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  • 8/3/2019 Glenn Ellison


    American Economic Association

    The Geographic Concentration of Industry: Does Natural Advantage Explain Agglomeration?Author(s): Glenn Ellison and Edward L. GlaeserSource: The American Economic Review, Vol. 89, No. 2, Papers and Proceedings of the OneHundred Eleventh Annual Meeting of the American Economic Association (May, 1999), pp. 311-316Published by: American Economic AssociationStable URL:

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  • 8/3/2019 Glenn Ellison


    EVOLUTION FTHEGEOGRAPHIC ONCENTRATIONFINDUSTRYtThe GeographicConcentrationof Industry:Does Natural

    Advantage ExplainAgglomeration?By GLENN ELLISON AND EDWARD L. GLAESER*

    Scholars in many fields of economics havebecome very interestedin Silicon Valley-styleagglomerations of individual industries (J.Vernon Henderson, 1988; Michael E. Porter,1990; Paul Krugman, 1991). These agglom-erations are striking features of the economiclandscape and may provide insights into thenature of the increasing-returns echnologiesand spillovers that are thought by many to bebehind endogenous growth and businesscycles.In our previous work (Ellison and Glaeser,1997), we noted that agglomerations mayarise in two ways. In addition to explanationsbased on localized industry-specificspillovers,there is a simpler alternative:an industrywillbe agglomerated if firms locate in areas thathave naturalcost advantages.Forexample, thewine industry (the second most agglomeratedindustry n our study) is surely affected by thesuitability of states' climates for growinggrapes. If firms' location decisions are highlysensitive to cost differences (as found byDennis Carlton [1983], Timothy J. Bartik[1985], and Henderson [1997], among oth-ers), then naturaladvantages may account fora substantialportion of observed geographicconcentration.

    In this paper we use the term "natural ad-vantage" fairly broadly. Some possible ex-amples would be that none of the more than100,000 shipbuilding workers in the U.S. in1987 worked in Colorado, Montana, or NorthDakota and that the highest concentration ofaluminumproduction (which uses electricityintensively) is in Washington (which has thelowest electricity prices). We will also speakof the concentration of the rubberand plasticfootwear industry in North Carolina,Florida,and Maine and its absence from Alaska andMichigan as possibly reflectingnaturaladvan-tages in the labor market. The industry is anintensive user of unskilled laborand faces tre-mendous competition from imports; hence wewould expect to see it locate in low-wagestates.The simplest way to find effects of naturaladvantages on industry locations is to regresseach industry's state-level employment onstates' resource endowments as in SukkooKim (1999). A problem with this approach,however, is that one can easily think of morepotentialadvantagesthanthere arestatesin theUnited States and fit each industry's employ-ment distribution perfectly. We identify ef-fects of naturaladvantageswithout overfittingby imposing cross-industry restrictions re-quiring the sensitivity of location decisionsto the cost of a particular nputto be relatedtothe intensity with which the industryuses theinput.Using such an approachto estimate the ef-fects of naturaladvantage on the 1987 loca-tions of four-digit manufacturing industries,we find that industry locations are related toresource and labor-marketnaturaladvantages.Our primary goal is to see how much of thegeographic concentration of industries re-ported in Ellison and Glaeser (1997) can be

    I Discussants: Yannis loannides, Tufts University;Thomas Nechyba, Stanford University; Sukkoo Kim,Washington University-St. Louis.* Ellison: Department of Economics, MassachusettsInstitute of Technology, Cambridge, MA 02139, andNBER; Glaeser:Departmentof Economics, HarvardUni-versity, CambridgeMA 02138, and NBER. We thank theNational Science Foundation for support through grantsSBR-9515076 and SES-9601764. Ellison was also sup-ported by a Sloan ResearchFellowship. We thank Yannisloannides for valuablecomments and David Hwang, SteveJens, Alan Sorensen, and Marcus Stanley for researchassistance.311

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    312 AEA PAPERS AND PROCEEDINGS MAY 1999attributed to natural advantages. In our pre-ferred specification, we attribute about one-fifth of the concentration o observable naturaladvantages. Given that we are using only asmall number of variables thatcaptureadvan-tages very imperfectly, we would guess thatatleast half of the concentration reported inEllison and Glaeser (1997) is due to naturaladvantages. Nonetheless, thereremaina num-ber of highly geographically concentratedin-dustries in which interfirm spillovers seemimportant.

    I. MeasuringGeographicConcentrationFollowing Ellison and Glaeser (1997), as-sume that industry i consists of K plants thatsequentially choose locations from the set ofstates S in order to maximize profits. Supposethat the profits received by plant k when lo-cated in state s, 7rik., are given bylog 7riks = log 7ris

    + gs(v, I , Vk-I) + 'qis + eiks

    where rri,is the expected profitabilityof locat-ing in state s given observed state-specificcosts, the function g, reflects the effect on theprofitability of state s of spillovers given thatplants 1, k - 1 have previously chosenlocations v1, ..., Vk , '1qis anunobservedcom-mon randomcomponent of the profitabilityoflocating in state s, and siks is an unobservedfirm-specific shock.Inourmodel, plantsin anindustrymay clus-ter relative to aggregate activity for three rea-sons: (i) more plants will locate in states withobservedcost advantages; ii) moreplantswilllocate in states with unobserved cost advan-tages; and (iii) plants will cluster if spilloversare geographically localized.Equilibrium geographic concentration iseasy to compute for a particularspecificationin which the importanceof unobservednaturaladvantages and spillovers are capturedby pa-rametersoyna and y' Ei [0, 1]. The importanceof unobserved naturaladvantages is reflectedin the variance of 1j,.Specifically, we assumethat 77, is such that 2 ( 1 _ y na)n e?7ie yna hasa x2 distribution with E(7ri,enis) -= r,, andVar(rr,e '1i-) ynai /( 1_- yna). Spillovers are

    of an all-or-nothing variety: with probabilityys a "crucial spillover" exists between eachpair of plants. If such a spillover exists be-tween plantk andplant f, thenplant k receivesnegative infinity profits if it does not locate inthe same state as plant f; otherwise its profitsare independentof E's location.When the plants choose locations to maxi-mize profits in this model, the expected shareof employment located in state s is E(Sis)Sis =,Y 7is/s,isr An index of geographicconcentration beyond that accounted for byobserved naturaladvantageis

    ~ S1,- - ~S7V) - H( Si s-is)/ i Si2s)s s1-H

    where H is the Herfindahl ndex of the plants'shares of industryemployment. The propertythatmakes this an appealingindex (which fol-lows from the same argumentas in Ellison andGlaeser [1997]) is that E(y) = yna + Sna sy y.The concentration index will reflect bothunobserved natural advantages and localizedspillovers. The index in Ellison and Glaeser(1997) is simply this index with the crudemodel where Si, is assumed to equal states's share of overall manufacturing employ-ment. As we better account for the effects ofnatural advantages on state-industry shares,we would expect the index of concentration oget smaller.Il. DoesNaturalAdvantageAffectIndustryLocation?

    In our empirical work we assume that av-erage state-industryprofitsarelog 7ris = a0 log(pop5) + a1I og(mfg5)

    6i xeYe,zeewhere pops and mfgs are the shares of totalU.S. population and manufacturing employ-ment in state s, t indexes inputs to the pro-duction process, yt, is the cost of input t instate s, and zf, is the intensity with which in-dustry i uses input t. The specification in-cludes multiplicative industry dummies, bi, to

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    VOL. 89 NO. 2 GEOGRAPHICCONCENTRATIONOF INDUSTRY 313account for the fact that observed cost differ-ences will affect location decisions more insome industries thanin others,both because ofdifferences in the magnitude of the plant-specific shocks and because of transportationcosts. For example, while one can easily imag-ine the fur industry concentrating n responseto moderatecost differences, it seems hard toimagine thatthe concrete industrywould con-centrate geographically even if there wereenormousdifferences in the costs of rocks andcoal. In the estimation, the multiplicative in-dustry effects are constrained to be nonnega-tive. Note that we have economized on thenumberof parametersby assuming thatthe ef-fect on industry profitabilityof the differencein the cost of a particular nputis proportionalto the intensity with which the industryusesthe input,rather han estimatinga separateco-efficient for each input for each industry.Given this specificationof the effects of nat-ural advantages, expected state-industryem-ployment shares are

    E(S,s)pop5 mfgs ' exp(-6i Y3eyeszei )

    s' spopa0mfgal exp (- bi f3eyes'zei)We estimate this relationship for the 1987state employment shares of four-digit man-ufacturing industries by nonlinear leastsquares using 16 interactions designed to re-flect advantages in natural resource, labor,and transportationcosts. We normalize all ofthe interaction variables to have a standarddeviation of 1 and choose their signs so thatthe ,3coefficients areexpected to be positive.A larger estimated 63value indicates thatvariation in the cost/intensity of use of thatinput has a larger effect in aggregate on thedistribution of industries.Results from estimating a base model with-out the multiplicative industry dummies arepresentedin Table IA. The first column givesthe name of the variable. Each is described intwo lines: the first being the state-level inputprice variable (usually a proxy ratherthan aprice) and the second being the industry-levelvariable reflecting intensity of use or the sen-sitivity of location decisions to input costs.

    The second column gives the coefficient,which should be interpretedas the percentageincrease in the state's share of total industryemployment caused by a one-standard-deviation increase in the explanatory variable;t statistics for the coefficient estimates aregiven in parentheses. Thus the coefficient of0.170 for the variable electricity price X elec-tricity usage means that the state's share ofindustry employment increases by about 17percent (e.g., from 10 percent to 11.7 percent)with a one-standard-deviation ncrease in thisvariable. Table lB shows the two industriesfor which the industrycomponentof each vari-able is largest.For example, electricity is mostimportant(as a fraction of value added) forprimaryaluminum and alkalies and chlorine.Table lB also shows the states where the inputcost is lowest (Washington, Idaho, and Mon-tana for electricity) and the state where it ishighest (Rhode Island).The first six variables in the table (a-f) aredesigned to reflect the costs of six commoninputs: electricity, natural gas, coal, agricul-turalproducts, ivestock products,and lumber.All are highly significant and of the expectedsign. The coefficients on several of these vari-ables are among the largestwe find, indicatingthat these variables reflect a substantialcom-ponent of naturaladvantage.The next six variables (g-l) relate to laborinputs. The first three are the average manu-facturingwage in the state interactedwith (g)wages as a shareof value added, (h) the frac-tion of industryoutputthatis exported,and(i)the fractionof U.S. consumptionof the outputgood that is imported. Interactions(h) and (i)examine whether industries that are morecompetitive internationally are more wage-sensitive. All of these variables, however, willnot matterif average wage differences are at-tributable o differences in laborproductivity.Interactions (g) and (i) are significant andhave the expected positive sign, but the coef-ficients are fairly small. We find no evidenceof exporting industries concentrating in low-wage states.The other laborinputvariablesaredesignedto capturedifferences in the relative prices ofdifferenttypes of labor. Variable (j) is the in-teraction of the share of the adult populationin the state without a high-school degree with

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    A. CoefficientState variable X industry variable (t statistic)(a) Electricity price X 0.170electricity use (17.62)(b) Naturalgas price X 0.117naturalgas use (6.91)(c) Coal price X 0.119coal use (4.55)(d) PercentagefarmlandX 0.026agricultural nputs (2.58)(e) Per capita cattle X 0.053livestock inputs (5.08)(f) Percentage timberlandX 0.152lumber inputs (11.98)(g) Average mfg wage X 0.059wages/value added (4.11)(h) Average mfg wage X -0.014exports/output (-1.28)(i) Average mfg wage X 0.036import competition (3.10)(j) Percentagewithout HS degree X 0.157percentageunskilled (7.38)(k) Unionization percentage X 0.100

    percentage precision products (12.17)(1) Percentagewith B.A. or more X 0.170percentageexecutive/professional (12.70)(m) Coast dummy X -0.031heavy exports (-2.20)(n) Coast dummy X 0.017heavy imports (0.92)(o) Population density X 0.043percentageto consumers (3.68)(p) (Income share - mfg share) X 0.025percentageto consumers (4.49)B. Industries where most Best statesVariablesa important SIC) [worst state]

    (a) Primaryaluminum (3334) WA, ID, MTAlkalies and chlorine (2812) [RI](b) Brick and clay tile (3251) AK, LA, TXFertilizer(2873-4) [HI](c) Cement (3241) MT, NV, WYLime (3274) [VT](d) Soybean oil (2075) NE, ND, SDVegetable oil (2076) [DC]

    TABLE 1-Continued.Industries where most Best statesVariables' important SIC) [worst state]

    (e) Milk (2026) SD, NE, MTCheese (2022) [MD](f) Sawmills (2421) AK, MT, IDWood preserving (2491) [DC](g) Industrialpatterns (3543) MS, NC, ARAuto stampings (3465) [MI](h) Oil and gas machinery (3533) MS, NC, ARRice milling (2044) [MI](i) Dolls (3942) MS, NC, ARTableware (3263) [MI](j) Apparel (23) MS, KY, WV

    Textiles (22) [AK](k) Machine tools (354) MI, NY, HIJewelry (391) [SD](1) Computers (357) DC, MA, CT(Periodicals) (2721) [WV](m) Rice milling (2044)Industrialgases (2813)(n) Nonferrous metals (3339)Petroleum refining (2911)(o) Potato chips (2036) DC, NJ, RIJewelry (3411) [AK](p) Potatochips (2036) FL, CA, NYJewelry (3411) [NC]

    aLetters n this column refer to state and industry vari-ables in partA of the table.

    the share of workers in the industry who areunskilled. Next, (k) is the interaction ofunionization in the state (as a proxy for thepresence of skilled workers) with the fractionof employees in the industrywho areprecisionproduction workers. Variable (1) is the inter-action of the fraction of the adult populationin the state with bachelors' degrees or moreeducation with the fraction of industryworkerswho are executives or professionals. All ofthese variableshave a powerful positive effect.The final four variables (m-p) relate totransportationcosts. The first two (m and n)are designed to examine whether industriesthat are intensive importers or exporters ofheavy goods tend to locate on the coast. Nei-therof the estimates is positive andsignificant.

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    VOL.89 NO. 2 GEOGRAPHICCONCENTRATIONOF INDUSTRY 315The next two variables (o and p) are meant tocapture the idea that firms will reduce trans-portation costs or improve their marketingbylocating closer to their customers. They arein-teractionsof the shareof the industry's outputthat is sold to consumers with population den-sity and with the difference between a state'sshareof income andits shareof manufacturingemployment. Both are significantlypositivelyrelated to employment.The coefficients on the natural advantagesin specifications that include multiplicativedummies for two-digit and three-digit indus-tries are similar. The tendency of labor-intensive industries to locate in low-wagestates appears more pronounced in these re-gressions, while estimates of the effects due tounskilled labor, import competition, and in-come share minus manufacturing share be-come insignificantor negative.

    III. Does NaturalAdvantageExplainAgglomeration?Ourgreatestmotivation for studyingnaturaladvantage is a desire to know whether it canaccount for a substantial portion of observedgeographic concentration. Table 2 illustratesthe effect on measuredgeographic concentra-tion of accountingfor observed naturaladvan-tages. Each row reportson the distributionofindustry agglomeration indexes - obtainedfrom a particularmodel of natural advantage.The firstrow describes the concentration ndexof Ellison and Glaeser (1997), which corre-spondsto the trivialmodel E(Si) = mfg,. Themean value of - in this model is 0.051. Onlya few industries have negative j's. (This isnoteworthy because the model has no trans-portation costs leading firms to spread outwhen serving local markets.)We regardthe28percentof industrieswith - > 0.05 as showingsubstantial agglomeration. For comparison,the y of the automobile industry (SIC 3711)is 0.127. Such extreme agglomeration is un-common but far from unique: 12.8 percent ofmanufacturing ndustrieshave a jygreater han0.1.The second row shows the results when weintroducethe 16 cost/intensity of use interac-tions but do not allow industries to differ inthe sensitivity of location decisions to ob-



    Mean Percentageof industries with j in rangeModel j 0.1A 0.051 2.8 39.9 29.2 15.3 12.8B 0.048 3.9 39.9 30.1 13.7 12.4C 0.045 3.1 42.9 29.4 13.5 11.1D 0.041 4.4 42.9 29.8 13.3 9.6

    Notes: Models A-D aredifferent models of naturaladvantage: A)no cost variables; (B) cost interactions ntroduced; C) cost inter-actions plus dummies for two-digit industries; D) cost interactionsplus dummies for three-digit industries.

    served cost differences. The mean -' declinesslightly to 0.048, and the overall distributionlooks quite similar. The third and fourth rowsdescribe the concentration indexes foundwhen we allow for multiplicativedummies foreach two- and three-digit industry, respec-tively. In these models, natural advantageshave greaterexplanatory power, reducing themean values of j' to 0.045 and 0.041, respec-tively. We conclude that 20 percent of mea-sured geographic concentration can beattributed to a few observable naturaladvantages.The fractionof industriesthatareextremelyagglomerated in this measure declines, butonly moderately: 9.6 percentof industriesstillhave - greaterthan 0.1 in the latterspecifica-tion. Another notable feature of the distribu-tion of - is that the index is negative for onlya very few industries.The findingthatvirtuallyall industries are at least slightly agglomeratedis apparently fairly robust to the introductionof measures of cost advantages.IV. Conclusion

    Industries' locations are affected by a widerange of naturaladvantages.About 20 percentof observed geographic concentration can beexplained by a small set of advantages. Wethink that this result is particularly notablegiven the limits on our explanatoryvariables.For example, nothingin ourmodel can explainwhy there is no shipbuildingin Colorado, norcan it predict that soybean-oil production isconcentrated in soybean-producing states, asopposed to being spreadamong all agricultural

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    316 AEA PAPERS AND PROCEEDINGS MAY 1999states. We hope that, in the future,others willprovide better estimates than we have beenable to give here. We conjecture that at leasthalf of observed geographic concentration isdue to naturaladvantages.At the same time, thereremain a large num-ber of highly concentrated industries where itseems that agglomeration must be explainedby localized intraindustryspillovers. Simplecost differences can not explain why the furindustry, the most agglomerated industry inour sample, is centered in New York. We seethe attempt to provide a clearer understandingof the sources of these spillovers as an impor-tant topic for future research. Some resultsalong these lines are describedin Guy Dumaiset al. (1997).

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    Exogenous Variables." Review of Econom-ics and Statistics, August 1983, 65(3), pp.440-49.Dumais, Guy; Ellison, Glenn and Glaeser,EdwardL. "Geographic Concentration asa Dynamic Process." National Bureau ofEconomic Research (Cambridge, MA)Working Paper No. 6270, November1997.Ellison, Glenn and Glaeser, Edward L. "Geo-graphic Concentration in U.S. Manufactur-ing Industries: A Dartboard Approach."9Journal of Political Economy, October1997, 105(5), pp. 889-927.Henderson, J. Vernon. Urban development:Theory,fact, and illusion. New York: Ox-ford University Press, 1988.

    _ "The Impact of Air Quality Regula-tion on Industrial Location." Annalesd'Economie et de Statistique, January-March 1997, (45), pp. 123 -37.Kim, Sukkoo. "Regions, Resources, and Eco-nomic Geography:Sources of U.S. RegionalComparative Advantage, 1880- 1987."Regional Science and Urban Economics,January1999, 29(1), pp. 1-32.Krugman, Paul. Geography and trade. Cam-bridge, MA: MIT Press, 1991.Porter,MichaelE. The competitive advantageof nations. New York: Free Press, 1990.