In a comprehensive and forward-looking investigation, researchers have meticulously analyzed the intricate relationship between innovation spillovers and economic growth in Pakistan, with a particular emphasis on the nation’s absorptive capacity. The groundbreaking study spans five decades, from 1972 to 2022, providing an unprecedented temporal depth to the evaluation of how domestic and foreign research and development (R&D) activities influence Total Factor Productivity (TFP) growth. This extensive timeline captures the evolving dynamics of Pakistan’s economic landscape and the pivotal role innovation plays in shaping sustainable development trajectories.
At the core of this investigation lies the concept of spillover effects in research and development, a phenomenon where knowledge created in one context or entity inadvertently enhances productivity elsewhere. The researchers have delved deep into distinguishing between domestic innovation impacts and those emanating from foreign sources—elements critical to understanding the diffusion of technological advancements in a globalized economy. Domestic R&D spillovers were meticulously quantified using robust proxies such as gross expenditure on R&D (GERD), registration of domestic patents, and investment in higher education R&D, anchoring the analysis in solid, measurable indicators of internal innovation efforts.
The complexity magnifies when foreign R&D spillovers enter the picture. International research effects, often transmitted through trade and foreign direct investment (FDI), were captured using trade-weighted foreign R&D stock figures alongside inflows of FDI. This approach acknowledges the multifaceted channels through which cross-border knowledge transfers materialize, encompassing technology embodied in imports and collaborative ventures that introduce novel processes and competencies. The study, therefore, integrates a nuanced perspective on how Pakistan’s economy absorbs and capitalizes on international innovation currents.
Methodologically, the researchers leveraged advanced econometric techniques to untangle these relationships. The adoption of Autoregressive Distributed Lag (ARDL) models allowed them to dissect both short-term responses and enduring impacts on productivity growth, thereby delivering a comprehensive picture of adjustment dynamics and sustained economic change. The ARDL framework is particularly suited for time series data spanning diverse stationary properties, strengthening the reliability of the derived inferences in this longitudinal study.
Deriving TFP growth indicators from both the Translog and Cobb-Douglas production functions adds significant rigor to the investigation. The Translog function, with its flexible form, captures potential nonlinearities and interactions between inputs, offering a refined understanding of productivity shifts. Meanwhile, the Cobb-Douglas specification provides a baseline benchmark frequently applied in productivity analysis. Employing these two complementary models ensures that findings are robust and not artefacts of methodological constraints.
Diving into the data sources, the study harnesses a rich mosaic of national and international records, seamlessly integrating statistics from the Pakistan Bureau of Statistics, Pakistan Economic Survey, and State Bank of Pakistan, alongside globally referenced datasets like the Penn World Table and World Development Indicators. Such a multidimensional dataset enhances the empirical strength of the research while providing policy-relevant insights grounded in both domestic economic realities and global benchmarks.
Intriguingly, the results underscore the differentiated impacts of domestic versus foreign innovation activities on Pakistan’s productivity landscape. Domestic R&D investments, particularly those channeled towards higher education and patent activities, manifest a significant positive influence on TFP growth. This outcome highlights the critical role of strengthening local knowledge institutions and innovation ecosystems as a pillar for economic advancement.
Conversely, the contribution of foreign R&D spillovers, mediated primarily through trade and FDI, reveals an essential external dimension to Pakistan’s innovation dynamics. Trade-weighted foreign R&D stock plays a salient role in augmenting productivity, suggesting that Pakistan’s integration into global supply and knowledge networks carries tangible benefits. Similarly, FDI not only injects capital but also catalyzes technological adoption and managerial know-how, reinforcing the importance of an open, conducive investment environment.
The study also brings to light the pivotal role of absorptive capacity—the ability of domestic firms and institutions to internalize and apply external knowledge effectively. Innovation inflows alone do not guarantee productivity gains unless complemented by robust human capital, infrastructure, and organizational capabilities that enable effective knowledge absorption. This nuanced finding calls for a holistic innovation policy framework encompassing both supply-side R&D initiatives and demand-side capacity-building measures.
From a policy perspective, the research provides valuable guidance for Pakistan as it seeks to navigate the complexities of innovation-driven growth. It advocates for sustained investment in domestic R&D ecosystems, especially targeting higher education and patent activity enhancement, empowering local knowledge production. Simultaneously, policies that facilitate increased openness to foreign technology via trade and FDI, without compromising local innovation development, emerge as critical levers for productivity gains.
The timeframe spanning half a century enriches the study’s relevance, positioning it as a key reference for understanding long-term structural changes and the evolving global economic context. The historical depth allows for observing how shifts in economic policy, trade liberalization, and technological advancements have shaped productivity trajectories, providing empirical grounding for future strategic planning.
Moreover, the integration of diagnostic and stability tests within the ARDL methodology underscores the researchers’ commitment to analytical rigor. This ensures the stability of the long-run relationships identified and validates the model specifications, enhancing confidence in the reported findings and their applicability to real-world policymaking.
The interplay between innovation spillovers and absorptive capacity resonates beyond Pakistan, offering lessons for other emerging economies striving to harness knowledge flows in the pursuit of sustainable development. The study reiterates the universal principle that innovation-driven growth is not solely about accessing new technologies but also about the domestic ecosystem’s readiness to transform such access into tangible productivity improvements.
In conclusion, this investigation not only sheds light on critical innovation dynamics within Pakistan but also contributes meaningfully to broader discussions on economic growth, technology transfer, and capacity-building. As economies worldwide grapple with accelerating technological change, understanding the conditions that maximize the productivity benefits of innovation remains paramount.
The study, published in Humanit Soc Sci Commun, encapsulates the intricate relationships between innovation spillovers, economic growth, and the crucial enabling role of absorptive capability. It provides empirical foundations to guide policymakers, researchers, and stakeholders committed to fostering a resilient and innovative economic future.
Subject of Research:
The role of domestic and foreign research and development spillovers on total factor productivity growth in Pakistan, with a focus on absorptive capacity between 1972 and 2022.
Article Title:
Innovation spillovers, economic growth and role of absorptive ability.
Article References:
Usman, M., Hameed, G., Almas, L.K. et al. Innovation spillovers, economic growth and role of absorptive ability. Humanit Soc Sci Commun 13, 465 (2026). https://doi.org/10.1057/s41599-026-06726-x
Image Credits: AI Generated
DOI: https://doi.org/10.1057/s41599-026-06726-x
