Abstract:

An attempt is made in this study to determine the major indicators of the inflow of FDI into Pakistan economy. Data from the time period 1985 to 2019 was used in this research. The level of stationary was checked through Augmented Dickey Fuller and Philips Perron tests. Using the analysis of the Autoregressive Distributed Lag (ARDL), it is identified that GDP, Inflation, Annual Average Exchange Rate and population Growth have a positive (significant) effect on FDI. Moreover, the empirical results also identified a negative but significant effect of financial development on FDI.

Key Words:

Foreign Direct Investment; Autoregressive Distributed Lag (ARDL) Model, GDP

Introduction

FDI is the investment by foreigners which accelerate the socio-economic activities in a country. Such investment creates employment opportunities; hence it contributes to the economic uplift of the country (Tendera W?aszczuk, 2015). 

FDI solve too many macroeconomic issues of the country or of the group of countries, through the creations of new opportunities, it also transfers skills and technologies which explores the available resources in the best way, which increase productivity and enhance the market size. These are the main indicators that push the economy on the track of economic development in the long term. Hence for the host country, FDI serves as the major source of external inflow, attracts sustained economic development in the host country through socio-economic innovations (World Investment Report, 2008). 

FDI aims to earn profit through accelerating socio-economic activities in the region, which benefits the country in the form of creating employment opportunities and influence its economic growth. Accelerating activities and creation of employment opportunities through the inflow of FDI also involve risk such as inflation risk, Horizon risk, Longevity risk, Reinvestment risk, liquidity risk and market risk etc. (Khan et al., 2019). 

FDI has cyclical economic trends, involves local investors and provides supply chain management which results in economies of scale and cost-benefit as well the expenditure help to attract FDI. The process expands government spending, which leads the economic growth with new employment opportunities and increases national incomes. The increase in national income indicates a rise in the economic growth of the nation, which then attracts foreign investors for investment (Foster & Magdoff, 2009). 

Amna et al. (2010) analyzed the time period 1981 to 2010. In their analysis, they checked different indicators of economic growth, such as the FDI   and inflations, which were taken as major indicators. They estimated the model while using the multi regression technique. The result was positive (significant)  with FDI   and inflation; however, the relationship between the explanatory variables such as inflations and FDI   was negative. 

Many prior studies highlighted the tremendous role plays by the global FDI   inflows in the economic development, as it brings capital, managerial skill, creates jobs opportunities, boosts competition in a local firm, increases productivity, and increase government revenues and much more (UNCTAD, 2014; Azam, 2015; Ahmad and Khan, 2018; Azam & Gavrila, 2015; Khan et al. 2014; Gupta & Singh, Balata et al., 2016; Khan and Khan, 2018).

Many studies show that in emerging economies, FDI is the major indicator and source of financial development. It also plays a vital role process of human resource development, technological progress, capital formation and international trade, standard living infrastructure (UNCTAD, 2006).

The literature also shows negative ties between FDI   and the growth of the economy. Durham (2004), in his study, analyzed the time series data. He concluded insignificant results also a negative relationship between FDI   and growths. He also concluded that the technology absorptions capacity of the receipt country is the major factor for the flow of FDI.

Ali Sharafat (2014) analyzed the time series data from 1972-2013. In their analysis, they used different techniques for the estimation, such as “Johansen co-integration technique and the Granger causality”. A negative impact was estimated on the GDP of Pakistan, which affects the growth rate negatively. The relationship was pedestal as long-run. It is due to day by day increasing trend of the world market size, which tensed the market competitions to pull FDI   in the host countries. Incapable environment and economic fundamentals for the inflows of the FDI badly affect the local economic indicators of the home country (Dunning 1993, Globeran & Shapiro 1999, Shapiro & Globermsn 2001).

Similarly, several studies have been conducted. Some observed positive impact while others got negative relationships of FDI. The nature of the variable used in the model had a great influence on the relationship. They hereby used politics, economics and technological advancement as a key indicator in the model. So still impact is still a controversial issue and its determinations are much sensitive for policymakers to contribute to the local economies.  

The history of Pakistan is hereby divided into two main phases. The first phase from 1947 to 1971. In the first phase, Pakistan lost Bangladesh as half of its resources. This was because of the two wars between Pakistan and India. Similarly, phase two started in 1972 and onward. The main issues faced by the country in the second phase were again war and political instabilities. The nature of war was different, and it fought against terrorism. These wars and military insurgencies between American and Afghan badly affected the economy of Pakistan; hence the political instabilities also put negative pressures on the economy. In this era, the trade restrictions imposed by the united nation due to the nuclear test by Pakistan also put pressure and negative impact on the economy. These instabilities had fluctuated the FDI in every time period. 

Hence to uplift the FDI, the government of Pakistan stepped many initiations, but the results are not achieved as many factors create hurdles in the way. The political instabilities, corruptions, diplomatic weak ties and inefficiency etc., are the major issues in the way to achieve a high rate of FDI.

This study hereby aims to underscore the importance of inflow of FDI   role in the development of developing country Pakistan. Therefore, this study is conducted to investigate the main determinants of the inflow of FDI into Pakistan with the following major objectives:

Objectives

i. To investigate the factors which affect the Inflow of FDI in Pakistan?

ii. How these variables affecting each other?




Literature Review

Balasubrammanyam and Sapsford (1996) and De Mello (1999) in their study accomplished that FDI   is the combinations of different economic indicators such as capital stock, knowledge and technology etc. the combination of such indicators, through management practices, skill development, transformations of techniques and trainings, persistently increase the available stock of the economy. Hence the relationship between FDI and GDP was concluded as positive (significant) by both of the studies observing the developing countries model. 

Khan (1997), in his analysis, identified different factors, such as political instabilities, law and order situations etc., as the main cause of the lower level of FDI in Pakistan. He made the recommendation in his study on the basis of the above identifies factors in order to improve it. 

Shah and Ahmad (2003), in their study, estimated the determinants of FDI   in Pakistan. They use different indicators as time series data and as determinants of FDI. They used time-series data from 1980- 1999 and applied OLS and ECM. It was a supply-side model. They concluded a positive influence of FDI   on the economy while considered socio-economic instabilities as one of the major hurdles in the way to level FDI in Pakistan. 

Aqeel and Nishat (2004) investigated time series data to find the factors which influence FDI   in Pakistan. They applied ECM to the data (1960 to 2004). They concluded that GDP has positive ties with FDI. 

Aqeel and Mohammed (2005), in their research study, analyzed the data from the time period 1961- 2003. The determinants of FDI in Pakistan were investigated. They used different variables such as exchange rate, tariff and tax rate etc., in their study.  They also used GDP per capita as a factor. The entire variable used in the study has a positive (significant) impact on FDI, while only wage rate general price indexes are negatively related to FDI. The study highlights the short run and the long-run role of these variables in inviting FDI   and its progress. 

Kok & Erso (2009) it is argued that the relationship of FDI   is positive with other variables such as total external debt and total debt services. The Gross capital formation affect on the FDI   is weak, and the effect with the FDI is zero, as concluded by (Blonigen 1997). Blomstrom also concluded an indirect relationship of FDI with gross fixed capital formation. His work is recognized by many researchers as he has done a lot of work. It is also concluded by Moody (1992) a negative correlation between infrastructure and electric power. It is argued by many researchers that inflation has a negative related to FDI, proved by many empirical results (Kok & Erso, 2009).

Falki (2010), in his research, investigated the relationship between FDI and GPD plus growth. He concluded that the correlation is positive and the impact is significant hence enhancing the productivities of the economy. FDI   also generate employment opportunities and improve macroeconomic indicators. 

Khan and Nawaz (2010) concluded that the gross domestic product growth rate shows a progressive effect on the inflow of FDI   in Pakistan. The result confirmed that export is the chief determining factor of FDI   in Pakistan. 

Wan et al. (2011), in their studies, explored different variables to determine the factors affecting FDI. Their results indicated that GDP, trade openness, the real growth rate of GDP, gross fixed capital formation, and per capita income are important and progressive influence on the inflow of FDI   in Pakistan. 

Akram (2014) investigate FDI determinants. He used electric power consumption, gross domestic fixed capital formations, total debt services and inflation as an independent variable while FDI  was taken as the dependent variable. All of the indicators showed a positive impact on the FDI  instead of one indicator known as inflation which showed a negative impact. 

The literature consists of the long-term ties of growth rate with FDI in Pakistan (Ahmad et al., 2003); the nature of variables in the economy fluctuates accordingly, which influence the impact of FDI on GDP (Mughal, 2008, Khan & Khan, 2011) it enhances the productivity and market structure (Zeb et al., 2013, Aqeel & Nishat, 2004) FDI inflows has positive contribution toward GDP (Rahman, 2014, Younus et al. 2014) the inflow of FDI is the main indicator which contributes to the economy of developing nations (Abdullah et al., 2015 & Dar et al., 2015). 

Methodology of the study 

The nature of this study is on time series data. Time series data of the period 1985 to 2019 are used for analysis. Keeping in view the above recent literature, this study used different variables, which are discussed onward, as a determinant of FDI in Pakistan. They used indicators such as GDP, inflations, financial development etc. as independent variables of FDI. 

Gross Domestic Product (GDP) per capita (current US dollar) is the monetary value of all the final goods and services produced in a country during a specific period of time of one year. 

Population (POP) is described as the annual percentage growth of Urban populations. As per the definitions of international labor organizations, the total labor force the group of people aged 15 who are economically active. The group of people known as labor force is, in fact, the supplier of labor to the market. In a true sense, these economically active people aged 15, even if unemployed, belong to this group. 

Inflation (INF) “the persistent increase in the price of goods and services is known as inflations. Also, the decrease in the purchasing power of the currency is known as inflations”. 

“Exchange rate is the value of one currency for the purpose of conversion to another currency. EXR rate as Rupees/Dollar rate as Rupees/Dollar”.

Financial development M2 (% of GDP) is used as an alternate for financial development data is from WDI.

Descriptive statistics are used to describe different prepositions of the study. Different methods and tests such as correlations for checking the assumptions data ADF test and PP test are used to check also for its measurement. Moreover, for the estimations of the model, ARDL and ARDL bound test is used.  This study used descriptive statistics to describe the dimension used in this study. The correlation is used to check the relationships and directions of different variables. ADF and PP test is used for checking data stationarity.  ARDL and ARDL bound test is used to estimate the model. 

Model Specification

Following the model used by Chaudhary (2017) and Azam et al. (2019 etc., the study used the given modified models:

In linear form

FDI_t  = ?_0  + ?_1 POP_t  +?_2 GDP_t  + ?_3 EXR_t  +?_4 FD_t+?_?(@5) INF_t  +? µ?_t-------------1

?FDI  ?_t is a dependent variable while population, gross domestic product, exchange rate, financial development and inflations are the independent variables used in the model. 

The equation (1) is used as a conditional ARDL (Autoregressive Distributed Lag) model.

In ARDL Form

FDI_t=?_0+?_(i=1)^(n_1)???_1i FDI_(t-i) ?+?_(i=1)^(n_2)???_2i POP_(t-i) ?+?_(i=1)^(n_3)???_3i GDP_(t-i) ?+?_(i=1)^(n_4)???_4i EXR_(t-i) ?+?_(i=1)^(n_5)???_5i FD_(t-i) ?+?_(i=1)^(n_6)???_6i INF_(t-i) ?+?_t--------------------------------------------------------------------2

Table 1 describes the statistics of all used variables and as defined above. 




Table 1. Descriptive Analysis

	FDI	FD	GDP	INF	POP	EXR
Mean	0.699579	49.21790	843.4319	9.079500	4.133053	6.113689
Median	0.612998	48.20324	649.8048	7.879657	4.091244	6.136581
Std. Dev.	0.483321	6.838261	444.9449	4.955728	0.445999	0.103486
Skewness	0.687800	0.136509	0.344886	0.724931	0.768855	-0.892680
Kurtosis	2.790380	1.917882	1.429269	2.871294	3.380203	2.926384

Source: Own processing of the data.

 

Table-2 shows the correlation between all the used variables. All high correlated pairs are deleted in order to avoid the problem of Multicollinearity in the model. The correlation coefficients of 0.70 or above are considered as high correlated Sun (, 2002). 

Table 2. Correlations Matrix

	FDI	FD	GDP	INF	POP	EXR
FDI	1.000000	0.3822232	0.608013	0.235555	0.612432	0.325536
FD		1.000000	0.694932	-0.035567	0.697743	0.635489
GDP			1.000000	-0.027676	0.886544	0.605466
INF				1.000000	0.034037	0.269000
POP					1.00000	0.6854345
EXR						1.00000

 

Source: Own processing of the data.

 

Table-3 shows the results of the unit root tests of all the variables used in the model. The result shows that FDI   and inflations are stationary at level (*). The variables such as GDP, population, financial development and exchange rates are stationary at first differences (**). ARDL model is hereby used.

Table 3. Unit Root Tests

Variables	ADF test Values (p-value)		PP test Values (p-value)
	At level P-value	At 1st Difference P-value	At level	At 1st difference	Decision
FDI	-4.021* (0.003)		-4.100* (0.0020)		*
GDP	-0.070* (0.95)	-4.558** ( 0.0002)	-0.215* (0.894)	-4.47** (0.0000)	**
POP	1.510* (0.900)	-5.815** (0.000)	1.976* (0.908)	-4.915** (0.0001)	**
INF	-5.062* (0.000)		-4.06* (0.0005)		*
FD	-2.346* (0.600)	-6.87** ( 0.000)	-1.61* (0.603)	-7.003** (0.000)	**
EXR	-2.508* (0.138)	-6.176** (0.000)	-2.7* (0.010)	-4.67** (0.000)	**

 

Source: own processing of the data. * indicates stationarity at level, ** indicates stationarity at 1^st difference.

 

ARDL Method After testing for the stationarity, the ARDL method is recommended. The studies hereby analyze both short-run and long-run estimates on the model for FDI. The long-run results of the estimated model, as shown in equation-2, are presented in table-4. The results show that all the used variables such as GPD, populations, inflations, exchange rate and financial development have a significant effect on foreign direct investment.  The result further reveals a positive impact of the variables GPD, populations, inflations and exchange rate on FDI while the financial development has a negative impact on FDI.

Conclusion and Recommendation

The study concluded that all the results show that all the used variables such as GPD, populations, inflations, exchange rate and financial development significant impact foreign direct investment. The result further reveals a positive impact of the variables GPD, populations, exchange rate and inflations on foreign direct investment. This result of positive impact on directed foreign investment is supported by the literature already mentioned above as by Ahmed et al. (2003), who concluded a significant and positive influence of GDP and other variables on FDI. These results are supported by recent literature such as Aqeel and Nishat (2004) etc. They concluded a positive relationship of FDI   with GDP.  The impact of financial development on FDI is negative while it is positive in the case of the exchange rate effect on FDI. Being a developing country, Pakistan is rich in natural resources as well as low labor cost. Before investment, the foreign companies see the currency exchange rates of Pakistan. At higher currency rates, the company receives more investment in Pakistan. 

Being an agrarian country, Pakistan needs high sophisticated technologies which could explore the available resources in viable ways. The attraction of FDI  is much important, which could enable the country to create new opportunities as the FDI  also transfer technologies and new research which explores the resources in the best way. Many macroeconomics issues such as unemployment and low rate of productivity etc., can best be solved this way.  Peace and political stability are important. 

References