This article addresses heterogeneity in determinants of economic growth in a data-driven way. Instead of defining groups of countries with different growth characteristics a priori, based on, for example, geographical location, we use a finite mixture panel model and endogenous clustering to examine cross-country differences and similarities in the effects of growth determinants. Applying this approach to an annual unbalanced panel of 59 countries in Asia, Latin and Middle America and Africa for the period 1971-2000, we can identify two groups of countries in terms of distinct growth structures. The structural differences between the country groups mainly stem from different effects of investment, openness measures and government share in the economy. Furthermore, the detected segmentation of countries does not match with conventional classifications in the literature.

economic growth, latent class models, panel time series, parameter heterogeneities
Time-Series Models; Dynamic Quantile Regressions (jel C22), Models with Panel Data (jel C23), Economic Growth and Aggregate Productivity (jel O4)
dx.doi.org/10.1080/00036846.2010.500274, hdl.handle.net/1765/26749
Applied Economics
Erasmus Research Institute of Management

Basturk, N, Paap, R, & van Dijk, D.J.C. (2012). Structural differences in economic growth: an endogenous clustering approach . Applied Economics, 44(1), 119–134. doi:10.1080/00036846.2010.500274