By Fredric T Howard (ed.)
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Additional info for Applications of fibonacci numbers : volume 8 : proceedings of 'The Eighth International Research Conference on Fibonacci Numbers and Their Applications', Rochester Institute of Technology, Rochester, New York, U.S.A., June 22-26, 1998
Up till that point, economic growth in China will effectively reduce measured world income inequality. 30 Trends in International Income Inequality Then comes the great reversal. Around the year 2020, China will surpass the mean logged income level. From that point on, if China continues to grow faster than the global average, it will be effectively increasing global income inequality as measured here. To take another case, Guatemala in 1998 recorded a GNP/RLC per capita of $1514, just above the global mean of the logged series.
The GNP/RLCR series implies that inequality was relatively stable over the period 1960-1998 in the world x-China, with a downward trend in inequality in recent 29 The International Structure of Income years. With China included in the analysis, weighted inequality has declined precipitously, but only due to China's expanded role in the global economy, surely a reasonable conclusion given that caveat. Incidentally, between-country inequality not weighted by population (results not shown here) has risen consistently over the period under all three methodologies.
I use base 10 logarithms, and plot all three time series on the same scale. 041 to the right, but the relative positions of the cases would not change. Table 1. S. CD (current dollar GDP) is substituted due to missing data in the primary series. KD (constant dollar GDP) is substituted due to missing data in the primary series. As a confirmation of the VarLog results, I also calculate international income inequality using weighted Gini coefficients. Gini coefficients are described in more detail below (see Figure 9).
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