Preliminary Results – Please Do Not Cite
Geographic Concentration in the U.S. Retail and Wholesale Sectors
Shawn D. Klimek*
Center for Economic Studies, U.S. Bureau of the Census
and
The Pennsylvania State University
David R. Merrell**
Center for Economic Studies, U.S. Bureau of the Census
and
Carnegie-Mellon University
November 8, 1999
__________________
We would like to thank seminar participants at the Center for Economic Studies for very
helpful suggestions. All conclusions here are those of the authors and do not represent
the opinions or official findings of the U.S. Bureau of the Census.
*Center for Economic Studies, U.S. Bureau of the Census, 4700 Silver Hill Road Stop
6300, Washington Plaza II Suite 211, Washington D.C. 20233-6300
(sklimek@ces.census.gov)
**5000 Forbes Avenue, Carnegie-Mellon University, H. John Heinz III School of Public
Policy and Management, Census Research Data Center, Room 238, Pittsburgh, PA 15213
(dmerrell@andrew.cmu.edu).
I.
Introduction
When looking at the economic landscape, one can point to a number of instances
where it appears that industries locate in the same geographic regions. High technology
industries seem to be located in Silicon Valley, automobiles in Detroit, financial
industries in New York and Chicago, and tires in Dayton—to give a few examples. In
the past, these stories of geographic industrial concentration have been taken as a rule of
thumb and were not given a lot of attention in the economics literature since Marshall
(1920).
More recently, however, interest has rekindled on the subject, and a good deal of
attention has focused on the geographic concentration of industries. Krugman (1991)
makes the case that this sort of concentration may be the general rule rather than an
exception—that the agglomeration of industries is more than merely a set of examples
that one can point out such as Silicon Valley. Rather, this sort of agglomeration of
industries could be the source of the increasing returns that models of international trade
and economic growth have at their core.
This renewed interest in the geographic concentration of industries seeks to explain
why agglomeration exists in the first place. Some of this attention focuses on the role
that technological spillovers play in industry concentration. Glaeser, Kallal, Scheinkman,
and Schleifer (1992) examine the role of technological spillovers in the growth of cities.
They find evidence that spillovers between industries may be more important than
spillovers within an industry. They also find evidence that competition and economic
diversity supports employment growth while specialization damages growth. Jaffe,
Trajtenberg, and Henderson (1993) provide evidence of technological spillovers using
patent citation data. This work also finds evidence of the geographic localization of
spillovers and finds further that these spillovers can be quite large and significant.
Other recent work takes a step back to examine not why these sorts of
agglomerations exist, but rather whether or not they are real. Ellison and Glaeser (1997)
examine U.S. manufacturing data for 1987 and ask the question: “How concentrated are
manufacturing industries?” Using a model of location choice that incorporates spillovers,
the authors derive measures of geographic concentration. These indices control both for
the size distribution of manufacturing plants and for the size distribution of the
geographic areas. Using these indices, the authors find that nearly all industries in their
study exhibit some degree of geographic concentration. Additionally, the authors find
that there is evidence of co-agglomeration of industries in the sense that there appears to
be location choices hinging on the upstream-downstream relationships among businesses.
In related work, Dumais, Ellison, and Glaeser (1997) treat the location of
manufacturing plants as a dynamic process. The idea is that the entry, exit, expansion,
and contraction of manufacturing plants will also affect measures of geographic industrial
concentration. Using the Census Bureau’s Longitudinal Research Database, these
authors find that the location choices of new plants and the differentials in the growth
rates of plants tend to reduce levels of geographic concentration in U.S. manufacturing.
Additionally, they find that the exit of plants tends to increase agglomeration.1
Virtually all of the research focusing on the agglomeration of industries uses data
on the manufacturing sector. In this paper, we extend the literature on geographic
concentration by measuring the agglomeration and co-agglomeration in the U.S.
wholesale and retail sectors in 1992. Together, these two sectors account for about as
much economic activity as manufacturing. In 1997, the wholesale and retain sectors
accounted for about 15% of all economic activity in the United States; in the same year,
manufacturing accounted for roughly 17% of economic activity. The shear size of the
wholesale and retail sectors makes them important to understand. Using a newly
constructed dataset containing the statistical universe of wholesale and retail
establishments, we use the Ellison-Glaeser geographic concentration indices to examine
patterns of agglomeration within and across the industries in these two sectors—using
calculations from the manufacturing sector as a benchmark.2
Our contributions are threefold. First, little is know about geographic concentration
outside of the manufacturing sector. In the spirit of Ellison and Glaeser, this paper
simply takes a step back to answer the primitive question: do establishments in the U.S.
wholesale and retail sectors tend to be geographically concentrated? We use the same
measures in manufacturing as a benchmark. Second, we focus on the co-agglomeration
of industries in retail and wholesale. To do this we calculate measures of coagglomeration for all possible combinations of industries and examine the distribution of
these measures. Third, the foundation of the U.S. statistical program has been the
Standard Industrial Classification (SIC) system. However, after 1997 all economic
census data will be collected under the new North American Industrial Classification
System (NAICS). The conversion to NAICS represents a significant change in the way
economic census data are collected and reported. This paper uses data from the 1992
1
Maurell and Sedillot (1999) use indices similar to Ellison and Glaeser (1997) to examine the geographic
concentration of French manufacturing plants. These authors find that patterns of geographic concentration
in France are very similar to the patterns in the U.S. data.
Economic Census that have been converted from SIC to NAICS and provides an
introduction to the new NAICS sectors, subsectors, and industries.
In Section II, we detail the model used by Ellison and Glaeser and the measure of
geographic concentration and co-agglomeration that is derived from it. Section III
describes the data. Section IV contrasts the results for manufacturing to retail and
wholesale and details the analysis of co-agglomeration. Section V concludes.
II.
The Model
There likely are many reasons why an industry may be geographically concentrated,
but two broad motivations spring to mind. First, depending on the type of industry, some
locations may present certain natural advantages over others. For example, industries
requiring large amounts of warehousing may locate near commercial naval ports or near
major highways. Second, certain industries may tend to be concentrated because of
technological spillovers that accrue; this certainly could explain the location of the
computer industry in Silicon Valley. However, in general, it likely is the case that the
concentration of industries is some amalgam of both of these broad motivations. Glaeser
and Ellison (1997) develop a model of location choice that incorporates both of these
motivations, and from that model, indices of geographic concentration are derived. We
present a shortened version of their model below.
A. Natural Advantages
Consider a model with N business units (in this case a census establishment in the
retail or wholesale sectors) and M geographic markets, which could be at the county,
These data are maintained at the Census Bureau’s Center for Economic Studies and are collected under
the Census of Wholesale Trade and the Census of Retail Trade programs.
2
state, or some other aggregate. For some industry, the kth business unit enters that region
(i) which maximizes profits. This can be expressed as the following:
log  ki  log  i  g i 1 ,..., k 1    ki
where log  ki is profit accruing to business unit k located in region i. Business unit k’s
profits are a function of log  i , the profit of the “typical” firm located in region i; this
profit also is a function of observable and unobservable regional characteristics. Profit
also is a function of g i 1 ,..., k 1  --the effects of spillovers from the other k-1 business
units located in region i. Finally, profit is a function of an idiosyncratic shock for
business unit k, located in region i. We assume that that the  ki are independent
 
Weibull random variables that also are independent of the  i . Further, it should be
clear that if g i 1 ,..., k 1   0  i, then the model reduces to a standard conditional
 
logistic model—conditioned on the realizations of the  i . Next, we impose the
following two parametric restrictions on the model:
(1)
E
1 ,...,  M
i
 xi
 j
j
(2)

 i
var 
  
 j
j


   na xi 1  xi  where  na  [0,1]


Equation (1) gives the probability of locating in region i. The parameter na is
interpreted as a measure of the importance of natural advantages in a given region. A
value of na close to zero suggests that the region does not exhibit natural advantages,
while a value near unity implies that the natural advantages of the region dominate all
other regions. In the latter case (viz., na =1), all k business units would find an optimum
by locating in that region.
Conditions (1) and (2) incorporate natural advantages into the location decisions
of businesses. Next is the incorporation of technological spillovers into the location
decision calculus. The idea is that locating near other facilities in the same industry could
represent lower transportation costs or even the transfer of knowledge across facilities.
Section II.B details how we incorporate spillovers.
B. Spillovers
Consider the following model of plant location that incorporates spillovers.
log  ki  log  i   ekl (1  u il )( )   ki
l k
The {eki} are Bernoulli random variables equal to one with probability s[0,1], and
equal to zero with probability 1-s. The variable uil is a dichotomous indicator variable
equal to one if establishment l is in region i, or equal to zero otherwise. The importance
of spillovers is captured by the probability parameter s.
C. Measures of Geographic Concentration and Co-agglomeration
For a single industry with M geographic regions (counties) and N business units
(establishments), Ellison and Glaeser use  as the measure of geographic concentration,
where  is defined as:


G  1   xi2  H
i

 
 


1   xi2 (1  H )
i


2
M
N

2
( si  xi )  1   xi   z 2j

i 1
i 1

 j 1
M
N



1   xi2 1   z 2j 
i 1
j 1



M
2
where si is the share of industry employment in region i, xi is the share of total
employment in region i, and zi is the share of establishment employment of the industry.
Ellison and Glaeser show that if the models in section IIA and IIB describe plants’
location decisions, then the measure of geographic concentration is a useful measure that
has several desirable properties. First, the index can be calculated easily with the
information in our dataset. Access to the establishment level data means that each
component of the index can be calculated by aggregating up to the county or industry
level. Second, the scale of the index allows comparisons to be made to a benchmark of
“no-agglomeration” when the expected value of  is equal to zero. Third, the index is
comparable across industries in which the size distribution of firms differs.
Ellison and Glaeser extend the model in Section II to examine the extent to which
industries are co-agglomerated. The measure c, defined below, shares the desirable
properties of ; to be sure, c and  share the same scale.





c  


r
G
  H   ˆ j w 2j (1  H j )


j 1
1   xi2 

i


r


1   w 2j 


j 1


where G 
and H 
 (s  x )

industries.
i
j
i
2
is area i's share of the aggregate employment in the r industries
w 2j H j is an establishment’s Herfindahl index of the aggregate of the r
If the measure c is equal to zero, then there are no spillovers or natural advantages
specific to the industry group; rather the natural advantages would be specific to an
industry—not the industry group. An alternative measure of co-agglomeration is :

c
 w ˆ
j
j
j
If the measure  is close to one, then all spillovers and natural advantages are group
specific rather than industry specific. On the other hand, a value of  close to zero
implies that the industries exhibit little co-agglomeration—that spillovers and natural
advantages are industry specific and not group specific. We use the measure  to analyze
to extent to which industries are co-agglomerated.3
C.
The Data
Our data come from two sources. First, we use establishment level data from the
1992 Economic Census. An establishment level observation is defined as a business or
an industrial unit located at a single physical location. Further, all establishments must
produce goods, distribute goods, or perform services.4 The Economic Census covers the
universe of retail and wholesale establishments in the United States. These observations
contain a wealth of information on the employment, sales, wages, industry and
geographic characteristics, inter alia, of business units. Second, to get information on
regional characteristics, we use data from Counties 1996. From this second source of
information, we construct total county employment for 1992. These two sources of data
To be sure,  measures the strength of co-agglomerative forces relative to agglomerative forces.
This definition is not always correct. The Census Bureau sometimes splits up very large "establishments"
into several establishments, especially when the products and industries these plants produce in are quite
varied. In our analysis, we do not exclude establishments that are broken out in this way.
3
4
provide the information necessary to construct the Ellison-Glaeser geographic
concentration indices.
This paper uses data on wholesale and retail establishments in the United States that
are classified using the new North American Industrial Classification System (NAICS);
all other work of which we are aware uses the Standard Industrial Classification (SIC)
system to define an industries. There are substantial differences between the NAICS and
SIC systems. In the following paragraph, we give a brief overview of the NAICS
taxonomy.
A NAICS subsector is the three-digit code—comparable to the two-digit SIC code.
There are two more detailed breakdowns, the five-digit NAICS code with is referred to as
the NAICS industry, and the six-digit NAICS code which is referred to as the U.S.
industry. The combination of NAICS industries and U.S. industries is comparable to the
old four-digit SIC industries. Under the SIC system, there were 459 four-digit industries
in manufacturing, under NAICS that number increased to the 474. NAICS leaves the
number of wholesale industries constant at 69, and increases the number of retail
industries from 64 to 72. More importantly, NAICS redefines the boundary between the
two sectors, which results in a number of establishments moving from wholesale to retail.
Klimek and Merrell (1999) provide a very detailed discussion on the differences between
the NAICS and SIC industry classification taxonomies.
For the analysis that follows, we compute our indices of geographic concentration
using the NAICS and U.S. industries—keeping mind that these levels of aggregation
correspond to the four-digit SIC levels. For our indices of co-agglomeration between
wholesale and retail establishments, we use the sub-sector code as the industry group of
analysis.
VI.
Geographic Concentration Results
First, we describe the general results for the manufacturing sector. Assuming the null
hypothesis of s=na=0 (viz., that there are no spillovers or natural advantages that would
give rise to geographic concentration), we compare the raw concentration
G   (si  xi )2 to the expected value of G under the null.5 We find a comparable, but
weaker result than Ellison and Glaeser using data for 1992. Of the 469 manufacturing
industries, 433 industries have a value of G that is greater than the expected value of G.
This implies that 433 (92.3%) manufacturing industries are more geographically
concentrated that what would be expected to arise if establishments were located
randomly. In contrast, 36 (7.7%) industries are more evenly distributed than what would
be expected if establishments were located randomly. Calculating the variance of G, we
check to see if the difference is significant.6 Of the 433 manufacturing industries where
the difference between G and E[G] is positive, only 12.2% are significantly different
which suggests that there is not a lot more geographic concentration than one might expect
to arise of plants were located randomly. This is in contrast to the 82.7% found by Ellison
and Glaeser using aggregate 1987 data. For the 36 manufacturing industries where the
difference is negative, none are significant—a result similar to Ellison and Glaeser.
The results for retail and wholesale are strikingly similar. Of the 130 retail and
wholesale industries, we find that the difference is positive for 128 industries (98.5%), and
5
r
r
j 1
j 1
E[G]  (1   xi2 )[ H  0 (1   w2j )    j w2j (1  H j )]
negative for just two industries (1.5%). The 128 retail and wholesale industries for which
the difference is positive, it is significantly positive for 35.2% of the industries. Like
manufacturing, when the difference is negative, it is not significantly different. So, under
the null hypothesis of “no spillovers or natural advantages,” the amount of geographic
concentration seems strikingly similar between the two sectors.
In Table 1 and Table 2, we list the fifteen most concentrated manufacturing,
wholesale and retail industries. Comparing the two tables contrasts the levels of
geographic concentration.7 Clearly, manufacturing is much more concentrated than retail
or wholesale. With the exception of Women's, Children's, and Infant's Clothing and
Accessories Wholesalers (NAICS 42233), the most concentrated manufacturing
industries have measures of  that are one and in a number of cases even two orders of
magnitude larger than the measures of  for the wholesale and retail sectors. The results
for manufacturing are quite reasonable in the sense that we see Silicon Valley computer
chips (NAICS 333295), Dalton, Georgia carpet manufacturing (NAICS 314110), and
Northern California wine producers (NAICS 312130) all having high levels of geographic
concentration. Although the numbers are far less dramatic than in the manufacturing
cases, our results confirm the conventional wisdom that in the retail sector, art dealers
(NAICS 45392) are concentrated and in the wholesale sector, jewelry wholesalers
(NAICS 42194) are concentrated geographically.
Table 3 and Table 4 present the other end of the distribution of geographic
concentration for manufacturing, wholesale, retail industries. These tables present the
6
The formula for var(G) is described on page 907 of Ellison and Glaeser (1997).
In these tables, the level of aggregation is different. In manufacturing, the six-digit NAICS code provides
much more detailed breakouts than the five-digit NAICS. In wholesale, the five-digit and six-digit are
identical. In retail, there are few differences between the five-digit and six-digit codes.
7
fifteen least concentrated industries. That some of these industries exhibit very slight
levels of concentration is not terribly surprising given that some of them are miscellaneous
industry groupings such as Other Major Household Appliance Manufacturing. Still,
others confirm conventional wisdom in the sense that it is believed that those industries
are not geographically concentrated—like Breweries (NAICS 312120) in manufacturing,
Department stores (NAICS 45211) in retail, and Dairy Product Wholesalers (NAICS
42243). What we do find interesting about the cross-sectoral comparisons is that the
wholesale and retail measures (in absolute terms) are still an order of magnitude (or two)
less concentrated than the manufacturing industries.
Tables 5, 6, and 7 provide our calculations of the presence of co-agglomeration in
manufacturing, retail, and wholesale (respectively) industries. In manufacturing and
retail, we use the three-digit NAICS industry group as the unit of observation, while in the
wholesale sector, we use the four-digit NAICS industry group as the unit of analysis. The
measures c and  will tell us the degree to five- and six-digit NAICS industries in
manufacturing, retail, and wholesale are co-agglomerated. To be sure, c is scaled
identically to ;  on the other hand measures the strength of co-agglomerative forces
relative to agglomerative forces. A value of =0 indicates that there is no coagglomeration and that the natural advantages and spillovers are industry specific.
Likewise, a value of =1 indicates that there are strong co-agglomerations and hence that
the natural advantages and spillovers are specific to groups of similar industries and not
industries themselves.
We find results similar to Ellison and Glaeser for manufacturing. That is, our
calculations indicate that there does exist some degree of co-agglomeration in
manufacturing—ranging from =0.088 in Textile Product Mills (NAICS 313) to a high of
=0.556 in Wood Products Manufacturing (NAICS 321). Retail subsectors and wholesale
industry groups, on the other hand, show markedly higher co-agglomeration than
manufacturing—with wholesale industry groups exhibiting generally more coagglomeration than retail industry groups. Retail subsectors range from a low of =0.206
in Non-store Retailers (NAICS 454) to a high of =0.760 in Clothing and Clothing
Accessory stores (NAICS 448). With the exception of the Beer, Wine, and Distilled
Alcoholic Beverage (NAICS 4228) industry group, all wholesale industry groups show
relatively larger degrees of co-agglomeration than either retail or manufacturing industry
groups. Wholesale industry groups range from a low of =-0.023 in Beer, Wine, and
Distilled Alcoholic Beverage (NAICS 4228) to a higher of =0.867 in Farm Product Raw
Materials (NAICS 4225). On balance, these calculations suggest that the natural
advantages and spillovers are to some degree group specific rather than industry
specific—though much more so in wholesale and retail than in manufacturing.
In addition to measuring geographic concentration for individual industries and
industry groups, we turn to the examination coagglomeration of pairs of industries. We
describe the distribution of  ic , where i  rr , rw, ww. r indicates a retail industry, and w
indicates a wholesale industry. There are 1830 pairs of retail (rr) industries. The
distribution of  rrc has a median value of .0001. The maximum value is .004. On the
c
other hand, there are 2346 pairs of wholesale (ww) industries. The distribution of  ww
has
a median value of .0006. The maximum value is .084. The most interesting pairs of
industries to examine are the pairs of retail industries and wholesale industries (rw).
c
There are 4209 possible rw pairs. The distribution of  wr
has a median value of .0001.
The maximum value is .022. Surprisingly, there seems to be very little evidence of
coagglomeration of industry pairs within or between the retail and wholesale sectors.
The median values of the  ic are very small, and even at the very top of the distribution,
few pairs of industries seem to be more than slightly coagglomerated.
VI.
Conclusion
In this paper, we present evidence on the existence of geographic concentration in the
wholesale and retail sectors. Using a newly constructed dataset containing the statistical
universe of wholesale and retail establishments for 1992, we find evidence that there is
some degree of agglomeration in these sectors—though far less than in manufacturing
industries. This suggests that natural advantages and spillovers within an industry are
less important in the location decisions for wholesale and retail establishments than for
manufacturing plants. Additionally, we find that wholesale and retail establishments are
far more co-agglomerated than manufacturing plants. This suggests that although there is
not a lot of agglomeration within individual industries, there are significant natural
advantages and spillovers within groups of similar industries. Similar to the results for
individual industries, the measures of coagglomeration for these two sectors are quite
week. Given the close buyer-seller relationship between wholesale and retail, we
expected this measure to be significantly higher, especially for industries across the two
sectors. In general, our results are support the hypothesis that retail and wholesale
industries are highly motivated to locate near the consumers of their products, and the
measures of coagglomeration and geographic concentration are an order of magnitude (or
two) smaller than in the manufacturing sector.
References
Dumais, G., Ellison, G., and Glaeser, E.L. (1997) “Geographic Concentration as a
Dynamic Process,” NBER Working Paper No. W6270.
Ellison, G. and Glaeser, E.L. (1997) “Geographic Concentration in U.S. Manufacturing
Industries: A Dartboard Approach,” Journal of Political Economy, v. 105, n. 5, pp. 889927.
Glaeser, E.L., Kallal, H.D., Scheinkman, J.A., and Schleifer, A. (1992) “Growth in
Cities,” Journal of Political Economy, v. 100, pp. 1126-1152.
Jaffe, A.B., Trajtenberg, M., and Henderson, R. (1993) “Geographic Localization of
Knowledge Spillovers as Evidenced by Patent Citations,” Quarterly Journal of
Economics, v. 108, pp. 577-598.
Klimek, S.D. and Merrell, D.R. (1999) “Industrial Reclassification from the Standard
Industrial Classification System to the North American Industry Classification System,”
Mimeo.
Krugman, P. (1991) “Increasing Returns and Economic Geography,” Journal of Political
Economy, v. 99, pp. 483-499.
Marshall, A. (1920) Principles of Economics: An Introductory Volume. 8th ed., London:
Macmillan.
Maurell, F. and Sedillot, B. (1999) “A Measure of the Geographic Concentration in
French Manufacturing Industries,” Regional Sciences and Urban Economics, v. 29, pp.
575-604.
Table 1. 15 Most Concentrated Industries in Manufacturing in 1992
NAICS
339913
339914
325110
333132
315232
333315
314110
325312
333295
336213
311311
312130
315292
336415
312210
Industry
Jewelers' Material and Lapidary Work
Costume Jewelry and Novelty Manufacturing
Petrochemical Manufacturing
Oil and Gas Field Machinery and Equipment Manufacturing
Women's and Girls' Cut and Sewn Blouse and Shirt Manufacturing
Photographic and Photcopying Equipment Manufacturing
Carpet and Rug Mills
Phosphatic Fertilizer Manufacturing
Semiconductor Machinery Manufacturing
Motor Home Manufacturing
Sugarcane Mills
Wineries
Fur and Leather Apparel Manufacturing
Guided Missle and Space Vehicle Propulsion Unit and Propulsion Unit Parts Manufacturing
Tobbaco Stemming and Redrying
H
0.22622
0.18289
0.20062
0.14495
0.13728
0.20169
0.11235
0.16002
0.15235
0.13651
0.13187
0.1196
0.10093
0.16927
0.13579
G
0.03513
0.0116
0.05349
0.01365
0.01952
0.09828
0.0115
0.06672
0.05992
0.04272
0.04375
0.0396
0.02251
0.10516
0.07023

0.19869
0.17391
0.15583
0.13344
0.12045
0.11544
0.10229
0.1002
0.09868
0.09825
0.09216
0.08359
0.0805
0.07192
0.07051
Table 2. 15 Most Concentrated Industries in Retail and Wholesale in 1992
NAICS
Industry
H
G

45392
44312
44523
45431
44832
45393
44313
45411
44711
45122
44121
44521
44522
44422
44813
Art Dealers
Computer and Software Stores
Fruit and Vegetable Markets
Fuel Dealers
Luggage and Leather Goods Stores
Manufactured Mobile Home Dealers
Camera and Photographic Supplies Stores
Florists
Gasoline Stations with Convenience Stores
Prerecorded Tape, Compact Discs, and Record Stores
Recreational Vehicle Dealers
Meat Markets
Fish and Seafood Markets
Nursery and Garden Centers
Children's and Infants' Clothing Stores
0.0005464
0.0025231
0.0014959
0.0002652
0.0012519
0.0005927
0.0007613
0.0049089
0.0000273
0.0002982
0.0010699
0.0003939
0.0011676
0.0002575
0.0005125
0.0075182
0.0066085
0.0048586
0.0032485
0.0038208
0.0030381
0.0029578
0.0070615
0.0019584
0.002203
0.0024381
0.0016698
0.002432
0.0014301
0.0016454
0.0070039
0.004121
0.0033859
0.0029965
0.0025864
0.0024572
0.0022093
0.0021903
0.0019387
0.0019137
0.0013783
0.0012828
0.001275
0.0011784
0.0011397
42233
42231
42194
42232
42234
42186
42192
42143
42141
42122
42246
42162
42199
42169
42151
Women's, Children's, and Infant's Clothing and Accessories Wholesalers
Piece Goods, Notions, and Other Dry Goods Wholesalers
Jewelry, Watch, Precious Stone, and Precious Metal Wholesalers
Men's and Boy's Clothing and Furnishing Wholesalers
Footwear Wholesalers
Transportation Equipment and Supplies (except Motor Vehicle) Wholesalers
Computer and Computer Peripheral Equipment and Software Wholesalers
Toy and Hobby Goods and Supplies Wholesalers
Photographic Equipment and Supplies Wholesalers
Home Furnishing Wholesalers
Fish and Seafood Wholesalers
Electrical Apparatus and Equipment, Wiring Supplies, and Construction Material Wholesalers
Other Miscellaneous Durable Goods Wholesalers
Other Electronic Parts and Equipment Wholesalers
Metal Services Centers and Offices
0.0015802
0.0014247
0.0018995
0.0023555
0.0089504
0.0026457
0.0048176
0.0012191
0.0080835
0.0011498
0.0017256
0.004921
0.0010158
0.0004492
0.0005439
0.10196
0.0734
0.05257
0.02681
0.02415
0.01275
0.01316
0.00906
0.01486
0.00775
0.00809
0.01117
0.00699
0.00642
0.00649
0.10092
0.07235
0.05096
0.02461
0.01542
0.01018
0.00843
0.00788
0.00689
0.00664
0.00641
0.00632
0.00601
0.006
0.00597
Table 3. 15 Least Concentrated Industries in Manufacturing in 1992
NAICS
322223
336419
325193
333311
332993
335228
311823
322122
327211
332995
337125
312120
326191
322215
323115
INDUSTRY
Plastics, Foil, and Coated Paper Bag Manufacturing
Other Guided Missile and Space Vehicle Parts and Auxiliary Equipment Manufacturing
Ethyl Alcohol Manufacturing
Automatic Vending Machine Manufacturing
Ammunition Manufacturing (Except Small Arms)
Other Major Household Appliance Manufacturing
Dry Pasta Manufacturing
Newsprint Mills
Flat Glass Manufacturing
Other Ordinance and Accessories Manufacturing
Household Furniture Manufacturing (Except Metal and Wood)
Breweries
Plastics Plumbing Fixture Manufacturing
Nonfolding Sanitary Food Container Manufacturing
Digital Printing
G
0.14846
0.10262
0.1108
0.05399
0.04284
0.08509
0.02737
0.07215
0.05176
0.16331
0.04612
0.04935
0.01364
0.04438
0.02657
H
0.16655
0.10698
0.1139
0.057
0.04529
0.08746
0.02974
0.07441
0.0538
0.1653
0.04802
0.05105
0.01525
0.04598
0.02812

-0.021385
-0.004661
-0.003487
-0.003049
-0.002477
-0.002426
-0.002359
-0.002332
-0.002137
-0.001944
-0.001867
-0.00165
-0.001586
-0.001569
-0.00153
Table 4. 15 Least Concentrated Industries in Retail and Wholesale in 1992
NAICS
INDUSTRY
H
G

45291
44411
45211
45311
45114
44412
44111
44511
45112
45113
44311
44211
44611
44419
44221
Warehouse Clubs and Superstores
Home Centers
Department Stores
Florists
Musical Instrument and Supplies Stores
Paint and Wallpaper Stores
New Car Dealers
Supermarkets and Other Grocery Stores (Except Convenience Stores)
Hobby, Toy, and Game Stores
Sewing, Needlework, and Piece Goods Stores
Appliance, Television, and Other Electronics Stores
Furniture Stores
Pharmacies and Drug Stores
Other Building Materials Stores
Floor Covering Stores
0.0010464
0.0005335
0.0001569
0.0000982
0.0006385
0.0003675
0.0000801
0.0000449
0.0003134
0.000275
0.0001434
0.000166
0.0000449
0.0000953
0.0002023
0.000966
0.000578
0.000278
0.000222
0.000769
0.000505
0.000219
0.000185
0.000458
0.000424
0.000300
0.000341
0.000234
0.000286
0.000420
-0.0000768
0.0000475
0.0001227
0.0001256
0.0001337
0.0001399
0.0001403
0.000141
0.0001465
0.0001508
0.0001583
0.0001769
0.0001903
0.0001922
0.00022
42281
42243
42114
42132
42249
42139
42185
42245
42241
42172
42242
42113
42294
42161
42111
Beer and Ale Wholesalers
Dairy Product Wholesalers (Except Dried or Canned)
Motor Vehicle Parts (Used) Wholesalers
Brick, Stone, and Related Construction Materials Wholesalers
Other Grocery and Related Products Wholesalers
Other Construction Materials Wholesalers
Service Establishment Equipment and Supplies Wholesalers
Confectionery Wholesalers
General Line Grocery Wholesalers
Plumbing and Heating Equipment and Supplies (Hydronics) Wholesalers
Packaged Frozen Food Wholesalers
Tire and Tube Wholesalers
Tobacco and Tobacco Products Wholesalers
Electrical Apparatus and Equipment, Wiring Supplies, and Construction Material Wholesalers
Automobile and Other Vehicle Wholesalers
0.0008481
0.0040736
0.0003582
0.0010916
0.0004425
0.0016305
0.0005527
0.0029584
0.00213
0.0009055
0.0015609
0.0021544
0.0060692
0.0003951
0.0009422
0.000436
0.004260
0.000637
0.001411
0.000875
0.002080
0.001021
0.003421
0.002598
0.001478
0.002236
0.002923
0.006841
0.001206
0.001832
-0.0004106
0.000204
0.0002813
0.0003251
0.0004365
0.0004581
0.0004727
0.0004769
0.0004795
0.0005794
0.0006847
0.000782
0.0008023
0.0008166
0.0008977
Table 5. Co-agglomeration in the Manufacturing Sector in 1992
NAICS Subsector
311
312
313
314
315
316
321
322
323
324
325
326
327
331
332
333
334
335
336
337
339
Subsector Name
Food Manufacturing
Beverage and Tobacco Manufacturing
Textile Mills
Textile Product Mills
Apparel Manufacturing
Leather and Allied Product Manufacturing
Wood Product Manufacturing
Paper Manufacturing
Printing and Related Support Activities
Petroleum and Coal Products Manufacturing
Chemical Manufacturing
Plastics and Rubber Product Manufacturing
Nonmetallic Mineral Product Manufacturing
Primary Metals Manufacturing
Fabricated Metal Product Manufacturing
Machinery Manufacturing
Computer and Electronic Product Manufacturing
Electrical Equipment, Appliance, and Component Manufacturing
Transportation Equipment Manufacturing
Furniture and Related Product Manufacturing
Miscellaneous Manufacturing
c
0.0007796
0.0011134
0.0061992
0.0022519
0.0060683
0.0017712
0.0024572
0.0013692
0.0012013
0.0023341
0.0011497
0.0006775
0.000808
0.0019546
0.0012297
0.0011377
0.0091815
0.000925
0.002574
0.0022254
0.0017886

0.12909
0.09942
0.44988
0.08855
0.27409
0.17066
0.55558
0.56612
0.39987
0.25006
0.06661
0.43528
0.23252
0.23956
0.3209
0.13835
0.5813
0.26285
0.15452
0.21514
0.13605
Table 6. Co-agglomeration in the Retail Sector in 1992
NAICS
441
442
443
444
445
446
447
448
451
452
453
454
Subsector Name
Motor Vehicle and Parts Dealers
Furniture and Home Furnishing Stores
Electronic and Appliance Stores
Building and Material and Garden Equipment Dealers
Food and Beverage Stores
Health and Personal Care Stores
Gasoline Stations
Clothing and Clothing Accessories Stores
Sporting Goods, Hobby, Book, and Music Stores
General Merchandise Stores
Misc. Store Retailers
Non-store Retailers
c
0.00018301
0.00013189
0.00052183
0.00012883
0.00006212
0.00014428
0.00037029
0.00040816
0.00031039
0.00010459
0.00015659
0.00037189

0.685
0.36183
0.54801
0.36029
0.22337
0.56657
0.35287
0.75964
0.49864
0.70607
0.21642
0.20636
Table 7. Co-agglomeration in the Wholesale Sector in 1992
NAICS
4211
4212
4213
4214
4215
4216
4217
4218
4219
4221
4223
4224
4225
4226
4227
4228
4229
Subsector Name
Motor Vehicles
Furniture
Lumber and Construction Materials
Professional and Commercial Equipment
Metal and Mineral
Electrical Goods
Hardware, Plumbing, and Heating Equipment
Machinery Equipment
Misc. Durable Goods
Paper and Paper Product
Apparel, Piece Goods and Notions
Grocery and Related Product
Farm Product Raw Materials
Chemical and Allied Products
Petroleum and Petroleum Products
Beer, Wine, and Distilled Alcoholic Beverage
Misc. non-durable Goods
c
0.000521
0.003543
0.000501
0.002282
0.002394
0.002086
0.00053
0.000566
0.003133
0.002009
0.050357
0.000577
0.002702
0.002086
0.000297
-0.000002
0.000352

0.62258
0.75618
0.51612
0.4764
0.40426
0.48732
0.41179
0.22522
0.27833
0.79227
0.79233
0.42155
0.86714
0.83773
0.21183
-0.0227
0.11621