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Structural Interactions and Long Run Growth. An Application of Experimental Design to Agent Based Models

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  • Tommaso Ciarli

Abstract

We propose an agent-based computational model defining the following dimensions of structural change?organisation of production, technology of production, and product on the supply side, and income distribution and consumption patterns on the demand side?at the microeconomic level. We define ten different parameters to account for these five dimensions of structural change. Building on existing results we use a full factorial experimental design (DOE) to analyse the size and significance the effect of these parameters on output growth. We identify the aspects of structural change that have the strongest impact. We study the direct and indirect effects of the factors of structural change, and focus on the role of the interactions among the different factors and different aspects of structural change. We find that some aspects of structural change?income distribution, changes to production technology and the emergence of new sectors?play a major role on output growth, while others?consumption shares, preferences, and the quality of goods?play a rather minor role. Second, these major factors can radically modify the growth of an economy even when all other aspects experience no structural change. Third, different aspects of structural change strongly interact: the effect of a factor that influences a particular aspect of structural change varies radically for different degrees of structural change in other aspects. These results on the different aspects of structural change provide a number of insights on why regions starting from a similar level of output and with initial small differences grow so differently through time.

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  • Tommaso Ciarli, 2012. "Structural Interactions and Long Run Growth. An Application of Experimental Design to Agent Based Models," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(5), pages 295-345.
  • Handle: RePEc:cai:reofsp:reof_124_0295
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    Cited by:

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    2. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Guerini, Mattia & Moneta, Alessio, 2017. "A method for agent-based models validation," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 125-141.
    4. Jean-Luc Gaffard & Mauro Napoletano, 2012. "Introduction - Improving the Toolbox: New Advances in Agent-Based and Computational Models," Post-Print hal-01053562, HAL.
    5. repec:hal:spmain:info:hdl:2441/53r60a8s3kup1vc9l564k4686 is not listed on IDEAS
    6. Martin Blom & Fulvio Castellacci & Arne Fevolden, 2014. "Defence firms facing liberalization: innovation and export in an agent-based model of the defence industry," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 430-461, December.

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    More about this item

    Keywords

    structural change; long run growth; ABM; DOE;
    All these keywords.

    JEL classification:

    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C99 - Mathematical and Quantitative Methods - - Design of Experiments - - - Other

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