1. Rhine-Waal University of Applied Sciences (Hochschule Rhein-Waal), Kleve, Germany; asmit676@gmail.com

* Correspondence: asmit676@gmail.com

Keywords: FDI, Granger causality, Engle-Granger, Macro-economy, GDP, Inflation

1. INTRODUCTION

Foreign Direct Investment (FDI) is seen as a crucial factor for economic growth, technological transfer, and employment creation for developing countries. It is considered crucial for a nation that has financial constraints. In the context of Nepal, a country between two economic giants India and China, FDI holds potential that can boost the economic development of the country. As Nepal is striving to transition from a least developed country to a developing country, understanding the factors that affect FDI inflow and factors that FDI affects becomes crucial. According to Nepal Rastra Bank (2022), FDI streamlines the flow of financial assets, technology, and intangible resources such as expertise in technology, management and organizational capabilities, and entry into foreign markets. This, in turn, contributes to a boost in production and efficiency within the hosting economy. The per-capita income of Nepal is low, with low savings and a steady increase in population over the recent decades. This factor might be responsible for low investment. In this scenario, emerging economies like Nepal should see FDI as a vital source of capital that can generate employment and economic development. Countries have liberalized their FDI policies to attract investments. Developing countries have also addressed domestic policies to maximize the benefits of FDI and foreign presence in the country. According to OECD (2002), the FDI and its importance to developing countries is evident. It triggers technological spillover, assists human capital formation and helps create a more competitive business environment and enhances enterprise development. From these factors, it is clear that FDI is important for a developing country like Nepal. There have been numerous studies on the FDI and its determinants for the SAARC nations (K. Rai & K. Sharma, 2020; Tahir & Alam, 2022). Here we are focusing on key determinants of FDI in Nepal. The priority sectors based on national needs are hydroelectricity (production and transmission), Infrastructure related to transport (fast track, railway, tunnel, cable car, metro rail, flyover and international airport), agricultural, food procession and herbs procession industries, tourism industries and mineral and productive sector industries (Nepal Rastra Bank, 2018).  Here, we aim to analyse two major factors responsible for FDI in Nepal i.e., macroeconomic factors and institutional factors and how FDI might affect these factors. Macroeconomic factors such as economic growth, inflation, per-capital income and infrastructure will be analysed and institutional factors such as political stability and corporate tax will be looked into.

2. LITERATURE REVIEW

It is generally seen that a country with higher economic growth attracts larger FDI due to a higher return on investment. Gross domestic product (GDP) often shows the economic growth of the country.  From Agrawal (2015) we have seen a unidirectional causality running from FDI to GDP. This has also been seen in Hammami (2017) as it shows the existence of unidirectional causality running from FDI stocks to economic growth. The effect of FDI on GDP is also shown by Tamilselvan & Manikandan (2015) as it shows that FDI has a significant impact on the GDP of India. The inflation rate affects domestic and international investment as it lowers the value of investment. It directly affects interest rates and that in turn affects the return on investment. Research done by Onyeiwu & Shresth (2004) based on the panel dataset for 29 African countries over 24 years, identified inflation as a significant factor for FDI flow among other factors. This plays a vital role in attracting FDI as it reduces the transaction cost of business and increases efficiency. According to Nepal Rastra Bank (2021), it allows for business operations and promotes the exchange of goods and services. It also reduces supply chain disruptions. A study by Neupane (2020) has shown that FDI contribution towards capital formation is not significant.  However, from the past studies on FDI in SAARC nations we have seen a long-run causal relationship between infrastructure development and FDI inflow (K. Rai & K. Sharma, 2020). This is one of the main indicators of investment. Higher per-capita income might indicate higher purchasing power of the country and hence will help in attracting investment. Higher per-capita income indicates a growing economy and attracts investments. The paper published by Tahir & Alam (2020) on FDI in SAARC nations showed that higher per-capita income showed a statistically significant positive impact on FDI inflow. Political stability can indicate a proper implementation of laws and stable laws. It can also act as an assurance for investors for their investment. It gives them a sense of security in their long-term investment. The literature on this is quite interesting as shown by Kurecic & Kokotovic (2017). It shows that there is a long-term relationship between political stability and FDI for the small economies while it found no empirical evidence of such relationship for larger and developed economies. Another research done by Sambharya & Rasheed (2015) suggested that the role played by governments in national economies has significant influence over FDI decisions. From the policy perspective, the results also showed that to attract FDI the government will need to improve the institutional environments of their countries. The research specified that improving levels of economic freedom can greatly facilitate the inflow of FDI. Corporate tax is considered one of the important aspects of investment. Research by Ang (2008) has shown that there is a negative relationship between corporate tax and FDI. The research by (K. Rai & K. Sharma, 2020) shows that there is a short-run bidirectional causal relationship between FDI inflows and corporate tax. It also showed that corporate tax is statistically significant in determining FDI inflows. The neo-classical theory developed by Solow and Swan could not explain the existence of multi-national corporations. It was Hymer (1960) who presented his work and we had an acceptable explanation for FDI and multinational companies. However, research done by Denisia (2010) on FDI theories concluded that all the empirical results pointed to the fact that there is no unified theoretical explanation and it is unlikely that a unified theory will emerge. Through time many other theories have been developed to explain FDI. The location-based theory extended by Popovici & Calin (2014) suggested that foreign investment decisions are influenced by country-level characteristics such as economic growth, infrastructure, government policy, natural resources etc. The research by Tahir & Alam (2022) showed political stability and FDI have a positive relation but are not statistically significant. It implies that improvement in political stability can increase investment but is not a strong factor in influencing FDI inflows. A study by Baccini et al. (2014) found that non-discriminatory tax cuts on direct investment profit increase FDI, but discriminatory tax cuts on selected government-sanctioned investment projects do not. The most well-known theory has to be from (Dunning, 1980) also known as the eclectic paradigm states that investing mostly depends on three factors. These factors are ownership, location and internalization advantage. Ownership means the greater the competitive advantage of the investing firm relative to those of other firms the more likely they are to invest. Location refers to the infrastructure, labor cost etc. advantages of investing in the country and internalization refers to alternative ways in which a company may create the exploitation of their core competencies given the locational attractions of different countries or regions. This might include many things such as integration of intermediate market to purchase of foreign corporation. In terms of the theoretical background for this study, it has considered the macroeconomic factors and the eclectic paradigm.

3. MATERIALS AND METHODS

The data of the variables has been obtained from World Bank open source and world development indicators. The data used for the study is from 1996 to 2022. Based on the review of the literature we have divided our variables into two parts. First are the macroeconomic variables which are economic growth, infrastructure development, inflation and per-capita income. The second part of the variables are the institutional factors which are political stability and corporate tax. The data for political stability was missing for the years 1997,1999 and 2001. For these years we have taken the average of available data from 1996 to 2006, since these were the years that political instability was high due to civil war. We have divided the variables into two groups as we are trying to see how different factors affect the FDI in Nepal. A detailed description of the variables considered in the study is given below:

Table 01: Description and source of the variables.

Name of the variable	Definition of the variable	Source
Foreign Direct Investment(FDI)	Foreign direct investment, net inflows, percentage of GDP	World Development Indicators(WDI, World bank)
Economic Growth	GDP-Constant	WDI, World Bank
Inflation Rate	Percentage change in CPI between two points at a time	WDI, World Bank
Infrastructure Development	Gross Fixed capital formation (% of GDP)	WDI, World Bank
Political Stability	Perception of the likelihood of political instability and politically motivated violence, including terrorism	Worldwide Governance Indicator (World Bank)
Per-Capita Income	GDP per capita (GDP of the country divided by mid-year Population)	WDI, World Bank
Corporate Tax	Tax on income, profits and capital gains(% of revenue)	WDI, World Bank

We have time series data from 1996 to 2022 and we will be studying interlinkage between them. To study the relationship we must first proceed with the unit root test or stationary test. Then we will move on to the Engle-Granger test and the Granger causality test.

3.1 Phillis-Perron Unit Root Test

In order to start the testing of the data we need to test the data for the level of their stationarity. One of the most popular unit root tests is the Phillip-Perron(PP) test. Phillips & Perron (1988) developed a unit root test that has become popular in the analysis of financial time series. The PP test regression equation is:

Here ∆ is the first difference operator,  is the random error term, Y = variable, T= linear trend, = constant. The null hypothesis is that a = 0 and the alternative hypothesis is that a < 0. Once the value for the test statistics is computed we will compare it with the critical value of the Phillip-Perron test. If the test statistic is greater (in absolute value) than the critical value at a 5% level of significance, then the null hypothesis of α = 0 is rejected and no unit root is present.

3.2 Cointegration test using Engle-Granger Methodology

Cointegration gives us a long-run relation linking the two indicators (Das & Ray, 2022) Cointegration is a test used to establish if there is a correlation between time series. It shows that two time series variables with at first level of difference I(1) can form a cointegration relationship.  According to this theory if there is some cointegration then there must be an Error Correction Mechanism (ECM). This process is shown by the following equations where the first step is to run normal OLS. If two series Y and X are both integrated of order one or I(1) and are related by the following equation:

From this equation, we can obtain a long-run relation between two series and then derive the estimated error term as . If the series  is found to be stationary then it is said that both the term is cointegrated from the Engle-Granger methodology. The stationarity of the error term  can be checked by the Augmented Dickey-fuller test (Dickey & Fuller, 1981) by the following equation:

We test for if f =0 against f < 0. If the hypothesis of f =0 is rejected with the critical value (level of significance) we can say that indicators X and Y are cointegrated.

3.3 Granger Causality Test

In our study, we can compare the relationship and the various factors that affect the FDI in Nepal. We are trying to check the relation between these variables and FDI. To check this we will conduct a Granger causality test between the FDI and other variables. Granger causality test (Granger, 1969) will be carried out by estimating the below-mentioned equation for variables x and y

Where ∆ stands for the difference operator. T indicates the figure for the lagged values of ∆y and ∆x which affect the current values of the two differenced indicators; v, , . , =1,2 becomes the equation disturbance terms with white noise properties. Based on these variables and empirical tools we have the following hypothesis for this study:

Macroeconomi variables

• FDI is cointegrated with economic growth.
• FDI is cointegrated with inflation.
• FDI is cointegrated with per-capita income.
• FDI is cointegrated with gross fixed capital formation.
• Institutional variables
• FDI is cointegrated with corporate tax.
• FDI is cointegrated with political stability.

4. RESULTS & DISCUSSIONS

Before analysing the data on the linkage between the FDI and variables we need to test the data. The series must be integrated at first level I (1) to carry out the Engle-Granger test. The Phillip-Perron test (PP) is used to test the unit root for the dependent and explanatory variables.

4.1 Results from Phillip-Perron Test

The table below shows the result of the PP test on the time series

Table 02: Result from the Phillip-Perron test.

Variables	Phillip-Perron Test (PP)- Z(Alpha)	Probability	Remark
FDI	-36.103	0.01	I(1)
Inflation	-30.31	0.01	I(1)
GDP	-24.35	0.01	I(1)
Gross Fixed Capital Formation	-19.23	0.033	I(1)
Per-Capita Income	-22.50	0.01	I(1)
Corporate Tax	-23.34	0.01	I(1)
Political Stability	-38.71	0.01	I(1)

As we can see from the result all the variables are stationary at the first level of difference at 5% level of significance.

4.2 Results of the Engle-Granger Test

A two-step Engle-Granger test was conducted to obtain the results to see if the variables have a long-term relation between them. In the first step, we conduct a regression between FDI and the variables. For the regression, the dependent variable was taken as FDI and the independent variables were macroeconomic and institutional variables.  In the second step, we conduct the ADF test on the residuals obtained from the previous results. This second step helps us to know if the residuals are stationary or not. This relation between the residuals helps us to determine if there is a long-term relation between the two variables.

Table 03: Results from Engle-Granger Test.

Dependent	Independent	Value of Test Statistics	Critical Value for Test Statistics (at 1%)	Adjusted R-Squared
FDI	GDP	-4.23	-2.66	0.70
FDI	GFCF	-4.03	-2.66	0.73
FDI	Inflation	-3.86	-2.66	0.73
FDI	Per-capita Income	-4.20	-2.66	0.70
FDI	Corporate tax	-3.86	-2.66	0.74
FDI	Political Stability	-3.89	-2.66	0.72

Table 03 shows the test result from the Engle-Granger test. The result clearly shows that there is a cointegration and long-term relation between the variables. From the test results, we can reject the null hypothesis at a 1% level of significance and it clearly shows that there is a long-term relation between FDI and all the variables.

4.3 Granger Causality Test

From the Engle-Granger test, we found that there is a long-term relation between the variables. Now from the Granger causality test, we can find the short-term relation and also the direction of causality between the variables. First, we are going to test if FDI causes any of our variables. The result from the test is as follows:

Table 04: Result from Granger Causality test at lag 2.

Independent	Dependent	Lag	F-Statistics
FDI	GDP	2	0.063*
FDI	GFCF	2	0.63
FDI	Inflation	2	0.70
FDI	Per-capita income	2	0.060*
FDI	Corporate tax	2	0.055*
FDI	Political Stability	2	0.94

Note: *Indicates significance at a 10% level of significance.

From Table 04 we can see that there is a short-term relation between GDP, per-capita income, and corporate tax with FDI. We can say that FDI Granger causes GDP, per-capita income and corporate tax in Nepal. FDI has a significant impact on GDP, per-capita income and corporate tax at lag 2 at 10% level of significance.  In Table 05 we look if FDI causes the same variable at lag 4. We get a very interesting observation as it shows that FDI Granger causes corporate tax at a 5% level of significance.

Table 05: Result from Granger Causality test at lag 4.

Independent	Dependent	Lags	F-Statistics
FDI	GDP	4	0.56
FDI	GFCF	4	0.36
FDI	Inflation	4	0.80
FDI	Per-capita income	4	0.54
FDI	Corporate tax	4	0.043**
FDI	Political Stability	4	0.65

Note: ** Indicates significance at a 5% level of significance.

Now we look if our variables affect the FDI of Nepal. We can also see if there is joint causality between the variables. From Table 06 we can see that our variables do not Granger cause FDI in Nepal.

Table 06: Result from Granger Causality test at lag 2.

Independent	Dependent	Lags	F-Statistics
GDP	FDI	2	0.27
GFCF	FDI	2	0.92
Inflation	FDI	2	0.76
Per-capita income	FDI	2	0.27
Corporate Tax	FDI	2	0.99
Political Stability	FDI	2	0.99

Now we look at our variables Granger causes any of our variables at 4 lag period. From Table 7 we can see that none of the variables significantly Granger cause FDI at 4 lag periods.

Table 07: Result from Granger causality test at lag 4.

Independent	Dependent	Lags	F-Statistics
GDP	FDI	4	0.68
GFCF	FDI	4	0.99
Inflation	FDI	4	0.54
Per-capita income	FDI	4	0.73
Corporate Tax	FDI	4	0.98
Political Stability	FDI	4	0.10

From the above Granger causality test, it is clear that there is no joint causality between FDI and our variables. Our variables also do not Granger cause FDI in Nepal. However, FDI Granger causes GDP, per-capita income and corporate tax.

5. CONCLUSION

The main purpose of the study was to determine whether there is a relation between FDI and the macroeconomic and institutional factors in Nepal. Also, we wanted to see what factors are affected by FDI inflow in Nepal.  The Engle-Granger test showed that there is long-term cointegration between all the variables and FDI. However, when ran a Granger causality test to see the short-term relation we saw that none of the variables cause FDI, rather FDI causes GDP, per-capita income and corporate tax. This might be due to several reasons such as the small size of Nepal’s economy and the lack of favourable investment scenarios that have failed to attract FDI. Past studies about FDI in SAARC have shown that a bigger market size (GDP), low inflation, and high per-capita income might be favourable to attract FDI. This might mean that Nepal’s GDP and other variables might not be big or favourable enough to attract FDI in the short term. The result has also shown that in the short term, FDI causes corporate tax significantly. This might be because as the FDI tends to increase the government increases the taxes on FDI. However, there is a long-term cointegration between these variables that shows that these factors are related to each other in the long term.  Proper development to encourage investment might result in these macroeconomic and institutional factors contributing significantly to FDI. The results have shown that FDI is important for Nepal as it causes GDP and also per-capita income. This means that FDI can be an important factor that can contribute towards the development of the country and improve the income level of the people. FDI should be taken seriously by the government as it has a significant impact on the GDP and per-capita income of Nepal. Therefore, from this result, it is clear that the government should improve its focus on economic growth and create a favourable environment that can increase the FDI inflow in Nepal.

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