Physical infrastructure and economic growth

ABSTRACT

The existing literature on the relationship between infrastructure and economic growth is inconclusive. We estimate the effect on GDP of three main categories of infrastructure – transport, electricity, and telecommunications – using data from 87 countries over the period 1992–2017. This study uses more recent data than previous research and includes new types of infrastructure such as mobile phones. Our main estimates use the PMG estimator that allows us to test for the weak exogeneity of the infrastructure variables. The key finding of the study is that an increase in infrastructure, especially electricity generation capacity and telecommunications, has large long-run positive effects on GDP, though the short-run effects are much smaller and are less than zero for roads and railways. Also, the effects of electricity and communication infrastructure are higher relative to the effects of transport infrastructure in developing economies than in industrialized economies.

KEYWORDS:

• Infrastructure
• growth
• mobile phones
• pooled mean group estimator

JEL CLASSIFICATION:

• O18
• O47

Acknowledgement

We wouldlike to thank Gal Hochman, Pravakar Sahoo, Fan Zhang, and ananonymous referee for their valuable comments and suggestions.

Disclosure statement

Govinda Timilsina is employed by the World Bank. This work was supported by a World Bank Research Support Grant (RSB), which funded Debasish Das. The views and interpretations are of the authors and should not be attributed to the World Bank Group and the organizations they are affiliated with. David Stern has no declarations to make..

Notes

¹ Elburz, Nijkamp, and Pels (2017) conducted a meta-analysis of 42 empirical studies published between 1995 and 2014 highlighting the sensitivity of results to various factors.

² See Ramey (2020) for a recent review of evidence for the United States.

³ We use the most recent classification: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.

⁴ The highest correlation between pairs of these demeaned variables is 0.52 between mobile and ifxed line telephones.

⁵ Following the suggestion of a referee, we also estimated the Pesaran (2006) CCEMG estimator for this sample. Because of all the additional regressors, there are only three degrees of freedom in each individual country regression. The results are in Table B1.

⁶ We also estimated cross-section growth models of the following form.

$\begin{array}{c}{y}_{\mathit{it}}-y_{\mathit{it}-5}={\alpha }_0+{\mathit{\delta }}^{\prime }{\mathit{z}}_{\mathit{it}-5}+e_{\mathit{it}}\end{array}$

where ${\mathit{z}}_{\mathit{it}-5}$ is a vector of the initial values of a set of variables including infrastructure variables and initial GDP per-capita. The latter variable controls for many unobserved determinants of growth. These regressions allow us to use a wider range of infrastructure variables that are not available as extensive time series, such as airports. On the whole, the coefficients of the infrastructure variables were statistically insignificant in these regressions, and we do not report them in this paper. This implies that higher levels of infrastructure are not associated with more rapid economic growth.

Additional information

Funding

The work was supported by the World Bank.