javier ordóñez. real unit labour costs in eurozone countries: drivers and clusters

Real unit labour costs in Eurozone countries:Drivers and clusters

Javier OrdóñezHector SalaJosé I. Silva

Eesti PankJune 18, 2014

Introduction:

Can nominal convergence last in the absence of real convergence?

Our view is that the EU policy mix has been successful in terms of nominal convergence but has not been conducive to overall growth nor to real convergence across the euro area.

The current economic crisis is the consequence of differences in competitiveness that generate real divergence and, therefore, growing account imbalances.

In this paper we take the real unit labour cost (RULC) as a relevant indicator of competitiveness and, as such, as a driver of real convergence. We examine to what extent our hypothesis of latent divergence forces holds by clustering the RULC according to its performance in a selection of 11 Eurozone economies.

Analytical decomposition:

RULC can be defined as:

Or alternatively,

(1)

Output per employee can be expressed as:

(2)

Inserting (2) in (1) and differentiating we get:

Empirical decomposition: data

Annual data (1980 – 2012).EC DG ECFIN

Empirical decomposition: data

Empirical decomposition: data

Empirical decomposition: data

Empirical decomposition: evolution by components

Empirical decomposition: scenarios

Empirical decomposition: evolution by components

Cluster analysis: methodology

To test for clusters we use the Phillips and Sul (2007, 2009) methodology.

These authors decomposed the variable of interest in two components, one common, one idyosincratic, both of which are time varying:

The time varying representation in can be used to separate common from idiosyncratic components in the traditional decomposition of panel data:

where git embodies systematic components, including permanent components that give rise to cross section dependence, and ait represents a transitory component.

Cluster analysis: methodology

This simple econometric representation can be used to analyze growth convergence by testing whether the factor loadings converge.

Phillips and Sul (2009) proposed a modification of the neoclassical growth model so that technological growth rates differ across and over time and are endogenously determined.

To account for temporal and transitional heterogeneity, Phillips and Sul (2009) introduced time-heterogeneous technology by allowing technological progress, , to follow a path of the form

Cluster analysis: methodology

Under this heterogeneous technology the individual transition path of log per capita real income evolves as:

is the initial level of log per capita real income

is the steady-state level of log per capita real income

is the time-varying spped of adjustment

Cluster analysis: methodology

This equation can be expressed in form of the time-varying representation:

This dynamic factor formulation involves:

1.A growth component common across countries (represents commonly available world technology such as the industrial an scientific revolution and internet technology).2.An individual transition factor which measures the transition path of a economy to the common steady-state growth path, μ

During transition depends on: 1.The speed of adjustment of convergence parameter, βit

2.The rate of technological progress, xit

3.And the initial endowment and steady-state level through the parameter ait

Cluster analysis: methodology

Phillips and Sul (2007) proposed to model the transition elements by the construction of a relative measure of the transition coefficients:

Next, these authors construct a cross-sectional mean square transition differential,

where

Cluster analysis: methodology

To formulate a null hypothesis of growth convergence, the authors proposed the following model for the transitions elements:

where:

δi is fixedσi > 0ξit is i.i.d(0,1) across i bay weakly dependent on t (introduces time-vaying and region-specific components to the model)L(t) is a slowly varying function which tend to infinity as t does (in practice log t)α determines the beahviour (convergence or divergence) of

Cluster analysis: methodology

The null hypothesis of convergence can be written as:

and the alternative (divergence):

or club convergence:

Cluster analysis: methodology

Phillips and Sul (2007) show that these hypothesis can be statistically tested by means of the following ‘log t’ regression model:

Advantages of this approach:

1.It is a test for relative convergence as it measures convergence to some cross sectional average in contrast to the concept of level convergence analyzed by Bernard and Durlauf (1995).2.This test does not depend on any particular assumption concerning trend stationarity or stochastic nonstationarity of the variables to be tested.

Cluster analysis

Cluster analysis

Concluding remarks

Since the oberved divergencies can be ascribed mainly to different technological levels, rather than to a wrong wage behaviour in the Periphery, internal devaluation policies are not the solution to surpass the current situation in the Eurozone. These policies have forced rebalancing of the external deficits, but they do not help convergence. And the reason is the same we have heard many times when economies embark in external devaluations: these are not genuine competitive gains, it is technology what matters.

Hence, looking retrospectively, the definition of the Maastricht criteria should have been probably more balanced towards the inclusion of some real convergence indicators to be fulfilled before joining the EMU. The extensive battery of indicators considered in the macroeconomic imbalance procedure (MIP) constitute a response to this void. We cannot abstain, however, to point out that this new set of indicative thresholds are formulated as a surveillance mechanism, and not as convergence targets. We wonder, in the current context, whether some real convergence indicators should also be targeted to safeguard, or at least strengthen, the process of European integration.