The Exogenous Variables of the Model

5.3.1. Origin of birth

For the purpose of this work, another important indicator for the robustness of the EU-SILC is the share of immigrants in the dataset. Contrary to the income data of EU-SILC, which can be compared with data of the same year, the available census data is only on-hand for the year 2001 or 2007. Hence, there could be a bias between the collected data and the real figures but we presume this error to be negligible.

Statistik Austria provides following information for the year 2007: 7,062,641 Aus- trians (85.1 percent), 240,217 from the EU15 (2.89 percent), 179,800 from the EU10 (2.17 percent), 375,191 Yugoslavs (4.52 percent) and 154,705 Turks (1.86 per- cent). The weighted EU-SILC dataset contains 5,798,354 Austrians (86.36 percent), 161,825 persons from the EU15 (2.41 percent), 148,562 from the EU10 (2.21 per- cent), 346,865 Yugoslavs (5.16 percent) and 107,203 Turks (1.60 percent)33_{. Hence}

the shares of the different nationalities in the EU-SILC seem to be more or less representative for the Austrian population, neglecting the slight differences due to an over-representation of Austrians.

5.3.2. Education level and labor market experience

A central statement of the human capital theory is that wage payments may depend on individual education level which is supposed to have a positive impact on salaries. Therefore we introduce a variable for testing if education has unequal influences on wages for Austrians and foreigners. Moreover, human capital theory implies that labor market experience has a positive relation with wages. In a comment on Jacob Mincer, Rosenzweig/Morgan (1976) state, that the sole use of age and age squared rather than work experience and experience squared in the structural equation creates a differential bias in the estimated returns to schooling. The authors argue, that ”age is not a good proxy for work experience since people of the same age who have spent a different number of years in school will also have different levels of labor force experience” (cp. Rosenzweig/Morgan 1976, p. 4). In this study, the extend of labor market experience will be measured with the number of years in

employment. It is assumed that working experience is rather a parabolic function than a linear one, which means that the extend of experience increases a lot at the beginning of the career and flattens with time. This will be discussed in Chapter 6. EU-SILC 2005 provides data for both variables, education and labor market ex- perience. On the one hand the highest educational level is recorded (p118000 ), on the other hand the number of years in employment is available (p033000 ). EU-SILC offers a detailed list of possible schooling careers, however the most interesting ques- tion is, if a degree in secondary school or university has unequal impact on wage of different nationality groups.

5.3.3. Other individual characteristics

Other individual characteristics that are introduced into the model are the sex of a person, a leading or important position in the company and a recent job change. The leading position in the enterprise is difficult to characterize, however in EU- SILC 2005 there was a question whether workers have to follow the participant’s orders (p020010 ) which is assumed to be a leading position. Moreover a variable that records a job change in the past twelve months (p034000 ) is available in the questionnaire. To reveal which influence this variable should have on wages, a simple statistics on the most affected layers of workers is offered in Table 5.3.

Table 5.3.: Job change and workers’ characteristics

Skilled Unskilled Leading Position No Lead. Pos. Total

Absolute Numbers 105 235 80 260 340

In Percent 30.88 69.12 23.53 76.47 100

A leading position in an enterprise means to be in charge of a number of employees, to be skilled means at least a graduation in secondary school (Austrian Matura).

(Source: EU-SILC 2005, own calculations)

Obviously most job changes are taking place among low-skilled workers who are not in a leading position within their company. 46.7% of the skilled workers say that the reason for the job change was an improvement of the employment, while this is true for 40.0% of the unskilled workers. This could be taken as a case where a job change means an improvement in wages. But whereas only 25.7% of the skilled workers were sacked directly by the employer or due to the end of reprieved employment contracts, some 34.0% of unskilled laborers lost their job this way and

had to change. In these cases a job change hardly is combined with an improvement in income. As mainly unskilled workers are affected by job changes we expect the regression coefficient to have a negative impact on wages. Of course a leading position should have a positive sign, being female should have a negative influence on wages.

5.3.4. Structural characteristics

However, the wage level is not only influenced by individual characteristics but also by structural attributes. Therefore a dummy for population agglomeration is introduced. Agglomeration is a contiguous set of local areas, each of which has a density superior to 500 inhabitants per square kilometer, where the total population for the set is at least 50,000 inhabitants.

Table 5.4.: Summary Statistics of the Individual and Structural Char- acteristics

Austria EU15 EU10 Fm. Yugoslavia Turkey

Sex 0.5210 0.6042 0.6443 0.4944 0.4594 Secondary School 0.1567 0.2000 0.2938 0.0939 0.0540 University Degree 0.0785 0.2978 0.1494 0.0223 0.0202 Agglomeration 0.2557 0.4680 0.6134 0.5861 0.5608 Job Change 0.0315 0.0297 0.0154 0.0425 0.0608 Leading Position 0.1882 0.1957 0.1391 0.0917 0.1081 Firm Size 0.2048 0.1744 0.1804 0.2550 0.2635

Labor Market Experience 20.18 18.08 17.42 17.89 16.00

Skilled Job 0.2091 0.2042 0.1494 0.1252 0.0675

Parental Education 0.0680 0.2042 0.1752 0.0201 0.0202

Note: All variables are dummies, apart from labor market experience which is measured in years (Sources: EU-SILC 2005, own calculations)

To test on the effects of generational mobility the parental education level is included in the calculations. This dummy equals 1 if one parent owns a degree in secondary school (Matura) or higher. Another dummy surveys the required skill for the sector in which the participant is employed. High skilled sectors are supposed to be human capital intensive, low skilled industries are assumed to be manual labor intensive. A list with all branches can be found in Appendix A.5. Finally there is a dummy on firm size included, which equals 1 if more than 50 workers are employed

in the participant’s company. All these dummy variables are expected to have a positive influence on the wage levels.

The summary statistics for all the described variables and all nationalities are provided in Table 5.4. In the further calculations the group of foreigners will be sum up apart from the EU15-citizen.