Master Thesis
Impact of immigration policies on real wages in
OECD countries
University of Groningen
Hildo Venhuizen
M.Sc. International Economics & Business Student number: 2762714
E-mail: h.o.venhuizen@student.rug.nl July 5, 2019
Supervisor: Co-assessor
Dr. A.A. Erumban Prof. dr. B. Los
Faculty of Economics and Business Faculty of Economics and Business
Abstract
The impact of immigrants on the economy gained new attention since the beginning of the European migration crisis in 2013. European citizens believe that immigrants are a burden to the economy, which ensured that far-right parties attracted more voters. However, attracting immigrants could be the answer to problems related to the ageing population and skill mismatches in OECD countries. This research contributes to the topic of selective immigration policies by analyzing the impact of immigration policies towards different immigrant groups on real wages in 17 OECD countries. The main findings are that liberal immigration policies towards asylum seekers increase real wages. Also, restrictive immigration policies regarding illegal immigrants and family reunification tend to have a positive impact on real wages.
1. Introduction
Immigration has become a sensitive topic in Organization for Economic Cooperation and Development (OECD) countries over the last decennia. Especially since the start of the European migration crisis in 2013. People fled their home- country to find better living conditions in Europe. Most of the refugees came from the Middle- East and Africa. They had to leave their countries because of wars, political instability or limited economic opportunities (Poddar, 2016). The European countries Hungary, Sweden, Austria and Germany faced the highest inflow of these immigrants. This led to rising concern in those countries, but also in other European countries.
The public opinion was and still is that immigrants are a burden to the economy because of the increased public spending on housing, food and integration into the labour market (Dullien, 2016). The International Monetary Fund (IMF) supports this opinion. The organization estimated that in the short- term, because of the inflow of immigrants, the budgetary expenses increased on average up to 0.1 percent of GDP in 2016 compared to 2014 (Aiyar et al., 2016). Another concern of the European citizens is that immigrants will substitute native workers and reduce social security benefits (Wike et al., 2016). This concern is justified for native workers who employ jobs in unskilled labour sectors. The IMF states that in the short-run immigrants will mainly find employment in unskilled jobs or the agricultural sector (Poddar, 2016). Even though the influx of immigrants is reduced, there is still not a perfect solution created for the European countries. As a response to the continuing public concern regarding immigrants, wing parties have attracted more voters. This is in line with the literature regarding right-wing parties. These parties, in general, gain support because of the fear that immigrants disrupt society and compete for the jobs of native workers. (Rodrik, 2017). Consequently, a common political idea of especially far right-wing parties is to introduce more restrictive immigration policies in order to stop the inflow of immigrants (Lubbers et al., 2002). By doing so, it alleviates the unrest concerning the negative impact of immigration.
population poses a substantial challenge for future economic development. The second problem of OECD countries is regarding skilled and unskilled workers. To identify this problem, the OECD released the OECD Skills for Jobs Database in 2017. This database provides information about skill shortages, surpluses and skill mismatches in OECD countries. According to this database, 50 percent of the high-skilled occupations are hard-to-fill, compared to less than 10 percent for low-skilled jobs. Besides this, approximately 35 percent of the total workers do not occupy a job that matches their qualifications (OECD, 2018). The problem with this is that labour shortages and skills mismatches could hamper potential economic growth (IME, 2018).
Individual member countries are more and more aware of these problems. An example of this is The Netherlands. The Central Bureau of Statistics published in 2018 an article that the shortage of workers hampers the growth of companies and that these workers should come from other OECD member countries or outside the OECD like China, India or Middle- East countries (CBS, 2018). Also, Germany and Austria published a report with recommended strategies to address the labour shortages, but mainly on how to overcome skill mismatches (BMWI, 2016). Attracting immigrants could solve these problems by complementing the existing workforce and fulfilling the jobs with immigrants that native workers are not able or not willing to occupy. Immigrants are only beneficial to the country when they are integrated into the host country and assimilated to the job market requirements. Initially, not all immigrants are necessarily beneficial to the economy. Refugees are an example of this. They need to leave their country for various reasons and seeking asylum in the host country. However, after a period of integration and assimilation, this group of immigrants could turn into labour immigrants and contribute to the economy.
Hence, it is reasonable to state that immigrants could have both a negative and positive impact on the economy of a country. Over the years, researchers have focused on finding solutions to mitigate the negative impact of immigration and to stimulate the positive dimensions. By doing so, the area of selective immigration policies has gained more attention. Selective immigration policies could be an essential tool in selecting only immigrants, who will complement the workforce, rather than substitute the native workers. To understand this, it is essential to discuss how immigration policies discourage or trigger the inflow of immigrants.
policies contain the actions a country undertake when the immigrants are already at the border or illegal in the country. Examples of these actions are border control and implementing tools to enforce rules that counter illegal immigration. The immigration control policy also includes the way how immigrants are deported back to their home country. For this to happen, countries need to make agreements with the sending-country to readmit the illegal immigrants. The second dimension is all about preventing immigrants from coming to the country rather than waiting until the immigrants are already at the border. This is possible by analyzing the causes of immigration flows. Usually, immigrants are coming from developing countries. Therefore, it would be helpful to improve the situation in those countries and help them in the developing process. More restrictive immigration policies will reduce the inflow of these immigrants and more liberal policies towards immigrants will encourage the inflow of the immigrants. Despite the pressure to implement more restrictive policies, Helbling and Kalkum (2018) have demonstrated that over the period 1980 till 2010 policies have become more liberal for either European countries or non- European countries.
Immigration policies could impact the inflow of immigrants, and therefore, it is also possible to implement policies which are targeting specific groups of immigrants. These policies aim is to attract immigrants who are beneficial for the economy. Studies find that the impact on wages differs between legal and illegal immigrants (Peri, 2012; Friedberg and Hunt, 1995; Hanson, 2007). Moreover, researchers find that governments should focus on attracting skilled workers because these immigrants increase wages (Czaika and Parsons, 2017; Boeri et al., 2012; Czaika, 2018; Signorelli, 2019). These studies provide information for policymakers by indicating that immigrants differ on how they impact the economy and what kind of immigration policy would be needed to mitigate the negative impact of these immigrants and to encourage the inflow of immigrants who positively affect the economy.
The best way to measure the impact of immigrants on wages is to use immigration flows. However, correctly measuring the impact of different groups of legal immigrants and illegal immigrants on wages is difficult. This is because there is only data for immigration flows regarding immigrants with economic reasons, social reasons and humanitarian reasons but not for immigrants with cultural reasons for example. Also, it is difficult to keep track of all immigrants who are entering a country. Therefore, estimations of immigration flows are needed to study the impact of immigrants on wages. This is especially the case for illegal immigrants. Using estimates could lead to biased results. Even though looking into immigration policies may not solve this problem, it will help us to assess the impact of selective immigration policies targeting different groups of immigrants. This provides complementary insights on the impact of immigration on wages. The immigration policies are easy to measure because the data can be obtained by indexing the legislation and measure the changes of the legislation regarding immigrants over time.
In this study, a quantitative research is conducted in order to analyze the impact of five different immigration policies on real wages for 17 OECD countries. By doing this, the impact of each immigration policy on real wages can be determined. The five immigration policies are reflecting a country’s acceptance towards labour immigration (economic reasons), family reunification (social reasons), asylum seekers (humanitarian reasons) and co-ethnicity (cultural reasons). The fifth policy is towards the control mechanism, which reflects the restrictiveness towards illegal immigrants who want to enter the country. These five dimensions are chosen because it covers the five most important policy fields regarding the acceptance of countries towards legal immigrants with different reasons to immigrate, and illegal immigrants (IOM, 2017).
shared colonial history, language, religion and/ or ancestry” (IOM’s GMDAC, 2019). Finally, illegal immigrants are “foreign nationals whose presence in the host countries is in violation of the law or who have violated a condition of legal entry into the country” (Chiswick, 1988).
Looking into these five immigration policies helps to determine which policies regarding these groups of immigrants have a positive impact on real wages and which do not. This is an essential contribution towards a more selective approach regarding granting immigrants’ access to the country. It also gives governments an explanation to justify their immigration policies and actively select immigrants who could contribute to the economy by increasing real wages. Therefore, the research question of this study is: Which immigration policies towards different immigration groups have a positive impact on real wages? The main findings are that liberal policies towards asylum seekers have a positive impact on real wages. The results further indicate that also restrictive immigration policies on illegal immigrants have a positive effect on real wages. Surprising results are observed regarding the policy towards family reunification. Namely, a more restrictive immigration policy for family reunification increases real wages. This could be explained by the fact that the group of immigrants that are attractive to a country, because of their complementary skills, are not allowed to bring their family as a result of the more restrictive immigration policies. The costs of leaving their family should be compensated by higher wages.
The remainder of this paper proceeds as follows: Section 2 provides a discussion of the current literature about the determinants of wages. This section also introduces immigration policies as determinant for wages and states the hypothesis. In the third section, the methodology of this paper will be discussed. The fourth section describes the dataset that is used for this research. After that, the results will be presented in section five, followed by a robustness check in section 6. The results and limitations will be discussed in section 7 and the last section concludes.
2. Literature review
Oswald, 1995), productivity (Akerlof, 1984; Shapiro and Stiglitz,1984; Van Biesbroeck, 2015), inflation (Groshen and Schweitzer, 1997; Kandil, 2006;) and GDP growth (Levanon et al., 2016; Guerriero, 2012). In the following paragraphs, these studies are discussed in some detail and the underlying channels through which the identified variables influence wages. Further, this section will outline the impact of immigration and introduces immigration policies as new determinant of wages.
2.1 Wages and unemployment rate
The link between the unemployment rate and wages has received much attention and is evolved over the years. Classical economists developed the first known theory regarding this relationship and is called the classical theory of unemployment. This theory implicates that unemployment depends on real wages and labour market rigidities. The classical economists assume that there is no involuntary unemployment when the market is free to adjust. Workers are able to work when they accept the going wage rate. If they do not want to work for the offered wages, they choose willingly to be unemployed. Nonetheless, involuntary unemployment occurs when labour market rigidities, such as minimum wage policies and labour unionization, prevent the market from adjusting to a new equilibrium. Therefore, labour market rigidities lead to wages above the equilibrium, which causes employers to hire fewer workers (Goodwin et al., 2006).
demand for labour and a low supply of labour because of low unemployment, firms will bid up the wages in order to attract workers. In periods with low demand for workers and a high supply of workers, firms will drive down the wages. However, the wages drive down slowly. When demand for labour is low, and unemployment is high, workers are not likely to work for lower wages than the current market price. This causes downward nominal wage rigidity (Phillips, 1958). The Phillips Curve is empirical tested in other studies as well, in Australia by Gruen et al. (1999) and also for OECD countries (Bhattarai, 2016; Turner and Seghezza, 1999). All these studies concluded that the Phillips Curve is empirically significant for these countries.
Although studies have proven that the Phillips Curve is empirically significant for some countries, the theory behind it was challenged by Friedman (1968) and Phelps (1968). They both argue that when wages keep going up, the demand for goods rises, which leads to price inflation. Ultimately, the real wages and the unemployment rate adjust and the equilibrium will move back to its original level with a higher inflation rate than before. The stagflation in the United States between 1973 and 1975 contradicted the Phillips Curve and proved that a country could have high inflation and high unemployment rates at the same time (Olsen, 1982). This stimulated other researchers to conduct further research on the theoretical and empirical relationship between wages and unemployment.
unemployment rate and the wage curve to the local unemployment rate. Also, the Phillips Curve uses nominal wage rates and the Wage Curve uses real wage rates.
2.2 Wages and productivity
Another determinant for wages is productivity. The relation between wages and productivity is well established in the literature. The neoclassical perspective suggests that real wages follow the pattern of marginal productivity of labour. The theory behind this is that if labour productivity increases with rigid wage prices, the demand for labour will increase because this will lead to higher profits. Assuming that the labour supply is fixed, increasing demand for labour will result in higher wages until the point that the real wages equals marginal productivity again (Meager and Speckesser, 2011; Van Biesebroeck, 2015).
Another relevant theory regarding the link between productivity and wages is the efficiency wage hypothesis. This theory claims the opposite and states that wages are affecting productivity. They argue that workers tend to shirk if wages are not high enough, which harms the productivity level of a firm. Higher wages would counteract this. Workers will have high costs when they lose their jobs and will therefore do not want to take the risk being caught shirking. Which implicates that higher wages lead to higher productivity. In this case, the wage level will be higher than the marginal product of labour and should be set at the lowest point at which employees are still encouraged to work rather than shirking (Akerlof, 1984; Shapiro and Stiglitz, 1984). Determining which theory is most accurate is hard to tell. Reasonable is to believe that both theories are relevant and depend on the country’s characteristics of the labour market (Millea, 2000).
Furthermore, productivity levels and wages vary between sectors. This could be explained, for instance, by the difference in usage of skilled labour and sophisticated production techniques, which increase productivity. In general, the manufacturing sector outpaces the service sector in terms of aggregate productivity growth. However, this contribution has become less significant. This is the result of the increasing aggregate productivity growth of the service sector. Because of this general trend, it is assumed that the economic structure of a country could explain the difference between the growth of productivity across countries (OECD, 2018). Finally, wages vary between countries because productivity levels are influenced by capital deepening, which refers to the increase of capital per worker in the economy. An increase in capital deepening will have a positive impact on productivity and hence, on wages (Acemoglu and Guerrieri, 2008). Countries with a higher capital deepening ratio tend to have higher wages.
Even though the literature shows an abundance of theoretical and empirical evidence that wages and productivity are firmly linked (Feldstein, 2008; Bosworth et al., 1994), this link seems to have weakened. Over the last thirty years, productivity growth has outpaced wage growth. Particularly in the United States, suggesting that changes in wages are not in tandem with productivity growth (Bivens and Mishel, 2015; Erumban and de Vries, 2016).
2.3 Wages and inflation
The relationship between wages and inflation is ambiguous. As with the relationship between wages and productivity, the relation appears to go both ways. The causal effect that inflation has a positive effect on wage rates could be explained by simple reasoning. When consumer prices rise and consequently, the cost of living increases, workers demand higher wages in order to prevent decreasing purchasing power. Most countries correct the wages by the inflation rate every year, to avoid decreasing purchasing power.
1997) which is the positive side of inflation. The negative effects of inflation are inflation costs caused by inaccurate information, uncertainty and extra wage costs. These extra wage costs have potential consequences like layoff of workers, which are the sand in the wheel of the labour market (Groshen and Schweitzer, 1997). A more recent study by Zhang and Hungfu (2016) finds that inflation causes increasing wage dispersion. They state that inflation decreases the real profits of a firm, which leads to lower high and low wages. The low wages will decrease even more because lower wages are calculated as the weighted average between high wages and unemployment insurance. The unemployment insurance will go down because of the higher unemployment rate, which has a result that the wage dispersion increases. Hence, this is another negative aspect of inflation.
The wage-price spiral demonstrates the opposite and proves that wage rates have a positive effect on inflation. This phenomenon occurs when higher labour costs are passed on to the consumer. Higher nominal consumer prices endanger the purchasing power of workers. Accordingly, workers will demand higher wages to sustain their purchasing power, which again leads to higher nominal prices. In this way, higher wages could cause inflation (Kandil, 2006). However, Hess and Schweitzer (2000) argue that there is “little reason to believe that wage inflation causes price inflation. It is often found that price inflation causes wage inflation”.
2.4 Wages and Gross Domestic Product
2.5 Wages and immigration
This section will give an overview of the literature regarding the impact of immigrants on wages. There are different definitions for the term immigration. According to the Eurostat’s definition, immigration is “the action by which a person establishes his or her usual residence in the territory of a Member State for a period that is, or is expected to be, of at least 12 months, having previously been usually resident in another Member State of a third country’’(Eurostat, 2019). However, this paper uses the definitions of the International Organization for Migration (IOM). The organization defines immigration as “A process by which non-nations move into a country for the purpose of settlement” (IOM, 2011). People have different primarily reasons to immigrate. We distinguish five different groups of immigrants. Some are moving because of family reunification; others are facing prosecution in their home country and are seeking asylum abroad. Another group of immigrants is called labour immigrants. This group is convinced that moving will give them more chance to earn a higher income in the host country. There is also a group of people that want to go to a country where there are more people of the same ethnicity. Finally, there is a group of immigrants who stay illegally in the country. It is acceptable to believe that immigrants have multiple motives to move into a country. For example, immigrants who have family reunification as primarily motive could also hope for higher income, which makes them also labour immigrants.
The theory concerning the impact of immigration on wages is extensive and makes a distinction between legal and illegal immigrants (Peri, 2012; Friedberg and Hunt, 1995; Hanson, 2007). Legal immigrants are immigrants who are granted temporary or permanent resident status. Illegal immigrants are “foreign nationals whose presence in the host countries is in violation of the law or who have violated a condition of legal entry into the country” (Chiswick, 1988). A simple supply and demand explanation would be that legal immigration increases labour supply in specific sectors of the economy, which will have a wage reducing effect for the native workers. Over and above that, illegal immigrant workers are likely less costly than native workers, which may also reduce overall wages. At the same time, the economy will expand because of increasing customer demand for goods and services. This will create opportunities in the long- run and thus mitigate the short-term negative effects on wages, like integration costs (Barwell, 2007; Ruhs and Vargas-Silva, 2019).
of the host country (Blau and Mackie, 2017). Immigrants could bring skills that substitute or complement the skills of native workers (Borjas, 1995). In the first case, when legal immigrants substitute the skills of native workers, the supply of workers is higher than the demand for workers. This will increase the competition for jobs. The wage equilibrium will adjust to a new equilibrium with lower wages. However, when the skills of legal immigrants complement the skills of the native workers, the legal immigrants could fill vacant jobs and solve problems concerning skill mismatches. Hence, legal immigrants could increase productivity and therefore wages, which is also supported by Peri (2012).
Legal immigrants could impact the economy in two other ways, through the output matrix and the use of technology. The higher supply of workers, especially low skilled workers, will lead to an expansion of the production of goods which require low skilled workers because the production costs are decreasing. The extra supply of workers would make the introduction of new technologies less necessary if it were implemented as a cost reduction tool. Further, skilled immigrants could bring in specific skills/ knowledge which stimulates technological innovation and improve the innovativeness of a firm (Dustmann et al., 2008).
The impact of legal immigrants on wage rates is empirically tested, and there is no clear evidence that the inflow of legal immigrants endangers the wages of native workers (Peri, 2014). Ottaviano and Peri (2008) researched this matter and concluded that in the short-run legal immigration has a small negative effect of 0.4 percent on wages but in the long run a positive effect of 0.6 percent.
illegal immigrant for lower wages (Hanson, 2007). Because of their illegal status, illegal immigrants can only perform a few specific jobs. This implicates that they will keep occupying low-skilled jobs and so remain substitutes for native workers. Therefore, it can be assumed that illegal immigrants have a negative effect on wages (Friedberg and Hunt, 1995).
Overall, the literature states that legal immigrants could have a positive effect on wages because of their skills, which could complement the skills of the native workforce. On the contrary to illegal immigrants. These immigrants are believed to perform low- skilled jobs and therefore substitutes for native workers. This will increase competition and reduce wages.
2.6 Wages and immigration policies
In the previous section, the link between immigrants and wages is outlined. As discussed, immigration is argued to have an impact on wages, as it increases the supply of workers in the domestic market. While this can be a general phenomenon, immigration policies can mitigate some of these effects, depending upon the substitutability and complementarity between immigrants and native workers. This is because more restrictive regulations reduce significant fast the inflow of immigrants (Ortega and Peri, 2012). How policies affect wages also depend upon the existing composition of the labour force in the host country. Selective immigration policies focusing on encouraging skilled immigration could complement the domestic labour force, which will solve problems related to labour shortages and skill mismatches and hence improve productivity. This will ensure higher wages, while for example, a liberal attitude towards illegal immigrants may reduce wages.
accordingly likely to change the policy if the voters organize in large numbers. The group which have the highest benefits of a change in policy will organize more effectively and therefore, will make a stronger statement against their government. So, for example, when employees are losing their jobs to immigrants or have to work for lower wages to compete with immigrants, they will most likely promote a more restrictive immigration policy.
Immigrants can be beneficial for a country, but countries should only open their doors for these workers whenever marginal productivity growth exceeds the marginal costs for adoption. Immigration policies are needed to regulate the immigration inflow, whenever the costs of immigration start to exceed the benefits (Straubhaar and Zimmermann, 1993). In order to reap up the benefits of immigration and mitigate the negative aspects, immigration policies around the world are changing towards a more selective approach. Currently, selective immigration policies are implemented in several countries in different forms and vary in terms of selection criteria (Koslowski, 2013). The modern literature mainly focuses on the impact of selective immigration policy regarding attracting skilled immigrants on wages (Boeri et al., 2012; Czaika and Parsons, 2017; Czaika, 2018; Signorelli, 2019). The common findings are that liberal immigration policies toward skilled- immigrants have positive effects on wages. Also, the current literature focused on the impact of immigration on wages as a whole and how immigration policies can stop the inflow of this as a group.
This research will analyze the impact of more liberal or restrictive immigration policies regarding these five groups on real wages in 17 OECD countries. Based on the reasons to immigrate, it can be assumed that more liberal immigration policies towards labour immigrants will attract required skilled workers and increase the demand for goods and services, which has a positive impact on real wages. Immigration policies regarding immigrants with other motives to immigrate are likely to have a less positive impact on real wages because these groups of immigrants do not necessarily participate in the labour market or have less complementary skills to the native workforce. The real wages are only positively affected because of the increase in demand for goods and services. Therefore, the positive impact of these immigrants on real wages is assumed to be lower. Accordingly, the following hypothesis will be tested:
Hypothesis: Liberal immigration policies towards labour immigrants will have a larger positive effect on real wages, compared to liberal immigration policies towards family
reunification, asylum seekers, co-ethnicity and illegal immigrants.
3. Methodology
The following section outlines the empirical strategy used to carry out this quantitative research. This section’s primary purpose is to describe the model specification and the variables that will be used. Information about the data sources will be discussed in the next section.
To test the hypothesis stated in the previous section, we employ a panel regression where real wages are regressed on various immigration policies. Due to the lack of availability of data for all the variables, some OECD countries are excluded from the analysis. These are Chile, Czech Republic, Estonia, Greece, Hungary, Israel, Latvia, Lithuania, Poland, Portugal, Slovak Republic and Slovenia. Our dataset covers 23 countries in total over the period 1991 till 2010. The model that will be used to analyze the effect of immigration policies on real wages is as follows:
"#$%&'() =b+ + b- "%.() + b/ 0%1() + b2 %34() + b5 '67()+ b8 9:76()
where i indicates the country and t represents the year. The dependent variable WAGE is the natural log of the average wage per worker corrected for inflation. The first five independent variables are related to various types of immigration policies, and the remaining variables are control variables. LAB is the immigration policy index towards labour immigrants, and a higher index score indicates stricter policy and a lower score indicates more liberal policy. We expect more liberal policies towards labour immigrants to have a positive impact on real wages. This is because labour immigrants are likely to bring skills which complement the native workforce; also, the economy is likely to expand as results of the increasing demand for goods and services. FAM indicates the immigration policy index concerning family reunification. A higher index score implies a stricter immigration policy and a lower index score indicates a more liberal policy towards family reunification. We expect that more liberal immigration policies will have a positive impact on real wages, because it may help to attract skilled workers and increase the demand for goods and services. ASY is the immigration policy index in relation to refugees who are seeking asylum, again a higher index score illustrates a stricter policy and a lower score means a more liberal policy. A liberal policy towards asylum seekers is expected to have a positive impact on real wages, because asylum seekers may reduce the labour shortages and by this improve productivity and again, expand the economy because of the increase in demand for goods and service. ETN stands for the immigration policy index in the field of immigrants with the same ethnicity. A higher index indicates a more restrictive policy and a lower index a more liberal policy. We expect that a more liberal policy on the field of co-ethnicity will have a positive impact on real wages, as a result of higher demand for goods and services, and it may help to attract skilled workers. The last variable regarding immigration policy, CONT, is the control mechanism, which represents policies that are used to stop illegal immigration. A stronger control mechanism would imply stricter control over illegal immigration to a country, which will have a positive effect on real wages because illegal immigrants are likely to substitute the native workers.
of the manufacturing sector in a country, measured as the share of employment in the manufacturing sector as a percentage of the total employment. CAPI is the capital deepening, measured as the ratio capital stock to employment. Thevariables PROD and GDP_c are taken in natural log form in our regression.
According to the existing literature, these variables are proven to be significantly relevant to determine real wages. The unemployment rate is expected to have a negative effect on real wages, as a higher unemployment indicates higher supply of workers. Both productivity and GDP per capita are expected to have a positive impact on real wages because higher productivity leads to more output and hence, higher marginal productivity. Higher GDP per capita is an indicator that the economy is growing and therefore, it is assumed that real wages increase. The literature implied that inflation was also an important determinant of wage changes. However, this research uses data on real wages, which takes out the effect of inflation and therefore excluded from the model. The impact of the economic structure on real wage depends on the size of the sectors. A higher share of the manufacturing sector in a country is expected to have a positive impact on real wages because the manufacturing sector tends to have higher productivity levels than in other sectors. As a result of this, it is safe to state that a higher share of the service sector in a country is expected to have a negative impact on real wages, because a higher share of service implicates a lower share of the manufacturing sector. Capital deepening is believed to have a positive impact on real wages because more capital will lead to an increase in productivity, which positively affects real wages.
4. Data
The following section provides an overview of the data which is used for conducting this research. The databases are discussed, and summary statistics are presented. By doing this, the reader gets a feeling for the data and is more able to understand this research.
4.1 Real wages
constant prices at 2017 USD Purchasing power parities (PPP) and constant prices at 2017 USD exchange rates. This research uses constant prices at 2017 USD PPP because it allows us to compare the real growth of the average wages over the years without the impact of inflation and with 2017 as the base year.
Figure 1 shows the upward slope of the real wages of 23 OECD countries. Since 1991 the real wages increased almost every year. Only in 2008, the growth was stagnated. Most likely because of the great recession which started in 2007. Looking into the data in more detail tells us that the real wages per worker were around 35000 US Dollars in 1991, which have consistently increased over the years, reaching nearly 45000 US Dollars in 2010. The real wage increase over the period 1991-2010 with almost 10000 US Dollars, equivalent to an annual increase of 1.3 percent. Figure A1 in the appendix provides more detailed information about the real wages across countries. In 1991 the real wages ranged from 14200 USD Dollars in Mexico to 50200 US Dollars in Switzerland. In 2010 these two countries are still the countries with the lowest and highest real wages, respectively 15300 US Dollars and 60400 US Dollars. Increasing real wages will stimulate customer spending and creates customer demand. The increased real wages in the OECD countries could, therefore, be interpreted as a reflection of the economic development that OECD countries as whole and individual have experienced.
Figure 1: Real average wages for 23 OECD countries between 1991-2010 in US Dollars.
4.2 Immigration policy
There are a variety of databases available which all measure immigration policies. Examples of immigration policy databases are the International Migration Policy And Law Analysis (IMPALA), Determinants of International Migration (DEMIG) and the Immigration Policies in Comparison (IMPIC) project. The IMPIC database measures immigration regulations and covers data for 33 OECD countries between 1980 and 2010 (Helbling et al., 2017). This database is selected because it covers the immigration policies on the five immigrants’ categories, which are analyzed in this research. The IMPIC is consists of regulations and control mechanism. The regulations are divided into Family, Labor, Asylum and co-ethnicity. The control mechanism measures, among other things, the immigration policy on the fields of illegal residence, carriers’ sanction, alien’s register, and aiding irregular documents. These regulations and control mechanism indexes can be used to measure the effect of immigration policies on real wages.
Countries which have more restrictive policies in these fields are likely to have fewer immigrants. Therefore, the indexes are suitable to analyze the effect of immigration policies concerning changes in the real wages of OECD countries. Countries are scored based on their restrictiveness regarding these five policies using a continuous variable with scores between 0-1 (0=less restrictive, 0-1=restrictive).
4.3 Unemployment rate
Data on the unemployment rate is obtained from the International Labour Organization (2018) database. This database covers 193 countries all over the world over the period 1991-2018. As mentioned in the literature review section, the unemployment rate is a measure to indicate the excess supply of labour, which has a negative relationship with real wages in a country. So, when the unemployment rate goes up, real wages will go down and vice versa. Figure A2 in the appendix demonstrates the average unemployment rate of 23 countries over the period 1991 till 2010. Over the years, the average unemployment rate has increased from 7.1 percent to 8.5 percent of the total labour force. Spain has the highest unemployment rate in both 1991 and 2010, and also has the highest average unemployment rate over 20 years. Luxembourg scores the best with respectively 1.5 percent unemployment rate in 1991 and 3.33 percent on average over 20 years.
4.4 Productivity level
The productivity level of a country is included in this research as the labour productivity per person employed based on 2018 US Dollars PPP. This data is gathered from The Conference Board Total Economy Database (2019). The database covers 123 countries between the years 1990 and 2018. It is interesting to provide information about productivity since the level of productivity has a positive influence on real wages. The average productivity per person employed of the 23 countries used in this research increased with 39,16 percent between 1991-2010. The average annual growth rate of the productivity levels in the 23 OECD is 1.76 percent. There are substantial differences between the increase in productivity levels over this period. For example, productivity levels increased the most in Canada over the period 1991-2010 with 125 percent compared with the production levels of New Zealand with only 6 percent (see figure A3 in the appendix).
4.5 GDP per capita
growth rate of GDP per capita is 1,77 percent. The GDP per capita did not increase in all OECD countries with this percentage. Austria was able to manage the most significant percentage growth over the years, with a 139 percent increase of GDP per capita. Meanwhile, Norway experienced the lowest percentage change, with only 18 percent increase in GDP per capita. More information about GDP per capita for individual countries is provided in figure A4 in the appendix.
4.6 Economic structure
Data about the economic structure of the 23 OECD countries are collected from the World Bank Group (2019). This database covers 193 countries and provides information about the share of the manufacturing sector and the share of the service sector over the period 1991 till 2018. Appendix figures A5 and A6 provide information about the change in the economic structure of the 23 OECD countries between the years 1991 and 2010. The average OECD share of the manufacturing sector has decreased by almost seven percentage points from 28.5 percent to 21.6 percent. One country does not follow this pattern. The data shows that the share of the manufacturing sector in Luxembourg increases by almost one percentage point. In the same period, the average share of the service sector increased by approximately ten percentage points. This increasing pattern is observed in all 23 countries.
4.7 Capital deepening
The ratio of capital stock to employment is used to measure the capital deepening in the 23 OECD countries. This data is achieved from The Conference Board Total Economy Database (2017). This database provides data regarding the capital stock and employment of 123 countries over the period 1990 till 2018. Figure A7 indicates that the capital deepening varies across countries and increases over time. On average, the capital deepening of the OECD countries increases by 33 percent. What stands out is that only in New Zealand the capital stock to employment ratio has decreased.
variable ETN has only 280 observations compared to 460 for the other variables. Table A2 and A3 in the appendix are included to present a more detailed overview of the differences across countries regarding these variables.
Table 1: Summary statistics
Variable N Mean Std. Dev. Min Max
WAGE (in thousands) 460 39.97 91.93 12.42 60.26
LAB Index 460 0.39 0.14 0.21 1.00 FAM Index 460 0.27 0.23 0.02 1.00 ASY Index 460 0.30 0.13 0.10 0.90 ETN Index 280 0.51 0.30 0.22 1.00 CONT Index 460 0.62 0.11 0.37 0.90 UNEMP (in %) 460 6.70 3.65 1.48 24.21
PROD (in thousands) 460 74.49 24.39 24.20 15.64
GDP (in thousands) 280 36.18 15.72 10.66 108.37
SERS 460 68.97 6.26 47.68 85.74
MANS 460 25.63 4.10 12.91 36.15
CAPI 460 255.16 75.92 25.23 36.15
5. Results
The following section presents the results of the model that was introduced in the methodology section. Before the regression results are presented, we first carry out tests to detect correlation and multicollinearity. After this, we select the most appropriate model for this research.
5.1 Correlation and multicollinearity
The VIF is implemented to detect multicollinearity. The results of the VIF will be interpreted as followed: VIF 0-1, the variables are not correlated, VIF 1-5, the variables are moderately correlated and finally, if the VIF is greater than 5 than the variables are highly correlated. Table 2 is estimated with the variables LnGDP_c, SERS, CAPI and without these variables. In line with the correlation matrix, the VIF detects multicollinearity. However, after removing the variables LnGDP_c, SERS, CAPI, the VIF changes radically and shows that the variables are moderately correlated. To avoid biased results, because of correlation and multicollinearity, these variables are removed. The impact of the correlation between the variables FAM and LAB will be addressed in the regression results section.
Table 2: Variance inflation factor with LnGDP, SERS, CAPI and without LnGDP, SERS, CAPI.
Variable VIF 1/VIF VIF 1/VIF
LAB 2.08 0.48 1.81 0.55 FAM 2.24 0.45 1.85 0.54 ASY 1.84 0.54 1.54 0.65 ETN 3.19 0.31 1.76 0.57 CONT 1.40 0.72 1.34 0.75 UNEMP 2.00 0.50 1.46 0.68 LnPROD 12.48 0.08 1.87 0.53 LnGDP 11.48 0.09 SERS 7.68 0.13 MANS 4.62 0.22 1.79 0.56 CAPI 4.51 0.22 Mean VIF 4.86 1.68 5.2 Model selection
economies of developed countries differ from the economies of developing countries. Also, in line with this, the policies towards immigrants in developed countries are different compared to the immigration policies of developing countries (Docquier et al., 2007). In the current dataset there is one developing countries, namely Mexico. Including a developing country might have an impact on the results.
Table 3: Pooled regression results Variables (1) (2) (3) (4) (5) LAB 0.547*** (0.106) 0.610*** (0.112) 0.070 (0.230) 0.046 (0.054) FAM -0.037 (0.079) -0.083 (0.085) -0.034 (0.039) -0.010 (0.038) ASY -0.854*** (0.148) -0.812*** (0.156) -0.059 (0.114) -0.072 (0.076) -0.029 (0.074) ETN -0.546*** (0.067) -0.517*** (0.068) -0.030 (0.052) -0.028 (0.040) -0.029 (0.040) CONT -0.059 (0.109) -0.016 (0.113) 0.333*** (0.058) 0.337*** (0.077) 0.320*** (0.077) UNEMP -0.001 (0.003) -0.003 (0.003) -0.007** (0.002) -0.007** (0.002) -0.008*** (0.002) LnPROD 0.250*** (0.055) 0.233*** (0.058) -0.206*** (0.042) -0.204*** (0.038) -0.218*** (0.036) MANS -0.002 (0.004) -0.006 (0.007) -0.023*** (0.007) -0.023*** (0.003) -0.024*** (0.003) Constant 8.110*** (0.672) 8.287*** (0.695) 13.338*** (0.497) 13.309*** (0.470) 13.517*** (0.444) N 280 260 240 240 240 R-Squared 0.473 0.481 0.476 0.475 0.473
Standard errors in parentheses: ***p<0.01, **p<0.05, *p<0.1
Table A5 in the appendix represents the Breusch- Pagan test statistic, which is used to test for the presence of random effects. The null hypothesis is that there are no random effects. Hence, rejecting H0 implies that random effects are present. Table A5 shows that the p- value of the Breusch and Pagan test is 0.00, which implicates that H0 should be rejected. This implicates that there are random effects. The Hausman test is performed to determine whether to use a fixed- or random effects model. The null hypothesis is that there are random effects. Otherwise, if H0 is rejected, then random effects are not present (Adkins et al., 2011). Appendix table A6 presents the results. The p-value of the Hausman test is 0.82. This suggests that H0 cannot be rejected at any significance level. Therefore, the random effects model will be used to perform the regressions. Next to this, the problem of heteroskedasticity needs to be addressed. The existence of heteroskedasticity leads to incorrect least squares estimator (Hill et al.,2012). Accordingly, robust standard errors are used to solve this problem.
5.3 Regression results
This section contains the regression results of the equation introduced in the methodology section. The sample consists of panel data for 17 OECD countries from 1991 till 2010. The preferred outcomes of the regression would be results, which support the hypothesis stated in the literature review section. So, the preferred results would be that the variable LAB would have a higher significant coefficient compared to the other variables. This would be positive for policymakers because it would justify why countries implement more liberal immigration policies towards immigrants, even though the citizens are requesting more restrictive policies. It would also help to only implement more liberal immigration policies towards the immigrant’s groups that are positively influencing the real wages.
FAM is significant. The Breusch- Pagan test and the Hausman tests indicate that the random effects model is the best model to use for this research, accordingly, Column 3 of table 4 will be used as leading results.
Analyzing the five different immigration policies gives, ceteris paribus, interesting insights regarding the influence of the policies on real wages in the 17 OECD countries. The model shows that the variable FAM has a significant positive impact on real wages. The table argues that a one unit increase of the variable FAM will lead to an increase in real wages with 0.210 percent. The variable ASY is significant and has a negative influence on real wages. An increase of one unit of the variable ASY leads to a decrease in real wages by 0.225 percent. Finally, the variable CONT has a significant positive impact on real wages. A one unit increase of this variable goes hand in hand with an increase in real wages of 0.280 percent. The remaining immigration policy variables LAB and ETN are not significant at any significance level.
Table 4: Random effects regression results Variables (1) (2) (3) LAB -0.070 (0.070) -0.059 (0.076) FAM 0.187*** (0.053) 0.183*** (0.050) ASY -0.225** (0.057) -0.146** (0.063) -0.161** (0.060) ETN 0.018 (0.072) 0.040 (0.074) 0.025 (0.073) CONT 0.241* (0.137) 0.208 (0.155) 0.280** (0.127) UNEMP -0.011** (0.004) -0.010** (0.004) -0.010** (0.003) LnPROD -0.033 (0.097) -0.080 (0.107) 0.050 (0.092) MANS -0.024*** (0.005) -0.027*** (0.005) -0.025*** (0.004) Constant 11.502*** (1.131) 12.170*** (1.229) 11.665*** (1.083) N 240 240 240 R-Squared overall 0.290 0.299 0.316 R-Squared between 0.112 0.150 0.153 R-Squared within 0.777 0.745 0.774
Standard errors in parentheses: ***p<0.01, **p<0.05, *p<0.1
6. Robustness check
This robustness check is carried out with only the 14 European countries from the original dataset, without the countries Sweden and Luxembourg which showed minimum variance in the immigration policies. These 14 European countries are chosen in order to check if the results are changing when the sample size is smaller and only with countries of the same continent. The results are presented in table 5. Several observations stand out while comparing the third columns of the tables 4 and 5 with each other. The variables FAM and CONT are in both regressions significant. The variable FAM has a less impact on real wages compared to the model with 17 countries. A one unit increase of the variable FAM increases real wages with 0.157 percent for these 14 countries. The immigration policy CONT is the control mechanism. This variable has a higher coefficient compared with the model in table 4 and has therefore, the most impact on real wages in this model. A one unit increase of the immigration policy control mechanism will increase real wages with 0.341 percent. Conflicting results are observed when looking at the variables ASY and ETN. The immigration policy Asylum is no longer significant in contrast to the immigration policy regarding Co-ethnicity, which turns out to be significant. A one unit increase of the variable ETN will lead to an increase of real wages with 0.182 percent.
Table 5: robustness check results Variables (1) (2) (3) LAB -0.223** (0.090) -0.092 (0.078) FAM 0.116** (0.055) 0.157*** (0.045) ASY 0.107 (0.198) -0.112 (0.162) -0.179 (0.154) ETN -0.326*** (0.093) 0.180*** (0.042) 0.182*** (0.038) CONT 0.428** (0.136) 0.285** (0.127) 0.341*** (0.103) UNEMP -0.015** (0.006) -0.008* (0.004) -0.007** (0.003) LnPROD -0.152** (0.067) -0.062 (0.087) -0.024 (0.088) MANS -0.006 (0.005) -0.021** (0.007) -0.020*** (0.006) Constant 12.197*** (0.671) 11.732*** (1.062) 11.166*** (1.031) N 180 180 180 R-Squared overall 0.696 0.563 0.531 R-Squared between 0.782 0.478 0.406 R-Squared within 0.612 0.742 0.772
Standard errors in parentheses: ***p<0.01, **p<0.05, *p<0.1
7. Discussion
The most interesting results for this research are the variables regarding the immigration policies and the impacts of these policies on real wages. The hypothesis that liberal immigration policies focused on labour immigrants, which is captured by LAB in our regression, will have a larger quantitative effect on real wages is not supported by the data. Moreover, this variable is not significant in this model. The variable CONT has the highest impact on real wages. As one expects, the relationship is negative, a one unit increase towards a more liberal policy tends to reduce real wages with 0.280 percent. In other words, higher wages may be observed if policies towards illegal immigrants are more restrictive. The variable FAM that presents the results for immigration policies regarding family reunification shows counter-intuitive results. A more restrictive immigration policy increases real wages with 0.183 percent. Finally, the variable ASY has a significant impact on real wages. The relationship is negative as the immigration policy becomes more restrictive with one-unit; real wages decrease with 0.161 percent. This implicates that a liberal immigration policy towards asylum seekers will increase real wages. The immigration policies regarding co-ethnicity is not significant in this model. Because of this, we cannot conclude that this policy affects real wages.
The results are partially in line with the leading literature about the impact of immigration on wages. The immigration policies regarding illegal immigrants and asylum seekers are showing expected results. Illegal immigrants tend to substitute the native workers and therefore increases the competition for jobs, which leads to lower wages (Hanson, 2007). The inflow of asylum- seekers might increase demand for goods and services, which causes the economy to expand and increases the demand for workers. Next to this, the complementary skills of asylum- seekers could increase productivity, which increases the real wages for all workers (Borjas, 1995; Barwell, 2007; Ruhs and Vargas-Silva, 2019). The impact of the immigration policy concerning family reunification on real wages provides counter intuitive results. The results implicate that a stricter policy towards family reunification increases real wages. One of the possible explanations might be that skilled- and unskilled workers who are needed to complement the native workers demand higher wages because they cannot bring their family. The cost of leaving their family should be compensated by the increase in economic wealth in order to immigrate to the host country.
an increase in unemployment decreases real wages (Blanchflower and Oswald, 1995). However, the variable LnPROD which measures the impact of productivity on real wages is not significant, even though a vast literature concludes that productivity determinants real wages (Meager and Speckesser, 2011; Van Biesebroeck, 2015). An explanation for this could be that the variable MANS causes the variable LnPROD to be insignificant because both variables control to some degree for productivity. It is possible that the variable MANS explains the results for both of the variables. The variable MANS also shows counter-intuitive results. A higher share of the manufacturing sector is argued to increase productivity, and therefore, real wages (OECD,2018). Nonetheless, the results indicate that a one unit increase in the share of manufacturing sectors decreases real wages with 0.025 percent. Although the results are different than expected, the explanation could be that in these 17 OECD countries, the service sector contributes more to the productivity level than the manufacturing share. Hence, when the share of manufacturing increases, the share of the more productive service sector most likely decreases, which leads to a lower productivity level and lower real wages.
8. Conclusion
Over the years, the focus of immigration policies has moved towards a more selective approach. Selective immigration policies could be helpful to attract only the immigrants who have a positive impact on wages and mitigate the negative influence of immigrants on wages. Studies have been conducted about how attracting skilled immigrants has a positive impact on wages (Boeri et al. 2012; Czaika and Parsons, 2017; Czaika, 2018) or distinguish the impact of legal and illegal immigrants on wages (Friedberg and Hunt, 1995; Hanson, 2007; Peri,2012). In the modern literature, it is assumed that a more liberal approach towards legal immigrants has a positive effect on wages. However, immigrants have various reasons to immigrate, and therefore, these groups of immigrants differ in terms of their skillset and consequently, have a different impact on wages. We categorize immigrants into five different groups. These groups are labour immigrants, family reunification, asylum seekers, co-ethnicity and illegal immigrants. This paper contributes to the field of selective immigration policy by analyzing the impact of immigration policies regarding these groups on real wages in 17 OECD countries over the period 1991 till 2010.
The aim of this paper is to provide an answer to the research question: Which immigration policies towards different immigration groups have a positive impact on real wages? In order to discuss this research question, we conducted a quantitative research to test the following hypothesis: Liberal immigration policy towards labour immigrants will have a larger positive effect on real wages, compared to liberal immigration policies towards family reunification, asylum seekers, co-ethnicity and illegal immigrants. The hypothesis is tested by using an economic model with as dependent variable real wages and explanatory variables, related to immigration policies on labour immigrants, family reunification, asylum seekers, co-ethnicity and illegal immigrants. This research finds no evidence to support this hypothesis. Instead, more restrictive immigration policies towards illegal immigrants have the largest positive impact on real wages. The results also show that a more restrictive policy towards family reunification and a more liberal policy towards asylum- seekers increases real wages. The immigration policies towards labour immigrants and co-ethnicity turned out to have an insignificant impact on real wages.
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