Microeconomics of the 21st Century: New Opportunities in Fundamental Analysis

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Abstract

Changes in economic reality have led to new theories in the area of the microlevel market decision making mechanism. The key trends in research that deals with microeconomics are demonstrated, to show in what aspects contemporary microeconomics reflects new conditions of the 21st century. With the help of the analysis of the current academic publications in microeconomics the behaviour aspects of the main economic agents have been clarified. New approaches and new ideas in current microeconomic investigations have been generalized. As far as consumers are concerned, new understanding has been developed in the area of rationality, utility satisfaction instead of utility maximization, new models of intertemporal choice with intertemporal transaction costs, attention and cognitive resources as new individual constraints. As for firms, new characteristics of business units have been shown in forms of digital ecosystems that blur the line between a firm and a market, different markets and industries. As a result, a classical well-structured firm has been giving way to a flexible digital division. New aspects have become inherent features of market interactions, price strategies and competition under information asymmetry. Digital economy allows to mitigate agents’ asymmetry in many dimensions through making information public, but at the same time digitalization creates barriers to optimal decision-making, e.g. non-adequate expert consultations, imprudently copying other’s social experience and rush herd behavior in networks.

About the authors

N. M. ROZANOVA

Moscow School of Economics, Lomonosov Moscow State University; Moscow State Institute of International Relations (MGIMO)

Author for correspondence.
Email: happyeconomics@list.ru
Moscow, Russia; Moscow, Russia

References

  1. Acemoglu, D., & Jackson, M. (2017). Social norma and the enforcement of laws. Journal of European Economic Association. Vol. 15. No. 2. Pp. 245–295.
  2. Ali, N., & Benabou, R. (2020). Image versus information. American Economic Journal: Microeconomics. Vol. 12. No. 3. Pp. 116–164.
  3. Apesteguia, J., Ballester, M., & Cuhadaroglu, T. (2023). A behavioral model of adaptation. Journal of Economic Behavior and Organization. Vol. 207. Pp. 146–156.
  4. Arcidiacono, P., Ellickson, P., Mela, C., & Singleton, J. (2020). The competitive effects of entry. American Economic Journal: Applied Economics. Vol. 12. No. 3. Pp. 175–206.
  5. Arrow, K., Bilir, K., & Sorensen, A. (2020). The impact of information technology on the diffusion of new pharmaceuticals. American Economic Journal: Applied Economics. Vol. 12. No. 3. Pp. 1–39.
  6. Asheim, B. (2018). Smart specialisation, innovation policy and regional innovation systems: what about new path development in less innovative regions? The European Journal of Social Science Research. Vol. 32. No. 1. Pp. 1–18.
  7. Baldwin, C., Bogers, M., Kapoor, R., & West, J. (2024). Focusing the ecosystem lens on innovation studies. Research Policy. Vol. 53. No. 104949. https://doi.org/10.1016/j.respol.2023.104949
  8. Biglaiser, G., & Cremer, J. (2020). The value of incumbency when platforms face heterogeneous customers. American Economic Journal: Microeconomic. Vol. 12. No. 4. Pp. 229–269.
  9. Billot, A., Mukerji, S., & Tallon, J-M. (2020). Market allocations under ambiguity. Revue Economique. Vol. 71. No. 2. Pp. 267–282.
  10. Braunerhjelm, P., & Lappi, E. (2023). Employees’ entrepreneurial human capital and firm performance. Research Policy. Vol. 52. No. 104703. https://doi.org/10.1016/j.respol.2022.104703
  11. Campbell, A., Leister, C.M., & Zenou, Y. (2020). Word-of-mouth communication and search. The RAND Journal of Economics. Vol. 51. No. 3. Pp. 676–712.
  12. Chandrasakhar, A., Larreguy, H., & Xandri, J.P. (2020). Testing models of social learning on networks. Econometrica. Vol. 88. No. 1. Pp. 1–32.
  13. Cohen, J., Ericson, K.M., Laibson, D., & Myles, J. (2020). Measuring time preferences. Journal of Economic Literature. Vol. 58. No. 2. Pp. 299–347.
  14. Echenique, F., Imai, T., & Saito, K. (2020). Testable implications of models of intertemporal choice. American Economic Journal: Microeconomics. Vol. 12. No. 4. Pp. 114–143.
  15. Eliaz, K., & Spiegler, R. (2020). Incentive-compatible advertising on nonretail platforms. The RAND Journal of Economics. Vol. 51. No. 2. Pp. 323–345.
  16. Ellickson, P., Grieco, P., & Khvastunov, O. (2020). Measuring competition in spatial retail. The RAND Journal of Economics. Vol. 51. No. 1. Pp. 189–232.
  17. Espana, V., Aparicio, J., Barber, X., & Esteve, M. (2024). Estimating production functions through additive models based on regression splines. European Journal of Operational Research. Vol. 312. No. 2. Pp. 684–699.
  18. Filippas, A., Horton, J., & Golden, J. (2019). Reputation inflation. NBER Working paper 25857. May. http://www.nber.org/papers/w25857
  19. Fischer, P., Heinle, M., & Smith, K. (2020). Constrained listening, audience alignment, and expert communication. The RAND Journal of Economics. Vol. 51. No. 4. Pp. 1037–1062.
  20. Francke, A.E., & Carrete, L. (2023). Consumer self-regulation: looking back to look forward. A systematic literature review. Journal of Business Research. Vol. 157. No. 113461. https://doi.org/10.1016/j.jbusres.2022.113461
  21. Frankel, A., & Kartik, N. (2019). Muddled information. Journal of Political Economy. Vol. 127. No. 4. Pp. 1739–1776.
  22. Galeotti, A., Golub, B., & Goyal, S. (2020). Targeting interventions in networks. Econometrica. Vol. 88. No. 6. Pp. 2445–2471.
  23. Halac, M., & Kremer, I. (2020). Experimenting with career concerns. American Economic Journal: Microeconomics. Vol. 12. No. 1. Pp. 260–288.
  24. Hanany, E., Klibanoff, P., & Mukerji, S. (2020). Incomplete information games with ambiguity averse players. American Economic Journal: Microeconomics. Vol. 12. No. 2. Pp. 135–187.
  25. Hashimzade, N., Kirsanov, O., & Kirsanova, T. (2023). Distributional effects of endogenous discounting. Mathematical Social Science. Vol. 122. Pp. 1–6.
  26. Herskovic, B., & Ramos J. (2020). Acquiring information through peers. The American Economic Review. Vol. 110. No. 7. Pp. 2128–2152.
  27. Huang, Y., Li, K., & Li, P. (2023). Innovation ecosystems and national talent competitiveness: a country-based comparison using fsQCA. Technological Forecasting and Social Change. Vol. 194. Issue. 4. No. 22733. https://doi.org/10.1016/j.techfore.2023.122733
  28. Jacobides, M., Cennamo, C., & Gawer, A. (2024). Externalities and complementarities in platforms and ecosystems: from structural solutions to endogenous failures. Research Policy. Vol. 53. No. 104906. https://doi.org/10.1016/j.respol.2023.104906
  29. Kamada, Y., & Kandori, M. (2020). Revision Games, Econometrica Vol. 88. No. 4. Pp. 1599–1630.
  30. Kuersteiner, G., & Prucha, I. (2020). Dynamic Spatial Panel Models. Econometrica. Vol. 88. No. 5. Pp. 2109–2146.
  31. Li, L., Tadelis, S., & Zhou, X. (2020). Buying reputation as a signal of quality: evidence from an online marketplace. The RAND Journal of Economics. Vol. 51. No. 4. Pp. 965–988.
  32. Linde, J., Gietl, D., Sonnemans, J., & Tuinstra, J. (2023). The effects of quantity and quality of information in strategy tournaments. Journal of Economic Behavior and Organization. Vol. 211. Pp. 305–323.
  33. Mauring, E. (2020). Informational cycles in search markets. American Economic Journal: Microeconomics. Vol. 12. No. 4. Pp. 170–192.
  34. Mullainathan, S. (2020). A memory-based model of bounded rationality. The Quarterly Journal of Economics. Vol. 117. No. 3. Pp. 735–774.
  35. Okuyama, R., & Tsujimoto, M. (2020). The importance of drug target selection capability for new drug innovation: definition, fostering process, and interaction with organizational management. Prometheus. Vol. 36. No. 2. Pp. 135–152.
  36. Paz, M.D.R., & Vargas, J.C.R. (2023). Main theoretical consumer behavioral models. A review from 1935 to 2021. Heliyon. Vol. 9. No. e13895. https://doi.org/10.1016/j.heliyon.2023.e.13895
  37. Shomalzadeh, K., Scherpen, J., & Camlibel M.K. (2023). A real-time balancing market optimization with personalized prices: from bilevel to convex. Operations Research Perspectives. Vol. 10. No. 100276. https://doi.org/10.1016/j.orp. 2023.100276
  38. Silva, F. (2020). The importance of commitment power in games with imperfect evidence. American Economic Journal: Microeconomics. Vol. 12. No. 4. Pp. 99–113.
  39. Sjodin, D., Liljeborg, A., & Mutter, S. (2024). Conceptualizing ecosystem management capabilities: managing the ecosystem-organization interface. Technological Forecasting & Social Change. Vol. 200. No. 123187. https://doi.org/10.1016/j.techfore.2023.123187
  40. Stornelli, A., Simms, Ch., Reim, W., & Ozcan, S. (2024). Exploring the dynamic capabilities of technology provider ecosystems: a study of smart manufacturing projects. Technovation. Vol. 130. No. 102925. https://doi.org/10.1016/j.technovation.2023.102925
  41. Sweeting, A., Jia, D., Hui, S., & Yao, X. (2020). Dynamic price competition, learning-by-doing and strategic buyers. NBER Working Paper 28272. December. http://www.bver.org/papers/w28272
  42. Wilson, A., & Vespa, E. (2020). Information transmission under the shadow of the future. American Economic Journal: Microeconomics. Vol. 12. No. 4. Pp. 75–98.
  43. Xie, X., Liu, X., & Blanco, C. (2023). Evaluating and forecasting the niche fitness of regional innovation ecosystems: a comparative evaluation of different optimized grey models. Technological Forecasting and Social Change. Vol. 191. Issue 4. No. 122473. https://doi.org/10.1016/j.techfore.2023.122473
  44. Zervas, G., Proserpio, D., & John, B. (2021). A First look at online reputation on Airbnb, where every stay is above average. Marketing Letters. Vol. 32. No. 1. Pp. 1–16.

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