Quantum mechanics

Engineering

Transport engineering

Materials science

Panel data

Time series

China

Archaeology

Aggregate (composite)

Economics

Computer science

Transportation infrastructure

Regional science

Physics

Transport infrastructure

Geography

Political science

Econometrics

Composite material

Econometric model

Causality (physics)

Machine learning

Research in Transportation Economics

This paper aims to explore the impact of transportation infrastructure on economic growth in China at different levels: aggregate and regional. Using a time series approach and panel data for 28 regions (where there are provinces also) over the time 1990–2017, the experimental findings confirms the economic theory of development choices. Although other studies have addressed this problem with the same data, our contribution has been to combine the aggregated results with the regional ones for policy analysis. In addition, we combine a Machine Learning technique capable of verifying causality through a supervised and an econometric approach. The results show that the contribution to the growth of transport investments is different from region to region, but we have highlighted how transport affects economic growth at the aggregate level. However, the lack of infrastructure maintenance eliminates the positive effects of investments over time in the medium term.

On the relationship between transportation infrastructure and economic development in China