Abstract
We explore whether finance influences the impact of labour market institutions on unemployment. Using a data set of 18 OECD countries over 1980–2004, we estimate a panel Vector AutoRegressive model. We check whether causalities from labour market variables to unemployment are affected by financial factors. In Belgium, Italy, Australia, Japan and Spain, accounting for financial indicators mitigates the benefits of labour market flexibility or makes it harmful to employment. In Austria, Canada, Finland and Portugal, it reduces its detrimental impact or makes it beneficial. In Ireland and Netherlands, both effects prevail, depending on the labour market indicator used.
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Notes
This time-series indicator is based on EPL scores obtained from Nickell et al. (2005) as well as on measures of structural reforms provided by the FRDB Database (the number of reforms passed each year in each country, whether they are directed towards more flexibility as well as whether they apply to all, or a large majority of professional categories, contract typologies etc).
As stressed by Kónya (2006), this definition implies causality for one period ahead.
As it is now well known, testing for non-causality is addressed in a time-series setting, in particular by Phillips (1995) in the context of a level VAR estimated using the Fully Modified estimator, as well as by Toda and Yamamoto (1995) who suggest augmenting the VAR by the maximal order of integration for the process being examined. The former method provides some very interesting results as far as asymptotic inference is concerned. The most important one is that statistical inference in this framework can be conducted by means of standard asymptotics. In this case, no unit root limit theory is required. Normal and mixed normal limit theory is applied to the stationary and non-stationary components of the VAR, respectively. This implies that optimal inference in level VARs and Wald test for non-causality can be obtained without prior knowledge of the number of unit roots or the order of cointegration rank in the system, and without the use of reduced rank Johansen-type regressions (which are subject to pretesting bias, since tests for cointegration ranks are extremely sensitive to the values of the nuisance parameters). The approach by Toda and Yamamoto (1995) also results in a standard Wald statistic for non-causality restrictions, although it requires some pretesting for determining the lag length of the VAR.
Note that our estimates do not allow to check whether the mitigation effect results from a rise or decline in financial development or banking concentration.
It is also true that reduced wage coordination becomes harmful to employment when introducing intermediated credit in the estimation (Part C of Table 3). But this effect appears less significant than the beneficial effect of introducing banking concentration.
The detrimental effect of reduced wage coordination in the case of Ireland is in line with Baker et al. (2004), who stress the role played by high wage coordination in the Irish employment success since the 1980s.
As pointed out by a referee, ‘possible breaks can affect causality tests if present in data’. For this reason it seems important to test for the presence of breaks in the data even if our approach does not require to pretest for unit roots and cointegration. We thus employed the panel data unit root test based on the Lagrangian multiplier (LM) principle developed by Im et al. (2005). This approach is very flexible since it can be applied not only when a structural break occurs at a different time period in each time series, but also when the structural break occurs in only some of the time series. It is also robust to the presence of structural breaks and more powerful than the popular Im et al. (2003)'s test in the basic scenario where no structural break is involved. Furthermore, as reported by Im et al. (2005), since the LM test loses little power when controlling for spurious structural breaks when they do not exist, this represents a reasonable strategy to control for breaks even when they are only at a suspicious level. Implemented on all series taken in level (labour market variables, LAB; financial variables FIN; unemployment, U), this test does not provide any clear evidence supporting the existence of a significant structural break in the data. Similar results are obtained with the approach of Lee and Strazicich (2003), employing minimum LM tests. We also performed a robustness check and estimated panel VAR models (composed of equations 1 and 2) including time-specific intercepts (β t ) in order to control for possible common macroeconomic shocks affecting all countries at period t. Our overall causality results, available upon request, are qualitatively unchanged.
The paper by Koskela and Stenbacka (2004) provides an interesting example of the emphasis put on interactions between labour market institutions and banking concentration in Scandinavian countries.
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We are grateful to a referee for his valuable remarks on an earlier draft of the paper. The usual disclaimers apply.
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Rault, C., Vaubourg, AG. Labour Market Institutions and Unemployment: Does Finance Matter?. Comp Econ Stud 54, 43–64 (2012). https://doi.org/10.1057/ces.2011.28
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DOI: https://doi.org/10.1057/ces.2011.28