIs will not reflect the concentration of their

constituent markets, a comparison of two market areas’ HHIs can

indicate which of the two has more concentrated constituent markets if

they have the same number of economically meaningful markets.

Our analysis uses observations from over 400 market areas. We find

that more concentrated market areas, in which producers

presumably

*adj.*

That can be presumed or taken for granted; reasonable as a supposition:

exercise more market power, exhibit more creative destruction. That is,

the hypothesis that producers use their market power to stabilize

industry structure finds no support from our observations. Instead,

market power apparently magnifies creative destruction. Determining

whether this

magnification

A measure of the effectiveness of an optical system in enlarging or reducing an image. For an optical system that forms a real image, such a measure is the lateral magnification *m*

is economically beneficial or representative

of other industries awaits our future research.

In the next section, we

summarize

*intr. & tr.v.* **sum·ma·rized**, **sum·ma·riz·ing**, **sum·ma·riz·es**

To make a summary or make a summary of.

**sum**

previous research related to

ours. We then discuss our data source and our measures of creative

destruction and concentration. Following that, we present our analysis

of the relationship between these two market characteristics.

Related literature

In a market with few substantial competitors, strategic

considerations can directly impact the rate of creative destruction.

Many authors have demonstrated that, in theory, a monopolist may act to

prevent its replacement by a potential entrant. For example, Dixit

(1980) showed that an incumbent monopolist might invest in excess

capacity to deter a potential entrant with a credible threat of fierce

competition. In general, concentration of sales among a few firms may

endow

*tr.v.* **en·dowed**, **en·dow·ing**, **en·dows****1. ** To provide with property, income, or a source of income.

**2. ****a.**

those firms with the ability to stabilize the industry structure

in a way that is

favorable

*adj.***1. ** Advantageous; helpful:

**2. ** Encouraging; propitious:

**3.**

to them. Gort’s (1963) finding that

firms in concentrated

manufacturing industries

*npl* →

*npl* →

have relatively stable

market shares supports this hypothesis. The present article provides

additional evidence on firms’ ability to suppress creative

destruction using their market power.

Our research builds upon many previous

empirical studies

that

document the relationship between productivity and creative destruction.

In the U.S. economy, the rate of creative destruction is large. Dunne,

Roberts, and Samuelson (1988) report that approximately 40 percent of

manufacturing plants operating in a given year cease production within

five years. A similar number of new plants replace them in that time, so

these shutdowns are associated with very little net loss of

manufacturing capacity. Instead, the large rates of creative destruction

apparently reflect the

reallocation

of capacity to more efficient

producers of more desirable products. Using similar data from four

manufacturing industries, Bartelsman and Dhrymes (1998) show that

productivity growth at incumbent plants contributes very little to

aggregate productivity growth. Instead, aggregate productivity growth

largely reflects the replacement of incumbent plants with relatively

more productive entrants. Campbell (1998) shows that drops in the plant

failure rate in manufacturing precede drops in plant entry and aggregate

productivity; and he builds a competitive model economy in which these

patterns reflect fluctuations in the quality of the ideas embodied in

new producers. These and other studies point to creative destruction as

a vital source of productivity growth.

In this article, we measure creative destruction in local markets

using a panel of Texas bars’ and restaurants’ March alcohol

tax returns. We measure annual sales creation as the sum of all sales

gains at establishments that entered or increased sales over the year.

Similarly, sales destruction is the sum of all sales losses at

establishments that exited or decreased sales. The sum of the two is

sales reallocation, our measure of creative destruction. Davis,

Haltiwanger, and Schuh (1996) (

hereafter

DHS

DHS Department of Human Services

DHS Department of Health Services

DHS Demographic and Health Surveys

DHS Dirhams

) developed these measures

of creative destruction and applied them to job flows within the U.S.

manufacturing sector. They consistently find that job reallocation

substantially exceeds manufacturing’s net job creation.

Approximately one in ten manufacturing jobs is destroyed each year, and

the number of jobs created each year nearly equals this, resulting in a

relatively small annual job loss for the sector as a whole.

The bars and restaurants we consider display even larger rates of

annual sales creation and destruction. Our sample covers the period from

1995 through 2001. In a typical year, sales destruction accounts for

between 10 percent and 15 percent of total industry sales, and sales

creation equals over 20 percent of industry sales. Hence, Texas

bars’ and restaurants’ alcohol sales grew between 6 percent

and 10 percent per year, while sales reallocation always exceeded 30

percent of sales.

Our empirical analysis also follows a great deal of work examining

how the structure of an industry influences the conduct of its producers

and its economic performance. The studies contained in Weiss (1990)

exemplify

*tr.v.* **ex·em·pli·fied**, **ex·em·pli·fy·ing**, **ex·em·pli·fies****1. ****a. ** To illustrate by example:

**b.**

this research, which takes the configuration of firms in a

market as a measure of its structure and uses this to explain variation

across markets in firms’ prices and profits. The HHI is a common

measure of market structure in this work. However, it is difficult to

say unequivocally that a high HHI indicates a lack of competition.

Peltzman (1977) among others noted that a market might be highly

concentrated because the most efficient firm can charge less than its

rivals can for the same good. In this case, a high HHI reflects the

proper operation of competition. Our finding that sales reallocation is

greater in market areas with higher HHIs suggests that high

concentration does not typically arise from the persistent competitive

success of one or a few firms.

Bars and restaurants serve local markets. In areas with larger

populations, more firms can operate and break even. Thus, we expect

concentration to be high in less-populated areas. (1) The wide variation

in population density across Texas is an important source of variation

in market areas’ measured HHIs, so this article also builds on

previous work that examines the effects of changes in population on

local service industries. Bresnahan and Reiss (1990) examine how the

population of isolated rural towns determines the number of active

automobile dealers. If incumbent monopolists can raise the cost of

rivals’ entry, then the lowest population that can support two

firms should be more than twice the population sufficient to induce a

firm to enter as a monopolist. In fact, their estimates of rivals’

entry costs are very close to the entry costs of monopolists, indicating

little if any entry

deterrence

. Campbell and Hopenhayn (2004) show that

larger U.S. cities have larger retail producers, including restaurants.

This is what we expect to see if competitors in large markets have

little market power, because they must sell more at a smaller

markup

to

recover their

fixed costs

*n.pl* the costs that do not change to meet fluctuations in enrollment or in use of services (e.g., salaries, rent, business license fees, and depreciation).

. Our results reinforce Bresnahan and

Reiss’s finding of no entry deterrence, and they also suggest that

larger markets’ heightened competition leads to less creative

destruction.

Texas alcohol tax returns

The state of Texas collects a 14 percent tax on the sale of alcohol

for on-premises consumption. Alcohol license holders file monthly tax

returns, and the Texas Alcoholic Beverage Control Board (TABC) makes

information on these returns publicly available. For each bar or

restaurant, this information includes the tax paid, its street address

and trade name, and separate identification numbers for its alcohol

license and the owner. Using the street addresses and alcohol license

identification numbers, we have linked the tax returns for a given

restaurant or bar together to form individual establishment histories.

Following the standard definition used by the U.S.

Census Bureau

, we

define an establishment as a physical location in which alcohol is

served. Hence, if a restaurant or bar’s owner sells it but the new

owner continues its operation without substantial interruption, tax

returns from the previous and new owners all belong to the same

establishment. We refer to this data set as the TABC panel, and we

explore other features of individual establishments’ histories in

Abbring and Campbell (2003).

Although we observe the establishments’ sales each month, we

focus here on annual changes in sales based on their March tax returns.

(2) As we noted above, the TABC panel displays substantial creative

destruction. Table 1 provides one perspective on the pace of creative

destruction among the TABC panel’s establishments. For March 2000,

it reports the number of operating establishments and classifies them

according to

*prep.***1. ** As stated or indicated by; on the authority of:

**2. ** In keeping with:

**3.**

past and future operation. If the establishment paid no tax

in the previous March, it is a birth. Otherwise, it is an incumbent. If

the establishment pays no tax in the following March it is a death, and

otherwise it is a survivor.

There were 6,176 establishments filing alcohol tax returns in March

2000. Of these, 12 percent did not pay tax in the previous March and 9.6

percent did not pay tax in the next March. The rate of death among those

establishments that are births, 19.7 percent, is double the overall rate

of death. This mimics many previous findings from manufacturing

industries that the likelihood of business failure declines with age.

Births are new establishments that have yet to accumulate either

experience or a stable clientele, so we expect them to be smaller than

the average incumbent. Similarly, we expect deaths to be less successful

and smaller than survivors. Table 2 reports the median and

interquartile

range

(

IQR

IQR Internet Quick Reference

IQR Individual Qualification Record

IQR Internal Quality Review

) of establishments’ March alcohol sales for all four

groups of establishments. Exactly half of the establishments have sales

at or below the median, and the IQR is defined as the length of the

interval that excludes the largest and smallest 25 percent of

establishments. As such, it measures the

dispersion

in chemistry, mixture in which fine particles of one substance are scattered throughout another substance. A dispersion is classed as a suspension, colloid, or solution.

of

establishments’ sizes. The mean and standard deviation, which are

more familiar measures of central tendency and dispersion, largely

reflect the sizes of a few very large firms. By construction, the median

and IQR are

invariant

to changes in the sizes of the largest and

smallest firms.

The median incumbent is 54 percent larger than the median birth,

and the median survivor is more than twice as large as the median death.

Although these differences are expected, their magnitudes are large.

Because deaths embody business ideas that have been tried and shown to

be wanting while births are largely untested, it is not surprising that

the median birth is 35 percent larger than the median death. The last

notable feature of table 2 is the substantial

heterogeneity

*n.*

The quality or state of being heterogeneous.

heterogeneity

the state of being heterogeneous.

in

establishment size. Not all establishments are born equal. The IQR of

births’ sales is nearly twice the median. The IQR of deaths’

sales is smaller than this but still

sizable

also **size·a·ble** *adj.*

Of considerable size; fairly large.

**siza·ble·ness**

*n.*

. Incumbents’ IQR is

substantially larger than that of births, so apparently establishment

heterogeneity increases as a birth cohort ages. This could reflect

firm-specific shocks to either cost or the popularity of its product

variety. In either case, such shocks should substantially impact the

rate of creative destruction.

Although we have focused on the year 2000, the features of tables 1

and 2 that we emphasize characterize every year of our sample. These are

high birth and death rates, incumbents and survivors’ large sizes

relative to births and deaths, and substantial size heterogeneity that

increases as a birth cohort ages.

Measuring creative destruction

Although birth and death rates provide one perspective on creative

destruction, they do not capture the ongoing reallocation of production

among incumbent survivors that is

concomitant

/con·com·i·tant/ () accompanying; accessory; joined with another.

*adjective*Accompanying, accessory, joined with another

with increasing

establishment heterogeneity. DHS suggest a simple measure of creative

destruction based on decomposing the net growth of an industry into

contributions by growing and shrinking firms. Although they apply their

methodology to observations of establishments’ employment

decisions, it can be applied to the sales data we have without

modification. We begin by measuring the growth rate of an

industry’s sales between two periods as the change in sales divided

by the average sales in the two periods. If we use S to

denote

*tr.v.*

**de·not·ed**,

**de·not·ing**,

**de·notes**

**1.**To mark; indicate:

**2.**

total

industry sales in March of year t, then this is

[NET.sub.t] = 2 x [S.sub.t]-[S.sub.t-1] / [S.sub.t]+[S.sub.t-1].

Here, we follow Davis, Haltiwanger, and Schuh (1996) and refer to

this as

net sales

growth. Similarly, the growth rate of an individual

establishment is

[g.sub.it] = 2 x [S.sub.it]-[s.sub.it -1] /

[S.sub.it]+[S.sub.it-1].

In this definition i is the index of the establishment, and

[s.sub.it] is the sales of establishment i in March of year t.

Standard growth rate measures place either of the two periods’

sales in the

denominator

. Instead, the denominators of [NET.sub.t] and

[g.sub.it] are the average of the two periods’ sales.

For values of

[S.sub.t] or [s.sub.it] near zero, this deviation from the standard

definition of a growth rate matters little. However, the standard growth

rate measures handle establishment births and deaths poorly, because

their denominators must equal zero in one of these two cases. In

contrast, [g.sub.it] is always well defined. If establishment i is a

birth, then [s.sub.it-1] = 0 and [g.sub.it] = 2; and if establishment i

is a death from year t – 1, then [s.sub.it] = 0 and [g.sub.it] = -2.

Finally, if establishment i is an incumbent, then -2 < [g.sub.it]

< 2. We use NET to measure industry

growth rates

because it equals

the size-weighted average of [g.sub.it], where size is measured with

([s.sub.it] + [s.sub.it-1])/2.

With these definitions in hand, we can

decompose

*v.* **de·com·posed**, **de·com·pos·ing**, **de·com·pos·es**

*v.**tr.***1. ** To separate into components or basic elements.

**2. ** To cause to rot.

*v.**intr.***1.**

NET, into the

weighted sum of growth rates for all establishments that grew or entered

minus the weighted sum of growth rates for all shrinking and exiting

establishments.

[NET.sub.t] = [[N.sub.t].

summation

n. the final argument of an attorney at the close of a trial in which he/she attempts to convince the judge and/or jury of the virtues of the client’s case. (See: closing argument)

over (i=1)] [w.sub.it] x

[g.sub.it] x I {[g.sub.it] > 0} – [[N.sub.t].summation over (i=1)]

[w.sub.it] x |[g.sub.it]| x I {[g.sub.it] < 0}.

Here, N is the number of establishments that produce in March of

either t or t – 1, [w.sub.it] = ([s.sub.it], + [s.sub.it-1]) /

([S.sub.t] + [S.sub.t-1]) is a weight proportional to the average of

establishment i’s size in the two years, and I{*} is an

indicator

function

that equals one if the condition in brackets is true. The first

term on the right-hand side is the weighted sum of growth rates for all

establishments that grew or entered between t and t – 1. Following DHS,

we call this the sales creation rate and denote it with PO[S.sub.t], for

“positive.” Similarly, the second term on the right-hand side

is minus the weighted sum of growth rates for all shrinking or exiting

establishments. This is the sales destruction rate, and we denote it

with NE[G.sub.t] for “negative.” With this notation, we can

express NE[T.sub.t] as PO[S.sub.t]- NE[G.sub.t]. DHS propose using the

sum of the sales creation and destruction rates as a measure of

reallocation. This is SU[M.sub.t] = PO[S.sub.t] + NE[G.sub.t]. It is the

sum of the absolute values of establishments’ growth rates.

If an industry’s establishments are identical and remain so

always, then SU[M.sub.t] = |NE[T.sub.t| and either PO[S.sub.t] or

NE[G.sub.t] equals zero. With simultaneous birth and death and

heterogeneity across establishments, SUM will generally exceed

|NE[T.sub.t]| and both PO[S.sub.t] and NE[G.sub.t] will be positive.

When applying these definitions to manufacturing establishments’

employment changes, DHS found that the rate of job reallocation greatly

exceeded the rate of job growth’s absolute value, even for narrowly

defined (four-digit standard industrial classification) industries.

By definition, these measurements associate creative destruction

with the expansion and contraction of individual plants. One might

consider a broader definition that also includes the reallocation of

sales (or jobs) within an establishment. If a shift in sales from beer

to wine within a given establishment contributes to sales reallocation,

then these measures miss this and underestimate creative destruction. Of

course, measurement of this definition of sales reallocation is

infeasible with only observations of establishments’ total sales.

However, previous experience measuring creative destruction suggests

that adopting this more expansive definition of sales reallocation would

add little to our analysis, even if it were feasible. Using Dutch

employment data that matches workers to specific jobs, Hamermesh,

Hassink, and van Ours (1996) find that accounting for simultaneous job

creation and destruction within employers changes the standard job

reallocation measure very little.

Another means of transferring resources between producers is the

outright sale of entire establishments from one producer to another.

When constructing establishment histories, we ignore such business

transfers, so our measures of creative destruction do not reflect them.

In this respect, our analysis follows DHS and others who have largely

focused on reallocation between establishments rather than between

firms. Our reason for doing so is simple: Many apparent business

transfers reflect corporate reorganizations, such as the incorporation

of a

sole proprietorship

, which has no practical consequences for the

establishment’s operation. To the extent we ignore economically

significant sales reallocation between firms, our measures

understate

*v.* **un·der·stat·ed**, **un·der·stat·ing**, **un·der·states**

*v.**tr.***1. ** To state with less completeness or truth than seems warranted by the facts.

**2.**

the true rate of creative destruction.

For each year of our sample excluding the first, table 3 reports

the rates of sales creation, destruction, growth, and reallocation for

the state of Texas as a whole. In addition, it reports the portion of

sales creation due to establishment births, the portion of sales

destruction accounted for by establishment deaths, and the portion of

sales reallocation accounted for by both births and deaths. We denote

these with

POS

[B.sub.t],

NEG

[D.sub.t], and

SUMB

SUMB Syracuse University Marching Band

[D.sub.t]. For all of

these statistics, the table’s final row reports average values

across years.

As with DHS’s measures of job reallocation, the rates of sales

reallocation vastly exceed the net growth rate of total industry sales.

In 1997, alcohol sales contracted very slightly, while the sales

creation and destruction rates both exceeded 17 percent. In the year of

greatest sales growth, 2000, the sales reallocation rate equals nearly

three times the rate of sales growth. In an average year, the rate of

sales reallocation is 36.4 percent. This greatly exceeds the average job

reallocation rate for the U.S. manufacturing sector measured by DHS,

19.4 percent. (3) A comparison of the average values of SUM with those

of SUMB[D.sub.t] indicates that establishment births, establishment

deaths, and the expansion and contraction of surviving incumbents all

contribute substantially to sales reallocation. In an average year,

births and deaths account for approximately half of sales creation and

destruction. Births and deaths play a much more prominent role in

creative destruction for this industry than they do in the U.S.

manufacturing sector. DHS report that manufacturing establishment births

account for 15.5 percent of annual job creation and manufacturing

establishment deaths account for 22.9 percent of annual job destruction.

(4) The expansion and contraction of surviving incumbents accounts for

the remainder of job reallocation.

Measures of concentration

We now consider the measurement of concentration in the local

markets of our sample. To do so, we must first define both

“concentration” and “market,” neither of which is

inherently unambiguous.

We consider a market area to be a particular

zip code

, and we

measure concentration using both producers located within that zip code

and those located nearby. The HHI for a given zip code’s market

area is constructed using sales of all establishments within 15 miles.

To measure the distance between two zip codes, we use location data from

the U.S. Census.

Figure 1 illustrates this measurement for an isolated city with

three market areas, labeled A, B, and C. For simplicity, suppose that

all of a market area’s producers are located at its central point.

We suppose that consumers are willing to travel no more than d = 7.5

miles to consume alcohol, so the circles around each market area contain

all consumers that could purchase at those areas. The circles around A

and B

intersect

, so some consumers could purchase in either market area.

The producers located in B are potential competitors to those located in

A, so it is appropriate to include them in the calculation of the HHI

for market area A. For the same reason, the producers in B should also

be included when calculating the HHI for market area C. Establishments

in B face competition from both A and C, so all three markets’

producers are included when calculating the HHI for market area B.

[FIGURE 1 OMITTED]

To summarize, the HHI for a given zip code z in year t is

[HHI.sub.zt] = [N.summation over (i=1)] I{d([z.sub.i],z) [less than

or equal to] 15} x [([s.sub.it]/[S.sub.zt]).sup.2],

where [S.sub.zt] is the total sales of alcohol at all zip codes

within 15 miles of z, [z.sub.i] is the zip code of establishment i, and

d([z.sub.i],z) is the distance between that establishment’s zip

code and z. If the

effective radius

of competition for bars and

restaurants is more or less than 15 miles, then our measure of the HHI

will respectively exceed or fall short of the true measure. By

construction, our measure of the HHI includes all establishments that

sell alcohol, but some of their relevant competitors may serve only food

and soft beverages. If so, then our measure of the HHI overstates

concentration.

When considering the

legality

*n.* *pl.* **le·gal·i·ties****1. ** The state or quality of being legal; lawfulness.

**2. ** Adherence to or observance of the law.

**3. ** A requirement enjoined by law. Often used in the plural.

of proposed mergers, the Department

of Justice and the Federal Trade Commission consider a market with an

HHI less than 1,000 to be “unconcentrated.” We restrict our

sample to market areas with average HHI (over the years of our sample)

less than 1,000, because these contain the vast majority of bars and

restaurants in Texas. Although our sample market areas’ HHIs

indicate that the markets are very competitive, we have not segmented

our observations further on the basis of cuisine or quality. Hence, we

believe that a market area’s HHI should be interpreted as merely

reflective of the HHIs of its more concentrated and economically

meaningful constituent markets.

There were 444 zip codes in Texas in which alcohol was served in

every year of our sample with average HHIs below 1,000. In our sample of

market areas, the median HHI is extremely low, 15, and the interquartile

range is 40. Hence, most of the market areas we consider display very

little concentration if they are not segmented further on the basis of

their product offerings.

The effects of concentration on creative destruction

With our measures of creative destruction and concentration in

hand, we are now prepared to consider the relationship between them. For

the 444 zip codes in our sample, we tabulated annual sales creation and

destruction rates. Their tabulation includes only establishments located

in that zip code. Figure 2 plots the averages of these sales creation

and destruction rates over time (on the vertical axis) against the

logarithm

[Gr.,=relation number], number associated with a positive number, being the power to which a third number, called the base, must be raised in order to obtain the given positive number.

of the zip code’s average HHI. Each circle and triangle

represent one zip code’s average sales creation and destruction

rates. To help visualize the relationships between these variables, the

solid and dashed lines plot smoothed versions of the raw sales creation

and destruction rates. (5)

[FIGURE 2 OMITTED]

Several features of the data immediately stand out in figure 2.

First and foremost, there is tremendous variability of sales creation

and destruction rates around their smoothed values. This is even after

averaging the data over seven years, so apparently market-specific

variables that we do not measure substantially impact the pace of sales

reallocation. Second, the smoothed sales creation and destruction rates

change with the HHI in very similar ways. The dashed plot of the

smoothed sales destruction rates is approximately equal to the solid

plot of the smoothed sales creation rates shifted down by 5 percentage

points.

Third, sales creation and destruction vary systematically with the

HHI. Increasing the HHI from 0 to approximately 100 increases the

typical sales creation and destruction rates by approximately 5

percentage points. Although there are relatively few zip codes with HHIs

greater than 100, it appears that increasing concentration further

decreases these rates. If we measure the instability of an

industry’s structure with the sales reallocation rate, then the

most stable industry structures are those with an HHI very close to

zero.

Although the smoothed sales creation and destruction rates in

figure 2 are suggestive, their patterns may simply reflect remaining

noise in the data. To measure the statistical significance of the

relationship, we have estimated

simple regression

equations of the form

[y.sub.j] = f([x.sub.j], [beta]) + [u.sub.j],

where [y.sub.j] is the relevant sales

statistic

*n* a value or number that describes a series of quantitative observations or measures; a value calculated from a sample.

statistic

a numerical value calculated from a number of observations in order to summarize them.

for market j,

[x.sub.j] is the logarithm of its HHI, [u.sub.j] is an error term with

an average value of zero, and f([x.sub.j], [beta]) is the average value

of y given x. This depends on the values of several unknown parameters,

which we group together and denote with [beta]. To estimate these

parameters using the data at hand, we follow the usual least squares

procedure. That is, we choose [beta] to minimize the sum of the squared

differences between [y.sub.j] and its predicted value, f([x.sub.j],

[beta]).

The simplest way of proceeding is to assume that f(x,[beta]) =

[[beta].sub.0] + [[beta].sub.1]x, so that the predicted values are a

linear function of x. Figure 2 suggests that such a specification would

be inappropriate for our data, because the effect of increasing

concentration on job creation and destruction is apparently small if the

HHI is already above 100. To evaluate the significance of this deviation

from a

linear regression

line, we also estimate a regression function

created by joining two lines together at an HHI of 100. The resulting

specification for the regression function is

f(x,[beta]) = [alpha] + [[delta].sub.0]x + [[delta].sub.100]I{x

> ln 100}(x – ln 100).

The coefficient [[delta].sub.0] gives the function’s slope at

the vertical axis and the coefficient [[delta].sub.100] gives the change

in its slope as the HHI passes through 100.

Figure 3 plots the markets’ average sales reallocation and the

estimated regression function against the logarithm of the HHI. The

relationship between the HHI and sales reallocation is as figure 2 leads

us to expect.

[FIGURE 3 OMITTED]

For POS, NEG, NET, and SUM, table 4 reports the estimated slopes

from the linear and

piecewise linear

regression functions. Beneath each

slope is its estimated standard error. (6) By construction, the

difference between the estimated slopes for POS and NEG equal the

corresponding slopes for NET, while their sums equal those for SUM. For

each slope, the final column reports the number of zip codes with an

average HHI that falls into the interval over which it applies. For both

sets of regressions, the table also reports the [R.sup.2] measure of

fit.

Consider first the linear regression function’s estimates. For

POS, NEG, and SUM, the slope estimates are positive and greatly exceed

their standard errors, indicating that they are statistically

significant. The estimated slope coefficients for POS and NEG both equal

half of the analogous estimate for SUM, 0.022. The regression predicts

that the sales reallocation rate will equal 36 percent when the HHI is

at its sample minimum, 6, and that this will rise to 42 percent when the

HHI equals 100. As figure 2 suggests, the positive effect of

concentration on sales reallocation increases sales creation and

destruction equally. Another perspective on the same result is that

concentration has no statistically or economically significant effect on

sales growth.

The piecewise linear regression functions also show that sales

creation, destruction, and reallocation are increasing with the HHI when

it is below 100. Although the estimated slopes are much greater than

their linear regression counterparts, their fitted values are quite

similar. Sales reallocation is predicted to equal 35 percent and 46

percent, respectively, when the HHI equals 6 and 100. As with the simple

linear regressions, sales creation and destruction contribute equally to

the increase in sales growth, so there is again no effect on sales

growth. For HHIs exceeding 100, the estimated slopes for sales creation

and reallocation are negative and highly statistically significant. The

estimated slope for sales destruction is also negative, but its

magnitude is only half that of sales creation’s slope and it is not

statistically significant. A simple consequence of this is that the

estimate of concentration’s effect on net sales growth is negative

and statistically significant. Apparently, increases in concentration

that push the HHI above 100 either have no effect or a negative effect

on creative destruction.

If the number of economically meaningful markets in a market area

is 20 or more, then an HHI of 500 would correspond to all markets being

served by monopolies. With an HHI of 1,000, half of the potential

markets would have no active firms. Thus, the behavior of the estimated

regression function may reflect changes of creative destruction within

markets, as well as changes in the number of active markets. For this

reason, we prefer to emphasize the positive effect of concentration on

creative destruction for market areas with HHIs below 100.

To better understand the sources of the estimated relationship

between concentration and creative destruction, we have also examined

two decompositions of sales reallocation. The first separates sales

reallocation due to births and deaths from that due to surviving

incumbents, and the second divides sales reallocation into the portions

due to establishments owned by small and large firms. We follow Dunne,

Roberts, and Samuelson (1988) and DHS and define a small firm as one

that controls a single establishment. Large firms control two or more

establishments. With both of these decompositions, we estimate the same

regression models as above using sales reallocation’s components as

the dependent variables. With either

decomposition

/de·com·po·si·tion/ () the separation of compound bodies into their constituent principles.

*n.***1.**

, the two

components’ estimated slopes must sum to the slope estimated for

all sales reallocation.

Table 5 reports the estimated slopes and their standard errors for

these two decompositions of sales reallocation. For reference, its first

column repeats the estimates of the slopes of SUM’s regression

function. Consider first the portion of SUM due to births and deaths. If

the HHI is less than 100, then changes in births and deaths account for

approximately half of the response of SUM to an increase in the HHI. The

effect on births and deaths of further increasing the HHI is large,

-0.018, but

imprecisely

*adj.*

Not precise.

**impre·cisely**

*adv.*

estimated. The effect on surviving incumbents is

much larger, -0.027, and it is statistically significant. Next, we turn

to the second decomposition of SUM. If the HHI is less than 100, small

firms account for nearly all of the response of SUM to a change in the

HHI. For more concentrated markets, the point estimates indicate that

establishments owned by small and large firms contribute equally to the

decrease in SUM. The simple linear regressions’ estimated slopes

qualitatively resemble those from the piecewise linear regressions when

the HHI is below 100. To summarize, the positive effect of concentration

on creative destruction that we emphasize apparently reflects the

expansion and contraction of establishments owned by small firms at all

stages of their lives.

Robustness

To ensure that our results do not merely reflect the exclusion of

relevant variables from the regressions, we have also estimated two

related specifications, which include additional industry

characteristics. In one, we included the average sales growth of alcohol

sales within 15 miles of the zip code. This accounts for the possibility

that market areas with fast growth systematically display more or less

creative destruction. Increases in this growth rate tend to increase

sales creation and decrease sales destruction by equal amounts, so it

has no substantial impact on sales reallocation. In the second, we

included the fraction of the market’s establishments that present

themselves to the public as bars. (7) Increasing bars’ market share

tends to increase sales creation, destruction, and reallocation. This is

particularly the case for sales reallocation due to births and deaths.

However, none of the coefficients in tables 4 and 5 substantially change

after including either of these two variables in the regressions.

For our final robustness check, we allowed the regressions’

intercepts to vary across the counties. In this way, we allow for the

effects of variation in counties’ permissiveness towards alcohol

consumption. The estimated slopes entirely reflect variation across zip

codes in the same county. Our 444 zip codes are in 44 counties. Ten of

these counties contain a single zip code in our sample, and so their

observations contribute nothing to our estimates. For the

simple linear

regression

estimates, the estimated coefficients are somewhat larger

than those reported in table 4, but the pattern of significance is

unchanged. For the piecewise linear regression functions, the slopes for

low concentration levels are again somewhat greater. The regression

functions’ slopes when the HHI exceeds 100 are much smaller than

those reported in table 4, and they are not statistically significant.

The associated confidence intervals are wide enough to encompass

regression functions with zero slopes and with constant slopes, so it is

difficult to characterize the slopes precisely. Nevertheless, the

results reinforce our decision to emphasize the positive relationship

between concentration and creative destruction evident across market

areas with lower values of concentration.

Conclusion

In this article, we have considered the

empirical relationship

between market concentration, measured with the HHI, and creative

destruction, measured with sales creation, destruction, and

reallocation. We find that increasing a market area’s concentration

increases creative destruction. Thus, more concentrated market

structures are the least stable in our dataset. Greater concentration

primarily increases creative destruction among small firms, but it

confers no apparent stabilization to their large competitors. This leads

us to question

oligopoly

see monopoly.

**oligopoly**

Market situation in which producers are so few that the actions of each of them have an impact on price and on competitors. Each producer must consider the effect of a price change on the others.

theory and competition

policy based

on the

premise that market power confers the ability to stabilize an

industry’s structure.

Our findings call for further empirical and theoretical research on

this topic. The outstanding empirical question is whether our results

also characterize other retail and service industries or bars and

restaurants in other states. The theoretical questions concern the

structural origins of our findings. Decreasing concentration apparently

decreases producer turnover. That is, competition

endogenously

*adj.***1. ** Produced or growing from within.

**2. ** Originating or produced within an organism, tissue, or cell:

creates

“barriers to entry.” We wish to determine whether this might

reflect firms’ strategic choices. Existing theories of creative

destruction in competitive industries, such as Hopenhayn’s (1992)

and Fishman and Rob’s (2003), are silent about the relationship

between concentration and creative destruction. By their nature, models

of perfectly competitive creative destruction assume that firms compete

anonymously. We believe that the reconciliation of these theories with

our observations will require dropping the anonymity assumption and

instead explicitly modeling firms’ strategic interactions.

Table 1 Establishment counts in March 2000 Survivors Death Total Incumbents 4,990 444 5,434 Births 596 146 742 Total 5,586 590 6,176 Table 2 Alcohol sales in March 2000 Incumbents Survivors Median IQR Median IQR $133,618 $213,457 $136,118 $216,936 Births Deaths Median IQR Median IQR $86,775 $171,914 $65,259 $117,914 Table 3 Sales, creation, destruction, growth, and reallocation rates Year POS NEG NET SUM 1995 23.3 19.4 3.9 42.7 1996 22.5 16.7 5.8 39.2 1997 17.3 17.3 0.0 34.6 1998 20.2 14.2 3.0 34.4 1999 21.4 14.6 6.8 36.0 2000 22.9 11.1 11.7 34.0 2001 22.2 11.6 10.5 33.8 Average 21.4 15.0 6.4 36.4 Year POSB NEGD SUMBD 1995 14.3 8.9 23.2 1996 12.9 9.2 22.1 1997 10.0 6.2 16.1 1998 10.4 6.1 16.6 1999 12.1 6.9 18.9 2000 10.4 4.9 15.3 2001 10.6 5.6 16.2 Average 11.5 6.8 18.3 Table 4 Regression slopes Interval POS NEG NET Estimated slopes 0<HHI<1,000 0.011 0.011 0.001 (0.003) (0.002) (0.002) Regression [R.sup.2] 0.02 0.02 0.00 Estimated slopes 0<HHI<100 0.023 0.018 0.005 (0.003) (0.003) (0.003) 100<HHI<1,000 -0.031 -0.014 -0.017 (0.009) (0.009) (0.007) Regression [R.sup.2] 0.04 0.03 0.00 Interval SUM N Estimated slopes 0<HHI<1,000 0.022 444 (0.004) Regression [R.sup.2] 0.02 Estimated slopes 0<HHI<100 0.041 384 (0.005) 100<HHI<1,000 -0.045 60 (0.016) Regression [R.sup.2] 0.04 Table 5 Regression slopes for sales reallocation and its components Births and Surviving Interval All deaths incumbents 0<HHI<1,000 0.022 0.012 0.011 (0.004) (0.0030 (0.002) 0<HHI<100 0.041 0.020 0.021 (0.005) (0.004) (0.003) 100<HHI<1,000 -0.045 -0.018 -0.027 (0.0160 (0.016) (0.007) Interval Small firms Large firms 0<HHI<1,000 0.021 0.001 (0.004) (0.002) 0<HHI<100 0.034 0.007 (0.006) (0.004) 100<HHI<1,000 -0.024 -0.022 (0.011) (0.008)

NOTES

(1) Geographic variation in population density is not the only

source of variation in concentration. If the diversity of tastes varies

across local markets, then markets with a more diverse population may

have a lower measured concentration because they demand a similar

diversity of restaurants and bars. Additionally, many Texas counties are

either “‘dry,” prohibiting the retail sale of alcohol or

“partially wet,” prohibiting it in some areas or for some

beverages. Whether a market area is partially wet or located near a

partially wet or dry area can clearly influence concentration.

(2) By using the March tax return, we enhance the comparability of

our results with those based on Economic Census records of mid-March

employment, such as the County Business Patterns.

(3) See table 2.1 of DHS.

(4) See figure 2.3 of DHS.

(5) If [y.sub.j] and [x.sub.j] denote the average sales creation or

destruction rate for market j and the logarithm of its HHI, then the

smoothed series is defined as the estimated intercept from the

[y.sub.l] = [alpha] + [beta][x.sub.l] +

[[epsilon].sub.l]. The estimation uses only the 10 percent of sample

markets with HHIs closest to market j’s and each market receives a

weight proportional to the absolute difference between its HHI and

market j’s. These local predictions use only a small portion of the

data and they display considerably more variance than ordinary linear

regression estimates. The considerable variation of the local

predictions for HHIs below 100 reflects this variance.

(6) We follow Conley (1999) and calculate standard errors that

account for a systematic relationship between the variance of the

regression function’s disturbance term and the HHI

(heteroskedaticity) and for correlation between the error terms of

markets that are geographically close to one another (spatial

correlation). Conley’s (1999) estimator requires a choice of

distance such that the regression function’s disturbances from two

markets farther apart than that distance are assumed to be uncorrelated.

We chose 15 miles. These estimated standard errors are uniformly lower

than those calculated under the assumption of uncorrelated disturbances

across markets.

(7) To measure this, we follow Abbring and Campbell (2003) and

examine the establishment’s trade name for the presence of several

words that indicate an emphasis on alcohol consumption and for the

absence of several words that indicate substantial food service.

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Jaap H. Abbring is a fellow of the Royal Netherlands Academy of

Arts and Seiences (KNAW) at Free University Amsterdam and a research

fellow of the Tinbergen Institute, Amsterdam. Jeffrey R. Campbell is a

senior economist at the Federal Reserve Bank of Chicago and a faculty

research fellow of the National Bureau of Economic Research (

NBER

NBER Nittany and Bald Eagle Railroad Company

). The

authors are grateful to Craig Furfine and Victor Stango for their

thoughtful comments. The National Science Foundation supported this

research through grant SES-0137048 to the NBER.