Wealth Mobility Lab, explained

To make the economics of mobility accessible to a general audience — not through dense academic prose, but through interaction, comparison, and visual storytelling. The goal is that every visitor leaves having learned something they did not know about how opportunity is distributed.

Income inequality is rising in most advanced economies, and with it, intergenerational mobility is falling. Yet public understanding of the link between the two is shallow. Most people can name the Gini index; few can explain why it matters for a child born today.

To compile a cross-country dataset from public sources; to visualise the relationship between inequality and mobility interactively; to let users simulate the effects of policy levers; and to provide a curated research library for further study.

The project follows a descriptive-comparative approach. Countries are compared across seventeen indicators of inequality, development, and mobility. The policy simulator uses a transparent linear model informed by the direction and magnitude of effects in peer-reviewed research.

Sources were chosen for three qualities: public availability, methodological transparency, and cross-country comparability. The World Bank, UNDP, OECD, and World Inequality Database meet all three. Where sources disagreed, the most recent and most cited estimate was used.

The design language draws from Financial Times Interactive, Our World in Data, and The Economist — editorial, restrained, data-forward. The colour system uses warm paper tones with gold accents to signal rigour without coldness. Every chart was designed to answer a question, not decorate a page.

Through this project, I learned to clean and structure cross-country data, to design interactive visualisations that respect a reader’s attention, and to communicate economic concepts in plain language. I also gained a deeper understanding of the mechanics of mobility itself.

The dataset covers 30 countries and is not exhaustive. Some indicators are approximate where recent data was unavailable. The policy simulator is illustrative, not predictive — it cannot capture the full complexity of real economies or the interactions between policies.

A natural extension is sub-national mobility data — the Chetty lab’s commuting-zone level work for the US, and similar efforts for the EU. Adding time-series to show how mobility has changed over decades would also deepen the analysis. A user-saved comparison feature is another direction.

To the researchers at Opportunity Insights, the OECD social mobility team, and the World Inequality Lab — whose work made this project possible. And to my teachers at Delhi Public School Sector 45, Gurgaon, who encouraged the curiosity that started it.