This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright © 2023 The Author(s). Published by Vilnius Gediminas Technical University ISSN 2669-2481 / eISSN 2669-249X BUSINESS, MANAGEMENT AND ECONOMICS ENGINEERING 2023 Volume 21 Issue 1 Pages 84–105 https://doi.org/10.3846/bmee.2023.18360 THE POTENTIAL OF MACROECONOMIC FACTORS IN SHAPING THE LANDSCAPE OF TECHNOLOGICAL DEVELOPMENT: A TESTIMONIAL FROM UPPER-MIDDLE-INCOME COUNTRIES Sadik ADEN DIRIR Faculty of Law, Economics, and Management, University of Djibouti, Djibouti Article History: Abstract. Purpose – the main goal of the paper is to examine the role of macroeconomic fac- tors in promoting the technological development of upper-middle-income countries. Research methodology – to carry on with the investigation the paper selected the expenditure in research and development as a proxy for technological advancement while GDP per capita, Final consumption expenditure, Domestic credit to the private sector, national income, and government transparency are selected as proxies for the macroeconomic indicators. More- over, a VECM approach is performed in order to capture the long-run and short-run rela- tionship among the variables. Additionally, a Granger causality test was used to observe the causality direction among the variables. Findings – the obtained results revealed that in the short run, all the selected variables have no prominent impact on R&D expenditure. However, the long run result, presented that the transparency situation of upper-middle-income countries, simultaneously the governments’ final consumption, the amount of credit provided to the private sector, and national income are unfavorably affecting technological development while the GDP is positively affecting the expenditure in R&D. Research limitations – the exclusive focus on macroeconomic factors and upper-middle-in- come countries as well as the fact of excluding the role of micro factors and low-income countries are the major limitation of the study. Practical implications – policymakers and nations looking to accomplish technological trans- formation in the age of digitization can benefit from the study’s findings. Originality/Value – since prior studies highlighted the link of macroeconomic factors with spe- cific sectors such as healthcare, education, and agriculture. Thus, giving little attention to or neglecting the information technology sector that compromises a more specific branch such as research and development. For that reason, this paper will bring light to this phenomenon. ■ received 23 December 2022 ■ accepted 09 May 2023 Keywords: macroeconomic factors, R&D, technological development, upper-middle-income countries. JEL Classification: O11, O32, O38.      Corresponding author. E-mail: sadikaden1999@gmail.com Introduction Technology is derived from the word technique, which denotes information, experience, tools, and instrument. Previous decades have seen the generation of knowledge and expertise across many facets of society (Bismala et al., 2020). The total amount of information on the methods and strategies employed in the creation of materials is known as science. The word “technology” has now been expanded to include both the methods and processes themselves as well as the body of information about them. This knowledge today encompasses not just manufacturing but also other facets of social life (Lee, 2018). http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.3846/bmee.2023.18360 https://orcid.org/0000-0002-8159-5442 mailto:karahan.kara@artvin.edu.tr mailto:sadikaden1999@gmail.com Business, Management and Economics Engineering, 2023, 21(1): 84–105 85 Cui (2016) in one of his studies states that as nations transition from agrarian to in- dustrialized societies, and then to post-industrial digital civilizations, many aspects of their features change, notably the importance placed on training and the work opportunities of labor. Contrasting the traits of modern society with the characteristics of manufacturing and agrarian civilizations has allowed everyone to track this progression. Although on a personal point, there may be prolonged life expectancies, extra leisure time, and a decline in hours spent on transportation, demographic change, and an increase in urbanization, the advance- ment of emerging technologies and scientific research has led to the higher significance of investigation and training, the complexity of institutions, bigger value of communication, higher levels of the employees in information, major roles for the mass communication, and a broader range of interdependence (Alam et al., 2019a). Research and development are the standard strategies for fostering technological ad- vancement, therefore appeals for additional R&D expenditure have become commonplace in both the governmental and general public spheres (Bengoa et al., 2017). It is easy to identify several breakthroughs as the main cause of the rise in living standards during the previous two centuries. Greater R&D, according to supporters, will provide new technical innovations that will promote wealth and economic development (i.e., an expansion in production per person) (Barkhordari et al., 2019). Some supporters even go to the extent of claiming that nations compete with one anoth- er for technical supremacy (Muscio & Ciffolilli, 2018). The nations who triumph in this compe- tition produce the most advanced technical goods, reaping the benefits of their achievement. These nations are thought to have better production and efficiency levels. It is anticipated that their employees would earn the greatest real earnings and thus have the best quality of life. The nations that succeed in achieving technological advancement could even be in a position to outperform others who are unable to reach the technological horizon (Chen et al., 2021). However, a significant portion of people who do not have such a pessimistic viewpoint nonetheless agrees that higher investment in R&D will boost future salaries and performance (Godil et al., 2021). In light of this, governments shouldn’t be reluctant to proactively promote and finance R&D efforts, particularly if private industry R&D is insufficient. One of the key factors in boosting a nation’s prosperity and competitiveness is its ex- penditure on research and development (Bor et al., 2010). Generally speaking, economic expansion is a long-term result of technical progress. Therefore, innovation increases overall factor productivity, which indicates an increase in overall output (Surani et al., 2017). Since Schumpeter, the importance of economic innovation practices in development has been extensively examined. Schumpeter is considered one of the pioneering economists who stud- ied the macroeconomic link between technological innovation and economic development in this setting (Hasan & Tucci, 2010). Through the beginning of the twentieth century, the Neoclassical growth paradigm had narrowed its attention to the contributions that capital formation, technical advancement, population expansion, and output made to long-term economic development. Although the concept stresses how technology fosters progress, it has been acknowledged that technological advancements are an exogenous component. R&D was regarded as an endogenous element in economic development frameworks in the late 1980s because of the groundbreaking work of Peng (2010). 86 S. Aden Dirir. The potential of macroeconomic factors in shaping the landscape of technological development: ... The major factor underlying technical advancements is R&D activity. R&D efforts yield ideas that are projected into products, which are then released onto the market through marketing. Nations can achieve greater technological levels and create new goods in this way to outperform others in terms of growth potential (Adomako et al., 2021). Therefore, governments should broaden the scope of R&D operations and devote additional GDP to R&D. Economic expansion concepts frequently challenge the idea that R&D expenditures are crucial to the cycle of economic growth (Beladi et al., 2021). Typically, the following issues occupy a central place in the discussion of innovation strate- gy: First, the use of the finest methods in public policy to encourage private-sector innovation with the broadest potential societal consequences. Second, the spread of technology (across nations and businesses). Third, the recognition of revolutionary breakthroughs and strategies for promoting them. Four, the significance of non-R&D innovation. Lastly, the relative contri- butions of public and private R&D (Prokop et al., 2021). The most urgent technology obsta- cles for upper-middle-income countries have been recognized, and they include expanding experience and understanding industrial activities, enhancing the availability of capital for high-growth, new sophisticated activities, enhancing the contribution of universities of higher learning to community business incubators, and enhancing the democratic institutions of innovations and research systems (Vrontis & Christofi, 2021). As matter of fact, prior studies highlighted the link of macroeconomic factors with specific sectors such as healthcare (Darvas, 2018), education (Olilingo & Putra, 2020), and agriculture (Czyżewski & Majchrzak, 2018). Thus, giving little attention to or neglecting the information technology sector that compromises a more specific branch such as research and develop- ment. As a consequence, this research aims to better investigate how macroeconomic factors, including a transparent government, contribute to technological development while taking into account the practical and dramatic elements of filling in the gaps of earlier literature. Based on this the paper answer question such as; Do macroeconomic factors contribute to the process of technological development in upper-middle-income countries? And how do these influences differ through the long-run and short-run aspects? To address these research questions, this study proposes a VECM model to assess the cointegration among the varia- bles in both the long-run and short-run periods. Additionally, the study employs a Granger causality test to examine the causality direction among the variables. The theoretical foundations of earlier studies reviews are explained in the upcoming sec- tion. The methodology section will include details on the data source, such as measurement details for each variable, and the research process adopted Moreover, the econometric model will be highlighted. Continuously, the discussion of empirical findings, data suitability, and results comes next. After validation of the findings, the implications, restrictions, and recom- mendations for further research are discussed. 1. Survey of the literature Expenditure in research and development (R&D) is regarded as one of the most crucial fac- tors in fostering economic progress. Coccia (2018) noted that by enhancing efficiency and expanding their base of knowledge, nations with adequate R&D expenditure may accomplish Business, Management and Economics Engineering, 2023, 21(1): 84–105 87 their desired economic development. The Goals for Sustainable Development (SDG9) now include creativity as one of its goals, and nations are urged to create a sustainable building, support an accessible and stable economy, and support innovation. Based on this R&D is the fuel to promote innovation. For instance, SDG9.5 urges nations to significantly expand public and corporate R&D expenditure. In different regions, East Asia and the OECD have the maximum R&D concentration; nationally, China and India have been the world’s largest innovation hubs for the past 10 years (Knoll et al., 2021). This approach had a substantial influence on R&D efforts in Russia, which changed to a market economy in 1991 after the Soviet Union fell apart. Industry groups, businesses, and local authorities have more influence now that state-owned firms are less dominant (Gokh- berg, 1999). Nevertheless, Russia’s R&D operations were adversely impacted by the country’s shift to a market economy. because the public’s straightforward contribution to R&D spend- ing as a share of GDP has declined by around 75%. Due to this, the employment of half the researchers and scientists was endangered (Schweiger et al., 2022). China’s technology infrastructure was not as strong in comparison to that of developed nations during the beginning of the 1980s. After the Chinese economy underwent structural reform in 1978, the fields of technology and science grew quickly (Chen et al., 2015). In this regard, the administration’s adoption of science and technology legislation in 1985 had a favorable impact on China’s advancement of its technical infrastructure. Furthermore, in 1996, the Act Encouraging Corporatization of Scientific and Technical Research and Inventions was adopted. These strategies have placed a strong emphasis on commercializing academic re- sults and building industry sectors’ capability for R&D and innovation (Yao et al., 2021). Wang and Wu (2015) examined the impact of firm and government R&D spending on China’s economic development in different research. Based on the report, all R&D invest- ments have a favorable impact on economic development, however, although there is a substantial association between enterprise R&D investments and growth, there is less link between government R&D investments and growth. Institutional variables may also contribute to the explanation of R&D expenditure (Wu et al., 2016). By enhancing the businesses’ potential for collaboration, a better institution- al framework may encourage R&D activities. Simultaneously, Audretsch and Belitski (2020) claimed that the administrative contexts in which enterprises function in addition to the char- acteristics of the company do matter for innovation. This idea was reinforced by (Wang et al., 2015), who also noted that the R&D expenditures approach, framework, and process needed to be in line with institutional requirements. Institutional contexts are regarded as the most significant stimuli for a creative activity for a number of reasons. First, organizational issues have an impact on R&D investments since they are hazardous and long-term investments. Nevertheless, better institutions aid in reducing the organization issue among decision-mak- ers thus assisting in boosting R&D spending. Second, the effectiveness of institutions spurs R&D spending by giving businesses exposure to a range of resources and innovations. Third, strong institutional qualities may draw international investments, enabling businesses to ob- tain external financing, reducing the amount of murky information, and offering incentives to businesses, which in turn encourages internal R&D investment. Last but not least, sound governance, such as robust intellectual property rights, offers investors safeguards and en- courages investment in R&D. 88 S. Aden Dirir. The potential of macroeconomic factors in shaping the landscape of technological development: ... Fiscal policy is one of the macroeconomic elements that influence research and devel- opment. This shows that increasing the quality and efficiency of public expenditure is a key strategy for preserving financial restraint (Muscio & Ciffolilli, 2020). The development of the required monitoring systems applied to specific expenditure areas, such as public actions in R&D, public schooling, health care, and infrastructure on a global scale, has advanced de- spite the systematic challenges raised by measuring the quality of public spending on R&D (Castellani et al., 2019). In a prior study, Cincera et al. (2008) used both techniques to estimate an average perfor- mance of the link between inputs and outputs (such as governmental R&D incentives to the business sector, R&D expenditures by educational institutions, and R&D carried out in public research organizations). Macroeconomic statistics from a panel of OECD nations provide the foundation of the experimental study. The study’s key findings may be summed up as follows. An indicator of the effectiveness of government expenditure on R&D is the link between in- puts and outputs. Contrarily, stability-focused economic regulations, a legal framework that protects rights (such as patents), an industrial framework focused towards high-tech industrial sectors, a more beneficial taxation system for global trade, and increased liberalization in the workforce and commodity marketplaces are discovered to favorably influence technological development. Additionally, high inflation rates and the proportion of government spending to overall consumption have a detrimental impact on technological development. As per Guceri and Liu (2019), a nation like the United Kingdom launched its first R&D tax motivational program in 2000 in an attempt to resolve its “economic output dilemma”, which refers to the UK private sector’s subpar overall factor productivity achievement in contrast to certain other advanced nations like the United States (US), France, and Germany. Firms find it simpler to adopt volume-based plans over cumulative plans, which concentrate benefits on the growth in R&D expenditure from a previous time span. Volume-based plans focus benefits on the total quantity of acceptable R&D completed in the time frame. Companies have been shown to employ stop-and-go tactics in evolutionary plans, potentially leading to inefficiencies (Ientile & Mairesse, 2009). The research by Guellec and van Pottelsberghe (2004) examined a group of 16 OECD nations and used, among other tools, an error-correction model (ECM) to calculate the ef- fect of both government and private R&D on total factor productivity (TFP). The model they used presupposed similar coefficients for each of the 16 nations in the investigation. Guellec and van Pottelsberghe determined that the production elasticity of company (private) R&D in OECD nations from 1980 to 1998 was equal to 13% and grew with time. They discovered that the long-term elasticity of research conducted by governments and universities was significantly higher for public research: roughly 17%. Additionally, their analysis emphasized the significance of “foreign” R&D operations for many nations, especially the smaller OECD nations included in their group. Countries with smaller populations seemed to be significantly more affected by “cross-border” ripples than bigger ones, which is in line with their greater proportions of global co-publication and co-patenting. But in order to reap such advantages, a lesser nation would eventually need to grow more R&D-intensive and specialized. Bottazzi and Peri (2007) similarly employed a VECM strategy, their attention was largely directed at how R&D may describe the dynamics of patents as a sign of innovative thinking Business, Management and Economics Engineering, 2023, 21(1): 84–105 89 as opposed to productivity (TFP). Additionally, they avoided directly addressing how govern- mental R&D spending affects the economy. Instead of R&D spending data, they used R&D employment data. No error-correction strategy was used in Khan and Luintel’s (2006) article. They elected to use a general output function approach instead, where country-specific intercepts and curves are permitted. They created association terms between a variety of factors, such as foreign direct investment and the percentage of high-tech businesses in trade, and the nation-spe- cific norms of the expertise stock indicators to account for variance in the slopes of the R&D variables per nation. The diversity of associated factors was then linked to the heterogeneity of the country-specific regression coefficients that were the outcome of this experiment. However, using such a strategy makes it impossible to examine prospective responses from production on the left side of the equation regarding information stocks and the many dy- namic relationships between output, productivity, and knowledge stocks on the right side. Public and private research investment complement one another in that it attracts globally mobile R&D, or what can be considered a domestic R&D “crowding-in” impact. Such studies include a variety of factors, including the possibility of excellent collaborators, employment prospects, and the existence of a regional expertise cluster, frequently supported by an infra- structure for knowledge and technology transfer (Cassiman & Veugelers, 2002). Panel data for 15 OECD nations are used by Bassanini et al. (2001), who additionally con- tain independent variables for the intensity of public and private R&D. They discover that private R&D has an advantageous projected effect (0.26) while public R&D has an adverse estimated effect (–0.37). The authors suggest that one explanation for the detrimental con- sequences of governmental R&D is the crowding out of private R&D activities. Additionally, they point out that publicly funded research may be more focused on developing fundamen- tal information than on immediately enhancing productivity. 2. Methodology 2.1. Data The study investigates the role of macroeconomic forces in shaping the landscape of tech- nological development in Upper middle-income countries. The selected countries are well known for their great social diversity, significant degrees of education, advanced national in- surance, free healthcare systems, accessibility to technology, and advanced legal frameworks. These mentioned characteristics reflect the transparency and partial administration of these countries. The technological development in upper-middle-income countries would likely de- pend on several factors, including the nature and scope of technological innovation, the level of investment in research and development, the extent of technology diffusion and adoption, and the impact of technological progress on economic growth and development. Addition- ally, technological development in upper-middle-income countries can be a sign of progress and advancement, as it can lead to increased productivity, improved competitiveness, and enhanced economic growth. It can also contribute to social and environmental development by improving living standards, promoting sustainable development, and addressing societal challenges such as poverty and inequality. 90 S. Aden Dirir. The potential of macroeconomic factors in shaping the landscape of technological development: ... Consequently, macroeconomic policies and conditions can have a significant impact on the level of investment in research and development, the availability of funding for innova- tion, the extent of technology diffusion and adoption, and the overall environment for tech- nological development in a country. For example, macroeconomic policies such as fiscal and monetary policies can impact the availability of funding for research and development (Tang et al., 2022). Inflation and interest rates can affect the cost of borrowing and the willingness of businesses to invest in new technologies (Chu et al., 2015). Government policies such as tax incentives, subsidies, and intellectual property protections can also influence the development and adoption of new technologies (Rao, 2016). Additionally, macroeconomic conditions such as economic growth, income levels, and the structure of the economy can affect the demand for technology and the ability of firms to innovate and compete (Das & Mukherjee, 2020). For instance, a growing middle class may create a larger market for new technologies, while a shifting economic structure may present new opportunities for innovation in specific sectors. With that in mind, the percentage of GDP spent on research and development is consid- ered a proxy for technological development. By investing in R&D, firms and governments can generate new knowledge and technologies that can drive innovation and economic growth. R&D activities can lead to the creation of new products and services, improved production processes, and the development of new materials, technologies, and systems that can be used across a range of industries. Because R&D is such a critical component of technological development, it is often used as a proxy for measuring the level of technological progress in a country or industry. Metrics such as R&D expenditure, patents granted, and scientific publica- tions are commonly used to track changes in the level of technological development over time. What is more, indicators such as GDP per capita, Final consumption expenditure, Domestic credit to the private sector, national income, and the CPIA transparency, accountability, and corruption in the public sector rating are selected as proxies for the macroeconomic indica- tors. Finally, to carry on with the study a vector error correction model is used to assess the cointegration between the variables. The model is used to analyze the long-run dynamics between two or more time series variables it is also important because it allows researchers and analysts to analyze the long-run relationships between multiple variables, while also accounting for the potential presence of short-term deviations from equilibrium. Similarly, the Granger causality test was applied to determine the nature of causality (unidirectional or bidirectional). The data are extracted from the World Bank Database, from the period 2000 to 2021. Table 1 presents the overall variables used in the study. Table 1. Summary of the variables (source: author’s computation) Notation Description Source Years 20 years From 2000 until 2020Countries Upper middle income RD Research and development expenditure (% of GDP) World Bank Database especially from World governance indicators and Development Indicators. TC CPIA transparency, accountability, and corruption in the public sector rating (1 = low to 6 = high) GDP The logarithm of the GDP per capita (current US$) FC The logarithm of the Final consumption expenditure (current US$) DC Domestic credit to the private sector (% of GDP) IC The logarithm of the adjusted net national income (current US$) Business, Management and Economics Engineering, 2023, 21(1): 84–105 91 2.2. Research process Figure 1 gives a general overview of the study’s structure. The flowchart thoroughly looks into the analytic methodologies employed for the study. Additionally, it provides a compre- hensive breakdown of the methodology utilized to draw logical conclusions throughout the study’s results phase. Figure 1. The analytical chart of the study 2.3. Model presentation To ascertain the interaction between the variables, the current study utilized the following economic functions: ( )= ∫ , , , ,t t t t t tRD TC GDP FC DC IC . (1) 2.4. Error correction term In order to evaluate the cointegration between the chosen variables, the research utilizes a vector error correction model. Additionally, we will conduct a Granger causality test to establish the relationship and direction between the variables. The VECM model may be referred to as a constrained VAR because cointegration is present in the model. The funda- mental presumption is that all indicators should be static within the appropriate direction or scale relative to the hypothesis that has to be met, notably in the first difference (Gujarati & Porter, 2010). It is possible to segregate the long-run and short-run elements of the data construction process using the VECM approach. Because it uses a VAR (Vector Autoregressive) method version. Consequently, the VECM model may be written as the following equation: 92 S. Aden Dirir. The potential of macroeconomic factors in shaping the landscape of technological development: ... − − − − = = − − − = ∆ = σ + γ ∆ + η ∆ + ξ ∆ + λ +…+ ∑ ∑ ∑ 1 1 11 1 1 11 ; k k t i t j t ji j k m t m t tm Y Y X R ECT u (2) − − − − = = − − − − − − = = = − − − = ∆ = σ + β ∆ + φ ∆ + η ∆ + ξ ∆ + ϑ ∆ + σ ∆ + +λ + ∑ ∑ ∑ ∑ ∑ ∑ 1 1 11 1 1 1 1 1 1 1 1 11 . k k t i t i j ti j k k k l t l m t m n t nl m n k l t l t to RD RD TC GDP FC DC IC ECT u (3) All of the variables that were utilized in the study are listed in the equation above. First, we examine the main variable, which consist of the research and development expenditure (RD), as well as the exogenous variables, which are the following: TC, GDP, FC, DC, and IC. The VECM equation has k–1 which suggest that the lag length is reduced by 1. Then we perceive β φ η ξ ϑ ω, , , , ,i j l m n p that denotes for the short-run dynamic coefficients of the model’s adjustment long-run equilibrium. Next, there is the −1tECT that signifies the error correction term. And finally, ut which is the residuals (impulses). 2.5. Granger causality test Additionally, it was intended to record how the different variables related to one another causally. The Granger causality test, recommended by (Granger, 1969), was performed to ascertain whether there is a causal link between the variables. Below a more comprehensive explanation of the model is provided: ( )− − = = + + m∑ 11,1 1 12,1 11 ; p t t t tl X a X a Y (4) ( )− − = = + +∑ 21,1 1 22,1 11 . p t t t tl Y a X a Y  (5) As illustrated in equations (4) and (5) is the model order, ( )=,1 , 1, 2ija i j are the coef- ficients of the model, and mt and ϵt denotes the residuals. Ordinary least squares can be used to estimate the coefficients, and F tests can identify the Causality relationship between X and Y. 2.6. Unit root test To ensure the stability and reliability of the data the study performed stationarity tests that consist of the Augmented Dickey-Fuller test (ADF) and the Phillips-Perron test (PP). Start- ing with the augmented Dickey-Fuller test, it assumes that u is a white noise error term. However, if u is autocorrelated we would need a drift version of the test which allows for higher-order lags. Accordingly, the test is augmented using p lags of the original series (Dickey & Fuller, 1979). Furthermore, the Phillips-Perron test corrects for any serial corre- lation and heteroskedasticity in the errors by some direct modification to the test statistics (Phillips & Perron, 1988). Below the equations for both tests are presented. Business, Management and Economics Engineering, 2023, 21(1): 84–105 93 − − = ∆ = y + m + α + β∆ +∑1 1 1 p t t t t i y y t y u ; (6) ( ) ( )−∆ = y + m + d +*1 ,   0 ,  ,t t t ty y t u u I ARMA p q∽ . (7) As per equation (6) p is used to augment the past autoregressive lags of the difference term. While m and at denotes the time trend parameter and also the intercept. In equation (7) yy consist of the initial term of the data while the term ut implies the stationarity at level I(0). Additionally, m* expresses the intercept while dt denotes the time trend. 3. Findings Table 2 provides a summary of the descriptive statistics, including the data used in this study and the statistical results of several parametric tests (Jarque-Bera, skewness, probability, and kurtosis). Each variable includes 21 samples of time series data for Upper Middle-Income countries between 2000 and 2021. According to real observations, the distribution is favorably skewed and the percentage of RD spans from 0.68 to 1.99 with a median of 1.22 and kurtosis of 2.05, and a standard deviation of 0.398%. GDP in upper-middle-income nations ranges from an average of 3.72% to a maximum of 4.03%. The maximum rate of economic growth cannot rise by more than 0.261% throughout the years since the standard deviation is less than 1. The results show that most of the variables, with the exception of transparency and domestic credit given to the private sector, have negatively skewed distributions. Additionally, the higher standard deviation value in DC illustrates the broad variation of domestic credit provided to the private sector and suggests that in nations with upper-middle incomes, the private sector needs more credit. Table 2. Descriptive statistics (source: author’s computation) RD TC GDP FC DC IC Mean 1.247327 3.376902 3.725607 12.91027 90.14900 12.98800 Median 1.223243 3.400350 3.838833 13.01254 82.21595 13.10075 Maximum 1.996084 3.681818 4.034850 13.18178 143.9245 13.25876 Minimum 0.684406 3.178571 3.292912 12.48955 58.64133 12.54162 Std. Dev. 0.398344 0.156642 0.261679 0.258046 27.50871 0.267380 Skewness 0.309693 0.053814 –0.601716 –0.565526 0.624437 –0.623666 Kurtosis 2.058733 1.809916 1.815042 1.744797 2.111440 1.812449 Jarque-Bera 1.163820 1.308894 2.614674 2.616912 2.153458 2.718938 Observations 22 22 22 22 22 22 A correlation matrix is a vital tool for establishing assumptions between variables before they are addressed. As a result, we can notice in Table 3 that every variable is closely related to the amount spent on research and development. This shows that RD tends to grow in value together with GDP, national income, final consumption of the government, transparency, and domestic lending to the private sector and vice versa. 94 S. Aden Dirir. The potential of macroeconomic factors in shaping the landscape of technological development: ... Table 3. Matrix of correlation (source: author’s computation) Variables RD TC GDP FC DC IC RD 1.000 TC 0.949 1.000 GDP 0.911 0.937 1.000 FC 0.911 0.932 0.998 1.000 DC 0.979 0.900 0.825 0.830 1.000 IC 0.902 0.925 0.998 0.999 0.817 1.000 In Table 4 stationarity test is performed by using the Augmented Dickey-Fuller test and Phillips–Perron test. The statistical results are contrasted with the MacKinnon critical values. Hence, the data is deemed to be non-stationary if the results show that the t statistic count is more than the MacKinnon critical value. On the other hand, it is said to be stationary if the value is less than the estimated MacKinnon critical value. Both tests revealed that all the variables are only stationary at first difference. Consequently, we will proceed with the model estimation since the variables displayed the absence of unit roots. Table 4. Unit root test (source: author’s computation) Variables Panel A: Augmented Dickey-Fuller test (ADF) test At level Note At first difference Note Decision RD 0.946 Not stationary –0.010** Stationary I (1) TC –0.039 Not stationary –2.836* Stationary I (1) GDP –1.681 Not stationary –2.714* Stationary I (1) FC –2.093 Not stationary –3.486** Stationary I (1) DC 1.388 Not stationary –3.031** Stationary I (1) IC –2.315 Stationary –2.523* Stationary I (1) Variables Panel B: Phillips–Perron test At level Note At first difference Note Decision RD 3.225 Not stationary 0.010*** Stationary I (1) TC 0.185 Not stationary –2.836** Stationary I (1) GDP –1.397 Not stationary –2.714* Stationary I (1) FC –1.628 Not stationary –3.486* Stationary I (1) DC 2.658 Not stationary –3.031*** Stationary I (1) IC –1.825 Not stationary –2.523** Stationary I (1) Note: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. One of the metrics used to assess the VAR model is the optimal lag to impose. A VAR system’s autocorrelation issues may be rectified by figuring out the ideal lag, which is helpful for demonstrating how long a variable takes to react to other variables. This test also verifies the accuracy of the information generated by the estimate of the Vector error correcting model. The metrics (LR), (AIC), (FPE), (SC), and (HQ) are evaluated to estimate lag candidates. Table 5’s findings show that lag 1 is the ideal latency for the paper. Business, Management and Economics Engineering, 2023, 21(1): 84–105 95 Table 5. Optimal lag selection (source: author’s computation) Lag LogL LR Df P FPE AIC HQIC SBIC 0 126.832 – – – 6.0e-14 –13.4258 –13.384 –13.129 1 239.797 225.93 36 0.000 1.5e-17* –21.9775* –21.691 –19.899* 2 630.604 781.61* 36 0.000 8.5e-34 –61.4005 –60.868* –57.542 3 2987.24 4713.3 36 0.000 – –319.916 –319.179 –314.573 Note: * indicates the lag order selected by the criterion. LR: sequential modified LR test statistic (each test at 5% level). FPE: final prediction error. AIC: Akaike information criterion. SC: Schwarz information criterion. HQ: Hannan–Quinn information criterion. The cointegration test aims to determine the cointegration of the non-stationary varia- bles. If there is cointegration, the investigation of the VECM model can be continued. Table 6 demonstrates a cointegration with statistical values above the threshold value for the Trace statistic test. As a result, we establish the existence of a long-term link between the variables. Hereby, we will continue with the error correction model. Table 6. Cointegration test (source: author’s computation) Hypothesized No. of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Prob.** None * 0.991415 240.1102 95.75366 0.0000 At most 1 * 0.980917 144.9549 69.81889 0.0000 At most 2 * 0.851999 65.77519 47.85613 0.0005 At most 3 0.595735 27.56450 29.79707 0.0886 At most 4 0.305444 9.450798 15.49471 0.3253 At most 5 0.102425 2.161162 3.841465 0.1415 Note: * Denotes rejection of the hypothesis at the 0.05 level and Trace test indicates 2 cointegrating eqn(s) at the 0.05 level. Table 7 presents the short-run results among the variables in proportion to the expenditure on R&D. The findings reveal no evidence of cointegration among the variables with R&D. This suggests during the short-run framework economic growth, government final consumption, do- mestic credit provided to the private sector, national income, and the government transparency do not contribute in the technological development of Upper-Middle-income countries. Never- theless, we observe a negative cointegration between R&D with GDP, FC, and IC. For instance, a 1% increase in expenditure on research and development decrease the GDP, government final consumption, and national income by 0.51%, 0.56%, and 0.64% respectively. Contrary to the short-run outcome which demonstrated no evidence of relationships among the variables. The long-run outcome displays the presence of cointegration among the varia- bles. Within this scope, we observe that TC, FC, DC, and IC are negatively affecting the expend- iture in R&D. This implies that the transparency situation of upper-middle-income countries, simultaneously the governments’ final consumption, the amount of credit provided to the private sector, and national income are unfavorably affecting the technological development. Nevertheless, the economic growth of upper-middle-income countries is revealed to support technological development. This is supported by the 1% increase in GDP which results in a 7.3% increase in expenditure on research and development. The results are displayed in Table 8. 96 S. Aden Dirir. The potential of macroeconomic factors in shaping the landscape of technological development: ... Table 7. Short-run estimates (source: author’s computation) VARIABLES D_RD D_TC D_GDP D_FC D_DC D_IC ECT (–1) –0.354 (0.542) 0.315 (0.334) 0.698*** (0.146) 0.548*** (0.146) –64.99 (40.86) 0.638*** (0.136) ∆RD (–1) 0.248 (0.768) –0.169 (0.474) –0.512** (0.206) –0.564*** (0.207) 67.26 (57.94) –0.647*** (0.193) ∆TC (–1) 0.334 (0.520) –0.127 (0.321) 0.107 (0.140) –0.0217 (0.140) –15.41 (39.19) 0.0225 (0.131) ∆GDP (–1) 2.004 (5.945) 1.245 (3.670) –5.765*** (1.596) –5.828*** (1.602) 371.6 (448.2) –6.712*** (1.496) ∆FC (–1) 2.232 (5.839) 0.603 (3.605) 1.807 (1.568) 2.036 (1.574) 88.85 (440.3) 2.477* (1.470) ∆DC (–1) –0.00435 (0.00652) 0.00555 (0.00403) 0.005*** (0.001) 0.00514*** (0.00176) –0.578 (0.492) 0.00506*** (0.00164) ∆IC (–1) –4.488 (4.841) –1.198 (2.988) 4.636*** (1.300) 4.609*** (1.305) –482.1 (365.0) 5.010*** (1.219) Constant 0.0518 (0.0759) 0.0152 (0.0468) 0.06*** (0.0204) 0.049** (0.0205) 0.0014 (5.723) 0.061*** (0.0191) Observations 20 20 20 20 20 20 R-squared 0.6043 0.5329 0.9137 0.8957 0.5991 0.9155 chi2 18.32339 13.6926 127.0069 103.0298 17.92952 129.9526 P > chi2 0.0189** 0.0901* 0.0000*** 0.0000*** 0.0218** 0.0000*** Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1. Table 8. Long-run estimates (source: author’s computation) Variables Coefficient Std.Dev T–statistics P–value TC –0.7061*** 0.03826 –18.45 0.000 GDP 7.346*** 0.67695 10.85 0.000 FC –3.2439*** 0.43344 –7.48 0.000 DC –0.0037*** 0.00036 –10.25 0.000 IC –4.6522*** 0.43611 –10.67 0.000 Constant 76.378 Note: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. The Impulse Response Function (IRF) displays how a variable responds to a shock (such as a stress of one standard deviation, a stimulus of one unit, etc.) over a specific period of time. A variable’s impact on another factor cannot be determined using the Granger causality or the VD. To ascertain an impact’s route, the IRF evaluation is crucial. The horizontal axis depicts time, while the vertical axis indicates the size of a variable’s responses to a shock. The red dotted line substitutes for the confidence bands at 5% significance while the blue line represents the IRF. Table 9 shows the responses of RD, TC, GDP, FC, DC, and IC to one standard deviation shock of RD. Within this scope, we witness that the impulse responses estimate in Table 9 shows that the amount of expenditure in research and development in Upper Middle-income countries would probably decrease as a result of GDP and government Business, Management and Economics Engineering, 2023, 21(1): 84–105 97 consumption, domestic credit to the private sector, and national income. On the other hand, it would seem that the transparency and corruption regulations would expand overall tech- nological development. Therefore, these areas require additional focus and funding to help boost technological progress during the coming ten years. Table 9. Impulse response’s function (source: author’s computation) Periods RD TC GDP FC DC IC 1 0.068002 0.000000 0.000000 0.000000 0.000000 0.000000 2 0.114941 0.027005 –0.017025 –0.017004 –0.014690 –0.032655 3 0.223534 0.073350 –0.045293 –0.075262 –0.029346 –0.065743 4 0.515151 0.176368 –0.096951 –0.204501 –0.090158 –0.135450 5 1.284428 0.452286 –0.248377 –0.550764 –0.258181 –0.334988 6 3.335901 1.194493 –0.664293 –1.483842 –0.703091 –0.872437 7 8.825649 3.179417 –1.778213 –3.982346 –1.892621 –2.309054 8 23.52164 8.491946 –4.759761 –10.66969 –5.078169 –6.154294 9 62.86333 22.71404 –12.74278 –28.57249 –13.60642 –16.44923 10 168.1844 60.78820 –34.11490 –76.50046 –36.43713 –44.01006 Table 10 depicts the variance and the decomposition outcome of all the explanatory variables of the model in combination with the research and development variable for the entire time frame. It can be noticed from the table that the time period selected was fixed to 10 years and divided to 5 years in order to evaluate the shock of each of factors on R&D. The results imply that the government final consumption, which is anticipated to increase from 1.415% in 2022 to 13.844% in 2031, would have a greater variance shock of 13.844% on the technological development. Additionally, transparency is expected to impose a greater variance shock of 8.74% on the technological progress of Upper Middle-income countries in 2031. The remaining factors GDP, domestic credit to the private sector, and national income will only cause a moderate shock on the technological development with variances of 2.75%, 3.14%, and 4.58% respectively. Table 10. Variance decomposition (source: author’s computation) Periods. RD TC GDP FC DC IC 1 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 2 87.31835 3.570180 1.418962 1.415562 1.056435 5.220512 3 76.46423 6.889924 2.640411 6.714036 1.214542 6.076858 4 71.98452 8.040337 2.536630 10.32167 1.988853 5.127994 5 69.30740 8.450564 2.566586 12.27213 2.651552 4.751768 6 67.88190 8.638975 2.664905 13.21743 2.952155 4.644643 7 67.28506 8.707765 2.718486 13.61327 3.070020 4.605407 8 67.05624 8.731383 2.740665 13.76513 3.116181 4.590397 9 66.96885 8.739889 2.749682 13.82256 3.133831 4.585192 10 66.93566 8.743067 2.753261 13.84422 3.140441 4.583350 98 S. Aden Dirir. The potential of macroeconomic factors in shaping the landscape of technological development: ... Following an examination of the cointegration between the predictor variables (RD) and the regressors (TC, GDP, FC, DC, and IC), the granger causality test will be performed to as- certain the relationship between the variables (Granger, 1969). According to the estimation results, we perceive a unidirectional relationship between all the variables with the research and development at 1% and 10% significance levels except for transparency which revealed no prominent causality. For instance, GDP is the driving force behind the level of RD invest- ment, rather than RD investment being the driving force behind GDP growth. This result may be due to a number of factors. For example, it could be that countries with higher GDP have more resources available to invest in research and development, and therefore see greater levels of RD investment. Alternatively, it could be that companies are more likely to invest in RD when they expect that economic conditions will be favorable for their products and services, which could lead to higher GDP growth. Next, as fiscal expenditure is one of the main ways that governments can support R&D investment in their countries. By providing funding, tax incentives, and other forms of support for R&D, governments can encourage businesses and other organizations to invest in research and development. In turn, this can lead to new technological innovations and improvements in productivity, which can contribute to economic growth. Third, the result behind unidirectional causation running from R&D to domestic credit provided to private sector may be due to the fact that R&D investment can lead to the development of new technologies and products, which can create new markets and opportunities for businesses. This can in turn lead to increased demand for credit to finance expansion and investment in these new markets. Additionally, R&D investment can lead to improvements in productivity and competitiveness, which can make businesses more creditworthy and therefore more likely to qualify for loans. Further, the outcome between national income and R&D is consistent with the idea that R&D in- vestment is a luxury good, meaning that as incomes rise, individuals and businesses are more likely to allocate resources towards R&D investment. Additionally, higher national income may provide more resources for governments to invest in R&D through public funding, tax incentives, or other policy measures. Hence, we conclude that technological development has a long-run relationship with the GDP, government final consumption, domestic credit provided to the private sector, and national income. See Table 11. Table 11. Granger causality test (source: author’s computation) Hypothesis F-statistic Prob. Decision Direction RD granger cause TC 4.472 0.107 Dismiss No causality TC granger cause RD 1.350 0.509 Dismiss RD granger cause GDP 0.237 0.888 Dismiss Unidirectional GDP granger cause RD 21.49 0.000*** Maintain RD granger cause FC 0.521 0.770 Dismiss Unidirectional FC granger cause RD 13.292 0.000*** Maintain RD granger cause DC 5.683 0.058* Maintain Unidirectional DC granger cause RD 3.135 0.205 Dismiss RD granger cause IC 1.814 0.404 Dismiss Unidirectional IC granger cause RD 19.85 0.000*** Maintain Note: ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Business, Management and Economics Engineering, 2023, 21(1): 84–105 99 In accordance with Table 12, the various diagnostic tests that consist of Jarque-Berra for normality, and stability conditions are performed. Based on the findings we perceive that all the variables are normally distributed across the model. What is more the eigenvalue for stability condition indicates that the VECM imposes 5-unit moduli. Table 12. The diagnostic test (source: author’s computation) Tests Prob Verdict Residual Normality Joint test (Jarque-Berra) 0.311 All the variables are normally distributed. Eigenvalue stability condition The VECM specification imposes 5-unit moduli Discussion Many emerging economies are currently on the forefront of development thanks to techno- logical advancements and transmission that have benefited and still benefit sizable portions of their populations. A number of nations, including China, India, Korea, Taiwan, Singapore, and, to some extent, Brazil, have followed their own technological trajectories. Even so, given the potential that modern inventions like solar technology, mobile phones, and even the Internet could help them revitalize their transition to the 21st century’s technological ad- vancement, the applications of technology continue to stay a utopian fantasy for significant portions of Africa, Asia, as well as Latin America. Since ancient times, technology has always remained at the center of progress, moving from one stage to another, from the industrial to the contemporary technological eras. The introduction of new goods, procedures, and services that improve the quality of lifestyle for both wealthy and less wealthy persons is the result of technology’s ongoing innovation. Re- search, innovation, and technology are crucial components of progress since the majority of items utilized in contemporary life are the result of technology, and engineering, which are derived through the resources that are extracted and analyzed in industries. It is necessary to examine the individuals and organizations that innovative culture in a certain field of technology, industry, or research level in order to comprehend how supporting the technology works. Actors often comprise people and groups functioning at various sizes, such as national and regional governments, city councils, colleges, for-profit and nonprofit businesses, startups, and technology users. The relationships and conduct of participants in an innovation system are governed by institutions, which are a collection of official and unof- ficial rules, conventions, decision-making processes, convictions, incentives, and expectations. Innovation frameworks are complex and adaptable because of the relationships between individuals and organizations throughout the numerous steps of the development process, which take place in various industries and at various scales. Achieving a technology shift can be significantly impacted by institutional policies and macroeconomic conditions. To guarantee that technology contributes as much as possible to sustainable growth, there are four important domains where authorities need to set up legislative, administrative, financial, and policy frameworks. Also, adequate regulation of macroeconomics such the national income, wealth generation, adequate fiscal policies, 100 S. Aden Dirir. The potential of macroeconomic factors in shaping the landscape of technological development: ... economic growth, trade, and financial tools assist in promoting innovation and R&D. Ac- cordingly, the goal of this paper was to examine how macroeconomic factors affect the process of technological development, particularly in upper-middle-income countries. With- in this framework, a VECM approach and granger causality test are performed by using yearly data of economic indicators concerning the upper-middle-income countries for the period 2000 to 2021. Within the paper, the expenditure in research and development is considered as a proxy for technological development and five macroeconomic factors are selected as explanatory variables. The findings demonstrated that in the short run there is no notable cointegration among the variables with R&D expenditure. In other words, during the short-run framework eco- nomic growth, government final consumption, domestic credit provided to the private sec- tor, national income, and government transparency do not contribute to the technological development of Upper-Middle-income countries. Nevertheless, the study detected that R&D expenditure has negative cointegration with the GDP, government final consumption, and national income. This may be explained by the fact that R&D expenditure is a relatively fixed cost for firms, meaning that they are less likely to increase spending on R&D when the econo- my is experiencing a downturn. Another possibility is that during periods of economic growth, firms may be more likely to invest in physical capital (such as machinery and equipment) than in R&D. Next, the negative relationship between R&D expenditure and government final consumption may be explained by the fact that governments often allocate resources towards social programs and infrastructure during periods of economic growth, rather than towards R&D. Additionally, during periods of economic contraction, governments may reduce spending on R&D in an effort to cut costs. Third, the negative relationship between R&D expenditure and national income may be due to the fact that as national income rises, firms may shift their focus towards other types of investment, such as physical capital, rather than R&D. Additionally, higher national income may lead to greater competition for resources, which may make it more difficult for firms to allocate resources towards R&D. Further, the long-term result displayed the presence of cointegration between the var- iables. According to the long-run outcome, we observed that the transparency situation of upper-middle-income countries, simultaneously the governments’ final consumption, the amount of credit provided to the private sector, and national income are unfavorably affect- ing the technological development. However, it turns out that the development of technol- ogy is supported by the economic growth of upper-middle-income nations in the long run. Furthermore, the causality test among the variables presented a unidirectional association between all of the variables, with research and development at 1% and 10% significant levels, with the exception of transparency, which showed no clear causation. Therefore, we draw the conclusion that the GDP, government final consumption, domestic credit given to the private sector, and national income are all positively correlated with technological development over the long term. The result obtained in the framework of economic growth and R&D is consistent with the study conducted by (Bozkurt, 2015). In his study, the author investigated the long- run and short-run relationship between R&D and economic growth in Turkey. The study uncovered that a 1% increase in GDP rises 1.6425% the fund allocated to R&D. Next, the Business, Management and Economics Engineering, 2023, 21(1): 84–105 101 negative result discovered in terms of government transparency and corruption agrees with the result obtained by (Alam et al., 2019b). In his research, the author used a GMM model on the institutional environment that influences R&D in emerging markets. He unveiled that the transparency and corruption levels of these countries decreased by 0.0021% the R&D. However, the present paper contradicts the findings obtained by (Rehman et al., 2020) in the scope of private credit provided to the domestic sector in proportion to R&D. The author examined the role of the public entities in supporting the R&D. The study showed that in OECD countries the public sector contributes by 0.3% increase in R&D. Finally, Kirca et al. (2021) examined the causality relationship between the national income per capita and R&D. According to the results of his bootstrap panel causality test, there is a unidirec- tional causal link between R&D spending and per capita income in Hong Kong and Korea. In contrast, in China and Turkey, there is a single direction of causation connecting per capita income to R&D spending. Important policy implications that could be extracted from the study are that first, fiscal policy can play an important role in supporting R&D investment. Governments can use pub- lic funding, tax incentives, and other forms of support to encourage businesses and other organizations to invest in research and development. By doing so, they can help to create a more favorable environment for technological development, which can contribute to eco- nomic growth. Next, policymakers should focus on increasing investment in R&D, as this can have a significant impact on technological development. This can be achieved through a range of measures, such as tax incentives, grants, and loans. Third, policymakers should also focus on promoting innovation, as this can drive technological development. This can be achieved through measures such as patent protection, technology transfer agreements, and research partnerships. Fourth, the presence of strong and accountable institutions can facilitate technological development. Policymakers should focus on strengthening institutions such as regulatory bodies, intellectual property offices, and science and technology ministries. Fifth, access to credit can be a crucial factor in enabling businesses to invest in R&D and de- velop new technologies. Policymakers should focus on enhancing access to credit, especially for small and medium-sized enterprises (SMEs), as these businesses can play a critical role in driving technological development. Lastly, a skilled workforce is critical to technological development. Policymakers should focus on promoting education and skills development, especially in science, technology, engineering, and mathematics (STEM) fields. Overall, policymakers and nations looking to accomplish technological transformation in the age of digitization can benefit from the study’s findings. The research also provides a systematic approach to the macroeconomic aspects that should be used to create an environment that is conducive to and supportive of technological growth. As a limitation of the study, the exclusive focus on only macroeconomic factors excluded the possibility of investigating the role of micro factors that may have an influence on technological develop- ment. Therefore, it is recommended that enlarging the scope of new research that involves microeconomic factors and governance indicators that affect technological development as well as a particular focus on the R&D of Low-income countries needs to be addressed since the current paper only considers upper-middle-income countries. 102 S. Aden Dirir. The potential of macroeconomic factors in shaping the landscape of technological development: ... Conclusions Technological development in upper middle-income countries varies widely depending on the specific country and sector in question. Generally speaking, upper middle-income countries tend to have more advanced technology and infrastructure than lower-income countries, but may not be as advanced as high-income countries. Many upper middle- income countries have made significant progress in developing their technology sectors, particularly in areas such as information technology, biotechnology, and renewable energy. These countries often have well-educated and skilled workforces, strong institutions, and policies that promote innovation and entrepreneurship. However, there are also challenges associated with technological development in upper middle-income countries. These may include issues such as limited access to financing for R&D, weak intellectual property pro- tections, and inadequate infrastructure. In addition, upper middle-income countries may face increasing competition from other countries, particularly emerging economies that are rapidly developing their own technology sectors. Within this framework, the current study investigated the potential of macroeconomic factors in shaping the landscape of techno- logical development by mainly focusing on upper-middle-income countries. Accordingly, the results uncovered that the transparency situation of upper-middle-income countries, simultaneously the governments’ final consumption, the amount of credit provided to the private sector, and national income are unfavourably affecting the technological develop- ment. Whereas, the economic growth of these countries is favourably supporting the re- search and development. Overall, while there is significant variation in the state of techno- logical development across upper middle-income countries, many of these countries have made progress in developing their technology sectors and are well-positioned to continue to do so with the right policies and investments. 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