Articles

Explainable AI for Foreign Direct Investment Analysis: Evidence from Central Asia

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Foreign direct investment (FDI) is an important factor in the economic development of Central Asian countries, where investment flows have traditionally been concentrated in resource-based sectors. In the context of a growing focus on diversification, the need to analyze and study the determinants of FDI is increasing.

This study examines the determinants of FDI inflows in Central Asian countries using machine learning methods (CatBoost) and explainable artificial intelligence (SHAP), and compares the results with a classical econometric approach based on a two-way fixed effects (TWFE) model. Given the limited availability of data, a transfer learning approach is applied: the model is first trained on a group of countries structurally similar to Central Asia and then fine-tuned on the regional sample.

The results show that key macroeconomic factors such as Trade (% of GDP), Current account balance (% of GDP), and several other macroeconomic variables remain significant across both methodologies. At the same time, ML identifies additional regional patterns, such as a higher importance for FDI of determinants including Adjusted savings: carbon dioxide damage (% of GNI), Urban population (% of total population), and Access to electricity (% of population), among others.

The findings indicate that XAI provides interpretable results that are consistent with classical methods and additionally allows for capturing nonlinearities and regional heterogeneity. The study extends the application of ML and XAI in data-constrained Central Asian settings and demonstrates the value of combining econometric and machine learning approaches in the analysis of FDI determinants.

Factors Affecting Investment Decisions of EU Investors in Vietnam

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EU investors were present in 18 out of 21 important economic sectors, focusing on manufacturing and processing industry 36.3%, refining and petrochemical 11%, textiles and garments 6.94%, electronics 6.4%, food processing 5.6%, cars and means of transport 5.2%; production and distribution of electricity and gas 20.7%, real estate 11%, information and communication 6.6% (GSO, 2020); contributed significantly to Vietnam’s economic growth. On the basis of that, attracting FDI is a subjective activity of the investee, that is, the investee will perform activities affecting the factors of the investee to increase the attractiveness of the investor. foreign investor. Thus, in order to effectively attract FDI from EU investors into Vietnam associated with the characteristics of each investor, it is necessary to assess the degree of influence of factors on FDI attraction, which is also the factors affecting the decision of EU investors. This study uses an exploratory factor analysis (EFA) model to analyze the factors affecting the investment decisions of EU investors in Vietnam, thereby proposing solutions to enhance the attractiveness of EU investors effective FDI from the EU into Vietnam in the coming time.