Journal of Service Science and Management, 2011, 4, 458-468
doi:10.4236/jssm.2011.44052 Published Online December 2011 (http://www.SciRP.org/journal/jssm)
A Comparative Analysis on Human Capital and
Wage Structure in the Spanish Hospitality Sector
Juan A. Campos-Soria, A. García-Pozo, José Luis Sánchez-Ollero, Carlos G. Benavides-Chicón
Faculty of Economics and Business Studies, University of Malaga, Malaga, Spain.
Received July 26th, 2011; revised September 10th, 2011; accepted October 15th, 2011.
ABSTRACT
This paper presents a comparative analysis of the wage structures in the Spanish hospitality sector and other private
service industries. Using data from the 2006 Spanish Wage Structure Survey (Encuesta de Estructura Salarial) [1], we
estimate human capital returns in the hospitality industries. These results have been compared to those obtained in
other private services. Education returns in these estimations are lower in the hospitality sector although there are sig-
nificant differences between regions. Similar results were found for the other human capital variables used. Finally, the
paper provides new empirical evidence on the regional wage gap in this sector. The main wage gap in the Canary Is-
lands and the Madrid region is due to differences in returns in the observed variables, whereas in other regions most of
the differences are due to the resources allocated.
Keywords: Human Capital, Tourism, Wage Differentials, Regional Analysis
1. Introduction
Tourism has been a determinant factor in the moderniza-
tion of the Spanish economy. However, many aspects of
this sector, such as labor market characteristics and re-
gional differences, have not been widely studied. Tou-
rism is a cross-sectional industry in which highly diverse
activities are grouped. This paper analyzes the hospitality
subsector which itself is composed of large and well-
differentiated subcategories1. In Spain, the hospitality in-
dustry is characterized by having a higher percentage of
women and younger workers, lower educational levels,
seasonality in contracts, a smaller percentage of employ-
yees, a greater percentage of foreign workers, and higher
than average working hours. However, regional differen-
ces are highly significant and the more precarious em-
ployment conditions tend to disappear in the more con-
solidated tourist destinations [2].
In this context, we estimate human capital returns and
quantify wage differences for tourism regions in Spain.
We also analyze whether wage differences at the regional
level may be accounted for by differences in returns and
observable characteristics. The estimations were perfor-
med using Oaxaca-Ramson wage estimations [3].
The paper is structured as follows: the methodology is
described in Section 2 and the variables that are included
in the wage equations. The next two sections outline the
database and present a descriptive analysis of the main
variables (Section 3). Empirical results are detailed in Se-
ction 4. Finally, a brief description of the evidence ob-
tained is provided and the main conclusions are formu-
lated.
2. Methodology
2.1. Theoretical Model and Econometric
Specification
The wage decomposition proposed by Oaxaca [4] and
Blinder [5] has been used widely in the field to study
discrimination in the labor market. We have based on it
to quantify the proportion of regional wage differences
due to differences in observable characteristics and the
proportion due to the return differences derived from su-
ch characteristics. An appropriate estimation of the re-
lationship between wages and different characteristics
observed between regions, such as human capital, assists
in interpreting important issues such as levels of regional
inequality and mobility incentives between regions [6].
Under the assumption that observable characteristics in
each region yield different returns, a wage equation has to
be estimated for each region. Taking a Mincer wage e-
quation as the starting point, Oaxaca and Ramson [3] pro-
1Hotels, campsites and other types of accommodation; restaurants, bars,
p
ubs and similar establishments; corporate dining rooms and catering.

A Comparative Analysis on Human Capital and Wage Structure in the Spanish Hospitality Sector459
pose a generalization of Oaxaca-Blinder’s decomposition
in order to assume a more flexible wage structure in the
absence of discrimination. The decomposition proposed
in [3] is as follows:
  


***
ˆ
ln ln jkjk jk
j
wwxx xxˆ
k

 
 

(1)
where

ln
j
w and

ln k w are the geometric means
of the logarithm of the wage in region j and k, respect-
tively; ´
j
x
and ´k
x
are vectors for the geometric means
of the observable characteristics for each region; and ˆ
j

and ˆk

are the vectors of the coefficients estimated for
each region separately, i.e. the estimated returns for ob-
servable characteristics. Finally, the non-discriminatory
wage structure can be understood as the returns of ob-
servable characteristics in competitive markets. If this
matches region j, then*ˆ
j

 ; if it matches region k,
then *ˆk

 . Any combination of wage structures in the
regions can also be considered as a non-discriminatory
wage structure, in which case

*ˆˆ
j
k
I

   

,
where Ω is the weighing matrix of such wage structures.
We assume two wage structures in the absence of dis-
crimination. The first corresponds to the Balearic Is-
lands

*ˆ
B
alearic
 . This region was chosen as the refe-
rence because of the large differences compared to other
regions specialized in “sun and beach” tourism, and the
relatively high weight of hospitality employment in the
total volume in the region. The second wage structure is
based on a weighted mean of the wage structures of the
six regions under analysis. The weights used in this case
are the relative weights each region has on the total
number of working people in the hospitality sector in the
six regions2, ; that is,
k

6
*
1
ˆ
nn
n





where “n” represents each of the regions. The use of dif-
ferent *

makes it possible to provide the non-discrimi-
natory wage structure taken as reference with more ro-
bust results.
2.2. Descriptions of the Variables
The wage equation estimated for each region has the fol-
lowing functional form (for reasons of simplicity sub-
scripts for individuals are omitted)


01 234
567 8
Log wrSExpTenGender
FTCSWOver Under
 

 
 

The dependent variable is the gross real wage per
hour3 expressed as logarithms. However, in order to iso-
late the effect of price differentials between regions, the
level of regional relative prices were estimated to obtain
the real wage/hour, i.e., the wage discounting the price
differential in 2006 in each of the regions studied. Ob-
servations of workers with a wage lower than the inter-
professional minimum wage and those younger than 16
or older than 65 years were eliminated from the original
sample. The right-hand side of equation (2), includes the
constant as well as the characteristics of the workers and
jobs. Human capital variables, such as mean schooling
years (S), previous experience (Exp)4 and tenure in the
firm (Ten), and the gender of the employee (Gender) are
derived from the characteristics of workers. Human
capital variables were constructed following the work of
[8]5. Gender is a dummy variable that takes value 1 if the
individual is a woman and 0 otherwise. This variable
attempts to measure the different wage levels between
men and women. Given the reduced size of the database
for some regions, no iterations were done between gen-
der and human capital variables to avoid problems of
multicollinearity
In order to control the characteristics of the job we
used the dummy variables type of contract, size of the
establishment, and the formal education of the worker in
relation to the job requirements.
The variable full-time and permanent contract (FTC)
takes value 1 when the worker has a full-time permanent
contract and zero in any other case. In this way, we at-
tempted to assess whether this type of contract involves
higher wages than to workers with temporary or part-
time contracts. Both types of contract are aggregated into
a single variable because in Spain they tend to be used
together in the hospitality sector to help employers adjust
the work supply to fluctuations in service demand.
The variable firm size (SW), takes value 1 when the
worker is employed in a firm with 20 or more employees,
and zero in any other case. In Spain, the mean firm size
in 2007 was 12.5 workers in hotel, and 3.9 in restaurants
[10]. This shows the great variability observed between
the two main hospitality subsectors in Spain. Thus, and
taking into account the availability of statistical data at a
3This variable is calculated with the data available in EES-2006. The
hours annually worked and the annual gross nominal wage received fo
r
the job are used in the calculations. It is also assumed that both 4.2
weeks and 30.42 days are equivalent to one month. Using the indexes
of relative prices by Autonomous Communities in [9], the real wage for
the year 2006 was obtained.
4This definition of potential previous experience (Expe r= age—years
of schooling—6 years—tenure in the firm) is generally closer to actual
p
revious experience in men and is likely to be overestimated in women
due to leaving and entering the labor market to raise children.
5Observations of workers whose declared mean schooling years were
clearly atypical are not included.
 
(2)
2In a study on gender wage differences, [7] uses the weighted mean o
f
men and women’s wage structure. Namely, Ω = lh·I, where lh is the
p
roportion of men in the sample.

A Comparative Analysis on Human Capital and Wage Structure in the Spanish Hospitality Sector
460
regional level, business size has been taken as an addi-
tional explanatory variable of regional wage differences
to control for the higher number of hotels in some tourist
destinations. Furthermore, economic theory suggests alter-
native explanations to the fact that until a certain size is
reached, wages increase with firm size (see [11]).
The variables Over and Under measure the fact that
the real wage per hour could be more influenced by the
type of job than by having a higher educational level.
Although the database used controls for the type of job,
there is a high correlation between educational level and
occupation, and this creates high multicollinearity. Thus,
following [12], occupation is taken into account indi-
rectly by calculating a mean of the most frequent level of
studies in each occupation. The mode is used as the sta-
tistical measure. However, this procedure is expanded in
cases where the mode represents a percentage lower than
40% of the total number of employees in each occupation,
defined at the level of two digits according to the Na-
tional Classification of Occupations –CNO94– [13]. In
these cases, the workers considered as appropriately edu-
cated are those who have obtained an educational level
corresponding to the mode as well as those who have
reached an adjacent educational level (immediately lower
or higher). Thus, for a given occupation, if the mean
number of study years required are 15 (undergraduates)
but the mode includes a reduced number of cases (less
than 40%), workers with secondary education (12 study
years) or postgraduates (17 study years) would be also
considered appropriately educated. Choosing the level of
studies immediately lower or higher than the mode was
done by taking into account which level has a greater
percentage of workers in each occupation.
Using this definition of appropriately educated work-
ers, the dummy variables for the workers with a level of
studies different from the one required by the occupation
are defined as follows. The variable that represents ove-
reducation (Over) takes value 1 if the worker has attained
an educational level higher than the mode of each occu-
pation (or the higher when two adjacent educational levels
are possible); and zero in any other case. Similarly, the
variable undereducation (Under) takes value 1 if the wor-
ker has an educational level lower than the mode of the
occupation (or the lower when two contiguous educational
levels are used); it will take value zero in any other case.
Finally, ε is a random disturbance term.
3. Database and Descriptive Analysis
The information used was taken from the Spanish Survey
of Wage Structure 2006 (Encuesta de Estructura Salarial,
EES-2006). The sample is representative at the regional
level (NUTS II), thus facilitating the present study.
Table 1 shows that workers in the Balearic Islands are
older and have higher tenure in the firm. Furthermore, 64%
of them have a full-time and permanent contract; this pe-
rcentage is only exceeded by the Canary Islands (65%).
Other labor characteristics of this region include lower
percentages of women (47%) and higher nominal gross
wages per hour. For these reasons, the Balearic Islands
have been taken as the reference region for wage differ-
rentials.
In the hospitality sector, mean schooling years is much
lower than in other private service sectors. It should be
pointed out that the Balearic and Canary Islands, which
are mature destinations for sun and beach tourism [14],
present the lowest mean schooling years of all the re-
gions under consideration. This is probably due to the
older mean age of workers who belong to the generation
that did not benefit from the current Spanish educational
system. In fact, this may account for the high proportion
of undereducated workers (higher than 30% in both re-
gions). We analyze this phenomenon later in this paper.
With the exception of the Balearic Islands, tenure is
lower in the hospitality sector than in other private ser-
vice industries. This suggests greater worker rotation in
this sector, as reported in other studies [15]. Except for
the Balearic and Canary Islands, 50% of employees are
women.
The percentage of workers suitably educated for their
job fluctuates between the highest in Valencia (67%) and
the minimum in the Balearic Islands (51%). These results
do not substantially differ from the estimations obtained
by [12] using a different database for the Spanish eco-
nomy as a whole; and are also consistent with [16] for the
hospitality sector in Andalusia. The percentage of over-
educated workers ranges between 12% in the Canary
Islands and 22% in Madrid. The highest level of under-
education is found in both Spanish archipelagos and the
lowest is found in Catalonia (15%) and the Community
of Valencia (16%).
Table 1 shows that the rate per hour received in the
hospitality sector is lower than the one received in other
private service sectors. In nominal terms, the wage re-
ceived by employees in the other regions as a whole is
almost 19% less than in the Balearic Islands. In real te-
rms, this difference decreases to 14.5% due to the differ-
rential in relative prices. The greatest difference is found
in Madrid where wages in the hospitality sector employ-
yees are 40.1% lower.
4. Empirical Results
4.1. Estimations
Table 2 shows the results of the estimations in the hos-
pitality sector and in other private service industries. The
value of the constant term indicates the part of the wage

A Comparative Analysis on Human Capital and Wage Structure in the Spanish Hospitality Sector
461
Table 1. Characteristics observed in the sample of the chosen regions for the hospitality sector.
Andalusia Balearic Island Canary IslandCatalonia Com. ValenciaCom. Madrid Other regions Spain
Variables H OS H OS H OS H OS H OS H OS H OS H OS
35.02 36.84 40.5837.98 39.17 36.8636.7238.0936.6537.85 38.5237.69 36.34 39.39 37.2638.32
Age
(10.93) (10.13) (11.60)(11.30) (10.53) (10.21)(11.06)(11.00)(10.93)(10.81)(11.31) (10.59) (11.01) (10.70) (11.13)(10.72)
7.63 10.54 6.89 10.05 6.74 9.46 8.07 10.807.41 10.037.8111.59 7.56 10.58 7.5010.71
S
(3.77) (4.59) (3.96)(4.73) (3.96) (4.57)(3.39)(4.35) (3.66)(4.58)(3.81) (4.53) (3.58) (4.48) (3.70)(4.53)
14.59 12.14 17.0613.76 18.77 13.6516.4013.1517.7613.84 17.1512.35 16.42 13.64 16.7613.17
Exp
(9.78) (9.41) (10.80)(10.59) (10.10) (9.62) (10.40)(10.60)(10.67) (10.54)(10.53) (9.82) (10.52) (10.17) (10.49)(10.14)
4.52 7.00 7.74 6.70 4.62 6.13 4.40 7.194.00 6.61 5.346.92 4.08 8.05 4.637.35
Ten
(6.99) (8.69) (9.62)(8.08) (6.93) (7.79)(6.67)(8.74) (6.57)(8.68)(7.07) (8.47) (6.18) (9.44) (6.92)(8.92)
0.52 0.54 0.47 0.64 0.49 0.58 0.55 0.620.56 0.55 0.650.58 0.62 0.58 0.580.58
Gender
(0.50) (0.50) (0.50)(0.48) (0.50) (0.49)(0.50)(0.48) (0.50)(0.50)(0.48) (0.49) (0.48) (0.49) (0.49)(0.49)
0.50 0.56 0.64 0.54 0.65 0.60 0.49 0.570.48 0.54 0.520.61 0.56 0.56 0.550.57
FTC
(0.50) (0.50) (0.48)(0.50) (0.48) (0.49)(0.50)(0.50) (0.50)(0.50)(0.50) (0.49) (0.50) (0.50) (0.50)(0.50)
0.81 0.81 0.79 0.68 0.79 0.76 0.76 0.840.75 0.77 0.770.90 0.52 0.70 0.670.78
SW
(0.40) (0.39) (0.41)(0.47) (0.41) (0.42)(0.43)(0.37) (0.44)(0.42)(0.42) (0.30) (0.50) (0.46) (0.47)(0.41)
0.20 0.22 0.15 0.14 0.12 0.16 0.20 0.180.17 0.16 0.220.20 0.19 0.18 0.180.19
Over
(0.40) (0.41) (0.36)(0.35) (0.33) (0.37)(0.40)(0.39) (0.37)(0.36)(0.42) (0.40) (0.39) (0.39) (0.39)(0.39)
0.21 0.22 0.34 0.27 0.32 0.26 0.15 0.200.16 0.21 0.170.21 0.17 0.18 0.200.20
Under
(0.41) (0.41) (0.47)(0.44) (0.47) (0.44)(0.36)(0.40) (0.37)(0.40)(0.38) (0.40) (0.38) (0.39) (0.40)(0.40)
8.66 12.51 10.0912.07 8.90 10.879.98 13.52 9.04 11.648.6214.59 8.17 12.65 8.8413.00
Nominal
wage (€/hour) (4.25) (9.94) (4.97)(8.41) (5.61) (8.28)(5.59)(9.99)(4.75)(7.64)(4.44)(11.98) (3.35) (8.73) (4.43)(9.75)
Price 96.99 103.26 89.88 108.13 99.93 102.36 97.83 100.00
8.93 12.90 9.77 11.69 9.91 12.109.23 12.509.05 11.648.4314.25 8.35 12.93 8.8413.00
Real wage
(€/hour) (4.38) (10.25) (4.81)(8.14) (6.25) (9.22)(5.17)(9.24)(4.76)(7.65)(4.34) (11.70) (3.38) (8.95) (4.43)(9.75)
Notes: Standard deviation values for the variables are shown in parentheses. Source: [1] and [9].
not affected by the independent variables. This compo-
nent depends on other variables, such as lifestyle, climate
and other differentiating characteristics of the regions
that have an effect on the conditions of employment and
wages, but which our specification was not able to in-
clude. The table shows that estimated values for the con-
stant in the hospitality sector are greater than in the pri-
vate sectors of services for all the regions. Furthermore,
in the hospitality sector, as in the other private services,
the maximum value of the constant term is found in the
the maximum value of the constant term is found in the
Community of Valencia and the lowest value in the
Community of Madrid.
4.1.1. Persona l Ch aracteristics
Education returns in these estimations are lower in the
hospitality sector. In addition, very significant regional di-
fferences are observed between the Canary Islands (5.5%)
and the Community of Valencia (1.8%). In Catalonia
(4.8%), the Balearic Islands (4.7%) and Madrid (4.2%),
education returns are higher than the mean for Spain
(3.2%). These results reveal regional heterogeneity.
Our results are similar to previous studies that also use
variables regarding job characteristics in their specifica-
tion. Introducing these additional variables reduce educa-
tional returns, because these variables capture the indi-
rect effect of education on wage and productivity. Ac-
cordingly, the education returns estimated in this work
represent the direct effect of education, once the effects
derived from the job characteristics are discounted.
The results of previous studies that specified the ori-
ginal Mincer equation showed education returns to be be-
tween 4.1% [17] when the estimation method used is or-
dinary least square and 5.6% [18] when instrumental va-
riable techniques are used. Nevertheless, in both papers,
education returns in the hospitality sector are less than
the estimates for most economic sectors.

Current Distortion Evaluation in Traction 4Q Constant Switching Frequency Converters 462
N
ote: Statistically significant at: *1%, **5% and ***10%. The standard errors and covariances are robust for heteroscedasticity.
Table 2. Results of the estimations.

A Comparative Analysis on Human Capital and Wage Structure in the Spanish Hospitality Sector
463
The returns estimated for previous experience and te-
nure in the firm are much lower than those for education.
Once again, the estimations for the hospitality sector
show returns to be quite lower than for the other private
service sectors. Thus, Table 2 shows that the returns from
previous experience are not statistically significant in the
hospitality sector of four regions (Andalusia, the Canary
Islands, Catalonia and Community of Valencia). On the
other hand, the returns from tenure in the firm are statis-
tically significant in all the regions, ranging from 1.1%
(the Balearic and Canary Islands) to 1.6% (Andalusia).
In order to compare the potential effect of not include-
ing the square of the variables previous experience and
tenure in the firm in our specification in order to avoid
multicollinearity, Table A.1 presents estimations for Spain
and the six aggregated regions6 which include these va-
riables. As can be observed, not including these varia-
bles does not affect the coefficients estimated for the re-
maining explanatory variables. Furthermore, if returns
are estimated using the sample mean of previous expe-
rience and tenure, the results obtained are very similar. In
the case of Spain, in the expanded specification, the re-
turns from previous experience and tenure are not statis-
tically different from the ones presented in Table 2.
The variable gender is statistically significant in all
cases7, and has a negative coefficient. Gender discrimi-
nation in the hospitality sector is greater in the Communi-
ty of Valencia, where women have wages up to 13% lo-
wer than men. In the other service sectors, gender dis-
crimination is greater in Catalonia, reaching 20.78%. Wi-
th the exception of the Balearic Islands, where wage dif-
ferences due to gender are similar in the hospitality sec-
tor and other services in the private sector, the effect of
gender on wages is less in the hospitality sector.
4.1.2. Job Cha r acteri stics
Except for the Balearic Islands (in all sectors) and the
Community of Valencia (in the hospitality sector), where
the estimated coefficient of the variable is not statistic-
cally different from zero, a full-time permanent contract
involves increased wages. This increase ranges from
13.91% in the Canary Islands and 5.91% in the other re-
gions.
The estimated coefficient for business size is statistic-
cally significant and positive in all cases. In the hospitali-
ty sector the wage premium of working in an establish-
ment of 20 workers or more ranges from 23.66% in
Catalonia to 7.65% in the Balearic Islands. Furthermore,
except for Catalonia, wage improvements are greater in
other service sectors than in the hospitality sector.
Finally, the estimated coefficients for educational mis-
match have the expected signs in all cases and it is only
statistically non significant in the aggregate of other re-
gions (hospitality sector) for the overeducated workers.
Overeducation involves a strong wage penalty which is
greater in other service sectors than in the hospitality
sector. On the other hand, undereducated workers earn
between 26.40% more (in the hospitality sector in Cata-
lonia) and 9.41% more (in other services in the Balearic
Islands).
The results indicate great regional variability both in
the hospitality sector and in the other private service se-
ctors. Nevertheless, our estimations for Spain are not su-
bstantially different from those of [12] using a different
database that included the public sector. These authors
estimate the wage premium associated with undereduca-
tion at 13% and wage reductions for overeducated work-
ers at 21%.
4.2. Decomposition of Regional Differences
The mean wage in the Balearic Islands hospitality sector
is higher than in the other tourist regions except for the
Canary Islands. In fact it is 15.91% higher than the mean
wage in the Community of Madrid, 9.35% higher than in
Andalusia, 7.94% higher than in the Community of Va-
lencia, and 5.84% higher than in Catalonia. However,
there is a negative differential of 1.4%8 compared to the
Canary Islands. In this section, the wage difference be-
tween the Balearic Islands and the main Spanish tourism
regions is decomposed following the methodology of [3].
Two different wage structures are assumed in the ab-
sence of discrimination. First, it is assumed that, in the
absence of discrimination, the prevailing wage structure
is that found in the Balearic Islands (model A). Then, a
weighted mean of the wage structures for the six regions
under analysis is used (model B). The aim is to obtain
robust results given the non-discriminatory wage struc-
ture used as a reference. Based on the estimations of the
wage equations in the hospitality sector for each region
(shown in Table 2), the relative contributions of the ob-
servable variables on the total wage differential are crea-
ted.
Tables 3 and Table A.2 show such contributions for
models A and B, respectively9, differentiating between
the constant and personal and job characteristics. The
results are robust given the non-discriminatory wage
structure taken as reference. In each table, the first co-
lumn in each region shows the portion of wage difference
due to differences in observable characteristics; the sec-
6For the six regions, the sampling means of the variable previous ex-
p
erience is 14.03 years and tenure in the firm is 6.99 years.
7The impact on the wage of the dummy variables when using a semilog
function was calculated by taking the antilogarithm of the correspond-
ing coefficient, subtracting 1 and multiplying by 100 [19].
8In all cases the reference is the real rate per hour worked in Euros.
9The results for model B are included in the Appendix because they are
similar to model A, both in signs and relative contributions.

A Comparative Analysis on Human Capital and Wage Structure in the Spanish Hospitality Sector
464
Table 3. Relative effect on regional wage difference (A model).
tion shows the wage difference due to differences in re-
turns; and the last column shows the total relative con-
tribution of each variable10. A positive value in a cell
indicates that the variable under study increases the wage
differential, whereas the opposite occurs when the sign is
negative. However, the interpretation of the signs is just
the reverse for the Canary Islands, as this is the only re-
gion that presents a favourable wage differential with
regard to the Balearic Islands. Thus, for example, the
difference in previous experience between these two re-
gions contributes 18.54% to wage differences in model A.
Given that *

is positive11, and in the Balearic Islands
for previous experience takes a mean value of 17.05
years (slightly less than in the Canary Islands [18.77
years]), this variable contributes an 18.54% decrease in
the wage difference.
In model A, the most of the wage gap in the Canary
Islands and the Community of Madrid is due to differen-
ces in returns of the observable characteristics (156.64%
and 68.79%, respectively), whereas in the other regions
differences in resources are more relevant. Specifically,
in Catalonia, this component contributes 108.29% to the
wage gap, whereas in the Community of Valencia and
Andalusia the contribution is 91.2% and 82.27%, respe-
cttively. Nevertheless, there are some differences. Al-
though in both models most of the wage gap in Andalu-
sia and the Community of Valencia is due to differences
in observable characteristics, the relative contribution of
differences in returns has a positive value in model A,
whereas it is negative in model B. However, this is not
especially relevant if we take into account that in both
models the relative weight and the sign of the non-dis-
criminatory and discriminatory effects are maintained in
both personal and job characteristics. In order to simplify
our description, from this point on we will only focus on
the results from model A.
The contribution of the constant is significant for all
the regions, which indicates that other factors different
from the explanatory variables included in the estima-
tions also have effects on wage differences. In the case of
Andalusia and the Community of Valencia, the differen-
ce in returns for these external factors decrease the wage
gap; however, the opposite occurs for the other regions,
including the Canary Islands12.
Personal characteristics help to reduce wage differen-
ces only in the Community of Madrid, although their e-
ffect is not very relevant (–6.81%). In the remaining
cases, they contribute to increasing the wage gaps, espe-
Andalusia Canary Islands Catalonia Valencia
(Community of)
Madrid
(Community of)
Factors
Charact. Returns Total Charact. ReturnsTotalCharact.ReturnsTotalCharact.Returns Total Charact. ReturnsTotal
Constant 0.00 –195.89 –195.89 0.00 –185.88 –185.880.00 198.71198.710.00–428.03 –428.03 0.00 111.83111.83
Personal
characteristics 25.88 292.90 318.77 –56.64 50.78–5.85 –33.86265.95232.0943.16459.70 502.86 4.92 –11.73 –6.81
S –75.42 241.68 166.26 0.00 122.70122.70–220.26 –22.49 –242.75–37.71333.14 295.43 –33.70 28.43–5.28
Exp 25.93 153.15 179.08 18.54 –126.17–107.640.00 316.52316.520.00132.58 132.58 0.00 10.8010.80
Ten 75.37 –47.68 27.69 –75.18 2.61–72.56 143.91–34.86109.0562.26–21.18 41.08 20.32 –15.005.32
Gender 0.00 –54.26 –54.26 0.00 51.6551.6542.496.77 49.2718.6115.15 33.77 18.30 –35.96 –17.66
Characteristics
of the job 56.39 –79.27 –22.88 0.00 291.73291.73142.07–472.88 –330.8048.04–22.86 25.17 26.28 –31.31 –5.02
FTC 0.00 –68.09 –68.09 0.00 188.16188.160.00 –116.81 –116.810.000.00 0.00 0.00 –29.76 –29.76
SW 0.00 –42.48 –42.48 0.00 84.3184.310.00 –416.56–416.56 0.00–39.68 –39.68 0.00 –8.41 –8.41
Over 9.61 17.76 27.38 0.00 –27.08–27.0817.3032.2449.552.349.88 12.22 4.93 6.6911.62
Under 46.78 13.54 60.31 0.00 46.3446.34124.7728.25153.0245.706.94 52.63 21.36 0.1721.53
Total 82.27 17.73 100.00 –56.64 156.64100.00108.21–8.21100.0091.208.80 100.00 31.21 68.79100.00
10If either the mean difference between the Balearic Islands and the re-
gion for a variable, or its estimated coefficient are not statistically dif-
ferent from zero at a 10% significance level, the relative impact is 0.
Similarly, if the coefficient estimated of a variable for the Balearic
Islands is not significantly different from the estimate for the region
under study, a zero is assigned to the contribution of the difference in
returns of that variable.
11In the case of previous experience, β* = 0.004864 in model A and β* =
0.001231 in model B.
12The reader should bear in mind that a negative sign in the cells of the
Canary Islands implies an increase in the wage gap in the Balearic
Islands when compared to this region.

A Comparative Analysis on Human Capital and Wage Structure in the Spanish Hospitality Sector465
cially in the Community of Valencia, Andalusia and Ca-
talonia (502.86%, 318.77% and 232.09%, respectively).
Such high contributions are basically due to the differen-
ce in returns. For example, the Community of Valencia
and Andalusia present far lower returns from education
and previous experience than the Balearic Islands, while
low returns from previous experience is the leading cause
in Catalonia. In the latter region, and in the Canary Is-
lands, the higher returns of education help to reduce the
wage differences by 22.4% and 122.70%, respectively.
All regions have returns from tenure in the firm higher
than in the Balearic Islands, which helps to reduce the
wage difference.
The fact of being woman has a negative contribution
to wages in the hospitality sector in every region (Table
2). However, the difference in returns translates into dif-
ferent contributions to the wage gap. In the case of An-
dalusia, the Canary Islands, and the Community of Ma-
drid, being a woman reduces the wage difference, whe-
reas in Catalonia and the Community of Valencia it in-
creases it. This is due to the fact that the returns of this
variable in these two regions in absolute values are
higher than in the Balearic Islands.
Regarding the differences in personal characteristics,
tenure in the firm contributes to increase the wage gap in
all regions, because the Balearic Islands have tenure va-
lues well above those found in the remaining destinations.
This is probably due to the Balearic tourist model being
well consolidated and the staff being stable. Exactly the
opposite occurs regarding schooling years, because all
the regions, except for the Canary Islands, have staffs
with higher educational levels, which reduces the wage
difference, especially in Catalonia and Andalusia (220.26%
and 75.42%, respectively). The greater number of wo-
men in the hospitality sector in Catalonia and the Com-
munities of Valencia and Madrid increases the wage gap
due to gender wage discrimination. Finally, differences
in previous experience contribute nothing in Catalonia
and the Communities of Valencia and Madrid.
The Canary Islands present some peculiarities. The in-
sular nature of both the Balearic and Canary Islands means
that no statistically significant differences are found in
the characteristics of workers and jobs. In fact, the wage
gap is only affected by differences in previous experien-
ce and tenure in the firm, whereas the differences in other
variables make no contribution. As the previous example
indicates, greater previous experience in the Canary Is-
lands causes an 18.54% reduction in the wage gap, whe-
reas less tenure contributes to increasing the wage dif-
ference by around 75.18%.
Significant differences in most job characteristics are
shown, except for the size of the business, because this
variable does not affect wage difference in any region.
Therefore, the unequal distribution of establishments
between regions regarding mean size does not seem to
have an effect on wage difference13. Except for the Ca-
nary Islands, the number of workers who are overedu-
cated for their job is greater in all regions than in the
Balearic Islands. This fact contributes to increasing the
wage gap, since overeducation has a negative effect on
wages. This also occurs with workers who are underedu-
cated for their jobs, although in this case the relative
contribution is greater for all the regions. The higher re-
lative weight is due to the fact that returns from under-
education is positive, and that the Balearic Islands have
the highest percentage of undereducation.
Regarding the difference in returns from job charac-
teristics, the type of contract and firm size make a nega-
tive contribution to the wage gap. The difference in re-
turn from the type of contract makes the greatest contri-
bution to reducing wage differences in all the regions,
except for the Community of Valencia. In this regard, the
contribution of the variable type of contract is 188.16%
and 116.81% in the Canary Islands and Catalonia, re-
spectively. Finally, the difference in returns from firm
size contributes to reducing the wage gap in all cases,
and is especially relevant in Catalonia and the Canary
Islands (416.56% and 84.31%, respectively). In the case
of Catalonia, the wage increase an individual receives
when changing from working in a small establishment to
one with more than 20 employees is almost triple the
returns a worker with similar characteristics would obtain
in the Balearic Islands (as shown in Table 2). In other
words, large establishments pay more in Catalonia than
in the Balearic Islands.
5. Conclusions
This article provides new empirical evidence on human
capital returns and regional wage differences in the Spa-
nish hospitality sector. The use of establishment-worker
paired data allows us to analyze regional disparities in
this context. This analysis focuses on the main tourist re-
gions in Spain. The differential behaviour of the labour
market and the significant structural differences that exist
between regions drives the need to study each region in-
dividually. Similarly, the regions analyzed present ma-
rked differences concerning the tourism segment they ha-
ve specialized in. The Community of Madrid is charac-
terised by being a cultural destination and the Capital of
Spain, whereas Valencia, Andalusia and Catalonia are
13As described in section 2, ideally the variables size of the establish-
ment and type of activity should have been included, making a distinc-
tion between hotels and restaurants. Including these variables would
make it possible to analyze the impact of the type of activity and busi-
ness size, respectively, on wage differences. In this way, we could have
estimated how the differences in the mean size of hotels in differen
t
regions affect wage differences.

A Comparative Analysis on Human Capital and Wage Structure in the Spanish Hospitality Sector
466
coastal destinations specialized in the sun and beach se-
gments with some cultural tourism and the Canary Is-
lands and the Balearic Islands are highly specialized in-
sular destinations with consolidated sun and beach tourism.
The estimations obtained for the different regions en-
able us to quantify the returns of human capital variables,
such as schooling years, previous experience and tenure
in the firm, as well as to compare their value to those
obtained from other private services. In all the regions,
returns from education are less in the hospitality sector
than in the other private service sectors. Although the
returns from education are less than those reported in
other studies, they are compatible with them, since we
introduced additional control variables related to the job
characteristics that reduce returns14. Similarly, the esti-
mated returns for previous experience and tenure in the
firm are less in the hospitality sector than in other sectors,
being in turn lower than those of the education.
Gender wage differences are found in all the regions.
In general, this problem is more significant in the hospi-
tality sector than other private services. These results
reveal a situation that can be generalised to most coun-
tries, i.e., the hospitality sector is characterized by lower
pay to workers with similar characteristics, and women
are especially penalised in this regard. The lower wages
women receive may be strongly affected by the greater
segregation they experience, as women are mainly found
in the worst-paid industries, establishments, jobs, func-
tional areas and responsibility levels. The works of [22],
[23], [24] or [25] reveal the importance of different types
of segregation on the gender wage gap in the hospitality
sector.
Regarding the job characteristics, there is a positive
wage premium associated with stability in the type of
contracts and establishment size. These results are com-
patible with other studies, as they point out that mean
wages increase in a large company as the number of em-
ployees with a contract based on union agreements in-
creases. This could be due to the negotiation system in
Spain, where specific worker agreements with the firm
are used to improve the labour conditions agreed at re-
gional levels [26]. Finally, the effects of the mismatch
between educational level and job requirements are com-
patible with estimates reported in the literature. In par-
ticular, overeducation has a negative effect on wages in
the hospitality sector, whereas undereducation has a po-
sitive effect.
The breakdown of the wage gap between the Balearic
Islands and each region serves to quantify the part of this
differential attributable to differences in productivity
from the part due to differences in returns of the observa-
ble characteristics. This is highly relevant from the stand-
point of the disparities between regional characteristics,
and has potential implications regarding labour mobility
between regions. The Balearic Islands was chosen as the
reference region because of the high relevance of em-
ployment in this sector in relative terms, and the high
number of hotel bed places and large establishments. The
results obtained are robust given the non-discriminatory
wage structure considered. The evidence helps to draw
conclusions that have relevant implications for economic
policies.
Wage gap decomposition in the hospitality sector shows
that, overall, wage discrimination is significant in all
Spanish regions, given that workers who work in identi-
cal conditions and who have identical characteristics
have different wages depending on the region where they
are employed. However, this is especially relevant in the
Canary Islands and the Community of Madrid. In these
two cases, most wages differences are accounted for dif-
ferences in the returns on characteristics, which mean
these two regions show higher distortions or failures in
the labour market. Wage discrimination is basically dri-
ven by differences in returns on personal characteristics
in Catalonia, Andalusia and the Community of Valencia.
In these last two cases, the increased wage gap is basi-
cally due to lower returns from education. Returns from
the job characteristics also have an influence, but in the
opposite direction, as they help to reduce wage differen-
ces compared to the Balearic Islands. This effect is stronger
in the case of Catalonia and Canary Islands.
Finally, it is worth noting that there are variables not
included in the wage equation specification that can be
determinants of regional differences, such as the regional
unemployment rate or to what extent the tourism develo-
pment model has been consolidated, among others. In
this regard, it is of interest to note that those regions
more specialized in the hospitality sector, such as the Ba-
learic Islands and the Canary Islands, present an employ-
ment pattern that is far from the stereotype in this sector
regarding precariousness and low returns from education.
This analysis may indicate that the level of tourism de-
velopment in a region could encourage greater stability
regarding contracts and better wages as the educational
status of the workers increases.
REFERENCES
[1] Instituto Nacional de Estadística (INE), Encuesta de Es-
tructura Salarial 2006 (Microdatos), Madrid: INE, 2007.
14See for example, [20], [3] or [2]. Nevertheless, the latter author in-
cludes additional personal characteristics, such as marital status and
number of children, and other characteristics related to human capital,
using dummy variables associated with the highest educational level
achieved by the worker.
[2] M. Fernández and Y. Pena-Boquete, “Condiciones de
Trabajo y Empleo en la Hostelería en España: Existe un
Modelo Nacional?” XVI Jornadas de la Asociación de

A Comparative Analysis on Human Capital and Wage Structure in the Spanish Hospitality Sector
467
Economía de la Educación, Palma de Gran Canaria,
2007.
[3] R. Oaxaca and M. R. Ransom, “On Discrimination and
the Decomposition of Wage Differentials,” Journal of
Econometrics, No. 61, 1994, pp. 5-21.
doi:10.1016/0304-4076(94)90074-4
[4] R. Oaxaca, “Male-Female Wage Differentials in Urban
Labor Markets,” International Economic Review, Vol. 14,
No. 3, 1973, pp. 693-709.
doi:10.2307/2525981
[5] A. Blinder, “Wage Discrimination: Reduced Form and
Structural Estimates,” Journal of Human Resources, No.
8, 1973, pp. 436-455. doi:10.2307/144855
[6] L. Serrano, “Salarios Regionales y Dotaciones de Capital
Humano,” Revista de Economía Aplicada, Vol. 28, No. 1,
2002, pp. 23-38.
[7] J. Cotton “On the Decomposition of Wage Differentials,”
Review of Economics and Statistics, No. 70, 1988, pp.
236-243. doi:10.2307/1928307
[8] M. Arrazola, J. Hevia, M. Risueño and J. F. Sanz, “Re-
turns to Education in Spain: Some Evidence on the En-
dogeneity of Schooling,” Education Economics, Vol. 11,
No. 3, 2003, pp. 293-304.
doi:10.1080/0964529032000148818
[9] J. Alcaide and P. Alcaide, “Avance de las Magnitudes
Económicas Españolas en 2008 y Serie Provisional del
Balance Económico Regional. Años 2000 a 2008,” Cua-
dernos de Información Económica, No. 208, 2009, pp.
1-65.
[10] Instituto de Estudios Turísticos (IET), El turismo español
en cifras, Madrid: IET, 2007.
[11] C. M. Schmidt and K. F. Zimmermann, “Work Charac-
teristics, Firm Size and Wages,” The Review of Econom-
ics and Statistics, Vol. 73, No. 4, 1991, pp. 705-710.
doi:10.2307/2109410
[12] H. Strauss and C. Maisonneuve, “The Wage Premium on
Tertiary Education: New Estimates for 21 OECD Coun-
tries,” OECD Economics Department Working Papers,
OECD Publishing, No. 589, 2007.
[13] Instituto Nacional de Estadística (INE), Clasificación Na-
cional de Ocupaciones, Madrid: INE, 1994.
[14] A. E. Aguiló and J. Alegre, “La Madurez de los Destinos
Turísticos de Sol y Playa,” Papeles de Economía Es-
pañola, No. 102, 2004, pp. 250-270.
[15] A. J. Marchante, B. Ortega and R. Pagán, “Educational
Mismatch and Wages in the Hospitality Sector,” Tourism
Economics, Vol. 11, No. 1, 2005, pp. 103-117.
doi:10.5367/0000000053297149
[16] D. Nickson, “Human Resource Management for the Hos-
pitality and Tourism Industries,” Oxford, UK: Butter-
worth-Heinemann, 2007.
[17] J. M. Pastor, J. L. Raymond, J. L. Roig and L. Serrano,
“El Rendimiento del Capital Humano en España,” Val-
encia: IVIE, 2006.
[18] A. Lillo-Bañuls and J. M. Casado-Díaz, “Rewards to
Education in the Tourism Sector: One Step Ahead,” Tou-
rism Economics, Vol. 16, No. 1, 2010, pp. 11-23.
doi:10.5367/000000010790872033
[19] R. Halvorsen and R. Palmquist, “The Interpretation of
Dummy Variables in Semilogarithmic Equations,” Am-
erican Economic Review, Vol. 70, No. 3, 1980, pp. 474-
475.
[20] A. Lillo-Bañuls and A. B. Ramón-Rodríguez, “Returns to
Education in the Spanish Tourism Labour Market,” Tou-
rism Economics, Vol. 11, No. 1, 2005, pp. 119-132.
doi:10.5367/0000000053297176
[21] C. Thrane, “Earnings Differentiation in the Tourism In-
dustry: Gender, Human Capital and Socio-Demographic
Effects,” Tourism Management, Vol. 29, No. 3, 2008, pp.
514-524. doi:10.1016/j.tourman.2007.05.017
[22] C. W. Ng and R. Pine, “Women and Men in Hotel Ma-
nagement in Hong Kong: Perceptions of Gender and Ca-
reer Development Issues,” International Journal of Hos-
pitality Management, No. 22, 2003, pp. 85-102.
[23] L. D. Delfim and J. Varejao, “Employment, Pay and Dis-
crimination in the Tourism Industry,” Tourism Economics,
Vol. 13, No. 2, 2007, pp. 225-240.
doi:10.5367/000000007780823186
[24] O. Skalpe, “The CEO Gender Pay Gap in Tourism Indus-
try: Evidence from Norway,” Tourism Management, No.
28, 2007, pp. 845-853.
doi:10.1016/j.tourman.2006.06.005
[25] J. A. Campos-Soria, B. Ortega and M. A. Ropero-García,
“Gender Segregation and Wage Difference in the Hos-
pitality Industry,” Tourism Economics, Vol. 15, No. 4,
2009, pp. 847-866.
doi:10.5367/000000009789955152
[26] R. Aláez, J. C. Longás and M. Ullibarri, “Diferencias Sa-
lariales en España: Un Análisis Sectorial/Regional,” Inve-
stigaciones Regionales, No. 3, 2003, pp. 5-24.

A Comparative Analysis on Human Capital and Wage Structure in the Spanish Hospitality Sector
468
7. Appendix
Table A.1. Results of the estimations for Spain (expanded model) and some chosen regions (expanded and basic models) in the hos-
pitality sector.
Expanded model Basic model
Factor Spain. 6 regions 6 regions
Coefficient 1.701* 1.641* 1.671*
Constant t-ratio (78.707) (56.190) (67.414)
Coefficient 0.032* 0.040* 0.039*
S t-ratio (16.172) (14.717) (14.653)
Coefficient 0.005* 0.006* 0.002
Exp t-ratio (3.373) (3.260) (3.394)
Coefficient –0.000** –0.000** –
Exp_Squared t-ratio (–2.038) (–2.439) –
Coefficient 0.014* 0.010* 0.012*
Ten t–ratio (8.087) (4.433) (15.545)
Coefficient –0.000 0.000 –
Ten_Squared t-ratio (–0.342) (0.979) –
Coefficient –0.113* –0.124* –0.124*
Gender t-ratio (–14.895) (–11.979) (–12.032)
Coefficient 0.065* 0.077* 0.076*
FTC t-ratio (8.228) (6.930) (7.134)
Coefficient 0.105* 0.125* 0.125*
SW t-ratio (14.296) (11.227) (11.246)
Coefficient –0.092* –0.136* –0.137*
Over t-ratio (–6.664) (–6.963) (–6.973)
Coefficient 0.140* 0.163* 0.163*
Under t-ratio (11.218) (9.989) (9.970)
Adjusted R2 0.254 0.265 0.264
F-Statistic 222.891* 140.807* 175.063*
Sum-squared residue 559.731 373.095 373.682
Obs. 6515 3874 3874
Significant at: * 1%. ** 5% and *** 10%. The standard errors and covariances are robust for heteroscedasticity.
Table A.2. Relative effect on regional wage difference (B model).
Andalusia Canary Islands Catalonia Valencia (Community of) Madrid (Community of)
Factors Charact. Returns Total Charact. ReturnsTotalCharact. ReturnsTotalCharact.Returns Total Charact. Returns Total
Constant 0.00 –187.59 –187.59 0.00 –200.81–200.81 0.00194.92194.920.00–461.05 –461.05 0.00 113.01113.01
Personal
characteristics 36.01 271.84 307.86 –95.47 85.64–9.8325.22211.13 236.3565.26472.47 537.74 11.44 –18.57–7.13
S –61.81 221.02 159.21 0.00 130.11 130.11–184.92–53.20–238.12–34.77 352.99 318.22 –29.15 23.81–5.33
Exp 7.56 163.92 171.48 6.10 –122.39–116.290.00319.17 319.170.00138.89 138.89 0.00 10.6710.67
Ten 90.26 –63.74 26.52 –101.57 23.18 –78.40176.54–69.57106.97 83.87 –39.62 44.25 25.68 –20.315.38
Gender 0.00 –49.36 –49.36 0.00 54.75 54.75 33.59 14.73 48.33 16.16 20.21 36.37 14.91 –32.75–17.84
Characteristics
of the job 71.19 –91.46 –20.27 0.00 310.64310.64170.46–501.73–331.2763.76–40.44 23.31 32.68 –38.55–5.88
FTC 16.36 –81.57 –65.20 0.00 201.29201.2933.00–147.58–114.5814.97–14.97 0.00 5.44 –35.51–30.07
SW 0.00 –39.04 –39.04 0.00 91.4791.470.00–415.39–415.390.00–46.55 –46.55 0.00 –9.30–9.30
Over 13.90 12.32 26.22 0.00 –31.78–31.7825.63 22.98 48.603.819.3613.17 7.52 4.2311.74
Under 40.93 16.83 57.76 0.00 49.66 49.66111.8338.27150.1044.98 11.72 56.69 19.72 2.0421.76
Total 107.21 –7.21 100.00 –95.47 195.47 100.00 195.68 –95.68 100.00 129.02 –29.02 100.00 44.12 55.88100.00
The weighted mean for the 6 regions is assumed as a non-discriminatory wage structure
6
*
1
ˆ
nn
n







.