2.1 Literature Review
From the literature review of this work, several issues concerning refugees in Jordan and their impact on the host community are theoretically as well empirically examined. Accordingly researches and studies on refugee influx are still growing especially on the case of Jordanian Economy. In addition, the topic an analysis of the impact of refugee influx on employment generation in Jordan is inadequately theorized. Thus, the study will seek to uncover the stand points from a point of attention to a new point of attention with respect to the debatable impact of refugee influx in Jordan and offer possible remedy for economic prosperity. The sections discuss the refugee influx in Jordan as well as the financial status of both the guest and the host communities. Accordingly, the impact of the refugees and response of the host and other organizations are equally examined in the literature.
2.2 Refugees Influx in Jordan
The large influx of refugees to Jordan was generally accompanied by demonstrations of solidarity, hospitality and tolerance from the host society towards the newcomers. Family ties as well as linguistic and cultural relations between refugees and the members of Jordanian host communities have facilitated the reception of refugees in the Kingdom of Jordan. However, the protracted nature of the conflicts is now affecting the relationship between the two communities. As of November 2015, over 633,000 refugees mainly from Syria have registered with the UNHCR in Jordan. The massive numbers of refugees over almost five years has had a serious impact on the already meager Jordanian national resources. All this has meant that Jordanians, who at first welcomed refugees, have become hostile: those who were originally welcome guests are now considered as refugees. The belief that refugees are thriving on scarce local resources is common amongst increasingly resentful host communities. There is also the widespread belief among less advantaged Jordanians that these refugees who are mostly Syrian refugees thrive thanks to a lavishly generous international community that is fulfilling their needs while leaving the host communities stranded and without resources. According to the Jordanian Economic and Social Council (undated), the Syrian crisis has cost the country USD 1.2 billion, and the financial burden is expected to rise to USD 4.2 billion by 2016. Accordingly, Jordan’s international trade has been gravely affected by the loss of one of the principal points of access to regional trade through Syria. However, a recent study reveals that the Syrian crisis has had a particularly negative impact on Jordanian structural vulnerabilities. The available services within the hosting communities have grown thinner, raising serious concerns over the availability of already limited resources: the chronic lack of safe drinkable water, for instance. The influx of refugees has also increased the demand on schools, sanitation, housing, food and energy to an intolerable extent. In particular, the arrival of Syrian refugees seems to have had a negative impact on Jordan’s housing sector. Rent prices have tripled or even quadrupled in border zones and other areas of high refugee density. As the majority of Syrians do not live in camps, this rise can be explained by the sharp increase in demand for housing and by the refugees’ capacity to afford higher prices by sharing housing with others to bring down costs. In addition, the protracted nature of the crisis is now aggravating the relationship between the two communities. It should be noted, however, that Syrian refugees are blamed for a variety of issues that have always plagued Jordan’s dysfunctional infrastructure and stagnant economic market. The job market constitutes another point of friction. Work permits are not being issued to Syrians, principally due to prohibitive costs and administrative obstacles. Non-Jordanians with legal residency and a valid passport can obtain a work permit only if the prospective employer pays a fee and shows that the job requires experience or skills not to be found among the Jordanian population. Nonetheless, a recent UNHCR survey reports that only 1% of visited refugee households had a member with a work permit in Jordan despite the official restrictions on the refugees (Achilli, 2016).
Graph: Refugee influx to Jordan (Source: data.unhcr.org)
Traditionally, Middle Eastern countries are known for their hospitality and they have practiced a relative open door policy towards non-nationals, and in particular toward individuals from other Arab nations who have traditionally been exempt from visa regulations (Barnes, 2009). However, Jordan is not a signatory of the 1951 Convention Relating to the Status of Refugees. Once in the country, an individual that would otherwise have been categorized as a refugee according to the Convention, are not necessarily granted refuge in host country. However, Refugee accommodation is generally a sensitive topic in Jordan and in the Middle East in general. The reluctance of signing the 1951 Convention is mostly attributed to the unresolved Palestinian issue. Jordan has hosted a sizeable population of Palestinian refugees for the last sixty years. In addition, Jordan has played host to other refugees before the influx of Syrian refugees, primarily Iraqi refugees of which approximately 30,000 are registered with UNHCR. The Government of Jordan registered 400,000 Iraqis as of March 2015 (data.unhcr.org). ‘They fear that future refugee populations like the Iraqis, if accorded the rights set down in the 1951 Convention may too end up remaining on their soil indefinitely’ (Barnes, 2009).
2.3 Financial Conditions of refugee in Jordan
Given the fact that cost is the most frequently reported barrier to accessing health services, it is important to examine household financial conditions. As expected, incomes were generally low, often less than 300 JD per month (approximately 450 US$).
Many households received financial support from humanitarian organizations, most commonly UNHCR, although CARITAS and the Danish Refugee Council were also mentioned as providing seasonal assistance (for holidays, support for heating in winter, etc.). Despite Jordanian labor laws that typically prohibit refugees who are mainly Syrian refugees from working in Jordan, the majority of participants reported that at least one member of their household was employed. Importantly, employment provides additional regular income to supplement the long-term costs of care and medications related to NCDs. Relatively low incomes, however, support the previous finding that these refugee households are ‘under considerable financial stress,’ which impacts their ability to pay for health services, especially for conditions that require continuous care (UNHCR, 2014).
2.4 Financial Deepening and Economic Growth in Jordan
Financial deepening is defined as the increased provision of financial services with a wider choice of services geared to all levels of society. It generally means an increased ratio of money supply to GDP, in other words, it refers to liquid money. The more liquid money is accessible in an economy, the more chances exist for continual growth (Shaw, 1973). Financial deepening stimulates higher investments, faster growth and more rapidly rising living standards. Jordan is a developing non-oil-producing country with limited natural resources and water. The Jordanian economy is one of the smallest in the Middle East with a GDP of JD 23, 851.6 million and a population of 6,530,000 (central bank of Jordan, 2013). The commodity producing sector represents 33.4% of the GDP while the greatest percentage is to the service producing sectors which represent 66.6% of the GDP according to the statistics of year 2013 (central bank of Jordan, 2015). Jordan is rapidly growing, both as a result of its population demographic and due to an influx of refugees over the past decades. However, since the beginning of the year 2011 following what is called ‘the Arab spring’. The linkage between financial deepening and economic growth is well documented in both the theoretical and the empirical literature. Accordingly, this has been seen as the causes of refugee influx in multiples that is affecting the growth of the Jordanian economy.
2.5 Impacts of the Syria Crisis on Jordan
It has been a challenge for the government and host communities to absorb the large influx of refugees. However, Jordan has been met by two shocks: first, a demographic shock due to the large influx of refugees; second, an economic shock as economic engagement with Syria has become limited (Bailey and Barbalet 2014). Economically it is the agricultural and food trade that has been severely affected with a 25 percent decrease in exports to Syria and 30 percent decline in imports from Syria. In addition, Syria was an important trade route for Jordan to access Turkey and Europe (MOPIC, 2013). The economic shock and decrease in GDP is compounded by an increase of its population. The majority of Syrian refugees live in the cities of Amman, Mafraq, Irbid and Zarqa which also have the highest number of poor Jordanian households (MOPIC, 2013). Worthy of note is that, there is concern that the pressure of an increased population has led to a deterioration of quality of public services, such as double shifting of schools and there being fewer physicians per person (MOPIC, 2013). There are also concerns that Syrian refugees are crowding Jordanians out of the labour market as they accept work for lower wages, which in turn puts downward pressure on wages, mainly in the informal sector (MOPIC, 2013). In a study conducted by ILO and Fafo, the unemployment rate of Jordanians increased from 14.5 to 22.1 per cent between 2011 and 2014, and increased from 19 to 35 per cent unemployment among young Jordanians (Stave and Hillesund, 2015). It is found that Syrian refugees ‘do to some degree’ push Jordanians out of the labour market (Stave and Hillesund, 2015:7). However, it can be difficult to isolate the impact of refugees from other factors that could affect unemployment rates since the level of unemployment was high in Jordan before the Syrian crisis. It could be argued that the Syrian refugee population has exacerbated pre-existing challenges.
2.6 Coordination of Responses to Refugee Situations
UNHCR is a unique agency in that it has a mandate to protect refugees and the institute of asylum. In terms of response to a refugee crisis, the mandate of UNHCR empowers the agency to take an active role in the ‘effective coordination of measures taken to deal with the refugee problem]’ (UNHCR, 2013). This also includes the right to ‘invite the co-operation of the various specialized agencies’ and facilitate ‘coordination of the efforts of private organizations concerned with the welfare of refugees’ to assist UNHCR in the performance of its mandate (UNHCR, 2013). UNHCR does this through its Refugee Coordination Model (RCM). This is a multifaceted approach that aims to provide predictable and effective response to ensure that the needs of refugees are met, as well as advocating on protection matters with the host government by UNHCR’s representative in country. Accordingly, at the operational level, a system of sectors is put in place to ensure service delivery to refugees, organized in thematic sectors such as Shelter, Health, Education and Water, Sanitation and Hygiene (WASH). These sectors are chaired or co-chaired by other UN agencies, the host government, or INGOs. However, the RCM therefore aims to be an inclusive model because host communities are also an important aspect of the refugee response.
2.6.1 Refugee Response in Jordan
In response to the influx of refugees, the refugee coordination model (RCM) was set up to ensure that the protection and material needs of refugees entering Jordan were addressed. This coordination structure led by UNHCR and brought together different UN agencies, government agencies, NGOs and INGOs to ensure that there was a consistent response in terms of quality and coverage, that refugees were assisted properly and had access to basic needs. The initial sectors and initially there were for health, education, WASH and basic needs. Each sector is coordinated by a Sector Working Group (working groups) that is chaired by a UN agency and an INGO with the exception of the Sector Working Group for Health which was chaired by the Ministry of Health.
Diagram: Refugee Coordination Model in Jordan (UNHCR 2015).
To accompany this coordination structure, according to one respondent, it is the policy of UNHCR to not build parallel systems and rather invest in capacity building and government services used by refugees.. Several Quick Impact Projects were also implemented to address the immediate needs of host communities. UNHCR was also present in Jordan in 2012, working with mostly Iraqi refugees. The refugee response plans were supported by the Government, and UNHCR and its partners such as WHO and UNICEF worked with government ministries such as Ministry of Health and Ministry of Education to ensure refugees had access to public services.
2.7 Empirical Literature
The influx of refugee is often viewed as a burden on the host country as resources that are meant for other productive purposes are committed towards catering for refugees (Black, 1994; Jacobsen, 1994; Rutinwa & Kamanga, 2003). However, a study by Black (1994), established that refugees have a strong negative effect on employment generation. This was reinforced by a study conducted by Rutinwa and Kamanga (2003), which showed that local individuals are driven out of employment as employers advocate for cheaper labour. Such an effect possess serious consequences of nations such as Jordan which has been struggling in terms of economic progress in which economic growth has been toiling below 3% since 2012 and was registered at 1.8% in 2017(Trade Economics, undated). This further puts a stumbling block on Jordan’s capacity to generate employment. Such has also attracted supported from scholars such as Kibreab (1997) and Sundquist (2000), who strongly argue that refugees have an adverse effect on employment generation.
Meanwhile, there is no common consensus among researchers about the impact of refugees on employment generation. Jacobsen (2002) argues that refugee impair employment generation whilst Rosenburg (2002), argues that the impact of refugees on any economic or social sphere is presumed to be determined by a lot of factors. For instance, the UN Refugee Agency (1997), contends that the magnitude of effects that is posed by refugees is determined by the size of the refugee population. This implies that the greater the refugee population, the greater the refugee impact. Other studies also contend that the refugee effects are determined by the level of economic development being experienced in that country (Kurimoto, 2005; Kuhlman, 1990). Studies have also shown that the impact of refugees especially on employment generation is high in low developed economies. Moreover, one can also argue that circumstances under which refugee will pose an effect on issues such as employment generation are many and diverse and tend to depend on economic situations prevailing in an economy (Kurimoto, 2005). On the contrary, it is also established by the UN Refugee Agency (undated) that the influx of refugees does not necessarily lead to economic challenges. This is because refugees tend to attract international agencies which offer relief assistances. Such relief often comes in huge financial packages with a total of US$25 million being disbursed to Malawi (UN Refugee Agency, 1997).Rutinwa and Kamanga (2003), however outlined possibilities of refugees contributing to economic development. However, from this analysis, it is therefore imperative that refugees do pose an effect on employment generation but circumstances under which they pose effects are not the same. Moreover, if a good analysis of the impact of refugees on employment generation is to be established in Jordan, then a study needs to be tailored to factor in circumstances and situations prevailing in Jordan. With a lack of consensus on the impact of refugee on employment creation, this study therefore seeks to analyse the impact of refugee on employment generation with regards to Jordan.
3.1 METHODOLOGY OF THE STUDY
The study includes three variables: unemployment, gross domestic product, number of Syrian refugees in Jordan .the experimental analysis, the quarterly data for each of the three variables were taken from the first quarter of 2012 to the fourth quarter of 2016. Data on the study variables were obtained from the annual reports of the Central Bank of Jordan, Publications of the International Monetary Fund, and publications issued by the Department of Statistics in Jordan and UNHCR.
3.3 Model Specification
The relationship between variables can be expressed in the form of an economic model as follows:
Unem= f(”0 +”1 NREF +”2 LGDP + ” )
4.1 Initial tests
In order to test the hypothesis of the study, preliminary tests will be conducted before the standard model is estimated in the study as follows:
4.2 Unit root test
To identify the timeliness of the time series of the study variables, and their degree of integration, we will use:
4.2.1 Test Augmented Dikey-Fuller (ADF), which takes the following formula:
Yt =A1+A2T+” Yt-1+ Yt-i+et
4.2.2 Test Phillips-perron (PP), which takes the following formula
‘yt = ”0 Dt + ”yt’1 + ut
If both the unit root tests give the same results then we are certain about the order of integration of series. The Augmented Dickey Fuller and Phillips-Perron (PP) tests are the two most widely used unit root test for stationary of data in literature.
4.3 Lag Length Criteria
It’s necessary to determine the Lag Length Criteria, in which the serial correlation is eliminated. In order to determine the Lag Length Criteria, there are several criteria, for example the Schwarz information criterion (SIC) Final prediction error (FPE), Akaike information criterion (AIC) and Hannan-Quinn criterion (HQ). These criteria specify the period in which the values of the criterions are as low as possible. There is also the (LR) test, which is concerned with testing the hypothesis that the parameters of the combined Lag Length Criteria are statistically unexplained using a distribution of Possible Lag Length Criteria and stops at a time when their parameters are explained (Gujarati and Porter, 2009).
4.4 CUSUM Stability Test
If Lag Length of variables for long periods of time have experienced internal or external disturbances, this may affect the behavior of these variables over time. It is known that the Jordanian economy has been subjected to many of these disorders, such as the entry of the Iraqi army to Kuwait in 1990, And the occupation of Iraq in 2003, which led to the resort of many Iraqis to Jordan, the Lebanese war, “Israel” in 2006, and the global financial crisis in 2008. In these circumstances it is necessary to ensure that there is no structural change in the form data during the study period, Make sure there are no sudden jumps or changes in data over time. The results of this test are shown in the form of a model curve estimated by the OLS. If the curve is within the range of two standard deviations (+2 S.E.) It means that the parameters are stable over the length of the period so the parameters can be estimated without having to segment them.
4.5 Co-integration test
After performing the unit root test for the time series, and the test of Lag Length Criteria, the joint integration test must be performed. Since the time series are not stationary at the level and are integrated in the first order, they may have an integral relationship in the long term. In general, if there is a common integration, this indicates a long-term linear combination between study variables, This combination is stable over time. This test is based on the null hypothesis that there is a (R) or less of the vectors of co-integration. Johansen (1995) has developed two main tests to test the combined integration of variables:
Pmax= Trace = -T
– Maximal Eigen Value:
pmax = -T Ln(1- r+1)
Where T is the number of observations, K is the number of variables and Eigen calculated super values, R number of co-integration vectors. In order to determine which of these vectors constitute the relationship of co-integration, complementary vectors are selected corresponding to the highest Eigen value.
4.6 Granger Causaltiy Test
The Granger test is one of the most important tests in time series analysis. It tests the existence or absence of causal relationship between two variables and the direction of this relationship, if any (mono or binary). (Engle & Granger, 1987). To estimate the effect of random shocks in any variable on other variables, we used two basic tools in the analysis: Variance Decomposition and Impulse Response Function, because any random shock in any variable can affect other variables of the model
4.7 EMPIRICAL RESULTS AND ANALYSIS
4.7.1 Unit Root Test
According to the doing (ADF) process, where all variables are stationarity at the first difference, depending on the appropriate Lag Length, which was determined at the minimum value of the AIC where the Lag Length was equal to one for all the variables shown in the table 1 and 2.
Table 1: Augmented Dickey Fuller unit root test results for stationarity of variables
First Differences At level
Intercept&trend Intercept Intercept&trend Intercept
-7.244057 -7.225265 -0.865464 -0.092558 UNE
-17.62413 -16.99404 0.061161 -1.392913 GDP
-5.893259 -6.161311 -3.180362 -1.876611 NOR
EViews 9.5 Used , Used Akaike Info Criterion (AIC) lag , Represents significant at 10%, 5%.
The calculated values of T in the Augmented Dickey-Fuller test are larger than the table values (absolute values) after taking the first difference at the level and thus the decision is to reject the null hypothesis (there is a unit root at the first difference), that’s mean the time series are stationary at I(1).
Table 2: Philips Perron unit root test results for stationarity of variables
First Differences At level
Intercept&trend Intercept Intercept&trend Intercept
-7.554771 -7.225265 -2.263227 -1.118961 UNE
-5.748628 -6.091207 -5.587036 -2.099797 GDP
-18.29071 -15.71419 -4.851410 -3.437025 NOR
EViews 9.5 Used ,unit root test with Newey’West using Bartlett kernel ,Represents significant at 10%, 5%.
The results of the study in the Philips Peron test are almost the same, with the exception of the number of Syrian refugees who are stationarity at level, which will strengthen the credibility of the results obtained in the ADF test.
4.7.2 Lag Length Selection Criteria
After conducting the tests to determine the optimal number of Lag Length Selection Test (LLST) it was concluded that there were Four periods as the results showed in the table 3.
Table 3: Lag Length Selection test results for stationarity of variables
HQ SC AIC FPE LR Lag
41.17782 41.31024 41.16320 1.51e+14 NA 0
41.28286 41.81255 41.22440 1.65e+14* 12.96919 1
40.69998 41.62693 40.59767 1.00e+14 16.85549 2
37.33044* 38.65466* 37.18428* 4.76e+12* 31.30549* 4
Likelihood Ratio Test(LR), Final Prediction Error Criterion(FPE),Hannan _ Quinn Criterion(HQ), Akaike Info Criterion(AIC), Schwarz Info Criterion(SIC).
4.7.3 CUSUM Stability Test
The figure above shows that the plot of CUSUM for the model under consideration is within the five per cent critical bound. This by implication suggests that the parameters of the model do not suffer from any structural instability over the period of study. That is, all the coefficients in the error correction model are stable.
4.7.4 Johansen cointegration test results (intercept and no trend)
After ascertaining that all study variables are stationarity at the first level, the Johansen Cointegration Test must be conducted where there can be a cointegration and long term stationarity relationship between the variables of the study. The results of which are shown in Table 4 and 5.
Table 4 : Unrestricted Cointegration Rank Test (Trace)
critical value Trace
No. of CE(s)
0.0023 29.79707 40.07235 0.859863 None*
0.8398 15.49471 4.699898 0.229312 At most 1
0.9147 3.841466 0.011402 0.000633 At most 2
The results indicate at least one cointegration.
Table 4 indicates the existence of a vector for the cointegration of the variables, because the value of the Trace Statistic is greater than the value of the critical value at 5%. It is integrated from the first d. The appropriate model for this type of data is the error correction model (ECM) .
Table 5 : Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
critical value Max-Eigen
No. of CE(s)
0.0003 21.13162 35.37246 0.859863 None*
0.7805 14.26460 4.699897 0.229312 At most 1
0.9147 3.841466 0.011402 0.000633 At most 2
The results indicate at least one cointegration.
Table 4 indicates the existence of a vector for the cointegration of the variables, because the value of the Max-Eigen Statistic is greater than the value of the critical value at 5%.
4.7.5 Granger causality Test
Null hypothesis F-Statistics P-Value Implication
NOR does not granger cause UNE 0.52366 0.6043 NOR is not causing UNE
UNE does not granger cause NOR 0.30443 0.7427 UNE is not causing NOR
GDP does not granger cause UNE 0.03789 0.9629 GDP is not causing UNE
UNE does not granger cause GDP 1.36981 0.2885 UNE is not causing GDP
GDP does not granger cause NOR 0.14848 0.8635 GDP is not causing NOR
NOR does not granger cause GDP 3.36408 0.0665 NOR is causing GDP
This study aimed an analysis of the impact of refugees’ influx on employment generation in jordan. And for the time period (2012Q1-2016Q4), using quarterly data. Cointegration was used as a model for study. The study findings indicated evidence of strong cointegration (long-run relationship) among the estimated variables in the model. A number of statistical tests were carried out (Unit Root Test). The study showed that all study variables are stationarity at the first difference Through the sleep test (ADF) while all variables are stationarity at the first difference or stationarity at the level when using the (PP) test, The Lag Length Selection test, as well as the Granger causality Test, on the time series of the study variables.
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