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Review of Regional Research

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08-12-2016 | Original Paper

An analysis of Okun’s law for the Spanish provinces

Author: Celia Melguizo

Published in: Review of Regional Research | Issue 1/2017

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Abstract

The inverse relationship between unemployment and Gross Domestic Product (GDP), commonly known as Okun’s law, has been traditionally analysed in the economic literature. Its application to Spain is particularly interesting due to the sharp effect that economic shocks have on unemployment. The purpose of this study is to analyse the relationship between unemployment and economic growth for the Spanish provinces between 1985 and 2013, a period characterized by economic booms and recessions that had a great impact on unemployment. After testing the time series properties of provincial GDP and unemployment, we specify and estimate the difference version of Okun’s law using VAR and panel VAR techniques. The obtained results point out that Spain’s provinces show large differences in their unemployment sensitivity to economic variations. In particular, provinces that show less diversified industries, a more developed services sector and higher rates of labour participation suffer from higher variations in unemployment rates.

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In appendix A, we highlight the differences in regional unemployment rates for Spain over the period. Maps show that unemployment rate differences among provinces are great and persistent.

Detailed information about the required data sets, the components and the sources of information are compiled in the table B.1 in the Appendix B.

Using CPI as a GDP deflator is a consequence of the lack of data on GDP deflation at the provincial level for part of the considered period. Hence provincial CPIs become the most suitable indicator to remove the effect of prices from the output. CPI is only available for provinces after 1993; we use the index for the provincial capitals for the previous years.

The EPA provides non homogeneous panel datasets. Occupation and participation data are furnished according to different criteria based on the time the information was collected. In this case, we follow De la Fuente (2012), who makes the required adjustments to link the 1976 to 1995 and 1996 to 2004 occupation and participation series to the 2005 to 2013 series. Differences are mainly due to sample replacement and methodological changes, such as questionnaire modifications and adjustments in the definition of occupation and unemployment. Annual and state adjustments are distributed among the provinces considering their weighting in the state occupation and labour force participation data.

Okun’s seminal work defined three different versions of the empirical relationship: the gap version, the specification in first differences and the dynamic one. In our analysis, we select the version in first differences. Literature has frequently resorted to the gap version, however it requires making strong assumptions on the unobserved macroeconomic variables (potential output and NAIRU). In addition, there is no agreement on the proper procedure to extract the trend component from the series and observe the effect of the cycle. These problems lead us to resort to the difference specification. Also, it is easier to reach stationary series when this version is used. Many analyses have resorted to this version. Among them, we find Mankiw (1994), Sogner and Stiassny (2002), Perman and Tavera (2005) Kosfeld and Dreger (2006), Knotek (2007), Gali et al. (2012) and Palombi et al. (2015a, 2015b).

Before estimating we need to perform unit root tests to know whether the series and panels with which we work are stationary. Stationarity ensures that the obtained results are not spurious. We obtain the panels and most series are generated by I(1) processes. Appendix C shows further information about the methodology and results obtained.

In order to apply PVAR technique, we resort to the Ryan Decker program, which is an update version of the Inessa Love original package, used in Love and Zicchino (2006), among others.

AIC, HQIC and SBIC are repectively the Akaike, Hannan-Quinn and Schwarz information criteria.

More lags have also been included in the specification and results are mostly the same.

As we orthogonalize the variables as Sims (1980) proposes and, thus, we assume that a GDP shock affect unemployment in the same period, we should express the VAR in the following way: ∆\(u_{t}\,\)= c + λ∆\(y_{t}\) + α(L) ∆\(u_{t}\,\)+ β(L) ∆\(y_{t}\,\)+ \(v_{t}^{u}\); ∆ \(y_{t}\,\) = c + γ(L) ∆ \(y_{t}\,\) + η(L) ∆ \(u_{t}\,\) +. \(v_{t}^{u}\)

We have estimated the gap version of Okun’s law using the Hodrick Prescott filter in order to check our specification. We aim to know if the results obtained are comparable with those obtained by the authors that consider the gap version. Appendix D shows that both versions provides us a similar province ordering regarding the value of the coefficient.

The ordering of the variables in the VAR model could determine the results obtained. For this reason, and in order to check GDP growth causes unemployment rate variations for most provinces, we show the results obtained when we change the ordering of the variables in the Appendix E.

Detailed information about the required data sets, the components and the sources of information are compiled in the table B.1 in the Appendix B.

Urban, south and coast are dummy variables. Urban variable takes the value 1 if one of the ten biggest Spanish municipalities is located in the province; and 0 otherwise. South variable takes the value 1 for the provinces belonging to Andalusia, Extremadura, Castile-La Mancha and Canary Islands NUTS 2 regions; and 0 otherwise. These regions are located in the south of Spain and are those that have traditionally had higher rates of unemployment over the years. Finally, coast takes the value 1 if the province is located in the coast; and 0 otherwise.

In addition to these variables, we consider wages, levels of education, trade unions and the employment share in the public sector as independent variables in the analysis. Results point out that these variables do not significantly affect unemployment sensitivity.

Diversification index is expressed as: \(\mathrm{D}_{\mathrm{i}}=\, -\overset{\mathrm{J}}{\underset{\mathrm{j}=1}\sum }\left [\frac{\mathrm{X}_{\mathrm{ij}}}{\mathrm{X}_{\mathrm{i}}}\ln \left (\, \frac{\mathrm{X}_{\mathrm{ij}}}{\mathrm{X}_{\mathrm{i}}}\right )\right ]\), where \(\mathrm{X}_{\mathrm{ij}}\) represents the total employment in industry j and province i, whereas \(\mathrm{X}_{\mathrm{i}}\) is the total employment in province i.

This number of lags is given by the following formula: int{4(T/100)^2/9}.

Other criteria are also used in order to obtain robust results. We consider the AIC criterion in the Levin Lin Chu and Im Pesaran Shin tests to select the lag length.

Unit root tests of the variables in levels are available from the author on request.

In Fig. E.1, for each province we show two graphs. The second graph is the graph that was represented in Fig. 5. Meanwhile, the first graph represents the Impulse Response Functions when variables are orthogonalized in the opposite direction. Thus, the first graph represent the effect that an economic growth shock has on unemployment rate variation after one period as it is indicated in the following equation: ∆ \(u_{t}\,\) = c + α(L) ∆ \(u_{t}\,\) + β(L) ∆ \(y_{t}\,\) + \(v_{t}^{u}\) ; ∆ \(y_{t}\,\) = c + λ∆ \(u_{t}\,\) + γ(L) ∆ \(y_{t}\,\) + η(L) ∆ \(u_{t}\,\) +. \(v_{t}^{u}\)

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Title: An analysis of Okun’s law for the Spanish provinces
Author: Celia Melguizo
Publication date: 08-12-2016
Publisher: Springer Berlin Heidelberg
Published in: Review of Regional Research / Issue 1/2017
Print ISSN: 0173-7600
Electronic ISSN: 1613-9836
DOI: https://doi.org/10.1007/s10037-016-0110-7