This study addresses the short-and long-term effects of infrastructure on exports and trade deficits in certain South Asian countries between 1990-2017. As you read, think about other countries where limited infrastructure capacity has affected their ability to develop.
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
Level | First difference | |||
---|---|---|---|---|
Levin Lin Chu test | IM Pesaran test | Levin Lin Chu test | IM Pesaran test | |
Export | 0.941 | − 0.118 | − 3.403*** | − 3.029*** |
Trade deficit | − 1.185** | − 0.802 | − 4.249*** | − 4.688*** |
Human capital | 1.089*** | 0.456*** | − 4.638*** | − 4.478*** |
Exchange rate | 0.070 | 0.534 | − 4.402*** | − 3.762*** |
Per capita GDP | 0.454 | 0.761 | − 4.139*** | − 3.211*** |
Institutional quality | − 5.636*** | − 4.804 | − 3.226*** | − 3.143*** |
Transport infrastructure | − 2.668*** | − 2.377*** | − 7.752*** | − 8.063*** |
Telecommunication infrastructure | − 1.801*** | − 2.286** | − 4.253*** | − 6.180*** |
Energy infrastructure | − 1.274*** | − 2.761*** | − 4.585*** | − 4.604*** |
Financial infrastructure | 0.176*** | − 1.593* | − 2.049*** | − 4.661*** |
Aggregate infrastructure | − 2.142*** | − 2.328*** | − 6.001*** | − 7.297*** |
****, ** and * denote the significance at 1%, 5%, and 10%, respectively. All variables are in natural log form. The results are based on intercept and trend
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)
Variables | Transport infrastructure | Telecommunication infrastructure | Energy infrastructure | Financial infrastructure | Aggregate infrastructure |
---|---|---|---|---|---|
Long-run results | |||||
Exchange rate | − 3.0573*** | − 0.5454*** | − 0.1234* | − 1.4267*** | − 0.7673** |
Std. error | 0.6979 | 0.0269 | 0.0994 | 0.3677 | 0.3298 |
Human capital | 0.1536 | 1.5653*** | 0.1917* | 0.1418 | 0.1670 |
Std. error | 0.3075*** | 0.1994 | 0.1175 | 0.5383 | 0.4494*** |
Per capita GDP | 1.7352 | 0.8590*** | 0.5565*** | 1.4300*** | 1.0565 |
Std. error | 0.2093 | 0.013 | 0.0386 | 0.2013 | 0.2349 |
Institutional quality | 1.6017*** | 0.0531*** | 0.1887* | 0.9098*** | 0.4234*** |
Std. error | 0.5920 | 0.0219 | 0.1171 | 0.1538 | 0.0513 |
Transport infrastructure | 1.3117*** | ||||
Std. error | 0.4232 | ||||
Telecommunication infrastructure | 0.4720*** | ||||
Std. error | 0.0155 | ||||
Energy infrastructure | 0.7733*** | ||||
Std. error | 0.2598 | ||||
Financial infrastructure | 0.2549*** | ||||
Std. error | 0.0628 | ||||
Aggregate infrastructure | 0.3267*** | ||||
Std. error | 0.0878 | ||||
Short-run results | |||||
Exchange rate | − 0.5190 | − 0.0306 | − 0.1256 | − 0.4713 | − 0.5334 |
Std. error | 0.4602 | 0.1472 | 0.2514 | 0.2414 | 0.4803 |
Human capital | 0.0592 | 0.9343 | 0.2223 | 0.1845 | 0.1577 |
Std. error | 0.1673 | 0.8129 | 0.4239 | 0.1946 | 0.2007 |
Per capita GDP | 2.1747* | 0.3917 | 0.9911*** | 1.0156 | 0.9119*** |
Std. error | 1.2822 | 0.3526 | 0.1564 | 0.1227 | 0.3089 |
Institutional quality | 0.1410 | 0.1099 | 0.0890 | 0.0864 | 0.3214** |
Std. error | 0.4021 | 0.0770 | 0.2367 | 0.1373 | 0.1544 |
Transport infrastructure | 0.0773 | ||||
Std. error | 0.1899 | ||||
Telecommunication infrastructure | 0.2901 | ||||
Std. error | 0.1239 | ||||
Energy infrastructure | 0.0853 | ||||
Std. error | 0.3017 | ||||
Financial infrastructure | 0.0494 | ||||
Std. error | 0.0509 | ||||
Aggregate infrastructure | 0.0874** | ||||
Std. error | 0.0489 | ||||
Constant | − 0.2699 | 1.7603 | 0.9300 | − 0.6134 | − 0.9540 |
Std. error | 0.6757 | 1.6136 | 0.5594 | 0.7883 | 0.1231 |
ECT(−1) | − 0.2954* | − 0.3867** | − 0.2478** | − 0.1220** | − 0.2429*** |
Std. error | 0.1496 | 0.2183 | 0.1421 | 0.0671 | 0.0683 |
Hausman test (P-values) | 0.4886 | 0.9995 | 0.2896 | 0.0063 | 0.4585 |
Pearson CD test (P-values) | 0.2679 | 0.4855 | 0.2749 | 0.3271 | 0.6453 |
Variables | Transport infrastructure | Telecommunication infrastructure | Energy infrastructure | Financial infrastructure | Aggregate infrastructure |
---|---|---|---|---|---|
Long-run results | |||||
Exchange rate | − 0.8650*** | − 0.6846** | − 0.8017*** | − 4.1071*** | − 3.4787*** |
Std. error | 0.2376 | 0.3581 | 0.2989 | 0.4929 | 0.7478 |
Human capital | − 0.0136 | − 0.1006 | − 0.0305 | − 1.3230* | 0.7265* |
Std. error | 0.2848 | 0.3298 | 0.3390 | 0.8826 | 0.4233 |
Per capita GDP | 1.3501*** | 1.3395*** | 1.3543*** | 3.0160*** | 2.7465*** |
Std. error | 0.1807 | 0.1735 | 0.1348 | 0.3011 | 0.6561 |
Institutional quality | − 0.2859 | − 0.6712** | − 0.5844*** | 0.7035 | 1.5687*** |
Std. error | 0.2488 | 0.3039 | 0.2682 | 0.7742 | 0.3632 |
Transport infrastructure | − 0.1078** | ||||
Std. error | 0.0554 | ||||
Telecommunication infrastructure | − 0.0464 | ||||
Std. error | 0.1958 | ||||
Energy infrastructure | − 0.0247 | ||||
Std. error | 0.1032 | ||||
Financial infrastructure | 0.3237*** | ||||
Std. error | 0.0802 | ||||
Aggregate infrastructure | − 0.4292*** | ||||
Std. error | 0.1751 | ||||
Short-run results | |||||
Exchange rate | 2.5839*** | 2.2407** | 2.5519*** | 2.5864 | 4.3304 |
Std. error | 0.1816 | 0.7701 | 1.0489 | 1.6439 | 2.9042 |
Human capital | 0.0903 | 0.3284*** | 0.0070 | − 0.9086 | 0.9416 |
Std. error | 1.4475 | 0.1039 | 1.3530 | 1.5923 | 0.8382 |
Per capita GDP | 3.3435*** | 3.3054*** | 3.5189*** | 3.0206** | 1.3757 |
Std. error | 1.1054 | 0.7950 | 1.0498 | 1.4649 | 0.4562 |
Institutional quality | − 1.6697 | − 1.1406 | − 1.4167 | − 0.6815 | − 1.6054 |
Std. error | 1.4140 | 1.1943 | 1.2166 | 0.9362 | 0.6058 |
Transport infrastructure | 0.2748 | ||||
Std. error | 0.3606 | ||||
Telecommunication infrastructure | − 0.5274** | ||||
Std. error | 0.2918 | ||||
Energy infrastructure | − 0.2832 | ||||
Std. error | 0.3510 | ||||
Financial infrastructure | 0.0257 | ||||
Std. error | 0.0802 | ||||
Aggregate infrastructure | 0.0113 | ||||
Std. error | 0.1587 | ||||
Constant | − 0.7986*** | − 0.5983*** | − 0.6599*** | 0.9704* | − 1.3201 |
Std. error | 0.1693 | 0.2918 | 0.1424 | 0.6286 | 1.7275 |
ECT(−1) | − 0.436*** | − 0.3745*** | − 0.4001*** | − 0.3108* | − 0.6665** |
Std. error | 0.181 | 0.1707 | 0.1622 | 0.2096 | 0.2864 |
Hausman test (P-values) | 0.8237 | 0.987 | 0.8060 | 0.2345 | 0.986 |
Pearson CD test (P-values) | 0.8372 | 0.5491 | 0.4414 | 0.6660 | 0.5327 |
In addition to that, the effect of other control variables such as exchange rate, human capital per capita GDP, institutional quality index is significant and negative in most of the columns except human capital which has the correct sign according to economics theory but insignificant. It is due to the fact that selected South Asian economies have insufficient human capital (i.e., decrease rate of enrollment in secondary school) and imports continuously rise up which may cause insignificancy. One can examine the empirical results of Table 3, that the influence of human capital on export is positive and significant. It is due to the reason that export enhances relative to the speed of human capital in South Asian economies, while in short run the influence of aggregate and all other sub-indices of infrastructure on trade deficit is insignificant. The values of ECT(−1) in Table 6 show slow adjustment to equilibrium position by trade deficit due to the above-mentioned reason.
Table 5 presents the Pedroni and Kao cointegration test results. The empirical results of Table 5 demonstrate the existence of a cointegration between dependent (i.e., export) and independent variables (such as ln_EXR, ln_HC, ln_IQ, ln_PGDP and ln_GINFRA) fully established in both (within-dimension and between-dimension) in all specifications because the v-statistic and the rho-statistics probability values are lower than the conventional level of significance, and also the ADF-statistic and PP-statistic indicate that their probability values are significant at 1% level of significance. The probability values of rho-statistic, v-statistic and ADF-statistic are also significant in case of trend and intercept (between-dimension and within-dimension). The PP-statistic (between-dimension and within-dimension) is significant at 1%, also ADF-statistic is significant at 1%.
Table 5 Pedroni and Kao cointegration test (export is dependent variable)
Export | Within-dimension (Panel) | Between-dimension (Group) | ||
---|---|---|---|---|
v-Statistic | − 1.4074* | 2.6440 | Group-rho | 2.4950*** |
rho-Statistic | − 1.534* | − 1.9146** | Group-PP | − 1.6704* |
PP-statistic | − 3.4253*** | 0.6203 | Group ADF | − 1.9146** |
ADF-statistic | − 1.178* | − 1.8524** | Kao test | − 2.22*** |
****, ** and * denote the significance at 1%, 5%, and 10%, respectively
We observed from the results of Table 5 that the cointegration is strong when an export use is a dependent variable in the analysis because most of the variables show significance (between-dimension, within-dimension and deterministic trend and intercept). Furthermore, Kao test in Table 5 clearly indicates that there is a long-run relationship between the dependent and independent variables in South Asian countries, because of the reason that all variables are significant. Here, we clearly reject the null hypothesis (of no cointegration) and accept an alternative hypothesis (presence of cointegration).
It can be seen from Table 6, this study also uses trade deficit as a dependent variable and apply Pedroni and Kao cointegration test. The results confirmed the presence of a cointegration fully conventional in both (within-dimension and between-dimension) in all specifications of v-statistics and rho-statistics because the v-statistic and the rho-statistics probability values are decreased than the conventional level of significance. The ADF-statistic and PP-statistic indicate that their probability values are significant at 1% level of significance. In the case of deterministic trend and intercept (between-dimension and within-dimension) the rho-statistics and v-statistics probability value shows significance at 1% level. The PP-statistic and ADF-statistics (between-dimension and within-dimension) is significant at 1%. We concluded from the results of Table 6 that the cointegration is also strong when the trade deficit used is a dependent variable in the regression analysis because most of the variables show insignificancy (between-dimension and within-dimension). Table 6 also shows the Kao cointegration test. The results show the dependent and independent variables are co-integrated, because whole variables are significant in all specifications.
Table 6 Pedroni and Kao cointegration test (trade deficit is dependent variable)
Within-dimension (Panel) | Between-dimension (Group) | |||
---|---|---|---|---|
v-Statistic | 1.852*** | − 1.7708** | Group-rho | 0.159 |
rho-Statistic | − 0.812 | 0.868 | Group-PP | − 2.214*** |
PP-statistic | − 2.323*** | 0.0472 | Group ADF | − 2.204*** |
ADF-statistic | − 1.526*** | 0.548 | Kao test | − 2.79*** |
****, ** and * denote the significance at 1%, 5%, and 10%, respectively