Abstract
The regional relationships between agglomeration and economic growth are expected to be different in different types of regions. In the literature of the new economic geography it is common to stress the importance of access to cities with agglomeration of economic activities in the form of firms and households in order to be able to explain regional growth. However, it is also well known that many rural areas are performing fairly well in terms of employment and economic opportunities.
The purpose of the present research is to analyze if concentration of population drives economic growth or if it is the other way around. A second question is if this relationship between concentration of population and growth is different in different types of regions.
In order to shed light on these two questions the economic performance of three types of Swedish regions (metropolitan-, cities- and rural regions) is related to changes in population densities.
In the empirical analysis the Shannon index is used in the measurement of regional concentration. By considering the effect of previous levels of the Shannon index on average wages we extract information on how regional concentration affects regional economic growth (expressed as growth in average wages). In the empirical analysis we employ a VAR Granger causality approach on regional Swedish yearly data from 1987 to 2006. From this analysis we are able to conclude that there are strong empirical indications that geographic agglomeration of population unidirectionally drives economic growth in metropolitan- and city regions. Concerning the rural regions no such indication is found in either direction. This is a fairly strong indication that urban regions are more dependent on economies of agglomeration compared to rural areas.
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Notes
- 1.
DVAR(2) is an abbreviation for differenced vector autoregressive model of order 2. Moreover, notice that even if there is only one single lag in a DVAR model this implies that the lag memory is infinite. For instance, the lag memory for an AR(1) parameter equal to β is equal to βs after s time periods. According to Enders (2004) one should apply a lag length that is maximally T1/3 where T is the number of observations, which implies that a maximum lag of T1/3 = 201/3 ≈ 3 lags should be applied. However, obviously, an information criteria (Schwarz-Bayes criteron) is applied to determine whether 1, 2 or 3 lags are appropriate.
- 2.
Sims, Stock, and Watson (1990) prove that these problems are eliminated asymptotically for VAR models. However, asymptotic results are not directly relevant in this paper since few observations are available.
References
Ayat L, Burridge P (2000) Unit root tests in the presence of uncertainty about the non-stochastic trend. J Econ 95(1):71–96
Bosker M (2007) Growth, agglomeration and convergence: a space-time analysis for European regions. Spat Econ Anal 2(1):91–110
Brulhart M, Mathys NA (2008) Sectoral agglomeration economies in a panel of European regions. Reg Sci Urban Econ 38:348–362
Ciccone A (2002) Agglomeration effects in Europe. Eur Econ Rev 46:213–227
Ciccone A, Hall RE (1996) Productivity and the density of economic activity. Am Econ Rev 86(1):54–70
Clements MP, Hendry DF (1996) Intercept corrections and structural change. J Appl Econ, Special issue: Econometric forecasting, 11(5):475–494
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427–431
Doan T (2007) RATS version 7, user’s guide. Estima, Evanston
Dolado J, Jenkinson T, Sosvilla-Rivero S (1990) Cointegration and unit roots. J Econ Surv 4(3):249–273
Elder J, Kennedy PE (2001) Testing for unit roots: what should students be taught? J Econ Educ 32(2):137–146, Spring
Elliott G, Rothenberg TJ, Stock JH (1996) Efficient tests for an autoregressive unit root. Econometrica 64:813–836
Enders W (2004) Applied econometric times series, Wiley series in probability and statistics. Wiley, Hoboken
Fujita M, Thisse JF (2002) Economics of agglomeration: cities, industrial location, and regional growth. Cambridge University Press, Cambridge
Fujita M, Krugman P, Venables AJ (1999) The spatial economy. Cities, regions and international trade. The MIT Press, Cambridge, MA
Glaeser EL, Kallal HD, Scheinkman JA, Shleifer A (1992) Growth in cities. J Pol Econ 100(6):1126–1152
Hamilton JD (1994) Time series analysis. Princeton University Press, Princeton
Hanson GH (2001) Scale economies and the geographic concentration of industry. J Econ Geogr 1:255–276
Henderson JV (1988) Urban development: theory, fact, and illusion. Oxford Univ. Press, New York
Hirschman AO (1958) The strategy of economic development. Yale University Press, New Haven
Holden D, Perman R (1994) Unit roots and cointegration for the economist. In: Rao BB (ed) Cointegration for the applied economist. St. Martin’s, New York, pp 47–112
Ilbery B, Bowler I, Clark G, Crockett A, Shaw A (1998) Farm-Based tourism as an alternative farm enterprise: a case study from the Northern Pennines, England. Reg Stud 32(4):355–364
Jacobs J (1984) Cities and the wealth of nations. Random House, New York
Johansen S (1995) Identifying restrictions of linear equations with applications to simultaneous equations and cointegration. J Econ 69:111–132
Johansson B, Klaesson J (2008) Landsbygdsföretagare måste fixa långa avstånd. In Ska hela Sverige leva? Formas Fokuserar
Jordbruksverket (2009) RA09:2, Nilsson, J., Petersson, M., and Petersson, L., Företagandet på Landsbygden. Stad eller land, gör det någon skillnad?
Kaldor N (1970) The case for regional policies. Scott J Pol Econ 17(3):337–348
Klaesson J, Pettersson L (2009) Urban–rural development in Sweden. In: Karlsson C, Andersson AE, Cheshire PC, Stough RR (eds) New directions in regional economic development. Springer, Berlin/Heidelberg
Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? J Econometrics 54:159–178
Labrianidis L (2004) The future of Europe’s rural peripheries, Economic geography series. Ashgate, Aldershot
Lucas RE (1988) On the mechanics of economic development. J Monetary Econ 22:3–42
Mizon GE (1995) A simple message for autocorrelation correctors: don’t. J Econ 69:267–288
Perron P (1988) Trends and random walks in macroeconomic time series. J Econ Dyn Control 12(12):297–332
Phillips PCB, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75:335–346
Polese M (2005) Cities and national economic growth: a reappraisal. Urban Stud 42(8):1429–1451
Quigley JM (1998) Urban diversity and economic growth. J Econ Perspect 12:127–138
Romer P (1990) Endogenous technological change. J Pol Econ 98:71–102
Romer PM (1994) The origins of endogenous economic growth. J Econ Perspect 8(1):3–22
Sbergami F (2002) Agglomeration and economic growth. Some puzzles, HEI working paper, No: 02/2002
Schön L (2000) En modern svensk ekonomisk historia: tillväxt och omvandling under två sekel, SNS förlag
Sims C (1980) Macroeconomics and reality. Econometrica 48:1–48
Sims CA, Stock JH, Watson MW (1990) Inference in linear time series models with some unit roots. Econometrica 58(1):161–182
SOU 2006:106 Fakta – omvärld – inspiration
Toda HY, Phillips PCB (1994) Vector autoregression and causality: a theoretical overview and simulation study. Econo Rev 13:259–285
Varga A, Schalk HJ (2004) Knowledge spillovers, agglomeration and macroeconomic growth: an empirical approach. Reg Stud 38(8):977–989
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Appendices
Appendix 1: The Elder and Kennedy Unit Root Testing Strategy
Due to the fact that the applied unit root tests in this study (ADF, PP, ERS, KPSS and DF-GLS) can be specified with a constant, or a constant and a linear time trend, it is necessary to apply a unit root testing strategy. For instance, unit root testing strategies are proposed by Perron (1988), Dolado et al. (1990), Holden and Perman (1994), Ayat and Burridge (2000), Enders (2004), and Elder and Kennedy (2001). However, due to the subsequently mentioned reasons, the unit root testing strategy by Elder and Kennedy (2001) is applied in this paper. The main advantage of this strategy in comparison to the other strategies is that Elder and Kennedy utilize prior economic theoretical knowledge regarding certain variables, instead of applying sequential significance testing. Based on economic theory, we know that the Shannon index is a function of time and that the per capita wages in an industrialized country grows over time in the long run. Therefore, there is unnecessary to test whether the variables are trending or not. Another advantage of the Elder and Kennedy (2001) approach is that this strategy does not consider outcomes of a unit root test that are not realistic, for example the simultaneous existence of a unit root and a deterministic trend (see Perron 1988, p. 304 and Holden and Perman 1994, p. 63). A third important reason why the Elder and Kennedy approach is attractive is that it can avoid the mass significance that is the consequence of repeated sequential testing. Consequently, the Elder and Kennedy (2001) approach is applied for this paper and the results are presented in the following Table (6.4) below.
Appendix 2: Impulse Response Functions
First of all, in the above graphs we can clearly observe that all shocks that these IRFs is exposed to, gradually dies away. This is an indication that the system is stable which is a necessary and important condition in Granger causality analysis. In the metropolitan regions, in Fig. 6.7 we can generally observe a positive response. Due to the fact that the significance is not equally strong in the city regions, as in the metropolitan regions, all responses on ΔW due to shocks in ΔS for the city regions are always within the error margin, and are thus not statistically significant. Consequently, we cannot draw any clear-cut conclusions from the graph in Fig. 6.8. However, the point estimates are almost all the time above zero, which is an indication that our theory may possibly be correct. In the last table, Fig. 6.9, the impulse response functions are continuously not significantly different from zero, which is what we expected based on our theory and on our prior analysis results.
Impulse response functions for the metropolitan regional model (response to Cholesky one s.d. innovations ± 2 S.E, that is, response of ΔW to ΔS)
Impulse response functions for the city regional model (response to Cholesky one s.d. innovations ± 2 S.E, that is, response of ΔW to ΔS)
Impulse response functions for the rural regional model (response to Cholesky one s.d. innovations ± 2 S.E, that is, response of ΔW to ΔS)
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Hacker, S.R., Klaesson, J., Pettersson, L., Sjölander, P. (2013). Regional Economic Concentration and Growth. In: Klaesson, J., Johansson, B., Karlsson, C. (eds) Metropolitan Regions. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32141-2_6
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