Impact of Russian Ukrainian War on Households in Egypt, IRUW 2024
Egypt, Arab Rep., 2024
Economic Research Forum
Identification
Survey ID Number
EGY-IRUW-2024-V1
Title
Impact of Russian Ukrainian War on Households in Egypt, IRUW 2024
Country
| Name | Country code |
|---|---|
| Egypt | EGY |
Series Information
Impact of Russian Ukrainian War on Households in Egypt is one of two comparative surveys, that include Egypt, and Kenya. The data were collected at one time in 2024 in the two countries.
Abstract
The war in Ukraine, which began in February 2022, has intensified several preexisting adverse global economic trends, including rising inflation, extreme poverty, increasing food insecurity, deglobalization, and worsening environmental degradation. Fuel and food shortages resulting from the war have aggravated post-pandemic inflation, which had already reached double digits in many parts of the world, eroding household purchasing power, hitting the most vulnerable hardest, and adding to social pressures. The reduction in exports of grains and oilseeds due to the conflict in Ukraine has impacted the consumption of other, more nutritious foods. The Middle East and North Africa (MENA) region is particularly vulnerable and volatile in response to these global food market disruptions, and as the crisis continues, its impact on African economies has deepened.
To better understand the shock's impact, ERF carried out a phone survey of approximately 2,000 households in Egypt and Kenya, focusing on how the war has affected them. The main objectives of this survey are to:
· Identify the factors contributing to food insecurity, particularly in female-headed households.
· Investigate how various households have coped with rising inflation and the scarcity of certain goods.
· Determine the extent to which households have benefited from government support programs.
The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between the two countries (Egypt and Kenya). Both surveys incorporate similar survey designs, with data on households and individuals within those households.
To better understand the shock's impact, ERF carried out a phone survey of approximately 2,000 households in Egypt and Kenya, focusing on how the war has affected them. The main objectives of this survey are to:
· Identify the factors contributing to food insecurity, particularly in female-headed households.
· Investigate how various households have coped with rising inflation and the scarcity of certain goods.
· Determine the extent to which households have benefited from government support programs.
The harmonization was designed to create comparable data that can facilitate cross-country and comparative research between the two countries (Egypt and Kenya). Both surveys incorporate similar survey designs, with data on households and individuals within those households.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Household and Individuals
Version
Version Description
V1.0: Version 1 of the Impact of Russian Ukrainian War on households surveys prepared for public dissemination.
Production
Date
2024-08
Scope
Notes
The ERF Impact of Russian Ukranian War on Households Survey includes a questionnaire that covers the demographic and household characteristics, education and children, labor market status, food security, income, social safety net, employment and unemployment detection, attitudes towards risks. Additionally, it includes:
• A worker module on occupation, job formality, impact of the war employment
• A farmer module on crops, inputs, harvest, prices, markets ...etc.
• A household enterprise module on industry, employment, sales/revenue, impact of the war on business,...etc.
• A worker module on occupation, job formality, impact of the war employment
• A farmer module on crops, inputs, harvest, prices, markets ...etc.
• A household enterprise module on industry, employment, sales/revenue, impact of the war on business,...etc.
Coverage
Geographic Coverage
National
Universe
The survey covered a national random sample of mobile phone users aged 18-64.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Economic Research Forum | ERF |
Producers
| Name |
|---|
| Economic Research Forum |
Funding Agency/Sponsor
| Name | Abbreviation |
|---|---|
| International Development Research Centre | IDRC |
Sampling
Sampling Procedure
The sample universe for the household survey was mobile phone users aged 18-64. Random digit dialing (RDD), within the range of valid numbers, was used, with up to three attempts if a phone number was not picked up/answered, was disconnected or busy, or picked up but could not complete the interview at that time. Samples were stratified by country-specific market shares of mobile operators. The sample is designed to cover at least 2000 unique households and individuals. A question is included in the survey for the number of phone numbers within the household to weight appropriately. Further weighting of the household and individual samples was done to reflect the demographic composition of the population as obtained by the most recent publicly available data with individual phone ownership and relevant demographic and labour market characteristics. In the individual interview, respondents who are employers or self-employed were asked to respond to either the household enterprise or farmer modules.
Response Rate
The total response rate is 18%.
It's important to note that our response rates exclude phones out of service, those disconnected or busy after repeated attempts, and individuals' ineligible for the survey. Responses reflect successful completions, regardless of whether they occurred on the first, second, or third attempt.
It's important to note that our response rates exclude phones out of service, those disconnected or busy after repeated attempts, and individuals' ineligible for the survey. Responses reflect successful completions, regardless of whether they occurred on the first, second, or third attempt.
Weighting
To reduce bias in a number of observable dimensions, inverse probability weighting was applied. The weights were created at two levels: Individual and household. The weights had the following inputs:
- Phone operators and their market shares provided by the data collection company
- Number of phones by operator for individuals (weighting for individuals) and household members (weighting for households).
- Representative data with comparable demographic and household characteristics for weighting non-response.
Household and individual weights were all winsorized at the 95th percentile to ensure that no outlier weight drove statistics.
Individual weights should be used for all analyses where the outcome is at the individual level. For household-level results (e.g. household income), the weighting used to generalize to households (e.g. X% of households are food insecure).
Variable names:
• Individual weight: ind_wt
• Household weight: hh_wt
Note: there is more details on the weights and sampling at the “ Impact of Russian Ukrainian War on Households in Egypt & Kenya_sampling Design” document at the documentation materials.
- Phone operators and their market shares provided by the data collection company
- Number of phones by operator for individuals (weighting for individuals) and household members (weighting for households).
- Representative data with comparable demographic and household characteristics for weighting non-response.
Household and individual weights were all winsorized at the 95th percentile to ensure that no outlier weight drove statistics.
Individual weights should be used for all analyses where the outcome is at the individual level. For household-level results (e.g. household income), the weighting used to generalize to households (e.g. X% of households are food insecure).
Variable names:
• Individual weight: ind_wt
• Household weight: hh_wt
Note: there is more details on the weights and sampling at the “ Impact of Russian Ukrainian War on Households in Egypt & Kenya_sampling Design” document at the documentation materials.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2024-03-26 | 2024-05-17 |
Data Collection Mode
Computer Assisted Telephone Interview [cati]
Data Collectors
| Name | Abbreviation |
|---|---|
| PHI Field & Tab | PHI |
Access policy
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| Economic Research Forum | ERF | erfdataportal@erf.org.eg | www.erf.org.eg |
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Economic Research Forum (ERF) - 21 Al-Sad Al-Aaly St., Dokki, Giza, Egypt | ERF | erfdataportal@erf.org.eg | www.erf.org.eg |
Confidentiality
To access the micro-data, researchers are required to register on the ERF website and comply with the data access agreement. The data will be used only for scholarly, research, or educational purposes. Users are prohibited from using data acquired from the Economic Research Forum in the pursuit of any commercial or private ventures.
Access conditions
Licensed datasets, accessible under conditions.
Citation requirements
The users should cite the Economic Research Forum as follows:
"OAMDI, 2024, Impact of Russian Ukranian War on Households Survey (IRUW) http://www.erfdataportal.com/index.php/catalog. Version 1.0 of the licensed data files; Egypt-IRUW-2024. Egypt: Economic Research Forum (ERF).”
"OAMDI, 2024, Impact of Russian Ukranian War on Households Survey (IRUW) http://www.erfdataportal.com/index.php/catalog. Version 1.0 of the licensed data files; Egypt-IRUW-2024. Egypt: Economic Research Forum (ERF).”
Disclaimer and copyrights
Disclaimer
The Economic Research Forum has granted the researcher access to relevant data following exhaustive efforts to protect the confidentiality of individual data. The researcher is solely responsible for any analysis or conclusions drawn from available data.
Copyright
(c) 2024, Economic Research Forum
Metadata production
DDI Document ID
DDI-EGY-IRUW-2024-V1
Producers
| Name | Abbreviation |
|---|---|
| Economic Research Forum | ERF |
Date of Metadata Production
2024-08
DDI Document version
Version 1