The theoretical systems of OFDI location determinants in global north and global south economies

The study addresses how determinants of outward foreign direct investment (OFDI) differ between global north and global south economies, using the United States (north) and China (south) as representative cases. Motivated by the rapid rise of OFDI from emerging (southern) economies since the early 2000s—despite their later start and more challenging institutional environments—the paper questions the applicability of mainstream OFDI theories (developed largely from northern-country experiences) to southern-country behavior. It posits that southern-country OFDI may be driven not only by market-seeking motives but also by energy security, infrastructure development, and political-strategic considerations, and seeks to empirically compare how host-country economic, energy, logistics, and political conditions shape the location of Chinese versus U.S. OFDI across 172 destination countries (2005–2019). The purpose is to refine the theoretical system of OFDI location determinants to account for north–south differences and offer policy insights.

Theoretical foundations are predominantly derived from research on northern countries. Classic perspectives include Hymer’s market imperfections, Vernon’s product life cycle, and Dunning’s eclectic (OLI) paradigm and IDP (Investment Development Path). While OLI and IDP have been widely used, evidence shows they insufficiently explain emerging-economy OFDI: southern MNEs often lack traditional ownership advantages and follow different development paths, deviating from IDP predictions. Complementary views emphasize resource-based motives (seeking scarce, valuable, irreplaceable resources) and institutional theory (governance, incentives, and state support reducing transaction costs). Empirical work shows: market size and trade openness attract FDI; logistics infrastructure quality enhances attractiveness; institutional quality generally promotes FDI though effects of corruption are mixed; and for emerging economies (e.g., China) resource-seeking is prominent, often targeting resource-rich countries and leveraging flexibility and adaptation advantages. However, systematic comparative analyses of OFDI theoretical systems and location determinants across north versus south are scarce, especially beyond single-industry studies. This gap motivates a comprehensive, cross-country comparison of determinants for China and the USA.

Design: A panel econometric analysis compares how host-country conditions affect China’s and the USA’s OFDI stocks across 172 destination countries, 2005–2019. Model: From a conceptual Y = E K L P framework (economy, energy, logistics, politics), log-linear panel specifications are estimated separately for China and the USA. To address heteroskedasticity and scale, variables are log-transformed. Equations include country fixed effects and time effects. Given serial correlation and potential endogeneity, fully modified OLS (FMOLS) for heterogeneous cointegrated panels is referenced, and a panel Vector Error Correction Model (VECM) is implemented to capture short- and long-run dynamics. Optimal lags are selected via information criteria; error-correction terms reflect long-run equilibrium. Variables: Principal component analysis (PCA) condenses 27 indicators into six composite indices: EC (economic market), EX (energy/export structure), ER (energy resources), LQ (logistics infrastructure quality), LC (logistics capacity), PO (political risk/governance). Sources include World Bank (WDI, governance), WTO, BP Statistical Review, Global Competitiveness Report, and national sources (MOC China; AEI for U.S. OFDI). KMO and Bartlett tests indicate PCA suitability (all KMO > 0.7; Bartlett p < 0.01). Total explained variances: EC 72.27%, EX 43.75%, ER 58.16%, LQ 84.45%, LC (as per table), PO 84.45% (per reported factor loadings table). Data: Dependent variables are bilateral OFDI stocks from China (COFDI) and the USA (UOFDI) to each host. Panel unit root tests (LLC, IPS, Fisher-ADF, Fisher-PP) confirm stationarity (all 1%); Kao cointegration tests indicate long-run relationships between OFDI and the determinants for both countries (p < 0.01). Stability is verified via inverse roots of AR characteristic polynomials. Dynamic relationships are further analyzed using panel Granger causality, impulse response functions, and variance decomposition over a 10-period horizon.

Long-run and short-run effects differ sharply between China and the USA. China (Table 8, VECM): - Error-correction (speed of adjustment): Lagged EC(-1) = -0.947***, indicating rapid convergence to long-run equilibrium. - Long-run coefficients: EX = 0.235**; ER = 1.760***; LQ = -0.279**; LC = -0.215**; EC = 0.126 (ns); PO = -0.268 (ns). Interpretation: Chinese OFDI is positively associated with host energy export structure and energy resource endowments, and negatively associated with logistics quality and capacity (suggesting targeting countries where China can build/upgrade infrastructure). Economy and politics are not significant in the long run. - Short-run (first differences): D(EX) = 0.211***; D(ER) = 1.789***; D(LQ) = -0.273***; D(LC) = -0.235***; D(PO) = -0.578**; D(EC) (ns). Politics displays a significant negative short-run impact, consistent with preference for higher-risk (lower PO) environments in the short term. USA (Table 9, VECM): - Error-correction: Lagged EC(-1) = -0.98***, also rapid convergence. - Long-run coefficients: ER = -0.04** (negative and significant); EC, EX, LQ, LC, PO are not significant. Interpretation: In aggregate, resource intensity deters U.S. OFDI, while broad economic, logistics, and political indices do not significantly affect long-run U.S. OFDI. - Short-run: D(EC) = 0.04** (positive); D(ER) = -0.04** (negative); other differenced variables not significant. Thus, economic market size drives U.S. OFDI in the short term; energy resources deter it in both horizons. Granger causality (Table 10): - China: OFDI exhibits Granger relationships with EC, ER, LQ, LC, PO (all significant), but not with EX. - USA: OFDI has Granger relationships with EC, EX, ER, LQ, PO; no causality with LC. Impulse responses: - China: One standard-deviation shocks to EC and ER lead to negative long-term effects on OFDI; shocks to LQ and LC yield positive long-term effects; PO mildly negative overall. Patterns align with VECM signs (energy/logistics salient, politics adverse in short term). - USA: ER shocks produce persistent negative long-term effects; EX and LQ shocks show positive long-term effects; PO exhibits weak, time-varying effects (early positive, mid negative, peaking negative around period 5). Variance decomposition (10 periods): - China: COFDI variation is largely self-explained (>92% by period 10). EC explains a substantial share of EX (≈28.07% to 27.71% over periods). EC and EX jointly contribute >15% to LQ; EC (>20%) and EX (>15%) contribute to LC (ER >5%). EX and LQ each explain >10% of PO, while other cross-effects are generally <10%. - USA: UOFDI, EC, and ER are mostly self-driven by period 10 (≈92.54%, 94.23%, 89.45%, respectively). EC explains ≈25.57% of EX. EC and EX explain >10% of LQ and LC; UOFDI’s contribution to LQ/LC is weak (>5%). EC, EX, ER, and LQ each explain >5–10% of PO; other contributions are <5%. Overall: China’s OFDI system is driven by energy (EX, ER) and conditions enabling infrastructure development (targeting weaker logistics), with sensitivity to political risk in the short run. The U.S. system is predominantly market-oriented (short-run EC), with energy-rich hosts deterring aggregate U.S. OFDI.

Findings support the hypothesis that OFDI location determinants differ between a representative southern economy (China) and a northern economy (USA). For China, results indicate a state-influenced, strategic pattern emphasizing energy security and infrastructure development—consistent with resource-based and institution-based perspectives tailored to emerging-economy contexts and partially outside traditional OLI ownership advantages. Negative associations with logistics quality/capacity suggest targeting locations where infrastructure investment is needed (and feasible), aligning with observed Chinese practices of building or upgrading host infrastructure as part of broader development initiatives. Short-run aversion to high political stability (negative PO effect) may reflect Chinese firms’ familiarity with and willingness to operate in riskier, resource-rich environments. For the USA, evidence points to market-seeking behavior: short-run economic size positively drives OFDI, and energy resource abundance is associated with lower aggregate U.S. OFDI (possibly reflecting industry composition and alternative modes of engagement). These differences imply that mainstream OFDI theories require refinement to account for southern-country behaviors, particularly state coordination, strategic resource access, and infrastructure-led approaches, whereas northern-country OFDI remains more aligned with classic market-driven paradigms.

The study refines the theoretical system of OFDI location determinants by contrasting China and the USA. Chinese OFDI is primarily driven by host energy endowments/exports and opportunities to develop logistics infrastructure, with nuanced responses to political risk, whereas U.S. OFDI is chiefly responsive to economic market size and is deterred by resource intensity at the aggregate level. Policy implications: host countries in the global south can attract Chinese OFDI by prioritizing infrastructure and energy sector collaborations; global north host economies can engage U.S. investors by reinforcing market size and openness. Target countries can pursue complementary strategies—e.g., leveraging Chinese capital for infrastructure while maintaining market-based ties with U.S. investors. Future research should extend analyses to additional countries/regions and incorporate spatial spillovers and external shocks for broader generalization.

- Generalizability: Only China and the USA are analyzed; OFDI policies and behaviors differ across countries and regions. - Omitted spatial dynamics and spillovers: The study does not examine spatial correlation or spillover effects of OFDI. - External shocks: Effects of shocks such as COVID-19 are not fully incorporated. - Aggregation: Sectoral heterogeneity may be masked in aggregate OFDI measures (notably for U.S. resource-seeking behavior).