Explainable and unexplainable wage gap between natives and immigrants with
equivalent education in the Norwegian labour market
Anna Latysheva
Thesis submitted for the degree of Master of Philosophy in Economics
30 credits
The Department of Economics The Faculty of Social Sciences
University of Oslo
May 2021
Acknowledgements
First of all, I want to thank my fantastic supervisor Ragnhild Camilla Schreiner for being available despite any life situations. Thank you for giving me the feedback that motivates and helps to understand the value of the thesis.
I also want to thank my dearest friends Maria and Charlotte, who corrected my grammar, challenged my vocabulary and helped me to formulate my thoughts in a proper way.
Last, but not least, I want to thank my partner Kristofer and my family for all the love and support during the whole program.
All the remaining mistakes are my own.
Abstract
The thesis’s purpose is to analyze and explain differences in wages between natives and immigrants conditioning on equivalent education. The raw gap at the range of 11.4% is high for non-western immigrants, but it is mainly explained by education, experience, occupation, firm and employment classification. The analysis finds evidence that there are some firms that pay generally better tend to not hire immigrants. Highly educated non-western immigrants experience the highest penalties. The research utilizes administrative data over the period 1995-2014. All the calculations are executed in R.
1 Introduction ... 2
2 Data, variables and descriptive statistics ... 5
2.1 Descriptive statistics ... 7
3 Empirical framework ... 11
3.1 The general regression model... 11
3.2 The general regression estimated on different samples sorted by education. ... 12
3.3 Estimating duration of disadvantage ... 13
3.4 Differences in wage growth across natives and immigrants among full-time workers 14 4 Results... 16
4.1 Results of the general regression model. ... 16
4.2 Impact of being an immigrant across education choices. ... 2
4.3 Importance of experience for natives and immigrants and wage increase. ... 7
4.4 Wage growth among full-time workers... 1
5 Discussion and conclusion ... 2
Bibliography... 4
Table of tables
Table 1 - Descriptive statistics: natives, western and non-western immigrants ... 7
Table 2 - Experience and wage distribution across the categories ... 8
Table 3– Regression results of the general model ... 1
Table 4– Wage gap across the educational categories. ... 3
Table 5 - Wage gap across the educational categories. (Estimated with firm fixed effects) .... 4
Table 6 - Wage gap during the experince. (Estimated with firm fixed effects) ... 1
Table 7- Wage gap during the experince. ... 1
Table 8- Wage growth differences during the experince. ... 2
Table of figures
Figure 1 - Experience fixed effects without firm fixed effects ... 7Figure 2- Experience fixed effects with firm fixed effects ... 7
1 Introduction
Norway is an attractive country for immigrants so, net immigration to Norway has been increasing since 1990. (Verner Holst Bloch, 2020) Per 9 March 2021, number of first- generation immigrants to Norway counted 800 094, which is an almost one fifth of Norwegian population. The most part of immigrants come from Asia and Europe. (SSB, 2021a) Norway is a country with relatively high wages, but does this also hold for immigrant workers? Do immigrants really take advantage of it?
Native-immigrant wage gap is a common issue on many labour markets. For instance, Kee explored Netherland’s labour market, in this particular study, only males were considered. He found an enormous wage gap between natives and different ethnic groups of immigrants. The gap was on the rate of 31-42% depending on ethnicity. The gap, which could not be explained by differences in characteristics varied a lot from 0.45 % to 11%, which was further stated to be discrimination. Employing Oaxaca decomposition, Kee finds that 6% and 11% of log wage difference between natives and Turks and Antilleans respectively is attributed to taste-based discrimination. Notwithstanding, he finds that Moroccans do not face discrimination at all and would earn 4 % more if they have had same characteristics as natives. The results are very different for different ethnicities, though the migration pattern and Netherlands’s migration policy are explanations for this. (Kee, 1995, p. 315)
A relatively new study from Austria finds raw native-immigrant wage gap of 15 log point.
Their sample included also second-generation immigrants. Decomposition of the gap revealed that a considerable part of the gap is due to human capital endowments and further controlling for occupation and job position reduces the gap to 3-5 log points. Authors conclude that their research is in line with evidence from other European countries. (Hofer, Titelbach, Winter- Ebmer & Ahammer, 2017, p. 119)
In this master thesis, I will consider whether it seems to be the case that long term immigrants with same personal characteristics earn less, more or equal to natives. Hardoy and Schøne (2011) find that returns to pre-immigration education are lower than domestic. That is why
this study focuses on the wage differences between natives and immigrants that acquired education in Norway.
The raw wage gap between natives and non-western immigrants is at the range of 11.4%, non-western immigrants earn less. On the contrary, the raw wage gap between natives and western immigrants is at 2.6%, western immigrants earn more than natives. However, these differences may be explained by some personal characteristics, like education, occupation, length of working experience, employment form – full-time or part-time employment. More precisely, I look at wage gap between western, non-western immigrants and natives within the same education, occupation, industry, experience, employment classification, age, gender, working during the same year in the same firm. In order to execute this analysis, further referred as the general regression model, a sequence of gradually augmented log-linear regressions with fixed effects are estimated.
The study utilizes rich administrative data. The thesis presents descriptive statistics prior to empirical analysis to show what kind of individuals the sample comprises of. The descriptive statistics leads to some important conclusions, like in which stage in life individuals are observed and whether they plan to stay in Norway over long period.
Further, this study is supplemented with more detailed analysis of the wage gap. Firstly, the analysis explores if the wage gap differs across the educational categories. The estimation revealed higher penalties among highly educated immigrants, especially non-western.
Secondly, the wage gap between natives and immigrants for different levels of experience are examined. Neither western nor non-western immigrants experience systematic penalties within a firm during the whole observed working experience. Non-western immigrants even have higher wage than natives during 2 first years of working experience. However,
estimation without firm fixed effects reveals a trace of a slight employer taste-based
discrimination. It seems like firms that pay generally high wage do not employ immigrants.
Lastly, the individual wage growth among full-time workers is analysed withing personal characteristics. The estimation did not uncover any reason to assume substantial difference in wage growth pattern. Methods used for supplementary analysis are based on the method used in general regression model.
Overall, differences in observable characteristics across natives and immigrants explain almost the entire wage gap. Any remaining differences might be explained by other factors, which can explain wage, like human capital characteristics, or it might be discrimination.
The low wage gap might be due to immigrants’ Norwegian education. The fact that the study considers only individuals, who completed some education in Norway makes the sample to a selected group compared to immigrants in general. Hardoy and Schøne (2011, p. 67) find that return to one year of education for non-western immigrants is 2.5% if education is acquired in the home country and 5.7% if education is obtained in Norway. The return to one year of education for natives is 6.8%. So, if individuals in the sample did not have Norwegian education, the gap would probably be larger.
The thesis is structured as follows: Chapter 2 explains how the sample was constructed and provides an overview of the data and employed variables; Chapter 3 explains the empirical framework used to estimate the wage gap between natives and immigrants; Chapter 4 reports, discusses and explains the results; Chapter 5 presents the conclusion and suggests areas for further research.
2 Data, variables and descriptive statistics
The data that was utilized in this thesis is based on several public registers managed by Statistics Norway. The process of analysis starts with comprehensive repeated cross-sectional data, which covers all years from 1995 to 2014. The employer-employee register includes all the work spells in Norway. Hence, absence from the data means that the individual was unemployed during that particular year. This thesis’s important contribution to the further analysis of this topic is that it reveals the problem from a slightly different point of view than in earlier research. Across the data analysis, the major restriction that was applied to data is ability to track education for all the individuals in the sample. The aim of this restriction is to assure the approximate equivalence of education since education is acquired in Norway.
In addition, a variable describing the number of years that individual has worked after last finished education, is calculated based on the existing data. The variable is further refereed as proxy variable for experience. Since the variable for experience is a proxy variable, there might be some disadvantages associated with exploiting it. For instance, experience acquired before the last graduation might be relevant for wage settlement after graduation.
As mentioned before, the data sample includes only individuals with education from Norway.
To reassure that education was obtained in Norway, study applies a variable which shows if an individual was studying during the year. The implementation of this variable gives a good approximation since, in order to be captured in the dataset, the individual must have been working in Norway. Unquestionably, this does not exclude the possibility that immigrants could have obtained higher education in their home country prior to immigration. In this case, they are overqualified. However, thesis’s aim is not to measure immigrants’ penalties due to overqualification, but to determine if immigrants with the same personal characteristics from Norway earn less, more or equal to natives.
Next, this study applies natural logarithm of annual salary as dependent variable. The annual salary is defined as the sum of all labour income during the year. If a worker had several work spells during the year, the total income is considered. Individuals that were self-employed are not included in the sample.
The key control variable is the proxy variable for work experience. Work experience is defined as a number of years that individuals have worked after last finished education.
Unfortunately, the proxy variable does not take into account the earlier experience, and it does not capture the relevance of previously acquired work experience. Even though the variable is not exact, it explains a significant share of log wage.
To answer the question of this thesis whether there is a native-immigrant wage gap, two main groups are considered: natives and first-generation immigrants. Further, immigrants are split into two mutually exclusive groups - western and non-western immigrants. There are different immigration rules for western and non-western immigrants, which leads to different
distribution of immigration reasons.
Non-western immigrants face strict requirements associated with immigration, while western immigrants can practically move and work in Norway without restrictions. As a result of immigrational policy, immigrants often behave differently on the labour market. That is why western and non-western immigrants are divided into categories and are analysed separately.
To determine the variable that indicates if immigrants are western or not, the definition from SSB was applied. The western immigrants include workers from EU / EEA countries, USA, Canada, Australia and New Zealand. (Høydahl, 2008, p. 69)
Additional control variables are utilized in the analysis: calendar year, occupation code based on the International Standard Classification of Occupations - ISCO-08, NACE code (industry classification), education code based on Classification of education (NUS), individual code for each firm, age and gender.
The data set utilizes both occupation and industry codes at the 2-digits level and education at 1 digit level. Occupation code is not available before 2003. Consequently, there are some observations with a missing occupation code. Excluding these observations would halve the analysed period. So, those were kept in the sample in order not to exclude individuals with longer experience. The age variable was constructed using the date of birth and variable for calendar year. A categorical variable for employment classification, which comprises of three types of employment was utilized. There is one category for full-time employment and two categories for part-time employment 11-53% and 53-80%.
2.1 Descriptive statistics
The final sample consists of totally 682 239 unique individuals. 8024 western immigrants, 22 911 non-western immigrants and consequently 651 304 of natives. The Table 1 reports detailed information about the workers in the sample. According to the table, non-western immigrants earn the least and western immigrants earn the most. Natives make a little less than western immigrants but more than non-western immigrants. Although, the standard deviation is high for all categories, which consequently means that there is a vast variation among yearly wages. The same applies to the experience. Wage reported in tables is normalized to 2014 using recorded inflation from SSB. (2021b)
Table 1 - Descriptive statistics: natives, western and non-western immigrants
Native Non-western
immigrant
Western immigrant
Wage (mean (SD)) 385863.70
(234421.64)
350870.71 (209216.03)
390550.81 (218181.72) Experience (mean (SD)) 6.53 (4.35) 5.51 (3.91) 5.72 (4.10)
Age (mean (SD)) 33.15 (9.29) 33.73 (9.13) 39.17 (11.12)
Female (mean) 0.54 % 0.53% 0.67%
Part/fulltime work (%)
11-53% 10.5% 15.5% 12.9%
53-80% 8.3% 9.6% 10.0%
more than 80% 81.2% 74.9% 77.1%
Occupation (%)
Armed forces and unspecified 16.8% 14.5% 18.8%
Elementary occupations 1.4% 3.6% 1.1%
Clerical support workers 3.9% 4.7% 4.1%
Craft and related trades workers 9.9% 6.9% 4.2%
Managers 5.2% 2.3% 4.6%
Plant and machine operators and
assemblers 4.0% 4.4% 1.6%
Professionals 14.5% 14.4% 23.6%
Service and sales workers 15.6% 25.9% 13.6%
Skilled agricultural, forestry and
fishery workers 0.3% 0.0% 0.1%
Technicians and associate
professionals 28.4% 23.4% 28.2%
Education (%)
Under high school 0.1% 3.9% 0.5%
High school 35.3% 41.5% 19.6%
Post-secondary non-tertiary 4.1% 3.3% 2.6%
Native Non-western immigrant
Western immigrant Undergraduate (Bachelor’s
degree) 44.3% 32.4% 51.1%
Graduate (Master’s degree) 15.3% 17.0% 21.8%
Postgraduate education 1.0% 1.9% 4.4%
Number of observations
n 4855541 126560 47825
Number of unique individuals 651304 22911 8024
The Table 2 reports more detailed information about experience and wage distribution across the categories. All three groups are likely distributed across the wage intervals with one exeption: western immigrant. This group is slightly overrepresented in the intervals indicating high wage.
Table 2 - Experience and wage distribution across the categories
Native Non-western immigrant Western immigrant
Wage (%)
Under 300 000 NOK 33.8% 40.4% 31.5%
300 000- 500 000 NOK 48.1% 45.1% 50.1%
500 000 -800 000 NOK 14.9% 12.2% 15.3%
800 000 -1 000 000 NOK 2.0% 1.4% 1.8%
Over 1 000 000 NOK 1.2% 0.8% 1.3%
Experience (%)
1-5 years 48.1% 58.0% 56.2%
6-10 years 31.8% 29.2% 29.2%
11-15 years 16.6% 11.2% 12.3%
16-19 years 3.5% 1.7% 2.4%
Number of observations
n 4855541 126560 47825
Number of unique individuals 651304 22911 8024
Difference in other variables can explaine the wage gap. Non-western immigrants are the youngest group, and western immigrant are the oldest group. However, the average
experience is almost equal for both groups. Low experience relatively to age among western immigrants may be explained by longer education or moving to Norway in older age.
Moreover, the fact that western immigrants have shorter work experience from Norway comparing with their age and relatively high wage might testify about some experience from home country.
Gender is evenly distributed among natives and non-western immigrants, however there is a considerable difference among western immigrants. There is a considerable smaller share of men among western immigrants. On average, women earn less than men, so a large share of women might have negative effect on average wage distribution. Even though, western immigrants are still in a group with highest labour income.
On the whole, the majority of employees work full-time. Immigrants are generally more inclined to work part-time, especially non-western immigrants. Regarding the occupation, technicians and associate professionals dominate in all three groups. Another occupation which is prevailing among all groups is armed forces and unspecified category. The reason for it are some observations from between 1995 and 2003. As mentioned earlier, the
occupation code is not available for this particular period, which means that occupation code is unspecified. Western immigrants are overrepresented among professionals, while non- western are mostly allocated to sales and service.
The former observation is in accordance with education distribution. 41.5% of non-western immigrants obtained education at the high school level. Non-western immigrants constitute the largest share of high school dropouts comparing with natives and western immigrants.
However, non-western group is relatively young. The explanation for low education might be the period when they are captured in the dataset. The large share of young people with low education might be a temporary situation caused by a pause from education and not a final decision not to study more. The mean age for natives and non-western immigrants is very similar. Correspondingly, the share of natives with lower education is approximately on the level with non-western immigrants.
Undergraduate is almost the most common education among all the groups. Generally, western immigrants seem to constitute the largest share among highly educated, for instance, among postgraduates, graduates and undergraduates. This remarkable coincidence provides evidence that we do not observe typical work immigrants. Norway is an equal country with a compressed wage system, which yields that the wage difference between high skilled and low skilled workers is much smaller than in unequal countries, for example, the USA. The most part of western counties are less equal than Norway as well. According to Roy model of migration, which describes how workers sort themselves among employment opportunities, immigrants to an equal country should be negatively selected. In other words, individuals who have low expected income in unequal or less equal source country because of low skills should be likely to migrate to an equal country like Norway. However, high skilled workers with high potential income are expected to choose less equal countries in order to take advantage of their proficiency. It is a positive selection. (Borjas, 2020, p. 280-282)
The data for the reason of immigration is almost entirely missing for western immigrants.
However, we can assume that the main reason for moving is not to work. Collating this argumentation with the fact that the average experience for western immigrants is long, almost on the par with natives, indicates that we observe long-term migrants.
There are two most common immigration reasons for non-western immigrants – family migration and asylum. Those who move to reunite with family do intend presumably to stay in Norway for a long time. Refugees move because living in their home country is dangerous, so they do not have a home to go back to. On the whole, the individuals observed in this thesis are apparently long-term migrants who came with the intention to stay in Norway. This makes the analysis even more meaningful and valuable. Possible penalties to observed
immigrants continue over a long period.
3 Empirical framework
In order to analyse the effects of being immigrant on one’s wage in Norwegian labour market, four analyses are executed. The method is based on the empirical framework further referred as general model. The analysis takes advantage of the data’s comprehensiveness and analyses are executed estimated on different but related samples.
3.1 The general regression model
The empirical strategy consists of estimating several regression equations. The purpose with the model is to start with a simple estimation to estimate a raw wage gap and then elaborate further by adding more control variables in order to explain the differences. The equations are constructed to be gradually complicating in order to enable following the dynamics of
coefficients’ change when more control variables are gradually included. Control variables are expected to explain wage differences between individuals and are correlated with immigration status.
In order to get a first impression of wage gap, a following simple linear regression was estimated:
log(𝑊𝑎𝑔𝑒𝑖𝑡) = 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 + 𝑛𝑜𝑛 − 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 + 𝜀𝑖𝑡 (1) Dependent variable is natural logarithm of wage, the independent variables of interest are 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 and 𝑛𝑜𝑛 − 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖. Two variables are added since western and non-western immigrants are expected to behave differently on labour market.
Those are indicator variables, that are active when a person is immigrant. These variables are designed to capture how much being an immigrant affects log wage. Therefore, the
coefficients can be interpreted as wage difference between natives and immigrants measured in percent.
As mentioned before, the initial equation is developed further by adding more control variables in order to explain wage variation within individuals. So, the final expression was constructed as following:
log(𝑊𝑎𝑔𝑒𝑖𝑡) = 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 + 𝑛𝑜𝑛 − 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 + 𝐹𝑒𝑚𝑎𝑙𝑒𝑖 + 𝐴𝑔𝑒𝑖𝑡+ 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒 𝐹𝐸 𝑖𝑡+ 𝑌𝑒𝑎𝑟 𝐹𝐸𝑡
+ 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝐹𝐸 𝑖𝑡+ 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 𝐹𝐸𝑖 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸𝑖𝑡+ 𝑂𝑐𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 𝐹𝐸𝑖𝑡+ 𝐹𝑖𝑟𝑚 𝐹𝐸𝑖𝑡 + 𝜀𝑖𝑡
(2)
The regression contains a dummy variable for gender, a linear variable for age and experience fixed effects. Fixed effects are preferred for adding experience as a linear regressor or by using Mincer regression. This decision was made in order not to condition on the form of relationship between wage and experience. The variable for experience is a calculated proxy variable, indicating how many years workers received wage after graduating. Calendar year fixed effects are also added, it helps to account for inflation and year specific incidence like, for instance, financial crisis or expansion of EU. By including occupation and industry fixed effects makes it possible to estimate the variation between individuals within the occupation and industry. The same applies for firm and education fixed effects. Including for example, education fixed effects yields calculating an intercept for every education category excluding the omitted one. The intercepts reflect the increase or decrease in wage, an optional category implies compared with the omitted one.
3.2 The general regression estimated on different samples sorted by education.
The drawback of estimating the general regression with education fixed effects is the
assumption that immigrant status affects wage in equal way across the educational categories.
Estimating the regression with education fixed effects implies calculating the wage increase connected with an education category compared with the omitted education category. The estimated intercepts – fixed effects are the same for both natives and immigrants. The purpose of this regression is to ascertain if the impact of being an immigrant is the same across the education categories. Therefore, all the individuals are divided into several groups according to their last finished education. The following equation of the general model was estimated for each of the education groups separately, both with and without firm fixed effects.
log(𝑊𝑎𝑔𝑒𝑖𝑡) = 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 + 𝑛𝑜𝑛 − 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 + 𝐹𝑒𝑚𝑎𝑙𝑒𝑖 + 𝐴𝑔𝑒𝑖𝑡+ 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒 𝐹𝐸 𝑖𝑡+ 𝑌𝑒𝑎𝑟 𝐹𝐸𝑡
+ 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝐹𝐸 𝑖𝑡+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸𝑖𝑡 + 𝑂𝑐𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 𝐹𝐸𝑖𝑡+ 𝐹𝑖𝑟𝑚 𝐹𝐸𝑖𝑡+ 𝜀𝑖𝑡
(3)
Based on estimates from this regression, the wage gap between natives and immigrants within education levels can be captured.
3.3 Estimating duration of disadvantage
The purpose of this analysis is to give an overview of the possible durability of this disadvantage. In order to capture this, the final equation of the general model is estimated separately for each group, natives, western and non-western immigrants. The experience fixed effects are the coefficients of interest here.
log(𝑊𝑎𝑔𝑒𝑖𝑡) = 𝐹𝑒𝑚𝑎𝑙𝑒𝑖 + 𝐴𝑔𝑒𝑖𝑡+ 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒 𝐹𝐸 𝑖𝑡 + 𝑌𝑒𝑎𝑟 𝐹𝐸𝑡 + 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝐹𝐸 𝑖𝑡+ 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 𝐹𝐸𝑖 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸𝑖𝑡 + 𝑂𝑐𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 𝐹𝐸𝑖𝑡 + 𝐹𝑖𝑟𝑚 𝐹𝐸𝑖𝑡+ 𝜀𝑖𝑡
(4)
It is essential to keep in mind that there are some crucial unobserved variables, and therefore not all effects are captured in the model. For instance, it is not possible to account for workers productivity. However, assuming that all the workers are perfect substitutes on the labour market, can discrimination be a case.
To measure the variation of disadvantage across experience, the final equation of the general regression is estimated on 19 samples sorted after year of experience. Experience fixed effects are excluded, since each sample consists of observations with same experience. The approach is similar to what was used in previous section.
log(𝑊𝑎𝑔𝑒𝑖𝑡) = 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 + 𝑛𝑜𝑛 − 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖+ 𝐹𝑒𝑚𝑎𝑙𝑒𝑖 + 𝐴𝑔𝑒𝑖𝑡+ 𝑌𝑒𝑎𝑟 𝐹𝐸𝑡+ 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝐹𝐸 𝑖𝑡
+ 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 𝐹𝐸𝑖+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸𝑖𝑡 + 𝑂𝑐𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 𝐹𝐸𝑖𝑡 + 𝐹𝑖𝑟𝑚 𝐹𝐸𝑖𝑡 + 𝜀𝑖𝑡
(5)
Wage gap across different working experiences is observed in these estimations. This allows to draw a conclusion about wages level and not only growth compared with first year of work.
Estimating this regression allows to evaluate if immigrants earn less than natives given experience.
3.4 Differences in wage growth across natives and immigrants among full-time workers
Wage growth is another factor of interest. This part of analysis attempts to distinguish if there is a so-called glass ceiling effect, which means migrants are less likely to be promoted than natives. Glass ceiling has been defined several times, some papers state that glass ceiling is lower probability to be promoted to manager positions. (Maume, 2004) Other state that glass- ceiling is limited access to highly paid occupations. (Dell’Aringa, Lucifora & Pagani, 2012) The data limitation of this study is the fact that occupation code reflects only promotions in the form of leadership position, but not all promotions involve leadership responsibilities.
That is why the study examines wage growth, which should be coherent with promotion.
Though, there are several ways to increase wage. Firstly, wage growth may be caused by career development. The worker negotiates a higher wage because he/she starts in a higher position or undertakes more responsibly in the current one. Secondly, an individual can increase the number of hours worked, which leads to increase in wage. By analysing the wage growth caused by increase of work hours, we observe the effect of personal choice rather than career development. The aim of this thesis is to define whether there is a wage gap between natives and immigrants with similar Norwegian education. Therefore, the wage increase that is caused by professional progress is more relevant for this study. Consequently, the
estimation can shed light on professional development opportunities for natives and immigrants.
The sample used to run this regression is different from the previous, here only full-time workers are included. The reason to exclude part-time workers is the inaccuracy of the variable for employment classification. As mentioned in the previous chapter, the variable is categorical and distinguishes between full-time workers, those who work 80% or more and two categories of part-time workers. The intervals for part-time workers are 11% - 53% and 53% - 80%. The intervals for part-time workers are fairly large and allow to double income,
fixed effects to isolate the effect of employment form is not correct, individuals can double their income within the interval. That is why people who work less than 80% are excluded.
The sequence of regressions is estimated in the same way as the general regression, starting with a simple one, including only binary variables for immigrants.
𝑊𝑎𝑔𝑒 𝑔𝑟𝑜𝑤𝑡ℎ 𝑖𝑡 = 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 + 𝑛𝑜𝑛 − 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 𝜀𝑖𝑡 (6)
Further, the regression estimated expands and looks like this:
𝑊𝑎𝑔𝑒 𝑔𝑟𝑜𝑤𝑡ℎ 𝑖𝑡 = 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖+ 𝑛𝑜𝑛 − 𝑤𝑒𝑠𝑡𝑒𝑟𝑛 𝑖𝑚𝑚𝑖𝑔𝑟𝑎𝑛𝑡𝑖 + 𝐹𝑒𝑚𝑎𝑙𝑒𝑖+ 𝐴𝑔𝑒𝑖𝑡 + 𝐸𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒 𝐹𝐸𝑖𝑡 + 𝑌𝑒𝑎𝑟 𝐹𝐸𝑡
+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸 𝑖𝑡+ 𝑂𝑐𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 𝐹𝐸𝑖𝑡+ 𝐹𝑖𝑟𝑚 𝐹𝐸𝑖𝑡 + 𝜀𝑖𝑡
(7)
The whole sequence of the regressions was estimated in the same way as the general regression model. Estimation of the augmented version of the equation allows observing differences in wage growth within the characteristics. In this section, the dependent variable is wage increase which is calculated individually and is defined as:
𝑊𝑎𝑔𝑒 𝑔𝑟𝑜𝑤𝑡ℎ𝑖𝑡 = 𝑊𝑎𝑔𝑒𝑖𝑡− 𝑊𝑎𝑔𝑒𝑖(𝑡−1) 𝑊𝑎𝑔𝑒𝑖(𝑡−1)
(8)
𝑊𝑎𝑔𝑒𝑖𝑡 is wage earned during the year of observation and 𝑊𝑎𝑔𝑒𝑖(𝑡−1) is wage from the previous year. Hence, the calculated value shows wage increase measured in percent of earlier wage. Coefficients to binary variables for immigrational status should reveal if wage increase among immigrants is lower than in the whole sample.
The control variables have almost the same meaning here as in the general regression model.
Adding year-fixed effects captures the year specific features, such as financial crisis, when comparably very few increased their labour income. The increase in wage most likely depends on experience. That is why experience fixed effects are included. Industry,
occupation, firm and education fixed effects allow comparing the wage increase within the characteristics.
4 Results
4.1 Results of the general regression model.
To define whether there is a wage gap between natives and immigrants, the general regression was estimated. The general regression is a log-linear regression with the natural logarithm of wage as the dependent variable. As mentioned in the previous chapter, the general regression model is not a single regression. It is a gradually expanding sequence of several regressions, the model starts with (1) and ends with (2).
The coefficients for binary variables for immigrants are the major interest of this research.
The intention was to follow these coefficients through the whole model while adding more control variables.
Below, Table 3 describes coefficient estimates for the general regression model, equations (1) and (2).
The model starts with simple regression, which includes only indicator variables for
immigrants (1). In this version, the model predicts non-western immigrants to earn 11% less than natives and western immigrants are predicted to earn 2.6% more than natives.
Controlling for gender had a minor but different impact on coefficients of interest – positive for western and negative for non-western immigrants. Adding a binary control variable for female allows estimating an average penalty of being a woman and insulate it from
coefficients for immigrational status. The coefficient for western immigrants has doubled.
This is obviously since the majority of western immigrants are women, which affected coefficient strongly before. The higher coefficient for western immigrants indicated that earnings of an average western immigrant is higher than a female with same immigration status. The coefficient for non-western immigrants became slightly lower, it might be caused by more equal earning between males and females. Estimation of equation (4), when the general regression model is estimated on natives, western and non-western immigrants separately supports this assumption. It reveals the evidence of more equal earnings among non-western immigrants.
Table 3– Regression results of the general model
Dependent variable:
log (Wage)
OLS felm
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Western immigrant 0.026*** 0.062*** -0.112*** -0.107*** -0.044*** -0.029*** -0.040*** -0.029*** -0.024*** -0.010***
(0.004) (0.004) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)
Non-western
immigrant -0.114*** -0.117*** -0.135*** -0.175*** -0.133*** -0.087*** -0.053*** -0.035*** -0.024*** 0.003
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Female -0.278*** -0.356*** -0.356*** -0.332*** -0.209*** -0.237*** -0.183*** -0.164*** -0.126***
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Age 0.031*** 0.025*** 0.020*** 0.018*** 0.013*** 0.012*** 0.010*** 0.011***
(0.00004) (0.00003) (0.00004) (0.00003) (0.00004) (0.00004) (0.00004) (0.00004)
Year FE × × × × × × ×
Experience FE × × × × × ×
Part/full time work
FE × × × × ×
Education FE × × × ×
Industry FE × × ×
Occupation FE × ×
Firm FE ×
Constant 12.543*** 12.692*** 11.720***
(0.0004) (0.001) (0.001)
Observations 5,029,926 5,029,926 5,029,926 5,029,926 5,029,926 5,029,926 5,029,926 5,005,356 4,330,657 4,330,657
R2 0.0005 0.030 0.151 0.238 0.278 0.407 0.430 0.463 0.449 0.542
Adjusted R2 0.0005 0.030 0.151 0.238 0.278 0.407 0.430 0.463 0.449 0.530
Residual Std. Error 0.809 (df = 5029923)
0.797 (df = 5029922)
0.745 (df = 5029921)
0.706 (df = 5029903)
0.687 (df = 5029885)
0.623 (df = 5029883)
0.611 (df = 5029878)
0.593 (df = 5005223)
0.569 (df = 4330493)
0.525 (df = 4220101)
Adding a linear variable for age negatively affected coefficients for immigration status.
However, the coefficient for age itself is positive, becoming a year older implies a 3% higher wage. As it was mentioned before, the western immigrants are the oldest group, and non- western immigrants are moderately older than natives. Therefore, controlling for age, affects coefficients for immigrants negatively, especially western immigrants. For western
immigrants adding age as a control variable led to fall in wage by 17%.
All subsequent controls are fixed effects, and the year fixed effects are the first ones to be added. Adding the year fixed effects implies calculating the average impact of working during a particular year among the whole sample. Utilizing year fixed effects makes it easy to take inflation into account as well as isolate the effect of some extraordinary incidents as for example uneven distribution of observations. Adding year fixed effects affected only non- western immigrants. One of possible explanations may be a large share of non-western immigrants working during recent years. In this way, their wage attracts the greatest effect of inflation. Consequently, when the effect of inflation is isolated, the coefficient decreases.
Including fixed effects for experience appears to explain the wage variation across western and non-western immigrant groups. Both coefficients for immigrant groups decrease. Fixed effects model is preferred to adding experience as linear or both linear and quadratic term, in order not to condition on the form of relationship. Another reason for careful use of the variable for experience is the fact that the variable is a proxy variable. Nevertheless, the estimated regression demonstrates that, the coefficient for western immigrants halves.
Adding fixed effects for part- and full-time workers leads to decrease in the coefficients for immigration status. As it follows from descriptive statistic, immigrants are more inclined to work part time than natives, especially non-western immigrants. Therefore, a considerable share of the wage gap between immigrants and natives is explained by employment classification. Now, the coefficients for immigration status do not include the variation in employment classification but displays the wage gap within the employment classifications.
Consequently, coefficients diminish, and the gap decreases. Adding fixed effects is preferred due to the categorical variable that indicates how much an individual works. There is no other reason to employ fixed effects instead of a linear variable than data limitations.
Education fixed effects affected binary variables for immigrational status differently. The
immigrants increased. This is caused by different distribution of immigrants among
educational categories. Western immigrants are generally higher educated, which results in higher wage. When the effect of higher education is isolated, the coefficient goes down. The situation is opposite with non-western immigrants, they have relatively lower education. Thus the coefficient for their immigrational status behaves differently, it increases.
Further, occupation and industry fixed effects are added to the regression. Hence, the regression coefficients in the column 9 in the Table 3 display the variation within the same occupation, education, experience, year, position characteristic and industry. The result here is equal coefficients for western and non-western immigrants, which are pretty low. The
regression predicts a 2.4% lower wage for immigrants than natives, and both coefficients are highly significant. Adding firm fixed effects does not result in a statistically significant result for non-western immigrants. Practically, adding firm fixed effects means estimating a firm- specific intercept per firm, which captures the effect of being employed in the particular firm comparing with an omitted firm. In this way, indicator variables for immigrational status do not include the effect of working for different firms. The impact of working for a particular firm is now isolated and is denoted by firm fixed effects. However, the coefficient for western immigrants is highly significant and implies a 1 % lower wage for immigrants.
To sum up, the general model explains the wage gap between natives and immigrants well.
The estimation started with a high wage gap between natives and non-western immigrants at the rate of 11.4%, which gradually disappeared when some relevant controls were added. The pattern for non-western immigrants was as expected - gradual diminishing of the wage gap with some minor, explainable fluctuations. The pattern is different for the western
immigrants, who by the start of the estimation earned more than natives and adding the same controls as for non-western immigrants resulted in a negligible, but negative wage gap.
Adding the control for age led to the change in gap’s direction. Adding age revealed an important relation, western immigrants earn generally more than natives, but western immigrant of the same age as native earns less.
4.2 Impact of being an immigrant across education choices.
The general conclusion from the previous section is that the wage gap between natives and
can vary across the education groups. The purpose of this section is to determine if the disadvantage of being an immigrant is the same across the education categories. Table 4 reports the results of the regression (3). Here the final equation of the general regression model both with and without firm fixed effects is applied to samples for each education category separately.
Table 4– Wage gap across the educational categories.
Dependent variable:
log (Wage) Lower
than High School
High School
Post- secondary non-tertiary
Bachelor’s degree
Master’s degree
Postgraduate education
(1) (2) (3) (4) (5) (6)
Western
immigrants 0.002 -0.038*** 0.0002 -0.017*** -0.020*** -0.025**
(0.069) (0.007) (0.017) (0.003) (0.005) (0.010) Non- western
immigrants 0.021 -0.010*** -0.037*** -0.018*** -0.049*** -0.079***
(0.026) (0.003) (0.010) (0.003) (0.004) (0.009) Female -0.093*** -0.184*** -0.234*** -0.161*** -0.135*** -0.133***
(0.025) (0.001) (0.004) (0.001) (0.001) (0.004) Age 0.005*** 0.010*** 0.008*** 0.009*** 0.013*** 0.013***
(0.001) (0.0001) (0.0002) (0.00005) (0.0001) (0.0003) Observations 6,877 1,487,289 172,079 1,920,316 694,526 49,570
R2 0.303 0.412 0.442 0.414 0.466 0.395
Adjusted R2 0.289 0.412 0.442 0.414 0.465 0.393
Residual Std.
Error
0.801 (df
= 6736)
0.654 (df = 1487135)
0.580 (df = 171929)
0.506 (df = 1920160)
0.507 (df
= 694373)
0.417 (df = 49425)
Note: *p**p***p<0.01
In general, there is a trend towards a larger wage gap for higher educated. The tendency is especially strong for non-western immigrants. Non-western immigrants with postgraduate education experience the lowest return to education - they earn 8% less than natives. The situation is analogous for those with master’s degree, they earn 5% less. On the contrary, western immigrants with postgraduate education make only 2.5 % less. Furthermore, western immigrants make approximately 2 % less than natives on average across all educational categories. The highest penalty for western immigrants is among those, who have a high
school education. It might be because the number of western immigrants with high school education is much lower than natives and non-western immigrants. Both groups of
immigrants with bachelor’s degree experience almost same, modest disadvantage at the rate of 1.7% and 1.8% respectively. However, there is no statistically significant impact for immigrants among those who have lower education than high school.
The picture is similar when the same regression is estimated with firm fixed effects, see results in Table 5. The main tendency is that wage gap is smaller. Estimation with firm fixed effects reports high wage differential in non-western immigrants’ favour among high school graduates and dropouts.
Table 5 - Wage gap across the educational categories. (Estimated with firm fixed effects)
Dependent variable:
log (Wage) Lower
than High School
High School
Post- secondary non-tertiary
Bachelor’s degree
Master’s degree
Postgraduate education
(1) (2) (3) (4) (5) (6)
Western
immigrants 0.092 -0.013* 0.014 -0.009*** -0.018*** -0.021**
(0.124) (0.007) (0.018) (0.003) (0.005) (0.009) Non- western
immigrants 0.101** 0.039*** 0.015 -0.004* -0.035*** -0.075***
(0.048) (0.003) (0.011) (0.003) (0.004) (0.009) Female -0.069* -0.133*** -0.167*** -0.120*** -0.097*** -0.108***
(0.036) (0.002) (0.005) (0.001) (0.001) (0.004) Age 0.007*** 0.010*** 0.010*** 0.010*** 0.014*** 0.013***
(0.002) (0.0001) (0.0002) (0.00004) (0.0001) (0.0003) Observations 6,877 1,487,289 172,079 1,920,316 694,526 49,570
R2 0.630 0.537 0.642 0.520 0.575 0.527
Adjusted R2 0.516 0.511 0.600 0.506 0.560 0.507
Residual Std.
Error
0.661 (df
= 5258)
0.597 (df = 1407678)
0.491 (df = 153930)
0.465 (df = 1869216)
0.460 (df
= 671110)
0.375 (df = 47531)
Note: *p**p***p<0.01
Higher penalties for highly educated immigrants are not expected.
A study from the USA analyses returns to human capital separately for blacks and whites – the minority group that might experience disadvantages and the majority. The measure of ability utilised is an Armed Forces Qualification Test. They state that individuals who obtain college degree reveal their ability entirely when they enter the labour market, the return to ability does not change with experience. While, for high school graduates, the situation is different, they enter the labour market with zero returns to ability, ability reveals when experience lengthens. An important conclusion Arcidiacono et al. come to is that high school graduates, unlike college graduates, are a subject to statistical discrimination. Blacks start with 6 percent lower wage and the gap just grow over time. So, their conclusion is that blacks’ motivation to obtain college degree is substantially higher than whites’ since blacks avoid discrimination entering the labour market for highly educated and reveal their true ability at the entrance. (Arcidiacono, Bayer & Hizmo, 2010, p. 77, 99-100)
The important inference from Arcidiacono’s et al. (2010) paper is that higher education
contributes to the easier revealing of human capital skills and, therefore to less discrimination.
If the situation is like in the Norwegian labour market, substantially lower wages among immigrants might insinuate their generally poorer human capital. Brekke and Mastekaasa (2008) study differences in the probability of full-time employment and wage between natives and non-western immigrants on the Norwegian labour market. They consider only individuals with master’s degree obtained in Norway. The work is especially seminal because they
observe grades, which they use to measure human capital characteristics. They find that immigrants have worse grades than natives averagely. They also find that higher grades increase the probability of full-time employment and affect wage positively. So, the conclusion might be that immigrants’ poorer human capital is the reason for a lower wage.
Nevertheless, Brekke and Mastekaasa find that despite the thorough control for both human capital and other relevant factors, the native-immigrant gap is significant both in earnings and employment. So, there is a difference in natives’ and immigrants’ human capital, but the wage gap is not proportional to the human capital gap.
A study of the Italian labour market, by estimation of quantile regression, finds an interesting
wages, are located in the highest deciles, experience the highest penalties. Penalties decline when authors control for occupation, but the trend remains. Assuming that those who earn most have high education, the results are analogous to the results of this thesis, that highly educated immigrants face the highest penalties. Researchers state that the penalty is
attributable to the glass ceiling - immigrants have limited access to highly paid occupations.
(Dell’Aringa et al., 2012)
This explanation is not suitable for the Norwegian situation because the wage gap reported in tables 4 and 5 are estimated within an occupation. The differences are not caused by choice of employer. It follows from the negligible difference of the coefficients in tables (4) when regressions are estimated without firm fixed effects and (5) with firm fixed effects.
Wage negotiation system in Norway - collective bargaining may be a possible explanation for this pattern. In Norway, wage is negotiated in several steps. It implies the wage negotiated by a union as well as a personal addition, negotiated on a firm level. Firstly, employer
organizations and employee organizations – unions negotiate wage centralized and then sectoral within occupation and industry. Union members can demand a centralized negotiated wage. Though, a worker is not obliged to be a union member to get a centralized negotiated wage. Secondly, workers have an additional right to negotiate an individual addition to wage locally. (Løken, Aarvaag Stokke & Nergaard, 2013, p.35) This step is often omitted among those, who’s work does not demand any special skills. However, highly educated workers are usually able to negotiate a sufficiently higher wage. Consequently, the larger gap between natives and immigrants might be due to immigrant’s weaker ability to negotiate.
Collective bargaining is obviously also a reason for the different pattern among those with lower education than bachelor’s degree comparing with results to Arcidiacono et al. (2010) Another explanation for the substantial higher penalties for individuals with master’s degree and postgraduate education among both immigrant groups might be a weak proficiency in Norwegian if their educational programs were taught in English. Norwegian language is essential in working life. Fossland and Aure (2011, p.136-137) find that access to advanced language courses needed for highly educated immigrants are at scarcity. Moreover, poor language skills may affect wage negotiation negatively.
High wage gap among highly educated immigrants, especially non-western group, are
hypothesis, which can explain the gap is lack of negotiation skills during the collective bargaining.
4.3 Importance of experience for natives and immigrants and wage increase.
In this section, the last equation of the general model is separately estimated for natives and both groups of immigrants and the experience fixed effects are the major focus for this
analysis. Graphs 1 and 2 depict the estimated intercepts on the experience fixed effects, when omitting the first year of experience. Experience fixed effects reflect the importance of
experience for the wage in the respective groups. The coefficients values express how much higher wage a worker gets after some work experience compared to the first year. The coefficients report wage increase measured in percent. The model is estimated with and without firm fixed effects.
Both graphs show relatively similar growth of red and blue lines which correspond with natives and non-western immigrants respectively. However, the blue line starts lower than the red one. The green curve, which reflects experience fixed effects for western immigrants, has a completely different development: here the salary stagnates (and even falls) over
experience.
Figure 1 - Experience fixed effects without firm fixed effects
Figure 2- Experience fixed effects with firm fixed effects
Red – Natives, Blue – Non-western, Green - Western
The wage increase after the second year of experience is high for all groups. The explanation here might be that job searching took some time and workers were not employed the whole first year. Consequently, the wage first year is smaller than if an individual worked a whole year. During the second year, the wage raise estimated both with and without firm fixed effects is 26.5% for natives, 21% for western immigrants 22.2% for non-western immigrants.
The wage growth for natives is the highest.
By analysing Graphs 1 and 2, the general trend appears to be logical for non-western immigrants and natives; a worker earns more as work experience lengthens and riches a plateau after nearly ten years.
As mentioned earlier, western immigrants have a slightly different pattern. Wage increase compared to the first year of working experience is lower than for other groups during the whole observation period, and the wage flattens out earlier. It might indicate that western immigrant had some working experience from the home country and reach the plateau earlier.
Another explanation might be that they are generally older than the rest of the sample and are therefore in the different stages of life.
Estimation without firm fixed effects gave a similar result as estimation with firm fixed effects, however the wage increase caused by experience is slightly higher.
Estimating the equation (5) on per experience year samples gave results reported in Table 6.
In this analysis all the control variables are included. Estimation with firm fixed effects reveals the waging pattern during the experience within a firm. So, the firm-specific characteristics are taken care of. Firstly, the estimation does not show a permanent
disadvantage for immigrants. As well as the wage gap does not appear after the 10th year for both categories of immigrants. The fact that there is no significant differences in wage after 9th year might be both due to perfect assimilation of immigrants on the labour market or lack of data on individuals with long experience.
For western immigrants, the results seem to vary. The disadvantage at approximately 3 % appears every other year. It does not seem to be a definite pattern. Western immigrants start with the like wage as natives while non-western immigrants start with 4.4% higher wage than natives. After the second year of experience, the advantage of non-western immigrants
declines to 1.7%, while western group experiences disadvantage relatively natives at the range of 2.7%.
Table 6 - Wage gap during the experince. (Estimated with firm fixed effects)
Dependent variable:
log (Wage)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)
Western
immigrants -0.009 -0.027*** -0.015 -0.028*** -0.004 -0.031*** -0.016 -0.017* -0.035*** 0.001 -0.004 -0.019 -0.009 0.007 -0.004 (0.009) (0.009) (0.009) (0.009) (0.009) (0.010) (0.010) (0.010) (0.011) (0.011) (0.012) (0.013) (0.014) (0.016) (0.018) Non-western
immigrants 0.044*** 0.017*** -0.010* 0.0001 -0.006 -0.017*** -0.016** -0.016** -0.008 0.004 0.004 -0.015* -0.005 -0.003 -0.005 (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.007) (0.007) (0.007) (0.008) (0.008) (0.009) (0.009) (0.010) (0.012) Female 0.001 -0.011*** -0.079*** -0.112*** -0.130*** -0.170*** -0.185*** -0.193*** -0.197*** -0.197*** -0.197*** -0.191*** -0.191*** -0.189*** -0.186***
(0.003) (0.003) (0.003) (0.003) (0.002) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.004) Age 0.028*** 0.016*** 0.013*** 0.012*** 0.011*** 0.011*** 0.009*** 0.008*** 0.007*** 0.005*** 0.004*** 0.003*** 0.001*** 0.001*** -0.001***
(0.0002) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) Observations 392,413 367,114 354,819 348,783 343,850 340,503 332,958 316,996 284,245 253,317 223,311 193,903 165,862 134,866 105,591 R2 0.531 0.558 0.561 0.566 0.574 0.504 0.505 0.512 0.524 0.528 0.533 0.549 0.553 0.562 0.579 Adjusted R2 0.465 0.490 0.491 0.494 0.502 0.418 0.418 0.425 0.436 0.436 0.438 0.452 0.453 0.454 0.465 Residual Std.
Error
0.647 (df = 343798)
0.598 (df = 318405)
0.550 (df = 305862)
0.537 (df = 299258)
0.522 (df = 294027)
0.548 (df = 290366)
0.533 (df = 283363)
0.512 (df = 268858)
0.496 (df = 239489)
0.479 (df = 212081)
0.468 (df = 185456)
0.448 (df = 159601)
0.440 (df = 135352)
0.430 (df = 108208)
0.416 (df = 82994)
Note: *p**p***p<0.01
The fact that non-western immigrants start with higher wage is coherent with the results of previous regression, see Graph 1 and 2. Non-western immigrants’ lower wage increase after second year of working experience compared to the first year’s wage estimated by regression (4) is obviously caused by higher starting salary. On the whole, the wage growth trend seems to be the same for natives and non-western immigrants. When non-western immigrants’ first year’s salary is higher than natives’, the increase next and further years comparing with the first wage first year, must not be like high to keep the same level as natives.
The Table 6 reports a modest decrease by 1-2% in wage level between 6 and 8 years among non-western immigrants. The gap is clearly not that high, but it is significant. A possible explanation for this could be a longer and more often parental leave for immigrants. However, it is not possible control for that given the data for disposition.
The same regression estimated without firm fixed effects gave different results. The wage gap observed earlier did not include features of the firm. In this estimation, we can also capture people’s preference when it comes to choose of workplace. Non-western immigrants still start somewhat higher than natives but experience a persistent disadvantage at 3-4% in the period from second to the ninth year. The situation is almost identical for western immigrants.
However, they do not start with a higher wage than natives.
The estimation with firm fixed effects did not reveal a visible sign of any discrimination.
Non-western immigrants seem to earn even more than natives within a firm, during some years. The fact that western immigrants earn substantially less than natives per given experience level is unexpected. However, a possible explanation may be the age of western immigrants. The coefficient for the western immigrant dummy variable implies wage gap relatively to natives when all other control variables are the same. Western immigrants are the oldest group, so they receive the highest deduction for age. One year implies a 1% increase in wage. Western immigrants are on average six years older than natives, so they averagely get a 6% higher wage due to age if all other controls are the same. Non-western immigrants’ age is in line with natives’, and these groups are generally easier to compare. Apparently, this is a reason for such an equal wage between these groups.