Introduction to Modern Economic Growth years of average schooling, which is much less than the 8.5 fold difference implied by the Mankiw-Romer-Weil analysis The consequence of this discussion is that the estimate for α that is implied by the Mankiw-Romer-Weil regressions is too high relative to the estimates that would be implied by the microeconometric evidence and thus likely upwardly biased The overestimation of the coefficient α is, in turn, most likely related to the possible correlation between the error term εj and the key right hand side regressors in equation (3.23) To recap, the comparison between the parameter estimates from the regression of (3.23) and the microeconometric Mincerian rates of return estimates to schooling imply that cross-country regression analysis is not necessarily giving us an accurate picture of the productivity differences and thus the proximate causes of income differences 3.5 Calibrating Productivity Differences What other approach can we use to gauge the importance of physical and human capital and technology differences? An alternative approach is to “calibrate” the (total factor) productivity differences across countries rather than estimating them using a regression framework These total factor productivity differences are then interpreted as a measure of the contribution of “technology” to cross-country income differences The calibration approach was first used by Klenow and Rodriguez (1997) and then later by Hall and Jones (1999) Here we follow Hall and Jones The advantage of the calibration approach is that the omitted variable bias underlying the estimates of Mankiw, Romer and Weil will be less important (since micro-level evidence will be used to anchor the contribution of human capital to economic growth) The disadvantage is that certain assumptions on functional forms have to be taken much more seriously and we explicitly have to assume no human capital externalities 3.5.1 Basics Suppose that each country has access to the Cobb-Douglas aggregate production function: (3.26) Yj = Kj1−α (Aj Hj )α , 135