Wage Inequality in Spain (1980-1990):
A Human Capital Analysis
Luis Díaz Serrano1
Abstract
During the 1980s, Spanish economy has experienced drastic changes in its labor market
performance. Such changes are mainly due to the 1984’s labor market reform, and the Spain’s
entry as a full member of the EU. On one hand, the liberalization process and the promotion of
fixed-term contracts have lead to an increase of the wage inequality. On the other hand, skill
biased technological change has lead to drastic changes in the skill structure of the workforce,
and also has contributed greatly to the increase of the wage inequality. In this paper, using the
EPF/1980 and 1990 I explore this phenomena, and obtaining positive returns to education on
the increase of wage inequality.
1. Introduction
In the 1980s, many developed economies experienced a rise in wage inequality. Most of
the empirical studies that try to find out which factors contribute to this wage inequality increase
and analyze the role of supply factors. However, because of the liberalization in the western
economies that took place in the 1980s, the demand side factors gained relevance in explaining
wage inequality, thus lowering the explanatory power of the supply factors.
In this chapter we explain wage inequality, concentrating only on the supply side. We
analyze the role of occupation, education, age and gender and how the evolution of the influence
of these supply side variables has changed during the 1980s. We follow the classic framework
dominated by human capital theory, since our empirical results show that worker’s attributes still
explain most wage inequality. We observe that wage inequality rose in Spain during the 1980s,
and hence this chapter attempts to answer the following questions:
1
Department of Economics, Universitat Rovira i Virgili.
P.O. Box: Avinguda de la Universitat 1
43204 Reus
Tel. 977 75 98 00 / Fax 977 75 98 10
e-mail: lds@fcee.urv.es
1
i)
How much of this wage inequality can be explained by supply factors?
ii)
Has the role of the supply variables explaining wage inequality changed?
iii)
Is the wage inequality level in Spain comparable to the level in other developed
economies?
iv)
Is wage inequality in Spain so high because low wages are too low or because high
wages are too high?
According to the aim mentioned above, this work is structured as follows. Section 2 reports on
the level of inequality in Spain in 1980 and 1990 using Gini’s and Theil’s indexes and wage
ratios. We also provide some international comparisons. In section 3 we analyze the role of some
supply variables explaining wage inequality in Spain and how the role of these variables have
evolved throughout the 1980s. We use the additive decomposition of Theil’s index. In section 4
overeducation and labor market segmentation are suggested as a plausible explanation for the
changes in wage inequality during the 1980s. Section 5 measures the inter-temporal changes in
the aggregated wage inequality level from 1980 to 1990. Finally, section 6 summarizes the main
conclusions drawn from this chapter.
2. The level of wage inequality in Spain: some international comparisons
During the 1980s wage inequality in Spain rose. Gini’s index shows an increase of 6.6
percent, from 0.28 in 1980 to 0.3 in 1990, decile wage ratios increase by a little over 9.4 percent,
from 3.4 in 1980 to 3.7 in 1990, and the corrected Theil’s index2 reports an increase of 25.6
percent, from 1.5 in 1980 to 1.9 in 1990. Such a difference displayed by the different measures
used suggests that we can affirm that effectively wage inequality has increased, but we cannot
quantify accurately the magnitude of such an increase (see table 1).
An international comparison of wage inequality shows that the level of inequality in the
Spanish labor market is quite high. It is similar to the levels in Portugal, the USA and the UK,
As Theil’s index depends on population size, two populations differing in size are not directly
comparable by this method. In order to make them comparable, we use the corrected Theil’s index
expressed as:
T
Tr 
• 100
log N
where T is Theil’s index and N is the population size.
2
2
but much higher than Australia, Sweden and Germany (see table 2). The measures of inequality
reported in table 2 should be independent of population size and the scale used to measure gross
earnings (hourly, monthly or yearly). The Gini and the corrected Theil indexes are appropriate
measures for a meaningful international comparison of the level of inequality. For the countries
mentioned previously, the level of wage inequality is calculated using full-time workers. In the
EPF/80 and EPF/90 there is no distinction between full-time and part-time workers, therefore we
select household heads, since they are the most similar to full-time workers in our data set. The
list of comparable countries is quite limited because of the scarcity
Table 1: Inequality measures for gross yearly wages on salaried workers (household heads).
1980
1990
% change
Gini
0.2814
0.3000
6.61
Theil
0.1475
0.1810
22.71
Corrected Theil
1.57
1.97
25.58
Wage ratio (1st decile / 9th decile)
3.40
3.72
9.41
Source: Author’s own computations based on EPF/80 and EPF/90.
of studies using the same measures and concepts. The level of inequality and its pattern of
growth from 1980 to 1990 in Spain is quite pronounced. According to the inequality measures
used for the international comparison (see table 2), each country occupies a different position in
the ranking list, however Spain always appears in the top four positions. Gini’s index places
Spain in fourth position for both 1980 and 1990 after USA, UK and Portugal. Corrected Theil’s
index places Spain in second position in 1980 after USA, and in first position in 1990. Finally the
wage ratio between the first and ninth decile places Spain in second position for both in 1980 and
1990 after USA. This pattern in the behavior of wage inequality in Spain may be a sign of a poor
performance of the Spanish labor market during the decade of the 1980s.
Now we will discuss why the level of inequality in Spain is so high. Is it because low
wages are too low? Or is it because high wages are too high? In other words, is the high level of
inequality mainly generated at the bottom or at the top of the wage distribution? To answer this
question we use an international comparison where the selected countries are ranked by interquantile ranks measures.
Table 3 reports the ratios between some of the selected upper and lower percentiles of the
wage distribution. As far as the bottom of the wage distribution is concerned, comparisons of the
1st percentile with regard to the median puts Spain in the middle of the ranking, while when the
10th percentile is compared Spain takes second place. On the other hand, at the top of the wage
3
distribution, when the 90th percentile is compared, Spain is in third place, climbing up to the
second place when the 99th percentile is used.
From the above comparisons we can conclude that in Spain low wages are quite low, but
also that high wages are quite high. Thus, contrary to other countries, a combination of both low
wages are very low and high wages are very high do shape wage inequality in Spain. In Portugal
the high level of inequality is mainly due to high wages are too high, and in USA it is because of
low wages are too low. The rest of countries, Sweden, the former W. Germany, Australia and
Canada show a bit more moderate behavior in the top and the bottom of the wage distribution.
Table 2: Inequality measures of gross yearly wages on salaried workers1 in several countries
(salaried males aged 25-54).
Gini
Theil
Corrected
Wage ratio
st
Theil
1 decile / 9th decile
Spain 19802
0.279
0.141
1.54
3.40
2
1990
0.292
0.175
1.95
3.72
Portugal 19863
0.295
0.168
1.45
3.17
2
West Germany 1984
0.205
0.071
0.83
2.38
UK 19862
0.296
n.a.
n.a.
n.a.
Sweden 19872
0.205
0.071
0.78
2.08
Australia 19852
0.202
0.087
0.97
2.42
Canada 19872
0.256
0.116
1.25
3.03
2
USA 1986
0.300
0.149
1.56
4.00
Sources: Author’s own computations based on EPF/80 and EPF/90 and for all indexes, Cardoso (1998)
for Portugal and all indexes. The other countries: Green, Coder and Ryscavage (1992) for Theil,
corrected Theil and wage ratio, and Bradbury (1993) for the Gini index.
Notes: (1) Both in Spain and in the other countries, only workers aged 25-54 are included.
(2) Gross yearly wages.
(3) Gross monthly wages.
Table 3: Ranking for several countries according to inequality measures at different points of the
log-wage distribution.
Ranking of low wage countries
Ranking of high wage countries
th
st
th
th
50 – 1
50 – 10
90th – 50th
99th – 50th
USA
1.76 USA
0.77 Portugal
0.76 Portugal
1.60
Canada
1.73 Spain
0.62 Spain
0.67 USA
1.31
Australia
1.55 Canada
0.63 Spain
1.28
0.61 USA
0.43 W. Germany
0.52 Canada
1.10
Spain
1.38 Australia
W. Germany
0.69 Portugal
0.39 Canada
0.48 Sweden
1.08
Portugal
0.67 W. Germany
0.35 Sweden
0.46 W. Germany 0.93
Sweden
0.67 Sweden
0.27 Australia
0.45 Australia
0.92
Sources: Author’s own computations based on EPF/90, Cardoso (1998) for Portugal and Green, Coder
and Ryscavage (1992) for the other countries.
4
3. What shapes wage inequality?
Once the high level of wage inequality in Spain is detected, we can consider which
factors shape wage inequality, and how their role has changed during the 1980s. According to
the human capital theory, in a competitive labor market, such a level of inequality should be
determined by supply variables. However, the structural economic changes during the 1980s
suggest that at the end of the decade, demand side also played an important role in explaining
wage inequality, thus reducing the explanatory power of the supply side variables.
In this section we will quantify the role of some factors, mainly supply and human capital
variables, that explain the wage inequality level during the 1980s. The supply variables
considered, for both 1980 and 1990, are occupation3, education, gender and age. In addition, in
1990, two other relevant factors associated with the demand side are considered - industry, and
whether workers are engaged in the public or private sector. To carry out our analysis, we use
Theil’s index, which is additively decomposable. Using its property of addition, the contribution
of the different factors explaining wage inequality is quantified, yielding some insight into the
causes of earnings inequality.
Consider an individual i who belongs to a population of size N, and yi be the share of
gross yearly wage of that individual with respect to that population, where
n
y
yi  0
i 1
i
 1.
Theil’s index is given by the expression
N
T   y i log( Nyi ) .
i 1
(1)
Let  is a discrete variable that divides population into G mutually exclusive groups. The
expression of Theil’s index in (1) can be additively decomposed as see Theil (1967):
3
In some sense occupation can be interpreted as a demand variable, since employers are the ones who
offer jobs associated with a given occupation. Nevertheless, the proper match worker-occupation, and
hence the good performance on-the-job, depends mainly on the worker’s human capital characteristics
and his/her abilities. Those individual’s characteristics are supply factor, that is why in this study as well
as in other studies occupation is assumed as a supply factor, specially for those jobs requiring a high level
of skills.
5
G
 Yg N  G
yN
   Yg  y i log  i g
T   Yg log 
 N  g 1 iS Y
 Y
g 1
g
 g 
 g
g




(2)
G
 TB (  )   Yg TW (  )g  TB (  )  TW (  )
g 1
where in (2)

yi is the share of wages of the individual i to total wages in the population (i=1 ... N)

Sg are mutually exclusive sub-groups of population (g=1 ... G)

Ng is the size of the sub-group g (g=1 ... G;
G
N
g 1

g
 N ), and
Yg is the share of wages earned by the group g to total wages in the population (g=1
... G; Yg 
G
 y ; Y
iS g
i
i 1
g
 1)
In expression (2) TB() is the component of total between-groups inequality. It measures
inequality exclusively due to belonging to a given population group. This component of the total
inequality is obtained comparing the average wage of each population group to the average wage
of the whole population, i.e., it is the percentage of the total wage variation that factor  is able to
explain by itself. The coefficient TW()g measures inequality explained by wage variations across
individuals within the same group, and its weighted aggregation TW() is the component of total
within-groups inequality, i.e., the percentage of the total inequality that cannot be attributed to
the factor .
Table 4 shows the results derived from Theil’s index decomposition for some selected
supply factors in 1980 and 1990. The demand factors considered in 1990 were not available for
1980. Nevertheless, since we are not interested in the role of the demand factors, we realize the
time comparisons just for the supply factors, i.e. human capital variables. The percentages of
total inequality explained separately by each variable cannot be aggregated. It means that the
percentage explained by two factors is not merely the sum of the percentages explained by each
of these factors separately. Inequality explained by two or more factors together should be
computed jointly. Considering each variable separately, in 1980 the variable with the greatest
6
explanatory power4 is occupation, explaining almost 47 percent of total wage inequality. It is
followed by education, which explains 31 percent of total inequality. On the other hand, the
individual’s innate attributes showed no relevance in the shaping of wage inequality, since
gender explains 3 percent, and age only 1.5 percent. In 1990, the same order in explanatory
power is maintained. However, the percentage explained by occupation falls to 36 percent, and
in the case of education it falls to 29 percent. Age and gender maintain their poor explanatory
power in wage inequality. The consideration of all supply factors together explains 66.2 percent
of the total inequality in 1980 and 62.6 percent in 1990. A similar result is reported by Cardoso
(1997) for the case of Portugal.
We can state that throughout the 1980s, human capital variables dominated the wage
formation process in the labor market. Nevertheless, the loss of some explanatory power between
1980 and 1990 by relevant variables, such as education and occupation, may explain the
importance gained by some other demand variables5. For 1990, the percentage explained by type
of industry explains almost 12 percent of total inequality. We consider this percentage relatively
low, compared to the results found out by Cardoso (1997), regarding Portugal, and Davis and
Haltiwanger (1991) for the USA.
This relatively low explanatory power of type of industry may be due to an aggregation
effect, since in the EPF/90 industry type is aggregated into ten categories. A lower level of
aggregation would have shown a higher percentage of the wage inequality explained by industry.
Working in the public or private sector shows no relevance in explaining wage inequality, and
geographical location, which allows for regional comparisons is also relatively irrelevant in
explaining wage inequality.
4. Looking for interactions
In the previous section was shown that both education and occupation are quite important
when explaining wage inequality. However, as human capital theory postulates better paid
occupations, which require high ability and skill levels, are usually associated with higher levels
of education, a significant explanatory power of the interaction between education and
4
Variables possessing a higher value of the between-group inequality component have higher explanatory
power, and hence a greater contribution to wage inequality. The percentage of the wage inequality
explained by each variable or group of variables is computed as TB ( ) / T  ·100.
5
Cardoso (1997) provides a detailed analysis of the relevance of demand side in explaining wage
inequality in Portugal. She find out that the type of industry and firm size are quite important. In Davis
and Haltiwanger (1991) a greater importance of these demand factors is also reported for the USA.
7
Table 4: Contributions of several factors to wage inequality. (Percentage of wage inequality
explained in parentheses).
T
TB()
TW()
Total inequality
Between-group
Within-group
Component inequality
Component inequality
1980
1990
1980
1990
1980
1990
Supply
(1) Occupation
0.068
(46.6)
0.065
(36.1)
0.078
(53.3)
0.115
(63.9)
0.147
(100)
0.181
(100)
(2) Education
0.046
(31.5)
0.053
(29.3)
0.101
(68.4)
0.128
(70.7)
0.147
(100)
0.181
(100)
(3) Age
0.002
(1.5)
0.006
(4)
0.145
(98.4)
0.178
(96.6)
0.147
(100)
0.181
(100)
(4) Gender
0.004
(9)
0.002
(0.9)
0.113
(97.1)
0.179
(99.0)
0.147
(100)
0.181
(100)
(1), (2)
0.077
(52.4)
0.086
(47.5)
0.070
(47.5)
0.095
(52.5)
0.147
(100)
0.181
(100)
Taken together
0.098
(66.2)
0.113
(62.6)
0.050
(33.7)
0.067
(37.3)
0.147
(100)
0.181
(100)
0.021
(11.6)
0.160
(88.4)
0.147
(100)
0.181
(100)
0.006
(3)
0.175
(96.7)
0.147
(100)
0.181
(100)
0.009
(5.0)
0.172
(95.0)
0.147
(100)
0.181
(100)
0.050
(27.6)
0.131
(72.4)
0.147
(100)
0.18 1
(100)
Demand
Industry
Public or private
Location
Taken together
0.003
(0)
Source: Author’s own computations based on EPF/80 and EPF/90.
8
occupation should be expected. The joint percentage of wage inequality explained by the two
factors is 52.5 percent in 1990 and 47.5 percent in 1980. It is thus an interesting exercise to
discover what percentage is explained by education or occupation by themselves, and what part
is explained by the interaction between them.
Let us consider two variables, say 1 and 2. The joint contribution of both which is not
equal to the sum of their marginal contributions can be expressed as
TB ( 1 , 2 )  TB ( 1 )  TB (  2 )  I ( 1 , 2 )
(3)
where I(1,2) reflects the interaction effect between the two factors. Expression (3) shows that
the joint contribution is equal to the sum of their marginal contributions if both variables are
independent, and hence they do not exert any interaction effect.
Following Cowell (1985), we may approach the marginal contribution of 1 and 2
controlling one over each other as
TB (  1 |  2 )  TB (  1 )  I (  1 ,  2 )
(4)
TB (  2 |  1 )  TB (  2 )  I (  1 ,  2 )
Combining of equation (2) and (3) yields the following expression of the decomposition
T  TB (  1 ,  2 )  TW (  1 ,  2 ) 
(5)
 TB (  1 )  TB (  2 )  I (  1 ,  2 )  TW (  1 ,  2 )
Replacing TB(1) and TB(2) as they are expressed in (4) into (5) it follows that
T  TB ( 1 |  2 )  TB (  2 | 1 )  I ( 1 , 2 )  TW ( 1 , 2 )
(6)
Results yielded by the decomposition (6) are shown in table 5. A negative value of the
interaction term implies that separate marginal contributions of education and occupation are
reinforced once they are considered jointly. From 1980 to 1990 the percentage of inequality
explained exclusively by occupation falls slightly from 21 percent to 18 percent, while for
education, it rises from 6 percent to almost 12 percent. The most interesting result is the high
interaction between education and occupation, explaining 25.3 percent in 1980 and 17.7 percent
in 1990 of the total wage inequality.
9
Such an important contribution of the interaction term between education and occupation
in 1980 implies that high wage occupations are associated with highly educated workers. This
fact, holds true in 1990 but its impact is much lower than in 1980. Therefore, it can be concluded
that respect to 1980, in 1990 higher educated workers were matched with lower paid
occupations. Such a phenomenon may suggest the existence of overeducation in the Spanish
labor market at the end of the 1980s.
During the 1980s, the supply of high educated workers increased dramatically. The
empirical studies for several countries show6 that, the labor market is not always able to absorb
such amount of new high educated workers. This fact leads to a mismatch where workers are
usually matched with occupations where the required level of education is lower than that they
posses. Hence, they earn lower wages than those they receive if they were correctly matched.
Table 5: Joint contributions of occupation and education1 explaining wage
inequality. (Percentage explained in parentheses)
T
TB(1|2)
TB(2|1)
I(1,2)
TW(1,2)
1980
0.031
(21.1)
0.009
(6.1)
-0.037
(25.3)
0.070
(47.5)
0.147
(100)
1990
0.033
(18.3)
0.021
(11.6)
-0.032
(17.7)
0.095
(52.5)
0.181
(100)
Source: Author’s own computations based on EPF/80 and EPF/90.
Notes: (1) 1: occupation, 2: education.
The decrease of the explanatory power of the interaction term between education and occupation
during the 1980s may be due to this mismatch between workers and occupations.
The deregulation process in the Spanish labor market since the early 1980s, and in
particular the 1984’s labor market reform, where the conditions for temporary contracts were
dramatically relaxed, favored a new overly permissive conditions for employers. The immediate
response to such a permissive law promoted and accelerated process of segmentation in the
Spanish labor market. As consequence precarious labor conditions took place, i.e. fixed-term
contracts associated with very low wages started to trap some segment of the workforce, mainly
young and low educated workers.
Although the intention of the 1984’s labor market reform was to create employment,
6
See Alba-Ramirez (1993) for a study of overeducation in Spain, or Hartog and Oosterbeek (1988) in The
Netherlands, all of them report the existence of overeducation in their respective labor markets.
10
paradoxically the unemployment rate7 did not fall and it promoted the segmentation process
mentioned previously. The existence of a segmented labor market, primary (high wage level) and
secondary (low wage level), is an explanation for the rising wage inequality in Spain during the
1980s. Moreover, the results obtained in section 2.2 also seem to support the theory of a
segmented Spanish labor market where low wages are very low and high wages are very high.
Since the explosion of fixed-term contracts promoted by the 1984’s labor market reform,
very atypical type of contracts compared to those existing in the early 1980s was introduced. As
a result a dual labor market was rapidly established in Spain. Antolin (1999) shows empirical
evidence that duality in the Spanish labor market is polarized between workers on permanent and
fixed-term job contracts. To show the inefficiency of the 1984’s labor market in Spain, Adam
and Canziani (1998) demonstrated that although a similar reform was adopted in Italy at the
Table 6: Gini’s indexes for several salaried population groups.
1980
1990
%
Age group
Under 25
25-35
35-45
45-55
Up to 65
0.246
0.244
0.270
0.303
0.307
0.306
0.273
0.287
0.315
0.319
24.4
11.9
6.3
3.9
3.9
Education
Lower primary
Primary
Secondary
University
0.253
0.212
0.239
0.257
0.302
0.242
0.249
0.229
19.4
14.1
4.1
-10.9
Source: Author’s own computations based on EPF/80 and EPF/90
same time as in Spain, in Italy no segmentation and more positive results were observed. García
(1998) also investigates the role of fixed-term contracts in worker flows and job turnover in
Spain. He finds out that fixed-term contracts lead to high job turnover and are used mainly for
rotation purposes. Alba-Ramírez (1998) analyzed rotations from temporary to permanent
7
Although some authors have observed a rigid response of wages to the unemployment rate, it may be
due to an aggregation effect, since it is expected that individual wages provide more flexible responses.
That is, wages on individual level are more sensible than wages on aggregated level when new policies
affecting the labor market are adopted.
11
employment in Spain, and explored the extent to which workers holding temporary contracts
tend to be trapped in temporary employment, thus favoring the existence of a dual labor market.
Additional evidence on how the temporary employment promoted by the 1984’s labor market
reform segmented the Spanish labor market can be found in Milner et al. (1995), Bentolila and
Dolado (1993), and Jimeno and Toharia (1993).
Table 6 shows the values of the Gini’s index value for different groups of workers,
categorized by age and education. It can be seen the increase in the wage inequality level by age
groups is quite high for the young workers, about 25 percent for individuals aged under 25, and
almost 12 percent for workers aged between 25 and 35 years old. Workers over 35 years old do
not experience so important increase in the wage inequality, about 6 and 4 percent.
Division by education groups also shows a high increase in the wage inequality level for
the less educated workers, almost 20 percent for individuals without primary education
completed, and 14 percent for individuals possessing primary education. Workers possessing
secondary education experience a low increase in the wage inequality level. Only individuals
possessing higher education experience decrease in the wage inequality level, around 11 percent.
These results support previous evidence on the segmentation process experienced in the Spanish
labor market during the 1980s, and confirm young and low educated workers are the most
affected by such a segmentation process. Since in a segmented labor market rotations from the
secondary to the primary market are quite unlikely, those workers trapped in temporary
employment are also trapped in low wage occupations. In both, young and low educated groups
of workers, are where the segmentation process polarized in a more important manner the level
of wages, and hence the wage inequality.
Table 7 reports quantile log-wage ratios for 1980 and 1990 for the whole population, age
and education groups. The full sample displays an increase in the polarization in the wage
distribution, especially in the bottom of the wage distribution. Compared to 1980, high wages are
slightly higher in 1990 since the ratio between the 99th percentile and the median only increased
by 0.21 percent. However, low wages fell dramatically since the ratio between the first percentile
and the median increased by about 9 percent. Comparisons by age and education groups also
allow us to observe the important changes during the 1980s in the bottom of the wage
distribution, and they are more pronounced as younger and lower education is the population..
This finding implies that the wage polarization within the younger and lower educated workers
caused by the segmentation process was more important in the bottom than in the top of the logwage distribution. That is, low wages fell more than high wages increased.
12
Table 7: Yearly log-wage Quantile ratios 1980-1990 (household heads).
50th - 1st
50th – 10th
90th – 50th
99th – 50th
1980
1990
%
1980
1990
%
1980
1990
%
1980
1990
%
1,1640
1,2661
8,77
1,0511
1,0972
4,39
1,0454
1,0500
0,44
1,0952
1,0975
0,21
Under 25
1,0523
1,1117
5,64
1,0341
1,0518
1,71
25-35
1,0414
1,0822
3,92
1,0415
1,0454
0,37
35-45
1,0487
1,0775
2,75
1,0450
1,0480
0,29
45-55
1,0524
1,0819
2,80
1,0498
1,0485
-0,12
55-65
1,0577
1,1005
4,05
1,0509
1,0499
-0,10
Lower Primary
1,0602
1,1302
6,60
1,0422
1,0480
0,56
Primary
1,0414
1,0929
4,95
1,0374
1,0434
0,58
Secondary
1,0415
1,0787
3,57
1,0357
1,0465
1,04
University
1,0374
1,0633
2,50
1,0480
1,0365
-1,10
Full sample1
Age group
Education
Source: Author’s own computations based on EPF/80 and EPF/90.
Notes: (1) Percentiles out of the full sample are not computed because division into groups provides a small sample in each percentile for some age groups.
13
During the 1980s the accelerated process of technological change led to demand for
skilled workers increasing dramatically. However, on one hand, the excess in the supply of
skilled workers led to the match between jobs and workers, according to on-the-job skill
requirements, to be done with a lower wage, and in the other hand, higher educated workers are
also reallocated to jobs with lower skill requirements. They are thus underpaid with respect to
their productive capacity. In other words, the process of overeducation of the workforce
experienced in the Spanish labor market during the 1980s caused an expansion of the supply of
skilled workers, and hence a decrease of the unitary price of skilled work. That is, employers can
offer and negotiate wages downwards, since they have many applicants for higher-skilled jobs.
In order to evaluate the changes during the 1980s in the match individuals’ educationjobs, we propose the following standard decomposition of aggregated change in the employment
by education levels. The use of this formula can allow us to reflect the reallocation of
employment according to the education level of the workforce between jobs, and it also can
allow reflect changes of proportions in the same concept within jobs8:
L
L 
LS    si  i     i  si
i
L i
 Li



(7)
where L is total employment, Li is employment in job i, Lsi is employment with education s in job
i, and i and si are coefficients reflecting the share of employment in job i to total employment,
and the share of employment with education level s to total employment in job i respectively.
The first term on the right hand side of the equation (7) expresses changes in the shares between
jobs, and the second term changes in the shares within jobs.
The results derived from decomposition (7) are reported in table 8. The negative sign of
the total change in aggregated employment of workers with lower-primary education reflects the
dramatic decrease in the demand for low educated workers. For all the other education groups,
the total change is positive, which means that for the rest of education groups demand
augmented. The highest increase in demand took place in the university group. Note that withinjob component dominates the between-job component across the whole of education levels, but
that this incidence also decreases systematically as the education level increases. This result can
be interpreted as a combined displacement and reallocation process.
Negative signs in the between and within components for lower primary workers suggest
8
This standard decomposition was proposed in Berman et al. (1994) to analyze changes in the proportion
of non-production workers across USA’s industries, and in Vieira (1999) to analyze changes in
proportions in Portuguese industries by education levels.
14
the extinction of these workers, i.e. older and low-educated workers are displaced to retirement,
and younger and low-educated workers are displaced to unemployment.
Workers possessing primary education display a negative sign in the between component,
and a positive sign in the within component. This fact can be interpreted as a concentration of
these workers in a few low-skilled jobs, where they replace the lower primary workers.
In the case of workers possessing secondary education, the between component shows
practically no effect, and the entire aggregated change is concentrated in the within component. It
means that the share of workers with secondary education has increased in jobs traditionally
occupied by these workers, however they do not experience any reallocation process.
Finally, the most interesting result concerns changes in the employment of workers
possessing university education. First, they display the most important increase in the aggregated
demand and the highest value in the between component. This fact implies that the share of
university workers in jobs traditionally occupied by these kind of workers increased, however
they also were reallocated in jobs usually occupied by less educated workers. These results
confirm that the most intensive reallocation process, were experienced by workers possessing
university education.
Table 8: Decomposition of the changes in aggregated employment
from 1980 to 1990 by education levels.
Between
Within Total change
Lower primary
Primary
Secondary
University
-0,668
-1,427
0,411
1,684
-10,996
4,723
3,174
3,098
-11,664
3,296
3,585
4,782
Source: Author’s own computations based on EPF/80 and EPF/90
The drop in the interaction effect between education and occupation in the Theil index
decomposition, may be explained of the results reported here in table 8 and combined with the
wage ratios presented in table 7.
5. Inter-temporal changes in the aggregated wage inequality level
In section 2.3, we point out the relevance of human capital variables (education and
occupation) in explaining wage inequality. We show that the contributions of these variables in
15
explaining wage inequality declined, while the total level of inequality rose. In section 2.4 we
also show that during the 1980s, interaction between education and occupation fell, due possibly
to the effect of overeducation.
The empirical evidence obtained here shows a clear temporal pattern in the behavior of
the variables, which explain the change in wage inequality in Spain. The level of wage inequality
is affected by several factors that act within each group. Such a temporal pattern of behavior in
the explanatory variables can be analyzed by the decomposition (2), since it is based on the
determination of the level of inequality explained between groups. However, temporal changes
in the population shares and on average wage levels affect not only between-group inequality,
but also within-group inequality. In order to detect these temporal patterns of behavior we use
the inter-temporal decomposition of Theil’s index used in Tsakloglou (1993) and expressed as:
T   Yg Tg    g Tg  log  g  g 
G
G
g 1
g 1
G

   g  g Tg  log  g  1   g  g  
g 1
 g 1

G
G


   g  g Tg  log  g  1  log Yg    g  g  log Yg 
g 1
g 1


G
(8)
where
 Tg is the value of the Theil’s index in group g.
 Y g is the average wage in group g.
 g 
Yg
Y
,
g 
Ng
N
. Ng is the population size for group g.
The values g, g and Tg used in expression (8) are defined as average values from period t to
t+1, i.e. g=0.5(gt + gt+1), g and Tg are analogously defined. Expression (8) can be simplified
to T=T1+T2-T3+T4, where T1 expresses the impact on T due to changes in the withingroup inequality. The T2-T3 is the impact on T due to changes in the population share. And
the T4 is the impact on T due to changes in the average wage between groups.
Table (9) shows the decomposition defined in expression (8). The positive values of the
explanatory variables associated to T4 imply a positive contribution to rising wage inequality,
i.e., the variables showing a positive sign generate inequality since the differential in the average
wage between groups increased from 1980 to 1990.
16
Education and age have reported positive signs associated to T4, which means that they
contributed to generate wage inequality between 1980 and 1990. Changes in the average wage
across age and education groups, account for around 21 and 18 percent respectively of the total
change in the aggregated wage inequality level. This result support the evidence that changes in
occurred in the age and skill structure of the workforce contributed more strongly on the changes
of the wage structure. In this case, and following the results obtained throughout the chapter, they
contributed to generate a wage inequality especially pronounced in the youngest and lowest
educated share of the workforce.
Occupation and gender display negative contributions to wage inequality. It means that
from 1980 to 1990, average wage differentials between groups were therefore a little more
equalized. Nevertheless, the equalizing effect of gender is quite slight. Changes in population
shares are irrelevant explaining the rise in the wage inequality, and the most important
contribution for either variable is due to increasing wage inequality within groups.
Table 9: Decomposition of the change in aggregated inequality from 1980
to 1990.
T1
T2-T3
T4
T
Occupation
0.037
0.001
-0.004
0.034
(108.8)
(9)
(-11.7)
(100)
Education
0.027
0.001
0.006
0.034
(79.4)
(9)
(17.7)
(100)
Age
0.029
-0.002
0.007
0.034
(85.2)
(-5.8)
(20.6)
(100)
Gender
0.033
0.002
-0.001
0.034
(97.1)
(5.8)
(-2.9)
(100)
Source: Author’s own computations based on EPF/80 and EPF90
6. Summary and concluding remarks
In this chapter we use a sample of salaried workers from the Family Budget Survey in
Spain for the years 1980 and 1990, to analyze the changes in wage inequality during the 1980s.
Our results show an increase in the wage inequality in Spain during that decade. We also
compare the level of wage inequality in Spain with that in some other countries (UK, USA,
Portugal, Canada, Germany, Sweden and Australia), and we find out that Spain together with
USA and Portugal has one the highest levels of wage inequality during the 1980s. We also
explore the causes of this increase in wage inequality, and we observe that the main reason is that
17
in 1990, low wages were lower and high wages were higher than in 1980. However, we observe
that the decrease in low wages is more important than the increase in high wages. In order to
understand the role of some supply variables in explaining wage inequality (age, gender,
education and occupation), we use the additive decomposition of Theil’s index. We show that
occupation and education are the most important variables in explaining wage inequality, but
with a decreasing explanatory power from 1980 to 1990. This result suggests that the demand
side variables gained some relevance in explaining the level of inequality.
In our analysis, we calculate the wage inequality level for separated groups of the
workforce. We observe an important increase in the wage inequality within the youngest
segments of the workforce during the 1980s. This result can be easily attributed to the 1984’s
labor market reform in Spain, where the regulation of temporary contracts was strongly relaxed.
In addition, interactions between supply factors are calculated, and we observe a strong fall in the
interaction between occupation and education from 1980 to 1990. To explain this result we use
wage ratios and a decomposition of the aggregated change in employment by education levels.
These two analysis suggest, as previously observed in Alba-Ramírez (1993), that the increasing
demand for educated workers during the 1980s has led to an excess in the supply of these
workers. Finally, we find out that in the inter-temporal decomposition of the aggregated level of
wage inequality, education and age display contribute positively to the rise of wage inequality.
This favors the hypothesis that after the introduction of fixed-term contracts follows the
segmentation of the labor market in Spain, where there is a low wage segment is composed by
young and low educated workers trapped in temporary employment. As we analyze in chapter 4,
this process of segmentation, where education plays an important role, is also accompanied by a
process of skill biased technological change, where once more education is a crucial variable.
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