Household Expenditure and Consumption Survey, 2023, Main Findings of Living Standards in the West Bank (Expenditure, Consumption and Poverty), 2023

The hhdata_wb file (West Bank, full year 2023) provides a comprehensive snapshot of each sampled household's social, economic and living‐condition profile. It covers all 4,992 households selected via a two‐stage stratified cluster sample across the 11 West Bank governorates, with data collection spanning January 2 through December 10, 2023. Each record-identified by ID00-captures not only basic demographics but also detailed information on housing quality, utility access, asset ownership, social assistance receipt and exposure to shocks. Together, these data form the household‐level backbone for linking to item‐level expenditure records and individual‐level demographic and income datasets.
Sampling and weighting procedures in hhdata_wb ensure that findings are representative of the entire West Bank population. Enumeration areas (primary sampling units) were selected with probability proportional to size, then 12 households per area were systematically chosen. Household weights adjust for both stages of selection, non‐response and post‐stratification to mid‐2023 population estimates by governorate and locality type (urban, rural, camp). This means any aggregate or disaggregated estimate-whether average food expenditure or the proportion of households without continuous water-can be reliably projected to the regional or governorate level.
At the core of the file are identification and composition variables
ID00 links to item‐level expenditure files;
ID01, region and loc_type pinpoint governorate (e.g. 15 = Nablus), West Bank vs. Jerusalem J1, and urban/rural/camp status;
QC3-QC5 record total, male and female household counts;
D0 indicates which household member (line number) provided the housing data.
These fields let you immediately filter or weight analyses by geography, settlement type, or household size.
The file's housing and infrastructure section (variables H1-H35) runs from dwelling type (villa vs. tent vs. caravan) and occupancy status, through detailed tenure categories (owned, rented, free of charge, in exchange for work), to how ownership was acquired and financed (inheritance, purchase, loan source, monthly installments, completed payments). It then documents structural features-age of building, wall/roof/floor materials, floor and land areas and counts of rooms, bathrooms, kitchens and garages-and utility connections: whether water and electricity reach kitchens, bathrooms or toilets; main sources of water (public network, Mekorot, well, tanker, bottled), interruptions and hours of availability; electricity sources (public grid, generator, solar) and outages; sewage disposal type; solid waste methods; and even whether soap and water are available for handwashing.
Beyond the physical home, hhdata_wb traces households' broader resilience and vulnerability. Variables A01-A03 capture receipt of social assistance (food, cash, medical aid, housing support, orphan sponsorship, etc.), its perceived importance, value, source and recurrence pattern. C-series questions record remittance receipts and uses, loans taken in the past decade, their purposes and repayment status, plus shocks such as property loss, theft, political arrests or movement restrictions. This rich information allows analysis of how households cope with income shortfalls, how social protection programs reach different population groups, and the interplay between assistance, debt and living standards.

West Bank (Full Year 2023 micro-data)
Weight variables: weight_wby_trim (final calibrated & trimmed probability weight for Q1-Q4) and its mean-one counterpart rw_wb. Use either for every 2023 West-Bank-only estimate.

The indata_wb file contains one record per person in sampled West Bank households for 2023, beginning with identifiers (household ID, governorate code, region, individual number and combined string ID), relationship to head, sex, birth date and age, and refugee status; it then captures health coverage (general and by source), chronic‐illness treatment, functional difficulties (seeing, hearing, mobility, cognition, communication, self‐care), recent service use (provider, maternal and child immunizations), and dietary indicators (daily fruit/vegetable, meat, and meal frequency). For women aged 15-49 it includes fertility and prenatal care details, and for all individuals it records educational history (attendance, highest grade, school type, failures, class size), labour‐force engagement (activity status, hours, job search, tenure, contract type, sector, months worked, work injuries, ISIC occupation/activity, and employer benefits), plus final person weights and locality type for population estimation.

West Bank (Full Year 2023 micro-data)
Weight variables: weight_wby_trim (final calibrated & trimmed probability weight for Q1-Q4) and its mean-one counterpart rw_wb. Use either for every 2023 West-Bank-only estimate.

This file contains item-level household expenditure and consumption data for the West Bank sample of PECS 2023. Each record represents one household (ID00) x one LocalCode-based item (ItemCode). Values are normalised to a single reference month so that totals can be directly summed across items and households. For food items the file provides weekly values (week_1 - week_4), monthly total weight of the item -kg or liter mostly- (total_weight_all_weeks) and monthly value (total_all_weeks). For other non-food goods the monthly value is derived from the original reporting period (monthly, yearly, or 3-year personal-transport). The file is designed to link, via ID00, with the accompanying household datasets.

West Bank (Full Year 2023 micro-data)
Weight variables: weight_wby_trim (final calibrated & trimmed probability weight for Q1-Q4) and its mean-one counterpart rw_wb. Use either for every 2023 West-Bank-only estimate.

The main_wb_Eng table is a household‐level summary of consumption and expenditure for all West Bank sample households over the full 2023 survey year. Each row corresponds to a unique household (ID00) and records its location (Governorate code, loc_type), composition (QC3 total members, num_children, num_adults), and design weight (rw_wb). Fiscal detail is captured in 30 “grp” columns-grp1 through grp30-each representing the monthly NIS value spent (or consumed) on a specific COICOP‐based category (e.g. grp1: cereals & bread; grp18a: transport; grp26: imputed rent for owner‐occupied housing; grp30: social protection). Four high‐level indicators-food_expenditure (sum of grp1-grp11), food_consumption (adds self-produced food grp12), total_expenditure, and total_consumption-distill these granular groups into broad measures of household spending and consumption.

Aggregation methodology
A. From transaction records to “grp” categories
Classify each raw transaction in the detailed spending file by ExpensesType:
“daily expenses” (purchased day-by-day),
“monthly expenses” (reference period = month),
“yearly expenses” (reference period = year), or
“Personal Transportation” (reference period = 3 years).
Daily items are first summed per household (ID00), per ItemCode and calendar Day. They are then binned into four “week” intervals (days 1-7 → week_1; 8-14 → week_2; etc.), summed again to yield each household's total_price in week_1…week_4, and finally collapsed into a single monthly equivalent total_all_weeks = week_1 + … + week_4.
Monthly, yearly, and transport items are summed per household-item, then converted to a common monthly equivalent:
monthly → leave as is;
yearly → divide total by 12;
Personal Transportation → divide total by 36.
Stack the two streams (daily vs. others) into one table.
Map each ItemCode to one of 30 groups (grp1…grp30) via R's case_when(...) using the PCBS code ranges (e.g. 101-144 → grp1; 3401-3420 → grp18a; 2220-2222 → grp25; etc.) and drop out‐of-scope codes.
B. Rent imputation & merging
Owner-occupied households (tenure H3 ∈ {1,4,5,6}) bring in their imputed rent (H10_1) in JOD or USD. This is converted to NIS by applying the survey-month‐specific exchange rate (JOD → NIS; USD → NIS) and stored as H10_1_NIS. A new “grp26” row is created for each household with total_all_weeks = H10_1_NIS.
Tenants (H3 ∈ {2,3}) bring their actual rent (H9_1) similarly converted to H9_1_NIS and added into group “grp14” (housing).
Both rent groups are merged into the stacked table, with missing values zero-filled.
C. Final household-level sums
Complete grid of all household × grp combinations is generated; any missing total_all_weeks or total_price is set to zero.
Per-household, per-grp sums of total_all_weeks become the final grp1…grp30 columns in main_wb_Eng.
D. High-level aggregates
food_expenditure = ∑ grp1 through grp11
food_consumption = food_expenditure + grp12 (self-produced food)
total_expenditure = ∑ grp1-grp11, grp13-grp24, grp27-grp29, and grp30
total_consumption = ∑ grp1-grp12, grp13-grp25, grp26 (imputed rent), and grp30
All sums use the monthly‐equivalent total_all_weeks per group and are joined back onto the hhdata_wb frame via ID00.
This pipeline ensures that every household's diverse spending rhythms-daily purchases, monthly bills, annual/three-year outlays-are harmonized into a consistent monthly base and organized into analytically coherent consumption and expenditure categories.

West Bank (Full Year 2023 micro-data)
Weight variables: weight_wby_trim (final calibrated & trimmed probability weight for Q1-Q4) and its mean-one counterpart rw_wb. Use either for every 2023 West-Bank-only estimate.

mincome_wb dataset
This dataset contains yearly income data at the individual level, with a unique identifier for each person (ind_ID) and their household (ID00). It includes:
Geographic Information:
ID01: Governorate code with labels (e.g., Jenin, Nablus).
region: Region (West Bank, Jerusalem J1).
Household Info:
QC3: Number of household members.
Raw Income Components (Original Currency):
Variables from I14_2_1 to I14_2_11, including cash and in-kind grants from government/non-government, remittances (domestic and abroad), rental income, investment income, etc.

West Bank (Full Year 2023 micro-data)
Weight variables: weight_wby_trim (final calibrated & trimmed probability weight for Q1-Q4) and its mean-one counterpart rw_wb. Use either for every 2023 West-Bank-only estimate.

yincome_wb dataset
This dataset contains yearly income data at the individual level, with a unique identifier for each person (ind_ID) and their household (ID00). It includes:
Geographic Information:
ID01: Governorate code with labels (e.g., Jenin, Nablus).
region: Region (West Bank, Jerusalem J1).
Household Info:
QC3: Number of household members.
Raw Income Components (Original Currency):
Variables from I15_2_1 to I15_2_11, including cash and in-kind grants from government/non-government, remittances (domestic and abroad), rental income, investment income, etc.
Total Yearly Income:
I15_Total: Total income over the past 12 months in original currency.
I15_1: Currency type used (NIS, JD, USD, Euro).
Converted Income in NIS:
Each component has a counterpart variable (e.g., I15_2_1_NIS) representing the same income converted to New Israeli Shekels (NIS) using predefined exchange rates.
I15_Total_NIS: Total yearly income in NIS.
Key Calculated Variables:
ind_ID: Created by combining ID00 and D1 to uniquely identify individuals within households.
Converted Values: All income components and total income were converted to NIS using monthly or average exchange rates.
These conversions were used to enable consistent aggregation and comparison across households.
Aggregation/Validation Notes:
If used in R, income values in NIS can be summed by household using group_by(ID00) to get household-level income.
Can be further linked with other datasets (e.g., consumption) via ID00 or ind_ID.

West Bank (Full Year 2023 micro-data)
Weight variables: weight_wby_trim (final calibrated & trimmed probability weight for Q1-Q4) and its mean-one counterpart rw_wb. Use either for every 2023 West-Bank-only estimate.

hhdata_3Qs ( Household-level file - Palestine, first three quarters 2023 )
This file gives a complete social, economic and living-conditions profile for every sampled household in the West Bank, Gaza Strip and Jerusalem J1 during Q1-Q3 2023. The two-stage stratified cluster design drew 5,304 units from the 2017 census frame; enumeration-area primary units were selected with probability proportional to size, and 12 dwellings were then chosen systematically inside each area. Fieldwork ran from 2 January to 6 October 2023, when Gaza interviewing was halted by the war; all West Bank interviewing continued through 10 December, so the file nevertheless represents the population of Palestine for the first nine months of the year. Each record, keyed by ID00, holds-
Identifiers (ID00, ID01, region, loc_type) that pinpoint governorate, region (WB, GS, J1) and urban/rural/camp status, and link to the other survey files;
Household composition counts (QC3-QC5) and the line number of the responding member;
Housing & infrastructure variables (H1-H35) covering tenure, construction materials, floor area, room counts, water and electricity sources and outages, sewage disposal, solid-waste arrangements and hand-washing facilities;
Social-protection & coping modules (A-, C- prefixes) that detail all cash/food/medical assistance received, its frequency and value, remittances, loans, shocks experienced and coping strategies.
Sampling weights embedded in hhdata_3Qs adjust for both selection stages, non-response and post-stratification to mid-2023 household totals by governorate and locality type, so any estimate derived from the file ­- from mean housing density in Gaza camps to the share of J1 households receiving food vouchers - can be projected to the nine-month Palestinian population.


West Bank + Gaza (First Three Quarters 2023 microdata)
Weight variables: weight3q_trim (final calibrated & trimmed probability weight for the pooled Q1-Q3 sample) and its mean-one counterpart rw. Because Q4 fieldwork in Gaza was cancelled due to the ongoing Israeli aggression, seasonality could not be modelled for Gaza; therefore, key expenditure- and consumption-based indicators were released only for the West Bank, not for Gaza or the national level.

indata_3Qs ( Person-level file - Palestine, first three quarters 2023 )
indata_3Qs holds a line-for-line roster of every individual who lived in the sample households during Q1-Q3 2023. For each person it records:
Core identifiers (ID00, ID01, region, D1, ind_ID) and relationship to head, sex, date of birth, age and refugee status;
Health coverage & status (five insurance sources, chronic-illness treatment, Washington-Group functional-difficulty matrix, six-month health-service use, provider, maternal care, child immunizations, basic diet indicators);
Fertility (prenatal care, births, infant deaths) for women 15-49;
Education (attendance history, highest grade, school type, failures, class size, literacy);
Labour-force details (current activity status, usual hours, job-search behaviour, unemployment duration, status in employment, workplace location, contract type, schedule, sector, months worked, occupational & industrial codes, injuries, employer benefits);
Final person weight (FWP) and locality type.

West Bank + Gaza (First Three Quarters 2023 microdata)
Weight variables: weight3q_trim (final calibrated & trimmed probability weight for the pooled Q1-Q3 sample) and its mean-one counterpart rw. Because Q4 fieldwork in Gaza was cancelled due to the ongoing Israeli aggression, seasonality could not be modelled for Gaza; therefore, key expenditure- and consumption-based indicators were released only for the West Bank, not for Gaza or the national level.

items_3Qs ( Item-level diary file - Palestine, first three quarters 2023 )
each record in items_3Qs represents a unique household (ID00) × one expenditure/consumption item (ItemCode) for the entire survey month that the diary covered. Weekly food figures are stored as four columns (week_1…week_4), which together sum to the single-month totals (total_weight_all_weeks, total_all_weeks). Non-food items appear once per household-item pair with their value already converted to a monthly equivalent (from a monthly, annual, or 3-year reference period, as appropriate). The dataset is therefore a pure ID00 × ItemCode matrix, designed to link to hhdata_3Qs and main_3Qs via ID00 for detailed budget analysis across Palestine.

West Bank + Gaza (First Three Quarters 2023 microdata)
Weight variables: weight3q_trim (final calibrated & trimmed probability weight for the pooled Q1-Q3 sample) and its mean-one counterpart rw. Because Q4 fieldwork in Gaza was cancelled due to the ongoing Israeli aggression, seasonality could not be modelled for Gaza; therefore, key expenditure- and consumption-based indicators were released only for the West Bank, not for Gaza or the national level.

main_3Qs ( Household consumption & expenditure summary - Palestine, first three quarters 2023 )
main_3Qs collapses every transaction reported by the diary into a single monthly-equivalent record per household. Each row lists location (ID01, loc_type), composition (QC3, num_children, num_adults), design weight (rw_pal) and 30 COICOP-based columns grp1…grp30, each giving the household's average‐month value in New Israeli Shekels:
grp1 cereals-bread … grp11 prepared meals,
grp12 self-produced food,
non-food groups grp13…grp25,
grp26 imputed rent for owner-occupied dwellings,
fiscal groups grp27 cash transfers, grp28 taxes, grp29 other non-consumption,
grp30 social-protection services.
Four headline indicators follow: food_expenditure, food_consumption, total_expenditure, total_consumption. Construction steps (classification of transactions, conversion of yearly and 3-year items to monthly values, rent imputation, zero-filling of absent groups, and group summations) are identical to the West Bank pipeline but applied to the full Palestine sample up to October 2023.

West Bank + Gaza (First Three Quarters 2023 microdata)
Weight variables: weight3q_trim (final calibrated & trimmed probability weight for the pooled Q1-Q3 sample) and its mean-one counterpart rw. Because Q4 fieldwork in Gaza was cancelled due to the ongoing Israeli aggression, seasonality could not be modelled for Gaza; therefore, key expenditure- and consumption-based indicators were released only for the West Bank, not for Gaza or the national level.

mincome_3Qs ( Monthly income file - Palestine, first three quarters 2023 )
For every adult who reported labour or retirement income in the reference survey month, mincome_3Qs lists nine cash or in-kind components (I14_2_1 … I14_2_9) in both original currency and shekel-converted form (_NIS suffix), plus the sum (I14_Total, I14_Total_NIS). Identifiers (ID00, ID01, region, ind_ID) permit direct merging with the person roster. Currency conversion uses the month-specific JD → NIS and USD → NIS rates published by the Palestine Monetary Authority.

West Bank + Gaza (First Three Quarters 2023 microdata)
Weight variables: weight3q_trim (final calibrated & trimmed probability weight for the pooled Q1-Q3 sample) and its mean-one counterpart rw. Because Q4 fieldwork in Gaza was cancelled due to the ongoing Israeli aggression, seasonality could not be modelled for Gaza; therefore, key expenditure- and consumption-based indicators were released only for the West Bank, not for Gaza or the national level.

yincome_3Qs ( Yearly income file - Palestine, first three quarters 2023 )
yincome_3Qs mirrors the structure of mincome_3Qs but covers the entire 12 months preceding interview. It records eleven annual income streams (I15_2_1 … I15_2_11) - wages, self-employment surplus, domestic and foreign remittances, property & investment returns, pensions, social transfers - in original currency and shekel terms, plus totals (I15_Total, I15_Total_NIS). The unique person key (ind_ID) lets researchers aggregate to household level or correlate yearly

West Bank + Gaza (First Three Quarters 2023 microdata)
Weight variables: weight3q_trim (final calibrated & trimmed probability weight for the pooled Q1-Q3 sample) and its mean-one counterpart rw. Because Q4 fieldwork in Gaza was cancelled due to the ongoing Israeli aggression, seasonality could not be modelled for Gaza; therefore, key expenditure- and consumption-based indicators were released only for the West Bank, not for Gaza or the national level.