Our data include all natives born between 1960 and 1980 who are still residents in Norway by age 32, and the purpose of our analysis is to examine how exposure to immigration during adolescence and young adulthood has affected relative prime age (33–36) earnings and employment outcomes (measured 1993–2016) by economic class background. Since we are going to use the variation in immigration patterns across commuting zones (travel-to-work areas) as the key source of identification, we need to assign each person to a commuting zone. Our definition of commuting zones follows Bhuller (2009), which divides Norway into 46 such zones. As we return to below, the size distribution of these zones is highly skewed. The largest city area around the capital alone accounts for 22.6% of the observations, whereas the four largest city zones taken together account for 44.2%. Since the commuting zone of actual residence in adulthood may have responded endogenously to immigration patterns, we assign each person to the strictly predetermined commuting zone inhabited at age 15/16. This information is obtained from educational registers containing addresses at the time of primary school completion.
Each person is assigned a class background on the basis of the average of his/her parents’ labor earnings (including self-employment earnings). To do this in practice, choices have to be made both with respect to which and how many earnings years to include in order to appropriately capture “class background.” These choices involve tradeoffs between concerns related to lifecycle bias (Solon 1999; Grawe 2006), attenuation bias (Solon 1992), consistency over time (Markussen and Røed 2020), and—in our case—possible simultaneity problems related to effects of immigration on parental ranks. Our limitation is that earnings data are observed from 1967 and onwards, while most of the parents used in our analysis are born between 1925 and 1960. In the main part of the paper, we apply four alternative ranking algorithms, all based on the sum of parental (wage-inflation-adjusted) earnings over a given period:
Parental earnings during their age 52–58
Parental earnings during their age 33–36Footnote 3
Parental earnings during the offspring’s age 7–15
Parental earnings during 1980–1992
Earnings obtained during the age of mid 50′s have been shown by Markussen and Røed (2020) to yield the highest correlation with lifetime earnings. For our purpose, they also have the advantage of being observed for almost all parents to the cohorts included in our analysis. A disadvantage is that they to some extent are measured in the same period as we measure offspring outcomes and well into the period with large immigration flows; hence, ages 52–58 measurement of parental earnings may entail some simultaneity problems. If immigration has effected the rank outcomes of offspring, it may as well have affected the earning rank of parents, making the interpretation of results regarding economic mobility less clear. By measuring parents at earlier ages or at a calendar time strictly prior to the immigration patterns used to identify the effects on offspring (starting in 1993), we reduce this potential source of simultaneity but at the cost of weaker association with lifetime earnings.
Given that immigration to Norway started before 1993 and that the spatial pattern of immigration may be persistent, none of the ranking criteria presented above can rule out completely that the migration patterns used to identify the causal impact on offspring’s economic mobility exhibit some statistical association with the sorting of parents into the different ranks. In the Appendix, we present results based on two additional ranking criteria: parents’ educational attainment (measured in 1990) and grandfathers’ earnings, respectively. These ranking criteria substantially reduce or (in the case of grandfather earnings) almost eliminate concerns that the class background itself has been influenced by immigration. However, as we explain in the appendix, the grandfather-based ranking criterion can be computed only for a subset of the data.
The ranks are in all cases computed from the earning distribution within each commuting zone. We use commuting zones (rather than the whole country) as the foundation for ranking in this paper to ensure that we compare offspring who, conditional on parental rank, have been exposed to similar overall economic and labor market developments. Had we used a national ranking algorithm, the distribution of classes would have varied considerably across commuting zones, implying that geographically differentiated economic trends could have affected different classes differently. For each annual birth cohort, the ranking is made separately for sons and daughters based on earning comparisons with all other parents to offspring born in the same year and living in the same commuting zone. Administrative registers ensure that 93–99% of all native birth cohorts are included in the dataset with appropriate information on both class background and commuting zone. In total, we have 1,116,827 observations that can be used in the empirical analysis.
In most of our empirical analysis, we divide the native population into five parental earnings classes (quintiles), with the aim of identifying separate immigration effects for the first, second, fourth, and fifth quintile relative to the third (middle) quintile. The choice of five classes in this context represents a compromise between the aim of allowing for sufficient variation in impacts across the class distribution and the need for including a wide range of class-specific fixed effects, which soak up more degrees of freedom the larger is the number of distinct classes.
For the offspring generation, we focus on three prime-age labor market outcomes:
Earnings rank: The rank position in the gender- and cohort-specific distribution of ages 33–36 earnings within the commuting zone, measured in percentilesFootnote 4
Earnings share: The total earnings obtained in the age 33–36 period divided by the gender- and cohort-specific average in the commuting zone
Employment: A dummy variable equal to one if average annual labor earnings obtained in the age 33–36 period exceeded approximately one third of average full-time-full-year earnings in NorwayFootnote 5
Measurement at age 33–36 is chosen as a compromise between the purpose of minimizing lifecycle bias (which calls for measurement at higher ages) and the ambition of being able to examine the more recent and immigration-exposed birth cohorts (which calls for measurement at younger ages). The earnings measure consists of all labor-related earnings, including business income from self-employment (but not capital income). Figure 2 shows how the bottom and top class quintiles have performed according to these outcomes, for offspring born from 1960 to 1980. Since a stable interpretation of class background across cohorts is a key concern when we wish to characterize changes in mobility over time, we use parental earnings obtained during age 52–58 as the ranking criterion in this figure.
Figure 2 confirms the finding in Markussen and Røed (2020) that people born into the lowest socioeconomic classes have fallen systematically behind over time. In particular, their average earning rank outcomes have dropped by approximately 2 percentiles, their relative earnings have declined by approximately 3%, and their employment propensity has fallen by approximately 2 percentage point relative to the cohort average. For the top quintile, the trends are less clear, although their earnings rank outcomes appear to have trended upwards.
Occupational class structure of native and immigrant employees
Before we examine the relationship between natives’ labor market outcomes and their exposure to immigration, we take a closer look at the kind of jobs that immigrants actually take in order to see which groups of natives they compete with in the labor market. To do this, we first need to characterize jobs in terms of their class status. From 2003, the Norwegian employee register contains detailed occupational codes, based on the International Standard Classification of Occupations (ISCO 88).Footnote 6 We use these auxiliary data to assess the class-structure of all occupations observed in our data (344 different occupations). This assessment is based on the population of employed adult natives, for which we have data on class background, i.e., we characterize each occupation’s socioeconomic status by computing the average class background of its native employees, again based on parental earnings during age 52–58. The parental classes are here defined in terms of earning decile rank (running from 1 to 10), with mean equal to 5.5; hence, the occupational status codes are also defined on this scale. Equipped with these occupational status codes, we compare the distribution of employees across occupational statuses for natives and immigrants. As the occupational structure varies considerably between immigrants from different origin countries, we start out by dividing the immigrant population into the three groups described in Section 3; i.e., (i) other rich countries, (ii) less developed countries (LDC), and (iii) Eastern Europe. We then compare the occupational class structure observed for natives and the three immigrant groups.
Figure 3 shows distribution functions for the resultant occupational class structure. Although individual class backgrounds vary from 1 to 10, the averages taken over occupations essentially vary between 4 and 7. A first point to note from Fig. 3 is that immigrant workers from less-developed countries (LDC) and Eastern Europe are heavily overrepresented in occupations typically held by natives from the lower classes, whereas immigrants from high-income countries are overrepresented in occupations held by natives with a high rank. A second point to note is that the class structures of the jobs held by immigrants from less-developed countries and from Eastern Europe are hardly distinguishable. Hence, in a social class context, immigrants from these two country groups compete in exactly the same segments of the labor market. Based on this observation, we aggregate these two immigrant groups into a single one. In our empirical analysis, we thus divide the immigrant population into two groups:
Low-income countries: Eastern Europe plus less-developed countries (LDC). The quantitatively most important countries in this group are Poland (2.8% of the adult population in Norway by 2016), Lithuania (1.1%), Somalia, Iraq, and the Philippines (all with 0.6% of the population)
High-income countries: Rest of the world. The most important countries in this group are Sweden (1.0% of the adult population by 2016), Germany (0.6%), Denmark (0.4%), Great Britain (0.3%), and the USA (0.2% of the population).
Based on the occupational structure described in Fig. 3, we expect immigrants from low-income countries to offer labor services of a type that primarily is a substitute for low-class native workers and a complement for high-class native workers, whereas immigrants from high-income countries offer services of a type that is a substitute for high-class and a complement for low-class native workers. The distinction between immigrants from high-income and low-income countries is also clear from the perspective of economic incentives for migration resulting in a low-pay job in Norway. While immigrants from high-income countries face a similar wage level in Norway as in their home country, immigrants from the low-income countries can obtain much higher earnings in Norway. For example, Bratsberg et al. (2020) show that average hourly wages in Norway exceeded those in Poland and Lithuania by factors of 4 and 6, respectively, in 2010.
Exposure to immigration
We measure the degree of exposure to immigration as the immigrant adult (age 25–55) population shares (from high-income and low-income countries, respectively) in each offspring’s childhood commuting zone by age 32. Given that the first birth cohort in our dataset is born in 1960 and that residential information for immigrants is available from 1992, age 32 is the lowest age at which we can measure the immigrant shares precisely for all the cohorts. However, as the immigration shares are stock variables, with moderate variation from year to year, these shares will to some extent pick up the overall exposure to immigrant labor market competition through adolescence and young adulthood. For more recent cohorts, we also have data for immigrant exposure earlier than at age 32, and these data show that there is a very high correlation between exposure at different ages. For example, for birth cohorts born after 1969, the correlation coefficients between immigrant exposure at age 22 and age 32 are 0.91 for immigration from both low-income and high-income countries.
Figure 4 illustrates that there has been considerable variation in exposure to immigrant population shares, both over the longitudinal and the cross-sectional dimensions, particularly for immigration from low-income countries. Mean exposure to immigration from low-income countries has varied from approximately 3–4% for the cohorts born in the early 1960s to 13% for the 1980 cohort, whereas exposure to immigration from high-income countries has remained fairly stable around 3%, yet with a slight increase for cohorts born after the mid-1970s.