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

West Bank and Gaza, 2023 - 2024

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PSE-PCBS-PECS-2023-V1.0

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Palestinian Central Bureau of Statistics

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Household Expenditure and Consumption

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DDI/XML JSON

Created on

Aug 11, 2025

Last modified

Aug 11, 2025

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Data file: Main Dataset for the West Bank, 2023

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.