*The Linear and Nonlinear Relationship between Infrastructure and FDI in India DOI: http://dx.doi.org/10.5772/intechopen.101612*

this paper used auto-regressive distributive lag model and error correction model techniques to cointegrate by using equations no 1&2. The study calculates the regressions techniques that FDI is substituted by sectoral FDI inflow (like by FDI in the primary sector, FDI in the service sector, and FDI in manufacturing and trading) to evaluate the probable long-run association regarding FDI inflow and cumulative infrastructure. As this paper investigates the two functional causalities between infrastructure and FDI inflow sector (like by FDI in the primary sector, FDI in the service sector, and FDI in manufacturing and trading), so for this determination of inverse impact, the study also take infrastructure as a dependent variable and then substitute the infrastructure into sub-indices of infrastructure (such as (T\_inf), (Te\_inf), (E\_inf), (F\_inf), (G\_inf-2020), (IQ\_inf), (PFDII), (FDIIS), (T\_inf) (H-inf)). The optimum lag length is grounded on AIC for measuring the present models of interest.

## **5. Linear cointegration outcomes (autoregressive distributed lag-ARDL)**

From **Table 5**, the significant level calculated with help of the F-statistics values, the Ho (Null hypothesis) rejected the there is no cointegration and **Table 5** suggested that there is a possible relationship between the FDI inflow, Infrastructure and all other control variables (such as Trade openness (export and import), Infrastructure intuitional quality and Human capital in infrastructure projects) exist in long-run. From equation no 1, the calculated F-statistics values are the upper bound critical value at 5% and 1% significant level with control variables. The outcomes are in the same order with preceding research studies [22, 23]. Furthermore, it was also examined in **Table 4**, the probable association exit in the long run between FDI, Infrastructure and control variables. From equation no 2, the calculated F-statistics value is higher than the upper bound critical factor value at 5% and 1% significant level


*\*\*\*,\*\*, and \* indicates significance level at per cent of "10%", "5%" and "1%" correspondingly. The "critical values" of intercept are* �*2.701,* �*2.730,* �*1.950 significant level at 1%, 5% and 10% correspondingly, where the "critical values" for Zivot-Andrews are* �*3.490,* �*6.351,* �*6.351 significant level at 1%, 5% and 10% 'correspondingly.*

#### **Table 5.**

*Unit root test outcomes.*

without considering the control variables of human capital and quality in infrastructure institutional body). The experiential outcomes of the certain test for equation no 1&2, the H1 (alternative hypothesis) accepted, the existence of cointegration between the chosen variables and Ho (Null hypothesis) rejected, due to no cointegration between the selected variables, according to Asiedu [24]. The study also checked the robustness for the determination of the long-run relationship between FDI and infrastructure projects. The study also used the depended-on variables with and without control variables, indicates in **Table 6** which gives constant outputs in both the cases (with control variables and without control variables).

**Table 6** illustrated that the association among the aggregates infrastructure, manufacturing sector and FDI inflow in the long run. Based on **Table 6**, the outcomes show the expected positive relationship among the chosen variables in the long run. The calculated F-statistics values are lower than higher bound critical factor value and significant at 5 per cent level with control variables are infrastructure institutional quality, human capital and export and import trade openness, while on the other side (i.e., opposite causality) the pragmatic outcomes of the bound test have advised robust relationship between total infrastructure and FDI inflow in the manufacturing sector (column 5–6). The predictable F-statistic value is higher than the upper bounds critical factor value at 5% and 1% correspondingly. Thus, the described outcomes disclose the existence of two functional associations between total infrastructure and manufacturing FDI. In this connection, the null hypothesis was rejected because there is no positive relationship between the selected variables. The current paper showed that two functional associations between total infrastructure FDI inflow in the primary sector in the long run in the column no 4 and 8. The empirical outcomes


#### **Table 6.**

*Cointegration outcomes (ARDL constraints test and error correction model result).*

#### *The Linear and Nonlinear Relationship between Infrastructure and FDI in India DOI: http://dx.doi.org/10.5772/intechopen.101612*

explored the positive association among the infrastructure, FDI in the primary sector in the long run and all the selected variables of the paper. The F-statistics is higher than the upper bound critical factor value at a 1 per cent significance level with consideration of with and without control variables.

**Table 6** indicates the two-function association between FDI inflow in the service sector and infrastructure in columns 4 and 8. The outcomes show the existence of two functional relationships between FDI and infrastructure development in India. The F-statistics values are greater than higher bound critical factor values in the case of with and without control variables of column 4. On the other hand, the Aggregate infrastructure to FDI services the values of assessed F-statistics are lesser than the greater bound critical factor values in case of without control variables and experiential greater than higher bound critical factor values at 10% significance level.

### **6. Granger causality method**

The granger causality test determines the long-run relationship between the condition for causality and the selected variables of the study according to Morley [22, 23]. The Confirmation of long-run existence between the variables indicators shows that there should be the minimum non-functional causality between the selected variables for this research [25]. Henceforth, VECM is employed to calculate the function of the long run and short-run causality relationship between total infrastructure and FDI inflow with the consistency of the service sector, manufacturing sector and the primary sector.

**Table 7** illustrates the long and short-run granger causality relationship from FDI inflow in different sectors and total infrastructure in Indian, the results show that the coefficient of error correction in long run period and the CEC term is strongly significant when cumulative FDI inflow FDI in the manufacturing sector, FDI in the primary sector and FDI in the service sector are used as dependent variables. Whereas the Fstatistics value does not indicate a significant impact on the selected variables during the short run, i.e., FDI inflow in selected sectors to total infrastructure. The empirical output of the Granger causality method estimator promoted the long-run causality exists from the study variables (FDI\_I, FDI\_T, FDI\_M, FDI\_S) to total infrastructure which advises the infrastructure play a significant role to attract FDI inflow in service sectors, primary sectors, and manufacturing sectors of India (1). However, there is no causality existing in the short-run (from FDI\_I, FDI\_T, FDI\_M, FDI\_S to total infrastructure) which discloses that the total infrastructure does not affect the ability to attract FDI inflow in sectors wise in the short run. The results in **Table 7** also indicates the short and long-run causality from total infrastructure to total FDI inflow. The output indicates that the long and short-run causality in current and significant level at 5 per cent. It means that in Indian total FDI inflow affects the availability of infrastructure and quality level.

Whereas the transportation infrastructure, telecommunication infrastructure, energy infrastructure, infrastructure in the power sector and financial infrastructure variables are used in this paper as dependent variables. This empirical output shows that total FDI inflow causes aggregates and sub-indices of infrastructure in the long run. The finding of the study reveals that inverse causality in FDI inflows indicates positive and significant effects on overall infrastructure sub-indices in the long-run period. Furthermore, the outcomes show that the sectors are FDI\_P, FDI\_S and FDI\_M are used as descriptive variables. The output demonstrated that the error correction model is significant at the level of 5 per cent while FDI\_P, FDI\_S and FDI\_M are used as independent variables. The outcomes also indicate that in the long


*<sup>\*\*\*, \*\*,</sup> and \* Indicates significance level at "10%", "5%" and "1%" correspondingly. Source: calculations of author.*

#### **Table 7.**

*Granger causality test output.*

run extension of FDI inflow, it can grow infrastructure quality and availability (1). Can grow infrastructure quality and availability (1).

Null hypothesis: The Diagnostic tests are not affected by the mention Econometric problem. Alternative: The Diagnostic tests are affected by the mention Econometric problem.

**Table 8** illustrates the determined causal relationship between GII and FDIP in the long run, in the same order where FDI\_S is used as a dependent variable. The fact that FDI\_ S is used as a dependent variable. This indicates that the impact of total


**Table 8.** *Diagnostic tests.*

### *The Linear and Nonlinear Relationship between Infrastructure and FDI in India DOI: http://dx.doi.org/10.5772/intechopen.101612*

infrastructure is positive but insignificant in the long run without considering the control variables, while significant considering the control variable. The empirical outcomes indicate that the spill-over effect of FDI inflow is more than infrastructure in the long run in Indian. The model's constancy is established by recursive estimation. They recommend that statistically valid inference can be drawn from the selected models. The rest of the diagnostic tests are indicated in **Table 8**.


*Note: \* DU\_FDI is time dummy variable confirmed for operational break in FDI\_I, FDI\_S, FDI\_P, and FDI\_M. \*\*\*p < 0.01, \*\* p < 0.05, \* p < 0.1.*

#### **Table 9.**

*Nonlinear effect of global infrastructure index on aggregate and disaggregate FDI inflow in India.*

### **7. Nonlinear cointegration test**

There may be a nonlinear relationship exist of time series variables, thus, after the newest methodology proposed by Shin et al. [21], this paper tested the cointegration method by exempting the linear relationship restriction. The outcomes are described in **Table 9**, which authorizes the cointegration relationship (attained negative and


*Note: \* DU\_FDI is time dummy variable confirmed for operational break in FDI\_I, FDI\_S, FDI\_P, and FDI\_M. \*\*\*p < 0.01, \*\* p < 0.05, \* p < 0.1.*

#### **Table 10.**

*Nonlinear effect of aggregate and disaggregate foreign direct investment inflow on global infrastructure index.*

#### *The Linear and Nonlinear Relationship between Infrastructure and FDI in India DOI: http://dx.doi.org/10.5772/intechopen.101612*

significant statistics of Error correction model) among the FDI\_I, FDI\_S, FDI\_P, FDI\_M and G\_I\_INF. Though, the co-movement of FDI\_I, FDI\_S, and G\_I\_INF is maintained by a significant PSS test. The feature that differentiates the non-linear auto-regressive distributive lag mode [25] from the traditional autoregressive distributive lag mode is the asymmetries testing. Fascinatingly, the outcomes illustrate that in the case of equation no 1 there is an indication of SR asymmetries, and in equation no 3 there exist LR asymmetries.

Likewise, **Table 10** explores the dependent variable is exchanged with an independent variable and the non-linear auto-regressive distributive lag model is assessed. The outcome of the paper is that cointegration exists when FDI inflow and FDI in the services sector are taken as descriptive variables, while unpredictably, the PSS F-Test does not sustain to Error correction term or model. Concerning the asymmetric relationship, only equation no 1 shows the existence of SR asymmetries, which is confirmation of the outcomes stated in **Table 9**. Thus, the paper infers that in the relationship of FDI and G\_I\_INF, traditional auto-regressive distributive lag may not be acceptable to rely upon and to articulate effective strategies, as it proceeds from asymmetric circumstances, which may lead to unsuitable policy measures. Hence, it is suggested to contemplate the non-linearities that may exist while testing linear modeling between the variables.

### **8. Conclusion**

The current paper determined to examine the linear and nonlinear cointegration between FDI inflow and total infrastructure, together with various sub-indices of infrastructure and sectoral FDI inflows of India. To accomplish this objective, the paper used Granger causality to determine the causal relationship between FDI inflow and infrastructure, while linear and nonlinear situations are used to find the cointegration relationship. The observation of the findings confirms the existence of the linear and nonlinear cointegration between the cumulative as well as sub-indices of infrastructure and aggregated and disaggregated FDI inflow. Additionally, the findings of asymmetric testing are motivating, which article mix outcomes in terms of the existence of SR and LR asymmetries in the appropriate manner as stated in **Tables 9** and **10**. So, we infer that in the fitting together of FDI and Global infrastructure index, traditional ARDL may not be acceptable to depend on and to articulate effective policies, as it proceeds from asymmetric circumstances, which may lead to a weedy policy assertion. Hence, it is suggested to study the non-linearities that may exist while testing linear modeling. Furthermore, the conclusions elaborate that to make the economy attract more FDI, the government shall further expand the system of infrastructure in education, quality of the institution and to promote the exports. Furthermore, the empirical results advise that improved quality and availability of infrastructure stocks are the most to attract high FDI inflow in the primary sector, services sector and manufacturing sector of India's economy in the long run. Hence, the emphasis of policies should be to progress both infrastructure facilities and to make available a conducive atmosphere for global investors to obtain high FDI because FDI inflows indicate to improve the quality and availability of infrastructure. This research concludes that, the current study offers a worthwhile understanding to policymakers and supervisors to consider the sectoral level FDI inflow in India, as an alternative of planning policies exclusively based upon aggregate FDI. Likewise, the study brings into the argument an exceptional measure of infrastructure index

(G\_I\_I) that incorporates the broader aspects together with telecommunication, energy, transportation and financial infrastructure. On the other hand, the previous secondary information deeply depends on only the telecommunication and IT infrastructure which may not be acceptable to represent the sundry dimensions of the infrastructure, reported by the Global Infrastructure Index 2020.
