The Impact of Refugees on Employment and Wages in Jordan 1
Belal Fallah, 2 Caroline Krafft, 3 and Jackline Wahba 4

Abstract: Starting in 2011, the Syrian conflict caused a large influx of refugees into Jordan. In
2015, there were an estimated 1.3 million Syrians in a country with just 6.6 million Jordanians.
This paper investigates the impact of the Syrian refugee influx on the Jordanian labor market.
Panel data from 2010 and 2016 combined with information on where the refugee influx wasconcentrated allow us to identify the impact of refugees on Jordanians’ labor market outcomes.
Overall, we find that Jordanians living in areas with a high concentration of refugees have had noworse labor market outcomes than Jordanians with less exposure to the refugee influx.

Keywords: Refugees; Labor Markets; Employment; Unemployment; Wages; Jordan
JEL Codes: J21, J31, F22, O15

1

Research supported by the British Academy through the Economic Research Forum (ERF). The authors
appreciate the helpful comments of discussant Susan Razzaz and participants in the December 2017 ERF“The Impact of Syrian Refugees Influx on Neighboring Countries” workshop, as well as participants in the
University of Minnesota, Department of Applied Economics Development Seminar. The authors would
like to thank Caitlyn Keo and Abu Siddique for research assistance.
2
College of Administrative Sciences and Informatics, Palestine Polytechnic University, Hebron, Palestine.
Email: bfallah2000@yahoo.com
3
Corresponding Author. Department of Economics and Political Science, St. Catherine University, 2004
Randolph Avenue, St. Paul, MN 55105, USA. Email: cgkrafft@stkate.edu
4
Department of Economics, University of Southampton, Southampton, SO171BJ, UK. Email:j.wahba@soton.ac.uk

1

1

Introduction
During the past decade, the number of people forcibly displaced has risen to unprecedented

levels. From 2007 to 2017, the number of displaced people rose from 43 million to 69 million(UNHCR 2018a). The highest growth took place between 2012-2015, due primarily to the Syrian
conflict. More than half of the Syrian population has been displaced internally or across borders.
Over 6.3 million Syrians have fled Syria since 2011, many seeking safety in neighboring countries
and beyond (UNHCR 2018a). This humanitarian crisis has generated public sympathy as well as
concern about the implications of such a massive flow of people.
Neighboring, developing countries host the vast majority (85%) of the world’s refugees,
including from the Syrian conflict (UNHCR 2018a). Jordan, which shares a border with Syria, hasexperienced a substantial influx of Syrians, with 1.3 million Syrians living in Jordan as of the 2015
Population Census (Department of Statistics (Jordan) 2015a). 5 Compared to a total population of
6.6 million Jordanians in 2015 (Department of Statistics (Jordan) 2015a), the Syrian influx
represents a major increase in Jordan’s population. The impact of such a massive influx of people
on members of the host community, in particular on their labor market outcomes, is a subject ofgreat importance and debate. This paper empirically investigates the impact of the Syrian refugee
influx on labor market outcomes in Jordan.
There is a large literature on the impacts of immigration on the labor market, mostly
focusing on voluntary and typically economic immigration. 6 See, for example, Peri (2016) for a5

As of March 2018, there were 659,000 Syrians in Jordan registered as refugees with the United
Nations High Commissioner for Refugees (UNHCR) (UNHCR 2018c). Not all Syrians within
Jordan are necessarily registered as refugees. However, since the vast majority of Syrians in
Jordan are either registered as refugees or fled Syria due to conflict or violence (Krafft et al.
2018), we refer to Syrians in Jordan synonymously as refugees (broadly defined).
6
See e.g. Kerr and Kerr (2011) and Dustmann et al. (2016).
2

review of the evolution of theory and evidence. However, the literature on the impact of refugees
on host labor markets is small but growing. For instance, one of the most studied cases is the effectof the 1980 Mariel boatlift from Cuba to Miami. Card’s (1990) seminal paper studied the impactof this influx on natives’ employment and wages and found no adverse effects. Several subsequent
papers, for example Clemens and Hunt (2017) and Peri and Yasenov (2017), have revisited thosefindings and in some cases, such as Borjas and Monras (2017), come to different conclusions.
Overall, the results from this literature suggest no or small negative impacts on natives.
A few papers have examined the impact of refugees on labor markets in host countries
from the developing world. Maystadt and Verwimp (2014) found Rwandan and Burundian refugee
inflows had a slightly negative impact on the employment outcomes of Tanzanian agricultural
workers, while Ruiz and Vargas-Silva (2016) showed that native Tanzanians adjusted to the
refugee flows by changing economic activities in the long run. Alix-Garcia et al. (2018) show thatnatives living near refugee camps benefit from new employment opportunities and favorable pricechanges. However, these studies examine the long-term effects (decades after the inflow) as
opposed to our focus here, which is on shorter-term effects. There may be a period of substantial
adjustment in the labor market in the wake of a refugee influx.
For the case of Syrian refugees, there is a very recent literature looking at the impact ofSyrian refugees in Turkey. Tumen (2016) examined the impact of Syrian refugee inflows in Turkey
and found small but statistically significant informal employment losses among natives in Turkey.
He focused on the first two years of the refugee inflows, an era in which both the decision to
migrate and the location choice within Turkey can be assumed to be exogenous. Bagir (2017)
analyzed the initial (primary) migration to Turkey’s borders, and (secondary) migration from theborders to the inner region of Turkey separately. He found statistically significant negative3

employment and wage effects on low-skilled and less experienced Turkish natives in the primary
migration. The secondary migration did not show a statistically significant negative employment
effect, however, it did generate significantly lower wages, particularly for low-skilled and lessexperienced informal Turkish workers. Ceritoglu et al. (2017) also found negative impacts of
Syrian refugee inflows on Turkish natives’ labor market outcomes: increasing unemployment andreducing labor force participation, informal employment and job finding rates among natives.
Similarly, Del Carpio and Wagner (2015) found large-scale displacement of natives by refugees
in the informal sector in Turkey.
In contrast, Cengiz and Tekguc (2017) argue that immigrants might bring capital andpurchasing power to local economies and shift labor demand to offset any negative labor supply
effects. Estimating a difference-in-difference model for Turkey, Cengiz and Tekguc (2017) as wellas Akgündüz, van den Berg, and Hassink (2015) found no sizable negative impact of Syrian
migrants on the native workforce. While their methods were similar to Ceritoglu et al. (2017), theirresults did not show that informal employment declined after 2012. In addition to looking at
employment effects, Cengiz and Tekguc (2017) look at wage effects. They find that there is a sharp
decline in wages in the early years of the refugee influx for low-skilled, predominantly informal
workers, yet it appears their wages quickly recovered. Finally, looking at residential construction
and the establishment of new companies, findings confirm that migrants cause a positive demand
shock that partially or totally offsets the labor supply shock.
There is little evidence on refugee impacts in Jordan. All the evidence to date is effectively
descriptive, looking at patterns of employment over time (Cookle 2017; Fakih and Ibrahim 2015;
Stave and Hillesund 2015). The Jordanian case is particularly interesting for several reasons. Until
2016, Syrians were not (officially) allowed to work (Razzaz 2017). Hence, similar to Turkey, if

4they did work, they did so in the informal sector. The Jordan Compact between the European
Union (EU) and Jordan in 2016 included humanitarian aid and macro financial assistance as wellas trade concessions by the EU. One of the concessions of the Jordanian government in return forthis assistance was allowing Syrian refugees in Jordan formal access to the labor market (European
Commission 2016). Hence, since 2016, Syrian refugees were allowed work permits in certain
sectors, such as agriculture, construction, food, and manufacturing (Razzaz 2017). These sectors
disproportionately employed migrant labor (and relatively few Jordanians) even prior to theconflict. Although there is a cap of 200,000 on the number of permits offered, only 87,141 hadbeen taken up by the end of 2017 (Ministry of Labour Syrian Refugee Unit 2018).
The main contribution of this paper is providing empirical evidence on the short-term
effects of large inflows of refugees on the natives’ labor market outcomes in a developing country
context. The paper additionally sheds light on the effects of allowing—at least in a limited way—
refugees to work legally, and how complementing legal work opportunities for refugees with aid
and trade opportunities may yield offsetting effects for natives’ labor market outcomes.
Economic theory would suggest that a large influx of refugees would yield a labor supply
shock in Jordan. First, refugees would displace natives (particularly initially in the informal sector),and this should lower employment and wages in the informal sector. Second, this might lead tocomplex effects on formal employment and wages depending on the complementarity between thetwo sectors and access of refugees, once they have work permits. A caveat to this theoreticalprediction is that the deal with the European Union that led to Jordan offering work permits also
included additional aid and trade concessions (European Commission 2016). These aspects of thedeal could generate additional labor demand among Jordanians, as could the general effort toprovide aid to refugees, as additional Jordanians work to provide services for refugees. The net

5effect of these labor supply and demand effects is, theoretically, ambiguous. Any potential impactof the Syrian influx will depend not only on the mechanisms of aid, additional labor demand inservices, and work permits, but also on the composition of the Syrian refugees in Jordan, and theother laborers (especially migrants) with whom they might compete.
Therefore, in this paper we empirically examine the impact of Syrian refugee inflows on
Jordanians’ labor market outcomes. We make use of nationally-representative panel data to capturelabor market outcomes before (2010) and after (2016) the Syrian influx. We study both theintensive and extensive margins of work as well as employment characteristics. Specifically, we
examine employment, unemployment, hours of work, and wages, as well as sector, formality,
economic activity, and occupation of employment. In additional models, we split our results along
dimensions that may shape labor substitutability, such as sex, age, and education level. We rely on
the variation in the share of Syrians by locality to identify the impact of exposure to refugees. We
additionally, in some models, control for geographical fixed effects or individual fixed effects. We
find there have not been negative effects on employment outcomes, but there have been slightshifts in the type of work Jordanians undertake. This finding has important implications for othercountries hosting refugees and considering whether to allow refugees to (legally) participate in thelabor market.
The remainder of the paper is organized as follows. Section 2 describes the data used in
the analysis. Section 3 introduces the empirical methodology. Section 4 presents the main results
followed by the sensitivity analysis in section 5. Section 6 discusses our results and concludes.

6

2

Data

2.1

Jordan Labor Market Panel Survey
The Jordan Labor Market Panel Survey (JLMPS) provides a unique opportunity to assess

the impact of the refugee influx on Jordan’s labor market. The initial wave of the JLMPS was
fielded in 2010 (primarily January-March), prior to the regional upheaval and Syrian conflict. 7
The data were nationally representative (after weighting to account for sample stratification along
geographic lines). A second wave of the JLMPS was fielded starting in December 2016 (the bulkof data collection finished by April 2017). Both waves of the JLMPS were a collaboration between
the Economic Research Forum (ERF) and the Jordanian Department of Statistics (DOS), which
was responsible for sampling and fieldwork.
The JLMPS 2016 tracked households from 2010, including individuals who split to form
new households. The 2016 wave also added a refresher sample that over-sampled neighborhoods
which were identified in the November 2015 population census as having a high proportion of
non-Jordanian households. Approximately 3,000 households were added with the refresher sample,
which stratified on governorate and urban/rural/(official) camps 8 as well as high vs. low proportionof households that were non-Jordanian. The 2016 sample weights, based on the 2015 census
population, take into account the initial wave sampling strategy, the refresher sampling strategy,
and account for attrition between the 2010 and 2016 waves on both the household and split
household levels. 9

7

See Assaad (2014) for more information on the JLMPS 2010. Data are publicly available fromERF Open Access Micro Data Initiative (OAMDI 2018a; b) at: http://www.erfdataportal.com/.
8
The official camps were Za’atari and Azraq.
9
See Krafft & Assaad (2018) for details on the data including sample design, attrition modeling,sample weights, and validation of the sample against other data sources. The appropriate weights
are used throughout our descriptive and multivariate results.
7

The panel structure of the JLMPS provides an enormous advantage in being able to observe
the impact of the refugee influx that occurred between waves. The JLMPS 2016 also includes a
substantial amount of retrospective data, including a re-designed labor market history that
improves on previous LMPSs in methods for collecting spells of non-participation and especiallyunemployment. 10 Assaad, Krafft, and Yassin (2018), using the Egypt Labor Market Panel Survey
and exploiting multiple waves of panel and retrospective data, assessed consistency andmeasurement error and proposed improvements (which were implemented in JLMPS 2016). The
labor market history data collect statuses starting from school exit or first job, whichever is first.
They capture, in sequence:(1) The date of school exit
(2) Any non-employment between school exit and first job (distinguishing between
unemployment and out of the labor force).
(3) First job start date and characteristics.
(4) Respondents are then asked if they have left that job, and if so, when and whether they
had a period of non-employment (again distinguishing unemployment and out of labor
force).
(5) They are then asked if they had a subsequent job, and if so, the start dates andcharacteristics.
Steps (4) and (5) repeat until all statuses are captured. The sequencing of statuses from initialforwards (rather than current, previous, and pre-previous) as well as the specific addition of
detection questions for non-employment (rather than just considering that as a status) were

10

The labor market history is asked of those who ever worked; we gather retrospective data on
unemployment for those who never worked from the unemployment module, which includes thestart date of unemployment.
8

innovations to improve data quality in response to challenges identified by Assaad, Krafft, andYassin (2018). We primarily exploit the 2010 to 2016 panel and 2016 retrospective data 11 to
examine a variety of labor market outcomes. We present the results from the repeated crosssections as a sensitivity analysis in Appendix D.

2.2

Analysis sample
Our analyses distinguish between two groups. First and foremost, in this paper we areinterested in how Jordanians’ labor market outcomes were affected by the influx of Syrian refugees.
We therefore focus most of our analyses on Jordanians 12 aged 15-64. 13 In order to understand thepotential impact of Syrian refugees on Jordan’s labor market, we, secondarily, descriptively
examine the labor market outcomes of Syrian refugees aged 15-64.
Labor markets in Jordan are highly segmented by sex and Jordanians’ female labor force
participation is near the lowest in the world (17%) (Assaad, Krafft, and Keo 2018). Very few
Syrian women in Jordan participate in the labor force (4%). Since the women who do participate
in Jordan tend to be the most educated and are unlikely to be competing with Syrians, we therefore
split all our results by sex and present only the results for men in the body of the paper. The results
for women are presented in an Appendix C. In additional sensitivity analyses, we also split the11

We restrict our analyses of the retrospective data to the period 2004-2017, centered around theyear 2010, which is the reference year for all our analyses.
12
The 2010 respondents were almost all Jordanian (92.5% of individuals, weighted. Individuals
of Palestinian origin who have Jordanian nationality are included in this group). There were asubstantial share of “Other Arab” respondents (5.0%), i.e. Palestinians, a small group of
Egyptian respondents (2.0%) and few “Other” respondents (0.1%). Just 0.5% of respondents
were Syrian in 2010. In 2016, the share of respondents who were Jordanian was 69.4%, followed
by 13.3% Syrian, 8.6% Other Arab, 6.7% Egyptian, and 2.1% Other.
13
In analyses that use retrospective data, we restrict individuals to be 15-64 in the retrospective
year in question. In the retrospective data we also exclude years spent outside of Jordan itself.
9

sample based on education level (basic or less vs. secondary and higher), since the less-educatedmay be disproportionately affected. We further split the sample by sector, public versus private,as there may be offsetting or sector-specific effects. These analyses are presented in Appendices
E and F. In appendices H and I we examine specifically labor market entrants, who may bedisproportionately affected by recent developments.

2.3

Labor market outcome variables
We examine a number of labor market outcomes. First, we focus on labor market status,classifying individuals as employed, unemployed, or out of the labor force (not shown). In
identifying the border between unemployment and non-participation, we require individuals tohave been actively searching for work during unemployment (within the past four weeks in thecontemporaneous data sources, within the period of non-employment for retrospective data). Workis defined in terms of market work in the past three months; those who do subsistence work only
are considered not working.
We then examine a number of outcomes among the employed, 14 including whether
individuals have formal work (with a contract or social insurance) or informal work (neither acontract nor social insurance). We also look at whether workers are in an “open sector,” that is, a
sector open to Syrians with work permits (agriculture, manufacturing, construction, food service,or domestic/cleaning work (Kelberer 2017)). While Jordanians may be facing competition in the
open sector, they may also be receiving more opportunities in other sectors, particularly the public
sector. For instance, additional provision of services and international funds may increase public

14

We undertook sensitivity analysis as to whether analyzing these outcomes unconditional on
employment, rather than among the employed, changed our results; it did not lead to substantive
changes (results available from authors on request).
10

sector employment, which is open exclusively to Jordanians, while displacement may occur in theprivate sector. We therefore examine the probability of employment in the private sector among
the employed (the complement necessarily being public sector work). 15 To specifically examine
whether aid is likely to be creating jobs in human services, we examine the probability of being
employed in the education or health care field among the employed. Further, we examine
occupations, specifically an outcome of being in a managerial or professional occupation among
the employed, in case there is occupational upgrading occurring. For all workers, we examine
hours per week, and for wage workers, we examine both hourly wages and monthly wages.

2.4

Identifying variation in the Syrian refugee influx
To assess the impact of the refugee influx, we identify off of variation in where Syrian

refugees settled. Syrians could enter Jordan from Syria up through mid-2013 through two official
border crossings along Jordan’s northern border as well as nearby informal crossings (Francis2015). In mid-2013 official crossings were closed due security concerns. Informal crossings were
subsequently closed as well. Most refugees (87%) are living in host communities (not camps),
although those in host communities may have initially passed through camps (Krafft et al. 2018).
The two main camps in Syria are located near the border; Za’atari was established in mid-2012 10
kilometers from the border, near the town of Mafraq, on desert land owned by the Jordanian air
force (Ledwith 2014; Reuters 2012). Azraq was subsequently opened in mid-2014 further inland,on a site that housed displaced Iraqis and Kuwaitis during the Gulf War (Oddone 2014).
Importantly for identification purposes, neither site was chosen for access to labor market

15

We include work in the small international and NGO sectors with public sector work to
capture the effect of aid on these two sectors together, as compared to the private sector.
11

opportunities. Syrians in host communities also are not equally distributed throughout the country.
Refugees are predominantly concentrated in the North of Jordan (in governorates along the borderwith Syria) and in the capital, Amman.
The refugee influx has thus differentially affected geographic areas within Jordan. We use
data from the 2015 census on the number of Syrian households in a particular locality as a measureof the refugee influx. Specifically, we use the percentage of households that are Syrian. We relyon the locality of residence in 2010 throughout our analyses, 16 using the retrospective residential
mobility data to identify 2010 residence even for observations from the 2016 wave. We use 2010
residence throughout in order to avoid estimation problems that might result from Jordanians
potentially relocating due to labor market or housing market pressures from the Syrian refugee
influx. 17
Localities are the fourth level of geographic disaggregation (Jordan is divided into
governorates, which contain districts, which contain sub-districts, which contain localities). There
are 958 localities in Jordan, although we typically cover only around half the localities within
Jordan in the JLMPS depending on the data and outcome used. Taking individuals as the unit ofanalysis, the median individual lives in a locality of 148,398 persons (that is, 50% of individuals
live in localities with more than 148,398 persons and 50% of individuals live in localities withfewer people). Again analyzing individuals, the 25th percentile individual lives in a locality of size
19,608 and the 75th percentile individual lives a locality of size 258,829. Although it is highly
debatable what a “local” labor market is, localities are a plausible size for a local labor market thatwould be, potentially, affected by a refugee influx. We investigate the question of what is a local

16

Individuals who were not in Jordan in 2010 are thus dropped.
Few Jordanians changed localities between 2010 and 2016; of the 18,669 individuals coveredby the retrospective 2016 data, only 475 report a different locality in 2016 than 2010.
1712

labor market further in examining, for those working outside the home, the percentage working intheir locality of residence, which is 40%. Thus, while many workers may cross locality borders,locality level shocks will definitely affect a substantial share of workers.
Although we have locality level data in the 2016 wave, and thus can use the 2010 locality
data based on the 2016 residential mobility for our panel and retrospective analyses, there is not
locality level data when using the repeated cross sections (Appendix D), that is when including the2010 wave. Therefore, we use sub-district level data on the percentage of households that are
Syrian, the next geographic level up, with the repeated cross-section. There are 89 sub-districts in
Jordan, 88 of which are in the JLMPS. On the sub-district level, 51% of workers who work outside
their home are working in their sub-district of residence. We also undertake sensitivity analyses
around different levels of geographic aggregation in Appendix K.
We use the number of Syrian households, rather than individuals, to account for the densityof working age men who might enter the labor market. 18 The refugee population is very young;
48% of the Syrian refugees in Jordan are young children (aged 0-14) (Krafft et al. 2018). The
young age of the refugee population is important to keep in mind in light of potential labor market
effects of the influx; young refugees are much more likely to be requiring services, such as
education, and receiving aid, than competing in the labor market. Refugee households are
predominantly nuclear, as 95% of household members are either the head, spouse, or offspring of
the head (Krafft et al. 2018). Thus, households are an ideal proxy for working age men, more sothan number of individuals. Unfortunately, we cannot examine directly the share of working-age

18

We tested the sensitivity of our main results to using the percentage of individuals Syrian
instead of households and the results were not substantively different.
13

individuals in the census because the census data are only available to us already geographicallyaggregated, not as individual microdata.
2.5

Control variables
Our models control for a number of important demographic differences among Jordanians.

We consider demographic differences because they may affect labor market outcomes over time,
be correlated with the refugee influx, or because certain demographic groups may be particularly
affected by the refugee influx. Our models account for respondents’ age and age squared. Sevenlevels of education are controlled for: (1) illiterate (reference) (2) read & write (3) basic (ten years)(4) secondary (two additional years) (5) post-secondary (two additional years beyond secondary)(6) university (four additional years beyond secondary) and (7) post-graduate. These sameeducation categories are included for mother’s and father’s education, although we aggregate postgraduate studies with university for parents. Since many labor market outcomes are predicated on
socio-economic status, parents’ background is a critical control. This information is available evenwhen the respondent’s parents are not in the household. As another proxy for socio-economic
background, we control for father’s employment status when the respondent was aged 15 as: (1)waged employee (2) employer (3) self-employed (4) unpaid worker (5) non-employed or (6) don’t
know. In some specifications we also control for geographic or individual fixed effects (in whichcase some invariant controls drop out of the models).

3

Methods
Our main models of the impact of the refugee influx on Jordanians’ labor market outcomesare linear difference-in-difference models. Denote outcomes as Yit, where i identifies an individual
and t denotes time. Further, denote with l a particular locality, so that 𝑆𝑆𝑙𝑙 is the control for the share
14

(percentage points) of households that were Syrian in the locality from the census in 2015. We
estimate:
𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛼𝛼𝑗𝑗 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖 + 𝛾𝛾𝛾𝛾𝑙𝑙 + 𝛿𝛿𝑡𝑡 𝑡𝑡 + 𝜃𝜃𝑡𝑡 𝑡𝑡 ∗ 𝑆𝑆𝑙𝑙 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖(1)𝛾𝛾 can be used to measure selection or endogenous placement of Syrians, whether they migrated towhere employment conditions were better prior to the influx. 𝛿𝛿𝑡𝑡 can be used to assess overall timetrends (specifically, trends for localities with no Syrians). t is operationalized sometimes as a single
control for 2016 (in the panel and repeated cross section models) and sometimes as a vector ofyears (in the retrospective models). The year 2010 is always the reference year regardless of thespecification. The covariate that measures the impact of the influx is the 𝜃𝜃𝑡𝑡 term on the interactionof share Syrian and time. In the retrospective models, 𝜃𝜃𝑡𝑡 can also be used to assess the parallel
trends assumption by comparing whether 𝜃𝜃𝑡𝑡 was different over time prior to the influx. We test

the parallel trends assumption with a joint test of the pre-2010 (2004-2009) 𝜃𝜃𝑡𝑡 interaction

coefficients. In the panel models and some of the retrospective models we also add individual fixed
effects, 𝜂𝜂𝑖𝑖 , to the specification above. Most of the specifications also include a number, j, of control

variables, 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖 , as discussed above.
4

Results
Results are presented first, in brief, in terms of descriptive statistics on the population andlabor force outcomes of Jordanians and Syrian refugees. We then present the multivariate models
using the retrospective data (where we can test parallel trends), and panel data. These are followed,
in the next section, by the various robustness checks.

15

4.1

Descriptive Statistics on the Population of Jordanians and Syrians in Jordan

There has been a substantial influx of nearly 1.3 million Syrians into Jordan between 2010 and2016. Although the Syrian population as a whole could create labor demand, the main labor supply
impact will be from the working-age population. We estimate that from 2010 to 2016, the numberof working-age Syrians rose from 19,000 to 644,000. The Syrian working age population was
about 16% the size of the Jordanian population in 2016, a substantial demographic shift. However,
not all working-age adults engage in the labor market. While there were 1.6 million Jordanians in
the labor force as of 2016 there were only 143,000 Syrians in the labor force in 2016. The Syrian
labor force in 2016 is equivalent to about 9% of the Jordanian labor force.
The Syrian refugee influx differentially affected different areas within Jordan. Figure 1
presents the sub-district level variation in the refugee influx. Specifically, it shows the percentage
of households that are Syrian as well as the number of households (based on the 2015 Census).
Our key covariate is the percentage of households that are Syrian, a relative measure, which could
vary either due to differences in the number of Syrian households or differences in total populationin an area. Both the number of Syrians and the percentage of households that are Syrian vary acrosssub-districts, ranging from a minimum of 0.5% or 6 households to a maximum of 82% or 29,905
households. The labor supply shocks were stronger near the Syrian border and Amman, but didvary substantially across sub-districts even in areas of overall concentration (and even more so
across localities). For example, in Amman governorate the sub-district level share of households
Syrian varied from a low of 0.6% to a high of 18%.

16

Figure 1. Number and percentage of households that are Syrian, by sub-district

Source: Authors’ calculation based on Census 2015

Focusing on the locality working-age Jordanians lived in as of 2010 (our main covariate
and sample), a sizeable proportion of individuals experienced relatively low levels of local labor
supply shocks; the 10th percentile is 2.5% Syrian and the 25th percentile 5.6%. The mean was
10.1% Syrian and the median 9.6%. On the high end, the 75th percentile was 12.5% Syrian and the90th percentile 21.6%. This variation in the local labor market shock is our key source ofidentification.

17

The year of arrival of refugees determines the timing of potential impacts on the labormarket. The JLMPS shows that very few Syrians (aged 15-59 19) in Jordan in 2016 arrived in 2011or earlier (8%). The influx began in 2012 (27%), peaked in 2013 (48%) and decreased thereafter(8% in 2014, 7% in 2015, and 3% in 2016/17). 20 Thus, labor market impacts are likely to havestarted in 2012 or 2013, although effects may have been delayed by the time it took for demand
for goods and services to generate additional labor demand, or for work permits and displacement
to occur.
The JLMPS data allow us to directly assess when the Syrians who did work in Jordan
started working. Work permits and aid and trade concessions were part of the Jordan Compact,
signed February 4, 2016 (International Rescue Committee 2017). Regulations for obtaining work
permits were relaxed starting in March of 2016 and work permit fees were waived starting in Aprilof 2016 (Dunmore 2016). Thus, by the time the JLMPS was fielding in December 2016-April 2017,
the work permit system had been operational for almost a year. Although work permits started in2016, Syrians reported starting work before then, presumably informally. Around a fifth of Syrians
in Jordan who had ever worked since arriving in Jordan started working in each of 2012 (23%),2013 (22%), and 2014 (21%). There was a slight decrease of starts, to 12%, in 2015, after arrivals
had tapered off, but an uptick to 17% in 2016/17, when work permits became available. Although
work permits allowed Syrians to work legally in Jordan, the majority were still working without apermit. We estimate 47,000 Syrians aged 15-59 21 worked with work permits. 22 In contrast,

19

Analyses of certain Syrian refugee outcomes are restricted to ages 15-59 as that was theuniverse for the in-migration section of the questionnaire.
20
The arrival timing observed in the JLMPS is consistent with UNHCR registration data as well
(UNHCR 2018c).
21
The age group for which we have data in the JLMPS.
22
This statistic is consistent with official reports of 37,000 permits issued from January 1, 2016
to January 1, 2017, the latter date in the midst of when JLMPS 2016 was in the field.
18

approximately 62,000 reported working without a permit. Thus, although some Syrians arepotentially competing for legal employment, most are not working legally, which limits the jobs
for which they might compete. Syrians are also in somewhat different types of work than
Jordanians. They are not holding the public sector jobs that employ 42% of working Jordanians.
Few working Jordanians (17%) are in the sort of informal or irregular wage work the working
Syrians hold (82%). Syrians may be competing somewhat more with non-Jordanians (primarily
economic immigrants). 23
4.2

Time trends in Jordanians’ labor market outcomes
As an initial, descriptive assessment of how host community labor markets have shiftedsince the refugee influx, Figure 2 presents Jordanians’ employment rates by sex for 2004-2017 andFigure 3 does likewise for the unemployment rate. Data are presented from the JLMPS 2016
retrospective labor market history as well as contemporaneous annual measures from the Jordanian
Employment and Unemployment Survey (EUS). These figures serve two purposes: first, they
demonstrate descriptively how the Jordanian labor market has been faring and second, they allow
us to assess the consistency of JLMPS retrospective data with contemporaneous EUS data. 24 EUS
employment rates tend to be slightly higher than JLMPS retrospective rates, 25 although they
converge towards the survey year. Unemployment rates are higher in the JLMPS, particularly for
women, although these also converge somewhat. The two data sources show relatively similar

23

For an investigation of Syrians’ impact on the labor market outcomes of other immigrants in
Jordan, see Malaeb & Wahba (2018).
24
For comparisons of the consistency of JLMPS 2010 and 2016 contemporaneous data with EUS
statistics see Assaad & Krafft (2018) and Krafft & Assaad (2018). Contemporaneous statistics
are generally close.
25
Measurement error is a concern with retrospective data and has been investigated with other
LMPSs (Assaad, Krafft, and Yassin 2018), leading to improvements in the design of the labormarket history for JLMPS 2016.
19

(fairly static) labor market trends. Labor markets are generally rigid in Jordan (Amer 2018; Assaad
and Krafft 2016; Yassine 2013). However, there is substantial variation in the panel andretrospective data to identify off of. For example, using the retrospective sample, we see thatamong those who reported being unemployed in 2010, 49% reported being employed in 2016.
Among those employed in 2010 in the retrospective data, as of 2016, 88% reported still being
employed, 9% were out of the labor force, and 3% were unemployed.

Figure 2. Jordanians’ employment rates (percentage of the population) by sex, ages 15-64,
2004-2017

Source: Authors’ calculations based on JLMPS 2016 retrospective data and EUS contemporaneous
data.
Note: No EUS data in 2004 or 2015 due to Census years

20Figure 3. Jordanians’ unemployment rates (percentage of labor force) by sex, ages 15-64,
2004-2017

Source: Authors’ calculations based on JLMPS 2016 retrospective data and EUS contemporaneous
data.
Note: No EUS data in 2004 or 2015 due to Census years
Notably, there is not a substantial or structural shift related to the start of the refugee influx
with either data source. Employment had been flat (JLMPS) or declining (EUS) prior to the refugee
influx for Jordanian men. Unemployment has likewise fluctuated without any very clear trend.
Whether looking at employment or unemployment, regardless of data source, there are not
structural changes. Such changes could, however, be localized or masked by shifting demographics
and other trends. As a further descriptive exploration of changes, using the JLMPS data from 2010
and 2016 in Appendix A we explore changes over time in our various labor market outcomes for
Jordanians. Among employed Jordanians, jobs became slightly more formal, shifted into the public

21

sector, were increasingly in health and human services, and decreasingly in competitive activities.
These results are suggestive of shifts in services potentially related to the refugee influx, inparticular additional labor demand that may be related to aid and services for refugees.
We investigate the potential for localized changes in Figure 4, which uses 2004 and 2015
Jordanian Census data and reports, on the governorate level, the unemployment rate of Jordanians
15+ in 2004 in 2015 (Department of Statistics (Jordan) 2015b, 2004), the percentage change in theunemployment rate, and the 2015 percentage of the population Syrian. The narrative in Jordan andeven in descriptive research is that worsening labor market outcomes for Jordanians are (at least
partly) a result of the Syrian influx (Francis 2015; International Rescue Committee 2017; Staveand Hillesund 2015). The results of the figure, however, show that, between 2004 and 2015,
unemployment decreased the most in the areas that had the most Syrians. This pattern has been
corroborated comparing 2010 and 2016 (Assaad, Krafft, and Keo 2018) and noted by other authors
as well (Ajluni and Kawar 2014). The contradiction between the popular wisdom in Jordan andeven the most basic descriptive results can be explained in part by difficulties in distinguishing thenegative effect of the Syrian conflict and deteriorating macroeconomic conditions associated withthe conflict from the Syrian refugees. The common narrative suffers from omitted variable bias.
In order to properly identify the labor market impacts of refugees, we now turn to our multivariate
models.

22

Figure 4. Unemployment rates in 2004 and 2015 (percentages), percentage change inunemployment rate from 2004 to 2015, and 2015 percentage of households Syrian, by
governorate

Source: Authors’ calculations based on 2004 and 2015 Census

4.3

Retrospective models
In this section we discuss the difference-in difference estimates using the retrospective data.

For each outcome, we present the results with various specifications: the first model includes only
the difference-in-difference estimates, locality share of refugees, and year dummies. The second
specification controls for individual observed characteristics, while the third adds locality fixed
effects to the individual controls. The fourth (our preferred specification) uses individual fixed
effects.

23

Table 1 presents the results on employment and unemployment for men. Since the omitted
year is 2010, the coefficient on percentage of households that are Syrian tests for endogenous
placement of Syrians relative to 2010, that is, whether Syrians may be in localities where there
were differential labor market outcomes preceding the influx. The year coefficients show timetrends overall (effectively for a locality with no Syrians), while the interactions between the year
and percentage of households Syrian prior to 2010 test for parallel trends and those after 2010
show the impact of Syrians. 26 Note that the percentage of households Syrian has a (potential) range
from 0-100, so the coefficients for this main effect and interaction show the labor market impact
for a one percentage point increase in the share of households that are Syrian in the locality.

26

In Appendix B we test the sensitivity of results to pooling the data instead of estimating single
year effects.
24

Table 1. Labor market status (linear probability model), men, retrospective data, 2004-2017(1)
Unemployed(2)-0.000(0.001)-0.000(0.001)-0.007(0.010)-0.005(0.010)-0.009(0.009)-0.005(0.006)-0.000(0.007)0.004(0.005)-0.002(0.005)-0.001(0.009)0.007(0.012)0.016(0.011)0.007(0.012)0.010(0.012)0.007(0.012)-0.009(0.010)-0.007(0.010)-0.012(0.009)-0.007(0.006)-0.002(0.007)0.003(0.005)-0.003(0.005)-0.001(0.009)0.007(0.011)0.016(0.011)0.007(0.012)0.010(0.012)0.010(0.012)-0.009(0.010)-0.007(0.010)-0.013(0.009)-0.008(0.006)-0.003(0.007)0.003(0.005)-0.002(0.005)-0.001(0.009)0.008(0.012)0.017(0.011)0.009(0.012)0.012(0.012)0.011(0.012)Int. year and % HH Syr.
Int. 2004 and % HH Syr. 0.001(0.001)0.001(0.001)0.001(0.001)

Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2004
2005
2006
2007
2008
2009
2011
2012
2013
2014
2015
2016
2017

(3)

(4)

(5)
Employed

(6)

(7)

(8)

0.001
(0.001)0.000(0.001)0.004(0.017)0.001(0.015)-0.006(0.013)0.000(0.008)0.002(0.008)0.006(0.006)-0.002(0.006)-0.002(0.011)0.008(0.014)0.018(0.015)0.014(0.017)0.020(0.019)0.019(0.021)0.024(0.020)0.019(0.019)0.016(0.016)0.017(0.013)0.007(0.009)-0.003(0.008)0.001(0.005)-0.001(0.010)0.010(0.013)0.006(0.015)0.015(0.017)0.017(0.018)0.019(0.017)0.007(0.017)0.008(0.016)0.009(0.014)0.014(0.010)0.005(0.008)-0.003(0.007)0.002(0.004)0.000(0.009)0.009(0.011)
0.003(0.011)0.010(0.014)0.015(0.015)0.003(0.014)0.006(0.018)0.007(0.016)0.009(0.014)0.014(0.011)
0.005(0.008)-0.002(0.007)0.002(0.004)0.001(0.009)0.009(0.011)0.004(0.011)
0.012(0.014)0.017(0.015)0.005(0.014)-0.588***(0.025)-0.489***(0.023)-0.391***(0.017)-0.286***(0.014)-0.195***(0.011)-0.105***(0.008)0.104***(0.006)0.202***(0.012)0.309***(0.015)0.404***(0.017)0.510***(0.023)0.611***(0.025)0.695***(0.027)0.001(0.001)-0.003*(0.001)-0.002*(0.001)-0.002*(0.001)-0.002(0.001)25

Int. 2005 and % HH Syr.
Int. 2006 and % HH Syr.
Int. 2007 and % HH Syr.
Int. 2008 and % HH Syr.
Int. 2009 and % HH Syr.
Int. 2011 and % HH Syr.
Int. 2012 and % HH Syr.
Int. 2013 and % HH Syr.
Int. 2014 and % HH Syr.
Int. 2015 and % HH Syr.
Int. 2016 and % HH Syr.
Int. 2017 and % HH Syr.
Controls
Locality FE
Individual FE
N (Person-Year Obs.)
R-sq.

(1)
Unemployed0.001*(0.001)0.001*(0.001)0.001(0.000)0.001(0.001)0.000(0.000)0.000(0.000)-0.000(0.001)-0.000(0.001)-0.001(0.001)0.000(0.001)-0.001(0.001)-0.001(0.001)

(2)

(3)

(4)

0.001*(0.001)0.002*(0.001)0.001(0.000)0.001(0.001)0.000(0.001)0.000(0.000)-0.000(0.001)-0.000(0.001)-0.001(0.001)0.000(0.001)-0.001(0.001)-0.001(0.001)X

0.001*(0.001)0.002*(0.001)0.001(0.000)0.001(0.001)0.000(0.000)0.000(0.000)-0.000(0.001)-0.000(0.001)-0.001(0.001)0.000(0.001)-0.001(0.001)-0.001(0.001)
X
X

0.001(0.001)0.001*(0.001)0.001(0.000)0.000(0.001)0.000(0.000)-0.000(0.000)-0.000(0.001)-0.000(0.001)-0.001(0.001)-0.000(0.001)-0.001(0.001)-0.001(0.001)
X

96543
0.001

94889
0.022

94889
0.066

X
96543
0.003

(5)
Employed-0.003(0.001)-0.002(0.001)-0.002(0.001)-0.001(0.001)-0.000(0.001)0.000(0.000)-0.000(0.001)-0.000(0.001)0.000(0.001)0.000(0.001)0.001(0.001)0.000(0.001)

(6)

(7)

(8)

-0.002*(0.001)-0.002*(0.001)-0.002*(0.001)-0.001(0.001)-0.001(0.001)0.000(0.000)0.000(0.001)-0.000(0.001)0.001(0.001)0.000(0.001)0.001(0.001)0.000(0.001)X-0.002*(0.001)-0.002*(0.001)-0.002*(0.001)-0.001(0.001)-0.001(0.001)0.000(0.000)0.000(0.001)-0.000(0.001)0.000(0.001)0.000(0.001)0.001(0.001)0.000(0.001)X
X-0.002(0.001)-0.002(0.001)-0.002*(0.001)-0.001(0.001)-0.001(0.001)0.000(0.000)0.000(0.001)-0.000(0.001)0.000(0.001)0.000(0.001)0.001(0.001)0.000(0.001)
X

96543
0.001

94889
0.256

94889
0.287

X
96543
0.192

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the 2010 locality level

26

The results for the parsimonious model show that the difference-in-difference estimates
capturing the impact of refugees (the coefficient on the interaction) on being out of the labor force
are all small, mixed in sign over time, and insignificant for all the years following the influx of the
Syrian refugees. This conclusion does not change even when controlling for individual
characteristics, locality fixed effects, or individual fixed effects.
The main advantage of using the retrospective data is that it can test the parallel trend
assumption for the years preceding the refugee shock. We undertake a joint test of the 2004-2009
interactions as a test of parallel trends. The joint test is insignificant for every outcome except
formality. Thus, with one exception, there are parallel trends. For men, formality was higher but
falling prior to the influx in areas that received a larger share of Syrians. Assuming this trend were
to continue, it would bias our results such that we would expect significantly lower formality postinflux in areas that had a larger influx.
Next, we focus on outcomes among the employed and explore the extent to which the
refugee shock has affected employment characteristics. Competition with native workers is
expected to be strongest in the informal sector. To test this hypothesis, we first estimate a model
for being employed in the formal sector. The results for men are reported in Table 2 and show no
significant effect (despite the potential non-parallel trend biasing towards a negative effect should
the trend continue). Nor is there a change in being in a managerial or professional occupation.

27

Table 2. Job formality and occupation (linear probability model), employed men, retrospective data, 2004-2017(1)
Formal(2)-0.003(0.002)-0.003(0.002)-0.038*(0.016)-0.032*(0.015)-0.032*(0.014)-0.031*(0.012)-0.018(0.011)-0.017*(0.008)0.003(0.005)0.010(0.007)0.010(0.009)0.019*(0.010)0.014(0.011)
0.012(0.014)0.015(0.014)-0.033(0.017)-0.028(0.016)-0.027(0.015)-0.026*(0.013)-0.010(0.011)-0.010(0.008)-0.003(0.005)0.005(0.007)0.006(0.009)0.017(0.010)0.010(0.011)0.010(0.014)0.017(0.015)-0.020(0.016)-0.018(0.015)-0.021(0.014)-0.023(0.013)-0.008(0.011)-0.012(0.008)-0.003(0.005)0.002(0.007)0.002(0.009)0.011(0.010)0.004(0.011)0.004(0.014)0.010(0.014)Int. year and % HH Syr.
Int. 2004 and % HH Syr. 0.000(0.001)
Int. 2005 and % HH Syr. -0.000-0.000(0.001)-0.000-0.000(0.001)-0.001

Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2004
2005
2006
2007
2008
2009
2011
2012
2013
2014
2015
2016
2017

(3)

(4)

(5)
(6)
(7)
Managerial/Professional Occupation

(8)

0.002
(0.001)0.001(0.001)-0.054***(0.011)-0.044***(0.010)-0.041***(0.009)-0.036***(0.008)-0.023***(0.005)-0.012**(0.004)0.004(0.002)0.014**(0.004)0.021***(0.006)0.032***(0.008)0.036***(0.009)0.046***(0.011)
0.051***(0.013)-0.033*(0.014)-0.025(0.013)-0.023(0.012)-0.021(0.011)-0.023*(0.010)-0.015(0.008)0.015**(0.006)0.021*(0.009)0.020(0.010)0.024*(0.011)0.028*(0.013)0.026*(0.012)0.024(0.012)0.001(0.008)0.004(0.007)0.007(0.006)0.003(0.005)0.001(0.006)0.000(0.004)0.004(0.003)0.003(0.006)0.004(0.007)0.005(0.007)0.006(0.012)0.000(0.012)-0.003(0.012)-0.001(0.009)0.003(0.008)0.007(0.007)0.002(0.006)-0.001(0.006)-0.001(0.004)0.006*(0.003)0.006(0.006)0.008(0.007)0.009(0.008)0.011(0.012)0.005(0.012)0.003(0.011)-0.002(0.007)-0.002(0.006)-0.001(0.005)0.000(0.004)0.000(0.003)0.001(0.001)0.001(0.001)
0.004(0.003)0.005(0.003)0.008(0.004)0.008(0.005)0.010(0.006)0.011(0.007)0.001*(0.001)
0.001

0.001
(0.001)0.001-0.000(0.000)-0.0000.000(0.001)-0.000-0.000(0.000)-0.00028

Int. 2006 and % HH Syr.
Int. 2007 and % HH Syr.
Int. 2008 and % HH Syr.
Int. 2009 and % HH Syr.
Int. 2011 and % HH Syr.
Int. 2012 and % HH Syr.
Int. 2013 and % HH Syr.
Int. 2014 and % HH Syr.
Int. 2015 and % HH Syr.
Int. 2016 and % HH Syr.
Int. 2017 and % HH Syr.
Controls
Locality FE
Individual FE
N (Person-Year Obs.)
R-sq.

(1)
Formal(0.001)0.000(0.001)0.001(0.001)0.001(0.001)0.001(0.001)-0.000(0.000)-0.000(0.001)-0.000(0.001)-0.000(0.001)-0.000(0.001)0.000(0.001)-0.000(0.001)

(2)

(3)

(4)

(0.001)0.000(0.001)0.001(0.001)0.000(0.001)0.001(0.001)0.000(0.000)0.000(0.001)0.000(0.001)-0.000(0.001)0.000(0.001)0.001(0.001)0.000(0.001)X(0.001)0.000(0.001)0.001(0.001)0.000(0.001)0.001(0.001)0.000(0.000)0.000(0.001)0.000(0.001)0.000(0.001)0.000(0.001)0.001(0.001)0.001(0.001)X
X(0.001)0.001**(0.001)0.001(0.001)0.001*(0.000)0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)
X

51123
0.002

50449
0.099

50449
0.209

X
51123
0.006

(5)
(6)
(7)
Managerial/Professional Occupation(0.001)(0.000)(0.001)0.000-0.001-0.000(0.001)(0.000)(0.000)
0.001
0.000
0.000
(0.001)(0.000)(0.000)
0.001
0.000
0.000
(0.001)(0.000)(0.000)0.001-0.0000.000(0.001)(0.000)(0.000)-0.000-0.000-0.000(0.000)(0.000)(0.000)-0.001-0.000-0.000(0.001)(0.000)(0.000)-0.0010.000-0.000(0.001)(0.000)(0.000)-0.0010.000-0.000(0.001)(0.000)(0.000)-0.001-0.000-0.001(0.001)(0.001)(0.001)-0.001-0.000-0.000(0.001)(0.001)(0.001)-0.001-0.000-0.000(0.001)(0.001)(0.001)
X
X
X
50732
0.003

50065
0.555

50065
0.588

(8)
(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)
X
X
50732
0.001

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the locality level29

We then examine whether the refugee shock affected the type of economic activity or sector(Table 3). Specifically, we estimate the probability of being employed in the open sector, where
refugees can get work permits, the health and human services sector, where aid flows and humanservice needs might be creating jobs, as well as the private sector, where refugees could potentially(legally) compete. The results show no effect on the open sector or health and human services
sector in our preferred specification. In our preferred specification there was a significant and,counter-intuitively, positive effect of local refugees on the probability of private sector
employment in 2012 and 2013, which disappears in later years. It may be that the influx of refugees
initially provided private sector employment opportunities, for example, in retail trades, whichthen diminished as refugees settled or were offset by subsequent increases in the public sector todeliver services.

30

Table 3. Job sector (linear probability model), employed men, retrospective data, 2004-2017

Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2004
2005
2006
2007
2008
2009
2011
2012
2013
2014
2015
2016
2017
Int. year and % HH Syr.
Int. 2004 and % HH Syr.
Int. 2005 and % HH Syr.

(1)

(2)

(3)
Open Sector-0.001(0.002)-0.000(0.002)0.010(0.011)0.007(0.011)0.006(0.010)0.010(0.009)0.006(0.009)0.007(0.007)-0.002(0.005)-0.008(0.006)-0.012(0.008)-0.010(0.009)-0.011(0.010)0.004(0.013)0.004(0.012)0.007(0.011)
0.005(0.011)
0.003(0.010)0.007(0.009)0.001(0.009)0.003(0.007)0.001(0.004)-0.004(0.005)-0.010(0.008)-0.009(0.009)-0.009(0.010)0.003(0.014)0.001(0.014)0.008(0.010)0.007(0.009)0.008(0.010)0.011(0.009)0.006(0.009)0.006(0.006)-0.002(0.004)-0.003(0.005)-0.006(0.008)-0.007(0.009)-0.006(0.010)0.004(0.013)0.002(0.014)-0.000(0.001)
0.000

0.000
(0.001)0.000-0.000(0.001)-0.000

(4)

(5)

(6)
(7)
(8)
Health and Human Services

(9)

(10)

(11)
(12)
Private Sector

0.000(0.003)0.000(0.002)0.002(0.001)0.001(0.001)0.026*(0.010)0.018(0.010)0.015(0.008)0.010(0.005)0.009*(0.004)0.003(0.003)-0.003(0.003)-0.009*(0.004)-0.016**(0.006)-0.018*(0.007)-0.021*(0.008)-0.027**(0.010)-0.031**(0.011)-0.003(0.009)-0.003(0.009)-0.005(0.008)-0.011(0.007)-0.008(0.007)-0.012*(0.005)0.005(0.003)0.008(0.004)0.008(0.005)0.009(0.007)0.006(0.008)0.003(0.008)0.003(0.008)0.008(0.009)0.006(0.008)0.003(0.007)-0.004(0.006)-0.001(0.007)-0.008(0.004)0.001(0.003)0.002(0.004)0.003(0.005)0.004(0.007)0.000(0.008)-0.002(0.008)-0.003(0.009)0.016(0.010)0.013(0.009)0.009(0.009)0.000(0.007)0.001(0.007)-0.005(0.004)0.000(0.003)-0.001(0.004)-0.002(0.005)-0.001(0.007)-0.005(0.009)-0.009(0.009)-0.011(0.009)-0.001(0.007)-0.002(0.006)-0.000(0.006)-0.000(0.004)0.001(0.003)-0.000(0.001)0.001(0.001)
0.003(0.002)0.004(0.003)0.004(0.006)0.005(0.007)0.006(0.007)0.007(0.008)-0.017(0.016)-0.012(0.015)-0.012(0.014)-0.008(0.011)-0.011(0.010)-0.006(0.008)-0.000(0.005)-0.007(0.009)-0.017(0.011)-0.010(0.013)-0.001(0.015)0.018(0.016)0.017(0.017)-0.004(0.015)-0.002(0.013)-0.003(0.013)-0.001(0.011)-0.010(0.010)-0.007(0.007)-0.001(0.006)-0.011(0.009)-0.025*(0.011)-0.024(0.012)-0.017(0.015)-0.006(0.017)-0.015(0.017)-0.019(0.015)-0.014(0.014)-0.007(0.011)-0.003(0.012)-0.009(0.011)-0.003(0.006)-0.000(0.004)-0.001(0.006)-0.008(0.008)-0.006(0.010)0.005(0.011)
0.015(0.013)0.011(0.013)0.022*(0.010)0.020*(0.009)0.017*(0.008)0.012(0.006)0.006(0.004)0.001(0.003)-0.006**(0.002)-0.011**(0.003)-0.019***(0.005)-0.018**(0.006)-0.020**(0.008)-0.025**(0.009)-0.030**(0.010)0.000(0.000)0.001-0.000(0.001)-0.000-0.001(0.001)-0.001-0.001(0.001)-0.001-0.000(0.000)-0.0000.003*(0.001)0.002*

0.003*(0.001)0.002*

0.002*(0.001)0.002*

0.000(0.001)
0.000

31

(1)

Int. 2006 and % HH Syr.
Int. 2007 and % HH Syr.
Int. 2008 and % HH Syr.
Int. 2009 and % HH Syr.
Int. 2011 and % HH Syr.
Int. 2012 and % HH Syr.
Int. 2013 and % HH Syr.
Int. 2014 and % HH Syr.
Int. 2015 and % HH Syr.
Int. 2016 and % HH Syr.
Int. 2017 and % HH Syr.
Controls
Locality FE
Individual FE
N (Person-Year Obs.)
R-sq.

(2)

(0.001)-0.000(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.000(0.000)-0.000(0.000)0.001(0.001)0.000(0.001)0.001(0.001)0.000(0.001)0.000(0.001)(3)
Open Sector(0.001) (0.001)-0.000-0.001(0.001) (0.001)-0.001-0.001(0.001) (0.001)-0.001-0.001(0.001) (0.001)-0.001-0.001(0.001) (0.001)-0.0000.000(0.000) (0.000)-0.000-0.000(0.000) (0.000)
0.000
0.000
(0.001) (0.000)-0.0000.000(0.000) (0.000)
0.000
0.000
(0.001) (0.001)-0.0000.000(0.001) (0.001)
0.000
0.000
(0.001) (0.001)
X
X
X

50813
0.000

50144
0.068

50144
0.189

(4)
(0.000)0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)
X
X
50813
0.003

(5)

(6)
(7)
(8)
Health and Human Services(0.001) (0.001) (0.001) (0.000)-0.000-0.001-0.001-0.000(0.001) (0.001) (0.001) (0.000)
0.000
0.000
-0.000-0.000(0.001) (0.000) (0.000) (0.000)
0.000-0.000-0.000-0.000(0.000) (0.000) (0.000) (0.000)
0.000
0.000
0.000
-0.000(0.000) (0.000) (0.000) (0.000)-0.000
0.000
0.000
0.000
(0.000) (0.000) (0.000) (0.000)-0.001* -0.000-0.000-0.000(0.000) (0.000) (0.000) (0.000)-0.001-0.000-0.0000.000(0.000) (0.000) (0.000) (0.000)-0.001-0.000-0.0000.000(0.000) (0.001) (0.001) (0.000)-0.001-0.000-0.0000.000(0.001) (0.001) (0.001) (0.000)-0.001-0.000-0.0000.000(0.001) (0.001) (0.001) (0.000)-0.001-0.0000.000
0.000(0.001) (0.001) (0.001) (0.000)
X
X
X
X
X
50813
50144
50144
50813
0.001
0.141
0.225
0.001

(9)
(0.001)
0.002(0.001)0.001(0.001)0.001(0.001)0.000(0.001)0.000(0.000)0.000(0.001)0.001(0.001)0.001(0.001)0.001(0.001)0.000(0.001)0.000(0.001)

51126
0.001

(10)

(11)
(12)
Private Sector(0.001) (0.001) (0.001)
0.002
0.001
0.000
(0.001) (0.001) (0.001)
0.001
0.000
0.000
(0.001) (0.001) (0.001)
0.001
0.001
0.000
(0.001) (0.001) (0.000)
0.000
0.000
-0.000(0.001) (0.000) (0.000)-0.0000.000
0.000(0.000) (0.000) (0.000)
0.000
0.000
0.000*(0.001) (0.001) (0.000)
0.001
0.001
0.001**(0.001) (0.001) (0.000)
0.001
0.001
0.000
(0.001) (0.001) (0.000)
0.001
0.000
0.000
(0.001) (0.001) (0.000)-0.000-0.0000.000(0.001) (0.001) (0.000)
0.000-0.0000.000(0.001) (0.001) (0.000)
X
X
X
X
X
50452
50452
51126
0.092
0.337
0.006

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the locality level

32

4.4

Panel models

One limitation of utilizing retrospective data is that the data lacks variables on some important
outcomes including wages 27 and hours worked. In addition, utilizing retrospective data might
produce biased or noisy estimates due to recall challenges. In this section we re-examine the impact
of Syrian refugees using the panel data for 2010 and 2016. In this model, we control for individual
fixed effects and use only observations for the individuals in both the 2010 and 2016 waves. Since
we control for individual fixed effects, the model only includes the 2016 year dummy (2010 is thereference year) and the share of refugees-2016 interaction variable (as well as controls for age).
The main limitation of using this model is that it does not allow us to investigate how the effectvaries over time as it utilizes only two points in time. In addition, we lack historical data, prior to2010, to re-test the parallel trend assumption.
Table 4 documents the panel results. The estimates of the impact of refugees are
statistically insignificant for employment and unemployment. However, the estimates are
significant for job formality, which increases with a larger shock. For each percentage point
increase in the share of the locality that is Syrian, the probability of formal employment increases
by 0.3 percentage points. 28 In addition to the positive effect on formality, in the panel models we
see a small but significant and positive effect of the shock on hourly wages; a percentage point
increase in the share Syrian leads to 0.9% higher wages in 2016. However, because hours have27

Around 86% of employed Jordanians were wage workers in 2016. Given the limited numberof non-wage workers we do not analyze them separately.
28
When estimating unconditional on employment (i.e. treating the non-employed as zeros) andestimating the probability of informal and formal employment, there are negative andinsignificant effects of the refugee shock on informal employment and positive and insignificant
effects of the refugee shock on formal employment.
33

(insignificantly) decreased, the monthly wage effect is positive but smaller and insignificant. There
are not significant occupation effects. Although the open sector and health and human services
sector effects are insignificant, the private sector effect is negative and significant; those who
experienced a greater refugee influx locally are less likely to work in the private sector (andtherefore more likely to work in the public sector). 29 Overall, these results suggest no negative
effects, but some potential shifts, particularly in terms of sector of work.

29

As with the shift in formality, when estimating unconditional on employment, there is anegative but insignificant change in private sector employment and a positive but insignificant
increase in public sector employment.
34

Table 4. Labor market outcomes (fixed effects linear probability and OLS models), men, panel data
Managerial/
perLn (monthlyProfessional
Health andPrivate
wage)
Occupation Open sector Human Serv. sector

Unemployed Employed

FormalLn (hourlyHours
wage)
week

0.033
(0.038)-0.083(0.049)0.153***(0.036)

0.337***(0.098)-2.592(1.783)0.131(0.126)0.024(0.023)-0.011(0.032)-0.004(0.018)-0.006(0.033)Int. year and % HH Syrian
Int. 2016 and % HH Syr. 0.001(0.001)
7363
N

0.001
(0.002)7394

0.003*(0.001)4786

0.009*(0.004)3863-0.135(0.121)4677

0.003(0.004)3924

0.001(0.001)4788

0.002(0.001)4789-0.001(0.001)4789-0.002*(0.001)
4808

Year (2010 omit.)
2016

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the locality level35

5Sensitivity Analyses
This section discusses a series of sensitivity analyses, starting with subgroup analyses by

education, sector, and location. These are followed by models assessing potentially differential
impacts on new entrants. Subsequently, instrumental variable models are discussed that canaccount for the potentially endogenous placement of refugees. We then test the sensitivity of ourresults to the definition of a labor market. Lastly, we present results on the potential role of aid,
government spending, and demand for services (specifically education) as a mechanism for the net
neutral impacts.

5.1

Sub-group analyses
Syrian refugees may be disproportionately competing with less educated Jordanians, thosein the private sector, and youth. Using the panel data and 2010 employment characteristics, inAppendix E we re-estimate our models distinguishing between the less and more educated subgroups. The results show no negative and significant results for the less educated. We undertake asimilar exercise in Appendix F for those employed in the private versus public sector in 2010.
There are not significant negative effects (and there are even some small positive effects) in theprivate sector. Appendix G investigates whether the result of non-effects may be driven by thecapital governorate, Amman, compared to elsewhere. There are consistently no significant
negative effects in Amman or other areas. Appendix H focuses on new entrants and does not findany significant negative effects. Appendix I estimates discrete time hazard models to assess
whether new entrants might be delayed in finding employment when they experience a higher
intensity refugee influx. There are not significant effects. Overall, the less educated, private sector,
and youth populations were not disproportionately affected by the refugee influx.

36

5.2

Instrumental variable models for endogenous placement of refugees
Since the majority of refugees do not live in camps, they have some choice in where they

locate and may select into areas with differential employment outcomes. The main effects of the
refugee influx in 2010 in our models generally suggest refugees located in places that werecomparable at that time, and with one exception the retrospective tests suggested parallel trends
hold. To analyze the sensitivity of our results to this issue, we instrument for the locality share of
refugees using, first, the percentage of Syrians and Egyptians from the 2004 census, and, second,the distance from the locality to Za’atari camp in kilometers. The models include the same controls,
except we cannot include locality-level fixed effects, since our instrument is at the locality level.
As a compromise between the tradeoffs between statistical power and controlling for important
geographic differences, we include district-level fixed effects to identify off of locality variation
within districts in the share of households that are Syrian. The details of the instruments and results
are presented in Appendix J. After accounting for the potential selection of refugees, there are notsignificant effects of refugee density on the local labor market.

5.3

Sensitivity of results to definition of a local labor market
As a final check on our results, we re-estimated our panel models with different definitionsof local labor markets. We calculated the share of households Syrian at the sub-district and districtlevels and used these in the place of locality. The results are presented in Appendix K. Generally,
the less-aggregated effects are greater in magnitude and more likely to be significant. They suggestthat our main model estimates are identifying the appropriate local labor market, as the results are
similar but more likely insignificant at higher levels of aggregation.

37

5.4

Education as a potential labor demand mechanism
While the Syrian influx could create a labor supply shock, demand for goods and services

from refugees, as well as foreign aid and government spending on services, could potentially create
additional labor demand, offsetting the labor supply shock. Such a situation is consistent with theoverall net neutral results in our models. Appendix L specifically explores potential labor demand
created through the education sector, from building new schools or adding shifts to existing schools.
The results suggest that such additions do increase employment in health and human services,consistent with the time trend results in Appendix A. Although we are not able to estimate a fully
parameterized model distinguishing labor demand and labor supply effects, the results aresuggestive of labor demand mechanisms.

6

Conclusion
This paper examines the impact of a refugee influx on natives’ labor market outcomes.

Using individual level panel data from Jordan before and after the Syrian refugee influx, we
estimated a variety of difference-in-difference models to study the effects of local labor market
shocks on natives’ labor market outcomes. An important strength of the analysis, relative to theliterature, is the potential in the individual level panel and retrospective data to both control for
individual fixed effects and test for parallel trends.
We found that Jordanians living in areas with high concentrations of refugees had no worse
labor market outcomes than Jordanians with less exposure to the refugee influx. This result held
across unemployment, employment, characteristics of employment (formality, occupation, open
sector, health and human services sector, private sector), hours, and wages. Employment andunemployment consistently showed no effects. The panel data results indicated a significant

38

increase in formality, hourly (but not monthly) wages, and a shift from the private to public sector
for workers with more exposure to the refugee influx.
Our findings contrast with most of the very recent literature on the impact of the Syrian
refugee influx, which to date had been limited to evidence from Turkey. Studies in Turkey tended
to find negative employment and wage effects, especially on low-skilled Turkish natives (Bagir
2017; Ceritoglu et al. 2017; Del Carpio and Wagner 2015; Tumen 2016), although other studies
suggested such results were estimation driven or transitory (Akgündüz, van den Berg, and Hassink
2015; Cengiz and Tekguc 2017). The global literature generally finds a similar mix of no or small
specific negative effects (Card 1990; Maystadt and Verwimp 2014; Peri 2016; Ruiz and VargasSilva 2016).
There are several channels that may have ameliorated any potentially negative impact ofthe massive influx of Syrian refugees. First, it is important to note that the composition of Syrians
in Jordan might have played an important role. The Syrians in Jordan are very young (Krafft et al.
2018). Almost half are under the age of 15. A quarter (23%) of Syrian refugee households in Jordan
have a female head (compared to 14% of Jordanian households). As a result, only 23% of Syrian
refugees—45% of men and 4% of women— are in the labor force. Only 38% of men and 3% of
women are employed (Krafft et al. 2018).
Furthermore, in February 2016, the Jordan Compact was signed with the aim of easing
Syrian refugees’ access to work permits and formalizing their work. The aim of this new system
was to create 200,000 new permits. However, the take-up of work permits by Syrians has beenvery low. According to the Ministry of Labor, by the end of 2017 only 87,141 work permits to
Syrians were issued (Ministry of Labour Syrian Refugee Unit 2018). Thus, few Syrians arecompeting in the (formal) labor market, making their labor supply effect relatively small.

39

In addition, despite the massive inflow of Syrian refugees, the number of non-Syrian
immigrants has not decreased in Jordan during the same period. According to the 2015 Population
Census of Jordan, Jordan hosted an additional 1.6 million non-Syrian foreigners (Department of
Statistics (Jordan) 2015a). Even before the refugee influx, economic immigrants in Jordan havetended to be on average low-skilled relative to Jordanians and many occupations in the Jordanianlabor market are reserved for Jordanian citizens alone, in particular in the public sector. Economic
immigrants are confined to the private sector and mostly in informal employment with no job
contract (Wahba 2014). As a result of this labor segmentation, Syrians mainly compete witheconomic immigrants in the informal sector. This limits the competition between refugees andJordanians. Indeed, Malaeb and Wahba (2018) controlling for the sorting of Syrians and economic
immigrants, found that economic immigrants were more likely to be pushed into informal
employment and worked fewer hours as a result of the influx of refugees.
The inflow of foreign aid has also been a potential mechanism for creating labor demand
among Jordanians. To help address the needs of the Syrian refugees, Jordan has received foreign
aid from multiple sources, including country donors and UN agencies. In 2016, for example,
Jordan received $1.65 billion in assistance towards the Jordan Response Plan for the Syria Crisis(Ministry of Planning and International Cooperation 2017). Net Official Aid (ODA) received as ashare of GNI has increased from 3.6% in 2010, before the Syrian conflict, to 7.3% in 2017 (WorldBank 2019). UN agencies directly provide aid to Syrian refugees in the forms of service delivery
or aid projects through nongovernmental organizations. For example, as of August 2016 the World
Food Program provided food vouchers of equivalent to $28.2 per refugee per month for 95
thousand refugees in camps and food vouchers equivalent to $14.1 per refugee per month to 425
thousand refugees in host communities (World Food Programme 2016). Aid is also channeled to40

the Jordanian government, to help offset the budget deficit, finance public projects, and support
country systems and services nationwide. For example, with assistance from partners, theJordanian Ministry of Education set up an Education Management Information System and a GIS
school mapping tool to aid in infrastructure planning (Ministry of Planning and International
Cooperation 2017). Both direct assistance to refugees and aid to the government can create labordemand, the latter disproportionately in the government and health and human services sectors.
The Jordan Compact, which included provisions to reform Jordan’s labor policy to grant
Syrian refugees formal work permits, also included aid and trade concessions. Specifically, the EUcommitted to enhance access of Jordanian exports to EU countries, temporarily relaxing the rulesof origin, potentially creating labor demand in tradeables (European Commission 2016).
The increase in demand for public services, in particular education and health, has resulted
in the Jordanian government increasing the provision of those services, which in turn increased
the demand for workers (almost exclusively Jordanians) in those sectors. For example, 209 schools
added double shifts in host communities and 45 schools were established in refugee camps,
employing Jordanian teachers (Ministry of Planning and International Cooperation 2017). Indeed,as shown in Appendix L, the percentage of new schools/new shifts built and shifts added for
Syrians is associated with a rise in health and human services employment.
Although we are not directly evaluating the impact of providing legal work opportunities
to refugees, our results do suggest that such a policy may be compatible with maintaining work
opportunities for natives as well. The inflow of foreign aid to Jordan to assist with some of the
refugees’ needs, as well as the conditions of the Jordan Compact, which included aid and tradeconcessions as well as support for Jordanians’ employment, may have played an important role increating labor demand for Jordanians. Further research to better understand the impact of the41

Jordan Compact and work permits will be important to inform the planned Global Compact on
Refugees. Specifically, such research could inform the importance of aid and other support forhost countries as well as how to include refugees in local labor markets (UNHCR 2018b).

42

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———. 2018b. “Labor Market Panel Surveys (LMPS). Version 1.1 of Licensed Data Files;
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Peri, Giovanni. 2016. “Immigrants, Productivity, and Labor Markets.” Journal of Economic
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45

ONLINE APPENDICES

Appendix A: Outcomes comparing 2010 and 2016 ........................................................................ 2
Appendix B: Pooling retrospective data ......................................................................................... 5
Appendix C: Results (retrospective and panel) for women .......................................................... 12
Appendix D: Cross-sectional model results.................................................................................. 20
Appendix E: Education: Sub-group analyses ............................................................................... 28
Appendix F: Sector: Sub-group analyses...................................................................................... 31
Appendix G: Amman versus elsewhere: Sub-group analyses ...................................................... 34
Appendix H: Entrants: Sub-Group Analyses ................................................................................ 37
Appendix I: Entrants: School-to-work transitions ........................................................................ 41
Appendix J: Instrumenting for potential endogenous placement of refugees............................... 45
Appendix K: Varying levels of geographic aggregation .............................................................. 56
Appendix L: New schools and labor demand in education .......................................................... 59
Appendices References ................................................................................................................. 621

Appendix A: Outcomes comparing 2010 and 2016

Comparing labor outcomes for Jordanians in 2010 to 2016 can illustrate national trends,
which may be linked to perceptions of the impact of refugees, as well as potential mechanisms
for impact. Figure 1, for outcomes in percentages or hours, and Figure 2, for log wages, present
how national outcomes have changed over time, by sex. The unemployment ratio (percentage ofthe population unemployed, distinct from the unemployment rate) rose (from 5% to 7%) while
the employment rate fell even further (from 39% to 33%), such that labor force participation (notshown) dropped as well. Among the employed, there are important changes over time thatsuggest possible mechanisms for the effects of the Syrian influx. Formality increased from 65%
to 66%, the share of workers in managerial or professional activities rose from 29% to 33%, theshare of workers in competitive activities fell from 29% to 27%, while the share of workers inhealth and human service activities rose from 17% to 19% . The private sector shrank from 61%
to 57% of employment (such that public sector employment rose from 39% to 43% of
employment). Hours per week fell slightly, from a mean of 46 to 43, but since hourly wages rose
10%, monthly wages still rose by 5%. These patterns generally held for both men and women.

2Figure 1. Outcomes in percentages or mean hours in 2010 and 2016, by sex, Jordanians
ages 15-64

Source: Authors’ calculations based on JLMPS 2010 and 2016

3

Figure 2. Log hourly and monthly real wages in 2010 and 2016, by sex, Jordanians ages 1564

Source: Authors’ calculations based on JLMPS 2010 and 20164

Appendix B: Pooling retrospective data

In the retrospective models, we estimate separately the interaction between the Syrian
influx and each year, in part because the Syrians arrived over a number of years, and in part
because the effects might vary over time. However, this estimation approach may underpower
our results. Therefore, in this appendix, we pool “pre-2011” and “2011+” in estimating thecentral interaction term. The results are presented in Table 1, Table 2, and Table 3. In our
preferred specification with individual fixed effects there is only one significant result; anegative effect on the probability of formality in areas that experienced a great influx. However,looking at the year-disaggregated results, this appears to be driven by higher formality in theseareas in 2004-2008 (not 2009 or 2010). We therefore prefer our non-pooled results that allow
separation of effects and comparison with 2010, immediately pre-influx.

5Table 1. Pooling 2011+: Labor market status (linear probability model), men, retrospective data, 2004-2017
Unemployed
Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2004
2005
2006
2007
2008
2009
2011
2012
2013
2014
2015
2016
2017

0.000
(0.001)0.000(0.001)0.007(0.007)0.009(0.006)0.006(0.005)0.003(0.004)0.006(0.004)0.007*(0.003)0.011(0.008)0.011(0.009)0.016(0.010)0.019*(0.010)0.019(0.010)0.013(0.010)0.012(0.010)0.005(0.007)0.008(0.006)0.004(0.005)0.001(0.004)0.005(0.005)0.006*(0.003)0.010(0.008)0.010(0.009)0.016(0.010)0.020*(0.010)0.020(0.010)0.015(0.010)0.015(0.010)0.005(0.007)0.008(0.006)0.004(0.005)0.001(0.004)0.005(0.005)0.006(0.003)0.012(0.008)0.011(0.009)0.017(0.010)0.021*(0.010)0.022*(0.010)0.017(0.010)0.017(0.010)

Employed

0.014(0.016)0.014(0.014)0.009(0.012)0.005(0.009)0.007(0.007)0.008(0.004)0.009(0.008)0.008(0.010)0.015(0.012)0.021(0.013)0.024(0.015)0.021(0.017)0.022(0.019)-0.001(0.001)-0.001(0.001)-0.007(0.012)-0.007(0.011)-0.007(0.009)0.001(0.007)-0.003(0.005)-0.008(0.005)-0.013(0.016)-0.018(0.017)-0.011(0.018)-0.008(0.018)-0.002(0.019)0.007(0.019)0.007(0.019)-0.017(0.010)-0.016(0.009)-0.013(0.008)-0.004(0.006)-0.007(0.005)-0.010*(0.004)-0.013(0.013)-0.015(0.014)-0.010(0.014)-0.009(0.014)-0.004(0.014)0.003(0.015)-0.012(0.015)-0.018(0.010)-0.018(0.009)-0.013(0.008)-0.004(0.007)-0.007(0.005)-0.010*(0.004)-0.013(0.013)-0.015(0.014)-0.009(0.014)-0.009(0.014)-0.003(0.014)0.004(0.015)-0.011(0.015)-0.606***(0.021)-0.509***(0.017)-0.409***(0.013)-0.302***(0.011)-0.206***(0.008)-0.110***(0.005)0.092***(0.013)0.188***(0.016)0.293***(0.018)0.394***(0.019)0.498***(0.022)0.603***(0.025)0.684***(0.027)

6

Int. 2011+ and % HH Syr.
Int. 2011+ and % HH Syr.
Controls
Locality FE
Individual FE
N (Person-Year Obs.)
R-sq.

-0.001(0.001)-0.001(0.001)X-0.001(0.001)
X
X

96543
0.000

94889
0.022

94889
0.065

-0.001(0.001)
X
X
96543
0.002

0.002
(0.001)

0.002(0.001)
X

0.002(0.001)
X
X

96543
0.001

94889
0.256

94889
0.286

0.001
(0.001)
X
X
96543
0.192

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the locality level7

Table 2. Pooling 2011+: Job formality and occupation (linear probability model), employed men, retrospective data, 20042017
FormalPercentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2004
2005
2006
2007
2008
2009
2011
2012
2013
2014
2015
2016
2017
Int. 2011+ and % HH Syr.
Int. 2011+ and % HH Syr.

-0.002(0.002)-0.003(0.002)-0.038***(0.010)-0.033***(0.009)-0.028***(0.008)-0.018*(0.007)-0.010(0.006)-0.006(0.006)0.007(0.010)0.016(0.010)0.015(0.010)0.021*(0.010)0.018(0.011)0.020(0.012)0.021(0.012)-0.036***(0.010)-0.031***(0.009)-0.024**(0.008)-0.015*(0.007)-0.005(0.006)-0.002(0.006)-0.001(0.010)0.009(0.010)0.009(0.010)0.016(0.011)0.013(0.012)0.016(0.012)0.022(0.013)-0.025*(0.010)-0.023**(0.009)-0.019*(0.007)-0.013*(0.006)-0.004(0.006)-0.004(0.006)-0.003(0.009)0.003(0.009)0.004(0.010)0.010(0.010)0.006(0.011)0.009(0.012)0.014(0.012)-0.001

0.000

0.000

Managerial/Professional Occupation.
0.003*(0.001)0.001(0.001)-0.039***(0.010)-0.033***(0.009)-0.027***(0.007)-0.023***(0.006)-0.015**(0.005)-0.007(0.004)0.015**(0.005)0.022***(0.006)0.029***(0.007)0.040***(0.009)0.047***(0.010)0.054***(0.011)0.060***(0.013)-0.022*(0.009)-0.018*(0.009)-0.019*(0.008)-0.012*(0.006)-0.012*(0.005)-0.007(0.004)0.026**(0.009)0.027**(0.010)0.029**(0.011)0.031**(0.011)0.031**(0.012)0.032**(0.012)0.029*(0.012)-0.002(0.006)-0.001(0.005)0.001(0.004)0.004(0.003)0.002(0.003)-0.000(0.003)0.002(0.008)0.001(0.009)0.005(0.009)0.006(0.009)0.001(0.011)-0.000(0.011)-0.003(0.011)-0.001(0.006)0.000(0.005)0.003(0.004)0.004(0.003)0.001(0.004)-0.001(0.003)0.006(0.008)0.006(0.009)0.009(0.009)0.011(0.009)0.007(0.011)
0.005(0.010)0.003(0.011)-0.004(0.006)-0.004(0.005)-0.003(0.005)-0.001(0.004)-0.001(0.003)-0.001(0.001)
0.002(0.002)0.003(0.003)0.004(0.004)0.006(0.004)0.007(0.005)0.008(0.006)0.008(0.007)-0.001*-0.002*0.000-0.000-0.0008

Controls
Locality FE
Individual FE
N (Person-Year Obs.)
R-sq.

(0.001)(0.001)X(0.001)
X
X

51123
0.002

50449
0.099

50449
0.209

(0.000)
X
X
51123
0.006

(0.001)(0.001)X(0.001)
X
X

50732
0.003

50065
0.555

50065
0.588

(0.000)
X
X
50732
0.001

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the locality level9Table 3. Pooling 2011+: Job sector (linear probability model), employed men, retrospective data 2004-2017
Open Sector
Percentage HH Syrian
Percentage of HH
Syr.
-0.001(0.002)
Year (2010 omit.)
2004
0.009
(0.006)2005
0.009(0.006)2006
0.003(0.006)2007-0.001(0.005)2008-0.005(0.006)2009-0.004(0.005)2011-0.011(0.009)2012-0.017(0.010)2013-0.014(0.011)
2014-0.016(0.011)2015-0.015(0.011)
2016-0.002(0.012)2017-0.000(0.012)Int. 2011+ and % HH
Syr.

Health and Human Services-0.001(0.002)0.010(0.007)0.010(0.006)0.003(0.006)-0.002(0.006)-0.008(0.006)-0.006(0.005)-0.004(0.009)-0.011(0.009)-0.010(0.010)-0.013(0.011)-0.011(0.011)-0.000(0.013)-0.001(0.014)0.005(0.006)0.006(0.005)0.002(0.005)-0.001(0.005)-0.007(0.006)-0.004(0.004)-0.009(0.009)-0.013(0.009)-0.012(0.010)-0.015(0.011)-0.011(0.011)-0.002(0.012)-0.001(0.013)0.027**(0.010)0.023*(0.009)0.019*(0.008)0.010*(0.004)0.004(0.003)0.000(0.002)-0.005(0.004)-0.011*(0.005)-0.015*(0.006)-0.019*(0.007)-0.022**(0.009)-0.025*(0.010)-0.029*(0.011)

0.002(0.001)0.001(0.001)-0.007(0.005)-0.006(0.005)-0.009*(0.004)-0.009*(0.004)-0.007(0.004)-0.007*(0.003)0.010(0.005)0.007(0.006)0.006(0.006)0.007(0.006)0.005(0.007)0.003(0.007)0.003(0.007)0.001(0.005)-0.001(0.005)-0.003(0.004)-0.003(0.004)-0.002(0.004)-0.005(0.003)0.002(0.006)-0.000(0.006)-0.001(0.006)0.001(0.006)-0.002(0.007)-0.004(0.007)-0.004(0.007)0.006(0.006)0.004(0.005)0.000(0.005)-0.002(0.004)-0.001(0.004)-0.004(0.003)-0.002(0.006)-0.006(0.007)-0.008(0.006)-0.006(0.007)-0.010(0.008)-0.012(0.008)-0.013(0.008)-0.004(0.005)-0.004(0.005)-0.003(0.005)-0.001(0.004)0.000(0.003)-0.000(0.001)-0.000(0.002)0.001(0.003)0.002(0.004)0.003(0.006)0.004(0.007)0.005(0.008)0.006(0.009)

Private Sector

0.001(0.003)0.001(0.003)0.009(0.010)0.011(0.009)0.005(0.009)-0.002(0.006)-0.001(0.006)-0.004(0.005)0.006(0.010)0.002(0.011)
0.003(0.012)0.007(0.012)0.012(0.012)0.024(0.013)0.023(0.013)0.024*(0.009)0.022*(0.009)0.014(0.008)0.005(0.006)0.000(0.006)-0.005(0.004)0.006(0.010)-0.002(0.010)-0.006(0.010)-0.007(0.011)-0.004(0.012)0.001(0.013)-0.007(0.014)0.004(0.009)0.008(0.009)0.006(0.007)0.001(0.006)-0.001(0.006)-0.002(0.004)0.009(0.007)0.007(0.007)0.006(0.008)0.008(0.008)0.014(0.009)0.020*(0.010)0.017(0.010)0.023**(0.007)0.021***(0.006)0.017***(0.005)0.013***(0.003)0.007**(0.002)0.001(0.001)-0.007(0.005)-0.011(0.006)-0.016*(0.006)-0.017*(0.008)-0.021*(0.009)-0.025*(0.010)-0.030**(0.011)10

Int. 2011+ and % HH
Syr.
0.001
(0.001)
Controls
Locality FE
Individual FE
N (Person-Year Obs.) 50813
R-sq.
0.000

0.000
(0.001)X

0.001(0.001)
X
X

50144
0.068

50144
0.189

0.000
(0.000)
X
X
50813
0.003

-0.001(0.000)-0.000(0.001)X

0.000(0.001)
X
X

50813
0.001

50144
0.141

50144
0.225

0.000
(0.000)
X
X
50813
0.001

-0.000(0.001)-0.001(0.001)X-0.001(0.001)
X
X

51126
0.000

50452
0.092

50452
0.337

0.000
(0.000)
X
X
51126
0.006

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the locality level

11Appendix C: Results (retrospective and panel) for women
In this appendix, we present the retrospective (Table 4, Table 5, and Table 6) and panel (Table 7)
models for women. In the fourth (our preferred) specification with individual fixed effects, theparallel trend assumption always holds in the retrospective models for women and none of the
refugee influx effects are significant. The panel results show a small but significant increase informality for women (as for men) in localities that experienced a greater influx.

12Table 4. Labor market status (linear probability model), women, retrospective data, 2004-2017
(1)

(2)

-0.000(0.001)-0.001(0.001)-0.016*(0.007)-0.012(0.007)-0.012(0.006)-0.010(0.006)-0.003(0.004)-0.002(0.003)0.004(0.003)0.003(0.004)0.006(0.004)0.009(0.006)0.011(0.007)0.010(0.007)0.010(0.007)-0.012(0.007)-0.009(0.007)-0.010(0.006)-0.007(0.005)-0.001(0.004)-0.001(0.003)0.005(0.003)0.006(0.004)0.009*(0.004)0.014*(0.006)0.016*(0.007)0.016*(0.008)0.016*(0.007)-0.011(0.007)-0.008(0.007)-0.009(0.006)-0.007(0.005)-0.002(0.004)-0.002(0.003)0.005(0.003)0.006(0.004)0.008(0.004)0.013*(0.006)0.016*(0.007)0.016*(0.008)0.015*(0.008)Int. year and % HH Syr.
Int. 2004 and % HH Syr. 0.000(0.001)
Int. 2005 and % HH Syr. 0.000

0.000(0.001)-0.0000.000(0.001)
0.000

Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2004
2005
2006
2007
2008
2009
2011
2012
2013
2014
2015
2016
2017

(3)
Unemployed

(4)

(5)

(6)

(7)
Employed(8)-0.000(0.001)-0.000(0.001)-0.105***(0.014)-0.087***(0.013)-0.071***(0.011)-0.054***(0.009)-0.034***(0.006)-0.018***(0.004)0.021***(0.004)0.036***(0.006)
0.055***(0.007)0.075***(0.010)0.093***(0.013)0.108***(0.014)0.121***(0.015)-0.010(0.007)-0.005(0.006)-0.004(0.005)-0.004(0.005)-0.007(0.004)-0.003(0.004)-0.000(0.002)0.004(0.003)0.008*(0.004)0.008(0.005)0.015*(0.006)0.021**(0.007)0.023**(0.008)-0.010(0.007)-0.007(0.006)-0.007(0.005)-0.007(0.005)-0.008(0.004)-0.004(0.004)0.002(0.002)0.010**(0.003)0.015***(0.004)0.019***(0.005)0.026***(0.006)0.035***(0.007)0.035***(0.008)-0.011(0.007)-0.008(0.006)-0.007(0.005)-0.007(0.005)-0.008(0.004)-0.004(0.004)0.002(0.002)0.010**(0.003)0.016***(0.004)0.020***(0.005)0.027***(0.006)
0.036***(0.007)
0.036***(0.007)-0.162***(0.013)-0.133***(0.011)-0.107***(0.008)-0.082***(0.007)-0.058***(0.006)-0.030***(0.004)0.025***(0.003)0.055***(0.005)0.084***(0.008)0.110***(0.010)0.140***(0.013)0.171***(0.015)0.193***(0.017)0.001(0.000)0.000-0.001(0.001)-0.001-0.001(0.001)-0.001-0.001(0.001)-0.001-0.000(0.001)-0.00013

(1)(0.001)
Int. 2006 and % HH Syr. 0.000(0.000)Int. 2007 and % HH Syr. 0.000(0.000)Int. 2008 and % HH Syr. -0.000(0.000)Int. 2009 and % HH Syr. -0.000(0.000)Int. 2011 and % HH Syr. 0.000(0.000)Int. 2012 and % HH Syr. 0.000(0.000)Int. 2013 and % HH Syr. 0.000(0.000)Int. 2014 and % HH Syr. 0.000(0.000)Int. 2015 and % HH Syr. 0.000(0.000)Int. 2016 and % HH Syr. 0.001(0.001)
Int. 2017 and % HH Syr. 0.000(0.001)
Controls
Locality FE
Individual FE
97581
N (Person-Year Obs.)
0.002
R-sq.

(2)
(0.001)0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)-0.000(0.000)0.000(0.001)0.000(0.001)
X

96367
0.066

(3)
Unemployed(0.001)0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)-0.000(0.000)0.000(0.001)0.000(0.001)
X
X
96367
0.117

(4)

(5)

(6)

(0.000)0.000(0.001)0.000(0.000)-0.000(0.000)-0.000(0.000)0.000(0.000)0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.001)0.000(0.001)0.000(0.001)X(0.001)-0.001(0.000)-0.000(0.001)-0.000(0.000)-0.000(0.000)0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.001)-0.000(0.001)(0.001)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.001)-0.000(0.001)
X

97581
0.002

96367
0.127

X
97581
0.027

(7)
Employed(0.001)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.001)-0.000(0.001)
X
X
96367
0.171

(8)
(0.001)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.001(0.000)-0.001(0.000)-0.001(0.000)-0.001(0.001)-0.001(0.001)
X
X
97581
0.054

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the locality level

14

Table 5. Job formality and occupation (linear probability model), employed women, retrospective data, 2004-2017

Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2004
2005
2006
2007
2008
2009
2011
2012
2013
2014
2015
2016
2017

(1)

(2)

0.006*(0.002)0.003(0.002)-0.019(0.027)-0.035(0.020)-0.009(0.024)-0.017(0.024)0.001(0.021)
0.023(0.018)0.002(0.005)0.014(0.012)0.039**(0.014)0.052***(0.015)0.062***(0.018)0.044*(0.019)0.023(0.023)-0.004(0.029)-0.010(0.024)-0.005(0.025)-0.010(0.023)0.012(0.019)0.035*(0.015)0.001(0.004)0.015(0.012)0.038**(0.013)0.049***(0.015)0.062***(0.017)0.041*(0.019)0.017(0.023)-0.010(0.025)-0.011(0.019)-0.007(0.025)-0.015(0.021)
0.003(0.016)0.023*(0.011)-0.001(0.004)-0.000(0.008)0.016(0.011)0.032*(0.013)0.037*(0.015)0.028(0.017)0.004(0.021)

0.002(0.002)0.002

0.003*(0.001)0.002*

Int. year and % HH Syr.
Int. 2004 and % HH Syr. 0.002(0.002)Int. 2005 and % HH Syr. 0.003*(3)
Formal

(4)

(5)

(6)
(7)
(8)
Managerial/Professional Occupation

0.012***(0.003)0.001(0.001)-0.002(0.002)-0.002(0.002)-0.002(0.002)-0.003(0.002)0.001(0.001)0.000(0.000)-0.000(0.000)-0.002(0.002)-0.002(0.002)-0.002(0.002)-0.002(0.002)-0.002(0.003)-0.003(0.003)-0.073(0.045)-0.077(0.045)-0.006(0.034)0.003(0.030)-0.008(0.023)-0.007(0.018)0.004(0.008)0.008(0.018)0.036(0.020)0.061**(0.022)0.060**(0.022)0.074*(0.029)0.080*(0.031)-0.023(0.023)-0.023(0.019)-0.013(0.017)0.002(0.015)0.013(0.011)0.009(0.010)-0.004(0.003)-0.005(0.011)0.011(0.012)0.018(0.013)0.013(0.013)0.021(0.020)0.016(0.021)-0.016(0.020)-0.026(0.017)-0.017(0.014)-0.009(0.011)-0.003(0.009)0.004(0.005)-0.005*(0.003)-0.004(0.007)0.006(0.011)
0.015(0.012)0.006(0.012)0.014(0.017)0.011(0.018)-0.013(0.012)-0.009(0.008)-0.007(0.007)-0.005(0.005)-0.004(0.003)-0.002(0.002)0.001(0.002)0.002(0.003)0.004(0.005)0.001(0.003)0.003(0.004)0.004(0.006)0.006(0.007)0.001(0.001)0.001-0.001(0.003)
0.002

0.002
(0.001)0.003*

0.002(0.001)0.003**

0.000(0.000)
0.000

15

(1)
(0.001)
Int. 2006 and % HH Syr. 0.002(0.001)
Int. 2007 and % HH Syr. 0.002(0.001)
Int. 2008 and % HH Syr. 0.001(0.001)
Int. 2009 and % HH Syr. -0.001(0.001)
Int. 2011 and % HH Syr. -0.000(0.000)Int. 2012 and % HH Syr. -0.001(0.001)
Int. 2013 and % HH Syr. -0.004*(0.002)Int. 2014 and % HH Syr. -0.004*(0.002)Int. 2015 and % HH Syr. -0.005**(0.001)
Int. 2016 and % HH Syr. -0.005***(0.001)
Int. 2017 and % HH Syr. -0.003*(0.002)Controls
Locality FE
Individual FE
9241
N (Person-Year Obs.)
0.010
R-sq.

(2)
(0.001)
0.002(0.001)
0.002(0.001)0.000(0.001)-0.001(0.001)0.000(0.000)-0.001(0.001)-0.003*(0.001)-0.003*(0.001)-0.004**(0.001)-0.004**(0.001)-0.002(0.002)
X

9146
0.173

(3)
Formal(0.001)
0.002(0.001)
0.002(0.001)0.000(0.001)-0.001(0.001)0.000(0.000)0.000(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.000(0.001)
X
X
9146
0.434

(4)

(5)

(0.001)0.001(0.000)0.000(0.000)0.000(0.000)0.000(0.000)-0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)X(0.003)-0.003(0.002)-0.002(0.002)-0.001(0.002)-0.001(0.001)-0.000(0.000)-0.001(0.001)-0.003(0.002)-0.005**(0.002)-0.005**(0.002)-0.006***(0.002)-0.006**(0.002)

X
9241
0.012

9150
0.025

(6)
(7)
(8)
Managerial/Professional Occupation(0.001)(0.001)(0.000)
0.001
0.002
0.000
(0.001)(0.001)(0.000)
0.001
0.001
0.000
(0.001)(0.001)(0.000)-0.0010.000
0.000(0.001)(0.001)(0.000)-0.001-0.0000.000(0.001)(0.000)(0.000)
0.000
0.000
0.000
(0.000)(0.000)(0.000)-0.001-0.0000.000(0.001)(0.000)(0.000)-0.002*-0.0010.000(0.001)(0.001)(0.000)-0.003**-0.002**-0.000(0.001)(0.001)(0.000)-0.003**-0.002**-0.000(0.001)(0.001)(0.000)-0.004***-0.003***-0.000(0.001)(0.001)(0.000)-0.004**-0.003**-0.000(0.001)(0.001)(0.000)
X
X
X
X
X
9059
9059
9150
0.690
0.769
0.010

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the locality level

16

Table 6. Job sector (linear probability model), employed women, retrospective data 2004-2017

Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2004
2005
2006
2007
2008
2009
2011
2012
2013
2014
2015
2016
2017
Int. year and % HH Syr.
Int. 2004 and % HH Syr.
Int. 2005 and % HH Syr.

(1)

(2)

(3)
Open Sector-0.003(0.002)0.001(0.002)0.077*(0.038)0.068*(0.031)
0.027(0.030)0.013(0.021)0.004(0.019)0.002(0.014)0.003(0.005)0.001(0.012)-0.009(0.014)-0.019(0.016)-0.008(0.023)0.002(0.025)0.001(0.026)0.056(0.033)0.048(0.026)0.031(0.028)0.014(0.018)-0.001(0.016)-0.005(0.013)0.007(0.004)0.010(0.010)0.004(0.012)0.004(0.013)0.020(0.020)0.035(0.023)0.041(0.024)0.035(0.032)0.025(0.025)0.016(0.026)0.005(0.018)-0.004(0.015)-0.005(0.013)0.009*(0.004)0.013(0.007)0.010(0.011)0.013(0.011)
0.031(0.018)0.029(0.020)0.033(0.023)-0.003(0.002)-0.003-0.004(0.002)-0.004*-0.002(0.002)-0.002

(4)

(5)

(6)
(7)
(8)
Health and Human Services

(9)

(10)

(11)
Private Sector-0.003(0.005)0.001(0.004)(12)-0.001(0.003)-0.007*(0.003)0.029(0.031)
0.027(0.027)0.023(0.023)0.009(0.010)0.005(0.006)0.002(0.003)-0.003(0.003)-0.007(0.006)-0.007(0.009)-0.008(0.013)-0.011(0.016)-0.013(0.019)-0.015(0.021)-0.110*(0.046)-0.098*(0.045)-0.021(0.038)0.011(0.033)0.002(0.026)0.004(0.021)0.001(0.010)-0.013(0.020)0.007(0.024)0.012(0.028)0.018(0.033)-0.012(0.039)-0.001(0.041)-0.073(0.042)-0.057(0.041)-0.017(0.036)0.018(0.028)0.015(0.024)0.016(0.020)-0.003(0.009)-0.018(0.015)-0.011(0.019)-0.016(0.023)-0.012(0.024)-0.047(0.031)-0.050(0.032)-0.027(0.040)-0.019(0.038)-0.002(0.033)0.018(0.024)0.012(0.020)0.017(0.016)-0.012(0.009)-0.026*(0.012)-0.033*(0.017)-0.044(0.023)-0.045*(0.022)-0.068*(0.028)-0.076**(0.029)0.006(0.010)0.005(0.010)0.003(0.007)0.004(0.005)0.003(0.004)0.001(0.002)0.003(0.005)-0.000(0.004)-0.002(0.005)-0.004(0.008)-0.005(0.009)-0.006(0.011)-0.007(0.012)0.000(0.054)0.015(0.048)-0.031(0.044)-0.037(0.036)-0.050(0.028)-0.041(0.024)0.008(0.010)0.023(0.018)0.021(0.023)0.032(0.024)0.055(0.032)0.043(0.033)0.049(0.038)-0.007(0.048)-0.005(0.042)-0.032(0.038)-0.044(0.032)-0.058*(0.024)-0.050*(0.022)0.010(0.011)0.028(0.019)0.034(0.025)0.049*(0.024)0.076**(0.029)0.064*(0.031)0.085*(0.036)-0.025(0.047)-0.034(0.041)-0.050(0.037)-0.048(0.030)-0.050*(0.019)-0.042*(0.018)0.019(0.010)0.044**(0.014)0.059**(0.021)0.077***(0.022)
0.113***(0.028)0.113***(0.030)0.132***(0.034)0.005(0.006)0.006(0.005)0.005(0.004)0.004(0.003)0.002(0.002)0.001(0.001)-0.000(0.001)-0.001(0.002)0.001(0.003)-0.001(0.003)-0.002(0.004)-0.006(0.005)-0.007(0.006)-0.001(0.001)-0.0010.007*(0.003)0.009*

0.007*(0.003)0.008**

0.004(0.003)0.005-0.000(0.000)-0.000-0.002(0.004)-0.002-0.002(0.004)-0.001-0.002(0.003)-0.000-0.000(0.000)-0.00017

(1)Int. 2006 and % HH Syr.
Int. 2007 and % HH Syr.
Int. 2008 and % HH Syr.
Int. 2009 and % HH Syr.
Int. 2011 and % HH Syr.
Int. 2012 and % HH Syr.
Int. 2013 and % HH Syr.
Int. 2014 and % HH Syr.
Int. 2015 and % HH Syr.
Int. 2016 and % HH Syr.
Int. 2017 and % HH Syr.
Controls
Locality FE
Individual FE
N (Person-Year Obs.)
R-sq.

(2)

(0.002)-0.002(0.002)-0.001(0.001)0.000(0.001)-0.000(0.001)0.000(0.000)0.001(0.001)0.001(0.001)0.001(0.001)0.001(0.002)0.001(0.002)0.001(0.002)(3)
Open Sector(0.002) (0.002)-0.003-0.001(0.002) (0.002)-0.001-0.000(0.001) (0.001)
0.000
0.001
(0.001) (0.001)-0.0000.000(0.001) (0.001)
0.000
0.000
(0.000) (0.000)
0.000-0.000(0.001) (0.000)
0.001-0.000(0.001) (0.001)
0.001-0.001(0.001) (0.001)-0.000-0.001(0.001) (0.001)
0.000-0.001(0.002) (0.001)-0.000-0.000(0.002) (0.001)
X
X
X

9155
0.005

9064
0.195

9064
0.399

(4)
(0.001)-0.001(0.001)-0.000(0.000)-0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)
X
X
9155
0.010

(5)

(6)
(7)
(8)
Health and Human Services(0.004) (0.003)(0.003)(0.000)
0.002
0.004
0.002
0.000
(0.003) (0.003)(0.003)(0.000)-0.000-0.000-0.001-0.000(0.002) (0.002)(0.002)(0.000)-0.000-0.001-0.001-0.000(0.002) (0.002)(0.002)(0.000)-0.001-0.002-0.002-0.000(0.002) (0.002)(0.002)(0.000)
0.000
0.000
0.001
-0.000(0.001) (0.001)(0.000)(0.000)0.002
0.001
0.002*
0.000(0.001) (0.001)(0.001)(0.000)
0.000
0.001
0.002
0.000
(0.002) (0.002)(0.001)(0.000)-0.000
0.001
0.002
0.000
(0.002) (0.002)(0.002)(0.000)-0.002-0.0000.001
0.000(0.003) (0.002)(0.002)(0.000)-0.002-0.0000.001
0.000(0.003) (0.002)(0.002)(0.000)-0.002-0.0000.001
0.000(0.003) (0.003)(0.002)(0.000)
X
X
X
X
X
9155
9064
9064
9155
0.002
0.199
0.424
0.005

(9)

(10)

(0.003)-0.001(0.003)-0.000(0.003)0.001(0.002)0.001(0.002)0.000(0.001)0.000(0.001)0.001(0.002)0.002(0.002)0.002(0.003)0.004(0.003)0.004(0.004)(11)
Private Sector(0.003)(0.002)-0.0010.000(0.003)(0.002)0.001
0.001(0.003)(0.002)0.003
0.003(0.003)(0.002)0.003
0.002(0.002)(0.002)0.000
0.000(0.001)(0.001)0.000-0.001(0.001)(0.001)0.001-0.001(0.002)(0.002)0.001-0.000(0.002)(0.002)0.001-0.001(0.002)(0.002)0.003
0.001(0.002)(0.003)0.002
0.000(0.003)(0.003)
X
X
X

9241
0.010

9146
0.193

9146
0.546

(12)
(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)0.000(0.000)0.000(0.000)
X
X
9241
0.003

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the locality level

18

Table 7. Labor market outcomes (fixed effects linear probability and OLS models), women, panel data

Year (2010 omit.)
2016
Int. year and % HH
SyrianInt. 2016 and % HH
Syr.
N

Hours perweek

Managerial/
Ln (monthly Professional
Health and Private
wage)
Occupation Open sector Human Serv. sector

Unemployed Employed

FormalLn (hourly
wage)

0.038*(0.019)-0.008(0.028)-0.344(0.192)0.117(0.780)15.274*(7.580)1.401*(0.708)-0.076(0.184)0.182(0.175)0.215(0.130)0.223(0.166)-0.001(0.001)7411

0.000(0.001)7412

0.004*(0.002)1071

0.010(0.016)979-0.138(0.143)1056

0.000(0.006)994

0.002(0.002)1193-0.001(0.002)1194-0.000(0.003)1194-0.004(0.004)1201

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the locality level

19Appendix D: Cross-sectional model results

In this appendix, we present the results treating the JLMPS 2010 and JLMPS 2016 waves
as two cross sections. Essentially, pooled cross-sectional difference-in-difference models are
estimated. We present the models across three specifications. The first has no controls, thesecond has controls, and the third adds sub-district (geographic) fixed effects (our preferred
specification). Since we lack locality identifiers for 2010, we use the share of Syrians at the subdistrict level and therefore cluster standard errors at this level as well. This also allows testing fora different definition of a local labor market. The coefficient of interest here is the interaction
between the 2016 round and the share of households in the sub-district that are Syrian. There areno significant employment or unemployment effects for men (Table 8) or women (Table 9).
Table 10 reports the results of examining the impact of Syrian refugees on men’s job
characteristics (and Table 11 does likewise for women). For formality, the estimate on theinteraction is positive and statistically significant (in the models with controls and sub-district
fixed effects); employed Jordanians are more likely to be engaged in formal work and therefore
less likely to be in informal work. However, from our results on employment, we know that they
are not less likely to be employed overall, so on the net this represents a shift in the types of jobs
Jordanians are doing rather than the net loss of employment. The estimates are statisticallysignificant for women in the model with fixed effects and of a similar magnitude. The tables also
show that the effects on occupation and sector are insignificant. Table 12 documents the results
for the wage and weekly hours worked models for men (both for waged and non-waged
workers). The estimated effect of refugees in 2016 for the wage model is statisticallyinsignificant. This finding holds for both hourly wage and monthly wage. Likewise, for hours

20

worked per week, the estimates are statistically insignificant across all specifications. As for
women (Table 13), the estimates are also statistically insignificant.

21

Table 8. Labor market status (linear probability model), men, cross-sectional data
Unemployed
Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2016-0.000 -0.000(0.000) (0.000)

Employed-0.001(0.001)-0.001(0.001)0.014 0.008 0.008 -0.102*** -0.095*** -0.092***(0.008) (0.010) (0.009) (0.022) (0.020) (0.018)

Int. 2016 and % HH Syr.
Int. 2016 and % HH Syr. -0.000 0.000 0.000 0.001(0.001) (0.001) (0.001) (0.002)
X
X
Controls
X
Sub-district FE
15253 15070 15070 15253
N
0.001 0.020 0.031 0.008
R-squared

0.001(0.001)
X
15070
0.324

0.001
(0.001)
X
X
15070
0.336

Source: Authors’ calculations based on JLMPS 2010 and 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the sub-district level

22

Table 9. Labor market status (linear probability model), women, cross-sectional data
Unemployed
Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2016
Int. 2016 and % HH Syr.
Int. 2016 and % HH Syr.
Controls
Sub-district FEN
R-squared-0.001(0.000)-0.000(0.000)0.024**(0.008)0.018*(0.008)0.000(0.000)0.000(0.000)
X

15553
0.004

15421
0.098

Employed-0.001(0.001)-0.001(0.001)0.020*(0.008)-0.027*(0.012)-0.045***(0.009)-0.043***(0.009)0.000(0.000)
X
X
15421
0.115

-0.000(0.001)0.000(0.000)
X

15553
0.002

15421
0.197

0.000
(0.000)
X
X
15421
0.205

Source: Authors’ calculations based on JLMPS 2010 and 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the sub-district level23

Table 10. Job characteristics (linear probability model), employed men, cross-sectional data
FormalPercentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2016-0.001 -0.002(0.002)(0.002)

Managerial/Professional
Occupation
0.001 0.000(0.001) (0.000)

Open Sector-0.001 -0.000(0.001)(0.001)

Health and Human
Serv.
0.000 -0.000(0.001)(0.001)

Private-0.000 0.000(0.003)(0.002)-0.009 -0.019 -0.019 0.029 0.018 0.019 -0.001 0.005 0.003 0.024* 0.021 0.015 -0.035 -0.037 -0.027(0.021)(0.020)(0.018)(0.019) (0.015) (0.014) (0.017)(0.016)(0.013)(0.011)(0.011)(0.011)(0.027)(0.027)(0.020)

Int. 2016 and % HH Syr.
Int. 2016 and % HH Syr.0.002 0.003* 0.002* -0.001 0.001 0.001 -0.000 -0.001 -0.000 -0.001 -0.000 -0.000 -0.001 -0.002 -0.001(0.001)(0.001)(0.001)(0.001) (0.001) (0.001) (0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)
X
X
X
X
X
X
X
X
X
X
Controls
X
X
X
X
X
Sub-district FE
9013 8930 8930 9004 8924 8924 9004 8924 8924 9004 8924 8924 9053 8970 8970
N
0.000 0.116 0.175 0.001 0.516 0.524 0.000 0.050 0.109 0.001 0.143 0.162 0.003 0.088 0.255
R-squared

Source: Authors’ calculations based on JLMPS 2010 and 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the sub-district level24

Table 11. Job characteristics (linear probability model), employed women, cross-sectional data
FormalPercentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2016
Int. 2016 and % HH Syr.
Int. 2016 and % HH Syr.
Controls
Sub-district FEN
R-squared

0.003 0.000(0.002) (0.001)

Managerial/Professional
Occupation

Open Sector

0.002 0.001(0.002) (0.001)

Private0.003(0.002)0.000(0.001)0.013 -0.027 -0.051* 0.145**(0.033) (0.025) (0.022) (0.049)

0.073*(0.028)0.068*(0.030)-0.089* -0.031 -0.024 0.080* 0.029 0.010 -0.058 -0.017 0.032(0.037) (0.026) (0.025) (0.033) (0.034) (0.032) (0.037) (0.030) (0.029)

0.001 0.001 0.002* -0.001(0.002) (0.001) (0.001) (0.004)
X
X
X
1932
1919
1919
2121
0.005 0.240 0.297 0.021-0.000(0.001)X

0.000(0.002)
X
X
2110
0.648

0.002 0.002 0.001(0.002) (0.001) (0.001)
X
X
X
2122
2111
2111
0.009 0.291 0.335

2110
0.633

-0.003 -0.001(0.002) (0.001)

Health and Human Serv.

-0.003 -0.004 -0.003(0.002) (0.002) (0.002)
X
X
X
2122
2111
2111
0.003 0.210 0.261-0.001 -0.000(0.002) (0.002)

0.002 0.003 0.002(0.002) (0.002) (0.002)
X
X
X
2131
2118
2118
0.002 0.228 0.344

Source: Authors’ calculations based on JLMPS 2010 and 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the sub-district level25

Table 12. Hours and wages (OLS model), employed (or wage-working) men, cross-sectional data
Ln (hourly wage)
Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2016
Int. 2016 and % HH Syr.
Int. 2016 and % HH Syr.
Controls
Sub-district FEN
R-squared

Hours per week

-0.001(0.002)-0.002(0.002)-0.030(0.053)-0.025(0.051)0.267***(0.056)0.170***(0.047)0.202***(0.049)-2.443* -1.641(1.183) (1.107)

0.001(0.002)0.002(0.002)X-0.065(0.074)-0.076(0.072)
X

7351
0.025

7278
0.144

0.001
(0.002)
X
X
7278
0.163

8834
0.009

8757
0.035

Ln (monthly wage)-0.002(0.002)-0.003(0.003)-1.895(1.044)0.188***(0.036)0.148***(0.038)0.165***(0.040)-0.052(0.065)
X
X
8757
0.060

0.002
(0.002)0.003(0.002)
X

7458
0.020

7383
0.154

0.002
(0.002)
X
X
7383
0.175

Source: Authors’ calculations based on JLMPS 2010 and 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the sub-district level26

Table 13. Hours and wages (OLS model), employed (or wage-working) women, cross-sectional data
Ln (hourly wage)
Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2016
Int. 2016 and % HH Syr.
Int. 2016 and % HH Syr.
Controls
Sub-district FEN
R-squared

0.002(0.003)-0.002(0.002)0.172(0.092)0.057(0.055)0.003(0.004)0.004(0.004)
X

1772
0.014

1762
0.227

Hours per week
0.008
(0.031)

0.049(0.031)

0.083(0.062)-0.219(1.116)0.239(0.895)0.003(0.004)
X
X
1762
0.276

0.009
(0.052)-0.005(0.046)
X

1911
0.000

1899
0.119

Ln (monthly wage)
0.003(0.004)0.000(0.003)0.390(1.054)0.214***(0.060)0.143**(0.046)0.145**(0.047)0.005(0.046)
X
X
1899
0.170

0.003
(0.003)0.003(0.003)
X

1792
0.038

1782
0.207

0.003
(0.003)
X
X
1782
0.281

Source: Authors’ calculations based on JLMPS 2010 and 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the sub-district level

27Appendix E: Education: Sub-group analyses
We distinguish individuals based on level of education in Table 14 and Table 15. Most
Syrian labor force participants have low levels of education themselves (Assaad, Krafft, and Keo2018) and are competing for informal and irregular jobs, which are likely to be held by lesseducated Jordanians (if any). Given the few females who work, we analyze only males for subgroup analyses. We present the panel results throughout our sub-group analyses, since some of
the analyses that follow (for instance, by sector) depend on the 2010 year status, and the panel
data, unlike the retrospective data, has wage and hours outcomes. We divide our sample into
those with a basic education or less and those with secondary or more (as of 2010). There are notsignificant results for either the less or more educated in terms of employment or unemployment.
The formality result becomes insignificant, but is larger for the less educated than the educated,suggesting they are particularly likely to shift out of informal work and into formal work. The
significant hourly wage effect persists (and is larger) for the less educated, and is positive but
insignificant for the more educated. The decrease in private sector (and increase in public sectorwork) is significant only for the more educated, unsurprising given the requirements of mostpublic sector jobs. Overall, our results do not suggest unique negative effects of the refugee
influx for the less educated.

28

Table 14. Labor market outcomes, hours, and wages (fixed effects linear probability and OLS models), by education (in 2010),
men, panel data

Year (2010 omit.)
2016
Int. year and % HH Syrian
Int. 2016 and % HH Syr.
N

Unemployed
Less
More
ed.
ed.

Employed
Less
More
ed.
ed.

Ln (hourly wage)
Less
More
ed.
ed.

Hours per week
Less
More
ed.
ed.

Ln (monthly
wage)
Less
More
ed.
ed.

0.044
(0.054)-0.002(0.039)-0.106(0.065)-0.046(0.055)0.181(0.164)0.469**(0.166)-0.211(2.009)-6.705(3.705)-0.088(0.216)0.371(0.365)-0.000(0.001)4755

0.002(0.001)2608

0.001(0.002)4776-0.001(0.003)2618

0.013*(0.006)2301

0.005(0.005)1562-0.207(0.144)2840-0.074(0.136)
1837

0.007(0.005)2337-0.001(0.004)1587

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the locality level29

Table 15. Job characteristics (fixed effects linear probability models), by education (in 2010), employed men, panel data

Year (2010omit.)
2016
Int. year and %HH SyrianInt. 2016 and %HH Syr.
N

FormalLess ed.
More ed.

Managerial/Profession
al Occupation
Less ed.
More ed.

Open sector
Less ed.
More ed.

Health and Human
Serv.
Less ed.
More ed.

Private sector
Less ed.
More ed.

0.144**(0.045)0.159**(0.052)0.004(0.016)-0.013(0.089)0.008(0.035)0.064(0.098)0.003(0.020)-0.005(0.024)0.010(0.031)-0.137**(0.053)0.004(0.002)2910

0.001(0.002)1876

0.000(0.001)2916

0.001(0.003)1872

0.000(0.002)2917

0.002(0.002)1872-0.000(0.001)2917-0.001(0.002)1872-0.002(0.002)2927-0.004*(0.002)
1881

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the locality level30Appendix F: Sector: Sub-group analyses
We examine the effects of employment by the sector of work in 2010 in Table 16 andTable 17. Those in the private sector in 2010 would be particularly likely to experience
competition from incoming Syrians. Again, we present results only for men, since few women
work. We use the panel data analyses for the best coverage of outcomes as well as being able tocondition on 2010 status. Keeping in mind the selected nature of the sample—we are comparing
those employed in the private sector and those employed in the public sector, so setting aside
those not employed in 2010—there are interesting unemployment and employment effects.
Those who were in the private sector and experienced a greater local labor market shock aresignificantly less likely to be unemployed. Those who had a greater shock and were in the public
sector are significantly more likely to be employed (the coefficient for those in the private sector
is of similar magnitude, albeit insignificant). The formality effects are insignificant, but ofgreater magnitude in the private sector. The increase in hourly wages is of a similar magnitudeacross sectors, but only significant in the public sector, where there was also a small (0.2) but
significant reduction in hours per week. There was a significant effect on the probability of beinga manager or professional only in the public sector. Curiously, there was a significant increase inbeing in the open sector for those in the private sector in 2010. It may be that the Jordan
Compact is creating greater employment opportunities for Jordanians in these activities, as wellas the Syrians who can acquire work permits in this sector. No other results were significant, but
the number of significant results by sector, particularly given the further division of the sample,
suggests that the Syrian refugee influx has had different effects, largely slight positive ones,
across sectors.

31

Table 16. Labor market outcomes, hours, and wages (fixed effects linear probability and OLS models), by sector (in 2010),
men, panel data

Year (2010 omit.)
2016
Int. year and % HH Syrian
Int. 2016 and % HH Syr.
N

Unemployed
PublicPrivatePublicEmployed
Private0.043**(0.016)

0.056***(0.011)-0.303***(0.045)-0.001(0.001)2356-0.002**(0.001)2364

0.003*(0.002)2357

Ln (hourly wage)
PublicPrivateHours per week
PublicPrivateLn (monthly wage)
PublicPrivate-0.245***(0.032)0.330**(0.121)0.503**(0.158)2.740(2.281)-6.712*(2.805)0.266**(0.090)0.021(0.250)0.003(0.002)2384

0.008*(0.004)2012

0.009(0.010)1313-0.207*(0.085)2054

0.037(0.207)2008

0.003(0.002)2036

0.005(0.012)1332

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the locality level32

Table 17. Job characteristics (fixed effects linear probability models), by sector (in 2010), employed men, panel data

PublicYear (2010 omit.)
2016
Int. year and % HH Syrian
Int. 2016 and % HH Syr.
N

FormalPrivateManagerial/Professional
Occupation
Open sector
PublicPrivatePublicPrivateHealth and Human
Serv.
PublicPrivatePrivate sector
PublicPrivate0.063(0.074)0.187***(0.047)0.051(0.028)0.041(0.031)-0.016(0.093)-0.065(0.044)-0.017(0.023)0.013(0.024)-0.040(0.073)-0.055**(0.020)0.001(0.001)2081

0.004(0.003)2063

0.003*(0.001)2080-0.002(0.003)2063

0.000(0.001)2082

0.005*(0.002)2063-0.000(0.002)2082-0.002(0.001)2063-0.001(0.001)2089-0.002(0.002)2070

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the locality level

33

Appendix G: Amman versus elsewhere: Sub-group analyses
Amman is the capital of Jordan – the seat of government – and also home to 39% of
Jordan’s population. Given these characteristics, Amman may also have a somewhat different
labor market and labor market response to the influx than elsewhere. We therefore undertook
sub-group analyses splitting the sample into Amman and “not Amman” (everywhere else). We
present the results for men in our main panel estimation, comparing Amman and “not Amman”
in Table 18 and Table 19. In neither Amman or outside Amman is there a negative effect of theinflux on employment or unemployment. Among the employed, shifts into more formal workand reductions in private sector/increases in public sector work occur in both areas but are larger
and significant in Amman.

34

Table 18. Labor market outcomes, hours, and wages (fixed effects linear probability and OLS models), by Amman (in 2010),
men, panel data

Year (2010 omit.)
2016
Int. year and % HH
SyrianInt. 2016 and % HH
Syr.
N

Unemployed
Not
Amman
Amman

Employed
Not
Amman
Amman

Ln (hourly wage)
Not
Amman
Amman

Hours per week
Not
Amman
Amman

Ln (monthly wage)
Not
Amman
Amman

0.087
(0.081)-0.012(0.037)0.010(0.106)-0.026(0.064)0.353**(0.110)0.237(0.241)-1.208(2.504)-5.425(4.461)

0.128(0.154)0.326(0.391)

0.000

0.004

0.001

-0.007

0.007

0.034

-0.131-0.077

0.004

0.006

(0.001)5966(0.003)1397(0.002)5992(0.006)1402(0.003)3158(0.027)705(0.090)3769(0.366)908(0.003)3193(0.028)731

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the locality level

35Table 19. Job characteristics (fixed effects linear probability models), by Amman (in 2010), employed men, panel data

Not
Amman

Amman

Managerial/Professional Occupation
Not
Amman
Amman

0.053
(0.070)0.111*(0.044)0.121***(0.035)-0.039(0.098)-0.080(0.109)-0.084(0.105)-0.068(0.068)0.029(0.046)-0.068(0.129)0.078(0.054)0.002(0.001)3832

0.011*(0.004)954

0.000(0.001)3835

0.005(0.010)953

0.001(0.001)3838

0.007(0.008)951-0.000(0.001)3838-0.003(0.004)951-0.001(0.001)3852-0.010*(0.004)956

FormalYear (2010 omit.)
2016
Int. year and % HH
SyrianInt. 2016 and % HH
Syr.
N

Amman

Health and Human
Serv.
Not
Amman
Amman

Private sector
Not
Amman
Amman

Open sector
Not
Amman

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the locality level36Appendix H: Entrants: Sub-Group Analyses
One further model is used to consider the potentially disproportionate impact of Syrian
refugees specifically on labor market entrants in Jordan. Unemployment is primarily a new
entrant phenomenon in Jordan. Labor markets are rigid, such that initial entry is highly
deterministic of subsequent labor market outcomes (Amer 2014; Assaad and Krafft 2016;
Mryyan 2014). Therefore, one of the sub-groups we examine as potentially disproportionately
impacted by the Syrian refugee influx are new entrants. In this section, we examine only labormarket entrants, those who left school or turned age 15 in the preceding five years, whichever
was later, using the repeated cross-section data. We use the repeated cross-section rather than our
preferred panel because we cannot observe any employment characteristics for those who werein school in 2010 and therefore could not estimate our models. The entrants are therefore those
who left school or turned 15 in 2005-2009 or 2012-2016. As elsewhere, we focus our results onmen. Table 20, Table 21, and Table 22 present the results of these models. In our preferred
specification (including controls and sub-district fixed effects) there are no significant effects.

37

Table 20. Entrants: Labor market status (linear probability model), men, cross-sectional data
Unemployed
Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2016
Int. 2016 and % HH Syr.
Int. 2016 and % HH Syr.
Controls
Sub-district FEN
R-squared-0.000(0.001)-0.001(0.001)

0.029(0.030)0.025(0.036)-0.002(0.002)-0.001(0.002)
X

2138
0.001

2096
0.044

Employed
0.000(0.002)0.000(0.001)

0.026(0.035)-0.225***(0.033)-0.193***(0.039)-0.194***(0.042)-0.001(0.002)
X
X
2096
0.089

0.002
(0.002)0.001(0.002)
X

2138
0.043

2096
0.142

0.002
(0.002)
X
X
2096
0.192

Source: Authors’ calculations based on JLMPS 2010 and 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the sub-district level38

Table 21. Entrants: Job characteristics (linear probability model), employed men, cross-sectional data
Managerial/Professional
Occupation

FormalPercentage
HH Syrian
Percentage
of HH Syr. -0.004(0.003)
Year (2010omit.)
2016-0.013(0.062)Int. 2016
and % HH
Syr.
Int. 2016
and % HH
Syr.
0.004
(0.003)Controls
Sub-district
FE
N
1188
R-squared 0.003-0.004(0.003)0.005**(0.001)0.002*(0.001)

Open Sector

0.000(0.003)0.000(0.002)

Health and Human Serv.

0.003*(0.001)

0.002(0.001)

Private0.003(0.005)0.003(0.003)-0.064(0.050)-0.006(0.046)

0.099*(0.049)0.012(0.025)0.006(0.027)-0.015(0.062)0.011(0.056)-0.006(0.060)0.047(0.033)0.011(0.024)0.012(0.025)0.019(0.050)0.048(0.048)-0.012(0.036)0.006(0.003)
X

0.003(0.003)X-0.008*** -0.002(0.002) (0.001)
X-0.001(0.001)X-0.002(0.002)-0.003(0.002)X-0.002(0.003)X-0.004** -0.003(0.002) (0.001)
X-0.003(0.001)X-0.005*(0.002)-0.005*(0.002)X-0.001(0.001)
X

1172
0.171

X
1172
0.296

1184
0.008

X
1169
0.627

1169
0.139

X
1169
0.241

1184
0.005

X
1169
0.306

1175
0.161

X
1175
0.400

1169
0.579

1184
0.003

1169
0.224

1191
0.004

Source: Authors’ calculations based on JLMPS 2010 and 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the sub-district level

39

Table 22. Entrants: Hours and wages (OLS model), employed (or wage-working) men, cross-sectional data
Ln (hourly wage)
Percentage HH Syrian
Percentage of HH Syr.
Year (2010 omit.)
2016
Int. 2016 and % HH Syr.
Int. 2016 and % HH Syr.
Controls
Sub-district FEN
R-squared

0.001(0.003)-0.001(0.003)0.358*(0.159)0.201*(0.098)-0.003(0.004)0.002(0.004)
X

1079
0.026

1066
0.184

Hours per week
-0.103(0.069)-0.100(0.061)

0.332***(0.097)-2.593(2.331)-1.177(1.806)-0.002(0.006)
X
X
1066
0.301

0.042
(0.089)0.013(0.090)
X

1150
0.006

1136
0.079

Ln (monthly wage)-0.004(0.004)-0.006(0.004)-2.677(2.072)0.242*(0.109)0.170(0.091)0.290***(0.080)0.056(0.100)
X
X
1136
0.145

0.002
(0.003)0.006(0.003)
X

1110
0.029

1095
0.209

0.001
(0.003)
X
X
1095
0.347

Source: Authors’ calculations based on JLMPS 2010 and 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s employment status, age, and age squaredStandard errors (in parentheses) clustered at the sub-district level40Appendix I: Entrants: School-to-work transitions

Since unemployment is a primarily new entrant phenomenon in Jordan and earlyoutcomes are highly deterministic of subsequent trajectory, the school to work transition of
Jordanian youth is of great concern. In this appendix we analyze school-to-work transitions overthe 2004-2016 period, 1 similar to the retrospective analyses, but with the outcome here being theprobability of obtaining a first job.
We specifically examine the duration of the school-to-work transitions using acomplementary log-log discrete-time hazard model. The underlying event. T, we are interested inmodeling (in this case, obtaining a first job) occurs at some point in time d. In this case, time isduration from school exit or age 15, whichever is later. 2 Duration-time, d, is distinct from
calendar time, t. However, some individuals are censored and have not yet obtained a first job.
Thus, we must use survival analysis, based on the idea of a hazard, hid, namely:ℎ𝑖𝑖𝑖𝑖 = Pr(𝑇𝑇𝑑𝑑 = 𝑑𝑑|𝑇𝑇𝑑𝑑 ≥ 𝑑𝑑)(1)

The hazard is the probability of individual i obtaining a first job at a particular duration, giventhat an individual has not yet done so. In a multivariate context, this gets modeled as thecomplementary log-log difference-in-difference model:ℎ𝑖𝑖𝑖𝑖 = 1 − exp[− exp( 𝜇𝜇𝑑𝑑 𝑑𝑑 + 𝛼𝛼𝑗𝑗 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝛿𝛿𝑡𝑡 𝑡𝑡 + 𝛾𝛾𝑆𝑆𝑙𝑙 + 𝜃𝜃𝑡𝑡 𝑡𝑡 ∗ 𝑆𝑆𝑙𝑙 )](2)Here the coefficients, once exponentiated, are hazard ratios, proportionately multiplying thebaseline hazards, 𝜇𝜇𝑑𝑑 .

1

Here we omit 2017 since we do not observe school exit in 2017 in our sample, since primary
fielding finished in April.
2
We restrict our analyses to those who exited in 2004-2016, parallel to the time frame for our
retrospective analyses.
41

We estimate the effect of Syrians in each year, which allows us to test for parallel trends
in this model, as in the retrospective data, as well as estimate the effect itself. Table 23 shows theresults in terms of hazard ratios; a hazard ratio less than one means a slower transition fromschool to work (specifically, a lower probability of obtaining a first job in each year if one has
not yet done so) while a hazard ratio greater than one is a faster transition (or higher probability).
The models are presented first without and then with controls. All specifications include thebaseline hazard, the probability of obtaining a job each year out from age 15 or school exit.
There are no significant refugee impacts for men, although after adding controls, there issome evidence that areas that had a larger refugee influx did, back in 2005/2006, have slower
school to work transitions, non-parallel trends, with joint significance for the 2004-2009
interactions. Overall, there does not appear to have been a negative impact of the refugee influx
on school-to-work transitions.

42

Table 23. School-to-work transition (hazard ratios from a complementary log-log discrete
time hazard model), men, retrospective data for 2004-2016
Year (2010 omit.)
2004
2005
2006
2007
2008
2009
2011
2012
2013
2014
2015
2016
Percentage HH Syrian
Percentage of HH Syr.
Int. year and % HH Syr.
Int. 2004 and % HH Syr.
Int. 2005 and % HH Syr.
Int. 2006 and % HH Syr.
Int. 2007 and % HH Syr.
Int. 2008 and % HH Syr.
Int. 2009 and % HH Syr.
Int. 2011 and % HH Syr.
Int. 2012 and % HH Syr.
Int. 2013 and % HH Syr.
Int. 2014 and % HH Syr.

1.189
(0.430)1.051(0.252)1.070(0.321)0.820(0.226)0.451**(0.133)0.540*(0.155)0.737(0.143)0.609(0.245)0.851(0.238)0.545*(0.137)0.777(0.230)0.753(0.163)1.280(0.470)1.258(0.299)1.256(0.377)0.870(0.243)0.478*(0.141)0.537*(0.161)0.745(0.145)0.700(0.257)0.930(0.251)0.609*(0.145)0.855(0.244)0.759(0.169)1.000(0.013)1.004(0.013)0.969(0.036)0.975(0.017)0.966(0.022)1.011(0.021)1.030(0.024)1.014(0.019)1.009(0.014)1.008(0.029)0.980(0.023)1.020(0.018)0.970(0.034)0.965*(0.017)0.957*(0.021)
1.005(0.020)1.028(0.024)1.013(0.018)1.009(0.014)0.995(0.023)0.973(0.022)
1.007(0.016)43

Int. 2015 and % HH Syr.
Int. 2016 and % HH Syr.
Controls
N (Person-year obs.)

1.008(0.021)1.021(0.013)
10594

0.993
(0.019)1.014(0.013)
X
10300

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include education level, mother’s education level, father’s education level, father’s
employment status, age, and age squaredStandard errors (in parentheses) clustered at the locality level

44Appendix J: Instrumenting for potential endogenous placement of refugees
As an additional robustness check for the potentially endogenous location decisions ofrefugees, we estimate two-stage least squares (2SLS) models, instrumenting for the locality shareof Syrians, based on two different instruments. The first instrument uses the 2004 population
census to calculate the percentage of households that are Syrian and Egyptian in a locality in
2004. 3 This is essentially an “ethnic enclave” approach, using historical shares of ethnic groups
to instrument for new arrivals, since new immigrants tend to migrate towards existing
communities (Bagir 2017; Card 2009). Although this instrument is common in the literature, one
potential threat to its exogeneity is that Syrians and Egyptians in 2004 were primarily migrant
workers, who may have been migrating to economically prosperous areas. Should these areas
remain more prosperous at the time of the refugee influx, the exclusion condition may not hold.
The second instrument is the distance, in kilometers, to the locality from Za’atari refugee
camp, Jordan’s largest camp. 4 While most Syrians are living in host communities, around a fifthpass through refugee camps before arriving in host communities (Krafft et al. 2018). Za’atarirefugee camp was opened in July 2012 in response to the rising refugee influx, and located in thedesert near the Syrian border. Its placement was unrelated to local labor market conditions,making it a plausibly exogenous instrument, although the proximity to the border, and thus
conflict may make areas closer to Za’atari predisposed to worse outcomes regardless of the local
share of refugees, such that the exclusion condition may not hold. Although there are potential

3

The share of non-Jordanians increased between 2004 and 2010, driven primarily by economic
migrants. We tested using the 2004 share of Syrians only, but found it to be a weak instrument.
Since both Syrians and Egyptians pre-2011 would have been relatively substitutable economic
migrants, we use the combination as our instrument.
4
Distance based on Google Maps. Distance to rural localities was not available, so for such
missing cases, the average sub-district distance was used. There are very few Syrians—and not
many Jordanians either—living in rural areas (Assaad, Krafft, and Keo 2018; Krafft et al. 2018).
45

threats to the exclusion of each instrument, they are likely to be biased in opposite directions,with the 2004 census Syrians and Egyptians linked to economic opportunity and the distance tothe border linked to economic downturn. Thus, we present both instruments separately. We
consider the instrumental variable estimates primarily as an additional robustness check,identifying off of alternative assumptions to the difference-in-difference model. Since our
instruments are at the locality level, we cannot include locality fixed effects, but do include
district fixed effects.
Figure 3 shows the number of households underlying the instrument and the instrument
itself for the first instrument, the percentage of households that are Syrian or Egyptian, by subdistrict (the instrument is used on the locality level, which has even more variation). There isclear geographic variation to identify off of, and while visibly correlated with the 2015 share ofhouseholds Syrian, this instrument also has a distinct pattern, concentrated in the center of thecountry, relative to identifying off of distance from the Za’atari refugee camp, near the border.

46

Figure 3. Number and percentage of households that are Syrian or Egyptian, by subdistrict, 2004 Census

Source: Authors’ calculation based on Census 2004

The first stage and second stage for both instruments are presented in Table 24 (the firststage showing the samples for the different outcomes, not the outcomes themselves). The
instruments are significant and sufficiently strong for the 2004 census instrument. F-statistics
range from 13.8 (for employment/unemployment samples) to 19.0 (for monthly wages sample).
The sign on the instrument is positive, as expected, indicating that as the share of Syrians andEgyptians in 2004 increases, so too does the share in 2015. The second stage of the 2SLS
estimates with the 2004 census instrument are all consistently insignificant, showing no
significant impact of more Syrians locally.

47

The instruments are consistently significant, but weak, for the distance instrument. Fstatistics range from 4.4 (for employment/unemployment samples) to 6.0 (for monthly wages
sample). The sign on the instrument is negative, as expected, indicating that each additional
kilometer from Za’atari reduces the percentage of households that are Syrian. The second stageof the 2SLS estimates are consistently insignificant.

48

Table 24. Instrumental variables 2SLS models, men

FormalLn (hourly Hours per
wage)
week

Managerial/
Health andLn (monthly Professional
Human
Privatewage)
Occupation Open sector Serv.
sector

0.396***(0.093)
X
4431
0.657
17.9750.000

0.390***(0.092)
X
3591
0.685
18.0360.000

0.391***(0.095)
X
4258
0.659
17.0600.000

0.393***(0.090)
X
3696
0.681
18.9780.000

0.382***(0.091)
X
4418
0.654
17.6970.000

0.380***(0.091)
X
4418
0.654
17.5880.000

0.380***(0.091)
X
4418
0.654
17.5880.000

0.386***(0.092)
X
4464
0.656
17.6480.000

0.000(0.007)
X
4431
0.169

0.004
(0.017)
X
3591
0.142

0.169
(0.273)
X
4258
0.056

0.007
(0.012)
X
3696
0.126

0.008
(0.005)
X
4418
0.567

0.009
(0.006)
X
4418
0.107

0.004
(0.005)
X
4418
0.173

0.005
(0.006)
X
4464
0.260

-0.057*(0.027)
X
8026
0.636
4.436
0.036

-0.072*(0.031)
X
4432
0.641
5.301
0.022

-0.073*(0.030)
X
3592
0.670
5.952
0.015

-0.074*(0.031)
X
4259
0.644
5.566
0.019

-0.072*(0.030)
X
3697
0.664
5.844
0.016

-0.072*(0.031)
X
4419
0.639
5.290
0.022

-0.073*(0.031)
X
4419
0.639
5.517
0.019

-0.073*(0.031)
X
4419
0.639
5.517
0.019

-0.071*(0.031)
X
4465
0.641
5.259
0.022

-0.002(0.012)
X
8026
0.299

0.007
(0.015)
X
4432
0.166

-0.056(0.052)
X
3592
0.059

0.105
(0.747)
X
4259
0.057

-0.038(0.025)
X
3697
0.063

0.013
(0.008)
X
4419
0.557

0.023
(0.015)
X
4419
0.055

0.003
(0.010)
X
4419
0.174

-0.005(0.013)
X
4465
0.268

UnemployedEmployedFirst stage: 2004 census % Syr. & Eg.
Percentage of HH Syr.
0.305***
0.305***or Eg. in 2004
(0.082)(0.082)ControlsXX
N (Obs.)
8024
8024
R-sq.
0.649
0.649
F-stat13.77613.776p-val.
0.000
0.000
Second stage: 2004 census % Syr. & Eg.
Percentage HH Syrian
Percentage of HH
Syr.
-0.004-0.005(0.004)(0.008)ControlsXX
N (Obs.)
8024
8024
R-sq.
0.039
0.297
First stage: distance to camp
Za’atari Camp
-0.057*(distance in km.)(0.027)
ControlsX
N (Obs.)
8026
R-sq.
0.636
F-stat4.436
p-val.
0.036
Second stage: distance to camp
Percentage HH Syrian
Percentage of HH
Syr.
0.006
(0.009)ControlsX
N (Obs.)
8026
R-sq.
0.031

49

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include district fixed effects, education level, mother’s education level, father’s education level, father’s employment status,
age, and age squaredStandard errors (in parentheses) clustered at the locality level50As a further investigation of the validity of our instruments, we explored whether there were pretrends (prior to 2011) that might relate these instruments and labor market outcomes. We
undertake these using the retrospective data, for men, presenting the results in Table 25 andTable 26. There is some potential evidence of pre-trends with significant interactions for earlier
years compared to 2010 for employment with the percentage Syrian and Egyptian in 2004
instrument, as well as significant negative interactions in terms of health and human services
employment. For the Za’atari distance instrument, there are some small significant pre-trends forformality and the private sector. Thus, as we noted, the two instruments may not be excludable,
but they are likely to be biased in different directions (e.g. migrant workers going to areas withhigher employment rates or effects of being near the Syrian border).

51Table 25. Instrument pre-trends for percentage Syrian or Egyptian in 2004 census (linear probability model), men,
retrospective data, 2004-2010

Percentage of HH Syr. or Eg. in 2004
Year (2010 omit.)
2004
2005
2006
2007
2008
2009

Unemployed-0.000(0.001)

Employed
0.002(0.002)Formal-0.002(0.003)

Managerial/
Professional
Occupation
0.002(0.002)

Open sector
0.007**(0.002)

Health and
Human
Serv.
0.002
(0.001)

Privatesector
0.006*(0.002)0.000(0.009)0.002(0.008)-0.000(0.007)-0.003(0.006)0.003(0.006)0.005(0.004)-0.008(0.013)-0.006(0.012)-0.005(0.010)0.006(0.008)-0.003(0.005)-0.007(0.005)-0.035**(0.012)-0.027*(0.011)-0.023*(0.009)-0.015(0.008)-0.002(0.007)-0.004(0.006)0.004(0.008)0.005(0.007)0.004(0.006)0.007(0.005)0.004(0.005)0.001(0.004)0.004(0.008)0.002(0.007)-0.001(0.007)-0.007(0.006)-0.012(0.007)-0.005(0.005)0.012(0.008)0.010(0.008)0.009(0.007)0.009(0.006)0.009(0.005)0.003(0.004)0.001(0.012)0.001(0.011)0.001(0.008)-0.006(0.008)-0.007(0.008)-0.005(0.005)0.001(0.001)0.001(0.001)0.001(0.001)0.001(0.001)0.001(0.001)0.000(0.001)-0.003*(0.001)-0.003*(0.001)-0.002(0.002)-0.003*(0.001)-0.001(0.001)-0.001(0.001)0.001(0.001)0.001(0.001)0.001(0.001)0.000(0.001)-0.001(0.001)0.000(0.001)-0.001(0.002)-0.001(0.002)-0.000(0.001)-0.001(0.001)-0.001(0.001)-0.000(0.001)0.000(0.001)0.001(0.001)0.001(0.001)0.001(0.001)0.001(0.001)0.000(0.001)-0.002(0.001)-0.002(0.001)-0.002*(0.001)-0.002*(0.001)-0.002**(0.001)-0.002*(0.001)

0.002(0.001)
0.002(0.001)
0.002(0.001)
0.002(0.001)0.001(0.001)0.000(0.001)

Int. Year and % of HH Syr. or Eg. in 2004
2004 # Percentage of HH Syr. or Eg. in 2004
2005 # Percentage of HH Syr. or Eg. in 2004
2006 # Percentage of HH Syr. or Eg. in 2004
2007 # Percentage of HH Syr. or Eg. in 2004
2008 # Percentage of HH Syr. or Eg. in 20042009 # Percentage of HH Syr. or Eg. in 2004

52

N
R-sq.

41642
0.047

41642
0.250

21632
0.153

21486
0.567

21517
0.140

21517
0.162

21632
0.272

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include district fixed effects, education level, mother’s education level, father’s education level, father’s employment status,
age, and age squaredStandard errors (in parentheses) clustered at the locality level53

Table 26. Instrument pre-trends for distance to Za’atari (linear probability model), men, retrospective data, 2004-2010

Year (2010 omit.)
2004
2005
2006
2007
2008
2009
Za’atari Camp (distance in km.)Int. Year and Za’atari Camp (distance in km.)2004 # Zaatari Camp
2005 # Zaatari Camp
2006 # Zaatari Camp
2007 # Zaatari Camp
2008 # Zaatari Camp
2009 # Zaatari CampN
R-sq.

Unemployed

Employed

FormalManagerial/
Professional
Occupation

Open sector

Health and
Human
Serv.

Privatesector

0.006(0.011)
0.012(0.009)0.006(0.009)0.005(0.007)0.010(0.007)0.008(0.005)-0.001(0.001)-0.019(0.014)-0.017(0.013)-0.015(0.012)-0.010(0.009)-0.013(0.007)-0.015*(0.006)0.000(0.001)-0.050***(0.015)-0.041**(0.015)-0.034**(0.012)-0.022*(0.009)-0.005(0.008)-0.004(0.008)-0.000(0.001)-0.005(0.008)-0.004(0.007)-0.001(0.006)0.005(0.005)-0.001(0.005)-0.002(0.004)-0.000(0.000)0.003(0.010)0.008(0.008)0.001(0.008)-0.008(0.008)-0.015(0.008)-0.011(0.007)-0.000(0.001)-0.003(0.008)-0.004(0.007)-0.008(0.006)-0.005(0.005)-0.000(0.005)-0.004(0.004)-0.001(0.001)0.027*(0.013)0.026*(0.012)0.023*(0.010)0.008(0.008)0.003(0.008)-0.001(0.006)0.001(0.001)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)
41649
0.049

-0.000(0.000)-0.000(0.000)-0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)
41649
0.250

0.000*(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)
21632
0.153

0.000
(0.000)0.000(0.000)0.000(0.000)-0.000(0.000)0.000(0.000)0.000(0.000)
21486
0.567

0.000
(0.000)-0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)
21517
0.137

0.000
(0.000)0.000(0.000)0.000(0.000)0.000(0.000)-0.000(0.000)-0.000(0.000)
21517
0.163

-0.000**(0.000)-0.000**(0.000)-0.000**(0.000)-0.000(0.000)-0.000(0.000)-0.000(0.000)
21632
0.270

54

Source: Authors’ calculations based on JLMPS 2016
Notes: *p<0.05; **p<0.01; ***p<0.001
Controls include district fixed effects, education level, mother’s education level, father’s education level, father’s employment status,
age, and age squaredStandard errors (in parentheses) clustered at the locality level55Appendix K: Varying levels of geographic aggregation
This appendix investigates the sensitivity of our results to varying the level of geographic
aggregation (sub-district or district rather than locality). We focus here on our main sample,
panel data for men. Appendix D, which presents the repeated cross-section data, is also ananalysis identifying off of variation in the share Syrian at the sub-district level. Before presenting
the model results, we discuss the degree of variation in localities within sub-districts or districts.
Overall, among Jordanians aged 15-64 (our sample), the median share of households Syrian in
their locality is 9.6%, with a standard deviation of 7.1 percentage points. Within sub-districts, theaverage standard deviation of locality-level percentage of households Syrian was 3.5 percentage
points, while within districts the average standard deviation of locality-level percentage ofhouseholds Syrian was 3.7 percentage points. This suggests variation across localities within
sub-districts and districts, but also, unsurprisingly, correlation within districts and sub-districts
and less variation than nationally.
Table 27 presents results of the panel data models for men using sub-district share ofhouseholds Syrian while Table 28 presents results with district share of households Syrian. In
both cases, results are similar to the main locality measures, although, unsurprisingly, statisticalsignificance is lost due for some results due to higher levels of aggregation (and clustering ofstandard errors).

56

Table 27. Sub-district share of households Syrian: Labor market outcomes (fixed effects linear probability and OLS models),men, panel data

Year (2010 omit.)
2016
Int. year and % HH
SyrianInt. 2016 and % HH
Syr.
N

Hours perweek

Managerial/
Health andLn (monthly Professional
Human
wage)
Occupation Open sector Serv.

Private sector

Unemployed Employed

FormalLn (hourly
wage)

0.040
(0.036)-0.091(0.057)0.157***(0.032)0.397**(0.117)-3.101*(1.304)0.145(0.112)0.019(0.026)-0.004(0.029)-0.006(0.016)-0.018(0.033)-0.000(0.001)7363

0.001(0.001)7394

0.002(0.001)4786

0.004(0.002)3863-0.077(0.096)4677

0.002(0.003)3924

0.001(0.001)4788

0.001(0.001)4789-0.000(0.001)4789-0.001(0.001)
4808

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the sub-district level57

Table 28. District share of households Syrian: Labor market outcomes (fixed effects linear probability and OLS models), men,panel data

Year (2010 omit.)
2016
Int. year and % HH
SyrianInt. 2016 and % HH
Syr.
N

Hours perweek

Managerial/
Health andLn (monthly Professional
Human
wage)
Occupation Open sector Serv.

Privatesector

Unemployed Employed

FormalLn (hourly
wage)

0.041
(0.038)-0.086(0.061)0.150***(0.034)0.395**(0.121)-3.339*(1.632)0.157(0.111)0.021(0.027)-0.007(0.030)-0.018(0.018)-0.009(0.034)-0.000(0.001)7363

0.001(0.002)7394

0.003*(0.001)4786

0.004(0.003)3863-0.054(0.130)4677

0.001(0.004)3924

0.001(0.001)4788

0.001(0.001)4789

0.001(0.001)4789-0.002(0.001)
4808

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the district level58Appendix L: New schools and labor demand in education
One potential mechanism for refugees creating labor demand is through use of health andhuman services, such as education. Education for Syrians is supported, financially, through both
donor aid and government funds (Brussels II Conference 2018). For example, in May 2016 $81.5
million was pledged specifically for education (Human Rights Watch 2016). In this appendix we
explore the potential role for such labor demand in the labor market in Jordan, examining therole of new schools and new shifts in Jordan. In host communities, Syrians can enroll in publicschools. If there are spots available in existing schools, after Jordanians have enrolled, Syrians
can take those spots; otherwise a second shift is added to schools. As of the 2016-2017 school
year, there were 209 schools on double shifts and 45 schools providing education to refugee
children in camps with Jordanian teaching staff (Ministry of Planning and International
Cooperation 2017). Donors, such as USAID, have helped finance expanding or renovating
schools as well as building new schools. Both the second shifts and additional schools have hired
additional teachers (Human Rights Watch 2016).
As a potential mechanism for creating labor demand, we use data on new schools. In the
Education Management Information System (EMIS) data from 2016/17, 5 an observation is a
shift within a school (so if a school operates two shifts, it has two observations), which isconsistent with the fact that a second shift would have additional hiring. We calculate thepercentage of observations (“schools,” which may be shifts within existing schools) that are“new,” established in 2011 or later based on their establishment date in the EMIS. We calculate
this measure at the sub-district level, the lowest level of geography available with the EMIS. We

5

See Assaad, Ginn, and Saleh (2018) for additional discussion of the EMIS data and analyses ofthe (non-)effect of Syrians on Jordanians’ education outcomes.
59interact this variable with the year (2016) in the panel data. The results are presented in Table 29.
In areas with more new schools as of 2016, for each percentage point increase in the share ofschools that are “new” there is a 0.3 percentage point increase in the probability of employment
in the health and human services sector (which includes education). There are not othersignificant effects. This multivariate evidence aligns with the patterns in Figure 1 in Appendix A,
suggesting a shift into such work between 2010 and 2016.

60

Table 29. Models including new schools: Labor market outcomes (fixed effects linear probability and OLS models), men, panel
data
Hours perweek

Managerial/
Ln (monthly Professional
Health and Private
wage)
Occupation Open sector Human Serv. sector

Unemployed Employed

FormalLn (hourly
wage)

0.042

-0.1090.207***

0.496**-2.8730.286*

0.023

0.023

-0.049-0.021(0.041)(0.065)(0.045)(0.163)(2.134)(0.117)(0.033)(0.041)(0.025)(0.042)-0.0000.001

0.003*

0.006-0.072

0.005

0.001

0.001

-0.001-0.001(0.001)
Int. year and % schoolsnewInt. 2016 and % schoolsnew-0.000(0.001)(0.001)(0.003)(0.100)(0.003)(0.001)(0.001)(0.001)(0.001)0.001-0.003-0.006-0.014-0.009-0.000-0.0020.003*

0.000(0.001)(0.002)(0.002)(0.006)(0.121)(0.005)(0.001)(0.002)(0.001)(0.001)

7363

7394

4786

3863

4677

3924

4788

4789

4789

4808

Year (2010 omit.)
2016
Int. year and % HH
SyrianInt. 2016 and % HH
Syr.

N

Source: Authors’ calculations based on JLMPS 2010 - JLMPS 2016 panel
Notes: *p<0.05; **p<0.01; ***p<0.001
Controlling for age and age squared in year
Standard errors (in parentheses) clustered at the sub-district level61

Appendices References
Amer, Mona. 2014. “The School-to-Work Transition of Jordanian Youth.” In The JordanianLabour Market in the New Millenium, edited by Ragui Assaad. Oxford, UK: Oxford
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Assaad, Ragui, Thomas Ginn, and Mohamed Saleh. 2018. “Impact of Syrian Refugees in Jordan
on Education Outcomes for Jordanian Youth.” Economic Research Forum Working Paper
Series No. 1214. Cairo, Egypt.
Assaad, Ragui, and Caroline Krafft. 2016. “Labor Market Dynamics and Youth Unemployment
in the Middle East and North Africa: Evidence from Egypt, Jordan and Tunisia.” Economic
Research Forum Working Paper Series No. 993. Cairo, Egypt.
Assaad, Ragui, Caroline Krafft, and Caitlyn Keo. 2018. “The Composition of Labor Supply and
Its Evolution from 2010 to 2016 in Jordan.” Economic Research Forum Working Paper
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Bagir, Yusuf. 2017. “Impact of the Syrian Refugee Influx on Turkish Native Workers: An Ethnic
Enclave Approach.” MPRA Paper No. 80803. Munich, Germany.
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for Syrian Children and Youth.” Brussels.
Card, David. 2009. “Immigration and Inequality.” The American Economic Review 99 (2): 1–21.
Human Rights Watch. 2016. “‘We’re Afraid for Their Future’: Barriers to Education for Syrian
Refugee Children in Jordan.” New York: Human Rights Watch.
Krafft, Caroline, Maia Sieverding, Colette Salemi, and Caitlyn Keo. 2018. “Syrian Refugees in
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