Time-series properties and empirical evidence of growth and infraestructure: revised version

After more than forty years studying growth, there are two classes of growth models that have emerged: exogenous and endogenous growth models. Since both try to mimic the same set of long-run stylized facts, they are observationally equivalent in some respects. Our goals in this paper are twofold First, we discuss the time-series properties of growth models in a way that is useful for assessing their fit to the data. Second, we investigate whether these two models successfully conforms to U.S. post-war data. We use cointegration techniques to estimate and test long-run capital elasticities, exogeneity tests to investigate the exogeneity status of TFP, and Granger-causality tests to examine temporal precedence of TFP with respect to infrastructure expenditures. The empirical evidence is robust in confirming the existence of a unity long-run capital elasticity. The analysis of TFP reveals that it is not weakly exogenous in the exogenous growth model Granger-causality test results show unequivocally that there is no evidence that TFP for both models precede infrastructure expenditures not being preceded by it. On the contrary, we find some evidence that infras- tructure investment precedes TFP. Our estimated impact of infrastructure on TFP lay rougbly in the interval (0.19, 0.27).

Technology adoption and effects of transfers across locations

This paper develops a two-period model with heterogeneous agents to analyze the e¤ects of transfers across locations on convergence, growth and welfare. The model has two important features. First, locations are asymmetric as it is assumed that there are more specialized occupations in the more developed one. Second, the returns on the investment to acquire new technology depend positively on the level of each region’s knowledge and on the level of the world knowledge assumed to be available to all. In one hand, the poor region has a disadvantage as it has a lower stock of knowledge. On the other hand, it has the advantage of not having yet exploited a greater stock of useable knowledge available in the world. Hence, there are two possible cases. When the returns are greater in the poor region, we obtain the following results: (i) the rich location grows slower; (ii) the transfers to the poor location enhances the country’s growth rate; and (iii) there is a positive amount of transfers to the poor region that is welfare improving. When the returns are greater in the rich region, the …rst two results are reversed and transfers to the rich region are welfare improving. In both cases, the optimal amount of transfer increases with the level of income disparity across regions and is not dependent on the level of the country’s economic development (measured by its income per capita). Barriers to the adoption of new technology available in the world can constrain the convergence process as it harms in greater length the poor region. The results do not change whether migration is allowed or not.