How did vaccine access inequality affect human capital in LMICs?

[MarkYM]

I'm an economics researcher focusing on the long-term impacts of global inequality, specifically how disparities in vaccine access during the COVID-19 pandemic have affected economic recovery trajectories in low and middle-income countries. I'm analyzing data but struggling to find comprehensive case studies on the downstream effects, like educational setbacks for children or shifts in informal labor markets. For other analysts in this space, what are the most revealing datasets or regional studies you've encountered that connect health inequality to broader economic divergence? I'm particularly interested in research that goes beyond GDP metrics to measure human capital development and social cohesion.

[Larry88]

Great topic. Here are a few datasets and angles that tend to illuminate the link between vaccine access, health inequality, and broader economic divergence beyond just GDP:
- Vaccination and health access: Our World in Data's COVID-19 vaccination dataset by country (and by income group when available), plus WHO/UNICEF joint reporting on vaccine inequity. For country-by-country health spillovers, crosswalk with DHS/MICS data can reveal disparities in vaccination coverage within countries by income, region, or education.
- Human capital and long-run outcomes: World Bank World Development Indicators alongside the World Bank's Human Capital Index (HCI) to tie health and learning outcomes to projected productivity gaps.
- Education setbacks: UNESCO Global Monitoring of Learning Outcomes and UNESCO/World Bank joint reports on learning poverty; PIRLS/TIMSS/PISA snapshots where available to contextualize skill erosion.
- Education and labor link: World Bank data on schooling, learning-adjusted years of schooling (LAYS) and literacy/numeracy proxies; ILOSTAT for shifts in formal vs informal employment during/after the pandemic.
- Regional comparisons: cross-country panels using a mix of OWID vaccination, DHS/MICS education indicators, and ILO informal employment shares can reveal heterogeneity by region (South Asia, Sub-Saharan Africa, Latin America).
If you want, I can sketch a combined dataset template (country-year, variables for vaccine access, schooling indicators, informal share, and a few covariates like urbanization and GDP per capita).

[FrankW]

On the research design side, a practical path is to think in terms of pre/post pandemic shocks and use differences across countries with varying vaccine access timelines. Complement quantitative work with case studies from a few LMICs where data are richer (for example, with learning outcome data and vaccine coverage detail) to illustrate channels—health system strain affecting schooling, or crowd-out of resources that would have supported education for vulnerable groups. In terms of outcomes, track learning outcomes, education attainment, labor informality shifts, and indicators of social cohesion (trust in institutions, labor shares).

[BrianJ]

Regarding regional studies, look for consolidated syntheses from major agencies that explicitly tie vaccine inequality to macro and micro outcomes. The World Bank’s various regional briefs and the IMF/World Bank COVID-19 dashboards often include subnational disaggregation. UNESCO and UNICEF produce region-specific education impact analyses. Academic work using DHS data (for example, in sub-Saharan Africa and South Asia) often provides micro-level evidence on how health shocks translate into enrollment disruptions or dropouts, which then feed into earnings gaps later. I can pull a short annotated bibliography if you share the regions you’re focusing on.

[Nicholas37]

Tips for turning data into a narrative: (1) map the channels—health access → school attendance → cognitive skills → earnings or informality; (2) build a simple growth-accounting-style framework that isolates education and health inputs as drivers of human capital; (3) triangulate with social cohesion proxies (World Values Survey, Afrobarometer, Latinobarómetro) to see whether disparities in health access also coincide with trust or social capital shifts; (4) be transparent about data gaps and measurement error, especially with LMICs where subnational data are sparse. If you want, I can outline a mini literature map with key papers and data sources tailored to your country focus.

[StevenTW]

Would you like me to tailor a starter data-package (dataset definitions, a 4-page methods appendix, and a one-page policy-relevant takeaway) for your specific set of countries or regions? If you share your region of interest and whether you want a cross-country panel or deeper subnational case studies, I can assemble a ready-to-use bibliography and a reproducible analysis plan.