The Political Determinants of Foreign Direct Investment: A Firm-Level Analysis

Arel‐Bundock, Vincent

doi:10.1080/03050629.2016.1185011

Cited by 18 publications

(3 citation statements)

References 67 publications

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“…The relationship between FDI flows and its determinants has generated a plethora of empirical papers. Whilst most of these research papers explore the host country's quality of institutions as determinants to be studied for FDI inflow (Sabir, Rafique, & Abbas, 2019;Tomelin, Amal, Hein, & Carpes Dani, 2018), other studies focused on determinants associated with political risk indicators of the host country (Arel-Bundock, 2017). However, these research papers' model specifications and empirical results are mixed and vary from one country to the other.…”

Section: Introductionmentioning

confidence: 99%

Foreign direct investment inflow: The drivers and motivations in MENA Region

Osabuohien-Irabor

2022

Econ. J. Emerg. Mark.

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Purpose ― Reasons why Multinational Enterprise (MNEs) engage in foreign direct investment (hereafter referred to as FDI) abroad have been of great interest to policy markets, academia and international portfolio investors. This examines FDI inflow motives to the Middle East and North Africa (MENA) region for the period 2005 to 2019. Design/methodology/approach ― This research paper applies both the static and dynamic panel methodologies such as SYS-GMM, fixed effects, and pooled OLS estimators to investigate the motivational factors of MNEs FDI inflows to MENA countries. Findings ― Although specificity applies to countries, estimated results suggest that MNEs in the MENA region are predominantly interested in serving both home and host markets. Other motives such as efficiency-seeking FDI vary across countries, indicating that FDI motives are not homogeneous among region members. This paper provides useful insight for both firms and host countries in the region. Originality/value ― This research paper investigates the factors that motivate MNEs to consider FDI decisions in MENA countries. Rather than investigate the individual countries within the region as done in existing literature, this research paper simultaneously examines MNEs' investment motivations in the MENA region. The findings are significant, plausible and in line with the economic development of most countries in the region.

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Section: Introductionmentioning

confidence: 99%

Foreign direct investment inflow: The drivers and motivations in MENA Region

Osabuohien-Irabor

2022

Econ. J. Emerg. Mark.

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“…Thus, forests are grown by bootstrapping, creating samples based on drawing observations from the initial set of data, then randomly selecting subsets of predictors (or independent variables), and using them to create the nodes and the tree. In the end, a majority vote is taken among all of the grown trees, the ensemble prediction is calculated, and the final prediction corresponds to the most common predicted value in the trees (Breiman 2001;Arel-Bundock 2017). Another very useful feature of random forest is the lack of an implicit assumption of linearity in the relationships between predictors and the dependent variables, which increases its ability to identify meaningful links between variables.…”

Section: Methodsmentioning

confidence: 99%

Solvency Risk and Corporate Performance: A Case Study on European Retailers

Horobeț

Curea

Popoviciu

et al. 2021

JRFM

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This paper proposes a new approach toward understanding the financial performance dynamics in the EU retail sector (pre-pandemic); we focus on the connection between indebtedness and solvency risk and other areas of corporate performance (e.g., liquidity, assets efficiency, and profitability). Its contribution resides in identifying the drivers behind solvency risk in a sector that went through significant transformations in recent decades, as well as the links between the various areas of performance of retailers, and their impacts on solvency risk, using the machine-learning random forest methodology. The results indicate a declining trend for solvency risk of EU food retailers after the global financial crisis and up until the beginning of the pandemic, which may reflect their maturity on the market, but also an adjustment to legal changes in the EU, meant to equalize the tax advantages of debt versus equity financing. Solvency risk accompanied by liquidity risk is a mark of the retail sector, and our results indicate that the most critical trade that EU retailers face is between solvency risk and liquidity, but is fading over time. The volatility of liquidity levels is an important predictor of solvency risk; hence, sustaining a stable and good level of liquidity supports lower risks of financial distress, and may mitigate the shock impacts for EU retailers. A higher solvency risk was accompanied by increased efficiency of asset use, but reduced profitability levels, which led to higher returns available to shareholders for high solvency risk retailers. Overall, retailers should focus on operational performance evidenced by financial indicator levels than on the volatility of these indicators as predictors of solvency risk.

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“…Foreign direct investment (FDI) inflow has been discussed by various researchers and it reflects the importance of FDI for economic development both at the regional level and the firm level (Arel-Bundock, 2017;Gasanova et al, 2017;Singh, 2021b). The decision of foreign inverstors on where to invest is based on various parameters, which leads to differences in FDI inflow at regional levels (Makojevic et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine-Learning Approach in Estimating FDI Inflow: Visualization of ML Models with LIME and H2O

Singh

2021

TalTech Journal of European Studies

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In advancement of interpretable machine learning (IML), this research proposes local interpretable model-agnostic explanations (LIME) as a new visualization technique in a novel informative way to analyze the foreign direct investment (FDI) inflow. This article examines the determinants of FDI inflow through IML with a supervised learning method to analyze the foreign investment determinants in Hungary by using an open-source artificial intelligence H2O platform. This author used three ML algorithms—general linear model (GML), gradient boosting machine (GBM), and random forest (RF) classifier—to analyze the FDI inflow from 2001 to 2018. The result of this study shows that in all three classifiers GBM performs better to analyze FDI inflow determinants. The variable value of production in a region is the most influenced determinant to the inflow of FDI in Hungarian regions. Explanatory visualizations are presented from the analyzed dataset, which leads to their use in decision-making.

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The Political Determinants of Foreign Direct Investment: A Firm-Level Analysis

Cited by 18 publications

References 67 publications

Foreign direct investment inflow: The drivers and motivations in MENA Region

Foreign direct investment inflow: The drivers and motivations in MENA Region

Solvency Risk and Corporate Performance: A Case Study on European Retailers

Interpretable Machine-Learning Approach in Estimating FDI Inflow: Visualization of ML Models with LIME and H2O

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