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The effect of demand-driven structural transformations on growth and technological change

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Abstract

The paper analyses the effect of the dynamics of consumption preferences on the dynamics of macro–economic growth. We endogenously derive micro–dynamics of consumption behavior as a result of the increase in the number of income classes. The different degrees of inertia in the adjustment of consumption levels to income changes affect firm selection and the dynamics of market structure, which is ultimately responsible for different regimes of macro–economic growth. We find, first, that higher heterogeneity in consumption preferences amplifies and accelerates market dynamics, leading to a swift shift from a Malthusian to a Kaldorian growth pattern. Second, consumption smoothing mainly affects the timing of such a take–off. Inertia in consumption delays the occurrence of a Kaldorian engine for growth.

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Notes

  1. We summarize and discuss in Section 3 the different growth patterns identified by the benchmark configuration.

  2. Although in the present paper we do not focus on the psychological drivers of consumption behavior, the results of this literature are implicitly taken into account when modelling the consumers selectivity.

  3. One of the reasons for this is that our model does not include an explicit financial market, so we are not explicitly able to model inter–temporal consumption choices.

  4. In line with large empirical evidence, starting from the seminal work by Kaldor (1957).

  5. The top asymptotic class corresponds to a hypothetical class. As the number of hierechically constructed managerial class rises with the size of the firms, the preferences of the highest managerial class approches those of this top asymptotic class.

  6. The model was implemented, run and analysed in the Laboratory for Simulation Development (LSD) (Valente 2008). The code of the model is available upon request.

  7. Each result is the average over 100 replicates to control for the influence of random events. We show the 95 % confidence interval in Fig. 1. To ensure a better visualization, we do not report the CI for the following figures, though they all show the same level of robustness. These are available from the authors on request.

  8. Due to space constraints, here we do not report a detailed analysis of the robustness check against randomness. However, we find that the within–time variance across all simulations for all value of υ max is at least 103 times larger than the variance across the 100 simulation run for the same value of υ max. The data are available from the authors.

  9. Average over 100 independent runs.

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Acknowledgments

The paper benefited from comments and suggestions on this or previous versions from S. Bruns, A. Chai, M. Guerzoni, A. Roventini and the participants of the 15th International Schumpeter Society Conference in Jena, 2014, as well as the detailed comments from two anonymous referees. We are indebted to the above-mentioned for improvements to the paper. All remaining errors are our own.

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Authors and Affiliations

  1. BETA (UMR-CNRS 7522), Université de Technologie de Belfort-Montbéliard, Belfort, France

    André Lorentz

  2. SPRU - Science Policy Research Unit, University of Sussex, Brighton, UK

    Tommaso Ciarli & Maria Savona

  3. University of L’Aquila, and LEM, Sant’Anna School for Advanced Studies, Pisa, Italy

    Marco Valente

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  1. André Lorentz
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  2. Tommaso Ciarli
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  3. Maria Savona
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  4. Marco Valente
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Correspondence to André Lorentz.

Appendix

Appendix

Table 1 Parameters setting. Parameter’s (1) name, (2) description, (3) value, and (4) empirical data range when its effect is not analysed in Section 4
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Lorentz, A., Ciarli, T., Savona, M. et al. The effect of demand-driven structural transformations on growth and technological change. J Evol Econ 26, 219–246 (2016). https://doi.org/10.1007/s00191-015-0409-5

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