The Institutional Factors Impact on Foreign Direct

The Institutional Factors Impact on Foreign Direct Investment (FDI) On

Indian Economic Growth.

Dr. R S Ch Murthy Chodisetty

Faculty of Management, Sreenidhi Institute of Science and Technology, Hyderabad

ABSTRACT

The study covers several aspects of FDI in the country, ranging from FDI patterns and FDI

drivers to FDI relations, growth and exports, taking into account several factors such as the

formation of raw equities, macroeconomic stability, institutional capital and human capital.

In recent years, the FDI has increased so greatly that it has surpassed all other metrics of

economic transactions. Countries are bidding for the highest levels of FDI, as they are the

cheapest foreign funding. The FDI rate has increased to the developed countries in the last

two decades, compared to the previous trend. There may have been a surprising rise of Asia

as big FDI recipients. In the 2014 industry review, the highest FDI for the service sector was

found. In the fields of training, accounting, infrastructure and telecoms, most of the FDI

inflows are generated. The self-employed industries authorize government investments in

chemical, metallurgical, automobile, Pharmaceutical and tourism sectors. The main recipient

is FDI, but FDI flows are subject to policy constraints. Despite the lack of restrictions on

FDI inflows in metallurgical, chemical, automotive, pharmaceutical and tourism industries,

FDI growth in those sectors was much lower than in the FDI markets for utilities and

telecoms.The study focuses on the impact of institutional influences on Indian foreign direct

investment.

Keywords:Foreign Direct Investment, Institutional Factors, Economic Growth.

JEL Codes:G1, F21, F43, O43, O47.

1. INTRODUCTION:

In several countries, including India, FDI's position for economic growth has been a hot

topic of debate. FDI is a key part of the world economy's global efforts. Economic and

technological forces are driving growth of international production. The continuing

liberalization of FDI and trade policies is also behind it. One feature of the world today is

the circulation of private capital flows in developing countries, particularly since the

1990s, in the form of foreign direct investment (FDI). Since the 1980s, MNCs have

emerged as major actors in the sense of globalization. In this sense, globalization gives

developing countries such as India a parallel opportunity to achieve rapid economic

growth through trade and investment. International trade expanded more rapidly than the

FDI in the 1970s, so far the major economic activities in international cooperation were

international trade. With the growth of marketing and global supply networks for

Manufacturing and Distribution in the mid-1980s, FDI has therefore begun to rise rapidly,

and has fundamentally changed.FDI flows include capital provided by foreign investors to

enterprises in another economy directly or indirectly, with an anticipation that they will

make better profit and participate in the management of the company in which they invest.

In proportion to their equity portfolios, foreign investors accumulate capital in host-

country companies Prachi Arora (2013)1. The previous Indian FDI definition differs from

High Technology Letters

Volume 27, Issue 1, 2021

ISSN NO : 1006-6748

http://www.gjstx-e.cn/226

that of the IMF as well as the UNCTAD WIR definition; the IMF definition comprises

ECBs. FDI inflows will preferably reflect the formation of capital, the formation of new

businesses in one factory, the increase in foreign equity held in existing firms, M&As in

existing companies and others.This is the empirical definition used by many countries to

distinguish between FDI and portfolio streams. FDI was defined as the' investment to

gather a lasting interest in a company which operates in the economy other than the

investor's by the International Monetary Fund (IMF),' the object of which is that of an

investor to have an effective corporate management voice (IMF, 1977). FDI is the process

through which residents of one country (source country) are acquired by assets in order to

monitor a business in another country (host country)'s production, distribution and other

productive activities.

2. REVIEW OF LITERATURE

Samina Sabir, Anum Rafique and Kamran Abbas (2018): This study investigates

the impact of institutional quality on Foreign Direct Investment (FDI) inflows using

panel data. The empirical results confirm that institutional quality has a positive

impact on FDI in Asian of countries. These countries sample period of 1996–2017

using the system Generalized Method of Moments (GMM) for analysis. The

magnitude of the coefficients of control of corruption, government effectiveness,

political stability, regulatory quality, rule of law, and voice and accountability for FDI

inflows are greater in developed countries than in developing countries. We conclude

that institutional quality is a more important determinant of FDI in developed

countries than in developed countries.

ZuhalKurul and A. Yasemin Yalta (2017)145

: In this paper, we revisit the relation

between institutional factors and foreign direct investment (FDI) inflows in

developing countries by employing a dynamic panel methodology, which enables us

to deal with the persistency of FDI flows and endogeneity issues. We also contribute

to the literature by using various measures of institutions to identify which aspects of

institutional quality affect FDI in the developing world. Our empirical findings based

on 113 developing countries over the period 2002–2012 show evidence that some

institutional factors matter more than others in attracting more FDI flows. We also

found that the financial crisis in 2008 and 2009 had a negative impact on FDI flows.

Viral Upendrabhai Pandya (2017)146

: This paper examines foreign direct

investment (FDI) inflows and its impact on economic growth in Australia. FDI

inflows are considered to be a vital source of economic growth or development for

any economy and it plays big role in growth in gross domestic product (GDP),

improvement in infrastructure, employment creation, export and trade performance.

This paper examines the relationship between FDI and economic growth of Australia

through regression analysis between FDI and different measures of economic growth.

Pradip Baija1(2017)144

: - The research paper titled, “FDI Inflows Road to India’s

Rapid Development”, recorded the Market Survey and the findings of survey

conducted by the federation of Indian chamber of commerce and industry

(FICCI),that 70 per cent of foreign investors surveyed were making profits from

their Indian operations. The survey noted that as manufacturing foreign investors



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were mostly dissatisfied with the infrastructure situation of the country, 42 per cent

rating the quality of parts and power facilities and 54 per cent of the companies rating

the condition of roads and highways.

Rao and Singh(2017)143

: - The study pointed out that "the regional concentration of

FDI is less of a concern if labor mobility is sufficient to ensure that workers can go

where new jobs are created." However, Top level points out that Indian labor mobility

is relatively low, "less than 0.5 percent of the population in rural and 4 percent of the

population in urban areas moved for reasons of economic consideration (or

employment).

3. Statement of the Problem:

In recent years emerging countries are attracting significant FDI inflows. The economic,

social development appear to the growing economies considerable making effects to

attract FDIs by focusing prospective sectors, institutional settings, policy changes.

Regulatory liberalization, investor guarantees, incentives and concessions etc.… but India

lagging behind in attracting the sizable amount of FDI. Over the past decade china doubled

its FDI and India over all FDI inflows show a significant growth trend. Despite the

dynamism of the country with enormous potential and increasing importance for FDI but

being the amount world’s top fastest growing countries (BRICS) failed to achieve the

expected FDI inflows compound with other sizable economic oriented countries. Indian

FDI Flows are growing year on year its observes the data from 2000 to till date. But the

Indian FDI growth is not as per the growth of global FDI growth. Many research scholars

have done research in this area but institutional factors role has not been studied, in the

progressive of FDI flows sector wise. All the sectors are not able to attract FDI flows

strongly due to bottlenecks in the form of Indian economic factors influence along with the

institutional factors.

4. Objectives of the Study:

1. To examine the institutional factors impact on Foreign Direct investment (FDI) flows in

to India.

5. Hypotheses of the Study:

H0: There is no institutional factors impact on Foreign Direct investment flows in to India.

H1: There isinstitutional factors impact on Foreign Direct investment flows in to India

6. Research Methodology:

6.1. Sources of Data:The secondary data were obtained from the annual reports of the ten public

sector banks. Additional data for analysis and verification were sourced from ww.moneycontrol.com.

The data were subjected to certain fundamental mathematical operations such as computing the ratios,

before being used for the analysis.

6.2. Period of the Study:

The study period is 15 years, between 2004 and 2018. Therefore, the trend in Indian FDI

influences for the period 2004 to 2018 is very necessary to look back and evaluate its



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potential factors that determine FDI attraction and thus provide a clear picture of India's

competitive status in FDI attraction.

6.3. Tools used in the Study:

Unit Root Test: The study has applied the unit root test for the stationary of the time

series data to remove the seasonality effect. The following are the statistical tests were

applied.

• Augmented Dicky Fuller test

• Phillip-perron test

ARDL: The Auto regressive distributed log methodology has been applied to know the

association between the independent variables and the dependent variable. The study

has considered the institutional factors (independent variables) association with the

Indian FDI (dependent Variable.

Ordinary least Square: The ordinary least square method has been applied to know

the selected economic factors influence on the dependent variable (FDI).

7.LIMITATIONS OF THE STUDY:

The present research focused only on secondary data and the required data from reputed

sources were not consistent and therefore the results based on the statistical analysis

may not be correct. For example, analyzing the significance of possible determinants of

FDI at national level is a highly complex task. And it was difficult to use the raw data

for business environmental considerations such as business regulations, government

efficiency, technological readiness, etc.,

Data from different sources such as FDI inflows from RBIs in India, which do not

exactly match UNCTAD FDI data, and similarly for IMF, world economic

perspectives, and UNCTAD economic indicators, also differed.

8. SCOPE FOR FURTHER RESEARCH:

The study has been emphasized on the foreign direct investments of India. The study

mainly focused on the Indian economic growth with contribution of sectoral investments.

The study has considered the FDI investments from the 2004 to 2015. The study made an

attempt to examine the sectors, economic factors and institutional dimensions were

considered in the study. The sectors were selected based on the higher FDI flows from the

period of 2004 onwards continuously.

In the study the following Institutional indicators included

• Control of Corruption

• Government Effectiveness

• Political Stability

• Rule of Law

• Regulatory Quality

• Voice and Accountability



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9. DATA ANALYSIS:

In the study IConsider Five types of Institutional Indicators from the Government website

for the analysis. The Factors are given below.

Control of Corruption

Government Effectiveness

Political Right Index

Regulatory Quality

Rule of Law

Voice &Accountability

Table – 1.0

Unit Root test with Augmented Dickey Fuller

Institutional Indicators Level 1st Difference 2

nd Difference

Control of Corruption 0.0013* - -

Government Effectiveness 0.0000* - -

Political Right Index 0.0000* - -

Regulatory Quality 0.0030* - -

Rule of Law 0.0682 0.0000* -

Voice &Accountability 0.0010* - -

*Significant at 5% level

Source: compiled on secondary data through E-views

The table stated the unit root test result under the Augmented Dickey Fuller test for the

institutional indicators of FDI are observed significant at the 5% level. The Control of

corruption, government effectiveness, political right index, regulatory quality and voice &

accountability are found to be significant at normal level and they are stationary. The rule of

law is observed to be non-significant at normal level but it is observed to be stationary in 1st

difference.

In this study following Institutions indicators include are Control of Corruption, Government

Effectiveness, Political Stability, Regulatory Quality, Rule of Law, and Voice and

Accountability. The unbundling of institutions allow us to examine which of these different

dimensions matter for FDI flows in developing countries.

1. Control of Corruption:



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Figure-1.1

Akaike Information Criteria Graph for Control of Corruption

10.48

10.52

10.56

10.60

10.64

10.68

10.72

10.76

10.80

ARDL(2, 0)

ARDL(4, 0)

ARDL(2, 1)

ARDL(1, 0)

ARDL(3, 0)

ARDL(4, 1)

ARDL(1, 1)

ARDL(3, 1)

ARDL(2, 2)

ARDL(4, 2)

ARDL(1, 2)

ARDL(2, 3)

ARDL(3, 2)

ARDL(1, 3)

ARDL(4, 4)

ARDL(4, 3)

ARDL(1, 4)

ARDL(2, 4)

ARDL(3, 3)

ARDL(3, 4)

Akaike Information Criteria

Source: Secondary Data.

The above Akaike Information Criteria graph illustrates the optimum selection criteria for the

Autoregressive Distributed Lag model to check the association between Foreign Direct

Investment and Control of Corruption.

The plot lines in the graph observed to have highest at lag period near to 10.80, there the

independent variable (control of corruption) seems to fit at lag 4 and the dependent variable

(FDI) observed to fit at lag 3. Hence, Akaike Information Criteria concludes that at lag (3.4)

the Autoregressive Distributed Lag model is optimum to evaluate ARDL with respect to FDI

and Control of Corruption

Table-1.2

Autoregressive Distributed Lag Model for Control of Corruption

Dependent Variable: FDI

Method: ARDL

Sample (adjusted): 1994 2018

Included observations: 25 after adjustments

Dependent lags: 3 (Fixed)

Dynamic repressors (4 lags, fixed): DCONTROL_OF_CORRUPTION

Fixed repressors: C

Variable Coefficient Std. Error t-Statistic Prob.*

FDI(-1) 0.128767 0.301813 0.426645 0.6753

FDI(-2) 0.210545 0.254029 0.828821 0.4194

FDI(-3) -0.143687 0.248252 -0.578797 0.5708

DCONTROL_OF_CORRUPTION 234.9695 269.9449 0.870435 0.3969

DCONTROL_OF_CORRUPTION(-1) 37.04804 242.2193 0.152953 0.8803

DCONTROL_OF_CORRUPTION(-2) -140.0988 257.9650 -0.543092 0.5946




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C 7.384580 17.97113 0.410914 0.6866

R-squared 0.637422 Mean dependent var 18.18908

Adjusted R-squared 0.643867 S.D. dependent var 43.41617

S.E. of regression 46.43434 Akaike info criterion 10.78767

Sum squared resid 34498.37 Schwarz criterion 11.22646

Log likelihood 125.8459 Hannan-Quinn criter. 10.90937

F-statistic 0.622682 Durbin-Watson stat 2.136874

Prob(F-statistic) 0.047304

*Note: p-values and any subsequent tests do not account for model selection.

Source: Secondary Data.

The above table illustrates the results of Autoregressive Distributed Lag model which

describes the direction of the depended variable with independent variable. The independent

variable coefficient (Control of Corruption) values for most of the lag are found to be

positive, indicating that an important strategy for increasing FDI inflows will be increase with

the improvement of quality of institutions and control corruption.R-square of the model is

0.63 that is above the recommended level (<0.60), implies model is strongly fit and Durbin

Watson Statistic value indicates that there is no Autocorrelation in the sample and lies within

the range of 1.5 to 2.5. Hence, concluded that the model is strongly fit and significant.

Pesaran Bound Test for Control of Corruption

F-Bounds Test Null Hypothesis: No levels relationship

Test Statistic Value Signif. I(0) I(1)

Asymptotic:

n=1000

F-statistic 2.143278 10% 3.02 3.51

K 1 5% 3.62 4.16

2.5% 4.18 4.79

1% 4.94 5.58

Actual Sample Size 25 Finite Sample:

n=30

10% 3.303 3.797

5% 4.09 4.663

1% 6.027 6.76

Source: Secondary Data

The above table represents Pesaran Bound test/ Wald Test for long run and short run

association between Foreign Direct Investment and Control of Corruption. Here, the F-

Statistic value is observed to be less than Pesaran critical value for bound test and falling in

the lower bound i.e. (2.143< 3.62) at 5% level of significance that means rejection of

alternative hypothesis and signifies there is Short run association between FDI and Control of

Corruption.

2. Government Effectiveness :



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Figure-1.2

Akaike Information Criteria Graph for Government Effectiveness

10.4

10.5

10.6

10.7

10.8

10.9

11.0

ARDL(4, 0)

ARDL(4, 1)

ARDL(1, 0)

ARDL(2, 0)

ARDL(1, 1)

ARDL(3, 0)

ARDL(2, 1)

ARDL(4, 2)

ARDL(3, 1)

ARDL(1, 2)

ARDL(4, 3)

ARDL(2, 2)

ARDL(4, 4)

ARDL(3, 2)

ARDL(1, 3)

ARDL(2, 3)

ARDL(3, 3)

ARDL(1, 4)

ARDL(2, 4)

ARDL(3, 4)



The above Akaike Information Criteria graph depicts the optimum selection criteria for the


Investment and Government Effectiveness.


independent variable (government effectiveness) seems to fit at lag 4 and the dependent

variable (FDI) observed to fit at lag 3. Hence, Akaike Information Criteria concludes that at

lag (3.4) the Autoregressive Distributed Lag model is optimum to evaluate ARDL with

respect to FDI and Government Effectiveness.

Table-1.3

Autoregressive Distributed Lag Model for Government Effectiveness


Method: ARDL



Dependent lags: 3 (Fixed)

Dynamic repressors (4 lags, fixed): DGOVT_EFFECTIVENESS

Fixed repressors: C


FDI(-1) 0.076918 0.278767 0.275921 0.0021

FDI(-2) 0.187181 0.209395 0.893914 0.0041

FDI(-3) -0.165670 0.218628 -0.757771 0.0026



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DGOVT_EFFECTIVENESS 2.728079 103.4850 0.026362 0.0013

DGOVT_EFFECTIVENESS(-1) 55.36249 94.85106 0.583678 0.0072


DGOVT_EFFECTIVENESS(-3) -27.34910 93.46337 -0.292618 0.0336


C 15.75698 12.62082 1.248491 0.0098





Log likelihood -127.3735 Hannan-Quinn criter. 11.03158





The above table describes the results of Autoregressive Distributed Lag model which


variable coefficient (Government Effectiveness) values are found to be positive for most of

the lag,this implies that government effectiveness plays a key role in contributing to the

inflow of foreign investment by attracting foreign firms to the host country.R-square of the

model is 0.73 that is above the recommended level (<0.60), implies model is strongly fit

andDurbin Watson Statistic value indicates that there is no Autocorrelation in the sample and

lies within the range of 1.5 to 2.5. Hence, concluded that the model is strongly fit and

significant.

Table-1.4

Pesaran Bound Test for Government Effectiveness



Asymptotic:

n=1000

F-statistic 2.388343 10% 3.02 3.51

K 1 5% 3.62 4.16

2.5% 4.18 4.79

1% 4.94 5.58


n=30

10% 3.303 3.797

5% 4.09 4.663

1% 6.027 6.76



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association between Foreign Direct Investment and Government Effectiveness. Here, the F-

Statistic value is observed to be less than Pesaran critical value for bound test and falling in

the lower bound i.e. (2.388< 3.62) at 5% level of significance that means rejection of

alternative hypothesis and signifies there is Short run association between FDI and

Government Effectiveness.

3. Political Stability :

Figure-1.4

Akaike Information Criteria Graph for Political Right Index

10.3

10.4

10.5

10.6

10.7

10.8

ARDL(4

, 4)

ARDL(4

, 0)

ARDL(4

, 1)

ARDL(4

, 2)

ARDL(1

, 4)

ARDL(2

, 4)

ARDL(1

, 0)

ARDL(2

, 0)

ARDL(3

, 0)

ARDL(1

, 1)

ARDL(4

, 3)

ARDL(3

, 1)

ARDL(3

, 4)

ARDL(2

, 1)

ARDL(1

, 2)

ARDL(3

, 2)

ARDL(2

, 2)

ARDL(1

, 3)

ARDL(3

, 3)

ARDL(2

, 3)



The above Akaike Information Criteria graph portrays the optimum selection criteria for the


Investment and Political Right Index.


independent variable (political right index) seems to fit at lag 3 and the dependent variable

(FDI) observed to fit at lag 2. Hence, Akaike Information Criteria concludes that at lag (2, 3)

the Autoregressive Distributed Lag model is optimum to evaluate ARDL with respect to FDI

and Political Right Index.



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Table-1.5

Autoregressive Distributed Lag Model for Political Right Index


Method: ARDL



Maximum dependent lags: 2 (Automatic selection)

Model selection method: Akaike info criterion (AIC)

Dynamic repressors (3 lags, automatic): POLITICAL_RIGHT_INDEX

Fixed repressors: C

Number of models evaluated: 8

Selected Model: ARDL(1, 0)

Note: final equation sample is larger than selection sample


FDI(-1) 0.055367 0.199715 0.277230 0.0039

POLITICAL_RIGHT_INDEX 4.761484 19.90335 0.239230 0.0129

C 13.93906 40.35533 0.345408 0.0327










The table represents Autoregressive Distributed Lag model which describes the direction of

the depended variable with independent variable.The independent variable coefficient

(Political Right Index) values is found to be positive, this implies public official and investors

to have a long term focus. R-square of the model is 0.87 that is above the recommended level

(<0.60), implies model is strongly fit and Durbin Watson Statistic value indicates that there is

no Autocorrelation in the sample and lies within the range of 1.5 to 2.5. Hence concluded that

the model is strongly fit and significant.



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Table-1.6

Pesaran Bound Test for Political Right Index



Asymptotic:

n=1000

F-statistic 7.457843 10% 3.02 3.51

k 1 5% 3.62 4.16

2.5% 4.18 4.79

1% 4.94 5.58


n=35

10% 3.223 3.757

5% 3.957 4.53

1% 5.763 6.48

Finite Sample:

n=30

10% 3.303 3.797

5% 4.09 4.663

1% 6.027 6.76



association between Foreign Direct Investment and Political Right Index. Here, the F-

Statistic value is observed to be less than Pesaran critical value for bound test and raising in

the greater bound i.e. (7.457< 3.62) at 5% level of significance that means acceptation of

alternative hypothesis and signifies there is Long run association between FDI and Political

Right Index.

4. Regulatory Quality:

Figure-1.7

Akaike Information Criteria Graph for Regulatory Quality

9.4

9.6

9.8

10.0

10.2

10.4

10.6

10.8

ARDL(4, 2)

ARDL(4, 4)

ARDL(4, 3)

ARDL(3, 2)

ARDL(1, 2)

ARDL(2, 2)

ARDL(3, 3)

ARDL(3, 4)

ARDL(1, 3)

ARDL(1, 4)

ARDL(2, 3)

ARDL(2, 4)

ARDL(4, 0)

ARDL(4, 1)

ARDL(1, 0)

ARDL(2, 0)

ARDL(3, 0)

ARDL(1, 1)

ARDL(2, 1)

ARDL(3, 1)




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Table-1.7

Autoregressive Distributed Lag Model for Regulatory Quality


Method: ARDL





Dynamic repressors (1 lag, automatic): REGULATORY_QUALITY

Fixed repressors: C





FDI(-1) 0.064254 0.198724 0.323332 0.0491

Regulatory_Quality 28.75564 52.21495 0.550717 0.0067

C 32.11204 19.52542 1.644627 0.0126








*Note: p-values and any subsequent tests do not account for modelSelection.


The above table depicts the results of Autoregressive Distributed Lag model which describes

the direction of the depended variable with independent variable. The independent variable

coefficient (Regulatory Quality) values is found to be positive, this implies increase the flow

of FDI and attract foreign investors and minimize the inefficient institutions associated with

corruption and mismanagement.R-square of the model is 0.41 that is below the recommended

level (<0.60) but acceptable andDurbin Watson Statistic value indicates that there is no

Autocorrelation in the sample and lies within the range of 1.5 to 2.5. Hence concluded that

the model is moderately fit and significant.



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Table-5.1.8

Pesaran Bound Testfor Regulatory Quality



Asymptotic: n=1000

F-statistic 7.612909 10% 3.02 3.51

K 1 5% 3.62 4.16

2.5% 4.18 4.79

1% 4.94 5.58

Actual Sample Size 28 Finite Sample: n=35

10% 3.223 3.757

5% 3.957 4.53

1% 5.763 6.48

Finite Sample: n=30

10% 3.303 3.797

5% 4.09 4.663

1% 6.027 6.76


The above table showsPesaran Bound test/ Wald Test for long run and short run association

between Foreign Direct Investment and Regulatory Quality. Here, the F-Statistic value is

observed to be less than Pesaran critical value for bound test and raising in the greater bound

i.e. (7.612< 3.62) at 5% level of significance that means acceptation of alternative hypothesis

and signifies there is Long run association between FDI and Regulatory Quality.

5. Rule of Law:

Figure-5.1.4

Akaike Information Criteria Graph for Rule of Law

9.0

9.2

9.4

9.6

9.8

10.0

10.2

ARDL(4, 1)

ARDL(4, 2)

ARDL(4, 3)

ARDL(4, 4)

ARDL(1, 3)

ARDL(1, 4)

ARDL(2, 3)

ARDL(2, 4)

ARDL(4, 0)

ARDL(1, 2)

ARDL(3, 3)

ARDL(3, 4)

ARDL(2, 2)

ARDL(3, 2)

ARDL(3, 1)

ARDL(1, 0)

ARDL(3, 0)

ARDL(2, 0)

ARDL(1, 1)

ARDL(2, 1)




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The above Akaike Information Criteria graph depicts the optimum selection criteria for the


Investment and Rule of Law.


independent variable (Rule of Law) seems to fit at lag 1 and the dependent variable (FDI)

observed to fit at lag 2. Hence, Akaike Information Criteria concludes that at lag (2, 1) the

Autoregressive Distributed Lag model is optimum to evaluate ARDL with respect to FDI and

Rule of Law.

Table-5.1.9

Autoregressive Distributed Lag Model for Rule of Law


Method: ARDL





Dynamic repressors (1 lag, automatic): RULE_OF_LAW

Fixed repressors: C





FDI(-1) 0.429110 0.174181 -2.463582 0.0210

Rule_of_law 491.1988 100.5659 4.884349 0.0001

C 35.31990 8.288585 4.261271 0.0003


AdjustedR-squared 0.649233 S.D. dependent var 50.60137






*Note: p-values and any subsequent tests do not account for model Selection.



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The above table illustrates the results of Autoregressive Distributed Lag model which


variable coefficient (Rule of Law) values is found to be positive, this indicator gives a clear

idea on the enforcement of contracts, enforcement of law by the courts, police and other law

enforcement agencies which will have an impact of the level of violence and crimes. R-

square of the model is 0.69 that is above the recommended level (<0.60), implies model is

strongly fit and Durbin Watson Statistic value indicates that there is no Autocorrelation in the

sample and lies within the range of 1.5 to 2.5. Hence concluded that the model is strongly fit

and significant.

Table-5.1.10

Pesaran Bound Testfor Rule of Law



Asymptotic: n=1000

F-statistic 22.45647 10% 3.02 3.51

K 1 5% 3.62 4.16

2.5% 4.18 4.79

1% 4.94 5.58


10% 3.223 3.757

5% 3.957 4.53

1% 5.763 6.48

Finite Sample: n=30

10% 3.303 3.797

5% 4.09 4.663

1% 6.027 6.76



association between Foreign Direct Investment and Rule of Law.

Here, the F-Statistic value is observed to be less than Pesaran critical value for bound test

and raising in the greater bound i.e. (22.456< 3.62) at 5% level of significance that means

acceptation of alternative hypothesis and signifies there is Long run association between FDI

and Rule of Law.

6. Voice and Accountability:

Figure-5.1.5

Akaike Information Criteria Graph for Voice and Accountability



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9.2

9.4

9.6

9.8

10.0

10.2

10.4

10.6

10.8

ARDL(4, 3)

ARDL(4, 4)

ARDL(4, 2)

ARDL(1, 4)

ARDL(3, 4)

ARDL(2, 4)

ARDL(3, 3)

ARDL(2, 3)

ARDL(2, 2)

ARDL(3, 2)

ARDL(1, 3)

ARDL(1, 2)

ARDL(4, 0)

ARDL(4, 1)

ARDL(1, 0)

ARDL(2, 0)

ARDL(2, 1)

ARDL(3, 0)

ARDL(1, 1)

ARDL(3, 1)



The above Akaike Information Criteria graph outlines the optimum selection criteria for the


Investment and Voice and Accountability.


independent variable (Voice and Accountability) seems to fit at lag 1 and the dependent

variable (FDI) observed to fit at lag 3. Hence, Akaike Information Criteria concludes that at

lag (3.1) the Autoregressive Distributed Lag model is optimum to evaluate ARDL with

respect to FDI and Voice and Accountability.

Table-5.1.11

Autoregressive Distributed Lag Model for Voice and Accountability


Method: ARDL





Dynamic repressors (1 lag, automatic): VOICE_AND_ACCOUNTABILITY

Fixed repressors: C





FDI(-1) 0.041200 0.205555 0.200432 0.0428

Voice_and_Accountability 41.08850 123.4451 0.332848 0.0420



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C 6.762934 50.67286 0.133463 0.0049










The above table portrays the results of Autoregressive Distributed Lag model which describes

the direction of the depended variable with independent variable. The independent variable

coefficient (Voice and Accountability) values are found to be positive, which implies, in

good governance and democracy, the state and its institutions, the public sector and civil

society work towards targeted objectives and develop effective strategies through strong

monitoring and reporting mechanisms.R-square of the model is 0.77 that is above the

recommended level (<0.60), implies model is strongly fit andDurbin Watson Statistic value

indicates that there is no Autocorrelation in the sample and lies within the range of 1.5 to 2.5.

Hence concluded that the model is strongly fit and significant.

Table-5.1.12

Pesaran Bound Testfor Voice and Accountability.



Asymptotic: n=1000

F-statistic 7.491594 10% 3.02 3.51

K 1 5% 3.62 4.16

2.5% 4.18 4.79

1% 4.94 5.58


10% 3.223 3.757

5% 3.957 4.53

1% 5.763 6.48

Finite Sample: n=30

10% 3.303 3.797

5% 4.09 4.663

1% 6.027 6.76




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association between Foreign Direct Investment and Voice and Accountability. Here, the F-

Statistic value is observed to be less than Pesaran critical value for bound test and raising in

the greater bound i.e. (7.491< 3.62) at 5% level of significance that means acceptation of

alternative hypothesis and signifies there is Long run association between FDI and Voice and

Accountability.

Table-5.1.13

Impact of Foreign Direct Investment on Institutional Indicators.


Method: Least Squares

Sample: 1990 2018

Included observations: 29

Variable Coefficient Std. Error t-Statistic Prob.

C -5.629325 92.16605 0.061078 0.0018

Dcontrol_of_Corruption 86.28174 127.5746 0.676324 0.0059

Dgovt_Effectiveness 78.09974 71.18082 1.097202 0.0244

Political_right_index -9.797831 16.17848 0.605609 0.0010

Regulatory_Quality 88.66404 113.7830 0.779238 0.0441

Rule_of_Law 307.3959 112.7686 2.725899 0.0123

Voice_and_Accountability 178.6707 274.1918 0.651627 0.0214











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10. FINDINGS OF THE STUDY

1. The study found with the help of ARDL model that the Institutional indicators had are

having the positive coefficient value and stated that Control of corruption and Govt.

effectiveness are institution indicators which shown short run association with FDI flows.

2. The study examined that Political stability f-statistic value is falling above the upper

peasant table (i.e., 7.4578 > 4.16) which states political stability has a long run

association with FDI flows.

3. Institutional Indicators such as Rule of law, Regulatory Quality and Voice &

accountability had a long run association with FDI flows.

4. Political stability is the institutional indicator shown negatively influenced on FDI flows

with -9.797 and states rise in political stability index will decrease the FDI growth with -

9.797 units.

5. Institutional indicators such as Rule of law (307.39) and Voice & Accountability (178.67)

had influenced high on FDI flows.

11. SUGGESTIONS OF THE STUDY

1. The study suggested that policymakers should ensure that funding is used optimally

and that projects are implemented in a timely manner. To attract more FDI inflows to

India, the government needs to retain investor confidence through strict controls on

inefficient bureaucracy, red-tapism, and rampant corruption. Finally, the government

must ensure quality FDI inflows rather than its magnitude. Policymakers must ensure

transparency and consistency in policy making, as well as an integrated long-term

development strategy.

2. The study also suggested that the government should take faster action to improve the

country's infrastructure to further diversify its businesses.

12. CONCLUSION OF THE STUDY

The results of the empirical analysis of the sector show that FDI creates sectoral growth,

while growth affects human capital and FDI inflows institutional quality. The study

observed that the high FDI inflows in the services, telecommunications, and automotive

sectors, corresponding to strong growth in the Indian economy. The metallurgical

industry continues to grow strongly, but with low FDI inflows. The main focus of the

study is on the overall impact and cause of FDI in the primary, secondary and tertiary

sectors. Another issue is the availability of data for all parameters before 2004. Therefore,

it is important to analyze sector-specific trends and impacts of FDI so that economic

growth can be accelerated to open up export-oriented areas through the growth of these

sectors. Thus, it can be concluded that India needs FDI to keep the economy growing and

developing. Expansion and development of the manufacturing industry, export-oriented

units, financial infrastructure and stability, and human capital development require FDI

13. REFERENCES:

1. Prachi Arora (2013), “Relation Between Inflow Of FDI and The Development Of

India's Economy”, International Journal of Trends in Economics Management &

Technology IJTEMT ISSN: 2321-5518; Vol. II, Issue III

2. Chandra, Nirmal (1991): "Growth of foreign capital and importance in Indian

manufacturing”', economic and political weekly, Volume 26, pp: 11-12.



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3. Chauhan Karuna and Kumar Surendra, 'Foreign Direct Investment and India's

Economic Growth, Prabandhiki, Volume 3, No. 1&2, 2009.

4. Khan, M.Y. (2003): “Indian Financial System”, Tata McGraw-Hill Publishing

Company Ltd, pp: 28-33.

5. Hsiu-Yun Lee a, Kenneth S. Lin b, Hsiao-ChienTsu (2009) “Home country effects of

foreign direct investment: From a small economy to a large economy”, ELSEVIER

Publications, Volume 26, Issue 5.



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Documents

The Institutional Factors Impact on Foreign Direct