Evaluating Total Factor Productivity Differences by a Mapping Structure in Growth Models

The article aims at providing a suitable measure of total factor productivity (TFP) levels within the conditional convergence framework by introducing unobserved heterogeneity in terms of a "mapping model". Our goal is twofold. First, we develop a generalized maximum entropy estimation procedure to account for ill-posed and ill-conditioned inference problems in estimating a conditional convergence regression with fixed effects and heterogeneous coefficients across regions. Second, we provide an endogenous spatial representation of unobserved fixed effects by using a multidimensional scaling technique. The proposed approach is applied to assess the existence of catching-up across Italian regions over the period 1960–1995 and to identify the effects of technology and geographic spillovers on the determination of TFP levels.