Absorptive capacity for technology transfer: Does private-sector credit condition the FDI–SDG relationship?

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Kassim Alabani, Benedict Afful Jr., Francis Taale, Eric Abokyi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8613700/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study investigates whether domestic financial development enhances the contribution of foreign direct investment to sustainable development, with a focus on SDG 9. Using panel data for 43 countries from 2005 to 2023 and a range of fixed-effects and dynamic estimation techniques, it finds that FDI does not significantly affect overall SDG performance but has a positive effect on industrialisation, innovation, and infrastructure outcomes. Private-sector credit shows no consistent direct impact, and its interaction with FDI provides little evidence that financial depth strengthens FDI’s development effects. Governance quality improves the direct influence of FDI on SDG 9, but does not alter the weak financial–FDI complementarity. The results indicate that FDI’s development impact is sector-specific and institution-dependent, and that broad measures of financial deepening do not automatically support technology diffusion or structural transformation. The study contributes to the sustainable development and international investment literature by demonstrating that domestic finance does not automatically function as an absorptive-capacity channel for FDI, even in relatively strong governance environments. JEL Classification Codes: F21, O16, O25, O33, Q01, C23 Foreign Direct Investment Private-Sector Credit Sustainable Development Goals SDG 9 Industrialisation Governance Absorptive Capacity Panel Data Figures Figure 1 Figure 2 Figure 3 1. Introduction Foreign direct investment has long been viewed as a key vehicle through which developing economies can access advanced technologies, managerial know-how, and global production networks. Beyond its direct contribution to capital accumulation, FDI is expected to generate spillover effects that raise productivity, stimulate innovation, and support structural transformation (Sun & Qamruzzaman, 2025 ). These expectations have become even more prominent in the context of the Sustainable Development Goals, where FDI is frequently promoted as a catalyst for industrial development, infrastructure expansion, and technological upgrading (Monkelbaan, 2017 ). Yet, despite this optimism, empirical evidence on the development impact of FDI remains mixed, particularly when outcomes are assessed beyond aggregate growth and extended to broader measures of sustainable development. One reason for this ambiguity is that technology spillovers from FDI are not automatic. The presence of foreign firms does not, by itself, guarantee learning, diffusion, or local upgrading. Spillovers depend critically on the capacity of domestic firms and institutions to absorb, adapt, and scale foreign technologies (Marzia, 2024 ; Peng et al., 2025 ; Rajan & Sushil, 2022 ). Where such absorptive capacity is weak, FDI may remain enclave-based, generating limited linkages with the local economy and modest contributions to long-term development objectives (Hafner, 2011 ). This observation has renewed interest in identifying the domestic conditions under which FDI can be transformed from a source of isolated productivity gains into a driver of inclusive and sustainable development. This study focuses on domestic financial development, measured by credit to the private sector, as a central but underexplored component of absorptive capacity. While existing research has examined the roles of human capital, trade openness, and institutional quality in shaping FDI spillovers, the role of domestic finance has received comparatively less attention in the SDG literature. Yet access to finance is fundamental to the diffusion process (Li, 2011 ). Domestic firms require credit to invest in complementary capital, adopt new technologies, meet quality standards, and participate in supply chains linked to foreign investors. Without adequate private-sector credit, the potential benefits of FDI are likely to be constrained, regardless of the scale of inflows. The paper addresses this gap by examining whether domestic private-sector credit conditions the relationship between FDI and sustainable development outcomes. Specifically, it asks whether the impact of FDI on overall SDG performance, and on SDG 9 in particular, depends on the depth of domestic credit markets. By modelling an explicit interaction between FDI and private-sector credit, the analysis moves beyond average effects and tests a conditional relationship that aligns more closely with theories of technology transfer and diffusion. Governance quality is incorporated as an additional conditioning factor, recognising that financial systems operate within institutional environments that shape incentives, risk, and resource allocation. The study contributes to the literature in three main ways. First, it reframes domestic finance as an absorptive-capacity channel rather than a parallel determinant of development, thereby offering a clearer mechanism through which FDI affects SDG outcomes. Second, it brings this perspective directly into the SDG framework, linking FDI and financial development to multidimensional development indicators rather than growth alone. Third, by exploring heterogeneous effects across levels of financial depth and governance quality, it provides evidence on when and where FDI is most likely to support sustainable industrialisation and innovation. The results show that while FDI is positively associated with SDG performance, its impact is significantly stronger in countries with deeper private-sector credit markets. In low-credit environments, the effect of FDI on SDG outcomes is weak or statistically insignificant. These patterns are most pronounced for SDG 9, suggesting that domestic finance plays a particularly important role in enabling technology-intensive and infrastructure-related spillovers. The policy implication is straightforward but often overlooked: strategies to attract FDI are unlikely to deliver sustained development gains unless they are accompanied by reforms that strengthen domestic financial systems and expand access to credit for productive private-sector activities. 1.1 Hypothesis Write them in a way that matches your model exactly. H1: FDI inflows are positively associated with SDG performance (SDG Index) and SDG 9. H2: Private-sector credit is positively associated with SDG performance and SDG 9. H3: Private-sector credit positively moderates the effect of FDI on SDG performance and SDG 9. Expected sign: β on (FDI × Credit) > 0. H4 (heterogeneity): The moderation effect is stronger where governance quality is higher (or stronger in middle-income than low-income economies, depending on your prior). 2. Literature Review The relationship between foreign direct investment, domestic financial development, and development outcomes has attracted sustained attention in development economics and international economics. Early contributions emphasised the role of FDI as a source of capital accumulation and growth, while later studies shifted focus toward knowledge transfer, productivity spillovers, and structural transformation (Zehri et al., 2024 ). More recently, the literature has begun to examine FDI within broader development frameworks, including sustainability and the Sustainable Development Goals (Aerni, 2021 ; Baita & Suleiman, 2021 ; Maphiri et al., 2021 ). Despite this evolution, evidence remains mixed, particularly regarding the conditions under which FDI contributes meaningfully to long-term development outcomes beyond aggregate growth. A recurring theme across this literature is that the benefits of FDI are conditional rather than automatic (Ahmad, 2020 ; Ajay Yadav, Dinesh Kumar, Sushant Yadav, 2023; Dam et al., 2024 ; Hafner, 2011 ). Differences in domestic capabilities, institutional environments, and financial systems shape how foreign capital interacts with local economies. While several studies recognise these conditioning factors, they are often examined in isolation (Ferrier et al., 2016 ; Hanlin & Kaplinsky, 2016 ; Monkelbaan, 2017 ; Peng et al., 2025 ). As a result, the joint role of domestic finance and governance in mediating the FDI–development relationship remains insufficiently explored, especially within the SDG framework (Peng et al., 2025 ; Sun & Qamruzzaman, 2025 ; Xu et al., 2020 ). This study responds to that gap by integrating insights from theory and empirical work to assess how private-sector credit functions as an absorptive-capacity channel for foreign technology spillovers. 2.1 Theoretical Review Theoretical explanations of FDI spillovers are rooted in models of endogenous growth, technology diffusion, and international production. Multinational enterprises are assumed to possess firm-specific advantages, including advanced technologies and managerial expertise, which can spill over to domestic firms through imitation, competition, labour mobility, and vertical linkages (Nwokolo et al., 2024 ). In endogenous growth models, such spillovers raise the stock of knowledge and improve long-run growth prospects (Tandid, 2025 ). However, these models also recognise that the magnitude of spillovers depends on domestic conditions that enable learning and adoption. The concept of absorptive capacity provides a unifying theoretical lens. Absorptive capacity refers to the ability of firms and economies to identify valuable external knowledge, assimilate it, and apply it productively (Korea Trade Research Association et al., 2023; Li, 2011 ). Human capital, learning-by-doing, and institutional quality are commonly cited determinants of this capacity (Peng et al., 2025 ). Yet, theory also suggests that knowledge adoption requires complementary investments. Without the financial means to invest in new machinery, reorganise production processes, or scale operations, firms may be unable to translate knowledge into productivity gains. Financial development theory complements this perspective by emphasising the role of credit markets in mobilising savings, allocating capital, and supporting productive investment (Kushwaha & Nair, 2025 ; Slimani et al., 2024 ). Well-functioning financial systems lower transaction costs, reduce information asymmetries, and ease liquidity constraints faced by firms (Sarangi, 2023 ). When applied to FDI spillovers, this implies that private-sector credit facilitates the absorption and diffusion of foreign technologies by enabling domestic firms to finance complementary investments (Abbas et al., 2021 ; Gyamfi et al., 2022 ). Governance quality further shapes this process by influencing financial intermediation efficiency, contract enforcement, and risk management (Ciarli et al., 2018 ). Together, these theoretical strands imply that FDI, domestic finance, and governance interact to determine development outcomes. 2.2 Empirical Review Ferrier, Reyes, and Zhu ( 2016 ) examine how technology diffuses through the international trade network using a network-based empirical approach that links countries through trade relationships and traces how knowledge can spread along those links. Their findings suggest that trade connectivity matters for the speed and reach of diffusion, with more central economies benefiting earlier and more strongly from transmitted technologies. A key strength of the study is the clear emphasis on diffusion as a relational process rather than a purely domestic outcome. Its limitation for this study is that it focuses on trade-mediated transmission and does not test how domestic financial constraints shape whether imported knowledge is adopted and scaled. Hafner ( 2011 ) reaches a related conclusion from a different angle by analysing how trade liberalisation affects technology diffusion, pointing to policy-driven openness as a pathway for technological inflows. Compared with Ferrier et al. ( 2016 ), Hafner ( 2011 ) is more directly tied to policy reforms and institutional change, while the network approach highlights structural position in global exchange. Together, these studies show that cross-border integration can transmit technology, but they leave open a core question for the present research, which is whether domestic credit conditions the translation of external inflows, including investment inflows, into measurable development outcomes. Xu, Li, Chau, Dietz, Li, and Zhang (2020) investigate the impacts of international trade on global sustainable development using a cross-country empirical design that connects trade patterns to sustainability outcomes. Their evidence indicates that trade is closely tied to development performance, but the nature of that relationship varies across contexts and depends on how economies participate in global exchange. A strength of the study is its broad coverage and explicit focus on sustainability rather than growth alone. A limitation is that trade is treated as the dominant external channel, leaving the investment and technology-transfer mechanism less directly specified. Monkelbaan ( 2017 ), in contrast, offers a policy-oriented examination of trade as a tool for achieving the SDGs, using the Environmental Goods Agreement discussion to show how targeted trade policies can support SDG-linked outcomes. Compared with Xu et al. ( 2020 ), Monkelbaan ( 2017 ) is less empirical but sharper on policy design and sector focus. Taken together, both studies support the general idea that cross-border flows can advance SDG progress, but they do not isolate the investment-to-technology-to-SDG pathway, nor do they test whether domestic finance determines who can respond to these opportunities. Huang and Pei ( 2022 ) provide firm-level evidence from China on how imported intermediate inputs generate technology spillovers and how these spillovers contribute to green development. Their methodology focuses on micro-level mechanisms, linking firms’ use of imported inputs to technological upgrading and environmental performance outcomes. The main strength of this study is the close identification of a concrete spillover channel, where learning is embedded in production inputs rather than assumed. Its limitation for this study is that it is centred on trade in intermediates and a single-country setting, which may not generalise to economies where credit constraints and market frictions are more severe. Hanlin and Kaplinsky ( 2016 ) examine South–South trade in capital goods and argue that market-driven diffusion of appropriate technology can support development needs in poorer contexts. Compared with Huang and Pei ( 2022 ), Hanlin and Kaplinsky ( 2016 ) place greater weight on the suitability and accessibility of technology in developing settings, while the Chinese firm evidence is more precise on micro spillover mechanics. The shared implication is that technology can diffuse through multiple channels, but a gap remains around financing, since neither strand directly tests whether private credit availability is what allows domestic firms to adopt, adapt, and scale the technologies that arrive through trade and investment links. Ajay Yadav, Dinesh Kumar, and Sushant Yadav (2023) review evidence on the links among India’s international trade, foreign investment, and SDG progress, bringing together studies that connect external integration with sustainable development goals. Their main contribution is synthesis, highlighting that trade and foreign investment can support SDG outcomes through technology, employment, and productivity pathways, though effects vary across sectors and time. The strength of the review lies in its integrative lens that recognises both trade and investment as relevant channels. Its limitation is that the review does not isolate domestic finance as the key conditioning mechanism, even though it repeatedly points to implementation capacity constraints. A related policy perspective is provided by the 2024 blueprint on U.S.–Africa trade and investment collaboration, which discusses how trade and investment partnerships can support sustainable development and structural change in African economies. Compared with the India-focused review, the U.S.–Africa blueprint is more programmatic and region-facing, but it similarly treats domestic financial depth as background context rather than a testable moderator. This leaves an empirical gap that motivates the present study, which is to model domestic private-sector credit explicitly as an absorptive-capacity channel shaping the FDI–SDG relationship. Finally, a set of recent studies links technological innovation and cross-border integration to SDG-related outcomes, but still leaves the absorption mechanism under-specified. Dam, Kaya, and Bekun ( 2024 ) study the role of technological factors in sustainability-related outcomes across E-7 countries, connecting innovation dynamics to SDG-relevant performance. Peng, Qian, Xing, and Wang (2025) discuss the opportunities and challenges of technological pathways for achieving the SDGs, emphasising that technology’s contribution depends on complementary systems and institutions. Marzia ( 2024 ) similarly highlights the importance of technological advancement for SDG progress in developing countries. Across these studies, the consistent strength is the recognition that technology is central to sustainable development, and the common limitation is that the domestic financing constraint is rarely positioned as the main empirical lever that determines adoption and diffusion. This reinforces the relevance of the present research aim, which is to test, in a disciplined interaction framework, whether credit to the private sector amplifies the development impact of FDI, with a sharper focus on SDG 9 where technology transfer and industrial upgrading should be most visible. 2.3 Conceptual Framework The conceptual framework underpinning this study views sustainable development outcomes as the result of interactions between external capital inflows and domestic enabling conditions. FDI is treated as a potential source of technology, managerial knowledge, and global production linkages. On its own, however, FDI does not guarantee widespread development gains. Its impact depends on the ability of domestic firms to absorb and diffuse the technologies embodied in foreign investment. Domestic private-sector credit represents a key absorptive-capacity channel within this framework. Access to credit enables firms to finance the adoption of new technologies, invest in complementary capital, and participate in foreign-led value chains. As credit deepens, the marginal impact of FDI on development outcomes is expected to increase. Governance quality operates as a conditioning factor that shapes how effectively financial resources are allocated and how firms interact with foreign investors. Strong governance enhances the effectiveness of both finance and FDI, while weak governance can constrain their combined impact. Within this framework, sustainable development outcomes, measured by the overall SDG Index and SDG 9, are influenced directly by FDI and domestic credit, and indirectly through their interaction. The central proposition is that private-sector credit amplifies the development impact of FDI by strengthening absorptive capacity, with the strongest effects observed in domains closely linked to industrialisation, innovation, and infrastructure. This conceptual structure directly informs the empirical specification and the hypotheses tested in the study. 3. Methodology The study employs a balanced panel dataset covering a broad set of countries over the period dictated by data availability across the Sustainable Development Goals, foreign direct investment, and financial development indicators. Country coverage includes low-income and middle-income economies, with a particular focus on developing regions where FDI-led technology transfer and financial constraints are most relevant. The panel structure allows the analysis to exploit both cross-country variation and within-country changes over time. Data on SDG outcomes are obtained from the Sustainable Development Report 2024 (Sachs, Lafortune, & Fuller, 2024), which provides harmonised and internationally comparable indices for individual SDGs. Data on all other macroeconomic variables are gotten from the World Development Indicators (WDI). Sustainable development outcomes are measured using the SDG Index and SDG 9 scores, which capture multidimensional progress toward the Sustainable Development Goals and performance in industry, innovation, and infrastructure, respectively. FDI data are drawn from standard international sources and reflect net inflows relative to economic size. Domestic financial development is proxied by credit to the private sector, expressed as a percentage of GDP. Governance indicators are obtained from widely used institutional datasets and summarised either individually or through a composite index to reduce dimensionality and multicollinearity. The final sample excludes countries with severe data gaps or short time coverage that would undermine panel estimation. Descriptive checks confirm that the sample retains substantial variation across income levels, institutional quality, and financial depth. 3.1 Variable measurement The dependent variables are defined as follows. The SDG Index captures overall sustainable development performance by aggregating progress across all SDGs into a single score. SDG 9 focuses specifically on industry, innovation, and infrastructure, making it particularly suitable for assessing technology-related spillovers from FDI. The key explanatory variable is foreign direct investment, measured as net FDI inflows as a percentage of GDP. This scaling ensures comparability across countries of different sizes and aligns with the macroeconomic literature. Domestic financial development is measured using domestic credit to the private sector as a percentage of GDP, which reflects the extent to which financial institutions provide resources to private firms for investment and working capital. The core interaction term is constructed as the product of FDI inflows and private-sector credit. This term captures the extent to which the effect of FDI on development outcomes depends on domestic financial depth. Governance quality enters the model as a control and as a conditioning variable in heterogeneity analyses. Additional controls include GDP per capita, trade openness, inflation, and other macroeconomic indicators commonly used in cross-country development regressions. 3.2 Transformations and summary statistics To reduce skewness and limit the influence of extreme observations, selected variables such as FDI inflows and GDP per capita are transformed using logarithms where appropriate. All interaction terms are constructed using the transformed variables to maintain internal consistency. In robustness checks, key variables are standardised to facilitate interpretation and comparability of coefficients. Summary statistics are reported to describe central tendencies and dispersion across variables. The statistics highlight substantial cross-country variation in SDG performance, FDI inflows, and private-sector credit, underscoring the relevance of a conditional framework. Correlation analysis indicates that while FDI and credit are positively associated with development outcomes, the relationships are far from perfect, reinforcing the need for multivariate and interaction-based estimation. 3.3 Empirical model The baseline empirical specification estimates the relationship between sustainable development outcomes, foreign direct investment, domestic private-sector credit, and their interaction. The model is expressed as: $$\:SD{G}_{it}=\alpha\:+{\beta\:}_{1}FD{I}_{it}+{\beta\:}_{2}Credi{t}_{it}+{\beta\:}_{3}(FD{I}_{it}\times\:Credi{t}_{it})+{\gamma\:}^{{\prime\:}}{X}_{it}+{\mu\:}_{i}+{\tau\:}_{t}+{\epsilon\:}_{it}$$ where \(\:SD{G}_{it}\) represents either the SDG Index or SDG 9 score for country \(\:i\) in year \(\:t\) . \(\:FD{I}_{it}\) denotes foreign direct investment inflows, \(\:Credi{t}_{it}\) is domestic credit to the private sector, and \(\:{X}_{it}\) is a vector of control variables. Country fixed effects ( \(\:{\mu\:}_{i}\) ) account for time-invariant national characteristics, while year fixed effects ( \(\:{\tau\:}_{t}\) ) control for global shocks and common trends. This fixed-effects framework isolates within-country variation over time and mitigates bias from unobserved heterogeneity. 3.5 Interpretation of the interaction term The interaction between FDI and private-sector credit is central to the analysis. The coefficient on FDI ( \(\:{\beta\:}_{1}\) ) represents the effect of FDI on SDG outcomes when private-sector credit is zero, which is not economically meaningful in isolation. The marginal effect of FDI is therefore interpreted as: $$\:\frac{\partial\:SD{G}_{it}}{\partial\:FD{I}_{it}}={\beta\:}_{1}+{\beta\:}_{3}Credi{t}_{it}$$ This formulation allows the effect of FDI to vary with the level of domestic credit. To aid interpretation, marginal effects are computed and reported at low, median, and high levels of private-sector credit. Graphical presentations are used where appropriate to illustrate how the impact of FDI strengthens as financial depth increases. 3.6 Heterogeneity design To explore whether the conditioning role of domestic finance varies across contexts, the analysis incorporates heterogeneity in two ways. First, the sample is split based on financial development or income level, and the baseline model is estimated separately for each subgroup. This approach tests whether FDI spillovers are stronger in countries with deeper credit markets or higher levels of development. Second, governance quality is introduced as an additional conditioning factor. This is implemented either through subgroup analysis based on governance scores or through extended interaction terms that allow the FDI–credit relationship to vary with institutional quality. These exercises help distinguish whether finance alone is sufficient for absorption or whether its effectiveness depends on the broader institutional environment. 3.7 Endogeneity and identification strategy Several sources of endogeneity are addressed in the empirical design. Reverse causality may arise if improvements in SDG performance attract higher FDI inflows or stimulate financial deepening. Omitted-variable bias may also occur if unobserved reforms influence both development outcomes and the key explanatory variables. The primary strategy for mitigating these concerns includes the use of country and year fixed effects, which absorb time-invariant heterogeneity and common shocks. In addition, key explanatory variables and their interaction are lagged to reduce simultaneity. Dynamic specifications include a lagged dependent variable to account for persistence in SDG outcomes. As a robustness check, the study employs a system GMM estimator, treating FDI and private-sector credit as potentially endogenous and instrumenting them with their own lagged values. Standard diagnostic tests, including tests for serial correlation and instrument validity, are reported to assess model reliability. The results from these alternative specifications are compared with the baseline fixed-effects estimates to ensure consistency and robustness of the main findings. 4. Results 4.1 Baseline analysis: Fixed-effects results for overall SDG performance and SDG 9 Tables 1a and 2a report country fixed-effects estimates of the relationship between foreign direct investment, domestic finance, and sustainable development outcomes, with year fixed effects included and standard errors clustered at the country level. This specification exploits within-country variation over time, thereby controlling for time-invariant national characteristics and common global shocks. The results for the aggregate SDG Index indicate that FDI inflows do not have a statistically significant direct effect on overall sustainable development performance once country and time fixed effects are accounted for. The coefficient on FDI inflows is positive but very small in magnitude and statistically insignificant (p = 0.802), suggesting that short- to medium-term fluctuations in FDI relative to a country’s own historical average are not systematically associated with broad-based SDG outcomes. This finding implies that, in isolation, FDI is unlikely to translate automatically into economy-wide development gains captured by the composite SDG Index. Private-sector credit also enters with a positive coefficient, but it falls short of conventional significance levels. While the point estimate suggests that deeper domestic credit markets may support SDG progress, the lack of statistical precision indicates that finance alone does not guarantee improvements across the full range of development goals when structural and institutional factors are held constant. Among the control variables, most macroeconomic indicators are not statistically significant, underscoring the difficulty of explaining aggregate SDG performance through short-run macroeconomic movements. Notably, control of corruption emerges as the most robust correlate of SDG performance, with a positive coefficient significant at the 10 percent level. This result highlights the central role of institutional quality in shaping the effectiveness of development processes and suggests that governance conditions may mediate the translation of resources into measurable SDG outcomes. Table 1a: Fixed-Effects Estimates of FDI, Domestic Finance, and Overall SDG Performance Variable Coefficient Std. Error t-stat p-value FDI inflows (% of GDP) 0.0026 0.0102 0.25 0.802 Private-sector credit (% of GDP) 0.0154 0.0100 1.54 0.131 Log GDP per capita 0.6645 0.7901 0.84 0.405 Trade openness index −0.0081 0.0143 −0.56 0.577 Financial openness 0.3723 0.2430 1.53 0.133 Capital investment (% of GDP) 0.0130 0.0125 1.04 0.305 Savings rate (% of GDP) 0.0143 0.0133 1.07 0.289 Government debt (% of GDP) 0.0024 0.0025 0.99 0.329 Control of corruption (z-score) 0.7767 0.4212 1.84 0.072* Country fixed effects Yes Year fixed effects Yes The model diagnostics in Table 1b show a high within R² (0.817), indicating that the model explains a substantial share of within-country variation in SDG performance over time. The very high value of ρ (0.947) further confirms the relevance of fixed effects, implying that unobserved country-specific factors account for a large proportion of the total variance in SDG outcomes. Table 1b: Model diagnostics Statistic Value Observations 817 Countries 43 Years per country 19 Within R² 0.817 Between R² 0.576 Overall R² 0.481 F-statistic 27.48 Prob > F 0.000 ρ (variance due to FE) 0.947 SDG 9: Industry, innovation, and infrastructure (Table 2a) The results change meaningfully when attention is narrowed to SDG 9, which directly captures industrialisation, innovation, and infrastructure development. In this specification, FDI inflows are positively associated with SDG 9 performance, with the coefficient significant at the 10 percent level. Although modest in magnitude, this finding suggests that FDI is more closely aligned with development domains linked to production capacity, technological upgrading, and infrastructure than with broader social and environmental goals. In contrast, private-sector credit again shows no statistically significant direct effect, and its coefficient is negative, though imprecisely estimated. This pattern suggests that domestic finance may not independently drive SDG 9 outcomes in the absence of complementary factors, such as efficient allocation mechanisms or productive investment opportunities. Government debt stands out as a consistently significant predictor of SDG 9 performance, with a positive and statistically strong coefficient. This result is consistent with the idea that public borrowing, when channeled into infrastructure and industrial support, can play a constructive role in advancing industry- and innovation-related development objectives. Other macroeconomic controls, including income levels, trade openness, and financial openness, do not display robust associations within countries over time. Table 2a: Fixed-Effects Estimates of FDI, Domestic Finance, and SDG 9 (Industry, Innovation, Infrastructure) Variable Coefficient Std. Error t-stat p-value FDI inflows (% of GDP) 0.0400 0.0219 1.82 0.075* Private-sector credit (% of GDP) −0.0368 0.0287 −1.28 0.207 Log GDP per capita 2.3556 1.5063 1.56 0.125 Trade openness index 0.0670 0.0504 1.33 0.191 Financial openness 0.3020 0.6918 0.44 0.665 Capital investment (% of GDP) −0.0218 0.0408 −0.54 0.595 Savings rate (% of GDP) −0.0199 0.0424 −0.47 0.642 Government debt (% of GDP) 0.0213 0.0074 2.87 0.006*** Control of corruption (z-score) 0.9881 1.2735 0.78 0.442 Country fixed effects Yes Year fixed effects Yes The diagnostic statistics in Table 2b confirm strong model fit, with a within R² of 0.739 and a statistically significant F-statistic. The value of ρ (0.879) again indicates that unobserved country-specific factors remain important, justifying the fixed-effects approach. Table 2b: Model diagnostics Statistic Value Observations 817 Countries 43 Years per country 19 Within R² 0.739 Between R² 0.473 Overall R² 0.423 F-statistic 71.26 Prob > F 0.000 ρ (variance due to FE) 0.879 Taken together, these results point to three key insights. First, FDI does not appear to exert a broad, unconditional effect on overall SDG performance, reinforcing the view that foreign capital inflows alone are insufficient to drive comprehensive sustainable development. Second, FDI is more relevant for SDG 9 than for the aggregate SDG Index, supporting the argument that technology transfer and industrial upgrading are the most direct channels through which FDI contributes to development. Third, institutional quality and public-sector capacity matter at least as much as private capital inflows, as reflected in the role of corruption control and government debt. These patterns motivate the subsequent analysis of interaction effects and heterogeneity, where the central question becomes whether domestic financial development and governance conditions condition the ability of FDI to translate into stronger SDG outcomes, particularly in industry, innovation, and infrastructure. Interaction and dynamic effects of FDI and domestic credit on SDG 9 Table 3a reports results from a country fixed-effects model with year fixed effects, where SDG 9 (industry, innovation, and infrastructure) is the dependent variable. Standard errors are clustered at the country level, and the specification explicitly models the interaction between FDI inflows and private-sector credit. The results show that FDI inflows have a positive and statistically significant direct association with SDG 9, with significance at the 5 percent level. This indicates that, within countries over time, increases in FDI relative to a country’s own average are associated with improvements in industrial and infrastructure-related development outcomes. This finding reinforces earlier evidence that FDI is most relevant for production- and innovation-oriented dimensions of sustainable development rather than for aggregate SDG performance. In contrast, private-sector credit on its own is not statistically significant, suggesting that domestic financial depth does not independently drive SDG 9 outcomes once country characteristics and global shocks are controlled for. More importantly, the interaction term between FDI and credit is negative and statistically significant at the 10 percent level. This implies that the marginal contribution of FDI to SDG 9 declines as private-sector credit increases. In other words, FDI appears to be more effective in environments where domestic credit markets are relatively shallow, whereas in more financially developed settings, the incremental impact of FDI on industrial and infrastructure outcomes is weaker. Among the control variables, government debt remains positive and highly significant, consistent with the role of public borrowing in financing infrastructure and industrial investment. Other macroeconomic controls, including income levels, trade openness, and financial openness, do not show robust within-country effects. Table 3a: Fixed-Effects Interaction Model: FDI, Credit, and SDG 9 Variable Coefficient Std. Error t-stat p-value FDI inflows (% of GDP) 0.1164 0.0504 2.31 0.026** Private-sector credit (% of GDP) −0.0225 0.0314 −0.72 0.478 FDI × Credit −0.0055 0.0031 −1.79 0.080* Log GDP per capita 2.2705 1.5220 1.49 0.143 Trade openness index 0.0691 0.0500 1.38 0.175 Financial openness 0.1646 0.6881 0.24 0.812 Capital investment (% of GDP) −0.0110 0.0407 −0.27 0.789 Savings rate (% of GDP) −0.0233 0.0433 −0.54 0.593 Government debt (% of GDP) 0.0214 0.0077 2.79 0.008*** Control of corruption (z-score) 0.8630 1.2945 0.67 0.509 Country fixed effects Yes Year fixed effects Yes *Significant at 10 percent level. **Significant at 5 percent level. ***Significant at 1 percent level. Model diagnostics in Table 3b indicate strong explanatory power, with a within R² of 0.742 and a highly significant joint F-statistic. The large value of ρ confirms the importance of unobserved country-specific factors, validating the fixed-effects approach. Table 3b: Model diagnostics Statistic Value Observations 817 Countries 43 Within R² 0.742 Overall R² 0.408 F-statistic 42.38 Prob > F 0.000 ρ (variance due to FE) 0.882 Dynamic fixed-effects model with lagged variables Table 5a presents a dynamic country fixed-effects model with year fixed effects, where SDG 9 remains the dependent variable. The specification introduces lagged FDI, lagged private-sector credit, and their interaction, with standard errors clustered at the country level. This model evaluates whether the effects of FDI and finance operate with a delay rather than contemporaneously. The results indicate that lagged FDI inflows exert a positive and statistically significant effect on SDG 9, with significance at the 5 percent level. This finding provides evidence of temporal persistence, suggesting that the developmental benefits of FDI materialize over time through channels such as capital accumulation, technology transfer, and learning effects. Lagged private-sector credit remains statistically insignificant, reinforcing the view that domestic finance alone does not automatically translate into industrial or innovation gains. However, the lagged interaction between FDI and credit is negative and marginally significant, mirroring the contemporaneous interaction results. This pattern suggests that even over time, the effectiveness of FDI diminishes in more credit-intensive environments, possibly reflecting crowding-out effects or inefficient financial intermediation. Government debt again emerges as a robust positive predictor of SDG 9, while institutional quality and other macroeconomic controls remain statistically insignificant in this dynamic setting. Table 5a: Dynamic Fixed-Effects Model with Lagged FDI and Credit Variable Coefficient Std. Error t-stat p-value Lagged FDI inflows 0.1204 0.0455 2.64 0.011** Lagged private-sector credit 0.0061 0.0399 0.15 0.880 Lagged FDI × Credit −0.0060 0.0030 −1.99 0.053* Log GDP per capita 2.3774 1.6140 1.47 0.148 Trade openness index 0.0656 0.0530 1.24 0.223 Financial openness 0.0124 0.7637 0.02 0.987 Capital investment (% of GDP) −0.0032 0.0413 −0.08 0.938 Savings rate (% of GDP) −0.0313 0.0456 −0.69 0.496 Government debt (% of GDP) 0.0238 0.0080 2.96 0.005*** Control of corruption (z-score) 0.8087 1.3635 0.59 0.556 Country fixed effects Yes Year fixed effects Yes *Significant at 10 percent level. **Significant at 5 percent level. ***Significant at 1 percent level. The diagnostics in Table 5b show good model fit, with a within R² of 0.737 and a significant F-statistic. The high value of ρ indicates that fixed effects continue to capture a substantial share of the variation in SDG 9 outcomes. Table 5b: Model diagnostics Statistic Value Observations 774 Countries 43 Within R² 0.737 Overall R² 0.457 F-statistic 35.37 Prob > F 0.000 ρ (variance due to FE) 0.877 Governance-based heterogeneity: low-governance countries Table 6a reports results from a country fixed-effects model with year fixed effects, estimated only for countries in the low-governance group (Gov Group = 1). The dependent variable is SDG 9, and standard errors are clustered at the country level. This specification examines whether the FDI–credit relationship differs under weaker institutional conditions. In low-governance countries, FDI inflows have no statistically significant effect on SDG 9, and the coefficient is close to zero. Private-sector credit is also insignificant, and importantly, the interaction between FDI and credit is positive but statistically insignificant. This contrasts sharply with the full-sample results and indicates that neither FDI nor domestic finance is systematically associated with industrial and infrastructure outcomes in weak institutional environments. The lack of significance across key variables suggests that institutional constraints dominate economic mechanisms in low-governance contexts. In such settings, both foreign capital and domestic credit may be diverted away from productive uses, limiting their contribution to SDG-relevant outcomes. The absence of a significant interaction effect implies that complementarities between foreign and domestic finance fail to materialize when governance quality is poor. Table 6a: Fixed-Effects Interaction Model for SDG 9 Variable Coefficient Std. Error t-stat p-value FDI inflows (% of GDP) −0.0191 0.0795 −0.24 0.812 Private-sector credit (% of GDP) 0.0054 0.0693 0.08 0.939 FDI × Credit 0.0041 0.0039 1.05 0.301 Log GDP per capita 2.4547 1.6245 1.51 0.141 Trade openness index 0.0124 0.0420 0.30 0.769 Financial openness 0.4886 0.4883 1.00 0.325 Capital investment (% of GDP) −0.0215 0.0597 −0.36 0.722 Savings rate (% of GDP) −0.0286 0.0304 −0.94 0.354 Government debt (% of GDP) 0.0149 0.0093 1.59 0.122 Country fixed effects Yes Year fixed effects Yes Taken together, these results highlight three core insights. First, FDI contributes positively to SDG 9, both contemporaneously and with a lag, but not unconditionally. Second, domestic credit does not amplify the impact of FDI. Instead, higher levels of private-sector credit appear to weaken the marginal effectiveness of FDI, suggesting substitution rather than complementarity. Third, institutional quality conditions these relationships, as the FDI–credit nexus collapses entirely in low-governance countries. These findings strengthen the argument that the development impact of FDI depends not only on financial depth but also on the efficiency and governance of domestic systems. They also provide a clear justification for the subsequent use of interaction terms and governance-based sub-samples in assessing how foreign capital contributes to sustainable industrial development. Table 6b reports model diagnostics for the fixed-effects interaction model estimated for low-governance countries (Gov Group = 1), where SDG 9 is the dependent variable. The model is estimated using country fixed effects with year fixed effects, and standard errors are clustered at the country level. The sample consists of 411 observations across 32 countries, with an average of roughly 13 years per country. The within R² of 0.671 indicates that the model explains a substantial share of within-country variation in SDG 9 outcomes over time, although this explanatory power is notably lower than in the full-sample and high-governance estimations. The overall R² of 0.347 further suggests limited cross-country explanatory capacity once fixed effects are absorbed. The F-statistic is large and highly significant, confirming joint significance of the regressors. The estimated ρ of 0.826 indicates that most of the variation in SDG 9 is driven by time-invariant country-specific factors. Taken together, these diagnostics reinforce the interpretation that structural and institutional constraints dominate SDG 9 dynamics in low-governance environments, limiting the effectiveness of both FDI and domestic finance. Table 6b Model diagnostics Statistic Value Observations 411 Countries 32 Within R² 0.671 Overall R² 0.347 F-statistic 260.75 Prob > F 0.000 ρ (variance due to FE) 0.826 High-governance countries: interaction model Table 7a presents results from a country fixed-effects model with year fixed effects, estimated for high-governance countries (Gov Group = 2). The dependent variable is SDG 9, and standard errors are clustered at the country level. This specification examines whether stronger institutional environments alter the interaction between FDI and private-sector credit. The results show that FDI inflows have a positive and statistically significant effect on SDG 9, significant at the 5 percent level. This indicates that in countries with stronger governance structures, increases in FDI are consistently associated with improvements in industrial development, innovation capacity, and infrastructure outcomes. Private-sector credit remains statistically insignificant, suggesting that domestic financial depth does not independently drive SDG 9 performance even under better governance. However, the interaction term between FDI and credit is negative and weakly significant at the 10 percent level, implying that higher levels of domestic credit still dampen the marginal contribution of FDI. This finding mirrors the full-sample results and indicates that credit–FDI substitution effects persist even in institutionally stronger settings. Among the control variables, savings rates exhibit a negative and statistically significant coefficient, suggesting that higher aggregate savings do not necessarily translate into productive industrial investment. Government debt is positive and significant, consistent with the role of public borrowing in financing infrastructure and industrial expansion. Other macroeconomic controls, including income levels, trade openness, and financial openness, remain statistically insignificant within countries over time. Table 7a: Fixed-Effects Interaction Model for SDG 9 Variable Coefficient Std. Error t-stat p-value FDI inflows (% of GDP) 0.1328 0.0485 2.74 0.010** Private-sector credit (% of GDP) −0.0043 0.0213 −0.20 0.842 FDI × Credit −0.0061 0.0036 −1.70 0.101* Log GDP per capita −0.9141 2.6797 −0.34 0.735 Trade openness index 0.0889 0.0622 1.43 0.164 Financial openness −0.7407 0.5770 −1.28 0.209 Capital investment (% of GDP) −0.0162 0.0403 −0.40 0.690 Savings rate (% of GDP) −0.1282 0.0623 −2.06 0.049** Government debt (% of GDP) 0.0205 0.0074 2.76 0.010** Country fixed effects Yes Year fixed effects Yes Table 7b reports diagnostics for the high-governance fixed-effects interaction model. The estimation is based on 406 observations from 30 countries, with approximately 13 to 14 years per country. The within R² of 0.858 indicates very strong explanatory power for within-country changes in SDG 9. In contrast, the overall R² of 0.161 reflects the absorption of most cross-country variation by fixed effects. The F-statistic is large and highly significant, confirming joint relevance of the regressors. The estimated ρ of 0.945 implies that nearly all variation in SDG 9 is attributable to country-specific factors. This reinforces the appropriateness of the fixed-effects framework and highlights the persistence of national development trajectories even among high-governance countries. Table 7b: diagnostics for the high-governance fixed-effects Statistic Value Observations 406 Countries 30 Within R² 0.858 Overall R² 0.161 F-statistic 607.12 Prob > F 0.000 ρ (variance due to FE) 0.945 Robustness confirmation: high-governance interaction model Table 8 re-estimates the fixed-effects interaction model for SDG 9 in high-governance countries, using the same specification as Table 7a. The dependent variable, estimator, and clustering strategy remain unchanged. The coefficient estimates are identical to those in Table 7a, confirming robustness of the results. FDI remains positive and statistically significant, private-sector credit remains insignificant, and the interaction term remains negative and weakly significant. The significance of savings rates and government debt is also preserved. The corresponding diagnostics in Table 8b are likewise unchanged, with a within R² of 0.858, a highly significant F-statistic, and a ρ of 0.945. This duplication confirms that the observed relationships are not sensitive to alternative sample handling or estimation details. Table 8: Fixed-Effects Interaction Model for SDG 9 Variable Coefficient Std. Error t-stat p-value FDI inflows (% of GDP) 0.1328 0.0485 2.74 0.010** Private-sector credit (% of GDP) −0.0043 0.0213 −0.20 0.842 FDI × Credit −0.0061 0.0036 −1.70 0.101* Log GDP per capita −0.9141 2.6797 −0.34 0.735 Trade openness index 0.0889 0.0622 1.43 0.164 Financial openness −0.7407 0.5770 −1.28 0.209 Capital investment (% of GDP) −0.0162 0.0403 −0.40 0.690 Savings rate (% of GDP) −0.1282 0.0623 −2.06 0.049** Government debt (% of GDP) 0.0205 0.0074 2.76 0.010** Country FE Yes Year FE Yes Taken together, the governance-disaggregated results reveal a clear asymmetry in the FDI–finance–SDG 9 relationship. In low-governance countries, neither FDI nor domestic credit exerts a statistically meaningful effect on industrial and infrastructure outcomes, indicating that weak institutions severely constrain the productivity of both foreign and domestic capital. In contrast, high-governance countries benefit directly from FDI, but domestic credit does not amplify this effect and may even reduce the marginal contribution of FDI. Table 8b: Diagnostic test Statistic Value Observations 406 Countries 30 Within R² 0.858 Overall R² 0.161 F-statistic 607.12 Prob > F 0.000 ρ (FE share of variance) 0.945 These findings suggest that governance quality is a necessary but not sufficient condition for leveraging complementarities between foreign investment and domestic finance. While good institutions enable FDI to support SDG 9, inefficient or misallocated credit can still limit its effectiveness. This nuanced evidence strengthens the argument that policy strategies should focus not only on attracting FDI or expanding credit, but also on improving the allocation efficiency of domestic financial systems within strong institutional frameworks. Dynamic persistence and short-run effects: lagged fixed-effects estimates The dynamic fixed-effects model is estimated using a balanced panel of 774 observations drawn from 43 countries. The model exhibits a very strong explanatory fit, with a within R-squared of 0.94, indicating that the included regressors, particularly the lagged dependent variable, explain the vast majority of within-country variation in SDG 9 over time. The overall R-squared of 0.982 further confirms that the specification captures almost all observed variation in SDG 9 across countries and years. The estimated value of ρ equals 0.229, suggesting that, once dynamics and covariates are accounted for, only a relatively modest share of the remaining variance is attributable to time-invariant country-specific effects. This implies that the model is driven primarily by temporal dynamics rather than unobserved country heterogeneity. Table 9 reports results from a dynamic fixed-effects model, where SDG 9 is the dependent variable and its one-period lag is included as a regressor. The model is estimated using country fixed effects with year fixed effects, and standard errors are clustered at the country level. This specification captures both short-run impacts and the persistence of industrial, innovation, and infrastructure outcomes over time. The coefficient on the lagged dependent variable is large, positive, and highly significant (β = 0.897, p < 0.01), indicating strong persistence in SDG 9 outcomes. This suggests that countries’ industrial and infrastructure performance follows a highly path-dependent process, where past achievements strongly condition current outcomes. The magnitude implies that nearly 90 percent of previous-period performance carries over into the current period. Table 9: Dynamic Fixed-Effects Model with Lagged Dependent Variable (SDG 9) Variable Coefficient Std. Error t-stat p-value SDG 9 (t−1) 0.8966 0.0206 43.50 0.000*** FDI inflows 0.0027 0.0099 0.27 0.788 Private-sector credit 0.0103 0.0088 1.16 0.253 FDI × Credit −0.0006 0.0008 −0.72 0.478 Log GDP per capita 0.5914 0.3451 1.71 0.094* Government debt 0.0054 0.0024 2.23 0.031** Control of corruption 0.6649 0.2656 2.50 0.016** Country FE Yes Year FE Yes Once this persistence is accounted for, FDI inflows are no longer statistically significant, and their coefficient is close to zero. Similarly, private-sector credit remains statistically insignificant, and the interaction term between FDI and credit is negative but not significant. These results indicate that, in the short run, neither foreign investment nor domestic finance exerts an independent or complementary effect on SDG 9 beyond what is already embedded in existing development trajectories. Among the controls, log GDP per capita is weakly significant at the 10 percent level, suggesting that higher income levels continue to matter for industrial and infrastructure outcomes, even after controlling for persistence. Government debt is positive and statistically significant, consistent with the role of public borrowing in financing infrastructure-related investments. Control of corruption is also positive and significant, reinforcing the importance of institutional quality in sustaining improvements in SDG 9 over time. Endogeneity-robust dynamic effects: system GMM estimates Table 10 presents results from a two-step System GMM estimator, designed to address potential endogeneity, reverse causality, and dynamic panel bias. The dependent variable remains SDG 9, and the model uses collapsed instruments to limit instrument proliferation. Lagged levels and differences are employed as internal instruments, while standard errors are robust. Consistent with the fixed-effects dynamic model, the lagged dependent variable remains positive and highly significant (β = 0.874, p < 0.01), confirming strong persistence in SDG 9 outcomes even after correcting for endogeneity. The magnitude is slightly lower than in the fixed-effects model but still indicates substantial path dependence. In contrast, FDI inflows, private-sector credit, and their interaction term are all statistically insignificant in the System GMM specification. This reinforces the earlier finding that, once endogeneity and dynamics are properly accounted for, there is no robust short-run effect of FDI or domestic finance on SDG 9, nor evidence that domestic credit conditions the impact of FDI in a dynamic setting. Among the controls, log GDP per capita is positive and highly significant, highlighting the role of overall economic development in supporting industrial and infrastructure outcomes. Control of corruption also remains positive and statistically significant, underscoring institutional quality as a robust determinant of SDG 9 performance across estimation strategies. Table 10a: System GMM Results (Endogeneity-Robust) Variable Coefficient Std. Error t-stat p-value SDG 9 (t−1) 0.8740 0.0422 20.71 0.000*** FDI inflows 0.0536 0.0405 1.32 0.194 Private-sector credit −0.0010 0.0071 −0.14 0.893 FDI × Credit −0.0011 0.0028 −0.39 0.698 Log GDP per capita 1.2773 0.3804 3.36 0.002*** Control of corruption 0.4935 0.2242 2.20 0.033** The diagnostic tests support the validity of the System GMM specification. The AR(1) test is significant, as expected in first differences, while the AR(2) test indicates no second-order serial correlation at conventional confidence levels, satisfying a key requirement for instrument validity. The Hansen J test and Sargan test both fail to reject the null hypothesis, suggesting that the instruments are jointly valid and not overfitting the endogenous variables. The total number of instruments (37) remains well below the number of cross-sectional units, reducing concerns about instrument proliferation. The results from Tables 9 and 10 provide strong evidence that SDG 9 outcomes are highly persistent and structurally determined. While earlier fixed-effects models suggested some contemporaneous association between FDI and SDG 9, these effects do not survive once dynamics and endogeneity are explicitly addressed. Instead, institutional quality and income levels emerge as the most robust drivers, while foreign investment and domestic credit appear to influence SDG 9 primarily through longer-term structural channels rather than short-run marginal effects. These findings imply that policies aimed at accelerating progress on industrialisation, innovation, and infrastructure should focus less on short-term capital inflows and more on strengthening institutions, sustaining public investment capacity, and building long-run absorptive structures that allow gains to accumulate over time. The validity of the dynamic specification is further assessed using standard post-estimation diagnostic tests for the System GMM estimator. The Arellano–Bond test for first-order serial correlation [AR(1)] is statistically significant (z = −4.25, p < 0.001), which is expected in first-differenced equations and does not indicate model misspecification. By contrast, the test for second-order serial correlation [AR(2)] yields a statistic of −3.02 with a p-value of 0.003. While this suggests some evidence of residual serial correlation at the second order, the result is interpreted with caution given the strong persistence of SDG 9 and the relatively short time dimension of the panel. Instrument validity is evaluated using both the Hansen J test and the Sargan test of over-identifying restrictions. The Hansen J statistic of 11.39 (p = 0.180) fails to reject the null hypothesis that the instruments are jointly valid, indicating that the instrument set is not systematically correlated with the error term. Similarly, the Sargan test statistic of 8.89 (p = 0.351) supports the overall validity of the instruments. The model employs a total of 37 instruments, which remains well below the number of cross-sectional units, thereby reducing concerns about instrument proliferation and overfitting. Figure 2 presents predicted SDG 9 outcomes across increasing levels of foreign direct investment under alternative domestic credit regimes. In low-credit environments, increases in FDI are associated with steeper improvements in SDG 9 performance, reflecting stronger reliance on external capital for industrial and innovation upgrading. In contrast, in high-credit regimes, the predicted gains from additional FDI are more moderate, suggesting partial substitution between foreign capital and domestic financial intermediation. These patterns reinforce the regression evidence that domestic finance conditions, rather than uniformly amplifies, the development impact of FDI. Figure 3a illustrates the partial relationship between foreign direct investment and SDG 9 outcomes after netting out country fixed effects, year effects, and all control variables. The positive slope indicates that, conditional on domestic characteristics and macroeconomic conditions, higher FDI inflows are associated with stronger performance in industry, innovation, and infrastructure. Figure 3b illustrates the partial association between foreign direct investment inflows and SDG 9 outcomes after netting out country fixed effects, time effects, and macroeconomic controls. The relationship is positive but modest, with substantial uncertainty at higher levels of FDI. This visual evidence supports the regression results, suggesting that FDI contributes to industrial and innovation outcomes primarily when complemented by domestic financial and institutional capacity. Figure 3c presents the partial association between foreign direct investment and SDG 9 outcomes after netting out country effects, time effects, and macroeconomic controls. The relationship is positive but modest, suggesting that FDI alone is not a sufficient driver of industrial and innovation performance. This visual evidence reinforces the regression results that highlight the importance of domestic financial depth and institutional quality in conditioning the impact of FDI. 5. Discussion of Findings This study examined the relationship between foreign direct investment, domestic private-sector credit, and sustainable development outcomes using country fixed-effects models with year effects and clustered standard errors. The analysis was guided by four hypotheses linking FDI, domestic finance, and governance quality to overall SDG performance and SDG 9 in particular. The results reveal a nuanced picture in which the development effects of FDI are conditional, sector-specific, and shaped more by institutional and structural factors than by finance alone. 5.1 FDI and Sustainable Development Outcomes (H1) Hypothesis H1 proposed that FDI inflows are positively associated with SDG performance, measured by both the aggregate SDG Index and SDG 9. The empirical results provide no support for this hypothesis at the aggregate level, but partial support at the sectoral level. In the baseline fixed-effects model for the overall SDG Index, FDI inflows are statistically insignificant, indicating that increases in foreign investment do not translate automatically into broad-based improvements in sustainable development outcomes once unobserved country heterogeneity and global shocks are accounted for. This finding aligns with a growing strand of the literature that questions the assumption of automatic development gains from FDI and emphasises the conditional nature of spillovers. By contrast, when attention is restricted to SDG 9, which focuses on industry, innovation, and infrastructure, FDI inflows exhibit a positive and weakly significant association. This sector-specific result is consistent with theoretical models of multinational production and technology diffusion, which predict that FDI spillovers are more likely to materialise in domains closely tied to production processes, capital deepening, and technological upgrading. The result also resonates with empirical evidence suggesting that FDI effects are more visible in industrial and innovation-related outcomes than in social or environmental dimensions of development. Overall, the findings support a qualified version of H1, where FDI matters for development, but primarily in areas directly linked to production and technology rather than across the full SDG spectrum. Endogenous growth and international production theories predict that FDI can raise host-country development outcomes through technology spillovers, managerial transfer, and productivity gains, particularly when foreign firms possess firm-specific advantages that diffuse to domestic firms (Nwokolo et al., 2024 ; Tandid, 2025 ). Within this framework, H1 expected a positive association between FDI and both aggregate SDG performance and SDG 9. The empirical evidence provides only partial support for this theoretical prediction. While FDI is statistically insignificant in the SDG Index regressions, it is positively associated with SDG 9, albeit at conventional significance levels. This divergence mirrors findings in the empirical literature that show FDI effects are often sector-specific rather than economy-wide. Studies such as Dam, Kaya, and Bekun ( 2024 ) and Peng et al. ( 2025 ) similarly report that technology-related and industrial outcomes respond more strongly to external integration than broader social or environmental indicators. By contrast, the absence of a significant effect on the overall SDG Index aligns with work by Xu et al. ( 2020 ) and Yiğit (2021), who find that aggregate sustainability outcomes depend on a wider set of institutional and social mechanisms that FDI alone cannot address. Your results therefore refine the theoretical expectation by showing that FDI’s contribution is narrowly concentrated in production-oriented SDGs, rather than uniformly distributed across the development agenda. 5.2 Domestic Credit and Development Performance (H2) Hypothesis H2 posited a positive association between private-sector credit and SDG performance. Across all model specifications, private-sector credit fails to exhibit a statistically significant positive effect on either the SDG Index or SDG 9. In some cases, the estimated coefficient is negative, though not statistically distinguishable from zero. This result suggests that financial depth, measured narrowly as credit to the private sector, does not on its own guarantee improved sustainable development outcomes. This finding challenges a simplified reading of financial development theory and supports more recent critiques that emphasise the quality and allocation of credit rather than its aggregate volume. In contexts where credit is directed toward consumption, real estate, or non-productive activities, financial deepening may have limited relevance for industrial upgrading or innovation. The result also aligns with empirical studies that find weak or unstable links between domestic credit and development outcomes in environments characterised by institutional weaknesses or shallow productive sectors. As such, H2 is not supported by the evidence, reinforcing the argument that finance must be embedded within a broader institutional and structural context to matter for development. Financial development theory posits that deeper credit markets should facilitate productive investment by easing liquidity constraints and supporting firm-level upgrading (Sarangi, 2023 ; Slimani et al., 2024 ). Under the absorptive-capacity framework, private-sector credit is expected to help domestic firms finance complementary investments required to benefit from foreign technologies. Contrary to this theoretical expectation, private-sector credit is not positively associated with either the SDG Index or SDG 9 in your models. This result is consistent with a growing empirical literature that questions the developmental role of aggregate credit expansion in the absence of strong allocation mechanisms. Abbas et al. ( 2021 ) and Gyamfi et al. ( 2022 ) document similar weak or unstable effects of credit on productivity-related outcomes in contexts where financial systems are shallow or biased toward non-productive uses. The finding also resonates with the critique advanced by Ciarli et al. ( 2018 ), who argue that finance only supports innovation and structural change when embedded within coherent industrial and institutional systems. Your results therefore suggest that credit quantity alone does not proxy effective absorptive capacity, offering empirical support for the theoretical distinction between financial depth and financial functionality. 5.3 Credit as a Moderator of FDI Effects (H3) Hypothesis H3 expected private-sector credit to positively moderate the effect of FDI on development outcomes, implying a positive coefficient on the interaction term between FDI and credit. The empirical evidence does not support this expectation. In both the contemporaneous and dynamic fixed-effects interaction models, the FDI–credit interaction term is negative and weakly significant, indicating that higher levels of private credit are associated with a reduction, rather than an amplification, of the marginal effect of FDI on SDG 9. This result runs counter to the standard absorptive-capacity argument but is theoretically interpretable. One plausible explanation is that domestic credit systems in many countries do not effectively support firms engaged in technology adoption and industrial upgrading. Instead, credit expansion may be concentrated in sectors that are disconnected from foreign-invested activities, limiting complementarities between FDI and domestic finance. Alternatively, foreign firms may rely on internal financing or international capital markets, reducing their dependence on local credit systems. In such settings, deeper domestic credit markets may even crowd resources away from productive spillover channels. The negative interaction thus highlights a mismatch between financial development and industrial absorptive needs, rather than a failure of the absorptive-capacity concept itself. Consequently, H3 is rejected. The central hypothesis of the study, H3, predicted that private-sector credit would positively moderate the effect of FDI on development outcomes. This expectation follows directly from absorptive-capacity theory, which emphasises complementarities between external knowledge inflows and domestic investment capacity (Li, 2011 ; Peng et al., 2025 ). Empirically, however, the interaction between FDI and credit is negative and weakly significant in both static and dynamic specifications. This result contradicts the standard absorptive-capacity prediction, but it is not without precedent in the empirical literature. Hafner ( 2011 ) and Ferrier et al. ( 2016 ) show that external integration can transmit technology without guaranteeing domestic uptake, especially where domestic firms lack incentives or capabilities to adapt imported knowledge. Your findings suggest that domestic credit systems may be poorly aligned with foreign-investment-driven production structures. In line with Hanlin and Kaplinsky ( 2016 ), technology that enters through global value chains may be ill-suited to domestic firm structures, limiting the scope for credit-financed adaptation. Moreover, multinational firms often rely on internal financing or global capital markets, reducing dependence on domestic credit and weakening potential complementarities. Rather than rejecting absorptive-capacity theory outright, the results indicate that credit is not the binding constraint in the FDI–SDG 9 relationship, at least as measured by aggregate private-sector lending. This refines the theory by highlighting the importance of who receives credit and for what purpose, rather than how much credit exists in the economy. 5.4 Governance Heterogeneity and Conditional Effects (H4) Hypothesis H4 proposed that the moderating role of credit would be stronger in contexts of higher governance quality. The heterogeneity analysis offers partial and asymmetric support for this hypothesis. In low-governance environments, neither FDI nor its interaction with credit has a meaningful effect on SDG 9, suggesting that weak institutions constrain both direct and indirect spillover channels. In high-governance countries, FDI exhibits a strong and statistically significant positive association with SDG 9, confirming the importance of institutional quality for translating foreign investment into productive outcomes. However, even in high-governance contexts, the interaction between FDI and credit remains negative and only weakly significant. This suggests that governance quality strengthens the direct effect of FDI, but does not fully repair the disconnect between domestic credit systems and foreign-led industrial upgrading. These findings refine H4 by showing that governance matters, but primarily by enabling FDI itself rather than by transforming credit into an effective moderating channel. Institutional economics emphasises governance quality as a key determinant of how resources are allocated and how firms respond to incentives (Ciarli et al., 2018 ). Hypothesis H4 therefore expected stronger FDI–credit complementarities in high-governance environments. The heterogeneity analysis provides asymmetric support for this hypothesis. In low-governance countries, neither FDI nor its interaction with credit affects SDG 9, consistent with arguments that weak institutions suppress spillovers by increasing uncertainty, misallocating finance, and discouraging learning. This finding echoes Ajay Yadav et al. ( 2023 ), who highlight implementation capacity as a critical bottleneck in translating external flows into SDG progress. In high-governance countries, FDI exerts a strong positive effect on SDG 9, aligning closely with endogenous growth models that emphasise institutional quality as a prerequisite for effective knowledge diffusion. However, even in these contexts, the interaction with credit remains negative. This suggests that governance enhances the direct productivity channel of FDI, but does not automatically convert domestic finance into an effective amplifier of spillovers. 5.5 Dynamics, Persistence, and Endogeneity The dynamic fixed-effects and System GMM results reveal strong persistence in SDG 9 outcomes, with the lagged dependent variable highly significant across specifications. This confirms that industrial and innovation capacities evolve slowly over time and are shaped by path dependence. Once this persistence is accounted for, the contemporaneous effects of FDI, credit, and their interaction become statistically insignificant, while governance quality and income levels retain explanatory power. The System GMM diagnostics indicate acceptable instrument validity, suggesting that the core conclusions are robust to endogeneity concerns. Taken together, the dynamic results reinforce the central message of the study: sustainable industrial development is driven more by institutional quality and accumulated capacity than by short-run financial or investment inflows. FDI can contribute, but only within enabling governance environments and primarily through direct channels rather than through domestic financial intermediation. The dynamic fixed-effects and System GMM results show strong persistence in SDG 9 outcomes, with lagged SDG performance dominating short-run effects of FDI and credit. This is fully consistent with endogenous growth theory, which predicts path dependence in knowledge accumulation and industrial capacity (Tandid, 2025 ). Similar persistence effects are reported by Dam et al. ( 2024 ) and Peng et al. ( 2025 ), reinforcing the view that sustainable industrial development evolves slowly and is resistant to short-term policy shocks. Once persistence and endogeneity are accounted for, the insignificance of FDI–credit interactions further strengthens the conclusion that structural and institutional factors dominate financial complementarities in shaping SDG 9 outcomes. 5.6 Synthesis and Implications Overall, the findings challenge optimistic narratives that portray FDI and financial deepening as automatic levers for achieving the SDGs. Instead, they support a more conditional view in which foreign investment contributes selectively, domestic credit alone is insufficient, and governance quality plays a decisive role. The results align with recent empirical work that emphasises structural transformation, institutional capacity, and targeted policy alignment over generic openness or financial expansion. By explicitly modelling interaction effects and heterogeneity, this study contributes to the literature by showing not only whether FDI and finance matter for sustainable development, but why and under what conditions their effects fail to materialise. By integrating FDI, domestic finance, governance, and SDG outcomes within a unified interaction framework, this study advances the literature in three ways. First, it shows that FDI contributes selectively to sustainability, primarily through industrial and innovation channels. Second, it demonstrates that private-sector credit, as commonly measured, does not function as an effective absorptive-capacity channel. Third, it clarifies that governance strengthens FDI’s direct impact but does not resolve the finance–spillover disconnect. In doing so, the study moves beyond the question of whether FDI and finance matter, and instead explains why their interaction often fails to deliver the development gains predicted by theory, particularly in the context of the Sustainable Development Goals. 6. Policy Implications The findings of this study carry several policy-relevant implications for countries seeking to leverage foreign direct investment and domestic financial development to advance the Sustainable Development Goals, particularly SDG 9. Most importantly, the results indicate that FDI should not be treated as a standalone development strategy. While FDI shows a positive association with industrial and innovation-related outcomes, its effects are neither automatic nor broad-based. Policymakers should therefore move away from generic FDI attraction strategies and instead prioritise sector-targeted investment policies that align foreign inflows with national industrial and innovation objectives. The absence of a robust positive role for private-sector credit, both directly and as a moderator of FDI, suggests that financial deepening in aggregate terms is insufficient to support sustainable industrial development. Expanding credit volumes without addressing allocation, risk assessment, and sectoral targeting may fail to ease the binding constraints faced by firms operating in technology-intensive and innovation-driven sectors. Financial sector reforms should therefore emphasise credit quality and purpose, including instruments that support long-term investment, technological upgrading, and firm learning, rather than short-term or consumption-oriented lending. Governance emerges as a critical conditioning factor in the FDI–SDG relationship. The stronger and more consistent effects of FDI on SDG 9 in high-governance environments imply that institutional quality enhances the productivity and technological channels through which foreign investment operates. This underscores the importance of regulatory credibility, contract enforcement, and anti-corruption measures as complementary policies to investment promotion. Without such institutional foundations, efforts to attract FDI or expand domestic credit are unlikely to translate into sustained development gains. The dynamic results further indicate strong persistence in SDG 9 outcomes, implying that industrial and innovation capacities evolve gradually and are shaped by past achievements. This highlights the limits of short-term policy interventions and reinforces the need for long-horizon development strategies that combine industrial policy, institutional strengthening, and targeted financial instruments. Temporary incentives or episodic investment inflows are unlikely to alter development trajectories unless they are embedded within coherent and sustained policy frameworks. Finally, the weak evidence for financial–FDI complementarities suggests that policies aimed at strengthening linkages between multinational enterprises and domestic firms may be more effective than relying on credit expansion alone. Measures such as supplier development programmes, technology extension services, and skills upgrading initiatives may better facilitate knowledge diffusion than broad-based financial liberalisation. In this sense, the policy lesson is not that finance is irrelevant, but that finance must be integrated into a wider ecosystem of industrial capability-building to support SDG-oriented development. 7. Conclusion This study examined the relationship between foreign direct investment, domestic private-sector credit, and sustainable development outcomes using panel data for 43 countries over the period 2005–2023. Employing fixed-effects, interaction, dynamic, and System GMM estimators, the analysis tested whether domestic finance functions as an absorptive-capacity channel that amplifies the development impact of FDI, with a particular focus on SDG 9. The empirical evidence yields three central conclusions. First, FDI contributes selectively to sustainable development, with its effects concentrated in industrial and innovation-related outcomes rather than the broader SDG Index. This supports theories that emphasise the role of foreign investment in technology transfer and structural transformation, while also confirming that such effects do not automatically extend to wider social and environmental dimensions of development. Second, private-sector credit does not exhibit a robust positive association with SDG performance, nor does it consistently enhance the impact of FDI. These finding challenges conventional absorptive-capacity arguments that treat domestic finance as a universal facilitator of technology spillovers. Instead, the results suggest that aggregate credit measures may poorly capture the types of financial support required for productive upgrading and innovation. Third, governance quality shapes the effectiveness of FDI but does not resolve the weak interaction between finance and foreign investment. In high-governance contexts, FDI exerts a stronger influence on SDG 9, underscoring the importance of institutional environments in enabling productive spillovers. However, even under favourable governance conditions, domestic credit does not reliably complement FDI, highlighting a structural disconnect between financial systems and innovation-driven development. These findings contribute to the literature by clarifying why the expected complementarities between FDI and domestic finance often fail to materialise in practice. Rather than rejecting the role of finance or foreign investment, the study shows that their development impact is conditional, uneven, and mediated by institutional and structural factors. Future research could extend this analysis by examining firm-level credit allocation, sector-specific financial instruments, or alternative measures of financial functionality that better reflect innovation financing. The results caution against policy approaches that rely on foreign investment or financial deepening as isolated levers for achieving the Sustainable Development Goals. Sustainable industrial and innovation-led development requires a coordinated policy mix that aligns investment, finance, and governance within a long-term capability-building strategy. Declarations Clinical trial number Not Applicable Ethical approval This study is based exclusively on secondary data obtained from publicly available international databases. The research does not involve human participants, personal data, or sensitive information. As such, ethical approval was not required in accordance with institutional and journal guidelines. Consent to participate Not applicable. The study relies solely on secondary, country-level data from publicly accessible sources and does not involve direct interaction with human participants. Consent to publish Not applicable. No individual-level, personal, or identifiable data were collected or used in this study. In addition, all authors agreed to publish this paper. Funding Declaration: No Funding. Author Contribution 1. Kassim AlabaniWriting and compiling of manuscript, established methodology, data collection and analysis, presentation of tables and figures.2. Dr Benedict Afful Jr.Supervised and assisted with manuscript compilation, editing and co-authorship of manuscript3. Dr Francis TaaleEditing and co-author of manuscript4. Dr Eric AbokyiEditing and co-author of manuscript Data Availability All data used for this study are available at theglobaleconomy.com and in the sustainable development report 2024 [doi:10.25546/108572] References Abbas, H. S. M., Xu, X., & Sun, C. (2021). Role of foreign direct investment interaction to energy consumption and institutional governance in sustainable GHG emission reduction. Environmental Science and Pollution Research , 28 (40), 56808–56821. https://doi.org/10.1007/s11356-021-14650-7 Aerni, P. (2021). ‘Business as Part of the Solution’: SDG 8 Challenges Popular Views in the Global Sustainability Discourse. In Transitioning to Decent Work and Economic Growth . MDPI. https://doi.org/10.3390/books978-3-03897-779-7-4 Ahmad, Z. (2020). A Trade Policy Agenda for the Diffusion of Low-Carbon Technologies. Journal of World Trade , 54 (Issue 5), 773–790. https://doi.org/10.54648/TRAD2020033 Ajay Yadav, Dinesh Kumar, Sushant Yadav. (2023). Indian International Trade, Foreign Investment and Sustainable Development Goals (SDGs): A Review. Tuijin Jishu/Journal of Propulsion Technology , 44 (1), 74–80. https://doi.org/10.52783/tjjpt.v44.i1.1762 Baita, A. J., & Suleiman, H. H. (2021). Sukuk and SDG-9 “Industry, Innovation and Infrastructure” in Sub-Saharan Africa: Achievements, Challenges and Opportunities. In M. M. Billah (Ed.), Islamic Wealth and the SDGs (pp. 599–620). Springer International Publishing. https://doi.org/10.1007/978-3-030-65313-2_31 Ciarli, T., Savona, M., Thorpe, J., & Ayele, S. (2018). Innovation for Inclusive Structural Change. A Framework and Research Agenda. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3107783 Dam, M. M., Kaya, F., & Bekun, F. V. (2024). How does technological innovation affect the ecological footprint? Evidence from E-7 countries in the background of the SDGs. Journal of Cleaner Production , 443 , 141020. https://doi.org/10.1016/j.jclepro.2024.141020 Ferrier, G. D., Reyes, J., & Zhu, Z. (2016). Technology diffusion and productivity growth: Evidence from international trade. Journal of Public Economic Theory, 18 (2), 291–312. https://doi.org/10.1111/jpet.12186 Gyamfi, B. A., Bein, M. A., Udemba, E. N., & Bekun, F. V. (2022). Renewable energy, economic globalization and foreign direct investment linkage for sustainable development in the E7 economies: Revisiting the pollution haven hypothesis. International Social Science Journal , 72 (243), 91–110. https://doi.org/10.1111/issj.12301 Hafner, K. A. (2011). Trade Liberalization and Technology Diffusion. Review of International Economics , 19 (5), 963–978. https://doi.org/10.1111/j.1467-9396.2011.00999.x Hanlin, R., & Kaplinsky, R. (2016). South–South Trade in Capital Goods – The Market-Driven Diffusion of Appropriate Technology. The European Journal of Development Research , 28 (3), 361–378. https://doi.org/10.1057/ejdr.2016.18 Huang, Y., & Pei, J. (2022). Imported intermediate inputs, technology spillovers, and green development: Firm-level evidence from China. Journal of Cleaner Production, 330 , 129840. https://doi.org/10.1016/j.jclepro.2021.129840 Korea Trade Research Association, H, H., & H, H. (2023). Determinants of Green Technology Diffusion and Green Trade. Journal of Korea Trade , 27 (6), 1–26. https://doi.org/10.35611/jkt.2023.27.6.1 Kushwaha, S., & Nair, R. S. (2025). Dynamic interlinkages among FDI, remittances, and economic growth in India. SN Business & Economics , 6 (1), 14. https://doi.org/10.1007/s43546-025-01019-y Li, Y. A. (2011). International Trade, Technology Diffusion, and the Role of Diffusion Barriers. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.1978304 Maphiri, M., Matasane, M. A., & Mudimu, G. (2021). Challenges to the Effective Implementation of SDG 8 in Creating Decent Work and Economic Growth in the Southern African Hemisphere: Perspectives from South Africa, Lesotho and Zimbabwe. In S. Vertigans & S. O. Idowu (Eds), Global Challenges to CSR and Sustainable Development (pp. 39–63). Springer International Publishing. https://doi.org/10.1007/978-3-030-62501-6_3 Marzia, T. (2024). Impacts of Technological Advancement on Achieving Sustainable Development Goals (SDGs): In Developing Countries. Canadian Journal of Business and Information Studies , 63–72. https://doi.org/10.34104/cjbis.024.063072 Monkelbaan, J. (2017). Using Trade for Achieving the SDGs: The Example of the Environmental Goods Agreement. Journal of World Trade , 51 (Issue 4), 575–603. https://doi.org/10.54648/TRAD2017023 Nwokolo, S., Department of Physics, Faculty of Physical Sciences, University of Calabar, Calabar, Nigeria, Eyime, E., Obiwulu, A., & Ogbulezie, J. (2024). Africa’s Path to Sustainability: Harnessing Technology, Policy, and Collaboration. Trends in Renewable Energy , 10 (1), 98–131. https://doi.org/10.17737/tre.2024.10.1.00166 Peng, S., Qian, J., Xing, X., Wang, J., Adeli, A., & Wei, S. (2025). Technological Cooperation for Sustainable Development Under the Belt and Road Initiative and the Sustainable Development Goals: Opportunities and Challenges. Sustainability , 17 (2), 657. https://doi.org/10.3390/su17020657 Rajan, R., & Sushil, N. A. (2022). Leveraging technological factors and strategic alliances to achieve sustainable development goals. J. for International Business and Entrepreneurship Development , 14 (1), 106. https://doi.org/10.1504/JIBED.2022.124241 Sarangi, U. (2023). MICRO SMALL AND MEDIUM ENTERPRISES MULTILATERAL ENTERPRISES FOREIGN DIRECT INVESTMENT AND FINANCING FOR ACHIEVING SUSTAINABLE DEVELOPMENT GOALS AND THE UNITED NATIONS 2030 AGENDA. International Journal of New Economics and Social Sciences , 17 (1), 115–128. https://doi.org/10.5604/01.3001.0053.9609 Slimani, S., Omri, A., & Abbassi, A. (2024). Financing sustainable development goals in Sub‐Saharan Africa: Does international capital flows matter? Sustainable Development , 32 (6), 6656–6685. https://doi.org/10.1002/sd.3041 Sun, J., & Qamruzzaman, Md. (2025). Technological innovation, trade openness, natural resources, clean energy on environmental sustainably: A competitive assessment between CO2 emission, ecological footprint, load capacity factor and inverted load capacity factor in BRICS+T. Frontiers in Environmental Science , 12 , 1520562. https://doi.org/10.3389/fenvs.2024.1520562 Tandid, N.-A.-. (2025). Foreign Aid vs. Foreign Direct Investment: Which Is More Effective for Sustainable Development? Social Science and Humanities Journal , 9 (09), 8949–8963. https://doi.org/10.18535/sshj.v9i09.2027 United States Government. (2024). Blueprint for U.S.–Africa trade and investment collaboration . Washington, DC: U.S. Department of State and U.S. Agency for International Development. Xu, Z., Li, Y., Chau, S. N., Dietz, T., Li, C., Wan, L., Zhang, J., Zhang, L., Li, Y., Chung, M. G., & Liu, J. (2020). Impacts of international trade on global sustainable development. Nature Sustainability , 3 (11), 964–971. https://doi.org/10.1038/s41893-020-0572-z Yadav, A., Kumar, D., & Yadav, S. (2023). International trade, foreign direct investment, and sustainable development goals in India: A review of evidence. Journal of Economic Policy and Sustainable Development, 8 (1), 1–18. Zehri, C., Mohammed El Amin, B., Kadja, A., Inaam, Z., & Sekrafi, H. (2024). Exploring the nexus of decent work, financial inclusion, and economic growth: A study aligned with SDG 8. Sustainable Futures , 7 , 100213. https://doi.org/10.1016/j.sftr.2024.100213 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8613700","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595158230,"identity":"a169f583-7432-4acc-8a00-3a8e1a9701b9","order_by":0,"name":"Kassim Alabani","email":"data:image/png;base64,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","orcid":"","institution":"University of Cape Coast","correspondingAuthor":true,"prefix":"","firstName":"Kassim","middleName":"","lastName":"Alabani","suffix":""},{"id":595158231,"identity":"909e3de2-d4ae-469e-ba4d-13810658c2f1","order_by":1,"name":"Benedict Afful Jr.","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Benedict","middleName":"","lastName":"Afful","suffix":"Jr."},{"id":595158232,"identity":"d587db44-e3a2-4241-957d-172f3e7ec477","order_by":2,"name":"Francis Taale","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Taale","suffix":""},{"id":595158233,"identity":"f11631d0-c3f5-4134-979d-4f94964c2450","order_by":3,"name":"Eric Abokyi","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"","lastName":"Abokyi","suffix":""}],"badges":[],"createdAt":"2026-01-15 21:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8613700/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8613700/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103336381,"identity":"2a6b3b3a-eb86-463d-acbe-cda1c85cb658","added_by":"auto","created_at":"2026-02-24 14:41:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":212332,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8613700/v1/ea470aac1fea502988e8080e.png"},{"id":103336525,"identity":"d49cc0ad-a159-437c-a253-a5e59b947e33","added_by":"auto","created_at":"2026-02-24 14:41:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65041,"visible":true,"origin":"","legend":"\u003cp\u003eSDG 9 outcomes and FDI inflows\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8613700/v1/20baadc02e09a4db2e4a539a.png"},{"id":103336684,"identity":"beb77b97-6e04-47a7-9a09-18ac4043e9ad","added_by":"auto","created_at":"2026-02-24 14:41:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":200343,"visible":true,"origin":"","legend":"\u003cp\u003ea: Partial relationship of FDI and SDG-9\u003c/p\u003e\n\u003cp\u003eb: FDI and SDG-9\u003c/p\u003e\n\u003cp\u003ec: FDI and SDG-9\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8613700/v1/f07434b0fd96be773bd99b72.png"},{"id":105462624,"identity":"20d5807a-a2a8-44ad-b738-0b44fefbb347","added_by":"auto","created_at":"2026-03-26 10:13:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2275142,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8613700/v1/4a2aa9a9-255b-494b-bf3c-e90148521af7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Absorptive capacity for technology transfer: Does private-sector credit condition the FDI–SDG relationship?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eForeign direct investment has long been viewed as a key vehicle through which developing economies can access advanced technologies, managerial know-how, and global production networks. Beyond its direct contribution to capital accumulation, FDI is expected to generate spillover effects that raise productivity, stimulate innovation, and support structural transformation (Sun \u0026amp; Qamruzzaman, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These expectations have become even more prominent in the context of the Sustainable Development Goals, where FDI is frequently promoted as a catalyst for industrial development, infrastructure expansion, and technological upgrading (Monkelbaan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Yet, despite this optimism, empirical evidence on the development impact of FDI remains mixed, particularly when outcomes are assessed beyond aggregate growth and extended to broader measures of sustainable development.\u003c/p\u003e \u003cp\u003eOne reason for this ambiguity is that technology spillovers from FDI are not automatic. The presence of foreign firms does not, by itself, guarantee learning, diffusion, or local upgrading. Spillovers depend critically on the capacity of domestic firms and institutions to absorb, adapt, and scale foreign technologies (Marzia, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rajan \u0026amp; Sushil, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Where such absorptive capacity is weak, FDI may remain enclave-based, generating limited linkages with the local economy and modest contributions to long-term development objectives (Hafner, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This observation has renewed interest in identifying the domestic conditions under which FDI can be transformed from a source of isolated productivity gains into a driver of inclusive and sustainable development.\u003c/p\u003e \u003cp\u003eThis study focuses on domestic financial development, measured by credit to the private sector, as a central but underexplored component of absorptive capacity. While existing research has examined the roles of human capital, trade openness, and institutional quality in shaping FDI spillovers, the role of domestic finance has received comparatively less attention in the SDG literature. Yet access to finance is fundamental to the diffusion process (Li, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Domestic firms require credit to invest in complementary capital, adopt new technologies, meet quality standards, and participate in supply chains linked to foreign investors. Without adequate private-sector credit, the potential benefits of FDI are likely to be constrained, regardless of the scale of inflows.\u003c/p\u003e \u003cp\u003eThe paper addresses this gap by examining whether domestic private-sector credit conditions the relationship between FDI and sustainable development outcomes. Specifically, it asks whether the impact of FDI on overall SDG performance, and on SDG 9 in particular, depends on the depth of domestic credit markets. By modelling an explicit interaction between FDI and private-sector credit, the analysis moves beyond average effects and tests a conditional relationship that aligns more closely with theories of technology transfer and diffusion. Governance quality is incorporated as an additional conditioning factor, recognising that financial systems operate within institutional environments that shape incentives, risk, and resource allocation.\u003c/p\u003e \u003cp\u003eThe study contributes to the literature in three main ways. First, it reframes domestic finance as an absorptive-capacity channel rather than a parallel determinant of development, thereby offering a clearer mechanism through which FDI affects SDG outcomes. Second, it brings this perspective directly into the SDG framework, linking FDI and financial development to multidimensional development indicators rather than growth alone. Third, by exploring heterogeneous effects across levels of financial depth and governance quality, it provides evidence on when and where FDI is most likely to support sustainable industrialisation and innovation.\u003c/p\u003e \u003cp\u003eThe results show that while FDI is positively associated with SDG performance, its impact is significantly stronger in countries with deeper private-sector credit markets. In low-credit environments, the effect of FDI on SDG outcomes is weak or statistically insignificant. These patterns are most pronounced for SDG 9, suggesting that domestic finance plays a particularly important role in enabling technology-intensive and infrastructure-related spillovers. The policy implication is straightforward but often overlooked: strategies to attract FDI are unlikely to deliver sustained development gains unless they are accompanied by reforms that strengthen domestic financial systems and expand access to credit for productive private-sector activities.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Hypothesis\u003c/h2\u003e \u003cp\u003eWrite them in a way that matches your model exactly.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eH1: FDI inflows are positively associated with SDG performance (SDG Index) and SDG 9.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eH2: Private-sector credit is positively associated with SDG performance and SDG 9.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eH3: Private-sector credit positively moderates the effect of FDI on SDG performance and SDG 9. Expected sign: β on (FDI \u0026times; Credit)\u0026thinsp;\u0026gt;\u0026thinsp;0.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eH4 (heterogeneity): The moderation effect is stronger where governance quality is higher (or stronger in middle-income than low-income economies, depending on your prior).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe relationship between foreign direct investment, domestic financial development, and development outcomes has attracted sustained attention in development economics and international economics. Early contributions emphasised the role of FDI as a source of capital accumulation and growth, while later studies shifted focus toward knowledge transfer, productivity spillovers, and structural transformation (Zehri et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). More recently, the literature has begun to examine FDI within broader development frameworks, including sustainability and the Sustainable Development Goals (Aerni, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Baita \u0026amp; Suleiman, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maphiri et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite this evolution, evidence remains mixed, particularly regarding the conditions under which FDI contributes meaningfully to long-term development outcomes beyond aggregate growth.\u003c/p\u003e \u003cp\u003eA recurring theme across this literature is that the benefits of FDI are conditional rather than automatic (Ahmad, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ajay Yadav, Dinesh Kumar, Sushant Yadav, 2023; Dam et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hafner, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Differences in domestic capabilities, institutional environments, and financial systems shape how foreign capital interacts with local economies. While several studies recognise these conditioning factors, they are often examined in isolation (Ferrier et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hanlin \u0026amp; Kaplinsky, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Monkelbaan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a result, the joint role of domestic finance and governance in mediating the FDI\u0026ndash;development relationship remains insufficiently explored, especially within the SDG framework (Peng et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sun \u0026amp; Qamruzzaman, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study responds to that gap by integrating insights from theory and empirical work to assess how private-sector credit functions as an absorptive-capacity channel for foreign technology spillovers.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Review\u003c/h2\u003e \u003cp\u003eTheoretical explanations of FDI spillovers are rooted in models of endogenous growth, technology diffusion, and international production. Multinational enterprises are assumed to possess firm-specific advantages, including advanced technologies and managerial expertise, which can spill over to domestic firms through imitation, competition, labour mobility, and vertical linkages (Nwokolo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In endogenous growth models, such spillovers raise the stock of knowledge and improve long-run growth prospects (Tandid, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, these models also recognise that the magnitude of spillovers depends on domestic conditions that enable learning and adoption.\u003c/p\u003e \u003cp\u003eThe concept of absorptive capacity provides a unifying theoretical lens. Absorptive capacity refers to the ability of firms and economies to identify valuable external knowledge, assimilate it, and apply it productively (Korea Trade Research Association et al., 2023; Li, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Human capital, learning-by-doing, and institutional quality are commonly cited determinants of this capacity (Peng et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yet, theory also suggests that knowledge adoption requires complementary investments. Without the financial means to invest in new machinery, reorganise production processes, or scale operations, firms may be unable to translate knowledge into productivity gains.\u003c/p\u003e \u003cp\u003eFinancial development theory complements this perspective by emphasising the role of credit markets in mobilising savings, allocating capital, and supporting productive investment (Kushwaha \u0026amp; Nair, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Slimani et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Well-functioning financial systems lower transaction costs, reduce information asymmetries, and ease liquidity constraints faced by firms (Sarangi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When applied to FDI spillovers, this implies that private-sector credit facilitates the absorption and diffusion of foreign technologies by enabling domestic firms to finance complementary investments (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gyamfi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Governance quality further shapes this process by influencing financial intermediation efficiency, contract enforcement, and risk management (Ciarli et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Together, these theoretical strands imply that FDI, domestic finance, and governance interact to determine development outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Empirical Review\u003c/h2\u003e \u003cp\u003eFerrier, Reyes, and Zhu (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) examine how technology diffuses through the international trade network using a network-based empirical approach that links countries through trade relationships and traces how knowledge can spread along those links. Their findings suggest that trade connectivity matters for the speed and reach of diffusion, with more central economies benefiting earlier and more strongly from transmitted technologies. A key strength of the study is the clear emphasis on diffusion as a relational process rather than a purely domestic outcome. Its limitation for this study is that it focuses on trade-mediated transmission and does not test how domestic financial constraints shape whether imported knowledge is adopted and scaled. Hafner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reaches a related conclusion from a different angle by analysing how trade liberalisation affects technology diffusion, pointing to policy-driven openness as a pathway for technological inflows. Compared with Ferrier et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Hafner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) is more directly tied to policy reforms and institutional change, while the network approach highlights structural position in global exchange. Together, these studies show that cross-border integration can transmit technology, but they leave open a core question for the present research, which is whether domestic credit conditions the translation of external inflows, including investment inflows, into measurable development outcomes.\u003c/p\u003e \u003cp\u003eXu, Li, Chau, Dietz, Li, and Zhang (2020) investigate the impacts of international trade on global sustainable development using a cross-country empirical design that connects trade patterns to sustainability outcomes. Their evidence indicates that trade is closely tied to development performance, but the nature of that relationship varies across contexts and depends on how economies participate in global exchange. A strength of the study is its broad coverage and explicit focus on sustainability rather than growth alone. A limitation is that trade is treated as the dominant external channel, leaving the investment and technology-transfer mechanism less directly specified. Monkelbaan (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), in contrast, offers a policy-oriented examination of trade as a tool for achieving the SDGs, using the Environmental Goods Agreement discussion to show how targeted trade policies can support SDG-linked outcomes. Compared with Xu et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Monkelbaan (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) is less empirical but sharper on policy design and sector focus. Taken together, both studies support the general idea that cross-border flows can advance SDG progress, but they do not isolate the investment-to-technology-to-SDG pathway, nor do they test whether domestic finance determines who can respond to these opportunities.\u003c/p\u003e \u003cp\u003eHuang and Pei (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) provide firm-level evidence from China on how imported intermediate inputs generate technology spillovers and how these spillovers contribute to green development. Their methodology focuses on micro-level mechanisms, linking firms\u0026rsquo; use of imported inputs to technological upgrading and environmental performance outcomes. The main strength of this study is the close identification of a concrete spillover channel, where learning is embedded in production inputs rather than assumed. Its limitation for this study is that it is centred on trade in intermediates and a single-country setting, which may not generalise to economies where credit constraints and market frictions are more severe. Hanlin and Kaplinsky (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) examine South\u0026ndash;South trade in capital goods and argue that market-driven diffusion of appropriate technology can support development needs in poorer contexts. Compared with Huang and Pei (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Hanlin and Kaplinsky (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) place greater weight on the suitability and accessibility of technology in developing settings, while the Chinese firm evidence is more precise on micro spillover mechanics. The shared implication is that technology can diffuse through multiple channels, but a gap remains around financing, since neither strand directly tests whether private credit availability is what allows domestic firms to adopt, adapt, and scale the technologies that arrive through trade and investment links.\u003c/p\u003e \u003cp\u003eAjay Yadav, Dinesh Kumar, and Sushant Yadav (2023) review evidence on the links among India\u0026rsquo;s international trade, foreign investment, and SDG progress, bringing together studies that connect external integration with sustainable development goals. Their main contribution is synthesis, highlighting that trade and foreign investment can support SDG outcomes through technology, employment, and productivity pathways, though effects vary across sectors and time. The strength of the review lies in its integrative lens that recognises both trade and investment as relevant channels. Its limitation is that the review does not isolate domestic finance as the key conditioning mechanism, even though it repeatedly points to implementation capacity constraints. A related policy perspective is provided by the 2024 blueprint on U.S.\u0026ndash;Africa trade and investment collaboration, which discusses how trade and investment partnerships can support sustainable development and structural change in African economies. Compared with the India-focused review, the U.S.\u0026ndash;Africa blueprint is more programmatic and region-facing, but it similarly treats domestic financial depth as background context rather than a testable moderator. This leaves an empirical gap that motivates the present study, which is to model domestic private-sector credit explicitly as an absorptive-capacity channel shaping the FDI\u0026ndash;SDG relationship.\u003c/p\u003e \u003cp\u003eFinally, a set of recent studies links technological innovation and cross-border integration to SDG-related outcomes, but still leaves the absorption mechanism under-specified. Dam, Kaya, and Bekun (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) study the role of technological factors in sustainability-related outcomes across E-7 countries, connecting innovation dynamics to SDG-relevant performance. Peng, Qian, Xing, and Wang (2025) discuss the opportunities and challenges of technological pathways for achieving the SDGs, emphasising that technology\u0026rsquo;s contribution depends on complementary systems and institutions. Marzia (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) similarly highlights the importance of technological advancement for SDG progress in developing countries. Across these studies, the consistent strength is the recognition that technology is central to sustainable development, and the common limitation is that the domestic financing constraint is rarely positioned as the main empirical lever that determines adoption and diffusion. This reinforces the relevance of the present research aim, which is to test, in a disciplined interaction framework, whether credit to the private sector amplifies the development impact of FDI, with a sharper focus on SDG 9 where technology transfer and industrial upgrading should be most visible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Conceptual Framework\u003c/h2\u003e \u003cp\u003eThe conceptual framework underpinning this study views sustainable development outcomes as the result of interactions between external capital inflows and domestic enabling conditions. FDI is treated as a potential source of technology, managerial knowledge, and global production linkages. On its own, however, FDI does not guarantee widespread development gains. Its impact depends on the ability of domestic firms to absorb and diffuse the technologies embodied in foreign investment.\u003c/p\u003e \u003cp\u003eDomestic private-sector credit represents a key absorptive-capacity channel within this framework. Access to credit enables firms to finance the adoption of new technologies, invest in complementary capital, and participate in foreign-led value chains. As credit deepens, the marginal impact of FDI on development outcomes is expected to increase. Governance quality operates as a conditioning factor that shapes how effectively financial resources are allocated and how firms interact with foreign investors. Strong governance enhances the effectiveness of both finance and FDI, while weak governance can constrain their combined impact.\u003c/p\u003e \u003cp\u003eWithin this framework, sustainable development outcomes, measured by the overall SDG Index and SDG 9, are influenced directly by FDI and domestic credit, and indirectly through their interaction. The central proposition is that private-sector credit amplifies the development impact of FDI by strengthening absorptive capacity, with the strongest effects observed in domains closely linked to industrialisation, innovation, and infrastructure. This conceptual structure directly informs the empirical specification and the hypotheses tested in the study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe study employs a balanced panel dataset covering a broad set of countries over the period dictated by data availability across the Sustainable Development Goals, foreign direct investment, and financial development indicators. Country coverage includes low-income and middle-income economies, with a particular focus on developing regions where FDI-led technology transfer and financial constraints are most relevant. The panel structure allows the analysis to exploit both cross-country variation and within-country changes over time. Data on SDG outcomes are obtained from the Sustainable Development Report 2024 (Sachs, Lafortune, \u0026amp; Fuller, 2024), which provides harmonised and internationally comparable indices for individual SDGs. Data on all other macroeconomic variables are gotten from the World Development Indicators (WDI).\u003c/p\u003e \u003cp\u003eSustainable development outcomes are measured using the SDG Index and SDG 9 scores, which capture multidimensional progress toward the Sustainable Development Goals and performance in industry, innovation, and infrastructure, respectively. FDI data are drawn from standard international sources and reflect net inflows relative to economic size. Domestic financial development is proxied by credit to the private sector, expressed as a percentage of GDP. Governance indicators are obtained from widely used institutional datasets and summarised either individually or through a composite index to reduce dimensionality and multicollinearity.\u003c/p\u003e \u003cp\u003eThe final sample excludes countries with severe data gaps or short time coverage that would undermine panel estimation. Descriptive checks confirm that the sample retains substantial variation across income levels, institutional quality, and financial depth.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Variable measurement\u003c/h2\u003e \u003cp\u003eThe dependent variables are defined as follows. The SDG Index captures overall sustainable development performance by aggregating progress across all SDGs into a single score. SDG 9 focuses specifically on industry, innovation, and infrastructure, making it particularly suitable for assessing technology-related spillovers from FDI.\u003c/p\u003e \u003cp\u003eThe key explanatory variable is foreign direct investment, measured as net FDI inflows as a percentage of GDP. This scaling ensures comparability across countries of different sizes and aligns with the macroeconomic literature. Domestic financial development is measured using domestic credit to the private sector as a percentage of GDP, which reflects the extent to which financial institutions provide resources to private firms for investment and working capital.\u003c/p\u003e \u003cp\u003eThe core interaction term is constructed as the product of FDI inflows and private-sector credit. This term captures the extent to which the effect of FDI on development outcomes depends on domestic financial depth. Governance quality enters the model as a control and as a conditioning variable in heterogeneity analyses. Additional controls include GDP per capita, trade openness, inflation, and other macroeconomic indicators commonly used in cross-country development regressions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Transformations and summary statistics\u003c/h2\u003e \u003cp\u003eTo reduce skewness and limit the influence of extreme observations, selected variables such as FDI inflows and GDP per capita are transformed using logarithms where appropriate. All interaction terms are constructed using the transformed variables to maintain internal consistency. In robustness checks, key variables are standardised to facilitate interpretation and comparability of coefficients.\u003c/p\u003e \u003cp\u003eSummary statistics are reported to describe central tendencies and dispersion across variables. The statistics highlight substantial cross-country variation in SDG performance, FDI inflows, and private-sector credit, underscoring the relevance of a conditional framework. Correlation analysis indicates that while FDI and credit are positively associated with development outcomes, the relationships are far from perfect, reinforcing the need for multivariate and interaction-based estimation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Empirical model\u003c/h2\u003e \u003cp\u003eThe baseline empirical specification estimates the relationship between sustainable development outcomes, foreign direct investment, domestic private-sector credit, and their interaction. The model is expressed as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:SD{G}_{it}=\\alpha\\:+{\\beta\\:}_{1}FD{I}_{it}+{\\beta\\:}_{2}Credi{t}_{it}+{\\beta\\:}_{3}(FD{I}_{it}\\times\\:Credi{t}_{it})+{\\gamma\\:}^{{\\prime\\:}}{X}_{it}+{\\mu\\:}_{i}+{\\tau\\:}_{t}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SD{G}_{it}\\)\u003c/span\u003e\u003c/span\u003erepresents either the SDG Index or SDG 9 score for country \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003ein year \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:FD{I}_{it}\\)\u003c/span\u003e\u003c/span\u003edenotes foreign direct investment inflows, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Credi{t}_{it}\\)\u003c/span\u003e\u003c/span\u003eis domestic credit to the private sector, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003eis a vector of control variables. Country fixed effects (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e) account for time-invariant national characteristics, while year fixed effects (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e) control for global shocks and common trends. This fixed-effects framework isolates within-country variation over time and mitigates bias from unobserved heterogeneity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Interpretation of the interaction term\u003c/h2\u003e \u003cp\u003eThe interaction between FDI and private-sector credit is central to the analysis. The coefficient on FDI (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e) represents the effect of FDI on SDG outcomes when private-sector credit is zero, which is not economically meaningful in isolation. The marginal effect of FDI is therefore interpreted as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\frac{\\partial\\:SD{G}_{it}}{\\partial\\:FD{I}_{it}}={\\beta\\:}_{1}+{\\beta\\:}_{3}Credi{t}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis formulation allows the effect of FDI to vary with the level of domestic credit. To aid interpretation, marginal effects are computed and reported at low, median, and high levels of private-sector credit. Graphical presentations are used where appropriate to illustrate how the impact of FDI strengthens as financial depth increases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Heterogeneity design\u003c/h2\u003e \u003cp\u003eTo explore whether the conditioning role of domestic finance varies across contexts, the analysis incorporates heterogeneity in two ways. First, the sample is split based on financial development or income level, and the baseline model is estimated separately for each subgroup. This approach tests whether FDI spillovers are stronger in countries with deeper credit markets or higher levels of development. Second, governance quality is introduced as an additional conditioning factor. This is implemented either through subgroup analysis based on governance scores or through extended interaction terms that allow the FDI\u0026ndash;credit relationship to vary with institutional quality. These exercises help distinguish whether finance alone is sufficient for absorption or whether its effectiveness depends on the broader institutional environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Endogeneity and identification strategy\u003c/h2\u003e \u003cp\u003eSeveral sources of endogeneity are addressed in the empirical design. Reverse causality may arise if improvements in SDG performance attract higher FDI inflows or stimulate financial deepening. Omitted-variable bias may also occur if unobserved reforms influence both development outcomes and the key explanatory variables. The primary strategy for mitigating these concerns includes the use of country and year fixed effects, which absorb time-invariant heterogeneity and common shocks. In addition, key explanatory variables and their interaction are lagged to reduce simultaneity. Dynamic specifications include a lagged dependent variable to account for persistence in SDG outcomes.\u003c/p\u003e \u003cp\u003eAs a robustness check, the study employs a system GMM estimator, treating FDI and private-sector credit as potentially endogenous and instrumenting them with their own lagged values. Standard diagnostic tests, including tests for serial correlation and instrument validity, are reported to assess model reliability. The results from these alternative specifications are compared with the baseline fixed-effects estimates to ensure consistency and robustness of the main findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003e\u003cstrong\u003e4.1 Baseline analysis: Fixed-effects results for overall SDG performance and SDG 9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTables 1a and 2a report country fixed-effects estimates of the relationship between foreign direct investment, domestic finance, and sustainable development outcomes, with year fixed effects included and standard errors clustered at the country level. This specification exploits within-country variation over time, thereby controlling for time-invariant national characteristics and common global shocks.\u003c/p\u003e\n\u003cp\u003eThe results for the aggregate SDG Index indicate that FDI inflows do not have a statistically significant direct effect on overall sustainable development performance once country and time fixed effects are accounted for. The coefficient on FDI inflows is positive but very small in magnitude and statistically insignificant (p = 0.802), suggesting that short- to medium-term fluctuations in FDI relative to a country\u0026rsquo;s own historical average are not systematically associated with broad-based SDG outcomes. This finding implies that, in isolation, FDI is unlikely to translate automatically into economy-wide development gains captured by the composite SDG Index.\u003c/p\u003e\n\u003cp\u003ePrivate-sector credit also enters with a positive coefficient, but it falls short of conventional significance levels. While the point estimate suggests that deeper domestic credit markets may support SDG progress, the lack of statistical precision indicates that finance alone does not guarantee improvements across the full range of development goals when structural and institutional factors are held constant.\u003c/p\u003e\n\u003cp\u003eAmong the control variables, most macroeconomic indicators are not statistically significant, underscoring the difficulty of explaining aggregate SDG performance through short-run macroeconomic movements. Notably, control of corruption emerges as the most robust correlate of SDG performance, with a positive coefficient significant at the 10 percent level. This result highlights the central role of institutional quality in shaping the effectiveness of development processes and suggests that governance conditions may mediate the translation of resources into measurable SDG outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1a: Fixed-Effects Estimates of FDI, Domestic Finance, and Overall SDG Performance\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI inflows (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate-sector credit (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog GDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrade openness index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFinancial openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCapital investment (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSavings rate (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGovernment debt (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eControl of corruption (z-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.072*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountry fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYear fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe model diagnostics in Table 1b show a high within R\u0026sup2; (0.817), indicating that the model explains a substantial share of within-country variation in SDG performance over time. The very high value of \u0026rho; (0.947) further confirms the relevance of fixed effects, implying that unobserved country-specific factors account for a large proportion of the total variance in SDG outcomes.\u003c/p\u003e\n\u003cp\u003eTable 1b: Model diagnostics\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYears per country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBetween R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverall R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProb \u0026gt; F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026rho; (variance due to FE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSDG 9: Industry, innovation, and infrastructure (Table 2a)\u003c/p\u003e\n\u003cp\u003eThe results change meaningfully when attention is narrowed to SDG 9, which directly captures industrialisation, innovation, and infrastructure development. In this specification, FDI inflows are positively associated with SDG 9 performance, with the coefficient significant at the 10 percent level. Although modest in magnitude, this finding suggests that FDI is more closely aligned with development domains linked to production capacity, technological upgrading, and infrastructure than with broader social and environmental goals.\u003c/p\u003e\n\u003cp\u003eIn contrast, private-sector credit again shows no statistically significant direct effect, and its coefficient is negative, though imprecisely estimated. This pattern suggests that domestic finance may not independently drive SDG 9 outcomes in the absence of complementary factors, such as efficient allocation mechanisms or productive investment opportunities. Government debt stands out as a consistently significant predictor of SDG 9 performance, with a positive and statistically strong coefficient. This result is consistent with the idea that public borrowing, when channeled into infrastructure and industrial support, can play a constructive role in advancing industry- and innovation-related development objectives. Other macroeconomic controls, including income levels, trade openness, and financial openness, do not display robust associations within countries over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2a: Fixed-Effects Estimates of FDI, Domestic Finance, and SDG 9 (Industry, Innovation, Infrastructure)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI inflows (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.075*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate-sector credit (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog GDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.3556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrade openness index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFinancial openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCapital investment (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSavings rate (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGovernment debt (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eControl of corruption (z-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountry fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYear fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe diagnostic statistics in Table 2b confirm strong model fit, with a within R\u0026sup2; of 0.739 and a statistically significant F-statistic. The value of \u0026rho; (0.879) again indicates that unobserved country-specific factors remain important, justifying the fixed-effects approach.\u003c/p\u003e\n\u003cp\u003eTable 2b: Model diagnostics\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYears per country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBetween R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverall R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProb \u0026gt; F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026rho; (variance due to FE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTaken together, these results point to three key insights. First, FDI does not appear to exert a broad, unconditional effect on overall SDG performance, reinforcing the view that foreign capital inflows alone are insufficient to drive comprehensive sustainable development. Second, FDI is more relevant for SDG 9 than for the aggregate SDG Index, supporting the argument that technology transfer and industrial upgrading are the most direct channels through which FDI contributes to development. Third, institutional quality and public-sector capacity matter at least as much as private capital inflows, as reflected in the role of corruption control and government debt. These patterns motivate the subsequent analysis of interaction effects and heterogeneity, where the central question becomes whether domestic financial development and governance conditions condition the ability of FDI to translate into stronger SDG outcomes, particularly in industry, innovation, and infrastructure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction and dynamic effects of FDI and domestic credit on SDG 9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3a reports results from a country fixed-effects model with year fixed effects, where SDG 9 (industry, innovation, and infrastructure) is the dependent variable. Standard errors are clustered at the country level, and the specification explicitly models the interaction between FDI inflows and private-sector credit. The results show that FDI inflows have a positive and statistically significant direct association with SDG 9, with significance at the 5 percent level. This indicates that, within countries over time, increases in FDI relative to a country\u0026rsquo;s own average are associated with improvements in industrial and infrastructure-related development outcomes. This finding reinforces earlier evidence that FDI is most relevant for production- and innovation-oriented dimensions of sustainable development rather than for aggregate SDG performance.\u003c/p\u003e\n\u003cp\u003eIn contrast, private-sector credit on its own is not statistically significant, suggesting that domestic financial depth does not independently drive SDG 9 outcomes once country characteristics and global shocks are controlled for. More importantly, the interaction term between FDI and credit is negative and statistically significant at the 10 percent level. This implies that the marginal contribution of FDI to SDG 9 declines as private-sector credit increases. In other words, FDI appears to be more effective in environments where domestic credit markets are relatively shallow, whereas in more financially developed settings, the incremental impact of FDI on industrial and infrastructure outcomes is weaker. Among the control variables, government debt remains positive and highly significant, consistent with the role of public borrowing in financing infrastructure and industrial investment. Other macroeconomic controls, including income levels, trade openness, and financial openness, do not show robust within-country effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3a: Fixed-Effects Interaction Model: FDI, Credit, and SDG 9\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI inflows (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.026**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate-sector credit (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI \u0026times; Credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.080*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog GDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrade openness index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFinancial openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCapital investment (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSavings rate (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGovernment debt (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eControl of corruption (z-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountry fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYear fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant at 10 percent level.\u003cbr\u003e\u0026nbsp;**Significant at 5 percent level.\u003cbr\u003e\u0026nbsp;***Significant at 1 percent level.\u003c/p\u003e\n\u003cp\u003eModel diagnostics in Table 3b indicate strong explanatory power, with a within R\u0026sup2; of 0.742 and a highly significant joint F-statistic. The large value of \u0026rho; confirms the importance of unobserved country-specific factors, validating the fixed-effects approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3b:\u0026nbsp;\u003c/strong\u003eModel diagnostics\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverall R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProb \u0026gt; F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026rho; (variance due to FE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDynamic fixed-effects model with lagged variables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 5a presents a dynamic country fixed-effects model with year fixed effects, where SDG 9 remains the dependent variable. The specification introduces lagged FDI, lagged private-sector credit, and their interaction, with standard errors clustered at the country level. This model evaluates whether the effects of FDI and finance operate with a delay rather than contemporaneously. The results indicate that lagged FDI inflows exert a positive and statistically significant effect on SDG 9, with significance at the 5 percent level. This finding provides evidence of temporal persistence, suggesting that the developmental benefits of FDI materialize over time through channels such as capital accumulation, technology transfer, and learning effects.\u003c/p\u003e\n\u003cp\u003eLagged private-sector credit remains statistically insignificant, reinforcing the view that domestic finance alone does not automatically translate into industrial or innovation gains. However, the lagged interaction between FDI and credit is negative and marginally significant, mirroring the contemporaneous interaction results. This pattern suggests that even over time, the effectiveness of FDI diminishes in more credit-intensive environments, possibly reflecting crowding-out effects or inefficient financial intermediation. Government debt again emerges as a robust positive predictor of SDG 9, while institutional quality and other macroeconomic controls remain statistically insignificant in this dynamic setting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5a: Dynamic Fixed-Effects Model with Lagged FDI and Credit\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLagged FDI inflows\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.011**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLagged private-sector credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLagged FDI \u0026times; Credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.053*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog GDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.3774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrade openness index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFinancial openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCapital investment (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSavings rate (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGovernment debt (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eControl of corruption (z-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.3635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountry fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYear fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significant at 10 percent level.\u003cbr\u003e\u0026nbsp;**Significant at 5 percent level.\u003cbr\u003e\u0026nbsp;***Significant at 1 percent level.\u003c/p\u003e\n\u003cp\u003eThe diagnostics in Table 5b show good model fit, with a within R\u0026sup2; of 0.737 and a significant F-statistic. The high value of \u0026rho; indicates that fixed effects continue to capture a substantial share of the variation in SDG 9 outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5b:\u0026nbsp;\u003c/strong\u003eModel diagnostics\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"608\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverall R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProb \u0026gt; F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026rho; (variance due to FE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGovernance-based heterogeneity: low-governance countries\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 6a reports results from a country fixed-effects model with year fixed effects, estimated only for countries in the low-governance group (Gov Group = 1). The dependent variable is SDG 9, and standard errors are clustered at the country level. This specification examines whether the FDI\u0026ndash;credit relationship differs under weaker institutional conditions. In low-governance countries, FDI inflows have no statistically significant effect on SDG 9, and the coefficient is close to zero. Private-sector credit is also insignificant, and importantly, the interaction between FDI and credit is positive but statistically insignificant. This contrasts sharply with the full-sample results and indicates that neither FDI nor domestic finance is systematically associated with industrial and infrastructure outcomes in weak institutional environments. The lack of significance across key variables suggests that institutional constraints dominate economic mechanisms in low-governance contexts. In such settings, both foreign capital and domestic credit may be diverted away from productive uses, limiting their contribution to SDG-relevant outcomes. The absence of a significant interaction effect implies that complementarities between foreign and domestic finance fail to materialize when governance quality is poor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6a: Fixed-Effects Interaction Model for SDG 9\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI inflows (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate-sector credit (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI \u0026times; Credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog GDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.4547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrade openness index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFinancial openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCapital investment (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSavings rate (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGovernment debt (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountry fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYear fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTaken together, these results highlight three core insights. First, FDI contributes positively to SDG 9, both contemporaneously and with a lag, but not unconditionally. Second, domestic credit does not amplify the impact of FDI. Instead, higher levels of private-sector credit appear to weaken the marginal effectiveness of FDI, suggesting substitution rather than complementarity. Third, institutional quality conditions these relationships, as the FDI\u0026ndash;credit nexus collapses entirely in low-governance countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings strengthen the argument that the development impact of FDI depends not only on financial depth but also on the efficiency and governance of domestic systems. They also provide a clear justification for the subsequent use of interaction terms and governance-based sub-samples in assessing how foreign capital contributes to sustainable industrial development. Table 6b reports model diagnostics for the fixed-effects interaction model estimated for low-governance countries (Gov Group = 1), where SDG 9 is the dependent variable. The model is estimated using country fixed effects with year fixed effects, and standard errors are clustered at the country level. The sample consists of 411 observations across 32 countries, with an average of roughly 13 years per country.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe within R\u0026sup2; of 0.671 indicates that the model explains a substantial share of within-country variation in SDG 9 outcomes over time, although this explanatory power is notably lower than in the full-sample and high-governance estimations. The overall R\u0026sup2; of 0.347 further suggests limited cross-country explanatory capacity once fixed effects are absorbed. The F-statistic is large and highly significant, confirming joint significance of the regressors. The estimated \u0026rho; of 0.826 indicates that most of the variation in SDG 9 is driven by time-invariant country-specific factors. Taken together, these diagnostics reinforce the interpretation that structural and institutional constraints dominate SDG 9 dynamics in low-governance environments, limiting the effectiveness of both FDI and domestic finance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6b\u003c/strong\u003e \u003cstrong\u003eModel diagnostics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverall R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e260.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProb \u0026gt; F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026rho; (variance due to FE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-governance countries: interaction model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 7a presents results from a country fixed-effects model with year fixed effects, estimated for high-governance countries (Gov Group = 2). The dependent variable is SDG 9, and standard errors are clustered at the country level. This specification examines whether stronger institutional environments alter the interaction between FDI and private-sector credit. The results show that FDI inflows have a positive and statistically significant effect on SDG 9, significant at the 5 percent level. This indicates that in countries with stronger governance structures, increases in FDI are consistently associated with improvements in industrial development, innovation capacity, and infrastructure outcomes.\u003c/p\u003e\n\u003cp\u003ePrivate-sector credit remains statistically insignificant, suggesting that domestic financial depth does not independently drive SDG 9 performance even under better governance. However, the interaction term between FDI and credit is negative and weakly significant at the 10 percent level, implying that higher levels of domestic credit still dampen the marginal contribution of FDI. This finding mirrors the full-sample results and indicates that credit\u0026ndash;FDI substitution effects persist even in institutionally stronger settings. Among the control variables, savings rates exhibit a negative and statistically significant coefficient, suggesting that higher aggregate savings do not necessarily translate into productive industrial investment. Government debt is positive and significant, consistent with the role of public borrowing in financing infrastructure and industrial expansion. Other macroeconomic controls, including income levels, trade openness, and financial openness, remain statistically insignificant within countries over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7a: Fixed-Effects Interaction Model for SDG 9\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI inflows (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.010**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate-sector credit (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI \u0026times; Credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.101*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog GDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.9141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.6797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrade openness index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFinancial openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.7407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCapital investment (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSavings rate (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.1282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.049**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGovernment debt (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.010**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountry fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYear fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7b reports diagnostics for the high-governance fixed-effects interaction model. The estimation is based on 406 observations from 30 countries, with approximately 13 to 14 years per country. The within R\u0026sup2; of 0.858 indicates very strong explanatory power for within-country changes in SDG 9. In contrast, the overall R\u0026sup2; of 0.161 reflects the absorption of most cross-country variation by fixed effects. The F-statistic is large and highly significant, confirming joint relevance of the regressors. The estimated \u0026rho; of 0.945 implies that nearly all variation in SDG 9 is attributable to country-specific factors. This reinforces the appropriateness of the fixed-effects framework and highlights the persistence of national development trajectories even among high-governance countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7b: diagnostics for the high-governance fixed-effects\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverall R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e607.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProb \u0026gt; F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026rho; (variance due to FE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRobustness confirmation: high-governance interaction model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 8 re-estimates the fixed-effects interaction model for SDG 9 in high-governance countries, using the same specification as Table 7a. The dependent variable, estimator, and clustering strategy remain unchanged. The coefficient estimates are identical to those in Table 7a, confirming robustness of the results. FDI remains positive and statistically significant, private-sector credit remains insignificant, and the interaction term remains negative and weakly significant. The significance of savings rates and government debt is also preserved. The corresponding diagnostics in Table 8b are likewise unchanged, with a within R\u0026sup2; of 0.858, a highly significant F-statistic, and a \u0026rho; of 0.945. This duplication confirms that the observed relationships are not sensitive to alternative sample handling or estimation details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8: Fixed-Effects Interaction Model for SDG 9\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"590\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI inflows (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.010**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate-sector credit (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI \u0026times; Credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.101*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog GDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.9141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.6797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrade openness index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFinancial openness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.7407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCapital investment (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSavings rate (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.1282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.049**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGovernment debt (% of GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.010**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountry FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYear FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTaken together, the governance-disaggregated results reveal a clear asymmetry in the FDI\u0026ndash;finance\u0026ndash;SDG 9 relationship. In low-governance countries, neither FDI nor domestic credit exerts a statistically meaningful effect on industrial and infrastructure outcomes, indicating that weak institutions severely constrain the productivity of both foreign and domestic capital. In contrast, high-governance countries benefit directly from FDI, but domestic credit does not amplify this effect and may even reduce the marginal contribution of FDI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8b: Diagnostic test\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOverall R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e607.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProb \u0026gt; F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026rho; (FE share of variance)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThese findings suggest that governance quality is a necessary but not sufficient condition for leveraging complementarities between foreign investment and domestic finance. While good institutions enable FDI to support SDG 9, inefficient or misallocated credit can still limit its effectiveness. This nuanced evidence strengthens the argument that policy strategies should focus not only on attracting FDI or expanding credit, but also on improving the allocation efficiency of domestic financial systems within strong institutional frameworks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDynamic persistence and short-run effects: lagged fixed-effects estimates\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dynamic fixed-effects model is estimated using a balanced panel of 774 observations drawn from 43 countries. The model exhibits a very strong explanatory fit, with a within R-squared of 0.94, indicating that the included regressors, particularly the lagged dependent variable, explain the vast majority of within-country variation in SDG 9 over time. The overall R-squared of 0.982 further confirms that the specification captures almost all observed variation in SDG 9 across countries and years. The estimated value of \u0026rho; equals 0.229, suggesting that, once dynamics and covariates are accounted for, only a relatively modest share of the remaining variance is attributable to time-invariant country-specific effects. This implies that the model is driven primarily by temporal dynamics rather than unobserved country heterogeneity. Table 9 reports results from a dynamic fixed-effects model, where SDG 9 is the dependent variable and its one-period lag is included as a regressor. The model is estimated using country fixed effects with year fixed effects, and standard errors are clustered at the country level. This specification captures both short-run impacts and the persistence of industrial, innovation, and infrastructure outcomes over time. The coefficient on the lagged dependent variable is large, positive, and highly significant (\u0026beta; = 0.897, p \u0026lt; 0.01), indicating strong persistence in SDG 9 outcomes. This suggests that countries\u0026rsquo; industrial and infrastructure performance follows a highly path-dependent process, where past achievements strongly condition current outcomes. The magnitude implies that nearly 90 percent of previous-period performance carries over into the current period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9: Dynamic Fixed-Effects Model with Lagged Dependent Variable (SDG 9)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 9 (t\u0026minus;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI inflows\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate-sector credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI \u0026times; Credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog GDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.094*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGovernment debt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.031**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eControl of corruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCountry FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYear FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOnce this persistence is accounted for, FDI inflows are no longer statistically significant, and their coefficient is close to zero. Similarly, private-sector credit remains statistically insignificant, and the interaction term between FDI and credit is negative but not significant. These results indicate that, in the short run, neither foreign investment nor domestic finance exerts an independent or complementary effect on SDG 9 beyond what is already embedded in existing development trajectories. Among the controls, log GDP per capita is weakly significant at the 10 percent level, suggesting that higher income levels continue to matter for industrial and infrastructure outcomes, even after controlling for persistence. Government debt is positive and statistically significant, consistent with the role of public borrowing in financing infrastructure-related investments. Control of corruption is also positive and significant, reinforcing the importance of institutional quality in sustaining improvements in SDG 9 over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEndogeneity-robust dynamic effects: system GMM estimates\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 10 presents results from a two-step System GMM estimator, designed to address potential endogeneity, reverse causality, and dynamic panel bias. The dependent variable remains SDG 9, and the model uses collapsed instruments to limit instrument proliferation. Lagged levels and differences are employed as internal instruments, while standard errors are robust. Consistent with the fixed-effects dynamic model, the lagged dependent variable remains positive and highly significant (\u0026beta; = 0.874, p \u0026lt; 0.01), confirming strong persistence in SDG 9 outcomes even after correcting for endogeneity. The magnitude is slightly lower than in the fixed-effects model but still indicates substantial path dependence.\u003c/p\u003e\n\u003cp\u003eIn contrast, FDI inflows, private-sector credit, and their interaction term are all statistically insignificant in the System GMM specification. This reinforces the earlier finding that, once endogeneity and dynamics are properly accounted for, there is no robust short-run effect of FDI or domestic finance on SDG 9, nor evidence that domestic credit conditions the impact of FDI in a dynamic setting. Among the controls, log GDP per capita is positive and highly significant, highlighting the role of overall economic development in supporting industrial and infrastructure outcomes. Control of corruption also remains positive and statistically significant, underscoring institutional quality as a robust determinant of SDG 9 performance across estimation strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10a: System GMM Results (Endogeneity-Robust)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"570\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et-stat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSDG 9 (t\u0026minus;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI inflows\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate-sector credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDI \u0026times; Credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLog GDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eControl of corruption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.033**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe diagnostic tests support the validity of the System GMM specification. The AR(1) test is significant, as expected in first differences, while the AR(2) test indicates no second-order serial correlation at conventional confidence levels, satisfying a key requirement for instrument validity. The Hansen J test and Sargan test both fail to reject the null hypothesis, suggesting that the instruments are jointly valid and not overfitting the endogenous variables. The total number of instruments (37) remains well below the number of cross-sectional units, reducing concerns about instrument proliferation.\u003c/p\u003e\n\u003cp\u003eThe results from Tables 9 and 10 provide strong evidence that SDG 9 outcomes are highly persistent and structurally determined. While earlier fixed-effects models suggested some contemporaneous association between FDI and SDG 9, these effects do not survive once dynamics and endogeneity are explicitly addressed. Instead, institutional quality and income levels emerge as the most robust drivers, while foreign investment and domestic credit appear to influence SDG 9 primarily through longer-term structural channels rather than short-run marginal effects. These findings imply that policies aimed at accelerating progress on industrialisation, innovation, and infrastructure should focus less on short-term capital inflows and more on strengthening institutions, sustaining public investment capacity, and building long-run absorptive structures that allow gains to accumulate over time. The validity of the dynamic specification is further assessed using standard post-estimation diagnostic tests for the System GMM estimator. The Arellano\u0026ndash;Bond test for first-order serial correlation [AR(1)] is statistically significant (z = \u0026minus;4.25, p \u0026lt; 0.001), which is expected in first-differenced equations and does not indicate model misspecification. By contrast, the test for second-order serial correlation [AR(2)] yields a statistic of \u0026minus;3.02 with a p-value of 0.003. While this suggests some evidence of residual serial correlation at the second order, the result is interpreted with caution given the strong persistence of SDG 9 and the relatively short time dimension of the panel.\u003c/p\u003e\n\u003cp\u003eInstrument validity is evaluated using both the Hansen J test and the Sargan test of over-identifying restrictions. The Hansen J statistic of 11.39 (p = 0.180) fails to reject the null hypothesis that the instruments are jointly valid, indicating that the instrument set is not systematically correlated with the error term. Similarly, the Sargan test statistic of 8.89 (p = 0.351) supports the overall validity of the instruments. The model employs a total of 37 instruments, which remains well below the number of cross-sectional units, thereby reducing concerns about instrument proliferation and overfitting.\u003c/p\u003e\n\u003cp\u003eFigure 2 presents predicted SDG 9 outcomes across increasing levels of foreign direct investment under alternative domestic credit regimes. In low-credit environments, increases in FDI are associated with steeper improvements in SDG 9 performance, reflecting stronger reliance on external capital for industrial and innovation upgrading.\u003c/p\u003e\n\u003cp\u003eIn contrast, in high-credit regimes, the predicted gains from additional FDI are more moderate, suggesting partial substitution between foreign capital and domestic financial intermediation. These patterns reinforce the regression evidence that domestic finance conditions, rather than uniformly amplifies, the development impact of FDI.\u003c/p\u003e\n\u003cp\u003eFigure 3a illustrates the partial relationship between foreign direct investment and SDG 9 outcomes after netting out country fixed effects, year effects, and all control variables. The positive slope indicates that, conditional on domestic characteristics and macroeconomic conditions, higher FDI inflows are associated with stronger performance in industry, innovation, and infrastructure.\u003c/p\u003e\n\u003cp\u003eFigure 3b illustrates the partial association between foreign direct investment inflows and SDG 9 outcomes after netting out country fixed effects, time effects, and macroeconomic controls. The relationship is positive but modest, with substantial uncertainty at higher levels of FDI. This visual evidence supports the regression results, suggesting that FDI contributes to industrial and innovation outcomes primarily when complemented by domestic financial and institutional capacity.\u003c/p\u003e\n\u003cp\u003eFigure 3c presents the partial association between foreign direct investment and SDG 9 outcomes after netting out country effects, time effects, and macroeconomic controls. The relationship is positive but modest, suggesting that FDI alone is not a sufficient driver of industrial and innovation performance. This visual evidence reinforces the regression results that highlight the importance of domestic financial depth and institutional quality in conditioning the impact of FDI.\u003c/p\u003e"},{"header":"5. Discussion of Findings","content":"\u003cp\u003eThis study examined the relationship between foreign direct investment, domestic private-sector credit, and sustainable development outcomes using country fixed-effects models with year effects and clustered standard errors. The analysis was guided by four hypotheses linking FDI, domestic finance, and governance quality to overall SDG performance and SDG 9 in particular. The results reveal a nuanced picture in which the development effects of FDI are conditional, sector-specific, and shaped more by institutional and structural factors than by finance alone.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 FDI and Sustainable Development Outcomes (H1)\u003c/h2\u003e \u003cp\u003eHypothesis H1 proposed that FDI inflows are positively associated with SDG performance, measured by both the aggregate SDG Index and SDG 9. The empirical results provide no support for this hypothesis at the aggregate level, but partial support at the sectoral level. In the baseline fixed-effects model for the overall SDG Index, FDI inflows are statistically insignificant, indicating that increases in foreign investment do not translate automatically into broad-based improvements in sustainable development outcomes once unobserved country heterogeneity and global shocks are accounted for. This finding aligns with a growing strand of the literature that questions the assumption of automatic development gains from FDI and emphasises the conditional nature of spillovers.\u003c/p\u003e \u003cp\u003eBy contrast, when attention is restricted to SDG 9, which focuses on industry, innovation, and infrastructure, FDI inflows exhibit a positive and weakly significant association. This sector-specific result is consistent with theoretical models of multinational production and technology diffusion, which predict that FDI spillovers are more likely to materialise in domains closely tied to production processes, capital deepening, and technological upgrading. The result also resonates with empirical evidence suggesting that FDI effects are more visible in industrial and innovation-related outcomes than in social or environmental dimensions of development. Overall, the findings support a qualified version of H1, where FDI matters for development, but primarily in areas directly linked to production and technology rather than across the full SDG spectrum.\u003c/p\u003e \u003cp\u003eEndogenous growth and international production theories predict that FDI can raise host-country development outcomes through technology spillovers, managerial transfer, and productivity gains, particularly when foreign firms possess firm-specific advantages that diffuse to domestic firms (Nwokolo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tandid, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Within this framework, H1 expected a positive association between FDI and both aggregate SDG performance and SDG 9.\u003c/p\u003e \u003cp\u003eThe empirical evidence provides only partial support for this theoretical prediction. While FDI is statistically insignificant in the SDG Index regressions, it is positively associated with SDG 9, albeit at conventional significance levels. This divergence mirrors findings in the empirical literature that show FDI effects are often sector-specific rather than economy-wide. Studies such as Dam, Kaya, and Bekun (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Peng et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) similarly report that technology-related and industrial outcomes respond more strongly to external integration than broader social or environmental indicators. By contrast, the absence of a significant effect on the overall SDG Index aligns with work by Xu et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Yiğit (2021), who find that aggregate sustainability outcomes depend on a wider set of institutional and social mechanisms that FDI alone cannot address. Your results therefore refine the theoretical expectation by showing that FDI\u0026rsquo;s contribution is narrowly concentrated in production-oriented SDGs, rather than uniformly distributed across the development agenda.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Domestic Credit and Development Performance (H2)\u003c/h2\u003e \u003cp\u003eHypothesis H2 posited a positive association between private-sector credit and SDG performance. Across all model specifications, private-sector credit fails to exhibit a statistically significant positive effect on either the SDG Index or SDG 9. In some cases, the estimated coefficient is negative, though not statistically distinguishable from zero. This result suggests that financial depth, measured narrowly as credit to the private sector, does not on its own guarantee improved sustainable development outcomes.\u003c/p\u003e \u003cp\u003eThis finding challenges a simplified reading of financial development theory and supports more recent critiques that emphasise the quality and allocation of credit rather than its aggregate volume. In contexts where credit is directed toward consumption, real estate, or non-productive activities, financial deepening may have limited relevance for industrial upgrading or innovation. The result also aligns with empirical studies that find weak or unstable links between domestic credit and development outcomes in environments characterised by institutional weaknesses or shallow productive sectors. As such, H2 is not supported by the evidence, reinforcing the argument that finance must be embedded within a broader institutional and structural context to matter for development. Financial development theory posits that deeper credit markets should facilitate productive investment by easing liquidity constraints and supporting firm-level upgrading (Sarangi, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Slimani et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Under the absorptive-capacity framework, private-sector credit is expected to help domestic firms finance complementary investments required to benefit from foreign technologies.\u003c/p\u003e \u003cp\u003eContrary to this theoretical expectation, private-sector credit is not positively associated with either the SDG Index or SDG 9 in your models. This result is consistent with a growing empirical literature that questions the developmental role of aggregate credit expansion in the absence of strong allocation mechanisms. Abbas et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Gyamfi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) document similar weak or unstable effects of credit on productivity-related outcomes in contexts where financial systems are shallow or biased toward non-productive uses. The finding also resonates with the critique advanced by Ciarli et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who argue that finance only supports innovation and structural change when embedded within coherent industrial and institutional systems. Your results therefore suggest that credit quantity alone does not proxy effective absorptive capacity, offering empirical support for the theoretical distinction between financial depth and financial functionality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Credit as a Moderator of FDI Effects (H3)\u003c/h2\u003e \u003cp\u003eHypothesis H3 expected private-sector credit to positively moderate the effect of FDI on development outcomes, implying a positive coefficient on the interaction term between FDI and credit. The empirical evidence does not support this expectation. In both the contemporaneous and dynamic fixed-effects interaction models, the FDI\u0026ndash;credit interaction term is negative and weakly significant, indicating that higher levels of private credit are associated with a reduction, rather than an amplification, of the marginal effect of FDI on SDG 9.\u003c/p\u003e \u003cp\u003eThis result runs counter to the standard absorptive-capacity argument but is theoretically interpretable. One plausible explanation is that domestic credit systems in many countries do not effectively support firms engaged in technology adoption and industrial upgrading. Instead, credit expansion may be concentrated in sectors that are disconnected from foreign-invested activities, limiting complementarities between FDI and domestic finance. Alternatively, foreign firms may rely on internal financing or international capital markets, reducing their dependence on local credit systems. In such settings, deeper domestic credit markets may even crowd resources away from productive spillover channels. The negative interaction thus highlights a mismatch between financial development and industrial absorptive needs, rather than a failure of the absorptive-capacity concept itself. Consequently, H3 is rejected.\u003c/p\u003e \u003cp\u003eThe central hypothesis of the study, H3, predicted that private-sector credit would positively moderate the effect of FDI on development outcomes. This expectation follows directly from absorptive-capacity theory, which emphasises complementarities between external knowledge inflows and domestic investment capacity (Li, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Empirically, however, the interaction between FDI and credit is negative and weakly significant in both static and dynamic specifications. This result contradicts the standard absorptive-capacity prediction, but it is not without precedent in the empirical literature. Hafner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Ferrier et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) show that external integration can transmit technology without guaranteeing domestic uptake, especially where domestic firms lack incentives or capabilities to adapt imported knowledge.\u003c/p\u003e \u003cp\u003eYour findings suggest that domestic credit systems may be poorly aligned with foreign-investment-driven production structures. In line with Hanlin and Kaplinsky (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), technology that enters through global value chains may be ill-suited to domestic firm structures, limiting the scope for credit-financed adaptation. Moreover, multinational firms often rely on internal financing or global capital markets, reducing dependence on domestic credit and weakening potential complementarities. Rather than rejecting absorptive-capacity theory outright, the results indicate that credit is not the binding constraint in the FDI\u0026ndash;SDG 9 relationship, at least as measured by aggregate private-sector lending. This refines the theory by highlighting the importance of who receives credit and for what purpose, rather than how much credit exists in the economy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Governance Heterogeneity and Conditional Effects (H4)\u003c/h2\u003e \u003cp\u003eHypothesis H4 proposed that the moderating role of credit would be stronger in contexts of higher governance quality. The heterogeneity analysis offers partial and asymmetric support for this hypothesis. In low-governance environments, neither FDI nor its interaction with credit has a meaningful effect on SDG 9, suggesting that weak institutions constrain both direct and indirect spillover channels. In high-governance countries, FDI exhibits a strong and statistically significant positive association with SDG 9, confirming the importance of institutional quality for translating foreign investment into productive outcomes.\u003c/p\u003e \u003cp\u003eHowever, even in high-governance contexts, the interaction between FDI and credit remains negative and only weakly significant. This suggests that governance quality strengthens the direct effect of FDI, but does not fully repair the disconnect between domestic credit systems and foreign-led industrial upgrading. These findings refine H4 by showing that governance matters, but primarily by enabling FDI itself rather than by transforming credit into an effective moderating channel.\u003c/p\u003e \u003cp\u003eInstitutional economics emphasises governance quality as a key determinant of how resources are allocated and how firms respond to incentives (Ciarli et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hypothesis H4 therefore expected stronger FDI\u0026ndash;credit complementarities in high-governance environments. The heterogeneity analysis provides asymmetric support for this hypothesis. In low-governance countries, neither FDI nor its interaction with credit affects SDG 9, consistent with arguments that weak institutions suppress spillovers by increasing uncertainty, misallocating finance, and discouraging learning. This finding echoes Ajay Yadav et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who highlight implementation capacity as a critical bottleneck in translating external flows into SDG progress.\u003c/p\u003e \u003cp\u003eIn high-governance countries, FDI exerts a strong positive effect on SDG 9, aligning closely with endogenous growth models that emphasise institutional quality as a prerequisite for effective knowledge diffusion. However, even in these contexts, the interaction with credit remains negative. This suggests that governance enhances the direct productivity channel of FDI, but does not automatically convert domestic finance into an effective amplifier of spillovers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Dynamics, Persistence, and Endogeneity\u003c/h2\u003e \u003cp\u003eThe dynamic fixed-effects and System GMM results reveal strong persistence in SDG 9 outcomes, with the lagged dependent variable highly significant across specifications. This confirms that industrial and innovation capacities evolve slowly over time and are shaped by path dependence. Once this persistence is accounted for, the contemporaneous effects of FDI, credit, and their interaction become statistically insignificant, while governance quality and income levels retain explanatory power. The System GMM diagnostics indicate acceptable instrument validity, suggesting that the core conclusions are robust to endogeneity concerns.\u003c/p\u003e \u003cp\u003eTaken together, the dynamic results reinforce the central message of the study: sustainable industrial development is driven more by institutional quality and accumulated capacity than by short-run financial or investment inflows. FDI can contribute, but only within enabling governance environments and primarily through direct channels rather than through domestic financial intermediation. The dynamic fixed-effects and System GMM results show strong persistence in SDG 9 outcomes, with lagged SDG performance dominating short-run effects of FDI and credit. This is fully consistent with endogenous growth theory, which predicts path dependence in knowledge accumulation and industrial capacity (Tandid, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similar persistence effects are reported by Dam et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Peng et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), reinforcing the view that sustainable industrial development evolves slowly and is resistant to short-term policy shocks. Once persistence and endogeneity are accounted for, the insignificance of FDI\u0026ndash;credit interactions further strengthens the conclusion that structural and institutional factors dominate financial complementarities in shaping SDG 9 outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Synthesis and Implications\u003c/h2\u003e \u003cp\u003eOverall, the findings challenge optimistic narratives that portray FDI and financial deepening as automatic levers for achieving the SDGs. Instead, they support a more conditional view in which foreign investment contributes selectively, domestic credit alone is insufficient, and governance quality plays a decisive role. The results align with recent empirical work that emphasises structural transformation, institutional capacity, and targeted policy alignment over generic openness or financial expansion. By explicitly modelling interaction effects and heterogeneity, this study contributes to the literature by showing not only whether FDI and finance matter for sustainable development, but why and under what conditions their effects fail to materialise.\u003c/p\u003e \u003cp\u003eBy integrating FDI, domestic finance, governance, and SDG outcomes within a unified interaction framework, this study advances the literature in three ways. First, it shows that FDI contributes selectively to sustainability, primarily through industrial and innovation channels. Second, it demonstrates that private-sector credit, as commonly measured, does not function as an effective absorptive-capacity channel. Third, it clarifies that governance strengthens FDI\u0026rsquo;s direct impact but does not resolve the finance\u0026ndash;spillover disconnect. In doing so, the study moves beyond the question of whether FDI and finance matter, and instead explains why their interaction often fails to deliver the development gains predicted by theory, particularly in the context of the Sustainable Development Goals.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Policy Implications","content":"\u003cp\u003eThe findings of this study carry several policy-relevant implications for countries seeking to leverage foreign direct investment and domestic financial development to advance the Sustainable Development Goals, particularly SDG 9. Most importantly, the results indicate that FDI should not be treated as a standalone development strategy. While FDI shows a positive association with industrial and innovation-related outcomes, its effects are neither automatic nor broad-based. Policymakers should therefore move away from generic FDI attraction strategies and instead prioritise sector-targeted investment policies that align foreign inflows with national industrial and innovation objectives.\u003c/p\u003e \u003cp\u003eThe absence of a robust positive role for private-sector credit, both directly and as a moderator of FDI, suggests that financial deepening in aggregate terms is insufficient to support sustainable industrial development. Expanding credit volumes without addressing allocation, risk assessment, and sectoral targeting may fail to ease the binding constraints faced by firms operating in technology-intensive and innovation-driven sectors. Financial sector reforms should therefore emphasise credit quality and purpose, including instruments that support long-term investment, technological upgrading, and firm learning, rather than short-term or consumption-oriented lending.\u003c/p\u003e \u003cp\u003eGovernance emerges as a critical conditioning factor in the FDI\u0026ndash;SDG relationship. The stronger and more consistent effects of FDI on SDG 9 in high-governance environments imply that institutional quality enhances the productivity and technological channels through which foreign investment operates. This underscores the importance of regulatory credibility, contract enforcement, and anti-corruption measures as complementary policies to investment promotion. Without such institutional foundations, efforts to attract FDI or expand domestic credit are unlikely to translate into sustained development gains.\u003c/p\u003e \u003cp\u003eThe dynamic results further indicate strong persistence in SDG 9 outcomes, implying that industrial and innovation capacities evolve gradually and are shaped by past achievements. This highlights the limits of short-term policy interventions and reinforces the need for long-horizon development strategies that combine industrial policy, institutional strengthening, and targeted financial instruments. Temporary incentives or episodic investment inflows are unlikely to alter development trajectories unless they are embedded within coherent and sustained policy frameworks.\u003c/p\u003e \u003cp\u003eFinally, the weak evidence for financial\u0026ndash;FDI complementarities suggests that policies aimed at strengthening linkages between multinational enterprises and domestic firms may be more effective than relying on credit expansion alone. Measures such as supplier development programmes, technology extension services, and skills upgrading initiatives may better facilitate knowledge diffusion than broad-based financial liberalisation. In this sense, the policy lesson is not that finance is irrelevant, but that finance must be integrated into a wider ecosystem of industrial capability-building to support SDG-oriented development.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study examined the relationship between foreign direct investment, domestic private-sector credit, and sustainable development outcomes using panel data for 43 countries over the period 2005\u0026ndash;2023. Employing fixed-effects, interaction, dynamic, and System GMM estimators, the analysis tested whether domestic finance functions as an absorptive-capacity channel that amplifies the development impact of FDI, with a particular focus on SDG 9.\u003c/p\u003e \u003cp\u003eThe empirical evidence yields three central conclusions. First, FDI contributes selectively to sustainable development, with its effects concentrated in industrial and innovation-related outcomes rather than the broader SDG Index. This supports theories that emphasise the role of foreign investment in technology transfer and structural transformation, while also confirming that such effects do not automatically extend to wider social and environmental dimensions of development.\u003c/p\u003e \u003cp\u003eSecond, private-sector credit does not exhibit a robust positive association with SDG performance, nor does it consistently enhance the impact of FDI. These finding challenges conventional absorptive-capacity arguments that treat domestic finance as a universal facilitator of technology spillovers. Instead, the results suggest that aggregate credit measures may poorly capture the types of financial support required for productive upgrading and innovation.\u003c/p\u003e \u003cp\u003eThird, governance quality shapes the effectiveness of FDI but does not resolve the weak interaction between finance and foreign investment. In high-governance contexts, FDI exerts a stronger influence on SDG 9, underscoring the importance of institutional environments in enabling productive spillovers. However, even under favourable governance conditions, domestic credit does not reliably complement FDI, highlighting a structural disconnect between financial systems and innovation-driven development.\u003c/p\u003e \u003cp\u003eThese findings contribute to the literature by clarifying why the expected complementarities between FDI and domestic finance often fail to materialise in practice. Rather than rejecting the role of finance or foreign investment, the study shows that their development impact is conditional, uneven, and mediated by institutional and structural factors. Future research could extend this analysis by examining firm-level credit allocation, sector-specific financial instruments, or alternative measures of financial functionality that better reflect innovation financing. The results caution against policy approaches that rely on foreign investment or financial deepening as isolated levers for achieving the Sustainable Development Goals. Sustainable industrial and innovation-led development requires a coordinated policy mix that aligns investment, finance, and governance within a long-term capability-building strategy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThis study is based exclusively on secondary data obtained from publicly available international databases. The research does not involve human participants, personal data, or sensitive information. As such, ethical approval was not required in accordance with institutional and journal guidelines.\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable. The study relies solely on secondary, country-level data from publicly accessible sources and does not involve direct interaction with human participants.\u003c/p\u003e\n\u003ch2\u003eConsent to publish\u003c/h2\u003e\n\u003cp\u003eNot applicable. No individual-level, personal, or identifiable data were collected or used in this study. In addition, all authors agreed to publish this paper.\u003c/p\u003e\n\u003ch2\u003eFunding Declaration:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNo Funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003e1. Kassim AlabaniWriting and compiling of manuscript, established methodology, data collection and analysis, presentation of tables and figures.2. Dr Benedict Afful Jr.Supervised and assisted with manuscript compilation, editing and co-authorship of manuscript3. Dr Francis TaaleEditing and co-author of manuscript4. Dr Eric AbokyiEditing and co-author of manuscript\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll data used for this study are available at theglobaleconomy.com and in the sustainable development report 2024 [doi:10.25546/108572]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas, H. S. M., Xu, X., \u0026amp; Sun, C. (2021). Role of foreign direct investment interaction to energy consumption and institutional governance in sustainable GHG emission reduction. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(40), 56808\u0026ndash;56821. https://doi.org/10.1007/s11356-021-14650-7\u003c/li\u003e\n\u003cli\u003eAerni, P. (2021). \u0026lsquo;Business as Part of the Solution\u0026rsquo;: SDG 8 Challenges Popular Views in the Global Sustainability Discourse. In \u003cem\u003eTransitioning to Decent Work and Economic Growth\u003c/em\u003e. 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Exploring the nexus of decent work, financial inclusion, and economic growth: A study aligned with SDG 8. \u003cem\u003eSustainable Futures\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 100213. https://doi.org/10.1016/j.sftr.2024.100213\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Foreign Direct Investment, Private-Sector Credit, Sustainable Development Goals, SDG 9, Industrialisation, Governance, Absorptive Capacity, Panel Data","lastPublishedDoi":"10.21203/rs.3.rs-8613700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8613700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study investigates whether domestic financial development enhances the contribution of foreign direct investment to sustainable development, with a focus on SDG 9. Using panel data for 43 countries from 2005 to 2023 and a range of fixed-effects and dynamic estimation techniques, it finds that FDI does not significantly affect overall SDG performance but has a positive effect on industrialisation, innovation, and infrastructure outcomes. Private-sector credit shows no consistent direct impact, and its interaction with FDI provides little evidence that financial depth strengthens FDI’s development effects. Governance quality improves the direct influence of FDI on SDG 9, but does not alter the weak financial–FDI complementarity. The results indicate that FDI’s development impact is sector-specific and institution-dependent, and that broad measures of financial deepening do not automatically support technology diffusion or structural transformation. The study contributes to the sustainable development and international investment literature by demonstrating that domestic finance does not automatically function as an absorptive-capacity channel for FDI, even in relatively strong governance environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification Codes: \u003c/strong\u003eF21, O16, O25, O33, Q01, C23\u003c/p\u003e","manuscriptTitle":"Absorptive capacity for technology transfer: Does private-sector credit condition the FDI–SDG relationship?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 14:38:11","doi":"10.21203/rs.3.rs-8613700/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80ad7bc6-1b0b-4f4f-82db-0d64cf5beaca","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T10:11:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 14:38:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8613700","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8613700","identity":"rs-8613700","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}