International Journal of Operations Management
Volume 1, Issue 1, October 2020, Pages 27-34
The Impact of Syrian Refugees on the Turkish Labor Market
1Huseyin Isiksal, 2Aliya Zhakanova Isiksal, 3Yossi Apeji
1,2 Near East University, North Cyprus
3 Girne American University
Abstract: The civil war in Syria has destabilized the whole Middle East along with neighboring regions. In this respect, the impact of Syrian refugees on Turkish labor market is one of the most important contemporary issues discussed in Turkey. This issue has both political and economic significance. Deriving from this point, the aim of this study is to research the empirical relationship between the Labor Market Indicator (LMI) and the growing number of Syrian Refugees in Turkey (RS) by using time series analysis. The data employs monthly data for the period from January 2012 to August 2017. Results of the ARDL bounds test suggest that the Labor Market Indicator and the number of Syrian Refugees are in a long-run relationship. The Gregory-Hansen cointegration test with a structural break confirms the robustness of the ARDL bounds test of cointegration. The Kalman filtering approach was designed to investigate the dynamic relationship between the Labor Market Indicators and the growing number of Syrian Refugees. The results show that the increase in the number of Syrian refugees negatively affects the Labor Market Indicator in Turkey, which implies that the inflow of Syrians has negative effects on labor market outcomes such as employment and unemployment in the country. These results also confirm the postulation of general labor migration theory, which holds that an influx of refugees negatively affects labor market outcomes in the harboring country.
Keywords: Kalman Filtering Approach, Syrian refugees, Turkish Labor Market.
The Syrian refugee crisis started in 2011 as a result of the political unrest and civil war in the country, which prompted a mass exodus of people to different countries. The number of refugees escalated when the radical group called Islamic State in Iraq and Syria (ISIS) claimed control over large populated areas in Syria (Işıksal, 2018b:91).Turkey shares an 822 km long border with Syria, which renders it vulnerable to the unprecedented entry of Syrians into the country. Another factor that has made Turkey a popular route for Syrian immigrants is the waiving of visa requirements between the two countries in 2009 (Işıksal, 2018a:20). Consequently, the refugee problem has become a considerable social and economic burden for Turkey with the arrival of at least three million Syrian refugees.
Deriving from these points, this study analyzes the effects of the inflow of Syrian refugees on labor market outcomes in Turkey such as employment, unemployment and the labor participation rate. According to the UNHCR, the majority of Syrians entering Turkey are unskilled and middle-aged. Therefore, it is only possible for them to enter the informal sector in Turkey, which increases the competition for jobs in the unskilled labor market. It is virtually impossible for the Syrian refugees to have direct access to the formal sector, because they do not have the required skills and educational qualifications that would make them eligible to apply for work permits. Furthermore, Turkey’s immigration policy does not provide refugee status to individuals arriving from non-European countries, which is an additional disadvantage for Syrians. The challenge for Turkey is to determine how these Syrians can be successfully integrated in a manner that does not have a negative impact on the employment status of the domestic labor market, while still increasing access to jobs for the Syrians.
The International Labor Organization (ILO) has been observing the challenges facing Syrians in Turkey and has found that Syrians who are privileged to work earn approximately $250 per month, which is the equivalent of 900 Turkish lira. This is significantly lower than the official minimum monthly wage, which is stipulated to be around 1,650 Turkish lira per month. This is because employers prefer Syrians as they earn less and work longer hours compared with their domestic counterparts. This threatens the balance of the labor market given that there could be an increase in unemployment among native workers if employers decide to replace them with Syrians.
Although studies on the effects of forced migration on the labor market have been conducted, the findings of these studies have not been able to definitively determine the type of effect that such migration has. This is simply because either there is no access to data regarding the refugees and their working conditions in the labor market or the data are of insufficient quality based on the methods of data collection.
On the other hand, the economic theory postulates that the entry of immigrants into a country negatively affects natives, because there is increased competition for jobs. This also results in unemployment among domestic workers and decreases the wages of those who successfully keep their jobs. In cases where there have been negative effects on the natives, the inflow has largely affected those in the informal sector and not those in the formal sector. Another noteworthy point is that the focus of previous studies has been how the inflow will affect the natives, while completely ignoring how the immigrants cope in terms of integration and the access to jobs when entering a host nation’s labor market.
In the present study, empirical research is conducted using the Kalman Filtering approach to predict the impact of the Syrian Refugees on the Turkish Labor Market.
The rest of this paper is organized as follows: Section 2 is literature review, Section 3 is data and methodology, Section 4 presents the results and discussions, and last section concludes the paper.
2. Literature Review
As a general study of refugees-labor market outcomes, Basu (2016) studied the economic implications of the inflow of immigrants by identifying both the negative and positive effects on labor market outcomes. The study found that the inflow of refugees could increase the strength of the labor force and could also provide skills that are lacking in the host nation’s labor market. Additionally, it was stated that the inflow of immigrants could have a negative impact on wages.
Dustmann et al. (2008) explored the cost and benefits of immigration on a hosting economy with a particular focus on wages and employment. Previous studies, such as Grossman and Hart (1982), have assumed that labor is categorized into immigrants and natives, stating that an immigrant cannot substitute a native and vice versa.
Altonji and Card (1991) viewed skills as the level of educational attainment, Card (2001)perceived skills based on the type of occupation and Borjas (2003)stated that skills are both the level of experience and educational attainment. The results of Dustmann et al.’s study identified that immigration affects unskilled workers more than skilled workers. The European Social Survey (2005) showed that average wages only declined in areas with large concentrations of immigrants, which subsequently increased unemployment in those areas. They also emphasized that the immigrants only worked in low skilled and low paying jobs, with no record of any formal employment. In terms of native unemployment, they noted that natives would only lose their jobs if they have similar skill sets to the immigrants.
In regard to the studies that have specifically addressed Turkey, the study by Del Carpio and Wagner (2015) for the World Bank examined the effects of the entry of displaced Syrians into the Turkish labor market, with particular emphasis on the informal sector. Data was based on the number of displaced Syrians living in Turkey at the time and the labor force survey (LFS). The results showed that there was an increase in unemployment among natives in the informal sector, while there was a surge in job opportunities for men with no significant level of education. Unskilled workers in the Turkish labor market experienced increasing unemployment and declining earning opportunities in the informal sector.
Similarly, Massimiliano and Samia (2015) indicated in a World Bank report that while the Syrian refugees in Turkey working in the informal sector have actually taken jobs from natives, they have also contributed positively to the labor market by increasing access to formal non-agricultural jobs, which has led to growth in the average wages of the natives.
Akgündüz et al. (2015) studied the impact of the influx of Syrian refugees on the Turkish labor market. Data on the number Syrian refugees in Turkey was sourced from the United Nations High Commission for Refugees (UNHCR). The results showed that there was a general increase in price levels, which meant that the purchasing power of wages in areas where the Syrian refugees were concentrated would drop. It was also revealed that there was a significant effect on native employment in the local labor market.
3. Data and Methodology
The aim of this study is to determine the long-run relationship between LMI (Labor Market Indicators) and LRS (Syrian Refugees represented in natural logarithm).Data were retrieved from the Turkish Statistical Institute (Turkstat), the Turkish Central Bank, and the United Nations High Commission for Refugees Syrian Response Database for the period between January 2012 and August 2017 on a monthly basis. The number of Syrian refugees is denoted by LRS and it is represented in the natural logarithm. Labor market indicators include Employment Rate and Unemployment Rate. In this study the labor market indicators are used compositely in that all variables are summed and compared to the total population.
This study hypothesizes that Syrian Refugees could be determinants of the Labor Market Indicator. Thus, the equation could be formulized as:
In Equation (1), it is assumed that there is a long-run relationship between Syrian refugees and the Labor Market Indicator.
3.3 Unit root tests with structural break
The unit root tests that take into account any structural break are employed in this study, such as Perron-Vogelsang (1999), Zivot Andrews (2002) unit root tests with one structural break and Clemente-Montanes-Reyes (CMR) (Clemente et. al.1998) with two structural breaks.
3.4 Bounds Test Approach
To find the long-run association between the variables, the bounds tests is employed by using the Autoregressive Distributed Lag (ARDL) approach that was proposed by Pesaran, Shin, and Smith (2001). The major advantage is that the regressors could be of different order of integration; thus, they could be I(0) or I(1), although they cannot be greater than order one. These tests are based on F-statistics derived from the ARDL approach. The critical values are provided for the lower bounds and upper bounds. There are three scenarios for the F-test proposed by Pesaran, Shin, and Smith (2001), namely , . When calculating the F-statistics, if it falls below the lower bounds this means that the null hypothesis of no long-run relationship cannot be rejected. However, if the F-statistics falls between the lower and upper levels, it means that the results are inconclusive. Finally, if the results are higher than the upper bounds, it means that the null hypothesis can be rejected and there is a valid long-run association between the variables (Pesaran et.al. 2001).
To perform the bounds test, the error correction model (ECM) should be created:
where ∆ is the first difference operator, LMI is the dependent variable, LRS is the natural logarithm of the independent variable; t is the maximum number of lags, and ɛ1t is the error term of the model. LMI is the dependent variable, and the null hypothesis of no co-integration, H0=σ1=σ2=0; it is tested against the alternative hypothesis, H0≠σ1≠σ2≠0.
To enhance the robustness of the ARDL bounds test, the paper employs the Gregory-Hansen cointegration. The main advantage of this test that it includes cointegration with one structural break (Gregory and Hansen, 1996) was applied. The structural change can take several forms; a simple case is that there is a level shift in the cointegration relationship, which can be modeled as a change in the intercept, where the equilibrium equation has shifted in a parallel fashion it is called as a level shift model denoted as follow:
where α1 is the slope coefficient, α are held constant. This implies that the equilibrium equation has shifted in a parallel fashion, ɛt is assumed to be I(0) error term φ is dummy variable.
3.5 Dynamic Kalman Filter Approach
After finding the association between Labor Market Indicator and the Syrian Refugees, this relationship is dynamically tested by using the Kalman Filtering Approach to reflect the time-varying relationship between LMI and LRS. The Dynamic Kalman Filtering approach is based on the estimation of the statistically significant relationship among the time series variables Labor Market and Syrian Refugees.
The Kalman filter is a recursive linear filter used to achieve the conditional density of observations by using the Bayes’ Theorem (Pasricha, 2006). In order to predict the following period’s value of the unobservable variable, the Kalman Filter uses current observations and the realized value for the next forecasts. Therefore, new information is used to upgrade the model estimates. The Kalman filter approach assumes the form of state space identification. The general model of a linear state-space equation indicating the dynamics of a system is:
In the present research is a 2 x 1 vector of unobserved state variables, ct, Zt, dt and Tt are conformable vectors and matrices, and where εt and νt are vectors of mean zero and Gaussian disturbances, respectively. Eq. (4) states that the unobserved state vector is expected to move over time as a first-order vector auto regression. The Kalman ﬁlter periodically estimates the parameters by updating the estimation by additional observation.
The Kalman ﬁlter is a repetitive algorithm which allows continuous updates of the one-step-ahead estimate of the state mean and variance are giving new observations. Provided with the one step ahead of state conditional mean, it is possible to formulate the one step ahead minimum mean square error estimate of yt.
Thus, the one step ahead prediction error is as follows
and the prediction error variance provided:
In summary, the Kalman filter identification for this study is as follows:
- EMPIRICAL RESULTS AND DISCUSSIONS
4.1. Conventional Unit root and Structural Break Test Results
In the empirical methodology, the series has been tested for stationarity. Zivot-Andrews and Perron-Vogelsang unit root tests results with one structural break are reported in Table 1. CMR unit root test results with two structural breaks are reported in Tables 2. The results show all the variables are stationary at first difference, thus LMI, lnRS variables are integrated of order I(1).
Table 1: Zivot-Andrews and Perron-Vogelsang with structural break unit root test result
Source: Authors’ own calculations.
Table 2: CMR unıt root test results with two structural breaks
|At level||BD 1||BD 2||At 1st difference||BD1||BD 2|
Source: Authors’ own calculations.
Since the start of the new millennium, Turkey has recorded significant developments in terms of observed macroeconomic and fiscal stability, which has translated into stable employment growth and improved income distribution rendering it an upper middle-income country. The poverty level has more than halved during this period, urbanization has increased, many foreign trade agreements have been signed and the country has developed with the implementation of well-structured laws and regulations according to European Union standards. Moreover, there has been significant growth in public infrastructure leading to increased access to public services. Turkey has also exhibited significant signs of recovery from the 2008/2009 economic crisis, particularly in terms of Gross Domestic Product (GDP) and Foreign Direct Investment (FDI) (Işıksal, A. et al., 70-71).
Nevertheless, from 2012 onwards, Turkey experienced a decrease in development that was evident in its shrinking per capita income which was around $9000, significant growth in unemployment and inconsistent policy reforms. This demonstrated that Turkey was unable to sustain its growth momentum, mainly because the country’s macroeconomic environment was unstable with stagnated growth in the European Region, along with the uncertain political climate of neighboring countries including Syria that has directly influenced the volume of exports and foreign direct investment.
In this connection, the refugee problem has become a considerable social and economic burden for Turkey. Although the actual number remains unknown, it is estimated that at least three million Syrian refugees are now residing in Turkey, which means that the country is now hosting the largest number of Syrian refugees (Turkish Ministry of Interior, 2016). The Turkish authorities have also spent more US $25 billion on these refugees (UNHCR 2015),which has placed significant strain on the country’s finances with increased social, economic and political demands. In other words, the Arab Spring revolts have developed into the “Turkish Autumn” (Işıksal, H. 2018c:214).
The political atmosphere between 2015 and 2016 has not helped the chances of recovery. The elections held in June and August of 2015, the cabinet reshuffle in May 2016, and the failed coup attempt in July 2016 have further halted policy reforms. The economic growth rate was 6.1% in 2015, which decreased to about 2.9% in 2016.The second half of 2016 saw an increase of 11.3%Turkey’s current account deficit due to a decline in the tourism industry. The volume of foreign direct investment inflows has also reduced because of domestic insecurity, macroeconomic imbalance, and the unstable global political climate. In the third quarter of 2016, Turkey experienced its worst growth rate in over a decade; however, in the fourth quarter, there were signs of recovery as a result of increased private consumption and net exports.
On the other hand, the labor market unemployment rate reached12.1% in November 2016, with a3.7%increase over November 2011, and it was Turkey’s recorded highest unemployment rate in over a decade. The rate of joblessness among young people in the 15-24 age bracket of 15-24 was 21.6% as of November 2016, which was reminiscent of the figure recorded in November 2009.
4.2 Cointegration Tests and Kalman Filtering Approach Results
Bounds tests are performed to check for the long-run relationship between LMI and LRS. Critical values using F-tests are extracted from studies by Pesaran et al. (2005).
Table 3: ARDL Bounds test results
|LMI, lnRS||(3, 3)||7.699***||Co-integration exist|
Bound Critical Value
Source: Authors’ own calculations.
Table 4: Gregory-Hansen cointegration test with one structural break test results
|At level||BD 1||t-statistic||5%|
Source: Authors’ own calculations.
According to Table 3, the F-statistics value is higher than the upper bound of the given critical values; thus, the null hypothesis of no-cointegration can be rejected. Therefore, it is found that there is a significant long-run relationship between LRS and LMI based on the analysis of the bounds test. The Gregory-Hansen cointegration with one structural break test results (Table 4) confirms the results of ARDL bounds test. After finding the long-run relationship between the LMI and LRS variables, this relationship is dynamically tested by using the Kalman Filtering approach to show the time-varying relationship between Labor Market Indicator and Syrian Refugees. When using the time-varying parameter (TVP), it is important to note that this parameter can change with every new observation (Koop &Potter, 2007).
The Kalman filter approach was used to analyze the dynamic relationship between the Labor Market Indicator and Syrian Refugees by employing level data, as represented in Figure 1.The empirical results show that Syrian Refugees have a significant impact on the Labor Market Indicator. Syrian refugees have a decreasing impact on the Labor Market Indicator, which implies that the inflow of Syrians has negative effects on labor market outcomes such as employment and unemployment in Turkey. These results also confirm the postulation of general labor migration theory, which implies that an influx of refugees negatively affects labor market outcomes.
Figure 1: Kalman filter approach by using level data
Source: Authors’ own calculations.
In summary, the empirical results prove that there is a dynamic relationship between the Labor Market Indicator and Syrian Refugees. The socio-political developments between January 2012 and August 2017 demonstrated that the Syrian refugees have a negative impact on the Turkish Labor Market and have increased unemployment rates.
Studying the effects of the entry of displaced Syrians on a host nation’s economy is essential in order to develop a key framework to mitigate against the humanitarian crisis spiraling out of control. This study investigated the link between the entry of displaced Syrians on the labor market indicator in Turkey. The data on the number of displaced Syrians living in both countries and labor force indicators (Employment, Unemployment) was used to empirically analyze the link between the entry of displaced Syrians and the labor market in Turkey.
The study investigated the relationship between Labor Market Participation and Syrian Refugees for the period from January 2012 to August 2017.
After testing for the stationarity by employing Zivot-Andrews and Perron-Vogelsang unit root tests results with one structural break and CMR unit root test results with two structural breaks the results show all the variables are stationary at first difference. The long-term relationship between the Labor Market Indicator and Syrian Refugees is investigated by using the bounds test developed by Pesaran and Gregory-Hansen cointegration test with one structural break. In regard to the results of the cointegration tests, it has been demonstrated that there is a significant long-run relationship between the Labor Market Indicator and Syrian Refugees. The Kalman filter approach also shows that there is a dynamic relationship between LMI and LRS, and LRS has a significant influence on LMI, particularly between January 2012 and August 2017.
The majority of Syrians in Turkey works in the informal sector and is predominantly working illegally, which prohibits them from having access to work permits. This means that the Syrians are participating in the labor market in the informal sector, which is increasing unemployment among natives. The incentive for employers to hire Syrians is simply that the labor costs are significantly lower. The report by the ILO outlined that the Syrians working in the informal sector earn an average of USD 250 monthly, which is considerably lower than the minimum wage in Turkey. Another incentive for employers to hire Syrians is that they are willing to work longer hours than natives.
It is clear that the Syrians have little or no access to the formal sector in Turkey, which is influenced by the fact that they are admitted into Turkey on a temporary basis and do not qualify for refugee status. Turkey’s immigration policy only offers refugee status to people arriving from the European region. Since Syrians are arriving from a non-European country, this policy has prohibited them from obtaining access to work permits. Also, it should be noted that most of the Syrians are low skilled and are therefore unable to compete in the formal labor market. As a result of these facts, it could be suggested that the competition in the informal sectors will continue to escalate, thus increasing unemployment rates in Turkey.
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