The chapter first discusses how productivity growth typically has been measured in classical productivity studies. We then briefly discuss how innovation and catch-up can be distinguished empirically. We next outline methods that have been proposed to measure productivity growth and its two main factors, innovation and catch-up. These approaches can be represented by a canonical form of the linear panel data model. A number of competing specifications are presented and model averaging is used to combine estimates from these competing specifications in order to ascertain the contributions of technical change and catch-up in world productivity growth. The chapter ends with concluding remarks and suggestions for the direction of future analysis. The literature on productivity and its sources is vast in terms of empirical and theoretical contributions at the aggregate, industry, and firm level. The pioneering work of Dale Jorgenson and his associates and Zvi Griliches and his associates, the National Bureau of Economic Research, the many research contributions made in U.S universities and research institutions, the World Bank and research institutes in Europe and other countries are not discussed here as our goal is by necessity rather narrow. We focus on work directly related to panel data methods that have been developed to address specific issues in specifying the production process and in measuring the sources of productivity growth in terms of its two main components of innovation (technical progress) and catch-up (efficiency growth), with emphasis given to one of the more important measures of the latter component and that is technical efficiency.