Productivity Paradox in Africa: Does Digitalization Foster Labor Productivity in African Economies?

Karacuka, Mehmet; Myovella, Godwin; Haucap, Justus Article — Published Version Productivity Paradox in Africa: Does Digitalization Foster Labor Productivity in African Economies? Journal of the Knowledge Economy Provided in Cooperation with: Springer Nature Suggested Citation: Karacuka, Mehmet; Myovella, Godwin; Haucap, Justus (2024) : Productivity Paradox in Africa: Does Digitalization Foster Labor Productivity in African Economies?, Journal of the Knowledge Economy, ISSN 1868-7873, Springer US, New York, NY, Vol. 16, Iss. 2, pp. 8374-8393, https://doi.org/10.1007/s13132-024-02200-8 This Version is available at: https://hdl.handle.net/10419/323661 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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MehmetKaracuka1,2 · GodwinMyovella3· JustusHaucap2 Received: 24 January 2024 / Accepted: 23 June 2024 / Published online: 24 July 2024 © The Author(s) 2024 Abstract How the advancement of information and communications technologies (ICT) and digitalization affect labor productivity is subject of an ongoing debate. While parts of the literature find the expected positive effects, other studies have found no effect, resulting in the so-called productiviy paradox. As most of the studies have focused on economically advanced economies such as OECD countries, evidence for less developed economies has been sparse.We use a digitalization composite index from a balanced panel of 40 Sub-Saharan African (SSA) economies, using data from 2006 to 2021, to assess the effect of digitalization on aggregate labor productivityin SSA economies. We employ generalized least squares (GLS) and system generalized methods of moments (GMM) methods to capture the effects of digitalization on labor productivity levels in agriculture, manufacturing, and service sectors. Our results show a weak association between digitalization and overall labor productivity. However, when sectors are analyzed separately, digitalization has a positive effect on labor productivity in agriculture and manufacturing sectors, whereas we find evidence for the productivity paradox in the service sector, with even a negative effect of digitalization on labor productivity. Keywords Digital economy· Labor productivity· Sub-Saharan Africa· Productivity paradox· Information and communications technology (ICT) * Mehmet Karacuka [email protected] Godwin Myovella myo[email protected] Justus Haucap [email protected] 1 Department ofEconomics, Ege University, Bornova, Turkey 2 DICE, Heinrich Heine University, Düsseldorf, Germany 3 University ofDodoma, Dodoma, Tanzania 8375 1 3 Journal of the Knowledge Economy (2025) 16:8374–8393 Introduction The so-called productivity paradox refers to the finding that, although there has been a rise in digitalization and in the usage of information and communications technology (ICT) over the past decades, productivity levels in advanced economies have stagnated and in some places even decreased, especially during the 1980s. Although there was an increase in productivity levels after the 1990s, many advanced economies have later again experienced a similar productivity paradox (Acemoglu etal., 2014; Brynjolfsson, 1993; Brynjolfsson etal., 2019). The lack of substantial productivity growth is considered a paradox because the combination of ever improving mobile technologies, unprecedented internet availability, and numerous new applications were expected to boost labor productivity (Van Ark, 2016; Polák, 2017; Gómez‐Tello etal., 2020). While there is a substantial body of literature that emphasizes the positive impacts of the digital revolution on productivity growth, especially in theory, even early studies found that the effects on growth and productivity where much smaller than expected (Baily & Chakrabarti, 1988; Loveman, 1994; Roach, 1987). For example, Roach (1987) noted that even though the amount of computing power per white-collar worker in the service industry increased dramatically between 1970 and 1980 in the US, the productivity development was rather flat. In the time from 1973 to the1980s US productivity growth was lower than between 1950 and 1973 (Brynjolfsson & Hitt, 1998). Even more dramatic is the shrinking productivity growth rate in the USA between 2011 and 2015, which stood at 0.5%, despite the advancements of digitalization with its vast amount of data collection and processing together with the proliferation of new apps (Papanyan, 2015). Productivity growth has also been sluggish in other developed nations (Capello etal., 2022; Watanabe etal., 2018), while there have been rapid advancements in some emerging economies such as Brazil, China, India, and Mexico. Still, the lack of productivity growth in many economies has become a significant concern and an issue of the global economic agenda (Colford, 2016; Van Ark, 2016). The unexpectedly low contribution of digital technology to world economic growth has been coined a productivity paradox (Solow, 1987; also Bemdt & Morrison, 1995; Brynjolfsson & Yang, 1996). A significant portion of literature on the impact of digitalization on labor productivity predominantly focuses on industrialized nations. The importance of computers, computer microprocessors, and productivity-enhancing computer software in driving productivity growth from the mid-1990s is well established in the literature. However, there is a notable lack of empirical evidence for developing nations with a focus on the relationship between the ICT revolution and labor productivity, specifically for Africa. For this reason, this study focusses exclusively on African economies and pays particular attention to the Internet and mobile telephony. We aim to explore how ICT, in terms of mobile phones, internet usage, and broadband technologies contribute to labor productivity in SubSaharan Africa, as the development of these technologies is supposed to be transforming the means organizations interact with labor (Byrne & Corrado, 2017). This study contributes to the analysis of the economic impacts of technology, by analyzing the effects of digitalization on total economy-wide labor 8376 Journal of the Knowledge Economy (2025) 16:8374–8393 1 3 productivity as well as effects on sector-level labor productivity in agriculture, manufacturing, and service sectors. Each of these sectors has its unique characteristics, challenges, and technological requirements due to the nature of work, skills requirements, and production processes. These specificities may result in different effects of digitalization. Hence, disaggregating the analysis may help governments and policy makers to identify the most beneficial policy measures through more targeted interventions with respect to digital technology adoption. This study also aims to present how the transition is made from the traditional path of development from agriculture to industry and finally to the service sector as pioneered by authors such as Fisher (1939) and Clark (1940). The remainder of this paper is organized as follows: the “Digitalization, Innovation, and Productivity” section briefly introduces the literature on digitalization, innovation, and productivity. The “Reasons Why Digitalization May Not Foster Productivity Growth” section describes some reasons why digitalization may not foster digitalization growth. The “Data and Methods” section describes data, methods, and empirical analysis. The “Empirical Results” section presents the empirical findings, and, finally, the “Conclusion” section provides conclusions and policy implications. Digitalization, Innovation, andProductivity Ever since Solow (1956) it has been widely acknowledged in the economic literature that technological progress is essential for productivity growth which, in turn, is a primary source of economic growth and prosperity and well-being (Papanyan, 2015; Jorgenson & Stiroh, 2000; Oliner & Sichel, 2000; Krugman, 1997; Solow, 1956; Romer, 1990; Grossman & Helpman, 1991). Paul Krugman (1997, p. 9) famously also said more than 25 years ago that, while productivity is not everything, in the long run it is almost everything. Mačiulytė-Šniukienė and Gaile-Sarkane (2014) point out that productivity is one of the key benchmarks to internationally compare the economic performance of different countries (see also Gomez etal., 2006; Frankel and Kendrick, 2024). Digitalization is among the fundamental drivers of technological change in the foreseeable future. Central to this development is the production and use of digital logic circuits and their derived technologies, including the computer, the smartphone, and the internet (Walwei, 2016). Digital technologies affect many aspects, including the production processes, and service delivery. It brings connectivity of people, workers, and machines (Walwei, 2016). Since the advent of the digital revolution, digital technologies and their derived technologies, including computers, cell phones, the internet, the use of big data, artificial intelligence, and mobile robots have facilitated computer-driven economies (Frey & Osborne, 2017). Brynjolfsson and McAfee (2012) pointed out that technological innovations are increasing tremendously in the area of digital technologies. Interestingly enough and rather remarkably, automatisation ternds do not only affect routine manufacturing tasks, but also high skilled jobs such as those performed by doctors, accountants, lawyers and many more. Thus, technological innovation is expected to significantly contribute to an increase in productivity, but there are sometimes also 8377 1 3 Journal of the Knowledge Economy (2025) 16:8374–8393 concerns about potential negative effects on employment. Frey and Osborne (2017) argue that occupations with the highest content of routine tasks are the most likely to be soon replaced by digitalization. Dachs (2018), however, argues that the size of the negative effects depends on the current production technology and the rate of substitution between input factors and the direction of technological change. Digitalization is said to facilitate more knowledge-based and decentralized production as a result of comprehensive internet connectivity. Like any other type of technological change, digitalization generally increases productivity and labor productivity in particular by working smarter through the new technologies and techniques of production (Brynjolfsson & Hitt, 1998). For example, cloud computing, remote working, infrastructure sharing and other developments faciliated by digitalization processes are likely to lower entry barriers in many markets, thereby intensifying product market competition and lowering consumer prices (Walwei, 2016). Kurt and Kurt (2015) further highlight that the ease and prevalence of communicating online as well as other developments in ICT “had a positive effect on load and productivity of labour force accelerated workflow and also increased the efficiency of production processes and output amounts.” As Kurt and Kurt (2015) further argue, countries “with higher populations and labor force have more opportunities to grow and develop if the labor force integrates the ICT technologies.” Several empirical studies provide evidence for a positive nexus between investments in digitalization and productivity performance, at the firm and industry levels. Gal etal. (2019) are among the studies that illuminate how major digital technologies (viz. high-speed broadband internet, simple and complex cloud computing services, Enterprise Resource Planning, and Customer Relationship Management software) affect firm productivity in 19 EU countries and Turkey. The study used 22 industries with data from 2010 to 2015 and revealed that industry-level digital adoption contributes to significant productivity returns at the firm level. Their results imply that digital adoption in the industry has contributed to increased productivity dispersion across firms. They further find that digitalization is on average more beneficial in manufacturing than service firms, and more wide-spread in industries that involve a high share of routine tasks. Bartel etal. (2007) have analyzed several plant-level mechanisms through which IT may promote productivity growth in industry valve manufacturing. Their analysis uncovered three main results. First, plants that adopt new IT-enhanced equipment also shift their business strategies by producing more customized valve products. Second, new IT investments improve efficiency in all stages of the production process by reducing setup times, run times, and inspection times. Consequently, it is less costly to switch production from one product to another and support the change in business strategy to more customized production. Third, the adoption of new ITenhanced capital equipment coincides with increases in the skill requirements of machine operators, notably technical and problem-solving skills, and with the adoption of new human resource practices to support these skills. Akerman, Gaarder, and Mogstad (2015) analyzed whether the adoption of broadband internet in firms enhances labor productivity and wages in Norwegian firms. They exploit firm-level information on value-added, factor inputs, and broadband, and found that broadband adoption favors skilled labor by increasing its relative 8378 Journal of the Knowledge Economy (2025) 16:8374–8393 1 3 productivity. Gal etal. (2019) point out that the nexus between investments in digital technologies and productivity growth is not without some complexities. It can be challenging to find reliable empirical measures. As the authors argue the impact may depend on other factors that complement digital technologies. Different factors enable the efficient development and deployment of resources. These include, among others, organizational capital and management skills (Basu etal., 2003; Bloom etal., 2012; Brynjolfsson & Hitt, 2000), human capital (Becker, 1964) and ICT-related skills, as well as a regulatory environment that supports the efficient allocation of resources. Reasons Why Digitalization May Not Foster Productivity Growth Despite the undeniable boost for economic transformation induced by digital technologies and innovations, productivity growth has not been as impressive as sometimes expected in many parts of the world (Goldfarb and Tucker 2019, OECD, 2015). In OECD economies, productivity growth has been fading over the past decades despite the massive diffusion of digital technologies (OECD, 2019, Chapter2). This observation is partly explained by issues such as premature deindustrialization, mismeasurement, and digital price deflation (Boussour, 2019; Byrne etal., 2016; McMillan etal., 2014). As a consequence of the global financial crisis and its aftermath, for instance, the reduction in credit availability has affected investment. In addition, there are sometimes structural reasons, such as a decline in business dynamism and poor performance of low-productivity firms (OECD, 2019, Chapter2). Premature industrialization and structural change have been identified as the major contributors to the sluggish growth in labor productivity (McMillan etal., 2014). As the authors argue the integration of the global economy and technology transfer may be a remedy for low growth rates. Even though globalization is progressing in developing countries, import quotas and tariffs have been lowered, foreign direct investments and exports are encouraged, and cross-border financial flows easier than in the past, the conditions for doing business are far from perfect in Sub-Saharan Africa. According to McMillan etal. (2014), labor has moved into the wrong sectors of the economies, from more productive to less productive activities, including most notably informal sectors. P remature de-industrialization has increased costs, as can be seen, for example, in Nigeria, Ghana, and South Africa. The patterns observed in the post-2000 period exhibit premature de-industrialization in these Sub-Saharan African economies. The share of employment in manufacturing has fallen by more than 5% but it was offset by a similar expansion in service sectors where labor productivity was nearly double that of manufacturing. South Africa and Ghana labor moved from a largely agriculture-based economy to economies with large service sectors. Boussour (2019) points out that productivity growth has gradually declined due to digital price deflation. The argument is that prices in traditional sectors of the economy have tended to increase in the recent decade whereas those in the digital sector have declined. Data of the US economy show that, in 2017, the price deflator for the overall economy increased by 1.9% while that of the digital economy declined by 2.2%. 8379 1 3 Journal of the Knowledge Economy (2025) 16:8374–8393 Moreover, mismeasurement is another issue in the digital economy that has biased macroeconomic statistics, particularly in the scope and estimation of GDP (Ahmad etal., 2017). Authors such as Boussour (2019) argue that mismeasurement is an explanation for the (measured) slow productivity growth in the post-recession era. The argument is based on the idea that official statistics cannot properly capture the productivity gains in information technology (IT) related goods and services. Prices and quality are the most critical factors whose impact is seen in the price deflators. On the contrary, however, it is argued that if original prices are not properly measured, price deflators would be over-estimated. This will consequently lead to an under-estimate of real economic output. Conversely, Byrne etal. (2016) point out that the changing structure of a rapidly evolving economy gives raise to another measurement problem. The rapid introduction of new products and services, such as Uber and Airbnb by businesses, can bias estimates of labor productivity. This is because faster transformations cause difficulties in adjusting official statistics. Byrne etal. (2016) and Ahmad etal. (2017) also conced, however, that mismeasurement is not a new issue. It has existed before the productivity slowdown, and there is no clear evidence whether this has become more servere over time. The authors further argue that even if there is mismeasurement, its extent is not sufficient to explain the widespread slowdown in productivity growth. Calvino and Criscuolo (2019) add that the uneven adoption and diffusion of digital tools across firms, industries, sectors, and countries may explain the digital productivity paradox. When there is uneven uptake in digital technologies, there may be a slowdown or even negative productivity growth. Evidence to illustrate this kind of nexus is taken from developed countries, such as the OECD economies, where many firms have access to high-speed broadband networks, and yet fewer of them have productivity-enhancing digital tools and applications, such as enterprise planning systems. Similar to other technological changes, digital transformation also needs complementary investments, such as process innovations, new systems, and business models (Haskel and Westlake, 2017). These, however, involve several trials and errors. In so doing, many firms enter and exit the business, depending on the business environment which differs across countries, which slows productivity growth (Brynjolfsson etal., 2019). Data andMethods Empirical Analysis This section describes the economic model and the econometric approach used to analyze the effects of digitalization on labor productivity in Sub-Saharan African economies. The approach follows Gust and Marquez (2004), but extends their study byusing panel regressions. Following Saia (2023) and Kouladoum et al. (2022) a digitalization composite index has been constructed from the four digital technology variables, namely mobile phone subscriptions per 100 inhabitants (Mobsub), fixed telephone subscriptions per 100 inhabitants (Fixsub), percentage of individuals using the internet, and mobile broadband subscriptions per 100 8380 Journal of the Knowledge Economy (2025) 16:8374–8393 1 3 inhabitants (Brb_sub). These technologies have been adopted mostly in the past two decades in Sub-Saharan Africa and exhibit an increasing trend despite the disparities between regions (Myovella etal., 2021). Moreover, they are chosen as measures of digitalization, because in contrast to some other more advanced measures they are widely available across Sub-Saharan African countries, sectors, and time. To study the effects of digitalization on labor productivity growth, we control for a variety of factors influencing labor productivity growth in SubSaharan Africa, including socio-economic, political, and infrastructure variables. In today’s knowledge-based economy, ICT-based tools, such as the internet, are expected to raise productivity. Morepver, ICT is a general purpose technology (GTP) that affects almost everything. This includes what and how economies produce and how they manage and organize the whole production process. Using the determinants of labor productivity, the study identifies the effect of the new digital variables on labor productivity, considering the following functional form: The dependent variable LPit denotes labor productivity (GDP per employed person), Xjit ( j=1….K) is a matrix with explanatory variables such as the digitalization index (digindx), human capital (HC), political stability (Pol_STBLTY), gross domestic investment (GDI), electricity infrastructure (ELECTRI), and trade openness (TOP). The subscript i denotes country i = 1, 2…m and at time t = 1,2,…T. where 𝜇t is the time effect captured by year dummy variables in the regression, vi is the country-specific effect invariant over time, and 𝜖it is the random error term in the equation, representing the net influence of all unmeasured factors. Human capital is an indispensable variable for the effective use of digital technologies, see (Lahouel etal., 2021; Cakar etal., 2021; Kouladoum etal., 2022). In the past two decades, when ICT is witnessed to have significantly grown and spread, researchers have observed a shift in labor demands towards skilled workers. Arvanitis and Loukis (2009) argue that skilled labor is a precondition for the use of ICT. Overall, there is an increase in total productivity since less labor is used to produce goods and services. In this study, human capital (HC) is measured by the human capital index based on years of schooling and returns on education. It is argued that policies that yield higher returns from education encourage innovation and foster the adoption of information and communication technologies. These policies also promote capital investment, both private and public, including telecommunications investment, and can potentially have a high impact on living standards (Papanyan, 2015). The contribution of human capital to labor productivity is theoretically widely accepted in both the macroeconomic and microeconomic literatures (see for example Nelson and Phelps, 1966; Barro and Sara-i-Martin, 2004). Other control variables include trade openness (TOP) which is defined as the sum of exports and imports of goods and services as a share of a country’s gross domestic product. Electricity (ELECTRI) is the percentage of the population that has access to electricity. Finally, gross fixed domestic investment (GDI) and a measure of political stability (Pol_STBLTY) are also included as control variables. (1) LP it =𝛼+𝜇t+ k ∑ j= 1 𝛽jXjit +vi+𝜖 it 8381 1 3 Journal of the Knowledge Economy (2025) 16:8374–8393 The empirical analysis takes into account differences among countries and changes over time. Data from several countries is used to increase the range of variation in the variables. It is important to take into account country-specific effects, as countries are likely to systematically be different due to different factors, such as weather, infrastructure, definition of inputs, productive efficiencies, cultural attitudes and many other factors that are difficult to measure and to observe directly (Table1). Econometric Method This study employs a generalized least square (GLS) method and it further tests the results using a system generalized methods of moments (GMM) which is a more robust estimator and has better estimation properties (Arellano & Bover, 1995; Blundell & Bond, 1998). The GLS model takes into account the non-spherical error structure under this specification. The variables under consideration are mainly economic variables; therefore, correlations may exist between regressors and error terms. When there is a certain degree of correlation between regressors and error terms, GLS is the most suitable tool for empirical investigation (see, e.g., Alvarez-Herranz etal., 2017). Furthermore, the rationale for using the system GMM as a more robust estimation technique is, first, that our number of countries is 40 which is greater than the number of years which equals 16 in the sample (Asongu etal., 2018; Myovella etal., 2020; Nchofoung & Asongu, 2022). Second, the system GMM method controls for possible endogeneity problems, as the literature has shown that analyzing the impact of digital Table 1 Definition of variables Variable name Definition Labor Productivity (LP) GDP per person employed (constant 2017 PPP $) Labor Productivity in Manufacturing (LPMAN) Value added per worker in the industrial sector (constant 2015 US$) Labor Productivity in Services (LPSERVICE) Value added per worker in the service sector (constant 2015 US$) Labor Productivity in Manufacturing (LPAgri) Value addes per worker in agriculture, forestry, and fishing (constant 2015 US$) Digitalization (digindx) Composite indicator constructed from mobile phone subscriptions per 100 inhabitants (Mobsub), fixed telephone subscriptions per 100 inhabitants (Fixsub), percentage of individuals using the internet, and mobile broadband subscriptions per 100 inhabitants (Brb_sub). Political Stability (Pol_STBLTY) Perception of the likelihood of political instability and/ or politically motivated violence, including terrorism. Electricity Infrastructure (ELECTRI) Percentage of the population with access to electricity. Human capital (HC) Human capital index, based on years of schooling and returns to education. Gross fixed capital formation per capita (GDI) Gross domestic investment, including land improvements, machinery, construction of roads, per capita. Trade Openness (TOP) Sum of exports and imports of goods and services as a share of a country’s gross domestic product 8388 Journal of the Knowledge Economy (2025) 16:8374–8393 1 3 integrated into production processes and are anticipated to enhance labor productivity, a desirable outcome. However, some parts of the literature suggests that despite increased investment in ICT technologies, productivity has not shown a positive impact. This study seeks to contribute to the existing body of knowledge by investigating the extent to which digitalization has facilitated growth in labor productivity in Sub-Saharan Africa. Utilizing annual data pertaining to various factors influencing labor productivity in Sub-Saharan Africa, an empirical analysis was carried out over a span of 16 years from 2006 to 2021. Two distinct models, the generalized least squares and a more robust system generalized method of moments, were employed to address potential endogeneity issues. We evaluated the impact of digitalization on overall labor productivity as well as on labor productivity in the agriculture, manufacturing, and service sectors. Each of these sectors exhibits unique characteristics, challenges, and technological requirements depending on the nature of the tasks, skill requirements, and manufacturing techniques, potentially leading to varying effects. The findings, particularly from the GMM model, indicate that digitalization may enhance labor productivity of the agriculture and manufacturing sectors. However, it has not enhanced labor productivity of the service sector and overall labor productivity, as the coefficients are negative and significant. The negative coefficients in labor productivity serve as an indicator for the presence of the productivity paradox in SubSaharan Africa. This phenomenon may be attributed to the underdevelopment of information and communication technologies in Africa, limited internet usage, and the presence of the digital divide. Furthermore, our empirical findings on various factors influencing labor productivity, such as political stability, electricity accessibility, human capital, gross domestic investment, and trade openness, demonstrate a favorable effect. Although some cutting-edge advanced digital technologies data were unavailable for analysis as a result of the nascent stage of the ICT sector in Africa, our empirical findings utilizing the current digital technologies carry significant implications for policymakers and governments. First, it is imperative for policymakers and governments to prioritize investment in robust digital infrastructure, including high-speed internet connectivity to enable widespread adoption across all sectors of the economy. Second, equal access to digital infrastructure in both rural and urban areas across SSA economies should be ensured to bridge the digital divide and promote inclusive productivity gains. For the manufacturing and agricultural sectors, which serve as the primary source of employment for most of the population in Africa, it is imperative to implement additional initiatives aimed at promoting the integration of digital technologies. It should be also stated that there are two ways that technology can affect productivity: Firstly, through the new tools, machineries, and equipment, and, secondly, also through a transformation of production organization. The first effect will be limited unless there are complementary organizational transformations. We believe that this effect comes with a time lag and with an increase in education, infrastructure, and human capital. Finally, in light of our findings, we recommend governments in Sub-Saharan Africa prioritize good governance, political stability, transparency, and the establishment of robust regulatory and policy frameworks. These measures will facilitate the seamless incorporation of digital technologies in various sectors, ultimately boosting labor productivity and fostering sustainable economic growth. 8389 1 3 Journal of the Knowledge Economy (2025) 16:8374–8393 Appendix Tables7 and 8 Funding Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK). Data Availability All data is available from public sources. Declarations Conflict of Interests The authors declare no conflict of interest. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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Table 7 List of Sub-Saharan African countries included in the sample Angola Cote d’Ivoire Liberia Rwanda Benin Equatorial Guinea Madagascar Senegal Botswana Eswatin Malawi Sierra Leone Burkina Faso Gabon Mali South Africa Burundi Gambia Mauritania Sudan Cameroon Ghana Mauritius Tanzania Central African Republic Guinea Mozambique Togo Chad Guinea Bissau Namibia Uganda Comoros Kenya Niger Zambia Congo Lesotho Nigeria Zimbabwe Table 8 Variance inflation factor Variable VIF 1/VIF Digindx 2.61 0.383 ELECTRI 2.61 0.382 HC 1.98 0.504 Pol_STBLTY 1.72 0.583 LnTOP 1.62 0.618 GDI 1.18 0.847 Mean VIF 1.95 8390 Journal of the Knowledge Economy (2025) 16:8374–8393 1 3 References Acemoglu, D., Autor, D., Dorn, D., Hanson, G. H., & Price, B. (2014). Return of the Solow Paradox? IT, productivity, and employment in US manufacturing. American Economic Review, 104(5), 394–399. https:// doi. org/ 10. 1257/ aer. 104.5. 394 Ahmad, N., Ribarsky, J., & Reinsdorf, M. (2017). 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