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IDB/Cornell Coronavirus Survey: 2020

By Department of Research and Chief Economist (VPS/RES/RES)

IDB/Cornell Coronavirus Survey: Labor, Food, Mobility & Perceptions Data

The IDB/Cornell Coronavirus Survey collects online responses about how the COVID-19 pandemic affected employment, labor markets, income, food security, and social distancing behavior and public knowledge about COVID-10 in Latin America and the Caribbean.

It was developed jointly by the Inter-American Development Bank (IDB) and Cornell University to inform evidence-based social and economic policy responses.

The survey’s modules were standardized across countries to allow pooled analyses and cross-country comparisons.

The first country launched the survey on March 27, 2020, and most responses were gathered during April 2020.

To recruit participants, social media campaigns using paid ads were used. Because all data come from an online survey, the most vulnerable populations—those without internet or social media access—are not represented. Still, the dataset captures respondents from diverse socioeconomic groups and subnational regions.

To enhance representativeness, weight variables are included:
- Within-country weights to correct sample bias
- Inter-country weights to adjust for differences in sample size and relative country population

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Metadata & use

Identifier https://doi.org/10.60966/8a9h-jk84
License Creative Commons Attribution–NonCommercial–NoDerivs 3.0 IGO
Related Knowledge Product
Citation

Bottan, Nicolas L., et al. (2020). IDB/Cornell Coronavirus Survey: 2020. IDB Open Data. https://doi.org/10.60966/8a9h-jk84
Published date 2020-01-07
Modified date 2026-06-25
Tags/Keywords Coronavirus · Food Security · Household Survey · Labor Market · Pandemic · Social Distancing · Social Media
Language
  1. Spanish
Temporal coverage 2020-2020
Country
Argentina
Bahamas
Barbados
Belize
Bolivia
Brazil
Chile
Colombia
Costa Rica
Dominican Republic
Ecuador
El Salvador
Guatemala
Guyana
Haiti
Honduras
Jamaica
Mexico
Nicaragua
Panama
Paraguay
Peru
Suriname
Trinidad & Tobago
Uruguay
Venezuela
Region Latin America and the Caribbean
Publisher
Inter-American Development Bank
Author
Bottan, Nicolas L.
Hoffmann, Bridget
Vera-Cossio, Diego A.
Data collection type Survey Data
Statistical type Cross-sectional Data
Data structure Structured Data
Data notes

What is this dataset?

This is the second round of the IDB/Cornell Coronavirus Survey, combining two rounds into a panel dataset. It captures how the COVID-19 pandemic affected households across multiple domains (income, assets, social program participation, behaviors) in Latin America and the Caribbean.

When and how was the survey conducted?

The first survey wave launched on March 27, 2020, with most responses collected during April 2020. Participants were recruited through paid social media advertising campaigns targeting adults across the participating countries.

How many countries are included?

The dataset covers 17 countries in Latin America and the Caribbean, including Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Jamaica, Mexico, Panama, Peru, Suriname, Trinidad & Tobago, Uruguay, and Venezuela.

What topics/modules does it include?

The survey modules include:
- Labor market impacts (job loss, income changes)
- Food security and nutrition
- Social distancing behaviors and compliance
- Knowledge and perception of COVID-19 symptoms, transmission, and policy
- Household assets and social program benefit receipt (in round 2)
- Political preferences and financial inclusion (in round 2)

What is the structure of the data & sampled?

Because it is a panel dataset, observations are linked across two rounds for households that responded both times.
The dataset provides harmonized individual-level responses that can be pooled across countries.
It includes:
- Pooled microdata (pooled_data_public)
- Variable labels and definitions (labels_pooled_data_public.xlsx)
- English and Spanish questionnaires for reference
Participants were recruited online via social media paid ads during the first round.

For the second round, the same households were followed up with, where possible. Weights are included to correct for sample bias within countries and adjust for differences in sample size across countries.

How does the dataset address sampling bias?

Since participation relied on internet and social media access, vulnerable groups without such access are underrepresented.
To reduce bias, the dataset includes post-stratification weights that correct for:
1. Demographic imbalance within countries, and
2. Differences in population size across countries.

What are the key limitations or caveats?

  • The survey is internet-based and does not reach the most vulnerable populations (without internet access).
  • Not all households from round 1 might respond in round 2, introducing potential attrition bias.
  • Some metadata or variables might not be fully comparable across countries.
  • Because data are from 2020 only, longer-term pandemic impacts beyond that year are not captured.
  • Self-reported information may be affected by recall or perception bias.

What research or policy questions can this dataset inform?

  • Examining changes over time in household income, job status, and vulnerability due to COVID-19
  • Measure short-term socioeconomic impacts of COVID-19
  • Compare responses by income group, gender, and education level
  • Evaluate social-distancing behavior and pandemic awareness
  • Analyzing how government support programs or assets buffer shocks
  • Cross-country comparisons of pandemic response outcomes
  • Studying behavioral responses (compliance, perceptions) across socioeconomic groups
  • Examine regional differences in the early COVID-19 experience in Latin America and the Caribbean

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