Migration and Regional Wage Disparities in Germany

1 Introduction

The magnitude of labor market disparities among German regions is almost as large as the one between Germany and other countries. There are significant differences between East and West Germany resulting from the economic reconstruction in the East after the fall of the Berlin Wall. Furthermore, labor market circumstances vary on a regional level. Between 1998 and 2009, the average wage level per hour ranged from about EUR 7 in regions of Mecklenburg-Western Pomerania to EUR 15 and above in some regions of Baden-Württemberg. During this time, the disparity between certain regional wages and the national mean rose for many regions. In some regions of Schleswig-Holstein, Thuringia, Bremen, and North Rhine-Westphalia below-average wages declined significantly. In contrast, there are regions, predominantly in Hesse, Bavaria and Baden-Württemberg, in which above-average wages increased. Average unemployment rates range from below 5 % in regions of Bavaria or Baden-Württemberg to above 20 % in some regions of Mecklenburg-Western Pomerania and Saxony. However, they remained relatively constant during this time.

There is no direct connection between regional labor market differences and the welfare level of the population in a particular region – especially since varying regional living costs reduce nominal wage differences. Many authors emphasize that economic disparities are inefficient. Taylor (1996) points out that they reduce national output and raise inflationary pressure. Elhorst (2003) adds that reducing disparities produces substantial social benefits. An important branch of research focuses on inter-regional migration as a mechanism for influencing labor market disparities; however, empirical evidence is mixed. It is questioned whether the influence is economically significant. Furthermore, the direction of the effect is unclear.

Traditional migration approaches assume that labor mobility reduces regional labor market disparities and can function as an adjustment mechanism. Möller (2001) describes different aspects of a regional adjustment process. When a region is hit by a severe adverse shock production is depressed and unemployment rises. High unemployment rates undermine the bargaining power of unions and individual employees. The wage pressure decreases and wages fall. Firms are able to offer lower prices. Consumption and – in the long run – labor demand increases. Furthermore, high emigration rates to other, more attractive regions will reinforce this process. Relative labor supply and unemployment decline.

Niebuhr et al. (2012) challenge traditional theories with new economic approaches such as New Economic Geography (NEG) established by Krugman (1991) and others. Increasing disparities may occur due to selective migration. The role of self-selection within the migration decision is an often emphasized issue (see Borjas 1987). Chiswick (1978) explains positive self-selection with the migration benefit which is higher for more able and higher motivated individuals. Hence, it is usually assumed that migrants are on average more educated or better skilled than individuals who choose to remain in their place of origin (Long 1973; Greenwood 1975; Chiswick 2000; Dustmann/Glitz 2011). In addition, economic migrants move to regions with above-average wage levels and low unemployment rates. Since additional high incomes are then spent in these regions, migration is assumed to lead to further prosperity. Furthermore, economically depressed regions with a net loss of the (highly skilled) population will suffer from a decreased demand for locally produced goods and services. Hence, disparities may increase due to migration.

This paper analyzes the relationship between migration and regional wage disparities in Germany for the period 1998 to 2009. It is examined whether migration and commuting contribute to increasing or decreasing disparities or have no significant economic influence. The analysis is based on the assumption of simultaneity between the regional labor market outcome and respective migration rates. It can be assumed that migration does not only influence the local labor market conditions but is also influenced by them. Attractive conditions may be an incentive for workers to move.

In the first step, a wage equation is estimated focusing on the influence of migration and commuting on the relative wage level of the “German Spatial Planning Regions” (Raumordnungsregionen, ROR). Dynamic GMM panel estimations are conducted to account for various simultaneities, for dynamic wage adjustments and for endogeneity due to regional fixed effects using data from the “Indicators and Maps on Urban Development in Germany and Europe” (Indikatoren und Karten zur Raum- und Stadtentwicklung in Deutschland und in Europa, INKAR) and the “German Socio-Economic Panel” (GSOEP). An error correction model provides a reconciliation of short-run and long-run effects of mobility on relative wages. The results show a significant small positive effect of regional migration on relative wage levels for both time frames. When the migration balance increases by 10 percentage point, the relative wage level increases by 0.0107 % in the long run. German regions seem to benefit from new citizens; however the effect is small. The migration balance considers both, internal migration within Germany and migration between Germany and other countries. When differentiating between Germans and transnational migration and only considering domestic migration, the effect on wages turns out to be smaller and negative. Hence, the above estimated positive effect only results from the combination of foreign and domestic migration.

The negative effect of internal migration would trigger an adjustment process when assuming that domestic migrants usually move to high-wage regions (although the impact is low due to the small estimated effect). Disparities lead to migration which decreases disparities. It is conceivable that employment prospects are important determinants of migration in Germany. Previous migration studies have argued that differences in economic opportunities between the source and destination regions due to income and unemployment rates are the main forces of migration. However, individual, family- and housing-specific factors highly influence the migration probability as well (Windzio 2004).

In the second step, a migration equation is estimated to analyze the influence of regional labor market circumstances on the domestic migration balance. Results indicate no effect of relative wage levels on migration. An adjustment process during which existing wage disparities decrease due to migration is not likely to occur in Germany in the next few years. However, the estimated positive effect of overall migration (both transnational and domestic) gives information about wage developments due to migration on a regional basis. In the last part of this study, German regions are named in which an already existing high gap between regional wages and the national mean is expected to increase in the coming years.

The outline of the paper is as follows: The second section reviews the relevant literature. In Section 3 the identification strategy is described. Section 4 presents the empirical analysis. It describes the econometric specification and presents the data set and the estimation results. Section 5 concludes.

2 Related literature

A lot of research has been carried out on labor mobility and inter-regional disparities. A classification can be reached by envisioning the issue’s simultaneity. The existing literature is divided into studies focusing on migration as the dependent variable and approaches that focus on the labor market outcome as the dependent variable.

Many previous studies take the traditional assumption “migration reduces disparities” as given. Here, it is questioned whether the effect of relative labor market circumstances on migration is significant enough to function as an adjustment mechanism. Decressin (1994) estimates the influence of shocks and regional differences of unemployment rates and salaries on migration flows in West Germany during the 1980s. Using a Least Squares Dummy Variables (LSDV) model with dummies for each region, he estimates a migration elasticity of 1.3 resulting from local salary increases (relative to those paid in another region) and of 3 due to increases of the local unemployment rate. His results show that the increase of unemployment in all regions contributes to an economically significant decrease of gross migration. In times of recession, migration is less likely to work as an adjustment mechanism. In contrast, Pissarides and McMaster (1990) show in their study on Great Britain between the years of 1961 and 1982 that this adjustment process worked so slowly that usually “compensating differentials” pre-exist. Applying a LSDV model in a first step, estimated effects of the regional wage level (unemployment rate) in relation to the average wage level (unemployment rate) on migration flows turn out to be low. With an adjustment equation, a second step estimation finds significant positive effects of relative unemployment rates on relative wage levels.

In light of Mundell’s “Theory of Optimum Currency Areas” Puhani (2001) assumes that a high degree of factor mobility will be conducive to the success of Europe’s currency union. Using Ordinary Least Squares (OLS) estimation, he estimates a migration elasticity with respect to the number of unemployed people in West German regions of only 0.00809 for the years 1985 to 1996. With respect to the regional GDP at purchasing power parities (which is used instead of wages due to data availability), he estimates a migration elasticity of only 0.00136. Hence, from a ceteris paribus 1 % change in the GDP of a West German region results a migration-induced population increase of 0.00136 %. He concludes that the degree of labor mobility in major European nation states appears to be too low to act as an adjustment mechanism at least in the short run. Focusing on West Germany, Windzio (2004) analyzes main moving determinants of migration flows between the superior more attractive south and the inferior north region. By using a three-level model, he considers individual and regional factors as well as the respective time frame. Following his results, the moving probability is lower for individuals living in regions with high unemployment rates and higher for individuals with an academic degree. Arntz (2005) analyzes main work-related moving motives of Germans with regard to qualifications for the years 1975 to 2001. Using a two- level nested logit model, she considers individual as well as regional destination-specific factors. Her results indicate that the skill composition of job flows highly matters for the relevance of respective determinants. While highly skilled job movers are responsive to regional wage differences, unemployment differences only influence the migration decision of less skilled workers. Furthermore, migration costs appear to decrease with education: the proportion of highly skilled movers increases significantly with migration distance.

Due to the historical background, many examinations on German migration focus on mobility from the Eastern to the Western part after the reunification. Considering West-East migration, net migration from East to West Germany between 1989 and 2007 amounted to 1.7 million people (Wolff 2009). Since 1990 data on individuals from Eastern Germany is included in the GSOEP. Considering the first two waves, Burda (1993) estimates binomial logit models with the dependent variable taking the value 1 when a person living in the East can imagine to move to West Germany or to West Berlin. While wages and wage increases appeared not to have any effect in these early years, a person’s age has a negative influence on the migration desire. Using a switching regression model and data from the IAB-employment sample, Brücker and Trübswetter (2004) find that migrants are positively selected with respect to unobserved abilities. Due to the longer observation period compared to Burda (1993), their results indicate that wage differentials and differences in employment opportunities were main forces of East-West migration after the German reunification.

Using the GSOEP waves 1990, 1991, 1996 and 1997, Hunt (2000) analyzes the determinants of emigration or commuting from East to West Germany with multinomial logit models. Her results indicate that migrants are on average younger and more skilled than non-migrants. Furthermore, using data on level of German Bundesländer from the years 1991 to 1996, she estimates the effect of regional labor market conditions on East to West migration with OLS regression. She explains a downward trend in East to West moving relative to within-west migration with the observed wage convergence during this time period. Hunt (2006) confirms these results. Again using data on level of German Bundesländer from the years 1991 to 2000, she estimates a fixed effects model and shows that rising wages reduce Eastern emigration. Furthermore, the SOEP indicates that young people are more sensitive to wage differences while older people are more sensitive to unemployment rates. Her results also show self-selection within the migration decision.

Regarding simultaneity, some studies focus on explaining the labor market situation with migration. Molho (1995) points out the dynamics of an adjustment process. His results show that for the UK in 1981, higher unemployment rates in remote areas can be explained by low out-migration rates caused by distance deterrence (and cumulative inertia). This is, however, only characterized by the sum of the distance from region i to other regions. Molho (1995) assumes that people in inaccessible areas who have fewer out-migration opportunities, stay unemployed for longer periods. Østbye and Westerlund (2007) estimate a neo-classical growth model with a System GMM estimator to consider endogeneity of the migration variable. The effect of migration on changes in GDP per capita is examined for Norwegian and Swedish counties over the time period 1980–2000. They show that migration adds to convergence of local labor market outcomes in Sweden but reduces convergence in Norway. However, convergence is measured by comparing the coefficient of the lagged dependent variable when it is a) controlled for migration and b) not controlled for migration. Therefore, the study does not measure convergence between regions, but over time.

Comparable to this study, Niebuhr et al. (2012) analyze the effects of labor mobility on German labor market disparities. A dynamic panel GMM estimation is conducted to consider simultaneity between mobility and the regional labor market outcome. The authors suggest that mobility decreases disparities, but only in unemployment rates. Wage disparities appear to remain unaffected. Interpretations are based on very strong assumptions. For a wage equation instead of wages in relation to the national mean, absolute wage rates are used. Although there is no per se connection to the relative wage position, an estimated negative migration effect on the absolute wage level is interpreted as a negative migration effect on regional wage disparities. This implies that migration mainly takes place from low- to high- wage regions. For the unemployment equation the relative unemployment rate is used but conclusions are also based on strong assumptions. A negative influence of the migration rate on the relative unemployment rate is understood as a negative effect on unemployment disparities. Only when people significantly react on regional labor market disparities by moving, the provided interpretation should be true. Otherwise results only give information about migration effects on the regional labor market outcome. Therefore, after estimating a relative wage equation, in a second step this study estimates a migration equation. Comparable to the previous studies in this section, it is analyzed whether migration rates of German regions are significantly influenced by the respective labor market outcome. In this case, migration may contribute to a process which may influence regional labor market disparities in the long run.

3 Identification strategy

In the first step of the empirical analysis the effect of migration on regional nominal wage levels is estimated. The regional value in relation to the national mean is used to consider the effect on the relative regional wage position. The relative regional wage level witwt equals the average wage level of region i wit divided by the average national wage level wt in time t. The same applies for the considered relative regional unemployment rate uitut. In the following model, no distinction will be drawn between migration and commuting. The considerations are based on balance rates. The migration balance is the difference between a region’s in- and out-mobility rates as the share of the regional population. Here, the estimation is carried out for the overall migration balance and the migration balance which only considers internal migration, ignoring migration between Germany and other countries. In the second step, a migration equation is estimated to analyze the influence of labor market disparities on domestic migration.

A simultaneous equation model (SEM) as given in eqs [1] and [2] describes theoretically the simultaneity between the wage and migration equation. However, in the empirical analysis the two equations are estimated separately, considering simultaneity by instrumenting possible endogenous variables as described below.

The wage eq. [1] describes the relation between different regional factors and the relative regional wage level of region i in t witwt. With regard to the research aim of this study, the effect of migration migBitis central here and the estimation aims at determining whether the effect is positive or negative. Furthermore, the relative regional unemployment rate uitut and the regional average rent per square meter Rit are assumed to be main determinants. While the relative unemployment rate is expected to have a negative influence, the average rent is assumed to have a positive influence on the local nominal wage level. Influencing regional varying living costs, local rents are usually relevant for salary negotiations.

Displayed by the vector Xit', four additional regional control variables are integrated in eq. [1]. First, the share of qualified employees is considered. The qualification structure of the local labor force is of main importance to the relative average wage level of a region. Second, a region’s population density per square kilometres is assumed to influence the wage level. It is mainly a proxy for a region’s degree of urbanization. The migration effect on wages probably varies between urban and rural areas and especially in regions with a high share of highly skilled workers. Therefore, it is important to control for these factors. Third, the share of women and fourth of self employed individuals is considered in Xit' due to an assumed strong influence on the local relative wage level. All remaining factors influencing the relative wage level of a region and for a certain time will be represented by regional effects αi, time effects τtw and by a structural error term εit.

The causal interpretation of the migration eq. [2] is related to the decision to move to region i in time t. Pissarides and McMaster (1990) derive a migration function on the basis of the migration probability of a single household. A household or a person moves when the gross gain from moving exceeds their costs. Costs depend on observable and unobservable individual characteristics which are randomly distributed among the population. Therefore, the migration balance is a positive function of the gross gain from moving to a region. Following John Hicks (1932), local differences in net economic advantages are the main determinants of moving gains. In the model the relative wage level witwt, the relative unemployment rate uitut and the regional average rent per square meter Rit are assumed to provide economic incentives to move to a region. A positive relation between wages and migration can be expected. A high unemployment rate is expected to reduce the employment opportunities of migrants and would therefore create a deterring effect. The same applies for the regional average rent per square meter. Various empirical studies estimate the effect of regional push and pull factors on migration. In traditional appraisals, the regional unemployment rate and the income or average nominal wage level are usually considered as main determinants (see Ritchey 1976; Greenwood 1975 for an overview). However, Renas and Kumar (1978) argue that nominal money income variables lead to a misspecification of the migration equation when local cost of living variables are not included. They show that variables measuring costs of living and the rate of change of these costs significantly influence migration. Empirical studies exist that measure the relevance of housing costs as a main component of the regional living costs. Cebula (2002) estimates a significant negative influence of the housing price index on the population change rate of U.S.-states. Furthermore, Pack (1973) shows that the lack of appropriate accommodation reduces the attraction of a city for migrants.

On the personal level, there are other main determinants of the moving gain such as year of birth, marital status and educational level. These determinants do not vary much over time and are treated as regional effects represented by ηi (Pissarides/McMaster 1990). In addition, time effects, displayed by υt, can be assumed due to varying migration patterns over time. All remaining factors influencing the migration rate will be represented by a structural error term eit.

When assuming simultaneity between eqs [1] and [2], the observed data does not represent the amount of migrant people exogenously influencing the endogenous wage variable. Neither does the data represent a given exogenous wage level influencing the endogenous migration rate exogenously. It is conceivable that regional migration rates do not only influence labor market outcomes but are also influenced by them. Individuals may move to regions with attractive labor market circumstances and thus influence the respective labor market circumstances. Furthermore, simultaneity can be assumed for the rent variable in both equations. Regarding eq. [1], the local nominal wage may not only determine housing costs. Usually regional housing costs also influence wage negotiations. Regarding eq. [2], rents do not only work as incentive or deterrent factor within the migration decision. Due to the influence on housing demand, migration rates also influence regional housing costs.

It can be shown that the migration variable in eq. [1] is generally correlated with the structural error of eq. [1] if witwt has a significant influence on migBit in eq. [2] and that the wage variable in eq. [2] is correlated with the structural error of eq. [2] if migBit has a significant influence on witwt in eq. [1]. Therefore, if simultaneity exists, the migration variable is endogenous in eq. [1] and the wage variable is endogenous in eq. [2] and the rent and unemployment variable are endogenous in both equations. In this case, an OLS estimation suffers from simultaneity bias (Wooldridge 2003). To identify the equations and to solve the problem of endogenous explanatory variables, instrumental variables are needed for eq. [1] as well as for eq. [2]. The estimation method is introduced in Section 4.1.

Estimating the wage equation, either a positive or negative effect of migration on the relative wage level will result. The respective effect can be explained within the neoclassical labor market model. Here, a distinction has to be drawn between labor supply and labor demand effects. The labor supply in a region increases due to a positive migration balance. Yet, when considering selective migration, the in-flow of qualified workers may raise productivity and may increase labor demand as well (Niebuhr 2012). Unfortunately the given data does not indicate the qualification composition of the migration flows. However, there is a broad agreement in literature that migration is selective. Hence, it can be assumed that both, the labor demand and supply curve will shift to the right. The extent of these shifts will determine the wage effect. As presented in Figure 2(a) (see electronic appendix at www.jbnst.de/en) a negative effect of migration on wages will result due to a higher shift of the supply curve. A positive effect will result due to a distinct right shift of the labor demand curve (see Figure 2(b) in the electronic appendix at www.jbnst.de/en).

In the long run, labor demand may increase due to more consumption of new high-income receivers. Consumption and resulting wage increases due to commuting should be lower than due to migration. It can be assumed that commuters spend more money at their places of residence rather than in their work region (Elhorst 2003).

The central research question of this study is whether migration increases regional wage disparities. Since only disparities between German regions and not between Germany and other countries are observed, the effect of internal migration on wages is central for the research question. How does a positive or negative wage effect of domestic migration relates to disparities? The second estimation analyzes whether or not a high relative wage level and/or a low unemployment rate provide incentives to move to a certain region. In the literature it is often assumed that workers vote with their feet and move to high-wage/low-unemployment regions (Borjas 2000). If internal migration is significantly triggered by the local wage level, a positive migration effect on wages will lead to increasing disparities resulting in a self-reinforcing process: High disparities lead to migration and further increase disparities. A negative migration effect on wages will lead to a reduction of disparities. Disparities, which lead to migration, are likely to result in a wage adjustment process. When people or households do not move due to regional wage differences, migration is not likely to have a significant effect on disparities.

4 Empirical analysis

4.2 Data and descriptive statistics

The examination about the effect of migration and commuting rates of German regions on regional wage disparities is based on data from the “Indicators and Maps on Urban Development in Germany” (INKAR, Federal Institute for Research on Building, Urban Affairs and Spatial Development Bonn) ^[1] and from the “German Socio Economic Panel” (GSOEP) ^[2] from the years 1998 to 2009. The examination is carried out on the regional level of the German Spatial Planning Regions (Raumordnungsregionen, RORs). RORs are classifications between German administrative districts and counties with a total of 96 regions. The INKAR dataset is supplied by the German Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR). It gives information on spatial development in several regional levels in the form of data, maps and charts. The data set provides a wide range of official regional figures about employment, population and age structure, mobility, education, living standards, environment, health, public budget, traffic and local economy in Germany. The GSOEP is supplied by the German Institute of Economic Research (DIW Berlin). It is a representative panel survey of private households in Germany. Among many other fields, it contains detailed information on household income and work hours.

SOEP data is used for differentiated wage information because the INKAR data set provides only information on gross earnings, not taking work hours into account. Average net hourly wages of SOEP respondents are merged to the INKAR data set on the regional level of RORs. For each ROR and year the number of wage observations in the SOEP for the considered time varies from 9 to 424. On average, the calculated wages are based on 100 observations. However, for RORs with few wage observations, the wages may not be fully representative and results may be biased to a certain extent. For further research it would be worthwhile to test results using other representative Data such as the employment history statistic of the IAB. For the following descriptive statistic part, more representative average wages are calculated using SOEP expansion factors. To consider dependencies between regions and for wages of one person over the observed years, averages are calculated using OLS estimation with robust standard errors. When estimating causal effects within the main regression analysis of this study, the use of sample weighting does not improve the results. The various econometric issues considered in the estimation already correct for heteroscedasticity and endogenous sampling (see Solon et al. 2013).

Wages are used in nominal form. Although the German federal statistical office is working on it, currently there is no representative data on regional differentiated prices available in Germany. As described in Section 3, in both estimated equations it will be controlled for local average rents. The BBSR supplies average rents, based on newspapers and internet advertisements. ^[3] Unfortunately, no data is available for years before 2004. However, for now and in contrast to SOEP information, it is the most representative available data base that includes all German regions. Therefore, average rents from 2004–2006 are used for the years before 2004 (BBSR-Wohnungsmarktbeobachtungssystem, IDN ImmoDaten GmbH).

Figures 3 and 4 (see electronic appendix at www.jbnst.de/en) give information about labor market disparities in Germany. For each ROR averages for the years 1998 to 2009 are displayed over German “Bundesländer” (NUTS 1 level). Figure 3 depicts averages of net hourly wages. In West Germany, shown by the first 10 columns, average wages range from about EUR 9 (Landshut) to EUR 15 (Ostwürttemberg). In the Eastern Bundesländer, shown by the last 6 columns, wages range from about EUR 7 (Mecklenburgische Seenplatte) to EUR 11 (Havelland-Fläming). Figure 4 depicts disparities in local unemployment rates. Unemployment rates in the East range from 12 % (Südthüringen) to 21 % (Mecklenburgische Seenplatte). In the West they range from 5 % (Oberland) to above 14 % (Emscher-Lippe).

While unemployment disparities do not vary much during the observed time period, the distance of regional wage rates to the national mean increased in many regions. Figure 1 relates average relative wage rates from 1998–2001 with average relative wage rates from 2006–2009 for West and East Germany. Most Eastern wages (see chart a)) remain below the average in the observed years, with a relative wage lower than 1. There are eight regions above the diagonal indicating further relative decreases. In the West (chart b)) there are many regions in the upper-right quadrant with a relative wage higher than 1 indicating that wages remained above average. Here, points under the diagonal indicate further relative increase. Ostwürttemberg is a high outlayer. Further decreases of under average wage rates are higher than in the East.

Figure 1:

Change of relative wage levels from 1998–2001 to 2006–2009. Source: SOEP.

The data set enables the observation of migration and commuting balances. With regard to migration, in addition to the overall migration balance, the rates for internal migration between different German regions are given. While migration flows are related to 100 residents of the respective ROR, commuting flows are related to 100 employees from the respective working population in the region. The mobility rates remain similar in the observed time period. Figure 5 (in the electronic appendix at www.jbnst.de/en) depicts the yearly change of the migration balance. Overall migration balances range from –1.38 to 1.76 %. Domestic migration balances range from –16.52 to 1.79 %. Commuting balances range from –43.91 to 30.44 %.

Additional regional control variables are used to estimate the influence of migration on wages and the effect of regional labor market circumstances on migration (see Section 4.1). Table 1 depicts the means and standard errors of the variables used in the estimation based on 96 RORs.

Table 1:

Mean and standard deviation of regional explanatory variables.

	Mean	Standard deviation
Wage per hour in EUR	10.23	(1.72)
Unemployment rate in %	10.04	(4.66)
Migration balance, related to residents	0.09	(0.43)
Within migration balance, related to residents	−0.06	(1.02)
Commuting balance, related to employees	−5.03	(11.67)
Rent per square meter in EUR	5.43	(0.85)
Population density, residents per km2	330.57	(494.15)
Share of women in %, related to residents	50.99	(0.42)
Share of self-employed in %, related to employees	11.05	(1.95)
Share of highly qualified employees in %, related to employees	7.83	(2.80)

^[4]

Figure 6 (see electronic appendix at www.jbnst.de/en) displays a scatter diagram relating relative wage levels (without using expansion factors) to migration balances. Although a distinct connection is not evident at first sight, a regression line illustrates a positive relation. Accordingly, wages in regions with high positive migration balances appear to be high.

5 Conclusion

This study provides an analysis of the effect of German regional mobility rates on regional wage disparities. The estimation uses data from the “Indicators and Maps on Urban Development in Germany and Europe” (INKAR) and the “German Socio Economic Panel” (GSOEP) from the years 1998 to 2009 on the basis of German Spatial Planning Regions (RORs). In a first step, a structural wage equation is carried out to estimate the effect of the regional migration and commuting balances on the relative regional wage level. Here, a first specification estimates the effect of the overall migration balance while a second specification considers only internal migration. The latter is relevant for analyzing the effect of migration on disparities between different German regions. All estimations of this study calculate short-run and long-term effects. Dynamic panel GMM estimations are conducted to account for simultaneity between the regional labor market situation and mobility, for dynamic wage adjustments and for endogeneity due to regional fixed effects.

Due to four or five endogenous variables in each estimation equation of this study, instrument proliferation can be assumed, also when the lag length is reduced to T-8 for basic estimations. Too many instruments may bias estimates and standard errors as well as may weaken the validity of the Hansen-Tests. The classical sign of too optimistic Hansen-test p-values are observed in the basic results. Following recommendations in the literature, for the main results of this study the instrument count is further reduced in two variants. In all cases this lowers the p-values to more realistic numbers and verifies basic results. However, the literature provides little guidance on how many instruments is too many. The research on instrument proliferation within the Difference and System GMM estimators is still in the early stages and as other authors claim, more methods are needed to further reduce the problem.

The results of the first specification indicate a small positive short-run and long-term wage effect of the regional overall migration balances. When the regional migration balance rate increases by 10 percentage point, the relative wage level increases by 0.0107 % in the long run. The yearly changes of the migration balance of German regions lie in an interval of –50 to +50 percentage points (which equals a change in the value of –0.5 to +0.5). Therefore, regions with a positive change rate of the migration balance can expect an increase of the relative wage rate of up to 0.054 %. Regions with a negative change rate of the migration balance are expected to deal with a respective negative effect. In sum, the regions seem to benefit from new citizens, although the effect is small. One reason for the positive effect may be a higher consumption demand. This might also provide an explanation for the fact that no wage effect of commuting could be found in any output of this study. Commuters may spend more money at their places of residence rather than in their work regions.

The results of the second specification show a small but negative wage effect of the migration balance which only considers migration between German regions. When the domestic migration balance in a region increases by 10 percentage points, the relative wage level decreases by 0.001 % in the long run. By comparing the results of specifications 1 and 2, it can be concluded that German regions seem to benefit mostly from the combination of domestic migration and foreign migration. However it has to be considered that both wage effects of overall and internal migration are very small.

In debates about migration, politicians often emphasize the fear that the regional labor market situation is worsened by migration, especially from other countries. The estimated small positive wage effect indicates that earning circumstances in German regions due not chance or may even improve with high positive migration balances. A possible explanation of the positive wage effect is selective migration. Self-selection can be considered in qualification but also in terms of unobserved characteristics such as general capability, motivation and courage. The moving incentive should be higher for individuals being more able or higher motivated in occupational field. This should especially apply with regard to the decision for relocating to another country. Hence, political debates about the impact of foreign migration should consider these results. As forecasted by the BBSR, the German population will decrease by about 2.52 % in the years 2010 to 2030, the number of employed people is expected to decrease by about 6.69 %. According to the ifo Institute, Germany may compensate the increasing labor demand by the unemployed people in the short run. However, labor reserves will not be able to fulfill the labor demand of firms in the long run. While for Germany as a whole, the GDP is supposed to increase by 1.14 % p.a. from 2010 to 2030, growth rates will highly differ on the regional level. The ifo Institute emphasize the demographical development as a main reason for the differences. Regions with a stable labor force potential will be better off (Berlemann et al. 2012). This stability may also be supported by foreign migration.

Migration literature assumes that domestic migrants move to regions with high wages and low unemployment rates. Focusing on work-related moving, Arntz (2010) confirms this assumption for Germany from 1975 to 2001. She shows that high-skilled job movers are mainly motivated by regional wage differences while less-skilled job movers are influenced by unemployment differences. As shown in studies presented in the second section, wage differences also mainly influence the migration flows from East to West Germany. It can be assumed that these crossing-boarder moves are primarily economically motivated moves. For the purpose of the research question, this study questions in a second step whether economic factors are main determinants of general migration within Germany. Only in this case, the estimated wage effect due to migration would be able to influence and to change regional wage disparities in the long run. Results indicate that the relative wage level shows no statistically significant influence on the regional domestic migration balance. Results probably differ from previous studies since the estimation considers migration that is not only motivated by economic factors. On average German households or individuals who decide to move, seem to have other reasons for moving such as family or housing-related issues.

Traditional migration literature assumes that labor mobility reduces regional labor market disparities. This conclusion is also made by Niebuhr et al. (2012) concerning unemployment rates. In this study when only considering internal migration within German regions, the estimated migration effect on relative wage levels turns out to be small and negative. Indeed, a negative effect may lead to an adjustment of disparities (although the impact would be low due to the small effect), but only if the migration rate due to wage disparities is high enough. Without analyzing this relationship, results remain hypothetical. Results of the second main estimation of this study indicate that German migration is not influenced by regional relative wage levels. An adjustment mechanism where disparities trigger migration and by this reduce existing disparities is not likely to occur in the coming years. Regional bindings might deter individuals leaving German regions with unattractive labor market circumstances. In addition, regional living expenses vary across Germany and thereby reduce earning disparities (Goebel et al. 2009). In this sense, disparities may still be too low to provide moving incentives.

On a single case basis, results can be used to determine how much the relative wage levels of regions may change due to migration. Since the estimated effect of the regional over-all migration balance on wages is positive, wages in regions with a positive balance will increase and decrease in regions with a negative balance. Hence, disparities will increase when regions with above-average wage levels are confronted with increases of the migration balance. In addition, disparities increase when regions with under-average wage levels are faced with decreases of the migration balances. For the analysis relative average wage levels are calculated using SOEP expansion factors. Table 5 (see electronic appendix at www.jbnst.de/en) lists regions in which a positive wage distance to the national mean may further increase in the coming years. Large increases of above-average wage levels due to a high average growth of the migration balance can be expected for Hamburg, Neckar-Alb, Rhein-Neckar and Munich. The average change rate of the migration balance (for the years 1998 to 2009) in the first three regions is 2 percentage points which implies an average increase of the relative wage level of 0.002 % due to the estimated wage effect of 0.00107. The average change rate in Munich is 5 percentage points. Hence, an average increase of the relative wage level of 0.005 % can be expected.

Large decreases of under-average wage levels due to a large amount of relocations can be expected in particular for regions of Brandenburg. Here occur the highest negative average change rates of the regional migration balance. In Uckermark-Barnim, Prignitz-Oberhavel and Oderland-Spree average change rates (for the years 1998 to 2009) range from –11 to –14 percentage points. The estimated wage effect of 0.00107 leads to average declines of the relative wage level of 0.012 to 0.015 %.