86038 Seeing is Believing Poverty in The Palestinian Territories 2014 Seeing is Believing Poverty in The Palestinian Territories 2014 Cover description: The cover illustrates the concentration of poor people in localities in the Palestinian Territories, by scaling (contracting or expanding) them according to the density of poor people per unit area, which is calculated with the method- ology by Gastner and Newman (2004). Table of Contents Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 1. Background and Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Country Context World Bank-PCBS Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 What is a Poverty Map? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Poverty Mapping: Methodology Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Main Data Sources and Technical Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Technical challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Choosing the appropriate consumption model 3. Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Building the Model Final Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4. Mapping The Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 A Fragmented Landscape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Visualizing Poverty in the Palestinian Territories . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Accessibility, mobility and poverty. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Pockets of poverty and prosperity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Poor areas, poor people. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Are poorer households also larger?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Does education pay off? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Unemployment goes hand in hand with poverty. . . . . . . . . . . . . . . . . . . . . . . . . . 44 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6. References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 7.  Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Poverty Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Merged Localities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Localities in the West Bank Isolated or Affected by the Barrier Wall . . . . . . . . . . . . . . 72 Percent of PCBS Localities Falling in Area C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Seeing is Believing – Poverty in The Palestinian Territories List of Maps Merged Localities – A zoom in of Hebron and Ramallah showing Map 1:  the localities that were merged together (in matching color) and those that were not (in white). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Map 2: A Divided Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Map 3: Punctuated by Barriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 . . . . . . . . . . . . . . . . . . . . . . . . . 28 Map 4: Localities Isolated or Affected by the Barrier Wall Map 5: Localities Falling in Area C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 A Fragmented Geography: A map of locality boundaries Map 6:  (Built-up areas) in the West Bank and Gaza. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Map 7: Merging localities in the West Bank. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 The Poorest Governorates in the West Bank are better off than most Map 8:  Governorates in Gaza: Boundaries of West Bank and Gaza and Regional Poverty Headcount Rates (2009 Poverty Map estimates) . . . . . . . . . . . . . 32 Map 9: Mapping Poverty in the Palestinian Territories . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Map 10: Mapping Mobility Restrictions in the West Bank. . . . . . . . . . . . . . . . . . . . . . . . . 35 Map 11: Pockets of Desperate Poverty: Relative Poverty in Gaza. . . . . . . . . . . . . . . . . . . . 36 Map 12: Islands of Prosperity: Relative Poverty in the West Bank. . . . . . . . . . . . . . . . . . . 37 Map 13: Low Rates of Poverty can Mask a Large Poor Population. . . . . . . . . . . . . . . . . . . 38 Map 14: Density of Poverty: Poor Population per Square km . . . . . . . . . . . . . . . . . . . . . . 39 Map 15: Poverty Appears to be Correlated with Higher Rates of Dependency . . . . . . . . . . 40 In the Palestinian Territories, more Educated Places are not Always Better off . . . 41 Map 16:  In Gaza, Education doesn’t Bear Fruit; in the West Bank, Map 17:  iv Limited Aaccess to Education keeps some Places Poor . . . . . . . . . . . . . . . . . . . . 42 Map 18: An Increasingly Educated Young Population. . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Not a Pretty Picture: Unemployment goes Hand in Hand with Poverty. . . . . . . . . 44 Map 19:  Map 20: Unemployment Level of Youth (15–30 years of age) . . . . . . . . . . . . . . . . . . . . . . 45  rivate Sector Dominant Source of Employment in the West Bank; Map 21: P but in Gaza, the Public Sector is Widespread . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 rregular and Self-Employment Correlated with Poverty in the West Bank; Map 22: I not in Gaza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Map 23: Areas Dominated by Agriculture and Manufacturing Tend to be Poorer . . . . . . . . 48 Map 24: Dominant Health Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 List of Tables Table 1:  Administrative Units in The Palestinian Territories . . . . . . . . . . . . . . . . . . . . . . . 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Table 2: Consumption Model for Gaza 2009 Table 3: Consumption Model for West Bank 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19  omparison between the Actual Data and the Model Estimates Table 4: C by Region, 2009. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23  omparison between the Actual Data and the Model Estimates Table 5: C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 by Governorate, 2009 List of Boxes Box 1: The Small Area Estimation Method Developed by ELL (2003) . . . . . . . . . . . . . . . . 6 Table of Contents v Acknowledgements This poverty map is a labor of love, the fruit of a very productive collaboration between the Palestinian Central Bureau of Statistics (PCBS) and the World Bank. The granular under- standing of the relationship between the unique fragmented geography of the Palestinian territories, and its poverty, health and education and important labor market outcomes is the result of the combined inputs and hard work of many over the last three years. It is our sincere hope that the data, analysis, and maps presented in this report are useful for pol- icy and program design and targeting for the Palestinian Authority and for development partners. The poverty map was officially launched on June 27, 2013 by the PCBS, and was presided over by H.E. the Prime Minister. The core World Bank team, led by Tara Vishwanath (Lead Economist, MNSED), comprises Brian Blankespoor (Environmental Specialist (GISP), Computational Tools – DECRG), Faythe Calandra (Program Assistant, MNSPR), Nandini Krishnan (Economist, MNSED), Meera Ma- hadevan (Consultant, MNSED) and Nobuo Yoshida (Senior Economist, PRMPR). Thanks also to Roy van der Weide (Economist, DECPI) for comments and suggestions and for sharing the work on mobility and access restrictions (joint with Brian Blankespoor). We are all very privileged to have worked on this project for the Palestinian Territories, and with a very com- mitted team from PCBS, led by Ms. Ola Awad, and we thank them. Very special thanks to the United Nations Office for the Coordination of Humanitarian Affairs (UN-OCHA oPT), and in particular Fuad Hudali and Yehezkel Lein, for sharing data and for many insightful conversations. Their commitment to collecting and sharing timely data is inspiring. Peer reviewer Peter Lanjouw (Research Manager, DECPI) provided very helpful comments, as did other colleagues; thank you. The team gratefully acknowledges the support and guidance of Mariam Sherman (Country Director, West Bank and Gaza), Bernard Funck (Sector Manager, MNSED) and Manuela Ferro (Sector Director, MNSPR). Cover design and all maps were painstakingly created by Brian Blankespoor. Many thanks. 1 Background and Context Country Context The Palestinian Territories have a uniquely fragmented geography, characterized by the isolation of Gaza from the rest of the world, and the man-made barriers to mobility within the West Bank. The internal mobility restrictions imposed by Israel, unique to the West Bank, play an important role in explaining spatial variations in outcomes within the West Bank. This is strikingly analogous to the role of Gaza’s external barriers in explaining the divergence between the West Bank and Gaza. These have consequences for poverty and economic development. Detailed analysis using a series of labor force and household surveys were undertaken as part of the West Bank and Gaza Poverty and Inclusion As- sessment, Coping with Conflict?. The analysis revealed that over the last decade, internal and external barriers have been associated with tremendous constraints to growth and investment, which is evident in high rates of unemployment, especially in Gaza and among women and youth. Over the same period, the territories have also witnessed large and widening gaps in pover- ty and labor market outcomes between the two territories of the West Bank and Gaza. Argu- ably, one of the most important reasons for this divergence is the external mobility restric- tions imposed on Gaza, which has been entirely “closed” with almost all movements across the border controlled by Israel. In practice, this means that few people and a limited number of goods are allowed to travel in and out; in particular, many inputs for commercial produc- tion are prohibited from entering the area.1 The lack of inputs and lack of access to markets have resulted in a virtual shut-down of the private sector, which in turn, has been associated with high levels of unemployment, under employment and higher rates of poverty in Gaza. The West Bank too is hampered by mobility restrictions, but of a different kind than Gaza. The West Bank is controlled by internal barriers in the form of road closures as well as exter- nal barriers. Goods and services still make it across the border, but transportation within the area is restricted and often encounters significant delays.2 As in Gaza, the mobility restric- tions hamper the growth potential of the private sector, albeit to a lesser extent. What is 1 Imports to Gaza declined in real terms by 47% and exports by 66% over the 2000–2008 period (source: PCBS). 2 Chapter 4, World Bank (2011) unique to these internal restrictions in mobility is that they which is in line with PCBS’s original request for TA to im- artificially create disadvantaged areas within the West Bank, prove the quality and comparability of survey instruments namely those areas where restrictions are most severe. and for continued assistance to create poverty maps using the most recent census and survey data to identify vulnera- These spatial disparities imply that poverty can vary widely ble groups. within the space of a few kilometers, and therefore, poverty estimates at a highly disaggregated level can reveal pock- This poverty mapping exercise is the latest result of the ets of extreme poverty, even in more prosperous areas, that collaboration between the World Bank and PCBS. This more aggregate analysis can mask. Such information is es- has involved technical assistance from the World Bank on pecially important for policy making, and for prioritizing the calculating small-area (locality level) poverty estimates for development efforts of the many international and national the Palestinian Territories. This also included training of Seeing is Believing – Poverty in The Palestinian Territories agencies working on the ground. A poverty map is a visual the PCBS staff on the methodology of poverty mapping, representation of precisely this kind of information. as well as the use of PovMap2, the software developed by the World Bank software for such work. Throughout this process, all the maps and analysis in this report have been World Bank-PCBS Collaboration replicated by both the World Bank and the PCBS teams. This Poverty Mapping exercise builds on a programmatic and comprehensive collaboration between the World Bank What is a Poverty Map? and the Palestinian Central Bureau of Statistics (PCBS). This collaboration began in 2010 with a request for Technical Poverty estimates are usually calculated using a nationally Assistance (TA) to validate and update methodology for representative household survey with consumption data. poverty measurement. Using a long series of Palestinian In the Palestinian Territories, the Palestine Expenditure and Expenditure and Consumption Surveys (PECS), the World Consumption Surveys (PECS) are designed to provide esti- Bank worked with the PCBS to create a fully consistent mates of poverty at the regional level (West Bank and Gaza), poverty series from 2004 to 2009, including a simulation strata level (Urban, Rural, Refugee Camp), and some larger of poverty estimates for Gaza in 2008 (due to the inability governorates. However, for policy makers, often, further dis- to complete data collection in Gaza that year). In October aggregation is needed. For instance, with limited resources, 2010, the Palestinian Authority publicly announced the 2009 what parts of a governorate should be prioritized for poverty poverty estimates in line with the new methodology and reduction programs? How do we identify poor and vulnera- international good practice.3 A series of four technical notes ble pockets to target social assistance? describe this body of work and were delivered to PCBS in August 2010. A core component of this TA involved several Poverty Mapping, using a methodology pioneered by the in-country capacity building exercises at the PCBS as well World Bank, can produce highly disaggregated databases as dedicated training for PCBS and Ministry of Social Affairs of welfare. Poverty Maps involve the estimation of pover- (MoSA) staff in using ADePT, a computational package for poverty analysis that the Research Group of the World Bank 2 has developed. 3 The new methodology used a reference household of 2 adults and 4 children. Since then, PCBS has recently expressed their interest in exploring a change in the reference household to 2 adults and 3 The analysis in the Poverty and Inclusion Assessment children. Their intension is to use this new reference household in revealed implications for survey design and methodology, future poverty estimates with 2009 as the base year. ty indicators at very detailed level (locality, enumeration This is because, at such lower levels of disaggregation, for area, and even households themselves) in order to identify instance, the community or village, the number of obser- pockets of poverty. This is a tool for effective and efficient vations in the survey is too small to produce statistically allocation of resources and programs according to the reliable estimates. The census on the other hand covers the greatest need, to achieve the broader development goal entire population and can therefore be reliable even at low- of poverty reduction. Poverty maps are not simply useful er levels of aggregation. However, the census usually covers as visual representations of poverty but also to understand only basic information like demographics, education and the relationship with a host of other important socio-eco- employment but not detailed information on consumption. nomic indicators such as health, education, labor market outcomes and social assistance. The methodology behind poverty mapping thus takes advantage of the strengths of the survey and the census. Poverty mapping relies on household survey and census In principle, it estimates consumption for every household data, making the most of the strengths of each, and com- covered by the census, and can therefore reliably produce pensating for their weaknesses. Certain key data require- measures of poverty for small areas. ments must be fulfilled to be able to construct a poverty map. Survey data must include detailed consumption data, This particular poverty mapping exercise makes use of the which is the basis for calculating poverty estimates, for most recent census, the General Census of Population and instance at the national and the regional level. However, Housing 2007. Two possible surveys were considered for the survey usually covers only a representative sample of the exercise—the PECS 2009 and 2010. The 2009 PECS was the population. This tradeoff between sample size and the chosen as it was the household survey closest to the census cost and time needed to collect quality consumption data year. The PECS 2007 was eschewed on account of it being implies that surveys cannot typically be used to calculate a crisis year in Gaza, and the PECS 2008 was not considered reliable poverty estimates for more disaggregated areas. because it did not cover Gaza. Background and Context 3 2 Poverty Mapping: Methodology Methodology The selection of the specific poverty mapping methodology is critical; numerous methods are available and have been documented by Bigman and Deichmann (2000). A method for Small Area Estimation (SAE) of poverty rates developed by Elbers et al. (2003) (henceforth referred to as ELL) has gained popularity amongst development practitioners around the world. This Palestinian poverty map implements the SAE method developed by ELL. It imputes consumption levels into census households based on a consumption model estimated from the household survey. In order for this to be possible, the consumption model must include explanatory variables (household and individual characteristics) that are available in both the census and the survey. By applying the estimated coefficients to the “common” variables from the census data, consumption expenditures of census households are imputed. Poverty and inequality statistics for small areas are then calculated with the imputed consumption of census households. One advantage of this method is that it not only estimates poverty incidence but also estimates standard errors of poverty estimates. Since poverty estimates are computed based on imputed consumption, they cannot escape imputation errors, and these errors are reflected in the standard errors. ELL analyzed the properties of such imputation errors in detail and derived a procedure to compute standard errors of pover- ty estimates. More details on the methodology are described in Box 1. Main Data Sources and Technical Challenges The Palestinian poverty map uses unit record Palestine Expenditure and Consumption Survey (PECS 2009) and the General Census of Population and Housing (2007). The census data covered roughly half a million households, while the household survey covered around 3,566 households in 2009. A wide range of house- hold information was collected including educational attainments, labor activities and occupation, and employment and housing conditions. As is the practice in all countries, the General Census of Population and Housing did not include household consumption and income levels, but its wide coverage of household characteristics is an advantage for imputing household consumption. |  Box 1  The Small Area Estimation Method Developed by ELL (2003) The method proposed by ELL has two stages. In the first part, a model of log per capita consumption expenditures () is estimated in the survey data: In ych = Xch’ + Z’ + uch where Xch’ is the vector of explanatory variables for household h in cluster c,  is the vector of associated regression coefficients, Z’ is the vector of location specific variables with  being the associated vector of coefficients, and uch is the regression disturbances due to the discrepancy between the predicted household consumption and the actual value. This disturbance term is decomposed into two independent components: uch = c + ech with a cluster-specific effect, c Seeing is Believing – Poverty in The Palestinian Territories and a household-specific effect, ech. This error structure allows for both a location effect—common to all households in the same area—and heteroskedasticity in the household-specific errors. The location variables can be at any level—for instance, district or village—and can be drawn from any data source that includes all the locations in the country. All parameters regarding the regression coefficients (b, ) and distributions of the disturbance terms are estimated by Feasi- ble Generalized Least Square (FGLS). In the second part of the analysis, poverty estimates and their standard errors are computed. There are two sources of errors involved in the estimation process: errors in the estimated regression coeffi- cients (b, ) and the disturbance terms, both of which affect poverty estimates and their levels of accuracy. ELL propose a way to properly calculate poverty estimates as well as measure their standard errors while taking into account these sources of bias. A simulated value of expenditure for each census household is calculated with predicted log expendi- tures Xch’b + Z’  and random draws from the estimated distributions of the disturbance terms, c and ech. These simula- tions are repeated 100 times. For any given location (such as a village), the mean across the 100 simulations of a poverty statistic provides a point estimate of the statistic, and the standard deviation provides an estimate of the standard error. Data Sources The ELL methodology calls for the creation of a consump- The Palestinian Territories are divided into two regions: the tion model using the household survey. The quality of the West Bank and Gaza. Each region is further subdivided into consumption model depends critically on the number governorates, and the lowest administrative unit within a of common variables in the census and survey, which are governorate is called a locality (see Table 1). The objective good predictors of consumption. Only these variables can of the poverty mapping exercise is to attempt, as far as be used in the regression model implemented in the ELL possible, to estimate poverty at the locality level. The PECS approach. This regression model identifies the significant includes detailed information on a wide array of socio-eco- determinants of poverty and the magnitude of their contri- nomic characteristics of households and their consumption, bution. The important criteria for a satisfactory model are which allows for in depth analysis, but on a smaller sam- having reasonable goodness-of-fit and plausible relation- ple. However, PECS is not representative at lower levels of ships between poverty and its correlates. The resulting esti- 6 aggregation and in particular, at the level of the locality. The mated coefficients are then combined with the correspond- census, on the other hand, collects information on a few ing variables calculated from census data to estimate or basic variables, but covers every single household in the predict consumption levels for all the households covered country. by the census. This imputed consumption is then aggre- gated at the desired level, locality in this case, to calculate data revealed that prices of goods and services vary con- poverty rates. siderably across locations in the West Bank, East Jerusalem (J1 governorate) and Gaza Strip. In general, prices appear The monthly consumption of households (obtained from to be lower in Gaza Strip compared to the West Bank and the Palestine Expenditure and Consumption Survey or higher in East Jerusalem (J1) compared to elsewhere. In PECS) is the main source of data for calculating pover- order to incorporate these price differences, the PCBS ty indicators in the Palestinian Territories. This survey is worked jointly with the World Bank to construct spatial regularly conducted by the Central Bureau of Statistics and price indices that would enable a meaningful comparison is available for the years 1996–1998, 2001, 2004–2011. The of living standards across the West Bank and Gaza Strip. In sampling frame of the PECS includes all the enumeration 2009, the reference household was changed to two adults areas of the Census-2007, which totaled 4,916 enumeration and three children (rather than four) to reflect the most areas distributed over all governorates of the West Bank common household composition. and Gaza Strip. In order to understand the distinct patterns of poverty and The poverty statistics calculated using the PECS were labor market outcomes in the West Bank and in Gaza as originally based on a poverty line definition first developed well as the differing nature of mobility and access restric- in 1998. The definition combines the concepts of both ab- tions in the two territories, separate consumption models solute and relative poverty and is based on a basic needs for the West Bank and Gaza were constructed for the pov- budget for a household of five people (two adults and erty map. three children). In addition to food, clothing, and housing, Poverty Mapping: Methodology the basic needs also include other necessities, including health care, education, transportation, personal care, and Technical Challenges housekeeping supplies. The poverty line is adjusted to re- The ELL poverty mapping methodology has been contin- flect the specific consumption needs of households based ually updated to improve statistical accuracy of poverty on their composition (household size and the number of estimates in response to findings from the latest studies children). by experts and researchers. To this end, the World Bank research department prepares a variety of documents and In 2010–2011, PCBS invested substantially in reviewing its manuals to inform development practitioners of the latest original (1998) poverty measurement and trends methodol- developments and methodological improvements in the ogy in order to meet international best practice standards, ELL method, and they provide recommendations so that which primarily involves the following: (a) adjusting for spa- the latest findings are reflected in the ongoing poverty tial price differences; (b) calculating poverty headcount at mapping exercise. These improvements are also reflected individual rather than household level; and (c) ensuring that in the updated versions of the PovMap2 software pro- poverty lines over time reflect the same purchasing power, duced by the World Bank to assist with application of the which necessitates that the poverty line is adjusted for price procedure. inflation using the official CPI. The Palestinian Poverty Mapping Exercise has faced three Costs of living were taken into consideration; individuals technical issues: (i) The choice of the survey year; (ii) Re- 7 living in different locations may face different prices for solving problems related to very small populations in some similar goods. When comparing the cost of living across census localities and the appropriate geographic boundar- locations using consumption based measures, the available ies for localities; and (iii) Whether to estimate locality level |  Table 1  Administrative Units in The Palestinian Territories Jenin 80 Tubas 21 Tulkarm 35 Nablus 64 561 Localities, 4916 census enumeration areas Qalqylia 34 West Bank Salfit 20 Seeing is Believing – Poverty in The Palestinian Territories Ramallah Palestinian Territories 75 Jericho 12 Jerusalem 51 Bethlehem 44 Hebron 92 Gaza North 5 Gaza City 5 Gaza Khan Younis 8 Rafah 4 Deir al - Balah 11 poverty rates for Jerusalem governorate given limited data and survey. In 2008, the PECS did not cover Gaza. There- availability and constraints to survey implementation. fore, the closest full survey was chosen: The 2009 PECS cov- ers 3,566 households and has an updated sampling frame Choice of survey year based on the 2007 census. In the case of the Palestinian Territories, 2007 was a census year as well as a year in which the PECS was conducted. Localities with small census population and choice of This would have been an ideal scenario for poverty map- locality boundaries ping—using the 2007 census and the 2007 PECS to impute In most countries, the geographic boundaries for areas poverty numbers at the locality level. However, 2007 was a of interest (village, community, locality etc.) are used to 8 crisis year in Gaza and the PECS had a smaller sample than visualize the poverty estimates in the form of a map. In the usual. More importantly, the sampling frame was based on case of the Palestinian territories, the parallel would be to the previous census of 1997, and it would have been very map the estimates of the model within locality boundaries. difficult to link the same geographic areas between census However, no official boundary map for localities currently exists, and different government institutions use different physical proximity were merged on the basis of similarity boundaries for their own purposes. PCBS uses the physical in observable characteristics). We worked with PCBS GIS built-up area of the enumeration area or primary sampling staff and the team responsible for the PECS, on a case by unit to demarcate boundaries for localities, and therefore, case basis, to implement this approach. First, we identified since these only cover inhabited areas, these naturally do localities that were below the minimum threshold of ob- not aggregate up to the entire geographic area of the servations, and a map was produced in order to identify its country. However, they do cover all the areas where Pales- neighbors with their respective number of observations and tinians live within the West Bank and Gaza. their observable characteristics. Then, if the two principles of contiguity and similarity were fulfilled, the localities were The availability of multiple geographic definitions for local- merged appropriately. ities and the lack of an official definition implied the need for a consensus on which definition would be adopted for We used local knowledge and information from the census the poverty map. Therefore, an expert committee was con- such as demographics, labor market outcomes, and spatial stituted that discussed the appropriate geographic defini- characteristics to identify similarities and, subsequently, we tion of a locality for the purposes of poverty mapping. The merged the most similar contiguous localities iteratively committee concluded that the PCBS definition, which is until an acceptable threshold was reached. For example, the basis for survey and census data, i.e., built-up area of many localities in South East Hebron did not meet the min- localities be used for the poverty map. The land outside imum sample size. Given the proximity of these locations, of the built-up area of the localities may include agricul- we considered each locality until both local knowledge and tural land, roads, Israeli settlements, and restricted military the census information substantiated a reasonable merged Poverty Mapping: Methodology areas; and this makes it difficult to delineate boundaries locality unit. Map 1 illustrates this outcome of this process in outside the built-up area. the case of two governorates, Hebron and Ramallah, where localities that were grouped together appear in the same Another important challenge was the presence of several color, while those that were not merged with others are in localities with very few households in the census, less than 10 white. One set of localities necessitated a ‘special’ merge, households in some cases. If the number of observations is which exhibited a similarity of observable characteristics, too low, then the simulated poverty rate for the locality can- but the two localities were not directly adjacent to each not be relied upon due to the likelihood of very high standard other.4 The results of the participatory mapping that have errors. In an attempt to balance the competing consider- the original locality identifier (ID) and the merged locality ID ations of maximizing disaggregated estimates, and minimiz- are in the Annex. ing standard errors, a threshold population of 200 households was agreed upon. Localities with below 200 households were Jerusalem governorate combined with geographically contiguous localities in order Jerusalem governorate covers East Jerusalem (J1, under to maintain statistical robustness for the poverty estimates. Israeli control) and the rest of Jerusalem governorate (J2). There are many settlements in J2 and consequently, many Two requirements were applied as part of this exercise: (i) merging-contiguity (small localities were to be merged 9 with neighbors with whom they shared boundaries); and (ii) similarity of observable characteristics (localities that did 4 The two localities are: Burqa (301185) and Badiw al Mu’arrajat not physically share built-up area boundaries but were in (301775). parts of the governorate are inaccessible to Palestinians. needed to be combined with geographically contiguous As a result, both census and survey data have extremely governorates, creating one consumption model for them. limited coverage of the Jerusalem governorate, ie, J1 and This was done to increase the reliability of the consumption J2. In addition, there were concerns about survey imple- models—the larger the number of observations, the smaller mentation in the governorate as a whole, given the diffi- the margin of error of the results. For instance, Hebron had culty for PCBS to access large parts. This poses significant enough observations to justify having one consumption challenges to estimate poverty at the governorate level, model. However, a single consumption model was created let alone at a locality level.5 Therefore, it was decided not for Nablus and Salfit to ensure that there were more than to include Jerusalem governorate in the poverty mapping 500 observations. exercise. Six distinct models were considered—for Jenin-Tubas-Jer- Seeing is Believing – Poverty in The Palestinian Territories icho, Tulkarm-Qalqylia, Bethlehem, Ramallah, Hebron and Choosing the appropriate consumption Nablus-Salfit. The estimates of poverty obtained from model these six models were compared with the corresponding Ideally, for a country this size, the consumption model poverty rate s from the PECS (and its associated confi- created using the household survey should be estimated dence intervals). Notwithstanding concerns about the at the national level, or in other words, one consumption representativeness of PECS at this level, the results were model for the entire country. In the case of the Palestin- not satisfactory. ian Territories, there are compelling reasons to consider a more disaggregated modeling approach—the evidently However, the information gained from this exercise helped large differences in consumption between the West Bank to refine models at the regional level (West Bank and Gaza and Gaza, and the fragmentation imposed by external and separately) to incorporate specific characteristics that are internal barriers that restrict access to services, markets, and salient in some areas but not in others. Variables that were employment, and therefore consumption. found to be important in the consumption models at gov- ernorate levels were included in the appropriate regional This is ultimately an empirically testable hypothesis, name- model through interactions with governorate dummies. ly that the consumption models were not only heteroge- This approach not only increased the R2s (48–49%) of the neous across the West Bank and Gaza, but within the West regressions but helped to produce robust and reliable Bank as well, or at the governorate level. The approach models. followed was to attempt to create consumption models at the governorate and the regional level, and through These models were then used to impute poverty rates at this exercise, to identify key variables that were pertinent locality level, using the census. The resulting poverty rates in some areas but not in others. This led to the incorpora- were highly robust with almost all of them having standard tion of a number of location variables and interactions as errors less than 5%. the process evolved to converge to the most appropriate model. 10 For models at the governorate level, one concern is the accompanying reduction in the number of observations available for the model. In some cases, governorate sample sizes in the PECS are below 500 households, and therefore 5 For more information, please refer to the PCBS poverty map report |  Map 1  Merged localities – A zoom in of Hebron and Ramallah showing the localities that were merged together (in matching color) and those that were not (in white) Hebron Ramallah Poverty Mapping: Methodology 11 3 Modeling Building the Model The first stage in setting up the model was the identification of variables common to the census and survey that were also important correlates or predictors of poverty. These form the potential pool of candidate variables for the consumption model and included:  Labor market indicators: Working-age males, working-age females, status of the head of the household with respect to the labor force, economic activity of the head of the household.  Demographic indicators: The number of adult males in the household, the number of adult females in the household, sex of the head of the household, the age of the head of the household, marital status head of household, the average household size, depen- dency ratio.  Education indicators: Educational level of the head of the household, the highest num- ber of years of schooling for household members.  Health indicators: The number of individuals with disabilities in the household.  Housing Indicators: Housing type, household density (number of household members per room), home ownership, durable goods such as (car, TV, cooking stove, etc.). The model was constructed in an iterative way using an OLS regression, adding one variable at a time. At each addition, every variable was tested for significance and retained in the model only if significant, and dropped otherwise. This process was then revised again based on whether these variables were significant in a GLS regression. The resulting model was then tested for stability by making sure the coefficients do not change dramatically with the addition or removal of any one variable. For a list of the variables that are used in each of the final models, please refer to the section “Models”. During this process, many models were constructed and discarded as unsatisfactory. Af- ter arriving at a satisfactory model for both the West Bank and Gaza, the coefficients from the model were then multiplied with census variables to and the standard errors of the final poverty rates were estimate consumption for all the households in the census. minimized. Since this exercise involved the imputation of This method also produces standard errors for each of the poverty from a survey to a census, the error that was most poverty estimates. crucial in determining the final standard errors was the sampling error of the survey, and great care was taken to be As a validation, poverty estimates simulated at governorate mindful of this. level were compared to actual poverty estimates at gover- norate level from the PECS. Significant differences indicat- The PECS has been used to calculate governorate level ed a problem with the model and the process was started poverty rates, but because of a small number of observa- again, until the simulation yielded poverty estimates at tions and high sampling errors below that level, it cannot be governorate level that were consistent with the PECS. reliably used to calculate locality level poverty rates. When Seeing is Believing – Poverty in The Palestinian Territories these locality level poverty estimates were imputed from Final Model the census, one of the steps taken to confirm their validity was to check whether the corresponding governorate level This section describes the final models used for poverty poverty estimate in the census was within the confidence mapping. The models are for 2009, separately for Gaza and intervals of the PECS governorate level estimates. the West Bank. One of the indicators of a good model is the adjusted R2, which is consistently high for the following models The variables used in each model have been described (always higher than 45%). In addition, the coefficients of all the earlier, and are labeled specifically in each of the following variables were checked to ensure that their magnitudes as well models. In addition to the variables available in the data- as sign were consistent with a rational economic explanation. sets, several household variables were interacted with loca- tion variables to reflect heterogeneity across regions. This Several consistency checks were run after these models also provided extra information in terms of which variables were produced to make sure that the models were stable were particularly driving consumption in certain regions. 14 Model: West Bank and Gaza |  Table 2  Consumption Model For Gaza 2009 R2 = 0.4821 adjR2 = 0.4737 Consumption Model Variables Coefficient Std. Err. Intercept 5.9989 0.2562 Dummy variable for whether a household has electricity 0.8635 0.2364 Asset Index 0.1093 0.0094 Dummy for whether the household owns a car 0.2714 0.0465 Dummy for whether a person completed above secondary school 0.1717 0.0267 No. of household members per room 0.2536 0.0357 Dummy for whether household belongs to Gaza City –0.1145 0.056 Dummy for whether household belongs to Rafah 0.1415 0.0414 Household size –0.1141 0.0211 Square of household size 0.0042 0.0012 Dummy for whether house is owned –0.2694 0.073 Enumeration area level mean of dummy for whether the household head works part-time 0.9457 0.202 Share of children in household 0.292 0.0583 Dummy for whether the household head is disabled, interacted with the dummy for governorate Gaza-North 0.1584 0.0376 Dummy for whether the household head is disabled, interacted with the dummy for governorate Khan Younes –0.2411 0.0644 Interaction term of enumeration area level mean full-time household head employment with ownership of home 0.6293 0.1194 Interaction term of dummy for governorate Gaza-North and locality type camp 0.2478 0.0964 Interaction term of dummy for governorate Gaza city and locality type camp –0.3338 0.104 Interaction term of dummy for governorate Khan Younes and locality type camp 0.2169 0.0739 Modeling Interaction term of enumeration area level mean of dummy for refugee with dummy for governorate Gaza city 0.3599 0.0881 Ratio of Variance of ETA Over MSE = 0.0058 GLS Variable Label Coefficient Std. Err. Intercept 6.1052 0.2218 Dummy variable for whether a household has electricity 0.7855 0.1999 Asset Index 0.1149 0.0091 Dummy for whether the household owns a car 0.2524 0.0602 15 Dummy for whether a person completed above secondary school 0.1711 0.0256 No. of household members per room 0.2626 0.0355 Dummy for whether household belongs to Gaza City –0.1613 0.0747 (Continued on next page) |  Table 2  Consumption Model For Gaza 2009 (continued) Dummy for whether household belongs to Rafah 0.1275 0.0525 Household size –0.1141 0.0196 Square of household size 0.0043 0.0012 Dummy for whether house is owned –0.2699 0.0755 Enumeration area level mean of dummy for whether the household head works part-time 0.7754 0.2199 Share of children in household 0.2907 0.0557 Dummy for whether the household head is disabled, interacted with the dummy for governorate Gaza-North 0.1317 0.0412 Dummy for whether the household head is disabled, interacted with the dummy for governorate Khan Younes –0.2138 0.0559 Interaction term of enumeration area level mean full-time household head employment with ownership of home 0.6222 0.12 Seeing is Believing – Poverty in The Palestinian Territories Interaction term of dummy for governorate Gaza-North and locality type camp 0.2541 0.0599 Interaction term of dummy for governorate Gaza city and locality type camp –0.309 0.1132 Interaction term of dummy for governorate Khan Younes and locality type camp 0.1878 0.0674 Interaction term of enumeration area level mean of dummy for refugee with dummy for governorate Gaza city 0.395 0.1006 |  Table 3  Consumption Model for West Bank 2009 R2 = 0.4821 adjR2 = 0.4737 Variable Label Coefficient Std. Err. Intercept 7.4618 0.1168 Dummy variable for whether a household has electricity –0.3524 0.0987 No. of adult females in the household –0.0592 0.0103 Asset index 0.0942 0.0064 Dummy for whether a household owns a car 0.2235 0.0213 Dummy for whether a person completed secondary school 0.0435 0.0217 Dummy for whether a person completed above secondary school 0.164 0.0221 No. of household members per room 0.15 0.0222 Enumeration area level mean of dummy for whether a head of household is working in finance 1.5031 0.3852 Enumeration area level mean of dummy for whether a head of household is working in manufacturing –0.5721 0.1099 Enumeration area level mean of dummy for whether a head of household is working in other –0.4396 0.1072 Dummy for governorate Jenin 0.2099 0.0457 Household size –0.068 0.0118 Household size squared 0.0021 0.0007 16 No. of working age males in household 0.0246 0.0079 Interaction term of asset index and governorate Bethlehem –0.0573 0.0133 (Continued on next page) |  Table 3  Consumption Model for West Bank 2009 (continued) Variable Label Coefficient Std. Err. Interaction term of enumeration area level mean of dummy for whether a head of household is working in agriculture with governorate –5.2353 0.9879 Ramallah Interaction term of enumeration area level mean of dummy for whether a head of household is working in agriculture with governorate Hebron –1.8494 0.3331 Interaction term of enumeration area level mean of dummy for whether a head of household is working in commerce with governorate Hebron 0.6855 0.1292 Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate 0.4316 0.1553 Nablus Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate 0.956 0.152 Qalqylia Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate –3.2228 1.269 Jericho Interaction term of enumeration area level mean of dummy for whether a head of household is working in trade and real estate with 2.196 0.6289 governorate Jenin Interaction term of enumeration area level mean of dummy for whether a head of household is working in trade and real estate with 1.7881 0.7193 governorate Ramallah Interaction term of enumeration area level mean of full-time household head employment with the dummy for ownership of a home 0.1345 0.0335 Interaction term of governorate Nablus and locality type camp –0.2475 0.0686 Interaction term of governorate Bethlehem and locality type urban 0.1479 0.0377 Interaction term of governorate Hebron and locality type rural 0.198 0.0743 Interaction term of enumeration area level mean of dummy for whether a head of household is unemployed with the dummy for governorate 4.6367 0.9509 Jericho Interaction term of enumeration area level mean of dummy for whether a head of household is unemployed with the dummy for governorate –0.5967 0.2097 Hebron Interaction term of asset index with the dummy for governorate Jenin and locality type urban 0.0537 0.0211 Interaction term of asset index with the dummy for governorate Tubas and locality type rural –0.1093 0.0243 Interaction term of asset index with the dummy for governorate Tulkarm and locality type camp –0.1095 0.039 Interaction term of asset index with the dummy for governorate Qalqylia and locality type urban 0.0611 0.0299 Modeling Interaction term of asset index with the dummy for governorate Hebron and locality type rural –0.0536 0.0264 Ratio of Variance of ETA Over MSE = 0.0083 GLS Variable Label Coefficient Std. Err. Intercept 7.435 0.1385 Dummy variable for whether a household has electricity –0.3526 0.1207 No. of adult females in the household –0.0596 0.0108 Asset index 0.094 0.0069 17 Dummy for whether a household owns a car 0.2218 0.0221 (Continued on next page) |  Table 3  Consumption Model for West Bank 2009 (continued) Dummy for whether a person completed secondary school 0.0431 0.0226 Dummy for whether a person completed above secondary school 0.1585 0.0231 No. of household members per room 0.1709 0.0238 Enumeration area level mean of dummy for whether a head of household is working in finance 1.6644 0.4243 Enumeration area level mean of dummy for whether a head of household is working in manufacturing –0.5851 0.1313 Enumeration area level mean of dummy for whether a head of household is working in other –0.5332 0.1224 Dummy for governorate Jenin 0.2064 0.0495 Household size –0.0617 0.0128 Household size squared 0.0017 0.0007 Seeing is Believing – Poverty in The Palestinian Territories No. of working age males in household 0.0256 0.0081 Interaction term of asset index and governorate Bethlehem –0.0591 0.0145 Interaction term of enumeration area level mean of dummy for whether a head of household is working in agriculture with governorate –5.2549 0.9827 Ramallah Interaction term of enumeration area level mean of dummy for whether a head of household is working in agriculture with governorate Hebron –1.7753 0.4067 Interaction term of enumeration area level mean of dummy for whether a head of household is working in commerce with governorate 0.6259 0.1626 Hebron Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate 0.4264 0.1824 Nablus Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate 0.9616 0.1812 Qalqylia Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate –3.4299 1.9478 Jericho Interaction term of enumeration area level mean of dummy for whether a head of household is working in trade and real estate with 2.1657 0.7399 governorate Jenin Interaction term of enumeration area level mean of dummy for whether a head of household is working in trade and real estate with 1.9103 0.7401 governorate Ramallah Interaction term of enumeration area level mean of full-time household head employment with the dummy for ownership of a home 0.1393 0.0363 Interaction term of governorate Nablus and locality type camp –0.2434 0.0855 Interaction term of governorate Bethlehem and locality type urban 0.1448 0.0449 Interaction term of governorate Hebron and locality type rural 0.1815 0.0864 Interaction term of enumeration area level mean of dummy for whether a head of household is unemployed with the dummy for governorate 4.9031 1.3347 Jericho Interaction term of enumeration area level mean of dummy for whether a head of household is unemployed with the dummy for governorate –0.538 0.2493 Hebron Interaction term of asset index with the dummy for governorate Jenin and locality type urban 0.0516 0.0229 Interaction term of asset index with the dummy for governorate Tubas and locality type rural –0.1013 0.0303 Interaction term of asset index with the dummy for governorate Tulkarm and locality type camp –0.1004 0.0427 18 Interaction term of asset index with the dummy for governorate Qalqylia and locality type urban 0.0598 0.0325 Interaction term of asset index with the dummy for governorate Hebron and locality type rural –0.0521 0.0307 Results The results are remarkably consistent with the poverty rates the survey confidence intervals, and are described in the derived from PECS, with all the model predictions lying with tables below. |  Table 4  Comparison between the Actual Data and the Model Estimates by Region, 2009 Region PECS Data Model West Bank 22% 21% Gaza Strip 38% 38% |  Table 5  Comparison between the Actual Data and the Model Estimates by Governorate, 2009 Survey Confidence intervals PECS 2009 Poverty Model Poverty Rate Governorate Estimates Std. Error Min. Max. 2009 1 Jenin 23% 3% 16% 30% 19% 5 Tubas 19% 9% –6% 44% 24% 10 Tulkarm 19% 2% 13% 24% 21% 15 Nablus 17% 4% 9% 24% 20% 20 Qalqylia 20% 6% 4% 36% 16% 25 Salfit 19% 2% 13% 24% 24% 30 Ramallah 8% 3% 1% 16% 9% 35 Jericho 26% 7% 8% 45% 31% 45 Bethlehem 10% 3% 3% 16% 17% 50 Hebron 28% 3% 22% 35% 30% 55 Gaza north 26% 7% 11% 42% 28% Modeling 60 Gaza city 37% 4% 28% 46% 38% 65 Deiralbalah 29% 8% 8% 50% 41% 70 Khan Younes 39% 4% 31% 47% 46% 75 Rafah 25% 4% 14% 35% 33% 19 4 Mapping the Results A Fragmented Landscape We begin by describing the physical landscape of the Palestinian territories, paying special attention to man-made barriers to movement and access. Following the Oslo Accords in 1993, the territories were divided into three areas: A, B and C. In Area A, which comprises heavily populated cities and towns, the Palestinian Authority (PA) has civil and security con- trol. The PA has civil autonomy but no security control in Area B; and no control whatsoever in Area C. More than half of the physical territory of the West Bank lies in Area C. Map 2 illustrates these three areas of varying PA control within the |  Map 2  A Divided Landscape Palestinian Territories. It also shows the various man-made restrictions on the mobility of goods and services within the West Bank. These are an important non-natural source of geographic fragmenta- tion. They include the barrier wall, settlements, (depicted in Map 2) as well as checkpoints, earth mounds Seeing is Believing – Poverty in The Palestinian Territories and other barriers (depicted in Map 3). In effect, Areas A and B look like a group of islands that are sepa- rated from each other by area C. The “boundaries” of these areas are largely shaped by the mobility restrictions in place. Palestinians routinely have to cross manned checkpoints and road gates to travel from home to work, or from home to school. Commercial traffic has to go through the same check- points, which induces a delay and in some cases imposes a “back-to- 22 |  Map 3  Punctuated by Barriers back” system where the truck load is transferred from one truck to the other (i.e. at these checkpoints the trucks themselves are not allowed to cross, only their load). Other clo- sure obstacles include road blocks, earth mounds, trenches, and the separation barrier wall. Three things are immediately evident from these two maps. First, the control of the Palestinian Authority and relatively free move- ment of Palestinians is restricted to small, non-contiguous islands within the West Bank. Secondly, moving between these ‘islands’ is further restricted by the presence of various types of checkpoints and barriers. Finally, Gaza remains isolated from the West Bank, with restrictions in place on the move- ment of people and goods in and out of Gaza. Mapping the Results 23 While checkpoints, roadblocks and other mobility restrictions have |  Map 4  Localities Isolated or Affected by the Barrier Wall varied in intensity over time, and have, on average, eased in much of the West Bank, the separation bar- rier wall, under construction since 1994, has steadily increased. When completed, it will encircle the West Bank. The wall roughly follows the 1949 Armistice or Green Line, but in many places, encroaches into Seeing is Believing – Poverty in The Palestinian Territories the West Bank. As a result, many communities have been isolated on one side of the wall, or lost access to agricultural lands, or have been split by the wall. Others have been adversely affected in terms of ac- cess to services, markets and other communities, as a result of being close to the wall. UNOCHA-oPT classifies these communities as be- ing “isolated” or “affected” by the wall, and the corresponding local- ities are depicted in Map 4 below. The barrier wall particularly affects certain parts of the West Bank such as Jerusalem governorate and Qalqilya city for instance, which is almost completely surrounded by the wall. In recent years, the barrier wall has also expanded in the Ra- mallah governorate (Blankespoor and van der Weide, 2012). 24 |  Map 5  Localities falling in area C Many localities in the West Bank also fall partially or completely within Area C. As the PA has no control over the parts of localities which fall in area C, it also cannot provide physical access to ser- vices—health, education, sanita- tion, water. Moreover, the presence of settlements also limits the ability of residents to move and access these types of services. Hence, many of these communities (some of which are isolated Bedouin communities) are vulnerable and depend largely on international non-governmental organizations and donor agencies for assistance. As Map 5 shows, many of the local- ities that lie predominantly in area C are in the Jordan valley or near the separation wall. Mapping the Results 25 Visualizing Poverty in the Palestinian Territories |  Map 6  A fragmented Geography: A map of locality boundaries (Built-up areas) in the West Bank and Gaza As a consequence of these delin- eated areas, and overlaid restric- tions on movement and access, it is no surprise that the locality bound- aries of built up areas within the West Bank look like a patchwork of Seeing is Believing – Poverty in The Palestinian Territories islands, whereas in Gaza, they are the contiguous areas, albeit isolat- ed from the West Bank and indeed, the rest of the world. Map 6 plots these built-up areas for the locali- ties in the West Bank and Gaza. 26 |  Map 7  Merging localities in the West Bank Many of these localities with small populations were merged with others to form larger, contiguous groupings that had adequate sample size to simulate poverty reliably (see Section 2). While Map 6 depicts all the localities in the West Bank and Gaza (barring those in Jerusalem governorate), Map 7 identifies those localities that were merged with others to form a group with sufficient number of observa- tions. One set of localities in Ramal- lah governorate was not merged to one of their nearest neighbors because the latter were relative- ly urbanized localities while the former consisted of predominantly Bedouin communities. Localities 301815 and 301775, Badiw al Mu’ar- rajat and Burqa (in the black oval) were merged with each other rather than their immediate neighbor, the locality of Deir Dibwan. Mapping the Results 27 |  We then map the boundaries of the 16 governorates in the West Map 8  The Poorest Governorates in the West Bank are better off than Bank and Gaza and the governor- most Governorates in Gaza: Boundaries of West Bank and Gaza ate level poverty estimates pro- and Regional Poverty Headcount Rates duced by PovMap2 (Map 8). The (2009 Poverty Map estimates) estimates are closely in line with PECS estimates for governorate poverty headcount rates (PECS is not representative at the level of smaller governorates), and this is the first aggregate check of the ro- Seeing is Believing – Poverty in The Palestinian Territories bustness of the simulation exercise. As expected, governorates in Gaza have on average, poverty rates significantly higher than those in the West Bank. Khan Younes, Deir- albalah and Gaza City have poverty rates higher than 33 percent; the highest in the territories. Within the West Bank, Jericho and Hebron are the poorest governorates. 28 |  Map 9  Mapping Poverty in the Palestinian Territories Next, we show the poverty map at the locality level for the West Bank and Gaza. The map below plots estimates of poverty head- count rates for the final list of localities (merged where neces- sary) that were included in the poverty mapping exercise. Map 9 is a visual representation of the poverty rates estimated at the locality level within the Palestinian Territories. It is also depicted on the back cover flap. The map of built up areas repre- senting localities closely resem- bles the ‘islands’ of Area A and B in Map 6, with vast parts of the Jordan valley having no Palestinian population. An important point to note is that for Jerusalem gover- norates, no locality boundaries of built-up area are plotted—instead, since J1 and J2 were excluded Mapping the Results from the poverty map analysis. The poverty map (Map 9) plots quintiles of poverty estimates for the West Bank and Gaza, with lighter shades denoting lower poverty rates. Other than two 29 islands of prosperity, all other localities in Gaza have difference between “accessibility” in a hypothetical world poverty rates upwards of 26 percent. This is in contrast where there are no obstacles, and “accessibility” in the real with the West Bank, where only Hebron governorate has world where, in this particular case, all the road closure obsta- a majority of localities with similarly high rates of pover- cles are in place. ty. Ramallah’s localities are predominantly much more prosperous than those in other governorates, in line with Blankespoor and van der Weide (2012) undertake this ex- the increasing concentration of government, business, ercise for the case of the Palestinian territories, focusing on and donors in Ramallah city. Many of the poorer localities measuring the intensity of mobility restrictions in the West within the West Bank are isolated Bedouin communities, Bank. This measure is based on detailed information on or communities in Area C that are cutoff from services and the locations of populated areas (with population counts), markets, or communities bordering settlements with the and the road network. This is combined with the precise Seeing is Believing – Poverty in The Palestinian Territories accompanying restrictions on mobility. locations of the road closure obstacles provided by UN- OCHA oPT and with estimates of the time it takes the cross Accessibility, mobility and poverty each of the obstacles. Their measure accounts for the fact The fragmented landscape, particularly in the West Bank, that different obstacles impact mobility differently. Certain and the accompanying restrictions to mobility and access obstacles (like road blocks and earth mounds) constitute can have implications for access to services, jobs and invest- a full stop to traffic; other important obstacles (like check- ment, and therefore for poverty rates. One way to quantify points and road gates) may permit traffic to pass through the effect of these man-made restrictions is through a “mo- them but will introduce a delay. In some cases, the delay bility restriction index”. The “mobility restriction index” is an- may be modest; in other cases it may be quite severe. The chored to the standard concept of an “accessibility index”, placement of the obstacle also matters critically. A check- which has a long history, see e.g. Deichmann (1997). The stan- point controlling traffic in and out of a major city clearly has dard accessibility index evaluates for a given origin (or loca- a larger impact on mobility than a checkpoint controlling tion point), the size of the population or the market that can access to a small community well away from a commercial be reached within a reference amount of time. A measure of route. All of this is taken into consideration by their mobility “mobility restriction” can then be obtained by evaluating the restriction index. 30 Map 10 shows how mobility restric- tions vary within the West Bank |  Map 10  Mapping Mobility Restrictions in the West Bank as of January 2009. This map was obtained by first estimating the mobility restriction index for each of the localities, and then smoothing these estimates over the continu- ous space. It can be seen that the restriction to mobility is particularly high around Nablus where a series of checkpoints around the city that have been in place since the second Intifada have effectively sealed it off from the rest of the West Bank. Elevated restrictions can also be observed around East Jerusalem, in the Jordan Valley region (especially the northern part), parts of the He- bron governorate, and the northern border of the Bethlehem gover- norate which acts as a gateway between the north and the south of the West Bank. Mapping the Results When compared with the poverty map, it is evident that poverty is correlated with more restricted areas when they overlap with area C. Localities in Hebron governorate and the Jordan valley that have high poverty rates and lie in area C also 31 |  Map 11  Pockets of Desperate Poverty: Relative Poverty in Gaza tend to face severe mobility restric- tions. Nablus and Qalqilya, which in contrast are heavily restricted, do not show correspondingly high rates of poverty. This could be because, unlike the small and isolated com- munities in the Jordan valley or the eastern part of Hebron, these are large population centers, and may have been able to adapt to these closures within their internal econo- Seeing is Believing – Poverty in The Palestinian Territories my. Moreover, PA services are likely well-functioning within these urban centers. Pockets of poverty and prosperity While the poverty map shows estimates of poverty with darker shades denoting higher poverty relative to the territories as a whole, Map 11 and Map 12 show relative poverty within each of the two regions. The former representation 32 may obfuscate important internal variation within the West Bank and |  Map 12  Islands of Prosperity: Relative Poverty in the West Bank Gaza. For instance, Gaza appears to be almost uniformly dark in Map 9, implying very high rates of pov- erty relative to the Palestinian ter- ritories as a whole. However, policy makers may need more nuanced information to target policies within Gaza. Therefore, Map 11 and Map 12 plot locality poverty rate quin- tiles for each of the regions individ- ually. The scales in the two panels are no longer comparable; instead each panel represents a ranking of localities by poverty, within that region. This representation helps to further identify pockets of severe poverty in the West Bank and Gaza, the darkest shades denoting areas where the majority of the people are poor. Mapping the Results 33 |  Map 13  Low Rates of Poverty can Mask a Large Poor Population Poor areas, poor people For the purposes of planning and targeting services and social as- sistance, in addition to identifying areas of high poverty, it may also be important to identify areas with a large number of poor people. A locality with a relatively low pov- erty rate could nevertheless have a large number of poor people because of its high population. Seeing is Believing – Poverty in The Palestinian Territories Map 13, when compared against the poverty map, illustrates the relationship between poverty headcount rates and the number of poor people. As expected, given the high density of population in Gaza, localities with high poverty rates also have a large population of poor people. Large cities in the West Bank such as Jeri- cho and Hebron with relatively high poverty also have a large number of poor people. In fact, they are in the highest range of poor population, but not in terms of poverty rates. In contrast, some of the localities in the Jordan valley (the eastern parts 34 of Hebron and Jericho governor- ates) have high rates of poverty but |  Map 14  Density of Poverty: Poor Population per Square km few poor people, as they are small, isolated communities. Another no- table locality is Qalqylia city, which is almost entirely enclosed by the barrier wall, which has a low poverty rate, but amongst the highest num- ber of poor people, many of them refugees. Another measure of the same theme is the density of poverty, or pockets where a large number of poor people are concentrated with- in a certain area. The import of this indicator is that policies targeted solely based on headcount rates could miss these types of high-den- sity areas because their poverty headcount rates may not be as high. Large population centers, such as Hebron and Nablus cities and many parts of Gaza, can have Mapping the Results up to tens of thousands of poor people within a square kilometer (Map 14). 35 | Map 15  Poverty Appears to be Correlated with Higher Rates of Are poorer households Dependency also larger? Two typical correlates of poverty are dependency ratio and house- hold size. In the Palestinian terri- tories as well, higher dependency ratios and larger household sizes (Map 15) are on average associ- ated with higher rates of poverty. In Hebron governorate in particu- Seeing is Believing – Poverty in The Palestinian Territories lar, these correlations are strong. However, in Gaza this relationship is not as evident, perhaps because poverty rates are uniformly high, and other factors are far more im- portant correlates. 36 Does education pay off? Map 16 depicts the proportion of heads of household in each locality |  Map 16  In the Palestinian Territories, more Educated Places are not Always Better off that have less than primary edu- cation. When compared with the poverty map, it is evident that in the West Bank, localities where more heads of household have low levels of education are more likely to also be poor. In Gaza by contrast, this relationship between poverty and education does not appear to hold as strongly. One possible explanation for the latter is the severe lack of employ- ment opportunities in Gaza, so that education does not guarantee a source of earnings. In contrast, the relatively better economic condi- tions in the West Bank allow for some positive returns to education from the labor market, which are reflected in household welfare Mapping the Results measures. 37 |  Map 17  In Gaza, Education doesn’t Bear Fruit; in the West Bank, Limited Aaccess to Education keeps some Places Poor Another measure of education is the dominant education level of heads of household in a given locality. This measure plots the most frequent level (modal value) of education reported by heads of household for each locality (Map 17). A few pockets of high levels of average education (higher than secondary) are plotted in blue and also correspond to localities with Seeing is Believing – Poverty in The Palestinian Territories low levels of poverty. In contrast, localities where many heads of household have primary education or less (in pink) are on average more likely to be very poor. The latter are predominantly in the eastern part of the West Bank, overlapping with area C, where ac- cess to education services may be very limited. In Gaza, it is striking that there is no locality where the most frequently reported level of education is primary or below. 38 More than 70 percent of people liv- ing in the Palestinian Territories are |  Map 18  An Increasingly Educated Young Population under the age of 30, and they are getting increasingly educated. Map 18 shows the dominant education level amongst youth, the education level most frequently reported of youths in a given locality. When compared to the education of the heads of household, the youth are in general, significantly better educated. Worryingly, there are still pockets in the West Bank where the dominant education level among youth is primary education or below. Many of these localities coincide with vulnerable communi- ties in Area C, with limited access to services including education. Mapping the Results 39 |   Map 19  Not a Pretty Picture: Unemployment goes Hand in Hand with Poverty Unemployment goes hand in hand with poverty The Poverty and Inclusion Assess- ment for the Palestinian Territories, Coping with Conflict?, highlights the primary importance of labor market outcomes, rather than health and education measures, in explaining poverty. This is sharply mirrored in Map 19, reflecting the Seeing is Believing – Poverty in The Palestinian Territories high correlation between unem- ployment rates and poverty at the locality level. Particularly in governorates such as Hebron in the West Bank, and in Khan Younes, Deiralbalah and Gaza City in Gaza, there is an almost one-to-one correspon- dence between unemployment and the poverty headcount ratio. Looking closely at these governor- ates in particular, the highest level of unemployment almost always coincides with the highest rate of poverty. 40 Map 20 plots the rates of unem- ployment among young people |  Map 20  Unemployment Level of Youth (15–30 years of age) aged 15–30 in the West Bank and Gaza. In general, the pattern mir- rors the adult unemployment rates, although the levels are higher. Mapping the Results 41 |   Map 21  Private Sector Dominant Source of Employment in the West Bank; But in Gaza, the Public Sector is Widespread Like the dominant education level, the modal value of the sector of employment, called the dominant sector of employment, is plotted in Map 21. The localities shaded in pink denote those where a ma- jority of household heads report- ed being employed in domestic private organizations. This appears to be the most widespread sector of employment in the West Bank, Seeing is Believing – Poverty in The Palestinian Territories unlike in Gaza. In Gaza, the private sector is the most important source of employment only in Gaza North, and parts of Rafah governorate. In the rest of Gaza, the public sector is the most frequent employer. In parts of the Hebron governorate, where high poverty rates prevail, the dominant employment sector is international organizations or NGOs. This is possibly due to the presence of such international organizations to provide aid and assistance. 42 The predominant employment status in the Palestinian territo- ries appears to be regular wage |  Map 22  Irregular and Self-Employment Correlated with Poverty in the West Bank; not in Gaza employment, in the private sector in the West Bank, and in the public sector in Gaza (Map 22B). In the West Bank, irregular wage employment and self-employment tend to be correlated with poverty. Particularly in localities in Ramallah where the dominant employment status is regular wage employment, there is also a very low incidence of poverty. In contrast, the localities in Ramallah where self-employment is the dominant form of employ- ment are marked by high rates of poverty. Similarly, in some of the south-eastern localities of Hebron, where irregular wage employment is the dominant employment sta- tus, a correspondingly high degree of poverty persists. Mapping the Results 43 |   Map 23  Areas Dominated by Agriculture and Manufacturing Tend to be Poorer Map 23 shows the most frequently reported industry of work in each locality. In Gaza, agriculture and commerce are dominant industries of employment in general. In con- trast, there is a lot of variation in the West Bank, with some localities dominated by manufacturing and construction as well. In the West Bank, localities where commerce is cited as the most frequent sector Seeing is Believing – Poverty in The Palestinian Territories of employment also tend to have relatively lower levels of poverty. As the previous sets of maps show, these are also likely regular, private sector work. In contrast, localities dominated by manufacturing in the West Bank and agriculture in the West Bank and in Gaza, tend to be associated with higher rates of poverty. 44 Map 24 depicts the dominant form of health insurance in each local- |  Map 24  Dominant Health Insurance ity group. The dominant health insurance was defined as the health insurance subscribed to by the majority, in each locality. The most common form of insurance appears to be provided by the government and this corresponds with a wide range of poverty levels all over West Bank. Mapping the Results 45 5 Conclusion Given the fragmented geography of the Palestinian Territories, the visualization of small-ar- ea poverty estimates is unique and has posed unique challenges. The presence of man- made barriers to mobility, the large parts of the West Bank that lie outside the control of the Palestinian Authority, and Gaza’s relative isolation imply that localities and communities living a few kilometers apart can have wide disparities in welfare. Even within Hebron, the poorest governorate in the West Bank, locality level estimates of poverty range from 14 percent to a whopping 83 percent. There is also a lot of variation in the number of poor people in Hebron governorate—from the heavily populated city of Hebron to small, isolated Bedouin communities in the south-eastern part of the governorate. The poverty map and estimates should be interpreted in relation to the unique nature of restrictions in place. For instance, Hebron city itself is divided into H1 and H2, with the latter under the control of the Israeli Defense Forces. The city has 11 permanently manned check- points. Many communities in the south eastern part of Hebron lie in large part in area C, and the resulting isolation and lack of access to services implies correspondingly high rates of poverty. Overall, thus, poverty and vulnerability are linked to and must be understood in relation to these types of restrictions. The poverty map is a visual illustration of estimated poverty indices at locality level. It is a powerful tool for policy makers and provides key information at a level of disaggregation that matters to prioritize the use of scarce resources in areas that need it most. It is important to remember that these are estimates, and are accompanied by standard errors. Therefore, the poverty map is in effect a range of poverty rates for each locality. The better the model and the quality of data, the smaller these errors, and the more accurate the estimates are likely to be. This report also provides cartographic representations of various correlates of poverty, which taken together with the poverty map are a striking visual story. These correlations illustrate the analysis in the poverty assessment for the West Bank and Gaza, Coping with Conflict?. Poverty goes hand in hand with labor market outcomes. Several localities with high levels of unemployment also lie in the highest quintile of poverty rates, and vice versa. While edu- cation matters in many parts of the West Bank, in Gaza, irrespective of education, poverty remains high. A sheer lack of jobs and insecure employment are the main drivers of welfare. The poverty map thus can be a very useful live monitoring Poverty maps are also useful to rank geographical areas and tool, provided it is regularly updated and linked to relevant communities for a phased roll-out of programs, but they are information such as geo-referenced datasets of market not a substitute for the identification of beneficiaries, which accessibility, facility locations (schools, hospitals and clinics), requires household or individual-level targeting. Secondly, agro-climatic information, road networks, and availability of the poverty estimates are based on consumption only, and services such as water and sanitation. As a combined and may not adequately capture other attributes of poverty or disaggregated database, it can serve as a tool for planning vulnerability. Thirdly, these estimates do not explain the purposes, especially in decentralized structures. Similarly, it causes of poverty—well designed surveys and careful anal- can provide a first stage filter for identification of project or yses will be needed to obtain diagnostics of the attributes program areas. This database cannot substitute for careful and causes of poverty, which are essential to design inter- policy design, but rather can serve as a guide for policy ventions. Seeing is Believing – Poverty in The Palestinian Territories prioritization. The poverty mapping exercise has also highlighted areas It is important to also recognize the limitations of the pov- for improvement in the census and the PECS. One import- erty map and its accompanying geo-referenced data and ant area that needs to be revisited is the sampling frame using care in applying it appropriately. Poverty maps have of the PECS to gain representativeness at the governor- become popular in contexts of social safety net programs. ate level and oversample small, isolated and vulnerable They are best suited to guide spatial targeting, for instance, communities, particularly in area C. Since the poverty map identifying pockets of high poverty rates or large popula- depends critically on the nature and amount of information tions of the poor. For instance, they could be combined that is commonly available in the survey and the census, the with the Ministry of Social Affairs’ database of current census instrument can also be redesigned to improve this beneficiaries to identify areas with inadequate coverage. aspect in looking forward to the next poverty map. 48 6 References Blankespoor, B. and R. van der Weide (2012), ‘Measuring the restrictions to mobility in the West Bank’.World Bank mimeo, Washington D.C. Bigman, D. and U. Deichmann. (2000), ‘Spatial indicators of access and fairness for the location of public facilities’, in Geographical Targeting for Poverty Alleviation. Method- ology and Applications, edited by D. Bigman and H. Fofack, World Bank Regional and Sectoral Studies, Washington DC. Deichmann (1997). Accessibility Indicators in GIS. United Nations, New York. Elbers, C., J.O. Lanjouw, and P. Lanjouw (2002). “Micro-level estimation of welfare,” Policy Research Working Paper Series no. 2911, The World Bank. Elbers, C., J.O. Lanjouw, and P. Lanjouw (2003). “Micro-level Estimation of Poverty and In- equality,” Econometrica, 71(1):355–364. Gastner, M.T. and Newman, M.E. (2004). “From The Cover: Diffusion-based method for pro- ducing density-equalizing maps.” Proceedings of the National Academy of Sciences of the United States of America 101, 7499–7504. Tarozzi, A. and A. Deaton (2009). “Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas,” Review of Economics and Statistics, 91(4), 773–792. World Bank (2011), “Coping with Conflict? Poverty and Inclusion in the West Bank and Gaza,” the World Bank, Washington, D.C. 7 Appendices Poverty Results Gaza 2009 Gaza 2009 Locality ID Poverty headcount rate Standard error Locality ID Poverty headcount rate Standard error 1 552681 69.27% 4.29% 21 653275 66.21% 2.92% 2 552695 31.16% 2.70% 22 703370 44.49% 2.79% 3 552740 39.69% 3.37% 23 703410 28.14% 2.64% 4 552755 6.73% 1.93% 24 703420 49.02% 2.69% 5 552790 29.57% 2.59% 25 703425 54.09% 3.16% Seeing is Believing – Poverty in The Palestinian Territories 6 602775 53.61% 4.03% 26 703430 43.60% 3.20% 7 602825 36.94% 3.62% 27 703445 39.70% 3.02% 8 602900 5.05% 1.20% 28 703470 49.20% 3.42% 9 602945 55.32% 3.51% 29 703485 60.89% 3.33% 10 603045 54.57% 4.25% 30 753490 30.70% 2.59% 11 653065 45.72% 3.48% 31 753495 33.51% 2.81% 12 653070 32.88% 2.80% 32 753500 53.88% 3.99% 13 653140 42.12% 2.95% 33 753505 52.32% 3.46% 14 653145 41.37% 3.16% Gaza-North 28.19% 1.46% 15 653180 40.92% 3.32% Gaza-City 38.26% 3.35% 16 653200 40.40% 3.91% Deiralbalah 40.64% 1.37% 17 653210 44.09% 3.50% Khan Younes 45.86% 1.58% 18 653215 34.28% 2.42% Rafah 33.45% 2.19% 19 653240 39.97% 2.76% Gaza 37.56% 1.34% 20 653250 40.76% 3.88% 52 West Bank 2009 West Bank 2009 Locality ID Poverty headcount rate Std. error Locality ID Poverty headcount rate Std. error 1 10005 18.50% 3.21% 40 10600 24.68% 3.42% 2 10010 17.85% 2.90% 41 10605 27.60% 2.64% 3 10030 13.79% 2.87% 42 10615 17.54% 2.91% 4 10035 13.94% 1.85% 43 10625 14.70% 2.15% 5 10045 22.91% 3.18% 44 50420 18.66% 3.29% 6 10050 17.62% 3.11% 45 50535 33.86% 3.15% 7 10055 15.60% 2.85% 46 50550 20.89% 4.07% 8 10060 16.33% 2.33% 47 50610 22.45% 2.55% 9 10080 24.71% 2.76% 48 50700 27.32% 3.37% 10 10095 21.03% 2.50% 49 50740 20.08% 3.11% 11 10120 16.07% 1.98% 50 50755 23.81% 2.72% 12 10125 31.35% 3.34% 51 100290 22.81% 2.56% 13 10140 17.91% 3.43% 52 100330 15.95% 2.74% 14 10145 18.78% 2.51% 53 100345 26.00% 3.64% 15 10180 14.37% 1.44% 54 100350 19.05% 2.95% 16 10185 10.47% 3.85% 55 100425 22.03% 3.06% 17 10190 22.24% 3.54% 56 100440 14.13% 2.43% 18 10215 24.23% 3.20% 57 100475 20.91% 2.83% 19 10220 15.45% 2.18% 58 100480 19.58% 2.39% 20 10245 19.78% 2.91% 59 100530 20.62% 2.40% 21 10265 18.34% 2.03% 60 100570 21.04% 2.27% 22 10275 18.18% 2.89% 61 100595 19.40% 2.69% 23 10300 20.43% 3.35% 62 100620 26.64% 3.66% 24 10305 32.17% 3.53% 63 100635 28.50% 4.47% 25 10310 34.83% 3.47% 64 100645 18.09% 1.93% Appendices 26 10320 31.82% 3.89% 65 100665 18.33% 2.14% 27 10340 25.62% 2.77% 66 100690 29.13% 3.46% 28 10370 14.21% 2.07% 67 100730 21.41% 3.45% 29 10395 24.76% 3.28% 68 100735 19.79% 2.63% 30 10405 31.04% 3.13% 69 100760 17.94% 2.15% 31 10415 19.41% 3.16% 70 100800 28.63% 3.33% 32 10435 9.44% 1.88% 71 100845 15.30% 2.72% 33 10445 28.01% 3.79% 72 100900 21.42% 3.14% 34 10465 21.91% 2.19% 73 100915 19.39% 3.29% 35 10500 14.96% 2.27% 74 150660 23.55% 3.88% 36 10505 14.77% 2.94% 75 150680 17.58% 2.77% 53 37 10510 19.69% 2.94% 76 150695 29.45% 3.48% 38 10520 16.03% 2.11% 77 150705 19.00% 2.65% 39 10565 21.61% 2.76% 78 150765 19.25% 2.91% (Continued on next page) West Bank 2009 (continued) West Bank 2009 Locality ID Poverty headcount rate Std. error Locality ID Poverty headcount rate Std. error 79 150775 23.77% 3.18% 118 151405 16.12% 2.92% 80 150785 14.32% 3.00% 119 151410 21.30% 3.43% 81 150805 16.99% 2.74% 120 151445 21.96% 3.11% 82 150810 19.62% 3.20% 121 200925 32.75% 3.50% 83 150820 12.05% 2.00% 122 200945 23.49% 3.63% 84 150825 36.79% 3.37% 123 200965 23.86% 2.84% 85 150835 17.69% 3.08% 124 200970 23.54% 3.41% 86 150855 19.34% 2.64% 125 200985 12.54% 2.32% Seeing is Believing – Poverty in The Palestinian Territories 87 150860 13.20% 2.70% 126 201020 16.44% 3.20% 88 150880 46.81% 4.58% 127 201040 16.31% 1.67% 89 150910 32.63% 3.04% 128 201055 17.89% 3.48% 90 150920 13.38% 1.68% 129 201085 18.32% 2.92% 91 150930 48.64% 4.50% 130 201100 16.59% 2.05% 92 150935 14.16% 2.65% 131 201125 6.54% 1.45% 93 150950 11.58% 2.61% 132 201155 4.66% 1.67% 94 150955 19.81% 3.11% 133 201175 15.00% 2.95% 95 150960 54.75% 4.66% 134 201255 8.76% 2.31% 96 150990 16.70% 2.75% 135 201260 10.92% 2.53% 97 151000 16.14% 2.56% 136 201280 9.67% 2.57% 98 151010 18.61% 3.12% 137 251250 24.80% 2.76% 99 151025 26.82% 3.67% 138 251275 28.07% 3.77% 100 151050 18.33% 2.72% 139 251295 24.41% 2.99% 101 151080 17.13% 2.75% 140 251300 22.35% 3.21% 102 151090 13.90% 2.16% 141 251305 17.27% 2.21% 103 151095 30.06% 3.48% 142 251310 41.17% 3.80% 104 151135 19.56% 2.54% 143 251315 20.43% 3.40% 105 151160 16.15% 3.33% 144 251320 18.91% 2.71% 106 151185 14.55% 2.03% 145 251340 19.84% 2.77% 107 151195 9.37% 2.62% 146 251360 23.95% 2.61% 108 151215 19.56% 2.95% 147 251370 19.14% 2.01% 109 151230 18.84% 3.32% 148 251395 33.08% 3.90% 110 151245 24.46% 3.15% 149 251400 31.51% 3.68% 111 151270 22.92% 2.89% 150 251425 26.53% 3.20% 112 151325 20.90% 2.83% 151 251430 27.18% 3.77% 113 151335 15.60% 2.48% 152 301455 10.71% 2.61% 54 114 151365 28.08% 3.30% 153 301460 10.25% 1.91% 115 151375 22.04% 3.06% 154 301470 9.07% 2.62% 116 151380 15.47% 2.80% 155 301480 9.33% 2.13% 117 151385 25.65% 3.41% 156 301485 4.25% 1.88% (Continued on next page) West Bank 2009 (continued) West Bank 2009 Locality ID Poverty headcount rate Std. error Locality ID Poverty headcount rate Std. error 157 301490 4.18% 1.21% 196 301805 11.46% 2.48% 158 301500 6.58% 1.95% 197 301810 1.86% 0.58% 159 301505 8.68% 2.40% 198 301815 41.59% 2.61% 160 301515 26.57% 3.49% 199 301820 10.74% 2.49% 161 301525 7.73% 1.96% 200 301825 4.40% 1.08% 162 301530 58.68% 4.30% 201 301830 8.39% 1.82% 163 301535 8.87% 2.05% 202 301850 16.18% 3.14% 164 301545 7.84% 1.96% 203 301855 22.40% 2.52% 165 301555 5.98% 1.47% 204 301890 7.41% 2.69% 166 301565 3.87% 1.56% 205 301895 26.02% 3.27% 167 301570 9.33% 2.15% 206 351110 39.78% 5.13% 168 301590 7.02% 2.02% 207 351140 29.96% 3.42% 169 301595 8.17% 1.69% 208 351690 27.40% 2.70% 170 301600 7.24% 1.50% 209 351840 40.56% 3.62% 171 301605 20.12% 3.21% 210 351865 45.18% 5.46% 172 301610 5.71% 1.37% 211 351920 27.19% 3.20% 173 301620 9.39% 2.03% 212 351975 33.33% 3.40% 174 301635 3.91% 1.27% 213 452170 18.12% 3.92% 175 301640 8.22% 2.68% 214 452175 15.26% 3.01% 176 301650 8.95% 1.74% 215 452180 14.41% 2.60% 177 301660 10.12% 2.48% 216 452185 22.76% 3.19% 178 301665 15.37% 2.98% 217 452195 23.43% 3.95% 179 301670 8.00% 2.11% 218 452208 13.79% 2.86% 180 301675 3.55% 1.12% 219 452210 6.19% 1.28% 181 301680 9.44% 1.74% 220 452225 19.64% 3.07% Appendices 182 301685 4.75% 1.53% 221 452230 8.63% 2.33% 183 301700 6.14% 1.59% 222 452235 16.46% 3.08% 184 301710 6.34% 2.07% 223 452240 8.76% 1.75% 185 301720 6.42% 1.95% 224 452255 4.65% 1.33% 186 301725 20.08% 2.93% 225 452265 18.21% 2.41% 187 301730 8.35% 2.46% 226 452270 10.82% 2.55% 188 301745 21.56% 2.77% 227 452275 21.84% 2.90% 189 301750 6.59% 1.89% 228 452280 26.81% 3.72% 190 301755 12.57% 2.71% 229 452285 26.66% 3.90% 191 301765 5.47% 1.46% 230 452300 30.11% 3.69% 192 301780 7.47% 1.81% 231 452325 10.91% 2.33% 55 193 301785 6.15% 1.56% 232 452360 17.51% 3.13% 194 301790 2.33% 0.63% 233 452385 17.86% 3.50% 195 301800 16.36% 3.26% 234 452400 30.50% 3.47% (Continued on next page) West Bank 2009 (continued) West Bank 2009 Locality ID Poverty headcount rate Std. error Locality ID Poverty headcount rate Std. error 235 452460 47.62% 4.69% 262 502910 18.39% 3.17% 236 452495 19.42% 3.42% 263 502920 19.50% 3.48% 237 452525 35.89% 4.23% 264 502950 26.25% 4.07% 238 452660 38.60% 4.27% 265 502960 33.55% 4.94% 239 502450 35.26% 3.02% 266 502970 34.18% 4.08% 240 502530 33.58% 3.16% 267 502980 16.82% 3.04% 241 502540 31.71% 2.75% 268 503090 21.40% 4.53% 242 502560 23.87% 2.86% 269 503100 33.93% 4.84% Seeing is Believing – Poverty in The Palestinian Territories 243 502615 40.61% 3.29% 270 503115 38.73% 4.81% 244 502620 42.33% 3.44% 271 503120 50.35% 3.46% 245 502630 21.11% 2.86% 272 503126 83.07% 4.80% 246 502635 32.52% 3.03% 273 503145 20.44% 4.38% 247 502640 27.72% 3.12% 274 503170 14.48% 3.34% 248 502655 45.24% 4.02% 275 503245 33.13% 3.20% 249 502680 20.56% 3.60% 276 503320 53.18% 3.69% 250 502685 35.75% 3.54% 277 503335 40.93% 4.23% 251 502750 33.17% 3.55% Jenin 19.30% 0.64% 252 502765 24.50% 4.15% Tubas 24.47% 1.50% 253 502780 18.67% 2.42% Tulkarm 20.81% 0.96% 254 502782 29.64% 4.05% Nablus 20.18% 0.75% 255 502810 26.03% 2.89% Qalqylia 15.82% 0.85% 256 502815 27.65% 2.97% Salfit 23.96% 0.85% 257 502835 18.39% 3.01% Ramallah 8.87% 0.58% 258 502840 21.39% 2.27% Jericho 31.28% 2.01% 259 502860 24.48% 4.46% Bethlehem 17.35% 0.83% 260 502895 20.41% 3.95% Hebron 29.88% 1.10% 261 502905 39.66% 3.68% West Bank 21.31% 0.46% 56 Merged Localities Region Governorate Locality Merge ID Original ID Region Governorate Locality Merge ID Original ID WB Bethlehem Al Walaja 452170 452170 WB Bethlehem Marah Rabah 452660 452500 WB Bethlehem Battir 452175 452175 WB Bethlehem Beit Fajjar 452525 452525 WB Bethlehem Al ‘Ubeidiya 452180 452180 WB Bethlehem Al Maniya 452660 452535 WB Bethlehem Ayda Camp 452185 452185 WB Bethlehem Kisan 452660 452565 WB Bethlehem Khallet an Nu’man 452225 452190 WB Bethlehem Arab ar Rashayida 452660 452660 WB Bethlehem Al ‘Aza Camp 452195 452195 WB Hebron Khirbet ad Deir 502450 502435 WB Bethlehem Al Khas 452225 452200 WB Hebron Surif 502450 502450 WB Bethlehem Al Haddadiya 452285 452205 WB Hebron Al ‘Arrub Camp 502530 502530 WB Bethlehem Khallet Hamameh 452208 452208 WB Hebron Beit Ummar 502540 502540 WB Bethlehem Bir Onah 452208 452209 WB Hebron Jala 502560 502545 WB Bethlehem Beit Jala 452210 452210 WB Hebron Hitta 502560 502550 WB Bethlehem Dar Salah 452225 452225 WB Hebron Shuyukh al ‘Arrub 502620 502555 WB Bethlehem Husan 452230 452230 WB Hebron Kharas 502560 502560 WB Bethlehem Wadi Fukin 452235 452235 WB Hebron Umm al Butm 502620 502575 WB Bethlehem Bethlehem (Beit Lahm) 452240 452240 WB Hebron Hamrush 502620 502580 WB Bethlehem Beit Sahur 452255 452255 WB Hebron Nuba 502560 502585 WB Bethlehem Ad Doha 452265 452265 WB Hebron Beit Ula 502615 502615 WB Bethlehem Al Khadr 452270 452270 WB Hebron Sa’ir 502620 502620 WB Bethlehem Ad Duheisha Camp 452275 452275 WB Hebron Halhul 502630 502630 WB Bethlehem Hindaza 452280 452280 WB Hebron Ash Shuyukh 502635 502635 WB Bethlehem Ash Shawawra 452285 452285 WB Hebron Tarqumiya 502640 502640 WB Bethlehem Artas 452300 452300 WB Hebron Beit Kahil 502655 502655 WB Bethlehem Nahhalin 452325 452325 WB Hebron Beit ‘Einun 502680 502680 WB Bethlehem Beit Ta’mir 452280 452335 WB Hebron Qlaa Zeta 502680 502681 WB Bethlehem Khallet al Louza 452280 452345 WB Hebron Idhna 502685 502685 Appendices WB Bethlehem Al Jab’a 452235 452355 WB Hebron Taffuh 502750 502750 WB Bethlehem Za’tara 452360 452360 WB Hebron Beit Maqdum 502765 502765 WB Bethlehem Jannatah 452385 452385 WB Hebron Al Baqa 502680 502778 WB Bethlehem Wadi Rahhal 452400 452400 WB Hebron Hebron (Al Khalil) 502780 502780 WB Bethlehem Jubbet adh Dhib 452385 452405 WB Hebron Al Bowereh (Aqabat Injeleh) 502782 502781 WB Bethlehem Khallet Sakariya 452235 452415 WB Hebron Khallet Edar 502782 502782 WB Bethlehem Khallet al Haddad 452400 452430 WB Hebron Deir Samit 502810 502810 WB Bethlehem Al Ma’sara 452400 452440 WB Hebron Bani Na’im 502815 502815 WB Bethlehem Wadi an Nis 452400 452445 WB Hebron Khallet Al Masafer 503126 502830 WB Bethlehem Jurat ash Sham’a 452460 452460 WB Hebron Beit ‘Awwa 502835 502835 WB Bethlehem Marah Ma’alla 452460 452470 WB Hebron Dura 502840 502840 57 WB Bethlehem Umm Salamuna 452460 452480 WB Hebron Qalqas 502782 502855 WB Bethlehem Al Manshiya 452660 452490 WB Hebron Sikka 502860 502860 WB Bethlehem Tuqu’ 452495 452495 WB Hebron Khirbet Salama 502860 502865 (Continued on next page) (continued) Region Governorate Locality Merge ID Original ID Region Governorate Locality Merge ID Original ID WB Hebron Wadi ‘Ubeid 502860 502870 WB Hebron Al Karmil 503215 503215 WB Hebron Fuqeiqis 502860 502875 WB Hebron Khallet Salih 503215 503225 WB Hebron Khursa 502895 502895 WB Hebron Adh Dhahiriya 503245 503245 WB Hebron Tarrama 502980 502900 WB Hebron At Tuwani 503215 503255 WB Hebron Al Fawwar Camp 502905 502905 WB Hebron Ma’in 503215 503260 WB Hebron Al Majd 502910 502910 WB Hebron An Najada 503215 503265 WB Hebron Marah al Baqqar 502860 502915 WB Hebron Anab al Kabir 503245 503295 WB Hebron Hadab al Fawwar 502920 502920 WB Hebron Khirbet Asafi 503215 503305 WB Hebron Deir al ‘Asal at Tahta 502970 502925 WB Hebron Mantiqat Shi’b al Batin 503215 503310 WB Hebron Al Heila 502782 502935 WB Hebron As Samu’ 503320 503320 Seeing is Believing – Poverty in The Palestinian Territories WB Hebron Wadi ash Shajina 502980 502940 WB Hebron Wadi Al Amayer 503320 503321 WB Hebron As Sura 502950 502950 WB Hebron Khirbet Tawil ash Shih 503215 503325 WB Hebron Deir Razih 502980 502955 WB Hebron Ar Ramadin 503335 503335 WB Hebron Ar Rihiya 502960 502960 WB Hebron Maghayir al ‘Abeed 503320 503345 WB Hebron Zif 503115 502965 WB Hebron Khirbet al Fakheit 503215 503350 WB Hebron Deir al ‘Asal al Fauqa 502970 502970 WB Hebron Khirbet Bir al ‘Idd 503320 503360 WB Hebron Khallet al ‘Aqed 502950 502975 WB Hebron Khirbet Zanuta 503335 503375 WB Hebron Imreish 502980 502980 WB Hebron Imneizil 503320 503380 WB Hebron Al Buweib 503126 503005 WB Hebron Arab al Fureijat 503335 503405 WB Hebron Beit ar Rush at Tahta 503090 503010 WB Jenin Zububa 10005 10005 WB Hebron Hadab al ‘Alaqa 502980 503040 WB Jenin Rummana 10010 10010 WB Hebron Beit Mirsim 503090 503075 WB Jenin Ti’innik 10010 10015 WB Hebron Beit ar Rush al Fauqa 503090 503090 WB Jenin At Tayba 10010 10020 WB Hebron Karma 502980 503095 WB Jenin Arabbuna 10055 10025 WB Hebron Beit ‘Amra 503100 503100 WB Jenin Al Jalama 10030 10030 WB Hebron Om Adaraj (Arab Al Kaabneh) 503126 503105 WB Jenin Silat al Harithiya 10035 10035 WB Hebron Wadi al Kilab 503090 503110 WB Jenin As Sa’aida 10045 10040 WB Hebron Om Ashoqhan 503115 503111 WB Jenin Anin 10045 10045 WB Hebron Khallet al Maiyya 503115 503115 WB Jenin Arrana 10050 10050 WB Hebron Kheroshewesh Wal 503115 503116 WB Jenin Deir Ghazala 10055 10055 Hadedeyah WB Jenin Faqqu’a 10060 10060 WB Hebron Om Al Amad (Sahel Wadi 503115 503117 WB Jenin Khirbet Suruj 10045 10070 Elma) WB Jenin Al Yamun 10080 10080 WB Hebron Yatta 503120 503120 WB Jenin Umm ar Rihan 10145 10085 WB Hebron Ad Deirat 503115 503125 WB Jenin Kafr Dan 10095 10095 WB Hebron Khashem Adaraj (Al- 503126 503126 WB Jenin Khirbet ‘Abdallah al Yunis 10145 10105 Hathaleen) WB Jenin Dhaher al Malih 10145 10115 WB Hebron Kurza 502980 503135 58 WB Jenin Barta’a ash Sharqiya 10120 10120 WB Hebron Rabud 503145 503145 WB Jenin Al ‘Araqa 10125 10125 WB Hebron Umm Lasafa 503215 503150 WB Jenin Al Jameelat 10140 10135 WB Hebron Al Burj 503170 503170 WB Jenin Beit Qad 10140 10140 WB Hebron Um Al-Khair 503126 503210 (Continued on next page) (continued) Region Governorate Locality Merge ID Original ID Region Governorate Locality Merge ID Original ID WB Jenin Tura al Gharbiya 10145 10145 WB Jenin Al Jarba 10415 10430 WB Jenin Tura ash Sharqiya 10145 10150 WB Jenin Az Zababida 10435 10435 WB Jenin Al Hashimiya 10145 10155 WB Jenin Fahma 10445 10445 WB Jenin Nazlat ash Sheikh Zeid 10145 10165 WB Jenin Az Zawiya 10395 10460 WB Jenin At Tarem 10145 10170 WB Jenin Kafr Ra’i 10465 10465 WB Jenin Khirbet al Muntar al 10145 10175 WB Jenin Al Kufeir 10405 10485 Gharbiya WB Jenin Sir 10405 10495 WB Jenin Jenin 10180 10180 WB Jenin Ajja 10500 10500 WB Jenin Jenin Camp 10185 10185 WB Jenin Anza 10505 10505 WB Jenin Jalbun 10190 10190 WB Jenin Sanur 10510 10510 WB Jenin Aba 10140 10195 WB Jenin Ar Rama 10500 10515 WB Jenin Khirbet Mas’ud 10245 10200 WB Jenin Meithalun 10520 10520 WB Jenin Khirbet al Muntar ash 10245 10205 WB Jenin Al Judeida 10565 10565 Sharqiya WB Jenin al ‘Asa’asa 10605 10585 WB Jenin Kafr Qud 10275 10210 WB Jenin Al ‘Attara 10625 10590 WB Jenin Deir Abu Da’if 10215 10215 WB Jenin Siris 10600 10600 WB Jenin Birqin 10220 10220 WB Jenin Jaba’ 10605 10605 WB Jenin Umm Dar 10245 10225 WB Jenin Al Fandaqumiya 10615 10615 WB Jenin Al Khuljan 10245 10230 WB Jenin Silat adh Dhahr 10625 10625 WB Jenin Wad ad Dabi’ 10140 10235 WB Jericho Marj Na’ja 351110 351045 WB Jenin Dhaher al ‘Abed 10245 10240 WB Jericho Az Zubeidat 351110 351110 WB Jenin Zabda 10245 10245 WB Jericho Marj al Ghazal 351110 351116 WB Jenin Ya’bad 10265 10265 WB Jericho Al Jiftlik 351140 351140 WB Jenin Kufeirit 10275 10275 WB Jericho Fasayil 351690 351510 WB Jenin Imreiha 10245 10285 WB Jericho Al ‘Auja 351690 351690 WB Jenin Umm at Tut 10305 10295 WB Jericho An Nuwei’ma 351840 351840 WB Jenin Ash Shuhada 10300 10300 WB Jericho Ein ad Duyuk al Fauqa 351840 351845 Appendices WB Jenin Jalqamus 10305 10305 WB Jericho Ein as Sultan Camp 351865 351865 WB Jenin Al Mughayyir 10310 10310 WB Jericho Jericho (Ariha) 351920 351920 WB Jenin Al Mutilla 10310 10315 WB Jericho Aqbat Jaber Camp 351975 351975 WB Jenin Bir al Basha 10320 10320 WB Jericho An Nabi Musa 351975 352075 WB Jenin Al Hafira 10320 10335 WB Jerusalem Rafat 401870 401870 WB Jenin Qabatiya 10340 10340 WB Jerusalem Mikhmas 401885 401885 WB Jenin Arraba 10370 10370 WB Jerusalem Qalandiya Camp 401900 401900 WB Jenin Telfit 10305 10385 WB Jerusalem Jaba’ (Tajammu’ Badawi) 401885 401910 WB Jenin Mirka 10395 10395 WB Jerusalem Qalandiya 401940 401915 WB Jenin Wadi Du’oq 10395 10400 WB Jerusalem Beit Duqqu 401930 401930 WB Jenin Fahma al Jadida 10395 10401 WB Jerusalem Jaba’ 401935 401935 59 WB Jenin Raba 10405 10405 WB Jerusalem Al Judeira 401940 401940 WB Jenin Al Mansura 10395 10410 WB Jerusalem Ar Ram & Dahiyat al Bareed 401945 401945 WB Jenin Misliya 10415 10415 WB Jerusalem Beit ‘Anan 401950 401950 (Continued on next page) (continued) Region Governorate Locality Merge ID Original ID Region Governorate Locality Merge ID Original ID WB Jerusalem Al Jib 401955 401955 WB Nablus Ein Shibli 150825 150885 WB Jerusalem Bir Nabala 401960 401960 WB Nablus Azmut 150910 150910 WB Jerusalem Beit Ijza 401980 401965 WB Nablus Nablus 150920 150920 WB Jerusalem Al Qubeiba 401980 401980 WB Nablus Askar Camp 150930 150930 WB Jerusalem Kharayib Umm al Lahim 402015 401985 WB Nablus Deir al Hatab 150935 150935 WB Jerusalem Biddu 401995 401995 WB Nablus Sarra 150950 150950 WB Jerusalem An Nabi Samwil 402025 402000 WB Nablus Salim 150955 150955 WB Jerusalem Hizma 402005 402005 WB Nablus Balata Camp 150960 150960 WB Jerusalem Beit Hanina al Balad 402025 402010 WB Nablus Iraq Burin 151050 150975 WB Jerusalem Qatanna 402015 402015 WB Nablus Tell 150990 150990 Seeing is Believing – Poverty in The Palestinian Territories WB Jerusalem Beit Surik 402020 402020 WB Nablus Beit Dajan 151000 151000 WB Jerusalem Beit Iksa 402025 402025 WB Nablus Rujeib 151010 151010 WB Jerusalem Anata 402040 402040 WB Nablus Kafr Qallil 151025 151025 WB Jerusalem Al Ka’abina (Tajammu’ 402005 402045 WB Nablus Furush Beit Dajan 150825 151030 Badawi) WB Nablus Madama 151050 151050 WB Jerusalem Az Za’ayyem 402065 402065 WB Nablus Burin 151080 151080 WB Jerusalem Al ‘Eizariya 402100 402100 WB Nablus Beit Furik 151090 151090 WB Jerusalem Abu Dis 402120 402120 WB Nablus Asira al Qibliya 151095 151095 WB Jerusalem Arab al Jahalin 402120 402125 WB Nablus Awarta 151135 151135 WB Jerusalem As Sawahira ash Sharqiya 402145 402145 WB Nablus Urif 151160 151160 WB Jerusalem Ash Sheikh Sa’d 402160 402160 WB Nablus Odala 151185 151180 WB Nablus Bizzariya 150660 150660 WB Nablus Huwwara 151185 151185 WB Nablus Burqa 150680 150680 WB Nablus Einabus 151195 151195 WB Nablus Yasid 150695 150695 WB Nablus Yanun 151270 151200 WB Nablus Beit Imrin 150705 150705 WB Nablus Beita 151215 151215 WB Nablus Nisf Jubeil 150705 150745 WB Nablus Ar Rajman 151270 151220 WB Nablus Sabastiya 150765 150765 WB Nablus Zeita Jamma’in 151230 151230 WB Nablus Ijnisinya 150785 150770 WB Nablus Jamma’in 151245 151245 WB Nablus Talluza 150775 150775 WB Nablus Osarin 151270 151265 WB Nablus An Naqura 150785 150785 WB Nablus Aqraba 151270 151270 WB Nablus Al Badhan 150805 150805 WB Nablus Za’tara 151325 151285 WB Nablus Deir Sharaf 150810 150810 WB Nablus Tall al Khashaba 151385 151311 WB Nablus Asira ash Shamaliya 150820 150820 WB Nablus Yatma 151325 151325 WB Nablus An Nassariya 150825 150825 WB Nablus Qabalan 151335 151335 WB Nablus Zawata 150835 150835 WB Nablus Jurish 151375 151345 WB Nablus Al ‘Aqrabaniya 150825 150840 WB Nablus Qusra 151365 151365 WB Nablus Qusin 150855 150855 WB Nablus Talfit 151375 151375 60 WB Nablus Beit Iba 150860 150860 WB Nablus As Sawiya 151380 151380 WB Nablus Beit Hasan 150825 150865 WB Nablus Majdal Bani Fadil 151385 151385 WB Nablus Beit Wazan 150855 150875 WB Nablus Al Lubban ash Sharqiya 151405 151405 WB Nablus Ein Beit el Ma Camp 150880 150880 WB Nablus Qaryut 151410 151410 (Continued on next page) (continued) Region Governorate Locality Merge ID Original ID Region Governorate Locality Merge ID Original ID WB Nablus Jalud 151410 151420 WB Ramallah Bani Zeid 301480 301480 WB Nablus Ammuriya 151405 151435 WB Ramallah Abwein 301485 301485 WB Nablus Duma 151445 151445 WB Ramallah Turmus’ayya 301490 301490 WB Qalqylia Falamya 200985 200905 WB Ramallah Al Lubban al Gharbi 301515 301495 WB Qalqylia Kafr Qaddum 200925 200925 WB Ramallah Sinjil 301500 301500 WB Qalqylia Jit 200945 200945 WB Ramallah Deir as Sudan 301505 301505 WB Qalqylia Baqat al Hatab 200965 200965 WB Ramallah Rantis 301515 301515 WB Qalqylia Hajja 200970 200970 WB Ramallah Jilijliya 301500 301520 WB Qalqylia Jayyus 200985 200985 WB Ramallah Ajjul 301525 301525 WB Qalqylia Khirbet Sir 200985 200995 WB Ramallah Al Mughayyir 301530 301530 WB Qalqylia Arab ar Ramadin ash 201040 201005 WB Ramallah Abud 301535 301535 Shamali WB Ramallah An Nabi Salih 301505 301540 WB Qalqylia Far’ata 201020 201015 WB Ramallah Khirbet Abu Falah 301545 301545 WB Qalqylia Immatin 201020 201020 WB Ramallah Umm Safa 301525 301550 WB Qalqylia Al Funduq 201085 201035 WB Ramallah Al Mazra’a ash Sharqiya 301555 301555 WB Qalqylia Qalqylia 201040 201040 WB Ramallah Deir Nidham 301505 301560 WB Qalqylia An Nabi Elyas 201055 201055 WB Ramallah Atara 301565 301565 WB Qalqylia Kafr Laqif 200965 201065 WB Ramallah Deir Abu Mash’al 301570 301570 WB Qalqylia Arab Abu Farda 201125 201070 WB Ramallah Jibiya 301600 301575 WB Qalqylia Izbat at Tabib 201055 201075 WB Ramallah Burham 301600 301585 WB Qalqylia Jinsafut 201085 201085 WB Ramallah Kafr Malik 301590 301590 WB Qalqylia Azzun 201100 201100 WB Ramallah Shuqba 301595 301595 WB Qalqylia Arab ar Ramadin al Janubi 201125 201105 WB Ramallah Kobar 301600 301600 WB Qalqylia Isla 201055 201115 WB Ramallah Qibya 301605 301605 WB Qalqylia Arab Al-Khouleh 201175 201116 WB Ramallah Silwad 301610 301610 WB Qalqylia Wadi ar Rasha 201155 201120 WB Ramallah Yabrud 301640 301615 WB Qalqylia Habla 201125 201125 WB Ramallah AL-Itihad 301620 301620 Appendices WB Qalqylia Ras at Tira 201155 201130 WB Ramallah Shabtin 301595 301625 WB Qalqylia Ras ‘Atiya 201155 201155 WB Ramallah Bir Zeit 301635 301635 WB Qalqylia Ad Dab’a 201155 201170 WB Ramallah AL-Doha 301675 301636 WB Qalqylia Kafr Thulth 201175 201175 WB Ramallah Ein Siniya 301640 301640 WB Qalqylia ud 201155 201190 WB Ramallah Silwad Camp 301640 301645 WB Qalqylia Al Mudawwar 201255 201205 WB Ramallah Deir Jarir 301650 301650 WB Qalqylia Izbat Salman 201255 201210 WB Ramallah Deir ‘Ammar Camp 301660 301660 WB Qalqylia Izbat al Ashqar 201255 201225 WB Ramallah Budrus 301665 301665 WB Qalqylia Beit Amin 201255 201255 WB Ramallah AL-Zaytouneh 301670 301670 WB Qalqylia Sanniriya 201260 201260 WB Ramallah Jifna 301675 301675 WB Qalqylia Atma 201280 201280 WB Ramallah Dura al Qar’ 301680 301680 61 WB Ramallah Qarawat Bani Zeid 301455 301455 WB Ramallah At Tayba 301685 301685 WB Ramallah Bani Zeid ash Sharqiya 301460 301460 WB Ramallah Al Jalazun Camp 301700 301700 WB Ramallah Kafr ‘Ein 301470 301470 WB Ramallah Abu Qash 301675 301705 (Continued on next page) (continued) Region Governorate Locality Merge ID Original ID Region Governorate Locality Merge ID Original ID WB Ramallah Deir Qaddis 301710 301710 WB Salfit Sarta 251340 251340 WB Ramallah Ni’lin 301745 301715 WB Salfit Izbat Abu Adam 251310 251355 WB Ramallah Ein Yabrud 301720 301720 WB Salfit Az Zawiya 251360 251360 WB Ramallah Kharbatha Bani Harith 301725 301725 WB Salfit Salfit 251370 251370 WB Ramallah Ras Karkar 301730 301730 WB Salfit Rafat 251395 251395 WB Ramallah Surda 301675 301735 WB Salfit Bruqin 251400 251400 WB Ramallah Al Janiya 301730 301740 WB Salfit Farkha 251370 251415 WB Ramallah Al Midya 301745 301745 WB Salfit Kafr ad Dik 251425 251425 WB Ramallah Rammun 301750 301750 WB Salfit Deir Ballut 251430 251430 WB Ramallah Kafr Ni’ma 301755 301755 WB Salfit Khirbet Qeis 251370 251440 Seeing is Believing – Poverty in The Palestinian Territories WB Ramallah Bil’in 301755 301760 WB Tubas Bardala 50420 50420 WB Ramallah Beitin 301765 301765 WB Tubas Ein el Beida 50420 50450 WB Ramallah Ein Qiniya 301780 301770 WB Tubas Kardala 50420 50455 WB Ramallah Badiw al Mu’arrajat 301815 301775 WB Tubas Ibziq 50535 50490 WB Ramallah Deir Ibzi’ 301780 301780 WB Tubas Salhab 50535 50525 WB Ramallah Deir Dibwan 301785 301785 WB Tubas Aqqaba 50535 50535 WB Ramallah Al Bireh 301790 301790 WB Tubas Tayasir 50550 50550 WB Ramallah Ein ‘Arik 301800 301800 WB Tubas Al Farisiya 50420 50551 WB Ramallah Saffa 301805 301805 WB Tubas Al ‘Aqaba 50550 50560 WB Ramallah Ramallah 301810 301810 WB Tubas Ath Thaghra 50610 50575 WB Ramallah Burqa 301815 301815 WB Tubas Al Malih 50420 50580 WB Ramallah Beit ‘Ur at Tahta 301820 301820 WB Tubas Tubas 50610 50610 WB Ramallah Beituniya 301825 301825 WB Tubas Kashda 50700 50650 WB Ramallah Al Am’ari Camp 301830 301830 WB Tubas Khirbet Yarza 50755 50656 WB Ramallah Qaddura Camp 301830 301835 WB Tubas Ras al Far’a 50700 50670 WB Ramallah Beit Sira 301850 301850 WB Tubas El Far’a Camp 50700 50700 WB Ramallah Kharbatha al Misbah 301855 301855 WB Tubas Khirbet ar Ras al Ahmar 50755 50720 WB Ramallah Beit ‘Ur al Fauqa 301890 301860 WB Tubas Wadi al Far’a 50740 50740 WB Ramallah At Tira 301890 301890 WB Tubas Tammun 50755 50755 WB Ramallah Beit Liqya 301895 301895 WB Tubas Khirbet ‘Atuf 50755 50790 WB Ramallah Beit Nuba 301895 301925 WB Tubas Khirbet Humsa 50755 50871 WB Salfit Deir Istiya 251250 251250 WB Tulkarm ‘Akkaba 100290 100250 WB Salfit Qarawat Bani Hassan 251275 251275 WB Tulkarm Qaffin 100290 100290 WB Salfit Qira 251295 251290 WB Tulkarm Nazlat ‘Isa 100330 100330 WB Salfit Kifl Haris 251295 251295 WB Tulkarm An Nazla ash Sharqiya 100345 100345 WB Salfit Marda 251300 251300 WB Tulkarm Baqa ash Sharqiya 100350 100350 WB Salfit Biddya 251305 251305 WB Tulkarm An Nazla al Wusta 100345 100355 62 WB Salfit Haris 251310 251310 WB Tulkarm An Nazla al Gharbiya 100345 100380 WB Salfit Yasuf 251315 251315 WB Tulkarm Zeita 100425 100425 WB Salfit Mas-ha 251320 251320 WB Tulkarm Seida 100440 100440 WB Salfit Iskaka 251315 251330 WB Tulkarm Illar 100475 100475 (Continued on next page) (continued) Region Governorate Locality Merge ID Original ID Region Governorate Locality Merge ID Original ID WB Tulkarm Attil 100480 100480 Gaza Deirelbalah Deir al Balah Camp 653200 653200 WB Tulkarm Deir al Ghusun 100530 100530 Gaza Deirelbalah Al Maghazi Camp 653210 653210 WB Tulkarm Al Jarushiya 100595 100545 Gaza Deirelbalah Al Maghazi 653215 653215 WB Tulkarm Al Masqufa 100595 100555 Gaza Deirelbalah Deir al Balah 653240 653240 WB Tulkarm Bal’a 100570 100570 Gaza Deirelbalah Al Musaddar 653250 653250 WB Tulkarm Iktaba 100595 100595 Gaza Deirelbalah Wadi as Salqa 653275 653275 WB Tulkarm Nur Shams Camp 100620 100620 Gaza Gaza-city Ash Shati’ Camp 602775 602775 WB Tulkarm Tulkarm Camp 100635 100635 Gaza Gaza-city Gaza 602825 602825 WB Tulkarm Tulkarm 100645 100645 Gaza Gaza-city Madinat Ezahra 602900 602900 WB Tulkarm Anabta 100665 100665 Gaza Gaza-city Al Mughraqa (Abu Middein) 602945 602945 WB Tulkarm Kafr al Labad 100690 100690 Gaza Gaza-city Juhor ad Dik 603045 603045 WB Tulkarm Kafa 100760 100710 Gaza Gaza-north Um Al-Nnaser (Al Qaraya al 552681 552681 WB Tulkarm Al Haffasi 100760 100715 Badawiya al Maslakh) WB Tulkarm Ramin 100730 100730 Gaza Gaza-north Beit Lahiya 552695 552695 WB Tulkarm Far’un 100735 100735 Gaza Gaza-north Beit Hanun 552740 552740 WB Tulkarm Shufa 100760 100760 Gaza Gaza-north Jabalya Camp 552755 552755 WB Tulkarm Khirbet Jubara 100760 100780 Gaza Gaza-north Jabalya 552790 552790 WB Tulkarm Saffarin 100800 100795 Gaza Khan younes Al Qarara 703370 703370 WB Tulkarm Beit Lid 100800 100800 Gaza Khan younes Khan Yunis Camp 703410 703410 WB Tulkarm Ar Ras 100845 100815 Gaza Khan younes Khan Yunis 703420 703420 WB Tulkarm Kafr Sur 100845 100845 Gaza Khan younes Bani Suheila 703425 703425 WB Tulkarm Kur 100915 100870 Gaza Khan younes Abasan al Jadida(as Saghira) 703430 703430 WB Tulkarm Kafr Zibad 100915 100895 Gaza Khan younes Abasan al Kabira 703445 703445 WB Tulkarm Kafr Jammal 100900 100900 Gaza Khan younes Khuza’a 703470 703470 WB Tulkarm Kafr ‘Abbush 100915 100915 Gaza Khan younes Al Fukhkhari 703485 703485 Gaza Deirelbalah An Nuseirat Camp 653065 653065 Gaza Rafah Rafah 753490 753490 Gaza Deirelbalah An Nuseirat 653070 653070 Gaza Rafah Rafah Camp 753495 753495 Appendices Gaza Deirelbalah Al Bureij Camp 653140 653140 Gaza Rafah Al-Nnaser (Al Bayuk) 753500 753500 Gaza Deirelbalah Al Bureij 653145 653145 Gaza Rafah Shokat as Sufi 753505 753505 Gaza Deirelbalah Az Zawayda 653180 653180 63 Localities in the West Bank Isolated or Affected by the Barrier Wall (PCBS built up area, original unmerged localities, UNOCHA-oPT definition)6 Locality ID Localities affected by the wall Locality ID Localities affected by the wall 10085 Umm ar Rihan 10145 Tura al Gharbiya 10105 Khirbet ‘Abdallah al Yunis 10150 Tura ash Sharqiya 10115 Dhaher al Malih 10165 Nazlat ash Sheikh Zeid 10120 Barta’a ash Sharqiya 10170 At Tarem 10175 Khirbet al Muntar al Gharbiya 10190 Jalbun Seeing is Believing – Poverty in The Palestinian Territories 10205 Khirbet al Muntar ash Sharqiya 10200 Khirbet Mas’ud 100780 Khirbet Jubara 10225 Umm Dar 201005 ‘Arab ar Ramadin ash Shamali 10230 Al Khuljan 201070 ‘Arab Abu Farda 10240 Dhaher al ‘Abed 201105 ‘Arab ar Ramadin al Janu 10245 Zabda 201280 ‘Azzun ‘Atma 10265 Ya’bad 251250 Deir Istiya 10310 Al Mughayyir 251310 Haris 10315 Al Mutilla 452230 Husan 10405 Raba 452235 Wadi Fukin 50420 Bardala 452325 Nahhalin 100250 ‘Akkaba 452355 Al Jab’a 100290 Qaffin 452465 Khallet ‘Afana 100330 Nazlat ‘Isa 502640 Tarqumiya 100425 Zeita 452175 Battir 100480 ‘Attil 452190 khallet an Nu’man 100530 Deir al Ghusun 10005 Zububa 100545 Al Jarushiya 10010 Rummana 100595 Iktaba 10020 At Tayba 100620 Nur Shams Camp 10025 ‘Arabbuna 100635 Tulkarm Camp 10030 Al Jalama 100645 Tulkarm 10040 As Sa’aida 100690 Kafr al Labad 10045 ‘Anin 100735 Far’un 10060 Faqqu’a 100815 Ar Ras 10070 Khirbet Suruj 100845 Kafr Sur 10125 Al ‘Araqa 100895 Kafr Zibad (Continued on next page) 64 6 UNOCHA also classifies Dura community in Hebron governorate as being affected by the barrier wall. The boundaries of Dura according to OCHA data fall in both Dura locality and Hebron city locality (according to PCBS data). Therefore, we only represent Dura locality as being af- fected by the wall, but not Hebron city (continued) Locality ID Localities affected by the wall Locality ID Localities affected by the wall 100915 Kafr ‘Abbush 503245 Adh Dhahiriya 201020 Immatin 503320 As Samu’ 201055 An Nabi Elyas 503321 Wadi Al Amayer 201100 ‘Azzun 503325 Khirbet Tawil ash Shih 201115 ‘Isla 503335 Ar Ramadin 201075 ‘Izbat at Tabib 503380 Imneizil 200985 Jayyus 503405 ‘Arab al Fureijat 201040 Qalqiliya 200905 Falamya 201120 Wadi ar Rasha 200925 Kafr Qaddum 201125 Habla 200945 Jit 201155 Ras ‘Atiya 301665 Budrus 201130 Ras at Tira 301710 Deir Qaddis 201170 Ad Dab’a 301715 Ni’lin 201190 ‘Izbat Jal’ud 301725 Kharbatha Bani Harith 201210 ‘Izbat Salman 301745 Al Midya 201175 Kafr Thulth 301755 Kafr Ni’ma 201260 Sanniriya 301760 Bil’in 201255 Beit Amin 301800 ‘Ein ‘Arik 251250 Deir Istiya 301805 Saffa 251275 Qarawat Bani Hassan 301810 Ramallah 251290 Qira 301820 Beit ‘Ur at Tahta 251295 Kifl Haris 301825 Beituniya 251300 Marda 301850 Beit Sira 251305 Biddya 301860 Beit ‘Ur al Fauqa 251310 Haris 301890 At Tira 251320 Mas-ha 301895 Beit Liqya 251330 Iskaka 301925 Beit Nuba Appendices 251340 Sarta 452209 Bir onah 251360 Az Zawiya 452210 Beit Jala 251370 Salfit 452225 Dar Salah 251395 Rafat 452208 Khallet Hamameh 251400 Bruqin 452400 Wadi Rahhal 251425 Kafr ad Dik 452415 Khallet Sakariya 251430 Deir Ballut 452240 Bethlehem (Beit Lahm) 301480 Bani Zeid 452255 Beit Sahur 301495 Al Lubban al Gharbi 452265 Ad Doha 301515 Rantis 452270 Al Khadr 301535 ‘Abud 452275 Ad Duheisha Camp 65 301595 Shuqba 452280 Hindaza 301605 Qibya 452300 Artas 503170 Al Burj 452385 Jannatah (Beit Falouh) (Continued on next page) (continued) Locality ID Localities affected by the wall 452480 Umm Salamuna 452445 Wadi an Nis 502435 Khirbet ad Deir 502450 Surif 502540 Beit Ummar 502550 Hitta 502560 Kharas 502585 Nuba 502615 Beit Ula 502925 Deir al ‘Asal at Tahta Seeing is Believing – Poverty in The Palestinian Territories 502935 Al Heila 502970 Deir al ‘Asal al Fauqa 503010 Beit ar Rush at Tahta 503075 Beit Mirsim 503090 Beit ar Rush al Fauqa 503105 Om Adaraj 503120 Yatta 503126 Kashem Adaraj (Al-Hathaleen 502640 Tarqumiya 502685 Idhna 502765 Beit Maqdum 502810 Deir Samit 502835 Beit ‘Awwa 502840 Dura 502910 Al Majd 201015 Far’ata 201035 Al Funduq 201085 Jinsafut 201065 Kafr Laqif 200970 Hajja 100900 Kafr Jammal 452170 Al Walaja 452200 Al Khas 452460 Jurat ash Sham’a 452470 Marah Ma’alla 452525 Beit Fajjar 502860 As Sikka 66 503360 Khirbet Bir al ‘Idd Percent of PCBS Localities Falling in Area C Locality Percent of locality Locality Percent of locality Code Locality Name area in area C Code Locality Name area in area C 10005 Zububa 0.556974 10225 Umm Dar 0.114143 10010 Rummana 0.610357 10230 Al Khuljan 0.013197 10015 Ti’innik 0.847732 10235 Wad ad Dabi’ 1 10020 At Tayba 0.469218 10240 Dhaher al ‘Abed 1 10025 ‘Arabbuna 0.709172 10245 Zabda 0.573726 10030 Al Jalama 0.784978 10265 Ya’bad 0.459703 10035 Silat al Harithiya 0.104891 10275 Kufeirit 0 10040 As Sa’aida 1 10285 Imreiha 1 10045 ‘Anin 0.680747 10295 Umm at Tut 0 10050 ‘Arrana 0.647262 10300 Ash Shuhada 0 10055 Deir Ghazala 0 10305 Jalqamus 0 10060 Faqqu’a 0.499992 10310 Al Mughayyir 0.061395 10070 Khirbet Suruj 1 10315 Al Mutilla 0.879746 10080 Al Yamun 0.00349 10320 Bir al Basha 0.906994 10085 Umm ar Rihan 1 10335 Al Hafira 0.982463 10095 Kafr Dan 0.062127 10340 Qabatiya 0.158225 10105 Khirbet ‘Abdallah al Yunis 1 10370 Arraba 0.236119 10115 Dhaher al Malih 1 10385 Telfit 0 10120 Barta’a ash Sharqiya 0.65301 10395 Mirka 0 10125 Al ‘Araqa 0 10400 Wadi Du’oq 0.839635 10135 Al Jameelat 0.116045 10401 Fahma al Jadida 0.432312 10140 Beit Qad 0 10405 Raba 0.069825 10145 Tura al Gharbiya 0.775159 10410 Al Mansura 0.966334 10150 Tura ash Sharqiya 1 10415 Misliya 0.000741 Appendices 10155 Al Hashimiya 0 10430 Al Jarba 0 10165 Nazlat ash Sheikh Zeid 0.707183 10435 Az Zababida 0.279886 503255 At Tuwani 1 10445 Fahma 0 10170 At Tarem 0.032801 10460 Az Zawiya 0.136736 10175 Khirbet al Muntar al Gharbiya 1 10465 Kafr Ra’i 0 10180 Jenin 0.128925 10485 Al Kufeir 0 10185 Jenin Camp 0 10495 Sir 0 10190 Jalbun 0.507959 10500 ‘Ajja 0.196207 10195 ‘Aba 0.922042 10505 ‘Anza 0.688927 10200 Khirbet Mas’ud 1 10510 Sanur 0.051339 10205 Khirbet al Muntar ash Sharqiya 1 10515 Ar Rama 0 67 10210 Kafr Qud 0 10520 Meithalun 0 10215 Deir Abu Da’if 0.138744 10565 Al Jadida 0 10220 Birqin 0 10585 al ‘Asa’asa 0.769768 (Continued on next page) (continued) Locality Percent of locality Locality Percent of locality Code Locality Name area in area C Code Locality Name area in area C 10590 Al ‘Attara 0.134759 100595 Iktaba 0.435849 10600 Siris 0 100620 Nur Shams Camp 0.290766 10605 Jaba’ 0.362854 100635 Tulkarm Camp 0 10615 Al Fandaqumiya 0.075434 100645 Tulkarm 0.40287 10625 Silat adh Dhahr 0.481306 100665 ‘Anabta 0.066476 50420 Bardala 0.822732 100690 Kafr al Labad 0.071841 50450 ‘Ein el Beida 0.855151 100715 Al Hafasa 0.566164 50455 Kardala 1 100730 Ramin 0.049029 50490 Ibziq 1 100735 Far’un 0.707448 Seeing is Believing – Poverty in The Palestinian Territories 50525 Salhab 0 100760 Shufa 0.818031 50535 ‘Aqqaba 0 100780 Khirbet Jubara 1 50550 Tayasir 0.025551 100795 Saffarin 0.059585 50575 Ath Thaghra 0.274079 100800 Beit Lid 0.023329 50580 Al Malih 1 100815 Ar Ras 0.340461 50610 Tubas 0.035748 100845 Kafr Sur 0.033191 50650 Kashda 0 100870 Kur 0 50656 Khirbet Yarza 1 150660 Bizzariya 0.013181 50670 Ras al Far’a 0 150680 Burqa 0.46601 50700 El Far’a Camp 0 150695 Yasid 0 50720 Khirbet ar Ras al Ahmar 1 150705 Beit Imrin 0 50740 Wadi al Far’a 0 150745 Nisf Jubeil 0 50755 Tammun 0.026124 100895 Kafr Zibad 0 50790 Khirbet ‘Atuf 0.378471 100915 Kafr ‘Abbush 0 50871 Khirbet Humsa 1 50551 Al Farisiya 1 100250 ‘Akkaba 0.879838 150765 Sabastiya 0.303854 100290 Qaffin 0.670512 150770 Ijnisinya 0 100330 Nazlat ‘Isa 0.624708 150775 Talluza 0 100345 An Nazla ash Sharqiya 0.114129 150785 An Naqura 0.140648 100350 Baqa ash Sharqiya 0.596284 150805 Al Badhan 0 100355 An Nazla al Wusta 0.835281 150810 Deir Sharaf 0.808779 100380 An Nazla al Gharbiya 0.430429 150820 ‘Asira ash Shamaliya 0.038296 100425 Zeita 0.627341 150825 An Nassariya 0 100475 ‘Illar 0.076417 150835 Zawata 0.401299 100480 ‘Attil 0.376622 150840 Al ‘Aqrabaniya 0.123505 100530 Deir al Ghusun 0.156145 150855 Qusin 0 100545 Al Jarushiya 0.655159 150860 Beit Iba 0.134714 68 100555 Masqufet al Hajj Mas’ud 0.893502 150865 Beit Hasan 0.026487 100570 Bal’a 0.033085 150875 Beit Wazan 0 (Continued on next page) (continued) Locality Percent of locality Locality Percent of locality Code Locality Name area in area C Code Locality Name area in area C 150880 ‘Ein Beit el Ma Camp 0 151385 Majdal Bani Fadil 0.580244 150885 ‘Ein Shibli 0.823524 151405 Al Lubban ash Sharqiya 0.641847 150910 ‘Azmut 0.00569 151410 Qaryut 0.292052 150920 Nablus 0.147035 151420 Jalud 0.206752 150930 ‘Askar Camp 0 151435 ‘Ammuriya 0 150935 Deir al Hatab 0.061931 151445 Duma 0.631792 150950 Sarra 0.184369 200965 Baqat al Hatab 0.234412 150955 Salim 0.283442 503305 Khirbet Asafi 1 150960 Balata Camp 0 200995 Khirbet Sir 0 150975 ‘Iraq Burin 0.121561 201005 ‘Arab ar Ramadin ash Shamali 1 150990 Tell 0 201020 Immatin 0.478706 151000 Beit Dajan 0.22824 201055 An Nabi Elyas 0.806387 151010 Rujeib 0.305072 201100 ‘Azzun 0.666551 151025 Kafr Qalil 0.425188 201115 ‘Isla 0.713731 151030 Furush Beit Dajan 1 201116 Arab Al-Khouleh 1 151050 Madama 0.188105 201075 ‘Izbat at Tabib 1 151080 Burin 0.51762 200985 Jayyus 0.194649 151090 Beit Furik 0.084128 201040 Qalqiliya 0.52357 151095 ‘Asira al Qibliya 0.051575 201070 ‘Arab Abu Farda 1 151135 ‘Awarta 0.25977 201105 ‘Arab ar Ramadin al Janu 1 151160 ‘Urif 0 201120 Wadi ar Rasha 1 151180 Odala 0.067626 201125 Habla 0.592784 151185 Huwwara 0.468488 201155 Ras ‘Atiya 0.642262 151195 ‘Einabus 0 201130 Ras at Tira 0.736465 151200 Yanun 0.626132 201170 Ad Dab’a 0.993623 151215 Beita 0.031985 201190 ‘Izbat Jal’ud 0.873275 Appendices 151230 Zeita Jamma’in 0 201210 ‘Izbat Salman 0.726244 151245 Jamma’in 0.062859 201205 Al Mudawwar 0.34479 151265 Osarin 0.061803 201225 ‘Izbat al Ashqar 0.43464 151270 Aqraba 0.1138 201175 Kafr Thulth 0.439822 151285 Za’tara 1 201260 Sanniriya 0.634202 151311 Tall al Khashaba 1 201255 Beit Amin 0.222249 151325 Yatma 0.49083 201280 ‘Azzun ‘Atma 0.928462 151335 Qabalan 0.104007 251250 Deir Istiya 0.427775 151345 Jurish 0 251275 Qarawat Bani Hassan 0.610683 151365 Qusra 0.142981 251290 Qira 0.026028 151375 Talfit 0 251295 Kifl Haris 0.372453 69 151380 As Sawiya 0.730612 251300 Marda 0.521183 (Continued on next page) (continued) Locality Percent of locality Locality Percent of locality Code Locality Name area in area C Code Locality Name area in area C 251305 Biddya 0.478562 301570 Deir Abu Mash’al 0.067818 251310 Haris 0.662607 301575 Jibiya 0 251315 Yasuf 0.275009 301585 Burham 0 251320 Mas-ha 0.659749 301590 Kafr Malik 0.804803 251330 Iskaka 0.286578 301595 Shuqba 0.737356 251340 Sarta 0.52984 301600 Kobar 0.105949 251360 Az Zawiya 0.564966 301605 Qibya 0.793831 251370 Salfit 0.106342 503150 Umm Lasafa 0.452902 251395 Rafat 0.538223 503170 Al Burj 0.436323 Seeing is Believing – Poverty in The Palestinian Territories 251400 Bruqin 0.297632 503215 Al Karmil 0.232089 251415 Farkha 0 503225 Khallet Salih 0.191997 251425 Kafr ad Dik 0.206389 503245 Adh Dhahiriya 0.016933 251430 Deir Ballut 0.819458 503260 Ma’in 0.696753 251440 Khirbet Qeis 0 503295 ‘Anab al Kabir 0.286103 352075 An Nabi Musa 1 503310 Mantiqat Shi’b al Batin 1 351116 Marj al Ghazal 0.669365 503320 As Samu’ 0.14376 351140 Al Jiftlik 1 503321 Wadi Al Amayer 0.409584 351510 Fasayil 0.708794 503325 Khirbet Tawil ash Shih 1 301455 Qarawat Bani Zeid (Bani Zeid * 0 503335 Ar Ramadin 0.624175 301460 Bani Zeid ash Sharqiya 0 503345 Maghayir al ‘Abeed 1 301470 Kafr ‘Ein 0 503380 Imneizil 1 301480 Bani Zeid 0.000454 503405 ‘Arab al Fureijat 0.999999 301485 ‘Abwein (Bani Zeid ash Sharqi* 0 200905 Falamya 0.845581 301490 Turmus’ayya 0.178478 200925 Kafr Qaddum 0.193679 301495 Al Lubban al Gharbi 0.685254 200945 Jit 0.804226 301500 Sinjil 0.262359 301610 Silwad 0.324286 301505 Deir as Sudan 0.073388 301615 Yabrud 0.204477 301515 Rantis 0.48314 301620 AL-Itihad 0.334802 301520 Jilijliya 0 301625 Shabtin 0.450997 301525 ‘Ajjul 0.019558 301635 Bir Zeit 0.065375 301530 Al Mughayyir 0.568513 301636 AL-Doha 0 301535 ‘Abud 0.558787 301640 ‘Ein Siniya 0.077432 301540 An Nabi Salih (Bani Zeid al g* 0.624548 301645 Silwad Camp 0 301545 Khirbet Abu Falah 0 301650 Deir Jarir 0.121826 301550 Umm Safa 0.717426 301660 Deir ‘Ammar Camp 0 301555 Al Mazra’a ash Sharqiya 0.022901 301665 Budrus 0.676608 70 301560 Deir Nidham 0.919499 301670 AL-Zaytouneh 0.079389 301565 ‘Atara 0.034948 502830 Khallet Al Masafer 0.144488 (Continued on next page) (continued) Locality Percent of locality Locality Percent of locality Code Locality Name area in area C Code Locality Name area in area C 301675 Jifna 0.001384 351690 Al ‘Auja 0.282275 301680 Dura al Qar’ 0.658896 351970 Deir al Qilt 1 301685 At Tayba 0.178973 351840 An Nuwei’ma 0.294061 301700 Al Jalazun Camp 0.173785 351975 Aqbat Jaber Camp 0.127579 301705 Abu Qash 0 352021 Deir Hajla 1 301710 Deir Qaddis 0.593384 351845 ‘Ein ad Duyuk al Foqa 0.151921 301715 Ni’lin 0.787901 351865 ‘Ein as Sultan Camp 0 301720 ‘Ein Yabrud 0.415723 351920 Jericho (Ariha) 0.183116 301725 Kharbatha Bani Harith 0.750441 100440 Seida 0 301730 Ras Karkar 0.596629 452185 ‘Ayda Camp 0.197781 301735 Surda 0.000004 452195 Al ‘Aza Camp 0 301740 Al Janiya 0.418553 452205 Al Haddadiya 0 301745 Al Midya 0.905015 452209 Bir onah 0.784869 301750 Rammun 0.229752 452210 Beit Jala 0.393844 301755 Kafr Ni’ma 0.078023 452225 Dar Salah 0.024182 301760 Bil’in 0.060017 452230 Husan 0.439333 301765 Beitin 0.580424 452180 Al ‘Ubeidiya 0.193905 301770 ‘Ein Qiniya 0.768954 452208 Khallet Hamameh 0.415654 301775 Badiw al Mu’arrajat 1 452400 Wadi Rahhal 0.458777 301780 Deir Ibzi’ 0.28905 452415 Khallet Sakariya 1 301785 Deir Dibwan 0.203989 452235 Wadi Fukin 0.75926 301790 Al Bira 0.289922 452240 Bethlehem (Beit Lahm) 0.106217 301800 ‘Ein ‘Arik 0.827866 452255 Beit Sahur 0.324249 301805 Saffa 0.480927 452265 Ad Doha 0 301810 Ramallah 0.063411 452270 Al Khadr 0.576425 301815 Burqa 0.570791 452275 Ad Duheisha Camp 0 Appendices 301820 Beit ‘Ur at Tahta 0.646639 452280 Hindaza 0.024303 301825 Beituniya 0.356308 452285 Ash Shawawra 0.233524 301830 Al Am’ari Camp 0 452300 Artas 0.269798 301835 Qaddura Camp 0 452325 Nahhalin 0.316471 301850 Beit Sira 0.698271 452335 Beit Ta’mir 0.109354 301855 Kharbatha al Misbah 0.681369 452345 Khallet al Louza 0.316089 301860 Beit ‘Ur al Fauqa 0.745514 452355 Al Jab’a 0.767717 301890 At Tira 0.771776 452360 Za’tara 0.252401 301895 Beit Liqya 0.659448 452385 Jannatah (Beit Falouh) 0.472378 301925 Beit Nuba 1 452405 Jubbet adh Dhib 1 351045 Marj Na’ja 0.562792 452430 Khallet al Haddad 0 71 351110 Az Zubeidat 0.910022 452465 Khallet ‘Afana 1 (Continued on next page) (continued) Locality Percent of locality Locality Percent of locality Code Locality Name area in area C Code Locality Name area in area C 452480 Umm Salamuna 0.813142 503100 Beit ‘Amra 0.103559 452490 Al Manshiya 0.917476 503105 Om Adaraj 0.601127 452495 Tuqu’ 0.573176 503110 Wadi al Kilab 0 452500 Marah Rabah 0.015302 503111 Om Ashoqhan 0.215132 452440 Al Ma’sara 0.796266 503115 Khallet al Maiyya 0.144908 452445 Wadi an Nis 0.862292 503116 Kheroshewesh Wal Hadadeyah 0 452565 Kisan 0.878482 503117 Om Al Amad (Sahel Wadi Elma) 0.169011 452535 Al Maniya 0.820975 503120 Yatta 0.024115 452660 ‘Arab ar Rashayida 0 503125 Ad Deirat 0.998857 Seeing is Believing – Poverty in The Palestinian Territories 502435 Khirbet ad Deir 0.884829 503126 Kashem Adaraj (Al-Hathaleen 1 502450 Surif 0.119912 502635 Ash Shuyukh 0.295323 502530 Al ‘Arrub Camp 0.17493 502640 Tarqumiya 0.51846 502540 Beit Ummar 0.404363 502655 Beit Kahil 0.141422 502545 Jala 0.018673 502681 Qla a Zeta 0.887566 502550 Hitta 0 502685 Idhna 0.553747 502555 Shuyukh al ‘Arrub 0.137773 502750 Taffuh 0 502560 Kharas 0.14251 502765 Beit Maqdum 0.09723 502575 Umm al Butm 1 502778 Al Baqa 1 502580 Hamrush 0.020239 502780 Hebron (Al Khalil) 0.298488 502585 Nuba 0 502781 Al Bowereh (Aqabat Injeleh) 0.36633 502615 Beit Ula 0.027619 502782 Khallet Edar 0.792711 502620 Sa’ir 0.165058 502810 Deir Samit 0.48028 502630 Halhul 0.267518 502815 Bani Na’im 0.158967 502925 Deir al ‘Asal at Tahta 0 502835 Beit ‘Awwa 0.276139 502935 Al Heila 0 502840 Dura 0.047723 502940 Wadi ash Shajina 0.710463 502855 Qalqas 0.355364 502950 As Sura 0 502865 Khirbet Salama 0.443132 502955 Deir Razih 0.808771 502870 Wadi ‘Ubeid 0 502960 Ar Rihiya 0 502875 Fuqeiqis 1 502965 Zif 0.980425 502895 Kharsa 0.001278 502970 Deir al ‘Asal al Fauqa 0.249479 502900 Turrama 0 502975 Khallet al ‘Aqed 0 502905 Al Fawwar Camp 0.000416 502980 Imreish 0.110137 502910 Al Majd 0.235582 503010 Beit ar Rush at Tahta 0.000105 502915 Marah al Baqqar 0 503040 Hadab al ‘Alaqa 0 502920 Hadab al Fawwar 0.253724 503075 Beit Mirsim 0.359482 503135 Kurza 0 72 503090 Beit ar Rush al Fauqa 0.346888 503145 Rabud 0.471884 503095 Karma 0.6694 502680 Beit ‘Einun 0.732306 (Continued on next page) (continued) Locality Percent of locality Locality Percent of locality Code Locality Name area in area C Code Locality Name area in area C 503210 Um Al Khair 1 452190 khallet an Nu’man 1 201015 Far’ata 0 452200 Al Khas 0.328656 201035 Al Funduq 0.902285 452460 Jurat ash Sham’a 0.614824 201085 Jinsafut 0.77182 452470 Marah Ma’alla 0.911323 201065 Kafr Laqif 0.603895 452525 Beit Fajjar 0.172462 200970 Hajja 0.659266 151220 Ar Rajman 0.999704 251355 ‘Izbat Abu Adam 1 502860 As Sikka 0.218311 100710 Kafa 0.919445 503005 Al Buweib 0.126362 100900 Kafr Jammal 0.150686 503265 An Najada 1 50560 Al ‘Aqaba 1 503350 Khirbet al Fakheit 0.994047 452170 Al Walaja 0.955492 503360 Khirbet Bir al ‘Idd 1 452175 Battir 0.688477 503375 Khirbet Zanuta 1 Appendices 73