Skip to main content

Advertisement

SpringerLink
Log in

Spatially blind policies? Analysing agglomeration economies and European Investment Bank funding in European neighbouring countries

  • Special Issue Paper
  • Published:
The Annals of Regional Science Aims and scope Submit manuscript

Abstract

Policies promoting development need to take into account the fact that globalisation has made space more rather than less important. To take full advantage of agglomeration economies that come with urban concentration, infrastructure plays a key role. For a long time development policies have focused on the provision of infrastructure. In this work, we analyse: first how urban concentration and infrastructure interact with each other for encouraging economic growth; and second whether policies promoting infrastructure have considered the spatial distribution of economic activity. As a case study, we focus on the European Investment Bank (EIB) projects financing infrastructures, for both the European Union and the EU neighbourhood. We perform panel data analysis considering different measures of infrastructure, and we also analyse the EIB projects. Our results suggest a relevant role of connectivity infrastructure (i.e. transport and communications) for agglomeration benefits to take place in European Neighbouring Policy countries. Our results also suggest that EIB funding in ENP countries is mostly country specific and displays no spatial dimension.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. See Romp and Haan (2007) and Straub (2008) for a review of the relevant empirical literature.

  2. Henderson (2000) finds a significant role of road density in defining urban concentration and the benefits and costs associated with it. Capello and Camagni (2000) and Royuela et al. (2010) consider telephone lines as a proxy for network integration of cities.

  3. Gifford (1996) emphasises the complexity behind sound infrastructure planning and development. Given increasing returns (due to, among other things, coordination effects, large set-up costs, and learning by doing), there is a potential “lock in” and path dependence, which can lead to lower level of productivity and welfare when new and better alternative systems become available but are difficult to adopt.

  4. Public policies are intimately linked with the provision of public goods. Governments decide on the systems and the frameworks from a social optimisation perspective, and also control the planning and development of these systems. Infrastructures are long-lived assets with high sunk costs and low marginal costs. Private investors only operate if they enjoy a sufficiently large cash flow, representing average rather than marginal cost (Wagenvoort et al. 2010). As stressed by Lundqvist and Mattsson (2002), integrated approaches are increasingly demanded for decision schemes capable of providing information on long-run effects of infrastructure, in line with a strong policy sensitivity that orientates national infrastructure systems.

  5. Clifton et al. (2013) analyse the evolution of EIB loans from prioritising funding utilities in member’s poorer zones to become more oriented in market development. Kollatz-Ahnen (2013) analyse the role of the EIB to foster growth inside the EU in the framework of the current crisis and efforts for recovery. Lesay (2013), relying on Critical Discourse Analysis, study the role of the EIB as a player in development efforts outside the EU. According to Langan (2014) EU–Africa ties through interventions by the EIB, although directed towards poverty reduction, serve more the commercial interests of Europe.

  6. This framework is common in other studies on the relationship between concentration and growth (such as Henderson 2000; Brülhart and Sbergami 2009 and Castells-Quintana and Royuela 2014). Durlauf et al. (2005) provide a detailed explanation of how to derive cross-country growth regressions from neoclassical economic growth theory.

  7. Primacy measures consider main metropolitan areas (including core city and satellite cities), a central concept for agglomeration economies and congestion costs. It has been shown that primacy correlates very highly with other measures of concentration (as the Hirschman-Herfindahl index for which there is very limited coverage) and reflects fairly well parameters behind Zipf’s law curves (the fact that when we rank cities from largest to smallest, rank times population size is approximately the same constant for all cities). The largest city in the country, therefore, delineates all other city sizes and is sufficient information to calculate any comparative index of national urban concentration (Henderson 2003).

  8. Ten countries joined the EU in 2004: Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia. Bulgaria and Romania joined in 2007 and according to the Commission, constitute part of the fifth enlargement. Finally, in 2013, Croatia became the \(28{\mathrm{th}}\) member state of the EU.

  9. By country, Israel displays the highest GDP per capita levels among all ENP countries, Moldova and Azerbaijan the lowest. Belarus is the richest among the Eastern ENCs, and Egypt is the poorest among the Southern ENCs. Regarding growth rates, Azerbaijan displayed the highest among all ENCs while Israel displayed the lowest. Tunisia was the fastest growing among the Southern ENCs, and Moldova was the slowest growing among Eastern ENCs.

  10. Armenia has the highest level of concentration among all ENPs, while Ukraine has the lowest. Among the Southern ENCs, Lebanon is the country with highest concentration and Tunisia the country with the lowest.

  11. For main tables and discussion of results, we have preferred to rely on our FE results for three main reasons. First, as we aggregate our data in 3-year periods, we have a small T (around 5 periods). IV-GMM estimations relying on lags imply an important loss of observations. Second, this also implies using variable transformations as instruments without a strong explanatory power. Finally, and related, the number of instruments becomes too large relative to the number of observations.

  12. Supplementary Material B reproduces main FE results in Table 1 showing: (i) coefficients for all considered controls, (ii) results without controls and (iii) some IV-GMM results. Controls appear with the expected sign and tend to be highly significant. Additionally, main results do not seem to be affected by the inclusion/exclusion of the considered controls.

References

  • Anderson W, Lakshmanan T (2007) Infrastructure and productivity: What are the underlying mechanisms? In: Karlsson C, Anderson W, Johansson B, Kobayashi K (eds) The management and measurement of infrastructure. Eduard-Elgar, Northhampton, pp 147–162

    Google Scholar 

  • Barca F (2009) An agenda for a reformed cohesion policy: a place-based approach to meeting European Union challenges and expectations. In: Independent report, prepared at the request of the European Commissioner for regional policy, Danuta Hübner, European Commission, Brussels

  • Barca F, McCann P, Rodríguez-Pose A (2012) The case for regional development intervention: place-based versus place-neutral approaches. J Reg Sci 52(1):134–152

    Article  Google Scholar 

  • Bertinelli L, Black D (2004) Urbanization and growth. J Urban Econ 56:80–96

    Article  Google Scholar 

  • Brülhart M, Sbergami F (2009) Agglomeration and growth: cross-country evidence. J Urban Econ 65:48–63

    Article  Google Scholar 

  • Camagni R, Capello R, Caragliu A (2013) One or infinite optimal city sizes? In search of an equilibrium size for cities. Ann Reg Sci 51:309–341

    Article  Google Scholar 

  • Capello R, Camagni R (2000) Beyond optimal city size: an evaluation of alternative urban growth patterns. Urban Stud 37:1479

    Article  Google Scholar 

  • Castells-Quintana D (2016) Malthus living in a slum: Urban concentration, infrastructure, and economic growth. J Urban Econ. doi:10.1016/j.jue.2016.02.003

    Google Scholar 

  • Castells-Quintana D, Royuela V (2014) Agglomeration, inequality and economic growth. Ann Reg Sci 52(2):343–366

    Article  Google Scholar 

  • Clifton J, Diaz-Fuentes D, Revuelta J (2013) Financing utilities: how the role of the European Investment Bank shifted from regional development to making markets. In: SSRN Working Paper

  • Diez T (2005) Constructing the self and changing others: reconsidering ‘Normative Power Europe’. Millenn J Int Stud 33(3):613–636

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Durlauf S, Johnson P, Temple J (2005) Growth econometrics. In: Aghion P, Durlauf S (eds) Handbook of economic growth. Elsevier, Amsterdam, pp 255–677

    Google Scholar 

  • Estache A (2010) Infrastructure finance in developing countries: an overview. EIB Pap 15(2):60–88

    Google Scholar 

  • European Commission (2003) The wider Europe neighbourhood. In: A new framework for relations with our eastern and southern neighbours. European Commission (COM 104 final). http://eeas.europa.eu/enp/pdf/pdf/com03_104_en.pdf

  • Fujita M, Krugman P, Venables A (1999) The spatial economy. MIT Press, Cambridge

    Google Scholar 

  • Gawrich A, Melnykovska I, Schweickert R (2010) Neighbourhood Europeanization through ENP: the case of Ukraine. J Common Mark Stud 48(5):1209–1235

    Article  Google Scholar 

  • Gifford JL (1996) Complexity, adaptability and flexibility in infrastructure and regional development: insights and implications for policy analysis and planning. In: Batten DF, Karlsson C (eds) Infrastructure and the complexity of economic development. Springer, Heidelberg, pp 169–186

    Chapter  Google Scholar 

  • Hakfoort J (1996) Public capital, private sector productivity and economic growth: a macroeconomic perspective. In: Batten DF, Karlsson C (eds) Infrastructure and the complexity of economic development. Springer, Heidelberg, pp 61–74

    Chapter  Google Scholar 

  • Henderson JV (2000) The effects of urban concentration on economic growth. In: NBER working papers series no. 7503. National Bureau of Economic Research, Cambridge, MA

  • Henderson JV (2003) The urbanization process and economic growth: the so-what question. J Econ Growth 8:47–71

    Article  Google Scholar 

  • Henderson JV (2005) Urbanization and growth. In: Aghion P, Durlauf S (eds) Handbook of economic growth, vol 1B. National Bureau of Economic Research Cambridge, Massachusetts, pp 1543–1591

  • Hewings GJD (2014) Spatially blind trade and fiscal impact policies and their impact on regional economies. Q Rev Econ Finance 54(4):590–602

    Article  Google Scholar 

  • Hyde-Price A (2006) ‘Normative’ power Europe: a realist critique. J Eur Public Policy 13(2):217–234

    Article  Google Scholar 

  • Kessides C (1996) A review of infrastructure’s impact on economic development. In: Batten DF, Karlsson C (eds) Infrastructure and the complexity of economic development. Springer, Heidelberg, pp 213–230

    Chapter  Google Scholar 

  • Kollatz-Ahnen M (2013) European intervention mechanisms for growth: budget and the European Investment Bank. Glob Policy 4(s1):41–49

    Article  Google Scholar 

  • Langan M (2014) A moral economy approach to Africa-EU ties: the case of the European Investment Bank. Rev Int Stud 40:465–485

    Article  Google Scholar 

  • Lesay I (2013) Discoursing development: the case of the European Investment Bank. Nova Econ 23(2):429–462

    Article  Google Scholar 

  • Lundqvist L, Mattsson LG (2002) National transport models: introduction and comparative analysis. In: Lundqvist L, Mattsson LG (eds) National transport models: recent developments and prospects. Springer, Heidelberg, pp 1–16

    Chapter  Google Scholar 

  • Newbery D (2012) Energy and infrastructure. In: Submission to the LSE growth commission Institute for government, London school of economics, London. http://tinyurl.com/c8qtahl

  • OECD (2009a) How regions grow. In: Organization for Economic Cooperation and Development Organization for Economic Cooperation and Development, Paris

  • OECD (2009b) Regions matter: economic recovery. In: Organization for Economic Cooperation and Development, Paris

  • OECD (2009c) Regions at a glance. Organization for Economic Cooperation and Development, Paris

  • Rodriguez-Pose A, Novak K (2013) Learning processes and economic returns in European Cohesion policy. Investig Reg 25:7–26

    Google Scholar 

  • Romp W, de Haan J (2007) Public capital and economic growth: a critical survey. Perspekt Wirtsch Ver Soc 8(s1):6–52

    Article  Google Scholar 

  • Royuela V, Suriñach J (2005) Constituents of quality of life and urban size. Soc Indic Res 74:549–572

    Article  Google Scholar 

  • Royuela V, Moreno R, Vayá E (2010) Influence of quality of life on Urban growth: a case study of Barcelona, Spain. Reg Stud 44(5):551–567

    Article  Google Scholar 

  • Royuela V, Ordóñez-Cuenca J (2016) Internal migration in a developing country: a panel data analysis of ecuador (1982–2010). Pap Reg Sci. doi:10.1111/pirs.12251

  • Straub S (2008) Infrastructure and growth in developing countries: recent advances and research challenges. In: Policy research working paper no. 4460. The World Bank

  • Wagenvoort R, de Nicola C, Kappeler A (2010) Infrastructure finance in Europe: composition, evolution and crisis impact. EIB Pap 15(1):16–39

    Google Scholar 

  • Wesselink E, Boschma R (2012) Overview of the European neighbourhood policy: its history, structure, and implemented policy measures. In: WP1/04 SEARCH working paper

  • World Bank (2009) World Development Report 2009: reshaping economic geography. World Bank, Washington, DC

Download references

Acknowledgments

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2010-2.2-1) under Grant Agreement No. 266834. We also acknowledge the support of ECO2013-41022R.

Author information

Authors and Affiliations

  1. Departamento de Economía Aplicada, Universidad Autónoma de Barcelona, Bellaterra, 08193, Barcelona, Spain

    David Castells-Quintana

  2. AQR-IREA Research Group, Universidad de Barcelona, Barcelona, Spain

    David Castells-Quintana & Vicente Royuela

Authors
  1. David Castells-Quintana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vicente Royuela
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vicente Royuela.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 477 KB)

Appendices

Appendix 1: Variables sources and definitions

Variable

Description

Source

Growth

GDP per capita growth (constant 2005 US$)

World Bank

Income

GDP per capita (constant 2005 US$)

World Bank

Investment

Gross capital formation (% of GDP)

World Bank

pop_g

Total population annual growth rate (three years average)

World Bank

Primacy

Population in the largest city (% of urban population)

World Bank

Sanitation

Improved sanitation facilities, urban (% of urban population with access)

World Bank

Water

Improved water source, urban (% of urban population with access)

World Bank

Teleph

Telephone lines (per 100 people)

World Bank

Cell

Mobile cellular subscriptions (per 100 people)

World Bank

Internet

Fixed broadband Internet subscribers (per 100 people)

World Bank

road_dens

Road density (km of road per 100 sq. km of land area)

World Bank

road_dens_2

Road density (km of road per 100 sq. km of agricultural land)

World Bank

road_paved_dens

Paved road density (km of paved road per 100 sq. km of land area)

World Bank

paved_road

Roads, paved (% of total roads)

World Bank

road_cong

Road congestion (km of road over total population)

World Bank

rail_density

Rail lines (total route-km) / Land area (sq. km)

World Bank

EIB_proyects

Total amount of EIB projects (€)

European Investment Bank

Appendix 2: EIB information

We recorded EIB projects signed since 1959 until 31/12/2013. The request was done through the EIB website (www.eib.europa.eu/projects/loans/list/index.htm), accessed on the \(20^{\mathrm{th}}\) of July 2014. All projects are assigned to one of the following sectors: 1-Telecom; 2-Composite infrastructures; 3-Education; 4-Health; 5-Solid waste; 6-Transport; 7-Urban development; 8-Water, sewerage; 9-Agriculture, fisheries, forestry; 10-Energy, industry; 11-Services; and 12-Credit lines. We consider the first eight sectors as potentially linked with infrastructure. Each of the 18,913 projects (10,899 projects since 1995) had information on the country, signature date, sector, description (89 % of the projects) and signed amount (in current €). For the descriptive statistics (see Supplementary material A), we built three alternative indicators of EIB investment:

  • As a % of GDP: we converted EIB investment into US dollars, by using the 2005 €/$ exchange rate. Finally, we divide this amount by GDP in current US dollars.

  • In terms of total population, using the GDP deflator and expressing the investment in terms of 2005 constant US dollars divided by total population.

  • In terms of urban population, using the GDP deflator and expressing the investment in terms of 2005 constant US dollars divided by urban population.

All indicators account for a span of five years (current and previous four years) in order to capture the time-to-build effect of investment.

Appendix 3: Result with other infrastructure variables

Dependent variable

(1) World

(2) ENPs

(3) World

(4) ENPs

growth

growth

growth

growth

\(\Delta \hbox {UC}\)

\(-0.349^{***}\)

\(-6.2156^{*}\)

\(-\)0.1909

\(-\)29.4048

(0.0634)

(3.1639)

(0.1550)

(19.0154)

\(\Delta \hbox {UC}^{*}\hbox {Infr}\)

\(0.0158^{***}\)

0.2442

0.0008

0.2827

(0.0057)

(0.1563)

(0.0039)

(0.1899)

Infr

\(0.0813^{***}\)

0.0002

\(0.1984^{*}\)

\(1.4820^{***}\)

(0.0271)

(0.1990)

(0.1071)

(0.2462)

Controls

Yes

Yes

Yes

Yes

Country fixed effects

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

Observations

669

58

650

58

N. of countries

172

15

168

15

  1. In columns (1) and (2) Infr is teleph. In columns (3) and (4) Infr is sanitation. Estimations using robust standard errors.
  2. * \(p<0.10\), ** \(p<0.05\), *** \(p<0.01\)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Castells-Quintana, D., Royuela, V. Spatially blind policies? Analysing agglomeration economies and European Investment Bank funding in European neighbouring countries. Ann Reg Sci 60, 569–589 (2018). https://doi.org/10.1007/s00168-016-0784-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00168-016-0784-3

Keywords

JEL Classification

Search

Navigation