Aggregation versus Heterogeneity in Cross-Country Growth Empirics.

The cross-country growth literature commonly uses aggregate economy datasets such as the Penn World Table (PWT) to estimate homogeneous production function or convergence regression models. Against the background of a dual economy framework this paper investigates the potential bias arising when aggregate economy data instead of sectoral data is adopted in macro production function regressions. Using a unique World Bank dataset we estimate production functions in agriculture and manufacturing for a panel of 41 developing and developed countries (1963-1992). We employ novel empirical methods which can accommodate technology heterogeneity, variable nonstationarity and the breakdown of the standard crosssection independence assumption. We then investigate the potential for biased estimates due to aggregation and empirical misspecification, relying on both theory and Monte Carlo simulations. We confirm substantial bias in the technology coefficients using data for a stylised aggregate economy made up of agricultural and manufacturing sectors and a matched PWT dataset.