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Logistics agglomeration and logistics productivity in the USA

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Abstract

Logistics agglomeration refers to the geographical concentration of logistics companies and activities. The growing trend of logistics agglomeration in the USA has drawn significant attention from both researchers and policy makers, and its benefits are being widely recognized. However, understanding of logistics agglomeration is still limited. The purposes of this paper are to confirm and quantify productivity gains for the logistics industry from the trend of agglomeration, using the County Business Patterns dataset for the US counties from 2007 to 2014. Panel unit root and panel cointegration test results suggest that a long-run equilibrium relationship between logistics agglomeration and logistics industry productivity exists. We then apply the panel fully modified ordinary least squares regression to estimate the long-run elasticity, and model estimation shows that the elasticity between concentration of logistics activities and industry productivity is 0.039. The study thus provides empirical evidence of the economic benefits associated this phenomenon and suggests that in a long run logistics agglomeration gives rise to higher industry productivity with an elasticity of 4%. In addition, our study results provide justification for policy makers to further invest in logistics agglomeration.

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References

  • Alises A, Vassallo JM (2015) Comparison of road freight transport trends in Europe. Coupling and decoupling factors from an input–output structural decomposition analysis. Transp Res Part Policy Pract 82:141–157. https://doi.org/10.1016/j.tra.2015.09.013

    Article  Google Scholar 

  • Aljohani K, Thompson RG (2016) Impacts of logistics sprawl on the urban environment and logistics: taxonomy and review of literature. J Transp Geogr 57:255–263

    Article  Google Scholar 

  • Allen WB (1997) The logistics revolution and transportation. Ann Am Acad Polit Soc Sci 553(1):106–116

    Article  Google Scholar 

  • Allen J, Browne M, Cherrett T (2012) Investigating relationships between road freight transport, facility location, logistics management and urban form. J Transp Geogr 24:45–57. https://doi.org/10.1016/j.jtrangeo.2012.06.010

    Article  Google Scholar 

  • Alstadt B, Weisbrod G, Cutler D (2012) Relationship of transportation access and connectivity to local economic outcomes. Transp Res Rec J Transp Res Board 2297:154–162. https://doi.org/10.3141/2297-19

    Article  Google Scholar 

  • Becattini G (1991) Il distretto industriale marshalliano come concetto socio-economico. In: Pyke F, Becattini G, Sengenberger W (eds) Distretti Industriali e Cooperazione tra Imprese in Italia. Banca Toscana, Studi e Informazioni, pp 51–65

  • Bowen JT Jr (2008) Moving places: the geography of warehousing in the US. J Transp Geogr 16:379–387. https://doi.org/10.1016/j.jtrangeo.2008.03.001

    Article  Google Scholar 

  • Breitung J (2001) The local power of some unit root tests for panel data. In: Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, pp 161–177

  • Brülhart M, Mathys NA (2008) Sectoral agglomeration economies in a panel of European regions. Reg Sci Urban Econ 38:348–362

    Article  Google Scholar 

  • Chhetri P, Butcher T, Corbitt B (2014) Characterising spatial logistics employment clusters. Int J Phys Distrib Logist Manage 44:221–241

    Article  Google Scholar 

  • Ciccone A, Hall RE (1996) Productivity and the density of economic activity. Am Econ Rev 86:54–70

    Google Scholar 

  • Cidell J (2010) Concentration and decentralization: the new geography of freight distribution in US metropolitan areas. J Transp Geogr 18:363–371. https://doi.org/10.1016/j.jtrangeo.2009.06.017

    Article  Google Scholar 

  • Combes P-P (2000) Economic structure and local growth: France, 1984–1993. J Urban Econ 47:329–355. https://doi.org/10.1006/juec.1999.2143

    Article  Google Scholar 

  • Cui J, Dodson J, Hall PV (2015) Planning for urban freight transport: an overview. Transp Rev 35:583–598. https://doi.org/10.1080/01441647.2015.1038666

    Article  Google Scholar 

  • Dablanc L, Rakotonarivo D (2010) The impacts of logistics sprawl: How does the location of parcel transport terminals affect the energy efficiency of goods’ movements in Paris and what can we do about it? Proc Soc Behav Sci 2(3):6087–6096

    Article  Google Scholar 

  • Dablanc L, Ross C (2012) Atlanta: a mega logistics center in the Piedmont Atlantic Megaregion (PAM). J Transp Geogr 24:432–442. https://doi.org/10.1016/j.jtrangeo.2012.05.001

    Article  Google Scholar 

  • Dablanc L, Ogilvie S, Goodchild A (2014) Logistics sprawl: differential warehousing development patterns in Los Angeles, California, and Seattle, Washington. Transp Res Record J Transp Res Board 2410:105–112

    Article  Google Scholar 

  • de Langen P (2004) The performance of seaport clusters; a framework to analyze cluster performance and an application to the seaport clusters of Durban, Rotterdam and the Lower Mississippi (No. ERIM PhD Series; EPS-2004-034-LIS)

  • Deng P, Lu S, **ao H (2013) Evaluation of the relevance measure between ports and regional economy using structural equation modeling. Transp Policy 27:123–133. https://doi.org/10.1016/j.tranpol.2013.01.008

    Article  Google Scholar 

  • Feitelson E, Salomon I (2000) The implications of differential network flexibility for spatial structures. Transp Res Part Policy Pract 34:459–479. https://doi.org/10.1016/S0965-8564(99)00028-2

    Article  Google Scholar 

  • Gambardella A, Giarratana MS (2010) Localized knowledge spillovers and skill-biased performance. Strateg Entrep J 4:323–339. https://doi.org/10.1002/sej.99

    Article  Google Scholar 

  • Gill I, Kharas H (2007) An east Asian renaissance. An east Asian renaissance: International Bank for Reconstruction and Development/World Bank

  • Graham DJ, Melo PS, Jiwattanakulpaisarn P, Noland RB (2010) Testing for causality between productivity and agglomeration economies. J Reg Sci 50:935–951. https://doi.org/10.1111/j.1467-9787.2010.00676.x

    Article  Google Scholar 

  • Granger CWJ (1981) Some properties of time series data and their use in econometric model specification. J Econom 16:121–130

    Article  Google Scholar 

  • Heitz A, Dablanc L (2015) Logistics spatial patterns in Paris. Transp Res Rec J Transp Res Board 2477:76–84. https://doi.org/10.3141/2477-09

    Article  Google Scholar 

  • Henderson JV, Shalizi Z, Venables AJ (2001) Geography and development. J Econ Geogr 1:81–105. https://doi.org/10.1093/jeg/1.1.81

    Article  Google Scholar 

  • Hesse M (2002) Location matters. Access magazine. Transportation Research at the University of California. No. 21, pp 22–26

  • Hesse M (2008) The city as a terminal: the urban context of logistics and freight transport. Ashgate Publishing, Ltd, Aldershot

    Google Scholar 

  • Hesse M, Rodrigue J-P (2004) The transport geography of logistics and freight distribution. J Transp Geogr 12:171–184

    Article  Google Scholar 

  • Hesse M, Rodrigue J-P (2006) Global production networks and the role of logistics and transportation. Growth Change 37(4):499–509

    Article  Google Scholar 

  • Hlouskova J, Wagner M (2006) The performance of panel unit root and stationarity tests: results from a large scale simulation study. Econom Rev 25:85–116. https://doi.org/10.1080/07474930500545504

    Article  Google Scholar 

  • Holl A, Mariotti I (2017) The geography of logistics firm location: the role of accessibility. Netw Spat Econ. https://doi.org/10.1007/s11067-017-9347-0

    Article  Google Scholar 

  • **g N, Cai W (2010) Analysis on the spatial distribution of logistics industry in the developed East Coast Area in China. Ann Reg Sci 45:331–350. https://doi.org/10.1007/s00168-009-0307-6

    Article  Google Scholar 

  • Labussière S, Nappi-Choulet I (2014) The city of tomorrow: new ways of using and sharing real estate, (Gallon, A. Trans.), ESSEC Business School: Real Estate and Sustainable Development Chair. Retrieved from http://chaire-immobilier-developpement-durable.essec.edu

  • Leigh NG, Hoelzel NZ (2012) Smart growth’s blind side: sustainable cities need productive urban industrial land. J Am Plan Assoc 78(1):87–103

    Article  Google Scholar 

  • Lindsey C, Mahmassani HS, Mullarkey M, Nash T, Rothberg S (2014) Industrial space demand and freight transportation activity: exploring the connection. J Transp Geogr 37:93–101

    Article  Google Scholar 

  • Mariotti I (2015a) Transport and logistics in a globalizing world: a focus on Italy. Springer, Berlin

    Book  Google Scholar 

  • Mariotti I (2015b) Transport and logistics in a globalizing world: an appraisal. Transport and logistics in a globalizing world. Springer, Berlin

    Book  Google Scholar 

  • Markusen A (1996) Sticky places in slippery space: a typology of industrial districts. Econ Geogr 72(3):293–313

    Article  Google Scholar 

  • Marshall A (1890) Principles of economics, 8th edn. Macmillan, London, p 1920

    Google Scholar 

  • Marshall A (1895) Principles of economics

  • Marshall A (1920) Industry and trade: a study of industrial technique and business organization; and of their influences on the conditions of various classes and nations. Macmillan

  • McKinnon A (2009) Logistics and land: the changing land use requirements of logistical activity. Citeseer, pp 767–775

  • Melo PC, Graham DJ, Noland RB (2009) A meta-analysis of estimates of urban agglomeration economies. Reg Sci Urban Econ 39:332–342

    Article  Google Scholar 

  • Melo PC, Graham DJ, Levinson D, Aarabi S (2017) Agglomeration, accessibility and productivity: evidence for large metropolitan areas in the US. Urban Stud 54:179–195. https://doi.org/10.1177/0042098015624850

    Article  Google Scholar 

  • Mori T, Nishikimi K (2002) Economies of transport density and industrial agglomeration. Reg Sci Urban Econ 32(2):167–200

    Article  Google Scholar 

  • Nelson CR, Plosser CR (1982) Trends and random walks in macroeconomic time series: some evidence and implications. J Monet Econ 10:139–162

    Article  Google Scholar 

  • Nittner T (2002) The additive model with missing values in the independent variable-theory and simulation. Sonderforschungsbereich 386, Paper 272. http://epub.ub.uni-muenchen.de/

  • Nittner T (2003) Missing at random (MAR) in nonparametric regression-A simulation experiment. Stat Methods Appl 12(2):195–210

    Google Scholar 

  • Pedroni P (2000) Fully modified OLS for heterogeneous cointegrated panels. Department of Economics Working Papers, vol 15, pp 93–130

  • Pedroni P (2001a) Purchasing power parity tests in cointegrated panels. Rev Econ Stat 83(4):727–731

    Article  Google Scholar 

  • Pedroni P (2001b) Fully modified OLS for heterogeneous cointegrated panels. In: Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, pp 93–130

  • Pedroni P (2010) Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bull Econ Stat 61(S1):653–670

    Article  Google Scholar 

  • Phillips PC, Hansen BE (1990) Statistical inference in instrumental variables regression with I (1) processes. Rev Econ Stud 57:99–125

    Article  Google Scholar 

  • Piore MJ, Sabel CF (1984) The second industrial divide. Basic Books, New York

    Google Scholar 

  • Polenske KR (2006) Clustering in space versus dispersing over space. In: The emerging digital economy. Springer, pp 35–54

  • Porter ME (1998) Clusters and the new economics of competition. Harv Bus Rev 76(6):77–90

    Google Scholar 

  • Programme UNHS (2013) Planning and design for sustainable urban mobility: policy directions: global report on human settlements 2013. Routledge

  • Radner R (1992) Hierarchy: the economics of managing. J Econ Lit 30(3):1382–1415

    Google Scholar 

  • Radner R, Van Zandt T (1995) Information processing in firms and returns to scale. In The economics of informational decentralization: complexity, efficiency, and stability. Springer US, pp 243–280

  • Reuer JJ, Lahiri N (2013) Searching for alliance partners: effects of geographic distance on the formation of R&D collaborations. Organ Sci 25:283–298. https://doi.org/10.1287/orsc.1120.0805

    Article  Google Scholar 

  • Rivera L, Sheffi Y, Welsch R (2014) Logistics agglomeration in the US. Transp Res Part Policy Pract 59:222–238. https://doi.org/10.1016/j.tra.2013.11.009

    Article  Google Scholar 

  • Rivera L, Gligor D, Sheffi Y (2016) The benefits of logistics clustering. Int J Phys Distrib Logist Manage 46:242–268. https://doi.org/10.1108/IJPDLM-10-2014-0243

    Article  Google Scholar 

  • Rodrigue J-P (2006) Challenging the derived transport-demand thesis: geographical issues in freight distribution. Environ Plan A 38:1449–1462. https://doi.org/10.1068/a38117

    Article  Google Scholar 

  • Rodrigue J-P, Comtois C, Slack B (2013) The geography of transport systems. Routledge, London

    Book  Google Scholar 

  • Romanelli E, Khessina OM (2005) Regional industrial identity: cluster configurations and economic development. Organ Sci 16:344–358

    Article  Google Scholar 

  • Sakai T, Kawamura K, Hyodo T (2015) Locational dynamics of logistics facilities: evidence from Tokyo. J Transp Geogr 46:10–19. https://doi.org/10.1016/j.jtrangeo.2015.05.003

    Article  Google Scholar 

  • Santa María MJ, Giner M, Fuster A (2004) Identificación de sistemas productivos locales en España: una aproximación desde el territorio a los fenómenos industriales. Documento de Trabajo, 1, 2004

  • Sheffi Y (2012) Logistics clusters: delivering value and driving growth. MIT Press, Cambridge

    Google Scholar 

  • Smith A (1776) An inquiry into the nature and causes of the wealth of nations, fifth edition. Edited by Edwin Cannon, Methuen, and London. Reprint in 1904

  • Syverson C (2004) Market structure and productivity: a concrete example. J Polit Econ 112:1181–1222

    Article  Google Scholar 

  • Targa F, Clifton K, Mahmassani H (2005) Economic activity and transportation access: an econometric analysis of business spatial patterns. Transp Res Rec J Transp Res Board 1932:61–71

    Google Scholar 

  • van den Heuvel FP, de Langen PW, van Donselaar KH, Fransoo JC (2013) Regional logistics land allocation policies: stimulating spatial concentration of logistics firms. Transp Policy 30:275–282. https://doi.org/10.1016/j.tranpol.2013.09.022

    Article  Google Scholar 

  • van den Heuvel FP, de Langen PW, van Donselaar KH, Fransoo JC (2014a) Proximity matters: synergies through co-location of logistics establishments. Int J Logist Res Appl 17:377–395. https://doi.org/10.1080/13675567.2013.870141

    Article  Google Scholar 

  • van den Heuvel FP, Rivera L, van Donselaar KH et al (2014b) Relationship between freight accessibility and logistics employment in US counties. Transp Res Part Policy Pract 59:91–105. https://doi.org/10.1016/j.tra.2013.11.002

    Article  Google Scholar 

  • Van Klink A, De Langen P (2001) Cycles in industrial clusters: the case of the shipbuilding industry in the Northern Netherlands. Tijdschr Econ Soc Geogr 92:449–463

    Article  Google Scholar 

  • Verhetsel A, Kessels R, Goos P et al (2015) Location of logistics companies: a stated preference study to disentangle the impact of accessibility. J Transp Geogr 42:110–121. https://doi.org/10.1016/j.jtrangeo.2014.12.002

    Article  Google Scholar 

  • Westerlund J (2007) Testing for error correction in panel data. Oxford Bull Econ Stat 69:709–748. https://doi.org/10.1111/j.1468-0084.2007.00477.x

    Article  Google Scholar 

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Authors and Affiliations

  1. Guanghua Management School of Peking University, Peking University, Summer Palace Road in Haidian District, Bei**g, 100871, China

    Bowen Sun & Haomin Li

  2. Institute of New Structural Economics at Peking University, Peking University, Summer Palace Road in Haidian District, Bei**g, 100871, China

    Qiuyun Zhao

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  1. Bowen Sun
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Correspondence to Bowen Sun.

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Sun, B., Li, H. & Zhao, Q. Logistics agglomeration and logistics productivity in the USA. Ann Reg Sci 61, 273–293 (2018). https://doi.org/10.1007/s00168-018-0867-4

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