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Regional specialisation and industry location in the long run: Spain in the US mirror (1856–2002)

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Abstract

This paper studies the long-run pattern of regional specialisation in Spain for the period 1856–2002. We have obtained an inverted U-shape trend in manufacturing specialisation which is similar to that in the USA. Using a model that nests factor endowments and increasing returns, we find that both the latter influence manufacturing location, albeit with variations over time, factor endowments being the most decisive. Manufacturing industries intensive in agricultural and mining inputs are located near regions with these factor endowments, while human capital location gained importance from 1965 onwards. Being located near the market was also significant for some industries. The inverted U-shape observed in manufacturing specialisation is due to the fact that the importance of immobile factors increased regional specialisation. However, when mobile factors increased, as was the case with skilled labour, and when this factor converged across regions, specialisation decreased. The fact that the importance of central markets diminishing also contributed to this trend.

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Notes

  1. The sources are explained in Sect. 1.1.

  2. We decided to use the autonomous regions (NUTS 2, in the EU classification of regions) because this is how Spain is currently organised and in fact has been since 1978. Moreover, in the case of the USA, Kim (1995, 1998) obtains similar patterns with different regional aggregations.

  3. Manufacturing data are significantly limited in the regions of the Basque Country and Navarra. We have no data for 1856 and we only have data related to corporate income tax paid by large companies between 1913 and 1929. In the case of the Basque Country, as this region began to gain importance as a manufacturing centre in 1870 and, moreover, the region is specialised in heavy industry, characterised by large companies, this data restriction is of only minor importance.

  4. Krugman (1991a, b) constructs this index to compare the regional specialisation pattern in Europe and the USA. We have divided the index by 2 to make it more easily comparable with other measures of concentration that take values from 0 to 1.

  5. Midelfart-Kanrvik et al. (2000a) explain that the differences in the size and geographical shapes of regions in the comparison of regional specialisation and concentration between the US and the EU are likely to increase the value of both measures of specialisation and also concentration for those using smaller geographical units, which is the case of the US, although time series trends can be compared. In our case, the effect would be to increase the value of specialisation and concentration for Spain.

  6. We have divided the data from Kim (1998) by 2 to make them comparable to our index of specialisation, which has also been divided by 2, along with other measures of concentration, so that it yields a value of between 0 and 1.

  7. In the case of the USA, the index has been constructed using employment data. In the case of Spain, added value data have been used with the exception of 1856, 1913 and 1929, when they are proxied using corporate income tax. Again we have divided Kim’s data by 2 for comparative purposes.

  8. See Hanson (2001) for a survey of the studies that consider the importance of increasing returns.

  9. We consider that regions where the agricultural sector is more important (with a larger share of the labour force) are specialised in agriculture or have the endowment of agriculture, as is also the case in Crafts and Mulatu (2005).

  10. We have calculated region i’s market potential as the sum of the GDP of region j inversely weighted by the circular distance between regions i and j and the GDP of region i inversely weighted by its own distance, proxied by 0.333 × √(area/Π), following Keeble et al. (1982), which gives a distance value of one-third of the radius of a circle of the same area as region i. We have calculated it for both the domestic market and the markets in neighbouring countries.

  11. Here we reproduce and summarise the Midelfart-Knarvik et al. model. See Midelfart-Kanrvik et al. (2000a, b) for more detail.

  12. In the estimation of the model for the EU, Midelfart-Knarvik et al. use a single time period for country and industry characteristics (close to 1990, from 1970–1997).

  13. We make the assumption that the intensity with which agriculture, mining and inputs are used has not changed over this period of the sample, but we are considering the relative differences in the intensity of use between industries.

  14. By this assumption, we are only considering the relative intensity of the use of skilled labour across industries, but not over time.

  15. In spite of considering productivity in 1955 for the previous years, as the same industries have always been found to record the highest and lowest productivity, we can consider that the ranking obtained is representative of the differences in productivity between them.

  16. We have obtained similar results using both kinds of instruments in the first period, but we have not been able to use market potential lagged 1 year for the second period because it is not a valid instrument (J-Hansen test). We present the results with market potential without region own market instrument in Table 10.

  17. We have checked the coefficients estimated are stable by means of a Chow test of structural stability.

  18. We have tested the impact of all market potential interactions individually and have obtained the same result.

  19. We present the adjusted coefficients with the IV estimation with time year dummies in order to clarify that even in this case, when more NEG factors were significant, H–O factors were still more important.

  20. In the case of the pooled IV estimation with time year dummies, the decrease in the coefficient of the NEG factor own sector intermediate inputs occurred throughout the period, but was particularly marked in the second period, 1965–2002.

  21. The exception is 1856, when the NEG factor coefficient is greater than H–O coefficients.

  22. There were also changes in the share of natural resources across regions over time, as we have commented on previously in the data section.

  23. The IV pooled estimation with time year dummies yielded a market effect for 1856, 1913 and 1929 due to the importance of being close to central markets for industries intensive in the use of their own industrial inputs. Only in 1856 was the NEG market factor more important than the H–O factors.

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Authors and Affiliations

  1. Facultad de Economía, Universidad de Valencia, 46022, Valencia, Spain

    Concha Betran

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Correspondence to Concha Betran.

Data appendix

Data appendix

1.1 Manufacturing regional specialisation

We used the income tax paid by industries called Contribución Industrial y de Comercio for 1856. In the case of 1913 and 1929, only tax data were available, obliging us to add a new tax on large companies to the foregoing source, the Contribución de Utilidades. We have elaborated an approach to both taxes in Betrán (1999) “Localización y difusión industrial en España durante el primer tercio del siglo XX”, Revista de Historia Industrial, 3, pp. 663–696 for 1913 and 1929. For the years from 1955 to 1993, we have data on manufacturing industrial added value by region elaborated by the Fundación BBV, from 1955 to 1993, Renta Nacional de España y su distribución provincial, Banco de Bilbao y Banco Bilbao-Vizcaya.

1.1.1 Regional data set

Share of Agricultural labour force (percentage): in Germán et al. (2001): Historia económica regional de España, siglos XIX y XX, Barcelona, Crítica, pp. 568–569 and Instituto National de Estadistica, INE, for 2002. The years before 1930 are from the male agricultural labour force because of census problems in the registration of women.

Mining Resources: Energy products, metal minerals and non-metal minerals are from Estadísticas Mineras y Metalúrgicas de España for 1861, 1913, 1929, 1955, 1965, 1974, 1985, 1994 and 2002.

Education: From 1856 to 1955, percentage of the population over 10 years old able to read and write, in Germán et al. (2001), p. 572. From 1965 to 2002, percentage of labour force with secondary and tertiary education from Mas et al. (2005): Capital humano, Series históricas, 1964–2004, Valencia, IVIE.

Market Potential: Domestic market: GDP per capita by region: factor cost GDP (in billions of 1986 pesetas) and Population (thousands of inhabitants) from Rosés et al. (2010), from 1860–1930, Fundación BBV from 1955–1999 in Germán et al. (2001), pp. 587 and 565 and Contabilidad Regional by the INE for 2002. Neighbouring countries market: France, the UK, Germany, the USA, Italy, and Portugal from Prados de la Escosura (2000): “International comparisons of real product, 1820–1990: an alternative data set”, Explorations in Economic History, 37, pp. 1–41. Population data from Maddison, A. web page. 1986 constant prices using implicit deflactor from Prados de la Escosura (2003): El progreso económico de España, 1850–2000, Bilbao, Fundación BBVA. To calculate distance equivalence to trade tariffs, we have followed Crafts’ methodology. Following Crafts (2005), 100% raising distance is equivalent to 80% raising trade tariff, being tariff = 1 + t. This calculation is obtaining from Estevadeordal et al. (2003): “The Rise and Fall of World Trade, 1870–1939”, The Quaterly Journal of Economics, vol. CXVIII, May, pp. 359–407. As Crafts, trade tariffs are taken from Bairoch (1993): Economics and world history: myths and paradoxes. Chicago, University of Chicago Press, p. 40, from 1820 to 1990, as there are no tariffs for Portugal, we have considered the same as in Spain. Normal distances (great circle distances) are considered from the Spanish integration in the EU in 1986, thus in 1993 and 2002 for the trade between Spain and the countries considered belonging to the UE. For the USA, the tariff for 2002 is of 2004 from Word Trade Organization and normal distances have been considered for 2002.

1.1.2 Manufacturing data set

Input–Output data: Agricultural Inputs over gross output, Mining inputs over gross output, Own sector intermediate inputs over gross output, Other sector intermediate inputs over gross output and Sales to industry over output are constructed from 1954 Input–Output Table: La estructura de la Economía Española, Tabla Input–Output, Instituto de Estudios Políticos, Madrid, 1958, for the years 1856, 1913, 1929 and 1955 (sector 1 and 5 and sector 8, 9 and 10, respectively, have the same input–output data), and 1985 Input–Output Table, Instituto National de Estadistica (INE), for the years 1965, 1973, 1985,1995 and 2002.

White-collar workers on employment from 1981 Census of Population, Instituto National de Estadistica (INE) for all the years. Sector 1 and Sector 2 are jointly.

Manufacturing industrial added value (VAB) by employment (L) by manufacturing industry in Fundación BBV, Renta Nacional de España y su distribución provincial, Banco de Bilbao y Banco Bilbao-Vizcaya, from 1955 to 1993 and Contabilidad Regional (INE) for 2002. For 1856, 1913, 1929 and 1955 we use 1955 data.

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Betran, C. Regional specialisation and industry location in the long run: Spain in the US mirror (1856–2002). Cliometrica 5, 259–290 (2011). https://doi.org/10.1007/s11698-010-0060-1

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