Why do people move to ? Analysis of determinants in source country

Rasa Svedaite-Palm

Thesis for the Master of Philosophy in Economics

Department of Economics UNIVERSITY OF

May 2016

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Why do people move to Norway? Analysis of determinants in source country

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© Rasa Svedaite-Palm

2016

Why do people move to Norway? Analysis of determinants in source country

Rasa Svedaite-Palm http://www.duo.uio.no/

Printed Reprosentralen,

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Acknowledgments

This thesis was written as a completion of the Master of Philosophy in Economics at the University of Oslo.

First and foremost, I am grateful to my supervisor Professor Karen Helene Ulltveit-Moe for her guidance and valuable feedback throughout the writing process.

I would also like to thank Margarita for the words of encouragement during our many coffee breaks and lunches.

To Zivile and Chris, thank you for the proofreading, for always checking in with me and supporting me emotionally throughout my studies.

Finally, I am grateful to my husband Aksel for his support, encouragement and endless patience, and to our son Matias for always putting things into perspective for me.

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Summary

In this thesis, I study the determinants of migration to Norway for the period of 2003 – 2013. I adapt the migration model developed by Clark et al. (2007) to Norway in order to explore the background factors in the source country that drive migration to Norway. I use panel data for 76 source-countries from 2003 to 2013 and run a panel data regression using a STATA statistical analysis tool in order to quantitatively assess the importance of these factors. I conclude that income, inequality, stock of previous immigrants and distance to the destination country are all significant factors affecting migration from EU/EEA-countries to Norway. For non-EU/EEA countries, stock of previous immigrants is found to be the single most important determinant affecting migration flow to Norway. In addition to this, I compare the results found by Clark et al. for the to migration determinants to Norway, concluding that the results differ significantly due to differences in the purpose of migration to the United States and Norway.

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Table of contents

1 Introduction ...... 1 2 Migration to Norway- Styled Facts ...... 3 3 International Migration: a Review of Theory and Empirical Evidence ...... 7 3.1 International migration theory ...... 7 3.2 Empirical Evidence on the Determinants of Migration ...... 11 4 Modelling migration to Norway ...... 16 4.1 Modelling migration ...... 16 4.2 Adapting model to Norway and data collected ...... 20 5 Econometric results ...... 27 5.1 Results ...... 27 5.2 Discussion ...... 32 5.3 Econometric Issues ...... 35 6 Conclusions ...... 38 References ...... 40

Table 1. Number of immigrants living in Norway, by country of origin ...... 3 Figure 1. Immigrants and born to immigrant parents, by country of origin ...... 4 Figure 2. Non-Nordic Immigrants, by reason for migration ...... 5 Table 2. Immigrants by reason for and country of background, 2013 ...... 6 Figure 3. Ricardo-Viner model ...... 8 Figure 4: Wage disparities in source and destination countries ...... 10 Table 3. Explaining U.S. Immigration ...... 19 Table 4. The Balanced Panel For Immigration to Norway, 2003 - 2013 ...... 22 Table 5. Numbers in the Panel and Total Immigration, 2003 - 2013 ...... 23 Table 6. Multicollinearity Diagnostics of Independent Variables ...... 24 Table 7. Immigration Rate Regression ...... 28 Table 8. Immigration Rate Regression- EU/EEA-countries ...... 30 Table 9. Reason for migration to Norway by origin, 2013 (Percent of total for each source) . 31 Table 10. Class of Admission to United States by Source Area, 1998 ...... 34

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1 Introduction

The massive migration flow into Norway and other European countries over the last several decades has precipitated debate about the gains and losses from immigration. More than a million migrants came into in 2015, causing a crisis in the recipient countries as they struggle to cope with the influx. Moreover, the open borders and single labor market - the founding principles of the - are threatened as the countries struggle to find a common solution for coping with mass immigration. has closed its border to and has reimposed its border controls with in 2015. Migration has huge political and economic implications for the destination countries, raising the question whether the of the developed countries can be sustained if the crisis continues and more refugees are admitted.

Immigration to Norway has increased significantly over recent years, and as of the beginning of 2016 there were 700 000 immigrants living in Norway. Economists predict that the increase in the number of refugees in Norway will threaten the welfare of Norwegian society, which is already affected by the potential economic crisis due to oil price reduction. Furthermore, as the government has tightened legislation and started returning refugees to in order to deter asylum seekers, society has become divided between those wanting to accept as many refugees into the country as possible, and those wanting to close the borders.

There is a great deal of economic literature on the implications of migration for the destination and source countries. Here, I will focus on the determinants of migration in the origin country. Several recent studies have examined the economic and demographic determinants of migration. Karemera et al. (2000) studied migration flows to North America and found that the population of the source country and the income in the receiving country are the two main determinants of migration to the United States and . Mayda (2010) and Pedersen (2008) analyze migration flows to OECD countries, while Clark et al. (2007) look at economic and demographic determinants in source countries that influence migration to the U.S. They find that income, inequality, stock of previous immigrants, distance and education are all important factors for migration decision making.

The main objective of this study is to adapt the model developed by Clark et al. (2007) to Norway, explore the background factors in the source country that drive migration to Norway,

1 and quantitatively assess the importance of these factors. For this purpose, I use panel data for 76 source-countries from 2003 to 2013 and then run a panel data regression using a STATA statistical analysis tool. I conclude that income, inequality, stock of previous immigrants and distance to the destination country are all significant factors affecting migration from EU/EEA-countries to Norway. For non-EU/EEA countries, stock of previous immigrants is found to be the single most important determinant affecting migration flow to Norway. In addition to this, I compare the results found by Clark et al. for the United States to migration determinants to Norway, finding that they differ significantly due to differences in the purpose of migration to the United States and Norway.

First, I will present the economic theory of migration by summarizing the main findings of previous theoretical and empirical works. Following this, I will describe the model developed by Clark et al. (2007) for migration to the United States, show the results of their study and explain how their model can be adapted to immigration to Norway. Moreover, I will describe the data collected for this study and explain the results obtained when analyzing determinants of migration to Norway. Finally, I will discuss how and why these results differ when analyzing EU/EEA countries and non-EU/EEA countries separately, and how the results differ from findings by Clark et al. I will conclude with section 6.

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2 Migration to Norway- Styled Facts

In this section, I will describe migration to Norway from 2003 to 2016. I will show the total numbers of migrants from different countries of origin, address the main reasons for migration and describe how and why they changed over the past 13 years.

As of 2003 there were over 277 000 immigrants living in Norway, while by 2016 the number reached almost 700 000. The enlargement of the European Union with central and Eastern European countries between 2004 and 2007 significantly affected migration flow to Norway. Table 1 shows that in 2003, before the enlargement of the European Union, there were approximately the same number of immigrants in Norway from EU/EEA countries (100 041 individuals) as from Asian countries (98 048 individuals). The number of immigrants for all regions of origin increased over the years from 2003 to 2013, but the number of immigrants from EU/EEA countries increased the most. In 2013, after the enlargement of the European Union, almost 50% of all immigrants living in Norway were from EU/EEA countries (273 197 individuals), surpassing the second largest group, Asian immigrants, by more than 100 000. African nationals were the third largest group of immigrants in 2013.

Table 1. Number of immigrants living in Norway, by country of origin

Country of Origin 2003 2005 2007 2009 2011 2013

EU/EEA countries 100 041 102 897 121 193 170 158 215 895 273 197

Europe excluding EU/EEA 30 624 34 857 38 360 42 876 45 989 50 061

Africa 28 612 33 972 39 495 46 392 55 593 67 571

Asia including 98 048 108 647 120 483 137 889 155 320 171 920

North America 8 139 7 807 7 988 8 626 9 185 9 814

South and 10 838 11 812 13 090 15 091 16 746 18 814

Oceania 960 1 053 1 222 1 562 1 771 1 945

Total Immigrants 277 262 301 045 341 831 422 594 500 499 593 322

Source: Statistics Norway, table 5184

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Figure 1. Immigrants and Norwegians born to immigrant parents, by country of origin

Source: Statistics Norway

Figure 1 illustrates how the number of immigrants in Norway grown significantly since 1970. We can see from the figure that the number of immigrants from and steadily increased over the years from 1970 to 2016. In contrast, the number of immigrants from increased slightly over the same period, while the number of immigrants from the excluding EU countries appears to be stable since 2000. The biggest change we can see is for the EU countries in Eastern Europe. The number of immigrants from these countries has increased dramatically since 2006. This is due to the expansion of the European Union in 2004 with , Malta, , , , , Hungary, , and . Workers from Cyprus and Malta have had no restrictions of movement since they joined EU in 2004, while for the remaining eight Eastern European countries these restrictions were removed in 2006, causing the increase in labor migration to Norway In total, European immigrants were the single largest immigrant group in Norway in 2016.

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Figure 2 shows how the immigration flow and reasons for migration to Norway changed from 1990 to 2014. In 1990, approximately 11 000 migrants came to Norway, while in 2014 the

Figure 2. Non-Nordic Immigrants, by reason for migration

Source: Statistics Norway number was almost five times higher, approximately 50 000 people.

In the period from 1990 to 2006, the main reasons for migration to Norway were family and refuge. In 2006, labor migration spiked, becoming the main reported purpose for migration to Norway, and surpassing family reunion. The increase in labor migration was caused by the removal of movement restrictions in 2006 for workers from the eight East European countries that have joined the EU in 2004. Labor has been the main reason for migration to Norway every year since 2006, followed by the family reunion and refuge. Labor immigration has been decreasing since 2011, while family immigration started to decrease in 2012.

Table 2 shows the number of immigrants who came to Norway in 2013 by the purpose of migration and the country of origin. Labor was the main purpose for immigration in 2013, when 23 530 individuals came to Norway because of work: 43% of the total immigrant flow in 2013. The majority of labor immigrants, over 90%, came to Norway from European countries, and only 2 318 individuals came from non-European countries for work purposes. A total of 17 337 individuals (32%) came as family immigrants, mostly from Europe and Asia, followed by African nationals. In addition, 7 083 of the immigrants in 2013 were

5 refugees. The absolute majority of these (97%) came from African and Asian countries. Education as the main purpose for migration was reported by 5 850 individuals. Of these, 3 182 came from Asia and 1 772 came from European countries.

Table 2. Immigrants by reason for immigration and country of background, 2013

Reported purpose for immigration

Country of origin Labor Family Refugee Education

Europe except Turkey 21 212 9 303 142 1 772

Africa 194 2 351 4 699 409

Asia including Turkey 1 403 4 428 2 169 3 182

North America 392 492 2 212

South and Central America 197 694 71 244

Oceania 132 69 0 31

Total 23 530 17 337 7 083 5 850

Total immigrants in 2013 54 518

Source: Statistics Norway, table 7113. Note: Includes only first-time immigrants with non-Nordic citizenship.

To conclude, most of the immigrants in Norway in 2013 were of European origin and came to Norway for work purposes or family reunion. The second largest group by origin was Asian immigrants who primarily moved to Norway for family reunion, education purposes or as refugees. The third largest immigrant group as of 2013 were African immigrants, who principally came to Norway as refugees or for family reunion.

In the next chapter, I will present the economic theory of international migration and summarize main findings of previous empirical works.

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3 International Migration: a Review of Theory and Empirical Evidence

3.1 International migration theory

In this section, I will review the theoretical literature on international migration. First, I will describe the Ricardo-Viner model of with labor mobility. Following this, I will explain the standard economic theory of migration - the human capital investment model developed by Sjaastad (1962). Finally, I will present the Roy (1952) model of self-selection, advanced by Borjas (1987) and a more current model of immigration, the Borjas (1987) model.

The Ricardo-Viner model or Specific Factor model explains the reasons for migration and its direction. The model was developed in order to describe the migration of workers from rural to urban areas, but can be used to explain international migration of labor when there are no restrictions for migration. This is a 2-2-3-model (Dixit and Norman, 1980 p.40): there are two countries (Home (h) and Foreign (f)), two goods are being produced (good 1 and good 2), and there are three factors of production (labor, capital in Home country and capital in Foreign country). We assume that capital is a country-specific factor and cannot move between countries, while labor is mobile across countries. The two countries use labor (L) and capital (K) to produce good 1 and good 2. Good 1 requires an input of labor and Home- country specific capital to be produced, good 2 requires an input of labor and Foreign country-specific capital in order to be produced. The production function for the two goods is:

yi = fi(Li,Ki), where i ∈ h, f

We assume a full employment of labor: Lh + Lf ≤ L. Moreover, the model assumes that countries decide on the output level that maximizes the profit, taking prices and wages as given.

Max ∑pifi (Li,Ki), subject to Lh + Lf ≤ L

In order to maximize the profit, labor will move between the two countries until its return is equalized between them (Feenstra, 2004 p.72): w = p1f1(Lh,Kh) = p2f2(Lf,Kf). See figure 3:

7 assuming we start with closed economies, the wages in Home and Foreign are wh and wf, respectively (point A and B). When we open for labor mobility, workers will move from

Figure 3. Ricardo-Viner model

p1f1 p2f2 A wh

C w w

B wf

Lh Lf

Foreign (country with lower wage) to Home (country with higher wage) until the return to labor is equalized between the two countries (point C).

A Specific Factor model is useful for studying international migration because it explains the direction of migration (people move from countries with a lower income to countries with a higher income until the return to labor is equalized), and the size of migration flow - the bigger the income difference, the greater is migration flow.

The Ricardo-Viner model is consistent with neoclassical principles of utility maximization for individuals, which states that an individual’s objective is to maximize his or her utility (income) subject to costs and other constraints. Therefore, people will only migrate if their expected return is higher in the destination country compared to the source country (Sjaastad, 1962).

Sjaastad (1962) describes migration as investment in human capital, where migration occurs due to spatial differences in net income. In accordance to Sjaastad, Hicks (1963, p.76) claims that "…differences in net economic advantages, chiefly differences in wages, are the main causes of migration". Sjaastad argues that a migrant’s objective is to maximize lifetime return

8 on his or her labor supply. The investment - moving to another location with a higher return to labor - will not only give return, but also entail costs. Sjaastad differentiates between the “money cost” and the “non-money cost” of migration, where the former is the direct financial cost of moving and the latter can be the forgone income while looking for a new job or the psychological cost of moving to a new environment. He claims that the cost of migration is directly related to the distance travelled, and therefore distance is a good proxy for the cost of migration. He argues that the longer the distance between the origin and destination country, the higher the travel costs for transportation and accommodation, but also the non-monetary costs such as loss of income in between jobs.

In addition, Sjaastad argues that age is an important determinant when making the investment decision to migrate because the goal of an individual is to maximize the present value of all his or her future income. Young people have higher life expectancy, and thus more years left to work and earn a higher income in the destination country. Therefore, the expected return on investment for them would be higher than for older migrants. In addition, Sjaastad claims that the psychological costs of migration increase with age because an individual has invested more in family and social networks in the home country. Becker (1993, p.85-86), argues that expected earnings is the most important determinant in migration decision making, and since the number of years left to work in the destination country affects the expected earnings, younger people would be more prone to migrate. He claims that “younger people have a greater incentive to invest because they can collect the return over more years”. Thus, one would expect that a high number of young, working-age people in the source country would positively affect migration flow.

Borjas (1987) in his analysis assumes that individuals make the migration decision by comparing income in source and destination countries, taking into account migration costs. People choose to migrate to where it is most “profitable”, i.e. to a country with the highest return for their specific skill set, net of migration cost. Furthermore, Borjas claims that the emigration rate of people with a specific skill set will be higher the higher the mean income in the destination country is, and lower the higher the mean income in the home country is or the higher the costs of migration. The predictions are in accordance with Sjaastad’s (1962) investment in human capital approach.

Additionally, Borjas (1987) advances Roy’s (1952) model for the case when income in the recipient country is higher than in the home country. According to the model, when the 9 destination country is relatively rich compared to the source country, there can be three ways for immigrant selection: positive (when people with the highest skill set emigrate), negative (when people with the lowest skill set emigrate) and “refugee” sorting (when the correlation of returns to a specific skill set between two countries is low or negative and individuals from the lowest income bracket in the home country enter into highest income bracket in the destination country).

Positive selection occurs when the high skill set is more valued in the destination country (d) than in the source country (s). This might occur when the source country has low wage dispersion (low inequality) and the destination country has high wage dispersion (high inequality). Figure 4.1 illustrates that when wage disparity in the source country is smaller than in the destination country, the (w1(d) curve is steeper than w(s)), only people with high skills will emigrate, as the return for high skill set is higher in the destination country: w1(d) > w(s) for the high skill set. Roy predicts that if the destination country has higher inequality than the source country, as illustrated in figure 4.1, an increase in the relative inequality (wage earning profile for source country shifts from w1(s) to w2(s)) will increase emigration.

Figure 4: Wage disparities in source and destination countries

4.2: Negative selection

4.1: Positive selection

w2(s) w (s)

w(d) 1

Earnings Earnings w (s) 2 w(d)

w1(s)

Skill level Skill level

Migrate Do not migrate Do not migrate Migrate

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Negative selection occurs when the source country is relatively unequal compared to the destination country (w1(s) is steeper than w(d)), see figure 4.2. Low skill workers will migrate to the destination country where wage dispersion is smaller, and the return on their skill set is higher: w(d) > w1(s) for low skill set. According to the Roy model, when the destination country is relatively equal compared to source country, as illustrated in figure 4.2, the increase in relative inequality (wage earning profile for source country shifts from w1(s) to w2(s)) will decrease migration.

In summary, the Roy model predicts that when the receiving country is relatively rich compared to the home country, the effects of inequality will follow an inverse U-curve: if the home country has relatively high inequality, the increase in relative inequality will reduce emigration, and conversely, if the home country has relatively low inequality, the increase in inequality will increase emigration.

To conclude, the theory of international migration is based on the behavioral assumption that people migrate from one country to another because it is economically beneficial for them to do so in terms of higher returns in the destination country for their specific skill set. However, migration constraints, both financial and psychological, exist. The financial constraint is the monetary costs of moving to another country (buying a plane ticket, moving your belongings); the psychological constraint is the language and cultural differences between the source and destination countries and the psychological difficulty of moving away from family and friends. In addition, there legal constraints of migration exist: regulations that limit or encourage moving to a particular country.

3.2 Empirical Evidence on the Determinants of Migration

In this section, I will look at the empirical evidence on the determinants of migration and on the factors that constrain migration. When surveying the empirical studies of international migration, it becomes apparent that empirical works of migration have come much further in explaining reasons for migration than theoretical works. Empirical researchers have successfully tested many more variables that affect migration than the few variables described in the theoretical models.

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Economic theory and empirical works suggests that there are several different factors that influence the decision to migrate, such as the income difference between the source and destination countries, income inequality, level of education, the cost of migration and legal immigration constraints (Bodvardsson and Van der Berg, 2013, p.27). Several recent empirical works have studied determinants of migration based on a single destination country, analyzing different factors in the source countries that affect migration. In one of the most comprehensive studies, Clark et al. (2007) study migration to the United States from 1971 to 1998 by analyzing effect of nine variables on their propensity to migrate to United States. The following migration determinants, economic and demographic, were included in the study: income, schooling years in source country, age composition of the source country, inequality, share of population living under poverty, distance to destination country, whether the source country is landlocked, whether it is English speaking and the numbers of previous immigrants in the destination country. Results obtained by Clark et al. will be provided and discussed in chapter 4. Prior to the study of Clark et al., researchers usually included some of the variables mentioned in their analysis, but often neglected one or more of the others. Here, I will provide the empirical evidence for each variable in turn, from studies prior to Clark et al. (2007).

Income

Economic theory suggests that an increase in real income in the destination country should increase immigration, while an increase in income in the source country should decrease emigration. Karemera et al. (2000) studies migration flows to North America from 1976 to 1986 and finds that the income of the receiving country is one of the two most influential determinants of migration (the population of the source country is another important determinant), and that the income in the destination country is positively related to immigration flow. Borjas (1987) has studied migrants to United States from 41 countries between 1951 and 1980, and obtained similar results. He finds that the immigration rate is negatively related to the income in the home country and the distance to the receiving country.

Education

Borjas (1991) in his study of migrants to the United States, Canada and predicts that emigration will increase with an increasing mean education level in the source country and decrease with an increasing variance of years of education in the source country. He found

12 that emigration increases with increasing education level in the source country. Mayda (2010) has studied migrants to fourteen OECD countries for 1980–1995, finding that income and education effects on migration tend to have opposite signs. If migration from a specific source country is decreasing with increasing income, then the level of education tends to have a positive effect on the migration rate, and vice versa.

Distance

Sjaastad (1962) suggests that distance between the source and destination country is a good proxy for the migration cost, both psychological and monetary. He claims that, as distance between countries increases, migration would decrease. Pedersen et al. (2008) studied migration flows to OECD countries and found that the distance between countries measured in kilometers is significant and negatively related to the migration flow. The result is in accordance with Borjas (1987) who studied migration to United States, finding that the distance from United States to the source country is negatively related to the emigration rate.

Landlocked and English speaking

The cost of migration is also associated with whether the country of origin is landlocked and whether it is English speaking. Kim and Cohen (2010) studies international migration flows to 17 Western countries and migration outflows from 13 of those countries. They find that whenever the origin or destination country was landlocked, the migration in- and outflows were reduced. The common official language between the countries had a significant positive effect on migration flow. Borjas (1987) finds that English proficiency in the origin country increases migration flow to the United States from that country. Karemera et al. (2010), in contrast, find no such evidence when studying migration to North America from 1976 to 1986. Moreover, Mayda (2010) finds that common language is not statistically significant when analyzing migration to fourteen OECD countries between 1980 and 1995.

Stock of previous immigrants

Several researchers suggested including the stock of previous immigrants into analysis. Massey et al. (1994) argue that a “cluster” of people from the same cultural background reduces the psychological cost of migration through a “family and friends effect”, and therefore positively affects the propensity to migrate from a particular country. Boyd (1989) claims that the “family and friends effect” affects migration rates through information sharing

13 and social and economic help in community networks in the destination country, thus increasing an individual’s utility in the destination country and decreasing the migration costs. Lundborg (1991) studied Nordic migrants moving to , finding that the previous stock numbers of expatriates in the destination country matters, and the significance of the factor increases with an individual’s age.

Inequality

Roy’s (1951) model suggests that an increase in inequality negatively affects migration rate when income into destination country is more equally distributed than in the source country (negative selection of immigrants). Increase in inequality positively affects migration when income in the source country is more equally distributed (positive selection of immigrants). Consistent with the model, Borjas (1987) finds that inequality in the source country negatively affects emigration, which implies negative selection of immigrants to the United States.

Age

Becker (1993) treats migration as an investment in human capital where the main objective of an individual is to maximize all of his or her future income net of migration costs. He claims that age matters when making the investment decision because younger individuals have more years to earn the higher income in the destination country. Therefore, I would expect that a higher share of young population in the source country would positively affect migration rate. Gallaway (1969) studies the impact of age on the geographic movement of male workers in the US and finds that for each additional year of age, a worker requires a higher earning difference in order to make the geographical move. Lundborg (1991) studied age effect on migration for migrants from , Norway and to Sweden, finding that elasticities of migration with respect to real wages at the receiving country are stable across different ages. Lundborg argues that this is due to short-distance, temporary migration, and that Becker’s argument regarding maximizing the present value of all future earnings applies better to a longer-distance, permanent migration.

To summarize, many different economic and demographic determinants of migration have been tested by empirical researchers. They have confirmed the predictions of international migration theory that income differences drive migration, but also suggested and successfully tested other determinants. In the next chapter, I will present the international migration model

14 developed by Clark et al. (2007) where they analyze the migration flow to United States from 1971 to 1998 and quantitatively assess the importance of migration determinants for migration decision making. They include all nine variables described in this section, and thus study a more comprehensive set of determinants than has been previously studied by any single researcher.

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4 Modelling migration to Norway

4.1 Modelling migration

In order to analyze migration to Norway, I am going to use the international migration model developed by Clark et al. (2007) for migration to the United States. It is therefore useful to present the model and the most important results of their research.

Clark et. al. (2007) set up a model and estimate coefficients for immigration determinants to the United States. They use panel data from 1971 to 1998 and isolate the economic and demographic variables in the source countries that affect migration rates to the United States. They model the decision to migrate based on the Roy (1951) model, and advanced by Borjas

(1987). In the home country (h), an individual (i) with a specific skill set (si) receives the wage wh(si). If the individual is to migrate to a destination country (d), the wage he or she would receive is wd(si). Migration cost, monetary and non-monetary, is c. An individual will migrate from country h to country d, if the net benefits function

Ii = wd(si) - wh(si) – c is positive.

The probability that an individual will migrate from country h to country d is

pi = Prob(Ii > 0).

That is, the probability of positive payoff considering different wages for the same skill set in the source and destination country, net of the migration cost.

It is assumed that for all individuals in country h, both wh(si) and wd(si) are normally distributed with means μh and μd, respectively. Summing all individuals in the home country h, the emigration probability (P) to destination country d is:

P = 1 – Ф (-μd + μh + c )/σI),

where Ф is the standard normal distribution function and σI is the standard deviation of the net 2 2 2 benefit function I, σI = σd + σh - 2σdσh.

The model implies that a higher average income in the destination country relative to the income in the source country would increase the probability of migration, while a higher cost

16 of migration would decrease the migration flow. Migration flow would also increase with an increased average skill set level in the home country if wage dispersion in the destination country is higher than wage dispersion in the source country (function wd(si) is steeper than wh(si)).

As a dependent variable, Clark et al. use the number of immigrants to the United States divided by the total population of the source country. The variable represents the propensity to migrate from the origin country to the United States. Independent variables consist of the relative income in the source country (GDP per capita in source country divided by GDP per capita in destination country), relative education level (mean years of education in the source country divided by mean years of education in the destination country), age composition of the population in the source country, inequality and four variables that represent costs of migration: distance from source to destination country, whether the source country is landlocked, whether it is English speaking and the stock of immigrants from the source country in the United States in the previous year.

Clark et al. uses the following specification in order to find the estimates of determinants of migration to the United States:

ln(mig/pop)j,t = β0 + β1 (yj/yUS)t + β2 (syrj/syrUS)t + β3agej,t + β4(ineqj/ineqUS)t +

2 β5(ineqj/ineqUS) t + β6povj,t + β7distj + β8landj + β9engj + β10(stockj,t-1/popj,t) +

2 β11(stockj,t-1/popj,t) + β12Xr,j,t(stockj,t-1/popj,t) + β13Xe,j,t(syrj/syrUS)t + β14Xd,j,t +

β15Xa,j,tcivj,t + β16Xirc,j,t + β17Xb

The dependent variable is the propensity to migrate to the United States from country j in year t, expressed as the total migrants from country j divided by the total population of the country in year t. Natural log is taken because the propensity to migrate is bounded at zero.

β1 is the coefficient on average income in country j relative to the United States, and is expected to be negative, because we expect emigration to decrease as income in the source country increases. The second term, (syrj/syrUS)t is the average years of education in country j relative to the United States. We expect emigration to increase when the mean years of schooling increases in the origin country. The coefficient on average years of schooling, β2, is expected to be positive. 17

Variable agej,t is the share of population aged 15–29 in the source country in year t. It is expected to have a positive effect on the propensity to migrate, thus Clark et al. expects β3 >0.

The fourth variable is inequality in the source country relative to inequality in the United States, represented by the Gini coefficient. According to the Roy model, when the recipient country is relatively rich compared to the source country, the effect of inequality will follow an inverse U-shape, as explained in section 3.1; therefore, Clark et al. enters inequality in quadratic form and expects β4 to be positive and β5 to be negative.

Variable poverty (povj,t) is the proportion of the source country population living in poverty. Complete data on the share of population living in poverty is not available for all origin countries; but, using cross-country estimates, Ravallion (2004) discovers that the share of population living in poverty in a country is inversely related to its income per capita squared. Therefore, Clark et al. use a proxy for poverty as suggested by Ravallion (2004), calculated as an inverse of the country’s income squared. Clark et al. expect β6 to be negative, because as poverty decreases, more people can afford to migrate.

Moreover, the authors expect β7 to be negative because migration costs increase with distance travelled. Variable landj is whether the home country is landlocked, and it is expected that migration costs increase if it is, therefore β8<0.

Migration costs are reduced if the source country is English speaking and if there are a lot of immigrants from the source country in the United States; therefore, β9 and β10 are expected to be positive. Clark et al. expect the “friends and relatives” effect to be smaller over time, so the stock variable enters in quadratic form and the coefficient β11 is expected to be negative.

The remaining variables in the Clark et al. study represent different migration policies in the United States and have no relevance to the case of Norway.

Table 2 illustrates the results found by Clark et al.

First, they estimate the model by running a fixed effect regression on the panel data of 81 countries from 1971 to 1998. They find that variables income, inequality, poverty and immigrant stock are all significant and have coefficient signs as expected, while education and share of young population are found to be insignificant (see first column of table 2).

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Second, Clark et al. estimate the model again using a “between” estimator, which allows the inclusion of variables with no time variation, see column (2).

In accordance with economic theory and previous empirical studies, Clark et al. find that emigration is negatively related to relative income in the source country. Propensity to emigrate is found to be positively related to inequality, but affects it at a decreasing rate (the square of the variable has a negative coefficient), as predicted by the Roy model, where the destination country is relatively rich. Previous immigrant stock also positively affects immigration, in accordance to theory that more expatriates in the destination country, the lower the psychological and financial costs of the migration.

Clark et al. find that in addition to income, distance also has a strong negative effect on migration. English speaking has a positive effect on migration, while a country being landlocked and the share of the young population in the sending country are found to be insignificant.

Table 3. Explaining U.S. Immigration

Immigration Rate Regression (81 countries, 1971-1998)

Dependent variable: Log (immigrants/source-country population)

Fixed Effects "Between" estimator

Constant -8.74 -16.89

(13.4) (8.4)

GDP per capita ratio (foreign/U.S.) -1.49 -1.77

(7.1) (2.6)

Schooling years ratio (foreign/U.S.) -0.02 3.08

(0.1) (4.0)

Share of population aged 15-29 (foreign) 0.49 10.32

(0.7) (1.6)

Inequality ratio (foreign/U.S.) 1.50 7.51

19

(3.5) (2.0)

Inequality ratio squared -1.18 -3.07

(6.2) (1.9)

Poverty: Inverse of income squared (foreign) -0.20 -0.33

(4.2) (4.2)

Distance from U.S. -0.09

(2.1)

Landlocked -0.33

(1.1)

English-speaking origin 0.31

(1.0)

Immigrant stock (t-1)/foreign population 8.63 89.90

(4.2) (5.9)

(Immigrant stock (t-1)/foreign population)2 -47.34 -418.74

(4.9) (8.4)

R squared (within) 0.15

R squared (between) 0.80

Source: Clark et al. “Explaining U.S. Immigration, 1971- 1998”. Note: t-values reported in parenthesis. Coefficients significant at 95% significant level are in bold.

4.2 Adapting model to Norway and data collected

In my study, I adapt the migration model developed by Clark et al. to better fit the case of Norway for the years 2003 to 2013. In addition to the variables mentioned in the previous section, I include a binary variable for whether the source country is in the EU/EEA in a particular year. Immigration is partly determined by individual incentives and constraints, and partly by legal constraints. It is natural to assume that a source country being a part of

20

EU/EEA would affect the migration rate to Norway, because there are no legal restrictions to move to Norway from these countries.

Even though Norway does not have English as an official language, I include the variable “English speaking” in the study. This is because most Norwegians speak English and thus, knowing a language that people in the destination country understand, might reduce both financial (easier to find a job) and psychological costs (easier to communicate with the local community) of migration. “English speaking” is a binary variable, where the source country is considered as an English-speaking country if English is the de jure or de facto official language of the country.

To estimate the model for migration to Norway, I used panel data for 76 countries covering 11 years from 2003 to 2013 (see Table 4). In total, there were immigrants from 220 countries in Norway in 2013 (Statistics Norway). Although it was not possible to include all of the countries of origin because one or more explanatory variables were not available for some of them, the 76 countries included in the study covers over 88% of all immigrants that moved to Norway over the 11 years studied (see Table 5).

Similarly to Clark et al, I use the following specification in the analysis:

ln(mig/pop)j,t = β0 + β1 (GDPj/GDPNO)t + β2 (eduj/eduNO)t + β3 agej,t + β4 (ginij/giniNO)t +

2 2 β5 (ginij/giniNO) t + β6 povj,t + β7 distt + β8 (stockj,t-1/popj,t) + β9 (stockj,t-1/popj,t) + β10landj +

β11engj + β12 euj,t

The dependent variable in the analysis, (mig/pop)j,t , is the number of country j nationals that moved to Norway in year t divided by the population of country j. The variable illustrates the propensity to migrate from a specific country to Norway. Data for the number of immigrants were obtained from Statistics Norway (ssb.no), while the countries’ population numbers were taken from the World Bank Databank. I take the natural logarithm of the dependent variable because propensity to migrate is bounded at zero.

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Table 4. The Balanced Panel For Immigration to Norway, 2003 - 2013

Countries in the Balanced Panel

EU/EEA Austria, , , Croatia, Czech Republic, Denmark, Estonia, Finland, , Germany, , Hungary, , Ireland, , Latvia, Lithuania, , Poland, , , Slovakia, Slovenia, , Sweden, , (27)

Europe excluding EU/EEA , , Macedonia, Russia, Turkey, (6)

Asia , , , , , , , , , , , , , , , , , (18)

North America Canada, , U.S. (3)

South America , , , , , (6)

Africa , , Congo, , , , , , , , , , , , (15)

Oceania Australia (1)

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Table 5. Numbers in the Panel and Total Immigration, 2003 - 2013

Year Immigrants in Sample Total Immigration Percent in Sample

2003 22 969 26 788 85,7 %

2004 24 010 27 863 86,2 %

2005 27 513 31 356 87,7 %

2006 33 326 37 429 89,0 %

2007 48 341 53 498 90,4 %

2008 53 393 58 820 90,8 %

2009 50 117 56 680 88,4 %

2010 58 767 65 065 90,3 %

2011 64 408 70 759 91,0 %

2012 60 545 70 013 86,5 %

2013 57 845 66 936 86,4 %

The first independent variable, (GDPj/GDPNO)t , is the ratio of average income in country j in year t relative to Norway. In order to calculate the ratio, I used purchasing power parity adjusted GDP per capita with constant 2011 prices, obtained from the World Bank Databank.

I expect migration to decrease when income in the source country increases, therefore β1 is expected to be negative.

The second term is the ratio of average years of education in country j compared to Norway. The variable used in regression is the mean years of schooling of people aged 25 and older. Education data was obtained from the UNESCO Institute for Statistics, where it is presented with yearly estimates for years after 2005 and with five-year intervals for the period before 2005. I have linearly interpolated the data in order to obtain yearly estimates. Migration rate is expected to increase as the mean years of schooling in the source country increases; therefore,

I expect β2 to be positive.

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Variable agej,t is the share of the population in the source country that is15-24 years of age. Data was obtained from the ’ World Population Prospects, where it was presented at five-year intervals. I have linearly interpolated the data in order to obtain yearly estimates. Young people are expected to be more prone to migrate; therefore, the coefficient is expected to have a positive effect on the migration rate.

The fourth variable, (ginij/giniNO)t , is the ratio of inequality in the source country relative to Norway. Inequality is measured by the Gini coefficient of household income. Data was obtained from the World Bank Databank and the WIDER Institute, combined and, where necessary, linearly interpolated between periods. Inequality also enters the equation in quadratic form because according to the Roy model, when the receiving country is relatively rich, effects of inequality will follow an inverse U-shape, as explained in section 3.1. I expect

β4 to be positive and β5 to be negative.

The next variable, povj,t, represents the share of the country of origin population living in poverty. I use the inverse of the country’s income squared as a proxy for poverty, the same way that Clark et al. did, as explained in the previous section. I expect the coefficient β6 to be negative, because as poverty decreases, more people would be able to afford to migrate. Including this variable in the regression might cause a multicollinearity problem because variable “poverty” could be correlated with income, causing coefficients on at least one of these variables to be inaccurately estimated (Stock and Watson, 2012, p.244). Table 6 shows the results for multicollinearity diagnostics from STATA. VIF (Variance inflation factor) measures how much the variance on the estimated regression coefficient is inflated due to collinearity and is used to show how severe multicollinearity is in a regression analysis.

Table 6. Multicollinearity Diagnostics of Independent Variables

Variable VIF GDP per capita ratio 2.96 Mean years of schooling 1.73 Share of population aged 15-24 4.07 Inequality ratio 1.38 Poverty: Inverse of income squared 1.26 Immigrant stock 1.17 Distance 2.14 Landlocked 1.06 English speaking 1.30 EU 2.79

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It is calculated by regressing kth variable on the remaining variables and then using the R- th 2 squared value obtained to calculate the VIF for the k variable: VIFk = 1 / (1 – Rk ). If VIF is more than 5, the variable is highly correlated with other variables. Table 6 shows that VIF is below 5 for all of the variables included in the regression, suggesting that none of the independent variables are linear combinations of the others.

Variable distt is the distance between Norway and the source country, and is a proxy for the cost of migration. For distance, I used the air distance, in kilometers, between Oslo and the capital city of the country of origin, obtained from the World Atlas (worldatlas.com). We expect migration to decrease as the cost of migration increases. Thus, the coefficient on distance is expected to be negative.

Variable, (stockj,t-1/popj,t), is the number of immigrants living in Norway from country j in year t -1, relative to the population of the origin country in year t. For the stock of immigrants, I used data obtained from Statistics Norway (ssb.no). I lagged the numbers one period so that the stock of immigrants in year t is the number of immigrants living in Norway on 31st December in year t -1. I expect the stock of previous immigrants to have a positive effect on migration as it reduces both psychological and monetary costs of migration, but I expect the effect to decrease as the stock increases. β8 is therefore expected to be positive and β9 is expected to be negative.

“Landlocked” is the binary variable for whether a country is entirely enclosed by land. If a country is landlocked, I expect the migration costs to increase, thus decreasing the migration flow. Therefore, I expect β10 to be negative.

Further, migration costs are expected to decrease if the source country is English speaking.

Variable engj is the binary variable for whether English is an official language or spoken by a significant number of the population in the origin country and it is expected to have a positive impact on migration flow. Therefore, I expect β11 to be positive.

The last variable, euj,t, is a dummy variable for whether country j is part of EU’s single market. In other words, if the origin country is part of EU, EEA or EFTA in year t. Norway applied restrictions on the free movement of labor to some of the new EU countries that joined the union in 2004, 2007 and 2013. Romania and Bulgaria, both joined EU in 2007, but the restrictions of moving to Norway freely were removed only in 2012, eight out of ten

25 countries that joined EU in 2004 did not have free access to work in Norway until year 2006, and Croatia joined EU in 2013, but Norway removed the restrictions of free movement of labor in 2014. In this study, a country is regarded as part of EU/EEA only from the year that all restrictions of moving to Norway were removed. Data was obtained from the official website of the European Union (europa.eu). I expect migration to increase as the legal constraint of migration is removed; therefore, β12 is expected to be positive.

In the next section, I will present the empirical results of the international migration model adapted to Norway.

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5 Econometric results

5.1 Results

I estimated the model on panel data for immigration to Norway by nationality for 76 countries from 2003 to 2013 using the “between” estimator model for the panel dataset. Using the “between” estimator rather than the “fixed effects” model, allows including variables that do not vary over time: whether a country is part of EU/EEA, distance between Norway and country of origin, whether the source country is landlocked and if it is predominantly English speaking. The results for estimation are shown in table 7.

The first column of table 7 shows regression results for all countries in the dataset. Only coefficients on previous immigrant stock and distance to Norway are significant at the 5% significance level. The coefficient on the previous year’s immigrant stock is significant and positive, while its square is negative, as expected. In addition, the distance from the source country to Norway affects the migration rate negatively, as predicted by the theory.

Columns (2) and (3) in table 7 show the regression results when EU/EEA countries and non- EU/EEA countries are analyzed separately. Being part of the European Single Market allows the free movement of goods and labor, and thus removes the legal constraint of migrating to Norway. All countries that were part of the EU/EEA as of December 31st 2013 are included in the second equation of table 7 and countries that were not part of EU/EEA at the end of 2013 are included in the third equation of table 7.

The results in column (2) show that the model fits data for EU/EEA countries much better than data for all countries. R-squared is 0.95, meaning that 95% of the data fits the regression line. All significant variables are of the expected signs. Relative income in the source country affects the migration rate negatively. Share of population aged 15-24 has a positive coefficient, implying that younger individuals are more prone to migrate. The coefficient on inequality ratio is positive (though significant only at 10% significance level), implying that an increase in inequality in the origin country positively affects the migration rate. This suggests negative immigrant selection in the source country.

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Table 7. Immigration Rate Regression

(76 countries, 11 years; Dependent variable: Log immigrants in year t/source-country population)

(1) All (2) EU/EEA countries (3) non-EU/EEA

Constant -15.49 -24.28 -15.86

(5.44) (4.37) (6.19)

GDP per capita ratio (foreign/Norway) -0.27 -2.64 1.47

(0.32) (2.43) (1.78)

Schooling years ratio (foreign/Norway) 1.11 -0.82 0.78

(1.77) (0.95) (1.24)

Share of population aged 15-24 (foreign) -3.24 38.57 6.03

(0.45) (2.29) (0.88)

Inequality ratio (foreign/Norway) 4.85 14.57 1.48

(1.43) (1.95) (0.48)

Inequality ratio squared (foreign/Norway) -1.44 -5.54 -0.45

(1.28) (1.95) (-0.45)

Poverty: inverse of income squared (foreign) 0.00 0.13 0.00

(0.97) (0.65) (1.62)

Distance from Norway -0.0001 -0.001 -0.0001

(2.91) (3.04) (1.31)

Landlocked 0.29 -0.11 0.77

(0.95) (0.38) (2.41)

English-speaking origin -0.21 -0.59 -0.24

(0.74) (1.08) (0.97)

Immigrant stock (t-1)/foreign population in t) 17.58 13.01 92.01

(5.83) (6.24) (3.68)

Immigrant stock squared -9.99 -6.99 -936.57

(4.41) (4.63) (2.10)

EU/EEA 0.61 2.25

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(1.23) (3.49)

R-sq (between) 0.7229 0.9547 0.5775

Number of observations 836 297 539

Notes: t-values are reported in parenthesis. Estimates in bold are significant at 5% significance level

The coefficient on inequality squared is negative in accordance to the Roy model. The previous year’s immigrant stock has a strong positive effect on the migration rate. As predicted, immigrant stock squared has a negative coefficient, showing that the “friends and relatives effect” diminishes as the number of immigrants from source country increases. Distance between the country of origin and Norway has a negative effect on immigration rate.

Being part of the EU/EEA has a strong positive effect on the propensity to migrate to Norway. The variable is included in this analysis because not all countries in the EU/EEA sample were members of EU/EEA for the whole period studied. 10 out of 27 countries in the sample joined the EU after 2003.

Variables “landlocked”, whether the origin country is primarily English speaking, average years of education and “poverty” are found to be insignificant. Mean years of schooling in the source country might be insignificant due to interpolation of the data between the five-year periods, which reduces the quality of the data. For the poverty variable, I used the inverse of average income squared as suggested by Ravallion (2004), but some researchers argue that this is a questionable proxy.

The last column of table 7 shows the regression results when only non-EU/EEA countries are studied. The results differ considerably from results obtained by Clark et al. for migration to United States and from the results for EU/EEA-countries only in this study.

Only two variables are found significant; the previous stock of immigrants, which has a strong positive effect on the migration rate, and whether the origin country is landlocked. Coefficient on the latter, however, is estimated to be positive, opposite to what was predicted by the theory. Relative income, education, share of young population, inequality, poverty, English speaking and distance variables are found insignificant. R squared for regression of non-EU/EEA sample is 0.58, meaning the model can explain 58% of the data, a number considerable lower than for the EU/EEA countries’ sample.

29

In order to capture the full effect of the economic determinants of migration from EU/EEA countries, I reestimate the regression for EU/EEA sample, only including the variables that were significant at 5% and 10% significance level in the second equation of table 7. All coefficients get attributed higher values and are estimated to be more significant, except for share of population aged 15-24, which is now found to be insignificant. This is in accordance to results found by Lundborg (1991), who studied migration flows of Nordic migrants to Sweden. He argued that age does not affect short distance, temporary migration.

Table 8. Immigration Rate Regression- EU/EEA-countries

(27 countries, 11 years. Dependent variable: Log immigrants in year t/source-country population)

EU/EEA-countries

Constant -23.63

(4.84)

GDP per capita ratio (foreign/Norway) -4.58

(3.92)

Share of population aged 15-24 (foreign) 26.87

(1.74)

Inequality ratio (foreign/Norway) 17.97

(2.54)

Inequality ratio squared (foreign/Norway) -6.56

(2.42)

Distance from Norway -0.001

(3.04)

Immigrant stock (t-1)/foreign population in t) 13.44

(6.57)

Immigrant stock squared -6.97

30

(4.40)

EU/EEA 1.34

(2.31)

R-sq (between) 0.9212

Notes: t-values are reported in parenthesis. Estimates in bold are significant at 5% significance level.

The results imply that the model well explains migration from EU/EEA countries to Norway, but fails to explain migration to Norway from non-EU/EEA countries. The reason for this might be that the model is misspecified. There might be other important determinants in addition to, or instead of, income, inequality, education, poverty and migration costs affecting migration, that are not included in this model. Furthermore, there is a strong reason to believe that migrants from EU/EEA-countries and migrants from non-EU/EEA countries fundamentally differ (see table 9), requiring two different models in order to understand factors influencing migration decision-making in non-EU/EEA countries.

Table 9. Reason for migration to Norway by origin, 2013 (Percent of total for each source)

Total immigration Work Family reunion Refuge Education Other

Europe 32 610 65.0 % 28.5 % 0.4 % 5.4 % 0.6 %

Others 21 908 10,6 % 37,2 % 33,0 % 18,6 % 0,5 %

Africa 7 706 2.5 % 30.5 % 61.0 % 5.3 % 0.7 %

Asia 11 243 12.5 % 39.4 % 19.3 % 28.3 % 0.5 %

North America 1 099 35.7 % 44.8 % 0.2 % 19.3 % 0.1 %

South America 1 208 16.3 % 57.5 % 5.9 % 20.2 % 0.2 %

Oceania 232 56.9 % 29.7 % 0.0 % 13.4 % 0.0 %

Total 54 518 43.2 % 32.0 % 13.5 % 10.7 % 0.6 %

Source: Statistics Norway, table 7113. Notes: First time immigrants to Norway by nationality.

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5.2 Discussion

In this section, I will discuss why the results differ significantly when I analyze EU/EEA countries and non-EU/EEA countries separately. Additionally, I will discuss why results in this study differ from results found by Clark et al. (2007) when studying migration to the United States.

The model in the study is based on an assumption that the sole reason for migration for an individual is to maximize his or her income net of migration costs. This is a very strong assumption and, while it explains the economic migration, it would fail to explain migration caused by war, family reunions, political reasons, etc. It is too simplistic to assume individuals do not consider factors other than income differentials when making the decision to move. Some of the other important determinants to consider could be safety in the destination country, weather conditions, culture and religious freedom, none of which are included in the model.

Table 9 illustrates the main motives for migration to Norway by origin in 2013, splitting the immigrants to “European” and “Others” first, then dividing “Others” into smaller regions. It shows that, for European nationals, the main purposes for migration were employment and family reunion: 65% of Europeans moved to Norway because of work and 28.5% for family reunion. In addition, 5.4% of European migrants came to Norway for the purpose of education and only 0.4% to seek asylum in 2013.

Additionally, the table shows that for immigrants other than European nationals only 10.6% were economic immigrants, while 33% were refugees. 37.2% moved to Norway to reunite with their families and 10.7% for education purposes. A significant share of immigrants, both European and non-European, came to Norway with the purpose of family reunion. However, this reason is probably strongly related to work immigration for EU/EEA-countries and to refuge for non-EU/EEA countries.

We can conclude that the reasons for migrating to Norway from Europe and from the rest of the world fundamentally differ, as the main purpose of migration for European nationals is employment, while for other nationals it is family reunion and refuge. The differences in the purpose of migration explain the inconsistency between the regression results for EU/EEA countries and non-EU/EEA countries. While European migrants are economic migrants,

32 moving to Norway because of higher returns on labor, reasons other than income maximization apply to non-Europeans. The model in this paper is based on the assumption that migrants geographically move in order to maximize the present value of all their future incomes. While this might be true for economic migrants, a model where the utility maximization is in focus would apply better to explain migration of non-European migrants, whose main purpose of migration is asylum. For further studies of migration flows from non- European countries, I would suggest including variables like source country stability, whether the country of origin is at war, and political and religious freedom.

Furthermore, I would like to discuss why there is a significant difference between the results found by Clark et al. for migration to the United States and the results for migration to Norway found in this study.

The sample of immigrants used for analysis for migration to the United States differs considerably from the sample of immigrants to Norway studied here. Table 10 shows the reasons for migration to the United States by country of origin in 1998. The share of refugees and asylees of all immigrants to the United States in 1998 were 8.3%, while in Norway in 2013 refugees accounted for 13.5% of all immigrants and 33% of immigrants of other nationalities than European. The share of immigrants who are refugees is much higher in Norway than in the United States, implying different determinants for migration decision- making between immigrants who move to the United States and immigrants who move to Norway.

In addition, most of the asylees and refugees that migrated to the U.S. in 1998 were of European origin (Singer and Wilson, 2007), while 64% of refugees who came to Norway in 2013 were from African countries (Statistics Norway).

Moreover, in the United States, European immigrants (including European asylees and refugees) were on average better educated and had higher household incomes than both average native-born and foreign-born populations of U.S. (Zong and Batalova, 2015). Unfortunately, there is no data currently available on the education of refugees who move to Norway. However, average schooling years in Norway in 2013 were at least twice as high as that for any African country included in the study (UNESCO Institute for Statistics), with the exception of South Africa (average schooling years in South Africa was 9.9 years, while in Norway it was 12.6 years), and thus it is safe to assume that refugees from African countries,

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Table 10. Class of Admission to United States by Source Area, 1998

(Percent of total for each source)

Source: Clark et al. (2007), Explaining U.S. immigration, 1971 – 1998

on average, are not better educated than an average native born in Norway. In addition, only approximately 50% of refugees in Norway were employed in 2013, while the number for the whole population was approximately 70% (Statistics Norway), implying refugees, on average, have a lower skill set than the average population in Norway.

To conclude, in the period studied, the share of refugees as of total immigration flow was significantly higher in Norway than in the U.S. In addition, there were important differences between refugees’ origin: the majority of U.S. refugees came from Europe, while most of the refugees who moved to Norway were African.

The results of the model imply that while the model well explains migration flows of income maximizing individuals, it fails to explain migration caused by other determinants, such as political prosecution and war.

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5.3 Econometric Issues

In this section, I will discuss potential econometric issues that could affect the regression results discussed in the previous sections and pose a threat to internal validity of the analysis. Multicollinearity was discussed in section 4.2, in this section I will in turn describe omitted variable bias, measurement errors, simultaneous causality bias and sample selection bias.

Omitted Variable Bias

Omitted variable bias occurs when a variable, that is correlated with the included independent variable and is a determinant of the dependent variable, is not included in the regression (Stock and Watson, 2012, p.224). The effect of the variables included in the model gets over- or underestimated, thus creating the “bias”. The underlying assumption for the model analyzed in this study was income maximization. Therefore, variables such as whether the source country is at war and the level of political or religious freedom in the source country were not included. However, if migration is not primarily driven by economic reasons, these are important determinants to consider, and excluding them from the analysis might cause omitted variable bias. I believe that omitted variables are the main reason the model studied here was not suitable for explaining migration to Norway from non- EU/EEA countries, as the migration from non- European countries is usually driven by other purposes than economical. Adding the omitted variables to the model would solve the omitted variable bias, however the data are not available.

Measurement errors

Measurement errors or errors-in-variables occur when the independent variables are measured imprecisely, which causes bias in the estimated coefficients (Stock and Watson, 2012, p.362). Several variables in the study were linearly interpolated between five- year periods. Among these were average years of schooling, share of population aged 15-24 in the source country and inequality ratio. When linearly interpolating, I assumed that the growth between periods was constant, which is a strong assumption and in most cases probably not a correct one. Say, data was available for periods one and three, but not for period two, and the value of the variable in period three (X3) was higher than the value in period one (X1). To obtain a balanced panel I estimated the value for period two (X2), using the following formula: X2 =

X1 + (X3 – X1) / 2. Which gives X1 < X2 < X3. However, the true X2 might be equal to or lower

35 than X1, resulting in measurement errors in the independent variables and bias in the estimated coefficients on the affected variables. Solution to the measurement errors is to obtain more accurate data, such that interpolation is avoided. However, better quality data was not available at the time of this study.

Simultaneous Causality

Simultaneous causality bias arises when both the independent variable affects the dependent variable, and the dependent variable affects the independent variable at the same time. Simultaneous equation bias leads to error term and the independent variable being correlated, and the estimators being inconsistent (Stock and Watson, 2012, p.367). The dependent variable in this analysis is the propensity to migrate to Norway from country i. One of the independent variables is the immigrant stock in Norway from country i. Simultaneous causality bias arises if the stock of previous immigrants in Norway affects the propensity to migrate to Norway and, simultaneously, the propensity to migrate affects the stock of immigrants in Norway. Simultaneous causality bias is avoided in the model by using the stock of immigrants from country i in the previous year, such that the propensity to migrate in the current year cannot affect the previous immigrant stock.

Sample Selection Bias

Sample selection bias arises when data for the analysis is selected in a way that the sample studied does not accurately represent the population that was intended to be analyzed (Stock and Watson, 2012, p.365). In this study, the population that was intended to be studied was all countries that Norway has received immigrants from over the years 2003 – 2013. There were immigrants from 220 countries in Norway in 2013 (Statistics Norway). However, only 76 countries were included in the analysis due to missing data for one or more variables for the remaining countries. Assuming data were missing mostly for low- income countries, a significant number of poor countries were excluded from the sample, leading to sample selection bias. In case of sample selection bias, the error term and the independent variables will be correlated, leading to bias in the estimated coefficients. Obtaining data for the missing countries would solve the sample selection bias; however the data was not available.

To summarize, in this section I have described four potential econometric issues that the model analyzed in this study could be affected by. The solution to three of them is to use

36 more accurate data when estimating the model. However, at the time of this study better quality data were not available.

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6 Conclusions

I have used panel data for 76 countries from 2003 to 2013 in order to study immigration to Norway. The main purpose of the study was to estimate economic and demographic determinants of migration to Norway and compare the results to the United States. European countries that were part of the EU or EEA as of 2013 were studied separately from the rest of the world. I used a standard economic model of migration, previously advanced by Clark et al. to study migration flow to the United States. The model includes relative income and education, relative inequality, the cost of migration and the stock of previous immigrants as independent variables. Legal constraints of migration are entered as a binary variable for whether a country was in the EU/EEA in a particular year.

When analyzing the sample of 76 countries as a whole, it is found that previous immigrant stock has a strong positive effect on migration, while the distance from the source to destination country affects migration negatively. Furthermore, being a member of the EU/EEA has a positive effect as it reduces the legal constraint of migration. Variables relative income, education, share of population aged 15-24, inequality ratio and poverty are found to be insignificant.

For EU/EEA countries, the results offer strong support for the migration model used. Most of the coefficients are of the expected signs. It is found that the relative income and distance to the destination country negatively affect migration flow, while income distribution (measured by inequality), previous immigrant stock and being part of EU/EEA have a strong positive effect on the migration rate as predicted by theory. In addition, income distribution in the source country is found to have a nonlinear effect on migration as predicted by the Roy model. Mean years of education, share of population and poverty variables are found to be insignificant, probably due to data interpolation between five-year periods.

For non-EU/EEA countries, the stock of previous immigrants in the destination country is found to be the single most significant determinant. All other economic and demographic determinants are found insignificant, and often of an opposite sign than expected. The divergent results can be explained by the different purpose of migration for individuals from EU/EEA countries and non-EU/EEA countries. For most of the European migrants, the main

38 objective of migration is employment, which is consistent with the underlying assumption for the model used that individuals migrate in order to maximize the present value of their expected income. However, this assumption is not correct for most of the migrants from non- EU/EEA countries, as the main reported purpose for migration is refuge and family reunion (the latter of which is most likely related to refuge). Therefore, a different model should be used in order to estimate the determinants of migration when the individuals have different objectives for migration other than income maximization. Thus, further research is needed that would include additional country specific variables.

39

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