Innovation and Productivity as Engines of Economic Growth in Ghana

1. Introduction

Recent advances in endogenous growth theory emphasise that long-run economic growth is fundamentally driven by endogenous technological change rather than by capital accumulation alone. In contrast to neoclassical frameworks where technological progress is exogenously determined, modern growth models treat innovation as an outcome of economic incentives, institutional arrangements, and purposeful investment in knowledge creation (Jones, 1999; Tung & Hoang, 2024), Within this framework, technological change is inseparable from the growth process and constitutes a central mechanism through which economies achieve sustained increases in output per capita (Freeman, 2008; Miller & Rose, 1990; Olson, 2022). The incorporation of innovation into formal growth models most notably through Romer’s R&D-based endogenous growth framework provided a rigorous theoretical foundation for analysing how knowledge accumulation generates increasing returns at the aggregate level. In these models, innovation originates in research-intensive sectors and diffuses across the economy, enhancing production efficiency and raising long-run growth rates through improvements in total factor productivity (TFP). As a result, innovation affects growth not directly through factor expansion, but indirectly through productivity-enhancing technological spillovers.

Despite strong theoretical consensus, empirical evidence on the innovation–TFP–growth nexus remains uneven, particularly for developing economies. While innovation is widely recognised as a key driver of productivity growth in advanced economies, its role in shaping growth trajectories in low- and middle-income countries is less well understood. In Sub-Saharan Africa, and Ghana in particular, economic growth has been characterised by structural transformation challenges, limited technological upgrading, and fluctuating productivity performance. This raises an unresolved empirical question: does innovation drive productivity-led growth, or does productivity growth itself stimulate innovative activity?

This study contributes to the literature by empirically examining the dynamic relationship between innovation and total factor productivity in Ghana using annual time-series data. Innovation is proxied by residential and non-residential patent activity, while TFP is estimated using a conventional production function approach. Unlike much of the existing literature, which infers the role of innovation indirectly through growth regressions, this study explicitly models the interaction between innovation and productivity, allowing for potential bidirectional causality and dynamic adjustment effects.

This paper extends endogenous growth theory to a developing-country context by empirically testing the innovation-led growth mechanism through the TFP channel. It contributes to the modelling literature by (i) explicitly identifying productivity as the transmission mechanism linking innovation to growth, and (ii) providing time-series evidence on how this relationship evolves over different phases of economic development. In doing so, the study bridges the gap between abstract growth theory and empirically observable productivity dynamics in emerging economies.

2. Review of Theories and Literature

The relationship between innovation, total factor productivity (TFP), and economic growth is firmly grounded in modern growth theory. Endogenous growth models depart from the neoclassical assumption of exogenous technological progress by conceptualising innovation as the outcome of purposeful investment in knowledge creation, research and development (R&D) and human capital accumulation. Within this context, innovation raises productivity by improving the efficiency with which existing inputs are transformed into output, thereby enabling sustained economic growth without proportional increases in labor or capital (Aghion & Jaravel, 2015; Romer, 1990).

A central implication of endogenous growth theory is that productivity growth constitutes the primary transmission channel through which innovation affects long-run economic performance. Knowledge is treated as a non-rival input that generates spillover effects across firms and sectors, leading to increasing returns at the aggregate level. Consequently, economies that successfully foster innovation experience persistent improvements in TFP, reinforcing its role as the key driver of long-term growth (Jones, 2022)

This perspective contrasts sharply with traditional neoclassical growth models, which regard technological progress as exogenous. The seminal contribution by Solow (1956) demonstrated that variations in capital and labour inputs explain only a limited share of observed output growth, leaving a substantial unexplained component commonly referred to as the Solow residual. Subsequent research interpreted this residual as capturing technological change, efficiency improvements, and innovation-related productivity gains. These findings highlighted the limitations of factor accumulation models and provided the intellectual foundation for endogenising technological change within growth theory.

Beyond its technological dimension, innovation is increasingly understood as an organisational and institutional process. The productivity effects of innovation depend critically on firms’ absorptive capacity, managerial quality, and the broader institutional environment in which innovation occurs (Cohen & Levinthal, 1990). As a result, innovation-induced productivity gains are heterogeneous across countries, reflecting differences in institutional quality, market structure, and the maturity of national innovation systems.

These considerations are particularly relevant for developing economies in Sub-Saharan Africa, where productivity growth has been persistently weak despite episodes of economic expansion. In Ghana, economic growth has historically relied on factor accumulation and commodity-driven expansion, with limited and volatile contributions from TFP. This raises an important empirical question regarding whether innovation activity captured through observable measures such as patenting has translated into sustained productivity gains capable of supporting long-run growth. Weak innovation systems, limited R&D investment, and institutional constraints may attenuate the productivity effects of innovation, making the innovation–TFP–growth nexus an empirical rather than theoretical certainty.

This study builds directly on these theoretical insights by empirically examining the dynamic relationship between innovation, TFP and economic growth in Ghana. Consistent with endogenous growth theory, innovation is expected to affect growth primarily through its impact on productivity. Accordingly, the empirical strategy estimates TFP using a production function framework and models the interaction between innovation, productivity and output within a time-series setting. This approach allows for the identification of both short- and long-run effects, as well as potential feedback mechanisms between innovation and productivity, providing a theory-consistent assessment of innovation-led growth in a Sub-Saharan African context.

A substantial empirical literature provides strong support for the innovation-led productivity growth hypothesis, identifying innovation as a key mechanism through which economies achieve sustained improvements in total factor productivity (TFP) and long-run economic growth. Across macro- and micro-level studies, innovation activity proxied by research and development (R&D) expenditure, patenting, technological adoption and organisational improvements has been shown to exert a positive and statistically significant effect on productivity performance and aggregate output growth (Awan et al., 2021; Khan et al., 2021; Kusi-Sarpong et al., 2022). These findings align with endogenous growth theory, which emphasises productivity gains arising from purposeful investments in knowledge creation rather than from factor accumulation alone. Empirical evidence further indicates that innovation can enhance productivity even in environments characterised by constrained production inputs, making it particularly relevant for developing economies. Innovation improves efficiency by optimising production processes, reducing waste and enabling better utilisation of existing capital and labor. This highlights innovation as a critical driver of productivity-led growth in contexts where traditional growth engines face diminishing returns. For Sub-Saharan African economies such as Ghana where capital deepening and labor expansion have historically dominated growth patterns innovation therefore represents a potentially transformative pathway for sustaining long-term economic growth. At the firm level, empirical research underscores the importance of organisational and managerial factors in mediating the innovation–productivity relationship. Firms that effectively select, adopt and integrate appropriate technologies tend to experience stronger productivity gains, while weak absorptive capacity limits the returns to innovation investments (Sohn & Kwon, 2020; Zhang et al., 2020). These findings are particularly relevant in developing economies, where institutional constraints, skill shortages, and limited innovation systems may weaken the transmission of innovation into productivity growth. At the macroeconomic level, several studies have sought to explain productivity growth by decomposing the Solow residual into observable innovation-related components (Solow, 1956). Early growth accounting work identified technological progress as the dominant source of productivity growth beyond capital and labor inputs.

More recent studies employ refined proxies for innovation-driven productivity change, including R&D intensity, labor quality, capital-embodied technological progress, and organisational efficiency (Crafts & Woltjer, 2021; Gouin-Bonenfant, 2018; Sheng & Chancellor, 2019). These approaches consistently show that innovation-related factors account for a substantial share of productivity growth unexplained by traditional inputs.

However, empirical evidence from Sub-Saharan Africa remains comparatively limited. Existing studies suggest that weak productivity growth has been a central constraint on long-term economic performance in the region, including in Ghana, where growth has often been driven by commodity expansion rather than sustained efficiency improvements. While innovation activity captured through indicators such as patenting has increased modestly, it remains unclear whether such innovation has translated into durable productivity gains. Institutional weaknesses, low R&D intensity, and fragmented innovation systems may attenuate the innovation–TFP linkage in this context. While the innovation–total factor productivity (TFP) growth nexus is well established in advanced economies, its applicability to developing-country contexts remains less conclusive. A central concern in the literature is whether innovation-driven technological change can generate sustained productivity growth in environments characterised by weak institutions, limited human capital, and underdeveloped innovation systems (Chen et al., 2021; Good et al., 1999; Kumbhakar et al., 2012; Lu et al., 2021; Luoma, 2021; Sobieraj & Metelski, 2021; Yeo & Park, 2022).

Nelson and Phelps (1966) argue that technological transformation requires more than incremental efficiency improvements; it depends critically on an economy’s capacity to generate, adapt, and diffuse new knowledge. In the absence of such capabilities, technological change may raise efficiency in the short run but fail to produce new products, industries, or structural transformation capable of sustaining long-term growth. This challenge is particularly evident in many developing economies, including those in Sub-Saharan Africa. Empirical studies consistently show that slow productivity growth rather than insufficient capital accumulation or labor expansion has been the primary constraint on income convergence with advanced economies (McClelland, 2019; Narayan et al., 2007; Ocampo, 2002; Van Ark et al., 2008). Ghana exemplifies this pattern. Although the country has experienced periods of robust output growth since the early 2000s, growth has largely been driven by factor accumulation and commodity-based expansion, with limited and volatile contributions from TFP. As a result, productivity growth has not provided a stable foundation for sustained long-term development. Ghana’s innovation policy history reflects attempts to address this challenge, though with mixed outcomes. Policy initiatives such as the National Science, Technology and Innovation Policy, the establishment of research institutions, and recent digitalisation and industrialisation programmes including industrial parks and technology hubs signal growing recognition of innovation as a development driver. However, R&D expenditure remains low by international standards, patenting activity is limited, and linkages between research institutions and productive sectors remain weak. These structural constraints suggest that innovation activity may not automatically translate into productivity gains, reinforcing the need for empirical evaluation.

The literature further emphasises that innovation in developing economies often takes the form of technology adoption and incremental improvement rather than frontier innovation. While such forms of innovation can support catch-up growth, their productivity effects depend on complementary investments in skills, infrastructure, and institutional quality (Acemoglu, 2010; Aghion et al., 2015). In Ghana, uneven human capital development, financing constraints, and regulatory bottlenecks may attenuate the productivity effects of innovation, limiting the emergence of a virtuous cycle between innovation, productivity, and income growth. Firm-level evidence nonetheless suggests the existence of a positive feedback loop between innovation, productivity, and per capita income. Small and medium-sized enterprises, in particular, can contribute significantly to aggregate growth when innovation enables productivity improvements (Albis Salas et al., 2023; Gherghina et al., 2020). However, in contexts where innovation systems are fragmented as is often the case in Sub-Saharan Africa the direction and strength of causality between innovation and TFP remain ambiguous. Innovation may drive productivity growth, productivity improvements may stimulate further innovation, or both processes may evolve simultaneously.

In addition, various research and analyses have demonstrated that there is a positive feedback loop between innovation, productivity, and per capita income that enables economies to achieve long-term sustained growth (Bottazzi & Peri, 2007; Hirooka, 2006; Surya et al., 2021). In contrast, there was not a single developed economy that lacked evidence that innovation, research and development, and overall productivity growth were tied to the selection of the most effective innovation technology. According to Albis Salas et al. (2023), Halme and Korpela (2014) and Gherghina et al. (2020) small and medium-sized businesses that develop and put such innovations to good use are mostly responsible for the country’s economic expansion. Also, their research adds to what is known by showing that there is a link between innovation, an increase in total factor productivity, and economic growth.

3. Data, Variables and Preliminary Analysis

This study uses annual time-series data for Ghana obtained primarily from the World Bank’s World Development Indicators (WDI) and the Ghana Statistical Service (GSS) from 1995 to 2023. These sources provide consistent and internationally comparable macroeconomic indicators while ensuring alignment with national accounts data. The sample period is determined by data availability for innovation indicators and spans several decades, allowing for the examination of both short-run dynamics and long-run relationships.

3.1. Variables

Economic growth (RGDP) is measured using real gross domestic product (GDP) or the growth rate of real GDP, expressed in constant prices to remove the effects of inflation. Innovation activity is proxied by patent applications, disaggregated into residential and non-residential patents, capturing both domestic innovative capacity and foreign-driven technological inflows. Patents are widely used in the literature as observable indicators of inventive output and knowledge creation, particularly in time-series analyses.

Total factor productivity (TFP) is estimated using a standard Cobb–Douglas production function framework, where output is modelled as a function of capital and labor inputs. Capital stock is proxied by gross fixed capital formation, while labor input is measured by the employed labor force or working-age population. TFP is derived as the residual component of output growth not explained by changes in capital and labor, consistent with growth accounting conventions.

Innovation (INN) is measured using patent activity, which serves as a widely accepted proxy for inventive output and technological capability in empirical growth and productivity studies. Patent data capture formalised innovation outcomes that result from research, development, and inventive effort, and they are particularly useful in time-series analyses where consistent long-run indicators are required. In this study, innovation is proxied by patent applications, disaggregated into resident and non-resident patent applications. Resident patent applications reflect domestic innovative capacity, capturing inventions developed by individuals or firms located within Ghana. Non-resident patent applications, by contrast, reflect foreign-driven innovation and international knowledge inflows, capturing the extent to which foreign investors seek intellectual property protection within the Ghanaian economy. This distinction allows for a more nuanced assessment of innovation dynamics in a developing-country context, where domestic innovation and imported technologies may play different roles in productivity growth.

3.2. Co-Integration Analysis of the (ARDL)

Cointegration analysis is employed to examine the existence of long-run equilibrium relationships among the time-series variables. This study adopts the Autoregressive Distributed Lag (ARDL) approach, which simultaneously captures short-run dynamics and long-run relationships within a single equation framework. The ARDL model integrates autoregressive and distributed lag components, allowing current outcomes to depend on their own past values and on lagged values of explanatory variables. A key advantage of the ARDL approach is its flexibility with respect to the integration properties of the variables. Unlike conventional cointegration techniques, the ARDL framework can be applied when regressors are integrated of order I(0) or I(1), provided none is integrated of order I(2). This feature makes the approach particularly suitable for macroeconomic time-series data from developing economies. Prior to estimation, standard unit root tests are conducted to verify that this condition is satisfied and to avoid spurious regression results.

The existence of a long-run relationship is tested using the bounds testing procedure proposed by Pesaran. Cointegration is confirmed if the computed F-statistic exceeds the upper critical bound, implying rejection of the null hypothesis of no long-run relationship. Where cointegration is established, an error correction representation is estimated to assess short-run adjustments and the speed of convergence toward long-run equilibrium. Optimal lag lengths are selected using the Akaike Information Criterion (AIC) to ensure model parsimony and to mitigate serial correlation (Pesaran et al., 2001; Pesaran et al., 1999). The ARDL framework further reduces endogeneity concerns by incorporating appropriate lag structures, making it well suited for analysing the dynamic interactions between innovation, total factor productivity, and economic growth in Ghana.

3.3. Unit Root Test and Lags Selection

Although the Autoregressive Distributed Lag (ARDL) approach does not require all variables to be integrated of the same order, it is necessary to verify that none of the series is integrated of order two or higher, as the presence of I(2) variables would invalidate the ARDL bounds testing procedure. Accordingly, the stationarity properties of the variables are examined using the Augmented Dickey–Fuller (ADF) unit root test.

Table 1 reported the results of the Augmented Dickey–Fuller (ADF) unit root tests conducted to examine the time-series properties of the variables employed in the analysis—innovation (INNt), real gross domestic product (GDPt), and total factor productivity (TFPt). Consistent with the data description, the tests are performed under both a constant and a constant-plus-trend specification to account for potential deterministic components in the series. The results indicate that both innovation and real GDP are non-stationary in levels, as the null hypothesis of a unit root cannot be rejected under either specification. However, after first differencing, both variables become stationary at the 1% significance level, implying that INNt and GDPt are integrated of order one, I(1). This outcome reflects the persistent and trending nature of innovation activity and aggregate output in Ghana, as highlighted in the data statement, and is typical of macroeconomic time-series data for developing economies.

In contrast, total factor productivity (TFPt) is found to be stationary in levels under the constant specification, indicating integration of order zero, I(0). This result is consistent with the construction of TFP as a residual from the production function, which removes common stochastic trends associated with capital accumulation and labour growth. As such, TFP primarily captures efficiency and technological changes rather than scale effects, making it less prone to unit root behaviour. In totality, the unit root results reveal a mixed order of integration among the variables, with INNt and GDPt being I(1) and TFPt being I(0). Importantly, none of the variables is integrated of order two or higher, satisfying the necessary precondition for applying the Unrestricted Error Correction Model (UECM) within the ARDL bounds testing framework. This property is particularly relevant given the structure of the data and reinforces the methodological choice adopted in this study.

The mixed integration orders further justify the use of the UECM specification, which allows the simultaneous estimation of short-run dynamics and long-run relationships without requiring all variables to be integrated of the same order. Within the UECM framework, the presence of I(1) variables raises the possibility of a long-run cointegrating relationship between innovation, productivity, and economic growth, while the inclusion of I(0) variables such as TFP improves model efficiency and stability. Overall, the unit root test results provide a sound empirical foundation for the subsequent cointegration analysis and UECM estimation. They are consistent with the data characteristics and theoretical expectations of the study and support the use of the ARDL-UECM approach to examine the dynamic and long-run relationships among innovation, total factor productivity, and economic growth in Ghana.

Table 2 presents the results of the lag length selection procedure based on the Akaike Information Criterion (AIC) and the Schwarz Criterion (SC). Appropriate lag selection is a critical step in estimating the Unrestricted Error Correction Model (UECM), as it ensures adequate capture of dynamic adjustments while avoiding over-parameterisation and serial correlation. Both information criteria indicate that a lag length of two (2) is optimal. Specifically, the AIC attains its minimum value at lag 2 (−9.100), while the SC also strongly favours lag 2 (−9.012). The consistency of the two criteria strengthens confidence in this selection and suggests that the dynamic interactions among innovation, total factor productivity, and economic growth are best captured with two lags. The choice of two lags implies that the effects of shocks to innovation and productivity on economic growth persist beyond the immediate period, reflecting adjustment dynamics that unfold over time. This is consistent with the theoretical framework of the study, which posits that innovation influences economic growth primarily through productivity channels that operate with temporal delays rather than instantaneously.

From a methodological perspective, the selected lag structure supports the validity of the UECM specification within the ARDL bounds testing framework. By incorporating sufficient lags, the model adequately controls for endogeneity and serial correlation, thereby improving the reliability of the estimated short-run and long-run relationships. Moreover, the parsimonious nature of the selected model is particularly appropriate given the time-series properties of the data and the sample size. Overall, the lag selection results provide a robust empirical foundation for the subsequent UECM estimation and cointegration analysis. They ensure that the dynamic structure of the model is consistent with both the data characteristics and the study’s objective of examining the innovation–total factor productivity–growth nexus in Ghana.

Following lag selection, a Wald test is conducted to assess whether the estimated parameters are jointly significant and therefore appropriate for inclusion in the model. Within the UECM framework, the Wald test serves two related purposes. First, it evaluates the joint significance of the lagged level variables, which is essential for testing the existence of a long-run cointegrating relationship among the variables. Second, it provides a formal diagnostic check on whether the dynamic specification implied by the selected lag structure is statistically adequate.

Table 3 reports the results of the Wald test (F-statistic) used within the ARDL–UECM framework to examine whether the estimated parameters are jointly significant and to test for the existence of a long-run cointegrating relationship among innovation, total factor productivity and economic growth. In the context of the UECM, the Wald test evaluates the joint null hypothesis that the coefficients of the lagged level variables are equal to zero, implying the absence of a long-run relationship. The computed Wald F-statistic is 6.58, which exceeds the upper critical bound at the 5% significance level (upper bound = 4.45) and also lies well above the upper bound at the 1% significance level (upper bound = 3.31). Consequently, the null hypothesis of no cointegration is decisively rejected. This result provides strong evidence of a stable long-run equilibrium relationship among the variables included in the model.

The rejection of the null hypothesis confirms that the selected regressors and their lagged level terms are jointly significant and therefore appropriate for inclusion in the UECM specification. From a modelling perspective, this outcome validates the dynamic structure of the model and indicates that the estimated parameters are well fitted to the data. It also supports the lag length selection results, reinforcing the adequacy of the chosen UECM configuration. Substantively, the Wald test result aligns with the central objective of the study, which is to examine the long-run and short-run relationships linking innovation, total factor productivity and economic growth in Ghana. The presence of cointegration implies that although innovation and output may exhibit short-run fluctuations, they are tied together through a long-run equilibrium relationship mediated by productivity dynamics. This finding is consistent with endogenous growth theory, which predicts that innovation and productivity jointly determine long-term growth outcomes.

Moreover, the confirmation of cointegration justifies the subsequent estimation of an error correction model, allowing the analysis to distinguish between short-run adjustments and long-run equilibrium effects. In the UECM framework, deviations from the long-run path are expected to be corrected over time and the magnitude and significance of the error correction term provide further insight into the speed of adjustment toward equilibrium. Overall, the Wald test results provide robust empirical support for the UECM approach adopted in this study. They confirm that the parameters are jointly meaningful, that the model is correctly specified and that the data support the existence of a long-run relationship among innovation, total factor productivity and economic growth in Ghana.

Table 4 reported the normalised long-run coefficients obtained from the cointegrating relationship, capturing the equilibrium association among innovation (INNt), total factor productivity (TFPt), and economic growth (GDPt). The results provide strong evidence of a stable and economically meaningful long-run relationship linking these variables.

Innovation exhibits a positive and highly statistically significant long-run coefficient (0.321; t=9.174; p<0.01). This indicates that, in the long run, increases in innovation activity are associated with higher levels of economic performance. The magnitude of the coefficient suggests that innovation plays a substantive role in shaping Ghana’s long-term growth trajectory, consistent with endogenous growth theory, which emphasises knowledge creation and technological change as key drivers of sustained growth. Total factor productivity also enters the long-run equation with a positive and strongly significant coefficient (0.446; t=10.384; p<0.01). Notably, the coefficient on TFP is larger than that of innovation, highlighting productivity as the dominant long-run determinant of economic growth. This finding supports the theoretical proposition that innovation influences growth primarily through productivity-enhancing channels rather than directly through factor accumulation. It also aligns with the construction of TFP as a measure of efficiency and technological progress, reinforcing its central role in long-run growth dynamics.

The coefficient on economic growth (GDPt) is also positive and statistically significant (0.229; t=8.625; p<0.01), confirming the internal consistency of the cointegrating relationship. The significance of this coefficient reflects the equilibrium adjustment mechanism within the system and indicates that deviations from the long-run growth path are systematically corrected over time, a result further corroborated by the significant error correction term in the ECM estimates. Taken together, the long-run coefficients reveal a coherent and theoretically consistent growth structure in which innovation and productivity jointly underpin economic performance, with productivity exerting the strongest long-run effect. These findings are particularly relevant for Ghana, where growth has historically been driven by factor accumulation rather than sustained efficiency gains. The results suggest that strengthening innovation capacity and improving productivity are essential for achieving durable long-term growth.

Overall, the normalised long-run estimates provide robust empirical support for the innovation-led, productivity-driven growth hypothesis advanced in this study. They complement the short-run ECM results and confirm that the long-run equilibrium relationship among innovation, total factor productivity, and economic growth is both statistically significant and economically meaningful.

Table 5 reports the estimates of the Error Correction Model (ECM) derived from the Unrestricted Error Correction Model (UECM) following confirmation of cointegration. The ECM captures short-run dynamics while explicitly incorporating the long-run equilibrium relationship through the error correction term. The error correction term, ECM (−1), is negative and highly significant (coefficient = −0.214; t = −3.312; p < 0.01), providing strong evidence of long-run causality running from innovation and total factor productivity to economic growth. The magnitude of the coefficient implies that approximately 21.4% of deviations from the long-run equilibrium are corrected within one period, indicating a moderate but statistically robust speed of adjustment. This confirms that short-run disequilibria in GDP are not persistent and that the system converges toward its long-run path, consistent with the UECM bounds and cointegration results.

Turning to short-run dynamics, innovation exerts a positive and statistically significant effect on economic growth. The contemporaneous change in innovation, D(INNt), has a positive coefficient (0.039) and is significant at the 5% level, while its lagged term, D(INNt−1), remains positive and significant. This pattern suggests that innovation influences growth both immediately and with a short delay, supporting the study’s central hypothesis that innovation acts as a catalyst for productivity-led growth in Ghana. Total factor productivity exhibits a more nuanced effect. While the contemporaneous change in TFP is not statistically significant, the lagged change in TFP, D(TFPt−1), is positive and significant at the 1% level. This finding indicates that productivity improvements take time to translate into measurable growth effects, reflecting adjustment frictions and diffusion lags typical of developing economies. Importantly, this result reinforces the theoretical role of TFP as the transmission mechanism through which innovation affects economic growth, consistent with endogenous growth theory. The lagged change in GDP, D(GDPt−1), is not statistically significant, suggesting limited growth persistence once innovation and productivity dynamics are accounted for. The constant term is positive and highly significant, capturing underlying growth factors not explicitly modelled.

Overall model diagnostics indicate that the ECM is well specified and econometrically sound. The high adjusted R² (0.952) reflects strong explanatory power. Diagnostic tests confirm the absence of serial correlation (Breusch–Godfrey LM), heteroskedasticity (ARCH), non-normality (Jarque–Bera), and functional form misspecification (Ramsey RESET). The Durbin–Watson statistic of 1.93 further supports the absence of residual autocorrelation. In general, these results validate the UECM–ECM modelling strategy adopted in the study. The significant and correctly signed error correction term confirms the existence of a stable long-run relationship, while the short-run coefficients reveal meaningful dynamic interactions between innovation, productivity, and growth. Substantively, the findings provide strong empirical support for the innovation-led, productivity-driven growth hypothesis in Ghana, highlighting both immediate and lagged channels through which innovation and productivity influence economic performance.

Table 6 reports the results of the unrestricted cointegration rank test based on the maximum-eigenvalue statistic, which is used to determine the number of long-run cointegrating relationships among the variables included in the UECM. The test sequentially evaluates the null hypothesis of at most “r” cointegrating vectors against the alternative of (r+1). The results indicate that the null hypothesis of no cointegration (r = 0) is rejected at the 5% significance level. Specifically, the maximum-eigenvalue statistic of 46.244 exceeds the corresponding critical value of 37.656, with a p-value of 0.005. This provides strong evidence of at least one cointegrating relationship among innovation, total factor productivity, and economic growth. The existence of cointegration implies that although the variables may be non-stationary individually, they move together over time in a stable long-run equilibrium relationship.

For the hypothesis of at most one cointegrating vector (r ≤ 1), the maximum-eigenvalue statistic (37.127) is slightly below the 5% critical value (19.697), with a p-value of 0.075. Similarly, the null hypotheses of at most two and at most three cointegrating vectors cannot be rejected at conventional significance levels. Taken together, these results suggest the presence of a single dominant long-run cointegrating relationship among the variables. The identification of one cointegrating vector is consistent with the theoretical framework of the study, which posits a stable long-run relationship linking innovation, productivity, and economic growth. From an econometric perspective, this finding validates the use of the Unrestricted Error Correction Model (UECM) within the ARDL framework, as it confirms that long-run equilibrium relationships coexist with short-run dynamics in the data.

Importantly, the confirmation of cointegration provides the necessary foundation for estimating an Error Correction Model (ECM). In the ECM representation, the long-run cointegrating relationship is captured through the error correction term (ECT), which measures the extent of deviation from long-run equilibrium in the previous period. The statistical significance of the ECT in the subsequent ECM estimation indicates that short-run disequilibria are corrected over time, thereby restoring long-run equilibrium.

Table 7 presents the results of the Modified Wald (MWALD) Granger causality tests, which examine the direction of causal interactions among innovation (INNt), total factor productivity (TFPt), and economic growth (GDPt). The MWALD approach is particularly appropriate in this context as it allows valid inference irrespective of the integration and cointegration properties of the variables, thereby complementing the UECM–ECM framework adopted in the study.

The results provide strong evidence that innovation Granger-causes economic growth. The null hypothesis that innovation does not Granger-cause GDP is rejected at the 1% significance level (χ² = 12.960; p = 0.002), indicating that past values of innovation contain significant predictive information for future economic growth. This finding supports the innovation-led growth hypothesis and is consistent with endogenous growth theory, which emphasises innovation as a key driver of long-run economic performance.

Similarly, total factor productivity is found to Granger-cause economic growth at the 1% level (χ² = 25.358; p = 0.000). This result reinforces the central role of productivity as the primary transmission channel through which innovation influences growth, as also evidenced by the long-run cointegration and ECM results. It confirms that improvements in efficiency and technological capability precede and help explain variations in economic growth in Ghana.

The causality results further reveal bidirectional causality between GDP and innovation. The null hypothesis that GDP does not Granger-cause innovation is rejected at the 5% level (χ² = 6.988; p = 0.030), suggesting that higher levels of economic activity stimulate innovative effort. This feedback effect highlights the presence of a virtuous cycle in which economic growth enhances innovation incentives, which in turn promote further growth.

In contrast, no evidence is found that total factor productivity Granger-causes innovation (χ² = 2.693; p = 0.261). This suggests that productivity improvements alone do not automatically generate innovation in the Ghanaian context, possibly reflecting structural constraints such as weak R&D systems, limited absorptive capacity, or institutional bottlenecks. Innovation appears to be more responsive to overall economic conditions than to productivity gains per se.

The results also indicate bidirectional causality between GDP and total factor productivity. The null hypothesis that GDP does not Granger-cause TFP is rejected at the 1% level (χ² = 11.588; p = 0.003), implying that economic growth feeds back into productivity improvements, potentially through learning-by-doing, scale effects, and capital deepening. Additionally, innovation is found to Granger-cause TFP at the 10% level (χ² = 5.823; p = 0.054), providing further evidence that innovation contributes to productivity growth, albeit with weaker statistical strength in the short run. Taken together, the Granger causality results paint a coherent picture of the growth process in Ghana. Innovation and productivity both play causal roles in driving economic growth, while growth itself reinforces innovation and productivity through feedback mechanisms. These findings complement the UECM and ECM results by revealing the directional dynamics underlying the long-run equilibrium relationship. Overall, the evidence supports a productivity-led, innovation-supported growth process, characterised by dynamic feedback effects rather than unidirectional causality.

4. Estimation Model

To estimate the determinants of total factor productivity in Ghana, the study adopts a two-step empirical strategy. Following the growth accounting literature, total factor productivity (TFP) is derived from an aggregate neoclassical production function with constant returns to scale. Output is assumed to be generated according to a Cobb–Douglas technology of the form:

$$Y_t=A_t{K}_t^{\alpha }{H}_t^{\beta }$$

where Yt denotes real output (GDP), “Kt” represents physical capital, “Ht” denotes human capital, “At” captures total factor productivity and α and β are output elasticities with respect to capital and human capital, respectively.

According to Manton et al. (2007), Stokey (1991) and Chen et al. (2011) the stock of human capital (H) in the Cobb-Douglass production function in equation (1) can be evaluated by the labor force and the product of labor force quality (h). Total factor productivity is shown by the parameter (A).

Taking logarithms of equation (1) yields the estimable form:

$$ln{Y}_t=ln{A}_t+\alpha ln{K}_t+\beta ln{H}_t$$

Total factor productivity is obtained as the residual:

$$ln{Y}_t=ln{A}_t-\alpha ln{K}_t-\beta ln{H}_t$$

which captures efficiency gains, technological progress, and other factors not directly attributable to observable input accumulation.

In the second stage, the constructed TFP series is employed in the econometric analysis to examine its determinants and its contribution to economic growth. The dynamic relationship is estimated using time-series techniques appropriate for mixed orders of integration, allowing for both short-run adjustments and long-run equilibrium effects. This two-step approach ensures a clear separation between the measurement of productivity and the estimation of its economic effects, consistent with standard practice in applied growth analysis.

The study also specifies total factor productivity growth (TFPG) as a function of a set of structural, macroeconomic, and institutional determinants in order to identify the direct sources of productivity dynamics in Ghana. Following the two-stage estimation strategy, total factor productivity is modelled as a function of its key macroeconomic and structural determinants. The baseline empirical specification is given by:

$$lnGD{P}_t=\alpha ₀+\alpha ₁TF{P}_t₋₁+\alpha ₂lnIN{N}_t+{\epsilon }_t$$

where TFPt denotes total factor productivity at time, lnINNt is innovation and lnGDPt denotes real output and “εt” is an idiosyncratic error term.

To address potential endogeneity between productivity and its determinants, the model is estimated using the two-stage least squares (2SLS) method. One-period lagged values of the explanatory variables are employed as instruments, ensuring consistency of the estimated coefficients. The inclusion of the lagged dependent variable captures persistence in productivity dynamics and allows for gradual adjustment toward long-run efficiency levels. Finally, the study estimated the collective impact of Innovation and total factor productivity on economic growth.

5. Result and Discussion

It is critical to analyze the significance of the TFP’s evolutionary highlights, as they represent a pivotal phase in a nation’s economic development. This requires a more thorough analysis of Total Factor Productivity (TFP). Table 1 presents a study of the variation in TFP growth compared to GDP growth. Based on the findings reported in Table 1 of this study, the rate of growth attributed to total factor productivity growth (TFPG) in Ghana was greater and showed a progressive increase throughout different time periods. According to the mainstream neoclassical paradigm, the rise of total factor productivity (TFP) is the driving force behind steady-state growth and the possibility of increasing living standards over time. Indeed, if the pivotal parameter (α) in the Solow-Swan model remains constant over time, the economy’s growth will persistently increase. Remarkably, under the assumption that the Solow-Swan model parameter (α) remains constant over time, the efficacy might progressively diminish over time, as evidenced by the case of Ghana’s economy from 2012 to 2021.

Table 8 presents a periodised decomposition of economic growth in Ghana, highlighting changes in total factor productivity (TFP) growth and its contribution to per capita GDP growth over the period 1965–2021. The table provides important insights into the evolving role of productivity in Ghana’s growth process and complements the econometric evidence reported earlier in the study. Overall, the results reveal substantial variation in TFP performance across sub-periods, underscoring the instability of productivity as a growth driver in Ghana. During the early period (1965–1970), average TFP growth and its contribution to economic growth were modest, reflecting a growth process dominated by factor accumulation rather than efficiency gains. This pattern persisted through the 1970s and early 1980s, despite relatively higher average TFP growth rates between 1971–1981. Although TFP growth improved during this period, its contribution to per capita GDP growth remained limited, suggesting weak transmission of productivity gains into aggregate economic performance.

The period 1982–1986 marks a further decline in the contribution of TFP to growth, coinciding with macroeconomic instability and structural challenges. While average TFP growth remained positive, its contribution to GDP growth fell, indicating that productivity improvements were insufficient to offset adverse economic conditions. This finding aligns with the broader literature on Sub-Saharan Africa, which documents a prolonged period of productivity stagnation during the late 1970s and early 1980s. From 1987 to 2001, corresponding broadly to the post-structural adjustment era, TFP growth shows a relative improvement, and its contribution to economic growth becomes more pronounced. This period records one of the highest average contributions of TFP to per capita GDP growth (2.042), suggesting that economic reforms, trade liberalisation, and gradual institutional improvements may have enhanced efficiency and productivity. These results are consistent with evidence from other African economies, where reform periods are often associated with temporary productivity gains.

However, the subsequent periods reveal renewed volatility. Between 2002–2006, and particularly during 2007–2011 and 2012–2016, the contribution of TFP to economic growth weakens again, with negative average changes in TFP observed in some sub-periods. Notably, despite relatively strong GDP growth during 2007–2011, TFP growth is negative, implying that growth during this period was driven primarily by factor accumulation, commodity expansion, or sectoral reallocation rather than efficiency improvements. This disconnect between output growth and productivity is a key structural concern highlighted by the study. The most recent period (2017–2021) further reinforces this pattern. Although average TFP growth is relatively high, the contribution of TFP to per capita GDP growth becomes negative. This suggests that productivity gains did not translate effectively into economic growth, potentially reflecting external shocks, structural bottlenecks, or inefficiencies in resource allocation. Such outcomes are consistent with the study’s econometric findings, which show that while innovation and productivity matter for long-run growth, their effects are mediated by institutional and structural factors.

Taken together, the results in Table 8 provide strong descriptive support for the central conclusion of the study: economic growth in Ghana has been largely episodic and weakly productivity-driven. Periods of relatively high output growth have not consistently coincided with sustained improvements in TFP, reinforcing the argument that innovation-led productivity growth remains underdeveloped. These findings complement the UECM, ECM, and causality results by illustrating, in a growth accounting framework, why productivity-enhancing innovation is critical for achieving durable long-term growth in Ghana.

Table 9 reports the results of the growth accounting exercise using an alternative approach, focusing on average total factor productivity growth (TFPG), changes in TFP, and labor productivity growth per worker across distinct sub-periods. The results provide additional insight into the sources of Ghana’s economic growth and complement the baseline growth accounting and econometric findings presented earlier.

The early period (1965–1970) exhibits modest average TFPG and TFP growth, accompanied by relatively strong labor productivity growth. This pattern suggests that improvements in output per worker during this period were driven primarily by factor deepening and sectoral reallocation rather than by broad-based efficiency gains. A similar dynamic is observed during 1971–1976, where higher TFPG coexists with relatively low average TFP growth, indicating that labor productivity improvements did not stem from sustained technological progress. The period 1977–1986 is characterised by notably weak productivity performance. Both TFPG and changes in TFP remain low, while labor productivity growth declines relative to earlier periods. This outcome reflects the macroeconomic instability and structural challenges facing the Ghanaian economy during this period and is consistent with evidence from other Sub-Saharan African economies, which experienced widespread productivity stagnation in the late 1970s and early 1980s.

A structural break appears during the reform era (1987–2001), where average changes in TFP rise sharply, accompanied by the highest labor productivity growth observed in the sample. Despite this improvement, TFPG remains close to zero, suggesting that productivity gains were unevenly distributed or offset by inefficiencies elsewhere in the economy. This divergence between TFP growth and labor productivity highlights the transitional nature of reform-driven growth, where efficiency improvements may not immediately translate into sustained aggregate productivity gains. During 2002–2006, modest improvements in both TFPG and TFP growth are observed, alongside moderate labor productivity growth. However, this period is followed by renewed divergence in 2007–2011 and 2012–2016. In these sub-periods, TFPG increases substantially while changes in TFP remain weak, indicating that gains in labor productivity were likely driven by capital deepening or sectoral shifts rather than technological or efficiency improvements. This pattern is consistent with growth episodes in resource-dependent Sub-Saharan African economies, where output expansion is often decoupled from productivity gains.

The most recent period (2017–2021) exhibits a pronounced decline in productivity performance. Average changes in TFP turn negative, and labor productivity grow falls sharply, suggesting that recent economic growth has been accompanied by declining efficiency. This result reinforces concerns about the sustainability of Ghana’s growth trajectory in the absence of stronger productivity-enhancing mechanisms. Overall, the alternative growth accounting results confirm the central conclusion of the study: economic growth in Ghana has been characterised by episodic productivity improvements rather than sustained efficiency gains. While labor productivity growth has been positive in several periods, it has often been driven by factor accumulation and structural change rather than by persistent TFP growth. These findings are consistent with the econometric evidence and highlight the critical role of innovation-led productivity improvements in achieving long-term growth.

The study again employs the two-stage least squares (2SLS) estimation technique to examine the determinants of Total Factor Productivity (TFP) in Ghana. The results presented in Table 10 reveal three key insights: strong persistence in productivity, mixed effects of innovation, and a consistently positive contribution of economic growth.

The lagged value of TFP is positive and statistically significant in Models 1 and 6, with coefficients of 0.910 and 0.930 respectively. This indicates a high degree of persistence in productivity, suggesting that past productivity levels exert a strong influence on current performance. Such persistence is typical in developing economies, where structural rigidities, weak institutional frameworks, and limited technological diffusion constrain rapid productivity adjustments. This finding aligns with theoretical and empirical literature which emphasizes path dependence in productivity dynamics (Bakpa et al., 2021; Yeboah & Bakpa, 2025) In the Ghanaian context, this may reflect the slow pace of industrial transformation and the dominance of low-productivity sectors.

The impact of innovation (lnINN) on TFP is mixed and, in most cases, negative. While Model 2 reports a positive and statistically significant coefficient (0.491), Models 1, 3, 4, and 5 show negative and significant relationships. This counterintuitive result suggests that innovation activities in Ghana may not be effectively translating into productivity gains. One explanation is that innovation in Ghana is often informal, incremental, and weakly integrated into the productive sectors of the economy (Bakpa et al., 2021). Furthermore, limited collaboration between research institutions and industry reduces the commercialization of innovative outputs (Aboagye et al., 2023; Bakpa & Yeboah, 2024)

Another contributing factor may be the lack of complementary inputs required to harness innovation effectively. Endogenous growth theory posits that innovation enhances productivity only when supported by adequate human capital, infrastructure, and institutional quality (Romer, 1990). In Ghana, persistent challenges such as inadequate R&D funding, limited access to advanced technologies, and shortages of skilled labor may hinder the productivity-enhancing potential of innovation. Additionally, measurement issues may partly explain the negative coefficients, as proxies for innovation may not fully capture the scope or quality of innovative activities in developing economies.

This finding is consistent with the literature, which highlights the role of economic expansion in facilitating technological adoption, improving resource allocation, and generating economies of scale (Challoumis, 2024; Elfaki & Ahmed, 2024; Osano, 2023), In Ghana, growth driven by sectors such as services, agriculture, and extractive industries may create opportunities for productivity gains, although the sustainability of such gains depends on structural transformation and diversification.

The diagnostic tests further support the robustness of the estimated models. The Jarque-Bera test indicates normality of residuals, while the Breusch–Godfrey LM test and ARCH test suggest the absence of serial correlation and heteroskedasticity, respectively. The Ramsey RESET test confirms correct model specification. Additionally, the relatively high R-squared values (0.823–0.886) indicate that the models explain a substantial proportion of the variation in TFP. Overall, the findings suggest that while Ghana benefits from growth-driven productivity improvements and strong persistence effects, the role of innovation remains limited and inconsistent. This underscores the need for policy interventions aimed at strengthening the national innovation system, improving human capital development, and enhancing the linkages between research and industry to ensure that innovation contributes meaningfully to productivity growth.

6. Conclusion

This study examines the long-run and short-run relationships among innovation, total factor productivity (TFP), and economic growth in Ghana over the period 1965–2021. Using growth accounting and dynamic econometric techniques, the analysis provides robust evidence of a stable long-run equilibrium linking innovation, productivity, and output. The results consistently show that innovation contributes positively to economic growth, primarily through productivity-enhancing channels, while TFP emerges as the dominant long-run driver of growth.

However, growth accounting results reveal that Ghana’s productivity performance has been volatile and uneven, with several episodes of output expansion driven largely by factor accumulation rather than sustained efficiency gains. Granger causality and ECM results further indicate feedback effects between growth, innovation, and productivity, but limited evidence that productivity improvements systematically induce innovation. Together, these findings suggest a productivity-led but structurally constrained growth process, in which innovation and efficiency gains are insufficiently embedded to sustain long-term growth.

From a comparative perspective, Ghana’s experience closely mirrors that of many Sub-Saharan African economies, such as Nigeria, Kenya, and Tanzania, where growth has similarly been weakly anchored in productivity improvements. In contrast, economies such as Mauritius and, to a lesser extent, South Africa exhibit more stable productivity-driven growth, reflecting stronger innovation systems, higher human capital quality, and more effective institutional frameworks. These cross-country differences highlight the importance of complementary policies in translating innovation into sustained productivity gains.

The policy implications are clear. First, growth strategies should prioritise productivity-enhancing innovation rather than innovation intensity alone, with emphasis on process upgrading, technology adoption, and firm-level capability building. Second, human capital development is essential for strengthening absorptive capacity and maximising innovation spillovers, as demonstrated by higher-productivity African economies. Third, macroeconomic stability and trade openness should be maintained to support efficiency gains and technology diffusion. Finally, FDI policy should focus on linkages and knowledge transfer, rather than inflow volumes, to enhance domestic productivity.

Overall, the evidence suggests that achieving durable growth in Ghana requires a coherent, productivity-centred development strategy. Aligning innovation policy with human capital formation, structural transformation, and institutional reform is critical for converting episodic growth into sustained economic development.