Feed the Future Nigeria Livelihoods Project - Baseline Survey, 2015
Nigeria, 2015
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Reference ID
NGA_2015_FNLP-BS_v01_EN_M_v01_A_OCS
Producer(s)
Gautam Bastian, Sreelakshmi Papineni
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Metadata
Created on
Sep 10, 2020
Last modified
Nov 08, 2022
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Identification
Survey ID Number
NGA_2015_FNLP-BS_v01_EN_M_v01_A_OCS
Title
Feed the Future Nigeria Livelihoods Project - Baseline Survey, 2015
Country
| Name | Country code |
|---|---|
| Nigeria | NGA |
Study type
Agricultural Survey [ag/oth]
Abstract
Feed the Future Nigeria Livelihoods Project (FNLP) is a multi-component development project based on the graduation model pioneered by Bangladesh Rural Advancement Committee (BRAC) that intends to help 42,000 very poor households across rural communities of northern Nigeria’s Sokoto and Kebbi states, and the Federal Capital Territory (FCT). FNLP is a 5-year program implemented by Catholic Relief Services (CRS). Both the program and the impact evaluation are funded by United States Agency for International Development (USAID).
This program approach is founded on an agriculture-led growth strategy that is expected to help vulnerable families diversify their income and grow assets while the community is strengthened by improving nutrition, water sanitation, and hygiene. The most vulnerable families receive cash transfers. A caseworker-led livelihood mentoring scheme also matches households with the resources they need to engage effectively in the local economy and break free from the cycle of poverty and malnutrition.
The impact evaluation, led by The World Bank’s Africa Gender Innovation Lab (GIL), is being conducted in Kebbi state in North-West Nigeria and will evaluate the impact of the overall program as well as two experiments that focus on the impact of the cash transfers and the caseworker mentoring scheme. Baseline data was collected for the FNLP starting in May 2015.
This program approach is founded on an agriculture-led growth strategy that is expected to help vulnerable families diversify their income and grow assets while the community is strengthened by improving nutrition, water sanitation, and hygiene. The most vulnerable families receive cash transfers. A caseworker-led livelihood mentoring scheme also matches households with the resources they need to engage effectively in the local economy and break free from the cycle of poverty and malnutrition.
The impact evaluation, led by The World Bank’s Africa Gender Innovation Lab (GIL), is being conducted in Kebbi state in North-West Nigeria and will evaluate the impact of the overall program as well as two experiments that focus on the impact of the cash transfers and the caseworker mentoring scheme. Baseline data was collected for the FNLP starting in May 2015.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Households
Scope
Notes
The scope of Feed the Future Nigeria Livelihoods Project (FNLP) includes:
- HOUSEHOLD: Household roster and demographics, Labor, Non-farm enterprises, Credit and savings, Household and agriculture assets, Housing, Food consumption and expenditure, Non-food expenditure, Food security, Safety nets, Economic shocks, Health, Risk aversion, Hyperbolic discounting, Aspirations and Women's survey.
- AGRICULTURE: Plot roster, Land inventory, Agricultural labor, Field crops and seed acquisition, Crop disposition, Animal holdings and costs, Agriculture by-product and Extension services.
- HOUSEHOLD: Household roster and demographics, Labor, Non-farm enterprises, Credit and savings, Household and agriculture assets, Housing, Food consumption and expenditure, Non-food expenditure, Food security, Safety nets, Economic shocks, Health, Risk aversion, Hyperbolic discounting, Aspirations and Women's survey.
- AGRICULTURE: Plot roster, Land inventory, Agricultural labor, Field crops and seed acquisition, Crop disposition, Animal holdings and costs, Agriculture by-product and Extension services.
Coverage
Geographic Coverage
Village level
Universe
Households in both FNLP villages and villages not receiving FNLP services but are part of the control group for the impact evaluation.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Gautam Bastian | World Bank |
| Sreelakshmi Papineni | World Bank |
Funding Agency/Sponsor
| Name | Role |
|---|---|
| The World Bank | Funding |
Sampling
Sampling Procedure
To determine which areas within Kebbi State would benefit from the FNLP program and to establish a sample of vulnerable households that will be part of the program and impact evaluation, CRS and GIL identified eligible communities and households in Kebbi using a number of steps. Detailed explanations of each stage in the process are provided in the baseline report (Attached in the Related Materials).
For the Impact Evaluation baseline survey, a sample of 2,400 EV households and 1,100 households equally divided between the VV and ML households was necessary based on power calculations. We sampled 2,074 of the 'Class B' households in FNLP treatment villages and 2,254 from FNLP control villages and sent this sample of 4,328 households to the survey firm to conduct a baseline survey.
For the Impact Evaluation baseline survey, a sample of 2,400 EV households and 1,100 households equally divided between the VV and ML households was necessary based on power calculations. We sampled 2,074 of the 'Class B' households in FNLP treatment villages and 2,254 from FNLP control villages and sent this sample of 4,328 households to the survey firm to conduct a baseline survey.
Response Rate
The number of household interviews completed was 3,976 for a household response rate of 92 percent.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2015-05-01 | 2015-06-01 | Baseline |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Processing
Data Editing
Data quality was ensured at several levels. At the tablet level, the questionnaire was programmed so that questions or sections could not be skipped by interviewers. Numerous quality checks were also built into the programming that identified inconsistencies and prevented interviewers from moving forward with the survey until errors were corrected. Logic checks and range checks were also included in the programming so that implausible entries were flagged to the interviewer at the time of surveying.
Monitoring of data collection activities was also conducted by several people. Supervisors monitored interviewer performance by observing interviews and conducting spot checks that consisted of assessing whether questions were being asked appropriately and providing immediate feedback to interviewers. The World Bank’s Project Manager and Field Coordinator also provided another layer of quality control, visiting each interviewer team at least twice each week to observe interviews and review household listings.
A final level of data quality control involved the use of quality control reports that were automatically generated using a quality-check file created by the research team at the World Bank. The file would scan the data for possible errors or large outliers as soon as data was downloaded from the server. The types of checks the file would make included the following: whether the household identifiers were unique within the dataset, whether interviews were being completed in their entirety, reviewing observations with duplicate values of a variable for which duplicates are uncommon, checking that no variables have only missing values, checking important skip patterns, range checks and interviewer comments. This helped with data accuracy as the report was reviewed at least every week by the research team throughout the data collection period and any errors could be sent back to the field team and rectified in real time while the data collection was still taking place.
Monitoring of data collection activities was also conducted by several people. Supervisors monitored interviewer performance by observing interviews and conducting spot checks that consisted of assessing whether questions were being asked appropriately and providing immediate feedback to interviewers. The World Bank’s Project Manager and Field Coordinator also provided another layer of quality control, visiting each interviewer team at least twice each week to observe interviews and review household listings.
A final level of data quality control involved the use of quality control reports that were automatically generated using a quality-check file created by the research team at the World Bank. The file would scan the data for possible errors or large outliers as soon as data was downloaded from the server. The types of checks the file would make included the following: whether the household identifiers were unique within the dataset, whether interviews were being completed in their entirety, reviewing observations with duplicate values of a variable for which duplicates are uncommon, checking that no variables have only missing values, checking important skip patterns, range checks and interviewer comments. This helped with data accuracy as the report was reviewed at least every week by the research team throughout the data collection period and any errors could be sent back to the field team and rectified in real time while the data collection was still taking place.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Gautam Bastian | World Bank | [email protected] |
| Sreelakshmi Papineni | World Bank | [email protected] |
Citation requirements
The use of the datasets must be acknowledged using a citation which would include:
- the identification of the Primary Investigator (including country name);
- the full title of the survey and its acronym (when available), and the year(s) of implementation;
- the survey reference number;
- the source and date of download (for datasets disseminated online).
Example:
Gautam Bastian, Sreelakshmi Papineni, World Bank. Nigeria - Feed the Future Nigeria Livelihoods Project 2015, Baseline Survey. Ref. NGA_2015_FNLP-BL_v01_M. Dataset downloaded from [URL] on [date].
- the identification of the Primary Investigator (including country name);
- the full title of the survey and its acronym (when available), and the year(s) of implementation;
- the survey reference number;
- the source and date of download (for datasets disseminated online).
Example:
Gautam Bastian, Sreelakshmi Papineni, World Bank. Nigeria - Feed the Future Nigeria Livelihoods Project 2015, Baseline Survey. Ref. NGA_2015_FNLP-BL_v01_M. Dataset downloaded from [URL] on [date].
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI Document ID
DDI_NGA_2015_FNLP-BS_v01_EN_M_v01_A_OCS_FAO
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | The World Bank | Documentation of the DDI |
| Office of Chief Statistician | OCS | Food and Agriculture Organization | Metadata adapted for FAM |
DDI Document version
NGA_2015_FNLP-BS_v01_EN_M_v01_A_OCS_v01