Geographic Concentration in the U.S. Retail and Wholesale Sectors

Preliminary Results – Please Do Not Cite Geographic Concentration in the U.S Retail and Wholesale Sectors Shawn D Klimek* Center for Economic Studies, U.S Bureau of the Census and The Pennsylvania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 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 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 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: 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 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 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 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, 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 π ki = log π i + g i (υ1 , ,υ k −1 ) + ε ki where log π ki is profit accruing to business unit k located in region i Business unit k’s profits are a function of log π 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 (υ1 , ,υ 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 {ε ki } are independent Weibull { } random variables that also are independent of the π i Further, it should be clear that if g i (υ1 , ,υ k −1 ) ≡ ∀ i, then the model reduces to a standard conditional logistic model— { } conditioned on the realizations of the π i Next, we impose the following two parametric restrictions on the model: (1) Eπ , ,π M πi = xi ∑π j j (2)   πi var  ∑ π  j j    = γ na xi (1 − xi ) where γ na ∈ [0,1]   Equation (1) gives the probability of locating in region i The parameter γ na is interpreted as a measure of the importance of natural advantages in a given region A value of γ 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., γ 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 π ki = log π i + ∑ ekl (1 − u il )(−∞ ) + ε ki l ≠k The {eki} are Bernoulli random variables equal to one with probability γ s∈[0,1], and equal to zero with probability 1-γ 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 γ 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 γ as the measure of geographic concentration, where γ is defined as:   G − 1 − ∑ xi2  H i   ≡ γ ≡   1 − ∑ xi2 (1 − H ) i   M N  2 ( si − xi ) − 1 − ∑ xi  ∑ z 2j ∑ i =1  i =1  j =1 M N    2 1 − ∑ xi 1 − ∑ z 2j  j =1  i =1   M 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 γ 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 γ c, defined below, shares the desirable properties of γ ; to be sure, γ c and γ share the same scale      γc ≡    r G  − H − ∑ γˆ 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 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 γ 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 λ: γc λ= ∑ w j γˆ j j If the measure λ is close to one, then all spillovers and natural advantages are group specific rather than industry specific On the other hand, a value of λ 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 λ 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, λ 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 not exclude establishments that are broken out in this way 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 γ s=γ 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 ) 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 r r j =1 j =1 E[G ] = (1 − ∑ xi2 )[ H +γ (1 − ∑ w2j ) + ∑ γ j w2j (1 − H j )] The formula for var(G) is described on page 907 of Ellison and Glaeser (1997) 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 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 γ that are one and in a number of cases even two orders of magnitude larger than the measures of γ 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 and Table present the other end of the distribution of geographic concentration for manufacturing, wholesale, retail industries These tables present the fifteen least concentrated industries That some of these industries exhibit very slight 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 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 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 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 γ c and λ will tell us the degree to five- and six-digit NAICS industries in manufacturing, retail, and wholesale are co-agglomerated To be sure, γ c is scaled identically to γ ; λ on the other hand measures the strength of co-agglomerative forces relative to agglomerative forces A value of λ=0 indicates that there is no coagglomeration and that the natural advantages and spillovers are industry specific Likewise, a value of λ=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 λ=0.088 in Textile Product Mills (NAICS 313) to a high of λ=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 λ=0.206 in Non-store Retailers (NAICS 454) to a high of λ=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 λ=-0.023 in Beer, Wine, and Distilled Alcoholic Beverage (NAICS 4228) to a higher of λ=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 c describe the distribution of γ i , 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 γ 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 γ 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 γ 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 c The median values of the γ i 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 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 γ 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 15 Most Concentrated Industries in Retail and Wholesale in 1992 NAICS Industry H G γ 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 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 γ -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 15 Least Concentrated Industries in Retail and Wholesale in 1992 NAICS INDUSTRY H G γ 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 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 γc 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 γ 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 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 γc 0.00018301 0.00013189 0.00052183 0.00012883 0.00006212 0.00014428 0.00037029 0.00040816 0.00031039 0.00010459 0.00015659 0.00037189 λ 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 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 γc 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 λ 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 ... 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. .. These authors find that patterns of geographic concentration in France are very similar to the patterns in the U.S data wholesale and retail sectors in 1992 Together, these two sectors account... 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,