BUS613: Advanced International Business

The measurement of infrastructure bears serious data limitations. Previous studies, for example, Straub, Roller and Waverman, Hoffmann, and Limao and Venables use several proxies for infrastructure such as, the aggregate data of road density, railways and airport facility for transport infrastructure, broadband and mobile connection for telecommunication infrastructure and electricity consumption and access to electricity, etc., are for energy infrastructure, which may be difficult to deliver a wide-ranging and true picture of infrastructure channel. However, some studies like Francois and Manchin, Sahoo and Dash, and Kumar relax the problematic assumption by employing PCA, but using PCA in a panel data tends to unduly restrict the set of countries and the data series that can be included in the analysis Donaubauer et al.. To overcome the above-mentioned limitation of previous studies, this study uses an inclusive index of infrastructure devised by Donaubauer et al., covering the data during 1990–2017 by applying Unobserved Component Analysis (UCM).

The aim of this research is to explore the long- and short-run impact of infrastructure on exports and trade deficit for selected South Asian economies by applying PMG estimator and cointegration techniques (i.e., Padroni and Kao test). The empirical results of the PMG approach confirmed the significant positive long-run impact of aggregate and all other sub-indices (i.e., transports, telecommunication, energy and financial) of infrastructure on exports. Most of control variables of this study also play significant role in export like, exchange rate (ln_EXR), human capital (ln_HC), per capita GDP (ln_PGDP) and institutional quality index (ln_IQ) while, in short run only aggregate infrastructure is significant. This is good news for policy-makers in South Asia who want to catch-up on developed economies and diminish the gap between domestic countries and advanced countries, in exports. The cointegration technique, like Padroni and Kao, examines strong cointegration between aggregate infrastructure and export. This study also used fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) cointegration approaches for robustness and to detect diagnostic problems of serial correlation, heteroskedasticity and most importantly endogeneity. Here, FMOLS and DOLS show consistent and robust results with our main models.

Similarly, we also examine the effect of aggregate and undermentioned sub-indices of infrastructure on trade deficit and apply the same undermentioned techniques. The empirical results of this study suggested that infrastructure including all sub-indices decreases trade deficit (i.e., the impact of infrastructure on trade deficit is negative and significant in long but insignificant in short run). Beside, infrastructure the undermentioned control variables have significant impact on trade deficit in long run but insignificant in short run. Furthermore, the Padroni, and Kao cointegration test suggested that there is strong cointegration between the dependent and independent variables in all of the columns. FMOLS and DLOS of cointegration gives robust and consistent results.

The findings recommended that quality and availability of infrastructure (aggregate and sub-indices) matters to enhance trade and decrease trade deficit in selected South Asian countries. Hence, efficient infrastructure (i.e., transport, energy, telecommunication and financial sector) arrangements should be the priority for policy-makers to ensure further increase in exports and decline trade deficit which is most important problem of South Asia. The present study highlighted that availability of infrastructure accelerates regional and intra-regional trade. However, we also find that the infrastructure decreases the trade deficit in South Asian economies.