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European Regional Science Association36th European CongressETII Zurich, Switzerland

26-30 August 1996

Ana FurtadoLondon School of Economics

London WC2A 2AE, UKFurtado@lse.ac.uk

Fax: (0171)9557412

INTERREGIONAL WAGE DIFFERENTIALS IN THE EUROPEAN UNION:A CROSS-SECTION ANALYSIS FOR ITALY, GERMANY,

SPAIN AND THE UNITED KINGDOM

Abstract: The microeconomic forces that influence regional disparities are not fully understood.This paper concentrates on one specific aspect of these inequalities - regional wage differentials -using microdata for four European countries on approximately 30000 individuals across 55regions. It main purpose is to contribute to the understanding of the importance of spatial factorsin the determination of wages.To test the hypothesis if regional wage differentials are equalising differences for cost of livingand amenities we followed the framework given by the Mincer type earnings function and thedata consists on 1991 microdatasets for Italy, Germany, Spain and the United Kingdom.Results support the view that wages for apparently similar workers can differ between regions.For the first time in a transeuropean context, using detailed regional specific variables, resultshighlight the importance of regional specific factors in accounting for wage differentials. Theseresults are consistent with the compensating differentials principle.

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I - INTRODUCTION

The spatial dimension of inequality has been a topic that although not old in the

researchers’ agenda has recently been the subject of increased attention. Before the world

recession of the 1930s it was believed that the spatial distribution of economic development was

determined by natural circumstances and that it was not feasible to change it. From the

theoretical point of view regional disequilibrium was thought to be only a temporary problem in

a general equilibrium system. Evidence has however been continuously pointing to the existence

of persistent spatial inequalities (Atkinson (1976), Dean (1978), Vanhove (1987) ). Factors such

as imperfect mobility of capital and labour, transitory shocks, institutional factors and industry

mix are usually pointed has the elements to be blamed.

Within the European Union (EU) the existence of a wide variation in living standards,

both between and within Member States, is a well documented fact (see for example, Cardoso

(1993), Cuadrado et al (1993), Dunford (1993)). It is also well known the extreme heterogeneity

of cultural, social and economic development compared with the US. As argued by Cheshire et al

(1994) “there is probably as much diversity within one of the larger European countries such as

France or Spain, as there is in the whole US, but even France is smaller than some large US

States”.

The purpose of this paper is not to analyse the overall regional dimension of EU

inequalities. On the contrary we concentrate on one specific aspect of these inequalities - wage

differentials - in order to demonstrate the importance of explicit spatial factors involved in the

determination of wages. The main reason for concern about spatial wage dispersion is that it is

probably one of the main determinants of spatial income inequality. This linkage between labour

income and total income is evident in simply looking at National Accounts data. In 1991

Compensation of Employees accounted for 51% of British, German, Italian and Spanish GDP

(OECD, National Accounts). In aggregate terms, compensation of employees varies considerably

across the regions of the EU. Concentrating again on the same 4 EU countries, 1988 REGIO Data

shows a regional dispersion (given by the Coefficient of Variation at the NUTS level I regions) of

60% for Italy and Spain and of almost 100% for Germany and the UK. Looking at microdata, the

1988 Labour Costs Survey shows that monthly earnings for manual and non-manual workers in

establishments of 10 or more employees ranged in Spain from 600 ECU in Canarias to 864 ECU

in Madrid (a 44% gap); in the UK from 1215 ECU in the East Midlands, to 1574 ECU in the

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South East (a 30% gap); in Italy from 935 ECU in Abruzzi-Molise to 1204 in Lazio (a 29% gap)

and in Germany from 1325 ECU in Bayern to 1956 ECU in Hamburg (a 25% differential). This

corresponds to an overall range of 600 ECU in Canarias (Spain) to 1656 ECU in Hamburg

(Germany) (a 176% differential).

This paper concentrates on the analysis of regional wage differentials in Italy, Germany,

Spain and the United Kingdom. Studies of regional wage differentials are rooted to the work of

Hanna (1951), Gallaway (1963), Hanushek (1973), Farber and Newman (1987, 1989), Dickie

and Gerking (1989 and 1994) and Montgomery (1993), among others for the case of the US and

more recently there has been a growing interest in such an analysis for the case of European

countries (for example, Shah and Walker (1983), Maier and Weiss (1986), Jackoby (1990),

Hemmings (1991), Lucifora (1991), Blackaby (1989), Blackaby and Murphy (1991, 1992,

1995), Moghadam (1990)). For both Continents, empirical work has claimed the existence of

significant interregional wage differentials. We take forward this debate and the contribution of

this paper can be seen in terms of an extension of more conventional analysis of interregional

wage differentials in a competitite framework, by the incorporation of specific spatial factors and

the application of this framework to the case of four EU Member States: Italy, Germany, Spain

and the UK.

The main reason for the lack of research in this area in a EU context has been the paucity

of appropriate data. The data used in this study is unique in that it contains for four European

Countries detailed information at the individual level (work and worker characteristics) to which

we added a set of regional specific characteristics.

The research, hopefully, throws light on the debate of regional inequalities by assessing

whether differences in the remunerations of an individual across regions are related to the

intrinsic spatial characteristics of the region.

After a brief description of the theory underlined on the studies of interregional wage

differentials, we begin the empirical analysis by investigating the extent to which Human Capital

adjusted regional wage differentials are still significant after having controlled for cost-of-living

differences. The answer turns out to be that not only are they significant, but that real wage

dispersion across the regions of the UK, Spain and Italy is even greater than nominal wage

dispersion. After that, a detailed analysis on the possible regional specific factors affecting wages

is undertaken. The results for the four countries reinforce the compensating differential principle

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reinforcing the notion that, controlling for everything else, workers living in regions with

disamenities are compensated with higher wages.

II - THEORETICAL FRAMEWORK

The theory demonstrates that the existence of persistent regional wage differentials is still

consistent with the equilibrium in the long run. If such is the case it is because regional wage

differentials merely reflect differences in cost-of-living, non-pecuniary factors, barriers to

regional mobility, industry mix and the non-homogeneity of labour.

To investigate such hypothesis we followed the framework given by the neoclassical

human capital theory, adopting the augmented Mincer (1974) type earnings function as

analytical tool. One of the biggest advantages of using a Mincer type earnings function with

micro data is the possibility of decomposing the overall regional earnings differential into its

various components: differences in individuals’ characteristics and differences in the rewards to

those characteristics.

Strongly embedded in a human capital framework, the earnings function augmented with

the introduction of regional specific characteristics, provides a practical way of assessing the

importance not only of the traditional factors accounting for wage differentials - work and

worker characteristics - but also of those specific spatial factors accounting for regional wage

differentials that still remain after adjusting for the later characteristics.

Following the work of Willis (1986) in what concerns the traditional earnings

specification, augmented with the inclusion of regional specific characteristics following

Blackaby and Murphy (1995), Dickie and Gerking (1995) and Gyourko and Tracy (1993) the

augmented earnings function relates individual earnings (Ei) to three main vectors: (i) X a vector

of Personal Human Capital characteristics, (ii) J a vector of job characteristics and Z a vector of

regional specific characteristics:

ln Eir = f(Xi,Ji,Zr) (1)

(where i refers to individual and r to the region of residence)

Human Capital characteristics are usually proxied by the inclusion of two variables,

schooling and experience, the theoretical foundations of which have been discussed in detail by

several authors (for example, Willis (1986)). According to the theory, positive coefficients are

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expected to the schooling and experience variable reflecting the positive effect of schooling on

wages. A negative coefficient on experience squared would reflect diminishing returns to

experience. The introduction of personal characteristics like sex, ethnical group and marital status

allows for differences in worker characteristics and is an improvement to the literature which

treated labour as homogeneous. In order to controll for industry mix and differences in

occupation, both variables are entered in the earnings equation in a dummy format. Regional

specific characteristics have been usually proxied by a regional dummy which would captured

the specificities of the region causing wage differentials.

III - REGIONAL SPECIFIC CHARACTERISTICS

It is in what concerns the regional factor that we concentrate the current study. It is well

known that for the individual, nominal wages may be only one of the many factors that enter the

individual utility function. Influencing individual’s economic decision, there are at least two other

main factors: area cost-of-living differences and amenities (climate, culture, environment, public

utilities, etc.). In this case, and if we take revealed preferences seriously, regional wages represent

compensating differentials, compensating individuals for the non pecuniary differences across

regions. This idea first developed by Adam Smith (1776) and since then often discussed at the

theoretical and empirical level, does not tell which regional specific characteristics should be

entered in an individuals’ utility function and which is its relative cost. Theory just tells us that

higher wages should be compensated for “bad” regional characteristics (entering in the utility

function of the individual with a negative sign). Emphasizing the importance of regional amenities

in the wage determination Roback (1988) specifies that “the implicit price of an attribute

represents both the marginal valuation to consumers and the marginal cost to firms (...) The

prices of crime, pollution and cold weather indicate that these attributes are indeed disamenities,

while clear days and, surprisingly, population density are found to be amenities.” In order to test

the importance of amenities in wage differences, regional specific characteristics included in our

analysis can be thought of as falling into four groups:

- Climate variables:

Average daily temperature (TEMP) and average hours of sunshine per day (SUN). Because

they are expected to represent an amenity a negative sign is expected in the wage equations if

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lower regional wages are compensated by more hours of sunshine and higher temperatures. On

the other hand, higher regional average rainfall (RAIN), gale days (GALE) or snow days (SNOW) are

expected to be compensated by higher regional wages. In this case a positive sign is expected in

the wage equation. Roback (1988), after controlling for regional prices, found coefficient of

0.0000052 (with a t-statistic of 1.47) for heating degree days1.

- Environmental variables:

In what concerns the environment one would expect that a “greener” environment (GREEN)

with lower levels of pollution (SMK, SLD, SOX) would represent an amenity. A positive sign for the

pollution variable would be expected in the wage equation representing the higher regional wage

necessary to compensate its workers for the high levels of pollution. The opposite would be

expected in what concerns a “greener” environment. Roback (1988), with or without controls for

regional prices, did not find any significance in the pollution variable, showing an unexpected

negative sign.

- Demographic variables:

According to the literature, we can find space for at least three different variables

influencing regional wages: they are population density (POP), a measure for the percentage of the

region which is urbanised (URB) and city size (CITYS). The effect of big cities, highly urbanised

areas or densely populated regions, on wages are usually seen as the result of economies of

agglomeration. However the net effect of economies of agglomeration on wages is not

straightforward:

- on one hand economies of agglomeration on the consumers side are associated in terms

of better infrastructures, better opportunities for specialisation and access to better information,

in which case City Size, or Population Density are regarded as positive amenities and a negative

sign for these variables should be expected in the wage equation;

- on the other hand economies of agglomeration at the production level are associated

with higher wages resulting from higher levels of productivity. In this case a positive sign is

expected in the wage equation.

So the point here is to detect what the net effect is. One more consideration should

however be taken into account when analysing the usual correlation established between wages

and City Size. Empirical studies have often found a positive correlation which can merely reflect

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the fact that wages are higher in bigger cities because prices are higher, or because unions have

more power or even because there is a bigger concentration of people who are better trained and

who have good skills. In order to avoid this simultaneity problem our analysis controls for all

these factors (regional prices, human capital characteristics and Union Density). The expected

sign for this variable will then capture the net effect resulting from economies of agglomeration

on the consumer and production side.

In what concerns Roback (1988) results, population density was found significant only

after controlling for regional price differences, with a coefficient of -0.000025. However at the

same time the author included a variable for the total number of people living in the region, which

was significant and with a positive sign, and another for the percentage change in the regional

population which was also statistically significant and positive. Blackaby and Murphy (1991)

using industry-regional fixed effects with 1982 micro data for the UK, and also controlling for

regional price effects, found a 0.0001 coefficient for Population Density. Others (for example

Alonso (1978), Quinn and McCormick (1981) and Kim (1991)) have been pointing to a positive

relationship between city size and nominal wages based upon two main arguments: on one hand,

big cities with decreased transport costs to the large consumption market, opportunities for

specialisation, entertainment, and greater innovation, i.e., local and urban agglomeration

economies will imply higher productivity originating higher wages. On the other hand, according

to the compensating differentials principle, workers living in big cities have highest transport and

land costs which should be compensated by higher wages.

In sum, whether city size (CITYS), percentage of the region which is urbanised (URB) or

population density (POP) are expected to have a positive or negative sign in the wage equation,

must stand as an empirical question.

- Regional prices:

Although the above described variables account for some of the cost of living differences,

regional price levels are important in determining the spatial allocation of human resources

between the regions. If the world was ruled under perfectly and homogeneous competitive

forces, one would expect a coefficient for the cost of living positive and close to unity, reflecting

regional wage differentials as equalising differences for cost-of-living. However given the above

described arguments it is not the case, and other factors are expected to influence the mechanism

of wage equalisation.

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Unfortunately, the lack of official regional price deflators for the three countries

concerned, is a problem which, after more than a decade of complains, still persist.

To solve the problem, we’ve relied on the literature (Bover et al (1989), Minford et al

(1988)) which notes the important role of the housing market in the adjustment process. Bover et

al (1989), for example, show that “Variations in housing costs are a major ingredient of regional

variations in the cost-of-living”. This is mainly because house prices are correlated with land

prices more generally and the latter are an important ingredient in the cost of locally provided

goods and services. Subsequently Blackaby and Murphy (1991) estimating an earnings function

for the UK with 1980-86 data, reports on the significance of regional house prices. Also, and in

the case of Spain, Antolin and Bover (1993) state that “house price differentials are one of the

most important elements in cost-of-living variation”. Empirical evidence, for the case of the UK,

also reinforces the importance of regional housing prices in accounting for cost-of-living

differentials with the existence of a very strong - 0.9% - correlation coefficient between British

regional cost-of-living differentials (from Rewards Regional Surveys) and British regional House

Prices (from Nationwide Building Society) differentials.

Given these conclusions it seems reliable to assume that cost-of-living differences

between regions are mainly affected by house price differentials and we use them to deflate

regional money wages. However, using regional housing prices to deflate wages in order to test

for the existence of compensating differentials we should bear in mind two important points:

first, that because the variance of regional house prices is, in principle, bigger than the variance of

regional consumer prices when using housing prices to deflate wage differentials, we are

overcompensating for regional price variation and so we can have an upward bias when

estimating regional real wage differentials. This upward bias is even stronger (for the same

reason) in the case of Germany for which we use housing land prices. Second, as argued by

Cheshire and Sheppard (1995), that regional amenities (or disamenities) can be capitalised into

house prices in which case testing compensating differentials with real wages would originate

simultaneity problems.

In the case of the UK, because Reward Regional Surveys Ltd. publish data on regional

cost-of-living (including and excluding housing costs), we have also used them to deflate British

money wages. This index is obtained through a survey conducted to approximately 100 British

towns quantifying the change in income required to hold the standard of living constant across

regions. One of the pitfalls of such an index is that because they are averages for each region they

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do not incorporate individual factors such as type of tenure and other important factors in

accounting for cost-of-living differences. However, given that the variable used in the estimations

is the deviation of the region from the national cost-of-living index, we can assume that it

indicates overall regional differences.

Both regional housing price deflators (housing land prices in the case of Germany) and

British regional cost-of-living are expressed as the proportional deviation from the national mean.

- Other variables:

According to the literature (for example, Blanchflower and Oswald, 1990; Blackaby and

Murphy, 1991; Stewart, 1995) unions bargaining power has been positively influencing wages.

For example Stewart (1995) reports that for all three skill groups (skilled, semi-skilled and

unskilled) the mean wage in log pay between 1984 and 1990 is greater in the union sector than in

the non-union sector. In fact trade unionism has been considered by the literature an institutional

feature of the labour market which allows workers’ to rise wages above competitive levels.

Union organisation is likely to affect the wage of both union and non-union workers. However if

in the union sector trade unions cause increases in wages, in the non-union sector the effect is

not in one direction. In the union sector higher wages set by trade unions reduce employment and

may increase the supply of labour to the non-union sector, and so place a downward pressure on

wages in this sector. On the other hand, if higher labour costs are not offset by increased

productivity in the union sector then this can cause demand to switch in favour of the non-union

products. If that is the case there can be an upward pressure on non-union wage rates. As noted

by Freeman and Medoff (1984) union and non-union wages can also move together due to

spillover and threat effects.

In order to capture the effect of unions on regional real wages, we have introduced an

“Union Density” variable measured as the percentage of workers in the regions covered by

unions agreements. Quinn and MacCormick (1981) in estimating a wage equation for the US with

regional specific variables found a highly significant union variable: “money wages rise by 2.7

percentage points for each 10-point increase in unionization” (Quinn and MacCormick, 1981).

Blackaby and Murphy (1991) also found significant and positive trade union effects on

industry-regional fixed effects.

Other regional specificity’s included in the regressions are crime rate (CRIME) and road

haulage (ROAD). Both are used in the context of testing for compensating differentials. A worker

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should be positively compensated if living in regions with higher levels of crime rate. If road

hoaulage is associated with an increase in the well being of workers then a negative sign should be

expected in the equation. Roback (1988) also includes crime rate as an explanatory variable in a

wage equation (after controlling for individual characteristics as well as price level and other

regional specific characteristics) and found a positive and statistically significant coefficient.

Regional specific variables definition and sources are shown in Annex 1 and their sample

means in Annex 22.

IV - THE DATA

The study uses four 1991 national micro data sets providing detailed information on

individual characteristics, their work conditions and region of residence. They all derive from a

nation-wide representative survey: the “Balanci delle Famiglie Italiane” including information on

almost 25000 individuals, the 1991 Wave of the German Socio Economic Panel with information

on almost 13000 individuals (from West and East Germany), the”Encuesta de Presupuestos

Familiares” including data for almost 72000 individual living in Spain at the time of the survey,

and finally, the General Household Survey using a sample of about 18384 individuals living in

Britain.

Annex 3 describes the data sets and lists the variables included in the model.

V - EMPIRICAL RESULTS

a) Regional wage gap: nominal or real?

Money wages are not in themselves sufficient evidence of inequalities. To measure

regional wage inequalities we should also take into account differences in cost-of-living and other

regional specific characteristics. The use of a measure of real regional wages, as opposed to

money wages, has long been argued as relevant (Johnson (1983), Shah and Walker (1983),

Montgomery (1993), Blackaby and Murphy (1995)). In fact, cost-of-living considerations play

an as important role as money wages in the workers’ decision making process, in particular in

what concerns migration and labour supply decisions.

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The first step of the analysis was the estimation of both regional nominal and real wage

differentials, after controlling for the standard human capital variables as well as worker and

working characteristics. Money wages were deflated by a regional price deflator aiming to

capture regional cost-of-living differentials and used as dependent variable in the augmented

earnings equation. The idea is to enquire on one hand whether regions showing money wage

advantages (or disadvantages) maintain their position in the regional wage ranking if regional cost-

of-living is taken into account, and on the other hand, what happens to wage dispersion across

regions when the analysis is pursued at the real level.

The coefficients on the Human Capital variables, demographic characteristics such as

marital status and ethnic group, as well as occupation and industry dummies (not presented in

the paper but available on request) appear mainly robust and with values comparable with those

reported in previous studies of wage differentials (for example, Blackaby and Murphy (1992),

Lucifora (1993), Gera and Gilles (1994)). Because we are now mainly concerned with the cost-

of-living effect, we will concentrate the discussion on the regional coefficients estimations.

We followed Krueger and Summers (1987) to correct for the bias existent in standard

deviations of estimated dummies coefficients: “Although for each industry i=(1...k) the estimated

wage differential $β is an unbiased estimate of the true wage differential β, the standard deviation

of $β is an upwardly biased estimate of the standard deviation of β. This bias occurs because $β

equals β+ε, where ε is a least squares sampling error.” In order to adjust for such bias they

suggest the following form:

( ) ( )SD ki

i

k

β β σ≅ − ÷=

∑var( $ ) $ 2

1

(2)

where $σ 2i is the standard error of $β i, and k is the number of regions.

Results are summarised in Table I3.

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Table I: Nominal and Real(*) regional wage differentials(**) - Italy, UK, Spain and Germany (1991)(***)

UK ITALY SPAIN GERMANYVariable 1(NW)(i)(r) 2(RW) (r) 3(RW)(b) (r) Variable 1(NW)(i) (r) 2(RW) (r) Variable 1(NW)(i) (r) 2(RW) (r) Variable 2(RW)

OLS OLS OLS OLS OLS OLS OLS OLS Coef. Coef. Coef. Coef. Coef. Coef. Coef. Coef.(t-st.) (t-st.) (t-st.) (t-st.) (t-st.) (t-st.) (t-st.) (t-st.)

N. Observ. 7458 7458 7458 N. Observ. 6466 6466 N. Observ. 15715 15715 N. Observ. 2385Adjusted R 0.506 0.494 0.507 Adjusted R 0.396 0.471 Adjusted R 0.419 0.399 Adjusted R 0.73REGION REGION REGION REGIONEast Anglia -0.132 2 0.112 3 0.074 6 Abruz 0.007(a) 7 1.014 3 Anda -0.172 16 0.571 10 Baden-Wu. 9 -0.058(a) (-4.79) (4.06) (2.68) (0.23)(a) (34.87) (-7.19) (23.9) (-1.45)(a)East Midlands-0.162 6 0.109 4 0.204 4 Basil -0.262 19 0.8942 7 Arag -0.038 (a) 7 0.643 7 Bayern 7 0.1481

(-7.93) (5.31) (9.96) (-5.80) (19.79) (-1.30) (a) (22.34) (3.69)North -0.211 10 0.089 6 0.119 5 Cala -0.068 16 1.243 1 Astu -0.112 13 0.339 15 Bremen 3 0.6076

(-9.14) (3.84) (5.15) (-1.98) (36.38) (-2.83) (8.55) (8.81)North west -0.160 5 0.068 7 0.043 7 Campa -0.035(a) 15 0.2199 17 Bale -0.022(a) 6 0.752 4 Hessen 6 0.1618

(-9.36) (3.96) (2.52) (-1.37)(a) (8.7) (-0.60) (a) (20.89) (3.92)Scotland -0.135 3 0.169 1 0.223 3 Emil -0.010(a) 10 0.32 15 Cana -0.101 11 0.523 13 Niedersa. 1 0.9822

(-6.84) (8.57) (11.27) (-0.42) (a) (13.24) (-3.28) 16.94 (23.49)

South West -0.152 4 0.047 8 0.006(a) 9 Friul 0.072 2 1.095 2 Cant -.014 (a) 5 0.366 2 Nordrhein-W. 5 0.3684(-7.76) (2.41) (0.31)(a) (2.20) (33.39) (-0.35) (a) (8.90) (9.28)

Wales -0.182 8 0.102 5 0.234 2 Ligur 0.108 1 0.462 14 Cast -0.096 10 0.532 12 Rheinl-Pf. 2 0.6558

(-7.68) (4.32) (9.87) (3.88) (16.57) (-3.91) (21.63) (15.22)West Midlands-0.188 9 0.042 9 0.026(a) 8 Lomb 0.042 4 -0.1928 19 Cata 0.028 (a) 2 0.188 16 Schlesw. 4 0.5836

(-9.93) (2.19) (1.37)(a) (1.75) (-8.12) (1.09) (a) (7.27) (12.10)Yorks&Humber-0.152 4 0.145 2 0.363 1 Marc 0.014(a) 6 0.976 4 Extr -0.222 17 0.812 1 Hamburg 8 ---

(-8) (7.61) (19.06) (0.47) (a) (34.27) (-6.69) (24.44) ASD 0.294

South East --- 1 --- 10 --- 10 Molis -0.005(a) 9 0.911 5 Gali -0.118 14 0.634 9 Selectivity Diagnostics

ASD(1) 0.016 0.02 0.118 (0.08) (12.99) (-4.40) (23.63) Reset X (4.61) 4.79Selectivity Diagnostics Piem 0.059 3 0.474 13 Manc -0.065 8 0.772 3 BJ X (5.99) 1579Reset X (4.61) 33.65 (2.37) (19.05) (-2.41) (28.61)BJ X (5.99) 1039 Pugl -0.108 18 0.479 12 Murc -0.162 15 0.668 5(a) not significant at 10% level (-4.25) (18.81) (-4.60) (18.93)

(b) regional wages deflated by regional house prices Sard -0.014(a) 11 0.877 9 Nava 0.072 1 0.571 11(r) Ranking of regions according to wage differential (-0.44) (a) (27.63) (1.88) (14.97)(1) Adjusted Standard Deviation Sici -0.093 17 0.784 10 Pais -0.009 (a) 4 0.362 14 (-3.58) (30.07) (-0.34) (a) (13.51)(*) British regional nominal wages are deflated by regional cpi. Tosc -0.022(a) 13 0.312 16 Rioj -0.077 9 0.66 6Spanish and Italian regional nominal wages are deflated by (-0.85)(a) (12.38) (-1.99) (17.02)regional house prices and German wages are deflated by Trent 0.057(a) 5 0.628 11 Vale -0.107 12 0.642 8regional housing land prices. (1.60)(a) (17.76) (-4.12) (24.69) (**) Dependent variable: ln hourly Wage (UK, Italy), Umbr -0.021(a) 12 0.898 6 Madr --- 3 --- 17ln Year Wage(Spain) (-0.67)(a) (28.36) ASD(1) 0.07 0.159

Independent variables: schooling, experience, tenure, Vene -0.025(a) 14 0.880 8 Selectivity Diagnostics

gender, marital status (excluding Spain), being in a part (-0.91)(a) (32.04) Reset X (4.61) 64.7or full-time job(excluding Spain), on a public or private job Lazio --- 8 --- 18 BJ X (5.99) 17369

(just for Spain), number of employees in the firm (UK), ASD(1) 0.076 0.309 (1) Adjusted Standard Deviation

occupation, industry and region of residence Selectivity Diagnostics (a) not significant at 10% level(***)For Germany regional wage differentials are only Reset X (4.61) 0.68 statistically significant in real terms (not in nominal terms) BJ X (5.99) 1298

(a) not significant at least at 10% level (r) Ranking of regions according to wage differential (1) Adjusted Standard Deviation

Figures on the right hand side of the tables, accounting for regional price differentials,

capture the percentage differential between the purchasing power of the wage a worker with a

given set of characteristics would receive in that region and the purchasing power of the wage

that a worker, with the same characteristics, would receive in the base region (South East for the

UK, Lazio for Italy, Madrid for Spain and Hamburg for Germany). The overall dispersion in

regional wages is given (in the last row of each table) by the Adjusted Standard Deviation.

It is clear from Table I that regional cost-of-living adjustments have a dramatic impact on

regional coefficients.

For the UK , all coefficients maintain the statistical significance but change sign. This

means that whereas in money terms the South East was the region which better remunerated its

employers, in real terms, its high cost-of-living offset such advantage and transform it in the

lowest real wage region after controlling for human capital and other worker and work conditions.

This is true both using the Reward Regional Survey regional cost-of-living deflator or the regional

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housing price deflator (columns 2 and 3, respectively). Scotland and Yorks and Humberside

however retain their position between the four highest wage regions (both in nominal and real

terms) and West Midlands as the second lowest wage region. The wage ranking position for all

the other regions changes a bit, with special reference, adding to the South East case, on the

South West which becomes worst off: it slides down from a 4th position to an 8th and 9th

position in real terms. Shah and Walker (1983) using the 1973 General Household Survey data,

and the Reward Regional Survey cost-of-living deflator, also find a strong effect of cost-of-living

adjustment in regional coefficients, and despite almost two decades separating the two data sets

used, the South East slides down from the top wage region to one of the lowest when analysed in

real terms.

The dispersion in wages across regions is also bigger in real than in nominal terms. For

example whereas in nominal terms from the lowest wage region (North) to the highest wage

region (South East) we find an almost 20% differential in real terms (wages deflated by housing

prices) the gap between the lowest real wage region (South West) and the highest real wage

region (Yorks and Humberside) goes to almost 36%. This is well supported by the Adjusted

Standard Deviation (ASD) which is found to be bigger in real (0.118) than in nominal terms

(0.016).

To find real wage dispersion bigger than nominal wage dispersion is supported by

previous empirical studies and usually explained in terms of three main factors: first, this might

simply result from econometric problems mainly related to omitted variables. The augmented

earnings function do not control, for example at the micro level, for differences in individual

ability and years of school education may not be sufficient to reflect all the relevant differences,

and at the aggregate level, regional dummies may not be sufficient to reflect regional specific

characteristics influencing regional wage dispersion; this might also indicate the presence of

significant disequilibrium wage premium across regions or, it may result from the use of a not

rigorous regional price deflator, as already pointed out by Johnson (1983): “to the extent that

area price levels are measured with error, the implicit coefficient of log wage on log price will be

biased down, thus increasing the estimated variance of log (wages/prices)”.

For the case of Italy, once again the regional deflated (by a regional housing price

deflator) wage gap diverges from the money wage gap. Main differences can be found in

particular in the case of Liguria which drops from the highest money wage region to a 14th place

14

in terms of deflated wages and also Lombardia which drops from a 4th place in nominal terms to

a 19th place in real terms. This fall is matched in the reverse direction by Calabria and Basilicata

which show in real terms a better position than in nominal terms. Calabria is the highest real

wage region and Lombardia the lowest. The dispersion of real regional wages also increases

(from an Adjusted Standard Deviation of 0.076 in nominal terms to 0.309 in real terms). For

example the gap between the highest (Liguria) and lowest (Basilicata) nominal wage region is

37% and between the highest (Calabria) and lowest (Lombardia) real wage region is almost one

and half log points (i.e., 150%).

In Spain Navarra, the region where people are better remunerated in nominal terms drops

to an 11th place when housing prices are taken into account. On the other extreme, Extremadura

which in nominal terms was the region showing lower remunerations, given its low cost of

housing, is in real terms the region taking the first place in the ranking order. Also, in terms of

dispersion, we found in Spain a real wage dispersion bigger than money wage dispersion: in

nominal terms from the highest (Navarra) to the lowest (Extremadura) wage region there is an

almost 30% gap whereas in real terms the gap is around 80% (80 log points). This corresponds

to an Adjusted Standard Deviation (ASD) of 0.07 in nominal terms and of 0.159 in real terms.

Finally, Germany is among these four countries, the one for which the estimation of

regional wage differentials at the Lander level change more dramatically after the use of a regional

wage deflator. Whereas at nominal terms we could not find regional wage differentials (results for

the regional dummies were not statistically significant) the same can not be said for the real level

where we find relevant wage differentials across the German Lander. However it is not a surprise

to find insignificant nominal wage differentials and it is a fact often referred to by the literature of

wage determination in Germany4. In Germany wage bargaining is centralised at the industry level

(Blanchflower and Oswald, 1994). There is little national wage bargaining, very little company

bargaining and almost no local bargaining. According to Fuerstenberg (1993) in 1988, from out of

more than 32000 registered collective agreements, only 25% were enterprise agreements (which

are different from plant agreements between work councils and management). These state-wide

wage agreements are limited to minimum wage standards and a few other matters. Each company

is free to pay higher rates and it is why almost all basic rate agreements are concluded at the

local-industry level. This means that regional wage differences are mainly due to a wage drift

15

between contractual wages (at the industry-state level) and effective wages (at the local-industry

level). This wage drift takes place between regions that are relatively small. Thus German Lander

are too large and too heterogeneous to account for such differences.

However it seems that in real terms even at the Lander level and after controlling for

Human Capital characteristics as well as other individual and job characteristics, there is still a

place for wage differences across regions. With the 1991 data and after controlling for work and

worker characteristics, we find Niedersachsen with the highest real wages and Baden

Wuerttemberg with the lowest, just followed by Hamburg.5

Dispersion of real regional wages (given by the Adjusted Standard Deviation) is also

found to be significant and around 0.294, which in comparison with the previous three countries,

comes just after the highest real wage dispersion found in Italy.

An immediate conclusion derived from this analysis is the importance of regional cost-of-

living differentials in the regional wage differential analysis. Although the dependence of the

results on the regional cost-of-living deflators used, which should bring some precaution on the

conclusions derived, we have shown that, even after controlling for Human Capital and

demographic characteristics, as well as worker and working conditions, money rewards but also

and more interestingly real rewards vary across regions. In particular:

- a considerable variation in the ranking of the regions is found when comparing money

with “real” wage differentials. Regions showing the highest wage differentials tend not to offer

such high ranking position when regional cost-of-living or regional housing price differentials are

taken into account;

- regional wage dispersion tend to be higher in “real” than in nominal terms;

- comparing regional real wage (deflated by regional housing prices) dispersion across

Italy, Spain and the UK (using the Adjusted Standard Deviation - ASD (see footnote number 2)),

returns to migration appear greater in Italy since the overall real wage differential for similar

workers in Italy when compared with the average regional wage, is bigger (0.309) than the

same difference in Spain (0.159), or in the UK (0.118);

- results for the four countries come in line with Roback (1988) conclusion that “cost-of-

living variations do not account for wage differences, but actually exacerbate them” . The

argument is that it is mainly regional differences in amenities which account for the wage

differences. This is so because amenities generate rent differences which by themselves comprise

16

a large proportion of cost-of-living differentials, i.e., cost-of-living is part of an equalising

difference paid for amenities.

We proceed with the following part of the paper analysing in more detail the effect of

regional specific characteristics in wage differentials.

b) Compensating differentials at the regional level

In order to test the importance of regional structural factors responsible for compensating

differentials, additional variables were included in the estimations described above. The aim is to

disentangle these effects and to determine whether the marginal impact of regional specific

factors remains after other variables are taken into account. Unfortunately it is difficult to

speculate on the relationship between regional wage differentials and regional specific variables

taken individually mainly due to three problems often referred to in the literature:

. the first relates to the fact that even with samples of at least 6000 observations for each

country, only at most 17 degrees of observation, which means 17 regional units, persist when

regional specific variables are introduced. As pointed out by Moulton (1990) and Blien (1995)

this implies that the standard errors of the aggregate variables are based on the wrong number of

observations originating biased (underestimated) standard errors if the random disturbances in the

regression are correlated;

. secondly, variable multicollinearity is also very common in hedonic studies, frequently

leading to insignificant and/or unstable parameter estimates. The methods available dealing with

multicollinearity - for example, factor analysis and ridge regression, or the most common case,

the omission of some of the most potential variables causing multicollinearity - make it difficult

to interpret the final results of the parameter estimates;

. finally it is possible that some regional specific variables are correlated across regions

causing correlation between the error term of different regions, i.e., spatial autocorrelation.

In what concerns the first problem, empirical studies trying to correct such bias have

found results similar to those found when nothing is done for what concerns the problem

(Dickens and Katz (1988), Blien (1995)). Because of the potential correlation between regional

specific variables (multicollinearity), for example, city size can be correlated with both crime rate

17

and pollution levels, after analysing the matrix of correlation of such variables we have opted to

include few variables at a time in each regression6 following two steps: first the aim was to find

the most similar set of regional specific characteristics showing a reliable performance for the

four countries; secondly, the aim was to find the best regional specific specification for each

country, both in terms of including the most representative set of regional specific characteristics

variables and in avoiding econometric problems, in particular multicollinearity.

We have used a method which combines a Bayesian approach with a more traditional

non-Bayesian proposal. A traditional classical “orthodox” approach a unique prior choice of the

functional form with the inclusion of the independent variables based on theoretical

considerations, and not taking into account previous studies, contrasts with a Bayesian

approach mainly because in the later empirical results from previous studies (on variables and

functional form) are used to derive a prior distribution. Our strategy, as explained by Andersson

(1994), is then Bayesian in drawing on cumulative experience of previous studies (mainly in the

choice of the specific regional variables with potential influence on regional wage differentials)

but using non-Bayesian principles mainly in what concerns the choice of the functional form

(based on the traditional Human Capital Model) and the concern on the traditional principles,

such as multicollinearity and heterokesdaticity, so that we do not obtain formalised posterior

distributions.

Extensive experimentation was done with many variables. It is clear the trade-off

between the generality and simplicity on the one hand and descriptive explanatory power on the

other. The equations reported below and the regional variables included were chosen to be

representative of the results and showing some light on theory predictions. Once again using a

semi-logarithmic function we regress real wages on the already defined work and worker

characteristics, but now substituting regional dummies variables by some regional specific

characteristics7.

The hypothesis to be tested is if regional wage differentials are equalising differences for

cost-of-living and amenities. The testing procedure consists in the estimation of the augmented

earnings functions used in the previous chapter (with real wages as dependent variables) with the

difference that now regional dummies will be replaced by the regional specific characteristics,

proxied by the variables explained in the previous section and listed in Annex 3. Disentangling

the regional effect in the wage determination process is expected to show some light on the

process in which regional wage differentials are originated after controlling for work and worker

18

characteristics. In particular it will permit us to test the existence of compensating differentials

compensating workers living in regions with relatively more “disadvantages” both in cost-of-

living differences and non-pecuniary factors.

As mentioned above one of the main problems in introducing regional specific variables in

a wage equation, specially when regional desegregation is not big enough, is the existence of

potential correlation between these variables. For example the percentage of people living in big

cities (URB) and size of the biggest city in the region (CITYS) as well as population density (POP)

and even pollution levels (SMK) are between the candidates to be strongly collinear. We have

approached this problem combining (i) the aim of finding a reasonably common pattern for the

four countries in what concerns regional specific characteristics influencing regional wages, with

(ii) the aim of having the best possible wage equation including regional specific characteristics

for each country. So two main questions have been asked before starting the estimation

procedure: (i) which sets of regional specific variables affect regional real wage differentials in

these four countries after taking into account human capital characteristics as well as job and

other individual characteristics? (ii) do we still see the same relationship if we drop the collinear

variables? After experimenting estimations with several combinations of variables we report

those results found to behave best both in terms of capturing the most important regional

specific effects on wages and with few chances of collinearity problems. For this latter aim we

have relied on the matrix of correlation in order not to include at the same time variables with

strong correlation coefficients. We have also always in mind to find the most common pattern of

regional specific characteristics across the four countries.

Before analysing the empirical results it is worth emphasing that these results should not

be seen as a casual relationship where regional wages are purely determined in terms of these

regional specific factors. Regional wage determination, as almost everything in economics, is a

complex process for which several factors account. In this analysis we are focusing on the spatial

side of the question trying to test, ceteris paribus, the importance of these regional specific

effects in accounting for wage differentials across regions.

Table II - A, B, C and D8 shows coefficients estimates and t statistics for the amenities

and disamenities included in the regression, also including work and worker characteristics, on the

log of real wages9. Looking across a row gives some indication of the robustness of a variable to

different specifications. Looking across a column gives some indication of the effect of different

regional specific variables in the wage equation. Three sets of variables were included

19

simultaneously in each equation in order to capture the effect of climate (temperature (TEMP),

rain (RAIN), hours of sunshine (SUN), snow (SNOW), gale days (GALE)), economies of

agglomeration (City Size (CITYS), Urban index (URB), Population Density (POP)) and

environmental characteristics (pollution (SMK, SLD or SOX) or protected areas (GREEN)).

Variables related to the housing market (percentage of rented dwellings (RDW), or variables such

as union density (UNION) crime rate (CRIME) or road haulage were just included after the three

other effects have been correctly specified and analysed due not only to the unavailability of

such variables for some of the countries, but also to the lower degree of importance and to the

probability that its effects were potentially picked up by the three other sets of variables.

Table II - Regional specific characteristics in wage equations after controlling for worker and work characteristics (*)(t statistics in parenthesis)A - UK 91 - Real Wage EquationReg.variables 1 2 3 4 5 6 7 8 9 10 11 (*)Dependent variable: log hourly Wage deflatedGREEN -0.0009 -0.0005 -0.0007 -0.0006 -0.001 -0.0004 --- --- -0.0006 -0.0004 -0.0008by regional consumer prices

(-6.91) (-3.23) (-4.86) (-3.78) (-7.38) (-2.73) (-3.94) (-3.35) (-6.16) Controls: School, Experience, Experience Squared, Tenure and URB -0.0458 -0.0527 -0.0277 -0.0664 -0.0229 --- -0.0574 -0.0653 --- -0.05711 -0.0268dummies for sex, marital status, full/part-time work,

(-5.04) (-5.83) (-2.96) (-6.94) (-2.42) (-6.47) (-7.32) (-6.85) (-2.88)number of employees in the firm, Employment stats,RAIN 0.00005 0.0002 0.000020.00001(a) 0.00003 0.00002 --- 0.00003 0.00002 --- --- Occupation and Industry

(7.45) (2.38) (2.65) (1.2) (3.68) (2.41) (5.71) (3.47) SNOW 0.0024 --- --- --- --- --- --- --- --- ---

(10.39) UNION 0.0017 --- 0.0006 --- --- --- --- ---

(8.37) (2.32)CRIME 0.0019 --- --- --- --- --- ---

(6.67) POP -0.00004 --- --- --- --- --- ---

(-8.06) CITYS -5E-09 --- --- --- --- ---

(-8.04) SUN -0.0411 --- --- --- ---

(-5.61) RDW 0.0016 --- --- --- ---

(6.71) SMK 0.0015 --- --- ---

(3.39) ROAD -0.0002 --- ---

(-10.85) TEMP -0.02345

(-13.54)GALE 0.0043

(10.89)R 0.486 0.493 0.486 0.489 0.49 0.49 0.49 0.483 0.492 0.505 0.505

B - SPAIN 91 - Real Wage EquationReg.var. 1 2 3 4 5 6 7 8 9 10 (*)Dependent variable: log hourly Wage deflated by regional housing pricesGREEN -0.015 -0.0166 -0.0261 -0.0131 -0.0238 -0.0181 -0.0105 -0.0482 --- 0.0065Controls: School, Experience, Experience Squared, Tenure and

(-4.42) (-4.89) (-6.94) (-3.97) (-7.10) (-5.28) (-3.11) (-12.70) (1.86) Dummies for Sex, Marital Status,Ethnical Group,Full/Part-time work,URB -0.5174 -0.5458 -0.5725 --- -0.1371 -0.6179 -0.5273 -0.4797 -0.5816 -0.2353Number of employees in the firm, Employment stats, Occupation

(-18.80) (-19.81) (-19.99) (-4.45) (-20.03) (-18.53) (-17.69) (-20.52) (-7.92)and IndustryRAIN -0.0001 -0.0002 -0.0001 -0.0002 -0.00005 -0.0002 --- --- -0.00007 -0.0001

(-7.94) (-11.75) (-8.41) (-13.66) (-2.97) (-10.53) (-4.59) (-4.99)SMK 0.0003 --- --- --- --- --- --- --- ---

(10.83) UNION 0.0035 --- --- --- --- --- --- ---

(6.83)CITYS -2E-07 --- --- --- --- --- ---

(-34.84) POP -0.0011 --- --- --- --- ---

(-25.63) SNOW -0.0017 --- --- --- ---

(-7.17) HUM -0.0034 --- --- ---

(-4.03)SUN 0.1206 --- ---

(20.36)TEMP 0.0174 ---

(7.61)RDW -0.0475

(-23.02)R 0.386 0.39 0.387 0.417 0.41 0.388 0.384 0.399 0.387 0.406

20

C - ITALY 91 - Real Wage Equation D - Germany 1991 - REAL Wage EquationsReg.variables 1 2 3 4 5 6 Reg.var. 1 2 3 4 5 6 7 8 9GREEN -0.0267 -0.0151 -0.02 -0.0182 -0.0208 -0.0115 GREEN -0.0024 -0.0099 -0.0035 -0.0034 -0.006 0.0018 0.0029 0.0018 0.003

(-24.63) (-14.86) (-20.08) (-14.27) (-17.27) (-9.98) (-2.51) (-11.40) (-3.88) (-3.59) (-5.09) (1.65) (2.78) (1.65) (2.41)URB -1.56 -0.5864 -0.6322 -1.8555 --- --- URB -0.0014 -0.0036 -0.0092 --- -0.0008 --- 0.0004(a) --- -0.0007(a)

(-32.04) (-11.61) (-12.40) (-35.45) (-3.16) (-9.37) (-13.54) (-1.79) (0.83) (-1.48)RAIN -0.0003 0.0001 -0.0003-0.00005(a) -0.0004 -0.0004 SUN -0.5168 -0.3657 -0.6038 -0.552 -0.5508 -0.3705 -0.3399 -0.3705 -0.4766

(-11.22) (5.79) (13.43) (-1.59) (-13.92) (-16.11) (-18.74) (-14.87) (-22.30) (-19.83) (-19.52) (-11.30) (-10.86) (-11.30) (-17.01)RDW -0.0588 --- --- --- --- SLD 0.1357 --- --- --- --- --- --- ---

(-38.43) (22.48)POP -0.0023 --- --- --- CRIME 1.8 0.4723 --- --- --- --- ---

(-36.40) (14.55) (6.03) TEMP 0.0521 --- --- TEMP -0.0621-0.0000003 --- --- ---

(12.21) (-5.13) (-9.59) UNION 0.0055 --- CITYS -0.0001-0.0000003-0.0000003 ---

(8.42) (-2.53) (-11.06) (-9.59) SOX -0.0022 POP -0.0001 ---

(-31.15) (-2.53)R 0.399 0.516 0.51 0.479 0.307 0.395 RAIN -0.0005(*)Dependent variable: log hourly Wage deflated by regional housing prices (-6.52)Controls: School, Experience, Experience Squared, Tenure and R 0.439 0.576 0.486 0.445 0.445 0.468 0.467 0.468 0.449Dummies for Sex, Marital Status, Ethnical Group, Part-time work, Dependent variable: log hourly Wage deflated by regional housing land pricesNumber of employees in the firm, Employment status, Occupation Controls: School, Experience, Experience Squared, Tenure and Dummies for Sex,and Industry. Marital Status, Nation, Employment status, number employees in firm, training requirements

Public or Private job, Occupation and Industry.

The model explains around 50% of variation in real regional wages in the UK and

Germany, 40% in Spain and 38% in Italy, values similar to those found by Roback (1982 and

1988) (around 40% to 50%). In what concerns heteroskedasticity, we could not (visually) detect

any differences in residual variance between the upper and lower reaches of the dependent

variable although the variance seemed somewhat smaller around the mean.

UK: Table II -A

It is clear the effect of climate, environmental and urbanisation variables on the wage

equation. The first three rows show a relatively stable behaviour of variables measuring the

impact of average rainfall (RAIN), percentage of environmental protected areas in the total area

of the region (GREEN) and percentage of people in the region living in big cities (more than

20000 inhabitants) in the wage determination process. For example, the coefficient of the RAIN

variable is positive and around 0.00002 signifying that an increase in one unit of rainfall in one

region is associated, ceteris paribus, with a 0.002% increase in the real wage of that region. This

comes in line with the compensating differential principle according to which bad weather

conditions should be compensated by higher wages. In what concerns the GREEN variable,

because it represents a regional amenity, the regional sign found for the estimated coefficient also

comes in line with the compensating differential principle. An increase in one unit of this variable

is associated, ceteris paribus, with a decrease of around 0.07% in the regional real wage. In what

concerns the variable picking up the effect of urbanisation, it is interesting to note that contrary

to what would be expected if the analysis was done at the nominal level, after taking into account

regional prices, and environmental variables it looks as though urban characteristics are, ceteris

paribus, connected with lower regional real wages. Exploring further this effect we use other

21

variables to capture the same effect. Again population density (POP) and size of the biggest cities

in the region (CITYS) come with a negative sign reinforcing the negative net effect that economies

of agglomeration have on the real wage of the region.

Some other variables also come in line with the compensating differential principle: most

climate variables perform well. Average daily temperature (TEMP) and sunshine days (SUN),

have a strong negative effect on wages (i.e., are net amenities); average gale days per annum

(GALE) and snow days (SNOW) have the expected positive signs, i.e., are net disamenities. In

principle, controlling for everything else, a person living in a region with higher temperatures,

less rain and fewer gale days is expected to receive a higher wage than one living in a region with

more pleasant climate conditions. Bad weather conditions are then, ceteris paribus, compensated

by higher wages. For example, controlling for everything else, in 1991 an additional degree on the

average temperature in one region is associated with a 2.3% reduction on the regional real wage.

The positive (0.0017) coefficient found to the Union Density variable (UNION) reflects the

positive impact of Unions on regional wages.

Crime rate (CRIME) and Pollution levels (SMK) come with the expected positive sign

reflecting the fact that, ceteris paribus, people are compensated in real terms for living in places

with higher levels of crime rate and higher levels of pollution.

Finally, we can say that the specification reported in column 3 is the one which, for

1991, best behaves and best reflects the effect of regional specific characteristics on British

regional wages. In particular this equation strongly supports the negative effect of environmental

and urban variables and the positive effect of rainfall and union density on the wage

determination process across British regions.

SPAIN (Table II - B):

Results on the effect of regional specific variables in the Spanish wage equation are

similar to those found for the UK with the exception of those concerning climate variables, where

we find signs opposite to those found in the UK, and usually reported in the literature (for

example Roback (1988)). According to our results, in Spain high levels of rainfall, humidity or

snowfall are associated with low wages, as well as regions with higher temperatures and more

hours of sunshine are compensated with higher wages. One plausible explanation is that given the

differences in weather conditions between the UK, a northern country, and Spain a Southern

country, what is considered a climate amenity in the UK is in Spain a climate disamenity.

22

In what concerns other specific regional characteristics, the results come in line with

those previously found in the UK: the environmental variable - protected areas (GREEN) -

comes with a robust negative sign, reflecting the fact that regions with more protected areas

(amenity) have, ceteris paribus, lower wages; reinforcing the effect of this environmental

variable, the pollution variable (SMK) comes with a positive and statistically significant sign,

reflecting the fact that more polluted regions are compensated with higher real wages. As in the

case of the UK, urban characteristics (URB), population density (POP) and size of the biggest

city in the region (CITYS) have a negative effect on real wages.

Finally, we can say that the specification reported in column 4 is the one that best

behaves and best reflects the effect of regional specific characteristics in the Spanish regional

wages. Regional wage differentials are then affected by regional specific characteristics such as

the percentage of the region which is a national park or protected area (GREEN), climate

conditions (RAIN), urban effects (URB) and population density (POP).

Italy (Table II-C):

As in the case of Spain, also in Italy climate variables have signs opposite to those found

in the UK: rainfall comes with a negative sign whereas temperature comes with a positive sign.

Once again the evident explanation is that rain is considered an amenity whereas days with higher

temperatures are considered disamenities. All other variables come in line with the compensating

differentials principle and with the results found for the other two countries: we find a negative

and stable coefficient for the GREEN variable as well as for the Urban variable (URB).

Once again this reflects that, ceteris paribus, workers receive a negative wage premium when

living in places with better environmental qualities and with the advantages of urban places. As

expected Unions have a positive influence on real wages. Not everything however behaves well

and we found the opposite sign in the pollution variable (SOX).

The equation which best seems to reflect the effect of regional specific characteristics in

the wage equation seems to be the one in column 3 where there is strong evidence of the negative

effect of an environmental variable, a climate variable and variables capturing the effect of

economies of agglomeration (URB and POP).

23

GERMANY (Table II - D):

The pattern found for the regional specific characteristics in the German regional real

wage determination process is very similar to the one found in the UK. The negative effect of the

environmental variable (GREEN) is clear, as well as the Urban variable and hours of sunshine.

Crime rate and Pollution levels come with a statistically significant and positive sign reinforcing

the compensating differentials principle. Population Density and the size of the biggest cities in

the region (CITYS) have, as found for the other countries, a negative effect on wages.

In this case, it is the equation reported in column 2 that best reflects the effect of regional

specific characteristics in regional real wages. In particular, characteristics such as the percentage

of the region which is National Park or Protected Area (GREEN), the percentage of the people in

the region living in big cities (URB), hours of sunshine (SUN) and pollution levels (SLD),

strongly influence real regional wages in Germany.

This combined evidence seems persuasive that regional differences in wages can be

largely accounted for by differences in regional amenities. And what can be said about these three

countries simultaneously?

Looking carefully the results for the three countries, and although there are differences in

the data and variables definition across the four countries, a common pattern is found, in

particular reinforcing the effect of environmental and climate variables in the regional wage

determination process:

(i) environmental variables, in particular our GREEN variable (Protected areas and

National Parks) behave consistently well in all four countries, with a negative sign between

around -0.02 for Spain and Italy and -0.001 for the UK. This means that ceteris paribus, regions

with more protected areas and National Parks are seen has having more amenities and because of

that people receive a negate wage premium. All these changes are to be understood as a ceteris

paribus change, holding other things constant;

(ii) climate variables, in general, behave well in the wage equations. After controlling for

work and worker characteristics, weather related variables behave accordingly to the

compensating differential principle. It is interesting to note that our results seem to indicate that

the same weather variable is classified has an amenity or disamenity differently in each country.

In northern countries such as the UK and Germany, rain, snow, humidity and gale days are seen

as disamenities and because of that regions with higher values of these variables, ceteris paribus,

24

are compensated with higher wages. On the contrary in southern countries such as Spain and

Italy the same variables are considered amenities and so, ceteris paribus, regions with higher

values of these variables receive a negative wage premium;

(iii) demographic variables as percentage of the region which is urbanised (URB) and city

size (CITYS) come always with a negative sign reflecting the argument that urban or more densely

populated places, due to economies of agglomeration at the consumption level, i.e., due to the

advantages of working population living in big cities, ceteris paribus, are associated with lower

real wages.

VI - CONCLUSION

It has long been noted that wages for apparently similar workers can differ between

regions. The results presented here not only support this view but reinforce it in two ways: first,

a nominal wage analysis is complemented through the use of regional price deflators. This reveals

both the significance of regional price variations - an issue previously rather negleted in the

literature - and modifies our judgement with respect to the form and extent of real earnings

differences. Results confirm the existence not only of nominal wage differentials but also of real

wage differentials for apparently similar workers. Also the rank order of the regions according to

its regional wage differentials changes when the analysis is pursued at the real level. This view is

supported not only for one specific country but for the four countries under analysis.

These results are consistent with Roback’s (1988) conclusions for the regions of the US:

controlling for regional cost-of-living differences does not eliminate regional wage differentials but

actually exacerbates them. Regional wage dispersion is found to be bigger in real than in nominal

terms in the four countries under analysis. The argument that amenities generate rent differences

which by themselves comprise a large proportion of cost-of-living differentials, i.e., cost-of-

living are part of an equalising difference paid for amenities, is also supported by the results

found in the second stage of the analysis where we test the importance of amenities in the

regional wage determination process.

The competitive theory pointing out that regional real earnings are compensated by

regional specific characteristics is strongly supported by our empirical results. Results for the

four countries support the idea that workers living in regions with disamenities (“bad” weather

25

conditions, high levels of crime rate and pollution) are compensated by a wage premium. The

inverse is also true in what concerns regional amenities (green spaces and “good” weather

conditions). It is also interesting to note that although there are differences across these four

countries with characteristics which are specific to every country, we have been able to find

some regional specific characteristics that, ceteris paribus, affect regional wages similarly in the

four countries under analysis. In particular two sets of variables behave strongly and

consistently well in the four countries - percentage of the region classified as National Park or

Protected Area (GREEN) and percentage of the people in the region living in urban places

(URB): ceteris paribus, workers with similar characteristics receive a negative real wage premium

when living in regions with more “GREEN” spaces, and when living in urban places (URB). The

importance of the variable measuring the level of regional union density suggests that workers in

these regions are receiving an economic rent, and calls for further investigation on the non-

competitive side of the story.

These results do not mean that compensating differentials are the only explanation for

adjusted Human Capital adjusted interregional real wage differentials. The coefficients of

determination found in the regression are around 50% suggesting that much has to be said about

interregional wage differentials. Problems related to the mix of regional aggregate variables with

individual data and the correlation between these variables are the weak points of such an

analysis. A note of caution should then be present in the analysis of the results concerning

regional specific characteristics.

These different random samples paint a uniform picture. Real wages are low, other things

constant, in areas with amenities. This is not for reasons of composition: it is not because one

region has disadvantages in terms of workforce composition, nor because it has more high wage

industries. Instead there appears to be some statistical link between regional specific

characteristics and pay.

Two particular aspects of the current debate on European regional policy can be

addressed from these results: first that redistribution policies between regions should take into

account the real side of the coin (and not only the nominal side); second, this study calls for

further research on the existence of differences in the patterns, and consequently on the

incentives, of interregional migration between different skill groups.

26

1 Heating degree days measured as the sum of daily negative departures per year from 65 degrees.2 Some general comments can be made on the regional characteristics for each of the four countries:

- for the UK, the South East is the region with the highest money average wages, as well as the highestcost-of-living, highest house prices, and the biggest city (London), and population density. In contrast Wales is thelowest wage region, but second lowest housing price region and the region with the fourth lowest cost-of-living.The North, a region with a very similar level of average wages as found in Wales, is the region showing the biggestproportion of rented dwellings;

- Madrid is for Spain, what the South East is for the UK: not only the highest wage Spanish region, butalso the region with the highest house prices and the bigger city and density of population. In contrast Extremadurais the lowest average wage region with considerably low house prices and without any city with more than 200 000inhabitants; the percentage of rented dwellings is relatively high in the high wage regions (Cataluna, Asturias,Madrid and Baleares);

- the highest average wage Italian region is Lazio, with the third highest level of house prices and thebiggest city (Rome). Basilicata is the lowest average wage region, with the lowest density of population but with arelatively high level of house prices. The percentage of rented dwellings is, as in the case of Spain, relatively highin the high wage regions (Campania, Piemonte, Lombardia, Lazio and Liguria);

- in Germany (West only) (excluding Berlin) Housing Land Prices are highest in Baden-Wurttemberg andlowest in Niedersachsen. Hamburg is the region with the highest population density, the biggest city and given theboundaries of the region (a city) all the population living in this region lives in a urban area. Surprisingly,Niedersachsen is the region for which our sample data shows the highest mean hourly wage, contrasting withRhein-Pfalz which shows the lowest mean hourly wage.3 Several diagnostics tests are also reported in the last rows of Table I. In general, with the exception of the RamseyReset Test for misspecification in the Italian equation, all the others exceed their critical values. This is however acommon occurrence, found in previous studies estimating wage equations with big microdata sets (Blackaby andMurphy (1991 and 1995), Wagner and Lorenz (1988)). The failure of the Ramsey Reset Test indicatesmisspecification problems in the estimated equations which is a common problem found in earnings functions ingeneral (see Wagner and Lorenz, 1988) and potentially related to the omission of variables measuring individuals’ability; The Bera Jarque Normality Test fails in all equations. Wagner and Lorenz (1988) have found the sameproblem and argue that the test is sensible to outliers. When correcting for these outliers, the hypothesis ofnormality is accepted and the estimated coefficients are found similar to those found when such correction is notdone.4 For further details of the German Labour Market system see Streeck (1988) and Kennedy (1980).5 We drop Berlin from this analysis because due to the 1990 German reunification data, in particular regionalspecific variables, as for example housing land prices, has become distorted.6Dickens and Katz (1988) suggested an alternative approach as a possible solution to the aggregation problem whichresults from the joint use of aggregate and individual variables. In this case coefficient of the regional dummiesresulted from the estimation of real wage equations (Table I) are regressed on the same regional specificcharacteristics. However given the reduced number of observations in sample and colinearity problems betweenseveral of the regional specific variables nothing conclusive could be obtained with such an approach.7 See Annex 1 for variables definition and sources.8 A for the UK; B for Spain, C for Italy and D for Germany.9 In order to test if regional housing prices constitute a good proxy for regional cost of living differentials we havecompared the coefficients of estimating the wage equation with the Reward Regional Survey cost-of-living indexwith the housing price coefficient. The results indicate that in fact housing prices are a good proxy for cost-of-livingdifferentials. First because the estimated coefficient for housing prices or consumer prices, came statisticallysignificant and similar (around 0.6 to the consumer price and 0.4 to the housing price) and second because estimatesfor other variables included in the regression also come similar when using both proxies for cost-of-livingdifferentials. So at least for the UK data seems to indicate that regional cost-of-living differentials are mainlyaccounted for differences in housing prices.

Acknowledgments: I would like to thank my PhD Supervisor, Professor Paul Cheshire, as well as ProfessorAndrew Oswald for helpful comments and suggestions. Financial support from the Portuguese Research Council -J.N.I.C.T.- under Programme PRAXIS XXI is gratefully acknowledged.Material from the General Household Survey was made available through the ESRC Data Archive has been used bypermission of the Controller of the HM Stationery Office with the usual disclaimer. Material from the “Bilanci delleFamiglie Italiane” was made available from the Bank of Italy. Material from the “Encusta de PresupuestosFamiliares” was made available from the Spanish National Institute of Statistics. Material from the Englishversion of the German Socio Economic Panel was made available from the Syracuse University in cooperation withthe German Institute for Economic Research in Berlin.

27

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31

Annex 1 - Regional variables sources and definitionsCountry Variable Description SourceUK91 RP Regional Consumer Price (a) Reward Regional Surveys

RPH Regional Housing Prices (including prices for detached Nationwide Building Societyand semi-detached houses, terraced houses,bungalows,flats and mansionettes) (in Pounds)

RDW Rented Dwellings (% of total dwellings) EUROSTAT-Regions(1994)RDPO Rented from Private Owner (% of total) Regional Trends (1993)RDLA Rented from Local Authority (% of total) Regional Trends (1993)RDHA Rented from Housing Association (% of total) Regional Trends (1993)TPOP Total Population EUROSTAT-Regions(1994)POP Density of Population EUROSTAT-Regions(1994)URB Percentage of people living in big cities( those with more own estimations (city population data from Census )CITYS Total population of the largest city Census

than 20000 inhabitants)UNION % of employees in the region who are members of trade Employment Gazette, January 1993

unions or staff associationSMK Smoking concentrations (micograms per cubic metre) Regional Trends (1993)SLD Sulphur Dioxide (micograms per cubic metre) Regional Trends (1993)GREEN Designated Areas as National Parks and Areas of Regional Trends (1993)

Outsanding Beauty (percentage of total area in region)RAIN Average annual rainfall (in mm) Monthly Weather ReportSUN Average hours of sunshine per day Monthly Weather ReportGALE Average gale days per annum Monthly Weather ReportSNOW Average snow days per annum Monthly Weather ReportTEMP Average daily temperature Monthly Weather ReportCRIME Notifiable offences recorded by the police(rates per 100 population)Regional Trends (1993)ROAD Road haulage Regional Trends (1993)

ITALY91 RP Regional consumer prices n.a.RPH Regional Housing Prices (including new and used housing) Consulente Immobiliare, 1991

(Thousand of Liras per Square Metre)RDW Rented Dwellings EUROSTAT-Regions(1994)POP Density of Population EUROSTAT-Regions(1994)URB Percentage of people living in big cities( those with more own estimations (city population data from Census )

than 20000 inhabitants)CITYS Total population of the largest city CensusUNION % of working population in the region who are members of trade Centro di Studi Sociali e Sindicali, 1994

unions or staff associationRAIN Average annual rainfall (in mm) ISTAT-Annuario Statistico ItalianoTEMP Average daily temperature ISTAT-Annuario Statistico ItalianoSOX Estimated emissions from combustion European Environmental Yearbook GREEN Designated Areas as National Parks and Areas of ISTAT-Annuario Statistico Italiano

Outsanding Beauty (percentage of total area in region)SPAIN 91 RP Regional Consumer Prices n.a.

RPH Regional Housing Prices(new houses) Sociedad de Tasacion(Thousand of Pesetas per square meter)

RDW Rented Dwellings EUROSTAT-Regions(1994)POP Density of Population EUROSTAT-Regions(1994)URB Percentage of people living in big cities( those with more own estimations (city population data from Census)

than 20000 inhabitants) INE-Anuario Estadistico, 1993CITYS Total population of the largest city INE-Anuario Estadistico, 1993UNION % of working population affected by collective bargaining Boletin de Estadisticas Laborales, 1988 and 1992SMK Total emissions of SO2 (thousand of tonnes) Medio Ambiente en Espana - MOPT 1991TEMP Average monthly temperature INE-Anuario Estadistico, 1993RAIN Average annual rainfall (in mm) INE-Anuario Estadistico, 1993SUN Average hours of sunshine per day INE-Anuario Estadistico, 1993HUM Average Monthly humidity (in %) INE - Anuario Estadistico, 1993SNOW Number of days with temperature below 0 INE - Anuario Estadistico, 1993

GERM91 RP Regional Consumer Prices n.a.RPH Regional Housing Land Prices(DM per square metre) Bundesforschungsanstalt fur Landeskunde und RaumordnungRDW Rented Dwellings n.a.POP Density of Population EUROSTAT-Regions(1994)URB Percentage of people living in big cities(those with more own estimations (city population data from Statistisches

than 20000 inhabitants) Bundesamt - Statistisches Janrbuch fur das Vereinte Deutschland)CITYS Total population of the largest city Statistisches Janrbuch fur das Vereinte Deutschland 1993TEMP Average monthly temperature Statistisches Janrbuch fur das Vereinte Deutschland 1992RAIN Average annual rainfall (in mm) Statistisches Janrbuch fur das Vereinte Deutschland 1992SUN Average hours of sunshine per day Statistisches Janrbuch fur das Vereinte Deutschland 1992GREEN Designated Areas as National Parks and Protected Statistisches Janrbuch fur das Vereinte Deutschland 1992

areas (percentage of total area in region)SLD Sulphur Dioxide (micograms per cubic metre-annual average) Norwegian Institute for Air Research - EMEP - Data Report 1991CRIME Notifiable offences recorded by the police(% of region pop.) Statistisches Janrbuch fur das Vereinte Deutschland 1992

(a) percentage comparison against the national average

32

Annex 2 - Means of regional specific variables

UK 1991 RP RDW TPOP POP CITYS URBUNION GREEN SMK SLDTEMP RAIN SUNGALESNOW CRIMEROAD RPHWeekW RDPORDLA RDHAEast Anglia 96 29 2087118 166 100200 0 28 7 12 17 9.7 442 3.58 2.7 12.05 7.9 83 57037 193 10 16 3East Midlands 93.5 29 4032540 258 285000 0.14 39 9 9 39 9.3 501 3.53 2.8 17.5 10.4 149 48616 199 8 19 2North 90.9 40 3095601 201 278000 0.09 51 38 10 28 8.5 866 3.75 6.8 25 13.5 104 50462 183 7 29 4North West 97.7 32 6399963 873 4810000.144 43 12 17 40 9.6 693 3.5 8 12.6 11.3 165 57286 204 6 22 4Scotland 90.4 47 5120895 65 689000 0.22 43 13 13 33 7.8 1129 3.38 11.2 25 11.6 148 49037 212 6 38 3South West 100.5 27 4722300 198 3970000.084 32 34 9 19 10 877 3.73 9 8.1 8.8 136 59913 198 10 15 2Wales 92.3 29 2886752 139 2940000.102 46 23 11 26 9.2 1143 3.7 10.5 13.4 9.4 92 46310 182 7 19 3West Midlands 97.5 32 5270265 405 10070000.249 40 12 16 39 9.7 620 3.3 4.8 17.8 9.7 143 56685 202 6 23 3Yorks&Humb 91.1 34 4980660 323 7170000.404 42 22 14 36 8.8 633 3.58 7.9 20 11.9 179 41915 198 8 24 2South East(b) 122.6 30 17639856 648 68900000.391 30 24 12 42 9.9 606 3.83 11.2 10.3 10.2 313 70148 258 8 18 4

GB 56889SPAIN 1991 RP(i) RDW TPOP POP CITYS URBUNION GREEN SMKTEMP RAIN HUM SUNSNOW RPHYearWAndalucia n.a. 9 6981440 80 6680000.265 54 3.9 156 17.5 441.9 62.3 7.7 25 111.3 978862Aragon n.a. 9 1191250 25 596000 0.5 49 0.75 430 13.1 421.7 64 6.7 52.8 118.5 1204030Asturias n.a. 14 1119890 106 2590000.231 37 0 142 13.1 823.8 82 4.9 17 149 1204972Baleares n.a. 12 681904 136 3210000.471 55 0 40 17.6 564 73 7.7 0 108 1099358Canarias n.a. 10 1491852 206 2110000.141 26 3.4 48 17.9 134.8 63.8 7.6 60 125.4 1087526Cantabria n.a. 9 524502 99 3780000.721 33 0 14 13.9 1066 72.5 4.5 14 160 1261543Castilha n.a. 9 2637404 28 342000 0.13 28 0.2 206 11.4 343.4 63.1 7 81.9 124.9 1226830La Mancha n.a. 6 1743060 22 127000 0 30 0.11 58 13.3 356 61 7 59 101.3 1101057Catalunha n.a. 16 6002840 188 16940000.319 43 2.2 127 15.6 507.4 66.3 7 44.5 199.5 1270575Valencia n.a. 7 3798715 163 7390000.265 44 0 49 17.4 466.8 65 7.3 5 110.6 1020517Extremadura n.a. 8 1123254 27 126000 0 60 0.4 3 16.6 351.2 62 7.7 17.5 83.2 976817Galicia n.a. 10 2796230 95 2640000.181 22 0.03 738 13.2 1187 75.2 5.8 27.8 110.3 1127556Madrid n.a. 12 4892940 612 31240000.639 41 0 51 14.1 341.9 54 5.9 10 234 1327113Murcia n.a. 7 1029847 91 3090000.464 42 1.1 26 17.8 364.5 60 8 5 102 1006028Navarra n.a. 7 521050 50 184000 0 43 0.2 9 11.2 837.5 76 5 60 142 1315066La Rioja n.a. 7 261768 52 0 0 32 0 2 13.4 430.1 73 6.2 45 112 1191865Pais Vasco n.a. 8 2127473 293 1800000.085 45 0 87 13 1336 77 4.7 3 161.4 1340866

NAT. 153ITALY 1991 RP(i) RDW TPOP POP CITYS URBUNION GREEN SOXTEMP RAIN PHYear WPiemonte n.a. 25 4292431 169 10250000.239 36.1 6.6 92.48 11.5 1322 3917.0 20983Lombardia n.a. 25 8851689 371 14780000.167 38.8 19.4 295.6 12.1 816.4 7500 20635Trentino n.a. 17 884455 65 0 0 15.2 19.8 3.55 11.5 516.6 3350 20732Veneto n.a. 18 4370870 238 3280000.185 41.2 5.3 242.4 13.2 676.2 2400 17870Friuli n.a. 19 1200132 153 0 0 47.6 5.8 61.75 13.8 912.6 2133 21144Liguria n.a. 22 1674162 309 7220000.431 44.5 11.5 141.3 16.3 --- 4166.7 21449Emilia Rom. n.a. 20 3915948 177 4270000.109 65.2 7.3 138.3 13.2 629.4 4266.7 19197Toscana n.a. 18 3517929 153 421000 0.12 45.9 5.5 133.8 14.7 1094 4250 18776Umbria n.a. 13 811776 96 0 0 52.8 2.1 23.08 13.4 951.3 2366.7 18156Marche n.a. 13 1425018 147 0 0 54.4 6.5 16.94 13 849.7 2266.7 18988Lazio n.a. 22 5133646 298 28170000.549 31.9 7 231.6 15.8 824.6 5933.3 21851Abruzzi n.a. 12 1252104 116 0 0 39.6 8.7 8.66 14 458 2166.7 20877 Molise n.a. 10 332850 75 0 0 38 1.3 1.66 16.6 411.3 2400 19681Campania n.a. 27 5628330 414 12000000.213 32.2 2.8 59.36 16.4 733.6 4600 20557Puglia n.a. 17 4026256 208 359000 0.15 46.3 0.8 107.7 16.1 503.8 3300 18727Basilicata n.a. 14 609512 61 0 0 51.9 17.6 3.61 11.9 444.5 1866.7 17181Calabria n.a. 12 2065960 137 1790000.087 53.9 8.9 58.04 18.7 607.3 1600 19477Sicilia n.a. 16 4961451 193 7290000.276 47.6 4.6 241.4 18.6 574.9 2466.7 19784Sardegna n.a. 14 1638120 68 2220000.136 42.1 17.8 107.2 16.3 473.1 2433.3 19440

NAT. 3336.0GERMANY 91 RP(i) RDW TPOP POP CITYS URBUNION GREEN SLDTEMP RAIN SUNCRIME RPHHour W

Baden-Wurttembergn.a. n.a. 9903027 277 5601000.118 n.a. 11 1.6 11.32 483 6.2 1.3 208 21.82Bayern n.a. n.a. 11500302 163 12064000.173 n.a. 30.84 3.1 9.41 893 5.98 1.4 161 21.98Bremen n.a. n.a. 682356 1689 5338000.809 n.a. 2.65 6.2 10.04 550 5.4 1.6 109 22.37Hessen n.a. n.a. 5806350 275 6200000.045 n.a. 30.08 5.5 8.98 578 5.9 1.2 164 22.88Niedersachsen n.a. n.a. 7433793 157 4972000.069 n.a. 17.72 6.3 10.15 546 5.96 1.4 69 25.5Nordrhein-Westfalenn.a. n.a. 17444864 512 5674000.365 n.a. 30.95 4.5 10.72 634 5.8 1.4 134 23.24Rheinl-Pfalz n.a. n.a. 3790586 191 180800 0 n.a. 24.1 3.4 10.7 563 5 1.2 96 21.06Schleswig-Holstein n.a. n.a. 2642976 168 2772000.175 n.a. 13.47 2.2 10.03 405 6.3 1.2 106 22.73Hamburg n.a. n.a. 1660245 2199 1603070 1 n.a. 8.87 5.8 10.06 763 6 1.7 145 22.32 NAT. 123

33

Annex 3 - National micro data sets description

a) Germany

The empirical analysis for German regions is based on the 1991 wave of the German

Socio-economic Panel (GSOEP) provided by the Deutsches Institut fuer Wirtschaftsforschung

(Berlin). The panel is derived from a nation-wide representative survey using a sample of about 5

000 randomly selected households for each year from 1984 onwards. Germans, foreigners living

in Germany and East Germans (from 1990 onwards) are represented in the survey. The 1991

wave provides annual data on over 12.941 individuals from 6.358 households (the response rate

is 70% for the individual schedule) including 3 991 individuals from East Germany, 3.968

children and 2221 foreigners.

The sample used in this study is confined only to West German employees reporting on

earnings and region of residence, a total of 6 259 wage earners (employees with a main job, full-

time or regular part-time) (including West German and foreigners).

b) United Kingdom

The empirical analysis for the case of British regions is based on the 1991 General

Household Survey (GHS) provided by the Data Archive (Essex). The data is derived from a

nation-wide representative survey using a sample of about 9623 randomly selected households

covering 18384 individuals aged 16 and over. Regional discrimination comes in terms of the 11

Standard regions. The sample used includes 7831 wage earners (employees as main job).

c) Italy

The empirical analysis for the case of Italian regions is based on the 1991 “Balanci delle

Famiglie italiane” provided by the Bank of Italy. The data is derived from a nation-wide

representative survey, conducted every two years from 1965 onwards and providing information

at the household and individual level.

The 1991 survey includes information on 283 Italian regions, 8 188 households and 24

930 individuals including 13 882 income earners. From the 13 882 income earners, 57.1% are

male, 42.9% are female, 17.6% are aged between 31 and 40 years old , 18% between 41 and 50

years old and 25.8% between 51 and 65 years old. 50.1% live in the North of the country, 29.8%

in the South and 20.1% in the Centre of the country. The sample used in this study includes

6706 wage earners (employees as main job).

34

d) Spain

We use the 1991 “Encuesta de Presupuestos Familiares” - a survey conducted every 10

years since 1958. The information was provided by the Spanish National Institute of Statistics

(INE). The data is derived from a nation-wide representative survey (corresponding to the period

from April 1990 to March 1991) using a sample of about 21 155 households randomly selected,

deriving the 72 123 individuals (51% is female, 13.9% are between 30 to 40 years old, 11.6% are

between 41 to 50 years old, and 17.1% between 51 to 65 years old).

The survey includes three types of information: household information (containing

information on household composition, type of accommodation, health etc.), individual

information (containing information on individual characteristics and some work characteristics)

and household expenditures. After all sample selections we end up with a sample of 19011 wage

earners (employees as main job).

Annex 3 - Variables available in the National microdata sets - 1990/91

ITALY SPAIN GERMANY UNITED KINGDOM

Micro Data Set "Bilanci delle Famiglie Italiane" "Encuesta de Presupuestos Familiares"German Socio Economic Panel General Household Survey

Model Specification1) Dependent v.WAGE (ln w) Net year earnings (net of taxes and Net year earnings (excluding meal, .Net or gross month earnings .Net or Gross Earnings (including

social contr. but including overtime , transport or material tickets, or any (excluding holiday bonus but commission, tips or tax refunds,holidays income, bonus) sick, pregnancy or work accident including overtime payments) overtime)

compensation). Income compensations .Bonus: 13th month, 14th month... .Bonus: christmas, 13 month...

. normal working hours per week Big problem: only information if working .Working hours per week .hour worked per week

. overtime working hours per week less than 13 hours last week . Overtime hours

2)Indiv. characteristics. sex (Dummy) .Sex .sex .sex .sex

. age .Age (born year) .age .age (born year) .age

. marital status (D) . marital status .Problem: only info.on the relation. .marital status .marital status

. nationality . birth country .nationality (German/Foreign) .born country and ethnic group (race)

.schooling .schooling . schooling .schooling .schooling

.experience .experience years as employee . experience = age-school years - 6 .experience:age-school years-6 .experience:age-school years-6

3) Job characteristics.full/part-time work (D) . Full or Part-time job . Full or Part-time .Full or Part-time(last year) Whether works full-time or part-time.Occupation(D) . Occupation . Occupation . Occupation . Occupation

.Public/Private work (D) . Public or Private Work .civil service

.Industry (D) Industry . Industry (15 CAE classification) .Industry (3-digit-code) .Industry

.Firm Size(number of workers) .Firm size (number employees) .Firm size: (no.employees)

.Training apprenticeship (D) .Present trainee .Apprenticeship hours scheeme

.Dual job holder .having main and secondary job . having main and secondary job

.Union membership .Union membership .Union membership

.job tenure .years with the present employer .job tenure .job tenure

4) Region . 20 NUTS II . 18 NUTS II 13 Lander (West and East) .11 Standard British regions