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Seeing is BelievingPoverty in The Palestinian Territories
2014
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Seeing is BelievingPoverty 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
Country Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
World Bank-PCBS Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
What is a Poverty Map? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 . Poverty Mapping: Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Main Data Sources and Technical Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Technical challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Choosing the appropriate consumption model . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 . Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Building the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
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
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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
List of Maps
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) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Map 2: A Divided Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Map 3: Punctuated by Barriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Map 4: Localities Isolated or Affected by the Barrier Wall . . . . . . . . . . . . . . . . . . . . . . . . 28
Map 5: Localities Falling in Area C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Map 6: A Fragmented Geography: A map of locality boundaries (Built-up areas) in the West Bank and Gaza . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Map 7: Merging localities in the West Bank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Map 8: The Poorest Governorates in the West Bank are better off than most 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
Map 16: In the Palestinian Territories, more Educated Places are not Always Better off . . 41
Map 17: In Gaza, Education doesn’t Bear Fruit; in the West Bank, Limited Aaccess to Education keeps some Places Poor . . . . . . . . . . . . . . . . . . . 42
Map 18: An Increasingly Educated Young Population . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Map 19: Not a Pretty Picture: Unemployment goes Hand in Hand with Poverty . . . . . . . . 44
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Table of Contents
Map 20: Unemployment Level of Youth (15–30 years of age) . . . . . . . . . . . . . . . . . . . . . 45
Map 21: Private Sector Dominant Source of Employment in the West Bank; but in Gaza, the Public Sector is Widespread . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Map 22: Irregular and Self-Employment Correlated with Poverty in the West Bank; 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
Table 2: Consumption Model for Gaza 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Table 3: Consumption Model for West Bank 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Table 4: Comparison between the Actual Data and the Model Estimates by Region, 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Table 5: Comparison between the Actual Data and the Model Estimates by Governorate, 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
List of Boxes
Box 1: The Small Area Estimation Method Developed by ELL (2003) . . . . . . . . . . . . . . . 6
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.
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)
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which is in line with PCBS’s original request for TA to im-
prove the quality and comparability of survey instruments
and for continued assistance to create poverty maps using
the most recent census and survey data to identify vulnera-
ble groups.
This poverty mapping exercise is the latest result of the
collaboration between the World Bank and PCBS. This
has involved technical assistance from the World Bank on
calculating small-area (locality level) poverty estimates for
the Palestinian Territories. This also included training of
the PCBS staff on the methodology of poverty mapping,
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
replicated by both the World Bank and the PCBS teams.
What is a Poverty Map?
Poverty estimates are usually calculated using a nationally
representative household survey with consumption data.
In the Palestinian Territories, the Palestine Expenditure and
Consumption Surveys (PECS) are designed to provide esti-
mates of poverty at the regional level (West Bank and Gaza),
strata level (Urban, Rural, Refugee Camp), and some larger
governorates. However, for policy makers, often, further dis-
aggregation is needed. For instance, with limited resources,
what parts of a governorate should be prioritized for poverty
reduction programs? How do we identify poor and vulnera-
ble pockets to target social assistance?
Poverty Mapping, using a methodology pioneered by the
World Bank, can produce highly disaggregated databases
of welfare. Poverty Maps involve the estimation of pover-
unique to these internal restrictions in mobility is that they
artificially create disadvantaged areas within the West Bank,
namely those areas where restrictions are most severe.
These spatial disparities imply that poverty can vary widely
within the space of a few kilometers, and therefore, poverty
estimates at a highly disaggregated level can reveal pock-
ets of extreme poverty, even in more prosperous areas, that
more aggregate analysis can mask. Such information is es-
pecially important for policy making, and for prioritizing the
development efforts of the many international and national
agencies working on the ground. A poverty map is a visual
representation of precisely this kind of information.
World Bank-PCBS Collaboration
This Poverty Mapping exercise builds on a programmatic
and comprehensive collaboration between the World Bank
and the Palestinian Central Bureau of Statistics (PCBS). This
collaboration began in 2010 with a request for Technical
Assistance (TA) to validate and update methodology for
poverty measurement. Using a long series of Palestinian
Expenditure and Consumption Surveys (PECS), the World
Bank worked with the PCBS to create a fully consistent
poverty series from 2004 to 2009, including a simulation
of poverty estimates for Gaza in 2008 (due to the inability
to complete data collection in Gaza that year). In October
2010, the Palestinian Authority publicly announced the 2009
poverty estimates in line with the new methodology and
international good practice.3 A series of four technical notes
describe this body of work and were delivered to PCBS in
August 2010. A core component of this TA involved several
in-country capacity building exercises at the PCBS as well
as dedicated training for PCBS and Ministry of Social Affairs
(MoSA) staff in using ADePT, a computational package for
poverty analysis that the Research Group of the World Bank
has developed.
The analysis in the Poverty and Inclusion Assessment
revealed implications for survey design and methodology,
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 children. Their intension is to use this new reference household in future poverty estimates with 2009 as the base year.
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Background and C
ontext
ty indicators at very detailed level (locality, enumeration
area, and even households themselves) in order to identify
pockets of poverty. This is a tool for effective and efficient
allocation of resources and programs according to the
greatest need, to achieve the broader development goal
of poverty reduction. Poverty maps are not simply useful
as visual representations of poverty but also to understand
the relationship with a host of other important socio-eco-
nomic indicators such as health, education, labor market
outcomes and social assistance.
Poverty mapping relies on household survey and census
data, making the most of the strengths of each, and com-
pensating for their weaknesses. Certain key data require-
ments must be fulfilled to be able to construct a poverty
map. Survey data must include detailed consumption data,
which is the basis for calculating poverty estimates, for
instance at the national and the regional level. However,
the survey usually covers only a representative sample of
the population. This tradeoff between sample size and the
cost and time needed to collect quality consumption data
implies that surveys cannot typically be used to calculate
reliable poverty estimates for more disaggregated areas.
This is because, at such lower levels of disaggregation, for
instance, the community or village, the number of obser-
vations in the survey is too small to produce statistically
reliable estimates. The census on the other hand covers the
entire population and can therefore be reliable even at low-
er levels of aggregation. However, the census usually covers
only basic information like demographics, education and
employment but not detailed information on consumption.
The methodology behind poverty mapping thus takes
advantage of the strengths of the survey and the census.
In principle, it estimates consumption for every household
covered by the census, and can therefore reliably produce
measures of poverty for small areas.
This particular poverty mapping exercise makes use of the
most recent census, the General Census of Population and
Housing 2007. Two possible surveys were considered for
the exercise—the PECS 2009 and 2010. The 2009 PECS was
chosen as it was the household survey closest to the census
year. The PECS 2007 was eschewed on account of it being
a crisis year in Gaza, and the PECS 2008 was not considered
because it did not cover Gaza.
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.
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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
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 (, ) 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 (, ) 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’ + 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 SourcesThe Palestinian Territories are divided into two regions: the
West Bank and Gaza. Each region is further subdivided into
governorates, and the lowest administrative unit within a
governorate is called a locality (see Table 1). The objective
of the poverty mapping exercise is to attempt, as far as
possible, to estimate poverty at the locality level. The PECS
includes detailed information on a wide array of socio-eco-
nomic characteristics of households and their consumption,
which allows for in depth analysis, but on a smaller sam-
ple. However, PECS is not representative at lower levels of
aggregation and in particular, at the level of the locality. The
census, on the other hand, collects information on a few
basic variables, but covers every single household in the
country.
The ELL methodology calls for the creation of a consump-
tion model using the household survey. The quality of the
consumption model depends critically on the number
of common variables in the census and survey, which are
good predictors of consumption. Only these variables can
be used in the regression model implemented in the ELL
approach. This regression model identifies the significant
determinants of poverty and the magnitude of their contri-
bution. The important criteria for a satisfactory model are
having reasonable goodness-of-fit and plausible relation-
ships between poverty and its correlates. The resulting esti-
mated coefficients are then combined with the correspond-
ing variables calculated from census data to estimate or
predict consumption levels for all the households covered
by the census. This imputed consumption is then aggre-
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Poverty Mapping: M
ethodology
gated at the desired level, locality in this case, to calculate
poverty rates.
The monthly consumption of households (obtained from
the Palestine Expenditure and Consumption Survey or
PECS) is the main source of data for calculating pover-
ty indicators in the Palestinian Territories. This survey is
regularly conducted by the Central Bureau of Statistics and
is available for the years 1996–1998, 2001, 2004–2011. The
sampling frame of the PECS includes all the enumeration
areas of the Census-2007, which totaled 4,916 enumeration
areas distributed over all governorates of the West Bank
and Gaza Strip.
The poverty statistics calculated using the PECS were
originally based on a poverty line definition first developed
in 1998. The definition combines the concepts of both ab-
solute and relative poverty and is based on a basic needs
budget for a household of five people (two adults and
three children). In addition to food, clothing, and housing,
the basic needs also include other necessities, including
health care, education, transportation, personal care, and
housekeeping supplies. The poverty line is adjusted to re-
flect the specific consumption needs of households based
on their composition (household size and the number of
children).
In 2010–2011, PCBS invested substantially in reviewing its
original (1998) poverty measurement and trends methodol-
ogy in order to meet international best practice standards,
which primarily involves the following: (a) adjusting for spa-
tial price differences; (b) calculating poverty headcount at
individual rather than household level; and (c) ensuring that
poverty lines over time reflect the same purchasing power,
which necessitates that the poverty line is adjusted for price
inflation using the official CPI.
Costs of living were taken into consideration; individuals
living in different locations may face different prices for
similar goods. When comparing the cost of living across
locations using consumption based measures, the available
data revealed that prices of goods and services vary con-
siderably across locations in the West Bank, East Jerusalem
(J1 governorate) and Gaza Strip. In general, prices appear
to be lower in Gaza Strip compared to the West Bank and
higher in East Jerusalem (J1) compared to elsewhere. In
order to incorporate these price differences, the PCBS
worked jointly with the World Bank to construct spatial
price indices that would enable a meaningful comparison
of living standards across the West Bank and Gaza Strip. In
2009, the reference household was changed to two adults
and three children (rather than four) to reflect the most
common household composition.
In order to understand the distinct patterns of poverty and
labor market outcomes in the West Bank and in Gaza as
well as the differing nature of mobility and access restric-
tions in the two territories, separate consumption models
for the West Bank and Gaza were constructed for the pov-
erty map.
Technical ChallengesThe ELL poverty mapping methodology has been contin-
ually updated to improve statistical accuracy of poverty
estimates in response to findings from the latest studies
by experts and researchers. To this end, the World Bank
research department prepares a variety of documents and
manuals to inform development practitioners of the latest
developments and methodological improvements in the
ELL method, and they provide recommendations so that
the latest findings are reflected in the ongoing poverty
mapping exercise. These improvements are also reflected
in the updated versions of the PovMap2 software pro-
duced by the World Bank to assist with application of the
procedure.
The Palestinian Poverty Mapping Exercise has faced three
technical issues: (i) The choice of the survey year; (ii) Re-
solving problems related to very small populations in some
census localities and the appropriate geographic boundar-
ies for localities; and (iii) Whether to estimate locality level
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poverty rates for Jerusalem governorate given limited data
availability and constraints to survey implementation.
Choice of survey yearIn the case of the Palestinian Territories, 2007 was a census
year as well as a year in which the PECS was conducted.
This would have been an ideal scenario for poverty map-
ping—using the 2007 census and the 2007 PECS to impute
poverty numbers at the locality level. However, 2007 was a
crisis year in Gaza and the PECS had a smaller sample than
usual. More importantly, the sampling frame was based on
the previous census of 1997, and it would have been very
difficult to link the same geographic areas between census
and survey. In 2008, the PECS did not cover Gaza. There-
fore, the closest full survey was chosen: The 2009 PECS cov-
ers 3,566 households and has an updated sampling frame
based on the 2007 census.
Localities with small census population and choice of locality boundariesIn most countries, the geographic boundaries for areas
of interest (village, community, locality etc.) are used to
visualize the poverty estimates in the form of a map. In the
case of the Palestinian territories, the parallel would be to
map the estimates of the model within locality boundaries.
However, no official boundary map for localities currently
Table 1 | Administrative Units in The Palestinian Territories
Pale
stin
ian
Terr
itorie
s
West Bank
Jenin 80
Tubas 21
Tulkarm 35
Nablus 64
Qalqylia 34
Salfit 20
Ramallah 75
Jericho 12
Jerusalem 51
Bethlehem 44
Hebron 92
Gaza
Gaza North 5
Gaza City 5
Khan Younis 8
Rafah 4
11
561
Loca
litie
s, 4
916
cens
us e
num
erat
ion
area
s
Deir al - Balah
9
Poverty Mapping: M
ethodology
exists, and different government institutions use different
boundaries for their own purposes. PCBS uses the physical
built-up area of the enumeration area or primary sampling
unit to demarcate boundaries for localities, and therefore,
since these only cover inhabited areas, these naturally do
not aggregate up to the entire geographic area of the
country. However, they do cover all the areas where Pales-
tinians live within the West Bank and Gaza.
The availability of multiple geographic definitions for local-
ities and the lack of an official definition implied the need
for a consensus on which definition would be adopted for
the poverty map. Therefore, an expert committee was con-
stituted that discussed the appropriate geographic defini-
tion of a locality for the purposes of poverty mapping. The
committee concluded that the PCBS definition, which is
the basis for survey and census data, i.e., built-up area of
localities be used for the poverty map. The land outside
of the built-up area of the localities may include agricul-
tural land, roads, Israeli settlements, and restricted military
areas; and this makes it difficult to delineate boundaries
outside the built-up area.
Another important challenge was the presence of several
localities with very few households in the census, less than 10
households in some cases. If the number of observations is
too low, then the simulated poverty rate for the locality can-
not be relied upon due to the likelihood of very high standard
errors. In an attempt to balance the competing consider-
ations of maximizing disaggregated estimates, and minimiz-
ing standard errors, a threshold population of 200 households
was agreed upon. Localities with below 200 households were
combined with geographically contiguous localities in order
to maintain statistical robustness for the poverty estimates.
Two requirements were applied as part of this exercise:
(i) merging-contiguity (small localities were to be merged
with neighbors with whom they shared boundaries); and (ii)
similarity of observable characteristics (localities that did
not physically share built-up area boundaries but were in
physical proximity were merged on the basis of similarity
in observable characteristics). We worked with PCBS GIS
staff and the team responsible for the PECS, on a case by
case basis, to implement this approach. First, we identified
localities that were below the minimum threshold of ob-
servations, and a map was produced in order to identify its
neighbors with their respective number of observations and
their observable characteristics. Then, if the two principles
of contiguity and similarity were fulfilled, the localities were
merged appropriately.
We used local knowledge and information from the census
such as demographics, labor market outcomes, and spatial
characteristics to identify similarities and, subsequently, we
merged the most similar contiguous localities iteratively
until an acceptable threshold was reached. For example,
many localities in South East Hebron did not meet the min-
imum sample size. Given the proximity of these locations,
we considered each locality until both local knowledge and
the census information substantiated a reasonable merged
locality unit. Map 1 illustrates this outcome of this process in
the case of two governorates, Hebron and Ramallah, where
localities that were grouped together appear in the same
color, while those that were not merged with others are in
white. One set of localities necessitated a ‘special’ merge,
which exhibited a similarity of observable characteristics,
but the two localities were not directly adjacent to each
other.4 The results of the participatory mapping that have
the original locality identifier (ID) and the merged locality ID
are in the Annex.
Jerusalem governorateJerusalem governorate covers East Jerusalem (J1, under
Israeli control) and the rest of Jerusalem governorate (J2).
There are many settlements in J2 and consequently, many
4 The two localities are: Burqa (301185) and Badiw al Mu’arrajat (301775).
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parts of the governorate are inaccessible to Palestinians.
As a result, both census and survey data have extremely
limited coverage of the Jerusalem governorate, ie, J1 and
J2. In addition, there were concerns about survey imple-
mentation in the governorate as a whole, given the diffi-
culty for PCBS to access large parts. This poses significant
challenges to estimate poverty at the governorate level,
let alone at a locality level.5 Therefore, it was decided not
to include Jerusalem governorate in the poverty mapping
exercise.
Choosing the appropriate consumption modelIdeally, for a country this size, the consumption model
created using the household survey should be estimated
at the national level, or in other words, one consumption
model for the entire country. In the case of the Palestin-
ian Territories, there are compelling reasons to consider a
more disaggregated modeling approach—the evidently
large differences in consumption between the West Bank
and Gaza, and the fragmentation imposed by external and
internal barriers that restrict access to services, markets, and
employment, and therefore consumption.
This is ultimately an empirically testable hypothesis, name-
ly that the consumption models were not only heteroge-
neous across the West Bank and Gaza, but within the West
Bank as well, or at the governorate level. The approach
followed was to attempt to create consumption models
at the governorate and the regional level, and through
this exercise, to identify key variables that were pertinent
in some areas but not in others. This led to the incorpora-
tion of a number of location variables and interactions as
the process evolved to converge to the most appropriate
model.
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
needed to be combined with geographically contiguous
governorates, creating one consumption model for them.
This was done to increase the reliability of the consumption
models—the larger the number of observations, the smaller
the margin of error of the results. For instance, Hebron had
enough observations to justify having one consumption
model. However, a single consumption model was created
for Nablus and Salfit to ensure that there were more than
500 observations.
Six distinct models were considered—for Jenin-Tubas-Jer-
icho, Tulkarm-Qalqylia, Bethlehem, Ramallah, Hebron and
Nablus-Salfit. The estimates of poverty obtained from
these six models were compared with the corresponding
poverty rate s from the PECS (and its associated confi-
dence intervals). Notwithstanding concerns about the
representativeness of PECS at this level, the results were
not satisfactory.
However, the information gained from this exercise helped
to refine models at the regional level (West Bank and Gaza
separately) to incorporate specific characteristics that are
salient in some areas but not in others. Variables that were
found to be important in the consumption models at gov-
ernorate levels were included in the appropriate regional
model through interactions with governorate dummies.
This approach not only increased the R2s (48–49%) of the
regressions but helped to produce robust and reliable
models.
These models were then used to impute poverty rates at
locality level, using the census. The resulting poverty rates
were highly robust with almost all of them having standard
errors less than 5%.
5 For more information, please refer to the PCBS poverty map report
11
Poverty Mapping: M
ethodology
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
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
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the model were then multiplied with census variables to
estimate consumption for all the households in the census.
This method also produces standard errors for each of the
poverty estimates.
As a validation, poverty estimates simulated at governorate
level were compared to actual poverty estimates at gover-
norate level from the PECS. Significant differences indicat-
ed a problem with the model and the process was started
again, until the simulation yielded poverty estimates at
governorate level that were consistent with the PECS.
Final Model
This section describes the final models used for poverty
mapping. The models are for 2009, separately for Gaza and
the West Bank. One of the indicators of a good model is the
adjusted R2, which is consistently high for the following models
(always higher than 45%). In addition, the coefficients of all the
variables were checked to ensure that their magnitudes as well
as sign were consistent with a rational economic explanation.
Several consistency checks were run after these models
were produced to make sure that the models were stable
and the standard errors of the final poverty rates were
minimized. Since this exercise involved the imputation of
poverty from a survey to a census, the error that was most
crucial in determining the final standard errors was the
sampling error of the survey, and great care was taken to be
mindful of this.
The PECS has been used to calculate governorate level
poverty rates, but because of a small number of observa-
tions and high sampling errors below that level, it cannot be
reliably used to calculate locality level poverty rates. When
these locality level poverty estimates were imputed from
the census, one of the steps taken to confirm their validity
was to check whether the corresponding governorate level
poverty estimate in the census was within the confidence
intervals of the PECS governorate level estimates.
The variables used in each model have been described
earlier, and are labeled specifically in each of the following
models. In addition to the variables available in the data-
sets, several household variables were interacted with loca-
tion variables to reflect heterogeneity across regions. This
also provided extra information in terms of which variables
were particularly driving consumption in certain regions.
15
Modeling
(Continued on next page)
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
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
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
Model: West Bank and Gaza
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Table 2 | Consumption Model For Gaza 2009
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
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
(continued)
(Continued on next page)
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
No. of working age males in household 0.0246 0.0079
Interaction term of asset index and governorate Bethlehem –0.0573 0.0133
17
Modeling
Interaction term of enumeration area level mean of dummy for whether a head of household is working in agriculture with governorate Ramallah
–5.2353 0.9879
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 Nablus
0.4316 0.1553
Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate Qalqylia
0.956 0.152
Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate Jericho
–3.2228 1.269
Interaction term of enumeration area level mean of dummy for whether a head of household is working in trade and real estate with governorate Jenin
2.196 0.6289
Interaction term of enumeration area level mean of dummy for whether a head of household is working in trade and real estate with governorate Ramallah
1.7881 0.7193
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 Jericho
4.6367 0.9509
Interaction term of enumeration area level mean of dummy for whether a head of household is unemployed with the dummy for governorate Hebron
–0.5967 0.2097
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
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
Dummy for whether a household owns a car 0.2218 0.0221
(Continued on next page)
Table 3 | Consumption Model for West Bank 2009
Variable Label Coefficient Std. Err.
(continued)
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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
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 Ramallah
–5.2549 0.9827
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 Hebron
0.6259 0.1626
Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate Nablus
0.4264 0.1824
Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate Qalqylia
0.9616 0.1812
Interaction term of enumeration area level mean of dummy for whether a head of household is working in construction with governorate Jericho
–3.4299 1.9478
Interaction term of enumeration area level mean of dummy for whether a head of household is working in trade and real estate with governorate Jenin
2.1657 0.7399
Interaction term of enumeration area level mean of dummy for whether a head of household is working in trade and real estate with governorate Ramallah
1.9103 0.7401
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 Jericho
4.9031 1.3347
Interaction term of enumeration area level mean of dummy for whether a head of household is unemployed with the dummy for governorate Hebron
–0.538 0.2493
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
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
Table 3 | Consumption Model for West Bank 2009 (continued)
19
Modeling
Results
The results are remarkably consistent with the poverty rates
derived from PECS, with all the model predictions lying with the survey confidence intervals, and are described in the
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
GovernoratePECS 2009 Poverty
Estimates Std. Error
Survey Confidence intervalsModel Poverty Rate
2009Min. Max.
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%
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%
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.
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Map 2 illustrates these three areas
of varying PA control within the
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
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-
Map 2 | A Divided Landscape
23
Mapping the R
esults
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.
Map 3 | Punctuated by Barriers
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Map 4 | Localities Isolated or Affected by the Barrier WallWhile checkpoints, roadblocks and
other mobility restrictions have
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
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).
25
Mapping the R
esults
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.
Map 5 | Localities falling in area C
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Visualizing Poverty in the Palestinian Territories
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
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.
Map 6 | A fragmented Geography: A map of locality boundaries (Built-up areas) in the West Bank and Gaza
27
Mapping the R
esults
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.
Map 7 | Merging localities in the West Bank
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We then map the boundaries of
the 16 governorates in the West
Bank and Gaza and the governor-
ate level poverty estimates pro-
duced by PovMap2 (Map 8). The
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-
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.
Map 8 | The Poorest Governorates in the West Bank are better off than most Governorates in Gaza: Boundaries of West Bank and Gaza and Regional Poverty Headcount Rates (2009 Poverty Map estimates)
29
Mapping the R
esults
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
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
Map 9 | Mapping Poverty in the Palestinian Territories
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islands of prosperity, all other localities in Gaza have
poverty rates upwards of 26 percent. This is in contrast
with the West Bank, where only Hebron governorate has
a majority of localities with similarly high rates of pover-
ty. Ramallah’s localities are predominantly much more
prosperous than those in other governorates, in line with
the increasing concentration of government, business,
and donors in Ramallah city. Many of the poorer localities
within the West Bank are isolated Bedouin communities,
or communities in Area C that are cutoff from services and
markets, or communities bordering settlements with the
accompanying restrictions on mobility.
Accessibility, mobility and povertyThe fragmented landscape, particularly in the West Bank,
and the accompanying restrictions to mobility and access
can have implications for access to services, jobs and invest-
ment, and therefore for poverty rates. One way to quantify
the effect of these man-made restrictions is through a “mo-
bility restriction index”. The “mobility restriction index” is an-
chored to the standard concept of an “accessibility index”,
which has a long history, see e.g. Deichmann (1997). The stan-
dard accessibility index evaluates for a given origin (or loca-
tion point), the size of the population or the market that can
be reached within a reference amount of time. A measure of
“mobility restriction” can then be obtained by evaluating the
difference between “accessibility” in a hypothetical world
where there are no obstacles, and “accessibility” in the real
world where, in this particular case, all the road closure obsta-
cles are in place.
Blankespoor and van der Weide (2012) undertake this ex-
ercise for the case of the Palestinian territories, focusing on
measuring the intensity of mobility restrictions in the West
Bank. This measure is based on detailed information on
the locations of populated areas (with population counts),
and the road network. This is combined with the precise
locations of the road closure obstacles provided by UN-
OCHA oPT and with estimates of the time it takes the cross
each of the obstacles. Their measure accounts for the fact
that different obstacles impact mobility differently. Certain
obstacles (like road blocks and earth mounds) constitute
a full stop to traffic; other important obstacles (like check-
points and road gates) may permit traffic to pass through
them but will introduce a delay. In some cases, the delay
may be modest; in other cases it may be quite severe. The
placement of the obstacle also matters critically. A check-
point controlling traffic in and out of a major city clearly has
a larger impact on mobility than a checkpoint controlling
access to a small community well away from a commercial
route. All of this is taken into consideration by their mobility
restriction index.
31
Mapping the R
esults
Map 10 shows how mobility restric-
tions vary within 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.
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
Map 10 | Mapping Mobility Restrictions in the West Bank
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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-
my. Moreover, PA services are likely
well-functioning within these urban
centers.
Pockets of poverty and prosperityWhile 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
Map 11 | Pockets of Desperate Poverty: Relative Poverty in Gaza
33
Mapping the R
esults
may obfuscate important internal
variation within the West Bank and
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.
Map 12 | Islands of Prosperity: Relative Poverty in the West Bank
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Poor areas, poor peopleFor 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.
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
Map 13 | Low Rates of Poverty can Mask a Large Poor Population
35
Mapping the R
esults
of Hebron and Jericho governor-
ates) have high rates of poverty but
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
up to tens of thousands of poor
people within a square kilometer
(Map 14).
Map 14 | Density of Poverty: Poor Population per Square km
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Are poorer households 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-
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.
Map 15 | Poverty Appears to be Correlated with Higher Rates of Dependency
37
Mapping the R
esults
Does education pay off?Map 16 depicts the proportion of
heads of household in each locality
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
measures.
Map 16 | In the Palestinian Territories, more Educated Places are not Always Better off
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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
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.
Map 17 | In Gaza, Education doesn’t Bear Fruit; in the West Bank, Limited Aaccess to Education keeps some Places Poor
39
Mapping the R
esults
More than 70 percent of people liv-
ing in the Palestinian Territories are
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.
Map 18 | An Increasingly Educated Young Population
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Unemployment goes hand in hand with povertyThe 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
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.
Map 19 | Not a Pretty Picture: Unemployment goes Hand in Hand with Poverty
41
Mapping the R
esults
Map 20 plots the rates of unem-
ployment among young people
aged 15–30 in the West Bank and
Gaza. In general, the pattern mir-
rors the adult unemployment rates,
although the levels are higher.
Map 20 | Unemployment Level of Youth (15–30 years of age)
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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,
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.
43
Mapping the R
esults
The predominant employment
status in the Palestinian territo-
ries appears to be regular wage
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.
Map 22 | Irregular and Self-Employment Correlated with Poverty in the West Bank; not in Gaza
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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
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.
45
Mapping the R
esults
Map 24 depicts the dominant form
of health insurance in each local-
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.
Map 24 | Dominant Health Insurance
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.
5
48
The poverty map thus can be a very useful live monitoring
tool, provided it is regularly updated and linked to relevant
information such as geo-referenced datasets of market
accessibility, facility locations (schools, hospitals and clinics),
agro-climatic information, road networks, and availability of
services such as water and sanitation. As a combined and
disaggregated database, it can serve as a tool for planning
purposes, especially in decentralized structures. Similarly, it
can provide a first stage filter for identification of project or
program areas. This database cannot substitute for careful
policy design, but rather can serve as a guide for policy
prioritization.
It is important to also recognize the limitations of the pov-
erty map and its accompanying geo-referenced data and
using care in applying it appropriately. Poverty maps have
become popular in contexts of social safety net programs.
They are best suited to guide spatial targeting, for instance,
identifying pockets of high poverty rates or large popula-
tions of the poor. For instance, they could be combined
with the Ministry of Social Affairs’ database of current
beneficiaries to identify areas with inadequate coverage.
Poverty maps are also useful to rank geographical areas and
communities for a phased roll-out of programs, but they are
not a substitute for the identification of beneficiaries, which
requires household or individual-level targeting. Secondly,
the poverty estimates are based on consumption only, and
may not adequately capture other attributes of poverty or
vulnerability. Thirdly, these estimates do not explain the
causes of poverty—well designed surveys and careful anal-
yses will be needed to obtain diagnostics of the attributes
and causes of poverty, which are essential to design inter-
ventions.
The poverty mapping exercise has also highlighted areas
for improvement in the census and the PECS. One import-
ant area that needs to be revisited is the sampling frame
of the PECS to gain representativeness at the governor-
ate level and oversample small, isolated and vulnerable
communities, particularly in area C. Since the poverty map
depends critically on the nature and amount of information
that is commonly available in the survey and the census, the
census instrument can also be redesigned to improve this
aspect in looking forward to the next poverty map.
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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.
6
7 aPPendiCes
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Poverty Results
Gaza 2009
Locality ID Poverty headcount rate Standard error
1 552681 69.27% 4.29%
2 552695 31.16% 2.70%
3 552740 39.69% 3.37%
4 552755 6.73% 1.93%
5 552790 29.57% 2.59%
6 602775 53.61% 4.03%
7 602825 36.94% 3.62%
8 602900 5.05% 1.20%
9 602945 55.32% 3.51%
10 603045 54.57% 4.25%
11 653065 45.72% 3.48%
12 653070 32.88% 2.80%
13 653140 42.12% 2.95%
14 653145 41.37% 3.16%
15 653180 40.92% 3.32%
16 653200 40.40% 3.91%
17 653210 44.09% 3.50%
18 653215 34.28% 2.42%
19 653240 39.97% 2.76%
20 653250 40.76% 3.88%
Gaza 2009
Locality ID Poverty headcount rate Standard error
21 653275 66.21% 2.92%
22 703370 44.49% 2.79%
23 703410 28.14% 2.64%
24 703420 49.02% 2.69%
25 703425 54.09% 3.16%
26 703430 43.60% 3.20%
27 703445 39.70% 3.02%
28 703470 49.20% 3.42%
29 703485 60.89% 3.33%
30 753490 30.70% 2.59%
31 753495 33.51% 2.81%
32 753500 53.88% 3.99%
33 753505 52.32% 3.46%
Gaza-North 28.19% 1.46%
Gaza-City 38.26% 3.35%
Deiralbalah 40.64% 1.37%
Khan Younes 45.86% 1.58%
Rafah 33.45% 2.19%
Gaza 37.56% 1.34%
53
Appendices
West Bank 2009
Locality ID Poverty headcount rate Std. error
1 10005 18.50% 3.21%
2 10010 17.85% 2.90%
3 10030 13.79% 2.87%
4 10035 13.94% 1.85%
5 10045 22.91% 3.18%
6 10050 17.62% 3.11%
7 10055 15.60% 2.85%
8 10060 16.33% 2.33%
9 10080 24.71% 2.76%
10 10095 21.03% 2.50%
11 10120 16.07% 1.98%
12 10125 31.35% 3.34%
13 10140 17.91% 3.43%
14 10145 18.78% 2.51%
15 10180 14.37% 1.44%
16 10185 10.47% 3.85%
17 10190 22.24% 3.54%
18 10215 24.23% 3.20%
19 10220 15.45% 2.18%
20 10245 19.78% 2.91%
21 10265 18.34% 2.03%
22 10275 18.18% 2.89%
23 10300 20.43% 3.35%
24 10305 32.17% 3.53%
25 10310 34.83% 3.47%
26 10320 31.82% 3.89%
27 10340 25.62% 2.77%
28 10370 14.21% 2.07%
29 10395 24.76% 3.28%
30 10405 31.04% 3.13%
31 10415 19.41% 3.16%
32 10435 9.44% 1.88%
33 10445 28.01% 3.79%
34 10465 21.91% 2.19%
35 10500 14.96% 2.27%
36 10505 14.77% 2.94%
37 10510 19.69% 2.94%
38 10520 16.03% 2.11%
39 10565 21.61% 2.76%
West Bank 2009
Locality ID Poverty headcount rate Std. error
40 10600 24.68% 3.42%
41 10605 27.60% 2.64%
42 10615 17.54% 2.91%
43 10625 14.70% 2.15%
44 50420 18.66% 3.29%
45 50535 33.86% 3.15%
46 50550 20.89% 4.07%
47 50610 22.45% 2.55%
48 50700 27.32% 3.37%
49 50740 20.08% 3.11%
50 50755 23.81% 2.72%
51 100290 22.81% 2.56%
52 100330 15.95% 2.74%
53 100345 26.00% 3.64%
54 100350 19.05% 2.95%
55 100425 22.03% 3.06%
56 100440 14.13% 2.43%
57 100475 20.91% 2.83%
58 100480 19.58% 2.39%
59 100530 20.62% 2.40%
60 100570 21.04% 2.27%
61 100595 19.40% 2.69%
62 100620 26.64% 3.66%
63 100635 28.50% 4.47%
64 100645 18.09% 1.93%
65 100665 18.33% 2.14%
66 100690 29.13% 3.46%
67 100730 21.41% 3.45%
68 100735 19.79% 2.63%
69 100760 17.94% 2.15%
70 100800 28.63% 3.33%
71 100845 15.30% 2.72%
72 100900 21.42% 3.14%
73 100915 19.39% 3.29%
74 150660 23.55% 3.88%
75 150680 17.58% 2.77%
76 150695 29.45% 3.48%
77 150705 19.00% 2.65%
78 150765 19.25% 2.91%
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West Bank 2009
Locality ID Poverty headcount rate Std. error
79 150775 23.77% 3.18%
80 150785 14.32% 3.00%
81 150805 16.99% 2.74%
82 150810 19.62% 3.20%
83 150820 12.05% 2.00%
84 150825 36.79% 3.37%
85 150835 17.69% 3.08%
86 150855 19.34% 2.64%
87 150860 13.20% 2.70%
88 150880 46.81% 4.58%
89 150910 32.63% 3.04%
90 150920 13.38% 1.68%
91 150930 48.64% 4.50%
92 150935 14.16% 2.65%
93 150950 11.58% 2.61%
94 150955 19.81% 3.11%
95 150960 54.75% 4.66%
96 150990 16.70% 2.75%
97 151000 16.14% 2.56%
98 151010 18.61% 3.12%
99 151025 26.82% 3.67%
100 151050 18.33% 2.72%
101 151080 17.13% 2.75%
102 151090 13.90% 2.16%
103 151095 30.06% 3.48%
104 151135 19.56% 2.54%
105 151160 16.15% 3.33%
106 151185 14.55% 2.03%
107 151195 9.37% 2.62%
108 151215 19.56% 2.95%
109 151230 18.84% 3.32%
110 151245 24.46% 3.15%
111 151270 22.92% 2.89%
112 151325 20.90% 2.83%
113 151335 15.60% 2.48%
114 151365 28.08% 3.30%
115 151375 22.04% 3.06%
116 151380 15.47% 2.80%
117 151385 25.65% 3.41%
(Continued on next page)
(continued) West Bank 2009
Locality ID Poverty headcount rate Std. error
118 151405 16.12% 2.92%
119 151410 21.30% 3.43%
120 151445 21.96% 3.11%
121 200925 32.75% 3.50%
122 200945 23.49% 3.63%
123 200965 23.86% 2.84%
124 200970 23.54% 3.41%
125 200985 12.54% 2.32%
126 201020 16.44% 3.20%
127 201040 16.31% 1.67%
128 201055 17.89% 3.48%
129 201085 18.32% 2.92%
130 201100 16.59% 2.05%
131 201125 6.54% 1.45%
132 201155 4.66% 1.67%
133 201175 15.00% 2.95%
134 201255 8.76% 2.31%
135 201260 10.92% 2.53%
136 201280 9.67% 2.57%
137 251250 24.80% 2.76%
138 251275 28.07% 3.77%
139 251295 24.41% 2.99%
140 251300 22.35% 3.21%
141 251305 17.27% 2.21%
142 251310 41.17% 3.80%
143 251315 20.43% 3.40%
144 251320 18.91% 2.71%
145 251340 19.84% 2.77%
146 251360 23.95% 2.61%
147 251370 19.14% 2.01%
148 251395 33.08% 3.90%
149 251400 31.51% 3.68%
150 251425 26.53% 3.20%
151 251430 27.18% 3.77%
152 301455 10.71% 2.61%
153 301460 10.25% 1.91%
154 301470 9.07% 2.62%
155 301480 9.33% 2.13%
156 301485 4.25% 1.88%
55
Appendices
West Bank 2009
Locality ID Poverty headcount rate Std. error
157 301490 4.18% 1.21%
158 301500 6.58% 1.95%
159 301505 8.68% 2.40%
160 301515 26.57% 3.49%
161 301525 7.73% 1.96%
162 301530 58.68% 4.30%
163 301535 8.87% 2.05%
164 301545 7.84% 1.96%
165 301555 5.98% 1.47%
166 301565 3.87% 1.56%
167 301570 9.33% 2.15%
168 301590 7.02% 2.02%
169 301595 8.17% 1.69%
170 301600 7.24% 1.50%
171 301605 20.12% 3.21%
172 301610 5.71% 1.37%
173 301620 9.39% 2.03%
174 301635 3.91% 1.27%
175 301640 8.22% 2.68%
176 301650 8.95% 1.74%
177 301660 10.12% 2.48%
178 301665 15.37% 2.98%
179 301670 8.00% 2.11%
180 301675 3.55% 1.12%
181 301680 9.44% 1.74%
182 301685 4.75% 1.53%
183 301700 6.14% 1.59%
184 301710 6.34% 2.07%
185 301720 6.42% 1.95%
186 301725 20.08% 2.93%
187 301730 8.35% 2.46%
188 301745 21.56% 2.77%
189 301750 6.59% 1.89%
190 301755 12.57% 2.71%
191 301765 5.47% 1.46%
192 301780 7.47% 1.81%
193 301785 6.15% 1.56%
194 301790 2.33% 0.63%
195 301800 16.36% 3.26%
West Bank 2009
Locality ID Poverty headcount rate Std. error
196 301805 11.46% 2.48%
197 301810 1.86% 0.58%
198 301815 41.59% 2.61%
199 301820 10.74% 2.49%
200 301825 4.40% 1.08%
201 301830 8.39% 1.82%
202 301850 16.18% 3.14%
203 301855 22.40% 2.52%
204 301890 7.41% 2.69%
205 301895 26.02% 3.27%
206 351110 39.78% 5.13%
207 351140 29.96% 3.42%
208 351690 27.40% 2.70%
209 351840 40.56% 3.62%
210 351865 45.18% 5.46%
211 351920 27.19% 3.20%
212 351975 33.33% 3.40%
213 452170 18.12% 3.92%
214 452175 15.26% 3.01%
215 452180 14.41% 2.60%
216 452185 22.76% 3.19%
217 452195 23.43% 3.95%
218 452208 13.79% 2.86%
219 452210 6.19% 1.28%
220 452225 19.64% 3.07%
221 452230 8.63% 2.33%
222 452235 16.46% 3.08%
223 452240 8.76% 1.75%
224 452255 4.65% 1.33%
225 452265 18.21% 2.41%
226 452270 10.82% 2.55%
227 452275 21.84% 2.90%
228 452280 26.81% 3.72%
229 452285 26.66% 3.90%
230 452300 30.11% 3.69%
231 452325 10.91% 2.33%
232 452360 17.51% 3.13%
233 452385 17.86% 3.50%
234 452400 30.50% 3.47%
(Continued on next page)
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West Bank 2009
Locality ID Poverty headcount rate Std. error
235 452460 47.62% 4.69%
236 452495 19.42% 3.42%
237 452525 35.89% 4.23%
238 452660 38.60% 4.27%
239 502450 35.26% 3.02%
240 502530 33.58% 3.16%
241 502540 31.71% 2.75%
242 502560 23.87% 2.86%
243 502615 40.61% 3.29%
244 502620 42.33% 3.44%
245 502630 21.11% 2.86%
246 502635 32.52% 3.03%
247 502640 27.72% 3.12%
248 502655 45.24% 4.02%
249 502680 20.56% 3.60%
250 502685 35.75% 3.54%
251 502750 33.17% 3.55%
252 502765 24.50% 4.15%
253 502780 18.67% 2.42%
254 502782 29.64% 4.05%
255 502810 26.03% 2.89%
256 502815 27.65% 2.97%
257 502835 18.39% 3.01%
258 502840 21.39% 2.27%
259 502860 24.48% 4.46%
260 502895 20.41% 3.95%
261 502905 39.66% 3.68%
West Bank 2009
Locality ID Poverty headcount rate Std. error
262 502910 18.39% 3.17%
263 502920 19.50% 3.48%
264 502950 26.25% 4.07%
265 502960 33.55% 4.94%
266 502970 34.18% 4.08%
267 502980 16.82% 3.04%
268 503090 21.40% 4.53%
269 503100 33.93% 4.84%
270 503115 38.73% 4.81%
271 503120 50.35% 3.46%
272 503126 83.07% 4.80%
273 503145 20.44% 4.38%
274 503170 14.48% 3.34%
275 503245 33.13% 3.20%
276 503320 53.18% 3.69%
277 503335 40.93% 4.23%
Jenin 19.30% 0.64%
Tubas 24.47% 1.50%
Tulkarm 20.81% 0.96%
Nablus 20.18% 0.75%
Qalqylia 15.82% 0.85%
Salfit 23.96% 0.85%
Ramallah 8.87% 0.58%
Jericho 31.28% 2.01%
Bethlehem 17.35% 0.83%
Hebron 29.88% 1.10%
West Bank 21.31% 0.46%
(continued)
57
Appendices
Merged Localities
Region Governorate Locality Merge ID Original ID
WB Bethlehem Al Walaja 452170 452170
WB Bethlehem Battir 452175 452175
WB Bethlehem Al ‘Ubeidiya 452180 452180
WB Bethlehem Ayda Camp 452185 452185
WB Bethlehem Khallet an Nu’man 452225 452190
WB Bethlehem Al ‘Aza Camp 452195 452195
WB Bethlehem Al Khas 452225 452200
WB Bethlehem Al Haddadiya 452285 452205
WB Bethlehem Khallet Hamameh 452208 452208
WB Bethlehem Bir Onah 452208 452209
WB Bethlehem Beit Jala 452210 452210
WB Bethlehem Dar Salah 452225 452225
WB Bethlehem Husan 452230 452230
WB Bethlehem Wadi Fukin 452235 452235
WB Bethlehem Bethlehem (Beit Lahm) 452240 452240
WB Bethlehem Beit Sahur 452255 452255
WB Bethlehem Ad Doha 452265 452265
WB Bethlehem Al Khadr 452270 452270
WB Bethlehem Ad Duheisha Camp 452275 452275
WB Bethlehem Hindaza 452280 452280
WB Bethlehem Ash Shawawra 452285 452285
WB Bethlehem Artas 452300 452300
WB Bethlehem Nahhalin 452325 452325
WB Bethlehem Beit Ta’mir 452280 452335
WB Bethlehem Khallet al Louza 452280 452345
WB Bethlehem Al Jab’a 452235 452355
WB Bethlehem Za’tara 452360 452360
WB Bethlehem Jannatah 452385 452385
WB Bethlehem Wadi Rahhal 452400 452400
WB Bethlehem Jubbet adh Dhib 452385 452405
WB Bethlehem Khallet Sakariya 452235 452415
WB Bethlehem Khallet al Haddad 452400 452430
WB Bethlehem Al Ma’sara 452400 452440
WB Bethlehem Wadi an Nis 452400 452445
WB Bethlehem Jurat ash Sham’a 452460 452460
WB Bethlehem Marah Ma’alla 452460 452470
WB Bethlehem Umm Salamuna 452460 452480
WB Bethlehem Al Manshiya 452660 452490
WB Bethlehem Tuqu’ 452495 452495
Region Governorate Locality Merge ID Original ID
WB Bethlehem Marah Rabah 452660 452500
WB Bethlehem Beit Fajjar 452525 452525
WB Bethlehem Al Maniya 452660 452535
WB Bethlehem Kisan 452660 452565
WB Bethlehem Arab ar Rashayida 452660 452660
WB Hebron Khirbet ad Deir 502450 502435
WB Hebron Surif 502450 502450
WB Hebron Al ‘Arrub Camp 502530 502530
WB Hebron Beit Ummar 502540 502540
WB Hebron Jala 502560 502545
WB Hebron Hitta 502560 502550
WB Hebron Shuyukh al ‘Arrub 502620 502555
WB Hebron Kharas 502560 502560
WB Hebron Umm al Butm 502620 502575
WB Hebron Hamrush 502620 502580
WB Hebron Nuba 502560 502585
WB Hebron Beit Ula 502615 502615
WB Hebron Sa’ir 502620 502620
WB Hebron Halhul 502630 502630
WB Hebron Ash Shuyukh 502635 502635
WB Hebron Tarqumiya 502640 502640
WB Hebron Beit Kahil 502655 502655
WB Hebron Beit ‘Einun 502680 502680
WB Hebron Qlaa Zeta 502680 502681
WB Hebron Idhna 502685 502685
WB Hebron Taffuh 502750 502750
WB Hebron Beit Maqdum 502765 502765
WB Hebron Al Baqa 502680 502778
WB Hebron Hebron (Al Khalil) 502780 502780
WB Hebron Al Bowereh (Aqabat Injeleh) 502782 502781
WB Hebron Khallet Edar 502782 502782
WB Hebron Deir Samit 502810 502810
WB Hebron Bani Na’im 502815 502815
WB Hebron Khallet Al Masafer 503126 502830
WB Hebron Beit ‘Awwa 502835 502835
WB Hebron Dura 502840 502840
WB Hebron Qalqas 502782 502855
WB Hebron Sikka 502860 502860
WB Hebron Khirbet Salama 502860 502865
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Region Governorate Locality Merge ID Original ID
WB Hebron Wadi ‘Ubeid 502860 502870
WB Hebron Fuqeiqis 502860 502875
WB Hebron Khursa 502895 502895
WB Hebron Tarrama 502980 502900
WB Hebron Al Fawwar Camp 502905 502905
WB Hebron Al Majd 502910 502910
WB Hebron Marah al Baqqar 502860 502915
WB Hebron Hadab al Fawwar 502920 502920
WB Hebron Deir al ‘Asal at Tahta 502970 502925
WB Hebron Al Heila 502782 502935
WB Hebron Wadi ash Shajina 502980 502940
WB Hebron As Sura 502950 502950
WB Hebron Deir Razih 502980 502955
WB Hebron Ar Rihiya 502960 502960
WB Hebron Zif 503115 502965
WB Hebron Deir al ‘Asal al Fauqa 502970 502970
WB Hebron Khallet al ‘Aqed 502950 502975
WB Hebron Imreish 502980 502980
WB Hebron Al Buweib 503126 503005
WB Hebron Beit ar Rush at Tahta 503090 503010
WB Hebron Hadab al ‘Alaqa 502980 503040
WB Hebron Beit Mirsim 503090 503075
WB Hebron Beit ar Rush al Fauqa 503090 503090
WB Hebron Karma 502980 503095
WB Hebron Beit ‘Amra 503100 503100
WB Hebron Om Adaraj (Arab Al Kaabneh) 503126 503105
WB Hebron Wadi al Kilab 503090 503110
WB Hebron Om Ashoqhan 503115 503111
WB Hebron Khallet al Maiyya 503115 503115
WB Hebron Kheroshewesh Wal Hadedeyah
503115 503116
WB Hebron Om Al Amad (Sahel Wadi Elma)
503115 503117
WB Hebron Yatta 503120 503120
WB Hebron Ad Deirat 503115 503125
WB Hebron Khashem Adaraj (Al-Hathaleen)
503126 503126
WB Hebron Kurza 502980 503135
WB Hebron Rabud 503145 503145
WB Hebron Umm Lasafa 503215 503150
WB Hebron Al Burj 503170 503170
WB Hebron Um Al-Khair 503126 503210(Continued on next page)
Region Governorate Locality Merge ID Original ID
WB Hebron Al Karmil 503215 503215
WB Hebron Khallet Salih 503215 503225
WB Hebron Adh Dhahiriya 503245 503245
WB Hebron At Tuwani 503215 503255
WB Hebron Ma’in 503215 503260
WB Hebron An Najada 503215 503265
WB Hebron Anab al Kabir 503245 503295
WB Hebron Khirbet Asafi 503215 503305
WB Hebron Mantiqat Shi’b al Batin 503215 503310
WB Hebron As Samu’ 503320 503320
WB Hebron Wadi Al Amayer 503320 503321
WB Hebron Khirbet Tawil ash Shih 503215 503325
WB Hebron Ar Ramadin 503335 503335
WB Hebron Maghayir al ‘Abeed 503320 503345
WB Hebron Khirbet al Fakheit 503215 503350
WB Hebron Khirbet Bir al ‘Idd 503320 503360
WB Hebron Khirbet Zanuta 503335 503375
WB Hebron Imneizil 503320 503380
WB Hebron Arab al Fureijat 503335 503405
WB Jenin Zububa 10005 10005
WB Jenin Rummana 10010 10010
WB Jenin Ti’innik 10010 10015
WB Jenin At Tayba 10010 10020
WB Jenin Arabbuna 10055 10025
WB Jenin Al Jalama 10030 10030
WB Jenin Silat al Harithiya 10035 10035
WB Jenin As Sa’aida 10045 10040
WB Jenin Anin 10045 10045
WB Jenin Arrana 10050 10050
WB Jenin Deir Ghazala 10055 10055
WB Jenin Faqqu’a 10060 10060
WB Jenin Khirbet Suruj 10045 10070
WB Jenin Al Yamun 10080 10080
WB Jenin Umm ar Rihan 10145 10085
WB Jenin Kafr Dan 10095 10095
WB Jenin Khirbet ‘Abdallah al Yunis 10145 10105
WB Jenin Dhaher al Malih 10145 10115
WB Jenin Barta’a ash Sharqiya 10120 10120
WB Jenin Al ‘Araqa 10125 10125
WB Jenin Al Jameelat 10140 10135
WB Jenin Beit Qad 10140 10140
(continued)
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Appendices
Region Governorate Locality Merge ID Original ID
WB Jenin Tura al Gharbiya 10145 10145
WB Jenin Tura ash Sharqiya 10145 10150
WB Jenin Al Hashimiya 10145 10155
WB Jenin Nazlat ash Sheikh Zeid 10145 10165
WB Jenin At Tarem 10145 10170
WB Jenin Khirbet al Muntar al Gharbiya
10145 10175
WB Jenin Jenin 10180 10180
WB Jenin Jenin Camp 10185 10185
WB Jenin Jalbun 10190 10190
WB Jenin Aba 10140 10195
WB Jenin Khirbet Mas’ud 10245 10200
WB Jenin Khirbet al Muntar ash Sharqiya
10245 10205
WB Jenin Kafr Qud 10275 10210
WB Jenin Deir Abu Da’if 10215 10215
WB Jenin Birqin 10220 10220
WB Jenin Umm Dar 10245 10225
WB Jenin Al Khuljan 10245 10230
WB Jenin Wad ad Dabi’ 10140 10235
WB Jenin Dhaher al ‘Abed 10245 10240
WB Jenin Zabda 10245 10245
WB Jenin Ya’bad 10265 10265
WB Jenin Kufeirit 10275 10275
WB Jenin Imreiha 10245 10285
WB Jenin Umm at Tut 10305 10295
WB Jenin Ash Shuhada 10300 10300
WB Jenin Jalqamus 10305 10305
WB Jenin Al Mughayyir 10310 10310
WB Jenin Al Mutilla 10310 10315
WB Jenin Bir al Basha 10320 10320
WB Jenin Al Hafira 10320 10335
WB Jenin Qabatiya 10340 10340
WB Jenin Arraba 10370 10370
WB Jenin Telfit 10305 10385
WB Jenin Mirka 10395 10395
WB Jenin Wadi Du’oq 10395 10400
WB Jenin Fahma al Jadida 10395 10401
WB Jenin Raba 10405 10405
WB Jenin Al Mansura 10395 10410
WB Jenin Misliya 10415 10415
Region Governorate Locality Merge ID Original ID
WB Jenin Al Jarba 10415 10430
WB Jenin Az Zababida 10435 10435
WB Jenin Fahma 10445 10445
WB Jenin Az Zawiya 10395 10460
WB Jenin Kafr Ra’i 10465 10465
WB Jenin Al Kufeir 10405 10485
WB Jenin Sir 10405 10495
WB Jenin Ajja 10500 10500
WB Jenin Anza 10505 10505
WB Jenin Sanur 10510 10510
WB Jenin Ar Rama 10500 10515
WB Jenin Meithalun 10520 10520
WB Jenin Al Judeida 10565 10565
WB Jenin al ‘Asa’asa 10605 10585
WB Jenin Al ‘Attara 10625 10590
WB Jenin Siris 10600 10600
WB Jenin Jaba’ 10605 10605
WB Jenin Al Fandaqumiya 10615 10615
WB Jenin Silat adh Dhahr 10625 10625
WB Jericho Marj Na’ja 351110 351045
WB Jericho Az Zubeidat 351110 351110
WB Jericho Marj al Ghazal 351110 351116
WB Jericho Al Jiftlik 351140 351140
WB Jericho Fasayil 351690 351510
WB Jericho Al ‘Auja 351690 351690
WB Jericho An Nuwei’ma 351840 351840
WB Jericho Ein ad Duyuk al Fauqa 351840 351845
WB Jericho Ein as Sultan Camp 351865 351865
WB Jericho Jericho (Ariha) 351920 351920
WB Jericho Aqbat Jaber Camp 351975 351975
WB Jericho An Nabi Musa 351975 352075
WB Jerusalem Rafat 401870 401870
WB Jerusalem Mikhmas 401885 401885
WB Jerusalem Qalandiya Camp 401900 401900
WB Jerusalem Jaba’ (Tajammu’ Badawi) 401885 401910
WB Jerusalem Qalandiya 401940 401915
WB Jerusalem Beit Duqqu 401930 401930
WB Jerusalem Jaba’ 401935 401935
WB Jerusalem Al Judeira 401940 401940
WB Jerusalem Ar Ram & Dahiyat al Bareed 401945 401945
WB Jerusalem Beit ‘Anan 401950 401950
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Region Governorate Locality Merge ID Original ID
WB Jerusalem Al Jib 401955 401955
WB Jerusalem Bir Nabala 401960 401960
WB Jerusalem Beit Ijza 401980 401965
WB Jerusalem Al Qubeiba 401980 401980
WB Jerusalem Kharayib Umm al Lahim 402015 401985
WB Jerusalem Biddu 401995 401995
WB Jerusalem An Nabi Samwil 402025 402000
WB Jerusalem Hizma 402005 402005
WB Jerusalem Beit Hanina al Balad 402025 402010
WB Jerusalem Qatanna 402015 402015
WB Jerusalem Beit Surik 402020 402020
WB Jerusalem Beit Iksa 402025 402025
WB Jerusalem Anata 402040 402040
WB Jerusalem Al Ka’abina (Tajammu’ Badawi)
402005 402045
WB Jerusalem Az Za’ayyem 402065 402065
WB Jerusalem Al ‘Eizariya 402100 402100
WB Jerusalem Abu Dis 402120 402120
WB Jerusalem Arab al Jahalin 402120 402125
WB Jerusalem As Sawahira ash Sharqiya 402145 402145
WB Jerusalem Ash Sheikh Sa’d 402160 402160
WB Nablus Bizzariya 150660 150660
WB Nablus Burqa 150680 150680
WB Nablus Yasid 150695 150695
WB Nablus Beit Imrin 150705 150705
WB Nablus Nisf Jubeil 150705 150745
WB Nablus Sabastiya 150765 150765
WB Nablus Ijnisinya 150785 150770
WB Nablus Talluza 150775 150775
WB Nablus An Naqura 150785 150785
WB Nablus Al Badhan 150805 150805
WB Nablus Deir Sharaf 150810 150810
WB Nablus Asira ash Shamaliya 150820 150820
WB Nablus An Nassariya 150825 150825
WB Nablus Zawata 150835 150835
WB Nablus Al ‘Aqrabaniya 150825 150840
WB Nablus Qusin 150855 150855
WB Nablus Beit Iba 150860 150860
WB Nablus Beit Hasan 150825 150865
WB Nablus Beit Wazan 150855 150875
WB Nablus Ein Beit el Ma Camp 150880 150880
Region Governorate Locality Merge ID Original ID
WB Nablus Ein Shibli 150825 150885
WB Nablus Azmut 150910 150910
WB Nablus Nablus 150920 150920
WB Nablus Askar Camp 150930 150930
WB Nablus Deir al Hatab 150935 150935
WB Nablus Sarra 150950 150950
WB Nablus Salim 150955 150955
WB Nablus Balata Camp 150960 150960
WB Nablus Iraq Burin 151050 150975
WB Nablus Tell 150990 150990
WB Nablus Beit Dajan 151000 151000
WB Nablus Rujeib 151010 151010
WB Nablus Kafr Qallil 151025 151025
WB Nablus Furush Beit Dajan 150825 151030
WB Nablus Madama 151050 151050
WB Nablus Burin 151080 151080
WB Nablus Beit Furik 151090 151090
WB Nablus Asira al Qibliya 151095 151095
WB Nablus Awarta 151135 151135
WB Nablus Urif 151160 151160
WB Nablus Odala 151185 151180
WB Nablus Huwwara 151185 151185
WB Nablus Einabus 151195 151195
WB Nablus Yanun 151270 151200
WB Nablus Beita 151215 151215
WB Nablus Ar Rajman 151270 151220
WB Nablus Zeita Jamma’in 151230 151230
WB Nablus Jamma’in 151245 151245
WB Nablus Osarin 151270 151265
WB Nablus Aqraba 151270 151270
WB Nablus Za’tara 151325 151285
WB Nablus Tall al Khashaba 151385 151311
WB Nablus Yatma 151325 151325
WB Nablus Qabalan 151335 151335
WB Nablus Jurish 151375 151345
WB Nablus Qusra 151365 151365
WB Nablus Talfit 151375 151375
WB Nablus As Sawiya 151380 151380
WB Nablus Majdal Bani Fadil 151385 151385
WB Nablus Al Lubban ash Sharqiya 151405 151405
WB Nablus Qaryut 151410 151410
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Region Governorate Locality Merge ID Original ID
WB Nablus Jalud 151410 151420
WB Nablus Ammuriya 151405 151435
WB Nablus Duma 151445 151445
WB Qalqylia Falamya 200985 200905
WB Qalqylia Kafr Qaddum 200925 200925
WB Qalqylia Jit 200945 200945
WB Qalqylia Baqat al Hatab 200965 200965
WB Qalqylia Hajja 200970 200970
WB Qalqylia Jayyus 200985 200985
WB Qalqylia Khirbet Sir 200985 200995
WB Qalqylia Arab ar Ramadin ash Shamali
201040 201005
WB Qalqylia Far’ata 201020 201015
WB Qalqylia Immatin 201020 201020
WB Qalqylia Al Funduq 201085 201035
WB Qalqylia Qalqylia 201040 201040
WB Qalqylia An Nabi Elyas 201055 201055
WB Qalqylia Kafr Laqif 200965 201065
WB Qalqylia Arab Abu Farda 201125 201070
WB Qalqylia Izbat at Tabib 201055 201075
WB Qalqylia Jinsafut 201085 201085
WB Qalqylia Azzun 201100 201100
WB Qalqylia Arab ar Ramadin al Janubi 201125 201105
WB Qalqylia Isla 201055 201115
WB Qalqylia Arab Al-Khouleh 201175 201116
WB Qalqylia Wadi ar Rasha 201155 201120
WB Qalqylia Habla 201125 201125
WB Qalqylia Ras at Tira 201155 201130
WB Qalqylia Ras ‘Atiya 201155 201155
WB Qalqylia Ad Dab’a 201155 201170
WB Qalqylia Kafr Thulth 201175 201175
WB Qalqylia ud 201155 201190
WB Qalqylia Al Mudawwar 201255 201205
WB Qalqylia Izbat Salman 201255 201210
WB Qalqylia Izbat al Ashqar 201255 201225
WB Qalqylia Beit Amin 201255 201255
WB Qalqylia Sanniriya 201260 201260
WB Qalqylia Atma 201280 201280
WB Ramallah Qarawat Bani Zeid 301455 301455
WB Ramallah Bani Zeid ash Sharqiya 301460 301460
WB Ramallah Kafr ‘Ein 301470 301470
Region Governorate Locality Merge ID Original ID
WB Ramallah Bani Zeid 301480 301480
WB Ramallah Abwein 301485 301485
WB Ramallah Turmus’ayya 301490 301490
WB Ramallah Al Lubban al Gharbi 301515 301495
WB Ramallah Sinjil 301500 301500
WB Ramallah Deir as Sudan 301505 301505
WB Ramallah Rantis 301515 301515
WB Ramallah Jilijliya 301500 301520
WB Ramallah Ajjul 301525 301525
WB Ramallah Al Mughayyir 301530 301530
WB Ramallah Abud 301535 301535
WB Ramallah An Nabi Salih 301505 301540
WB Ramallah Khirbet Abu Falah 301545 301545
WB Ramallah Umm Safa 301525 301550
WB Ramallah Al Mazra’a ash Sharqiya 301555 301555
WB Ramallah Deir Nidham 301505 301560
WB Ramallah Atara 301565 301565
WB Ramallah Deir Abu Mash’al 301570 301570
WB Ramallah Jibiya 301600 301575
WB Ramallah Burham 301600 301585
WB Ramallah Kafr Malik 301590 301590
WB Ramallah Shuqba 301595 301595
WB Ramallah Kobar 301600 301600
WB Ramallah Qibya 301605 301605
WB Ramallah Silwad 301610 301610
WB Ramallah Yabrud 301640 301615
WB Ramallah AL-Itihad 301620 301620
WB Ramallah Shabtin 301595 301625
WB Ramallah Bir Zeit 301635 301635
WB Ramallah AL-Doha 301675 301636
WB Ramallah Ein Siniya 301640 301640
WB Ramallah Silwad Camp 301640 301645
WB Ramallah Deir Jarir 301650 301650
WB Ramallah Deir ‘Ammar Camp 301660 301660
WB Ramallah Budrus 301665 301665
WB Ramallah AL-Zaytouneh 301670 301670
WB Ramallah Jifna 301675 301675
WB Ramallah Dura al Qar’ 301680 301680
WB Ramallah At Tayba 301685 301685
WB Ramallah Al Jalazun Camp 301700 301700
WB Ramallah Abu Qash 301675 301705
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Region Governorate Locality Merge ID Original ID
WB Ramallah Deir Qaddis 301710 301710
WB Ramallah Ni’lin 301745 301715
WB Ramallah Ein Yabrud 301720 301720
WB Ramallah Kharbatha Bani Harith 301725 301725
WB Ramallah Ras Karkar 301730 301730
WB Ramallah Surda 301675 301735
WB Ramallah Al Janiya 301730 301740
WB Ramallah Al Midya 301745 301745
WB Ramallah Rammun 301750 301750
WB Ramallah Kafr Ni’ma 301755 301755
WB Ramallah Bil’in 301755 301760
WB Ramallah Beitin 301765 301765
WB Ramallah Ein Qiniya 301780 301770
WB Ramallah Badiw al Mu’arrajat 301815 301775
WB Ramallah Deir Ibzi’ 301780 301780
WB Ramallah Deir Dibwan 301785 301785
WB Ramallah Al Bireh 301790 301790
WB Ramallah Ein ‘Arik 301800 301800
WB Ramallah Saffa 301805 301805
WB Ramallah Ramallah 301810 301810
WB Ramallah Burqa 301815 301815
WB Ramallah Beit ‘Ur at Tahta 301820 301820
WB Ramallah Beituniya 301825 301825
WB Ramallah Al Am’ari Camp 301830 301830
WB Ramallah Qaddura Camp 301830 301835
WB Ramallah Beit Sira 301850 301850
WB Ramallah Kharbatha al Misbah 301855 301855
WB Ramallah Beit ‘Ur al Fauqa 301890 301860
WB Ramallah At Tira 301890 301890
WB Ramallah Beit Liqya 301895 301895
WB Ramallah Beit Nuba 301895 301925
WB Salfit Deir Istiya 251250 251250
WB Salfit Qarawat Bani Hassan 251275 251275
WB Salfit Qira 251295 251290
WB Salfit Kifl Haris 251295 251295
WB Salfit Marda 251300 251300
WB Salfit Biddya 251305 251305
WB Salfit Haris 251310 251310
WB Salfit Yasuf 251315 251315
WB Salfit Mas-ha 251320 251320
WB Salfit Iskaka 251315 251330
Region Governorate Locality Merge ID Original ID
WB Salfit Sarta 251340 251340
WB Salfit Izbat Abu Adam 251310 251355
WB Salfit Az Zawiya 251360 251360
WB Salfit Salfit 251370 251370
WB Salfit Rafat 251395 251395
WB Salfit Bruqin 251400 251400
WB Salfit Farkha 251370 251415
WB Salfit Kafr ad Dik 251425 251425
WB Salfit Deir Ballut 251430 251430
WB Salfit Khirbet Qeis 251370 251440
WB Tubas Bardala 50420 50420
WB Tubas Ein el Beida 50420 50450
WB Tubas Kardala 50420 50455
WB Tubas Ibziq 50535 50490
WB Tubas Salhab 50535 50525
WB Tubas Aqqaba 50535 50535
WB Tubas Tayasir 50550 50550
WB Tubas Al Farisiya 50420 50551
WB Tubas Al ‘Aqaba 50550 50560
WB Tubas Ath Thaghra 50610 50575
WB Tubas Al Malih 50420 50580
WB Tubas Tubas 50610 50610
WB Tubas Kashda 50700 50650
WB Tubas Khirbet Yarza 50755 50656
WB Tubas Ras al Far’a 50700 50670
WB Tubas El Far’a Camp 50700 50700
WB Tubas Khirbet ar Ras al Ahmar 50755 50720
WB Tubas Wadi al Far’a 50740 50740
WB Tubas Tammun 50755 50755
WB Tubas Khirbet ‘Atuf 50755 50790
WB Tubas Khirbet Humsa 50755 50871
WB Tulkarm ‘Akkaba 100290 100250
WB Tulkarm Qaffin 100290 100290
WB Tulkarm Nazlat ‘Isa 100330 100330
WB Tulkarm An Nazla ash Sharqiya 100345 100345
WB Tulkarm Baqa ash Sharqiya 100350 100350
WB Tulkarm An Nazla al Wusta 100345 100355
WB Tulkarm An Nazla al Gharbiya 100345 100380
WB Tulkarm Zeita 100425 100425
WB Tulkarm Seida 100440 100440
WB Tulkarm Illar 100475 100475
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(continued)
Region Governorate Locality Merge ID Original ID
WB Tulkarm Attil 100480 100480
WB Tulkarm Deir al Ghusun 100530 100530
WB Tulkarm Al Jarushiya 100595 100545
WB Tulkarm Al Masqufa 100595 100555
WB Tulkarm Bal’a 100570 100570
WB Tulkarm Iktaba 100595 100595
WB Tulkarm Nur Shams Camp 100620 100620
WB Tulkarm Tulkarm Camp 100635 100635
WB Tulkarm Tulkarm 100645 100645
WB Tulkarm Anabta 100665 100665
WB Tulkarm Kafr al Labad 100690 100690
WB Tulkarm Kafa 100760 100710
WB Tulkarm Al Haffasi 100760 100715
WB Tulkarm Ramin 100730 100730
WB Tulkarm Far’un 100735 100735
WB Tulkarm Shufa 100760 100760
WB Tulkarm Khirbet Jubara 100760 100780
WB Tulkarm Saffarin 100800 100795
WB Tulkarm Beit Lid 100800 100800
WB Tulkarm Ar Ras 100845 100815
WB Tulkarm Kafr Sur 100845 100845
WB Tulkarm Kur 100915 100870
WB Tulkarm Kafr Zibad 100915 100895
WB Tulkarm Kafr Jammal 100900 100900
WB Tulkarm Kafr ‘Abbush 100915 100915
Gaza Deirelbalah An Nuseirat Camp 653065 653065
Gaza Deirelbalah An Nuseirat 653070 653070
Gaza Deirelbalah Al Bureij Camp 653140 653140
Gaza Deirelbalah Al Bureij 653145 653145
Gaza Deirelbalah Az Zawayda 653180 653180
Region Governorate Locality Merge ID Original ID
Gaza Deirelbalah Deir al Balah Camp 653200 653200
Gaza Deirelbalah Al Maghazi Camp 653210 653210
Gaza Deirelbalah Al Maghazi 653215 653215
Gaza Deirelbalah Deir al Balah 653240 653240
Gaza Deirelbalah Al Musaddar 653250 653250
Gaza Deirelbalah Wadi as Salqa 653275 653275
Gaza Gaza-city Ash Shati’ Camp 602775 602775
Gaza Gaza-city Gaza 602825 602825
Gaza Gaza-city Madinat Ezahra 602900 602900
Gaza Gaza-city Al Mughraqa (Abu Middein) 602945 602945
Gaza Gaza-city Juhor ad Dik 603045 603045
Gaza Gaza-north Um Al-Nnaser (Al Qaraya al Badawiya al Maslakh)
552681 552681
Gaza Gaza-north Beit Lahiya 552695 552695
Gaza Gaza-north Beit Hanun 552740 552740
Gaza Gaza-north Jabalya Camp 552755 552755
Gaza Gaza-north Jabalya 552790 552790
Gaza Khan younes Al Qarara 703370 703370
Gaza Khan younes Khan Yunis Camp 703410 703410
Gaza Khan younes Khan Yunis 703420 703420
Gaza Khan younes Bani Suheila 703425 703425
Gaza Khan younes Abasan al Jadida(as Saghira) 703430 703430
Gaza Khan younes Abasan al Kabira 703445 703445
Gaza Khan younes Khuza’a 703470 703470
Gaza Khan younes Al Fukhkhari 703485 703485
Gaza Rafah Rafah 753490 753490
Gaza Rafah Rafah Camp 753495 753495
Gaza Rafah Al-Nnaser (Al Bayuk) 753500 753500
Gaza Rafah Shokat as Sufi 753505 753505
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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
10085 Umm ar Rihan
10105 Khirbet ‘Abdallah al Yunis
10115 Dhaher al Malih
10120 Barta’a ash Sharqiya
10175 Khirbet al Muntar al Gharbiya
10205 Khirbet al Muntar ash Sharqiya
100780 Khirbet Jubara
201005 ‘Arab ar Ramadin ash Shamali
201070 ‘Arab Abu Farda
201105 ‘Arab ar Ramadin al Janu
201280 ‘Azzun ‘Atma
251250 Deir Istiya
251310 Haris
452230 Husan
452235 Wadi Fukin
452325 Nahhalin
452355 Al Jab’a
452465 Khallet ‘Afana
502640 Tarqumiya
452175 Battir
452190 khallet an Nu’man
10005 Zububa
10010 Rummana
10020 At Tayba
10025 ‘Arabbuna
10030 Al Jalama
10040 As Sa’aida
10045 ‘Anin
10060 Faqqu’a
10070 Khirbet Suruj
10125 Al ‘Araqa
Locality ID Localities affected by the wall
10145 Tura al Gharbiya
10150 Tura ash Sharqiya
10165 Nazlat ash Sheikh Zeid
10170 At Tarem
10190 Jalbun
10200 Khirbet Mas’ud
10225 Umm Dar
10230 Al Khuljan
10240 Dhaher al ‘Abed
10245 Zabda
10265 Ya’bad
10310 Al Mughayyir
10315 Al Mutilla
10405 Raba
50420 Bardala
100250 ‘Akkaba
100290 Qaffin
100330 Nazlat ‘Isa
100425 Zeita
100480 ‘Attil
100530 Deir al Ghusun
100545 Al Jarushiya
100595 Iktaba
100620 Nur Shams Camp
100635 Tulkarm Camp
100645 Tulkarm
100690 Kafr al Labad
100735 Far’un
100815 Ar Ras
100845 Kafr Sur
100895 Kafr Zibad
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
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Locality ID Localities affected by the wall
100915 Kafr ‘Abbush
201020 Immatin
201055 An Nabi Elyas
201100 ‘Azzun
201115 ‘Isla
201075 ‘Izbat at Tabib
200985 Jayyus
201040 Qalqiliya
201120 Wadi ar Rasha
201125 Habla
201155 Ras ‘Atiya
201130 Ras at Tira
201170 Ad Dab’a
201190 ‘Izbat Jal’ud
201210 ‘Izbat Salman
201175 Kafr Thulth
201260 Sanniriya
201255 Beit Amin
251250 Deir Istiya
251275 Qarawat Bani Hassan
251290 Qira
251295 Kifl Haris
251300 Marda
251305 Biddya
251310 Haris
251320 Mas-ha
251330 Iskaka
251340 Sarta
251360 Az Zawiya
251370 Salfit
251395 Rafat
251400 Bruqin
251425 Kafr ad Dik
251430 Deir Ballut
301480 Bani Zeid
301495 Al Lubban al Gharbi
301515 Rantis
301535 ‘Abud
301595 Shuqba
301605 Qibya
503170 Al Burj
(Continued on next page)
Locality ID Localities affected by the wall
503245 Adh Dhahiriya
503320 As Samu’
503321 Wadi Al Amayer
503325 Khirbet Tawil ash Shih
503335 Ar Ramadin
503380 Imneizil
503405 ‘Arab al Fureijat
200905 Falamya
200925 Kafr Qaddum
200945 Jit
301665 Budrus
301710 Deir Qaddis
301715 Ni’lin
301725 Kharbatha Bani Harith
301745 Al Midya
301755 Kafr Ni’ma
301760 Bil’in
301800 ‘Ein ‘Arik
301805 Saffa
301810 Ramallah
301820 Beit ‘Ur at Tahta
301825 Beituniya
301850 Beit Sira
301860 Beit ‘Ur al Fauqa
301890 At Tira
301895 Beit Liqya
301925 Beit Nuba
452209 Bir onah
452210 Beit Jala
452225 Dar Salah
452208 Khallet Hamameh
452400 Wadi Rahhal
452415 Khallet Sakariya
452240 Bethlehem (Beit Lahm)
452255 Beit Sahur
452265 Ad Doha
452270 Al Khadr
452275 Ad Duheisha Camp
452280 Hindaza
452300 Artas
452385 Jannatah (Beit Falouh)
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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
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
503360 Khirbet Bir al ‘Idd
(continued)
67
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Percent of PCBS Localities Falling in Area C
Locality Code Locality Name
Percent of locality area in area C
10005 Zububa 0.556974
10010 Rummana 0.610357
10015 Ti’innik 0.847732
10020 At Tayba 0.469218
10025 ‘Arabbuna 0.709172
10030 Al Jalama 0.784978
10035 Silat al Harithiya 0.104891
10040 As Sa’aida 1
10045 ‘Anin 0.680747
10050 ‘Arrana 0.647262
10055 Deir Ghazala 0
10060 Faqqu’a 0.499992
10070 Khirbet Suruj 1
10080 Al Yamun 0.00349
10085 Umm ar Rihan 1
10095 Kafr Dan 0.062127
10105 Khirbet ‘Abdallah al Yunis 1
10115 Dhaher al Malih 1
10120 Barta’a ash Sharqiya 0.65301
10125 Al ‘Araqa 0
10135 Al Jameelat 0.116045
10140 Beit Qad 0
10145 Tura al Gharbiya 0.775159
10150 Tura ash Sharqiya 1
10155 Al Hashimiya 0
10165 Nazlat ash Sheikh Zeid 0.707183
503255 At Tuwani 1
10170 At Tarem 0.032801
10175 Khirbet al Muntar al Gharbiya 1
10180 Jenin 0.128925
10185 Jenin Camp 0
10190 Jalbun 0.507959
10195 ‘Aba 0.922042
10200 Khirbet Mas’ud 1
10205 Khirbet al Muntar ash Sharqiya 1
10210 Kafr Qud 0
10215 Deir Abu Da’if 0.138744
10220 Birqin 0
(Continued on next page)
Locality Code Locality Name
Percent of locality area in area C
10225 Umm Dar 0.114143
10230 Al Khuljan 0.013197
10235 Wad ad Dabi’ 1
10240 Dhaher al ‘Abed 1
10245 Zabda 0.573726
10265 Ya’bad 0.459703
10275 Kufeirit 0
10285 Imreiha 1
10295 Umm at Tut 0
10300 Ash Shuhada 0
10305 Jalqamus 0
10310 Al Mughayyir 0.061395
10315 Al Mutilla 0.879746
10320 Bir al Basha 0.906994
10335 Al Hafira 0.982463
10340 Qabatiya 0.158225
10370 Arraba 0.236119
10385 Telfit 0
10395 Mirka 0
10400 Wadi Du’oq 0.839635
10401 Fahma al Jadida 0.432312
10405 Raba 0.069825
10410 Al Mansura 0.966334
10415 Misliya 0.000741
10430 Al Jarba 0
10435 Az Zababida 0.279886
10445 Fahma 0
10460 Az Zawiya 0.136736
10465 Kafr Ra’i 0
10485 Al Kufeir 0
10495 Sir 0
10500 ‘Ajja 0.196207
10505 ‘Anza 0.688927
10510 Sanur 0.051339
10515 Ar Rama 0
10520 Meithalun 0
10565 Al Jadida 0
10585 al ‘Asa’asa 0.769768
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Locality Code Locality Name
Percent of locality area in area C
10590 Al ‘Attara 0.134759
10600 Siris 0
10605 Jaba’ 0.362854
10615 Al Fandaqumiya 0.075434
10625 Silat adh Dhahr 0.481306
50420 Bardala 0.822732
50450 ‘Ein el Beida 0.855151
50455 Kardala 1
50490 Ibziq 1
50525 Salhab 0
50535 ‘Aqqaba 0
50550 Tayasir 0.025551
50575 Ath Thaghra 0.274079
50580 Al Malih 1
50610 Tubas 0.035748
50650 Kashda 0
50656 Khirbet Yarza 1
50670 Ras al Far’a 0
50700 El Far’a Camp 0
50720 Khirbet ar Ras al Ahmar 1
50740 Wadi al Far’a 0
50755 Tammun 0.026124
50790 Khirbet ‘Atuf 0.378471
50871 Khirbet Humsa 1
100250 ‘Akkaba 0.879838
100290 Qaffin 0.670512
100330 Nazlat ‘Isa 0.624708
100345 An Nazla ash Sharqiya 0.114129
100350 Baqa ash Sharqiya 0.596284
100355 An Nazla al Wusta 0.835281
100380 An Nazla al Gharbiya 0.430429
100425 Zeita 0.627341
100475 ‘Illar 0.076417
100480 ‘Attil 0.376622
100530 Deir al Ghusun 0.156145
100545 Al Jarushiya 0.655159
100555 Masqufet al Hajj Mas’ud 0.893502
100570 Bal’a 0.033085
Locality Code Locality Name
Percent of locality area in area C
100595 Iktaba 0.435849
100620 Nur Shams Camp 0.290766
100635 Tulkarm Camp 0
100645 Tulkarm 0.40287
100665 ‘Anabta 0.066476
100690 Kafr al Labad 0.071841
100715 Al Hafasa 0.566164
100730 Ramin 0.049029
100735 Far’un 0.707448
100760 Shufa 0.818031
100780 Khirbet Jubara 1
100795 Saffarin 0.059585
100800 Beit Lid 0.023329
100815 Ar Ras 0.340461
100845 Kafr Sur 0.033191
100870 Kur 0
150660 Bizzariya 0.013181
150680 Burqa 0.46601
150695 Yasid 0
150705 Beit Imrin 0
150745 Nisf Jubeil 0
100895 Kafr Zibad 0
100915 Kafr ‘Abbush 0
50551 Al Farisiya 1
150765 Sabastiya 0.303854
150770 Ijnisinya 0
150775 Talluza 0
150785 An Naqura 0.140648
150805 Al Badhan 0
150810 Deir Sharaf 0.808779
150820 ‘Asira ash Shamaliya 0.038296
150825 An Nassariya 0
150835 Zawata 0.401299
150840 Al ‘Aqrabaniya 0.123505
150855 Qusin 0
150860 Beit Iba 0.134714
150865 Beit Hasan 0.026487
150875 Beit Wazan 0
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Locality Code Locality Name
Percent of locality area in area C
150880 ‘Ein Beit el Ma Camp 0
150885 ‘Ein Shibli 0.823524
150910 ‘Azmut 0.00569
150920 Nablus 0.147035
150930 ‘Askar Camp 0
150935 Deir al Hatab 0.061931
150950 Sarra 0.184369
150955 Salim 0.283442
150960 Balata Camp 0
150975 ‘Iraq Burin 0.121561
150990 Tell 0
151000 Beit Dajan 0.22824
151010 Rujeib 0.305072
151025 Kafr Qalil 0.425188
151030 Furush Beit Dajan 1
151050 Madama 0.188105
151080 Burin 0.51762
151090 Beit Furik 0.084128
151095 ‘Asira al Qibliya 0.051575
151135 ‘Awarta 0.25977
151160 ‘Urif 0
151180 Odala 0.067626
151185 Huwwara 0.468488
151195 ‘Einabus 0
151200 Yanun 0.626132
151215 Beita 0.031985
151230 Zeita Jamma’in 0
151245 Jamma’in 0.062859
151265 Osarin 0.061803
151270 Aqraba 0.1138
151285 Za’tara 1
151311 Tall al Khashaba 1
151325 Yatma 0.49083
151335 Qabalan 0.104007
151345 Jurish 0
151365 Qusra 0.142981
151375 Talfit 0
151380 As Sawiya 0.730612
(continued)
Locality Code Locality Name
Percent of locality area in area C
151385 Majdal Bani Fadil 0.580244
151405 Al Lubban ash Sharqiya 0.641847
151410 Qaryut 0.292052
151420 Jalud 0.206752
151435 ‘Ammuriya 0
151445 Duma 0.631792
200965 Baqat al Hatab 0.234412
503305 Khirbet Asafi 1
200995 Khirbet Sir 0
201005 ‘Arab ar Ramadin ash Shamali 1
201020 Immatin 0.478706
201055 An Nabi Elyas 0.806387
201100 ‘Azzun 0.666551
201115 ‘Isla 0.713731
201116 Arab Al-Khouleh 1
201075 ‘Izbat at Tabib 1
200985 Jayyus 0.194649
201040 Qalqiliya 0.52357
201070 ‘Arab Abu Farda 1
201105 ‘Arab ar Ramadin al Janu 1
201120 Wadi ar Rasha 1
201125 Habla 0.592784
201155 Ras ‘Atiya 0.642262
201130 Ras at Tira 0.736465
201170 Ad Dab’a 0.993623
201190 ‘Izbat Jal’ud 0.873275
201210 ‘Izbat Salman 0.726244
201205 Al Mudawwar 0.34479
201225 ‘Izbat al Ashqar 0.43464
201175 Kafr Thulth 0.439822
201260 Sanniriya 0.634202
201255 Beit Amin 0.222249
201280 ‘Azzun ‘Atma 0.928462
251250 Deir Istiya 0.427775
251275 Qarawat Bani Hassan 0.610683
251290 Qira 0.026028
251295 Kifl Haris 0.372453
251300 Marda 0.521183
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Locality Code Locality Name
Percent of locality area in area C
251305 Biddya 0.478562
251310 Haris 0.662607
251315 Yasuf 0.275009
251320 Mas-ha 0.659749
251330 Iskaka 0.286578
251340 Sarta 0.52984
251360 Az Zawiya 0.564966
251370 Salfit 0.106342
251395 Rafat 0.538223
251400 Bruqin 0.297632
251415 Farkha 0
251425 Kafr ad Dik 0.206389
251430 Deir Ballut 0.819458
251440 Khirbet Qeis 0
352075 An Nabi Musa 1
351116 Marj al Ghazal 0.669365
351140 Al Jiftlik 1
351510 Fasayil 0.708794
301455 Qarawat Bani Zeid (Bani Zeid * 0
301460 Bani Zeid ash Sharqiya 0
301470 Kafr ‘Ein 0
301480 Bani Zeid 0.000454
301485 ‘Abwein (Bani Zeid ash Sharqi* 0
301490 Turmus’ayya 0.178478
301495 Al Lubban al Gharbi 0.685254
301500 Sinjil 0.262359
301505 Deir as Sudan 0.073388
301515 Rantis 0.48314
301520 Jilijliya 0
301525 ‘Ajjul 0.019558
301530 Al Mughayyir 0.568513
301535 ‘Abud 0.558787
301540 An Nabi Salih (Bani Zeid al g* 0.624548
301545 Khirbet Abu Falah 0
301550 Umm Safa 0.717426
301555 Al Mazra’a ash Sharqiya 0.022901
301560 Deir Nidham 0.919499
301565 ‘Atara 0.034948
Locality Code Locality Name
Percent of locality area in area C
301570 Deir Abu Mash’al 0.067818
301575 Jibiya 0
301585 Burham 0
301590 Kafr Malik 0.804803
301595 Shuqba 0.737356
301600 Kobar 0.105949
301605 Qibya 0.793831
503150 Umm Lasafa 0.452902
503170 Al Burj 0.436323
503215 Al Karmil 0.232089
503225 Khallet Salih 0.191997
503245 Adh Dhahiriya 0.016933
503260 Ma’in 0.696753
503295 ‘Anab al Kabir 0.286103
503310 Mantiqat Shi’b al Batin 1
503320 As Samu’ 0.14376
503321 Wadi Al Amayer 0.409584
503325 Khirbet Tawil ash Shih 1
503335 Ar Ramadin 0.624175
503345 Maghayir al ‘Abeed 1
503380 Imneizil 1
503405 ‘Arab al Fureijat 0.999999
200905 Falamya 0.845581
200925 Kafr Qaddum 0.193679
200945 Jit 0.804226
301610 Silwad 0.324286
301615 Yabrud 0.204477
301620 AL-Itihad 0.334802
301625 Shabtin 0.450997
301635 Bir Zeit 0.065375
301636 AL-Doha 0
301640 ‘Ein Siniya 0.077432
301645 Silwad Camp 0
301650 Deir Jarir 0.121826
301660 Deir ‘Ammar Camp 0
301665 Budrus 0.676608
301670 AL-Zaytouneh 0.079389
502830 Khallet Al Masafer 0.144488
(continued)
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Locality Code Locality Name
Percent of locality area in area C
301675 Jifna 0.001384
301680 Dura al Qar’ 0.658896
301685 At Tayba 0.178973
301700 Al Jalazun Camp 0.173785
301705 Abu Qash 0
301710 Deir Qaddis 0.593384
301715 Ni’lin 0.787901
301720 ‘Ein Yabrud 0.415723
301725 Kharbatha Bani Harith 0.750441
301730 Ras Karkar 0.596629
301735 Surda 0.000004
301740 Al Janiya 0.418553
301745 Al Midya 0.905015
301750 Rammun 0.229752
301755 Kafr Ni’ma 0.078023
301760 Bil’in 0.060017
301765 Beitin 0.580424
301770 ‘Ein Qiniya 0.768954
301775 Badiw al Mu’arrajat 1
301780 Deir Ibzi’ 0.28905
301785 Deir Dibwan 0.203989
301790 Al Bira 0.289922
301800 ‘Ein ‘Arik 0.827866
301805 Saffa 0.480927
301810 Ramallah 0.063411
301815 Burqa 0.570791
301820 Beit ‘Ur at Tahta 0.646639
301825 Beituniya 0.356308
301830 Al Am’ari Camp 0
301835 Qaddura Camp 0
301850 Beit Sira 0.698271
301855 Kharbatha al Misbah 0.681369
301860 Beit ‘Ur al Fauqa 0.745514
301890 At Tira 0.771776
301895 Beit Liqya 0.659448
301925 Beit Nuba 1
351045 Marj Na’ja 0.562792
351110 Az Zubeidat 0.910022
(continued)
Locality Code Locality Name
Percent of locality area in area C
351690 Al ‘Auja 0.282275
351970 Deir al Qilt 1
351840 An Nuwei’ma 0.294061
351975 Aqbat Jaber Camp 0.127579
352021 Deir Hajla 1
351845 ‘Ein ad Duyuk al Foqa 0.151921
351865 ‘Ein as Sultan Camp 0
351920 Jericho (Ariha) 0.183116
100440 Seida 0
452185 ‘Ayda Camp 0.197781
452195 Al ‘Aza Camp 0
452205 Al Haddadiya 0
452209 Bir onah 0.784869
452210 Beit Jala 0.393844
452225 Dar Salah 0.024182
452230 Husan 0.439333
452180 Al ‘Ubeidiya 0.193905
452208 Khallet Hamameh 0.415654
452400 Wadi Rahhal 0.458777
452415 Khallet Sakariya 1
452235 Wadi Fukin 0.75926
452240 Bethlehem (Beit Lahm) 0.106217
452255 Beit Sahur 0.324249
452265 Ad Doha 0
452270 Al Khadr 0.576425
452275 Ad Duheisha Camp 0
452280 Hindaza 0.024303
452285 Ash Shawawra 0.233524
452300 Artas 0.269798
452325 Nahhalin 0.316471
452335 Beit Ta’mir 0.109354
452345 Khallet al Louza 0.316089
452355 Al Jab’a 0.767717
452360 Za’tara 0.252401
452385 Jannatah (Beit Falouh) 0.472378
452405 Jubbet adh Dhib 1
452430 Khallet al Haddad 0
452465 Khallet ‘Afana 1
(Continued on next page)
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Locality Code Locality Name
Percent of locality area in area C
452480 Umm Salamuna 0.813142
452490 Al Manshiya 0.917476
452495 Tuqu’ 0.573176
452500 Marah Rabah 0.015302
452440 Al Ma’sara 0.796266
452445 Wadi an Nis 0.862292
452565 Kisan 0.878482
452535 Al Maniya 0.820975
452660 ‘Arab ar Rashayida 0
502435 Khirbet ad Deir 0.884829
502450 Surif 0.119912
502530 Al ‘Arrub Camp 0.17493
502540 Beit Ummar 0.404363
502545 Jala 0.018673
502550 Hitta 0
502555 Shuyukh al ‘Arrub 0.137773
502560 Kharas 0.14251
502575 Umm al Butm 1
502580 Hamrush 0.020239
502585 Nuba 0
502615 Beit Ula 0.027619
502620 Sa’ir 0.165058
502630 Halhul 0.267518
502925 Deir al ‘Asal at Tahta 0
502935 Al Heila 0
502940 Wadi ash Shajina 0.710463
502950 As Sura 0
502955 Deir Razih 0.808771
502960 Ar Rihiya 0
502965 Zif 0.980425
502970 Deir al ‘Asal al Fauqa 0.249479
502975 Khallet al ‘Aqed 0
502980 Imreish 0.110137
503010 Beit ar Rush at Tahta 0.000105
503040 Hadab al ‘Alaqa 0
503075 Beit Mirsim 0.359482
503090 Beit ar Rush al Fauqa 0.346888
503095 Karma 0.6694
Locality Code Locality Name
Percent of locality area in area C
503100 Beit ‘Amra 0.103559
503105 Om Adaraj 0.601127
503110 Wadi al Kilab 0
503111 Om Ashoqhan 0.215132
503115 Khallet al Maiyya 0.144908
503116 Kheroshewesh Wal Hadadeyah 0
503117 Om Al Amad (Sahel Wadi Elma) 0.169011
503120 Yatta 0.024115
503125 Ad Deirat 0.998857
503126 Kashem Adaraj (Al-Hathaleen 1
502635 Ash Shuyukh 0.295323
502640 Tarqumiya 0.51846
502655 Beit Kahil 0.141422
502681 Qla a Zeta 0.887566
502685 Idhna 0.553747
502750 Taffuh 0
502765 Beit Maqdum 0.09723
502778 Al Baqa 1
502780 Hebron (Al Khalil) 0.298488
502781 Al Bowereh (Aqabat Injeleh) 0.36633
502782 Khallet Edar 0.792711
502810 Deir Samit 0.48028
502815 Bani Na’im 0.158967
502835 Beit ‘Awwa 0.276139
502840 Dura 0.047723
502855 Qalqas 0.355364
502865 Khirbet Salama 0.443132
502870 Wadi ‘Ubeid 0
502875 Fuqeiqis 1
502895 Kharsa 0.001278
502900 Turrama 0
502905 Al Fawwar Camp 0.000416
502910 Al Majd 0.235582
502915 Marah al Baqqar 0
502920 Hadab al Fawwar 0.253724
503135 Kurza 0
503145 Rabud 0.471884
502680 Beit ‘Einun 0.732306
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Appendices
Locality Code Locality Name
Percent of locality area in area C
503210 Um Al Khair 1
201015 Far’ata 0
201035 Al Funduq 0.902285
201085 Jinsafut 0.77182
201065 Kafr Laqif 0.603895
200970 Hajja 0.659266
251355 ‘Izbat Abu Adam 1
100710 Kafa 0.919445
100900 Kafr Jammal 0.150686
50560 Al ‘Aqaba 1
452170 Al Walaja 0.955492
452175 Battir 0.688477
Locality Code Locality Name
Percent of locality area in area C
452190 khallet an Nu’man 1
452200 Al Khas 0.328656
452460 Jurat ash Sham’a 0.614824
452470 Marah Ma’alla 0.911323
452525 Beit Fajjar 0.172462
151220 Ar Rajman 0.999704
502860 As Sikka 0.218311
503005 Al Buweib 0.126362
503265 An Najada 1
503350 Khirbet al Fakheit 0.994047
503360 Khirbet Bir al ‘Idd 1
503375 Khirbet Zanuta 1
(continued)
