The impact of artificial intelligence applied in businesses on economic growth, welfare, and social disparities

Socol, Adela; Marin-Pantelescu, Andreea; Attila, Tamas-Szora; Cioca, Ionela Cornelia Article The impact of artificial intelligence applied in businesses on economic growth, welfare, and social disparities Amfiteatru Economic Journal Provided in Cooperation with: The Bucharest University of Economic Studies Suggested Citation: Socol, Adela; Marin-Pantelescu, Andreea; Attila, Tamas-Szora; Cioca, Ionela Cornelia (2024) : The impact of artificial intelligence applied in businesses on economic growth, welfare, and social disparities, Amfiteatru Economic Journal, ISSN 2247-9104, The Bucharest University of Economic Studies, Bucharest, Vol. 26, Iss. 66, pp. 475-493, https://doi.org/10.24818/EA/2024/66/475 This Version is available at: https://hdl.handle.net/10419/300605 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Innovative Application of AI in Business Impacting Socio-Economic Progress AE Vol. 26 • No. 66 • May 2024 475 THE IMPACT OF ARTIFICIAL INTELLIGENCE APPLIED IN BUSINESSES ON ECONOMIC GROWTH, WELFARE, AND SOCIAL DISPARITIES Adela Socol1, Andreea Marin-Pantelescu2, Attila Tamas-Szora 3 and Ionela Cornelia Cioca4 1)3)4) University “1 Decembrie 1918” of Alba Iulia, Alba Iulia, Romania. 2) Bucharest University of Economic Studies, Bucharest, Romania. Please cite this article as: Socol, A., Marin-Pantelescu, A., Tamas-Szora, A. and Cioca, C., 2024. The Impact of Artificial Intelligence Applied in Businesses on Economic Growth, Welfare and Social Disparities. Amfiteatru Economic, 26(66), pp. 475-493. DOI: https://doi.org/10.24818/EA/2024/66/475 Article History Received: 19 December 2023 Revised: 12 February 2024 Accepted: 18 March 2024 Abstract Previous literature on the impact of the application of artificial intelligence in businesses on economic growth, welfare, and social disparities is scarce, due to limited data and the recent dynamics of the field. To contribute to this gap, the study employs the percentage of large enterprises in the European Union (EU-27) using artificial intelligence technologies in production for the year 2021. The results obtained by static analysis (Feasible Generalized Least Squares method) indicate positive relationships between the application of artificial intelligence in large enterprises in European Union countries and economic growth, while the analysis of welfare and social disparities leads to mixed results: increasing the average net income per person, respectively, the poverty threshold and decreasing the number of people at risk of poverty and unemployment. Spatial analysis (Spatial Lag Model) of the economic and social impact of artificial intelligence applied in a country's large enterprises on neighbouring countries leads to robust results for economic growth and net average income per person, whose levels are positively influenced, through spillover effects and spatial interactions between states. At the microeconomic level, the study highlights the need for rapid adaptation of enterprises to artificial intelligence, and from the perspective of public policies, the need for transparent and sustainable regulations. Keywords: Artificial intelligence (AI); enterprises, growth; welfare; unemployment; European Union. JEL Classification: O11, B55, O33, C58, O52  Corresponding author, Andreea Marin-Pantelescu – e-mail: [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2023 The Author(s). AE The Impact of Artificial Intelligence Applied in Businesses on Economic Growth, Welfare, and Social Disparities 476 Amfiteatru Economic Introduction On its way to becoming ubiquitous in the contemporary world (Liefooghe and van Maanen, 2023), artificial intelligence (AI) profoundly influences the economy and society (Luan et al., 2020; Dinu, 2021). Businesses are tempted to adopt AI technologies at a rapid pace, as artificial intelligence is the major source of innovation (Pelau et al., 2021), contributes to performance and revenue growth and reduces costs for most enterprise functions (Ruiz-Real et al., 2020), leads to better governance and optimised decision-making processes (Gînguță et al., 2023), increased capacity for data collection and analysis, efficient management of malfunctions or crises, supervision, safety and increased productivity of employees, etc. (Sipola, Saunila and Ukko, 2023). It is estimated that by 2026 the compound annual growth rate of the global enterprise artificial intelligence technology market will be around 35% to $53.06 billion, compared to $4.68 billion in 2018 (Herrmann, 2022). Delineating the scope, valences and economic and social impact of artificial intelligence used in enterprises is challenging and a critical topic, exposed to major information and societal risks, as well as ethical aspects (Tăchiciu, 2019; Meghișan-Toma et al., 2022; Fulop et al., 2023). The influence of artificial intelligence on the economy and society is still largely unknown, but early studies suggest that all social systems, including economics, politics, science, and education, are impacted by artificial intelligence technologies (Luan et al., 2020). Concerns are emerging in the public, academic, and political domains regarding the impact of the application of artificial intelligence in enterprises on the well-being of employees, jobs and, implicitly, of people at risk of poverty and social exclusion (Acemoglu and Restrepo, 2019; Lu, 2022). Studies analysing the influence of artificial intelligence on jobs and income distribution reveal heteroclite results, generate polemics, and open chapters of unforeseen social and economic changes, whose impact and effects are difficult to estimate at present. In the context of technology-driven economic development theories, the study aims to answer the following research questions: 1. To what extent is economic growth in the member states of the European Union (EU27) influenced by the application of artificial intelligence technologies in businesses? 2. Does the deployment of artificial intelligence technologies in enterprises in the European Union (EU-27) impact welfare and social disparities in income and unemployment? 3. Does the implementation of artificial intelligence technologies in companies in the European Union (EU-27) in one country influence economic growth, welfare, and social disparities in neighbouring countries? The central objective of the study is to analyse the impact of artificial intelligence in businesses on economic growth, respectively, welfare, and social disparities in terms of income (people at risk of poverty or social exclusion, poverty threshold) and unemployment, by developing empirical research at the European Union (EU) level by 2021. This paper addresses a topic rarely explored and not adequately clarified in the previous literature, contributing to it in several ways. First, it empirically studies, based on static and spatial analysis, the situation of a conglomerate of countries represented by the EU member states, at the intersection between artificial intelligence in enterprises and socioeconomic development. Second, the study is based on the theory of endogenous economic growth Innovative Application of AI in Business Impacting Socio-Economic Progress AE Vol. 26 • No. 66 • May 2024 477 theory (Romer, 1990), in which innovation, R&D and human capital are considered catalysts for economic growth. Third, the study addresses an identified literature gap on the social implications of adopting artificial intelligence technologies in enterprises. The research also presents managerial implications in terms of public policies, whose relevance in the current context is defining for the transparent, reliable, secure, and sustainable future configuration of artificial intelligence in enterprises. The following sections of the paper are organised as follows: section 1 briefly presents the relevant literature, section 2 describes the data used and the research methodology, section 3 analyses the results obtained, section 4 contains the discussion, and the final section concludes on own contributions, limitations, and future research directions. 1. Literature review 1.1. Implementing artificial intelligence in enterprises Artificial intelligence is used in enterprises to increase production and efficiency, in areas such as machine learning, learning, robotics, neural networks, and continuous learning, which with the help of ERP (Enterprise Resource Planning) applications uses artificial intelligence in financial management, production process, customer service, sales, analytics, forecasting (Kunduru, 2023). Artificial intelligence is used in enterprises in other areas and subfields: decision support systems, big data, cloud computing, graphics and tensor processing units, metaheuristics, etc. (Herrmann, 2022). The implementation of artificial intelligence in enterprises is most often associated with benefits for them, in terms of organisational efficiency, entrepreneurial culture, and decision-making processes (Ransbotham et al., 2021). Although most studies highlight the benefits of using artificial intelligence for enterprises, there are also points of view that contradict this hypothesis, namely: at the initial stage of implementation, enterprises need to involve all relevant stakeholders, because the impact of artificial intelligence is far-reaching, then the value system is questioned, and thirdly, controlled experiments need to be done, as the equipment only imitates human thinking, which generates various risks (Holtel, 2016). 1.2. The link between artificial intelligence applied in enterprises and economic growth Studies on the influence of the implementation of artificial intelligence technologies in enterprises on economic growth are quite limited and are based, in the absence of quantitative data, mainly on theoretical reasoning, value judgments, or forecasts. On the one hand, the application of artificial intelligence in enterprises contributes to improving product quality and productivity growth, work efficiency, increased customer satisfaction, having beneficial effects on the economy and ensuring long-term economic growth (Gonzales, 2023). Artificial intelligence is an important innovation in science and technology, can be considered a new factor of production, influences economic growth through three channels: automating complex physical tasks, supplementing the workforce, and promoting innovation in almost all industries (Yugang, 2019). The most used information capturing the intensity of artificial intelligence application in enterprises refers to industrial robots, artificial intelligence patents, and artificial intelligence start-ups (Furman and Seamans, 2019). An analysis developed for 77 countries between 1993 AE The Impact of Artificial Intelligence Applied in Businesses on Economic Growth, Welfare, and Social Disparities 478 Amfiteatru Economic and 2019 (Gong et al., 2023) shows that industrial robots can stimulate economic growth, but structural changes in the labour market and job losses are concerns that need to be focused on public policies. On the other hand, skeptics of the field raise legitimate questions about the effects robots (as substitutes for labour-based production and conventional equipment) on economic development and launch ideas that demand for labour is likely to decline, threatening a decline in wages, savings, and economic well-being of current and future generations (Sachs, Benzell and LaGarda, 2015). Based on the previous literature, the study's first research hypothesis assumes the following: H1: The application of artificial intelligence in enterprises positively influences economic growth in the Member States of the European Union. 1.3. The impact of artificial intelligence in enterprises on welfare and social disparities The current wave of technological change based on advances in artificial intelligence has created widespread fear of further increases in inequality and job losses (Ernst, Merola and Samaan, 2019). While the incipient literature addresses emerging concepts such as the 'digital divide 1.0/2.0' generated by inequalities in digitalisation (Harambam, Aupers and Houtman, 2013), new artificial intelligence technologies are likely to lead to evolved forms of gaps, superior in nature and intensity, which could massively contribute to widening social differences in individuals' well-being. Beyond the hyperbole and exaggerations of approaches specific to the application of artificial intelligence in companies, concerns about their effects on the well-being of individuals are a topic of current interest, addressed by previous literature, especially from the perspective of the labour market and unemployment (Mutascu, 2021). The complex facets of welfare (or well-being) – social, material, financial, economic, personal, etc. – reflect relative, subjective, multidimensional, and difficult-to-scale notions that imply, in a broad sense, a high satisfaction of human existential and cultural needs (Polak, 2021a). The perception of well-being is directly related to a pejorative side of wellbeing, represented by the phenomenon of poverty and well-being related to working conditions and the environment (Wiśniewski, 2018). Social income disparities affect contemporary economies, which are subject to profound social and economic challenges (Manta et al., 2023) and where new technologies and artificial intelligence are perceived as generating risks and possible scenarios, where there will be "winners and losers" (Goralski and Tan, 2020). Social inequalities in income are expected not only within a nation state, but also from a comparative international perspective, with artificial intelligence expected to contribute to poverty reduction in poor countries, while in developed countries it is expected to lead to significant job losses (Goralski and Tan, 2020). According to a study developed for the United States between 2011 and 2021, different types of technology influence the labour market inhomogeneously: industrial robots and software are associated with lower individual wages, while occupational exposure to AI technologies leads to higher individual wages (Fossen, Samaan and Sorgner, 2022). The literature on the link between artificial intelligence and poverty is relatively new and contains a small number of studies (Mhlanga, 2021), mainly based on theoretical reasoning and without benefiting from empirical or econometric approaches, amid a reduced series of Innovative Application of AI in Business Impacting Socio-Economic Progress AE Vol. 26 • No. 66 • May 2024 479 data and the recent expansion of the field, still not explored in scientific research. New technologies have enabled unprecedented growth in labour incomes, but at the same time represent disruptive elements of the labour market, as technology has increased productivity, which in turn has led to strong GDP per capita growth, but labour gains can come in bursts and favour some sectors over others, leads to polarisation of earnings, favours highly skilled workers, and disadvantages those with low skills (Peralta-Alva and Roitman, 2018). Innovation has the potential to reduce poverty (Pansera and Martinez, 2017). In contrast, studies on the influence of artificial intelligence on the labour market and unemployment are numerous and generally fall into two antagonistic currents, but many of them do not provide categorical results, but outline specific situations for certain industries (Georgieff and Hyee, 2021). An economic dystopia of extreme inequality and class conflict (Berg, Buffie and Zanna, 2018) was suggested a few years ago by some researchers, concerned about the expansion of robots in enterprises where workers would become serfs working on behalf of robot masters, in a new form of "economic feudalism" (Freeman, 2015) or workers would even lose their jobs (Oravec, 2019). There is a consistent literature addressing the risk of job automation and its impact on employment (Frey and Osborne, 2017; Haiss, Mahlberg and Michlits, 2021). In the absence of reliable data on the application of artificial intelligence technologies in enterprises, many empirical studies in recent years have focused on capturing the phenomenon of artificial intelligence through indicators such as the number of industrial robots in enterprises or artificial intelligence patents, which have been analysed in interdependence with unemployment and associated phenomena, such as "replacement effect" or job creation ("displacement effect"), (Mutascu, 2021). Based on the analysis of the period 2012-2019 in 23 OECD countries and for 36 occupations, Georgieff and Hyee (2021) conclude that there is no clear relationship between exposure to artificial intelligence and employment in all occupations. Research developed for 74 countries and the period 20042016 (Fu et al., 2021) shows that industrial robot deployment has heterogeneous effects between developed and developing economies. In developed economies, robots improve labour productivity and total employment, whereas in developing economies there is no clear evidence of these effects. The impact of robots on income inequality also reveals mixed results. It appears that the increased adoption of robots is making the rich richer, although there is no evidence of the so-called technological unemployment (Fu et al., 2021). Technological progress could lead to job losses in some sectors, but even when that happens, other sectors will expand and contribute to overall employment and wage growth (Acemoglu and Restrepo, 2019). The use of artificial intelligence has a favourable effect on the labour market, although the number of jobs in companies decreases, workers retrain to other professions, thus the unemployment rate is decreasing (Lu, 2022). On the basis of the previous literature, the second hypothesis of the work is formulated: H2: The application of artificial intelligence in companies negatively impacts the welfare of people and contributes to widening social disparities in the Member States of the European Union. The expected meanings of the influence of the application of artificial intelligence in enterprises on welfare and social disparities in income and unemployment are: expectations of a decrease in average net income per person, an increase in the number of people at risk AE The Impact of Artificial Intelligence Applied in Businesses on Economic Growth, Welfare, and Social Disparities 480 Amfiteatru Economic of poverty or social exclusion, an increase in the poverty threshold, and an increase in the annual unemployment rate. The European Union represents an area of analysis in which common policies aim to reduce economic and social inequalities between member states. Based on common policies of free movement of goods, capital, and labour, the third hypothesis of the study is configured: H3: The application of artificial intelligence in businesses in one European Union country influences economic growth, welfare and social disparities in neighbouring countries. Based on the results of the previous research presented, the research model is proposed (Figure no. 1), which captures the influence that the application of artificial intelligence in enterprises has on economic growth, welfare, and social disparities (in terms of income and unemployment), in the EU member countries (2021). Figure no. 1. Theoretical model of research Source: authors projection 2. Research methodology The study develops multiple regression models for cross-sectional data for EU member states and the year 2021, with the aim of determining whether the deployment of artificial intelligence in enterprises impacts economic growth, welfare, and social disparities (represented by income inequalities, risks related to poverty levels, and unemployment). The study develops multiple regression models (Feasible Generalized Least Squares, FGLS) and spatial analysis (Spatial Lag Model, SLM) for cross-sectional data for EU member states (EU-27) and 2021 for all variables included in the analysis. The aim is to determine whether the implementation of artificial intelligence in businesses impacts economic growth, welfare, and social disparities. The choice of the FGLS analysis method was based on its potential to manage aspects of heteroscedasticity and which, even under conditions of homoscedasticity of errors, leads to more efficient estimates than the least squares method, through the ability to manage various other forms of correlation between variables. In the first phase of the research, the data were analysed from the perspective of classical regression assumptions, respectively, whether they are affected by multicollinearity, Innovative Application of AI in Business Impacting Socio-Economic Progress AE Vol. 26 • No. 66 • May 2024 481 heteroscedasticity, non-normal distribution, or non-stationarity of variables. Endogenity is an aspect often ignored in economic studies that leads to inconsistent estimates. Endogeneity occurs either when two variables influence each other simultaneously, or when relationships between two or more variables are influenced by a factor that is not considered, or when dynamic endogeneity occurs, if the present values of a variable are influenced by its past values or those of other variables (Chatterjee and Nag, 2023). Based on these considerations, the applied strategy was to apply theoretical reasoning to identify the relationships between the variables, followed by the analysis of the Durbin-Wu-Hausman test to identify the endogeneity of the variables. A spatial analysis was chosen for the efficient management of endogeneity (Petrovici et al., 2023), as it can capture spatial autocorrelation and control endogeneity problems that may arise due to spatially correlated errors or omitted variables. Spatial analysis can also manage cross-sectional dependence, given that data are susceptible to intra-state effects generated by common unobservable factors or common features. The study applies the Spatial Lag Model, which has the ability to effectively control data endogeneity, due to observations available for only one year and without the possibility of using dynamic analysis models. The Spatial Lag Model is used to determine to what extent values recorded in a country are influenced by those in neighbouring countries. Based on the latitude and longitude of the European Union’s countries, the matrix of spatial weights W (spwmat in Stata) was built, which shows the interactions of a state with its neighbours, the spatial gap model was developed (Pisati, 2001) and diagnostic and post-estimation tests for spatial dependence were performed for all analysis models specified in equations 2-6 (using the Moran index, of the Rho spatial coefficient, Wald, Likelihood Ratio and Lagrange Multiplier tests): 𝑦 = 𝜌𝑊𝑦 + 𝛽𝑋 + 𝑒 (1) in which 𝜌 is the autoregressive spatial dependence parameter, Wy is the spatial lag of the dependent variable y, X represents the independent variables, and e shows the errors. The core explanatory variable of this study captures the application of artificial intelligence technologies in enterprises, by the percentage of large enterprises, which have 250 or more employees, and which use artificial intelligence technologies in production activity (AI_PROD_LARGE), (Eurostat, 2023f). The influence of the application of artificial intelligence in enterprises on economic growth is studied using the dependent variable GDPPP (euro) - Gross Domestic Product per capita, expressed in euro / person, at market prices (reference 2010), (Eurostat, 2023a). To capture the social impact of the application of artificial intelligence in enterprises, welfare phenomena and social disparities are targeted, and four distinct dependent variables are used:  Average net income per person (annual), (INCOMEPP - Euro/person), (Eurostat, 2023b), captures the average income of the population in the EU.  People at risk of poverty or social exclusion (POVERTY - Thousand people), (Eurostat, 2023c), groups individuals living in households with very low work intensity (less than 20% of the available time in the last 12 months), with an equivalent total disposable income below the threshold of 60% of the national median income and are in severe material deprivation (Guio et al., 2021).  The at-risk-of-poverty threshold indicator (THRESHOLD, expressed in the Purchasing Power Standard) (Eurostat, 2023d) is set at 60% of the median equivalised disposable income AE The Impact of Artificial Intelligence Applied in Businesses on Economic Growth, Welfare, and Social Disparities 482 Amfiteatru Economic at national level, after social transfers and refers to a household of two adults and two children under 14 years of age (Guio et al., 2021).  The annual unemployment rate (UNEMPL), expressed as a percentage, captures the percentage of people looking for a job (Eurostat, 2023e). The control variables used in the analysis models are the following: Gross domestic expenditure on research and development by the higher education system, expressed in millions at the Purchasing Power Standard (R&D_EXPENSES), (Eurostat, 2023g); Government expenditure on education, expressed as a percentage of Gross Domestic Product (EDUC_EXPENSES), (World Bank, 2023); Education, represented by the average of school years, expressed in years (EDUCATION) (United Nations Development Programme, 2023); Labour productivity per person employed, percentage of EU-27 total (2020 reference based on Purchasing Power Standard million, current prices), (LABPRODPP), (Eurostat, 2023h); Sum of exports and imports of goods and services, measured as a share of Gross Domestic Product (TRADE), (World Bank, 2023); Inflation, annual consumer price index, expressed as a percentage (INFLA), (World Bank, 2023); GINI coefficient of disposable income inequality, scale from 0 to 100 (GINI), (Eurostat, 2023i); Annual growth in government spending, based on constant local currency and expressed as a percentage (GOVEXPG), (World Bank, 2023); Life expectancy at birth, expressed in years (LIFEEXP), (United Nations Development Programme, 2023); Patent applications, residents, expressed in absolute values (PATENTS), (World Bank, 2023); Tax burden (TAX_BURDEN), as total tax revenue relative to GDP / % of GDP (Heritage Foundation, 2023). To study the influence of the implementation of artificial intelligence in enterprises on economic growth, welfare, and social disparities in the EU, the following models of multiple linear regression are developed. In equations 2-6, indicators of economic growth, welfare and social disparities are alternatively considered as dependent variables of the model, as follows: GDPPP (euro)i,t =∝0+ ∝1AI_PROD_LARGEi,t +∝2R&D_EXPENSESi,t +∝3EDUCATIONi,t + ∝4LABPRODPPi,t +∝5TRADEi,t +∝6INFLAi,t + ui,t (2) INCOMEPPi,t =∝0+ ∝1AI_PROD_LARGEi,t +∝2R&D_EXPENSESi,t + ∝3EDUC_EXPENSESi,t +∝4EDUCATIONi,t +∝5LABPRODPPi,t +∝6TRADEi,t + ∝7INFLAi,t +∝8GINIi,t + ui,t (3) POVERTYi,t =∝0+ ∝1AI_PROD_LARGEi,t +∝2R&D_EXPENSESi,t +∝3EDUC_EXPENSESi,t + ∝4EDUCATIONi,t + ∝5TRADEi,t +∝6INFLAi,t + ui,t (4) THRESHOLDi,t =∝0+ ∝1AI_PROD_LARGEi,t +∝2R&D_EXPENSESi,t +∝3EDUCATIONi,t + ∝4TRADEi,t +∝5GINIi,t +∝6GOVEXPGi,t +∝7LIFEEXPi,t + ui,t (5) UNEMPLi,t =∝0+ ∝1AI_PROD_LARGEi,t +∝2PATENTSi,t +∝3EDUCATIONi,t + ∝4GDPPP (euro)i,t +∝5GOVEXPGi,t +∝6TRADEi,t +∝7TAX_BURDENi,t + ui,t (6) where i represents the country, t is the year, α0 is constant (intercept), α1,2,3,4,5,6,7,8 are the coefficients of the estimated parameters, and ui,t is the error. 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