Convergence tool · Measuring global development disparities · Browse the public repositoryBrowse repositoryRepository
This interactive tool estimates how long it would take countries to converge with a chosen target, across a range of development indicators. The target can be another country, an income or human development group, or the world average. Data run through 2023. Each area is then projected forward from that level at a constant rate, equal to the average annual rate it recorded over the preceding years. The length of the window over which that rate is computed is yours to choose. Two convergence scenarios are provided: a static one, where the target stays fixed at its 2023 level, and a dynamic one, where it keeps growing at its own rate, just like the comparison countries.
| Country | Average annual growth rate between | Years to catch up (Static scenario) |
Years to catch up (Dynamic scenario) |
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Note: The vertical axis is on a log scale, so a constant growth rate plots as a straight line and a catch-up is simply the point where two lines meet. The horizontal axis still runs to whichever comparison country takes the longest, so a country that catches up quickly occupies only a narrow slice on the left.
Objective and interpretation. The aim of the tool is to assess whether recent trajectories have been fast enough to close existing gaps and to make the scale of the remaining effort visible. Bear in mind that it is in no way a credible forecast, as constant growth is never a realistic long-run assumption.
Methodological note. The number of years required to close the gap is estimated from each country's average annual (geometric) growth rate computed over the selected window. This rate is extrapolated forward at a constant pace, starting from each country's own 2023 level. Projected levels far out on the horizon should therefore be read as a simple extrapolation, not a forecast. The underlying equations are detailed in the front page of the public repository.
Measuring development. Development is a multidimensional concept, and one must be aware of the limitations of each indicator used. For instance, GDP per capita only captures the monetary dimension of development and still remains an imperfect proxy for it. A high gross domestic product per capita says nothing about how that income is distributed among the population (assumed here to be uniform) and may conceal important inequalities. What is ultimately done with that income matters perhaps as much as its level. As such, accounting for other dimensions (education, health, and so on) is equally important, and additional variables are provided to that end. Keep in mind, however, that these indicators (e.g., mean years of schooling) also carry limitations. Future releases might incorporate additional indicators to provide a more comprehensive view.
Related literature. For a more realistic approximation of income convergence, beta-convergence models let the speed of catch-up slow down as the gap narrows. See, for example: Barro and sala-i-martin (1992), Barro (2015), Johnson and Papageorgiou (2020), Patel et al., (2021) or this recent blog post (2025).