Introduction to Modern Economic Growth In the next subsection, we will see why the conclusion that technology differences are minor and physical and human capital differences are the major proximate cause of income per capita differences should not be accepted without further investigation 3.4.3 Challenges to the Regression Analyses of Growth Models There are two major (and related) problems with this approach The first relates to the assumption that technology differences across countries are orthogonal to all other variables While the constant technology advances assumption may be defended, the orthogonality assumption is too strong, almost untenable We not only expect A¯j to vary across countries, but also to be correlated with measures of shj and skj ; countries that are more productive will also invest more in physical and human capital This has at least two reasons The first is a version of the omitted variable bias problem; as we will discuss in detail later in the book, technology differences are also outcomes of investment decisions Thus societies with high levels of A¯j will be those that have invested more in technology for various reasons; it is then natural to expect the same reasons to induce greater investment in physical and human capital as well Second, even ignoring the omitted variable bias problem, there is a reverse causality problem; complementarity between technology and physical or human capital imply that countries with high A¯j will find it more beneficial to increase their stock of human and physical capital In terms of the regression equation (3.23), this implies that the key right hand side variables are correlated with the error term, εj Consequently, ordinary least squares regressions of equation (3.23) will lead to upwardly biased estimates of α and β In addition, the estimate of the R2 , which is a measure of how much of the cross-country variability in income per capita can be explained by physical and human capital, will also be biased upwards The second problem relates to the magnitudes of the estimates of α and β in equation (3.23) The regression framework above is attractive in part because we can gauge whether the estimate of β was plausible We should the same for the estimate of α, the coefficient on the investment rate in human capital, shj We will now see that when we perform a similar analysis for α, we will find that it is too large relative to what we should expect on the basis of microeconometric evidence 131