Government Artificial Intelligence readiness and brain drain: influencing factors and spatial effects in the European Union member states
Abstract
In the swiftly advancing field of Artificial Intelligence (AI), a field where every country aims to keep pace, significant disparities are observed in how different nations adopt AI. This study explores the deep, yet insufficiently studied, effects of AI on societal, economic, and environmental aspects. It particularly examines how brain drain influences governmental AI implementation capabilities, addressing a gap in existing literature. The study investigates the interplay between government AI implementation and brain drain, factoring in macroeconomic conditions, governance quality, educational levels, and R&D efforts. Utilizing 2022 data from European Union countries, the research employs instrumental-variables regressions (2SLS and LIML) to counteract endogeneity and uses clustering methods for categorizing countries based on their government AI levels, alongside spatial analysis to detect cross-national spillovers and interactions. The findings reveal brain drain’s detrimental effect on governmental AI preparedness, highlight clustering tendencies, and identify spatial interdependencies. This paper underscores the need for strategic policy-making and institutional reforms to bolster government AI capabilities. It advocates for a paradigm shift in government frameworks post-New Public Management era, tailored to the new challenges posed by AI. The research, however, is limited to a single year and region, with constraints on data availability and indicator breadth.
Keyword : government AI readiness, brain drain, government expenditure, spatial effects, spillover effects, human capital
How to Cite
Iuga, I. C., & Socol, A. (2024). Government Artificial Intelligence readiness and brain drain: influencing factors and spatial effects in the European Union member states. Journal of Business Economics and Management, 25(2), 268–296. https://doi.org/10.3846/jbem.2024.21136
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Agrawal, A., Gans, J., & Goldfarb, A. (2019). The economics of artificial intelligence: An agenda. University of Chicago Press. https://doi.org/10.7208/chicago/9780226613475.001.0001
AIoD Platform. (2019). About the AI-on-Demand Platform. https://aiod.eu/about
Al-Mushayt, O. S. (2019). Automating E-government services with artificial intelligence. IEEE Access, 7, 146821–146829. https://doi.org/10.1109/ACCESS.2019.2946204
Anselin, L., Syabri, I., & Kho, Y. (2006). GeoDa: An introduction to spatial data analysis. Geographical Analysis, 38(1), 5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x
Altuzarra, A., Gálvez-Gálvez, C., & González-Flores, A. (2019). Economic development and female labour force participation: The case of European Union countries. Sustainability, 11(7), Article 1962. https://doi.org/10.3390/su11071962
Arntz, M., Gregory, T., & Zierahn, U. (2019). Digitalization and the future of work: Macroeconomic consequences (IZA Discussion Paper No. 12428). SSRN. https://doi.org/10.2139/ssrn.3411981
Bălan, M., & Olteanu, C. (2017). Brain drain in the globalization era: The case of Romania. Annals of the “Constantin Brâncuşi” University of Târgu Jiu, Economy Series, 3. https://www.utgjiu.ro/revista/ec/pdf/2017-03/03_MARIANA%20BALAN,%20COSMIN%20OLTEANU.pdf
Banerjee, A.V., & Duflo, E. (2019). Good economics for hard times (1st ed.). Public Affairs.
Basri, E., & Box, S. (2010). International mobility of the highly skilled: Impact and policy approaches. In The innovation for development report 2009–2010 (pp. 109–135). Palgrave Macmillan. https://doi.org/10.1057/9780230285477_5
Belotti, F., Hughes, G., & Mortari, A. P. (2017). Spatial panel-data models using Stata. The Stata Journal, 17(1), 139–180. https://doi.org/10.1177/1536867X1701700109
Berger, S. (2022, November). Brain drain, brain gain and its net effect. (Knomad Paper 46). Knomad. https://www.knomad.org/sites/default/files/2022-11/knomad_paper_46_brain_drain_brain_gain_and_its_net_effect_sandra_berger_november_2022.pdf
Bose, N., Haque, M. E., & Osborn, D. R. (2007). Public expenditure and economic growth: A disaggregated analysis for developing countries. The Manchester School, 75(5), 533–556. https://doi.org/10.1111/j.1467-9957.2007.01028.x
Bredt, S. (2019). Artificial Intelligence (AI) in the financial sector – Potential and public strategies. Frontiers in Artifical Intelligence, 2, Article 16. https://doi.org/10.3389/frai.2019.00016
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
Calderaro, A., & Blumfelde, S. (2022). Artificial intelligence and EU security: The false promise of digital sovereignty. European Security, 31(3), 415–434. https://doi.org/10.1080/09662839.2022.2101885
Can, M. (2023). Under the leadership of our president: ‘Potemkin AI’ and the Turkish approach to artificial intelligence. Third World Quarterly, 44(2), 356–376. https://doi.org/10.1080/01436597.2022.2147059
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2017). Artificial intelligence and the ‘good society’: The US, EU, and UK approach. Science and Engineering Ethics, 24, 505–528. https://doi.org/10.1007/s11948-017-9901-7
Ciftci, C., & Durusu-Ciftci, D. (2022). Economic freedom, foreign direct investment, and economic growth: The role of sub-components of freedom. The Journal of International Trade & Economic Development, 31(2), 233–254. https://doi.org/10.1080/09638199.2021.1962392
Czaika, M., & de Haas, H. (2015). The globalization of migration: Has the world become more migratory? International Migration Review, 48(2), 283–323. https://doi.org/10.1111/imre.12095
Dignum, V. (2019). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer. https://doi.org/10.1007/978-3-030-30371-6
Djeffal, C., Siewert, M. B., & Wurster, S. (2022). Role of the state and responsibility in governing artificial intelligence: A comparative analysis of AI strategies. Journal of European Public Policy, 29(11), 1799–1821. https://doi.org/10.1080/13501763.2022.2094987
Docquier, F., & Rapoport, H. (2012). Globalization, brain drain, and development. Journal of Economic Literature, 50(3), 681–730. https://doi.org/10.1257/jel.50.3.681
Doran, N. M., Puiu, S., Bădîrcea, R. M., Pirtea, M. G., Doran, M. D., Ciobanu, G., & Mihit, L. D. (2023). E-government development – A key factor in government administration effectiveness in the European Union. Electronics, 12(3), Article 641. https://doi.org/10.3390/electronics12030641
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan. Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Eggers, W. D., Schatsky, D., & Viechnicki, P. (2017). AI-augmented government. Deloitte University Press. https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/artificial-intelligence-government.html
Elhorst, J. P. (2017). Spatial panel data analysis. In S. Shekhar, H. Xiong, & X. Zhou (Eds.), Encyclopedia of GIS (pp. 2050–2058). Springer. https://doi.org/10.1007/978-3-319-17885-1_1641
European Commission. (2018). Coordinated plan on artificial intelligence. https://digital-strategy.ec.europa.eu/en/library/coordinated-plan-artificial-intelligence
European Commission. (2020). White paper on artificial intelligence: A European approach to excellence and trust. https://commission.europa.eu/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en
European Commission. (2021a). Horizon Europe. https://research-and-innovation.ec.europa.eu/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-europe_en
European Commission. (2021b). New rules for Artificial Intelligence – Questions and answers. https://www.eumonitor.eu/9353000/1/j9vvik7m1c3gyxp/vli5fy8jickv?ctx=vg9pjpw5wsz1&v=1
European Commission. (2022). Joint research Centre data catalogue. AI Watch Index 2021. https://data.jrc.ec.europa.eu/dataset/e3757f41-fe54-4330-946d-ae897686164f
European Parliament. (2021a). AI for public services. https://www.europarl.europa.eu/RegData/etudes/BRIE/2021/662936/IPOL_BRI(2021)662936_EN.pdf
European Parliament. (2021b). Challenges and limits of an open source approach to Artificial Intelligence. https://www.europarl.europa.eu/RegData/etudes/STUD/2021/662908/IPOL_STU(2021)662908_EN.pdf
European Parliament. (2023). EU AI Act: First regulation on artificial intelligence. https://www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence
European Tertiary Education Register. (2019). How are European higher education institutions funded? New evidence from ETER microdata. https://www.eter-project.com/uploads/analytical-reports/ETER_AnalyticalReport_02_final.pdf
European Union. (2012). Consolidated version of the Treaty on the Functioning of the European Union. Article 45. Official Journal of the European Union C 326/288 (26.10.2012). https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:12012E/TXT:en:PDF
Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. https://doi.org/10.1002/9780470977811
Fund for Peace. (2022). Fragile States Index. https://fragilestatesindex.org/indicators/e3/
Frankowska, A., & Pawlik, B. (2022). A decade of artificial intelligence research in the European Union: A bibliometric analysis. In C. Biele, J. Kacprzyk, W. Kopeć, J. W. Owsiński, A. Romanowski, & M. Sikorski (Eds.), Lecture notes in networks and systems: Vol. 440. Digital interaction and machine intelligence. MIDI 2021 (pp. 52–62). Springer. https://doi.org/10.1007/978-3-031-11432-8_5
Frățilă, A., Păunescu, M., Nichita, E.-M., & Lazăr, P. (2023). Digitalization of Romanian public administration: A panel data analysis at regional level. Journal of Business Economics and Management, 24(1), 74–92. https://doi.org/10.3846/jbem.2023.18574
Gesk, T. S., & Leyer, M. (2022). Artificial intelligence in public services: When and why citizens accept its usage. Government Information Quarterly, 39(3), Article 101704. https://doi.org/10.1016/j.giq.2022.101704
Gomes de Sousa, W., Pereira de Melo, E. R., De Souza Bermejo, P. H., Sousa Farias, R. A., & Oliveira Gomes, A. (2019). How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 36(4), Article 101392. https://doi.org/10.1016/j.giq.2019.07.004
Gupta, K. P. (2019). Artificial intelligence for governance in India: Prioritizing the challenges using analytic hierarchy process (AHP). International Journal of Recent Technology and Engineering, 8(2), 3756–3762. https://doi.org/10.35940/ijrte.B3392.078219
Heritage Foundation. (2022). Index of Economic Freedom. https://www.heritage.org/index/
Ibrahim, M., & Arundina, T. (2022). Practical panel modelling. KNEKS. https://kneks.go.id/storage/upload/1675248927-[FINAL]%20Practional%20Panel%20Modeling%20-%20Applications%20in%20Islamic%20Banking%20and%20Finance_PDF.pdf
Ionescu, A. M., Clipa, A. M., Turnea, E. S., Clipa, C. I., Bedrule-Grigoruta, M. V., & Roth, S. (2022). The impact of innovation framework conditions on corporate digital technology integration: Institutions as facilitators for sustainable digital transformation. Journal of Business Economics and Management, 23(5), 1037–1059. https://doi.org/10.3846/jbem.2022.17039
Jacobs, K. (2023). Future of work: Upskilling. Work, 74(1). https://doi.org/10.3233/WOR-236000
Johnson, H. G. (1965). The economics of the “brain-drain”: The Canadian case. Minerva, 3, 299–311. https://doi.org/10.1007/BF01099956
Kapur, D., & McHale, J. (2005). Give us your best and brightest: The global hunt for talent and its impact on the developing world. Center for Global Development. https://www.cgdev.org/sites/default/files/9781933286037-Kapur-Hale-best-and-brightest.pdf
Labra, R., & Torrecillas, C. (2018). Estimating dynamic panel data. A practical approach to perform long panels. Revista Colombiana de Estadistica, 41(1), 31–52. https://doi.org/10.15446/rce.v41n1.61885
Le, A. H., & Kim, T. (2020). The effects of economic freedom on firm investment in Vietnam. Journal of Asian Finance, Economics and Business, 7(3), 9–15. https://doi.org/10.13106/jafeb.2020.vol7.no3.9
Lepori, B., Reale, E., & Spinello, A. O. (2018). Conceptualizing and measuring performance orientation of research funding systems. Research Evaluation, 27(3), 171–183. https://doi.org/10.1093/reseval/rvy007
Margetts, H., & Dorobantu, C. (2019). Rethink government with AI. Nature, 568, 163–165. https://doi.org/10.1038/d41586-019-01099-5
Mehr, H. (2017). Artificial intelligence for citizen services and government. https://ash.harvard.edu/files/ash/files/artificial_intelligence_for_citizen_services.pdf
Mikhaylov, S. J., Esteve, M., & Campion, A. (2018). Artificial intelligence for the public sector: Opportunities and challenges of cross-sector collaboration. Philosophical Transactions of the Royal Society A, 376(2128), 1–26. https://doi.org/10.1098/rsta.2017.0357
Millard, J. (2017). European strategies for e-Governance to 2020 and beyond. In A. Ojo & J. Millard (Eds.), Public administration and information technology: Vol. 32. Government 3.0 – next generation government technology infrastructure and services (pp. 1–25). Springer. https://doi.org/10.1007/978-3-319-63743-3_1
Montoya, L., & Rivas, P. (2019, November 15-16). Government AI readiness meta-analysis for Latin America and The Caribbean. In Proceedings of 2019 IEEE international symposium on technology and society (ISTAS). Medford. https://doi.org/10.1109/ISTAS48451.2019.8937869
Nguyen, M. L. T., & Bui, N. T. (2022). Government expenditure and economic growth: Does the role of corruption control matter? Heliyon, 8(10), Article e10822. https://doi.org/10.1016/j.heliyon.2022.e10822
Noja, G. G. (2018). Flexicurity models and productivity interference in C.E.E. countries: A new approach based on cluster and spatial analysis. Economic Research-Ekonomska Istraživanja, 31(1), 1111–1136. https://doi.org/10.1080/1331677X.2018.1456356
OECD. (2023). OECD AI Policy Observatory. https://oecd.ai/en/
Offer, A. (2022). Understanding the private–public divide. Markets, governments, and time horizons. Cambridge University Press. https://doi.org/10.1017/9781108866415
Ojo, A. (2019). Next generation government – hyperconnected, smart and augmented. In L. M. Camarinha-Matos, H. Afsarmanesh, & D. Antonelli (Eds.), IFIP advances in information and communication technology: Vol. 568. collaborative networks and digital transformation. PRO-VE 2019 (pp. 285–294). Springer. https://doi.org/10.1007/978-3-030-28464-0_25
Ojo, A., Mellouli, S., & Zeleti, F. A. (2019, June 18–20). A realist perspective on AI – era public management. An analysis of mechanisms, outcomes and challenges of AI solutions in the public sector. In Proceedings of the 20th Annual International Conference on Digital Government Research (pp. 159–170). Dubai. https://doi.org/10.1145/3325112.3325261
Okulich-Kazarin, V., Goloborodko, A., & Kravets, O. (2020). What is the priority of the political rhetoric of the Russian presidents: Growth of bureaucracy or economic freedom? In M. J. Lomott, K. Łyskawa, P. Polychronidou, & A. Karasavvoglou (Eds.), Springer proceedings in business and economics. Economic and financial challenges for Balkan and Eastern European countries (pp. 315–327). Springer. https://doi.org/10.1007/978-3-030-39927-6_20
Oxford Insights. (2019). Government Artificial Intelligence. Readiness Index 2019. https://oxfordinsights.com/wp-content/uploads/2023/12/ai-gov-readiness-report_v08.pdf
Oxford Insights. (2022). Government AI Readiness Index 2022. https://oxfordinsights.com/wp-content/uploads/2023/11/Government_AI_Readiness_2022_FV.pdf
Pan, Y., & Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, Article 103517. https://doi.org/10.1016/j.autcon.2020.103517
Piasecki, R., Wolnicki, M., & Betancourt, E. W. (2021). Artificial Intelligence in the context of global resource mobility. What can be expected from it? Comparative Economic Research-Central and Eastern Europe, 24(3), 93–107. https://doi.org/10.18778/1508-2008.24.23
Pisati, M. (2001). Tools for spatial data analysis. Stata Technical Bulletin, 60, 21–37.
Qin, Y., Xu, Z., Wang, X., & Skare, M. (2023). Artificial intelligence and economic development: An evolutionary investigation and systematic review. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-023-01183-2
Reis, J., Santo, P. E., & Melão, N. (2019). Artificial intelligence in government services: A systematic literature review. In Á. Rocha, H. Adeli, L. P. Reis, & S. Costanzo (Eds.), Advances in intelligent systems and computing. Vol. 930. New knowledge in information systems and technologies. WorldCIST’19 2019 (pp. 241–252). Springer. https://doi.org/10.1007/978-3-030-16181-1_23
Robles, P., & Mallinson, D. J. (2023). Artificial intelligence technology, public trust, and effective governance. Review of Policy Research. https://doi.org/10.1111/ropr.12555
Sabry, R. A. W. O. (2021). Artificial intelligence role for advertising campaigns development. International Journal of Artificial Intelligence and Emerging Technology, 4(1), 1–16. https://doi.org/10.21608/ijaiet.2021.187258
Siar, S. V. (2013). From highly skilled to low skilled: Revisiting the deskilling of migrant labor (PIDS Discussion Paper Series, No. 2013-30). Philippine Institute for Development Studies (PIDS). http://hdl.handle.net/10419/126949
Singh, J., & Krishna, V. V. (2018). Trends in brain drain, gain and circulation: Indian experience of knowledge workers. Science, Technology and Society, 20(3), 300–321. https://doi.org/10.1177/0971721815597132
Spence, M. (2021). Government and economics in the digital economy. Journal of Government and Economics, 3, Article 1000020. https://doi.org/10.1016/j.jge.2021.100020
Szarowska, I., (2022). Relationship between government expenditure and economic growth in Visegrad Group. Financial Internet Quaterly, 18(4), 12–22. https://doi.org/10.2478/fiqf-2022-0024
Thanh, S. D., Hart, N., & Canh, N. P. (2020). Public spending, public governance and economic growth at the Vietnamese provincial level: A disaggregate analysis. Economic Systems, 44(4), Article 100780. https://doi.org/10.1016/j.ecosys.2020.100780
Thomas, P., & Murdick, D. (2020). Patents and artificial intelligence: A primer. CSET data brief. Center for Security and Emerging Technology. https://doi.org/10.51593/20200038
Trishch, R. M., Sichinava, A., Bartos, V., Stasiukynas, A., & Schieg, M. (2023). Comparative assessment of economic development in the countries of the European Union. Journal of Business Economics and Management, 24(1), 20–36. https://doi.org/10.3846/jbem.2023.18320
Ullah, S., Akhtar, P., & Zaefarian, G. (2018). Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data. Industrial Marketing Management, 71, 69–78. https://doi.org/10.1016/j.indmarman.2017.11.010
Vicsek, L. (2021). Artificial intelligence and the future of work – lessons from the sociology of expectations. International Journal of Sociology and Social Policy, 41(7/8), 842–861. https://doi.org/10.1108/IJSSP-05-2020-0174
Voda, A. D., Dobrota, G., Dobrota, D., & Dumitrascu, D. D. (2022). Error correction model for analysis of influence of fiscal policy on economic growth in EU. Journal of Business Economics and Management, 23(3), 586–605. https://doi.org/10.3846/jbem.2022.16242
Walker, A., & Maltby, T. (2012). Active ageing: A strategic policy solution to demographic ageing in the European Union. International Journal of Social Welfare, 21(s1), S117–S130. https://doi.org/10.1111/j.1468-2397.2012.00871.x
Wang, Z., Ma, D., Zhang, J., Wang, Y., & Sun, D. (2023). Does urbanization have spatial spillover effects on poverty reduction: Empirical evidence from rural China. Economic Research-Ekonomska Istraživanja, 36, Article 2167730. https://doi.org/10.1080/1331677X.2023.2167730
Wang, X., & Cui, X. (2022). PPP financing model in the infrastructure construction of the park integrating artificial intelligence technology. Computational Intelligence and Neuroscience, Article 6154885. https://doi.org/10.1155/2022/6154885
Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The dark sides of artificial intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration, 43(9), 818–829. https://doi.org/10.1080/01900692.2020.1749851
World Bank. (2022a). General government final consumption expenditure (current US$). https://data.worldbank.org/indicator/NE.CON.GOVT.CD
World Bank. (2022b). GDP (current US$). https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?view=chart
World Economic Forum. (2023). For inclusive growth, leaders must embrace a global and open economic future. https://www.weforum.org/agenda/2023/01/finance-inclusive-growth-wef23/
Yamashita, I., Murakami, A., Cairns, S., & Galindo-Rueda, F. (2021). Measuring the AI content of government-funded R&D projects: A proof of concept for the OECD Fundstat initiative. (OECD Science, Technology and Industry Working Papers, No. 2021/09). OECD. https://doi.org/10.1787/7b43b038-en
Zajko, M. (2022). Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates. Sociology Compass, 16(3), Article e12962. https://doi.org/10.1111/soc4.12962
AIoD Platform. (2019). About the AI-on-Demand Platform. https://aiod.eu/about
Al-Mushayt, O. S. (2019). Automating E-government services with artificial intelligence. IEEE Access, 7, 146821–146829. https://doi.org/10.1109/ACCESS.2019.2946204
Anselin, L., Syabri, I., & Kho, Y. (2006). GeoDa: An introduction to spatial data analysis. Geographical Analysis, 38(1), 5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x
Altuzarra, A., Gálvez-Gálvez, C., & González-Flores, A. (2019). Economic development and female labour force participation: The case of European Union countries. Sustainability, 11(7), Article 1962. https://doi.org/10.3390/su11071962
Arntz, M., Gregory, T., & Zierahn, U. (2019). Digitalization and the future of work: Macroeconomic consequences (IZA Discussion Paper No. 12428). SSRN. https://doi.org/10.2139/ssrn.3411981
Bălan, M., & Olteanu, C. (2017). Brain drain in the globalization era: The case of Romania. Annals of the “Constantin Brâncuşi” University of Târgu Jiu, Economy Series, 3. https://www.utgjiu.ro/revista/ec/pdf/2017-03/03_MARIANA%20BALAN,%20COSMIN%20OLTEANU.pdf
Banerjee, A.V., & Duflo, E. (2019). Good economics for hard times (1st ed.). Public Affairs.
Basri, E., & Box, S. (2010). International mobility of the highly skilled: Impact and policy approaches. In The innovation for development report 2009–2010 (pp. 109–135). Palgrave Macmillan. https://doi.org/10.1057/9780230285477_5
Belotti, F., Hughes, G., & Mortari, A. P. (2017). Spatial panel-data models using Stata. The Stata Journal, 17(1), 139–180. https://doi.org/10.1177/1536867X1701700109
Berger, S. (2022, November). Brain drain, brain gain and its net effect. (Knomad Paper 46). Knomad. https://www.knomad.org/sites/default/files/2022-11/knomad_paper_46_brain_drain_brain_gain_and_its_net_effect_sandra_berger_november_2022.pdf
Bose, N., Haque, M. E., & Osborn, D. R. (2007). Public expenditure and economic growth: A disaggregated analysis for developing countries. The Manchester School, 75(5), 533–556. https://doi.org/10.1111/j.1467-9957.2007.01028.x
Bredt, S. (2019). Artificial Intelligence (AI) in the financial sector – Potential and public strategies. Frontiers in Artifical Intelligence, 2, Article 16. https://doi.org/10.3389/frai.2019.00016
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
Calderaro, A., & Blumfelde, S. (2022). Artificial intelligence and EU security: The false promise of digital sovereignty. European Security, 31(3), 415–434. https://doi.org/10.1080/09662839.2022.2101885
Can, M. (2023). Under the leadership of our president: ‘Potemkin AI’ and the Turkish approach to artificial intelligence. Third World Quarterly, 44(2), 356–376. https://doi.org/10.1080/01436597.2022.2147059
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2017). Artificial intelligence and the ‘good society’: The US, EU, and UK approach. Science and Engineering Ethics, 24, 505–528. https://doi.org/10.1007/s11948-017-9901-7
Ciftci, C., & Durusu-Ciftci, D. (2022). Economic freedom, foreign direct investment, and economic growth: The role of sub-components of freedom. The Journal of International Trade & Economic Development, 31(2), 233–254. https://doi.org/10.1080/09638199.2021.1962392
Czaika, M., & de Haas, H. (2015). The globalization of migration: Has the world become more migratory? International Migration Review, 48(2), 283–323. https://doi.org/10.1111/imre.12095
Dignum, V. (2019). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer. https://doi.org/10.1007/978-3-030-30371-6
Djeffal, C., Siewert, M. B., & Wurster, S. (2022). Role of the state and responsibility in governing artificial intelligence: A comparative analysis of AI strategies. Journal of European Public Policy, 29(11), 1799–1821. https://doi.org/10.1080/13501763.2022.2094987
Docquier, F., & Rapoport, H. (2012). Globalization, brain drain, and development. Journal of Economic Literature, 50(3), 681–730. https://doi.org/10.1257/jel.50.3.681
Doran, N. M., Puiu, S., Bădîrcea, R. M., Pirtea, M. G., Doran, M. D., Ciobanu, G., & Mihit, L. D. (2023). E-government development – A key factor in government administration effectiveness in the European Union. Electronics, 12(3), Article 641. https://doi.org/10.3390/electronics12030641
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan. Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Eggers, W. D., Schatsky, D., & Viechnicki, P. (2017). AI-augmented government. Deloitte University Press. https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/artificial-intelligence-government.html
Elhorst, J. P. (2017). Spatial panel data analysis. In S. Shekhar, H. Xiong, & X. Zhou (Eds.), Encyclopedia of GIS (pp. 2050–2058). Springer. https://doi.org/10.1007/978-3-319-17885-1_1641
European Commission. (2018). Coordinated plan on artificial intelligence. https://digital-strategy.ec.europa.eu/en/library/coordinated-plan-artificial-intelligence
European Commission. (2020). White paper on artificial intelligence: A European approach to excellence and trust. https://commission.europa.eu/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en
European Commission. (2021a). Horizon Europe. https://research-and-innovation.ec.europa.eu/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-europe_en
European Commission. (2021b). New rules for Artificial Intelligence – Questions and answers. https://www.eumonitor.eu/9353000/1/j9vvik7m1c3gyxp/vli5fy8jickv?ctx=vg9pjpw5wsz1&v=1
European Commission. (2022). Joint research Centre data catalogue. AI Watch Index 2021. https://data.jrc.ec.europa.eu/dataset/e3757f41-fe54-4330-946d-ae897686164f
European Parliament. (2021a). AI for public services. https://www.europarl.europa.eu/RegData/etudes/BRIE/2021/662936/IPOL_BRI(2021)662936_EN.pdf
European Parliament. (2021b). Challenges and limits of an open source approach to Artificial Intelligence. https://www.europarl.europa.eu/RegData/etudes/STUD/2021/662908/IPOL_STU(2021)662908_EN.pdf
European Parliament. (2023). EU AI Act: First regulation on artificial intelligence. https://www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence
European Tertiary Education Register. (2019). How are European higher education institutions funded? New evidence from ETER microdata. https://www.eter-project.com/uploads/analytical-reports/ETER_AnalyticalReport_02_final.pdf
European Union. (2012). Consolidated version of the Treaty on the Functioning of the European Union. Article 45. Official Journal of the European Union C 326/288 (26.10.2012). https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:12012E/TXT:en:PDF
Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. https://doi.org/10.1002/9780470977811
Fund for Peace. (2022). Fragile States Index. https://fragilestatesindex.org/indicators/e3/
Frankowska, A., & Pawlik, B. (2022). A decade of artificial intelligence research in the European Union: A bibliometric analysis. In C. Biele, J. Kacprzyk, W. Kopeć, J. W. Owsiński, A. Romanowski, & M. Sikorski (Eds.), Lecture notes in networks and systems: Vol. 440. Digital interaction and machine intelligence. MIDI 2021 (pp. 52–62). Springer. https://doi.org/10.1007/978-3-031-11432-8_5
Frățilă, A., Păunescu, M., Nichita, E.-M., & Lazăr, P. (2023). Digitalization of Romanian public administration: A panel data analysis at regional level. Journal of Business Economics and Management, 24(1), 74–92. https://doi.org/10.3846/jbem.2023.18574
Gesk, T. S., & Leyer, M. (2022). Artificial intelligence in public services: When and why citizens accept its usage. Government Information Quarterly, 39(3), Article 101704. https://doi.org/10.1016/j.giq.2022.101704
Gomes de Sousa, W., Pereira de Melo, E. R., De Souza Bermejo, P. H., Sousa Farias, R. A., & Oliveira Gomes, A. (2019). How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 36(4), Article 101392. https://doi.org/10.1016/j.giq.2019.07.004
Gupta, K. P. (2019). Artificial intelligence for governance in India: Prioritizing the challenges using analytic hierarchy process (AHP). International Journal of Recent Technology and Engineering, 8(2), 3756–3762. https://doi.org/10.35940/ijrte.B3392.078219
Heritage Foundation. (2022). Index of Economic Freedom. https://www.heritage.org/index/
Ibrahim, M., & Arundina, T. (2022). Practical panel modelling. KNEKS. https://kneks.go.id/storage/upload/1675248927-[FINAL]%20Practional%20Panel%20Modeling%20-%20Applications%20in%20Islamic%20Banking%20and%20Finance_PDF.pdf
Ionescu, A. M., Clipa, A. M., Turnea, E. S., Clipa, C. I., Bedrule-Grigoruta, M. V., & Roth, S. (2022). The impact of innovation framework conditions on corporate digital technology integration: Institutions as facilitators for sustainable digital transformation. Journal of Business Economics and Management, 23(5), 1037–1059. https://doi.org/10.3846/jbem.2022.17039
Jacobs, K. (2023). Future of work: Upskilling. Work, 74(1). https://doi.org/10.3233/WOR-236000
Johnson, H. G. (1965). The economics of the “brain-drain”: The Canadian case. Minerva, 3, 299–311. https://doi.org/10.1007/BF01099956
Kapur, D., & McHale, J. (2005). Give us your best and brightest: The global hunt for talent and its impact on the developing world. Center for Global Development. https://www.cgdev.org/sites/default/files/9781933286037-Kapur-Hale-best-and-brightest.pdf
Labra, R., & Torrecillas, C. (2018). Estimating dynamic panel data. A practical approach to perform long panels. Revista Colombiana de Estadistica, 41(1), 31–52. https://doi.org/10.15446/rce.v41n1.61885
Le, A. H., & Kim, T. (2020). The effects of economic freedom on firm investment in Vietnam. Journal of Asian Finance, Economics and Business, 7(3), 9–15. https://doi.org/10.13106/jafeb.2020.vol7.no3.9
Lepori, B., Reale, E., & Spinello, A. O. (2018). Conceptualizing and measuring performance orientation of research funding systems. Research Evaluation, 27(3), 171–183. https://doi.org/10.1093/reseval/rvy007
Margetts, H., & Dorobantu, C. (2019). Rethink government with AI. Nature, 568, 163–165. https://doi.org/10.1038/d41586-019-01099-5
Mehr, H. (2017). Artificial intelligence for citizen services and government. https://ash.harvard.edu/files/ash/files/artificial_intelligence_for_citizen_services.pdf
Mikhaylov, S. J., Esteve, M., & Campion, A. (2018). Artificial intelligence for the public sector: Opportunities and challenges of cross-sector collaboration. Philosophical Transactions of the Royal Society A, 376(2128), 1–26. https://doi.org/10.1098/rsta.2017.0357
Millard, J. (2017). European strategies for e-Governance to 2020 and beyond. In A. Ojo & J. Millard (Eds.), Public administration and information technology: Vol. 32. Government 3.0 – next generation government technology infrastructure and services (pp. 1–25). Springer. https://doi.org/10.1007/978-3-319-63743-3_1
Montoya, L., & Rivas, P. (2019, November 15-16). Government AI readiness meta-analysis for Latin America and The Caribbean. In Proceedings of 2019 IEEE international symposium on technology and society (ISTAS). Medford. https://doi.org/10.1109/ISTAS48451.2019.8937869
Nguyen, M. L. T., & Bui, N. T. (2022). Government expenditure and economic growth: Does the role of corruption control matter? Heliyon, 8(10), Article e10822. https://doi.org/10.1016/j.heliyon.2022.e10822
Noja, G. G. (2018). Flexicurity models and productivity interference in C.E.E. countries: A new approach based on cluster and spatial analysis. Economic Research-Ekonomska Istraživanja, 31(1), 1111–1136. https://doi.org/10.1080/1331677X.2018.1456356
OECD. (2023). OECD AI Policy Observatory. https://oecd.ai/en/
Offer, A. (2022). Understanding the private–public divide. Markets, governments, and time horizons. Cambridge University Press. https://doi.org/10.1017/9781108866415
Ojo, A. (2019). Next generation government – hyperconnected, smart and augmented. In L. M. Camarinha-Matos, H. Afsarmanesh, & D. Antonelli (Eds.), IFIP advances in information and communication technology: Vol. 568. collaborative networks and digital transformation. PRO-VE 2019 (pp. 285–294). Springer. https://doi.org/10.1007/978-3-030-28464-0_25
Ojo, A., Mellouli, S., & Zeleti, F. A. (2019, June 18–20). A realist perspective on AI – era public management. An analysis of mechanisms, outcomes and challenges of AI solutions in the public sector. In Proceedings of the 20th Annual International Conference on Digital Government Research (pp. 159–170). Dubai. https://doi.org/10.1145/3325112.3325261
Okulich-Kazarin, V., Goloborodko, A., & Kravets, O. (2020). What is the priority of the political rhetoric of the Russian presidents: Growth of bureaucracy or economic freedom? In M. J. Lomott, K. Łyskawa, P. Polychronidou, & A. Karasavvoglou (Eds.), Springer proceedings in business and economics. Economic and financial challenges for Balkan and Eastern European countries (pp. 315–327). Springer. https://doi.org/10.1007/978-3-030-39927-6_20
Oxford Insights. (2019). Government Artificial Intelligence. Readiness Index 2019. https://oxfordinsights.com/wp-content/uploads/2023/12/ai-gov-readiness-report_v08.pdf
Oxford Insights. (2022). Government AI Readiness Index 2022. https://oxfordinsights.com/wp-content/uploads/2023/11/Government_AI_Readiness_2022_FV.pdf
Pan, Y., & Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, Article 103517. https://doi.org/10.1016/j.autcon.2020.103517
Piasecki, R., Wolnicki, M., & Betancourt, E. W. (2021). Artificial Intelligence in the context of global resource mobility. What can be expected from it? Comparative Economic Research-Central and Eastern Europe, 24(3), 93–107. https://doi.org/10.18778/1508-2008.24.23
Pisati, M. (2001). Tools for spatial data analysis. Stata Technical Bulletin, 60, 21–37.
Qin, Y., Xu, Z., Wang, X., & Skare, M. (2023). Artificial intelligence and economic development: An evolutionary investigation and systematic review. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-023-01183-2
Reis, J., Santo, P. E., & Melão, N. (2019). Artificial intelligence in government services: A systematic literature review. In Á. Rocha, H. Adeli, L. P. Reis, & S. Costanzo (Eds.), Advances in intelligent systems and computing. Vol. 930. New knowledge in information systems and technologies. WorldCIST’19 2019 (pp. 241–252). Springer. https://doi.org/10.1007/978-3-030-16181-1_23
Robles, P., & Mallinson, D. J. (2023). Artificial intelligence technology, public trust, and effective governance. Review of Policy Research. https://doi.org/10.1111/ropr.12555
Sabry, R. A. W. O. (2021). Artificial intelligence role for advertising campaigns development. International Journal of Artificial Intelligence and Emerging Technology, 4(1), 1–16. https://doi.org/10.21608/ijaiet.2021.187258
Siar, S. V. (2013). From highly skilled to low skilled: Revisiting the deskilling of migrant labor (PIDS Discussion Paper Series, No. 2013-30). Philippine Institute for Development Studies (PIDS). http://hdl.handle.net/10419/126949
Singh, J., & Krishna, V. V. (2018). Trends in brain drain, gain and circulation: Indian experience of knowledge workers. Science, Technology and Society, 20(3), 300–321. https://doi.org/10.1177/0971721815597132
Spence, M. (2021). Government and economics in the digital economy. Journal of Government and Economics, 3, Article 1000020. https://doi.org/10.1016/j.jge.2021.100020
Szarowska, I., (2022). Relationship between government expenditure and economic growth in Visegrad Group. Financial Internet Quaterly, 18(4), 12–22. https://doi.org/10.2478/fiqf-2022-0024
Thanh, S. D., Hart, N., & Canh, N. P. (2020). Public spending, public governance and economic growth at the Vietnamese provincial level: A disaggregate analysis. Economic Systems, 44(4), Article 100780. https://doi.org/10.1016/j.ecosys.2020.100780
Thomas, P., & Murdick, D. (2020). Patents and artificial intelligence: A primer. CSET data brief. Center for Security and Emerging Technology. https://doi.org/10.51593/20200038
Trishch, R. M., Sichinava, A., Bartos, V., Stasiukynas, A., & Schieg, M. (2023). Comparative assessment of economic development in the countries of the European Union. Journal of Business Economics and Management, 24(1), 20–36. https://doi.org/10.3846/jbem.2023.18320
Ullah, S., Akhtar, P., & Zaefarian, G. (2018). Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data. Industrial Marketing Management, 71, 69–78. https://doi.org/10.1016/j.indmarman.2017.11.010
Vicsek, L. (2021). Artificial intelligence and the future of work – lessons from the sociology of expectations. International Journal of Sociology and Social Policy, 41(7/8), 842–861. https://doi.org/10.1108/IJSSP-05-2020-0174
Voda, A. D., Dobrota, G., Dobrota, D., & Dumitrascu, D. D. (2022). Error correction model for analysis of influence of fiscal policy on economic growth in EU. Journal of Business Economics and Management, 23(3), 586–605. https://doi.org/10.3846/jbem.2022.16242
Walker, A., & Maltby, T. (2012). Active ageing: A strategic policy solution to demographic ageing in the European Union. International Journal of Social Welfare, 21(s1), S117–S130. https://doi.org/10.1111/j.1468-2397.2012.00871.x
Wang, Z., Ma, D., Zhang, J., Wang, Y., & Sun, D. (2023). Does urbanization have spatial spillover effects on poverty reduction: Empirical evidence from rural China. Economic Research-Ekonomska Istraživanja, 36, Article 2167730. https://doi.org/10.1080/1331677X.2023.2167730
Wang, X., & Cui, X. (2022). PPP financing model in the infrastructure construction of the park integrating artificial intelligence technology. Computational Intelligence and Neuroscience, Article 6154885. https://doi.org/10.1155/2022/6154885
Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The dark sides of artificial intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration, 43(9), 818–829. https://doi.org/10.1080/01900692.2020.1749851
World Bank. (2022a). General government final consumption expenditure (current US$). https://data.worldbank.org/indicator/NE.CON.GOVT.CD
World Bank. (2022b). GDP (current US$). https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?view=chart
World Economic Forum. (2023). For inclusive growth, leaders must embrace a global and open economic future. https://www.weforum.org/agenda/2023/01/finance-inclusive-growth-wef23/
Yamashita, I., Murakami, A., Cairns, S., & Galindo-Rueda, F. (2021). Measuring the AI content of government-funded R&D projects: A proof of concept for the OECD Fundstat initiative. (OECD Science, Technology and Industry Working Papers, No. 2021/09). OECD. https://doi.org/10.1787/7b43b038-en
Zajko, M. (2022). Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates. Sociology Compass, 16(3), Article e12962. https://doi.org/10.1111/soc4.12962