metadata: authors: 0: author: Azevedo,Joao Pedro Wagner De
1: author: Yang,Judy
2: author: Inan,Osman Kaan
count: Syrian Arab Republic,Türkiye
docna: 0: docna: What are the impacts of Syrian refugees on
            host community welfare in Turkey ? a subnational poverty analysis
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Poverty Impact Evaluation,Poverty Monitoring & Analysis,Hydrology,Inequality,Poverty Assessment,Poverty Diagnostics,Poverty Lines,Small Area Estimation Poverty Mapping,Rural Labor Markets
teratopic: Social Development
abstracts: cdata!: In recent years, Turkey has been host to
            more than two million Syrians seeking refuge. Initially
            concentrated in the southeastern regions, these refugees now
            reside throughout the country. There are many questions from
            policy makers regarding the impact of the population of
            Syrians Under Temporary Protection on the host community.
            This paper examines the impact of migrants on regional host
            communities from a poverty perspective. The paper does not
            find any negative impacts on poverty for the host community
            from the increasing population of Syrians Under Temporary
            Protection as of 2013, despite the high poverty rates
            experienced among the recent migrants.
geo_reg_key: 517191,517198,517208,517202
repnme: repnme: What are the impacts of Syrian refugees on
            host community welfare in Turkey ? a subnational poverty analysis
display_title: What are the impacts of Syrian
            refugees on host community welfare in Turkey a subnational
            poverty analysis
listing_relative_url: /research/2016/01/25838806/impacts-syrian-refugees-host-community-welfare-turkey-subnational-poverty-analysis
topicv3: Labor Markets m1326488 855,Water m1326522 137,Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707,Education m1327048 470
publishtoextweb_dt: 2016-01-28T00:00:00Z
historic_topic: Social Development
no_of_pages: 38
author_keys: 0: author_key: 675142
1: author_key: 973296
2: author_key: 1335589
model_extractions: WPS7542 Policy Research Working Paper 7542 What Are the Impacts of Syrian Refugees on Host Community Welfare in Turkey? A Subnational Poverty Analysis _Joao Pedro Azevedo Judy S. Yang Osman Kaan Inan_ Poverty and Equity Global Practice Group January 2016
Policy Research Working Paper 7542

# **Abstract**

In recent years, Turkey has been host to more than two million Syrians seeking refuge. Initially concentrated in the southeastern regions, these refugees now reside throughout the country. There are many questions from policy makers regarding the impact of the population of Syrians Under Temporary Protection on the host community.

This paper examines the impact of migrants on regional host communities from a poverty perspective. The paper does not find any negative impacts on poverty for the host community from the increasing population of Syrians Under Temporary Protection as of 2013, despite the high poverty rates experienced among the recent migrants.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jazevedo@worldbank.org or jyang4@worldbank.org or oinan@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
What Are the Impacts of Syrian Refugees on Host Community Welfare in Turkey? A Subnational Poverty Analysis Joao Pedro Azevedo Judy S. Yang Osman Kaan Inan December 7, 2015 The World Bank<sup>1</sup> JEL Codes: F22, I3, O15, R23 Keywords: Turkey, Syria, Poverty, Refugees, Forced Migration This paper has benefitted from comments by participants from presentations made in World Bank Washington, DC and Ankara offices, more specifically from the Poverty and Equity Global Practice’s Europe and Central Asia Team and the Turkey Country Office as well as inputs by our colleagues in the Social Protection and Labor Global Practice and the Social, Urban, Rural, and Resilience Global Practice. The team thanks Martin Raiser, William Wiseman for their guidance and support. We are also thankful for comments and assistance with data received from JoAnna P. De Berry, Ximena Del Carpio, Doreen Triebe, Zeynep Durnev Darendeliler, Jose Montes, and Minh C. Nguyen. The usual disclaimer applies. This paper is a product of the FY2015 Turkey Poverty Team in the World Bank’s Poverty and Equity Global Practice. This piece is part of a collaboration with the Urban, Rural, and Social Development GP supporting Turkey’s Regional Development Strategy (RDS) 2014-2023.

> 1Contacts: Joao Pedro Azevedo (Lead Economist, jazevedo@worldbank.org); Judy S. Yang (ET Consultant, jyang4@worldbank.org); Osman Kaan Inan (Junior Professional Associate, oinan@worldbank.org)

> This paper has benefitted from comments by participants from presentations made in World Bank Washington, DC and Ankara offices, more specifically from the Poverty and Equity Global Practice’s Europe and Central Asia Team and the Turkey Country Office as well as inputs by our colleagues in the Social Protection and Labor Global Practice and the Social, Urban, Rural, and Resilience Global Practice. The team thanks Martin Raiser, William Wiseman for their guidance and support. We are also thankful for comments and assistance with data received from JoAnna P. De Berry, Ximena Del Carpio, Doreen Triebe, Zeynep Durnev Darendeliler, Jose Montes, and Minh C. Nguyen. The usual disclaimer applies.  This paper is a product of the FY2015 Turkey Poverty Team in the World Bank’s Poverty and Equity Global Practice. This piece is part of a collaboration with the Urban, Rural, and Social Development GP supporting Turkey’s Regional Development Strategy (RDS) 2014-2023.
# **INTRODUCTION**

In 2015, an estimated 2.2 million Syrians Under Temporary Protection (SUTPs) were residing in Turkey, the majority arriving in the country over the last 4 years.<sup>2</sup> Turkey’s national population is roughly 75 million; recent refugees account for approximately 3 percent of the population. For a country that has never experienced such a large-scale, sudden inflow of foreigners, demographic changes in the composition of the population and labor force will yield unprecedented implications.

This paper examines, as data allows, the relationship between the size of the foreign-born population and host community poverty rates in Turkey. First, this paper finds the poverty rates of ‘recent migrants’ near the Syrian border (NSB) significantly increased from 2009 to 2013. Second, the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians. Third, with respect to poverty, the results show no negative impacts on the host community as a result of the increasing size of the foreign-born population. The impact of SUTPs has been both positive and negative. Overall, a significant negative impact on host communities’ welfare is not observed in the data. This paper’s scope of analysis includes the country as a whole using a nationally representative survey. While regional case studies may reveal salient stresses on public services and job displacement, nationally, there is no significant impact. Over the period of 2009 to 2013, the poverty rates of host community households have stayed relatively stable near the Syrian border; despite the high poverty rates experienced among the recent migrants.

By country of origin, the displacement of Syrians is one of the largest in recent history. As a result of the civil war that began in 2011, Syrians started to leave their homes and look for safety in neighboring countries across the region. By November 2015, about 4.3 million Syrians were seeking refuge in primarily Turkey, Lebanon, Jordan, Iraq, and the Arab Republic of Egypt.<sup>3</sup> The only other time in the last half century that the world experienced a larger group of refugees from a single country is the case of Afghan refugees during the 1980s to 1990s. Refugee displacements of this size are rare. Consequently, they are not well studied and their impacts are not well understood. Moreover, the case of Afghan refugees in Pakistan is different, since they were stigmatized to a larger extent, which limited their movement in Pakistan. While there is a large literature on the role of immigrants on the native-born population in terms of labor market competition, there is a limited amount of studies that examine the effect from displaced populations. Many conclusions from the traditional literature on the study of immigrants’ impact on natives cannot be applied to the case of Syrians in Turkey. There are many differences between the inflow of Syrians and other flows of extended family and economic immigrants. First, the sheer volume of Syrian refugees and the short timeframe in which they entered Turkey is unprecedented. For the case of Syrians in Turkey, or displaced populations in general, large movements of refugees are not restricted due to humanitarian reasons. Second, formal immigration processes are controlled, limited, and regulated by destination countries. Therefore, results from literature on “immigrants” are very different than a focus on displaced or refugee populations.

Recent literature on the labor market effects of SUTPs estimates negative impacts on host community employment rates. The negative displacement results are largest for the young, women, informal workers,

> 2 United Nations High Commissioner for Refugees (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal

> 3 (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal

2
and the less educated (Ceritoglu, Tunculer, Torun, and Tumen, 2015; Del Carpio and Wagner, 2015). The economic effects of SUTPs not only vary across different segments of the labor market, there are also strong regional differences in their economic effects. Using synthetic modelling methods, Ozturkler and Goksel (2015) estimate the impact of Syrian refugees on local prices, wages, inflation, and services in 10 cities with large refugee populations. Some of the salient negative effects have been increases in rental prices, increases in inflation at border cities, illegal hiring by small business, and decreases in wages. However, in some cities (Gaziantep, Adana, Kahramanmaras, and Mardin), the presence of refugees has improved the trade balance, and economic activity in these areas are projected to increase as economic integration with MENA deepens. Orhan and Gundogar (2015) also note both positive and negative aspects of SUTPs.

A primary contribution of this paper is the estimation of poverty at the sub-national level and among population groups of interest. Since migration, geographic, and welfare variables do not exist in a single data set, imputation techniques are required to overcome these limitations and to compute household level poverty. The imputation of income poverty is done using the Turkish Labor Force Survey (LFS), and with information and modeling parameters determined from the Survey of Income and Living Conditions (SILC). More details and validation of this methodology is discussed throughout this paper.

While explicit identification of Syrians in available surveys is not feasible, there is evidence of an increase in the amount of foreign-born individuals that is being captured in the LFS. The arrival year of foreign-born migrants is available in the data which allows for identification of “Settled Migrants” and “Recent Migrants”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the country, it is not surprising that they are accessible to interviews by the LFS.

Despite data limitations, there are strong and significant trends in the poverty rates for the recent foreignborn, especially for those near the Syrian border. In 2013, recent migrants near the Syrian border were the poorest group<sup>4</sup> in Turkey. While this statistic in itself is not initially surprising, fluctuating welfare trends of recent migrants over time is noteworthy. In previous years, migrant households in Turkey tend to have much lower poverty on average than even the host community. Across comparison groups and time, the poverty rates of recent migrants is higher than among the host community in only one instance: in 2013 near the Syrian border. This sudden change in the historically stable pattern implies that the LFS is able to capture at least a part of the incoming SUTPs who have significantly different socioeconomic profiles in comparison the previous economic migrants.

Throughout history, immigration to Turkey has been relatively limited and consisted mostly of those of Turkish heritage. In the early 20<sup>th</sup> century, immigration was encouraged by the government as a method to increase the population. Since 1970, immigration has slowed down and has been even discouraged at times. Many immigrants to Turkey are of Muslim Turkish background, since the government prioritized preserving a national identity. This is likely why “migrants” had very similar or even lower poverty rates than the host community.

The sharp degradation of welfare among recent migrants in 2013 illustrates the severity of poverty that is arising very likely from a growing population of Syrian refugees. The Syrian refugee inflow to Turkey

> 4 Based on grouping by host community, established migrant, recent migrant.

3
began in April 2011 and has been continuing at an increasing pace as the conflict in Syria expands.<sup>5</sup> The Turkish government has provided a tremendous amount of support in the form of shelter and essential items to help sustain the livelihood of large numbers of refugees. However, aid funds are not limitless and refugees face hardships that will persist over the long-term. The refugee camp population in Turkey has been stable since March of 2013 as the physical capacity of the camps have been exhausted.<sup>6</sup> This saturation has resulted in a steep increase in the number of Syrians living outside camps across Turkey. The proportion of Syrian refugees living outside camps increased from 53 percent to 87 percent between March 2013 and November 2014.<sup>7</sup> In addition, even though refugees living outside camps continue to be concentrated near the Syrian border (64 percent), the dispersion of Syrians across the country has expanded, especially in major urban centers such as Istanbul and Ankara.

The results in this paper are limited to 2013 due to changes in the 2014 LFS that make poverty estimations incomparable to previous years.<sup>8</sup> Therefore our results may provide only a partial insight into the impact of SUTPs, since the dispersion of Syrians across Turkey has increased greatly in 2014 and 2015. The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics.

Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013.

The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare.

# **1. DATA AND TECHNICAL ISSUES**

## **_Data Sets_**

Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty,

> 5 UNCHR, 2013pg 13

> 6 UNHCR (22 November 2013), UNHCR (15 September 2014)

> 7 UNHCR (22 March 2013),  Erdogan (2014), pg 14

> 8 This technical issue will be discussed further later in the paper.

4
does not have migration or geographic identifiers. The SILC contains geographic identifying variables, (at the NUTS1 level) but still lacks migration variables.

The data set this study uses is the Labor Force Survey (LFS) since there is an adequate availability of both migration and geographic variables. The LFS is representative at the NUTS2 level which corresponds to 26 regions in Turkey. One caveat is that income in the LFS refers to only wage income from employment,<sup>9</sup> and is an insufficient measure of income that should be used for welfare measurement. For example, important sources of income such as social assistance, asset liquidation, or remittances are missing. Therefore, income in the LFS is imputed with a few assumptions using information from the SILC. The NUTS1 spatial effects of the SILC are a good proxy for NUTS2 welfare dynamics in the LFS which increases the accuracy of the imputation model. However, since the original sample frame of the LFS does not account for the recent influx of foreign migrants in Turkey, the labor market characteristics of recent migrants might not be representative of the actual SUTP population. Therefore, results of the imputation could be interpreted as upper bound estimates for recent migrants. More details of survey techniques used to complete this exercise are available in the Annex. As a result, imputed poverty is measured as income poverty. Another advantage of using the LFS is the availability of CPI at the NUTS2 level in Turkey which allows for spatial deflation of different price levels across the country.

Table 1. Survey Comparison and Data Availability

||Years<br>Available|Migration<br>Variables|Income or Consumption|Geographic Identifier|Spatial<br>Deflation|
|---|---|---|---|---|---|
|**HICES**|2003-2012|No|Consumption, income|National, urban/rural|No|
|**SILC**|2009-2012|No|Income|NUTS1|No|
|**LFS**|2009-2013|Yes|Imputed Income|NUTS2|Yes|

However, there are other issues for consideration when using the LFS. Principally, there is a low number of sample points that are migrant households. Moreover, the study cannot identify migrant households and individuals that are specifically Syrian refugees. Foreign migrants are defined as those who were born abroad and have lived abroad for at least more than 12 months. Some Turkish-born households have also lived abroad for over a year, and these individuals are not considered to be migrants. Amongst foreign-born individuals, only the ones who have been in the country for more than 12 months are included in the sample which underrepresents the actual number of foreign migrants in the region. In addition, no specific procedure is adopted by the enumerators if the household does not speak Turkish. Given that a majority of Syrian refugees do not speak Turkish, the language barrier might result in the removal of Syrian households from the sample. Finally, refugee camps are not included in the sample frame, which limits the study to only examining recent migrants who do not live in refugee camps.

> 9 Wage income is only available for regular and casual employees in the LFS which accounts for around 60% of total employment. There is no other monetary income value for the rest of the working population.

5
# ~~we awe~~

<!-- Start of picture text --> we awe<br><!-- End of picture text -->
Host community households are those whose head of household were born in Turkey, or born outside Turkey but did not live abroad for more than a year. Conversely, migrant households are defined as those whose head of household was born abroad and has lived abroad for more than 12 months. The duration of a migrant household’s stay in Turkey is also based on when the head of household arrived in Turkey. Three thresholds are tested: 2, 3, and 4 years. The 4 year cut-off is preferred to maximize the sample size of recent migrant households.

Table 2. Population Groups for Comparison

||Group1|Group2|Group3|Group4|Group5|Group6|
|---|---|---|---|---|---|---|
|Geographic<br>Location||Near the Syrian Bord|er|T|he Rest of the Count|ry|
|Status|Host Community|Settled Migrant<br>Households|Recent Migrant<br>Households|Host<br>Community|Settled Migrant<br>Households|Recent Migrant<br>Households|
|Years in Turkey|Head of<br>Household born<br>in Turkey|Arrived in<br>Turkey more<br>than: 2, 3, or 4<br>years|Arrived in<br>Turkey less than:<br>2, 3, or 4 years|Head of<br>Household born<br>in Turkey|Arrived in<br>Turkey more<br>than: 2, 3, or 4<br>years|Arrived in<br>Turkey less than:<br>2, 3, or 4 years|

# **_Are Syrian Refugees being captured using the LFS?_**

While variables covering all topics of interest (migration, welfare, and geography) are available or can be imputed into the LFS, it may be unclear, ex-ante, if SUTPs are adequately included in the survey.

Despite a small sample of foreigners and other concerns, there is evidence that the LFS sample does include some “recent foreign” migrants, especially in the border regions (NUTS2) [TRC1-Gaziantep, Adiyaman, Kilis, TRC2-Sanliurfa, Diyarbakir, TRC3-Mardin, Batman, Sirnak, Siirt TR63-Hatay, Kahramanmaras, Osmaniye] (Map 1).

7
Figure 1. Change in Recent Migration <!-- Start of picture text --> Recent Migrants (1 or less than 1 year in Turkey) Recent Migrants (2 or less years in Turkey)<br>60.00% 60.00%<br>50.00% 50.00%<br>40.00% 40.00%<br>30.00% 30.00%<br>20.00% 20.00%<br>10.00% 10.00%<br>0.00% 0.00%<br>2010 2011 2012 2013 2010 2011 2012 2013<br>-10.00% -10.00%<br><!-- End of picture text -->

**Recent Migrants (3 or less years in Turkey)**

**Recent Migrants (4 or less years in Turkey)**

<!-- Start of picture text --> 60.00% 60.00%<br>50.00% 50.00%<br>40.00% 40.00%<br>30.00% 30.00%<br>20.00% 20.00%<br>10.00% 10.00%<br>0.00% 0.00%<br>2010 2011 2012 2013 2010 2011 2012 2013<br>-10.00% -10.00%<br>Regions Bordering Syria Rest Regions Bordering Syria Rest<br><!-- End of picture text --> _Source_ : LFS 2009-2014. World Bank author’s calculations.

Figure 1 illustrates the percentage increase of _recent foreign migrants_ when compared to the level of _recent foreign migrants_ present in Turkey in 2009 (baseline year omitted). The dashed line shows the trends in the border regions and the solid line shows trends in the rest of the country.

While there is an uptick in recent migrants near the Syrian border regions, it is important to remember that the sample size in the LFS is small, especially among recent migrants (Table 3). Notice, that the sample of recent migrants is relatively constant from 2009 to 2012, but in 2013 and 2014 there is a clear acceleration in the growth of the number of recent migrants being included in the survey. Since the number of recent migrant households begins to increase substantially in the 2013 LFS, there is evidence pointing to the representation of SUTPs in the survey. The analysis in this paper will be as transparent and rigorous as possible under the data conditions and limitations. This will translate into high standard errors in our statistical estimates, but estimates and especially trends of poverty rates can still be informative.

8
Table 3. Number of households (HH) in the sample

||Host<br>Community|Settled Migrant<br>HH<br>(>4 years in<br>Turkey)|Recent Migrant<br>HH<br>(<=4 years in<br>Turkey)|Settled Migrant<br>HH<br>(>3 years in<br>Turkey)|Recent Migrant HH<br>(<=3 years in<br>Turkey)|
|---|---|---|---|---|---|
|2009|133,710|2,127|54|2,142|39|
|2010|141,597|2,216|58|2,223|51|
|2011|142,203|2,101|57|2,111|47|
|2012|143,756|2,086|68|2,098|56|
|2013|143,572|2,363|120|2,374|109|
|2014|147,175|2.523|359|2.553|329|

_Source_ : LFS 2009-2014. World Bank author’s calculations.

The rate of increase in the recent migrant population of labor force surveys between 2011 and 2013 is parallel to observed trends in the government and UNHCR data regarding the SUTPs (Figure 2 in the annex). At the level, the LFS is not comparable to the government and UNHCR data since the sampling structure was not planned to represent the entire SUTP population. However, Figure 2 shows that the change between 2012 and 2013 in the recent migrant population of the LFS has a similar slope to the increase in the SUTP population from administrative data. The trend in the recent migrant population near the Syrian border is almost identical to the movement in the UNHCR and government numbers.

A caveat to the analysis in this paper is the small sample of foreign-born households in the LFS. In the 2009 survey, there are only 58 foreign-born households, and only three are located near the Syrian Border. Applying sampling weights, these three households represented 1,368 people in the population. By 2013, the number of surveyed foreign households increased substantially to 120, with 34 households in the NSB region. In 2014, the sample of foreign-born almost tripled from the year before and the estimated foreignborn population over the age of 15 reached 1.3 million. One very important change in the survey that cannot be emphasized enough is that the sampling of the LFS changed in 2014. Therefore the population estimates in 2014 are only of the population aged 15 and over. From 2009 to 2013, estimates reflect the entire population. However, estimates of the number of households should not be affected by this sampling change.

Table 6 through Table 9 highlight statistics on host community and foreign-born households and population. An interesting trend is the movement of the local population away from the East. In 2013, 16.6 percent of host community households were located near the Syrian border. This value dropped to 15.7 percent in 2014. The recently (<=4 years in Turkey) arrived foreign households, are also mobile and able to move inland. In 2013, 25.7 percent of recent foreign households were located Near the Syrian Border compared to 22.2 percent in 2014. However, the sampling frame of the LFS also changed in 2014 which might have impacted population trends.

9
# **2. POVERTY TRENDS**

## **_Imputation_**<sup>**_11_**</sup>

Household-level poverty is calculated using imputed income from the LFS. The LFS is ideal due to the availability of regional and migrant information, but only has wage data which is insufficient to capture total income for welfare measurement. Thus total income needs to be imputed. The SILC data set can also be used to study regional poverty rates, but only at the NUTS1 level. Another disadvantage of the SILC is that spatial deflation in Turkey is only available at the NUTS2 level (LFS data), and not at the NUTS1 level since TUIK computes regional CPIs only at the NUTS2 level. Income and household characteristics from the SILC are used to produce a model to impute total income into the LFS.

The imputation model is an OLS with regionally deflated incomes at the NUTS2 level and time-invariant characteristics. Regressions are conducted using household-level data. For each year, the model specified from the SILC may vary. For every household, total household income is imputed 100 times. At the regional and comparison group level, poverty rate estimates and standard errors are computed from these 100 imputations. The international $5/day per capita 2005PPP poverty line is used. One way to interpret the poverty rates from imputed income is simply the conditional welfare based on observables.

Observed income poverty can be computed using the SILC but only at the NUTS1 level and without differentiation between the host and foreign-born populations. As a validation exercise, observed poverty computed using the SILC and imputed poverty using the LFS can be compared.

Figure 7 illustrates this comparison and shows that, at the NUTS1 level, imputed poverty rates using the LFS are very similar to observed poverty in the SILC. One caveat is that 2012 and 2013 LFS uses models computed in the 2011 SILC due to a lag in the SILC data availability and changes in the LFS survey methodology.

## **_Estimates of Poverty_**

Table 4 lists results of imputed poverty rates by geographic location, comparison group, and year. Results are shown using the OLS imputation model with time-invariant characteristics and regionally deflated incomes. Recall that in the LFS, the number of recent migrant households in the sample is small when broken down to geographical groups. An implication of small sample size is high standard errors in our poverty estimates for this group, especially in 2009. However, in 2013, the number of foreign-born households increased significantly which allows for more precise estimates of migrant poverty rates.

In principle, the migration and assimilation literature sets expectations that migrant households are generally worse off than host community households upon arrival. The rate of assimilation of migrants depend on their education, language, and opportunities. The children of migrant households also do better than their parents. However, in Turkey, migrant households traditionally have not been worse off than host community households. This is due to the homogeneity of migrants which tend to be of Turkish heritage.

> 11 See the Annex for further details on the survey-to-survey imputation methodology.

10
However, recent migrant households near the Syrian border in 2013 are distinctively poorer than any previous group of migrant households.

Table 4. Imputed Poverty Rates (LFS)

|||Ne<br>|arSyrian Bor<br>|der<br>|Re<br>|st ofthe Coun<br>|try<br>|
|---|---|---|---|---|---|---|---|
|||Host<br>Community|Settled<br>Migrants<br>(>4years)|Recent<br>Migrants<br>(<=4years)|Host<br>Community|Settled<br>Migrants<br>(>4years)|Recent<br>Migrants<br>(<=4years)|
|2009|Poverty Rate|44.2|27.5|15.6|21.1|9.1|7.3|
||S.E.|0.8|6.7|22.8|0.3|0.7|5.0|
|2010|Poverty Rate|42.6|23.8|27.1|20.8|10.7|5.2|
||S.E.|0.7|6.4|19.3|0.3|0.8|3.2|
|2011|Poverty Rate|40.9|12.5|17.5|19.3|9.3|13.3|
||S.E.|0.7|5.4|25.6|0.3|0.7|4.9|
|2012|Poverty Rate|41.1|21.3|37.0|18.4|7.0|13.2|
||S.E.|0.7|5.1|22.5|0.3|0.7|4.5|
|2013|Poverty Rate|40.3|17.7|46.7|17.9|6.6|16.1|
||S.E.|0.8|5.8|7.9|0.3|0.5|6.7|

_Notes_ : Regionally deflated, OLS imputation model, with time-invariant characteristics. The Near Syrian Border region (NSB) includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, TR62. Time in Turkey is based on the date of arrival of the head of household. _Source_ : LFS 2009-2014. World Bank author’s calculations.

Figure 2 illustrates poverty trends in 2009 and 2013, by region and migrant status. Regions are divided into the near Syrian border region, and the rest of the country. The NSB includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, and TR62. The population is also divided into host communities, recent migrant households, and settled migrant households.

Poverty is higher in regions near the Syrian border, for both host communities and migrants. The South Eastern region of Turkey has been historically the poorest area of the country. However, between 2009 and 2013, poverty has declined for host communities and settled migrant households. On the other hand, poverty of recent migrant households in the NSB region has been increasing, and significantly so in 2013 (Figure 3). The sharp increase in the poverty rates of recent migrant households is evident. Near the Syrian border, recent migrant households had a poverty rate of 15.6 percent (caveat on the small sample size and large standard error) in 2009, and a rate of 46.7 percent in 2013. There was a parallel increase among the recent migrant households in rest of the country, from 7.3 percent to 16.1 percent over the four years.

11
Figure 2. Poverty Estimates, by year and migrant status <!-- Start of picture text --> 2009<br>80<br>70<br>60<br>50<br>44.2<br>40<br>30<br>27.5<br>20 21.1<br>15.6<br>10 9.1 7.3<br>0<br>Host Community Settled Migrants Recent Migrants Host Community Settled Migrants Recent Migrants<br>(>4 years) (<=4 years) (>4 years) (<=4 years)<br>Near Syrian Border Rest of the Country<br>2013<br>80<br>70<br>60<br>50<br>46.7<br>40 40.3<br>30<br>20 17.7 17.9 16.1<br>10<br>6.6<br>0<br>Host Community Settled Migrants Recent Migrants Host Community Settled Migrants Recent Migrants<br>(>4 years) (<=4 years) (>4 years) (<=4 years)<br>Near Syrian Border Rest of the Country<br>Poverty Rate (%)<br>Poverty Rate (%)<br><!-- End of picture text --> Notes: Regionally deflated, OLS imputation model, time-invariant. Vertical line represents standard error. Poverty line is $5/day PPP per capita. The Near Syrian Border region (NSB) includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, TR62. Time in Turkey is based on the date of arrival of the head of household. Results displayed at the 95% Confidence Interval _Source_ : LFS 2009-2014. World Bank author’s calculations.

It is important to understand which characteristics or circumstances may be driving the large differences in poverty between our six comparison groups. Changes over time are also worthy to note since the number of foreign households in the LFS increases dramatically in 2013 and 2014. Table 10 illustrates the mean summary statistics for our comparison groups in 2009, 2013, and 2014 that are located near the Syrian border.<sup>12</sup> Table 11 shows complementary statistics for households located in the rest of the country.

Recent migrant households are younger and have more children, and a higher dependency ratio. Dependency in migrant households result not from the elderly but from children. Families of recent migrants are much larger. In 2013, recent migrant households near the Syrian border had on average 3.1 adults, 2.4 children, and 0.4 elderly. The large number of children is indicative of entire families moving to Turkey from Syria. The composition is in contrast to working age individuals who are more likely to be economic migrants. These individuals would migrate without families, and send remittances home to their

> 12 The years 2009 and 2014 are the earliest and latest available surveys in the LFS that are usable for measuring migration. However, due to data availability on Syrian refugees and changes to the survey in 2014, regression analysis will use only 2011 and 2013 LFS data.

12
origin countries. Compared to host community or settled migrant households, recent migrant households are younger. In 2013, heads of households in over half of recent migrant households were aged 25-39. In host communities, about a third of household heads are in this age group. Moreover, recent migrant households near the Syrian border are less educated. The heads of household in 76% of recent migrant households NSB had no education, and none of them had post-secondary education. The majority of recent migrant households are also not registered with the social security institution. In 2013, only 20.7 percent of recent foreign-born households near the Syrian border were registered. In 2014, this rate dropped to only 2.3 percent of households. Registration to social security is related to a person’s main job and the formality of the occupation. This rate decreased, indicating a lack of formal jobs. Moreover, the recent jobs were mostly blue-collar. From 2013, to 2014, there is a clear trend of increasing informality among these workers.

To summarize, the high poverty rates observed among recent migrant households near the Syrian border can be related to a few descriptive characteristics: larger family size, higher dependency ratios, less education, younger families, informality, and less social protection.

Interestingly, poverty rates of settled migrant households are actually lower than rates among host communities, and significantly so. In 2009, poverty rates for both settled and recent migrants (large sample error for recent migrants) are substantially lower than the host communities. In 2013, settled migrants’ poverty rate is less than half of the host community’s rate while recent migrants became the poorest group. This decoupling between the poverty rates of settled and recent migrants over the four years indicates that the type of recent migrants changed over that time span. The increase in the poverty rate of this new type of recent migrants suggest that the cohort, in 2013, is capturing the incoming SUTPs, particularly in the NSB regions.

There is weak evidence that the welfare of migrant households in Turkey does improve over time in the long-run. Near the Syrian border regions, the poverty rates of migrant households who have entered Turkey <10 or 10-19 years ago are not significantly different. There could be a number of reasons for this, including wealthier families being able to move farther west to Istanbul or Europe in the long run. In 2013, the poverty rates of recent migrants do increase substantially, while this cannot be attributed directly to the influx of Syrian refugees, recent migrants in the NSB regions are very likely to be SUTPs. However, migrant households in Turkey are still very dynamic, with poverty rates generally lower than those of host communities. Since the nationalities of migrants from 20-30 years ago are much different than the composition in recent years, no clear conclusions on assimilations can be drawn yet.

Figure 5 illustrates the poverty rate of only host community households. Poverty rates are illustrated by geographic area as well as characteristic groups. Among the selected characteristics, host community households whose head is a blue collar worker have higher poverty rates, while male headed households have lower poverty rates. Geographically, the host community in the NSB region have poverty rates almost twice as high as their counterparts in the rest to the country, and this does not vary significantly by their characteristics groups.

Overall, no matter whether poverty rates are calculated for selected host community households whose head of household is a blue collar worker, female head, male head, or head with low education; poverty rates among these households do not increase in 2013. This pattern holds for both in the near Syrian border region and in the rest of the country.

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# **3. ANALYSIS**

Has the increase in Syrian refugees impacted the welfare and socioeconomic conditions of the host community? Summary statistics in the previous section showed clear trends of increasing poverty among recent migrants throughout the country, both near the Syrian border and across rest of the country. While the poverty rates of recent migrant households spiked in 2013, poverty of host community households maintained a relatively constant level in the whole country. From these trends, it appears that there at least has not been an increasing trend in poverty among the host community over the latest years.

The empirical model is shown in Equation 1. Regressions are estimated at the NUTS2-year level and using data from only the years 2011 and 2013. The dependent variable of interest is the host community poverty rate by region and year, where the poverty rate is based on spatially deflated imputed household income. Unlike the computation of the poverty rates, “recent migrant” information is not used for the analysis. Only the host community poverty rates are calculated using the LFS and the number of Syrians are taken from government sources. In that respect, the regression dos not suffer from the small sample size or any other limitations relating to the estimation of poverty rates for recent migrants.

The year 2013 is selected since there is available administrative information on the stock of Syrian refugees across regions. The choice of 2011 is rather arbitrary since we assume no foreign-born population in the year 2011 or before. The explanatory variable of interested is the proportion of SUTPs relative to the population (���). The assumption is that the proportion equals zero for all regions in 2011. This is a simplistic but reasonably accurate estimate since the majority of SUTPs arrive in 2012. Moreover, official estimates of the number of Syrians in Turkey were also not available until starting in 2012. Overall, in 2013, Syrians were still very concentrated in only a few regions. Out of 26 regions, Syrians comprised more than 1 percent of the population in only 7 regions. Estimations cannot be conducted at the household-level data since the survey-to-survey imputation methods used to compute poverty rates yield only group level estimates of poverty rates, not household level poverty rates.

The ideal methodology would be to measure the causal effects of the influx (shock) of Syrians under temporary protection as a proportion of the population onto the poverty rates of the host community. However, there are some concerns regarding endogeneity. The foreign-born in Turkey have freedom of movement and it is very likely that households prefer to settle in locations that are more welcoming or offer better economic opportunities. Regional differences in social and economic conditions can influence their decisions to locate. Del Carpio and Wagner (2015) discuss these concerns and propose an instrumental variable approach. Two different, but complementary IVs are proposed: distance and time from Syrian regions to Turkish regions. In this paper, regional aggregation and time dimension of the data limits the data to only 52 data points. There are not enough rank conditions and IV is not feasible.

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Moreover, there is an added level of complexity of estimation error arising from imputation estimates as well as from the regression. Running regressions at this level of aggregation can only provide a general sense if host community poverty rates are correlated to the size of the foreign-born population.

The most likely scenario is that refugees are attracted to locations with healthy economies and attractive wages, or locations that show promising growth and good job opportunities. These unobservable circumstances would be negatively associated with poverty rates of the host community. The variable of interest _Rry_ would be negatively correlated to these types of unobservable conditions. Therefore �� would be positively biased. In other words, estimates are reasonably lower bounds.

# **_Results_**

Table 5 illustrates results from the empirical estimation of Equation (1). There is no evidence that poverty rates of the host community are linked to the proportion of the foreign-born population. OLS regressions without any control variables do show a positive correlation between the proportions of foreign-born and host community poverty. However, this is primarily due to regions in the East and near the Syrian border historically being the poorest regions in the country. Once region and time controls are added to the regressions, the correlation between the host community poverty and the size of the foreign-born no longer exist. Similarly when controls on average characteristics of the regional population are included into the specification, there is no relationship between the explanatory variable of interest and the dependent variable.

Table 5. OLS Regression Results, the Host Community

|(1)|(2)|(3)|
|---|---|---|
|Proportion of Foreign-Born in the Population<br>0.957**<br>(0.423)|-0.0910<br>(0.126)|-0.00385<br>(0.0753)|
|Constant<br>0.203***<br>(0.0386)|0.108***<br>(0.00177)|0.505<br>(0.289)|
|Year Dummy|X|X|
|Region Dummies|X|X|
|Labor and Demographic Controls||X|
|Observations<br>52|52|52|
|R-squared<br>0.091|0.944|0.992|
|Notes: * p<0.10, ** p<0.05, *** p<0.01.<br>_Source_: LFS 2011 and 2013. World Bank author’s ca|lculations.||

Del Carpio and Wagner (2015) find some negative impacts, displacement effects, from the increase in foreign-born from 2011 to 2014. The displacement effect is largest among low-skill, agricultural, and loweducated workers. There are various reasons why results in this paper are neutral compared with those of DCW (2015). Their analysis is based on the 2014 LFS, which exhibits a much larger foreign-born population than the 2013 survey that is used in this paper. Unfortunately, due to changes in survey collection 15
methods, particularly the exclusion of children from the LFS data set in 2014, this paper is not able to use the 2014 data set to impute household-level poverty rates.<sup>13</sup> Our analysis is limited to 2013.

Another difference is that this paper’s analysis is conducted at the regional level. Regional aggregation smooths away household differences. Therefore, regressions are run on poverty rates of different subsamples of the host community. Table 13 and Table 14 present results of regressions on sub-samples based on the characteristics of the head of the household. We consider gender, education, and work type. Del Carpio and Wagner (2015) found the impact of SUTPs varies across different groups of the labor force. It is possible that some households are more susceptible or vulnerable than others in managing externalities from large in-flows of SUTPs. However, results from these regressions continue to show that across various groups, the proportion of foreign-born did not have a significant effect on poverty.

A third reason is substitution of labor yielded no net effects at the household level. Not all impacts are negative to the host community. As Del Carpio and Wagner (2015) point out, the influx of refugees also generated better paying jobs that were filled by local Turkish workers. It is possible that at the household level, the net labor market effects from the influx of refugees remained insignificant as some low skilled members lost jobs but then better jobs became available. Summary statistics show that from 2009 to 2013, the share of employed adults in a host community household near the Syrian border actually increased from 24 to 27 percent, and the share of unemployed adults dropped from 5 to 4 percent (Table 10). The share of host community heads of household working in Agriculture, Forestry, Fishing, and Mining also significantly declined near the Syrian border from 2009 to 2013. In 2009, 16 percent of heads of household in this group worked in this sector, and declined to 12 percent in 2013. The share of workers in white collar jobs also significantly increased and blue collar jobs declined during this period. This would result in insignificant impacts from an increasing foreign-born population at the household level.

In the shorter time period, from 2011 to 2013, the characteristics of host communities in near the Syrian border did not regress (Table 16). For example, the proportion of workers in a household working white collar jobs increased from 30.9 percent in 2011 to 34.4 percent in 2013. The share of workers registered with a social security institution also increased from 46.1 to 52.4 percent.

One caveat is that over this period, Turkey experienced healthy growth, and direct impact of these effects whether through displacement effects or increased growth due to the influx of Syrians is not completely obvious. For example, in the rest of the country, which is a less popular destination for migrants, the share of adults employed also increased from 32 to 37 percent from 2009 to 2013. In other words, improved conditions of host community families could be due to a period of healthy growth. Would host community families have stopped working in agriculture anyway even if there were no Syrians or did Syrians start taking these jobs? Whether or not host communities would be better off is not clear, but what can be said is that the poverty rate has not gone up.

While poverty rates in 2014 are not available, a comparison of the host community characteristics in 2013 and 2014 may give insights to trends. Looking at a more compressed period from 2013 to 2014, there is some evidence that host community families changing in their labor market characteristics. For example, the proportion of workers with a permanent contract type declined from 36.2 percent in 2013 to 35.1 percent

> 13 Household size is not reported for the entire household, only the 15+ segment. Dependency ratios and per capita income cannot be computed using the 2014 LFS.

16
in 2014. The share of workers registered with social security in host community households declined but the change is not significant. In terms of the number of hours worked, and the proportion of workers in blue or white collar jobs, there is no statistical difference between 2013 and 2014.

# **4. CONCLUSION**

The movement of Syrian refugees is one of the largest passages of refugee populations in recent history. With millions of people leaving Syria and settling in Turkey, concerns about externalities onto the native population are very salient. This paper addressed the poverty impacts of SUTPs on the host communities and found no evidence that the increase in foreign-born population from 2011 to 2013 resulted in higher poverty rates among the host community. As recent literature has noted, the SUTPs have both positive and negative impacts. While some types of people may be more likely to be displaced by Syrians in the labor market, Syrians are consumers and renters, they also open businesses and create jobs. Local Turkish citizens have also benefited as employers and sellers. In some border cities, the balance of trade has improved as exports to the Middle East increased. On the other hand, analysis of only the recent migrants clearly demonstrates that the group’s poverty profile is worsening between 2009 and 2013. Moreover, descriptive characteristics suggest that their conditions might have worsened in 2014.

As this unprecedented event continues, the integration of Syrians into the Turkish labor market, access to public services, changing demographics, and socioeconomic impacts should be monitored closely. Especially with an increasing rate of SUTP migration to Turkey during 2014 and 2015 and the continued conflict in the region, the inflow of Syrians will be one of the most critical short, medium, and possibly long term policy issues in the country. In addition, Turkey’s role as a pathway to Europe for those escaping conflict in the Middle East makes the issue an international phenomenon. In this respect, the healthy incorporation of SUTPs that will protect the wellbeing of host communities while satisfying the humanitarian necessity of helping Syrians will be among the more important development issues of today and the foreseeable future.

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# **REFERENCE**

AFAD (2013). “Syrian Refugees in Turkey, 2013. Field Survey Results”, Republic of Turkey, Prime Ministry, Disaster and Emergency Management Presidency.

Ceritoglu, Evren, H., Burcu Gurcihan Yunculer, Huzeyfe Torun, and Semih Tumen. (2015). “The Impact of Syrian Refugees on Natives’ Labor Market Outcomes in Turkey: Evidence from a Quasi-Experimental Design”, MRPA Paper No. 61503, posted January 23, 2015.

Del Carpio, Ximena V. and Mathis Wagner. (2015). “The Impact of Syrians Refugees on the Turkish Labor Market”, World Bank Working Paper 7402, August 2015.

Del Carpio, Ximena V., Doreen Triebe, and Mathis Wagner. (2015). “Short-term Impacts of Syrians Refugees on the Turkish Labor Market”, Working Paper, May 2015.

Erdogan, M. Murat. (2014). “Syrians in Turkey: Social Acceptance and Integration Research”, Executive Summary and Report, November 2014.

Kirisci, Kemal. (2003). “Turkey: A Transformation from Emigration to Immigration” November 1, 2003. (http://www.migrationpolicy.org/article/turkey-transformation-emigration-immigration) Orhan Oytun, and Sabiha Senyucel Gundogar (2015). “Effects of the Syrian Refugees on Turkey”, ORSAM Report No 195, January 2015.

Ozturkler, Harun, and Turkmen Goksel. (2015). “The Economic Effects of Syrian Refugees on Turkey: A Synthetic Modelling”, ORSAM Report No: 196, January 2015.

UNHCR (2013). “Countries Hosting Syrian Refugees, Solidarity and Burden-Sharing, Background papers for the High Level Segment”. Provisional Release, September 2013.

UNHCR, Turkey Syrian Refugee Daily Sitrep, (22 March 2013) UNHCR, Turkey Syrian Refugee Daily Sitrep, (22 November 2013) UNHCR, Turkey Syrian Refugee Daily Sitrep, (15 September 2014) 18
# **FIGURES**

Figure 2. SuTPs Living Outside Camps vs Recent Migrants in the LFS <!-- Start of picture text --> 1400000 90000<br>GOV<br>80000<br>1200000<br>UNHCR<br>70000<br>1000000<br>UNHCR (registered)<br>60000<br>800000<br>50000 LFS-Near Syrian<br>Border<br>40000<br>600000<br>LFS-National<br>30000<br>400000<br>LFS-Other Regions<br>20000<br>200000<br>10000<br>0 0<br>12/31/2011 12/31/2012 12/31/2013<br><!-- End of picture text --> _Sources_ : AFAD 2013, MOI, UNHCR, Turkey LFS 19
# **TABLES**

Table 6. Population, by Comparison Groups

|||Host<br>Community|Settled Migrant HHs<br>(>4 years in Turkey)|Recent Migrant HHs<br>(<=4 years in<br>Turkey)|
|---|---|---|---|---|
|2009|Near the Syrian Border|13,645,091|32,177|1,368|
|Population|Rest of the Country|55,441,281|956,218|27,345|
|2013|Near the Syrian Border|14,442,691|18,099|29,846|
|Population|Rest of the Country|57,129,706|952,052|50,989|
|2014|Near the Syrian Border|9,942,906*|28,272*|99,243*|
|Population|Rest ofthe Country|45,700,589*|1,004,532*|210,658*|

_Notes_ : *The population in 2014 is only of the population aged 15+. In 2014, the population weights exclude individuals under 15 due to changes in the LFS _. Source_ : LFS 2009-2014. World Bank author’s calculations.

Table 7. Population Shares, by Comparison Group

||||Host<br>Community|Settled Migrant<br>HHs (>4 years in<br>Turkey)|Recent Migrant HHs<br>(<=4 years in<br>Turkey)|
|---|---|---|---|---|---|
|2009|Population|Near the Syrian Border|19.8%|3.3%|4.8%|
|Share||Rest of the Country|80.2%|96.7%|95.2%|
|2013|Population|Near the Syrian Border|20.2%|1.9%|36.9%|
|Share||Rest of the Country|79.8%|98.1%|63.1%|
|2014|Population|Near the Syrian Border|17.9%|2.7%|32.0%|
|Share||Rest of the Country|82.1%|97.3%|68.0%|

_Notes_ : In 2014, the population weights exclude individuals under 15 due to changes in the LFS _Source_ : LFS 2009-2014. World Bank author’s calculations.

Table 8. Households, by Comparison Group

|||Host<br>Community|Settled Migrant<br>HHs (>4 years in<br>Turkey)|Recent Migrant HHs<br>(<=4 years in<br>Turkey)|
|---|---|---|---|---|
|2009|Near the Syrian Border|3,046,843|9,744|399|
|Population|Rest of the Country|15,623,820|315,158|10,433|
|2013|Near the Syrian Border|3,384,455|6,439|5,064|
|Population|Rest of the Country|16,947,974|349,414|14,678|
|2014|Near the Syrian Border|3,176,654|10,917|18,575|
|Population|Rest of the Country|17,047,500|417,846|64,973|

_Notes_ : In 2014, the population weights exclude individuals under 15 due to changes in the LFS _._ However, this should not affect estimates of the number of households.

_Source_ : LFS 2009-2014. World Bank author’s calculations.

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Table 9. Household Shares, by Comparison Groups

|||Host<br>Community|Settled Migrant<br>HHs (>4 years in<br>Turkey)|Recent Migrant HHs<br>(<=4 years in<br>Turkey)|
|---|---|---|---|---|
|2009 Population|Near the Syrian Border|16.3%|3.0%|3.7%|
|Share|Rest of the Country|83.7%|97.0%|96.3%|
|2013 Population|Near the Syrian Border|16.6%|1.8%|25.7%|
|Share|Rest of the Country|83.4%|98.2%|74.3%|
|2014 Population|Near the Syrian Border|15.7%|2.5%|22.2%|
|Share|Rest of the Country|84.3%|97.5%|77.8%|

_Notes_ : In 2014, the population weights exclude individuals under 15 due to changes in the LFS _._ However, this should not affect estimates of the number of households.

_Source_ : LFS 2009-2014. World Bank author’s calculations.

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Figure 3. Poverty Estimates 2009-2013 Near the Syrian Border <!-- Start of picture text --> Host communities Near the Syrian Border<br>80<br>70<br>60<br>50<br>40 44.2 42.6 40.9 41.1 40.3<br>30<br>20<br>10<br>0<br>2009 2010 2011 2012 2013<br>Settled Migrant Households Near the Syrian Border (>4 years in Turkey)<br>80<br>70<br>60<br>50<br>40<br>30<br>27.5<br>23.8<br>20 21.3<br>17.7<br>12.5<br>10<br>0<br>2009 2010 2011 2012 2013<br>Recent Migrant Households Near the Syrian Border (<=4 years in Turkey)<br>80<br>70<br>60<br>50<br>46.7<br>40<br>37.0<br>30<br>27.1<br>20<br>17.5<br>15.6<br>10<br>0<br>2009 2010 2011 2012 2013<br>Poverty Rate (%)<br>Poverty Rate (%)<br>Poverty Rate (%)<br><!-- End of picture text --> _Notes_ : Regionally deflated, OLS imputation model, with time-invariant characteristics. Vertical line represents standard error. Poverty line is $5/day PPP per capita. The Near Syrian Border region (NSB) includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, TR62. Time in Turkey is based on the date of arrival of the head of household. Results displayed at the 95% Confidence Interval. _Source_ : LFS 2009-2014. World Bank author’s calculations.

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Figure 4. Poverty Estimates 2009-2013 Rest of the Country <!-- Start of picture text --> Host communities<br>80<br>70<br>60<br>50<br>40<br>30<br>20 21.1 20.8 19.3 18.4 17.9<br>10<br>0<br>2009 2010 2011 2012 2013<br>Settled Migrant Households in the Rest of the Country (>4 years in Turkey)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10 9.1 10.7 9.3<br>7.0 6.6<br>0<br>2009 2010 2011 2012 2013<br>Recent Migrant Households (Rest of the Country, <=4 years in Turkey)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>16.1<br>13.3 13.2<br>10<br>7.3<br>5.2<br>0<br>-10<br>2009 2010 2011 2012 2013<br>Poverty Rate (%)<br>Poverty Rate (%)<br>Poverty Rate (%)<br><!-- End of picture text --> _Notes_ : Regionally deflated, OLS imputation model, with time-invariant characteristics. Vertical line represents standard error. Poverty line is $5/day PPP per capita. The Near Syrian Border region (NSB) includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, TR62. Time in Turkey is based on the date of arrival of the head of household. Results displayed at the 95% Confidence Interval.

_Source_ : LFS 2009-2014. World Bank author’s calculations.

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Figure 5. Host Community Poverty Trends, by Geographic Region and Group All Host Community Households <!-- Start of picture text --> Rest of the Country Near the Syrian Border<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40% 44% 43% 41% 41% 40%<br>30% 30%<br>20% 21% 21% 19% 18% 18% 20%<br>10% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Low Educated Households (Head has no or primary level education)<br>Rest of the Country Near the Syrian Border<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50% 51.4% 48.3% 47.0% 48.3% 48.4%<br>40% 40%<br>30% 29.6% 28.7% 26.2% 25.1% 25.1% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Female Headed Households<br>Rest of the Country Near the Syrian Border<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40% 40% 37% 37% 36% 36%<br>30% 30%<br>20% 20%<br>10% 16% 17% 14% 13% 12% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Poverty Rate (%) Poverty Rate (%)<br>Poverty Rate (%) Poverty Rate (%)<br>Poverty Rate (%) Poverty Rate (%)<br><!-- End of picture text --> 24
# Male Headed Households

<!-- Start of picture text --> Rest of the Country Near the Syrian Border<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40% 45% 43% 41% 42% 41%<br>30% 30%<br>20% 22% 21% 20% 19% 19% 20%<br>10% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Head of Households: Blue Collar<br>Rest of the Country Near the Syrian Border<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50% 50% 49% 48% 48% 47%<br>40% 40%<br>30% 28% 28% 27% 26% 25% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Poverty Rate (%) Poverty Rate (%)<br>Poverty Rate (%) Poverty Rate (%)<br><!-- End of picture text --> _Notes_ : OLS imputation method, regionally deflated. Results displayed at the 95% Confidence Interval. _Source_ : LFS 2009-2013. World Bank author’s calculations.

25
# Table 10. Descriptive Statistics, Near the Syrian Border (2009, 2013, 2014)

|||2009<br>Near the Syrian Border|||2013<br>Near the Syrian Bo|rder||2014<br>Near the Syrian Bo|rder|
|---|---|---|---|---|---|---|---|---|---|
||Host<br>Community|Settled Migrant HHs<br>(>4 years in Turkey)|Recent<br>Migrant HHs<br>(<=4 years in|Host<br>Community|Settled<br>Migrant HHs<br>(>4 years in|Recent Migrant<br>HHs<br>(<=4 years in|Host<br>Community|Settled<br>Migrant HHs<br>(>4 years in|Recent<br>Migrant HHs<br>(<=4 years in|
||||Turkey)||Turkey)|Turkey)||Turkey)|Turkey)|
|Sample of Households|21,324|68|3|21,467|51|34|22,791|71|88|
|Households|3,046,842|9,744|398,844|3,384,455|6,439|5,063|3,176,654|10,917|18,575|
|Population*|13,645,090|32,177,378|1,367,570|14,442,691|18,099|29,846|9,942,906|28,272|99,243|
|N children|1.6|1.1|0.6|1.4|0.8|2.4|-|-|-|
|N adults|2.4|1.4|2.6|2.3|1.4|3.1|2.5|2.3|4.7|
|N old|0.5|0.8|0.2|0.5|0.5|0.4|0.6|0.3|0.7|
|0-14|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|15-24|2.3%|0.0%|40.0%|1.6%|0.0%|0.0%|1.1%|1.0%|2.3%|
|25-39|36.2%|38.8%|0.0%|31.6%|34.9%|53.1%|29.0%|44.7%|29.8%|
|40-54|34.0%|6.4%|37.0%|35.6%|29.9%|37.7%|35.8%|30.5%|33.7%|
|55+|27.5%|54.8%|23.0%|31.2%|35.2%|9.1%|34.1%|23.8%|34.2%|
|1-Male, 0-Female|84.9%|67.1%|100.0%|83.9%|54.4%|87.9%|86.1%|80.1%|82.7%|
|None|25.8%|37.1%|60.0%|22.9%|19.4%|75.6%|22.1%|21.6%|51.3%|
|Primary|44.9%|26.0%|0.0%|42.4%|29.9%|8.2%|43.8%|20.0%|23.5%|
|Secondary|22.4%|31.4%|40.0%|24.7%|30.3%|16.2%|24.1%|24.6%|20.7%|
|<br>Post-Secondary|6.9%|5.5%|0.0%|9.9%|20.4%|0.0%|10.2%|35.6%|4.6%|
|Share of Employed Adults|22.5%|26.7%|0.0%|25.0%|31.8%|20.6%|35.9%|47.5%|23.8%|
|<br>Share of Unemployed Adults|3.7%|2.1%|25.0%|2.3%|3.4%|4.6%|3.7%|1.2%|7.7%|
|Total Hours Worked in the Whole Family|52.96|51.40||51.50|55.04|58.77|51.58|57.95|47.77|
|Mean Hours Worked out of all working individuals|52.09|52.62|50.93|50.46|50.41|58.81|50.88|56.21|51.98|
|Share Workers White Collar|29.1%|44.2%||33.4%|40.0%|40.8%|32.9%|51.3%|20.9%|
|Share Workers Blue Collar|53.6%|38.1%||47.1%|35.1%|50.8%|44.7%|24.3%|48.4%|
|Share with Permanent Contract Type|35.1%|33.3%|0.0%|36.4%|34.6%|33.8%|33.6%|45.6%|11.8%|
|Regular or casual employee|60.3%|44.9%||65.3%|70.9%|80.5%|65.0%|79.1%|81.1%|
|Employer|7.1%|9.4%||6.2%|15.6%|7.4%|6.5%|13.4%|6.1%|
|Self employed|32.3%|43.0%||28.4%|13.6%|12.2%|28.2%|6.2%|12.8%|
|Unpaid family worker|0.3%|2.7%||0.2%|0.0%|0.0%|0.4%|1.3%|0.0%|
|1-9 employees|51.8%|38.6%||45.3%|40.9%|76.3%|44.2%|49.6%|56.4%|
|<br>10-49 employees|14.7%|15.9%||17.4%|15.2%|15.3%|14.1%|4.3%|14.9%|
|50+ employees|16.2%|27.8%||17.7%|19.0%|0.0%|19.2%|22.1%|0.0%|
|Share Agriculture, forestry, fishing-Mining and quarrying|16.6%|8.2%||13.6%|0.0%|5.1%|12.1%|1.0%|15.3%|
|<br>Share Manufacturing Industry|13.8%|25.8%||12.7%|27.6%|21.8%|13.7%|4.6%|15.0%|
|Share Electricity Gas Steam Water and Sewerage|0.7%|0.0%||1.0%|0.0%|0.0%|1.1%|0.0%|0.0%|
|, , ,<br>Share Construction|<br>8.2%|<br>5.9%||<br>10.0%|<br>0.0%|<br>18.6%|<br>8.4%|<br>7.7%|<br>18.1%|
|Share Commerce and services|25.1%|25.3%||22.7%|22.6%|35.0%|22.4%|30.7%|17.7%|
|Share Others|18.4%|17.2%||20.4%|24.9%|11.1%|20.0%|31.6%|3.2%|
|Registered with Any SS institution related to your main job|50.2%|56.5%||62.1%|64.6%|20.7%|63.4%|82.9%|2.3%|
|<br>Share of workers registered with any social security institution|43.0%|56.5%||51.4%|50.1%|20.7%|51.3%|61.1%|2.3%|
|<br>related toyour mainjob||||||||||

_Notes:_ Population estimates in 2014 are of the 15+ population. _Source_ : LFS 2009-2014. World Bank author’s calculations.

26
# Table 11. Descriptive Statistics, Rest of the County (2009, 2013, 2014)

|||2009<br>Rest of the Countr|y||2013<br>Rest of the Country|||2014<br>Rest of the Country||
|---|---|---|---|---|---|---|---|---|---|
||Host<br>Community|Settled<br>Migrant HHs<br>(>4 years in|Recent<br>Migrant HHs<br>(<=4 years in|Host Community|Settled Migrant<br>HHs (>4 years<br>in Turkey)|Recent<br>Migrant HHs<br>(<=4 years in|Host<br>Community|Settled Migrant<br>HHs (>4 years in<br>Turkey)|Recent<br>Migrant HHs<br>(<=4 years in|
|||Turkey)|Turkey)|||Turkey)|||Turkey)|
|Sample of Households|112,386|2,059|51|122,105|2,312|86|124,384|2,452|271|
|Households|15,623,820|315,158|10,432|16,947,974|349,414|14,678|17,047,500|417,846|64,974|
|Population*|55,441,280|956,217|27,345|57,129,706|952,052|50,989|45,700,589|1,004,532|210,658|
|N children|0.9|0.5|0.6|0.8|0.4|0.9|-|-|-|
|N adults|2.1|1.8|1.8|2.0|1.4|2.4|2.1|1.6|3.1|
|N old|0.5|0.8|0.2|0.6|0.8|0.2|0.6|0.8|0.2|
|0-14|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|15-24|1.9%|0.7%|17.3%|1.6%|0.5%|10.4%|1.5%|0.7%|5.1%|
|25-39|32.0%|25.9%|57.6%|30.0%|24.2%|55.4%|27.9%|22.8%|50.8%|
|40-54|35.4%|29.8%|15.8%|35.2%|26.0%|19.0%|35.3%|27.0%|36.1%|
|55+|30.7%|43.6%|9.4%|33.1%|49.4%|15.2%|35.3%|49.6%|7.9%|
|1-Male, 0-Female|86.0%|80.3%|83.7%|84.6%|76.1%|84.9%|84.3%|76.3%|81.8%|
|None|12.2%|10.4%|2.1%|12.2%|10.5%|5.4%|12.0%|11.5%|20.1%|
|Primary|47.4%|25.5%|7.3%|43.9%|22.8%|12.5%|44.0%|21.7%|21.2%|
|Secondary|27.6%|48.3%|63.8%|28.5%|49.4%|55.2%|28.7%|46.7%|40.8%|
|Post-Secondary|12.8%|15.7%|26.8%|15.3%|17.2%|26.8%|15.8%|22.1%|20.4%|
|Share of Employed Adults|28.7%|32.0%|39.9%|31.6%|35.0%|35.5%|41.4%|44.1%|37.3%|
|<br>Share of Unemployed Adults|2.8%|3.5%|10.3%|1.9%|4.0%|8.9%|2.5%|3.4%|8.7%|
|Total Hours Worked in the Whole Family|51.69|50.59|49.45|50.40|49.26|54.73|50.03|48.68|51.59|
|Mean Hours Worked out of all working individuals|50.30|49.57|49.25|48.80|48.01|52.32|48.60|47.79|51.52|
|Share Workers White Collar|34.9%|42.0%|55.7%|35.6%|45.6%|28.2%|35.6%|43.9%|24.5%|
|Share Workers Blue Collar|43.0%|34.4%|20.9%|39.4%|28.0%|48.1%|37.5%|29.2%|49.5%|
|Share with Permanent Contract Type|39.5%|40.1%|38.6%|40.0%|39.9%|49.0%|38.4%|41.1%|40.6%|
|Regular or casual employee|61.3%|75.7%|69.0%|64.5%|74.9%|89.3%|65.8%|78.7%|89.9%|
|Employer|9.1%|8.5%|7.2%|7.5%|9.4%|0.9%|7.6%|7.4%|1.3%|
|Self employed|29.2%|15.5%|13.3%|27.5%|15.0%|6.8%|25.9%|13.2%|8.7%|
|Unpaid family worker|0.4%|0.4%|10.5%|0.5%|0.6%|3.0%|0.7%|0.7%|0.0%|
|1-9 employees|41.2%|30.2%|48.5%|37.8%|28.2%|30.8%|38.0%|28.1%|39.3%|
|10-49 employees|16.3%|18.1%|18.0%|16.7%|18.7%|29.4%|13.9%|16.7%|22.9%|
|50+ employees|20.5%|28.1%|10.1%|20.5%|26.7%|16.1%|21.1%|28.3%|11.5%|
|Share Agriculture, forestry, fishing-Mining and quarrying|11.0%|2.8%|5.7%|11.0%|2.2%|0.0%|9.4%|2.1%|0.7%|
|<br>Share Manufacturing Industry|16.0%|24.6%|1.6%|15.4%|21.5%|45.0%|15.1%|21.0%|32.7%|
|<br>Share Electricity, Gas , Steam, Water and Sewerage|0.6%|0.4%|0.0%|0.9%|0.5%|0.0%|0.9%|0.4%|0.8%|
|<br>Share Construction|6.3%|4.2%|11.5%|6.3%|5.2%|1.7%|6.6%|6.2%|11.2%|
|Share Commerce and services|25.5%|25.4%|45.4%|23.3%|24.4%|13.8%|22.7%|21.3%|9.9%|
|Share Others|18.5%|18.9%|12.5%|18.2%|20.0%|15.8%|18.3%|22.0%|18.6%|
|Registered with Any SS institution related to your main job|67.4%|76.4%|25.0%|73.9%|83.9%|48.5%|75.2%|83.4%|25.5%|
|<br>Share of workers registered with any social security institution related to|54.7%|58.9%|20.0%|57.5%|62.2%|37.1%|57.1%|62.2%|20.7%|
|your mainjob||||||||||

_Notes:_ Population estimates in 2014 are of the 15+ population. _Source_ : LFS 2009-2014. World Bank author’s calculations.

27
Table 12. Proportion of SUTPs by NUTS2 regions in 2013

|||Percent of SUTPs in the<br>totalpopulation<br>|
|---|---|---|
|TR10|Istanbul|2.8%|
|TR21|Tekirdag|0.0%|
|TR22<br>|Balikesir<br>|0.0%<br>|
|TR31|Izmir|0.6%|
|TR32|Aydin|0.1%|
|TR33|Manisa|0.0%|
|TR41|Bursa|0.5%|
|TR42|Kocaeli|0.5%|
|TR51|Ankara|0.6%|
|TR52|Konya|3.5%|
|TR61|Antalya|0.4%|
|TR62|Adana|4.9%|
|TR63|Hatay|16.9%|
|TR71|Kirikale|0.1%|
|TR72|Kayseri|0.8%|
|TR81|Zonguldak|0.0%|
|TR82|Kastamonu|0.0%|
|TR83|Samsun|0.1%|
|TR90|Trabzon|0.0%|
|TRA1|Erzurum|0.0%|
|TRA2|Agri|0.0%|
|TRB1|Malatya|0.1%|
|TRB2|Van|0.1%|
|TRC1|Gaziantep|22.4%|
|TRC2|Sanliurfa|11.0%|
|TRC3|Mardin|11.3%|

_Sources_ : AFAD 2013, MOI, UNHCR 28
# Table 13. OLS

||Blue Collar<br>|Households<br>||Households<br>|with Low Edu<br>|cation<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(1)|(2)|(3)|
|Proportion of Foreign-Born in the Population|1.123*|-0.128|-0.0154|1.098*|-0.108|0.0367|
|Constant|(0.512)<br>0.279***|(0.0857)<br>0.190***|(0.0763)<br>-0.724|(0.517)<br>0.258***|(0.0890)<br>0.161***|(0.0707)<br>0.238|
||(0.0447)|(0.00121)|(0.553)|(0.0465)|(0.00126)|(0.459)|
|Observations|52|52|52|52|52|52|
|R-squared|0.095|0.959|0.995|0.086|0.959|0.994|
|Year Controls||X|X||X|X|
|Regional Controls||X|X||X|X|
|Labor and Demographic Controls|||X|||X|

Notes: * p<0.10, ** p<0.05, *** p<0.01. _Source_ : LFS 2011 and 2013. World Bank author’s calculations.

# Table 14. OLS

||Female Hea<br>|ded Household<br>|s<br>|Male Heade<br>|d Households<br>|<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(1)|(2)|(3)|
|Proportion of Foreign-Born in the Population<br>Constant|0.898**<br>(0.325)<br>0.153***<br>(0.0352)|0.00125<br>(0.209)<br>0.0520***<br>(0.00296)|0.0348<br>(0.0653)<br>0.555**<br>(0.247)|0.969**<br>(0.436)<br>0.211***<br>(0.0388)|-0.107<br>(0.112)<br>0.119***<br>(0.00159)|-0.0393<br>(0.0675)<br>0.601**<br>(0.211)|
|Observations|52|52|52|52|52|52|
|R-squared|0.095|0.910|0.973|0.092|0.948|0.993|
|Year Controls||X|X||X|X|
|Regional Controls||X|X||X|X|
|Labor and Demographic Controls|||X|||X|

Notes: * p<0.10, ** p<0.05, *** p<0.01. _Source_ : LFS 2011 and 2013. World Bank author’s calculations.

29
Table 15. Host Community Near the Syrian Border, 2009 & 2013 Means Comparison Test

||mean(2013)-|two-sided|2013-|2009-|
|---|---|---|---|---|
||mean(2009)|means test p-<br>value|mean|mean|
|N children|-0.377|0.0000|1.28|1.66|
|N adults|-0.176|0.0000|2.24|2.42|
|N old|0.060|0.0000|0.58|0.52|
|**Characteristics of the head of household**|||||
|0-14|0.0%|.|-|-|
|15-24|-0.6%|0.0000|1.5%|2.1%|
|25-39|-5.6%|0.0000|27.6%|33.2%|
|40-54|0.9%|0.0491|35.4%|34.5%|
|55+|5.3%|0.0000|35.5%|30.2%|
|1-Male, 0-Female|-1.5%|0.0001|82.5%|84.0%|
|None|-4.7%|0.0000|23.6%|28.4%|
|Primary|-2.1%|0.0000|41.6%|43.7%|
|Secondary|3.2%|0.0000|24.5%|21.4%|
|Post-Secondary|3.7%|0.0000|10.3%|6.6%|
|**Al workers in the household**|||||
|Share of Employed Adults|3.4%|0.0000|25.5%|22.2%|
|Share of Unemployed Adults|-1.2%|0.0000|2.4%|3.6%|
|Total Hours Worked in the Whole Family|-1.62|0.0000|51.31|52.93|
|Mean Hours Worked out of all working individuals|-1.71|0.0000|50.45|52.16|
|Share Workers White Collar|5.6%|0.0000|34.4%|28.8%|
|Share Workers Blue Collar|-7.8%|0.0000|45.9%|53.7%|
|Share with Permanent Contract Type|2.9%|0.0000|36.2%|33.3%|
|1-9 employees|-8.8%|0.0000|44.3%|53.1%|
|10-49 employees|3.1%|0.0000|17.5%|14.4%|
|50+ employees|3.5%|0.0000|18.5%|15.1%|
|Share Agriculture, forestry, fishing-Mining and<br>quarrying|-5.9%|0.0000|12.2%|18.0%|
|Share Manufacturing Industry|1.1%|0.0109|13.2%|12.1%|
|Share Electricity, Gas , Steam, Water and Sewerage|0.2%|0.0470|1.0%|0.8%|
|Share Construction|1.5%|0.0000|9.7%|8.2%|
|Share Commerce and services|-1.3%|0.0126|23.6%|24.9%|
|Share Others|2.2%|0.0000|20.8%|18.5%|
|Registered with Any SS institution related to your|15.7%|0.0000|63.5%|47.8%|
|main job|||||
|Share of workers registered with any social security<br>|11.6%|0.0000|52.4%|40.8%|
|institution related to your main job|||||

_Notes_ : 2009 N = 21,324, 2013 N = 21, 467 _Source_ : LFS 2009 & 2013. World Bank author’s calculations.

30
Table 16. Host Community Near the Syrian Border, 2011 & 2013 Means Comparison Test

||mean(2013)-<br>mean(2011)|two-sided<br>means test<br>p-value|2013-<br>mean|2011-<br>mean|
|---|---|---|---|---|
|N children|-0.097|0.0000|1.28|1.38|
|N adults|-0.084|0.0000|2.24|2.33|
|N old|0.021|0.0048|0.58|0.56|
|**Characteristics of the head of household**|||||
|0-14|0.000|.|-|-|
|15-24|-0.002|0.0434|0.01|0.02|
|25-39|-0.014|0.0009|0.28|0.29|
|40-54|0.001|0.7810|0.35|0.35|
|55+|0.016|0.0007|0.36|0.34|
|1-Male, 0-Female|-0.009|0.0123|0.83|0.83|
|None|-0.002|0.6283|0.24|0.24|
|Primary|-1.2%|0.0124|41.6%|42.7%|
|Secondary|0.4%|0.3095|24.5%|24.1%|
|Post-Secondary|1.0%|0.0007|10.3%|9.4%|
|**Al workers in the household**|||||
|Share of Employed Adults|1.0%|0.0000|25.5%|24.6%|
|Share of Unemployed Adults|0.1%|0.3775|2.4%|2.3%|
|Total Hours Worked in the Whole Family|-0.526|0.0085|51.31|51.83|
|Mean Hours Worked out of all working individuals|-0.542|0.0014|50.45|50.99|
|Share Workers White Collar|3.5%|0.0000|34.4%|30.9%|
|Share Workers Blue Collar|-3.2%|0.0000|45.9%|49.1%|
|Share with Permanent Contract Type|-0.1%|0.7832|36.2%|36.4%|
|1-9 employees|-2.6%|0.0000|44.3%|46.9%|
|10-49 employees|1.3%|0.0048|17.5%|16.3%|
|50+ employees|1.6%|0.0004|18.5%|16.9%|
|Share Agriculture, forestry, fishing-Mining and quarrying|-1.4%|0.0002|12.2%|13.6%|
|Share Manufacturing Industry|1.3%|0.0009|13.2%|11.9%|
|Share Electricity, Gas , Steam, Water and Sewerage|-0.1%|0.3614|1.0%|1.1%|
|Share Construction|1.2%|0.0005|9.7%|8.4%|
|Share Commerce and services|-1.4%|0.0055|23.6%|25.0%|
|Share Others|0.7%|0.1739|20.8%|20.1%|
|Registered with Any SS institution related to your main job|8.0%|0.0000|63.5%|55.5%|
|Share of workers registered with any social security<br>|6.3%|0.0000|52.4%|46.1%|
|institution related to your main job|||||

_Notes_ : 2011 N = 21,460, 2013 N = 21, 467 _Source_ : LFS 2011 & 2013. World Bank author’s calculations.

31
Table 17. Host Community Near the Syrian Border, 2013 & 2014 Means Comparison Test

||mean(2014)-|two-sided|2014-|2013-|
|---|---|---|---|---|
||mean(2013)|means test p-<br>value|mean|mean|
|N children*|-1.284|-|-|1.28|
|N adults|0.050|0.0006|2.29|2.24|
|N old|0.024|0.0017|0.60|0.58|
|**Characteristics of the head of household**|||||
|0-14|0.0%|.|0.0%|0.0%|
|15-24|-0.1%|0.3616|1.4%|1.5%|
|25-39|-0.3%|0.5363|27.4%|27.6%|
|40-54|0.7%|0.1266|36.1%|35.4%|
|55+|-0.3%|0.4658|35.2%|35.5%|
|1-Male, 0-Female|2.4%|0.0000|85.0%|82.5%|
|None|-0.3%|0.4101|23.3%|23.6%|
|Primary|1.3%|0.0054|42.9%|41.6%|
|Secondary|-0.6%|0.1661|23.9%|24.5%|
|Post-Secondary|-0.2%|0.4396|10.1%|10.3%|
|**Al workers in the household**|||||
|Share of Employed Adults|11.0%|-|36.5%|25.5%|
|Share of Unemployed Adults|1.5%|0.0000|3.8%|2.4%|
|Total Hours Worked in the Whole Family|0.190|0.3610|51.50|51.31|
|Mean Hours Worked out of all working individuals|0.251|0.1554|50.70|50.45|
|Share Workers White Collar|-0.7%|0.2275|33.8%|34.4%|
|Share Workers Blue Collar|-0.1%|0.8321|45.8%|45.9%|
|Share with Permanent Contract Type|-1.1%|0.0273|35.1%|36.2%|
|1-9 employees|1.6%|0.0042|45.9%|44.3%|
|10-49 employees|-3.4%|0.0000|14.2%|17.5%|
|50+ employees|0.9%|0.0512|19.4%|18.5%|
|Share Agriculture, forestry, fishing-Mining and quarrying|1.4%|0.0002|13.6%|12.2%|
|Share Manufacturing Industry|-0.2%|0.6378|13.0%|13.2%|
|Share Electricity, Gas , Steam, Water and Sewerage|0.1%|0.4251|1.1%|1.0%|
|<br>Share Construction|-1.2%|0.0005|8.5%|9.7%|
|Share Commerce and services|-1.3%|0.0113|22.3%|23.6%|
|Share Others|0.4%|0.4353|21.1%|20.8%|
|Registered with Any SS institution related to your main job|-1.1%|0.0785|62.5%|63.5%|
|Share of workers registered with any social security<br>|-0.8%|0.1496|51.6%|52.4%|
|institution related to your main job|||||

_Notes_ : *In 2014, the LFS no longer captures information about children 14 and under. N2013 = 21,467 for most variables, N2014=22,791 for most variables. Variables are at the household level. _Source_ : LFS 2013 & 2014. World Bank author’s calculations.

32
# **ANNEX. SURVEY-TO SURVEY IMPUTATION METHODOLOGY**

## **1. Methodology**

The estimates of poverty and welfare status of the households rely on survey data with a complex consumption module containing large set of detailed questions on prices and quantities consumed. Given its complexity the collection and analysis of that data involves significant investment of time, money and analytical efforts. On the other hand, there is a need for timely poverty estimates for evidence-based policies in the face of the high cost of fielding comprehensive surveys to track income and/or expenditure has led to the development of a variety of approaches for estimating poverty in the absence of consumption expenditure or income data.

In this section, we explore the survey-to-survey approach, which used the common observed assets and household characteristics in order to impute a proxy for welfare. The survey-to-survey imputation requires that at least one previous comparable survey contains household-level income or consumption information. The method draws upon the imputation literature (see Brick and Kalton, 1996 for a discussion of various techniques), which utilizes non-missing data in a larger data set to predict the values for missing variables, and from the poverty targeting literature (see Grosh and Baker, 1995) which seeks proxies for poverty status from household characteristics. The survey-to-survey imputation method extends that to using common variables in two data sets, only one of which contains consumption to predict consumption values in the second data set. A common application of this method in poverty analysis is “poverty mapping,” which uses consumption and poverty estimates from a household survey imputed into census data to achieve very fine levels of geographic disaggregation. (see Rao, 2003, for a discussion of the general technique of small area estimation, and Elbers et al., 2002, specifically related to poverty mapping.) For examples of surveyto-survey imputation for poverty analysis, see Stifel and Christiaensen (2007), Tarozzi (2007), Grosse et al (2009), and Douidich et al (2013).

A more formal presentation of the S2S model is as follows. There are surveys: in Survey 1 there are information on the income or consumption as well as the set of household characteristics ���. In the Survey 2, the same set of household characteristics are also observed, and those characteristics are comparable between the two surveys.

The (log) of per capita household income or consumption is modeled for the first survey as: ��� ��′������� where ��� is the per capita income or consumption of household _h_ residing in area _c_ , ��� are household and area/location characteristics, and ��� ��� ���� is the residual composed of the area component �� and the household component ���, which have zero expectations and are independent of each other.

In the second survey where there is no income or consumption information, a set of common variables ��� will be use to impute the income or consumption for each household in the second survey using the estimated points and their distributions estimated from the survey 1. Since each estimated point is fluctuated from an assumed normal distribution with its standard errors, the imputation is done through a number of simulation in order to preserve the error structure of the correlates.

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R A= <u>ad, ho")</u> hy) yr HBS /LSMS Develop projection model (regress C on X) Labor Force Survey or other frequent surveys C=F(X) C: Consumption X: Household variables (e.g. education, employment) C=F(X): Projected Consumption data
## <u>Inputs:</u>

1. Household Survey with consumption or income welfare aggregates

2. Project data/Other survey data without welfare aggregates 3. Set of harmonized common variables in both surveys

## <u>Outputs:</u>

1. Set of imputed welfare variables for project data/other survey for each household in the data

2. Imputed welfare variables can be used for poverty, distributional analysis (quintiles or more), profiling of the poor or group of interest

## <u>Models:</u>

1. Ordinary Least Squares (OLS)

2. Probit 3. Multiple Imputation (MI) Table 18. Model Specification

||**Variables**|
|---|---|
|**Demographic**|Share of children,share of adults,share of adults squared and share of old(omitted)|
|**Characteristics of head**|Age, gender,and level of education|
|**Interactions with urban dummy variable**|Level of education of the head,age of the head|
|**Geography**|Dummies for regions at NUTS 1 level(12 regions)|
|**Interactions with Geography**|Level of education of the head, age of the head interacted with regions at NUTS 1<br>level(12 regions)and urban-rural division|

# **1. Validation and Robustness Check**

Figure 7. External Validation, NUTS1 Level $5/day PPP– Observed (SILC) & Imputed (LFS), 2007 <!-- Start of picture text --> LFS<br>SILC<br><!-- End of picture text --> 35
_Source_ : LFS, SILC, imputed OLS, regionally deflated, authors’’ calculations

# Table 19. Robustness Checking

<!-- Start of picture text --> 2009 2013 2009 2013 2009 2013<br>Model Probit OLS OLS<br>Specification Mean Time Invariant – Not Regionally Deflated Time Invariant – Regionally Deflated<br>NSB - Host community 45.98 42.76 45.86 41.89 44.16 40.30<br>NSB - Recent migrants 17.81 49.01 16.44 47.82 15.63 46.70<br>NSB -Settled migrants 28.33 19.13 28.25 18.60 27.50 17.74<br>Rest - Host community 20.17 17.37 20.01 17.08 21.06 17.90<br>Rest - Recent migrants 6.06 13.23 5.88 13.23 7.30 16.13<br>Rest - Settled migrants 8.37 5.98 8.19 5.83 9.14 6.56<br>Figure 8. Robustness Checking<br>Near Syrian Border - Near Syrian Border - Near Syrian Border - Rest of the country - Rest of the country - Rest of the country -<br>Turkey<br>Host community Recent migrants Settled migrants Host community Recent migrants Settled migrants<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Type<br>Probit - Not Regionally Deflated<br>OLS - Not Regionally Deflated<br>OLS - Regionally Deflated<br>Poverty Rate ($5 PPP, %)<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br><!-- End of picture text --> 36