The coefficient of variation, also called the relative standard deviation, confirms that the dispersion of the respective wage is greatest at the individual level with 0. At the level of higher-education institutions and fields of study, there are relatively similar results, with values of 0. In Fig. The anonymised higher-education institutions are sorted in ascending order of the mean wage of their graduates. For some values, the confidence intervals are rather large due to small cluster sizes.
However, as these cases are unsystematically spread across the distribution, this does not change the overall pattern. Against the background of the two types of higher-education institutions in Germany, it is noteworthy that the differing wage levels do not simply represent institutional differences between universities and universities of applied sciences; in fact, the distributions of the two types are rather similar see also Fig.
Mean wages across higher-education institutions. There is also considerable wage variation depending on the field of study, as can be seen in the distribution of fields of study sorted by the mean wage of graduates Fig. On average, English and American studies pay the least, and human medicine pays the most.
Footnote 12 These results are consistent with the state of research that graduates of fields of study with high occupational specificity earn higher wages in the labour market than graduates of fields of study with low occupational specificity. In summary, there are notable wage variations along both dimensions. Additional analyses showed that the standard deviation of the individual wage attributable to differences between higher-education institutions is much smaller than the standard deviation attributable to differences within them.
This also holds true for wage differences by field of study, indicating once again a similar relevance of higher-education institutions and fields of study in wage differences among graduates. Mean wages across fields of study. However, higher-education institutions differ in the study programmes they offer and in the size of their graduation cohorts per field of study.
Consequently, fields of study differ in the higher-education institutions where they are available. Therefore, the question arises of how the two dimensions are related and whether the composition of fields of study accounts for the wage variation among higher-education institutions.
Figure 3 shows the dispersion in the form of coefficients of variation for field-of-study-specific mean wages related to the different higher-education institutions. Here, the extent of differences across fields of study can be seen when looking at specific higher-education institutions.
The institutions are sorted according to their specific value of this variation. Only higher-education institutions with at least two fields of study are presented, and again, only the mean wages of fields of study with at least five observations are calculated. This explains the considerable reduction in the number of higher-education institutions in this figure.
For the presented institutions, three important aspects can be noted. First, and not surprisingly, the strong differences across fields of study are present even within higher-education institutions.
Second, the wage dispersion across fields of study is not the same for each institution. This might be explained by the number and kinds of fields of study offered at each institution or represented in our data. Third, there is again no clear pattern of differences between universities and universities of applied sciences. Coefficient of variation of mean wages across fields of study, by higher-education institutions. Only fields of study with at least five observations are used for calculating the mean, and only institutions with more than two fields of study are displayed.
And again, the variation across higher-education institutions is not at all homogeneous across fields of study. In general, the results indicate that even if the field of study is held constant, there are still wage differences among graduates of different higher-education institutions.
Comparing both figures, differences by field of study within institutions seems to be more relevant than differences by higher-education institutions within fields of study. This is in line with our expectations because subject-oriented vocationalism is a well-known characteristic of the German labour market. Coefficient of variation of mean wages across higher-education institutions, by field of study. Only institutions with at least five observations are used for calculating the mean, and only fields of study at more than two institutions are displayed.
So far, two horizontal dimensions of the higher-education system have been the focus. However, wage-generation processes take place in the labour market, which is itself heterogeneous.
This result emphasises regional wage differences not only between East and West but also on a less aggregated level. Therefore, it is not only important where students study and which subject, but also where they work as graduates. In this respect, mobility behaviour in the labour market has already gained considerable attention see Ganesch et al.
In large part, they are graduates who never left the federal state where they achieved their higher-education entrance certificate and, to a lesser extent, graduates who moved to begin their studies and stayed. These differences in mobility behaviour can be explained partly by regional labour-market conditions.
Therefore, it may also be related to the mean wage of graduates from specific higher-education institutions. This possibility is not covered by controlling for the place of study or mobility, but only by considering the workplace in any form.
Of course, these analyses of mobility patterns are not sufficient to make causal claims. However, they support the argument of the relevance of regional labour markets in wage variations among graduates of different higher-education institutions. Regional differences. The descriptive results indicate that there is considerable wage variation by higher-education institution as well as variation by field of study.
However, when studying both dimensions and looking at wage variations by higher-education institutions within a field of study, the variation becomes significantly smaller. Thus, wage differences between higher-education institutions can be attributed in great part, but not completely, to the fact that they offer different fields of study or offer them in different compositions.
Simultaneously, regional wage differences seem to be related to wage variation by institution as the mobility behaviour of graduates varies across institutions and is known to be dependent on regional labour-market conditions.
So far, we have neglected this selection problem, and the dimensions were not considered simultaneously. Now we draw on crossed random-effects models that include all these aspects. The estimation results are shown in Table 3. When including individual-level characteristics to consider selection in higher-education institutions M1 as well as the combined indicator of type of degree and higher-education institution M2 , the standard deviations at both the aggregate and individual levels decreases slightly.
This is a surprisingly small reduction, given that selection in higher education institutions is considered particularly important in international research.
However, it supports the expectation that selection is not as important for wage differences among higher-education institutions in Germany.
Footnote 15 Despite the fact that our models have not been specified for measuring the effects of the following variables, the estimated coefficients are mostly in line with known findings. The coefficients for grade of higher-education entrance certificate, gender and age point in the typical direction and are statistically significant at conventional levels, while parental education is not.
Regarding the types of degrees offered at different types of institutions, the results show no significant differences between universities and universities of applied sciences for graduates with traditional degrees and even significantly lower wages for state examination degrees.
Footnote 16 For bachelor graduates, the results are consistent with the current state of the research, showing significantly lower wages in general and even more so for graduates of universities. However, it needs to be acknowledged that a selection-on-observables approach was taken, and although the most evident individual-level determinants were included in the model, it is possible that not all relevant aspects were considered.
We might therefore underestimate the relevance of selection, so the results should not be interpreted in a causal way. In the next model M3 , the aggregate level is extended by the field of study as a factor cross-classified with higher-education institution.
The standard deviation for the level of higher-education institutions shows a significant decrease to 0. This is a clearly smaller value than the 0. Hence, there are two important things to note. First, wage differences among graduates of specific higher-education institutions are promoted by differences in the available fields of study, but they are not completely accounted for by these. This is consistent with the descriptive results above. Second, a comparison of the dimensions indicates that, in Germany, what someone studies is much more important for wage variation than where they study.
Given the strong links between fields of study and occupations in the labour market, this was expected. In the final set of models, the dimension of the labour-market region was added as the final piece of the puzzle. It was operationalised in the form of the regional level of monthly earnings as fixed effect M4a and as a further crossed random factor using the categorical identifier M4b.
Thus, while the relevance of the field of study is relatively unaffected by the regional labour market, the wage level of the location where graduates enter the labour market for their first job seems to contribute to wage differences among graduates of higher-education institutions, but not to wage differences among graduates of different fields of study. In the model with the labour-market region as a third crossed random factor M4b , the wage intercepts of the administrative regions are based on the wage information collected from the graduates in the sample.
Due to the proximity to the dependent variable and the fact that not only the regional wage level, but also all other regional aspects are represented by the administrative region dummies, a strong decrease of wage variation at the level of higher-education institutions could be expected when compared to the model with two additive crossed random factors and the external wage indicator M4a. However, in both models, the reduction pattern looks rather similar.
To sum up, wage variations among graduates depending on their alma mater are considerable. The reduction of variance between the models with different indicators shows that this variation can be associated to a small extent with selection based on individual-level characteristics, to a greater extent with regional labour-market differences and, most of all, with differences between fields of study. Against the backdrop of wage heterogeneity between not only graduates of different educational levels but also within the group of higher-education graduates, the focus of this study was on the labour-market relevance of the specific higher-education institution in Germany.
We described wage variations among graduates of different alma maters and examined to what extent this wage heterogeneity can be associated with differences in represented fields of study and in the regional conditions under which graduates enter the labour market for their first job, while also considering selection processes.
It has been argued that, other than possible mechanisms based on institutional characteristics, the field-of-study composition can be associated with wage variation between higher-education institutions. Moreover, the mobility behaviour of German graduates is characterised by great proportions entering the labour market in the region where their alma mater is located. As there are wage differences not only between East and West Germany but also among less aggregated spatial units, such as administrative regions, wage variations from higher-education institutions can be associated with regional wage levels.
For the empirical analyses, cross-classified random-effects models were applied to data on the cohort from the DZHW Graduate Panel. Results indicated that there is considerable wage variation attributable to higher-education institutions, which is especially related to the fields of study offered and the labour-market region in which graduates take up their first job; only to a lesser extent is the variation due to selection based on individual-level characteristics. Beyond the dimensions considered in these analyses, the residual level of wage variation associated with higher-education institutions is miniscule.
However, there are also some limitations regarding data, operationalisation and the methodological approach that should be mentioned. First, the data are quite unbalanced, with many cells consisting of only a few observations. On the one hand, this situation results from the fact that not every field of study is offered at every higher-education institution and the size of a field of study varies considerably.
On the other hand, a reason can be found in the sampling design of the Graduate Panel and varying response rates. Especially when considering higher-education institutions, fields of study and labour-market regions simultaneously, the number of observations in the respective cells drops significantly. In fact, crossed random-effects models can deal with unbalanced data, but their power decreases with many small cells.
Therefore, a more balanced and larger dataset containing the relevant information in comparable detail would be advantageous. Unfortunately, such data are, to our knowledge, not yet available in Germany. Second, only the higher-education institution of graduation has been considered, but not whether graduates studied at other institutions in prior years.
Because the aim of the study was not to prove causal mechanisms, such as human capital arguments, this is of minor importance. Otherwise, the time spent at each institution would have been an obvious aspect to consider. Third, there are several issues regarding the operationalisation of the regional labour market. It is questionable whether the studied administrative regions are the best representation of regional labour markets, because the latter do not necessarily follow administrative borders.
Especially if wage differences are of interest, administrative regions can be too broad, and more narrow regional classifications such as planning regions, labour-market regions or even counties can be more suitable see, for example, Ganesch et al. Hence, a comparison of different spatial operationalisations is an interesting task for further research.
An additional aspect that needs to be considered when adding external spatial data is the temporal dimension. Spatial classification codes and indicators tend to change over time, and they should match with the points of time represented in the data.
In our case, the graduates entered the labour market between and Yet the regional indicator we used was available only from on, so there was a small delay. This indicates that we have used a sufficient proxy for the labour-market situation despite the time lag and the economic crisis.
Footnote 18 Fourth, following our argument regarding potential endogeneity problems, an external indicator might be useful not only for the regional wage level but also for field-of-study-specific wages. Given an adequate database, this might be another option for further research. Finally, we used a selection-on-observables approach to address selection into higher-education institutions.
Although the most prominent aspects have been included, relevant aspects might still be missing. University of California. Johanna Posch. Analysis Group. Select Format Select format.
Permissions Icon Permissions. Abstract Italy and Germany have similar geographical differences in firm productivity—with the North more productive than the South in Italy and the West more productive than the East in Germany—but have adopted different models of wage bargaining.
Issue Section:. You do not currently have access to this article. Download all slides. Sign in Don't already have an Oxford Academic account? You could not be signed in. Sign In Forgot password? Don't have an account? A highly paid worker in this occupation might coordinate all of the advertising for a large, multinational corporation, and another worker might make much less overseeing the classified ads department of a local newspaper. Additional factors, such as industry of employment, education level, and job performance might also contribute to differences in pay.
Generally, the more technical an industry is, the better paid its managers are. Varying education levels also contribute to big wage differences for these workers. For example, some jobs that typically require a masters or doctoral degree at the entry level may be open to natural sciences managers who have advanced education.
These jobs are likely to pay more than other jobs for natural sciences managers that require a bachelor's degree or less education. And sales managers can boost their wages through bonuses or commissions made by meeting performance goals.
Many sales and financial workers are paid a commission, usually after selling a specific amount of goods or services. A commission may be earned in addition to or instead of a salary and can lead to big differences in pay among workers in the same occupation. Real estate brokers , for example, earn most of their income from commissions on property sales—usually a percentage of those sales—so those who sell expensive properties or have higher sales volumes are likely to earn more than those who don't.
See table 4. In addition to performance-based pay, factors such as experience, industry of employment, and education level may also play a role in large wage differences for sales, business, and financial occupations. For example, management analysts who have several years of experience often command high pay as they take on additional responsibilities, such as overseeing teams of other analysts. The clientele that an industry serves may also influence wage potential for workers in an occupation.
And the diverse education levels of supervisors might lead to variations in their pay. For example, those who supervise wholesale and manufacturers' sales representatives of technical and scientific products may be more likely to have a bachelor's degree or higher-and may earn more-than those who supervise telemarketers.
Varying education levels is among the reasons for big wage differences in certain science , math , and engineering occupations. Jobs requiring more advanced education are more likely to have higher pay. Some geoscientists , for example, have a bachelor's degree, and others have a master's or doctoral degree. Credentials, experience, and industry of employment might influence wages as well.
Actuaries , for example, must pass a series of exams over several years to become fully qualified. When they first start out, they usually work as trainees and have lower wages than experienced actuaries. Gradually, trainees receive higher salaries as they gain credentials. Even highly educated workers in these occupations might make less if they are in entry-level positions. For example, biochemists and biophysicists who have a Ph. And in some occupations, wages vary by industry.
Wages for occupations related to law , teaching , and air transportation vary widely due to a number of factors. Job tasks for these workers vary by levels of authority, from handling simple infractions or disputes to presiding over complex legal cases on appeal, which may contribute to wage differences. Additional factors affecting wages for the occupations in the table include worker qualifications, industry of employment, and job location.
Ninety other countries have at least two and up to several dozen minimum wages, which can vary by region, industry, occupation, experience or a combination of those factors.
For instance, Panama, a country of about 4. Within each sector, minimums vary by region there are two and by type of business within each region. Public-sector workers, interestingly, are exempt from minimum wage rules , and generally earn less than their private-sector counterparts. One thing rapidly became clear from our review: No two countries have exactly the same minimum wage systems, and many have combinations of features that defy easy categorization.
A few examples:. On the other hand, 21 countries have no generally applicable minimum wage at all, though some have set minimums for specific occupations or industries. In five countries that have national minimum wage systems, we could find no reliable information on how they function. Finally, there were three countries North Korea, Syria and Vatican City for which we could find no reliable, current information on their minimum wage systems, or indeed if any even exist.
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