Abstract
In recent years, there has been a growing interest in the geographic aspects of development or the question of where economic activities take place. There is an extensive literature in urban economics, location theory and economic agglomeration. In fact, many economic activities are concentrated geographically and most people in advanced countries or regions live in densely populated metropolitan areas. The main issue is how to explain this concentration. Most of the references assume two approaches, first nature (Sachs 2000) and second nature (Krugman 1993, 1999; Venables 2003), which are also identified as Sachs’ (first nature) and Krugman’s approach (second nature). Krugman’s New Economic Geography abstracts from natural conditions. It states that agglomerations can be explained by second nature alone (i.e. by man-made agglomeration economies due to increasing returns to scale and transportation costs), which arises endogenously in the economic process.
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Notes
- 1.
- 2.
However, it must be said that this indicator has important drawbacks. On the one hand, regions that are specialized in high-value added sectors will automatically display greater GDP values, while it could not necessarily reflect in the true level of spatial agglomeration of firms and workers. On the other hand, the level of GDP per km2 in a region like Madrid is possibly overstated because many workers commute everyday from neighboring Castilian provinces; as a result, the level of agglomeration in these Castilian provinces would be understated. In addition, it is known that first and second nature factors have different effects in different industries, as stated in Alonso-Villar et al. (2004). Using aggregate GDP does not allow analyzing this issue properly.
- 3.
In spatial econometric applications, some authors prefer to exclude those Spanish regions without neighbours (e.g. Márquez and Hewings 2003), since it is politically debatable how to connect them to the rest of the system.
- 4.
This data are available in the INE webpage: http://www.ine.es
- 5.
We have specified the spatial weights matrix, W, such that each element is set equal to 1 if province j has a common border with i, and 0 otherwise. Similar results have been observed with other specifications. These include an inverse distance matrix (such that each element w ij is set equal to the inverse of the squared distance between provinces i and j), and a matrix obtained from a 200 km distance threshold to define a province’s neighborhood set (as stated in Rey and Montouri 1999).
- 6.
We follow a general-to-specific modeling strategy. In a first regression, we include the complete set of first nature variables. In a step-by-step sequenced process, we exclude the variable with the lowest t-statistic and estimate the remaining equation again. This procedure is repeated until all coefficients are significantly different from zero at the 10% level.
- 7.
In semi-logarithmic equations, the dependent variable changes by [exp(b) − 1] ⋅100% if the explanatory variable changes from zero to one unit, where b is the explanatory variable coefficient.
- 8.
As shown in Anselin (1999), DWH test is consistent with spatially autocorrelated OLS residuals.
- 9.
Non-contemporary dependence between regressors and the error terms lead to asymptotically unbiased estimators only in absence of temporal autocorrelation. However, in our case it is difficult to suppose time independence between the error terms what could somewhat affect our results.
- 10.
The goodness of the instruments can be proved with the help of the Sargan test, which contrasts the null hypothesis that a group of s instruments of q regressors is valid. This is a Chi-2 test with (s–q) degrees of freedom that rejects the null when at least one of the instruments is correlated with the error term (Sargan 1964). In our case, we can clearly accept the null with a confidence level of 0.99. All the computations can be obtained upon request from the authors.
- 11.
This test has been constructed in the same fashion as in Burridge (1980). The spatial weight matrix is specified as in foot note 7.
- 12.
We have also estimated a groupwise heteroskedastic error model. In general, both GLS and LM estimations produce signifficant variance coefficients in each subspace, but cannot absorb all the heteroskedasticity and spatial dependence still present in the residuals.
- 13.
Factors have been extracted using principal components and rotated with Varimax method.
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Acknowledgements
Coro Chasco acknowledges financial support from the Spanish Ministry of Education and Science SEJ2006–02328/ECON and SEJ2006–14277-C04–01. The comments received by three anonymous referees are also gratefully acknowledged.
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Yrigoyen, C.C., López Garcı́a, A.M. (2010). Evolution of the Influence of Geography on the Location of Production in Spain (1930–2005). In: Páez, A., Gallo, J., Buliung, R., Dall'erba, S. (eds) Progress in Spatial Analysis. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03326-1_19
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