Greenfield investment and job creation in Ghana: a sectoral analysis and geopolitical implications of Chinese investments

The paper situates a long-standing debate over whether multinational corporations (MNCs) are malign or beneficial for host countries, noting their global scale and potential for productivity spillovers, export growth, employment, and broader development gains. Against this background, Ghana provides a relevant case given recent political stability, rising FDI inflows, and its role as AfCFTA secretariat. The central research question asks whether Greenfield investment leads to significant formal-sector job creation in Ghana, addressing a gap where prior studies often treat FDI monolithically rather than distinguishing Greenfield from Brownfield/M&As. The study underscores Ghana’s growing attractiveness for FDI and the policy interest in job creation outcomes. It previews sectoral heterogeneity, noting varying job impacts across sectors and differences in source-country sectoral focus: Chinese firms tend to invest in Automotive OEM and business machines/equipment, while U.S. firms concentrate on Food & Beverages, Metals, Healthcare, software, and IT services.

The literature traces MNC activity in Ghana from the colonial era to post-independence reforms aimed at attracting capital, technology, and diversification. Ghana enacted successive investment laws (e.g., Capital Investment Act 1963; GIPC Acts 1994 and 2013) and pursued ERP/SAP liberalization, which reshaped FDI inflows across services, manufacturing, tourism, construction, agriculture, and exports. Empirical studies generally find positive FDI effects on employment and growth in Ghana (e.g., Osei 2019; Ato-Mensah and Long 2021; Awunyo-Vitor and Sackey 2018), though some note delayed or indirect effects via growth. Sectoral patterns and source-country differences matter: Chinese firms have influenced manufacturing (plastics, steel, construction materials, paper, wigs), while U.S. and South African firms engage more in agriculture and services; Indian firms employ more in agriculture. Governance quality often correlates with FDI, though some evidence suggests Chinese SOE FDI may flow more to weaker-institution environments. Broader debates weigh FDI’s role in poverty reduction, human rights, and distributional impacts, with critiques highlighting potential for inequality, governance risks, and limited benefits when complementary domestic capabilities are lacking. The review underscores the need to distinguish entry modes (Greenfield vs. M&A) and the importance of absorptive capacity, linkages, and sectoral context to realize employment benefits.

Data: Project-level Greenfield FDI data were obtained from fDi Markets for Ghana from June 2003 to September 2020, covering 500 projects by 386 unique firms. For each project, jobs created (dependent variable) and capital investment in USD millions (Capin) were recorded as repeated cross-sections (not panel data). Annual macro-level controls for Ghana (time series, 2003–2020) were merged by project year: GDP (USD, World Bank WDI), labor force participation rate (LFPR, WDI), gross capital formation (GCF, % of GDP, WDI), inflation (annual CPI change, Statista), tax burden, government integrity, business freedom, and property rights (indices 0–100, Heritage Foundation). Variables are summarized in Table 2 of the paper. Empirical strategy: Ordinary least squares (OLS) regressions were estimated using Stata 16. Given nonlinearity, log transformations were employed where appropriate. Four models were specified: - Model 1 (linear-linear bivariate): Jobs_i = β1 + β2 Capin_i + μ_i. - Model 2 (log-log bivariate): ln(Jobs_i) = β1 + β2 ln(Capin_i) + μ_i. - Model 3 (log-log with economic controls): ln(Jobs_i) = β1 + β2 ln(Capin_i) + β3 GDP_t + β4 GCF_t + β5 LFPR_t + β6 Infl_t + μ_i. - Model 4 (log-log with governance/regulatory controls added): ln(Jobs_i) = β1 + β2 ln(Capin_i) + β3 GDP_t + β4 GCF_t + β5 LFPR_t + β6 Infl_t + β7 Taxburd_t + β8 Govinteg_t + β9 Buzfr_t + β10 Proprit_t + μ_i. Sectoral analysis: To examine sectoral heterogeneity and the role of domestic investment in fixed assets, an interaction model assessed how GCF relates to sector-specific job creation: ln(Jobs_i) = β1 + β2 ln(Capin_i) + Σ β2i Sector_i + (Sector_i × GCF_t) + μ_i, using sector classifications from fDi Markets. The model evaluated which sectors, in conjunction with GCF, significantly correlate with jobs. Diagnostics and robustness: Multicollinearity was assessed using VIF (no severe multicollinearity detected in Models 3–4). Heteroskedasticity was tested using the Breusch–Pagan/Cook–Weisberg test; evidence of heteroskedasticity led to the use of robust standard errors for inference.

- Descriptive statistics (N=500 projects): mean Jobs=197 (SD 457; min 0; max 5000); mean Capin=$98.9m (SD $525.2m; min $0.1m; max $7900m). Largest project: Eni SpA ($7.9b, 2007). Regression results (Table 4): - Model 1 (linear-linear, bivariate): Jobs_i = 153.4 + 0.445 Capin_i; Capin coefficient positive and significant (p<0.001). Interpretation: each additional $1m in capital investment predicts ~0.445 more jobs. R²=0.262. - Model 2 (log-log, bivariate): ln(Jobs_i) = -8.012 + 0.719 ln(Capin_i); elasticity 0.719, p<0.001. R²=0.515. - Model 3 (log-log with economic controls): ln(Jobs_i) = -30.45 + 0.711 ln(Capin_i) + 1.70e-11 GDP + 0.038 GCF + 0.307 LFPR − 0.047 infl. Capin remains positive and highly significant (p<0.001). Controls: GDP (p=0.042), GCF (p=0.008), LFPR (p=0.005) positive and significant; inflation negative (p=0.007). R²=0.535. - Model 4 (adding governance/regulatory controls): Capin remains positive and highly significant (p<0.001); GDP, GCF, LFPR, inflation become insignificant. Governance/regulatory indices: Government integrity negative (coef ≈ −0.0510, p=0.002); Property rights negative (coef ≈ −0.201, p=0.003); Business freedom positive (coef ≈ 0.0291, p=0.023); Tax burden not significant. R²=0.554. F-tests indicate overall model significance (p=0.000). Sectoral effects with GCF interactions (Table 5): Out of 32 sectors, 13 show positive and significant associations with job creation when considering GCF’s effect, including highly significant clusters (p<0.001): Automotive OEM, Business Machines & Equipment, Consumer Products, Food & Beverages, Metals, and Textile; others significant at p<0.05–0.01 include Building Materials, Ceramics & Glass, Electronic Components, Healthcare, Industrial Equipment, Non-automotive Transport OEM, Rubber. Model R²≈0.735. Overall: Greenfield capital investment has a statistically significant, positive association with jobs in Ghana. Government investment in fixed assets (GCF) is associated with higher jobs in sectoral analyses, while higher inflation correlates negatively with jobs. Governance integrity and property rights indices exhibit negative correlations with jobs in the full model; business freedom correlates positively.

The findings directly address the question of whether Greenfield FDI creates jobs in Ghana’s formal sector: across multiple model specifications, Greenfield capital investment is positively and significantly associated with employment, with elasticities around 0.71–0.72 in log-log models. Incorporating macroeconomic controls suggests that stronger GDP, higher gross capital formation (GCF), and higher labor force participation are associated with higher job creation, whereas inflation is detrimental. When governance and regulatory indices are included, business freedom (ease of starting and operating businesses) remains a positive correlate of job creation, while government integrity and property rights indices are negatively associated, consistent with perspectives that some MNCs may prefer environments with weaker institutions, though this paradox warrants careful interpretation. Sectoral heterogeneity indicates that targeted government investment in fixed assets can amplify job creation in particular sectors. Policy implications include: continuing to attract Greenfield FDI; prioritizing infrastructure and fixed asset investment that supports productive activities; maintaining low inflation; streamlining business procedures; and strengthening sector-specific infrastructure to spread FDI beyond traditional hubs. The geopolitical analysis underscores that aligning foreign investments—especially from China and the U.S.—with national development strategies and international standards is critical to maximize benefits and manage risks.

The study concludes that Greenfield investment by multinational companies positively and significantly contributes to job creation in Ghana. Distinguishing Greenfield from Brownfield investments is important for understanding employment impacts. Sectoral results highlight especially strong job creation effects in Consumer Products, Food & Beverages, Industrial Equipment, and Non-automotive Transport OEM (and, in extended sectoral analysis with GCF, additional sectors including Automotive OEM, Business Machines & Equipment, Metals, and Textile). Effective management of government investment in fixed assets is essential to support job creation without fueling inflation, which correlates negatively with employment. The paper also emphasizes that China’s sizable investments—focused on infrastructure, natural resources, and manufacturing—have stimulated growth, jobs, and technology transfer but raise concerns regarding sustainability, transparency, and environmental impacts. Policymakers should foster responsible investment aligned with national priorities, ensure adherence to international norms, and safeguard long-term interests as Ghana deepens engagement with foreign investors, including China and the U.S.