electricsheepafrica/cgap-smallholder-survey-cote-divoire-2016

CGAP Smallholder Household Survey - Côte d'Ivoire (2016)

Dataset Description

Nationally representative household survey of smallholder farming families in Côte d'Ivoire, conducted by the Consultative Group to Assist the Poor (CGAP) in 2016.

Dataset Summary

Metric	Value
Country	Côte d'Ivoire
Year	2016
Households Surveyed	~3,000
Total Records	11,674

Tables

Table	Rows	Columns	Description
household	3,019	274	Household-level characteristics
single	2,949	932	Single respondent detailed survey
multiple	5,706	455	Repeated measures (members, plots, livestock)

Usage

from datasets import load_dataset

# Load household-level data
household = load_dataset("YOUR_USERNAME/cgap-smallholder-civ", "household", split="train")

# Load single respondent data
single = load_dataset("YOUR_USERNAME/cgap-smallholder-civ", "single", split="train")

# Load multiple respondent data
multiple = load_dataset("YOUR_USERNAME/cgap-smallholder-civ", "multiple", split="train")

With Pandas

import pandas as pd
df = pd.read_parquet("hf://datasets/YOUR_USERNAME/cgap-smallholder-civ/household.parquet")

Features

The dataset covers:

• Demographics: Household composition, age, gender, education levels
• Agriculture: Crops cultivated, land holdings, livestock, farming practices
• Financial Services: Bank accounts, mobile money usage, savings groups, credit access
• Income & Expenditure: Income sources, consumption patterns
• Assets: Household and productive assets
• Shocks: Economic shocks and coping mechanisms

Documentation

Official CGAP User Guide

For detailed variable definitions, survey methodology, and questionnaire documentation, see the official CGAP user guide:

📖 Côte d'Ivoire User Guide

Data Dictionary

This repository includes comprehensive data dictionaries with statistics for every variable:

• DATA_DICTIONARY.md - Human-readable markdown format
• data_dictionary.json - Machine-readable JSON format

The data dictionary includes for each variable:

• Data type (numeric/categorical)
• Non-null counts and missing value percentages
• Value ranges (min, max, mean, median) for numeric columns
• Unique value counts and distributions for categorical columns

Suggested ML Tasks

• Financial inclusion prediction (binary classification)
• Income/wealth classification
• Household segmentation (clustering)
• Agricultural productivity prediction
• Vulnerability/poverty risk assessment

Source

• Publisher: Consultative Group to Assist the Poor (CGAP)
• Host: World Bank Microdata Library
• URL: https://www.cgap.org/research/data/smallholder-families-data-hub

Citation

@misc{cgap_smallholder_civ_2016,
  title={CGAP Smallholder Families Household Survey - Côte d'Ivoire},
  author={{Consultative Group to Assist the Poor}},
  year={2016},
  publisher={World Bank Microdata Library}
}

License

Subject to World Bank Microdata Library Terms of Use. Intended for research and educational purposes.