The purpose of this paper is to provide evidence on the impact of internet use –as a proxy for Information and Communication Technologies (ICT)– on Total Factor Productivity (TFP) for a group of 12 Latin American countries. Unlike other studies for the region, this paper considers a more recent and extended period in which the internet is no longer a newly introduced technology. This relationship is analyzed by estimating a two-way panel data model. The results obtained provide evidence of a positive, albeit inelastic, response. A 1% increase in the percentage of households with broadband access (PHBA) is associated with a 0.064% increase in productivity.

The history of humanity is littered with technological innovations that have brought about significant improvements in living standards, as well as periods of strong economic growth and revolutions in productivity. Examples include the First and Second Industrial Revolutions. More recently, economics historians have highlighted, as a paradigmatic case, the rise of Information and Communication Technologies (hereafter ICT), which took place in the last quarter of the 20th century and whose momentum continues to the present day.

Although most of the specialized literature reports a positive impact of ICT on economic growth and productivity, some studies suggest that this impact is limited or even null (Fernández-Portillo et al., 2020). Similarly, Niebel (2018) argues that it is also unclear a priori whether the impact of ICT on economic growth is more significant in emerging economies than in developed ones. In the same vein, Steinmueller (2001) proposes the "leapfrog hypothesis," according to which ICT has the potential to support a "leapfrog" or "big push" strategy to reduce productivity and production gaps between developed and developing countries.

In general, the benefits of ICT for economic growth and productivity include strengthening collaboration among economic agents, improving the innovation capacity of an economy, facilitating access to products and services, opening up new employment opportunities and providing marginalized communities with access to information and resources for business development (Pradhan et al., 2017a). In this respect, internet access is critical to achieving productivity gains. It can provide not only direct economic benefits resulting from increased innovation and productivity in the economy (Kim et al., 2021) but also others related to better access to healthcare services and improved education, as well as more efficient energy consumption (Ben Lahouel et al., 2021). In this context, greater access to the Internet enables productivity improvements that lead to the optimization of production methods and improvements in the quality of human capital.

The scenario of continuous developments has revived the debate on the "productivity paradox," an idea popularized by Solow (1987, p. 36) in his famous comment: "Computers are everywhere except in productivity statistics," alluding to the low correspondence between ICT development and productivity progress experienced by the US economy between the 1970s and 1980s.

Possible explanations for the paradox generally include four main conjectures (Brynjolfsson et al., 2019). The first relates to false expectations, which implies a mismatch between the actual impact of technological progress on the overall system and what was initially expected of it. Thus, even if the invention brings about a significant change in a specific sector, the impact on a general level may be small.

The second explanation emphasizes the statistical issue and is related to measurement errors. The idea is simple: new technologies provide benefits not reflected in current productivity statistics. This implies that the tools and techniques used to measure the phenomenon cannot do so accurately. Thus, there would be a problem with the measurement and/or data collection instrument that underlies the statistics.

A third way of looking at the phenomenon is to assume that the benefits of ICT are redistributed between businesses and customers but without increasing total output. Some authors support the concept of "rent dissipation" to explain situations in which "both those seeking to be one of the few beneficiaries, as well as those who have attained some gains and seek to block access to others, engage in these dissipative efforts, destroying many of the benefits of the new technologies" (Brynjolfsson et al., 2019, p. 29).

Finally, the fourth conjecture points to time lags. According to this idea, a long period of time is needed to take full advantage of a new technology. It is also possible to think that the longer the time lag, the wider the scope of application of the invention. In this respect, the need to reorganize production affects the productivity distribution of adopting plants during the diffusion of a major new technology (Juhász et al., 2020).

The literature on this research topic continues to proliferate as digital technologies continue to advance in leaps and bounds (Vu et al., 2020). Refining statistical techniques to collect and compile more specific statistical series is also contributing to the development of the field.

One of the issues that has provided the most support for the return of this discussion –which some authors call the modern Productivity Paradox debate— has been, among others, the remarkable progress in Artificial Intelligence (hereafter AI) achieved in recent years. In this context, authors such as Zhang et al. (2024) point out that, despite the rapid development of AI-related technology, its relationship with productivity growth remains weak in the macroeconomic literature. In the future, AI will bring drastic changes to how we produce and all aspects of daily life. Machine Learning is perhaps one of the most disruptive fields within AI, as it implies a much more profound change than the advances made a few years ago with the emergence of traditional computing.

Nevertheless, the development of AI and its subsequent evolution would have been impossible without the widespread use of the Internet. Today, devices and objects are increasingly connected to the network, leading to a large-scale convergence between ICT and the economy (Figueroa Hernández et al., 2021), favoring productivity development. Meanwhile, Internet access allows cell phones to a large extent to increase competitiveness (Soylu et al., 2022).

From a conceptual point of view, the relationship between productivity and Internet use and how the Internet can improve productivity has multiple facets, the most important of which are the significant reduction in transaction costs, the increase in the efficiency of businesses and the increase in competition (Sánchez et al., 2006).

Although recent evidence is relatively abundant for developed countries, studies for Latin American countries are scarcer and refer to the country level to a greater extent (case analysis). Thus, the objective of this paper is to contribute to the modern debate on the Productivity Paradox for a group of Latin American countries (Argentina, Brazil, Bolivia, Chile, Colombia, Costa Rica, Ecuador, Guatemala, Mexico, Paraguay, Peru and Uruguay), for the period 2000-2019. Both the choice of cross-sections and the time period are restricted based on the availability of information.

The document is organized as follows: after this introduction, the second section deals with theoretical aspects and a bibliographic review. The third section presents the data and methodology. The fourth section presents the results obtained, and the fifth section contains the conclusions and possible ways to continue this work.

Technological progress plays a crucial role in both exogenous and endogenous growth models. However, while the latter develops explanations in this respect, the former assumes that the rate of progress is determined outside the model because their assumptions prevent the economy from allocating resources to finance technological progress. In particular, exogenous growth models assume a production function with constant returns to scale and perfect competition in the different markets so that the payment of each factor is equal to its marginal product. Using Euler's theorem, this implies that the total product of the economy is equal to the sum of the quantity of factors multiplied by their respective marginal productivities. Thus, there are no resources available to finance technological progress.

Broadly speaking, the link between ICT and economic growth can be seen in efficiency gains and productivity improvements by economic agents (Nair et al., 2020). A simple way of incorporating the issue into the framework of growth models is presented by Barro and Sala-i-Martin (2004), who use a simple leader-follower model to analyze the effect of innovation and technological imitation on the rate of economic growth. The importance of this model lies in the fact that it can be interpreted in a novel way to reveal how ICT penetration can drive economic growth in both the economy leading the innovation as well as in the follower economies, thus providing a useful point of reference and starting point for analyzing the phenomenon and obtaining policy recommendations.¹ In this model, the growth of the leading economy (economy 1) is driven by its innovations, while the growth of the follower economy (economy 2) depends on imitating the innovations made in the leading economy.

According to Barro and Sala-i-Martin's model (2004, p. 296), the growth rate of the leading economy can be written as follows:

(1)

where θ > 0 and ρ > 0 since they are parameters related to household preferences. Meanwhile, 0 < α > 1 indicates the elasticity of the marginal product of the intermediate goods. An important assumption made by the authors is that these three parameters are similar for both economies (the leader and the follower). Furthermore, A₁ is a productivity parameter representing the quality of governance and the level of technology. L₁ is the labor endowment, and η is the unit cost of inventing a new variety.

With respect to the follower company, the growth rate will be:

(2)

Where N₁ is the number of varieties of intermediate products developed in the leading economy and N₂ is the number of products introduced in the follower economy. At all times, N₂ ≤ N₁. In turn, μ is a positive parameter that determines the convergence speed, and (N₂ / N₁)* is a ratio that indicates when the two countries are in a steady state.

Several conclusions can be drawn given equations (1) and (2). First, the growth rate of the leading economy can be increased by reducing the cost of invention (η), by increasing the labor force L₁ (to which ICT can directly contribute), or by increasing the productivity parameter A₁, the latter through actions that promote "e-governance, accountability, public-private partnerships, and learning from best practices around the world, ICT improves the quality of governance and thus productivity" (Vu, 2011, p. 360).

Finally, the follower economy will have a growth rate that depends fundamentally on the growth rate of the leading economy (γ₁) and on μ. This last result is relevant when considering a conceptual breakdown of the current technological transformations and their impact on growth. On the one hand, there is Industry 4.0, which refers to a form of production based on the incorporation of 4.0 technologies, focusing mainly on automation, robotization of processes and real-time data; on the other hand, there is the digital services economy based mainly on the use of platforms (e-commerce). Some authors, such as Capello and Lenzi (2023), show that the adoption of Industry 4.0 technology and automation could be associated with regional economic growth in the places where the transformation is taking place, while the effects of digitization more generally spread and diffuse across all regions, without giving those regions where the transformation of the digital services economy prevails a significant growth advantage over others.

In general, the existing empirical literature regarding ICT, productivity and economic growth has been grouped according to different criteria (Fernández-Portillo et al., 2020; Vu et al., 2020): effects of ICT on economic growth and/or total factor productivity (TFP) –including causality tests—, transmission channels, and differential impact of ICT, depending on the type of country.

However, it is also possible to find authors who divide the studies into two categories: those that examine the relationship between ICT and TFP –here making a subdivision between country-level studies and company-level studies— and those that examine the relationship between ICT and labor productivity (Shahnazi, 2021).

In the case of Latin American countries, the empirical literature focuses on the analysis of individual cases at the country or firm level, except the papers by Hofman et al. (2016) and Quiroga-Parra et al. (2017).

Hofman et al. (2016) analyze the impact of ICT on economic growth in five Latin American countries (Argentina, Brazil, Chile, Colombia and Mexico) from 1990-2013. The authors are also interested in explaining the gap in GDP per capita relative to the US economy. They find that the main reason for the persistence of the labor productivity gap is the growing ICT gap, which is counteracting improvements in human capital in Latin America.

Quiroga-Parra et al. (2017) conducted a comparative analysis of ICT use between six Latin American countries (Argentina, Brazil, Chile, Colombia, Peru and Mexico) and seven developed countries. The results show the significant technology, knowledge, productivity and innovation gaps in the region; the authors indicate this as the first evidence that ICT is a causal reason for the low productivity and quality of life in Latin America. Although they do not develop an econometric model, they carry out the main comparisons based on correlations for different measures of ICT access and use.

In contrast to these two papers, this paper considers a more recent and extended period when the Internet is no longer an emerging technology and a larger number of cross-sections at the country group and aggregate economy levels. In addition, the panel data methodology is helpful for international comparisons with other papers.

At the company level and for the Ecuadorian case, Arévalo-Avecillas et al. (2017) conducted a correlational study in which they reached three main conclusions. On the one hand, there is an evident influence between IT investment and the productivity of organizations. On the other hand, the adoption of a website shows a significant and positive relationship based on the performance of products and services, and finally, there is no relationship whatsoever with the performance of business processes. To conclude, the use of e-commerce does not show a significant relationship with productivity.

For the Mexican economy, Figueroa Hernández et al. (2021) studied the impact of the Internet on economic growth for the period 1990-2018. Using a single-equation model estimated by Ordinary Least Squares (OLS), they find a positive effect of the variables: number of Internet users and fixed telephone subscriptions on growth.

Using a quadratic model, for the case of Nicaragua, Brenes-Gonzalez (2023) estimates the semi-elasticities of the percentage of households using the Internet on the economic growth of that economy. The estimated coefficient of the linear variable is 0.0373.

In the case of other economies, recent empirical contributions are generally more productive, perhaps due to the greater availability and specificity of data. In this respect, Gopane (2020) examines the impact of digitization on labor productivity using econometric modeling based on an endogenous growth model for the case of the so-called BRICS bloc (Brazil, Russia, India, China and South Africa). The study's findings confirm the new productivity paradox, as the author concludes that accelerated digitalization does not manifest itself in productivity growth.

For the case of the Organization for Economic Cooperation and Development (OECD) countries, Kurniawati (2021) works with a sample of 24 countries for the period 2000-2018. The methodology used is based on panel cointegration and vector error correction models. The results above show that innovation development is highly significant in promoting economic growth in the later years of his sample.

Previous studies for European or OECD countries are those of Nair et al. (2020) and Pradhan et al. (2017b, 2018, 2019a, and 2019b). In all cases, using different methodologies, time periods, and country groups, they found positive effects of ICT on economic growth.

Adeleye et al. (2022), for example, test the so-called leapfrog hypothesis for eight economies in the South Asian Association for Regional Cooperation (SAARC) from 2000 to 2020. This hypothesis is tested using unbalanced panel data. The key variables are real GDP per capita and four ICT indicators (cell phones, fixed telephones, fixed broadband and Internet users). The primary estimation method is the Quantile Method of Moments Regression (MM-QR). The results show that ICT (individual indicators and composite index) has a statistically significant positive effect on economic growth. Likewise, the leapfrog hypothesis is valid for cell phones and composite index models and only for the lower quantiles in the case of fixed broadband.

Odhiambo (2022) suggests studying the relationship between ICT, income distribution and economic growth in the case of Sub-Saharan African countries for the period 2004-2014. The estimation technique used is the Generalized Method of Moments (GMM). The author finds evidence that, in general, an increase in ICT development leads unconditionally to increased economic growth in the countries studied.

Lyu et al. (2023) recently found evidence of a positive relationship between the digital economy and ecological TFP in the case of the Chinese economy. Their principal findings show that the digital economy has, on the one hand, a positive direct impact and, on the other hand, a spatial spillover effect on green U-shaped TFP. The origin of these effects is mainly related to the role of the digital economy as an agent for promoting the progress of green technology. Furthermore, the reported results of the heterogeneity analysis show that the digital economy is the fundamental factor for cities whose economies are based on natural resources, thus breaking the so-called natural resource curse.

Haldar et al. (2023) worked with a sample of 16 emerging markets from 2000-2018. Using GMM estimates with instrumental variable and fixed effect regressions, they find that ICT not only monotonically increases economic growth but also exerts an effect on growth by increasing the efficiency of financial development. However, the authors caution that ICT accentuates the adverse effects of trade on economic growth.

The literature on the relationship between ICT vs. labor productivity is extensive. In general, a positive effect of ICT use on labor productivity is documented, even for those with lower levels of education (Lee et al., 2020).

Moreover, it emerges as another important issue related to the question of the sensitivity of jobs to computerization and the widespread diffusion of robotics in different sectors (Lorenz et al., 2023; Acemoglu and Restrepo, 2020; Frey and Osborne, 2017). In this regard, it is important to note that "ICT can affect cost reduction by saving labor and capital. This can affect the flexibility of the production process and induce increasing returns to scale" (Shahnazi, 2021, p. 345) (see Figure 1).

Finally, Table 1 provides a synthesis of empirical works regarding the impacts of ICT on productivity and growth:

Different sources were considered for this analysis: the Penn World Table 10.0 (PWT) (Feenstra et al., 2015), the International Telecommunication Union (ITU) reporting to the United Nations, the World Bank Open Data, the Ibero-American and Inter-American Science and Technology Indicators Network (RICYT), and the Worldwide Governance Indicators (WGI) produced by the World Bank (see Table 2).

An attempt is made to determine the evolution of the TFP variable as a function of PHBA. Adopting this variable as a proxy for ICT responds to the availability of data the ITU provides for the group of countries for which we intend to provide evidence. In this respect, PHBA allows for a broader sample than other measures commonly used for this purpose (e.g., number of telephones or total number of Internet users), covering a greater number of periods and Latin American economies. In addition, three control variables commonly mentioned in the literature are included: gross fixed capital formation (as a percentage of GDP) –(FBC)—, R&D expenditure (as a percentage of GDP) –(GID)—, and rules of the game (RG).).² The latter attempts to incorporate an important institutional aspect, reflecting perceptions of the extent to which agents trust and abide by the rules of society, and in particular, the quality of contract compliance, property rights, police and courts, as well as the likelihood of crime and violence. In this respect, some of the most critical aspects in the construction of this metric are: protection of property and intellectual rights, fairness of judicial processes, organized crime and its cost to economic activity, judicial independence, confidence in police forces, risk of expropriation and contract modifications (IHS Markit World Economic Service), and a law and order component (Political Risk Services International Country Risk Guide). It is constructed as an index on a scale ranging from -2.5 to 2.5 (-2.5 representing worse performance and weak governance, 2.5 representing better performance and strong governance).

Thus, the proposed model is as follows:

(3)

(4)

Note that this is a panel model based on two-way error components (Baltagi, 2021), which can be estimated using simple methods such as MCO. Given its formulation, the estimation of the coefficients will allow us to obtain elasticities, except for RG, which will be semi-elastic.

Since this is an unbalanced panel, only fixed effects can be considered. In more general terms, the proposed modeling implies that in the case of a general model as follows:

(5)

where N_t (N_t ≤ N) denotes the number of individuals observed in year t, with

D_t, a matrix of order N_t × N, can be constructed from an identity matrix, I_N, omitting the rows corresponding to individuals not observed in year t. In this way, it is possible to define:

(6)

where is n × N y ∆₂ = diag [ D_tι N ] = diag [ ι N_t ] is n × T. The matrix provides the dummy variable structure for the incomplete data model (unbalanced panel).

Table 3 shows the main statistics of the variables involved in the analysis.

As can be seen, there is a wide discrepancy in the PHBA variable, which reflects the marked difference between countries in terms of PHBA. In this regard, while countries such as Argentina and Chile have values between 85 and 90% for the last year of the sample, others such as Guatemala, Paraguay and Peru have values below 40%.

On the other hand, the TFP has not developed uniformly across economies (see Figure 2). Although a positive trend can be observed in some of them, the trend is clearly downward in the case of Brazil and Mexico.

Table 4 summarizes the main estimation results. The estimation was performed using MCO and robust standard errors based on the use of a White matrix, which resolves the dependence between different cross sections.

First of all, it can be observed that all the variables have the signs expected a priori, according to the theoretical level, and at the same time, they show statistical significance. The coefficient of the variable PHBA shows that a 1% change in it is associated with a 0.064% change in the dependent variable (TFP). This indicates that the contribution, although in the direction suggested by the theory, has a strong inelasticity, implying that the increase in the percentage of households with broadband has a modest impact on productivity improvements. This value is very close to that reported by Niebel (2018), who, although he calculates the elasticity of economic growth with respect to ICT capital, could consider these measurements to be close due to the significant impact of TFP on growth. The sample used by the author consists of countries with different levels of development. The overall value estimated in this study is 0.089 for the global sample; however, a more exhaustive analysis allows him to present the results according to groups of countries. He concludes that these values are 0.066, 0.059 and 0.084 for developing, emerging and developed countries, respectively. In the same direction, Mashadihasanli and Zülfikar (2023) obtain a similar value for the elasticity of economic growth with respect to the number of Internet users per hundred people. In particular, using a GMM estimation, they conclude that the value is 0.0477 for a sample of 35 countries (developed and developing) and for the period 2001-2021.

Moreover, all the control variables also have coefficients with the expected signs and some degree of statistical significance. For the first of these, FBC is used as a proxy for investment, and the result is also inelastic, 0.205, meaning that a 1% change in investment implies a 0.205% change in TFP. This result is reasonable and makes sense, among other factors, because of the law of diminishing marginal returns.

Something similar occurs in relation to the R&D expenditure variable, which has a coefficient value of 0.03. However, this variable was only significant at 10%. The reported value is in line with estimates for developed countries. In this respect, Piekkola (2022) estimates the elasticity of output with respect to R&D expenditure for Finland to be between 0.036 and 0.039.

Concerning the institutional control variable RG, it is also significant at 5%, and it should be remembered that in this case, its coefficient is semi-elastic as it enters the model at the level of assuming values between -2.5 and 2.5. The results, therefore, show a positive contribution of the RG to the TFP. This means that a 1% improvement in RG has an approximate impact of 0.0052% on the change in TFP.

In terms of Figure 1, the control variables FBC and RG positively impact productivity, FBC via inputs and RG via the process of obtaining outputs. In this respect, Shahnazi (2021) argues that ICT can affect the process through cost reduction by reducing or saving inputs (labor and capital). At this point, it is clear that the degree of flexibility of the production process will depend to a large extent on institutional aspects related to the "rules of the game."

Meanwhile, the validation tests of the model are satisfactory, there is residual normality, and the level of explanation is adequate (the joint variations in the explanatory variables explain 93% of the variations in the PFP). The problem of residual dependence between cross-sections is solved using the White procedure.

Finally, the annex presents the effects by cross-section and period, respectively. Concerning the former, only Bolivia, Brazil, Peru and Uruguay have negative values, which means that these idiosyncratic values are added to the intercept of their respective equations. At the same time, the effects by period reflect the changes associated with the intercept in each of the reference years.

This paper was the first contribution to the modern debate on the productivity paradox. In this context, it addressed the question of how families' use of the Internet might affect the economy's productivity. The topic has attracted renewed interest from researchers because of recent technological advances, many of which have been made possible by the spread of the Internet.

This impact is analyzed for a sample of Latin American countries (Argentina, Brazil, Bolivia, Chile, Colombia, Costa Rica, Ecuador, Guatemala, Mexico, Paraguay, Peru and Uruguay) from 2000 to 2019. The effect is examined using a two-way panel data model. The availability of information has largely determined the choice of time period and cross-sectional units.

The results obtained from a model allowed us to quantify the impact of increased internet use by families on the economy's productivity. In particular, the estimated value shows that the contribution is positive but inelastic. In other words, changes in the percentage of households with broadband lead to equal but less than proportional changes in productivity. Specifically, a 1% increase in PHBA generates a 0.064% increase in TFP.

Likewise, the model also included three control variables to avoid bias in the estimates. These are gross fixed capital formation as a percentage of GDP (FBC), R&D expenditure (GID) and RG, all indicated in the theory. The results show that all of them have a positive impact, as expected, and some degree of statistical significance.

The first approach presented in this paper could be continued and extended in several directions. First, as the availability of data increases and international databases are updated, it will be possible to replicate the work over a more extended period of time and, if a balanced panel is available, even to apply other techniques rejected here because only an unbalanced panel of data is available. Second, econometric time series methodology could be used in addition to that used to measure the phenomenon under consideration. However, longer time series would be desirable for this purpose. An auto-regressive distributed lag model (ARLM) could be an appropriate tool to analyze the impact of the issue of lagged Internet use on productivity. Furthermore, other countries with a similar level of development could be included in the group considered. The interest of this paper was limited to Latin American countries. Finally, it would be desirable to deepen the analysis of the channels through which Internet use can affect the productivity of the economy, given the significant developments that it is experiencing and is expected to experience in the future in areas such as AI, for which long statistical series will begin to be available in the coming years.

In any case, countries will need to allocate more resources to public policies aimed at the systematic and detailed collection of information that will allow them to continue analyzing the so-called "productivity paradox."

The author is grateful for the comments and suggestions of two anonymous reviewers who helped improve the article. However, any remaining errors are the sole responsibility of the author.

Acemoglu, D. and Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6). https://doi.org/10.1086/705716

Adeleye, B. N., Jamal, A., Adam, L. S. and Oyedepo, T. (2022). ICT leapfrogging and economic growth among SAARC economies: Evidence from method of moments quantile regression. Journal of Global Information Technology Management, 25(3). https://doi.org/10.1080/1097198x.2022.2094184

Arévalo-Avecillas, D. X., Padilla-Lozano, C. P., Bustamante-Ubilla, M. A. and Vidal-Silva, C. L. (2017). Contrastación de la paradoja de la productividad por el uso de las Tecnologías de Información: el caso ecuatoriano. Información Tecnológica, 28(1). https://doi.org/10.4067/s0718-07642017000100017

Baltagi, B. H. (ed.) (2021). Econometric analysis of panel data. Editorial Sringer. https://doi.org/10.1007/978-3-030-53953-5_9

Barro, R. and Sala-i-Martin, X. (eds.) (2004). Economic growth. MIT Press.

Ben Lahouel, B., Taleb, L., Ben Zaied, Y. and Managi, S. (2021). Does ICT change the relationship between total factor productivity and CO2 emissions? Evidence based on a nonlinear model. Energy Economics, 101, 105406. https://doi.org/10.1016/j.eneco.2021.105406

Brenes-González, H. A. (2023). El uso del internet y su relación con el crecimiento económico: análisis para Nicaragua. Revista Científica Profundidad Construyendo Futuro, 19(19). https://doi.org/10.22463/24221783.4030

Brynjolfsson, E., Rock, D. and Syverson, C. (2019). Artificial Intelligence and the modern productivity paradox. In Agrawal, A., Gans, J. and Goldfarb, A. (coord.). The Economics of Artificial Intelligence (p. 23). University of Chicago Press. https://doi.org/10.7208/chicago/9780226613475.003.0001

Capello, R. and Lenzi, C. (2023). 4.0 technological transformations: heterogeneous effects on regional growth. Economics of Innovation and New Technology, 33(5). https://doi.org/10.1080/10438599.2023.2204523

Feenstra, R. C., Inklaar, R. and Timmer, M. P. (2015). The next generation of the Penn World Table. American Economic Review, 105(10). https://doi.org/10.1257/aer.20130954

Fernández-Portillo, A., Almodóvar-González, M. and Hernández-Mogollón, R. (2020). Impact of ICT development on economic growth. A study of OECD European Union countries. Technology in Society, 63. https://doi.org/10.1016/j.techsoc.2020.101420

Figueroa Hernández, E., Pérez Soto, F. and Godínez Montoya, L. (2021). El impacto del internet en el crecimiento económico de México. In Rózga Luter, R. E., Serrano Oswald, S. E. and Mota Flores, V. E. (coords.) (2021). Innovación, turismo y perspectiva de género en el desarrollo regional (vol. V). UNAM-IIEC.

Frey, C. B. and Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114. https://doi.org/10.1016/j.techfore.2016.08.019

Gopane, T. J. (2020). Digitalisation, productivity, and measurability of digital economy: Evidence from BRICS. Digital economy. In Bach Tobji, M. A, Jallouli, R., Samet, A., Touzani, M., Alecsandru Strat, V. and Pocatilu, P. (coord.). Emerging Technologies and Business Innovation (p. 27). Springer. https://doi.org/10.1007/978-3-030-64642-4_3

Haldar, A., Sucharita, S., Dash, D. P., Sethi, N. and Chandra Padhan, P. (2023). The effects of ICT, electricity consumption, innovation and renewable power generation on economic growth: An income level analysis for the emerging economies. Journal of Cleaner Production, 384, 135607. https://doi.org/10.1016/j.jclepro.2022.135607

Hofman, A., Aravena, C. and Aliaga, V. (2016). Information and communication technologies and their impact in the economic growth of Latin America, 1990-2013. Telecommunications Policy, 40(5). https://doi.org/10.1016/j.telpol.2016.02.002

Juhász, R., Squicciarini, M. P. and Voigtländer, N. (2020). Technology adoption and productivity growth: Evidence from industrialization in France (No. w27503). National Bureau of Economic Research.

Kaufmann, D., Kraay, A. and Mastruzzi, M. (2010). The worldwide governance indicators: Methodology and analytical issues. World Bank Policy Research Working Paper, (5430).

Kim, J., Park, J. C. and Komarek, T. (2021). The impact of mobile ICT on national productivity in developed and developing countries. Information and Management, 58(3). https://doi.org/10.1016/j.im.2021.103442

Kurniawati, M. A. (2021). ICT infrastructure, innovation development and economic growth: A comparative evidence between two decades in OECD countries. International Journal of Social Economics, 48(1). https://doi. org/10.1108/ijse-05-2020-0321/v2/review1

Lee, J. W., Song, E. and Kwak, D. W. (2020). Aging labor, ICT capital, and productivity. Japan and Korea. Journal of the Japanese and International Economies, 58 (1). https://doi.org/10.1016/j.jjie.2020.101095

Lyu, Y., Wang, W., Wu, Y. and Zhang, J. (2023). How does digital economy affect green total factor productivity? Evidence from China. Science of The Total Environment, 857. https://doi.org/10.1016/j.scitotenv.2022.159428

Lorenz, H., Stephany, F. and Kluge, J. (2023). The future of employment revisited: How model selection affects digitization risks. Empirica, 50(2). https://doi.org/10.1007/s10663-023-09571-2

Mashadihasanli, T. and Zülfikar, H. (2023). The impact of information and communication technologies on economic growth: The case of selected European countries. Journal of Research in Economics, 3(3). https://doi.org/10.29228/jore.28

Nair, M., Pradhan, R. P. and Arvin, M. B. (2020). Endogenous dynamics between R&D, ICT and economic growth: empirical evidence from the OECD countries. Technology in Society, 62. https://doi.org/10.1016/j.techsoc.2020.101315

Niebel, T. (2018). ICT and economic growth-comparing developing, emerging and developed countries. World Development, 104. https://doi.org/10.1016/j.worlddev.2017.11.024

Odhiambo, N. M. (2022). Information technology, income inequality and economic growth in sub-Saharan African countries. Telecommunications Policy, 46(6). https://doi.org/10.1016/j.telpol.2022.102309

Palos-Sanchez, P., Reyes-Menendez, A. and Saura, J. R. (2019). Modelos de adopción de Tecnologías de la Información y cloud computing en las organizaciones. Información Tecnológica, 30(3). https://doi.org/10.4067/s0718-07642019000300003

Piekkola, H. (2022). Innovation-driven growth policies for Finland. Informe de antecedentes para el Consejo de Política Económica. https://www.doria.fi/bitstream/handle/10024/188112/Piekkola-2022.pdf?sequence=1

Pradhan, R. P., Arvin, M. B., Bahmani, S. and Bennett, S. E. (2017a). The innovation growth link in OECD countries: Could other macroeconomic variables matter? Technology in Society, 51. https://doi.org/10.1016/j.techsoc.2017.08.003

_________, Arvin, M., Nair, M., Bennett, S. and Bahmani, S. (2017b). ICT-finance growth nexus: Empirical evidence from the Next-11 countries. Cuadernos de Economía, 40(113). https://doi.org/10.1016/j.cesjef.2016.02.003

Pradhan, R. P., Mallik, G. and Bagchi, T. P. (2018). Information communication technology (ICT) infrastructure and economic growth: A causality evinced by cross-country panel data. IIMB Management Review, 30(1). https://doi.org/10.1016/j.iimb.2018.01.001

_________, Arvin, M. B., Nair, M., Bennett, S. E. and Bahmani, S. (2019a). Short-term and long-term dynamics of venture capital and economic growth in a digital economy: A study of European countries. Technology in Society, 57(1). https://doi.org/10.1016/j.techsoc.2018.11.002

_________, Arvin, M. B., Nair, M., Bennett, S. E. and Hall, J. H. (2019b). The information revolution, innovation diffusion and economic growth: an examination of causal links in European countries. Quality and Quantity, 53(3). https://doi.org/10.1007/s11135-018-0826-2

Quiroga-Parra, D. J., Torrent-Sellens, J. and Murcia-Zorrilla, C. P. (2017). Information technology in Latin America, its impact on productivity: A comparative analysis with developed countries. Dyna, 84(200). http:// dx.doi.org/10.15446/dyna.v84n200.60632

Sánchez, J. L., Rata, B. M., Duarte, A. R. and Sandulli, F. D. (2006). Uso de internet y paradoja de la productividad: el caso de las empresas españolas. Cuadernos de Economía y Dirección de la Empresa, (26). https://www.redalyc.org/pdf/807/80702603.pdf

Shahnazi, R. (2021). Do information and communications technology spillovers affect labor productivity? Structural Change and Economic Dynamics, 59. https://doi.org/10.1016/j.strueco.2021.09.003

Solow, R. M. (1987). We’d better watch out. New York Times Book Review, 36. Soylu, Ö. B., Adeleye, B. N., Ergül, M., Okur, F. and Lorente, D. B. (2022). Investigating the impact of ICT-trade nexus on competitiveness in Eastern and Western European countries. Journal of Economic Studies, 50(4). https://doi.org/10.1108/jes-12-2021-0638

Steinmueller, W. E. (2001). ICT and the possibilities for leapfrogging by developing countries. International Labour Review, 140(2). Portico. https://doi.org/10.1111/j.1564-913x.2001.tb00220.x

Vu, K. M. (2011). ICT as a source of economic growth in the information age: Empirical evidence from the 1996-2005 period. Telecommunications Policy, 35(4). https://doi.org/10.1016/j.telpol.2011.02.008

Vu, K., Hanafizadeh, P. and Bohlin, E. (2020). ICT as a driver of economic growth: A survey of the literature and directions for future research. Telecommunications Policy, 44(2). https://doi.org/10.1016/j.telpol.2020.101922

Zhang, Y., Wang, T. and Liu, C. (2024). Beyond the modern productivity paradox: The effect of robotics technology on firm-level to total factor productivity in China. Journal of Asian Economics, 90, 101692. https://doi.org/10.1016/j.asieco.2023.101692