european union: a diverging union?

Journal of Post Keynesian Economics / Summer 2013, Vol. 35, No. 4 537© 2013 M.E. Sharpe, Inc. All rights reserved. Permissions: www.copyright.com

ISSN 0160–3477 (print) / ISSN 1557–7821 (online)DOI: 10.2753/PKE0160-3477350403

GrIGOrIS ZArOtIADIS AND ArIStEA GKAGKA

European Union: a diverging Union?

Abstract: Standard growth theory emphasizes the closure of gaps: as interna-tionalization proceeds, socioeconomic, structural characteristics of different countries become similar. Despite the fact that the European Union (EU) represents a historical experiment of a region of gradually strengthening inter-nationalization, a wide range of EU studies reject the convergence hypothesis, showing an unclear development of standard deviation in time. Many of the studies find that something went wrong in the 1980s, yet they describe it as the result of a temporary effect. In the present paper, we show that the puzzle of the 1980s is not a short-term break in a continuous trend, but a complete altera-tion of the process, a structural shift toward a persisting period of continuous divergence! The previous trend of closing the gap among the member countries was reversed completely: in 2010, the coefficient of variation returned to higher levels than those of 1960. Second, all previous gains of labor vanished: in the period 1980–2005, real wages lost about 35 percent against per capita gross domestic product (GDP). The empirical findings we provide support our main suspicion: apart from confirming the Organization for Economic Cooperation and Development (OECD) observation of growing and persisting inequality in all Western economies, the gradual transition of the European free trade area into an economic and monetary union, accompanied by the prevalence of a specific policy, explains the prevalence of a period of deepening divergence since the beginning of the 1980s.

Key words: σ-convergence, domestic inequality.

JEL classifications: O47, F15

With respect to the effectiveness of internationalization, the neoclassical paradigm can be summarized as follows: opening up markets and enhanc-ing the degree of international competition will boost economic growth and initiate convergence. Since the 1980s there has been vigorous debate

Grigoris Zarotiadis is assistant professor of economics in the Department of Econom-ics, Aristotle University of thessaloniki. Aristea Gkagka is visiting lecturer, School of Management and Economics, technological Educational Institute (tEI) of Epirus.

538 JOURNAL OF POST KEYNESIAN ECONOMICS

(romer, 1986) concerning the growth effects resulting from interna-tionalization, as well as the convergence tendencies in a progressively globalized environment. Subjective reasons—answering the specific questions relates strongly to various sociopolitical interests—but also objective ones—such as differences in the underlying theoretical assump-tions, the variables used, the sample, and the statistical data, as well as the econometric techniques applied—generated a variety of, to a certain extent, controversial empirical results and arguments.

Although the dominant position in the relevant literature seems to be that trade contributes significantly to the strengthening of growth, both the sign and the causality of the estimated effects vary with respect to country and time period (Khalafalla and Webb, 2001), indicating that a range of time and region-specific socioeconomic conditions are of great importance (Chuang, 2002; Levine and renelt, 1992).1 then again, the literature on convergence is even more contradictory, albeit standard growth theory unquestionably insists on the closure of gaps: as internationalization proceeds, the socioeconomic, structural characteristics of different countries become similar. thereby, region/country-specific steady state growth rates also become alike; in place of confidence in “conditional convergence,” there arises a certainty of unconditional closure of cross-country inequality!2 Indeed, many au-thors concentrated on σ-convergence in Europe and provided evidence for closing of the gaps.3 Yin et al. (2003) study σ-convergence of real gross domestic product (GDP) per capita for the period 1960–95. Driven by the different integration levels within this period, they distinguish the European Union (EU) among the EU6, EU9, EU12, and EU15 and provide evidence for convergence, except for the period 1980–85. Also Hoen (2000), who uses data on six core European countries (Germany, France, Italy, the Netherlands, Belgium, and Denmark), provides results that are in accordance with the neoclassical paradigm: GDP per capita converges in the period 1970–85. Barro and Sala-i-Martin (1991, 1995) found the same among European regions for a more extended period

1 Kali et al. (2007) gather all of the different conceivable reasons for obtaining di-versified empirical results. they refer to the work of Grossman and Helpman (1991), Matsuyama (1992), rodriguez and rodrik (2001), Walde and Wood (2005), and Yanikkaya (2003).

2 See the theoretical analysis and literature review provided by Jones (2001).3 Some studies provide evidence of σ-convergence in Europe (Basile et al., 2001;

Desli, 2009; Fingleton, 2003; Yin et al., 2003), although some of them find subperiods of weak divergence.

EUROPEAN UNION: A DIvERGING UNION? 539

(1950–90). Veiga (1999), who focuses on NUtS II regions4 of twelve European countries, also provides evidence for convergence until the late 1970s.5

However, a wide range of studies reject the convergence hypothesis for the European Union, despite the fact that we are dealing with a re-gion of gradually strengthening internationalization. Most of them show an unclear development of standard deviation over time (Barrios and Strobl, 2005; Basile et al., 2001; Cappelen et al., 2003; López-Bazo et al., 1999; Neven, 1995; Neven and Gouyette, 1994), while others (e.g., Neven, 1995) identify different patterns of convergence in northern and southern Europe, especially during the period 1975–90.

the contradictory results arise partly from using dissimilar sets of countries, but, basically, from covering different time periods. the pic-ture we get from the aforementioned relevant studies is that something is going wrong in the 1980s! Many of the studies anticipated it,6 but they thought of it as the result of a temporary effect: the big 1980–82 reces-sion, resulting from continuously increasing oil prices, or the accession of southern European countries (Greece, Portugal, and Spain) (Neven and Gouyette, 1994), were thought to be the underlying reasons. Indeed, this could be a valid conclusion for someone studying the period lasting, at most, until the beginning of the 1990s. In the present paper, we take into consideration annual data up to 2010. We thus show that the problem with the 1980s is not a short-term break in a continuous trend, but a complete alteration of the process, a structural change from a previous sustained convergence into a persisting period of continuous divergence!

In the present paper, the European Union serves as a historical experi-ment for the formation of an almost perfectly internationalized environ-ment. Bearing in mind the subsequent institutional steps that have been

4 the nomenclature of territorial units for statistics, abbreviated as NUtS (from the French Nomenclature des Unités territoriales Statistiques) is a geographical clas-sification subdividing the territory of the European Union into regions at three differ-ent levels (NUtS I, II, and III, respectively, moving from larger to smaller territorial units). NUtS I stands for the national level of the member state. the classification is made for the purpose of collection, development, and harmonization of EU regional statistics, socioeconomic analyses of the regions, and the framing of EU regional policies.

5 Moreover, some studies look at the standard deviation of many different mea-sures. For instance, Boldrin and Canova (2001) study several indicators, such as labor productivity, income per capita, and GDP per capita in the EU15, and find support for the convergence hypothesis.

6 Giannias et al. (1999), for instance, speak of a convergence process that is dis-rupted in the early 1980s.


taken in the past five decades, we consider the EU15 as the outcome of a regionally evolving internationalization process.7 After presenting the data and methodology, we focus on two distinct questions: Can we ob-serve any convergence among the member states? Do we see a narrowing of inequalities, or not? Finally, we proceed with a panel regression and draw the respective conclusions.

Data and methodology

For the needs of the present paper, we employed mainly data on real GDP per capita (level and the rate of change) and on real wages (real compensation per employee), for the EU15 as a whole and for each member country as well, in the period 1960–2010. We used the Eurostat database,8 combined with that of the OECD (Organization for Economic Cooperation and Development).9 For employees’ compensation in par-ticular we used the AMECO database.10 In addition, we considered data on annual real GDP per capita growth for the world economy as a whole. For that reason, we used the World Development Indicators 2007 (World Bank—WDI data set).11

the analysis can be divided into two parts. the first part is quite simple, yet it serves the ultimate goal of the present study in a satisfactory way. Initially, in order to check the validity of projections based on the neoclas-sical paradigm, we study the development of cross-country inequalities. We estimate the coefficient of variation (standard deviation divided by the mean) of real GDP per capita among the different countries of the EU15 and the euro area, on an annual basis, and we check the charac-teristics of the derived time series (1960–2010). Similarly, we proceed

7 In contrast to Yin et al. (2003), we consider all of these countries together, over the whole period (1960–2006), regardless of the time of accession. Economic and political cooperation always evolves much earlier than the official agreement. the reason for not taking all twenty-five countries is also straightforward: political reasons kept the newest members completely apart from the core European Union until the late 1990s.

8 European Commission, Directorate General for Economic and Financial Affairs, “European Economy: Annual Economic report for 1997,” no. 63, 1997.

9 Organization for Economic Cooperation and Development, OECD Factbook 2008: Economic, Environmental and Social Statistics, available at www.oecd.org ).

10 AMECO is the annual macroeconomic database of the European Commission’s Directorate General for Economic and Financial Affairs, available at http://ec.europa.eu/economy_finance/indicators/annual_macro_economic_database/ameco_en.htm.

11 World Bank, World Development Indicators, available at http://data.worldbank.org/data-catalog/world-development-indicators.


to analyze σ-convergence for real wages. Convergence can be defined in different ways. First, convergence can arise when growth rates are nega-tively associated with initial levels of the variable under consideration, for example, per capita income. this is the concept of σ-convergence and has been proposed by Baumol (1986) and Barro and Sala-i-Martin (1991, 1992). Second, convergence occurs when the dispersion of the cross-sectional distribution has decreased over time. this is the concept of β-convergence and has been proposed by Barro and Sala-i-Martin (1995). the reason we choose to study σ-convergence and not β-convergence is twofold: first, it is easier, as we do not have to proceed with a regression, concerning ourselves with control variables and the data we are going to use; second and more important, it is the most direct way to verify the extent to which we had a closure of gaps or not.12

We focus on the members of the EU 15, because they went through a long-lasting, continuous process of unification. this resulted in a gradual matching of their structural socioeconomic characteristics, which implies an analogue homogenization of steady states. therefore, based on the standard growth theory, we would expect convergence of GDP per capita to occur unconditionally: the standard deviation (alternatively coefficient of variation) should be falling. then again, standard trade theory also provides arguments for convergence: as the degree of openness increases, product- as well as factor-prices should equalize too.

Next, we consider annual GDP per capita (y) and compensation per employee (w), in real terms, in order to look at the broad development of inequalities within each country. We use the ratio w/y as an indicator of the relative strength of labor income. When w/y increases (decreases), real wages become higher (fall behind) relative to per capita income. Note that, w/y is not a typical measure of inequality.13 Alternatively,

12 On the contrary, even if someone finds significant β-convergence (conditional or not), he may conclude that some tendencies could eventually result in actual convergence.

13 If we assume a quasi-perfectly competitive labor market, real wage should be equal to the marginal product of labor: w = dY/dL. Moreover, if we suppose that labor is the only factor contributing to the production of wealth, in other words the whole of the population are workers, w/y could also be written as (dY/dL)/(Y/L), expressing the elasticity of production with respect to labor input. Under these very specific condi-tions, w/y should constantly be equal to 1: as all citizens are workers, their wage is the average income, or equivalently, as labor is the only production factor, elasticity of production with respect to labor cannot be anything else but 1. Otherwise, there are two conceivable reasons for observing a falling w/y: (1) labor’s position in the imper-fect labor market is worsening, meaning that w does not keep pace with the gains in labor productivity, and/or (2) the share of workers in the total population is decreas-ing. Apparently, the opposite is true for an increasing w/y.


adjusted wage share, which is defined as the ratio of real compensation per employee to real GDP per person employed, w/ye, is usually used as an indicator of functional income distribution and a “fair share” for workers. this is because a declining wage share usually implies that a larger share of the economic gains is directed into profits. Not only may this be seen as unfair, but it can also have an adverse effect on future economic growth (ILO, 2008).

In the second part of the main study, we proceed with a cointegration analysis (vector error correction model), to establish the degree in which functional income distribution (w/ye) can be related to the country’s growth rate, the country’s degree of openness, governmental spending, and social expenditures. relevant unit roots and cointegration tests led us to apply an autoregressive distributed lag (ArDL) specification that allows for mixed order of integration of the variables (Greene, 2003; Pesaran et al., 2001). Next, we estimated the error correction model with the pooled mean group (PMG) estimators developed by Pesaran et al. (1999) for dynamic panel data. In addition to PMG estimators, results were attained using the mean group (MG) and dynamic fixed-effects (DFE) estimator.

Inequalities and growth dynamic in the European Union

Real wage and GDP convergence

Standard theory declares that as the process of internationalization evolves, cross-regional inequalities fade out. From a static point of view, mainstream trade analysis implies for open economies the equaliza-tion of factors’ remuneration in real terms. At the same time, in terms of a dynamic approach, the steady state of all participating economies becomes more and more similar. therefore, convergence is a straight-forward conclusion. Does this apply to the core of the European Union (EU15 and euro area)?

Convergence can be measured directly, either by the standard deviation (σ) of a variable or by the coefficient of variation defined as the stan-dard deviation divided by the mean (σ/μ). In our analysis, we employ the second method,14 in accordance with many comparable empirical studies: Beckfield (2004), Fingleton (2003), Kenny (2005), rowthorn

14 According to Kenny (2005), for variables that trend upward or downward across time—as real wage and per capita income—the coefficient of variation might provide a better reflection of convergence or divergence.


and Kozul-Wright (1998), Soukiazis (2003), Studer (2008), tsagkanos et al. (2006), and Veiga (1999).

table A.1 in the Appendix provides stationarity tests and trend estima-tions for the annual coefficient of cross-country variation (σ/μ) of real wages per employee (w) and real GDP per capita (y), in the EU15 and euro area, for the period 1960–2010. In the case of real wages, the KPSS test provides us with a significantly estimated negative trend, meaning a convergence tendency. the picture is quite different for σ/μ of real GDP per capita: especially for the EU15, we have the reproduction of a significant positive trend, meaning divergence, with all four different methods.

Yet, trend estimations alone can lead us to incomplete conclusions. For that reason, we present Figures 1 and 2. In both figures, the horizontal axis corresponds to the years (1960–2010) and the vertical axis to the values of the estimated coefficient of variation (σ/μ). Figure 1 refers to the EU15 and Figure 2 refers to the euro area. the left-hand side of both figures shows a declining coefficient of variation for real compensation per employee. the right-hand side depicts the same indicator for real GDP per capita. We can see that the trend is not uniform: σ/μ in the euro area starts at 0.43 in 1960, and falls continuously to 0.31 in 1980. By contrast, in the next three decades, per capita income clearly diverges, as σ/μ grows back to 0.44 in 2010. the case for the EU15 is comparable.

the picture we get from Figures 1 and 2 is convincing: there are two obviously different periods. In the 1960s and 1970s, a convergence took place for real wages, as well as for per capita income. Yet from the beginning of the 1980s the picture changes dramatically: the coefficient of variation of w shows a noticeable stagnation. In the case of y, starting again from the 1980s, σ/μ rebounds and follows an upward tendency of divergence, which is so strong that the trend we estimated for the whole period is slightly positive. Putting all these together, there is an apparent structural change after 1980: cross-country inequality starts to rise again, above any previous convergences. Using the Perron test, structural change appears in 1982 in all cases, except for σ/μ of y in the euro area, where structural change is estimated for 1981.

Domestic distribution of income

recently the OECD published two studies claiming that economic growth in developed countries goes together with a persisting deepening of domestic inequality (OECD, 2008, 2011). We depict below the annual development of w/y and w/ye for the EU as a whole (once for the EU15 and then for the euro area). As already mentioned, these ratios serve


Fig

ure

1 σ

-con

verg

ence

of

w a

nd y

in th

e E

U 1

5


.30

.32

.34

.36

.38

.40

.42

.44

.46

6065

7075

8085

9095

0005

10

REALGDPPERCAPITA

.24

.26

.28

.30

.32

.34

.36

6065

7075

8085

9095

0005

10

REALCOMPENSATIONPEREMPLOYEE

Fig

ure

2 σ

-con

verg

ence

of

w a

nd y

in th

e eu

ro a

rea


as indicators for the position of labor in the distribution of produced income.

Figures 3 and 4 confirm the OECD findings, yet only for the second half of the period: being in remarkable conformity with the develop-ment of cross-country inequality (σ-divergence), labor’s relative income improves only during the first two decades of the period under examina-tion. Starting in the 1980s, European workers get a progressively smaller share of produced output. As the share of workers is, if anything, not decreasing, the worsening of labor’s position in the imperfect labor market is the only reasonable justification for the fact that real wages lost about 35 percent compared to GDP per capita over the past three decades. Furthermore, here the Perron test shows a significant structural change in the development of w/y and w/ye around 1981 (1978 in the case of the euro area).

A closer look at the development of adjusted wage share

Econometric specification and estimation techniques

the main message of the above paragraphs is the structural break that took place around the beginning of the 1980s: a previous period of converging differences gave way to a profound cross-country diver-gence and a worsening of labor’s relative remuneration. there is no way to justify this complete alteration of the process by any temporary effect. the 1980–82 recession or the accession of southern European countries (Greece, Portugal, and Spain) could be blamed only for short-term breaks in a continuous trend. We suspect that the basic underlying reason is the gradual transition of the European free trade area into an economic and monetary union, accompanied by the prevalence of a specific policy.

In this last part of the paper, we take a closer look at cross-time de-velopment of w/ye in each EU15 country, focusing on the following explanatory variables:15

w y gr ep gov soc openeit i i it i it i it i it i it( ) = + + + + + +

+

θ θ θ θ θ θ

α0 1 2 3 4 5

11 2 3 4i i i i i i i itMS MT EU t u+ + + +α α α , (1)

15 For a more detailed presentation of explanatory variables, see the Appendix.


1.2

1.3

1.4

1.5

1.6

1.7

6065

7075

8085

9095

0005

10

W/Y(EU-15)

EU_W

Y

1.2

1.3

1.4

1.5

1.6

1.7

1.8

6065

7075

8085

9095

0005

10

W/Y(EA-12)

EA_W

Y

Fig

ure

3 L

abor

’s p

ositi

on in

the

EU

15

and

euro

are

a


580

600

620

640

660

680

700

6065

7075

8085

9095

0005

10

W/Ye(EU-15)

ADJ_EU

560

580

600

620

640

660

680

700

6065

7075

8085

9095

0005

10

W/Ye(EA-12)

ADJ_EA

Fig

ure

4 A

djus

ted

wag

e sh

are

in E

U 1

5 an

d eu

ro a

rea


where gr is the growth rate of real GDP per capita in country i, ep is the rate of dependent employment,16 gov and soc gives the rate of govern-ment spending and social expenditures over GDP, open is the country’s degree of openness, MS, MT, and EU are dummies showing the creation of the European Monetary System, the agreement upon the Maastricht criteria, and the country’s accession to the European Union, respectively. Finally, t stands for time and u for the error term.17 Bear in mind that the index i = 1,2,...,15 tracks for the cross-section dimension of the data set and stands for the EU15 countries, while t is the index for the time dimension running from 1960 to 2010.

Before proceeding with the estimation of the above equation, we first tested for unit roots and cointegration relationships among our variables. the relevant panel unit root tests proved the nonstationary character of our variables,18 and we verified cointegration.19 As the gr is I(0) while the other variables of interest are I(1), we chose to proceed with an auto-regressive distributed lag specification that allows for mixed order of integration of the variables (Greene, 2003; Pesaran et al., 2001).

the general ArDL (p, q1, q2, q3, q4, q5) model specification of Equa-tion (1) is:

w y w y gr epeit i i e i t j

j

p

ji i t jj

q

ji i t j( ) = + + +−=

−=

−∑ ∑α λ δ δ( ) , , ,1

10

2

1

jj

q

ji i t jj

q

ji i t jj

q

ji i t

gov

soc open

=−

=

−=

∑ ∑

∑

+ +

+ +

03

0

3

40

5

2

4

δ

δ δ

,

, , −−=

∑ + + + + +jj

q

i i i i i i i itMS MT EU t0

1 2 3 4

5

β β β β ε . (2)

Based on Equation (2), the general form of the corresponding error cor-rection model (ECM) used for the estimations is the following:

∆( ) = ( ) − − − − − −

−w y w y MS MT EUe

it i ei t i ki it i i i i i i iφ θ θ α α α α[, 1 0 1 2 3 4X tt

w yi ei t j

j

p

kji i t jj

q

it

k

]

,, ,

−

− ( ) − +−

=

−

−=

−

∑ ∑λ δ ε∆ ∆1

1

0

1

X (3)

where X is a vector for the k explanatory variables,

16 It is the part of the employment rate that refers to employees, after excluding those who are self employed.

17 Note that the panel we are using is an unbalanced one. Yet this should not be a problem, because missing observations do not relate to an idiosyncratic, time varying error.

18 Unit root tests are available upon request. Using ADF Fisher and PP Fisher tests and asymptotic chi-square distribution, we found that all variables are stationary in first differences, with the exception of gr.

19 Panel cointegration tests are available upon request. We used Kao residual cointegration tests based on the Akaike and Schwarz criteria.


θα

λθ

δ

λ0

0

1

1 1ii

iki

kjij

q

i

k

=−

=−

=

−

∑, , and φi = 1 – λi.

More specifically, λi and δkji are the short-run elasticity coefficients, θki is the long-run elasticity vector of the dependent variable with respect to the kth explanatory variable, and φi is the error correction coefficient. the last one measures the speed of adjustment or convergence toward the long-run equilibrium. the estimated model is relevant to the cointegra-tion hypothesis if the estimated coefficient ¬φi is negative and statistically significant. the closer the absolute value of this coefficient is to zero, the less adjustment there is, so weak convergence is observed.

If we substitute the X vector variable in Equation (3), then the ECM equation becomes:

∆( ) = ( ) − − − − −−

w y w y gr ep gov soceit i e

i t i i it i it i it iφ θ θ θ θ θ[, 1 0 1 2 3 4 iit i it

i i i i i i i

i ei t j

j

open

MS MT EU t

w y

− −

− − − − −

− ( ) −=

θ

α α α α

λ

5

1 2 3 4 ]

,∆

11

1

10

1

20

1

3

1 2p

ji i t jj

q

ji i t jj

q

ji

gr ep

gov

−

−=

−

−=

−

∑ ∑ ∑− − −

−

δ δ

δ

∆ ∆

∆

, ,

ii t jj

q

ji i t jj

q

ji i t jj

q

soc open, , ,−=

−

−=

−

−=

−

∑ ∑− +0

1

40

1

50

13 4 5

δ δ∆ ∆∑∑ + εit .

(4)

the aforementioned error correction model can be estimated in dif-ferent ways. traditional time-series models do not take into account the information on the cross-country correlation in the data. Dynamic fixed effect models control for country fixed effects but impose the same coefficients for all countries.20 Pesaran and Smith (1995) showed that pooling produces inconsistent estimates of the parameter values unless the slope coefficients are identical.21 to tackle this issue, Pesaran and Smith (1995) proposed a mean group (MG) estimator involving estimat-ing the coefficient of each cross-section and then taking their average. Although consistent, the MG estimator does not take into account that some of the parameters may be the same across countries, implying that

20 It is well known that with a small time dimension, dynamic fixed-effects esti-mators give biased and inconsistent estimates of the parameters. However, when the number of observations over time is large enough, the asymptotic bias of the estimator is likely to be rather small (Baltagi, 2005).

21 the inconsistency does not disappear even when the sizes of the cross-section and the time periods are large.


its estimates, especially in small samples, are likely to be inefficient and strongly affected by the presence of outliers.

An intermediate choice between imposing slope homogeneity and no restrictions is the pooled mean group (PMG) estimator proposed in Pesaran et al. (1999), which combines the characteristics of the pooled estimators (namely, the fixed effects) with those of the mean group es-timator.22 the PMG estimator treats the short- and long-run dynamics differently, and is a suitable method for studying long-term tendencies and short-term adjustment. More specifically, the short-run dynamics are allowed to differ across countries but the long-run effects are constrained to be the same. Formally, the PMG estimator imposes the restriction that the long-run coefficients are the same across units.

the PMG estimators have two key advantages over the commonly used estimators in the literature. Compared to the static fixed-effects estimator, the PMG estimator allows for dynamics while the static fixed-effects model does not. In comparison to the dynamic fixed-effects estimator, the PMG estimator allows the short-run dynamics (shocks) and error variances to differ across the cross-sections. Another pertinent advantage is that the underlying ArDL structure dispenses with the importance of the unit root pretesting of the variables in question. As long as there is a unique vector that defines the long-run relationship among the variables of interest, it is of no consequence whether these variables are either I(0) or I(1) since the PMG estimates of an ArDL specification will yield consistent estimates.

thus, the error correction model in Equation (4) is estimated with the pooled mean group estimators developed by Pesaran et al. (1999) for dynamic panel data. In addition to PMG estimators, results are obtained using the MG and dynamic fixed-effects estimator (DFE),23 and are reported in table 1 in order to facilitate comparison.24

22 there is an increasing use of PMG estimates in applied econometric work. As Ar-paia and turrini (2008) mention, PMG estimates have recently been used in the analysis of the effects of institutions on innovation and growth (OECD, 2001), the effects of social protection on growth (Arjona et al., 2002), for modeling the euro area demand for money (Golinelli and Pastorello, 2002), to analyze the wealth effects in the consump-tion function (Barrel and Davis, 2004), to explore the impact of policies on fertility rates (D’Addio and Mira D’Ercole, 2005), to identify the determinants of the sovereign risks in the gold standard (Cameron et al., 2006), to analyze the link between fiscal policies and the trade balance (Funke and Nickel, 2006), and to investigate the effects of finan-cial intermediation of economic activity (Loyaza and ranciere, 2006).

23 the DFE method is similar to the generalized method of moments (GMM) procedure.

24 the analytical results for each country are available upon request.


Tab

le 1

E

stim

ates

of

EC

M E

qu

atio

ns

(4.1

) an

d (

4.2)

Dep

ende

nt v

aria

ble:

firs

t diff

eren

ce o

f (w

/ye)

i,t

Exp

lana

tory

var

iabl

es:

Est

imat

ed c

oeffi

cien

ts

Long

run

coe

ffici

ents

, θki

MG

PM

GD

FE

MG

PM

GD

FE

Gro

wth

rat

e of

rea

l p.c

. GD

P, g

r i,t

–389

.913

–1,3

09.0

41–5

41.0

60–4

15.8

29–2

27.5

65–1

20.6

84

129.

444

173.

674

181.

194

225.

531

274.

174

232.

205

Rat

e of

(de

pend

ent)

em

ploy

men

t, ep

it –1

45.6

3440

2.02

521

6.06

5–4

63.3

8442

3.35

320

2.86

8

543.

439

127.

210

190.

240

1,17

5.32

129

1.23

020

0.68

4

Gov

ernm

ent s

pend

ing

over

GD

P, g

ovi,t

47.1

3594

.963

14.6

49–9

95.1

071,

311.

456

60.4

67

380.

258

93.2

3413

2.94

71,

100.

035

308.

969

141.

158

Soc

ial e

xpen

ditu

res

over

GD

P, s

oci,t

–112

.678

–208

.700

93.3

83–2

9.31

2–1

,407

.391

112.

392

313.

310

99.2

5014

1.73

363

7.11

730

1.13

9814

9.97

2

Deg

ree

of o

penn

ess,

ope

n i,t

–201

.591

–191

.526

–170

.029

–59.

964

–386

.135

–167

.498

145.

715

46.5

6649

.728

604.

342

121.

978

54.1

96

Eur

opea

n M

onet

ary

Sys

tem

(du

mm

y), M

Si

–6.8

14–1

6.80

8–9

.560

–3.3

06–4

5.53

3–1

4.93

9

3.55

48.

268

15.8

732.

740

17.1

9116

.910

Maa

stric

ht (

dum

my)

, MT

i15

.061

4.62

9–5

.196

73.2

8715

.669

–5.8

92

13.0

025.

888

10.3

2648

.458

10.9

8210

.908

EU

acc

essi

on (

dum

my)

, EU

i,t

–13.

119

–37.

246

13.9

77–6

.098

–92.

319

11.3

43

8.62

45.

683

14.0

287.

362

20.1

1214

.699

Tim

e, t

0.42

02.

052

–0.2

36–1

.507

16.0

620.

032

3.62

31.

144

1.41

112

.998

3.42

91.

502


Con

verg

ence

coe

ffici

ents

, ϕi

–0.6

17–0

.158

–0.1

82–0

.640

–0.0

82–0

.170

0.14

40.

031

0.02

00.

126

0.02

40.

020

Sho

rt r

un c

oeffi

cien

ts, δ

kji

Firs

t diff

eren

ce o

f gro

wth

rat

e of

rea

l p.c

. GD

P, ∆

(gr i,

t)—

——

116.

798

–114

.460

–80.

654

85.1

7436

.493

24.7

60

Firs

t diff

eren

ce o

f rat

e of

(de

pend

ent)

em

ploy

men

t, ∆(

epi,t)

693.

354

898.

354

650.

652

607.

669

652.

604

501.

287

275.

438

136.

261

99.0

9526

5.72

910

0.48

410

8.47

2

Firs

t diff

eren

ce o

f gov

ernm

ent s

pend

ing

over

GD

P,

∆(go

v i,t)

76.0

9048

5.38

844

5.51

735

4.75

651

3.39

243

1.56

4

180.

183

128.

985

48.8

2011

5.98

412

1.54

348

.936

Firs

t diff

eren

ce o

f soc

ial e

xpen

ditu

res

over

GD

P, ∆

(soc

i,t)

199.

178

26.3

2811

2.46

1–8

8.61

553

.617

111.

519

113.

465

121.

527

47.2

0394

.422

143.

477

47.5

43

Firs

t diff

eren

ce o

f deg

ree

of o

penn

ess,

∆(o

pen i

,t)—

——

–46.

698

–0.9

816.

247

134.

222

34.7

3527

.885

Inte

rcep

t18

0.86

684

.844

102.

609

445.

382

–7.8

7490

.675

151.

023

18.2

5722

.380

173.

546

2.75

022

.457

R2

0.30

620.

3309

0.36

170.

0160

0.14

690.

3635

Not

es:

Stan

dard

err

ors

are

repo

rted

in it

alic

s. S

tatis

tical

ly s

igni

fican

t est

imat

ed c

oeffi

cien

ts a

re s

how

n in

bol

dfac

e.


Results

Working with an ArDL model, the results may be sensitive to the choice of lag length. therefore, we have to use the relevant criteria to obtain the optimal lag length for the variables. In our study, the selection of the appropriate lag lengths p and qk was made using the Akaike, Schwarz Bayesian, and Hannan–Quinn criteria. However, in many empirical studies, the authors prefer to use the whole setting of the explanatory variables in the short-run section of the model (e.g., Combes et al., 2011; Pesaran et al., 1999; tan, 2006). In that case, the choice of lag length is based on the relevant literature and they usually use one lag for each one of the explanatory variables.

Equation (1) was specified with two alternative versions depending on the specification of the ArDL model. therefore, we had to estimate two different versions of Equation (4), depending on whether the selection of the appropriate lag length and the specification of the ArDL model were based on the relevant criteria or on a deliberate selection. the first case is based on the aforementioned three criteria—Akaike information criterion (AIC), Schwarz Bayesian criterion (SBIC), and Hannan–Quinn criterion (HQC). All three criteria proposed the specification ArDL (1,0,1,1,1,0) model:

w y w y gr ep epeit i i e

i t i it i it i i t

i

( ) = + ( ) + + + +− −α λ δ δ δ

δ, ,1 10 20 21 1

30 ggov gov soc soc open

Mit i i t i it i i t i it

i

+ + + + +− −δ δ δ δ

β31 1 40 41 1 50

1

, ,

SS MT EU ti i i i i i it+ + + +β β β ε2 3 4 .

(2.1)

Equation (2.1) leads to the corresponding error correction model:

∆( ) = ( ) − − − − −−

w y w y gr ep gov

soc

eit i e

i t i i it i it i it

i

φ θ θ θ θ

θ

[, 1 0 1 2 3

4 iit i it i i i i i i i

i it i

open MS MT EU t

ep go

− − − − − −−

θ α α α αδ δ

5 1 2 3 4

20 30

]

∆ ∆ vv socit i it it− +δ ε40 ∆ .

(4.1)

In the second case, the model is specified deliberately as an ArDL (1,1,1,1,1,1) model:

w y w y gr gr epeit i i e

i t i it i i t i it

i

( ) = + ( ) + + + +− −α λ δ δ δ

δ, ,1 10 11 1 20

21 eep gov gov soc soci t i it i i t i it i i t, , ,− − −+ + + + +1 30 31 1 40 41 1

50

δ δ δ δ

δ ii it i i t i i i i i i i itopen open MS MT EU t+ + + + + +−δ β β β β ε51 1 1 2 3 4, . (2.2)


therefore, the corresponding error correction model used for the es-timation is:

∆( ) = ( ) − − − − −−

w y w y gr ep gov

soc

eit i e

i t i i it i it i it

i

φ θ θ θ θ

θ

[, 1 0 1 2 3

4 iit i it i i i i i i i

i it i

open MS MT EU t

gr ep

− − − − − −−

θ α α α αδ δ

5 1 2 3 4

10 20

]

∆ ∆ iit i it i it i it itgov soc open− − − +δ δ δ ε30 40 50∆ ∆ ∆ .

(4.2)

In table 1 we present the estimations of Equations (4.1) and (4.2). We apply PMG methodology and we also present the results of MG and DFE for purposes of comparison. Nevertheless, the joint Hausman test suggests that the PMG results are more appropriate (see Appendix A.4 for the relevant tests).25

the first thing to note is that the degree of openness has a clear and strong negative effect in w/ye in the long run (in the short run, open is insignificant). this is not surprising, if we consider that European firms compete with low-wage economies. Moving on, the formation of EMS also has a significantly negative effect. As becomes obvious from Figure 4, the structural change observed in the late 1970s and the early 1980s is more consistent with the establishment of the monetary union than with the Maastricht agreement ten years later.26

On the other hand, government spending seems to improve the rela-tive position of labor income, both in the short- and the long-run period too (short-run effects are more profound). Also, social expenditures seem to improve the relative position of labor in the short term. But long-run effects are clearly negative. this disparity may reflect the fact that social expenditures point toward aggregate consumption with short-run positive effects, while they might lead to budget deficits that negatively affect the long-run perspective. then again, government spending, including public investment, is more likely to have positive

25 Note that the adjustment or convergence coefficient is statistically negative, sug-gesting that any deviation of w/ye from the value implied by the long-run equilibrium relationship with the explanatory variables brings about a correction in the opposite direction. In particular, the error correction coefficient is –0.158 under the PMG framework, meaning a slow adjustment from longrun disequilibria.

26 Another variable that appears to be important is the rate of dependent employ-ment. Both in the short- and the long run period, increases of epi,t arise along with relative gains for employees (increases of w/ye). this implies that, for the given period (1960–2010), adjustment in European labor markets resulted mainly from the demand side.


effects in the long run too, provided that the government and the public sector work efficiently.27

Last but not least, the “growing unequal” hypothesis of the OECD is verified! truly, the figures in the first part show that functional income distribution worsens after the 1980s. Yet, as the estimations prove, this is probably the effect of intra-EU structural changes (EMS, governmen-tal spending, and degree of openness), while, an increase in real GDP growth is still associated with a more than proportional decrease in wages compared to nonlabor income, in both the long- and short-run period (although criteria propose not to include the short-run effect). Note that the same evidence derives from the alternative MG and DFE estimates: in all cases, the short and long-run elasticity coefficients of w/ye with respect to real GDP growth are significantly larger than 1.

Other empirical studies have reached similar conclusions.28 trott (2011) and Giovannoni (2010) refer to the countercyclical behavior of wage shares, going down in good economic times and up during recessions. Also Giovannoni (2006) provides an analysis for the case of American labor and wage shares, with comparable results. the immediate explana-tion is that productivity is more responsive to growth than wages, while, during downturns, wages are sticky but productivity collapses. the In-ternational Labour Office (ILO) (Global Wage report—2008) focuses on longer-lasting relations. It reports that a 1 percent GDP growth has been associated on average with a 0.05 percent decrease in the wage share and it identifies three possible factors as the underlying causes: first, the weakening of trade unions; second, labor-saving, skill-based technical progress, which, by the way, is the explanation favored by the

27 Moreover, social expenditures should have a positive effect on w/ye, if they are associated with an emphasis on “employment-oriented policies” and alter the bal-ance between active and passive social expenditures toward a greater emphasis on the former rather than the latter (Arjona et al., 2002). Active policies are introduced in order to encourage increased employment by the beneficiaries of such spending, while passive policies are transfers of consumption from one group in society to another, in the form of either cash transfers or services. to the extent that the former mechanism is important, the more active spending in the total of social spending, the more posi-tive or less negative should be the effect on w/ye. In our study, the variable of social expenditures, soc, belongs to the passive policies (see Appendix A.3). therefore, a negative sign could be expected.

28 real GDP per capita growth in the euro area does not show any increasing trend since the early 1980s. At first glance, this may call into question the validity of the specific empirical finding. Yet consider that, besides the growth rate, other factors also affect the ratio w/ye. then again, all the estimations we provide lead to the same conclusion, which, incidentally, confirms the OECD publications: Europe is growing unequally (OECD, 2008) and, moreover, inequality keeps rising (OECD, 2011).


International Monetary Fund (IMF, 2007a, 2007b); third, globalization—the ILO reports that over the past decade the countries in which trade has grown as a percentage of GDP are also the countries with the fastest declines in wage share. It should be noted that the latter agrees with the estimated coefficient for the country’s openness in table 1.

Conclusions

Neoclassical growth theory places great emphasis on the closure of gaps. As internationalization proceeds, the socioeconomic, structural charac-teristics of different countries become similar: in place of confidence in “conditional convergence,” the certainty arises of unconditional closure of cross-country inequality! In general, the present paper focuses on the historical experiment of the European Union in order to test the validity of these standard theoretical expectations.

A wide range of EU studies show an unclear development of standard deviation of per capita income over time, indicating that something went wrong in the 1980s. But they describe it as the result of a temporary fac-tor (1980–82 recession, continuously rising oil prices, or the accession of southern European countries).

In the present paper, we take into consideration a longer period with annual data up to 2010 and we see that we are not dealing with a short-term break in a continuous trend, but with a complete alteration in the process! We find clear evidence for a profound structural change re-garding the distributional patterns that took place in the first half of the 1980s. During the first two decades of the period we studied (1960s and 1970s), both inter and intraregional inequality narrowed: the coefficient of variation of GDP per capita fell strongly and labor remuneration grew substantially relative to nonlabor income. this picture changed completely after 1980. the previous trend of closing the gap among the countries was reversed completely: in 2010, the coefficient of variation grew back to the levels of 1960. At the same time, all previous gains of labor vanished: in the period 1980–2010, real wages lost more than 35 percent against GDP per person employed. In accordance with recent OECD and ILO publications, European economies’ development has gone hand in hand with a deepening of cross-factor inequality.

In the last part of this paper we contribute to the discussion of the reasons behind this structural change. the overall empirical conclusions are simple and clear-cut: as real GDP growth strengthens, wages fall further behind compared to nonlabor income—the “growing unequal” hypothesis. At the same time, degree of openness also has a clear negative effect: the more


domestic production competes with foreigners the less w/ye is. Moreover, the formation of the EMS (European Monetary System) has also had a significant negative effect on w/ye. In 1974, the ECU (European Currency Unit) was defined and on March 13, 1979, the EMS entered into force, according to an agreement made on the same day between the central banks of the member countries. Obviously, the mere fact that EMS oc-curred just before the emergence of a persisting period of continuous divergence justifies the significance of the specific dummy. Yet, this is not simply a coincidence: the fact that cutting down government spend-ing is also highly related to the relative worsening of w/ye confirms the political importance of the above estimations.

In fact, empirical findings support our main suspicion: apart from the “growing unequal” hypothesis that refers to all Western economies, the gradual transition of the European free trade area into an economic and monetary union, accompanied by the prevalence of a specific policy, explains the occurrence of a period of deepening divergence since the beginning of the 1980s.

Finally, we saw that international divergence and disparities in the remuneration of different classes and production factors proceeded together in the core of the European Union. So, an interesting question arises: is there any underlying relationship between these two kinds of inequality? Does the one affect the other or are they both affected by some deeper structural change? Our findings speak for the second alternative, especially regarding the post-EMS era. Nevertheless, as the conformity of the two kinds of inequality appears throughout the whole period (1960–2010), and given that as far as we are aware, there are no relevant references in the literature, this could make an interesting proposal for further research.

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[Appendix follows]


Tab

le A

.1

σ-co

nve

rgen

ce o

f re

al w

ages

per

em

plo

yee

and

rea

l GD

P p

er c

apit

a o

ver

tim

e

Sta

tiona

rity

Est

imat

ion

of tr

end

AD

F (

AIC

)A

DF

(S

IC)

PP

KP

SS

AD

F (

AIC

)A

DF

(S

IC)

PP

KP

SS

Coe

ffici

ent

t-st

atis

ticC

oeffi

cien

tt-

stat

istic

Coe

ffici

ent

t-st

atis

ticC

oeffi

cien

tt-

stat

istic

EU

-15

σ/μ

of w

(G

DP

de

flato

r)a

–2.5

56–2

.556

–2.7

310.

213§

0.00

00.

485

0.00

00.

485

0.00

00.

485

–0.0

01–7

.547

***

σ/μ

of y

–3

.339

*–2

.504

––2.

504

0.22

2§0.

000

4.07

8***

0.00

04.

803*

**0.

000

4.80

3***

0.00

00.

861

σ/μ

of y

gro

wth

ra

te–6

.720

§–6

.720

§–6

.781

§0.

073†

–0.0

07–0

.330

–0.0

07–0

.330

–0.0

07–0

.330

–0.0

08–0

.381

Eur

o ar

ea

σ/μ

of w

(G

DP

defl

ator

) –2

.807

–2.8

07–2

.930

0.18

1§0.

000

–0.5

400.

000

–0.5

400.

000

–0.5

40–0

.001

–8.2

17**

*

σ/μ

of y

–3

.329

*–3

.329

*–3

.493

*0.

219†

0.00

050

,531

***

0.00

05.

531*

**0.

000

5.53

1***

0.00

14.

090*

*

σ/μ

of y

gro

wth

ra

te–7

.187

§–7

.187

§–7

.234

§0.

088

0.00

50.

079

0.00

50.

079

0.00

50.

079

0.00

20.

037

Not

es: † ,

‡ an

d §

deno

te re

ject

ion

of th

e nu

ll hy

poth

esis

of u

nit r

oots

for A

ugm

ente

d D

icke

y–Fu

ller (

AD

F) a

nd P

hilli

ps–P

erro

n (P

P) te

sts

and

reje

ctio

n of

the

null

hypo

thes

is o

f sta

tiona

rity

for t

he K

PSS

(Kw

iatk

owsk

i–Ph

illip

s–Sc

hmid

t–Sh

in) t

est a

t the

10

perc

ent,

5 pe

rcen

t, an

d 1

perc

ent s

igni

fican

ce le

vel,

resp

ectiv

ely.

* ,

** a

nd **

* de

note

sig

nific

ant a

t the

10

perc

ent,

5 pe

rcen

t, an

d 1

perc

ent l

evel

s, r

espe

ctiv

ely.

a W

e es

timat

e re

al w

ages

by

defla

ting

the

nom

inal

com

pens

atio

n pe

r em

ploy

ee in

two

way

s: o

nce

we

use

a G

DP

defla

tor

and

then

a fi

nal c

onsu

mpt

ion

defla

-to

r. r

esul

ts d

o no

t dif

fer

sign

ifica

ntly

.


Tab

le A

.2

w/y

in t

he

EU

-15

and

th

e eu

ro a

rea

ove

r ti

me

Sta

tiona

rity

Est

imat

ion

of tr

end

AD

F (

AIC

)A

DF

(S

IC)

PP

KP

SS

AD

F (

AIC

)A

DF

(S

IC)

PP

KP

SS

Coe

ffici

ent

t-st

atis

ticC

oeffi

cien

tt-

stat

istic

Coe

ffici

ent

t-st

atis

ticC

oeffi

cien

tt-

stat

istic

w/y

in E

U-1

5–2

.249

–2.2

49–2

.138

0.21

0‡–0

.001

–2.3

51**

–0.0

01–2

.351

**–0

.001

–2.7

54**

*–0

.005

–7.4

85**

*

w/y

in e

uro

area

–2.3

10–2

.310

–2.2

490.

216‡

–0.0

01–2

.552

**–0

.001

–2.5

52**

–0.0

01–3

.256

***

–0.0

06–6

.504

***

Not

es:

† , ‡ ,

and

§ de

note

rej

ectio

n of

the

null

hypo

thes

is o

f un

it ro

ots

for A

ugm

ente

d D

icke

y–Fu

ller

(AD

F) a

nd P

hilli

ps–P

erro

n (P

P) te

sts

and

reje

ctio

n of

the

null

hypo

thes

is o

f st

atio

nari

ty f

or th

e K

PSS

(Kw

iatk

owsk

i–Ph

illip

s–Sc

hmid

t–Sh

in)

test

at t

he 1

0 pe

rcen

t, 5

perc

ent,

and

1 pe

rcen

t sig

nific

ance

leve

ls,

resp

ectiv

ely.

* ,

**, a

nd **

* de

note

sig

nific

ant a

t the

10

perc

ent,

5 pe

rcen

t, an

d 1

perc

ent l

evel

s, r

espe

ctiv

ely.


A.3: Definition of Explanatory Variables in Table 1

the explanatory variables of the econometric estimation are defined as follows:

• Realcompensationperemployee(w): real compensation per employee results out of nominal compensation per employee divided by GDP price deflator. It is measured in Mrd euro (1 Mrd = 1,000 million).

• RealGDPpercapita(y): real GDP per capita is defined by nominal GDP per capita deflated with GDP price deflator. It is measured in Mrd euro (1 Mrd = 1,000 million).

• RealGDPperpersonemployed(ye): real GDP per person employed is defined by nominal GDP per person employed deflated with GDP price deflator. It is measured in Mrd euro (1 Mrd = 1,000 million).

• Dependent employment (ep): dependent employment refers to employees, after we excluded those who are self-employed. they are measured in 1,000 persons. For the calculation of the rate of dependent employment (ep) we divide the number of dependent employees (e) by the number of total population (p).

• Openness(open): the degree of openness is the sum of exports and imports divided by the quantity (2*GDP). Both exports and imports are in real terms, as we deflated the nominal exports and imports by the exports and imports deflator, respectively. Openness is measured in Mrd euro (1 Mrd = 1,000 million).

• GovernmentexpendituresoverGDP(gov): the nominal govern-ment expenditures were deflated with GDP price deflator and divided by GDP. the real government expenditures are measured in Mrd euro (1 Mrd = 1,000 million). this variable refers to the expenditures of general government. total general government expenditure is the sum of:• Intermediateconsumption• Grosscapitalformation• Othertaxesonproduction,payable• Subsidies,payable• Propertyincome,payable,• Currenttaxesonincomeandwealth,payable• Socialbenefitsotherthansocialtransfersinkind,payable,• Socialtransferin-kindrelatedtoexpenditureonproductssup-

plied to households via market producers, payable• Othercurrenttransfers,payable


• Adjustmentforthechangeinthenetequityofhouseholdsonpension funds reserves

• Capitaltransfers,payable• Acquisitionsofnonfinancialassets

• SocialexpendituresoverGDP(soc): the nominal social expendi-tures were deflated with GDP price deflator and divided by GDP. the real social expenditures are measured in Mrd euro (1 Mrd = 1,000 million). this variable refers to the following three major categories of social expenditures:• Socialtransfersin-kind: they are equal to the individual consumption expenditure of

general government. they consist of individual goods and ser-vices provided as transfers in-kind to individual households by government units. they include:• Social benefits in-kind.They are intended to relieve the

household of the financial burden of social risks or needs. they include the following cases:• Socialsecuritybenefits,reimbursements.Thesebenefits

consist of reimbursement by social security funds of ap-proved expenditures made by households on specific goods or services.

• Other social security benefits in-kind.These consist oftransfers in-kind provided to households by government units that are similar in nature to social security benefits in-kind but are not provided in the context of social insurance schemes. Social assistance benefits in-kind include, if not covered by a social insurance scheme, for instance, social housing, dwelling allowances, and reduction of transport prices (provided that there is a social purpose).

• Transfersof individualnonmarketgoodsorservices.Theyconsist of goods or services provided to individual house-holds free or at prices that are not economically significant, by nonmarket producers of government units. they cover, for instance, education and cultural services.

• Socialbenefitsother thansocial transfers in-kindpaidbythegeneral government:• Socialsecuritybenefitsincash• Privatelyfundedsocialbenefits,forexample,retirementpen-

sions paid by an autonomous pension fund• Unfundedemployeesocialbenefits


• Social assistance benefits in cash, for instance, children’sallowance, welfare payments, and services

• Subsidiespaidbythegeneralgovernment:• Subsidiesonproducts:subsidiespayableperunitofagood

or a service produced or imported• Othersubsidiesonproduction:consistofsubsidiesexcept

subsidies on products that resident producer units may receive as a consequence of engaging in production. they include, for instance, payments on the employment of people who have been unemployed for long periods

Note that investment grants are not treated as subsidies, they are part of the capital transfers.

Finally, we use three dummy variables, MS, MT, and EU, in order to capture the changes in economic policy:

• EMS (European Monetary System): this is a dummy variable for European Monetary System. It entered into force on March 13, 1979. So, for the years 1960–78, it takes the value of 0 and for the remaining years it takes the value of 1.

• Maastricht: dummy variable for the treaty of Maastricht (formally the treaty on European Union [tEU]), which was signed on February 7, 1992 by the members of the European Community in Maastricht, Netherlands. For the years 1960–92, it takes the value of 0 and for the years 1993–2010, it takes the value of 1.

• EU (European Union): dummy variable for the year of accession of each member country separately. the years before the accession take the value of 0 and the remaining years take the value of 1.

A.4: Joint Hausman test

regarding the estimation of Equation (4.1), the joint Hausman test gives the following results:

• ComparingPMGwithMG: Hausman test: χ2

9 = 193.71, p-value = 0.000. therefore, the test suggests that PMG results are more appropriate than MG results.

• ComparingDFEwithPMG: Hausman test: χ2

9 = 3.25, p-value = 0.954. therefore, the test sug-gests that PMG results are more appropriate than DFE results.

• ComparingDFEwithMG: Hausman test: χ2

9 = 35.28, p-value = 0.000. therefore, the test sug-gests that DFE results are more appropriate than MG results.


Overall, the joint Hausman test confirms our choice to use PMG estima-tion rather than MG or DFE estimation techniques.

regarding the estimation of Equation (4.2), the joint Hausman test gives the following results:

• ComparingPMGwithMG:Hausman test: χ2

9 = 227.58, p-value = 0.000. therefore, the test sug-gests that PMG results are more appropriate than MG results.

• ComparingDFEwithPMG:Hausman test: χ2

9 = 95.44, p-value = 0.000. therefore, the test sug-gests that DFE results are more appropriate than PMG results.

• ComparingMGwithDFE:Hausman test: χ2

9 = 6.84, p-value = 0.654. therefore, the test suggests that DFE results are more appropriate than MG results.

Overall, the joint Hausman test suggests that DFE results are more ap-propriate than PMG or MG results.

european union: a diverging union?

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