Growth Regression Revisited: IV and GMM

Barro and Sala-i-Martin (2004) analyzed the empirical determinants of growth. They used a cross-sectional empirical framework that considered growth from two kinds of factors, initial levels of steady-state variables and control variables (e.g., investment ratio, infrastructure). Recent literature suggests that GMM estimation of dynamic panel data models produce more efficient and consistent estimates than OLS (ordinary least squares) or pooled regression models. Following Cellini (1995), we also consider co-integration and error-correction methods for the growth regression. We extend the previous research for Asian countries of Kim (2009) to developed countries. Following the implications of semi-endogenous growth theory, we regressed output growth on a constant, one-year lagged output (initial income) and the determinants of steady-state income [investment rate, population growth, the quadratic (or linear) function of R&D intensity]. The regression suggests faster significant convergence. This contradicts with that of MRW (1992), which asserts the speed is lower when considering broad concept of capital including human capital. The coefficients for the determinants of steady-state income, especially for the quadratic function of R&D intensity, are significant and occur in the expected direction. Our results suggests that adopting appropriate growth policy, an economy can grow more rapidly through transition dynamics or changing