Trade Capacity

Abstract

This study investigates the short- and long-run impact of infrastructure on export and trade deficit in selected South Asian countries during 1990–2017 by using Pooled Mean Group (PMG) estimator and cointegration techniques like Pedroni and Kao test. The empirical results of the PMG approach confirmed the existence of a significant long-run impact of aggregate and sub-indices of infrastructure (i.e., transport, telecommunication, energy and financial sector) on export and trade deficit. The findings suggested that infrastructure positively promotes exports while negatively affecting trade deficit. The relationship between infrastructure and export is a worthy bulletin for South Asian economies to encourage the quantity of exports and catch-up on established economies. The control variables of exchange rate, human capital, per capita GDP and institutional quality enhance exports and retard trade deficit significantly in the long run. Furthermore, the Pedroni and Kao test indicates strong evidence of cointegration in selected variables. Fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) support robust and consistent results to the main model of this study. Furthermore, the study recommended that in the long run aggregate and sub-indices of infrastructure promote exports and decrease trade deficit in selected South Asian economies.

Source: Faheem Ur Rehman, Abul Ala Noman, and Yibing Ding, https://journalofeconomicstructures.springeropen.com/articles/10.1186/s40008-020-0183-x
This work is licensed under a Creative Commons Attribution 4.0 License.

Introduction

Structuralists consider that availability of infrastructure plays important role in markets connectivity and trade promotion while the lack of infrastructure disrupts markets and retards trade. Infrastructure makes a huge difference in the process of development and the comparative edge of an economy, particularly in trade. Researchers estimated that poor infrastructure penalizes international trade. Countries with better infrastructure (such as Singapore and Hong Kong) perform well in international trade and punch above their weight while countries with weak infrastructure (such as Bhutan and Pakistan) perform poorly in the external sector. This means infrastructure is crucial for trade promotion and global economic integration.

Despite the fact that infrastructure affects the cost of production and level of trade, many international trade theories overlooked the role of infrastructure. Traditional international trade theories assumed zero transportation and energy cost which hardly justify the ground realities at a time when infrastructure services play a dominant role in regional as well as international trade. Hoekman and Nicita argue that 10% decrease in transport costs increase trade by 6% while 10% increase in overall investment in infrastructure contribute 5% to exports in developing countries. On the other hand, lack of infrastructure increases the cost of production, reduces profitability, and causes unnecessary delay in economic activities.

South Asian poor performance in the external sector is attributed to a number of variables including lack of skilled labor, meager foreign direct investment, shortage of capital, etc.; however, rarely any study focused on the role of infrastructure despite its significant contribution to trade and business. It is difficult to understand the South Asian external sector performance without understanding the role of infrastructure in the region. For example, lack of energy, transport, and communication and its related infrastructure adversely affect inter-regional and international trade in South Asia. Keeping in view the importance of physical infrastructure in a robust external sector, Asian Development Bank report advised South Asia to focus on investment in infrastructure in order to boost exports and tackle the perennial trade deficit. Therefore, in this paper we are trying to examine whether infrastructure affects international trade, particularly exports, and reduces trade deficit in selected South Asian countries.

Previous studies have some shortcomings to better understand the role of infrastructure in international trade by using the individual aggregate data of landline and mobile connectivity for telecommunication - and the total length of roads and the number of aircraft departures for transport infrastructure cost. Some recent studies devised principal components analysis (PCA). However, 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. Therefore, in this study we use a new Global Infrastructure Index based on annual dataset of 30 indicators of the quantity and quality of infrastructure and sub-indices on transport, communication, financial and energy to better understand the role of physical infrastructure in promoting exports and curtailing trade deficit in selected South Asian countries. This study uses the Pooled Mean Group (PMG) technique to examine the long- and short-run impact of infrastructure on exports and trade deficit. The superiority of the PMG procedure over other econometric techniques is that it allows for both short-run and long-run results. In addition, it also suggests the speed of adjustment to the long run. We also employ the Padroni and Kao cointegration test to examine the cointegration between the variables of our interest. Fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) are also used for further robustness and to obtain long-run coefficients of cointegration.

Rest of the paper is organized as: Sect. 2 presents infrastructure services and trade in South Asia, Sect. 3 reports data source and description of the variables, Sect. 4 provide the detail about econometric methodology and Sect. 5 consists of results and discussion, Sect. 6 shows robustness check with alternative methodologies, while conclusion and policy implications is accommodated in Sect. 7.

Infrastructure services and trade in South Asia

Infrastructure remained a big hurdle for South Asia to reap potential and develop rapidly. For example, 40% of firms in India, 45% in Pakistan, 60% in Bangladesh, and 75% in Nepal reported that inadequate infrastructure is a major obstacle to their pursuit to grow rapidly. Infrastructure deficit in South Asia is ever increasing and has reached a level where it hurts the domestic economy as well as the external sector of the region. The gap between supply and demand for infrastructure is continuously on the rise.

In South Asia, road density varies considerably. Road density is highest in Bangladesh despite the fact that just 30% of its roads are paved and more than 60% of its rural population lack access to all-season roads. The road density is lowest in the tough terrain Bhutan and Nepal. Meanwhile India has the world's second longest road network (i.e., 3.5 million km road network, and has 70,000-km-long national highway network), but road quality in India leaves much to be desired. More than half of its roads are not paved and a great deal of highways have just two lanes. Road network is shattered in rural India areas, where only 60% of the population has access to all-weather roads. Road condition in Pakistan are no different than any other part of South Asia. Total length of roads in Pakistan was 269,618 km in 2016, out of which 63% was paved. Around 60% of the road network is in poor condition due to poor maintenance and vehicle overloading, etc. The share of national highways and motorways in total road network for Pakistan is just 4.2%, but together the two handles more than 85% of Pakistan's total traffic. Poor road quality not only contributes to the cost of production, but it also retards the much needed connectivity in the movement of people and goods. Resultantly, poor transport infrastructure keeps domestic as well as international trade on hold.

Besides transportation, the shortage of energy and its related infrastructure is a huge constraint for inter-regional and international trade in South Asia. Garsous argues that the energy sector has a more significant impact than any other sector of infrastructure on international trade. Investing in the energy sector is crucial for development and for securing a high trade balance.

Access to electricity is a good indicator to understand the quality of energy infrastructure. Recently, the access to electricity in the selected South Asian countries has improved, however, the access to the basic input of electricity is still a big issue in South Asia, particularly in Bangladesh and India where a quarter of their population has yet to get access to electricity. Access to electricity in Nepal and Sri Lanka is more than 90% while it is around 99% in Pakistan (see Fig. 1), but despite a broad access to electricity, rampant load shedding and short supply of electricity has deprived Pakistan to excel on the economic front.

per capita fixed broadband

Data description and source

To assess the impact of infrastructure on exports and trade deficit over the period of 1990–2017, we rely on a new Global Infrastructure Index used by Donaubauer et al.. The detail regarding this infrastructure index is given by Donaubauer et al.. Most importantly the devised index contains further four sub-indices of infrastructure, i.e., transport, telecommunication, financial and energy to better understand the role of physical infrastructure in enhancing exports and decreasing trade deficit in selected South Asian countries (i.e., Bangladesh, Bhutan, Nepal, India, Pakistan, Sri Lanka). This new Global Infrastructure Index contains 30 indicators in order to cover all the important dimensions of quality and quantity infrastructure. The Unobserved Components Model (UCM) is used to determine the weight given to each component in the construction of the index. Similarly, we uses Quality of Institution Index (ln_QI), which is a composite index constructed on data collected from the International Country Risk Guide (ICRG). The developed index takes six variables of institutional quality like, law and order situation, corruption, government stability, investment profile, bureaucratic quality and democratic accountability are taken into consideration for the aim of to cover all the key extents of institution quality, by taking the average of all these six variables. The details about this index are found in Rehman and Ding. 

Furthermore, this is a panel data study and heterogeneity would be a major concern since the panel is a combination of time-series and cross-sectional data. The size of the countries in the present study is not homogenous, thus, we convert the selected monetary variables into per capita form such as export, trade deficit, exchange rate and per capita GDP. In the case of the nominal form of the monetary variables, the variation of the variables might also be due to the change in price. Thus, the analysis fails to capture the actual impact of the variables on trade. So the undermentioned variables are divided by their respective country's population.

The selected variables naming, Global Infrastructure Index and trade deficit consist of negative values which we convert into positive by dividing − 1 before taking natural log (LN). It is important to standardize the measurement of the variables, as it will improve fitness and homogeneity. The natural logarithm is a reliable method of the many methods. This study has taken the initiative to standardize the measurement in order to gain better and more meaningful interpretation as well. Table 1 presents the selected dependent and independent variables, notation, data description in braces and the sources. Moreover, all variables are converted into natural logs.

Theoretical justification

Infrastructure plays a vital role in promoting trade and curing trade deficit. Transport infrastructure can help a country to connect its remote area domestically and to business areas world wide at low cost. Good quality of energy infrastructure promotes capital-intensified industrialization and thus reduces production cost. Marketing is one of the most important tools of promoting products to capture the market which can be made possible through telecommunication. Better financial infrastructure helps to solve financial and liquidity barriers in the way of trade.

Econometric methodology

Y_{it} = \mathop \sum \limits_{j = i}^{p} \gamma_{ij} Y_{i, t - 1} + \sum \emptyset_{ij} Z_{i, t - j} + \mu_{t} + \varepsilon_{it} ,

where number of cross sections i = 1, 2, …. N and time t = 1, 2, 3 …. T. Z_it is a vector of K × 1 regressors, γ_ij is a scalar, μi is a group-specific effect. The disturbance term is an I(0) process if the variables are I(1) and co-integrated then a major characteristic of co-integrated variables is their rejoinder to any deviance from long-run equilibrium. This characteristic infers error correction dynamics of the variables in the system are swayed by the deviance from equilibrium. So it is common to re-parameterize above equation into the error correction equation as:

\( \Delta Y_{it} = \theta_{i,} y_{i,t - j} - \beta_{i} Z_{i,t - j} \mathop \sum \limits_{j = i}^{p - 1} \gamma_{ij} \Delta y_{i, t - j} + \sum \emptyset_{ij} \Delta Z_{i, t - j} + \mu_{t} + \varepsilon_{it} .\)              (2)

The error correction parameter θ_i indicates the speed of adjustment. If θ_i = 0, then there is no evidence that variables have long-run association. It is expected that θ_i is negative and statistically significant under the prior supposition that variables indicate a convergence to long-run equilibrium in case of any disturbance.

With increase in time period of analysis, dynamic panels; non-stationarity is a very important issue and in present study this issue has been taken into consideration by applying Levin, Lin and Chu (LLC) and Im, Pesaran and Shin (IPS) unit root tests. The condition is: when all the chosen variables in the model are stationary at I(1), I(0) or a mixture of I(0) and I(1). PMG being an ARDL-model is sensitive to the selection of lag length and hence, we utilize the Akaike Information Criteria (AIC) to obtain our optimal lag length. On the basis of the above model, we consider the following hypothesis.

H0

There is no impact of infrastructure on exports in South Asia.

H1

There is a significant impact of infrastructure on exports in South Asia.

H01

There is no impact of infrastructure on the trade deficit in South Asia.

H2

There is a  significant impact of infrastructure on the trade deficit in South Asia.

b. Panel cointegration tests

In order to examine the presence of a long-run convergence among our variables of interest, we carry out a panel cointegration test. The objective of the panel cointegration test is to combine information on similar long runs across the various panel members. Pedroni suggested seven cointegration tests for panel data on the basis of the cointegrating residuals of εit, three of which are considered to be group mean panel cointegration tests and are based on the between-dimension. They are devised by dividing the numerator by the denominator before adding over the N-dimension. The other four, referred to as panel cointegration tests, are based on the within-dimension and are formulated by adding both the numerator and the denominator over the N dimension. Moreover, the Kao test is also being used for cointegration between dependent and independent variables, on the foundation of Eq. (1) with the test for the null hypothesis of no cointegration being considered.

H0

There is no cointegration between infrastructure and exports in South Asia.

H1

There is significant cointegration between infrastructure and exports in South Asia.

H01

There is no cointegration between infrastructure and trade deficit in South Asia.

H2

There is significant cointegration between infrastructure and trade deficit in South Asia.

Empirical results and discussions

Prior to observing the potential long- and short-run impact of infrastructure on export and trade deficit, it is essential to create the order of integration among the selected variables because if the variable(s) are integrated of order I(2) the results do not remain valid. For this reason, Levine et al. and Im et al.  unit root tests are employed to examine the order of integration among the chosen variables. The results in Table 2 point out that all variables are either integrated of order I(1) or I(0) and no one of the variables is integrated of order I(2) or above, which clearly support the Pooled Mean Group (PMG) estimation procedure rather than other alternative cointegration technique.

Table 2 Unit root test results

The descriptive statistics of the explanatory variables is shown in Appendix 2. Appendix 3 presents the Pearson correlation coefficients among all the selected variables of the present study. It can be seen from Appendix 2 that there is strong positive correlation between the export and all others explanatory variables. On the other hand, there is a strong negative correlation between trade deficit and all other independent variables. In the subsequent regression, in order to alleviate the interference of multicollinearity on the regression results, there is no multicollinearity problem in our selected variables.

The empirical results in Table 3 show the outcomes of the PMG heterogeneous panel procedure. The result exhibits notable variations subject to the method of estimation. The PMG estimation result shows that a plausible long-run impact of aggregate and sub-indices of infrastructure (transport, telecommunication, energy, and financial sector) on export is positive and significant at 1% level in selected South Asian economies. The significant role of aggregate and all other sub-indices of infrastructure in exports confirm the findings of Donaubauer et al. and Brooks and Menon. Thus we reject the null hypothesis of no impact of independent variables on dependent variables, rather we accept alternative hypotheses. The empirical results are consistent with the opinion that, infrastructure matters to trade mainly because they decrease the cost of trade and ensure the ease of doing business in host economies. Lower trade costs raise the potential for increased export markets. This study uses the South Asian economies which are less developed countries. So, Garsous argues that the larger the number of developing countries in the sample, the more likely a positive impact of infrastructure on trade is likely to be observed. This would lead to the conclusion that the less developed the country, the more likely infrastructure will matter. Andrés et al. and Asif and Rehman found that infrastructure development has been a main determinant in reducing Asia's trade costs and thereby export expansion. Among the infrastructure the effects of other control variables, i.e., exchange rate (ln_EXR), human capital (ln_HC), per capita GDP (ln_PGDP), institutional quality index (ln_IQ) on exports is significant in all columns. It indicated that the undermentioned control variables increase the export significantly, except exchange rate which is consistently negative and significant in the present results. It presents that, when the  exchange rate of a host economy increases, automatically the price seems to be high. So export will decrease. The results are in the line of Sahoo and Dash and Ayogu. Similarly, institutional quality has a significant positive impact on export. It signifies that better quality of institutions significantly encourage export in the domestic economy. These empirical results negate the claim of Khan et al. that institutional quality does not contribute to export in South Asian economies. Furthermore, it can be seen in the lower half of Table 3, that in the short run, most independent variables are insignificant except aggregate infrastructure (ln_GINFRA), which is significant in both the short and long run.. The values of ECT₍₋₁₎ in Table 3 show slow adjustment to equilibrium position by exports. Likewise, in the present study, most of the developing countries experience persistent low economic growth; it is very likely that such a long-run relationship exists. However, there is little evidence to suggest their speed of adjustment to the long-run steady state should be the same.

Table 3 Pooled Mean Group method results (export is dependent variable)

Robustness with alternative methodologies

As it is apparent from the fact that the selected sample countries contain heterogeneous properties, like different market size, per capita GDP and exchange rate and they are not exactly the same, therefore, there is probability that standard error may not be normally distributed. In this regard, serial correlation, heteroskedasticity and also the problem of endogeneity may occur. For robustness check, and also for any undermentioned problems, this study uses fully modified least squares (FMOLS) and dynamic ordinary least square (DOLS) estimators. Pedroni proposes FMOLS and DOLS to attain the long-run cointegrating coefficients. In the existence of "unit root variables", the impact of super consistency may not control the endogeneity problem effect of the regressors if ordinary least squares (OLS) is used.

FMOLS estimator was formerly proposed in work by Phillips and Hansen to postulate optimal estimates of cointegrating regressions. This approach adjusts least squares to account for "serial correlation" effects and for the "endogeneity" in the regressors that result from the presence of a cointegrating association. Furthermore, the asymptotic behavior of FMOLS in models with full rank I(1) regressors, models with I(1) and I(0) regressors, models with unit roots, and models with only stationary regressors.

The fully modified (FM) estimator was originally devised to evaluate cointegrating links directly by modifying traditional OLS. One reason, this technique has proven beneficial in practice is that one can use the FMOLS corrections to determine how imperative these effects are in an empirical practice. This has assisted to make the approach less of a "black box" for practitioners. In cases where there are main differences with OLS the source or sources of those differences can generally be easily located and this in turn helps to stipulate the researcher with additional information about important features of the data. Contemporary simulation experience and empirical research indicates that the FM estimator performs well in relation to other procedures of estimating cointegrating relationships.

DOLS and FMOLS are superior to the OLS for many reasons; (1) OLS estimates are super-consistent, but the t-statistic obtained without stationary or I(0) terms are only approximately normal. Even though, OLS is super-consistent, in the presence of "a large finite sample bias' convergence of OLS can be low in finite samples (2) OLS estimates may suffer from serial correlation, hetero skedasticity since the omitted dynamics are captured by the residual so that inference using the normal tables will not be valid - even asymptotically. Therefore, "t" statistics for the OLS estimates are useless (3) DOLS and FMOLS take care of endogeneity issues by adding the leads and lags (DOLS). In addition, white Heteroskedastic standard errors are used. FMOLS does the same using a nonparametric approach, see Arize et al., and Arellano and Bond.

To overcome these problems, we applied FMOLS, and DLOS methods. These models are capable of dealing with above diagnostic issues attributable to the Padroni and Kao cointegration test and also to the PMG estimator. The results are reported in Tables 7 and  8 which are consistent with main models; however, there is reasonable improvement in explanation power of some of the indicators due to error correction.

Table 7 Fully modified OLS results (export is dependent variable)

Table 8 Fully modified OLS results (trade deficit is dependent variable)

Conclusion and policy implications

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.