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Journal of Economic Interaction and Coordination

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31-07-2019 | Regular Article

Innovation, finance, and economic growth: an agent-based approach

Authors: Giorgio Fagiolo, Daniele Giachini, Andrea Roventini

Published in: Journal of Economic Interaction and Coordination | Issue 3/2020

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Abstract

This paper extends the endogenous growth agent-based model in Fagiolo and Dosi (Struct Change Econ Dyn 14(3):237–273, 2003) to study the finance–growth nexus. We explore industries where firms produce a homogeneous good using existing technologies, perform R&D activities to introduce new techniques, and imitate the most productive practices. Unlike the original model, we assume that both exploration and imitation require resources provided by banks, which pool agent savings and finance new projects via loans. We find that banking activity has a positive impact on growth. However, excessive financialization can hamper growth. Indeed, we find a significant and robust inverted U-shaped relation between financial depth and growth. Overall, our results stress the fundamental (and still poorly understood) role played by innovation in the finance–growth nexus.

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For an introduction to agent-based computational economics, see Tesfatsion and Judd (2006) and LeBaron and Tesfatsion (2008). Fagiolo and Roventini (2017); Dawid and Delli Gatti (2018); Dosi and Roventini (2019) survey agent-based models in macroeconomics with emphasis on economic policy.

We do not attach any meaning to the x and y dimensions. A 2-dimensional lattice is chosen only for descriptive reasons.

In the first period, agents allocate a fraction \(\gamma _2\) of their savings to create the initial equity of the bank.

Nonetheless, in Sect. 4.4 we test the consequences of different returns to scale regimes.

This assumption is made for consistency with the original FDM. In reality, firms do not stop production while performing R&D and we tested the consequences of production during sailing in “Appendix A.” Overall our results are not qualitatively affected.

Here one can imagine that more “risk averse” explorers would like to have larger savings, or borrow more resources, than the expected exploration cost. That is, they could apply a sort of safety buffer. We test the consequences of this assumption in “Appendix A.” Our results are not significantly affected by such refinement. Notice also that such dependence of exploration upon savings, combined with the way in which banks provide loans (see Sect. 3.5), makes the probability that a firm starts exploring increasing in the amount of savings.

The same happens if the explorer arrives, by chance, to an already known island.

One could relax such an assumption allowing firms to move only in the directions where the productivity of the new technologies should be higher than the one they currently master. We will consider this setting in future versions of the model.

One could imagine that bankrupt explorers should be more penalized, e.g., letting them go back to the origin or introducing some form of bankruptcy cost they should pay when restarting production. However, the cumulation of knowledge and the dynamic increasing returns which are generated while the explorer was sailing let the bankrupt agent fall behind with respect to the technological frontier. This constitutes already a fairly large penalization.

The probability of such an outcome is negligible, as the productivity of a newly discovered island is linked to the last production carried out by the explorer. For this reason, we make this assumption for keeping the setting as simple as possible. However, in future versions of the model we will consider a more sophisticated setting in which an explorer can continue to sail when she arrives on an already known island with lower productivity, or she can come back to her initial island.

In the case of multiple maxima, miner i chooses one of them at random.

Also in this case we assume that imitators do not produce during sailing for consistency with the FDM. An analysis of the consequences of production during sailing can be found in “Appendix A.” See also footnote 5.

One can also assume that adoption should be further favored by reducing the time and hence the cost, it takes to be performed. We analyze such scenario in “Appendix A.” Our results are robust to lower time for adoption.

We assume that the per-period share of production c consumed for imitation equals that for exploration.

Since “asymmetric information is a defining characteristic of credit markets” (Dell’Ariccia 2001, see also Bhattacharya and Thakor 1993; Van Damme 1994), this is the only viable way to introduce it in the model. Indeed, given our simple setting, if banks were able to distinguish between explorers and imitators, their information would be perfect. Nonetheless, we investigate the role of information asymmetry in Sect. 4.4.

Since a bankrupt agent may hold bank equity shares, we assume for simplicity that in such a case the shares are redistributed to the other shareholders proportionally to their positions. Investigating the effects of bank ownership structure goes beyond the objectives of the present analysis.

We define net position as the difference between agent’s deposit and outstanding loan. If liquidity is not enough to satisfy depositors, then it is distributed proportionally to net positions. A firm with negative net position does not receive anything.

The source code of the model, written in C++, is available upon request.

All our results do not significantly change if one increases Monte Carlo sample sizes. Extensive tests show that the MC distributions of the statistics of interest are sufficiently symmetric and unimodal. Thus, we can use MC sample averages to get meaningful synthetic indicators.

The Basel II capital requirement is 8%, while in our baseline parametrization we fix it to 10%. Our choice captures the additional capital buffer that risk averse banks hold in order to reduce their solvability risk.

For a critical discussion of empirical validation of agent-based models, see Fagiolo et al. (2017).

See Fagiolo and Dosi (2003) for more details.

We thank an anonymous referee for having pointed out this.

Such an exercise can also be considered as a preliminary—and admittedly very partial—sensitivity analysis of the model. Indeed, we test the robustness of our findings when the value of one or two parameters is changed keeping all the other parameters as in the baseline parameterization in Table 1.

We further tested the effect of lower levels of innovation cumulativeness; overall the results are consistent with the intuition provided here. This analysis can be found in “Appendix A.”

Notice also how, from Eq. (1), individual production decreases with the number of miners when \(\alpha <1\).

We also tested how the economy reacts to changes in bank setup costs and in the number of banks. Overall, our results are robust and consistent with the effect of credit on the exploration–exploitation trade-off. For this reason, we do not report such analyses, which are nevertheless available from the authors upon request.

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Title: Innovation, finance, and economic growth: an agent-based approach
Authors: Giorgio Fagiolo
Daniele Giachini
Andrea Roventini
Publication date: 31-07-2019
Publisher: Springer Berlin Heidelberg
Published in: Journal of Economic Interaction and Coordination / Issue 3/2020
Print ISSN: 1860-711X
Electronic ISSN: 1860-7128
DOI: https://doi.org/10.1007/s11403-019-00258-1

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