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A Nonlinear Relationship: Unemployment and Economic Growth (Construction Sector) in Spain, 1995.1–2012.2

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Abstract

A quadratic version of the first-difference Okun’s Law model was estimated for Spain (1995.Q1-2012.Q2). An accelerationist version of Okun’s Law was obtained, which allowed us to calculate variable Okun coefficients as well as critical points in the relationship between construction sector growth and the variation in overall unemployment. The optimal economic growth rate was determined to be 7.38 %. By applying principal components, it is demonstrated that this sector led the economic process after 1995.

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Notes

  1. Balassa claims that this was the case in Argentina, Brazil, Chile, Colombia, India, Israel, Korea, Mexico and Taiwan.

  2. It is not the objective of this document to discuss the most suitable economic growth model, nor to propose another one, but rather to apply a nonlinear econometric technique that overcomes linear econometric issues, as well as to provide an important economic introspection.

  3. This is how Arellano and Bentolila (2009) referred to the surprising economic expansion from 1995 to 2005.

  4. Okun uses linear regressions in his three models.

  5. Knotek (2007) and Virén (2001), among others.

  6. In the same theoretical vein, further research has been done for the United States and other developed countries, such as Friedman and Watcher (1974), Gordon and Clark (1984), Evans (1989), Prachowny (1993), Weber (1995), Attfield and Silverstone (1997), Knotek (2007), Owyang and Sekhposyan (2012), Cazes et al. (2012), and Ball et al. (2012).

  7. The other two are the output gap model and the fitted trend and elasticity model.

  8. The first reform of the Workers’ Statute was introduced in 1984 with the main objective of reducing the high level of unemployment by encouraging temporary work.

  9. See section II in the Appendix.

  10. The partial correlation coefficient is −0.55 (t = −5.29). As can be seen, Fig. 3 is far from showing a clear linear fit. Nonetheless it is negative.

  11. Variables in different units are made comparable through the following normalization procedure: \( \frac{x_{it-}{\overline{x}}_{{}_t}}{SD} \), where SD = standard deviation and \( {\overline{x}}_{{}_t} \) = arithmetic median.

  12. As mentioned, this has been noted by Silvapulle et al. (2004)and Knotek (2007). Palley (1993) noticed this fact even earlier and thus reformulated Okun’s Law to take into account these asymmetries for the United States economic cycle.

  13. As suggested by a referee.

  14. Pazos (1977), García et al. (1997), Kumbhakar (1997), Hernández et al. (2001), Troncoso (2001), Pech et al. (2002), Díaz (2003), Ferrer et al. (2004), Rebollar et al. (2007), Casas et al. (2009).

  15. Variables specified this way are stationary. It is therefore appropriate to use OLS. See Table  3.

  16. R2 = 0.79; DW = 1.61; JB = 0.24(0.88); LM(2) = 0.90(0.41); ARCH(2) = 0.25(0.77); WHITE(n.c) = 1.96(0.12); RESET(2) = 1.95(0.15). d is a dummy (2008.4, 2009.1, and 2012.2 = 1, and 0 otherwise) that captures the unemployment outliers (highest values).

  17. An explanation of this could be that above the optimum, economic growth would be increasingly based on labor productivity, which would require fewer workers.

  18. This feature has already been mentioned.

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Acknowledgements

Center of Modeling and Economic Forecasting, School of Economics, National Autonomous University of Mexico (UNAM). This article is part of the research project Mexico: growth, cycle and labor precariousness 1980-2020 (IN302514), DGAPA, UNAM. We thank Catalina Libreros for her technical assistance.

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  1. National Autonomous University of Mexico, Mexico, Mexico

    Eduardo Loría & Emmanuel Salas

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  1. Eduardo Loría
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Correspondence to Eduardo Loría.

Appendices

Appendix

Table 4 A. Eigenvalues and variance explained by the principal components 1995.1–2012.2
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Table 3 A. Unit root tests, 1995.1–2012.4
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Methodology and Statistics

Principal Components

Principal component analysis is a multivariate statistical technique to select information contained in a set of p variables of interest in m new independent variables. By means of linear combination of the original variables it is possible to achieve a space reduction. In this regard, Shlens (2005) proposes principal components as a standard, non-parametric method to extract information, as it is a statistical technique that synthesizes information or reduces the dimension (i.e. number of variables), losing as little information as possible.

In general, there is a reduced set of factors that can explain most of the total variability of an information set. The contribution of the other factors is typically minor. Thus, one of the issues at hand is determining the number of factors that should be preserved, in order to observe the principle of parsimony (U. de Oviedo 2014). Several criteria exist for determining the number of factors that should be kept. One of the best known and used is the Kaiser (1960) rule, which suggests maintaining only those factors whose eigenvalues are greater than unity (Horn 1965). This criterion can sometimes overestimate the number of components that should be selected; for this reason, Cattell (1966) suggests representing in a coordinate plane the eigenvalues (vertical axis) and the component number (horizontal axis) and selecting the higher values in the graph.

In Table 4 the main economic sectors are considered: a) agriculture, livestock and fisheries, b) industry and energy, c) services and d) construction. Following Horn (op.cit.), construction has the highest eigenvalue (2.27) which explains almost 57 % of the variance.

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Loría, E., Salas, E. A Nonlinear Relationship: Unemployment and Economic Growth (Construction Sector) in Spain, 1995.1–2012.2. Int Adv Econ Res 20, 439–453 (2014). https://doi.org/10.1007/s11294-014-9496-6

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