diferences on wages and inmigration in spain€¦ · web viewuniversity of barcelona diferences...

UNIVERSITY OF BARCELONA

DIFERENCES ON WAGES AND INMIGRATION IN

SPAINMAR Agustín Alarcón

Josep González Calvet

June, 2008

The present work examines the wage structures of the native and immigrant population in the Spanish labor market. Moreover, we intend to analyze the provenience of the wage differential empirically observed among the native and immigrant inhabitants. We differentiate two big groups of immigrants, those from EU-15, North America and Oceania, and the ones from other areas around the world. We find that the latter group presents lower average wage and a more compressed salary structure than the former. The immigrant from developed countries show bigger wage and a more disperse salary structure. The analysis of the salary determinant factors reveal the existence of wage discrimination, that is to say those same characteristics are valued in a different way depending if the individual is native or foreigner. Furthermore, it exist other factors that also depart from the individual characteristics of the employees and generate segregating behaviors’ of the immigrants in some labor market segments.

W A G E S A N D I M M I G R A T I O N I N S P A I N | 2

1.1. IINTRODUCTIONNTRODUCTION..

Since the middles of the 90’s, immigration in Spain is a phenomenon of major importance in national demography and economy. According to the estimations of National Institute of Statistics (INE), 11,10% of the population resident in Spain is foreigner (Survey on the Active Population (EPA), 1st trimester 2008).

The purpose of this work is to analyze the differences on the wage structures of the immigrant and native populations in the Spanish labour market.

We observe that the immigrant wages are very different from the Spanish ones. The average wage of the immigrant employee is systematically less than the one of the Spanish. This is shown by the statistical tests done (test of student “t” to compare the average of independent samples), for both total of population and sectors, by level of studies and labour category and firms size, etc. Nevertheless, it should be distinguished inside the immigrant population to big groups: (1) those from EU-15 and North America who have average wages bigger than the Spanish; and (2) the ones from the rest of the world (rest of Europe, Africa, Asia, and rest of America) who present wages systematically smaller and being the most numerous one. In Table 1 we may check the results in detail.

Table 1. Wage per hour by nationality and geographical area.

Nationality by geographical area

Average wage €/h

Percentile 05 (€/h)

Percentile 95 (€/h)

Variation Coefficien

t

Total 11,65 4,90 26,05 0,74

Spain 11,73 4,90 26,13 0,73

Foreigner 9,55 4,35 21,90 1,07

EU-15 without Spain 14,90 4,77 39,17 1,02

Rest of Europe 8,01 4,13 13,69 0,53

North America* 20,19 4,77 39,88 0,66


Rest of America 7,79 4,06 13,27 1,24

Africa 7,78 4,49 12,58 0,44

Asia 8,8 4,82 14,64 0,92

Oceania* 6,60 - - 0,09

All foreigner except the ones from EU-15

8,02 4,18 13,89 0,94

*The sample for the citizens of Oceania and North America is too small and cannot be considered representative. Source: Own elaboration through EES 2002 (INE).

The EU immigrants have bigger wages than the rest of employees, either Spanish or foreigners. More specifically, their wage is 27% greater than the Spanish one and 56% greater than the one of the whole of foreigners (almost doubles the salary of the Africans and the rest of America). Their large wage and the quantitative importance, deviates to an upper position the immigrant average salary. This situation is due to a much smaller average salary of the immigrants of the rest of the world, who are more numerous group, of 8,02 €/h, representing 68,4% of the Spanish group salary.

Emphasize that the smaller percentile (5% of the employees have a smaller wage) is quite similar to every group, between 4 and 4,9 €/h. This fact which is perhaps reflecting the results of the legislation of minimum salary and the collective negotiation, which pretends to equalize the incomes of the lower categories in every sector, and the existence of inter-professional minimum salary of 2,8 €/h, in October 2002.

If we observe the percentile of the upper wages (95), the salary disparities are very significative: the Spanish employees who have bigger salaries are paid almost double when compared with the foreigners groups outside EU, and the foreigners’ employees of the


EU who are best paid have a salary three times bigger than the employees outside of the EU.

The wage dispersion among the foreigner employees of outside EU is smaller than the one of the Spanish employees (except the rest of America), or the one of the EU-15, although it should be referred the differentiated behavior of the immigrant of the rest of America, whose salary dispersion is the bigger (deviation coefficient of 1,24), the variation coefficient1 is 0,73 for the Spanish group and 0,94 for the foreigners outside EU. The latter group presents a high score because of the wage behavior of the rest of America. The immigrant of the EU-15 have a bigger dispersion coefficient (1,02), which is revealing us that these employees are less concentrated in determined jobs. We could refer that the foreigners outside of EU are more concentrated in jobs with low and middle wages rather than EU immigrants.

The salary by nationality and its distribution are represented in graph 1. The upper and lower bounds show us the maximum and minimum wage. The central box represents the population and horizontal line is the average.

The above referred points can be clearly illustrated: the salary among the Spanish, foreigners of the EU-15 and foreigners outside EU are different. The minimum salaries are similar for all groups. The foreigner employees of outside the EU are paid low and middle wages. The graph can also show us that the salary dispersion among the foreigner employees outside EU is lesser than the one of the Spanish employees and EU-15.

Graph 1. Wages by nationality with geographical areas.

1 Variation coefficient = standard deviation divided by the average.


The number of employees with low wages (salary 60% lesser than the average salary) is 1.816.300, of which 1.717.025 have Spanish nationality, 99.275 are foreigners from outside EU and 12.850 are foreigners of EU-15. Therefore, the proportion of employees with low salaries is bigger in the case that foreigners from outside EU (58,76%) than in the case of Spanish (36,61%) or foreigners of EU (26,50%).

Graph 2. Distribution of salaries in Spain.

1. Spain.2. EU-15.3. Rest of

Europe.4. North

America.5. Rest of

America.


Deriving from this first overview, we may refer some quite clear conclusions. The employees with nationality from countries outside EU are paid less than the Spanish and, almost half of them are concentrated in jobs with low incomes (less than 60% of average salary of the economy). The foreigners from EU are paid 27% more than the Spanish and 56% more than the rest of foreigners.

Given the referred salary differential and being in all cases statistically significant, as confirmed by the tests for comparing the averages of the independent averages, we try to check the causes of the wage differential observed between immigrants and natives. Therefore, we ask ourselves a core question: Does salary discrimination exists? Can labor segregation be referred?

Recently, there have been done a relevant amount of studies on immigration. There exists significant literature on determinant factors that motivate an individual to change his residency place for finding a job: Ahn, De La Rica, and Ugidos (1999), Abellán (1998), Pons, Paluzie, Silvestre and Tirado (2007), Amuedo-Dorantes and De La Rica (2005).

In opposition, the thematic of the salary differential2 from an immigration overview didn’t have such a prolific development in terms of literature. It exist a study for Spain from Simón, Ramos y Sanromá (2007), that examines the origin of the differences on salary structures of the native and immigrant populations in the Spanish labor market. They give special attention on the effect of the segregation of the immigrants by sort of occupation and establishment. The result they obtain is that the discrepances in salary distributions can be explained, greatly, by the differences relative to the observed individual characteristics. For Catalonia,

2 The thematic salary inequality by means of gender, and more specifically salary discrimination by means of gender, has been quite prolific and exists relevant literature. Please check: del Río Otero, Gradín and Cantó (2004), García-Crespo, (2001), García-Pérez and Jimeno (2007), Alaez, Ullibarri and Miren (2001), de la Rica y Ugidos (1995), Alcalá y Hernández (2007), de la Rica, Dolado y Llorens (2007), Pérez and Hidalgo (2001).


González-Calvet (2006) does a similar analysis, concluding that there is no statistical evidence of salary discrimination by nationalities. The observed salary differentials could be explained by the same characteristics of the immigrant employees, from the jobs and by the economical environment of the firms that they work in. Also it is referred the existence of differentiated labor segments occupied mainly by foreigners.

This outline of immigrant concentration in jobs with poor conditions can be observed in other countries, as demonstrated by the work of Peri and Sparber (2007) for USA. The authors try to test the hypothesis of Borjas (2006) and Borjas and Katz (2005) for whom the immigration reduce between 5% and 10 % of the USA citizens’ that didn’t finish their secondary school studies. They demonstrate that this hypothesis does not accomplish. They explain why the immigrants and natives with low level of formal education may not be perfectly substitutes. If it is so, the immigrants cannot compete directly with the natives and the scenario of salary diminution predicted by Borjas and Katz won’t be generated. The cause referred by the authors is that the immigrant skills are different, neither are profficient in the countries language, nor know the culture, therefore, they concentrate in jobs that are labor-intensive (construction, house/building cleaning, restaurant and hotel business, etc.). Consequently, there won’t be any impact/effect on wages, except the production of a segregation effect in determined tip of jobs.

Carrasco, R.; Jimeno, J.F.; Ortega, C. (2006) analyze empirically the effects of immigration in various aspects of the Spanish labor market. Through the use of different data bases, they evaluate the impact of the immigration fluxes on employment rates and native employees salaries. The relevant result of the work is that, in agreement with the study of Peri and Sparber for USA, the absence of a negative effect statistically significant of the immigration in both variables.

Finally, Deutsch y Silber (2007) study the salary differential among natives and immigrants for the Israeli case, extending the classical


methodology of Oaxaca-Blinder3 to the study of salary dispersion. They observe the increasing of a global dispersion of salaries between 1990 and 1998 in Israel and conclude that this situation is strongly linked with the process of massive immigration which took place in the beginnings of the 90’s.

The structure of this work is as follows. Next to this introduction, in the section 2 we expose the data base used and we describe the reality of the immigration in Spain. In section 3 we do an empirical analysis, identifying the explanatory factors of the salary differential and we test the existence of salary discrimination through the traditional methodology and salary decomposition of Oaxaca-Blinder. In the last section we discuss the results and we present the conclusions.

2. IIMMIGRATIONMMIGRATION ININ S SPAINPAIN..

2.1.Database: Information and limitation .

To analyze the differences in wages, we use the micro data in Encuesta de Estructura Salarial de 2002 (EES). The Wage Structure Survey (EES) is an inquiry about the structure and distribution of wages done with a four-year periodicity in all the members of the European Union. The main novelty of the EES is that compared to other surveys on this topic it gathers the wages individually, among other many variables. Related to the employee. That makes it possible to establish relations between wage and other variables that can contribute to determine its amount, such as level of studies, antiquity, type of contract or occupation. This survey helps to cover a great gap in wage statistical information. Before the first EES, from 1995, there were just aggregate wages of companies

3 We shall explain and use this methodology in section 4.


or establishment at disposal, or at the most of more or less homogeneous groups among establishments, but never individual information4.

Moreover, it connects wage level with some other variables that affects the employees of an establishment or a company as a group, connections which had not been taken into account before: the market for which the company produces, the existence of a collective bargaining agreement and its scope, or if it public or private owned.

The EES geographical scope includes the country, with aggregate data for Ceuta and Melilla.

Its population scope covers all employed employees that provide their services in centres of contribution of 10 or more employees and who have been registered during October 2002. Chairmen, members of the board of directors and in general all staff whose remuneration is basically no wage but commission or benefits.

Regarding sectoral coverage, those centres of contribution that have their economic activity included in one of these large sectors: Industry, Construction and Services.

Agricultural, farming and fishing activities, civil service, Defence and compulsory Social Security, domestic service and non-territorial organizations are excluded.

The wage concept we are using is gross wage per hour, calculated with the EES-02 data for annual gross wage and annual labour time. This magnitude differs from the wage per hour calculated by the INE (National Institute for Statistics) with the wage and labour time data in the month of October, from the same survey, since the annual magnitude includes extra month’s pays and holidays. INE’s methodology underestimates approximately in a 14% the wage per hour. We have calculated an alternative wage per hour to the one from INE’s, the wage per hour under agreed annual work, in 4 Some authors had used indirect sources such as the Survey on Family Budget or, previously, the Survey on Life and Labour


which worked time is approximated by this magnitude. One problem arouses with this calculation, that overtime is not included. We have solved it with a coefficient calculated with overtime in October (see Annex 1).

The resulting final sample has a size of 189,366 observations. We are considering immigrants those people with no Spanish citizenship, requirement that 5,600 people fulfil, 2.94% of the sample, which represents 217,430 people, which represents 8.20% of the wage-earning foreigners referred.

Finally, two limitations of the EES must be emphasized, which have already been implicitly mentioned in the previous pages, and that can affect the results in this paper. On the one hand, the EES does not cover some productive activities such as the agricultural sector, civil service and domestic service. On the other hand, the EES excludes establishments with lees than 10 employees. Both limitations affect immigrants and Spanish wage earners who work in these labour segments. The final impact that it has in each group depends on whether the exclusion affectin the same way to both groups.

To alleviate these limitations and taking also into consideration that the EES covers only those immigrants who pay Social Security, that is, legal immigrants, and that it will tend to sub represent the foreigners wage earners, we are going to use another database for year 2002 that offers individual information in detail of the features of the employees and their activity, divided into branch activity and company size. For that reason we are going to base on the information from the EPA, the Survey on Economically Active Population, to make an approximation of the real situation of immigration in Spain in 2002 (to compare it with the data from the EES) and in the first quarter of 2008, to show a view of the development of immigration until today.


2.2. Immigration in the Spanish labour market.

According to the EPA the foreign population represents an 11.10% of the total Spanish populations in 2008’s first quarter. In the fourth quarter in 2002, a year for which have detailed information on wages, the ratio was a 4.6% of foreigners, from the same source.

It is significant that, under the data from the EPA in the first quarter of 2008 the immigrant population is concentrated in those segments of younger labour age, with a longer labour time horizon5. 90.89% of immigrant employees are between 19 and 55 years old, whereas in the Spanish population just reaches a 64.64%. If we limit this comparison to the youngest groups the difference is more evident: 53.15% of the foreign population is between 16 and 34 years old, whereas 29.40% of the Spanish population fills in this segment. In 2002 the situation was very similar, though the differences were smaller. The population between 16 and 54 was 89.1% of the total of the immigrant, 67.1% of the Spanish. On the other hand, 32.9 of the Spanish population was older than 55, and just 10.9% of the immigrant.

Table 2. Foreign population and age groups. I Quarter 2008- IV Quarter 2002

CONCEPT/ PERIOD

I Quarter 2008

(%)IV Quarter

2002(%)

Total Population 45.109,50 100% 41.324,8 100%

Spanish citizenship

39.703,80 88,02% 39.433,4 95,42%

Foreign citizenship

5.007,00 11,10% 1.891,4 4,58%

5 The incorporation of immigrants has a very important rejuvenating effect on the labour population, which is showing in the Social Security accounts


Double citizenship

398,7 0,88% - -

Spanish

16-24 years-old

3.833,00 11,53% 4.751,7 14,4%

De 25 a 34 años

5.938,20 17,87%

17.381,8 52,7%De 35 a 44 años

6.198,50 18,65%

De 45 a 54 años

5.515,40 19,59%

De 55 y más años

11.754,10 35,36% 10.856,9 32,9%

Foreigners

De 16 a 24 años

732,5 16,28% 303,6 17,46%

De 25 a 34 años

1658,70 36,87%

1.244,9 71,6%De 35 a 44 años

1156,30 25,70%

De 45 a 54 años

541,5 12,04%

De 55 y más años

409,7 9,11% 189,9 10,9%

Source: Own elaboration with the data in EPA (INE).

These data are reflected in the figures of economically active population, where their weight of immigrants is more important than their demographic weight and has increased considerably in the last years. As a result, the immigrants were 6.33% of the wage earning population in the fourth quarter in 2002, and in the first in 2008 were already15.26%. However, the weight of the immigrant population in the employed population is not as high because it has more difficulties to find a job and therefore their unemployment rate


higher. In the last quarter in 2002 the foreign population in Spain was 6.36% of the employed population. In the first quarter in 2008 and despite the change of cycle which is being experienced and which has increased immigrant unemployment rate from 9 to 14% in just three months, the foreign population is already a 14.41% of the employed population.

Table 3. Economically active population and employed population. IV Quarter 2002 – I Quarter 2008.

CONCEPT / PERIODFirst trimester 2008 Fourth trimester 2002

Total Active Population 22.576,5 19.037,2

Spanish Citizenship 18.905,3 17.773,3

Foreigner Citizenship

3445,8 1.261,9

Proportion of Foreigners in the total population

15,26% 6,63%

Total rate of Activity 59,35% 54,6%

Spanish Citizenship 56,88% 53,7%


76,6% 72,6%

Población ocupada total

20.402,3 16.825,4



2.941,1 1.069,9

Proportion of Foreigners in the

14,41% 6,36%


total population


Most part of the employed population works for a company as wage earning employee. In this point there is also an important difference between the Spanish and the immigrant population, 82.72% and 90.17% respectively.

Table 4. Wage earning population and wage rate. I Quarter 2008 – I Quarter 2002.

CONCEPT/PERIOD First trimester 2008 Fourth trimester 2002

Total Active Population 17.105,9 13.698,9



2.651,9 939,8

Proportion of Foreigners in the total population

15,5% 6,9%

Tasa de salarización total

83,84% 81,4%

Spanish Citizenship 82,72% 81,0%


90,17% 87,8%


Another difference among Spanish and non-Spanish employees is that almost all of the wage earning immigrants work in the private sector (97.81%) whereas a relevant part of Spanish employees are in public sector (19.81%).

Table 5. Wage earning employees by institutional sectors. I Quarter 2008 – IV Quarter 2002.


CONCEPT/PERIOD First trimester 2008

Fourth trimester 2002

Employees in the total private sector

13.944,2 11.057,0


Foreigner Citizenship 2.595,6 916,4

% foreigners in total employes of the private sector

18,61% 8,3%

Employees in the private sector in % of total

81,52% 80,7%

% spanish employees in the private sector

78,37% 79,5%

% foreigner employees in the private sector

97,88% 97,5%

Employees in the public sector 2.873,2 2.641,9


Foreigner Citizenship 31,26 23,4

Employees of public sector in % of total

16,80% 19,3%

% spanish employees in the public sector

19,81% 20,5%

% foreigner employees in the public sector

1,18% 2,5%


The activities in which foreigners work are mainly, according to the EPA, trade and the hotel industry (25%), construction (22.7%) and other services (17.5%). These other services are basically done by women (91.7%)6.

6 In 2005 domestic service employed 320,000 foreign women.


The following table shows the activity sectors in which foreigners work and the rate that they are among the total employed in each sector. We can observe that the labour force is concentrated in construction, other services and agriculture.

In 2002 that was not so differentiated as nowadays, since immigrants were more distributed among the different sectors and were more represented in the industrial activities. Nevertheless, trade and the hotel industry, construction and other services were already concentrating a 54% of the employed labourers. Although foreigners were just a 6.5% of the employed total population (see table 2) in construction, other services and agriculture, immigrants were already more than a 10% rate of employed workers.

Table 6. Immigrant participation rate per sectoral activity. I Quarter 2008 – IV Quarter 2002.

Period I trimester 2008 IV trimester 2002

Activity sector/magnitude % sobre extranj. ocupados

% sobre ocupación sector

% sobre extranj. ocupados

% sobre ocupación sector

Agriculture, forestry and fishing 5,87% 30,07% 3,9% 10,4%

Food and beverage industry, textil, leather, Word, paper 4,31% 12,78% 8,4% 5,8%

Extractive industries, refinery, chemical, cork, metal, energy and water

5,16% 11,27% 9,8% 7,0%

Machinery, electric equipment, transporttion machinery, manufacturing industry

3,07% 9,18% 6,2% 4,2%

Construction 22,65% 27,84% 19,4% 12,1%


Comerse and restaurant and hotel business 25,00% 18,83% 24,6% 8,2%

Transports 4,60% 9,92% 1,9% 1,7%

Financial intermediation, real estate activities 8,25% 11,12% 9,2% 5,3%

Public administration, education and health 3,61% 2,77% 6,6% 3,0%

Other services (personnel, domestics, etc.) 17,49% 36,47% 9,8% 10,9%


If we introduce the variable of gender, we will limit even more the profile of the immigrant. The activity and occupation rate are higher among women with no Spanish citizenship than by Spanish women employees because the rate of women with an age for working is higher and because the rate of activity among immigrant women is higher for every age group except the 25 to 34 range, in which the rates are similar. The activity rate is 47.11% in Spanish women, 6.89% in foreign women. This rate shows the higher participation of foreign women in the labour market in comparison to the Spanish. 44.68% of the immigrant employed population is of this gender, 43.58% in he case of Spanish employees.

In the first quarter in 2008 the employment is concentrated in other services (personal, domestic services, etc) where 35.9% of immigrants are working, trade and the hotel industry, where 34.1% work.

The activity of foreign men is focused in other activities. 40.3% of these foreigners perform there work in construction and 17.7% in trade and the hotel industry. The rest distribute themselves in agricultural or extraction activities (8%), transport (6.9%), financial intermediation and property activities (5.3%) and other industries.


In 2002 30.1% of employed foreign men worked in construction and 18.3% in trade and the hotel industry. Their weight in construction was already important, 12.1% of its employees.

Table 7. Activity rates, by gender and nationality. IV Quarter 2008.

Activity rate Spanish Foreigners

Total 56,88% 76,60%

Men 67,04% 85,33%

Women 47,11% 67,84%


Finally we are considering the labour category. According to the information in the EPA, there is a clear differentiation between categories where men and women are employed. In the first quarter in 2008 50.1% of foreign women were employed as non-qualified employees and 30.2% as service employees in catering, personal service, or shop assistant7. Regarding foreign male employees 38.6% have a qualified job in industry or construction and 26.5% are non-qualified. Directive posts, technical or professional are occupied by a 7% of foreign male immigrants, compared to a 32% in Spanish born employees.

In the fourth quarter in 2002 this profile was not as defined and some characteristics stood out. Among women, there was a 29.4% of

7 Women’s predominance in domestic service, and specially foreign women’s predominance, has been settling down since long ago. And the CES’s Review has emphasized it so in the first quarter of 2006: “in 2005 682,882 people worked in domestic service; more than a 90% of these were women. In more than half the cases they had a foreign citizenship. The previous decreasing tendency of employment in this branch was reversed in 1994 and since then it has been increasing gradually at a higher level than those employed in the rest of sectors.” (P.1)


non-qualified employees and a 31% in catering, personal service, or shop assistant. Nevertheless 32% were employed in technical, professional or directive posts, with levels similar to those of national women. Regarding foreign male employees 26.7% were hired in qualified posts in industry and construction. For directive, technical or professional posts 19% of foreign male immigrants were hired.

To sum up, if we want to determine the profile of an average immigrant in Spain we can emphasize the following characteristic. He is 32 year-old and comes to Spain to work as an employee in the private sector. If he is a man he shall work in the construction but may also be employed either in trade or in the hotel industry. If she is a woman she shall work in other services (personal, domestic and other) and trade and the hotel industry.

3. MMETHODOLOGYETHODOLOGY.

3.1.The equation of the salaries .

As we have already mentioned, the lower remuneration of the immigrants vs. the Spanish natives is an empirical fact that can be easily observed. This situation raises the question whether this minor remuneration of the immigrants is justified in terms of productivity or if, in addition, has some relation with some type of wage discrimination. According to the human capital theory, salary determination is related to the levels of productivity reached by the employees and this is related to the human capital of the individual. Therefore, on a perfectly competitive market, the salary should correspond with the value to the marginal product of the employee, so the presence of wage differences among natives and immigrants would be justified by differences in productivity, derived from differences in human capital.


Nevertheless, as we have previously observed, none of the standard models has sufficient explanatory capacity, nor provides, statistically speaking, unquestionable contrasts. This situation has led to the usage of wider empirical models that provide elements of different theories and estimate salary equations that consider other individual variables of the employee and his/her labour environment, according to the approach of the Lancaster characteristics model (1966).

Starting from a salary equation that relates the salary received by the employee with a set of observed variables available that, according to some or other theories, determine the salary level, we intend to identify which characteristics are statistically significant in this relation and which is the impact in the salary level. Hence, when comparing the estimate salaries for natives and immigrants, we will be in a position to determine which part of the estimated salary differences is due to differences in allowances and which part is derived from different salaries among identical characteristics. This second component would include the portion of the salary gap against the immigrants that cannot be explained by differences in allowances and in productivity that, therefore could be due to discriminatory behaviours.

In order to carry on this analysis and quantify precisely the impact of the characteristics, a semi-logarithmic regression is performed, following the salary equation proposed by Mincer (1974) to contrast the human capital theory including other individual or labour-environment-related variables (sector, company, etc) as suggested the Lancaster characteristics approach. Empirically, this is the selection technique for the quantitative analysis of the salary conditioning factors8.

8 The use of a semi-logarithmical regression could mask problems related to the heteroscedasticity of data, originated by the self-selection of the individuals, extent manifested by some authors like, for instance, Blackburn, M.L. (2007). Madariaga (2007) contrasts this hypothesis for the EES-2002 data rejecting it for any significance level.


The criteria used to distinguish the immigrant employees from the native ones is the nationality criteria provided by the “Encuesta de Estructura Salarial” (Salary Structure Survey). Thus, in a strict sense, the object of the study is the salary differences between Spanish and foreign individuals.

We estimate a salary equation of the semi-logarithmic type inspired in the Mincer empirical model (1974), having the salary logarithm depending lineally on the explanatory variables used. These variables can be numeric, such as age, antiquity or antiquity square, or categorical, such as qualifications, nationality or sex. Both types of variables can be incorporated into the model by means of specific techniques (fictitious variables). The equation specification is as follows:

Coefficients obtained from this analysis show the impact on the logarithm wage-hour related to the existence or non-existence of a categorical variable, such as sex or nationality, or the impact of the increment in one unit in a quantifiable variable, such as age or antiquity, having the rest of the variables unchanged. That is to say that the coefficients show the percentage of variation of the salary among two individuals identical except for one variable.

The significant variables in the determination of the salaries and the impact quantification can be obtained from this analysis. Additionally, such analysis permits to determine whether the nationality variable is significant and, in case it is, estimate its impact on the salary.


In order to obtain the significance of the nationality variable in the determination of the salary, we have estimated the equation with and without this variable, with the objective of comparing results. To do so, we have introduced this variable according to the maximum detail permitted by the data (eight geographical regions: Spain, EU-15 without Spain, Rest of Europe, Asia, Oceania, North America, Rest of America and Africa).

4.2. The Oaxaca-Blinder decomposition.

In a second stage of the empirical analysis, we have applied the technique developed by Oaxaca (1973) and Blinder (1973) that evaluates the differential of the salary in the mean of the distribution of characteristics, from MCO estimations of the mincerian equations, quantifying the salary discrimination suffered by the average immigrant compared to the average native.

Y is the wage/hour. Zj is the vector of individual characteristics considered relevant to explain salary differences. B is the return ratio (the percentage of change in the salary) of such characteristics


and U j is the error term. Once the model is estimated, we are in

position to predict the estimated salary of each immigrant, inm.,

together with the salary that he/she would obtain if his/her

characteristics were paid as a native, inm.j, so that:

inm.j = exp ( inm.* Zinm.j)

inm.j = exp ( nat.* Zinm.j)

The difference in salary ( inm.j - inm.j) represents the estimation that

the model provides regarding the salary discrimination suffered by the immigrant employee j.

The average salary breach observed is divided into two components. The first component would quantify the retribution that the market provides to the difference in the average allowance between both collectives and, the second component would gather the different allowances that the market provides to immigrants and natives when applying their average characteristics.

From that second term, the equation proposed by Oaxaca (1973) to measure the aggregated discrimination is:


.

This technique presents limitations due to the fact that it only analyzes the average of the salary distribution. On one hand, it does not permit to identify the differences in terms of discrimination that arise along the salary distribution. On the other hand, it implies the assumption that the aggregation of the individual discrimination levels is performed according to the same weight, independently from the supported discrimination level.

Taking into account these limitations, several studies have applied a wide set of econometric techniques with the objective of including distributive aspects in the analysis of the comparison of the salary distribution. Based upon Juhn, Murphy and Pierce (1991, 1993), several studies suggest that the salary structure is not constant along the salaries range. Nevertheless, the Juhn et. al. approach also presents errors in the discrimination measurement. Their estimations are not precise, since they do not consider the effects produced by the reordering of the individuals. Those estimations will be erroneous to a greater or smaller extent depending on the importance of such mobility source provoked by the discount of the discrimination phenomenon. It is to be accepted the necessity of regulatory measures in the calculation of the salary discrimination. The Jenkins approach (1994) tends towards that direction.

Another alternative approach to mitigate these limitations is to perform the analysis based on quartiles, so that the variation in the differences along the salary distribution can be calculated. García et. al. (2001) perform this type of analysis by deciles in order to quantify


the salary breach man-woman in the Spanish case and check that such gap and its discriminatory component increase with the salary.

4. RRESULTSESULTS.

4.1.Salary determinants.

The analysis with all the individuals of the sample, introducing all variables previously announced except the variable nationality, gives us an adjustment quite acceptable, R2 corrected = 0,616 (that is to say, the proposed regression explains 61,6% of the observed salaries), and it is significant with very high probability.

The salary that each individual would have, from whom we didn’t know any of the studied characteristics, is 3,96 €/h. This is the departure salary9, in which we would sum or take off the impact which correspond to each one of the own individual characteristics.

We should refer that when doing this regression, not every single estimated coefficient are significant, namely the explanatory variable “education level”. The remaining of the used variables is significant with confidence level >95% and their relative importance is quite different10.

9 This departure salary is given by the regression constant β0.

10 The estimated coefficient allows to obtain the salary bounty, that is to say that indicate the percentage of the change produced by the increasing the variable by one unit (or the presence of a characteristic) but, given that the variables are measured in different units, the coefficients value does not reflect the relative importance of the factor. Therefore, the standardized coefficients must be computed, comparable between them, in order to measure the relative weight of the different elements that affect the salary.


4.1.1.Main variables .The variable that has more influence when explaining the salary differential of our sample is the antiquity (with an relative weight of 9,83%), mostly in the first years. It follows the next factors, by importance order in the relative impact on the salary: belonging to a high labor category (from technician till managerial, with a relative weight between 4,9% and 7%), working in a firm with more than 200 employees (4,7%), working in the financial sector (4,2%), to have a bachelor degree (3,8%), working in the construction sector (3,78%), the age (2,8%), to have a university diploma (2,7%), to have responsibility position (2,5%), and from here, sector, level of average studies, etc., the importance is lesser each time. Please check the table of the annex 2 to look up at the standardized coefficients and the relative

4.1.2.The level of studies .Two categories of the variable “educative level” are not significant (incomplete primary school and secondary school without graduation title). The small size of these groups can explain, in part, the lost of significance. Nevertheless, for the remaining of the education scale categories, the significance is very high (>99%). This fact correspond and is in agreement with largely accepted theory of human capital developed by Becker (1964), for which the level of education reached plays a core role in the determination of salaries. We have done a new analysis putting apart Catalonia, Madrid and Basque Country – Navarra from the rest of Spain, and we referred that all the categories of the variable “educative level” are significant for these regions, but when the analysis is done for the rest for the rest of Spain, once again the two previous educative levels appear to be non-significant.

We try to check why are those coefficients of the variable “level of studies” losing significance when analyzing the regression for the rest of Spain. In order to do so, we do all over again the analysis for


the rest of Spain but now eliminating the variable “occupational category”, because we observe a high correlation between both variables and perhaps the lost of significance of the education coefficients has to do with it. Nevertheless, the results of the analysis without the variable “occupational category” still present non-significant coefficient.

Through the work of Madariaga, R. (2007), we know that for the employees with full-time permanent labor contracts, the variable “level of studies” is significant for Spain as whole. Effectively, we carry out again the analysis for those individuals, and we obtain high significant coefficients. In order to know what it is causing the lost of coefficients significance, if part-time or if temporal contracts, we repeat the analysis introducing separately both categories. When introducing the category “temporal contract”, the coefficients under analysis don’t lose significance, in opposite, every single one is significant with high probability. The results of introducing the category “part-time” are quite evident, the two levels of the study referred above are not significant and the other two (primary school completed and graduate school) are significant only with 95% and 90% respectively.

This suggests that the part-time employees from the “rest of Spain”, representing 54,39% of the part-time employees of our sample, have characteristics that guide their salary level but, those characteristics have little to do with the education level regulated for the primary and secondary educative levels.

4.1.3.Salary Bounty .The salary bounty or salary differential11 of a variable or characteristics, that is to say the percentage from which the salary will change if all the other factors maintain constants and the

11 In order to convert the coefficient β into differential or expected salary bounty, expressed in %, we apply the following transformation: % salary bounty = (eβ-1)*100.


variable that we analyze increases by one unit (or if the characteristic is present), it is also indicated in the table of the annex 2, calculated from the regression coefficients. Thus, the employees who take part in managing tasks have an expected salary differential of 110,79% respect to the non-qualified employees, that is, their salary is more than double of all the other characteristics.

The jobs that require bachelor degree have a salary differential of 81,95% more, those which require “diplomatura” a 60,22% more, the technician a 35,43% more, with respect to the non-qualified employees. Now looking at the variable level of studies, the differential is smaller. The individuals possessing a bachelor degree have a differential of 29,26% over the employees without any studies, a 21,96% over the “diplomados” and those with middle studies (high school and technical studies) present a differential of 14,66% and 13,82% respectively.

Working in big firm (over 200 employees) present a salary bounty of 22,82% in relation to small firms (10 till 49 employees).

Respect to the sectors, the financial one has a salary bounty of 57,65%, the one of electricity and water of 52,6%, the construction sector has a differential of 26,1%, transportation and services of 28,86% and commerce with 14,98%, all of them compared with the textile sector, which shows the lesser remuneration.

The permanent contract shows an expected salary differential of 5,82% with respect to the temporal one.

The expected differential of being woman respect to be man is -16,29%, similarly as in the other factors.

All the previous results are quite similar to the ones obtained in other works, like Madariaga (2007), De Cabo and Garzón (2007).

4.2.Nationality Effect.


When introducing the variable nationality, with the maximum detail that the data allow us (eight geographic regions: Spain, EU without Spain, North America, Rest of America, Oceania, Africa and Asia), the regression adjustment just presents small variation (Corrected R2

= 0,617). That is, the variable nationality simply does not add any explanatory capacity to the model.

The coefficient of the variable Rest of America is not significant. The variables EU-15 without Spain, Rest of Europe, Rest of America, Africa and Oceania are significant. The rest of coefficients do not present a substantial variation in relation to the estimation without taking into account the nationalities.

The relative weight of the variable nationality is too low for explaining the expected salary (between 0,00% and 0,39%). The salary differentials with respect to the Spanish are: 9,89% for foreigners from EU-15, 0,002% for the foreigners of the rest of EU, -4,83% for those from the rest of America, -2,03% for the foreigners from Africa, 1,34% for those from Asia, -31,59% for ones of Oceania and 11,60% for the foreigners of North America. In relation to the last two numbers, we should refer that they do not have any statistical relevance, due reduced size of the sample (we just have 3 observations with belongs to Oceania, representing 210 individuals, and 50 observations for North America, representing 1.885 individuals).

The numbers seems to indicate that being immigrant is not a characteristic that induce much variation in the expected salary, except in the case of the immigrants from EU-15. In any case, the relative importance of the factor nationality in relation to the rest of factors is very low, and the model does not gain explanatory capacity. The fact that the salary differential by nationality is so low, or not being significant in some cases, suggests that, similarly to the rest of factors, nationality does not produces much variation in


salary, what seems to advocate the non-existence of discriminatory behaviours12.

Now, when doing separately the same analysis for each group of nationalities, we may observe the regression constant, that determines the initial salary without characteristics, is bigger for immigrants from countries in development (β0=1,721, for the rest of America and β0=1,709 for Africa) than for the Spanish (β0=1,357) or for the immigrant from EU (β0=1,596). At the same time, the adjustment capacity of the model is smaller in the regressions of the countries in development (Corrected R2=0,441 for the rest of America and 0,365 for Africa) than for Spain (R2=0,617) and from EU-15 without Spain (R2=0,613).the differences in constant values together with the biggest average salary of the Spanish and the ones from EU-15 imply that in case these last two, the individual characteristics are much more important when determining the salary, rather than if the immigrants are from countries in development. This suggests the existence of two different markets, one in which the employee characteristics are taken into account, meaning that the salary will be determined by the characteristics of the employee (its qualities or its skills), and another one, where the main group of immigrant is from countries in development, that functions in a different manner and in which the personnel characteristics are not so important (academic formation, experience, occupation, etc.). The characteristics have less influence in the determination of salary because, by type of employments that the immigrant will be inserted in, other elements are more relevant, other factors that do not appear in the EES-02, as the individual physical appearance, etc. This difference, that can be quickly

12 We understand by salary discrimination the situation of an individual who is paid a different salary from another individual, even being equal individuals with the all the same characteristics and environmental variables that may affect the labor productivity. The only difference between them can be a individual characteristic that has nothing to do with labor capacity, as gender, religion, nationality or race.


noticed, suggest the existence of discriminatory behaviors or, even segregating13, which take us to do a further analysis.

4.3.Analysis by quartiles.

In order to do a further study, and following the work of García, Hernández, y López (2001), we did an analysis of sample by quartiles. García et. al. did an analysis by deciles for approaching the salary differential by gender, but we do it by quartiles due to the reduced osize of the immigrant population. In the first quartile is represented an important part of the immigrant from outside EU (46,27%). (The tests on equality of the averages allow us to conclude that for this quartile all the nationality groups are different. Also the tests on the homogeneity of the samples of Kolmogorov-Smirnov and Mann-Whitney take us to the same conclusion).

When doing this regression for the first quartile, both the salary differentials and the relative weight of the variables, explain that the salary varies with respect to the analysis of all sample. The introduction of the variable nationality does not improve the adjustment of the model (R2 corrected=0,109, does not vary), and the coefficients for the different groups of nationality all are significant, except for the groups “North America” and “Oceania”, but this non-significance can be due to a low representativeness of the data of these groups. The salary bounty of the variable nationality is too low for every group, fact that suggests that being immigrant is not a characteristic that takes the expected average salary to vary much (please check table 1 of annex 3).

According to the international literature on immigration (Chiswick, 1978, Borjas, 1999, Peri y Sparber, 2007) and looking at the results, 13 There exists segregation when different tasks are assigned to individuals that are just different because of the race, gender, religion or nationality, even if the rest of individual characteristics are the same.


we may suggest that for the employees of the first quartile, their salary level is not determined by its characteristics (regulated formation level, employment formation, qualities, etc.). This means that the immigrants that establish in the origin country in jobs, in which their education level is not taken into account, but when their labor adaptation, linguistic and cultural, the education level seems to be adjusting. These jobs are usually the ones with lower remuneration, labor-intensive and those which require high labor flexibility (long labor journeys, high availability, etc.). If we calculate the average of antiquity among the immigrant of this quartile, we observe that barely reaches a year (0,69), while for whole immigrant population is 1,43 years.

The variable with more weight when explaining the salary in this quartile is the antiquity (246,61%). It follows as explanatory variables, working in the construction sector (182,25%),working in a firm with over 200 employees (100,9%), working in other industries (82,09%) and working in the restaurant and hotel business (77,63%).

The fact that the constant presents high value (β0=1,702) indicates that exists other factors which are not being taken into account in the regression, and at the same time are influencing in the determination of salary for this quartile. If we take into account the sectors which have more weight when determining the salary in this quartile (construction, etc.), these other factors which we didn’t take into account an determine the initial salary are connected with the physical characteristics of the individual like his physical strength, the capacity to do long labor journeys, etc.

Now, it is important to refer that in this quartile the difference by gender is much less than whole sample (-2.89% and -16,29% respectively), what could suggest the non-existence of discrimination in this quartile. Please take note that 36,36% of the female working population is concentrated in this quartile. 54,79% of the immigrant working females concentrate in this quartile. (Also it is an important


aspect the high percentage representing the domestic work of this group, as we referred before).

When doing the regression for the last quartile, the one where the biggest proportion of immigrants from EU-15 (32,66%) is included, the results are different. (Please check table 2 of annex 3). At the time we add the variable nationality to the regression, the explanatory capacity of the model increases but just slightly (R2

corrected passes from 0,314 to 0,316). All nationalities are significant, except Rest of Europe. The conclusion achieved is the same one of the first quartile: the low relative weight that these variables have to explain the expected salary suggests that being immigrant is not a characteristic that influenciates the salary, except in the case of immigrants from EU-15, which salary bounty is 15,87%

In this quartile, the variables with more weight when explaining salary are high labor category (management, positions that require university formation, technician, between 17% and 9%), the age (9,9%), working in the financial sector, antiquity, etc.

As it may be observed, is given more importance to the personnel characteristics of the employee and its education than to its physical capacities.

The fact of doing the analysis by quartiles promotes relevant changes in the relative weights of the factors that explain the salary, suggesting the existence of different behaviors that point out the existence of differentiated segments of the labor market. For the immigrants of the EU, the personnel characteristics have bigger weight when determining the salary than for the rest of population. In opposition, for the immigrants from outside EU, the explanatory capacity of the personnel characteristics is lesser, if moving in one market segment where exist low differentiation, low specialization and little information about the employee skills. Therefore, the information that we have available till now points out that what explains the salary differential between natives and


immigrants is the segregation14 of the immigrants in some sectors, labor categories, types of firms, etc., more than a pure salary discrimination. It is worth to advance further in this analysis to push out doubts.

4.4.Is there salary discrimination?

In this second section, we will apply the basic decomposition of Oaxaca (1973) and Blinder (1973) for detecting the existence of salary discrimination. In order to do so, we are going to decompose the salary differential among natives and immigrants in an explanatory part of the personnel characteristics of the individuals and the part caused by the discriminatory dealing of the foreigners or favoritism of the Spanish.

In the first column of the table 9, it is shown the total coefficients of doing a regression separately for each group (Spanish and foreigners). It is indicating the observed salary differentials.

The differences because of being native or foreigner (pure discrimination) are indicated by the constants of the regression, column 2.

In the third column, we show why part of the differences is given by a difference in the personnel characteristics of the employees. In order to do so, we do an analysis for each group applying the corresponding coefficients to the other group. Finally, in the fourth column, we express the differences caused by the discrimination when receiving different remunerations for the same characteristics.

14 There exists segregation when different tasks are assigned to individuals that are just different because of the race, gender, religion or nationality, even if the rest of individual characteristics are the same.


The results are the following ones:

Table 9. Analysis of the salary differentials between natives and immigrants.

Difference of the

salary per hour

natives-immigrant

s

Difference of constants of

the regression

(pure discriminati

on)

Difference explained

by the characteris

tics

Non-explained difference

(discrimination)

Total population

0,32

Salary differential = + 27,43%

-0,203

-63,44%

0,192

0,193

60% a 60,3%

0,331 Discrimination

0,332 Favoritism

103,44% a 103,75%

Source: own elaboration.

Approximately 40% of the salary differentials between natives and immigrants are promoted by discriminatory behaviors to the latter.

The characteristics of the foreigners are not valued as much as the ones of Spanish (almost double in favor of the Spanish (103,44%)). Although, due to the non-knowledge of the characteristics, it is more valued to the foreigners (constant difference = 63,44%).

The interpretation is that existence of factors which are not observed in the sample and are more relevant for the foreigners than for the Spanish, therefore the constant of the regression is bigger (please pray attention that the constant takes into account the impact of all variables that did not enter in the regression). An element to be


considered among these factors is the legislation of the minimum salary or the convention deals on minimum salaries. For many foreigners outside EU, their wage is fixed upon these conditions, independently of their characteristics. In opposition, the observed factors in the EES-02 are more important for the Spanish. As a result, the foreigners are paid less. Finally, the different behavior of the managers to natives and immigrants explains the remaining 40% of the salary differential.

These results, together with the data of the EPA on qualification, sectors where the immigrants work, categories, etc., indicate clearly the existence of relevant segregating behaviors. The foreigners end in sectors and categories where the characteristics, that we normally give importance, are less valued, deriving many of them of the ideas of human capital theory. For the foreigners, their academic degree, etc., are not that much valued but other aspects. For example, in the sector of construction what counts is the physical aspect and capacity for physical work. The same appears in the domestic jobs (which is not present in the sample), where the flexibility, working for little money, doing long labor journeys, etc., is what counts. But another thing to keep in mind is the existence of regulated minimum salaries (or by convention) which must be paid even if the characteristics are not valued. The difference of constants can reflect the equalizing impact of the minimum salary and conventions.

As it was previously argued when we described the profile of the average immigrant, the biggest part of the immigrant is concentrated in jobs with salaries above the average. We ask what it may happens if applied the decomposition of Oaxaca-Blinder to the lower quartile. Formulating the question in another way, the immigrants that concentrate in this quartile are treated in an equal way as the Spanish in this kind of employment? If it is so, it would have more sense talking about labor market segmented by salary levels and labor segregation of the immigrants more than salary discrimination by nationalities.


Table 10. Analysis of the salary differentials between natives and immigrants in the lower quartile (<25%).

Difference of the

salary per hour

natives-immigrants

Difference of constants of

the regression

(pure discriminati

on)

Difference explained

by the characteris

tics

Non-explained difference

(discrimination)

Total population

0,014

Salary differential =

0,81%

0,025

+179,51%

0,005

0,006

35,9% a 42,9%

-0,017

-0,018

-122,07% a -129,25%

Source: own elaboration.

The first that we may observe is that the salary differential when looking just at this quartile is much inferior (1,77%). As we said before, in the salary lower bound, the salary are quite similar, the salary differentials between immigrants and foreigners come basically from the middle and high salaries.

Therefore, even observing the existence of a total discrimination between 48% and 59%, being the salary differential among the groups so reduced, the discrimination results to be irrelevant.

Now we are in capacity to answer the question that we set up at the beginning of this work: does it exists salary discrimination? According to the analysis of Oaxaca-Blinder, approximately 40% of the salary differential between natives and immigrants are promoted by discriminatory behaviors against the immigrants. The characteristics of the foreigners are much less valued than the ones of the Spanish. Consequently, the observed salary


differential is not due totally to pure salary discrimination; the results are very conclusive with respect to what the salary differences explain by segregating behaviors of the immigrants in determined types of employment.

5. CCONCLUSSIONSONCLUSSIONS.

This paper studies the differences in the wage structure of natives and foreign employees in the Spanish labour market. Our starting point is the micro data from the EES, Wage Structure Survey, in 2002 and the data from the EPA (IV Quarter 2002, I Quarter 2008). After making a first characterization of the immigrant population in Spain, we ascertained that we can divide the population into two groups: 1) coming from the EU-15 and North America, with higher wages than Spanish employees, and 2) coming from the other areas of the world (Africa Asia and rest of America), with lower wages than the Spanish. Given this empirical evidence, we expect to find out the origin of these wage differences.We are estimating a wage equation for this that connects the logarithm of wage earned by the employee with another group of variables that we have at our disposal and that, according to one or other theories, determine the wage level, with the goal of identifying which characteristics are statistically significant in this relation and which is their impact in the wage level. In spite of what we could have expected, the variable citizenship contributes almost nothing as an explanatory variable of the model and, for some groups, has a low significance. In all the cases except for the EU-15 countries it means a low wage differential. Therefore it suggests that there should be no wage discrimination as far as the citizenship goes. Nevertheless, a deeper analysis allows us to appreciate that the foreign labour force is much less valued for its human capital characteristics and others.


The application of the decomposition of Oaxaca-Blinder confirms the previous results. According to this analysis, approximately 40% of the salary differences between native and immigrants are caused by discriminatory behaviors against these last ones and their characteristics are half valued than the one of the natives. The analysis by quartiles emphasizes this conclusion. We observe that in the first quartile, the salary differential is too small (0.81%), even if exists salary discrimination, this is irrelevant. Finally, we observe segregating behaviors of the immigrants in this quartile.

Finally, we must refer again the limitations of the micro data employed in the study for the analysis of the immigration phenomenon. The data base presents an incomplete sector cover and just includes immigrants that present a legal working contract with firms with 10 or more employees. Consequently, an important part of the foreigner employees are not represented: the ones working in small firms, in domestic jobs, in agriculture, etc. or that ones who are not legal. The inequality factors which we identified would probably have an negative impact in the salary of the greatest part of these employees of the present study.

Another important limitation which we must refer is that the analysis was done by taking as reference the data of the EES for the year 2002, and as we told, the migratory phenomenon is getting more and more important.


6. BBIBLIOGRAPHYIBLIOGRAPHY.

ABELLÁN-COLODRÓN, C. “La ganancia salarial esperada como determinante de la decisión de emigrar”. Investigaciones Económicas, January 1998, v.22, iss. 1, pp. 93 -117. Universidad de Oviedo.

AHN, N., DE LA RICA, S., UGIDOS, A. “Willigness to Move for Work and Unemployment Duration in Spain”. Economica, August 1999, v.66, iss. 263, pp. 335-57. CEMFI; Universidad País Vasco and Instituto de Economía Pública.

AKERLOF, G. (1981). “Labor Contracts as Partial Gift Exchange”. Quartely Journal of Economics, vol. 97, pp. 543-569. Reimpreso en Akerlof, G. Yellen, J. (1986).

ANDRÉS, J., GARCÍA, J. (1991). “Una interpretación de las diferencias salariales entre sectores”. Investigaciones económicas. Vol. 15, nº1, pp. 143-67.

ALAEZ-ALLER, R., ULLIBARRI-ARCE, M. “Discriminación salarial por sexo: un análisis del sector privado y sus diferencias regionales


en España”. Información Comercial Española Revista de Economía, December 2000-January 2001, iss. 789, pp. 117-38. Universidad Pública de Navarra.

ALCALÁ, F; HERNÁNDEZ, P.J. “Estructura empresarial, educación y salarios: la dinámica reciente”. Cuadernos Económicos de I.C.E. December 2007, iss. 74, pp. 73-100. Universidad de Murcia.

AMUEDO-DORANTES, C, DE LA RICA, S. (2005) “Immigrants’, Responsiveness to Labor Market Conditions and its Implications on Regional Disparities: Evidence from Spain”, IZA Discussion Paper 1557.

BECKER, G.S. (1964). Human Capital. Columbia University Press, New York.

BORJAS, J. (1994). “The Economics of Immigration”. Journal of Economic Literature, 32 (4), pp. 1667-1717.

BORJAS, J. (1999). “The Economic Analysis of Immigration” en Ashenfelter y Card (eds.) Handbook of Labor Economics, vol. 3A, ed. North-Holland.

BORJAS, J. (2006). “Native Internal Migrationand the Labor Market Impact of Immigration” Journal of Human Resources, 41 (2), pp.221-258.


BORJAS, J., KATZ, L. (2005). “The Evolution of the Mexican-Born Workforce in the United States”. NBER Working paper 11281, April 2005.

CARRASCO, R., JIMENO, J.F., ORTEGA, C. (2006). “The effect of immigration on the labor market performance of native-born employees: Some evidence for Spain”. Journal of Population Economics. (Publicado en ensayos sobre los efectos de la inmigración en España, Dolado, J. y Vázquez, P. (eds.), FEDEA).

CHISWICK, R.B. (1978). “The effect of Americanization on the Earnings of Foreign-born Men”, Journal of Political Economy, 86 (5), pp-81-87.

CONSEJO ECONÓMICO Y SOCIAL. (Primer trimestre 2006). “Panorama sociolaboral de la mujer en España”. Número 43. Boletín elaborado por el área de estudios y análisis.

DE CABO, G. y GARZON, M. (2007): Estudio de la discriminación salarial por razón de sexo, Fundación Tomillo – Instituto de la Mujer, Madrid.

DE LA RICA, S; DOLADO, J; LLORENS, V. “¿Techos o suelos? Diferenciales salariales por género y educación en España”. Cuadernos Económicos de I.C.E., December 2007, iss. 74, pp. 39-71. Universidad País Vasco and IZA; Universidad Carlos III and IZA; LECG Consulting Spain S.L.


DE LA RICA, S; UGIDOS, A. “¿Son las diferencias en capital humano determinantes de las diferencias salariales observadas entre hombres y mujeres?” Investigaciones Económicas, September 1995, v. 19, iss. 3, pp. 393-414. Universidad País Vasco.

DEL RÍO OTERO, C; GRADÍN LAGO, C; CANTÓ SÁNCHEZ, O. “El enfoque distributivo en el análisis de la discriminación salarial por razón de género”. Junio 2004. Universidad de Vigo. Working Paper.

DEUTSCH, J; SILBER, J. “Earnings Functions and the Measurement of the Determinants of Wage Dispersion: Extending the Blinder-Oaxaca's Approach”. August 2007.

DOERINGER, P.B., PIORE, M.J. (1971). Internal labor markets and manpower analysis. Lexington: Health Lexington Books. Traducción española: Mercados internos de trabajo y análisis laboral. Madrid: Ministerio de Trabajo y Seguridad Social, 1985.

DOLADO, J.J., BENTOLILA, S. (1992). “Who are the insiders? Wage setting in spanish manufacturing firms”. Banco de España, Working paper nº 9229.

DUNLOP, J.T. (1944). Wage Determination under Trade Union. Traducción española del capítulo 3 en Alba, A. (comp.) (1992).

FERNÁNDEZ, M., MONTUENGA, V. (1997). “Salario y productividad sectorial: ¿existe evidencia de un comportamiento dual?”. Cuadernos económicos del ICE, nº 63.


GARCÍA, J., HERNÁNDEZ, P., LÓPEZ, A. (2001). “How wide is the gap? An investigation of gender wage differences using quantile regression”. Empirical Economics, (26), pp. 149-168.

GARCÍA-CRESPO, DOLORES. Promotions in the Spanish Labor Market: Differences by Gender. Oxford Bulletin of Economics and Statistics, December 2001, v.63, iss.5, pp. 599-615. Universidad de Málaga.

GARCÍA-PÉREZ, J.I; JIMENO, J.F. Public Sector Wage Gaps in Spanish Regions. Manchester School, 2007, v.75, iss. 4, pp. 501-31. Universidad Pablo de Olavide, Fcea and FEDEA; Bank of Spain and IZA.

GONZÀLEZ-CALVET, MIR, J. GIL, P. (2002). La desigualtat salarial a Catalunya (1995-2000). Barcelona: Fundació Jaume Bofill.

GONZÀLEZ I CALVET, J. (2006) “Diferències salarials i immigració a Catalunya”. Barcelona.

GROSHEN, E. (1991). “Five reasons why wages vary among employers”. Industrial relations, 30, 3, pp.350-381.

JAUMANDREU, J. (1994). “Diferencias de coste laboral: razones y evidencia para la industria española”. Revista de Economía y Sociología del Trabajo. 25-26, pp.122-131.


J.SIMÓN, H; RAMOS, R; SANROMÁ, E. “Segregación laboral y estructuras salariales de nativos e inmigrantes en España. Un análisis con datos emparejados empresa-trabajador”. WP-EC 2007-03.

JOVANOVIC, B. (1979). “ Job Matching and the Theory of Turnover”. Journal of Political Economy, part 1, vol.87, nº5, pp. 972-990.

KEYNES, J.M. (1936). The General Theory of Employment, Interest and Money. London: MacMillan. Traducción española: La teoría general del empleo, el interés y el dinero. Ediciones Aosta. Madrid, 1998.

LANCASTER, K.J. (1966). “A new approach to consumer theory”, Journal of Political Economy, 74, pp. 132-157.

LINDBECK,S., SNOWER, D. (1986). “Wage setting, Unemployment and Insider-Outsider Relations”. American Economic Review, vol. 76, nº2, pp.235-239.

MADARIAGA SÁNCHEZ, R. (2007). Diferencias salariales en España: datos, teoría y contrastes empíricos. Tesis doctoral. Universitat de Barcelona.

MCDONALD, I.M., SOLOW, R. (1981). “Wage Bargaining and Employment”. American Economic Review, 71, pp.896-908. Traducción española en Alba, a. (comp.) (1992).


MCLINLEY,L., BLACKBURN. (2005). “Estimating wage differentials without logarithms”. Labour Economics. 14, pp. 73-98.

MEIXIDE, A. (1988). El mercado de trabajo y la estructura salarial. Madrid, Centro de Publicaciones Ministerio de Trabajo y Seguridad Social.

MINCER, J. (1974). Schooling, experience and earnings. Columbia University, New York.

MORENO, G., RODRÍGUEZ, J.M., VERA, J. (1996) “La participación laboral femenina y la discriminación salarial en España (1990-1991)”. CES, colección de estudios número 29.

PÉREZ, S., HIDALGO, A. (2000). Los salarios en España. Madrid: Fundación Argentaria-Visor.

PISSARIDES, C.A. (1979). “Job Matchings with State Employment Agencies and Random Search”, Economic Journal, vol. 89, nº356, pp.818-833.

PISSARIDES, C.A. (1985). “Job Search and the Functioning of Labour Markets”, Labour Economics, 1985, pp.159-85, Surveys in Economics series London and New York: Longman.


PISSARIDES, C.A. (1989). “The Search Equilibrium Approach to Fluctuations in Employment”, American Economic Review, 1985, vol.78, nº2, pp. 363-368.

PERI, G; SPARBER, C. “Task Specialization, Comparative Advantages, and the Effects of Immigration on Wages”. NBER Working Paper No. 13389. September 2007.

PONS,J; PALUZIE,E; SILVESTRE, J;TIRADO, D. Testing the New Economic Geography: Migrations and Industrial Agglomerations in Spain. Journal of Regional Science, May 2007, v.47, iss.2, pp. 289-313. CAEPS and Universidad de Barcelona; Universidad de Zaragoza.

RECIO, A. (1988). Capitalismo y formas de contratación laboral. Madrid: Ministerio de Trabajo y Seguridad Social.

RECIO, A. (1997). Trabajo, personas, mercados. Barcelona, Icaria.

ROSEN, S. (1984). “Teoría de las diferencias igualadoras”, en Ashenfelter y Layard (comps.), Manual de economía del trabajo, vol.1, Madrid: Ministerio de Trabajo y Seguridad Social, 1991.

ROY, A. (1951). “Some thoughts on the Distributions of Earnings”. Oxford Economic Papers, vol. 3, pp.135-146.


SOLOW, R.M. (1991). El mercado de trabajo como institución social. Madrid: Alianza Editorial, 1992.

SPENCE, M. (1973). “Job Market Signalling”. Quarterly Journal of Economics, vol. 87, nº3, pp. 355-379.

TOHARIA, L. (comp.)(1999). El mercado de trabajo: Teorías y aplicaciones. Compilación e introducción de Luis Toharia. Madrid, Alianza Editorial.

7. AANNEXNNEX.

7.1. ANNEX 1.

EXPLANATORY NOTE FOR COMPUTING THE SALARY.

For computing the hour-salary, it can be used a measure of the hour-salary, computed from the data of the month of October or from the annual data given by the questionnary. What it is expected, is that


the differences between calculating the hour-salary in terms of all year or in terms just for the month of October are too short.

The calculus of salary per hour can be done either by following a methodology that the National Institute of Statistics uses or through the Annual Journey Agreement. Both methods derive from the concepts used in the Questionnaire of the EES.

Concepts of Journey used in the calculus.

1. The variable Annual Journey Agreement is the number of hours that an employee must work during a year. For each employee this journey can be different. By other part, if it is agreed 100 hours/year, this does not mean those hours will be done every week, but it may be distributed in a different manner in a year term. The extra hours are counted apart, and are not counted as taking part for the year as whole.

2. The variable Weekly Journey makes reference “to the number of working hours that each employee worked in a normal week of October of 2002” that is, if October is the low season and worked few hours, when taking the journey of October and multiplying by44,5 working weeks that existed in 2002, the annual journey would be underestimated. Now, if is high season and worked more hours, the annual journey would be overestimated.

3. The variable Extra Hours makes reference to the extra hours done in the month of October and, because it is extra, does not take part of the normal monthly journey of October, that is, are not part of the weekly journey.

Concept of Annual Gross Salary.


The Annual Gross Salary gathers the total payments effectively paid to a employee during the year 2002. It does not take into account the time that the individual was working and, thus, presents relevant differences due to the existence of individuals that didn’t work all year or did not work complete journey.

The calculus of the Equivalent Annual Salary and the Salary per hour through the method of NIS.

The calculus of the Equivalent Annual Salary through the method of NIS (SAE_INE) reveals some insufficiencies. It adjusts the Annual Gross Salary by the time effectively worked (number of months and days), without adjusting the journey.

For the individuals that worked all year, the SAE_INE coincide with the variable Annual Gross Salary of the sample.

Fort he individuals which did not work all year, the formula would be the following one:

SAE_INE = 12 * (Annual Gross Salary / number of worked months)

That is, 12 months are taken of 30 days as reference and computed the number of worked months.

SAE_INE = 12 *( Annual Gross Salary / (Number of worked months + Number of worked days / 30)

It is not usefull to take the annual salary for doing comparisons between employees and we must adjust the journey. This is the reason why NIS computes the salary per hour (SAL_HORA_INE) from the data of the month of october, manner which by the way is quite satisfactor but presents some limitation:


To calculate the worked hours, sum the normal hours and the extra ones.

The monthly profits of october are not corrected neither by the proportional part of extra payments (june and december) nor vcations. By other part, it could be some exception if to some individual of the sample, the liquidation was done in the month of october.

SAL_HORA_INE = total profits of te month of october/ worked hours in october.

Calculus of a more adjust salary per hour.

The salary of the month of October does not collcet the proportional part of the fix extra payments, reason why the salary per hour should be corrected according to this variable. The fix extra payments are expressed in annual terms in the proportion over the gross salary will be:

Proportion of extra payments = Extra Fixed Payments / Annual Gross Salary

Corrected Gross Salary of october by extra payments =

Total Profits October * (1 + Proportion of extra payments)

SAL_HORA_INE_AJUST = Corrected Gross Salary of october by extra payments / worked hours in october

Other approaches of the Salary per hour

Other approach of the salary per hour can be calculated through the equivalent annual salary of the sample and the agreed annual


journey, giving an approximation of the extra hours through the ones done in October.

Salary per hour according to the Agreed Annual Journey = SAE_HJAP = SAE_INE/ Agreed Annual Journey * (1+proportion of extra hours done in October).

The calculus employed in this last version of the salary per hour for believing this approximates best to the true value. This magnitude is approximately 14% greater to the estimation to the salary per hour done by the NIS. Nonetheless, their dispersion indicators are identical.

7.2. ANNEX 2.

Estimated coefficients of the salary ecuation

Non-Standardized coefficients

Standardized

coefficients

t of Stude

nt

Sig.

Salary bounty in %

Factor weight in %

B Error típ. Beta

Constant 1,376 0,022 - 61,168

0,000 - -

Age 0,005 0,000 0,111 352,885

0,000 0,538 2,806

Sex -0,178 0,000 -0,163

-571,5

79

0,000 -16,288 -4,128

Antiquity 0,022 0,000 0,388 430,393

0,000 2,221 9,830

Antiquity20,000 0,000 -0,191

-233,7

45

0,000 -0,036 -4,839

Management 0,746 0,001 0,197 681,276

0,000 110,789 4,989


Ocup.Univ.Sup. 0,599 0,001 0,277 662,904

0,000 81,950 7,009

Ocup.Univ.1er ciclo 0,471 0,001 0,200 495,4

070,000 60,217 5,074

Technician 0,303 0,001 0,201 538,282

0,000 35,432 5,102

Administrate employees 0,089 0,001 0,055 163,7

860,000 9,279 1,395

Cualif. Serv. 0,062 0,001 0,041 117,785

0,000 6,442 1,040

Cualif. Constr. 0,090 0,001 0,048 142,908

0,000 9,459 1,215

Cualif. Ind. 0,092 0,000 0,071 192,102

0,000 9,613 1,797

Primarios 0,035 0,022 0,029 1,553 0,120 3,550 0,739

Estudios sec. Sin tít. 0,024 0,023 0,004 1,068 0,2

86 2,433 0,109

EGB complete 0,046 0,022 0,039 2,066 0,039 4,752 0,995

Bachillerato 0,137 0,022 0,078 6,088 0,000 14,657 1,978

FP medio 0,102 0,022 0,051 4,536 0,000 10,730 1,283

FP superior 0,129 0,022 0,068 5,760 0,000 13,818 1,717

Diplomatura 0,199 0,022 0,107 8,834 0,000 21,962 2,708

Bachelor degree 0,257 0,022 0,150 11,42

10,000 29,264 3,811

Extractive 0,360 0,002 0,043 161,990

0,000 43,389 1,082

Food and beverage 0,119 0,001 0,042 109,9

790,000 12,638 1,064

Paper 0,148 0,001 0,044 126,382

0,000 15,968 1,112

Chemistry 0,314 0,001 0,077 240,522

0,000 36,887 1,943


Cork 0,222 0,001 0,046 153,431

0,000 24,829 1,162

Metal 0,225 0,001 0,080 200,932

0,000 25,196 2,027

Machinery 0,231 0,001 0,056 173,689

0,000 25,996 1,427

Electrical machinery 0,186 0,001 0,041 132,8

160,000 20,502 1,046

Transp. Mat. 0,269 0,001 0,083 225,483

0,000 30,905 2,110

Other industries 0,178 0,001 0,060 159,2

120,000 19,465 1,514

Electricity and wáter 0,423 0,002 0,068 240,7

660,000 52,615 1,734

Construction 0,232 0,001 0,149 230,195

0,000 26,097 3,781

Commerce 0,140 0,001 0,099 147,967

0,000 14,982 2,502

Restaurant and hotel business 0,116 0,001 0,051 107,7

450,000 12,271 1,289

Transportation and commerce 0,254 0,001 0,103 239,6

410,000 28,864 2,613

Finances 0,455 0,001 0,168 402,442

0,000 57,648 4,256

Real estate and services 0,091 0,001 0,059 94,41

20,000 9,482 1,490

Education 0,180 0,001 0,072 162,561

0,000 19,707 1,836

Health 0,116 0,001 0,062 111,528

0,000 12,277 1,581

Social activities 0,118 0,001 0,040 102,815

0,000 12,561 1,003

D20A49 0,052 0,000 0,042 131,379

0,000 5,350 1,074

D50A99 0,120 0,000 0,077 256,552

0,000 12,740 1,940

D100A199 0,166 0,001 0,093 321,705

0,000 18,091 2,369


DMES200 0,205 0,000 0,185 486,855

0,000 22,811 4,691

Responsability 0,122 0,000 0,100 350,560

0,000 13,014 2,526

Conv. Sector -0,001 0,000 -0,001 -2,944 0,0

03 -0,097 -0,022

Con. Empresa 0,069 0,000 0,053 174,707

0,000 7,159 1,339

Catalonia 0,102 0,000 0,077 295,481

0,000 10,775 1,940

País Vasco -Navarra 0,091 0,001 0,044 160,9

990,000 9,569 1,107

Madrid 0,059 0,000 0,042 157,203

0,000 6,094 1,056

Permanent contract 0,057 0,000 0,048 156,0

410,000 5,824 1,215

Part-time journey -

0,029 0,000 -0,017-

64,774

0,000 -2,850 -0,438

Non-Standardized coefficients

Standardized

coefficients

t of Stude

nt

Sig.

Salary bounty

in %

Factor weight in %

B Error típ.

Beta

Coefficients by nationalities

EU without Spain 0,094 0,001 0,015 63,03

20,000 9,892 0,390

Rest of Europe 0,000 0,002 0,000 0,012 0,991 0,002 0,000

North America 0,110 0,008 0,004 14,568

0,000 11,598 0,090

Rest of America -

0,050 0,001 -0,010-

41,638

0,000 -4,835 -0,260

Africa -0,021 0,001 -0,003

-13,90

9

0,000 -2,030 -0,087


Asia 0,013 0,003 0,001 4,042 0,000 1,345 0,025

Oceania -0,380 0,023 -0,004

-16,84

8

0,000 -31,596 -0,104

7.3. ANNEX 3.

Table 1. Coefficients of the estimation for the lower quartile.

Coefficients of the estimation for the lower quartile (ln salary<=25%)

Non- standardized Coefficients

Standardized

coefficients

t of Stude

nt

Sig. Salary bounty

in %

Weight of the factor in %


B Error típ. Beta

Constant 1,702 0,012 145,026

0,000

Age 0,001 0,000 0,058 68,097 0,000 0,093 75,096

Sex -0,029 0,000 -0,086 -

95,7130,00

0 -2,890 -111,244

Antiquity 0,008 0,000 0,190 100,114

0,000 0,762 246,614

Antiquity2 0,000 0,000 -0,101 -57,967

0,000 -0,019 -

130,857

Management -0,028 0,005 -0,004 -5,780 0,00

0 -2,726 -5,611

Ocup.Univ.Sup. -0,015 0,002 -0,008 -8,621 0,00

0 -1,464 -9,798

Ocup.Univ.1er ciclo 0,008 0,002 0,004 4,929 0,00

0 0,831 5,415

Technicians 0,021 0,001 0,027 31,273 0,000 2,121 35,592

Administrative employees 0,020 0,000 0,041 42,590 0,00

0 1,975 53,175

Cualif. Serv. 0,006 0,000 0,015 15,008 0,000 0,645 19,892

Cualif. Constr. 0,027 0,001 0,042 43,131 0,000 2,725 54,844

Cualif. Ind. 0,010 0,000 0,024 23,522 0,000 1,045 30,743

Primarios -0,069 0,012 -0,194 -5,887 0,00

0 -6,656 -251,340

Estudios sec. Sin tít.

-0,063 0,012 -0,032 -5,322 0,00

0 -6,080 -41,919

Bachillerato -0,072 0,012 -0,203 -6,156 0,00

0 -6,949 -264,053

EGB -0,050 0,012 -0,083 -4,296 0,00

0 -4,905 -107,579

FP medio -0,054 0,012 -0,081 -4,648 0,00

0 -5,297 -105,259


FP superior -0,061 0,012 -0,081 -5,167 0,00

0 -5,873 -104,815

Diplomatura -0,041 0,012 -0,040 -3,493 0,00

0 -4,014 -52,071

Bachelor degree

-0,036 0,012 -0,035 -3,043 0,00

2 -3,508 -45,746

Extractive 0,081 0,003 0,022 29,109 0,000 8,415 28,709

Food and beverage 0,003 0,001 0,004 4,084 0,00

0 0,349 5,317

Paper 0,035 0,001 0,031 34,193 0,000 3,573 40,273

Chemistry 0,096 0,002 0,038 49,443 0,000 10,083 49,822

Cork 0,064 0,002 0,031 38,598 0,000 6,563 39,645

Metal 0,066 0,001 0,050 56,102 0,000 6,838 65,431

Machinery 0,077 0,002 0,034 43,114 0,000 8,050 44,348

Electrical machinery 0,053 0,002 0,024 30,098 0,00

0 5,436 31,011

Transp. Mat. 0,067 0,002 0,027 35,048 0,000 6,905 35,642

Other industries 0,066 0,001 0,063 67,405 0,00

0 6,778 82,092

Electricity and water

-0,005 0,004 -0,001 -1,349 0,17

7 -0,528 -1,314

Construction 0,072 0,001 0,140 86,989 0,000 7,471 182,248

Commerce 0,018 0,001 0,045 25,536 0,000 1,839 58,746

Restaurant and hotel business 0,035 0,001 0,060 43,180 0,00

0 3,563 77,634

Transportation and commerce 0,037 0,001 0,035 37,151 0,00

0 3,721 45,956

Finances 0,060 0,003 0,015 19,267 0,000 6,229 19,093


Real estate and services 0,012 0,001 0,030 16,672 0,00

0 1,230 38,364

Education -0,022 0,001 -0,017 -

18,4320,00

0 -2,131 -22,014

Health -0,003 0,001 -0,004 -3,722 0,00

0 -0,334 -5,529

Social activities -0,043 0,001 -0,049 -

46,7460,00

0 -4,222 -63,384

D20A49 0,015 0,000 0,040 44,555 0,000 1,507 52,101

D50A99 0,023 0,000 0,044 52,502 0,000 2,292 57,777

D100A199 0,028 0,001 0,046 55,605 0,000 2,848 60,285

DMES200 0,033 0,000 0,078 80,248 0,000 3,315 100,896

Responsability 0,018 0,001 0,030 35,046 0,000 1,847 38,466

Conv. Sector -0,018 0,000 -0,049 -

59,5840,00

0 -1,749 -63,759

Con. Empresa -0,036 0,000 -0,065 -

80,8600,00

0 -3,494 -84,870

Catalonia 0,015 0,000 0,032 40,682 0,000 1,540 41,306

País Vasco -Navarra 0,010 0,001 0,009 11,090 0,00

0 1,042 11,842

Madrid -0,016 0,000 -0,033 -

40,9340,00

0 -1,540 -42,714

Permanent contract 0,020 0,000 0,058 64,701 0,00

0 2,026 75,857

Part-time journey -

0,051 0,000 -0,122-

147,987

0,000 -4,979 -

158,655

EU without Spain

-0,005 0,002 -0,003 -3,435 0,00

1 -0,526 -3,322

Rest of Europe -0,010 0,001 -0,005 -6,661 0,00

0 -0,986 -6,447

North America - 0,010 0,000 -0,255 0,79 -0,257 -0,246


0,003 8

Rest of America

-0,017 0,001 -0,016 -

20,5930,00

0 -1,671 -20,203

Africa -0,007 0,001 -0,004 -5,955 0,00

0 -0,681 -5,798

Asia 0,012 0,003 0,004 4,728 0,000 1,215 4,573

Oceania -0,005 0,023 0,000 -0,237 0,81

3 -0,545 -0,229

Table 2. Estimated coefficients of the estimation for the upper quartile.

Estimated coefficients of the estimation for the upper quartile (ln salary>=75%)

Non-standardized coefficients

Standardized

coefficients

t of Stude

ntSig.

Salary bounty

in %

Weight of

factor in %

B Error típ. Beta

Constant 2,509 0,004 - 685,387

0,000 - -

Age 0,007 0,000 0,185 202,501

0,000 0,654 9,892

Sex -0,100 0,001 -0,132

-178,48

8

0,000 -9,506 -7,048

Antiquity 0,005 0,000 0,156 60,537 0,000 0,481 8,307

Antiquity2 0,000 0,000 -0,121 -51,299

0,000 -0,011 -6,474

Management 0,434 0,002 0,328 254,032

0,000 54,397 17,506

Ocup.Univ.Sup. 0,279 0,002 0,318 165,014

0,000 32,158 16,937


Ocup.Univ.1er ciclo 0,144 0,002 0,150 82,239 0,00

0 15,536 8,019

Technician 0,134 0,001 0,173 91,529 0,000 14,388 9,239

Administrative employees 0,067 0,002 0,057 42,279 0,00

0 6,920 3,041

Cualif. Serv. 0,038 0,002 0,022 20,758 0,000 3,917 1,149

Cualif. Constr. 0,036 0,002 0,020 19,675 0,000 3,713 1,050

Cualif. Ind. 0,009 0,001 0,009 5,952 0,000 0,863 0,484

Primarios -0,156 0,001 -0,155

-144,53

1

0,000 -14,468 -8,258

Estudios sec. Sin tít.

-0,147 0,004 -0,023 -

35,2090,00

0 -13,706 -1,251

Bachillerato -0,144 0,001 -0,140

-139,25

3

0,000 -13,420 -7,447

EGB -0,096 0,001 -0,092

-100,54

0

0,000 -9,196 -4,917

FP medio -0,125 0,001 -0,085

-101,00

4

0,000 -11,707 -4,514

FP superior -0,111 0,001 -0,099

-108,27

9

0,000 -10,538 -5,302

“Diplomatura” -0,058 0,001 -0,069 -

64,2040,00

0 -5,624 -3,654

Extractive 0,220 0,004 0,044 51,025 0,000 24,634 2,366

Food and Beverage 0,092 0,003 0,045 29,416 0,00

0 9,616 2,384

Paper 0,099 0,003 0,044 31,453 0,000 10,456 2,359

Chemistry 0,136 0,003 0,072 44,529 0,000 14,550 3,832

Cork 0,084 0,003 0,031 25,024 0,00 8,709 1,632


0

Metal 0,034 0,003 0,020 11,143 0,000 3,489 1,051

Machinery 0,045 0,003 0,018 13,777 0,000 4,593 0,983

Electrical machinery 0,038 0,003 0,014 11,455 0,00

0 3,902 0,758

Transp. Mat. 0,028 0,003 0,018 9,076 0,000 2,790 0,985

Other industries 0,076 0,003 0,032 23,483 0,00

0 7,864 1,708

Electricity, wáter 0,218 0,003 0,091 66,656 0,00

0 24,337 4,833

Construction 0,095 0,003 0,069 31,217 0,000 9,921 3,670

Commerce 0,123 0,003 0,109 42,065 0,000 13,102 5,806

Restaurant and hotel business

-0,003 0,004 -0,001 -0,856 0,39

2 -0,304 -0,053

Transportation and commerce 0,101 0,003 0,076 33,886 0,00

0 10,666 4,028

Finances 0,165 0,003 0,162 56,873 0,000 17,986 8,614

Real estate and services 0,101 0,003 0,080 34,237 0,00

0 10,626 4,242

Education -0,005 0,003 -0,004 -1,627 0,10

4 -0,482 -0,237

Health 0,022 0,003 0,021 7,263 0,000 2,194 1,143

Social activities 0,050 0,003 0,025 15,800 0,000 5,077 1,350

D20A49 -0,010 0,001 -0,011 -

10,6220,00

0 -0,995 -0,575

D50A99 0,009 0,001 0,009 9,311 0,000 0,943 0,467

D100A199 0,020 0,001 0,017 18,742 0,000 2,026 0,920

DMES200 0,031 0,001 0,045 33,921 0,00 3,114 2,400


0

Responsability 0,075 0,000 0,110 154,006

0,000 7,744 5,850

Conv. Sector -0,011 0,001 -0,015 -

15,9300,00

0 -1,075 -0,816

Con. Empresa 0,010 0,001 0,014 15,091 0,000 1,015 0,762

Catalonia 0,038 0,001 0,045 63,127 0,000 3,829 2,420

País Vasco-Navarra

-0,026 0,001 -0,023 -

31,3660,00

0 -2,521 -1,214

Madrid 0,053 0,001 0,064 85,399 0,000 5,443 3,399

Permanent contract 0,006 0,001 0,005 6,813 0,00

0 0,617 0,281

Part-time journey 0,108 0,001 0,076 106,63

30,00

0 11,433 4,060

EU without Spain 0,147 0,002 0,043 65,136 0,00

0 15,872 2,275

Rest of Europe 0,004 0,008 0,000 0,461 0,645 0,353 0,016

North America 0,029 0,008 0,002 3,641 0,000 2,932 0,126

Rest of America 0,125 0,005 0,017 26,066 0,00

0 13,370 0,905

Africa -0,016 0,006 -0,002 -2,475 0,01

3 -1,591 -0,086

Asia 0,209 0,012 0,011 16,893 0,000 23,246 0,585

diferences on wages and inmigration in spain€¦ · web viewuniversity of barcelona diferences...

Documents