Hostname: page-component-848d4c4894-4hhp2 Total loading time: 0 Render date: 2024-05-23T06:44:03.477Z Has data issue: false hasContentIssue false
  • English
  • Français

TIME ALLOCATION, THE DYNAMICS OF INTERACTION, AND TECHNOLOGY ADOPTION

Published online by Cambridge University Press:  20 December 2017

Orlando Gomes
Orlando Gomes*
Affiliation:
Lisbon Accounting and Business School (ISCAL/IPL) and Business Research Unit (UNIDE/ISCTE-IUL)
*
Address correspondence to: Orlando Gomes, Lisbon Accounting and Business School (ISCAL/IPL), Av. Miguel Bombarda 20, 1069-035 Lisbon, Portugal; e-mail: omgomes@iscal.ipl.pt.
Get access Rights & Permissions [Opens in a new window]

Abstract

Inspired by recent literature that approaches the dissemination of knowledge from a social interaction perspective, the article explores the dynamics of a prototypical optimal control growth problem structured upon the following features: (i) the model economy is populated by a large number of rational agents; (ii) each agent allocates time, optimally, among production and social interaction; (iii) knowledge spreads through the contact with others; (iv) the propagation of ideas follows two steps—in a first stage, interaction promotes the acquisition of theoretical knowledge and, in a second stage, it works as a catalyst for the successful implementation of the theory to practical productive uses; (v) interaction contributes not only to the diffusion of a given state of technical knowledge—it fosters, as well, the growth of ideas and techniques. The model allows for the endogenous determination of optimal trajectories concerning the allocation of time and the intensity of interaction; moreover, a long-term endogenous growth rate for the economy is derived, with optimal growth being essentially driven by the state of techniques and by the forces that shape the human interaction process.

Keywords

Social InteractionKnowledge DiffusionOptimal Time AllocationEndogenous GrowthBasic and Applied Research
Type
Articles
Information
Macroeconomic Dynamics , Volume 23 , Issue 6 , September 2019 , pp. 2150 - 2190
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Financial support from the Lisbon Polytechnic Institute, under project MacroModel, is gratefully acknowledged. I also thank two anonymous referees for insightful comments and suggestions. The usual disclaimer applies.

References

REFERENCES

Acemoglu, D., Ozdaglar, A., and Yildiz, E. (2011) Diffusion of innovations in social networks. In Chong, E., Farrell, J., Camacho, E., and Polycarpou, M. (eds.), Proceedings of the 50th IEEE Conference on Decision and Control (CDC-ECC), pp. 2329–2334. Orlando, FL: Institute of Electrical and Electronics Engineers (IEEE).Google Scholar
Aghion, P. and Howitt, P. (1992) A model of growth through creative destruction. Econometrica 60, 323–351.Google Scholar
Akcigit, U., Hanley, D., and Serrano-Velarde, N. (2013) Back to Basics: Basic Research Spillovers, Innovation Policy and Growth. NBER working paper 19473, National Bureau of Economic Research.Google Scholar
Angeletos, G. M. and La'O, J. (2013) Sentiments. Econometrica 81, 739–779.Google Scholar
Arts, S., Cassiman, B., and Veugelers, R. (2012) Mind the Gap: Capturing Value from Basic Research: Boundary Crossing Inventors and Partnerships. CEPR discussion paper 9215.Google Scholar
Barro, R. J. and Sala-i-Martin, X. (1997) Technological diffusion, convergence and growth. Journal of Economic Growth 1, 1–26.Google Scholar
Benhabib, J., Perla, J., and Tonetti, C. (2014) Catch-up and fall-back through innovation and imitation. Journal of Economic Growth 19, 1–35.Google Scholar
Benhabib, J. and Spiegel, M. M. (2005) Human capital and technology diffusion. In Aghion, P. and Durlauf, S. N. (eds.), Handbook of Economic Growth, vol. 1a, pp. 935–966. Amsterdam and San Diego: Elsevier, North-Holland.Google Scholar
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., and Hwang, D. U. (2006) Complex networks: Structure and dynamics. Physics Reports 424, 175–308.Google Scholar
Bosworth, D. and Jobome, G. (2003) The rate of depreciation of technological knowledge: Evidence from patent renewal data. Economic Issues 8, 59–82.Google Scholar
Boucekkine, R., Saglam, C., and Vallée, T. (2004) Technology adoption under embodiment: A two-stage optimal control approach. Macroeconomic Dynamics 8, 250–271.Google Scholar
Comin, D. A. and Mestieri, M. (2013) Technology Diffusion: Measurement, Causes and Consequences. NBER working paper 19052, National Bureau of Economic Research.Google Scholar
Cozzi, G. and Galli, S. (2014) Sequential R&D and blocking patents in the dynamics of growth. Journal of Economic Growth 19, 183–219.Google Scholar
Daley, D. J. and Kendall, D. G. (1964) Epidemics and rumors. Nature 204, 1118.Google Scholar
Daley, D. J. and Kendall, D. G. (1965) Stochastic rumours. Journal of the Institute of Mathematics and Its Applications 1, 42–55.Google Scholar
Davis, C. and Hashimoto, K. I. (2015) R&D subsidies, international knowledge diffusion, and fully endogenous productivity growth. Macroeconomic Dynamics 19, 1816–1838.Google Scholar
Durlauf, S. N. (2005) Complexity and empirical economics. Economic Journal 115, F225–F243.Google Scholar
Durlauf, S. N. (2012) Complexity, economics and public policy. Politics, Philosophy and Economics 11, 45–75.Google Scholar
Evangelatos, N. and Carayannis, E. (2014) Innovation diffusion: An epidemiological perspective. International Journal of Social Ecology and Sustainable Development 5, 22–30.Google Scholar
Fishman, A., Gandal, N., and Shy, O. (1993) Planned obsolescence as an engine of technological progress. Journal of Industrial Economics 41, 361–370.Google Scholar
Fogli, A. and Veldkamp, L. (2012) Germs, Social Networks and Growth. NBER working paper 18470, National Bureau of Economic Research.Google Scholar
Gersbach, H., Sorger, G., and Amon, C. (2009) Hierarchical Growth: Basic and Applied Research. Working paper 0912, Department of Economics, University of Vienna.Google Scholar
Granovetter, M. (1978) Threshold models of collective behavior. American Journal of Sociology 83, 1420–1443.Google Scholar
Gurley, N. and Johnson, D. K. N. (2017) Viral economics: An epidemiological model of knowledge diffusion in economics. Oxford Economic Papers 69, 320–331.Google Scholar
Huo, L., Huang, P., and Guo, C. X. (2012) Analyzing the dynamics of a rumor transmission model with incubation. Discrete Dynamics in Nature and Society article ID 328151, 21 pages.Google Scholar
König, M. D. (2014) Technology Cycles in Dynamic R&D Networks. Working paper 178, Department of Economics, University of Zurich.Google Scholar
König, M. D., Lorenz, J., and Zilibotti, F. (2016) Innovation vs. imitation and the evolution of productivity distributions. Theoretical Economics 11, 1053–1102.Google Scholar
Kosfeld, M. (2005) Rumours and markets. Journal of Mathematical Economics 41, 646–664.Google Scholar
Lafond, F. (2015) Self-organization of knowledge economies. Journal of Economic Dynamics and Control 52, 150–165.Google Scholar
Lamberson, P. J. (2010) Social learning in social networks. B.E. Journal of Theoretical Economics 10 (1), article 36.Google Scholar
Lee, I. H. and Lee, J. (1998) A theory of economic obsolescence. Journal of Industrial Economics 46, 383–401.Google Scholar
López-Pintado, D. (2013) Influence networks. Games and Economic Behavior 75, 776–787.Google Scholar
Lucas, R. E. (2009) Ideas and growth. Economica 76, 1–19.Google Scholar
Lucas, R. E. and Moll, B. (2014) Knowledge growth and the allocation of time. Journal of Political Economy 122, 1–51.Google Scholar
Luttmer, E. G. J. (2011) Technology diffusion and growth. Journal of Economic Theory 147, 602–622.Google Scholar
Luttmer, E. G. J. (2015) Four Models of Knowledge Diffusion and Growth. Working paper 724, Federal Reserve Bank of Minneapolis.Google Scholar
Maki, D. P. and Thompson, M. (1973) Mathematical Models and Applications, with Emphasis on Social, Life, and Management Sciences. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Manski, C. F. (2000) Economic analysis of social interactions. Journal of Economic Perspectives 14, 115–136.Google Scholar
Mokyr, J. (2002) The Gifts of Athena. Princeton, NJ: Princeton University Press.Google Scholar
Morales, M. F. (2004) Research policy and endogenous growth. Spanish Economic Review 6, 179–209.Google Scholar
Mukoyama, T. (2003) Innovation, imitation and growth with cumulative technology. Journal of Monetary Economics 50, 361–380.Google Scholar
Mukoyama, T. (2004) Diffusion and innovation of new technologies under skill heterogeneity. Journal of Economic Growth 9, 451–479.Google Scholar
Mukoyama, T. (2006) Rosenberg's ‘Learning by Using’ and technology diffusion. Journal of Economic Behavior and Organization 61, 123–144.Google Scholar
Nekovee, M., Moreno, Y., Bianconi, G., and Marsili, M. (2007) Theory of rumor spreading in complex social networks. Physica A 374, 457–470.Google Scholar
Nelson, R. R. and Phelps, E. S. (1966) Investment in humans, technological diffusion, and economic growth. American Economic Review 56, 69–75.Google Scholar
Pastor-Santorras, R., Castellano, C., Van Mieghem, P., and Vespignani, A. (2015) Epidemic processes in complex networks. Reviews of Modern Physics 87, 925–979.Google Scholar
Perla, J. and Tonetti, C. (2014) Equilibrium imitation and growth. Journal of Political Economy 122, 52–76.Google Scholar
Peyton-Young, H. (2006) The diffusion of innovations in social networks. In Blume, L. E. and Durlauf, S. N. (eds.), The Economy as an Evolving Complex System III: Current Perspectives and Future Directions, pp. 267–282. New York: Oxford University Press.Google Scholar
Peyton-Young, H. (2009) Innovation diffusion in heterogeneous populations: Contagion, social influence, and social learning. American Economic Review 99, 1899–1924.Google Scholar
Piqueira, J. R. C. (2010) Rumor propagation model: An equilibrium study. Mathematical Problems in Engineering Article ID 631357, 7 pages.Google Scholar
Prettner, K. and Werner, K. (2014) Human Capital, Basic Research and Applied Research: Three Dimensions of Human Knowledge and their Differential Growth Effects. Center for European Governance and Economic Development Research Discussion Papers 186, Department of Economics, University of Goettingen.Google Scholar
Raurich, X., Sanchez-Losada, F., and Vilalta-Bufi, M. (2015). Knowledge Misallocation and Growth. Macroeconomic Dynamics 19, 1540–1564.Google Scholar
Rosen, S. (1975) Measuring the obsolescence of knowledge. In Juster, F. T. (ed.), Education, Income and Human Behavior, pp. 199–232. New York: Carnegie Foundation and Columbia University Press.Google Scholar
Staley, M. (2011) Growth and the diffusion of ideas. Journal of Mathematical Economics 47, 470–478.Google Scholar
Stokey, N. L. (2015) Catching up and falling behind. Journal of Economic Growth 20, 1–36.Google Scholar
Thompson, K., Estrada, R. C., Daugherty, D., and Cintron-Arias, A. (2003) A Deterministic Approach to the Spread of Rumors. Dept. of Biological Statistics & Computational Biology, Cornell University, Technical report BU-1642-M.Google Scholar
Wang, Y. Q., Yang, X. Y., Han, Y. L., and Wang, X. A. (2013) Rumor spreading model with trust mechanism in complex social networks. Communications in Theoretical Physics 59, 510–516.Google Scholar
Xue, J. and Yip, C. K. (2012) Factor substitution and economic growth: A unified approach. Macroeconomic Dynamics 16, 625–656.Google Scholar
Zanette, D. H. (2002) Dynamics of rumor propagation on small-world networks. Physical Review E 65, 041908.Google Scholar
Zhao, L. J., Wang, J. J., Chen, Y. C., Wang, Q., Cheng, J. J., and Cui, H. X. (2012) SIHR rumor spreading model in social networks. Physica A 391, 2444–2453.Google Scholar