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Model evaluation and causality testing in short panels: The case of infrastructure provision and population growth in the Brazilian Amazon

Diana Weinhold, Eustáquio J. Reis

[April 1999]

Abstract: In this paper we examine the relationship between the growth of infrastructure and population growth in the Amazon using a panel of 295 municipalities over the period from 1975 to 1985. While many econometric models of economic and demographic processes in the Amazon have assumed that infrastructure development is an exogenous variable, attracting immigrants and increasing populations, other political economy models predict that in fact the causality goes in the reverse direction with growing populations demanding better infrastructure. We show that contemporaneous cross-section analysis does indeed confirm a strong positive correlation between infrastructure and urban population. However, we argue that a simple contemporaneous correlation does not give us empirical evidence of causal relationship between these two variables. We therefore develop a modified form of the traditional Granger causality tests to suit the short time series that we have available. Our out-of-sample forecasting tests show clearly that information on infrastructure does not improve forecasts of future population growth. On the other hand, our forecasts of future infrastructure development are significantly improved when we include information on past urban population growth. We thus conclude that the empirical evidence supports the hypothesis that growing urban populations lead to more infrastructure development, rather than vice versa.