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    • Introduction The rapid economic growth that

    2018-10-23

    Introduction The rapid economic growth that emerging and developing economies experienced in the last decades, specially in the run-up to the global financial crisis of 2008–2009, gave a new life to the debate about economic convergence – i.e., whether poorer countries tend to grow faster than the richer ones, then converging in living standards. Discussions about the risk of decay of the supremacy of the U.S. and other advanced economies (Eichengreen, 2011; Subramanian, 2011), and the prospects of the developing world growth (O’Neill, 2011; Rodrik, 2011a) abound. Rodrik (2011a,b) documented that manufacturing industries exhibit unconditional convergence in labor productivity. Differences between the results with country-level data and those obtained for industrial sectors can be associated to the process of technology dissemination and capital mobility. In particular, the results obtained by Rodrik (2011a,b) suggest that technology and capital spread easier across industrial sectors than across countries. Therefore, sectors are more likely to face similar steady-states than countries. In this Sulindac sulfide paper we bring evidence that speed of labor productivity convergence changes across countries, identifying key drivers of the convergence process. Our basic questions are: Can we identify a multiple regime dynamics in industry productivity growth across countries? Are country-specific features related to the industry productivity growth? In what magnitude? Using the same data set as Rodrik (2011a,b) – UNIDO\'s INDSTAT 4, available for a wide range of countries –, we provide a novel semi-parametric specification for convergence equations and show that the speed of convergence varies systematically with country-specific characteristics. We investigate whether unobserved heterogeneity in the convergence coefficient at the country level is associated with geographic conditions, trade openness, political institutions and education. In principle, these variables might affect the steady-state and the process of capital accumulation. Hence, the goal is to test if these variables change the convergence process, at the country level, on top of the mechanisms identified in Rodrik (2011a,b). We consider the flexible smooth transition model with multiple groups and multiple transition variables proposed by Medeiros and Veiga (2005). This model allows that each group has distinct dynamics controlled by a linear combination of known variables. This specification is very flexible and nests several linear and nonlinear models and can be as well interpreted as a semi-parametric model. We found evidence that the laws of motion for growth are different across countries and those with political institutions that are more permissive converge faster. Less democratic states grow at higher paces according to our estimates, a result that is compatible with those documented in Barro (1996, 1999). The speed of convergence also has a non-monotonic relationship with trade openness and education, suggesting faster convergence at the extremes. Differences across countries in the convergence coefficient is not only of statistical significance, but also economically meaningful. The lower and upper values of the estimated convergence coefficient are, respectively, −3.7% and −2.8% per year, which means that the half life to productivity convergence varies in a range of about 7 years (between 18 and 25 years). The mechanisms through which these elements affect convergence are beyond the scope of the paper but, in contrast to the results found in Rodrik (2011a,b), we found salient features at the country level that affect income and growth.
    Data Our industrial database is the same as in Rodrik (2011a,b). We use data from UNIDO\'s INDSTAT 4 database, which provides industrial statistics for a wide range of countries at the ISIC 4-digit level (UNIDO, 2011). These statistics cover a series of variables, including value added and employment, for up to 127 manufacturing industries per country. As in Rodrik (2011a,b), because of data availability we take 1990 as the starting point for the Sulindac sulfide empirical work. To maximize the number of observations, we estimate pooled regressions using rolling 10-year distances (with 1990 as the starting date) for each industry data available. Our sample includes 127 industries, 49 countries and 8 periods (total of 13,296 observations).