Driving Factors of Foreign Direct Investment in Kosovo: The Roles of Market Access and Government Support

FDI is an important source of capital for developing countries, helping to accelerate economic growth, improve infrastructure and transfer advanced technologies. In the case of Kosovo, FDI represents a key opportunity to address economic development challenges, particularly in a context of high dependence on remittances and a still-developing private sector. Kosovo has a strategic geographical position in the heart of the Balkans, offering foreign investors easy access to the European Union (EU). It is well connected to major regional ports as follows:

- The distance between Kosovo and Albania via National Road/E8512 is 244.5 km, taking 2 hours and 51 minutes (https://www.distance.to/Kosovo).

- The distance from Kosovo to Tivat, Montenegro, is 343 km, taking 3 hours and 29 minutes (www.rome2rio.com/s/Pristina/Tivat).

- As for the distance from Kosovo to Thessaloniki (Greece), the air line is 270.13 km, taking 167.85 minutes, and the driving route is 325.36 km, taking 4 hours and 5 minutes (https://www.distance.to/Pristina/Thessaloniki).

A young, skilled workforce and low labor costs are important incentives for foreign investment. Investors are particularly attracted by the availability of an educated workforce. Kosovo is also competitive in sectors such as information technology (IT) (https://kit-ks.com/), wood processing, metal manufacturing, corporate outsourcing, tourism and agriculture. Businesses can be registered quickly (in just two days), facilitating rapid market entry. The government actively promotes investment opportunities, regularly informing investors about favorable investment laws. In addition, Kosovo offers international arbitration for legal disputes, ensuring fast and flexible resolution of commercial conflicts. The Law on Foreign Investment grants investors the right to seek arbitration for damages arising from legal actions or omissions directed against them. If necessary, disputes can be resolved through arbitration based on established procedures.

The research problem in this study is to identify and assess specific factors that influence the growth of FDI in Kosovo as follows:

- Ease of access to local markets: Studying how the ease of access to local markets, including business regulation and licensing procedures, influences the incentive for companies to enter the Kosovo market and make investments;

- Size of the available market: Despite Kosovo having a small market, its strategic position in the Balkans and regional trade agreements, such as the Central European Free Trade Agreement (CEFTA) (https://cefta.int/about/), provide access to a wider market. This is an attractive point for investors targeting the region as a whole;

- Ease of access to market information: Kosovo has made efforts to improve the digitalization of administrative services, facilitating the process of business registration and licensing. The government, through institutions such as the Kosovo Investment and Enterprise Support Agency (KIESA) (https://kiesa.rks-gov.net/), provides information platforms for foreign investors;

- Government committed to promoting exports: The Kosovo Government has initiated programs to support exports, especially for sectors with high potential such as agriculture and manufacturing. This commitment demonstrates the commitment to creating a supportive environment for investments aimed at exporting products.

Several studies have investigated the FDI in Kosovo. Bajrami and Zeqiri [1] found that attracting FDI should be accompanied by development and an increase in the level of human capital as a prerequisite for attracting FDI to Kosovo. Haliti et al. [2] showed that corruption control, political stability, and the absence of violence and terrorism have a positive and significant effect on attracting FDI flows, while distance to the border (doing business) has shown a negative effect on FDI flows in Kosovo from 2009 to 2016. Whereas Sahiti et al. [3] analyzed the potential determinants of FDI and empirical results revealed that low corporate tax rates, low wages, Gross Domestic Product (GDP) growth rate and high interest rates are associated with FDI attraction in Kosovo.

While Conahan et al. [4] emphasized that FDI remains uncertain due to domestic and international political challenges, the economic consequences of the COVID-19 pandemic has particularly hit many of the main countries of origin of FDI in Kosovo. Islami et al. [5] showed that there is a positive relationship between FDI inflows and GDP growth, while there is a negative relationship between FDI inflows and Kosovo's trade balance.

The trend of FDI in Kosovo has been unstable over the last two decades, reflecting the failures of the promotional strategies of the Investment Promotion Agency in Kosovo (IPAK) [6]. According to the Central Bank of Kosovo [7], the first official data on FDI dating back to 2004 was only 40.7 million euros. In the following years, FDI flows showed major changes. In 2007, 440.7 million euros were registered, while in 2008 there was a decrease of 17%, with an inflow of 369.9 million euros. The weakest year for FDI was 2014, with only 151.2 million euros, showing a decrease of 46.3% compared to 2013. On the contrary, the year 2022 marked a new record, with 732 million euros of FDI, an increase of 43% compared to the previous year. In the period from January to October in 2023, Kosovo attracted 700.7 million euros of FDI. In August 2024, the FDI was 560.9 million euros, bringing the total to 6,598.2 million euros from 2007 to October 2024 [7].

In terms of FDI sources, Germany led the way for 2022 with 194.2 million euros, followed by Switzerland (155.2 million euros) and the USA (93 million euros). Albania contributed 79.8 million euros, while Austria and the Netherlands provided 44.7 million and 43.7 million euros, respectively. Considerable amounts also came from Turkey (39 million euros), Slovenia (17.8 million euros) and other countries (89.5 million euros). The countries that have invested the most in Kosovo from 2007 to 2024 include Austria (1,105.4 million euros), Germany (3,395.6 million euros), Slovenia (464.4 million euros), Great Britain (870.2 million euros), Switzerland (100.5 million euros), Turkey (1133.0 million euros), Albania (1301.8 million euros), Luxembourg (49.7 million euros), Serbia (1351.8 million euros), USA (59.1 million euros), France (200.6 million euros), Bulgaria (118.6 million euros), North Macedonia (114.4 million euros), Bosnia and Herzegovina (8.3 million euros), Croatia (56.6 million euros), Slovakia (32.2 million euros), Norway (186.8 million euros), Italy (269.6 million euros), Hungary (213.4 million euros), Canada (208.1 million euros), and United Arab Emirates (120 million euros). The top six countries bringing FDI to Kosovo are Germany, Serbia, Austria, Albania, Turkey and Great Britain [7].

In terms of economic activities, the real estate sector dominated with 523.7 million euros in 2022, followed by financial and insurance activities (53.9 million euros), energy and gas supply (44.9 million euros) and extractive industry (52.4 million euros) [7].

The wholesale and retail trade, manufacturing and information and communication sectors contributed less than 20 million euros each. The top economic sectors of FDI between 2007 and 2024 are financial and insurance activities (3,394.6 million euros), real estate activities (993.7 million euros), construction (420.30 million euros), manufacturing (407 million euros), and electricity and gas (256.8 million euros) [7]. The data show that, although there has been significant growth in recent years, FDI in Kosovo continues to face structural challenges and dependence on limited economic sectors. Increasing diversification and strengthening attraction strategies remain critical for the country's economic future. These studies provide an important basis for understanding the challenges and opportunities for attracting FDI in Kosovo, identifying key areas for improvement.

In this study, the factors influencing FDI were adapted according to the paradigms of Hymer [8] and Dunning [9]. As for ownership, the technology and expertise that firms bring to Kosovo can be analyzed as part of the advantages of ownership. As for location, the variables (AMS, LCDB, EADM, EAMI, and GEPEX) represent location advantages, which are essential for attracting FDI to a market like Kosovo. As for internalization, the research assesses whether foreign firms prefer to control their own operations due to the lack of reliable information or government support.

Specific factors that most influence the preference for location (AMS, LCDB, EADM, EAMI, and GEPEX) and internalization were identified in this study, such as market transparency and institutional support in the Kosovo context. While ownership was not directly researched, it was considered that foreign firms in Kosovo bring technology and expertise and it is recommended to be investigated in future research.

In this study, data from the Tax Administration of Kosovo and the Statistical Office of Kosovo were used to identify the base of foreign investors with branches in Kosovo. The sampling process was carried out through the stratified random sampling method, ensuring a balanced representation of investors from different sectors and regions of the country. From a population of 450 registered foreign investors, all investors were contacted via Google Forms, social media, phone calls, and physical communications where possible. Responses were collected randomly from 148 investors, who constitute the sample for this study. To minimize bias and increase the responsiveness of the data, investors from all major regions of Kosovo were included, including Pristina, Ferizaj, and other important economic regions.

It was ensured that investor data was treated with complete confidentiality, using agreements signed with the companies to maintain the privacy of sensitive information. By using a combination of online (Google Forms) and offline (physical and telephone) methods, representativeness was maximized and the potential for bias that could occur from complete reliance on a single method was reduced. The structured questionnaire was structured in two parts. In the demographic part, it included questions to collect basic data on the investor profile, such as economic sector, region of operation, and investment experience in Kosovo. The investors came from Albania, Austria, Switzerland, Germany, France, Great Britain, Croatia, Hungary, Italy, Norway, Saudi Arabia, North Macedonia, Slovenia, Serbia, Turkey, and the USA. As for the content section, a five-point Likert scale (1 = Strongly agree; 2 = Agree; 3 = Neutral; 4 = Slightly agree; 5 = Disagree) was used to measure investors’ perceptions on key variables of the study. This scale was applied to questions related to the main factors of the research, i.e., EADM, AMS, LCDB, EAMI, and GEPEX. The data were analyzed using SPSS 26.

The methodology used in this study includes a wide range of data analysis steps and techniques to achieve the study's objective, which is to analyze the impact of key factors contributing to the growth of FDI in Kosovo, including EADM, AMS, LCDB, EAMI, and GEPEX.

At the same time, this study aims to:

• Examine the role of LCDB in attracting foreign investment.
• Assess the importance of EAMI and its impact on foreign investors' decision-making.
• Identify ways to improve information and services for investors in order to create a more favorable environment for FDI.
• To provide recommendations for policies and reforms that can contribute to the growth of FDI in Kosovo.

Meanwhile, to comply with the study objectives, two study hypotheses were established as follows:

Hypothesis 1 (H1): EADM, AMS, LCDB, EAMI, and GEPEX are positively and statistically significantly related to the growth of FDI in Kosovo.

Hypothesis 2 (H2): EADM, AMS, LCDB, EAMI, and GEPEX have a statistically significant and positive impact on the growth of FDI in Kosovo.

Primary quantitative data collected randomly through a structured questionnaire instrument were used. In a population of 450 respondents, 148 foreign investors operating in Kosovo from 2018 to 2023 were selected randomly. Two classical tests were used to test the reliability of the data. The Breusch-Pagan test [32] uses the following hypotheses, according to Statology [33]:

• Null hypothesis (H0): There is homoscedasticity (the residuals are distributed with equal variance).
• Alternative hypothesis (Ha): There is heteroscedasticity (the residuals are not distributed with equal variance).

If the p-value of the test is smaller than a specified significance level (e.g., α = 0.05), then the null hypothesis is rejected and it can be concluded that heteroscedasticity is present in the regression model, according to Statology [33]. The steps to perform the Breusch-Pagan test are as follows:

Step 1: Fit the regression model.

Step 2: Calculate the squared residuals of the model.

Step 3: Fit a new regression model using the squared residuals as the response variable.

Step 4: Calculate the chi-square test statistic (X²) as n*R² of the new model, where n is the total number of observations, and R² is the coefficient of determination of the new model using squared residuals as the response values.

If the corresponding p-value of the chi-square statistic with p (the number of predictor variables) degrees of freedom (df) is smaller than the specified significance level (e.g., α = 0.05), then the null hypothesis is rejected and it can be concluded that heteroscedasticity is present.

The other test used in this study is the diagnostic collinearity test. The analysis of multicollinearity through eigenvalue and condition index, as well as variance proportions, is connected to the work by Bollinger [34], who developed methods for diagnosing multicollinearity and assessing the stability of regression models. A smaller eigenvalue indicates strong correlations between the independent variables and the potential for high multicollinearity. A condition index greater than 30 suggests the possibility of significant multicollinearity problems and requires intervention. Variance proportions indicate how much variance is explained by the other variables and can help identify problematic variables.

To analyze multicollinearity through eigenvalue and condition index, the following formula was used:

Condition index =$\sqrt{\frac{\lambda max}{\lambda min}}$                    (3)

where, λmax is the largest eigenvalue (the largest variance) of the covariance matrix of independent data, and λmin is the smallest eigenvalue (the smallest variance) of the covariance matrix of independent data. The condition index is calculated by comparing the different eigenvalues. A high condition index (usually greater than 30) indicates the possibility of high multicollinearity and can be a sign that the models are highly sensitive to any independent variable. Variance proportions represent the percentage of variance for each variable explained by the other independent variables in the model and are related to the eigenvalue.

To calculate the variance proportions, the following formula was used:

 $Variance~proportion=~\frac{\lambda i}{\mathop{\sum }_{i=1}^{p}\lambda i}$                   (4)

where, λi is the eigenvalue for dimension i, and p is the number of independent variables. This formula shows how each dimension (independent variable) contributes to the total variance of the model. High values of variance proportions indicate that the dimension is closely related to others, which may suggest high multicollinearity.

To explain the complex interactions between the factors that attract FDI and the growth of FDI in Kosovo, Kendall's tau-b, which is a correlation coefficient, was used.

H1 aims to assess whether there are strong and statistically significant relationships between these factors, i.e., the dependent variable (FDI) and independent variables (EADM, AMS, LCDB, EAMI, and GEPEX). Kendall's tau-b is a nonparametric correlation coefficient used to measure the strength and direction of the relationship between two ordinal or interval variables. It is particularly useful when the data does not have a normal distribution and includes a limited number of cases or ranges (148 investor cases in this study) [35].

Kendall's tau-b is calculated according to the following formula [35]:

$T=\frac{\left( D-D \right)}{\sqrt{\left( C+D+{{T}_{X}} \right)*\left( C+D+{{T}_{Y}} \right)}}$                 (5)

where, C is the number of concordant pairs (where one variable increases when the other increases), D is the number of discordant pairs (where one variable increases and the other decreases), and T_X and T_Y are the numbers of equalities in each variable. In addition, τb takes values from -1 (perfect negative relationship) to +1 (perfect positive relationship), with 0 indicating no relationship. A statistically significant value indicates that the variables have a statistically valid relationship. For H1, this method can answer the following questions:

- Is there a statistically significant relationship between each factor (EADM, AMS, LCDB, EAMI, and GEPEX) and FDI?

- What is the strength and direction of the relationship?

For H2, multiple linear regression was used. H2 aims to assess the overall and individual impact of factors of EADM, AMS, EAMI, LCDB, and GEPEX on FDI in Kosovo. This model measures the degree of impact of each factor on the dependent variable (FDI). It also explains how much of the variance in FDI is explained by the independent variables. Linear regression, first developed by Galton [36] in the late 19^th century, has evolved into a key statistical method [37]. Linear regression was initially used to study the inheritance of physical traits, such as human height, and it became known as "regression to the mean" when statistical trends demonstrated that traits tend to average across generations [38]. The goal of linear regression is to analyze the relationship between a dependent variable (outcome) and one or more independent variables (predictors). Simple linear regression focuses on the relationship between one independent variable and the dependent variable, whereas multiple linear regression uses multiple independent variables to provide a more accurate prediction.

The equation of simple linear regression is as follows:

$y=a+\beta x+~\varepsilon i~$                                (6)

where, y is the dependent variable (or response variable), a is the intercept term (where the regression line intersects the axis of the dependent variable), βx is the regression coefficient (the weight given to the independent variable), and ℇi is the error term that represents different causes (variables) that are not included in the model.

H2 includes the dependent variable (FDI) and independent variables (EADM, AMS, LCDB, EAMI, and GEPEX). This statistical model is particularly suitable when it is required to analyze how several factors (independent variables) simultaneously affect a single dependent variable, providing a complete picture of the interrelated relationships.

Multiple linear regression has the following general form [39]:

$y=~\beta 0+\beta 1x1+\beta 2x2+\beta 3x3+\beta 4x4+~\beta nxn+~\varepsilon i$                   (7)

where, y is the dependent variable, which in this case represents FDI; x₁, x₂, ..., xₙ are the independent variables, representing various factors such as EADM, AMS, LCDB, EAMI, and GEPEX; Β₀ is the intercept term, which indicates the value of FDI when all the independent variables are zero; Β₁, Β₂, Β₃,..., Βₙ are the regression coefficients that indicate the expected change in FDI for a one-unit increase in each independent variable; and ε is the error term, which represents the random factors or influences that are not included in the model.

As for the regression, Analysis of Variance (ANOVA) tests whether the overall model is statistically significant; regression coefficients measure the individual impact of each factor; R-squared (R²) explains the percentage of variance in FDI that is explained by the model; βi>0 indicates a positive impact of the factor on FDI; p-value <0.05 indicates statistical significance; and R² is the percentage of variation in FDI explained by the model.

For H2, this model can answer the following questions:

- How much do all factors together affect FDI growth?

- Which variables are most important and have the greatest effect?

This model helps determine the impact that the independent variables have on the dependent variable and provides a deeper understanding of how these relationships may influence the study outcomes.

The comparative analysis of the findings of this study with other studies on FDI drivers highlights several relevant themes. Key points include market size, ease of doing business, access to market information, and government attention to export promotion. Below is a structured discussion based on the findings of this study and existing research.

- EADM (p = 0.008, r = 0.666): The results of this study suggest that EADM significantly influences FDI inflows in Kosovo. This finding aligns with the study by Li [41], which emphasizes the role of diplomatic relations in reducing barriers to market entry. Similarly, Bardakas et al. [42] argued that a favorable business environment and institutional quality are crucial. This study is also supported by the main findings of Petrović-Ranđelović et al. [43] in the Balkans, which confirm that market size is an important determinant of FDI inflows in the Western Balkan countries. Similarly, Sahiti et al. [22] found that most FDI in Kosovo targets real estate, leasing, and business sectors, suggesting sector-specific opportunities despite market size limitations.

- AMS (p = 0.000, r = 0.335): The findings of this study show a positive but weak relationship between AMS and FDI. This aligns with studies by Atobatele [44], Asiedu [45], and Asbullah et al. [46], which emphasize the importance of market size for attracting FDI in Africa. Similar dynamics have been observed in Oman, which indicates that FDI flows are positively influenced by the market size [47]. Unlike this study, market size and availability of low-cost inputs are, respectively, the least desirable factors [48]. Despite Kosovo's small consumer base, integration into broader Balkan markets could enhance its FDI appeal. Similar to this study, market size and low input costs have a smaller but positive impact on increasing the motivation to invest. Statistical analysis reveals that these factors do not affect investors from different countries equally [48]. This is consistent with Gashi [27], who highlights trade liberalization's potential for integrating Kosovo’s industries into global production networks. The results of this study are similar to the results of the study by Asbullah [46], which indicate that market size has a positive impact on FDI; for this reason, governments should increase the size of market access, increase the infrastructure that facilitates market access, and open up trade with neighboring and more distant countries in order to increase FDI.

- LCDB (p = 0.023, r = -0.187): Interestingly, a negative relationship between LCDB and FDI was found in this study, indicating potential areas for policy improvement. This result diverges from studies by Mottaleb and Kalirajan [49], who found that low operational costs increase FDI inflows. It suggests that factors beyond direct costs, such as administrative efficiency and investment security, may play a more critical role in Kosovo, as evidenced by findings from Piwonski [50]. Additionally, Kelmendi [25] identified low labor costs and streamlined registration procedures as key advantages, though issues like corruption and law enforcement remain critical concerns.

- EAMI (p = 0.111, r = 0.131): This study indicates that EAMI is statistically insignificant in attracting FDI. The positive, but statistically insignificant, relationship in the results of this study echoes research that emphasizes the need for transparent and easily accessible information for investors. Weak information channels can limit the effectiveness of FDI. This result contrasts with the findings from Jaiblai and Shenai [51], who demonstrated that transparent and accessible market data significantly boosts FDI. Kosovo could benefit from investing in centralized, easily accessible economic and trade information systems, as recommended by Artige and Nicolini [52]. The Kosovo Chamber of Commerce [31] also stressed promoting accurate market data to boost investor confidence.

- GEPEX (p = 0.047, r = 0.615): GEPEX is positively significant. GEPEX emerged as a positive factor, aligning with Kyereboah-Coleman and Agyire-Tettey [53], who emphasized the role of government policies in stimulating FDI. The findings of this study suggest that the Kosovo government should further increase export incentives and promote competitive policies for both domestic and foreign businesses because the effect of attracting investors can be even greater, similar to successful practices in countries such as India and Brazil. The Kosovo government could adopt policies similar to those in India and Brazil [52]. Furthermore, Kastrati and Vokshi [30] called for reducing administrative barriers and combating corruption, which can improve FDI-friendly policies.

Bevan and Estrin [54] argued that access to local and regional markets is a key motivator for FDI. The results of this study for EADM (B=0.167 for all) support this argument, showing that EADM is an important factor for FDI. Blonigen [55] suggested that market size is critical for attracting FDI, especially in emerging markets. The results of this study (B=0.375 for all; r=0.289 for all) support this theory, showing a strong positive relationship and impact of market size on FDI. Wang et al. [56] proposed that LCDB is critical for attracting FDI. The results of this study (B=−0.037 for all; r=−0.283 for all) show a negative relationship and impact, suggesting that in the case of Kosovo, this factor is not that important. Beugelsdijk et al. [57] estimate the effects of increased vertical and horizontal activity of US MNEs in 44 host countries over the period 1983–2003, also using traditional total FDI figures as a benchmark. Consistent with the existing literature, we find no significant effects of horizontal or vertical FDI in developing countries.

The results of this study for EAMI (B=0.106 for all; r=0.029 for all) show a weak and non-significant impact, suggesting that this factor is not perceived as decisive in Kosovo. Dunning mentioned the role of government in creating location advantages for attracting FDI. This factor falls within the framework of factors that the Eclectic Paradigm of Production "a powerful general framework for explaining and analyzing not only the economic rationale of economic production, but many organizational issues and influences related to MNE activity [58]. The analysis takes as its starting point the growing convergence between theories of international trade and production, and argues the case for an integrated approach to international economic involvement, based both on the location-specific endowments of countries and on the specific ownership assets of firms. In pursuing this approach, Dunning presents a systematic explanation of the foreign activities of firms in terms of their ability to capture markets to their advantage [59].

The results of this study for GEPEX (B=−0.304 for all; r=−0.138 for all) indicate a negative impact and poor perceptions of government policies. This suggests the need for improvements in government initiatives to promote exports.

AMS is the most important factor and is supported by existing literature. Alfaro et al. [60] argued that the impact of market size on attracting FDI is more limited in developing countries. It emphasizes that other factors, such as institutional transparency and infrastructure, are more decisive than the local market in countries with small economies. Morisset [61] found that low business costs do not necessarily have a positive impact on attracting foreign investment, especially when institutions are unstable and the legal framework is uncertain. Bevan and Estrin [12] provided a contrasting result in their study of Central and Eastern Europe, finding that the impact of market size and business costs is lower than the impact of trade relations and integration into international organizations such as the EU. Campos and Kinoshita [62] argued that the quality of institutions and political stability are more important than access to local markets in attracting FDI to developing countries. Bellak et al. [63] emphasized that government export promotion policies are effective only when combined with other investment-friendly policies, showing that market access alone is not sufficient.

EADM has a moderate positive impact, also in line with theories. GEPEX and LCDB are not as effective in Kosovo as expected, contrary to the suggestions of some authors. EAMI has a weak impact and is not essential in this case. However, AMSAMS is undervalued in developing countries and those with small markets. LCDB is enough without a strong institutional framework. The effects of GEPEX are limited if not accompanied by overall economic stability and support.

As in the case of this study, the creation of Investment Promotion Agencies (IPAs) was considered one of the main policy initiatives for attracting FDI [16]. Countries were encouraged to develop the activities of IPAs because they attract inward FDI; sub-national regional IPAs attract FDI in particular towards less developed areas; IPAs in less developed regions increase FDI inflows by up to 71%; impacts are concentrated in knowledge-intensive sectors and IPAs work best for less experienced investing companies.

The findings of this study confirm that while Kosovo shares several FDI-attracting characteristics with other developing economies, targeted policy improvements could enhance its investment climate. These include expanding market integration, reducing bureaucratic obstacles, and enhancing transparency. Future studies could explore sector-specific FDI dynamics and incorporate broader regional comparisons to provide deeper insights.

Nakije Kida, Remzi Smajli, Delvina Gjuraj, Vesa Morina, Julinda Morina: Validation, Supervision, Writing – original draft, Methodology. Nakije Kida, Remzi Smajli, Delvina Xhuraj: Writing – review and editing, Nakije Kida, Vesa Morina dhe Julinda Morina; Writing – original draft, Visualization. Remzi Smajli, Vesa Morina dhe Julinda Morina, Nakije Kida, Delvina Gjuraj: Software, Sources, Project administration, Conceptualization. Nakije Kida, Julinda Morina, Remzi Smajli dhe Delvina Xhuraj: Methodology, Investigation, Formal analysis, Data curation.