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Empirical Economics

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10-07-2017

Do neighboring municipalities matter in industrial location decisions? Empirical evidence from Spain

Authors: Angel Alañon-Pardo, Patrick J. Walsh, Rafael Myro

Published in: Empirical Economics | Issue 3/2018

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Abstract

This paper focuses on industrial location, assuming that entrepreneurs not only consider the advantages associated with a certain municipality, but also those coming from nearby areas. Exploratory analysis reflects the existence of spatial patterns in the creation of manufacturing establishments and sheds light on the geographical scope on which agglomeration economies operate in industrial location. Spatial Probit models and standard Probit models with spatially lagged explanatory variables are estimated to test whether neighboring municipalities’ location decisions and characteristics, including agglomeration economies, matter in industrial location choices. Results show that neighboring municipalities location decisions and characteristics help to explain location decisions of new establishments for 11 manufacturing industries in Spanish municipalities (NUTS V) over the period 1991–1995.

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See Rosenthal and Strange (2004) for a review on the nature and sources of agglomeration economies.

Hoover’s classification also included internal economies of scale.

“And presently subsidiary trades grow up in the neighborhood, supplying it with implements and materials, organizing its traffic, and in many ways conducing to the economy of its material (Marshall 1890)”.

“A localized industry gains a great advantage from the fact that it offers a constant market for

skill. ... Employers are apt to resort to any place where they are likely to find a good choice of workers with the special skill which they require, while men seeking employment naturally go to places where there are many employers who need such skills as theirs (Marshall 1890)”.

“Great are the advantages which people following the same skilled trade get from near neighborhood to one another. The mysteries of the trade become no mysteries; but are as it were in the air ... if one man starts a new idea, it is taken up by others and combined with suggestions of their own; and thus it becomes the source of further new ideas (Marshall 1890)”.

See Holmes (1999) and Bartlesman et al. (1994) for evidence about shared input markets. Jaffee (1989), Acs et al. (1992), Jaffe et al. (1993), and Audretsch and Feldman (1996) provide evidence on the relevance of human capital and knowledge spillovers. Evidence on labor market pooling can be found in Baumgartner (1988), Diamond and Simon (1990), Moretti (2000), Costa and Kahn (2001).

See Henderson (2003), Glaeser et al. (1992) or Duranton and Puga (2005).

See Miller (2004) for more information on Tobler’s law.

See Arauzo-Carod et al. (2010) for a review on methods and results of empirical studies in industrial location.

There are some works following this way such as Viladecans-Marsal (2004), Autant-Bernard (2006) and LeSage et al. (2011). While LeSage et al. (2011) addresses spatial autocorrelation by estimating a spatial autorregresive Probit model to study the decisions of reopen after Hurricane Katrina, the other papers model spatial effects including spatially lagged explanatory variables. However, these other papers do not fully control for spatial dependence through the error term or the likelihood function (Anselin 1988). Viladecans-Marsal (2004) use an OLS IV estimator to analyze the role of agglomeration economies in most crowded Spanish municipalities. Autant-Bernard (2006) analyses the location of R&D establishments in French NUTS 2 using a conditional logit model. However, neither of the latter two papers use a full spatial econometric model, as we do here.

Called random, since it follows from the random utility framework. See Guimarães et al. (2004) for an extension of the random utility framework.

See Arauzo-Carod (2002), Holl (2004a, b) and Guimarães et al. (2004).

See Anselin (1988) for more information about spatial autocorrelation and Spatial Econometrics techniques.

By implementing robust estimation techniques, applying spatial filters or enlarging or improving the dataset, etc.

SAR models include a spatially lagged dependent variable, Wy, as one of the explanatory variables, that is \({ y = \rho Wy + X\beta +\varepsilon }\), where y is a nx1 vector of observations on the dependent variable and Wy is an nx1 vector of spatial lags for the dependent variable (where again, W is an SWM). The parameter \(\rho \) is the spatial autoregressive coefficient that indicates the strength of spatial dependence, X is an nxk matrix of observations on the (exogenous) explanatory variables with an associated \(\beta \) kx1 vector of regression coefficients, and \(\varepsilon \) is an nx1 vector of normally distributed (N(0, \(\sigma 2\))) random error terms.

SEM models deal with spatial dependence through a spatially lagged error term, which uses a non- spherical error: \({ y = X\beta + u}\), where \(u = \lambda Wu + \varepsilon \), and \(\varepsilon \sim \hbox {N}\)(\({ 0},\sigma 2 I n\)). \(\lambda \) is a coefficient on the spatially correlated errors. See Anselin (1988) for additional details.

We choose that period because of the availability of data for the dependent and independent variables.

If we were not interested on the location decisions but in the creation of new manufacturing establishments, there are several ways to estimate spatial count data models. Kaiser and Cressie (1997) developed a Poisson auto-model which allows positive spatial dependencies in multivariate count data by specifying conditional distributions as truncated or Winsorized Poisson probability mass functions, and Poisson spatial interaction models are estimated in Lesage et al. (2007) and in Fischer and Griffith (2008) to analyze origin-destination patent citation data.

See Fleming (2004) for a more complete discussion on the advantages and disadvantages of different spatial Probit estimation techniques.

Following Lesage and Pace (2009), we employ a normal prior distribution for the \(\beta \) parameters, which are conditional on an inverse gamma distribution for \(\sigma ^{2}\). The spatial parameters, \(\lambda \) and \(\rho \), have uniform prior distributions.

Unlike the case of a continuous dependent variable, where Lagrange multiplier tests can be used to choose between the two models.

See Hayter (1997), Guimarães et al. (2000), Figueiredo et al. (2002) and Guimarães et al. (2004) for more information about locational determinants.

Local tax data are not available for small municipalities due to statistical secrecy, and, as argued before, we should not use NUTS III in order to avoid MAUP or ecological fallacy problems.

The economic interpretation of \(\lambda Wu\) in SEM models is not so straightforward.

In order to work with spatially continuous data, we consider 7906 municipalities, that is, we ignore the municipalities which belong to Balearic Islands or to Canary Islands.

The influence of MAUP on location analysis is addressed in Pablo-Martí and Muñoz-Yebra (2009).

All manufacturing establishments must be registered in REI before starting up its activities. See Mompó and Monfort (1989) for a description of REI.

During the nineties regional governments started managing REI delegations, and data about new establishments are neither provided in a timely fashion for all the regions nor in a friendly format to be processed.

As Anselin (1992) points out binary variables take on only the values 1 and 0, areal units with observations 1 are often referred to as colored Black. Black–Black (BB) join counts is the number of times a join, colored area, is contiguous to another Black unit. See also Cliff and Ord (1980) for technical details.

See Cliff and Ord (1980) or Anselin (1988) for more information about Moran’s I statistic.

As most of spatial statistics are significant, in order to reduce the length of Tables 1 and 2, we will only show results for 1980, 1985, 1990, 1995, 1998, and for the years in which some of the spatial statistics are not significant.

As our dataset comprises 19 years and 11 manufacturing industries, due to length limitations, the descriptive analysis only include the number of municipalities in which there was creation of manufacturing establishments, the number of manufacturing establishments created per year, and the maximum of establishments created in a given year (see “Appendix” section).

If we could disaggregate the Food industry, results would probably differ.

The coefficient estimates from the SARP models (Table 7) cannot be interpreted as representing how changes in the explanatory variables affect location decisions. In order to do so, direct and indirect effects have to be estimated (Tables 9, 10). See LeSage et al. (2011) for more information.

We must bear in mind that these studies were not carried out using the same methodology and do not use exactly the same dataset, thus full comparison is not possible.

Acs ZJ, Audretsch DB, Feldman MP (1992) Real effects of academic research: comment. Am Econ Rev 82(1):363–367

Aghion P, Bulanger JY, Cohen E (2011) Rethinking industrial policy. Bruegel, issue 2011/04 June

Alañón-Pardo A (2002) Estimación del valor añadido per cápita de los municipios españoles en 1991 mediante técnicas de econometría espacial. Ekonomiaz 51:172–194

Anselin L (1988) Spatial econometrics. Methods and models. Kluwer Academic, DordrechtCrossRef

Anselin L (1992) SpaceStat tutorial, a book for using SpaceStat in the analysis of spatial data. University of Illinois, Urbana-Champaign

Anselin L (2002) Under the Hood. Issues in the specification and interpretation of spatial regression models. Agric Econ 17:247–267CrossRef

Arauzo-Carod J (2002) Determinants of industrial location. An application for catalan municipalities. In: Working paper \(\text{N}^{\circ }\) 138, Estudios sobre la Economía Española, FEDEA, Madrid

Arauzo-Carod JM, Liviano D, Manjón M (2010) Empirical studies in industrial location: an assessment of their methods and results. J Reg Sci 50(3):685–711CrossRef

Arita T, McCann P (2000) Industrial alliances and firm location behaviour: some evidence from the US semiconductor industry. Appl Econ 32:1391–1403CrossRef

Arrow KJ (1962) Economic welfare and the allocation of resources for invention. In: Nelson RR (ed) The rate and direction of inventive activity. Princeton-University Press, Princeton, pp 609–626

Audretsch D, Feldman M (1996) R&D spillovers and the geography of innovation and production. Am Econ Rev 86:630–640

Autant-Bernard C (2006) Where do firms choose to locate their R&D? a spatial conditional logit analysis on french data. Eur Plan Stud 14(9):1187–1208CrossRef

Bailey TC, Gatrell AC (1995) Interactive spatial data analysis, 2nd edn. Longman, London

Bartlesman EJ, Caballero RJ, Lyons RK (1994) Customer and supplier-driven externalities. Am Econ Rev 84(4):1075–1084

Baumgartner JR (1988) Physicians’ services and the division of labor across local markets. J Polit Econ 96(5):948–982CrossRef

Carlton D (1983) The location and employment choices of new firms: an econometric model with discrete and continuous endogenous variables. Rev Econ Stat 65:440–449CrossRef

Cliff A, Ord K (1980) Spatial processes: models & applications. Pion, London

Diamond CA, Simon CJ (1990) Industrial specialization and the returns to labor. J Lab Econ 8(2):175–201CrossRef

Costa DL, Kahn ME (2001) Power couples. Quart J Econ 116:1287–1315

Duranton G, Puga D (2000) Diversity and specialisation in cities, why, where and when does it matter? Urban Stud 37:533–555CrossRef

Duranton G, Puga D (2005) From sectoral to functional urban specialization. J Urban Econ 57(2):343–370CrossRef

Ellison G, Glaeser E (1997) Geographic concentration in US manufacturing industries, a dartboard approach. J Polit Econ 105:889–927CrossRef

Ellison G, Glaeser EL, Kerr WR (2010) What causes industry agglomeration? Evidence from coagglomeration patterns. Am Econ Rev 100(3):1195–1213CrossRef

Figueiredo O, Guimarães P, Woodward D (2002) Home-field advantage: location decisions of Portuguese entrepreneurs. J Urban Econ 52:341–361CrossRef

Fischer MM, Griffith DA (2008) Modeling spatial autocorrelation in spatial interaction data: an application to patent citation data in the European Union. J Reg Sci 48(5):969–989CrossRef

Fleming M (2004) Techniques for estimating spatially dependent discrete choice models. In: Iin Anselin L, Florax R, Rey S (eds) Advances in spatial econometrics, methodology, tools and applications. Springer, Berlin, pp 145–167CrossRef

Glaeser E, Kallal H, Scheinkman J, Shleifer A (1992) Growth in cities. J Polit Econ 100:1126–1152CrossRef

Guimarães P, Figueiredo O, Woodward D (2000) Agglomeration and the location of foreign direct investment in Portugal. J Urban Econ 47:115–135CrossRef

Guimarães P, Figueiredo O, Woodward D (2004) Industrial location modeling: extending the random utility framework. J Reg Sci 44:1–20CrossRef

Hayter R (1997) The dynamics of industrial location, the factory, the firm and the production system. Wiley, New York

Henderson JV (1997) Externalities and industrial development. J Urban Econ 42:449–470CrossRef

Henderson JV (2003) Marshall’s scale economies. J Urban Econ 53:1–28CrossRef

Holl A (2004a) Manufacturing location and impacts of road transport infrastructure: empirical evidence from Spain. Reg Sci Urban Econ 34:341–364CrossRef

Holl A (2004b) Transport infrastructure, agglomeration economies, and firm birth. Empirical evidence from Portugal. J Reg Sci 44:693–712CrossRef

Holmes T (1999) Localization of industry and vertical disintegration. Rev Econ Stat 81:314–325CrossRef

Hoover E (1948) The location of economic activity. McGraw-Hill, New York

Jacobs J (1969) The economy of cities. Vintage, New York

Jaffee AB (1989) Real effects of academic research. Am Econ Rev 79(5):957–970

Jaffe AB, Trajtenberg M, Henderson R (1993) Geographic localization of knowledge spillovers as evidenced by patent citations. Quart J Econ 108:577–598CrossRef

Jofré-Montseny J (2009) The scope of agglomeration economies: evidence from Catalonia. Pap Reg Sci 88(3):575–590CrossRef

Kaiser M, Cressie N (1997) Modeling Poisson variables with positive spatial dependence. Stat Probab Lett 35:423–432CrossRef

Krugman P (1991a) Geography and trade. MIT Press, Cambridge

Krugman P (1991b) Increasing returns and economic geography. J Polit Econ 99:484–499CrossRef

Lesage J, Pace RK (2009) Introduction to spatial econometrics. Chapman & Hall/CRC, Boca Raton

LeSage JP, Pace RK, Lam N, Campanella R, Liu X (2011) New Orleans business recovery in the aftermath of Hurricane Katrina. J R Stat Soc Ser A 174(4):1007–1027CrossRef

Lesage JP, Fischer MM, Scherngell T (2007) Knowledge spillovers across Europe: evidence from a Poisson spatial interaction model with spatial effects. Pap Reg Sci 86(3):393–422CrossRef

Lesage J (1997) Bayesian estimation of spatial autoregressive models. Int Reg Sci Rev 20:19–35CrossRef

Lesage J (2000) Bayesian estimation of limited dependent variable spatial autoregressive models. Geogr Anal 32:19–35CrossRef

Marshall A (1890) Principles of economics, ed of 1920. MacMillan, London

McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105–142

Mcmillen D (1995) Spatial effects in Probit models: a Monte Carlo investigation. In: Anselin L, Florax R (eds) New directions in spatial econometrics. Springer, Berlin, pp 189–228CrossRef

Miller HJ (2004) Tobler’s first law and spatial analysis. Ann Assoc Am Geogr 94(2):284–289CrossRef

Mompó A, Monfort V (1989) El registro industrial como fuente estadística regional: el caso de la Comunidad Valenciana. Econ Ind 268:129–140

Moretti E (2000) Estimating the social return to higher education In: NBER Working Paper No 9018

Ottaviano G, Puga D (1998) Agglomeration in the global economy: a survey of the new economic geography. World Econ 21(6):707–731CrossRef

Pablo-Martí F, Muñoz-Yebra C (2009) Localización empresarial y economías de aglomeración: el debate en torno a la agregación espacial. Investig Reg 15:139–166

Pinkse J, Slade M (1988) Contracting in space: an application of spatial statistics to discrete-choice models. J Econ 85:125–154CrossRef

Romer P (1986) Increasing returns and long run growth. J Polit Econ 94:1002–1037CrossRef

Rosenthal S, Strange W (2003) Geography, industrial organization and agglomeration. Rev Econ Stat 85:377–393CrossRef

Rosenthal S, Strange W (2004) Evidence on the nature and sources of agglomeration economies. In: Henderson J, Thisse JF (eds) Handbook of urban and regional economics, vol 4. Elsevier, Amsterdam, pp 2119–2171

Simmie J (1998) Reasons for the development of ‘Islands of innovation’: evidence from Hertfordshire. Urban Stud 35:1261–1289CrossRef

Smith T, Lesage J (2002) A Bayesian probit model with spatial dependencies. Webpage of Smith T at the University of Pennsylvania, School of Engineering and Applied Science. http://www.seas.upenn.edu/tesmith/sprobit.pdf. Accessed 15 July 2011

Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B (Stat Methodol) 64:583–639. doi:10.1111/1467-9868.00353 CrossRef

Suárez-Villa L, Alrod W (1998) Operational strategy, R&D and intrametropolitan clustering in a polycentric structure. The advanced electronics industries of the Los Angeles Basin. Urban Stud 34:1343–1380CrossRef

Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234–240CrossRef

Unwin DJ (1996) GIS, spatial analysis and spatial statistics. Prog Hum Geogr 20(4):540–551CrossRef

Van Soest D, Gerking S, Van Oort F (2006) Spatial impacts of agglomeration externalities. J Reg Sci 46:881–899CrossRef

Viladecans-Marsal E (2001) La concentración territorial de las empresas industriales: un estudio sobre el tamaño de las empresas y su proximidad geográfica. Pap Econ Española 89–90:308–321

Viladecans-Marsal E (2003) Economías externas y localización del empleo industrial. Revista Econ Aplicada 31:5–32

Viladecans-Marsal E (2004) Agglomeration economies and industrial location: city-level evidence. J Econ Geogr 4:562–582CrossRef

Title: Do neighboring municipalities matter in industrial location decisions? Empirical evidence from Spain
Authors: Angel Alañon-Pardo
Patrick J. Walsh
Rafael Myro
Publication date: 10-07-2017
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 3/2018
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-017-1307-5

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