Generalized Correlation and Kernel Causality with Applications in Development Economics
Hrishikesh D. Vinod
Fordham University - Department of Economics
November 24, 2013
Newly developed generalized measures of correlation, GMC (Y|X) use Kernel regressions to overcome its linearity assumption. If GMC (Y|X) sufficiently exceeds GMC (X|Y), X better predicts Y than vice versa, or X "kernel causes" Y. The new causality is supported by some simulations. Development economists, including the Nobel winner W. A. Lewis, have explained observed correlation coefficients. We use recent data for 198 countries for nine variables: poverty, growth, inequality, human development index, gender inequality index, measures of governance, entrepreneurship, economic complexity and foreign aid to study 72 possible (X,Y) pairs to assess kernel causalities. The results suggest that foreign aid can hurt and that countries should promote greater entrepreneurship to improve their scores on various desirable indicators. The results seem to support Jagdish Bhagwati's greater growth policy over Amartya Sen's redistribution. We also find support for Angus Deaton's criticism of foreign aid.
Number of Pages in PDF File: 36
Keywords: bootstrap, poverty reduction, foreign aid, entrepreneurship
JEL Classification: O57, O10, C15, C31working papers series
Date posted: November 7, 2013 ; Last revised: November 25, 2013
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