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The relationship between income and growth rates has been an elementary problem of research on economic convergence. In the present paper, we study growth-income paths in a new perspective. We assess the similarities of transitional growth trajectories with the use of novel concept-based model. Further, we group economies on the basis of the assessed similarities and we evaluate within-group growth-income relationships. The obtained results point to distinct patterns of development, which help to understand the puzzles of absolute convergence and divergence. Among others, we find evidence of a humped-shaped path of long-run transitional growth. Simultaneously, we identify countries which got stuck in poverty and in the middle-income trap.

In this paper, we study economic growth and its volatility from an episodic perspective. We first demonstrate the ability of the genetic algorithm to detect shifts in the volatility and levels of a given time series. Having shown that it works well, we then use it to detect structural breaks that segment the GDP per capita time series into episodes characterized by different means and volatility of growth rates. We further investigate whether a volatile economy is likely to grow more slowly and analyze the determinants of high/low growth with high/low volatility patterns. The main results indicate a negative relationship between volatility and growth. Moreover, the results suggest that international trade simultaneously promotes growth and increases volatility, human capital promotes growth and stability, and financial development reduces volatility and negatively correlates with growth.

We would like to thank Aart Kraay for sharing us the replication codes for testing the instrument strength in the inequality-growth regressions with the system GMM estimator, and two anonymous reviewers for helpful comments on early version of our paper. AbstractThe objective of the paper is to verify if income inequality impedes the growth rates in OECD countries in the period of 1990-2014 and to reveal whether the choice of the income inequality measure determines the sign and the strength of the estimated relationship. We use system GMM to estimate parameters of a dynamic panel growth model. The research indicates that income inequality negatively affects economic growth. We also find evidence that various measures of inequality bring the different scale of consequences for economic growth, with measures that give more weight to the middle part of the distribution being the weakest predictor of GDP growth. Simultaneously, we present the test of weak instruments, which helps to explain these differences. K E Y W O R D Seconomic growth, generalized method of moments, income inequality, inequality measures J E L C L A S S I F I C A T I O N S O11, O15

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