Schneider, Florian (2024): Do robots boost productivity? A quantitative meta-study.
![]() |
PDF
MPRA_paper_123392.pdf Download (2MB) |
Abstract
This meta-study analyzes the productivity effects of industrial robots. More than 1800 estimates from 81 primary studies are collected. There is strong evidence that the empirical literature on the productivity effect of robots suffers from a substantial positive publication bias. This finding is observed across all measures of productivity used in the primary literature and is robust to several modern meta-analytic estimators. Beyond publication bias, there is only limited evidence for a productivity-increasing effect of robots, which so far have exerted at best a marginal boost. My analysis of the drivers of heterogeneity among the findings of primary studies points to adjustment costs at low intensities of robot use as well as diminishing returns at more advanced levels of robotization. My findings are robust to addressing model uncertainty through Bayesian model averaging. Finally, several explanatory factors for the emergence of a productivity paradox in the context of robotics are discussed.
Item Type: | MPRA Paper |
---|---|
Original Title: | Do robots boost productivity? A quantitative meta-study |
Language: | English |
Keywords: | robots; technology; IFR; meta-analysis; publication bias; productivity; growth; Solow-paradox |
Subjects: | O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O11 - Macroeconomic Analyses of Economic Development O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O12 - Microeconomic Analyses of Economic Development O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O14 - Industrialization ; Manufacturing and Service Industries ; Choice of Technology O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O33 - Technological Change: Choices and Consequences ; Diffusion Processes O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O40 - General O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O47 - Empirical Studies of Economic Growth ; Aggregate Productivity ; Cross-Country Output Convergence |
Item ID: | 123392 |
Depositing User: | Florian Schneider |
Date Deposited: | 07 Feb 2025 11:34 |
Last Modified: | 07 Feb 2025 11:34 |
References: | Acemoglu, D. (2024). “The simple macroeconomics of AI”. In: Economic Policy. Acemoglu, D., Anderson, G. W., Beede, D. N., Buffington, C., Childress, E. E., Dinlersoz, E., Foster, L. S., Goldschlag, N., Haltiwanger, J. C., Kroff, Z., Restrepo, P., and Zolas, N. (2022). “Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey”. In: NBER Working Papers, no. 30659. Acemoglu, D., Lelarge, C., and Restrepo, P. (2020a). “Competing with Robots: Firm-Level Evidence from France”. In: AEA Papers and Proceedings, vol. 110, pp. 383–388. Acemoglu, D., Manera, A., and Restrepo, P. (2020b). “Does the US Tax Code Favor Automation?” In: Brookings Papers on Economic Activity, pp. 231–285. Acemoglu, D. and Restrepo, P. (2018a). “Artificial intelligence, automation, and work”. In: The economics of artificial intelligence: An agenda. University of Chicago Press, pp. 197–236. Acemoglu, D. and Restrepo, P. (2018b). “Modeling Automation”. In: AEA Papers and Proceedings, vol. 108, pp. 48–53. Acemoglu, D. and Restrepo, P. (2018c). “The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment”. In: American Economic Review, vol. 108, no. 6, pp. 1488–1542. Acemoglu, D. and Restrepo, P. (2019). “Automation and new tasks: How technology displaces and reinstates labor”. In: Journal of Economic Perspectives, vol. 33, no. 2, pp. 3–30. Acemoglu, D. and Restrepo, P. (2020). “Robots and Jobs: Evidence from US Labor Markets”. In: Journal of Political Economy, vol. 128, no. 6. Ackerberg, D. A., Caves, K., and Frazer, G. (2015). “Identification properties of recent production function estimators”. In: Econometrica, vol. 83, no. 6, pp. 2411–2451. Adachi, D. (2024). “Robots and Wage Polarization: The effects of robot capital by occupation”. Afonso, O., Neves, P. C., and Pinto, T. (2020). “The non-observed economy and economic growth: A meta-analysis”. In: Economic Systems, vol. 44, no. 1, 100746. Agarwal, R., Audretsch, D., and Sarkar, M. (2010). “Knowledge spillovers and strategic entrepreneurship”. In: Strategic entrepreneurship journal, vol. 4, no. 4, pp. 271–283. Aghion, P. and Howitt, P. (1992). “A Model of Growth Through Creative Destruction”. In: Econometrica, vol. 60, no. 2, pp. 323–351. Albinowski, M. and Lewandowski, P. (2024). “The impact of ICT and robots on labour market outcomes of demographic groups in Europe”. In: Labour Economics, vol. 87, 102481. Alguacil, M., Lo Turco, A., and Mart´ınez-Zarzoso, I. (2022). “Robot adoption and export performance: Firm-level evidence from Spain”. In: Economic Modelling, vol. 114, 105912. Almeida, D. and Sequeira, T. N. (2023). “Are Robots, Software, ICT and physical capital related to productivity? A panel quantile approach”. In: Economics of Innovation and New Technology, pp. 1–18. Almeida, D. and Sequeira, T. N. (2024). “Robots at work: new evidence with recent data”. In: The Manchester School. Andrews, D., Criscuolo, C., and Gal, P. N. (2016). “THE BEST VERSUS THE REST: THE GLOBAL PRODUCTIVITY SLOWDOWN, DIVERGENCE ACROSS FIRMS AND THE ROLE OF PUBLIC POLICY”. In: OECD Productivity Working Papers, vol. 5. Andrews, I. and Kasy, M. (2019). “Identification of and Correction for Publication Bias”. In: American Economic Review, vol. 109, no. 8, pp. 2766–2794. Antonietti, R., Cattani, L., and Pedrini, G. (2023). “Robots and the productivity of local manufacturing systems in Emilia- Romagna: the mediating role of occupational similarity and complexity”. In: European Planning Studies, vol. 31, no. 7, pp. 1397–1421. Antonioli, D., Marzucchi, A., Rentocchini, F., and Vannuccini, S. (2024). “Robot adoption and product innovation”. In: Research Policy, vol. 53, no. 6, 105002. Autor, D. and Dorn, D. (2013). “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market”. In: The American Economic Review, vol. 103, no. 5, pp. 1553–1597. Autor, D., Dorn, D., Katz, L. F., Patterson, C., and Reenen, J. V. (2017). “Concentrating on the Fall of the Labor Share”. In: American Economic Review, vol. 107, no. 5, pp. 180–185. Autor, D., Dorn, D., Katz, L. F., Patterson, C., and Van Reenen, J. (2020). “The fall of the labor share and the rise of superstar firms”. In: The Quarterly Journal of Economics, vol. 135, no. 2, pp. 645–709. Baily, M. N., Gordon, R. J., Nordhaus, W. D., and Romer, D. (1988). “The productivity slowdown, measurement issues, and the explosion of computer power”. In: Brookings Papers on Economic Activity, vol. 1988, no. 2, pp. 347–431. Ballestar, M. T., Cami˜na, E., D´ıaz-Chao, ´A., and Torrent-Sellens, J. (2021). “Productivity and employment effects of digital complementarities”. In: Journal of Innovation & Knowledge, vol. 6, no. 3, pp. 177–190. Ballestar, M. T., D´ıaz-Chao, ´A., Sainz, J., and Torrent-Sellens, J. (2020). “Knowledge, robots and productivity in SMEs: Explaining the second digital wave”. In: Journal of Business Research, vol. 108, pp. 119–131. Baskaran, T., Feld, L. P., and Schnellenbach, J. (2016). “Fiscal federalism, decentralization and economic growth: A metaanalysis”. In: Economic Inquiry, vol. 54, no. 3, pp. 1445–1463. Basu, S., Fernald, J. G., and Shapiro, M. D. (2001). “Productivity growth in the 1990s: technology, utilization, or adjustment?” In: Carnegie-Rochester Conference Series on Public Policy. Vol. 55. 1. Elsevier, pp. 117–165. Bekhtiar, K., Bittschi, B., and Sellner, R. (2024). “Robots at work? Pitfalls of industry-level data”. In: Journal of Applied Econometrics, vol. 39, no. 6, pp. 1180–1189. Bettiol, M., Capestro, M., Di Maria, E., and Ganau, R. (2024). “Is this time different? How Industry 4.0 affects firms’ labor productivity”. In: Small Business Economics, vol. 62, no. 4, pp. 1449–1467. Bijlsma, M., Kool, C., and Non, M. (2018). “The effect of financial development on economic growth: a meta-analysis”. In: Applied Economics, vol. 50, no. 57, pp. 6128–6148. Birke, D. (2009). “The economics of networks: A survey of the empirical literature”. In: Journal of Economic Surveys, vol. 23, no. 4, pp. 762–793. Bom, P. R. and Rachinger, H. (2019). “A kinked meta-regression model for publication bias correction”. In: Research synthesis methods, vol. 10, no. 4, pp. 497–514. Bonfiglioli, A., Crino, R., Fadinger, H., and Gancia, G. (2024). “Robot imports and firm-level outcomes”. In: The Economic Journal, vol. 134, no. 664, pp. 3428–3444. Bresnahan, T. F. and Trajtenberg, M. (1995). “General purpose technologies ‘Engines of growth’?” In: Journal of Econometrics, vol. 65, no. 1, pp. 83–108. Bresnahan, T. F., Brynjolfsson, E., and Hitt, L. M. (2002). “Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence”. In: The Quarterly Journal of Economics, vol. 117, no. 1, pp. 339– 376. Bruno, R. L. and Cipollina, M. (2018). “A meta-analysis of the indirect impact of foreign direct investment in old and new EU member states: Understanding productivity spillovers”. In: The World Economy, vol. 41, no. 5, pp. 1342–1377. Brynjolfsson, E. (1993). “The productivity paradox of information technology”. In: Communications of the ACM, vol. 36, no. 12, pp. 66–77. Brynjolfsson, E., Benzell, S., and Rock, D. (2020). “Understanding and Addressing the Modern Productivity Paradox”. In: Economics Faculty Articles and Research. Brynjolfsson, E. and Hitt, L. M. (2000). “Beyond Computation: Information Technology, Organizational Transformation and Business Performance”. In: Journal of Economic Perspectives, vol. 14, no. 4, pp. 23–48. Brynjolfsson, E., Rock, D., and Syverson, C. (2019). “Artificial Intelligence and the Modern Productivity Paradox”. In: The Economics of Artificial Intelligence: An Agenda. Ed. by A. Agrawal, J. Gans, and A. Goldfarb. University of Chicago Press, pp. 23–60. Brynjolfsson, E., Rock, D., and Syverson, C. (2021). “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies”. In: American Economic Journal: Macroeconomics, vol. 13, no. 1, pp. 333–372. Büchel, B. and Floreano, D. (2018). “Tesla’s problem: overestimating automation, underestimating humans: Elon Musk’s Tesla has serious production problems”. In: IMD Research & Knowledge. Byrne, D. M., Fernald, J. G., and Reinsdorf, M. B. (2016). “Does the United States have a productivity slowdown or a measurement problem?” In: Brookings Papers on Economic Activity, vol. 2016, no. 1, pp. 109–182. Calì, M. and Presidente, G. (2022). Robots For Economic Development. Kiel, Hamburg: ZBW - Leibniz Information Centre for Economics. Camiña, E., Díaz-Chao, Á., and Torrent-Sellens, J. (2020). “Automation technologies: Long-term effects for Spanish industrial firms”. In: Technological Forecasting and Social Change, vol. 151. 119828. Cao, Y., CHEN, s., and Tang, H. (2021). “Robots, Productivity, and Firm Exports”. In: SSRN Electronic Journal. Capello, R., Lenzi, C., and Perucca, G. (2022). “The modern Solow paradox. In search for explanations”. In: Structural Change and Economic Dynamics, vol. 63, pp. 166–180. Cardona, M., Kretschmer, T., and Strobel, T. (2013). “ICT and productivity: conclusions from the empirical literature”. In: Information Economics and Policy, vol. 25, no. 3, pp. 109–125. Carlaw, K. I. and Lipsey, R. G. (2003). “Productivity, technology and economic growth: what is the relationship?” In: Journal of Economic Surveys, vol. 17, no. 3, pp. 457–495. Cazachevici, A., Havranek, T., and Horvath, R. (2020). “Remittances and economic growth: A meta-analysis”. In: World Development, vol. 134. 105021. Cetrulo, A. and Nuvolari, A. (2019). “Industry 4.0: revolution or hype? Reassessing recent technological trends and their impact on labour”. In: Journal of Industrial and Business Economics, vol. 46, no. 3, pp. 391–402. Cette, G., Devillard, A., and Spiezia, V. (2021a). “Growth Factors in Developed Countries: A 1960-2019 Growth Accounting Decomposition”. In: Comparative Economic Studies, pp. 1–27. Cette, G., Devillard, A., and Spiezia, V. (2021b). “The contribution of robots to productivity growth in 30 OECD countries over 1975–2019”. In: Economics Letters, vol. 200. 109762. Chang, L., Taghizadeh-Hesary, F., and Mohsin, M. (2023). “Role of artificial intelligence on green economic development: Joint determinates of natural resources and green total factor productivity”. In: Resources Policy, vol. 82, 103508. Chen, P., Gao, J., Ji, Z., Liang, H., and Peng, Y. (2022). “Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities”. In: Energies, vol. 15, no. 15. 5730. Chen, S., Mu, S., He, X., Han, J., and Tan, Z. (2024). “Does industrial robot adoption affect green total factor productivity? – Evidence from China”. In: Ecological Indicators, vol. 161. 111958. Crafts, N. (2004). “Steam as a General Purpose Technology: A Growth Accounting Perspective”. In: The Economic Journal, vol. 114, no. 495, pp. 338–351. Cui, H., Liang, S., Xu, C., and Junli, Y. (2024). “Robots and analyst forecast precision: Evidence from Chinese manufacturing”. In: International Review of Financial Analysis, vol. 94, 103197. Dauth, W., Findeisen, S., Suedekum, J., and Woessner, N. (2018). “Adjusting to Robots: Worker-Level Evidence”. In: Opportunity and Inclusive Growth Institute Working Paper, no. 13. Dauth, W., Findeisen, S., Suedekum, J., and Woessner, N. (2021). “The Adjustment of Labor Markets to Robots”. In: Journal of the European Economic Association, vol. 19, no. 6, pp. 3104–3153. DeCanio, S. J. (2016). “Robots and humans – complements or substitutes?” In: Journal of Macroeconomics, vol. 49, pp. 280– 291. Demena, B. A. and Bergeijk, P. A. van (2017). “A meta-analysis of FDI and productivity spillovers in developing countries”. In: Journal of Economic Surveys, vol. 31, no. 2, pp. 546–571. Deng, L., Plümpe, V., and Stegmaier, J. (2024). “Robot adoption at German plants”. In: Jahrbücher für Nationalökonomie und Statistik, vol. 244, no. 3, pp. 201–235. DeStefano, T. and Timmis, J. (2024). “Robots and export quality”. In: Journal of Development Economics, vol. 168, 103248. Dewan, S. and Min, C.-k. (1997). “The substitution of information technology for other factors of production: A firm level analysis”. In: Management Science, vol. 43, no. 12, pp. 1660–1675. Díaz-Chao, Á., Ficapal-Cusí., P., and Torrent-Sellens, J. (2021). “Environmental assets, industry 4.0 technologies and firm performance in Spain: A dynamic capabilities path to reward sustainability”. In: Journal of Cleaner Production, vol. 281. 125264. Dixon, J., Hong, B., and Wu, L. (2021). “The Robot Revolution: Managerial and Employment Consequences for Firms”. In: Management Science, vol. 67, no. 9, pp. 5586–5605. Dottori, D. (2021). “Robots and employment: evidence from Italy”. In: Economia Politica, vol. 38, no. 2, pp. 739–795. Doucouliagos, H. (2011). “How large is large? Preliminary and relative guidelines for interpreting partial correlations in economics”. In: Deakin University School Working Paper Economic Series, no. 2011/5. Doucouliagos, H. and Laroche, P. (2003). “What Do Unions Do to Productivity? A Meta–Analysis”. In: Industrial Relations: A Journal of Economy and Society, vol. 42, no. 4, pp. 650–691. Doucouliagos, H. and Stanley, T. D. (2013). “Are all economic facts greatly exaggerated? Theory competition and selectiv- ity”. In: Journal of Economic Surveys, vol. 27, no. 2, pp. 316–339. Doucouliagos, H. and Ulubaşoğlu, M. A. (2008). “Democracy and Economic Growth: A Meta-Analysis”. In: American Journal of Political Science, vol. 52, no. 1, pp. 61–83. Du, L. and Lin, W. (2022). “Does the application of industrial robots overcome the Solow paradox? Evidence from China”. In: Technology in Society, vol. 68. 101932. Duan, D., CHEN, s., Feng, Z., and Li, J. (2023). “Industrial robots and firm productivity”. In: Structural Change and Economic Dynamics, vol. 67, pp. 388–406. Duan, J., Das, K. K., Meriluoto, L., and Reed, W. R. (2020). “Estimating the effect of spillovers on exports: a meta-analysis”. In: Review of World Economics, vol. 156, no. 2, pp. 219–249. Eder, A., Koller, W., and Mahlberg, B. (2023). “The contribution of industrial robots to labor productivity growth and economic convergence: a production frontier approach”. In: Journal of Productivity Analysis. Edquist, H. and Henrekson, M. (2006). “Technological breakthroughs and productivity growth”. In: Research in Economic History. Emerald Group Publishing Limited, pp. 1–53. Egger, M., Smith, G. D., Schneider, M., and Minder, C. (1997). “Bias in meta-analysis detected by a simple, graphical test”. In: BMJ, vol. 315, no. 7109, pp. 629–634. Eicher, T. S., Papageorgiou, C., and Raftery, A. E. (2011). “Default priors and predictive performance in Bayesian model averaging, with application to growth determinants”. In: Journal of Applied Econometrics, vol. 26, no. 1, pp. 30–55. Fan, H., Hu, Y., and Tang, L. (2021). “Labor costs and the adoption of robots in China”. In: Journal of Economic Behavior & Organization, vol. 186, pp. 608–631. Feld, L. P. and Heckemeyer, J. H. (2011). “FDI and Taxation: A meta-study”. In: Journal of Economic Surveys, vol. 25, no. 2, pp. 233–272. Feldkircher, M. and Zeugner, S. (2009). “Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging”. In: IMF Working Papers, vol. 09, no. 202. Feldkircher, M. and Zeugner, S. (2012). “The impact of data revisions on the robustness of growth determinants—a note on ‘determinants of economic growth: Will data tell?’” In: Journal of Applied Econometrics, vol. 27, no. 4, pp. 686–694. Fernández, C., Ley, E., and Steel, M. F. (2001). “Benchmark priors for Bayesian model averaging”. In: Journal of Econometrics, vol. 100, no. 2, pp. 381–427. Fernández-Macías, E., Klenert, D., and Antón, J.-I. (2021). “Not so disruptive yet? Characteristics, distribution and determinants of robots in Europe”. In: Structural Change and Economic Dynamics, vol. 58, pp. 76–89. Filippi, E., Bann`o, M., and Trento, S. (2023). “Automation technologies and their impact on employment: A review, synthesis and future research agenda”. In: Technological Forecasting and Social Change, vol. 191. 122448. Foster, A. D. and Rosenzweig, M. R. (2010). “Microeconomics of technology adoption”. In: Annu. Rev. Econ., vol. 2, no. 1, pp. 395–424. Fragkandreas, T. (2021). “Innovation-Productivity Paradox: Implications for Regional Policy”. In: Background Paper for the OECD-EC High-Level Expert Workshop Series “Productivity Policy for Places. Fu, X., Bao, Q., Xie, H., and Fu, X. (2021). “Diffusion of industrial robotics and inclusive growth: Labour market evidence from cross country data”. In: Journal of Business Research, vol. 122, pp. 670–684. Furman, J. and Orszag, P. (2018). “A firm-level perspective on the role of rents in the rise in inequality”. In: Toward a Just Society: Joseph Stiglitz and Twenty-First Century Economics. Columbia University Press, pp. 19–47. GCEE (2016). Annual Economic Report 2015/16: Focus on Future Viability. German Council of Economic Experts. George, E. I. (2010). “Dilution priors: Compensating for model space redundancy”. In: Borrowing Strength: Theory Powering Applications–A Festschrift for Lawrence D. Brown. Vol. 6. Institute of Mathematical Statistics, pp. 158–166. Gong, C., Yang, X., Tan, H., and Lu, X. (2023). “Industrial Robots, Economic Growth, and Sustainable Development in an Aging Society”. In: Sustainability, vol. 15, no. 5. 4590. Gorg, H. and Strobl, E. (2001). “Multinational companies and productivity spillovers: A meta-analysis”. In: The Economic Journal, vol. 111, no. 475, pp. 723–739. Graetz, G. and Michaels, G. (2018). “Robots at Work”. In: The Review of Economics and Statistics, vol. 100, no. 5, pp. 753– 768. Grossman, G. M. and Helpman, E. (1991). “Quality Ladders in the Theory of Growth”. In: The Review of Economic Studies, vol. 58, no. 1, pp. 43–61. Growiec, J. (2023). “What will drive global economic growth in the digital age?” In: Studies in Nonlinear Dynamics & Econometrics, vol. 27, no. 3, pp. 335–354. Guarascio, D., Piccirillo, A., and Reljic, J. (2024). “Will Robots Replace Workers? Assessing the Impact of Robots on Employment and Wages with Meta-Analysis”. In: SSRN Electronic Journal. Gunby, P., Jin, Y., and Robert Reed, W. (2017). “Did FDI Really Cause Chinese Economic Growth? A Meta-Analysis”. In: World Development, vol. 90, pp. 242–255. Gustafson, R. L. (1961). “Partial Correlations in Regression Computations”. In: Journal of the American Statistical Association, vol. 56, no. 294, p. 363. Havranek, T., Herman, D., and Irsova, Z. (2018a). “Does daylight saving save electricity? A meta-analysis”. In: The Energy Journal, vol. 39, no. 2, pp. 35–61. Havranek, T., Horvath, R., and Zeynalov, A. (2016). “Natural Resources and Economic Growth: A Meta-Analysis”. In: World Development, vol. 88, pp. 134–151. Havranek, T. and Irsova, Z. (2010). “Meta-analysis of intra-industry FDI spillovers: Updated evidence”. In: Czech Journal of Economics and Finance, vol. 60, no. 2, pp. 151–174. Havranek, T. and Irsova, Z. (2011). “Estimating vertical spillovers from FDI: Why results vary and what the true effect is”. In: Journal of International Economics, vol. 85, no. 2, pp. 234–244. Havranek, T., Irsova, Z., and Zeynalova, O. (2018b). “Tuition fees and university enrolment: a meta-regression analysis”. In: Oxford Bulletin of Economics and Statistics, vol. 80, no. 6, pp. 1145–1184. Havranek, T., Rusnak, M., and Sokolova, A. (2017). “Habit formation in consumption: A meta-analysis”. In: European economic review, vol. 95, pp. 142–167. Havranek, T. (2015). “Measuring Intertemporal Substitution: The Importance Of Method Choices And Selective Reporting”. In: Journal of the European Economic Association, vol. 13, no. 6, pp. 1180–1204. Havranek, T., Stanley, T. D., Doucouliagos, H., Bom, P., Geyer–Klingeberg, J., Iwasaki, I., Reed, W. R., Rost, K., and Aert, R. C. M. (2020). “Reporting guidelines for meta-analysis in economics”. In: Journal of Economic Surveys, vol. 34, no. 3, pp. 469–475. He, X., Teng, R., Feng, D., and Gai, J. (2024). “Industrial robots and pollution: Evidence from Chinese enterprises”. In: Economic Analysis and Policy, vol. 82, pp. 629–650. Henmi, M. and Copas, J. B. (2010). “Confidence intervals for random effects meta-analysis and robustness to publication bias”. In: Statistics in Medicine, vol. 29, no. 29, pp. 2969–2983. Hong, S. and Reed, W. R. (2024). “Meta-analysis and partial correlation coefficients: A matter of weights”. In: Research Synthesis Methods, vol. 15, no. 2, pp. 303–312. Hötte, K., Somers, M., and Theodorakopoulos, A. (2023). “Technology and jobs: A systematic literature review”. In: Technological Forecasting and Social Change, vol. 194. 122750. Hötte, K., Theodorakopoulos, A., and Koutroumpis, P. (2024). “Automation and taxation”. In: Oxford Economic Papers. Huang, G., He, L.-Y., and Lin, X. (2022). “Robot adoption and energy performance: Evidence from Chinese industrial firms”. In: Energy Economics, vol. 107. 105837. Huang, K., Liu, Q., and Tang, C. (2023). “Which firms benefit from robot adoption? Evidence from China”. In: Journal of Asian Economics, vol. 86. 101612. Huang, R., Shen, Z., and Yao, X. (2024). “How does industrial intelligence affect total-factor energy productivity? Evidence from China’s manufacturing industry”. In: Computers & Industrial Engineering, vol. 188. 109901. Hulten, C. R. (1978). “Growth Accounting with Intermediate Inputs”. In: The Review of Economic Studies, vol. 45, no. 3, pp. 511–518. IFR (2020). World Robotics 2020: Industrial robots. Frankfurt am Main: VDMA Services GmbH. IFR (2023). World Robotics 2023: Industrial robots. Frankfurt am Main: VDMA Services GmbH. Inklaar, R., J¨ager, K., O’Mahony, M., and Ark, B. van (2020). “European productivity in the digital age: evidence from EU KLEMS”. In: Measuring Economic Growth and Productivity. Elsevier, pp. 75–94. Ioannidis, J. P. A., Stanley, T. D., and Doucouliagos, H. (2017). “The Power of Bias in Economics Research”. In: The Economic Journal, vol. 127, no. 605, F236–F265. Irsova, Z., Bom, P. R. D., Havranek, T., and Rachinger, H. (2024). Spurious Precision in Meta-Analysis. Irsova, Z., Doucouliagos, H., Havranek, T., and Stanley, T. D. (2023). “Meta–analysis of social science research: A practi- tioner’s guide”. In: Journal of Economic Surveys, vol. 38, no. 5, pp. 1547–1566. Irsova, Z. and Havr´anek, T. (2013). “Determinants of horizontal spillovers from FDI: Evidence from a large meta-analysis”. In: World Development, vol. 42, pp. 1–15. Iwasaki, I. and Koˇcenda, E. (2024). “Quest for the general effect size of finance on growth: a large meta-analysis of worldwide studies”. In: Empirical Economics, vol. 66, no. 6, pp. 2659–2722. Iwasaki, I. and Tokunaga, M. (2016). “Technology transfer and spillovers from FDI in transition economies: A meta-analysis”. In: Journal of Comparative Economics, vol. 44, no. 4, pp. 1086–1114. Jäger, A., Moll, C., and Lerch, C. (2016). Analysis of the impact of robotic systems on employment in the European Union - 2012 data update. Luxembourg: Publications Office of the EU. Jäger, A., Moll, C., Som, O., and Zanker, C. (2015). Analysis of the impact of robotic systems on employment in the European Union. Publications Office of the EU. Jeffreys, H. (1961). Theory of Probability (3rd. edition). Oxford, U.K.: Oxford University Press. Ji, Y.-b. and Lee, C. (2010). “Data envelopment analysis”. In: The Stata Journal, vol. 10, no. 2, pp. 267–280. Jones, B. F. and Liu, X. (2024). “A framework for economic growth with capital-embodied technical change”. In: American Economic Review, vol. 114, no. 5, pp. 1448–1487. Jones, C. I. (2002). “Sources of US economic growth in a world of ideas”. In: American Economic Review, vol. 92, no. 1, pp. 220–239. Jung, J. H. and Lim, D.-G. (2020). “Industrial robots, employment growth, and labor cost: A simultaneous equation analysis”. In: Technological Forecasting and Social Change, vol. 159. 120202. Jungmittag, A. and Pesole, A. (2019). “The impacts of robots on labour productivity: A panel data approach covering 9 industries and 12 countries”. In: JRC Working Papers Series on Labour, Education and Technology. Jurkat, A., Klump, R., and Schneider, F. (2022). “Tracking the Rise of Robots: The IFR Database”. In: Jahrbücher für Nationalökonomie und Statistik, vol. 242, no. 5-6, pp. 669–689. Jurkat, A., Klump, R., and Schneider, F. (2023). “Robots and Wages: A Meta-Analysis”. In: ZBW - Leibniz Information Centre for Economics. Jurkat, A., Klump, R., and Schneider, F. (2024). “Wie Roboter die Welt (und das Wirtschaften) verändern: Ein Überblick ¨uber Daten, Forschungsergebnisse und wirtschaftspolitische Strategien”. In: Perspektiven der Wirtschaftspolitik, vol. 25, no. 2, pp. 130–152. Kaldor, N. (1961). “Capital accumulation and economic growth”. In: The Theory of Capital: Proceedings of a conference held by the International Economic Association. Springer, pp. 177–222. Kass, R. E. and Raftery, A. E. (1995). “Bayes Factors”. In: Journal of the American Statistical Association, vol. 90, no. 430, pp. 773–795. Katz, M. L. and Shapiro, C. (1994). “Systems competition and network effects”. In: Journal of economic perspectives, vol. 8, no. 2, pp. 93–115. Kim, Y. E. and Loayza, N. V. (2019). “Productivity Growth : Patterns and Determinants across the World”. In: World Bank Policy Research Working Paper Series. Klomp, J. and Valckx, K. (2014). “Natural disasters and economic growth: A meta-analysis”. In: Global Environmental Change, vol. 26, pp. 183–195. Klump, R., Jurkat, A., and Schneider, F. (2021). “Tracking the rise of robots: A survey of the IFR database and its applications”. In: MPRA Paper, no. 111812. Koch, M., Manuylov, I., and Smolka, M. (2021). “Robots and Firms”. In: The Economic Journal, vol. 131, no. 638, pp. 2553– 2584. Kohli, R. and Devaraj, S. (2003). “Measuring information technology payoff: A meta-analysis of structural variables in firm-level empirical research”. In: Information Systems Research, vol. 14, no. 2, pp. 127–145. Kromann, L., Malchow-Møller, N., Skaksen, J. R., and Sorensen, A. (2020). “Automation and Productivity – A Cross– Country, Cross–Industry Comparison”. In: Industrial and Corporate Change, vol. 29, no. 2, pp. 265–287. Krugman, P. R. (1997). The Age of Diminished Expectations: U.S. Economic Policy in the 1990s. MIT press. Leigh, N. G. and Kraft, B. R. (2018). “Emerging robotic regions in the United States: insights for regional economic evolution”. In: Regional Studies, vol. 52, no. 6, pp. 804–815. Leitner, S. M. and Stehrer, R. (2019). “The automatisation challenge meets the demographic challenge: In need of higher productivity growth”. In: wiiw Working Paper, no. 171. Leone, F. (2022). Foreign ownership and robot adoption. CEP Discussion Papers. London, UK: London School of Economics and Political Science. Centre for Economic Performance. Levinsohn, J. and Petrin, A. (2003). “Estimating Production Functions Using Inputs to Control for Unobservables”. In: The Review of Economic Studies, vol. 70, no. 2, pp. 317–341. Ley, E. and Steel, M. F. (2009). “On the effect of prior assumptions in Bayesian model averaging with applications to growth regression”. In: Journal of Applied Econometrics, vol. 24, no. 4, pp. 651–674. Li, B. and Zhou, C. (2024). “Robot adoption and urban total factor productivity: evidence from China”. In: Technological and Economic Development of Economy, vol. 30, no. 5, pp. 1–22. Li, D., Jin, Y., and Cheng, M. (2024). “Unleashing the power of industrial robotics on firm productivity: Evidence from China”. In: Journal of Economic Behavior & Organization, vol. 224, pp. 500–520. Li, J., Ma, S., Qu, Y., and Wang, J. (2023). “The impact of artificial intelligence on firms’ energy and resource efficiency: Empirical evidence from China”. In: Resources Policy, vol. 82, 103507. Li, Y., Zhang, Y., Pan, Han, M., and Veglianti, E. (2022). “Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms”. In: Technology in Society, vol. 70. 102034. Liang, F., Paulo, R., Molina, G., Clyde, M. A., and Berger, J. O. (2008). “Mixtures of g priors for Bayesian variable selection”. In: Journal of the American Statistical Association, vol. 103, no. 481, pp. 410–423. Lim, J.-H., Stratopoulos, T. C., and Wirjanto, T. S. (2013). “Sustainability of a firm’s reputation for information technology capability: The role of senior IT executives”. In: Journal of Management Information Systems, vol. 30, no. 1, pp. 57–96. Lin, C., Xiao, S., and Yin, Z. (2022). “How do industrial robots applications affect the quality upgrade of Chinese export trade?” In: Telecommunications Policy, vol. 46, no. 10. 102425. Liu, J., Chang, H., Forrest, J. Y.-L., and Yang, B. (2020). “Influence of artificial intelligence on technological innovation: Evidence from the panel data of China’s manufacturing sectors”. In: Technological Forecasting and Social Change, vol. 158. 120142. Liu, J., Liu, L., Qian, Y., and Song, S. (2022a). “The effect of artificial intelligence on carbon intensity: Evidence from China’s industrial sector”. In: Socio-Economic Planning Sciences, vol. 83. 101002. Liu, J., Qian, Y., Yang, Y., and Yang, Z. (2022b). “Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China”. In: International Journal of Environmental Research and Public Health, vol. 19, no. 4. Liu, L., Yang, K., Fujii, H., and Liu, J. (2021). “Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel”. In: Economic Analysis and Policy, vol. 70, pp. 276–293. Lu, Y. and Zhou, Y. (2021). “A review on the economics of artificial intelligence”. In: Journal of Economic Surveys, vol. 35, no. 4, pp. 1045–1072. Luan, F., Yang, X., Chen, Y., and Regis, P. J. (2022). “Industrial robots and air environment: A moderated mediation model of population density and energy consumption”. In: Sustainable Production and Consumption, vol. 30, pp. 870–888. Lyu, W. and Liu, J. (2021). “Artificial Intelligence and emerging digital technologies in the energy sector”. In: Applied Energy, vol. 303, 117615. Malmquist, S. (1953). “Index numbers and indifference surfaces”. In: Trabajos de Estad´ıstica, vol. 4, no. 2, pp. 209–242. Malovaná, S., Hodula, M., Bajzík, J., and Gric, Z. (2024). “Bank capital, lending, and regulation: A meta–analysis”. In: Journal of Economic Surveys, vol. 38, no. 3, pp. 823–851. Manjón, M. and Manez, J. (2016). “Production function estimation in Stata using the Ackerberg–Caves–Frazer method”. In: The Stata Journal, vol. 16, no. 4, pp. 900–916. Matthess, M. and Kunkel, S. (2020). “Structural change and digitalization in developing countries: Conceptually linking the two transformations”. In: Technology in Society, vol. 63. 101428. McGuckin, R. H., Streitwieser, M. L., and Doms, M. (1998). “The effect of technology use on productivity growth”. In: Economics of Innovation and New Technology, vol. 7, no. 1, pp. 1–26. Mebratie, A. D. and Bergeijk, P. A. van (2013). “Firm heterogeneity and development: A meta-analysis of FDI productivity spillovers”. In: The Journal of International Trade & Economic Development, vol. 22, no. 1, pp. 53–74. Meyer, K. E. and Sinani, E. (2009). “When and where does foreign direct investment generate positive spillovers? A metaanalysis”. In: Journal of International Business Studies, vol. 40, pp. 1075–1094. Mondolo, J. (2021). “The composite link between technological change and employment: A survey of the literature”. In: Journal of Economic Surveys. Montobbio, F., Staccioli, J., Virgillito, M. E., and Vivarelli, M. (2023). “The empirics of technology, employment and occupations: Lessons learned and challenges ahead”. In: Journal of Economic Surveys. Musk, E. (2018). Twitter Post. https://x.com/elonmusk/status/984882630947753984. Nordhaus, W. D. (2021). “Are we approaching an economic singularity? information technology and the future of economic growth”. In: American Economic Journal: Macroeconomics, vol. 13, no. 1, pp. 299–332. OECD (2024a). OECD Data Explorer: Annual labour force survey, summary tables. OECD (2024b). OECD Data Explorer: Average annual hours actually worked per worker. OECD (2024c). OECD Data Explorer: Productivity growth rates. Oh, D.-h. (2010). “A global Malmquist-Luenberger productivity index”. In: Journal of productivity analysis, vol. 34, pp. 183– 197. Olley, G. S. and Pakes, A. (1996). “The Dynamics of Productivity in the Telecommunications Equipment Industry”. In: Econometrica, vol. 64, no. 6, p. 1263. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hr´objartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews”. In: BMJ (Clinical research ed.), vol. 372, no. 71. Parente, S. L. and Prescott, E. C. (1994). “Barriers to technology adoption and development”. In: Journal of political Economy, vol. 102, no. 2, pp. 298–321. Park, C.-Y., Shin, K., and Kikkawa, A. (2021). “Aging, automation, and productivity in Korea”. In: Journal of the Japanese and International Economies, vol. 59. 101109. Petrin, A., Poi, B. P., and Levinsohn, J. (2004). “Production function estimation in Stata using inputs to control for unobservables”. In: The Stata Journal, vol. 4, no. 2. Philbeck, T. and Davis, N. (2018). “THE FOURTH INDUSTRIAL REVOLUTION: SHAPING A NEW ERA”. In: Journal of International Affairs, vol. 72, no. 1, pp. 17–22. Picchio, M. and Ubaldi, M. (2024). “Unemployment and health: A meta–analysis”. In: Journal of Economic Surveys, vol. 38, no. 4, pp. 1437–1472. Pinheiro, A., Sochirca, E., Afonso, O., and Neves, P. C. (2023). “Automation and off(re)shoring: A meta-regression analysis”. In: International Journal of Production Economics, vol. 264. 108980. Pisková, L., Dobranschi, M., Semerád, P., and Otavová, M. (2024). “Impact of Robot Installations on Employment and Labour Productivity in Automotive Industry”. In: Central European Business Review, vol. 13, no. 2, pp. 53–68. Polák, P. (2017). “The productivity paradox: A meta-analysis”. In: Information Economics and Policy, vol. 38, pp. 38–54. Poole, C. and Greenland, S. (1999). “Random-effects meta-analyses are not always conservative”. In: American Journal of Epidemiology, vol. 150, no. 5, pp. 469–475. Qi, J. and Zhang, Z. (2023). “Robot Application and Adjustment of Export Product Scope: Can We Have Both Efficiency and Quality?” In: China Finance and Economic Review, vol. 12, no. 1, pp. 67–88. Qian, W. and Wang, Y. (2022). “How Do Rising Labor Costs Affect Green Total Factor Productivity? Based on the Industrial Intelligence Perspective”. In: Sustainability, vol. 14, no. 20. 13653. Raudenbush, S. W. (2009). “Analyzing effect sizes: Random-effects models”. In: The Handbook of Research Synthesis and Meta-Analysis, vol. 2, pp. 295–316. Ren, H., Zhou, S., Hu, A., and Cheng, H. (2018). “Industrial Robots and Jobs Turnover: Evidence from Chinese Firm Level Data”. In: SSRN Electronic Journal. Restrepo, P. (2023). “Automation: Theory, Evidence, and Outlook”. In: Annual Review of Economics, vol. 16. Ridhwan, M. M., Ismail, A., and Nijkamp, P. (2024). “The real exchange rate and economic growth: a meta-analysis”. In: Journal of Economic Studies, vol. 51, no. 2, pp. 287–318. Ridhwan, M. M., Nijkamp, P., Ismail, A., and M Irsyad, L. (2022). “The effect of health on economic growth: a metaregression analysis”. In: Empirical Economics, pp. 1–41. Rodrigo, R. (2021). Robot Adoption, Organizational Capital, and the Productivity Paradox. Romer, P. M. (1990). “Endogenous technological change”. In: Journal of Political Economy, vol. 98, no. 5, Part 2, S71–S102. Rovigatti, G. and Mollisi, V. (2018). “Theory and practice of total-factor productivity estimation: The control function approach using Stata”. In: The Stata Journal, vol. 18, no. 3, pp. 618–662. Schwab, K. (2016). The fourth industrial revolution. Genf: World Economic Forum. Schweikl, S. and Obermaier, R. (2020). “Lessons from three decades of IT productivity research: towards a better understanding of IT-induced productivity effects”. In: Management Review Quarterly, vol. 70, no. 4, pp. 461–507. Shen, Y. and Zhang, X. (2023). “Intelligent manufacturing, green technological innovation and environmental pollution”. In: Journal of Innovation & Knowledge, vol. 8, no. 3. 100384. Skilton, M. and Hovsepian, F. (2018). The 4th Industrial Revolution: Responding to the Impact of Artificial Intelligence on Business. Cham: Palgrave Macmillan US. Soliman, K. (2021). “Are Industrial Robots a new GPT? A Panel Study of Nine European Countries with Capital and Quality-adjusted Industrial Robots as Drivers of Labour Productivity Growth”. In: EIIW Discussion Paper, no. 307. Solow, R. (1987). “We’d better watch out”. In: New York Times Book Review. Solow, R. (1956). “A contribution to the theory of economic growth”. In: The Quarterly Journal of Economics, vol. 70, no. 1, pp. 65–94. Somohano-Rodríguez, F. M. and Madrid-Guijarro, A. (2022). “Do industry 4.0 technologies improve Cantabrian manufacturing smes performance? The role played by industry competition”. In: Technology in Society, vol. 70. 102019. Song, J., Wang, Y., and Wang, J. (2022). “The Impact of SO2 Emissions Trading Scheme on Firm’s Environmental Performance: A Channel from Robot Application”. In: International Journal of Environmental Research and Public Health, vol. 19, no. 24. 16471. Stanley, T. D. and Doucouliagos, H. (2015). “Neither fixed nor random: weighted least squares meta-analysis”. In: Statistics in Medicine, vol. 34, no. 13, pp. 2116–2127. Stanley, T. D., Doucouliagos, H., and Havranek, T. (2024). “Meta-analyses of partial correlations are biased: Detection and solutions”. In: Research Synthesis Methods, vol. 15, no. 2, pp. 313–325. Stanley, T. D., Doucouliagos, H., and Steel, P. (2018). “Does ICT generate economic growth? A meta-regression analysis”. In: Journal of Economic Surveys, vol. 32, no. 3, pp. 705–726. Stanley, T. D., Ioannidis, J. P. A., Maier, M., Doucouliagos, H., Otte, W. M., and Bartoˇs, F. (2023). “Unrestricted weighted least squares represent medical research better than random effects in 67,308 Cochrane meta-analyses”. In: Journal of Clinical Epidemiology, vol. 157, pp. 53–58. Stanley, T. D. and Doucouliagos, H. (2017). “Neither fixed nor random: Weighted least squares meta-regression”. In: Research Synthesis Methods, vol. 8, no. 1, pp. 19–42. Stanley, T. D. and Doucouliagos, H. (2012). Meta-regression analysis in economics and business. Vol. 5. Routledge Advances in Research Methods. London and New York, NY: Routledge. Stapleton, K. and Webb, M. (2023). “Automation, Trade and Multinational Activity: Micro Evidence from Spain”. In: SSRN. Starovatova, D. A. (2023). “The relationship between robots and labour productivity: Does business scale matter?” In: Journal of New Economy, vol. 24, no. 1, pp. 81–103. Steel, M. F. J. (2020). “Model Averaging and Its Use in Economics”. In: Journal of Economic Literature, vol. 58, no. 3, pp. 644–719. Stiebale, J., Suedekum, J., and Woessner, N. (2024). “Robots and the rise of European superstar firms”. In: International Journal of Industrial Organization, vol. 97, 103085. Stiroh, K. J. (2005). “Reassessing the Impact of IT in the Production Function: A Meta-Analysis and Sensitivity Tests”. In: Annales d’ ´Economie et de Statistique, no. 79/80. 529. Sun, W., Zhang, Z., Chen, Y., and Luan, F. (2023). “Heterogeneous effects of robots on employment in agriculture, industry, and services sectors”. In: Technology in Society, vol. 75. 102371. Swan, T. W. (1956). “Economic growth and capital accumulation”. In: Economic Record, vol. 32, no. 2, pp. 334–361. Syverson, C. (2017). “Challenges to mismeasurement explanations for the US productivity slowdown”. In: Journal of Economic Perspectives, vol. 31, no. 2, pp. 165–186. Triplett, J. E. (1999). “The Solow productivity paradox: what do computers do to productivity?” In: The Canadian Journal of Economics/Revue canadienne d’Economique, vol. 32, no. 2, pp. 309–334. Ugur, M., Churchill, S. A., and Luong, H. M. (2020). “What do we know about R&D spillovers and productivity? Metaanalysis evidence on heterogeneity and statistical power”. In: Research Policy, vol. 49, no. 1. 103866. Upchurch, M. (2018). “Robots and AI at work: the prospects for singularity”. In: New Technology, Work and Employment, vol. 33, no. 3, pp. 205–218. Valickova, P., Havranek, T., and Horvath, R. (2015). “Financial Development and Economic Growth: A Meta-Analysis”. In: Journal of Economic Surveys, vol. 29, no. 3, pp. 506–526. van Aert, R. C. M. (2023). “Meta-analyzing partial correlation coefficients using Fisher’s z transformation”. In: Research Synthesis Methods, vol. 14, no. 5, pp. 768–773. Venturini, F. (2022). “Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution”. In: Journal of Economic Behavior & Organization, vol. 194, pp. 220–243. Vermeulen, B., Kesselhut, J., Pyka, A., and Saviotti, P. P. (2018). “The Impact of Automation on Employment: Just the Usual Structural Change?” In: Sustainability, vol. 10, no. 5. 1661. Vries, G. J. de, Gentile, E., Miroudot, S., and Wacker, K. M. (2020). “The rise of robots and the fall of routine jobs”. In: Labour Economics, vol. 66, no. 101885, pp. 1–18. Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., and Trichina, E. (2021). “Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review”. In: The International Journal of Human Resource Management, vol. 33, no. 6, pp. 1237–1266. Vu, K., Hanafizadeh, P., and Bohlin, E. (2020). “ICT as a driver of economic growth: A survey of the literature and directions for future research”. In: Telecommunications Policy, vol. 44, no. 2. 101922. Wang, J. C. (2022). “Essays on Trade, Technology, and Banking”. Doctoral dissertation. Cambridge, Massachusetts: Harvard University Graduate School of Arts and Sciences. Wang, J., Wang, Y., and Song, J. (2023). “The policy evaluation of China’s carbon emissions trading scheme on firm employment: A channel from industrial automation”. In: Energy Policy, vol. 178. 113590. Wang, T., Zhang, Y., and Liu, C. (2024). “Robot adoption and employment adjustment: Firm-level evidence from China”. In: China Economic Review, vol. 84. 102137. Wang, E.-Z., Lee, C.-C., and Li, Y. (2022). “Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries”. In: Energy Economics, vol. 105. 105748. Weyerstrass, K. (2018). “How to Boost Productivity in the EU”. In: EconPol Policy Brief, no. 8. Wooldridge, J. M. (2009). “On estimating firm-level production functions using proxy variables to control for unobservables”. In: Economics Letters, vol. 104, no. 3, pp. 112–114. Wooster, R. B. and Diebel, D. S. (2010). “Productivity spillovers from foreign direct investment in developing countries: A meta-regression analysis”. In: Review of Development Economics, vol. 14, no. 3, pp. 640–655. World Bank (2024). The World by Income and Region. Wu, Q. (2023). “Sustainable growth through industrial robot diffusion: Quasi–experimental evidence from a Bartik shift– share design”. In: Economics of Transition and Institutional Change, vol. 31, no. 4, pp. 1107–1133. Wu, T., Yan, N., Wang, J., and Chen, J. (2024). “Industry spillover effects of robot applications on labor productivity: Evidence from China”. In: Economic Analysis and Policy, vol. 84, pp. 1272–1286. WWCLEG (2016). Guide to scoring evidence using the Maryland Scientific Methods Scale: Updated June 2016. Tech. rep. What Works Centre for Local Economic Growth. Xia, L., Han, Q., and Yu, S. (2024). “Industrial intelligence and industrial structure change: Effect and mechanism”. In: International Review of Economics & Finance, vol. 93, pp. 1494–1506. Xie, X. and Yan, J. (2024). “How does artificial intelligence affect productivity and agglomeration? Evidence from China’s listed enterprise data”. In: International Review of Economics & Finance, vol. 94. 103408. Yang, S. and Liu, F. (2024). “Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system”. In: Ecological Economics, vol. 216. 108021. Yang, Z. and Shen, Y. (2023). “The impact of intelligent manufacturing on industrial green total factor productivity and its multiple mechanisms”. In: Frontiers in Environmental Science, vol. 10. 1058664. Yasar, M., Raciborski, R., and Poi, B. (2008). “Production Function Estimation in Stata Using the Olley and Pakes Method”. In: The Stata Journal: Promoting communications on statistics and Stata, vol. 8, no. 2, pp. 221–231. Zeugner, S. and Feldkircher, M. (2015). “Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R”. In: Journal of Statistical Software, vol. 68, no. 4, pp. 1–37. Zhang, F., Zhang, Q., and Wu, H. (2023a). “Robot adoption and export performance: evidence from Chinese industrial firms”. In: Journal of Manufacturing Technology Management, vol. 34, no. 6, pp. 896–916. Zhang, L., Gan, T., and Fan, J. (2023b). “Do industrial robots affect the labour market? Evidence from China”. In: Economics of Transition and Institutional Change. Zhang, L. and Shen, Q. (2023). “Carbon Emission Performance of Robot Application: Influencing Mechanisms and Heterogeneity Characteristics”. In: Discrete Dynamics in Nature and Society, vol. 2023, pp. 1–18. Zhang, Q., Zhang, F., and Mai, Q. (2022). “Robot adoption and green productivity: Curse or Boon”. In: Sustainable Production and Consumption, vol. 34, pp. 1–11. Zhang, Y., Wang, T., and Liu, C. (2024). “Beyond the modern productivity paradox: The effect of robotics technology on firm-level total factor productivity in China”. In: Journal of Asian Economics, vol. 90. 101692. Zhang, Z. and Deng, F. (2023). “How can artificial intelligence boost firms’ exports? evidence from China”. In: PLOS ONE, vol. 18, no. 8, e0283230. Zhao, P., Gao, Y., and Sun, X. (2022). “How does artificial intelligence affect green economic growth? Evidence from China”. In: The Science of the Total Environment, vol. 834. 155306. Zhao, X. and Yang, S. (2022). “Does Intelligence Improve the Efficiency of Technological Innovation?” In: Journal of the Knowledge Economy, pp. 1–25. Zhao, Y., Said, R., Ismail, N. W., and Hamzah, H. Z. (2024). “Impact of industrial robot on labour productivity: Empirical study based on industry panel data”. In: Innovation and Green Development, vol. 3, no. 2. 100148. Zhou, P., Han, M., and Shen, Y. (2023). “Impact of Intelligent Manufacturing on Total-Factor Energy Efficiency: Mechanism and Improvement Path”. In: Sustainability, vol. 15, no. 5. 3944. Zhou, R. and Zhang, Q. (2024). “The Impact of Industrial Robots on the Sustainable Development of Zombie Firms in China”. In: Sustainability, vol. 16, no. 5. 2180. Zhou, W., Zhuang, Y., and Chen, Y. (2024). “How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology”. In: Energy Economics, vol. 131. 107355. Zhu, H. and Zhang, X. (2021). “The Impact of Robots on Labor Productivity and Employment: Evidence from the Three Largest Economies”. In: SSRN Electronic Journal. Zhu, H., Sang, B., Zhang, C., and Guo, L. (2023). “Have Industrial Robots Improved Pollution Reduction? A Theoretical Approach and Empirical Analysis”. In: China & World Economy, vol. 31, no. 4, pp. 153–172. Zhu, M., Liang, C., Yeung, A. C., and Zhou, H. (2024). “The impact of intelligent manufacturing on labor productivity: An empirical analysis of Chinese listed manufacturing companies”. In: International Journal of Production Economics, vol. 267. 109070. Zigraiova, D. and Havranek, T. (2016). “Bank competition and financial stability: Much ado about nothing?” In: Journal of Economic Surveys, vol. 30, no. 5, pp. 944–981. Zigraiova, D., Havranek, T., Irsova, Z., and Novak, J. (2021). “How puzzling is the forward premium puzzle? A metaanalysis”. In: European Economic Review, vol. 134, 103714. Zolas, N., Kroff, Z., Brynjolfsson, E., McElheran, K., Beede, D., Buffington, C., Goldschlag, N., Foster, L., and Dinlersoz, E. (2021). “Advanced Technologies Adoption and Use by U.S. Firms: Evidence from the Annual Business Survey”. In: National Bureau of Economic Research. 68 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/123392 |