ECONOMETRICS REPORT factors affecting FDI in developing countries from 2011 2019

Rationale

In today's era of rapid advancements, econometrics plays a crucial role in addressing socioeconomic issues through its quantitative application of statistical and mathematical models This discipline utilizes data to formulate theories, test hypotheses, and predict future trends based on historical information By conducting statistical tests on real-world data, econometrics allows for a thorough comparison with existing economic theories As students of economics, we recognize the significance of studying and researching econometrics to enhance our understanding of these complex relationships.

Foreign direct investment (FDI) plays a crucial role in fostering an open international economic system and driving development However, the advantages of FDI are not uniformly distributed among countries, sectors, or communities Effective national policies and a robust international investment framework are essential for attracting FDI to more developing nations and maximizing its developmental benefits Host countries face challenges that require them to create a transparent and effective policy environment for investment, as well as to enhance their human and institutional capacities for successful implementation.

Therefore, our group has come to a decision of choosing this topic for our report:

“ Factors Affecting FDI in Developing Countries from 2011 - 2019”.

Objectives

This report will analyse the impact of following factors on FDI in developing countries from 2011

- 2019: Gross fixed capital formation; Gross domestic product per capita; Labour force;

Profit tax; Transport services; Inflation, GDP deflator and Political stability and absence of violence/terrorism.

This report provides a comprehensive overview, with detailed descriptions in each section for enhanced understanding It concludes with a well-reasoned summary and offers actionable recommendations aimed at boosting foreign direct investment in developing countries.

Factors Affecting FDI in Developing Countries from 2011 - 2019 GROUP 12 download by : skknchat@gmail.com

Subjects and scope of study

• Subjects of this study are the Factors Affecting FDI in Developing Countries from 2011 - 2019 period Factors examined in this study include: Gross fixed capital formation;

Gross domestic product per capita; Labour force; Profit tax; Transport services; Inflation, GDP deflator and Political stability and absence of violence/terrorism with the sample of 525 observations.

• Scope of study: Observations are taken from 82 developing countries from 2011 – 2019.

The report contains the following contents:

• Section 1 Overview of the topic

• Section 3 Estimated model and Hypothesis testing

This report may contain errors due to time constraints and challenges in data collection and comprehension We welcome your feedback to enhance our group's performance.

Through this report, we sincerely appreciate and value the insights and guidance that Dr Vu Thi Phuong Mai provided us.

OVERVIEW OF THE TOPIC

Definition of Foreign Direct Investment

Foreign Direct Investment (FDI) refers to the net transfer of funds aimed at acquiring physical capital, like factories and machinery, exemplified by Nissan's establishment of a car factory in the UK Recently, FDI has expanded to encompass the acquisition of assets and shares, providing investors with a management stake in companies.

The World Bank defines Foreign direct investment as:

Foreign direct investment (FDI) refers to the net inflows of capital aimed at acquiring a significant management stake—typically 10 percent or more—in a business located outside the investor's home country This investment encompasses equity capital, reinvested earnings, other long-term capital, and short-term capital, as detailed in the balance of payments (World Bank).

Foreign direct investment (FDI) differs from portfolio transfers, such as relocating financial capital to foreign bank accounts, which are classified as indirect investment However, if portfolio transfers result in a foreign investor gaining control over a management share in a company, this can qualify as FDI due to the change in ownership.

Foreign direct investment in developing countries

A developing country, often referred to as a low and middle-income country (LMIC), is characterized by a less developed industrial base These nations are keenly focused on attracting foreign direct investment (FDI) to stimulate economic growth and transformation To achieve this, they are actively enhancing the key factors that influence foreign investors' location decisions.

2.2 Foreign direct investment in developing countries

Foreign Direct Investment (FDI) plays a crucial role in developing countries by not only increasing capital formation but also facilitating the transfer of technology, skills, and innovative practices It enhances access to international marketing networks, benefiting enterprises linked to transnational systems Domestic firms can also gain from these advantages if the local environment is supportive The effectiveness of FDI in boosting productivity and competitiveness is influenced by the strength of supply chains between foreign affiliates and local businesses, as well as the ability of domestic firms to leverage spillover effects Consequently, effective policies are essential for attracting transnational corporations and maximizing the benefits of FDI.

Economic theories

This study aims to identify the key factors influencing the rise of Foreign Direct Investment (FDI) inflows in developing countries The analysis focuses on FDI as the dependent variable, while independent variables include gross fixed capital formation, GDP per capita, labor force, profit tax, transport services, inflation as measured by the GDP deflator, and political stability, including the absence of violence and terrorism.

3.1 Gross fixed capital formation (% of GDP)

Gross fixed capital formation encompasses various investments, including land improvements, purchases of machinery and equipment, and the construction of essential infrastructure such as roads, railways, schools, hospitals, and commercial buildings Additionally, net acquisitions of valuables are classified as capital formation under the 1993 System of National Accounts (SNA) This metric is expressed as a percentage of GDP, highlighting its significance in economic analysis.

The relationship between Gross Fixed Capital Formation (GFCF) and Foreign Direct Investment (FDI) is often analyzed by expressing FDI as a percentage of GFCF, serving as an indicator of foreign involvement in domestic capital formation However, the influence of foreign firms on domestic investment extends beyond their initial investment size, encompassing factors such as the frequency of interactions between domestic and foreign companies In developing countries, local firms frequently face "binding constraints" that limit their access to foreign markets and technology, ultimately hindering their growth and investment potential (Rodrik, 2006) The entry of multinational corporations can play a crucial role in alleviating these constraints and fostering local capital formation.

Multinational enterprises (MNEs) can provide domestic companies with access to advanced technologies and expanded foreign markets By forming arm’s length relationships with more efficient firms, domestic businesses can leverage extensive international distribution networks, ultimately enhancing the profitability of their investments.

3.2 Gross Domestic Product per capita

GDP per capita is calculated by dividing the gross domestic product (GDP) by the mid-year population GDP itself represents the total gross value added by all resident producers within an economy, adjusted for product taxes and excluding subsidies not accounted for in product value All figures are presented in current U.S dollars.

The relationship between GDP per capita and foreign direct investment (FDI) is significant, as highlighted by Callen (2008), who notes that GDP per capita serves as a key indicator of the economic well-being of a country's citizens This metric is vital for investors, as it reflects the purchasing power of the population and can influence their investment decisions Asiedu (2002) supports this notion, demonstrating a positive correlation between GDP per capita and FDI, suggesting that higher GDP per capita enhances FDI prospects in host countries Furthermore, a study by Kureþiü, Luburiü, and Šimoviü (2015) explored this relationship in the transitional economies of Central and Eastern Europe, emphasizing the need for more research in this area.

Between 2011 and 2019, various factors influenced Foreign Direct Investment (FDI) in developing countries, with a particular focus on GDP per capita as a key determinant This economic indicator helps explain the varying levels of attractiveness for FDI among different nations.

Such cases are well applied under developing economies as GDP is one of the dominating determinants in attracting FDI especially from developed countries.

The labor force, defined as the currently active population aged 15 and older, consists of individuals who provide labor for goods and services within a specific timeframe This group includes both employed individuals and those unemployed but actively seeking work, as well as first-time job seekers However, not all workers are accounted for; unpaid workers, family members assisting in businesses, and students are typically excluded, and in some nations, military personnel are not considered part of the labor force Additionally, the size of the labor force fluctuates throughout the year due to the seasonal nature of certain jobs.

The quantity and quality of laborers are vital factors influencing foreign direct investment (FDI) inflows from foreign affiliates in developing countries This paper focuses on the positive relationship between the labor force participation rate and the increase in FDI inflows, highlighting its significance in attracting investment.

According to World Bank’s Investment Climate Surveys, about 10 percent of respondents rate transport as a “major” or “severe” constraint to investing.

Transport encompasses a wide range of services, including sea, air, land, internal waterway, pipeline, space, and electricity transmission, provided by residents of one economy to another This sector involves the transportation of passengers and freight, as well as the rental of carriers with crews and various support and auxiliary services Additionally, postal and courier services are integral to the transport industry, with data represented as a percentage of commercial services.

Research indicates that high-quality transport infrastructure significantly drives Foreign Direct Investment (FDI) inflows Well-developed transportation systems are essential for attracting investors, as they enhance connectivity and facilitate efficient movement of goods and services.

From 2011 to 2019, various factors influenced Foreign Direct Investment (FDI) in developing countries, with transport infrastructure being a critical determinant Efficient transportation and communication systems enable foreign firms to reduce operational costs, thereby enhancing the productivity potential of their investments This, in turn, stimulates FDI inflows, as highlighted by Morriset (2000) and Jordaan (2004) Krugman (1991) emphasized that a robust transportation network allows firms in manufacturing regions to access broader input and product markets Additionally, research by Loree & Guisinger (1995) and Addison & Heshmati (2003) supports the notion that well-developed infrastructure positively impacts inward FDI Wheeler and Mody (1992) further concluded that transport infrastructure is a dominant factor in attracting FDI to developing nations.

To enhance the quality of transport services, it is essential to continually improve them to satisfy the increasing demands of citizens and businesses for superior service, enhanced accessibility, and more cost-effective solutions (EU, 2017).

Profit tax represents the taxes on profits that businesses are required to pay, and the total tax rate reflects the overall tax burden on a business, differing from the statutory tax rate applied to the tax base The calculation of business tax rates involves dividing the actual taxes paid by the commercial profit Taxes serve as a primary revenue source for governments, with their structure and contribution shaped by government policies and economic changes Tax policies consider distributional effects, economic efficiency, and the challenges of tax administration, with no universally ideal tax level However, taxation significantly influences economic behavior and competitiveness, and it is generally believed that appropriate profit taxation can enhance foreign direct investment (FDI) inflows in developing countries.

There is no universal agreement among researchers, economists, and practitioners regarding a definitive definition of inflation Nonetheless, the World Bank characterizes inflation as the annual growth rate of the GDP implicit deflator, which reflects the overall rate of price changes within the economy.

Related published researches

Foreign direct investment (FDI) has been extensively studied, focusing on the reasons firms pursue FDI, the attractiveness of certain countries for investment, and the choice of entry modes Faeth (2009) reviews various theoretical models and econometric studies that explore FDI determinants, including early research by Robinson (1961) and Wilkins (1970), neoclassical trade theory by MacDougall (1960) and Kemp (1964), ownership advantages as outlined by Vernon (1966) and Buckley & Casson (1976), and aggregate variables studied by Scaperlanda & Mauer (1969) Additionally, the OLI paradigm proposed by Dunning is a significant framework in understanding FDI determinants.

Theories of Foreign Direct Investment (FDI) have evolved over the years, with key contributions highlighting horizontal and vertical FDI models (Markusen, 1984; Helpman, 1984) Determinants of FDI are explored through various frameworks, including the Horizontal FDI, Vertical FDI, and Knowledge-Capital Model (Markusen, 1997; Markusen & Venables, 1998), as well as diversified FDI and risk diversification models (Rugman, 1975; Hanson et al., 2001) Additionally, policy variables have been identified as significant determinants of FDI (Bond & Samuelson, 1986; Black & Hoyt, 1989; Haufler & Wooton, 1999).

Dunning's OLI paradigm, developed in 1979 and 1980, is a widely recognized model that elucidates foreign direct investment (FDI) and the location choices of multinational enterprises (MNEs) by integrating ownership, location, and internalization advantages Ownership advantages highlight the competitive edge MNEs possess through proprietary technology or unique intangible assets, surpassing domestic firms Location advantages encompass specific benefits offered by a location, such as favorable tax regimes, reduced production and transport costs, and lower risks Internalization advantages pertain to the firm's capacity to internalize operations, thereby minimizing transaction costs Dunning further categorizes FDI into four primary types based on investment motives: resource-seeking FDI, which targets natural, physical, or human resources; market-seeking FDI, aimed at accessing domestic or regional markets; efficiency-seeking FDI, focused on optimizing production through economies of specialization; and strategic-asset-seeking FDI, intended to enhance competitive positioning.

From 2011 to 2019, various factors influenced Foreign Direct Investment (FDI) in developing countries, including a company's regional or global strategy and its integration into foreign networks These networks often encompass critical assets such as technology, organizational capabilities, and access to markets.

Foreign Direct Investment (FDI) is influenced by a variety of factors, including market size, factor costs, transport costs, political environment, exchange rates, trade openness, tax rates, infrastructure, and property rights Empirical studies indicate that these determinants vary significantly across different regions and countries, with some factors positively affecting FDI inflows in certain nations while having neutral or negative impacts in others Consequently, it is crucial for host countries to identify the specific factors that influence their FDI attractiveness and implement strategies to enhance their appeal compared to other nations.

MODEL SPECIFICATION

Methodology in the study

1.1 Method to derive the model

We employ ordinary least squares (OLS) for multiple regression analysis, a method developed by German mathematician Carl Friedrich Gauss OLS possesses significant statistical properties under specific assumptions, making it one of the most robust and widely used techniques in regression analysis due to its unbiased and efficient estimators.

1.2 Method to collect and analyse data

This article analyzes a panel dataset from 2011 to 2019, encompassing key economic indicators such as Foreign Direct Investment (FDI) net inflows, GDP per capita, total labor force, profit tax, transport services, gross fixed capital formation, inflation measured by the GDP deflator, and political stability alongside the absence of violence and terrorism across 82 countries The secondary data used in this analysis is sourced from the highly reliable World Data Bank, ensuring precision and credibility in the findings.

The analysis is carried out using Stata to analyse the dataset and interpret the correlation matrix between variables.

Theoretical model specification

According to previous published researches, our group has established a function to analyse the relationship between:

• FDI: Foreign direct investment, net inflows (current US$)

• GDP per capita (current US$)

• Profit tax (% of commercial profits)

• Transport services (% of service exports)

• Gross fixed capital formation (% of GDP)

• Political Stability and Absence of Violence/Terrorism

• βj 1 : the intercept term of the model

• βj2: the regression coefficient of GDP per capita

• βj3: the regression coefficient of Labour force

• βj 4 : the regression coefficient of Profit tax

• βj5: the regression coefficient of Transport services

• βj6: the regression coefficient of Gross fixed capital formation

• βj7: the regression coefficient of Inflation

• βj 8 : the regression coefficient of Political Stability and Absence of Violence/Terrorism

• Ui: The disturbance term of the model, represents other factors that affects FDI but are not mentioned in the model

• : the estimator of U i - residuals term

No Variables Meaning Unit sign

1 FDI Foreign direct investment, net inflows Ten million USD

2 GDP GDP per capita Hundred USD +

3 LAB Labour force Million people +

Factors Affecting FDI in Developing Countries from 2011 - 2019 GROUP 12

6 GFCF Gross fixed capital formation to GDP % +

Political Stability and Absence of

The dataset was collected from the official website of World Bank, including 137 developing countries in the period from 2011 - 2019 There are 791 observations in general.

2.3.2 Statistical description of the variables

To obtain comprehensive details about all the variables, including the number of observations (Obs), average value (Mean), standard deviation (SD), minimum value (Min), and maximum value (Max), execute the command “SUM.”

Running the command “CORR” to analyse the correlation between the variables, we have the table of correlation as below:

• The correlation between lnfdi and lngdp is 0.421, which is positive and mediumly high.

It can be interpreted that GDP per capita has a positive effect of FDI; an increase in GDP per capita results in a significant increase in FDI.

The analysis reveals a strong positive correlation of 0.664 between foreign direct investment (FDI) and the labor force, indicating that an increase in the labor force significantly boosts FDI levels.

The correlation between lnfdi and lntax is 0.12, indicating a positive relationship This suggests that an increase in profit tax has a slight positive effect on foreign direct investment (FDI), implying that higher profit taxes may lead to a modest rise in FDI levels.

The correlation between lnfdi and lntrans is 0.03, indicating a positive relationship This suggests that an increase in transport services significantly enhances foreign direct investment (FDI).

The correlation between lnfdi and lngfcf is 0.07, indicating a strong positive relationship This suggests that an increase in Gross Fixed Capital Formation (GFCF) relative to GDP significantly enhances Foreign Direct Investment (FDI), demonstrating the positive impact of GFCF on attracting FDI.

The correlation between lnfdi and lninf is -0.005, indicating a significantly low negative relationship This suggests that inflation, as measured by the GDP deflator, adversely impacts foreign direct investment (FDI), with a decrease in inflation leading to a substantial increase in FDI.

The correlation between lnfdi and lnpol is -0.03, indicating a weak negative relationship This suggests that a decline in political stability and an increase in violence or terrorism can lead to a significant rise in Foreign Direct Investment (FDI).

Also, we can see something from the correlation between independent variables:

The analysis reveals that the independent variables exhibit no excessively high correlations The strongest positive correlation is observed between lnfdi and lnlab at 0.664, while the most significant negative correlation is found between lnfdi and lnpol, measuring -0.03.

ESTIMATED MODEL AND STATISTICAL INFERENCE

Estimated Model

Run the command “REG” to compute estimation result, given in table:

According to the estimated result from Stata using the Ordinary Least Squares (OLS) method, we have the SRF as below:

The coefficient of determination R 2 = 0.7523 means 75.23% of the variation in FDI can be explained by 7 explanatory variables in the model; 24.77%left can be explained with factors outside the model.

Also, the adjusted R 2 is 0.7490, not so different from R 2

• The constant term is estimated to be 1 = -1.419906.

The estimated regression coefficient for GDP per capita (lngdp) is 0.7419004, indicating that a 1% increase in GDP per capita is associated with a 0.7419004% rise in foreign direct investment (FDI), while keeping other variables constant This finding aligns with our expectation that higher GDP per capita positively impacts FDI inflows in developing countries.

• The regression coefficient of lnlab is estimated to be 3 = 0.6684977 Holding other explanatory variables unchanged, if Labour force increases by 1%, the expected value of

FDI will increase by 0.6684977% This result is in sync with our expectation that the number of people in working age has a positive influence on FDI inflows of a developing country.

• The regression coefficient of lntax is estimated to be 4 = 0.13112 Holding other explanatory variables unchanged, if The Percentage of Profit tax in the total Commercial

Service (TAX) increases by 1%, the expected value FDI will increase by 0.13112% This

Between 2011 and 2019, the impact of various factors on Foreign Direct Investment (FDI) in developing countries was analyzed, revealing that while the overall positive effect was lower than anticipated, a well-structured tax policy could significantly enhance FDI levels.

The regression coefficient for lntrans is estimated at -0.636074, indicating that a 1% increase in the percentage of transport services within service exports (TRANS) is associated with a 0.636074% increase in expected Foreign Direct Investment (FDI), assuming other explanatory variables remain constant However, this result is not statistically significant at the 5% confidence level, which may be due to limitations in the model or data collection methods.

The regression coefficient for Gross Fixed Capital Formation (GFCF) is estimated at 0.4170271 This indicates that, with other variables held constant, a 1% increase in the GFCF to GDP ratio is expected to result in a 0.4170271% rise in Foreign Direct Investment (FDI) This positive relationship has been supported by previous research findings.

The regression coefficient for lninf is estimated at 0.2494664, indicating that a 1% increase in the annual inflation rate is associated with a 0.2494664% rise in foreign direct investment (FDI), assuming other variables remain constant This positive correlation between inflation and FDI was unexpected, suggesting the need for further research to explore this relationship in greater depth.

The regression analysis reveals that the coefficient for lnpol is 0.1213802, indicating that a 1% increase in GDP per capita (GDP) is associated with a 0.1213802% rise in Foreign Direct Investment (FDI), while keeping other variables constant This confirms the anticipated positive relationship between Political Stability and FDI.

• The Explained Sum of Squares (ESS) represents the variation of the estimated lnfdi about their sample mean, or explained by the regression model: ESS = 608.131843.

• The Residual Sum of Squares (RSS) represents the variation of the estimated lnfdi about their sample mean, or explained by the regression model: RSS = 200.203829.

The Total Sum of Squares (TSS) quantifies the variation of the estimated log foreign direct investment (lnfdi) around the sample mean, as explained by the regression model In this case, the TSS is calculated to be 808.335671, with a degree of freedom of n - 1 equaling 254.

Hypothesis Testing

2.1 Testing the significance of an individual coefficient βjj

According to the results from Stat using OLS regression analysis method, we obtained the confidence interval for the regression coefficients of each variable at a significant level of 5% as below:

The regression analysis indicates that the variables lngdp, lnlab, lngfcf, lntax, lninf, lnpol, and the constant term have statistically significant coefficients at the 5% level, as the value 0 is excluded from their confidence intervals.

• For lntrans, the value 0 does belong to the confidence interval The regression coefficient of this variable is not statistically significant at 5% level of significance.

• n: the number of observations or sample size, n = 525

• a: the significant level, a = 0.05, for the two-tailed test, a/2 = 0.025

According to test statistic, = βjj − 0 / (βjj ) of each variable at the significance level of 5% obtained from the results, we have:

• For the variable lngdp, its absolute value is |ts| = 21.93 > 1.96, we can reject H 0 Therefore, the regression coefficient of lngdp is statistically significant at 5% level of significance.

• For the variable lnlab, its absolute value is |ts| = 30.19 > 1.96, we can reject H 0 Therefore, the regression coefficient of lnlab is statistically significant at 5% level of significance.

• For the variable lntax, its absolute value is |ts| = 3.96 > 1.96, we can reject H 0 Therefore, the regression coefficient of lngdp is statistically significant at 5% level of significance.

• For the variable lntrans, its absolute value is |ts| = -1.72 < 1.96, we cannot reject H 0 Therefore, the regression coefficient of lntrans is not statistically significant at 5% level of significance.

• For the variable lngfcf, its absolute value is |ts| = 4 > 1.96, we can reject H 0 Therefore, the regression coefficient of lngfcf is statistically significant at 5% level of significance.

• For the variable lninf, its absolute value is |ts| = 2.03 > 1.96, we cannot reject H 0 Therefore, the regression coefficient of lninf is statistically significant at 5% level of significance.

• For the variable lnpol, its absolute value is |ts| = 2.87 > 1.96, we can reject H 0 Therefore, the regression coefficient of lnpol is statistically significant at 5% level of significance.

The P-value is the lowest significance level at which the Null Hypothesis H0 can be rejected.

• For the variables lngdp, lnlab, lntax, lninf, lngfcf, lnpol, their P-value is less than 0.05 Therefore, their regression coefficients are statistically significant at 5% level of significance.

The variable lntrans has a P-value greater than 0.05, indicating insufficient evidence to reject the null hypothesis (H0) Consequently, the regression coefficient for lntrans is not statistically significant at the 5% significance level.

2.2 Testing the significance of the model

The F-test of overall significance indicates whether your linear regression model provides a better fit to the data than a model that contains no explanatory variables.

Calculate the test statistic: Fs = ( (1−R 2 )( −1))

Because Fs > Fc, we can reject H 0 The overall model is statistically significant at 5% significance level.

The OLS regression analysis conducted using Stata revealed a P-value of P (Fs > Fc) = 0.000, which is less than the 0.05 threshold Consequently, we can reject the null hypothesis (H0) and affirm that the overall model demonstrates statistical significance at a 5% significance level.

2.3 Testing the assumptions of the classical model

2.3.1 Test the existence of multicollinearity

According to this result, we have:

• VIF (lnpol), VIF (lnlab ), VIF (lngdp ), VIF (lntrans ), VIF (lngfcf), VIF (lntax), VIF (lninf) are 1.61, 1.33, 1.31, 1.19, 1.15, 1.14, 1.07 respectively They are all smaller than 10.

• Mean VIF of 7 variables is 1.26 < 10.

In conclusion, there is imperfect multicollinearity between variables but is negligible This would not have a significant influence on the result of the model, so we can ignore it.

2.3.2 Testing the existence of heteroskedasticity

H1: unrestricted heteroscedasticity Running the command “ESTAT HETTEST”

Prob > chi2 = 0.1522 > 0.05 H0 cannot be rejected We accept that there is no

Heteroscedasticity Phenomenon in the model.

2.3.3 Test the normal distribution of random errors

Stating the hypothesis: H 0 : Normal distribution of random errors

H 1 : Non-normal distribution of random variable Running the command “SKTEST R”

Prob>chi2 = 0.3234 > 0.05 H0 cannot be rejected We accept that there is no non - normal distribution of random errors.

In conclusion, we cannot detect Multicollinearity, Heteroscedasticity or Non-normal Distribution of Random Errors in the model Therefore, the results obtained from it can be trusted

Developing countries increasingly view Foreign Direct Investment (FDI) as a crucial driver for economic development, modernization, income growth, and job creation In response, these nations have liberalized their FDI policies to attract more investment while also strategizing on domestic policies to optimize the benefits of foreign entities within their economies This study aims to explore the relationship between FDI and various influencing factors such as gross capital formation, GDP per capita, labor force, transport services, and profit tax Additionally, it examines not only the positive impacts of these determinants on FDI in developing countries but also the potential drawbacks for host economies, encompassing both economic and non-economic concerns.

To fully harness the advantages of Foreign Direct Investment (FDI), it is crucial for host countries to implement effective policies Foreign investors are primarily influenced by three key factors: the per capita gross domestic product, the level of gross fixed capital formation, and the overall political stability of the country Consequently, it is essential for authorities in host nations to take strategic actions that enhance these factors to attract and maximize FDI benefits.

To boost GDP per capita, it is essential to establish robust infrastructure A reliable power system and well-maintained roads are crucial for a nation’s capacity to produce and transport goods, as well as for businesses to deliver services effectively Investing in comprehensive infrastructure, including telecommunications, enables significant economic growth and enhances per capita income.

Investment in physical capital, including plants, machinery, and raw materials, is essential for GFCF growth, and this investment relies heavily on financial capital Advanced technology is often embedded in capital goods that facilitate a country's technological advancement However, limited or costly access to finance can hinder investment, especially for small and medium-sized enterprises and the informal sector A robust financial sector promotes economic growth by ensuring that capital is actively utilized, directed toward the most beneficial opportunities, and that risks are managed effectively.

To achieve political stability, governments must intensify their commitment to strengthening the rule of law and promoting good governance by combating corruption and improving policy and regulatory frameworks, particularly in competition Careful sequencing and pacing of reforms are essential to support domestic producers, especially during capital market integration Rapid liberalization of capital markets has previously led to financial crises that severely hindered growth over the last decade.

Investment plays a crucial role in driving economic growth and competitiveness across nations According to various studies, including those from the NBER and OECD, effective investment strategies can significantly enhance a country's development potential The World Bank's Global Investment Competitiveness Report highlights the importance of a favorable investment climate, which is influenced by factors such as labor force participation, tax policies, and overall GDP growth Moreover, understanding the dynamics of gross savings and domestic investment ratios is essential for policymakers aiming to boost income per capita Comprehensive data from reputable sources, including the IMF and World Bank, provides valuable insights into labor market trends and investment patterns that can guide strategic economic decisions.

Abbreviations

TAX Profit tax (% of commercial profits)

Transport services (% of service exports)

GFCF Gross fixed capital formation (% of GDP)

INF Inflation, GDP deflator (annual %)

POL Political stability and absence of violence/terrorism

FDI Foreign direct investment, net inflows (current US$)

List of countries

Albania Algeria Angola Argentina Armenia

Azerbaijan Bangladesh Belarus Bolivia Bosnia and

Botswana Brazil Bulgaria Cambodia Cameroon

Congo, Rep Costa Rica Rep.

Ethiopia Gabon Georgia Ghana Guatemala

Guinea Guatemala Guinea Honduras India

Indonesia Iraq Jamaica Jordan Kazakhstan

Kenya Kyrgyz Republic Lao PDR Lebanon Madagascar

Malaysia Maldives Mali Mauritania Mauritius

Mexico Moldova Mongolia Montenegro Morocco

Mozambique Myanmar Namibia Nicaragua Zambia

Paraguay Peru Philippines Romania Russian

Senegal Serbia Sierra Leone South Africa Sri Lanka

Sudan Tanzania Thailand Togo Tunisia

Evaluator Nguyễn Nguyễn Lê Vũ

Linh Khanh Nhật Linh Ngọc Ly Hạnh Nguyên

Tiêu đề	Factors Affecting FDI in Developing Countries from 2011 - 2019
Tác giả	Nguyen Linh Khanh, Nguyen Nhat Linh, Le Ngoc Ly, Vu Hanh Nguyen
Người hướng dẫn	Dr. Vu Thi Phuong Mai
Trường học	Foreign Trade University
Chuyên ngành	Econometrics
Thể loại	Report
Năm xuất bản	2021
Thành phố	Ha Noi

Định dạng
Số trang	44
Dung lượng	324,88 KB