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Econometric Analysis of the Impact of Immigration on the German Economy

11168 words (45 pages) Business Assignment

1st Dec 2020 Business Assignment Reference this

Tags: Business AssignmentsEconomics

Abstract

For long, the issue of immigrants coming into high-income countries has kept governments and natives concerned and researchers interested. This paper contributes to the existing vast discussion on the impact of immigration on the receiving country’s macroeconomic indicators by examining the effect that immigrants have had on Germany’s household consumption expenditure and unemployment rate, while, also analyzing if the 2015 European refugee crisis has had any significant effect yet. The study estimates two models over the period 1983-2017 using Auto-regressive Distributed Lag (ARDL) Bounds testing approach. The econometric analysis of this paper revealed that immigrants can be associated with negative effects on household consumption expenditure, while, they remain insignificant to any change in the unemployment rate in both, short and long run. Further, the 2015 refugee crisis appeared to have a positive impact on household consumption expenditure but had no significance on unemployment. The findings were consistent with the existing papers and theoretical background for the most part.

1. Introduction

In the last few decades, the world has witnessed various changes in the socio-economic and demographic trends. The most significant of such changes is the sharp rise in foreign-born population, particularly in high-income countries. The constantly increasing proportion of foreign-born population in many high-income countries is an issue that concerns not only the government but also the natives of the receiving countries. UN (2017) estimates that between 1980 and 2017, the foreign-born population in the world increased by 2.5 times to almost 258 million, with more than three-fifth of these living in high-income countries (UN, 2017). The share of immigrants in the population of OECD countries has risen from 9% in 1995 to almost 13% in 2006 (Longhi, et al., 2010, p.820).

The transnational migration from low and middle-income countries to high-income countries has many motives, such as, family reunification, desire for a better standard of living or even escaping a war struck country (Longhi, et al., 2010). The consequences for the receiving country range from demographic and social to economic and political changes. The native population is particularly unhappy about the rise in immigrants and have thus given rise to political debates and demand for policy reforms. In the last decade, only the political parties which supported right-wing ideologies and promised to restrict immigration in the country attracted the most support from the natives. This rise of right-wing populist political parties has not only promoted the rhetoric of anti-immigration but, also as a consequence, has normalized the idea of closed borders among the natives. Increasing proportion of natives now believe that restricting foreign-born immigration will drastically reduce crime rate and spur economic growth (Kubrin, 2013).

The rhetoric of anti-immigration arises primarily from two factors; economic and cultural (Tabellini, 2018). Natives believe that more immigrants lead to an increased competition in the labor market and therefore reduce employment opportunities for them, and at the same time, natives also consider immigrants a threat to their cultural and social cohesion (Tabellini, 2018). The consequences of this rising concern of the natives can be seen through the support and popularity for the ‘Border Wall’ in the US, the ‘Brexit’ movement in the UK and the rise of AfD in Germany[1].

Amongst the OECD countries, Germany has arguably had one of the most open borders for refugees and asylum seekers over the years[2]. It receives the second highest number of asylum applications, after the US, with almost 198,000 applicants in 2017 (OECD, 2018a). According to Eurostat (2019), Germany has the highest numbers of foreign-born[3] people among the EU-28 states with almost 14 million people in 2018 (Eurostat, 2019). Given the large number of immigrants it is not surprising that Germany is also a home for many anti-immigration movements, most notably, Patriotic Europeans Against the Islamisation of the Occident (PEGIDA)[4]. According to a survey conducted by Pew Research Center (2018), more than 58% of the people surveyed in Germany believed that the country should let fewer or no immigrants in, in contrast, only 29% of the Americans surveyed think the same.

For decades, the issue of immigration has been at the center of all political and scholarly debates in Germany. Many right-wing groups have taken to the streets demanding stricter policies on immigration and are simultaneously known to have committed hate crimes against refugees and people of different races (Benček & Strasheim, 2016). Their fear of immigrants range from increased job losses, due to availability of cheaper labor, to increased crime and cultural disharmony. This paper attempts to examine this very fear of the natives and analyse if immigration has any effect on Germany’s household consumption expenditure and unemployment rate, at the same time, the paper will also analyse if the 2015 Refugee Crisis has had any effect on the dependent variables so far. The importance of this study, both, to the government and the natives, cannot be unstated, with the former requiring it to formulate better employment and immigrant policies, and latter, to understand the reality better. This paper argues that there might be short term negative effects of immigration, but in the long-term, immigration will have no effect on the two dependent variables.

The paper is divided in 9 sections. The second section reviews the existing literature along with the theoretical issues involved in this study. Section 3 and 4 provide historical and theoretical background, with data sources, methodology and model specification in the fifth section. Sixth section analyses the data used and provides the result of all misspecification tests performed. Model estimation done using the Auto-regressive Distributed Lag (ARDL) Bounds testing approach is given in section 7. Finally, the paper ends with results discussion and conclusion in section 8 and 9 respectively.

2. Literature Review 

Until the late 1990s there were very little empirical studies on the impact of immigration, Bauer & Zimmermann (1995, p.95) went on to call it a ‘black hole’ in economics. They believed that this was primarily because of the lack of desired time series data and the limited interest of researchers on the topic. However, since then, the topic has gained a lot of traction. Presently, there are hundreds of studies examining the impact of immigration on the receiving country’s labor market and they vary across methodology and the kind of data used.

The scholarly and political discussions on the impact of immigration are based on three major aspects; immigrants’ performance in the receiving country’s economy, their effect on the natives’ unemployment, and finally, the right immigration policy that benefits receiving country the most (Borjas, 1994)[5]. Borjas (1994) believes that the perception that all immigrants have an adverse effect on the native labor market is incorrectly formed. Immigrants have varied levels of productivity and skills, a highly productive immigrant, one who can easily adapt to local conditions, can have a significant positive effect for the natives, while, a low skilled immigrant who can’t adapt will only increase the costs of income maintenance programs (Borjas, 1994). Altonji and Card (1989) also found that the effect of immigration depends largely on their skill composition, and that there exists a moderate correlation between the less skilled natives and the immigrants, in particular, an increase in immigration increases the fraction of immigrants in low-skilled labor force (Altonji & Card, 1989). 

The topic is primarily examined using cross-country data (panel data) or time series data, with most of the papers using the former (Shan, et al., 1999). Damette & Fromentin (2013) used panel cointegration approach to study the effect of migration on the labor markets of OECD countries and found no evidence of a significant effect in the long run. On the other hand, Angrist and Kugler (2003) also studied a panel of 18 OECD countries over 1983 to 1999 and found a small but negative effect of immigrants on natives’ employment. Use of time series analysis for this topic is fairly recent, however, more and more studyies are switching to time series analysis. Shan, et al. (1999) used Granger causality testing procedure to study Australia and New Zealand, and concluded that there is no causal relationship. Ortega and Peri (2009) derived a pseudo-gravity model and found no correlation between immigration and per capita income in 22 OECD countries. In a similar study on OECD countries, Boubtane, et al. (2013) found that immigrants can actually be linked with higher GDP and economic prosperity in the host country.

Weiske (2019) used data from the Current Population Survey (CPS) to analyse the macroeconomic effects of immigration in the US and found that immigration is insignificant to the US business cycles, in addition, he pointed out various problems associated with studying the effects of immigration; (1) immigration has to be treated as an endogenous variable since the decision to migrate depends on the conditions of both, the home and the destination country[6] leading to biasedness in estimation; (2) natives can switch to other sectors or move to other regions which might not be affected by immigrants; (3) change in wages in the short run is highly dependent on the rate of capital adjustment following an immigration shock.

Fertig & Schurer (2007) discuss the importance of heterogeneity, assimilation of immigrants and the quantitative role played by attrition bias while exploring the impact of immigration in Germany. They found a fair degree of heterogeneity, with regard to assimilation, in the probabilities for both annual income and unemployment. According to Buhr & Weber (1996), much of the increase in government spending on social security can be attributed to migrants.  This gives rise to another major concern of the natives, i.e., high utilization of social security benefits by the migrants, which increases the burden on the state and therefore on the taxes paid by the natives (Bauer, et al., 2005). Bonin (2005) uses skill group approach to analyse the impact of immigration on the labor market for native Germans, and found that a 10% increase in the immigrants’ proportion in total workforce would normally reduce the natives’ wage by less than 1%. In addition, he points out that the effect slightly worsens as a result of low-skilled and old aged immigration, which interestingly is in sharp contrast to the results of similar studies on the US labor market (Bonin, 2005). Natives fear that more immigrants would translate to job losses and lower wages, however, Schmidt (1993) showed that the immigrants in Germany are far less likely to get a white-collar job or become civil servants as compared to the native Germans, which suggests that natives do not run a risk of losing high-skilled or government jobs as a result of an immigration shock. Bentolila, et al. (2008) analyzed how immigration affects the New Keynesian Phillips Curve and found that had it not been for a huge influx of immigrants in Spain, the inflation would have risen by 2.5% after the fall in unemployment.

OECD (2018a) estimates that the employment rate of immigrants in OECD countries was 67% in 2017 and the gap between the unemployment rate of the natives and immigrants was about 3%. Winkelmann & Zimmermann (1993) made one of first attempts to examine the impact on employment and unemployment in Germany. They studied a period of 10 years and based their analysis on 1,830 males, which included 586 foreigners. They found that unemployment would increase substantially as a result of immigration, however, Mühleisena & Zimmermann (1994) found no evidence to support this. According to Bauer, et al. (2005) there isn’t much evidence to support the notion that immigration has any adverse effect on wages and unemployment in Germany, which is consistent with studies on other European countries or the US. They however conclude that the effect largely depends on the competitiveness of the labor markets, as, a less competitive market will have higher risks for natives as compared to a highly competitive market (Bauer, et al., 2005). 

The existing literature is quite vast and comprehensive, it covers almost all aspects of a labor market and how immigration can have an impact. Most of the papers have a similar conclusion, i.e., low or insignificant effect of an immigration shock on the local labor market, with a few exceptions. A major takeaway is that, the effect on labor market depends a lot on factors like, age, sex, skill and education level of the immigrants, and also on the macroeconomic and the labor market conditions of the receiving country.

Although the impact of immigrants on an economy is a well-researched topic, this study attempts to contribute to the existing literature by analyzing the short and long run effects of immigration on unemployment rate and household consumption expenditure for the period 1983 to 2017, thereby, also including the effect of the 2015 Refugee Crisis, which the existing papers lack. Further, by also examining the impact on household consumption expenditure, the study is including an important aspect of the economy, which is often left out, the households. In addition to this, the study will also attempt to provide a more comprehensive analysis by focusing entirely on the German economy, as most existing papers instead aim to provide a comparison of the impact on various OECD economies than a single country analysis.

3. Historical Background

3.1 Immigration in West and East Germany

The Berlin Wall, manifested in 1961, stood as a divide between East and West Germany[7] for over 28 years. It was not only a physical disturbance but also a cultural and most importantly an ideological divide (Leventhal, 2010). Since its inception, Germany faced a many-sided migration and labor market experience with low supply of skilled labor and yet high and rising unemployment (Bauer, et al., 2005). Until the 1973, it recruited low skilled labor from southern and eastern Europe[8], most of whom were later given German citizenships (Fertig & Schurer, 2007). By the 1980s, the political changes allowed for a fresh period of immigration in Germany with a high inflow of asylum seekers along with east-west migration (Bauer, et al., 2005). Foreign-born people’s share had risen to almost 8% of the total population and an equivalent share of the labor force, by 1989 (Bauer, et al., 2005).

3.2 Immigration in Reunified Germany

Figure 1 shows that the number of foreign-born persons coming into Germany each year from 1980 to 2018 has an upward trend with two major events of sharp increase, 1991 and 2014. After the fall of Berlin Wall and the Reunification of Germany, the country witnessed a spike in internal migration and an inflow of almost 400,000 refugees and asylum seekers (Bauer, et al., 2005). This was followed by a period of restrictive immigration policies put in place by the Federal Government, and as a consequence, there was a dramatic decline in immigration of foreign-born persons each year up to 2005. After years of political impasse, a new immigration law was passed in 2004 allowing highly skilled workers and entrepreneurs who promised to generate local employment, to immigrate (Bauer, et al., 2005). Germany’s stance on immigrants and in particular asylum seekers relaxed when Angela Merkel assumed office in 2005 and the country started taking more asylum applications. In the first few months of 2015, close to 500,000 refugees, most of them fleeing war-struck Syria, arrived on European shores, giving rise to the European refugee crisis of 2015 (Holmes & Castañeda, 2016). Germany with its relatively relaxed immigrant policy let in most of the refugees which spiked the number of foreign-born persons coming into Germany between 2015 and 2017 (see figure 1).

4. Theoretical Background

Multiple theories can help explain the relationship between foreign-born migrants and receiving country’s macroeconomic indicators. According to the Neoclassical approach, the primary reason for migration between two regions is the difference in their demand and supply of labor (see Ravenstein, 1889). Regions where the labor demand exceeds supply tend to have a higher equilibrium wage and regions with excess supply tend to have lower, as a result, labor migrates from a region with excess supply to a region with excess demand. In this process, the wage in the previously excess demand region fall until the difference in wages is equal to the cost of migration (Hicks, 1932, as cited in Bauer and Zimmermann, 1995). Harris & Todaro (1970, as cited in Bauer and Zimmermann, 1995) extended this model by dropping the assumption of full employment, now, a migrant base his/her decision on the probability of employment and the expected earnings.  

Figure 3: Push Migration with Fixed Wages
Source: Bauer & Zimmermann (1995, p.100)

Figure 2: Push and Pull Migration
Source: Bauer & Zimmermann (1995, p.100)

The theory of ‘push and pull’ factors of migration provide simple dynamics of the impact of immigration on the receiving country. From the figure 2, if the demand for labor increases in the host country, the price for labor starts rising, in an effort to control inflation the host country might choose to allow immigration, as a result, the supply curve shifts downward, increasing output and decreasing price. This shift of equilibrium point from A to B is known as pull migration (Bauer & Zimmermann, 1995). On the other hand, if the supply curve shifts downward, as a result of an immigration shock, with no change in demand, the equilibrium will move from A to C. This movement is known as push migration[9] (Bauer & Zimmermann, 1995). Conversely, if the real wages are fixed at W, EF will represent the level of unemployment in the host country (see figure 3). An immigration shock in this case will result in greater unemployment and prices[10] with constant output (Bauer & Zimmermann, 1995). Hence, a push migration, in such a scenario, would cause stagflation (Bauer & Zimmermann, 1995). Germany experiences both, push and pull factors of migration, and as Heckel, et al. (2008) point out, there is some degree of wage stickiness present across Europe, as a result, the second scenario, given in figure 3, can be experienced to some extent.  

High-income countries, like Germany, tend to have a high demand for low-skilled labor with insufficient supply, and although, it might be fair to assume that immigration will lower the average wage, Longhi, et al. (2010) believe that, the lower wages will not only help firms expand production but also allow them adopt labor intensive techniques which weren’t possible earlier, this effect can be seen in figure 2, which shows an increase in output and fall in wages. The increased employment, production and consumer demand associated with bigger population will yield larger tax revenue and as a consequence, greater public services (Longhi, et al., 2010). Cheaper immigrant labor can also translate to higher productivity levels (or GDP per hour worked), which will induce more investment until the rate of return to capital is restored to national average in the long run (Longhi, et al., 2010). Nickell (2010) believes that natives shouldn’t be affected by immigrants in the long-run as the economy will be bigger and can hence accommodate more workers. 

To predict the effect that immigrants have on unemployment in the receiving country is rather tricky. As Jean & Jiménez (2011) explain, immigrants increase both labor supply and demand, but, not necessarily simultaneously. The occurrence of these increases depends on multiple factors, which include, rate of immigrant assimilation and labor market policies put in place by the host country’s government (Jean & Jiménez, 2011). For instance, in the presence of high level of dualism in labor market protectionism, the employment gap between foreign-born and natives reduces, and the wage gap rises, at the same, high tax wedges cause employment rate of immigrants to fall (Causa & Jean, 2007, as cited in Jean & Jiménez, 2011). In addition, the impact of immigration on unemployment will be lower if a change in employment policy increases how wages respond to unemployment (Jean & Jiménez, 2011). According to Nickell (2010), in the short run, unemployment rises after an immigration shock and it stays high if the employment policies are restrictive, however, in the long run, immigrations helps to increase the flexibility of local labor market and reduces the equilibrium rate of unemployment (Nickell, 2010). In short, an economy should witness negative effect on average wages and a positive effect on employment, output and productivity, however, these effects are expected to die out in the long run.

5.    Data and Methodology

5.1 Variables and Data Sources

This study uses an original dataset compiled using 35 annual observations of time series data of 7 variables over the period 1983-2017. Data for 6 of the 7 variables is obtained from the enormous database of Organization for Economic Co-operation and Development (OECD) while the data for immigration in Germany is obtained from the database of Federal Statistical Office, Germany called Genesis Destatis[11]. OECD data is known for its robust and routinely reported statistics which is widely accepted and used by governments, financial markets and businesses, suggesting its fair degree of reliability and comparability (Reinhardt, et al., 2002). Since each country defines its immigrants differently (see Bauer & Zimmermann, 1995), Genesis Destatis was used to obtain the time series on immigrants. Zühlke, et al. (2004) presents evidence for the robustness of data obtained from Federal Statistical Office and its various research centers.

For empirical analysis, this study uses log differenced form of household consumption expenditure (HHEXP) to represent Germany’s annual percentage change in consumption. Unemployment rate (UNEMPR) and employment rate (EMPR)[12] are included to account for the absorption role that a labor market plays in an economy (Hausmann & Rigobon, 2003), while, a log differenced form of unemployment rate (UNEMPC) is included to examine the rate of change of unemployment. As suggested by Bentolila, et al. (2008), the study includes inflation rate (INFL) to analyse if immigration has any effect on the Phillips curve. GDP per hour worked is used as a proxy for productivity level (PROD). CRISIS, a dummy variable, that takes the value 0 for years 1983-2014 and the value 1 for 2015-2017, is included to account for the European refugee crisis of 2015[13]. Finally, IMMGTN is the number of foreign-born persons coming into Germany each year from Asia, America, Africa and Australia and Oceania and also people who are ‘Stateless, unknown, uncertain, not specified’ (Federal Statistical Office, 2019). Migrants from European countries were not included because of the freedom of movement and work within the EU countries, as inferred from Nickell (2010).

5.2 Problems

Number of immigrants is a challenging statistic for a number of reasons; poor monitoring of registration and deregistration of new and outgoing migrants, large number of undocumented and unrecorded migrants. This leads to different sources telling largely different figures of immigration (Bauer & Zimmermann, 1995). Further, the fall of Berlin Wall and the German Reunification in 1990 not only limits the availability and consistency of the macroeconomic statistics of Germany but also raise questions on its integrity. Before 1990, West and East Germany were two separate countries with two contrasting political systems, as a result, a simple aggregation of the data doesn’t give the true picture of the German economy, henceforth, the results should be interpreted with caution.

5.3 Methodology

Although most papers employ panel data analysis, this study uses time series analysis and employs the Auto-regressive Distributed Lag (ARDL) Bounds testing approach, developed by Pesaran and Shin (1999) and Pesaran et al. (2001). The general ARDL (p,q) model is given by,  

Yt=γ0i+∑i=1p δiYt-i+∑i=0q βi'Xt-i+εit

Here, the dependent variable (Y) is represented as a function of its on lagged variables, current and lagged values of explanatory variables (X). This procedure is adopted for four broad reasons; (1) its simplicity, when compared to multivariate cointegration techniques like Johansen cointegration, Bounds test allows cointegration to be estimated using OLS; (2) Bounds test doesn’t require variables to be tested for stationarity beforehand, unlike VAR technique, variables can be used in an ARDL model irrespective of their order of integration, hence, regressors can be stationary I(0), integrated of order one I(1) or even mutually cointegrated, however, it is necessary to ensure that none of the variables are integrated of order two I(2); (3) this technique gives more robust results in finite or small samples; (4) ARDL uses one of the information criterion, e.g. Akaike info criterion (AIC), to choose the appropriate lag length for each regressor, as opposed to VAR, where all regressors have the same lags (Frimpong & Oteng-Abayie, 2006).

Figure 4: ARDL Bounds testing procedure

The ARDL Bounds testing procedure, developed by Pesaran, et al. (2001), is used to estimate the short and long-run impact of immigration on unemployment rate and household consumption expenditure for the period 1983 to 2017. Broadly, the procedure has four steps (see the figure 4 above), first, perform unit root tests to ensure that none of variables used are integrated of order two I(2), second, specify the correct ARDL model, tested for all the misspecification tests, third, perform a bounds test for cointegration and if cointegration among the regressors is found, the final step is to use error correction and estimating a long run model, else, estimating only the short-run model. This study uses EViews (version 10) to perform all data analysis and takes help from Startz (2015).

5.4 Model Specification 

5.4.1  Model 1

HHEXPt=κ0+∑i=1h κiHHEXPt-i+∑i=0j πiIMMGTNt-i+∑i=0f ψiPRODt-i+∑i=0n ϕiUNEMPRt-i+CRISIS+ε2t

The purpose of this model is to examine the relationship between the annual change in household consumption expenditure (HHEXP), number of immigrants (IMMGTN), GDP per hour worked (PROD) and the rate of unemployment (UNEMPR). CRISIS is again the time dummy.

5.4.2  Model 2

UNEMPCt=β0+∑i=1p βiUNEMPCt-i+∑i=0q γiIMMGTNt-i+∑i=0r αiINFLt-i+∑i=0v ρiEMPRt-i+CRISIS+ε1t

This model is based on the traditional Phillips curve according to which unemployment and inflation have an inverse relationship (Bentolila, et al., 2008). Similar to Bentolila, et al. (2008) this study will also analyse how immigration affects the Phillips curve of Germany by estimating the same model without IMMGTN and CRISIS[14]. The primary model examines the correlation between the percentage change in unemployment rate (UNEMPC), number of immigrants (IMMGTN), inflation (INFL), employment rate (EMPR) and the time dummy (CRISIS).

6. Data Analysis

6.1  Preliminary Analysis

 The a-priori analysis of the data involved in this study uses descriptive statistics given in table 1. The table shows a summary of mean, median, maximum, minimum, standard deviation, skewness, kurtosis, Jarque-Bera and probability. From this table, it can be seen that all thevariableshave a standard deviation lower than 1 (except INFL), and the maximum and minimum values are near the mean, except for IMMGTN, which has a few outlier values from the 2015 refugee crisis (see series and box plots in appendix 2-figure2.c). Further, probability suggests that all the variables, except INFL[15], are normally distributed. Hence, it can be concluded that there is a fair degree of stability in the variables used.

6.2  Stationarity Tests

It is important to ensure that all the variables used in an ARDL model are either stationary I(0), integrated of order one I(1) or mutually cointegrated. If any variable used is integrated of order two I(2), the F-stat for bounds test, as provided by Pesaran et al. (2001), cannot be considered valid and the regression might give spurious results (Frimpong & Oteng-Abayie, 2006). Hence, the following stationarity tests are performed.

6.2.1  Graphical Analysis

A quick inspection of all the series plots (see appendix 2) show that EMPR and PROD have a clear upward trend, and the trend line in figure 1 suggests that IMMGTN also has a small but upward trend. While the plot of INFL seems to suggest no trend, the plots for UNEMPC and UNEMPR show a downward trend. Hence, for all the variables (except INFL), it is difficult to conclude stationarity, as a consequence, unit root tests are performed.

 6.2.2  Unit Root Test

This study employs both, the conventional Augmented Dickey-Fuller test (ADF) and the Kwiatkowski-Phillips-Schmidt-Shin test (KPSS) to test for the unit root processes in the series. ADF test is performed using two lag selection criteria; Akaike Info Criterion and Schwarz Info Criterion with maximum lags being 9. The bandwidth used for the KPSS test is given by Newey-West Bandwidth.

The null and alternative hypothesis for the Augmented Dickey-Fuller test is given by,

H0: the process has a unit root

H1: the process doesn’t have a unit root

And the null and alternative hypothesis for Kwiatkowski-Phillips-Schmidt-Shin test is,

H0: process is trend stationary

H1: it is a unit root process

Because of the different hypothesis, rejecting the null in ADF test and failing to reject the null in KPSS test, suggests stationarity. Table 2 below provides results from the two tests.

The test statistics from the ADF test suggest that only HHEXP and UNEMPC can be said to be stationary at levels, i.e. I(0), at 1% significance level for both the lag length criteria. As a result, the test was extended to include the 1st differenced form of all the variables. The results of ADF test with 1st differenced form suggests that all the variables, except UNEMPR, can be strongly concluded to be integrated of order one, i.e. I(1). Since, the ADF test for UNEMPR was only weakly conclusive, KPSS test was used with level, 1st difference and 1st difference with trend. The test results from the KPSS test coincide with that from ADF test for the most part, however, it further suggests that UNEMPR is stationary in 1st difference with trend form. Therefore, it can be strongly concluded from the above unit root tests that all the variables involved in this study are integrated of order one with HHEXP and UNEMPC also stationary at levels.

6.3  Misspecification Tests

It is crucial to ensure that the models analyzed in this study are not misspecified before proceeding to estimation. If the models are misspecified and do not fulfill the classical linear regression assumptions (as stated in Brooks, 2014), the estimation results along with the t, F and LM stats won’t be valid. The two models were run as ARDL in EViews and the residuals from their regressions were used for the following misspecification tests.

i. Linearity  

The two models do satisfy the linear combination assumption of various parameters, moreover, the unit root tests in section 6.2.2 provided evidence that all the variables used are at most integrated of order one. Therefore, they will be run in differenced form. The models will be further tested for cointegration using bounds test in section 7.1.

ii. Multicollinearity

Table 3 below provides the correlation between the variables used in this study[16].

According to Kennedy (2003, p.209), a correlation coefficient of more than 0.8 or 0.9 suggests high correlation between any two variables. From the table it can be seen that except for one value which is above 0.8, none have a high correlation coefficient. This method does not necessarily detect perfect multicollinearity; however, it does suggest if any additional information about the data is needed (Kennedy, 2003, p.209). In addition to this, it should also be noted that EViews displays ‘Near Singular Matrix’ in case it finds perfect multicollinearity in the model and doesn’t show the output. Therefore, it can be concluded that the assumption of no perfect multicollinearity is not violated.     

iii. Zero Conditional Mean

The study uses Ramsey RESET test to test for functional form, endogeneity and any omitted variables, among various things. Table 4 below provides the test results. The probability values for both the models are above 0.05, hence, the null hypothesis of correct functional form is failed to reject, which suggests that the models satisfy the third assumption of zero conditional mean. 

iv. Homoscedasticity

This study adopts the standard Breusch-Pagan Godfrey test for homoscedasticity and the results are given in table 5 below. 

The null hypothesis in the Breusch-Pagan-Godfrey Test says that the residuals are contemporaneously homoscedastic, hence, failure to reject the null satisfies the assumption. From the above table 5, it can be noted that the probability values for both the models are well above 0.05 and thus the null is failed to reject at 5% significance level.

v. Serial Correlation

Breusch Godfrey Serial Correlation LM Test[17] is performed based on 2 lags to test for serial correlation. The results of the test are, 

Since, all the probability values are above 0.05, the null hypothesis of no serial correlation is failed to reject at 5% significance level. Thus, satisfying the fifth assumption.

vi. Normality

The study adopts 2 methods to test for normality of the residuals, first, a visual inspection of the residual histograms, second, the Jarque-Bera test. The residual histograms of the two models given in appendix 3 do seem to have a bell-shape, however, their normality can’t be concluded with certainty from the visual inspection. As a result, normality is further examined using the Jarque-Bera test.

The null hypothesis for this test says that the residuals are normally distributed. From the above table 7, it can be seen that for both the models the probability value is greater than 0.05. The null can’t be rejected at 5% significance level, therefore, it can be concluded that the assumption of normality is satisfied.

vii. Stability Test

In addition to the tests above, an Auto-regressive Distributed Lag model is also subject to Cumulative Sum (CUSUM) test and the Cumulative Sum of Square (CUSUM of squares) test. The purpose of these tests is to ensure the stability of coefficients and check for any structural breaks (Salha & Sebri, 2014). The null and alternative hypothesis for these tests is given by,

H0: Parameters are stable

H1: Parameters are not stable

Appendix 4 gives the result of these tests both, with and without the dummy variable (CRISIS)[18]. If the blue line lies within the straight red lines, the hypothesis will be failed to reject, suggesting stability in parameters. As it can be seen in the given figures, the null is not rejected for either of the models hence, it can be concluded that the parameters are stable.

7.   Model Estimation

The section 6.3 confirms that both the models are correctly specified, therefore, the study proceeds to model estimation and results discussion.

7.1 Bounds Test

As the variables are integrated of different orders, it is necessary to perform the bounds test in the specified ARDL models to test for cointegration among variables. The null and alternative hypothesis of the bounds test is given by,

H0: no cointegrating equation

H1: cointegration is present

According to Belloumi (2014), if the test statistic is lower than the critical value of I(0) the null hypothesis can’t be rejected, however, if the test statistic is greater than the critical value of I(1), the null will be rejected, suggesting a cointegrating equation. Any other outcome of the test will be considered inconclusive (Belloumi, 2014). From table 8 below, the results of the test show that for both the models the null hypothesis is rejected at 1% significance level which suggests that there exists cointegrating equation. As a result, both the models will also be tested using an error correction model (ECM).    

7.2 Estimation Results

7.2.1  Model 1

The short-run ARDL model as given by Pesaran et al. (2001) is,

∆HHEXPt-1=β0+β1HHEXPt-1+β2IMMGTNt-1+β3PRODt-1+β4UNEMPRt-1+∑i=1r α1i∆HHEXPt-i+∑i=1y α2i∆IMMGTNt-i+∑i=1u α3i∆PRODt-i+∑i=1j α4i∆UNEMPRt-i+β5CRISIS+τTREND + εt

 Where ∆ is the difference operator.

The model has an unrestricted constant and unrestricted trend as fixed regressors, and the lag length (1,0,1,0) is selected using the Akaike Info Criterion (AIC). This model is used to examine the impact of GDP per hour worked, unemployment rate and more importantly the number of immigrants and 2015 refugee crisis on the annual change in household consumption expenditure. From the table 9 below, the results of the short run analysis show that all the explanatory variables including the time dummy, CRISIS, and @TREND are statistically significant at 5% significance level. 

The error correction model for long run ARDL estimation is given by,

∆HHEXPt-1=β0+∑i=1b β1i∆HHEXPt-i+∑i=1n β2i∆PRODt-i+∑i=1m β3i∆UNEMPRt-i+∑i=1x β4i∆IMMGTNt-i+β5CRISIS+τTREND+λECTt-1+εt

Where ECT is the error correction term. CointEq(-1)(given in appendix 5) is negative and statistically significant with a coefficient of -1.026. Although it is widely believed that this coefficient can’t be below -1, Olczyk & Kordalska (2016) and Loayza & Ranciere (2005) argue that a strong ECT effect, i.e. lower than -1 but greater than -2, suggests that any discrepancy between an immigration shock and the trend is reduced within a year. Further, it indicates a high degree of absorption capacity in one of the sectors (Olczyk & Kordalska, 2016). The result of the long run analysis given in table 10 below shows that all the explanatory variables are statistically significant at 1% significance level in the long run.

7.2.2  Model 2

The short-run ARDL model as given by Pesaran et al. (2001) is,

∆UNEMPCt-1=λ0+λ1UNEMPCt-1+λ2INFLt-1+λ3EMPRt-1+λ4IMMGTNt-1+∑i=1b π1i∆UNEMPCt-i+∑i=1n π2i∆INFLt-i+∑i=1m π3i∆EMPRt-i+∑i=1x π4i∆IMMGTNt-i+λ5CRISIS+εt

Model 2 is aimed at examining the impact of number of immigrants, employment rate, inflation and CRISIS on the change in unemployment rate. Its ARDL estimation includes unrestricted constant and no trend with a lag length (2,1,0,1) selected using Akaike Info Criterion (AIC). In the table 11, the results of the short run estimation suggest that the current value of EMPR, one period lagged values of UNEMPC and INFL, and the differenced value of UNEMPC, are significant at 5% significance level, while, others, including IMMGTN and CRISIS are not statistically significant. Further, the same model without IMMGTN and CRISIS was analyzed and the results in table 12 suggest that the coefficients are quite similar with similar statistical significance.

The error correction model for long run ARDL estimation is given by, 

∆UNEMPCt-1=π0+∑i=1b π1i∆UNEMPCt-i+∑i=1n π2i∆INFLt-i+∑i=1m π3i∆EMPRt-i+∑i=1x π4i∆IMMGTNt-i+π5CRISIS+ λECTt-1+εt

CointEq(-1) (given in appendix 6) is negative and statistically significant with a coefficient of -0.755,which implies that the reversion to long-run equilibrium is at an adjustment speed of 75.5%. This also suggests high degree of absorption capacity of the German economy. The results of the long run analysis, given in table 13, suggest that both INFL and EMPR are significant at 1% significance level, while, IMMGTN is only weakly significant at 10% level. Table 14 again shows that the coefficient for INFL is quite similar with similar statistical significance.

8. Results Discussion

Both the models showed presence of cointegration in the bounds test and were thus examined for both, short and long run. The results of the analysis present a very different picture than the initial argument of this paper. In the first model, both GDP per hour worked (PROD) and the unemployment rate (UNEMPR) have an expected effect on the change in household expenditure. In both short and long run, as the productivity level rises, household expenditure changes positively and, as unemployment rate rises, the expenditure falls. This can be attributed to fact that, higher GDP per hour worked implies higher income and hence, higher consumption, similarly, households will contract their consumption if unemployment increases. As against the argument of this paper, number of immigrants coming into Germany has a negative effect of -0.03% on household expenditure, in both short and long run. This however, is consistent with the assumptions given in theoretical background in section 4, i.e., a higher proportion of workers willing to work for lower wages in the receiving country’s total workforce will drive the average wage downwards. A fall in average wage would translate to lower income and hence, lower household expenditure. Coefficient of CRISIS is interestingly positive and significant at 5% level. This suggest that the years of refugee crisis, 2015-2017, have so far had a positive effect. @TREND is negative which suggests that the change in household expenditure has a downward trend, which can also be seen in appendix 2 figure 2.a. This however, contradicts the assumption laid in section 4 that the output, and therefore income and expenditure, should rise in the long run. Lack of empirical studies on the impact of immigration on household expenditure makes it difficult to say if these findings are consistent with the existing literature.  

The second model examined how the change in unemployment rate is affected by immigration, inflation, employment rate and the 2015 refugee crisis. The results for this model contradict some of the theoretical assumptions. For instance, the relationship of inflation on unemployment, as suggested by the Phillips curve, is inverse, while, the analysis above suggests that inflation has a positive effect on the change in unemployment rate in Germany. This means that, a 0.1% increase in inflation would cause the annual change in unemployment rate to rise by 0.43%[19] in the short run, this effect however, reduces in the long run by 0.1%. Having said that, this contradiction with the theory of the traditional Phillips curve is not new, in fact, lack of empirical fit is the primary criticism that the theory faces (Gali & Gertler, 1999). In comparison, the results of this model without IMMGTN and CRISIS suggest that the effect of inflation on unemployment reduces slightly in the short run and stays almost the same in the long run. Employment rate, a measure of how well the available labor resource in an economy is employed, is negatively correlated with UNEMPC, in both short and long run. Number of immigrants (IMMGTN), on the other hand, is not statistically significant in the short run and only weakly significant at 10% level in the long run. This finding is consistent with Damette & Fromentin (2013).  As Jean & Jiménez (2011) explain, this can be because of the fact that, integration of immigrants into the receiving country’s labor market is usually imperfect and their assimilation is slow and gradual. In addition, inclusion of immigrants into the workforce is restricted by various employment policies which further slows down the process (Jean & Jiménez, 2011). The statistical insignificance of the dummy variable (CRISIS) suggests that the 2015 European refugee crisis hasn’t had an effect on Germany’s unemployment so far.

9.   Conclusion

For long, the issue of immigrants coming into high-income countries has kept both, governments and natives, equally concerned. At the core of this issue, is the debate surrounding the effect that immigrants have on the host economy. This paper attempted to examine and discuss the effect that immigrants have on Germany’s annual change in household consumption expenditure and unemployment. Germany, a country with, almost 14 million foreign-born people (Eurostat, 2019), increasing number of natives wanting fewer or no immigrants (Pew Research Center, 2018), rising anti-immigrant sentiment (Dostal, 2015), was undoubtedly an appropriate choice of country for such an analysis.

The existing literature on the topic was vast and covered almost all aspects, however, this paper tried to fill a void by including the effect of the recent refugee crisis of 2015. The findings of this study revealed that although, immigration has a negative effect on the household consumption expenditure, its impact on the country’s unemployment rate was insignificant in both, short and long run. The results were in line with the existing literature for second model, while, it is difficult to say so for the first. This paper did not account for various factors like; (1) high degree of institutional changes that took place in reunified Germany after 1991, (2) varied effects of high and low skilled immigrants, (3) employment and work visa policies, (4) aging native population[20]. As a result, there exists a vast scope for further research on this topic.

In conclusion, this paper finds both, slightly negative and insignificant effects of immigration on Germany’s macroeconomic indicators. Although, the 2015 European refugee crisis hasn’t had any effect on the country’s unemployment yet, it did positively affect the household expenditure. With the number of people fleeing war-struck countries on a perpetual rise, and more and more people migrating to OECD countries in search of asylum, the governments are faced with a constant dilemma. Providing asylum to the ones in need might seem like an obvious choice for the governments but the rising anti-immigrant sentiment among voters has held them back. The results of this paper suggest that immigrants haven’t had any worrying effect on the German economy yet and letting them in might not be as bad as natives believe.

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Appendix

 

Appendix 1: Data description

Appendix 2: Series plots

Figure 2.1

Figure 2.b

 

Figure 2.c

Figure 2.d

 

Figure 2.e

Figure 2.f

 

Figure 2.g

Appendix 3: Histogram of Residuals for Normality

Model 1

 

 

Model 2

 

Appendix 4: CUSUM Test and CUSUM of Squares Test

(The straight red lines represent the critical bounds at 5% significance level)

Model 1 (without CRISIS)

With CRISIS

Model 2 (without CRISIS)

With CRISIS

Appendix 5: ECM Regression of Model 1

Appendix 6: ECM Regression of Model 2


[1] AfD or ‘Alternative for Germany’, is a right-wing extremist political party in Germany which rose to power mainly because of its anti-immigration stand, in particular after the 2015 refugee crisis (see Decker, 2016)

[2] Germany had maintained ‘open arms policy’ even when the EU had started to close its borders after the 2015 refugee crisis (Benček & Strasheim, 2016).

[3] Born outside the EU.

[4] PEGIDA is an anti-Islam and anti-immigrants, right-wing extremist, political movement (see Dostal, 2015).

[5] The Economics of Immigration by George J. Borjas (1994), is a key paper on immigration economics. 

[6] Bauer and Zimmermann (1995) call this the ‘push and pull migration’.

[7] West Germany was formally called the Federal Republic of Germany, while, German Democratic Republic was the official name of East Germany.

[8] The period is known as the ‘guest-worker recruitment era’ (Fertig & Schurer, 2007).

[9] Asylum seekers and refugees, which Germany receives many of, are considered a part of push migration (Bauer & Zimmermann, 1995).

[10] And, in case of unemployment benefits, greater government deficits as well.

[11] For complete data description and individual sources, see appendix 1.

[12] If any discouraged person stops looking for work, s/he is excluded from ‘unemployed persons’. Hence, it should be noted that the unemployment rate can fall even without any increase in employment rate.

[13] Figure 1 and discussion in section 3.2 clearly suggest an exponential rise in the number of immigrants after 2014.

[14] Model 2 without IMMGTN and CRISIS is only for comparison and will not be fully analyzed.

[15] An exploratory data analysis (EDA) showed that the INFL has a bell-shaped distribution and suggested that it can be considered to be normally distributed.

[16] Correlation between the dependent variables need not be analyzed.

[17] Durbin-Watson test can’t be used with lagged dependent variables.

[18] CUSUM tests, when performed with a dummy variable, only give out results for the years with dummy=1.

[19] Inflation (INFL) is not log transformed (see appendix 1).

[20] As Muyskena, et al. (2011) note, Europe is heading towards an old age crisis with increasing proportion of population above-65 years old. Young and skilled immigrants can help solve this problem (see Muyskena, et al., 2011).

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