Political and productive capacity characteristics as outward foreign direct investment push factors from BRICS countries

This study focuses on push factors—home country characteristics driving outward foreign direct investment (OFDI)—from BRICS (Brazil, Russia, India, China, and South Africa) nations. While pull factors (host country attributes attracting investment) are also important, this research addresses a gap in the literature by specifically examining the domestic conditions within BRICS countries that propel their firms to invest abroad. OFDI is considered beneficial for home countries, offering advantages like access to new markets, intermediate goods, and technologies at reduced costs, ultimately boosting productivity, output, and competitiveness. The last decade has witnessed a significant surge in OFDI from developing countries, with BRICS nations accounting for a substantial 62% of this increase. Existing research often treats BRICS countries as a homogeneous group or focuses on individual countries, neglecting the nuances of their unique characteristics and the combined impact of political stability and productive capacity. This study addresses this limitation by empirically examining these two factors, deriving a political stability index from the International Country Risk Guide (ICRG) data, and using a panel autoregressive distributed lag (PARDL) model combined with impulse response function (IRF) analysis to analyze the data from 2000 to 2018. This methodology offers a novel approach to understanding OFDI push factors from BRICS economies.

The theoretical framework draws upon Dunning's eclectic paradigm (OLI model), which considers ownership, location, and internalization advantages for OFDI decisions. Later extensions incorporate institutional factors and an investment development path, highlighting the role of market size, resources, FDI type, and government policies at different stages of economic development. Studies on individual BRICS countries confirm the effects of institutional and political variables, but this study uniquely examines them as a group using a PARDL model. Previous research on individual BRICS nations often employed various methodologies and indicators without incorporating political stability and productive capacity. Existing literature has demonstrated the significant influence of political risk (ICRG) on inward FDI and the impact of productive capacity on sustainable development. This study adds a new dimension by empirically integrating these under-researched variables into the analysis of OFDI push factors for BRICS countries. Previous studies either focused on single countries or used broad categorization of developing countries. This research aims to bridge these gaps by providing a comprehensive analysis specifically tailored to the unique characteristics of BRICS economies.

The study utilizes balanced panel data for BRICS countries from 2000 to 2018 to empirically analyze the relationship between home-country push factors (explanatory variables) and OFDI stock (dependent variable). Diagnostic tests, including multicollinearity and cross-sectional dependency (CSD) checks, were conducted. The PARDL model was employed due to its ability to handle dynamic short- and long-run analyses simultaneously, accommodating mixed integration levels and variable lag selections. The study also included a principal component analysis (PCA) to derive a political stability index (PSI) from the ICRG's twelve sub-components of political risk. To mitigate multicollinearity, only the first four principal components were initially considered, but ultimately all components were retained to provide a more comprehensive measure. The resulting PSI was incorporated into the PARDL model alongside other economic indicators such as trade openness, inward FDI, GDP, real exchange rate, real interest rate, technological progress, and the Productive Capacity Index (PCI). Following the PARDL analysis, an IRF analysis of a vector autoregression (VAR) model was performed to assess the dynamic effects of shocks in each explanatory variable on OFDI stock over time. Several econometric tests were implemented, such as CSD tests (Pesaran scaled LM, Pesaran CD, Breusch-Pagan LM), and unit root tests (Im, Pesaran, and Shin (IPS), Augmented Dickey Fuller (ADF), Phillips-Perron (PP), and Levin-Lin-Chu (LLC)) to confirm stationarity of the time series. Panel cointegration tests (Pedroni, Kao, Westerlund) determined long-term relationships between the variables, which allowed for using panel cointegration tests. Optimal lag selection was determined using various criteria (MMSC, Hansen J statistic, AIC, BIC, HQIC). Three different estimators within the PARDL framework—Mean Group (MG), Pooled Mean Group (PMG), and Dynamic Fixed Effects (DFE)—were employed to address potential heterogeneity and biases in the data. A Hausman test helped determine the most suitable estimator among PMG and MG.

The PARDL model results demonstrated significant short- and long-term relationships between OFDI and its push factors. The MG estimator was identified as the most appropriate based on the Hausman test. Key findings include: A positive and significant relationship between trade openness (LEIGDP), inward FDI (LIFDI), GDP (LGDP), and real exchange rate (LEXCH) with OFDI (LOFDIS) in both short and long term. A negative and significant effect of the real interest rate (LRIR) on OFDI, indicating that lower capital costs incentivize outward investment. The PCI demonstrated a significant positive influence on OFDI in both the short and long runs, highlighting the importance of productive capacity. Technological progress (LTechno) also significantly positively influenced OFDI. The PSI (Lindex) showed a significant positive long-term effect on OFDI, indicating that political stability greatly impacts a country's ability to undertake OFDI. The IRF analysis confirmed the significance of the long-term impact of PSI, PCI, technology, and GDP shocks on OFDI. Specifically, positive shocks to the PSI and PCI resulted in prolonged positive effects on OFDI, while shocks to technology and GDP had similar positive, albeit slightly shorter-lived, effects. A robustness check was performed using the World Bank's Governance Political Stability (GPS) data as an alternative control variable. The results were consistent and confirmed that the study's own PSI was a more appropriate and comprehensive measure of political stability.

The findings robustly support the hypothesis that both political stability and productive capacity are crucial push factors driving OFDI from BRICS countries. The significant long-term impacts of these factors are particularly noteworthy. The study addresses the gap in the literature by providing empirical evidence on the interconnectedness of political stability and productive capacity, with the long-term relationship between these and OFDI being especially strong. The results underscore the importance of government policies aimed at improving the business environment within BRICS countries, which helps them to invest abroad. This research contributes to a more nuanced understanding of OFDI dynamics from emerging market economies, going beyond previous studies that often overlooked the combined influence of political stability and productive capacity and only looked at a single country or grouped developing countries.

This study provides strong empirical evidence highlighting the crucial role of political stability and productive capacity as push factors for OFDI from BRICS countries. The findings demonstrate significant positive long-term effects of these factors on OFDI stocks. Policymakers in BRICS nations should prioritize policies that enhance these elements to foster a more conducive investment climate, encouraging further outward investments and economic growth. Future research could explore the individual components of political stability in more detail, potentially using alternative methodologies to address multicollinearity issues, or extend the analysis to other groups of emerging economies. A disaggregated analysis of specific sectors and industries could also reveal interesting insights.

The study's time frame (2000-2018) might limit the generalizability of the findings to other periods. Although the study employed a robust methodology to address potential endogeneity and heterogeneity, some biases might still be present. The reliance on secondary data from the ICRG and UNCTAD, which might have their own limitations and potential biases could also lead to some inaccuracies. The PCA approach to creating a Political Stability Index (PSI) involved inherent subjective choices regarding the selection of component variables and weights assigned, although efforts were made to create a comprehensive index using multiple component variables. Further research can explore the limitations caused by these.