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Endogenous fluctuations, markups, capacity and credit constraints

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Abstract

In the present paper endogenous fluctuations have been generated by referring to endogenous markups, variable capacity utilization, and credit constraints. As one can detect, they are the same ingredients as those used to generate the so called selffulfilling cycles. With respect to this strand of literature, three changes are introduced. First of all, credit constraints are conceived within the Minsky’s financial instability hypothesis. Secondly, markups may have different dynamic patterns and impacts. Finally, heterogeneous agents are assumed to form evolutionary expectations. The results of these interacting aggregate demand and supply aspects are endogenous fluctuations obtained by means of simulations. Robust limit cycles and interesting comovements between variables are achieved in this medium-run model.

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Notes

  1. The statement refers to mainstream literature, as the macroeconomic impact of uncertainty is at the core of alternative schools of thoughts such as the Post-Keynesian one, whose methodological tenets are basically incompatible with the literature we are just mentioning.

  2. The notion of medium run grounds on the idea of Solow (2000); see also Ferri (2002).

  3. Here we especially refer to the emphasis on quantity adjustments and their connection to output and capital excess capacity.

  4. One cannot overlook the fact that Schumpeter (1934) himself emphasized the fundamental role of credit.

  5. The roots of this literature obviously trace back to a broader number of authors. Among others Fisher (1933) and Schumpeter (1939). On the issue of endogenous financial instability, the interested reader may find in Toporowski (2005) a good synthesis of the different positions which are strictly related to the view we hold in this paper.

  6. Further developments can be found in Bernanke et al. (1996, 1999). A critical review of the concept is presented in Coric (2011).

  7. See Asunama (2011) as an example of credit rationing which emerges without asymmetric information, in a context of economic depression.

  8. In the case, the countercyclical behavior depends on the assumption of rigid prices and decreasing marginal productivity of labor.

  9. This type of production function is a business cycle version of the long-run variety suggested by the endogenous growth theory (see Aghion and Howitt 1998), where capital replaces employment.

  10. One can use other distributions. For instance, Gilchrist and Williams (2005), refer to a (log) normal distribution. Liu and Wang (2014) refer to a Pareto distribution.

  11. The percentage of operative plants (i.e. \(n_{t})\) is determined after the idiosyncratic shock has been revealed. In some other models, the variable \(s_{th}\) works ex ante and in this case conditional expectations must be used (see Krause and Lubik 2007).

  12. The time subscript is maintained in cases exogenous shocks are to be considered.

  13. We are stressing the fact that (fundamental) uncertainty implies wider consequences than limited contract enforcement. Hence the lack of information due to uncertainty creates interacting and intertwined rigidities. In this respect Eq. 9, is itself a simplistic way to deal with the issue of uncertainty as conceived by Minsky. Nevertheless it is coherent with the setup of the paper where different kinds of quantity adjustments operate through all sectors of the economy (integrating both real and financial sides). This equation makes explicit the quantity constraint due to the financial side of the economy.

  14. See Fendoglu (2014) on the link between credit spreads and uncertainty. Gaiotti (2013), Gu and Wright (2011), Huang et al. (2014) and Kocherlatoka (2000) can be quoted among most recent references supporting the framework we suggest, justifying empirically and theoretically the emergence of endogenous credit cycles and slow recoveries. On the link between credit and resource reallocation see Barlevy (2003).

  15. The presence of financially constrained consumers is documented in Cynamon and Fazzari (2008), while the case of investment is dealt with in Fazzari et al. (2008).

  16. Layard et al. (1991) discuss the literature on the cyclical behaviour of the markup, stressing the absence of strong conclusions. For instance, the authors present both contributions that underline the effects due to oligopolistic behaviour (it may be more difficult to enforce collusion between oligopolistic firms during booms because the incentive for undercutting are greater at this time and therefore this leads to downward pressure on the markup) and contributions emphasizing the role of a changing elasticity of demand. On the negative correlation between markups and the number of competitors, see also Jaimovich (2007). One may also find further reference in Chevalier and Scharfstein (1996), Do Santos et al. (2005), Edmond and Veldkamp (2009), Lewis and Poilly, (2012), Seegmuller (2009) and Zhang (2007).

  17. The weak nonlinearities of the model only allows to generate closed orbits.

  18. Lengnick and Wohltmann (2013) introduce a changing gap.

  19. This selection mechanism can be interpreted as an evolutionary one, as stressed by De Grauwe (2008).

  20. The parameters used in the expectation formula are \(\upgamma =400\) (in the weight formula) and \(\uprho =0.7\), a parameter used in the equation referring to the attractiveness of the different forecasting strategies.

    Table 8 Parameters for the model with heterogeneous agents
    Full size table

  21. The same holds true for the parameters related to expectations. In fact, \(\uprho \) can vary from \(-\)90 to 30 %, while \(\upgamma \) can change \(\pm 100\) %.

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Authors and Affiliations

  1. University of Bergamo, Bergamo, Italy

    Piero Ferri, Annalisa Cristini & Anna Maria Variato

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  1. Piero Ferri
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  2. Annalisa Cristini
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  3. Anna Maria Variato
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Correspondence to Anna Maria Variato.

Appendix

Appendix

The following are the multipliers used in the linearized model:

$$\begin{aligned} N1= & {} \frac{\partial n_t }{\partial sth_t }=-\frac{1}{2\sigma }; \\ CU1= & {} \frac{\partial A_t }{\partial n_t }=\sigma _0 +z_0 -2\sigma _0 n_{0;} ; \\ S1= & {} \frac{\partial \omega _t }{\partial \mu _t }=\frac{z_0 }{\mu _0^2 *A_0 }; \\ S2= & {} \frac{\partial \omega _t }{\partial A_t }=\frac{z_0 }{\mu _0 A^{2}_0 }; \\ P1= & {} \frac{\partial th_t }{\partial P_t }=\frac{w_0 }{P^{2}_0 }; \end{aligned}$$

The suffix 0 stands for the stationary values that can be obtained in the following straightforward manner. A labor share equal to 0.60 has been assumed, with wages equal to 0.35. Furthermore, in a business cycle perspective about 82 % of the plants are operating on average. By assuming a steady state value of \(\hbox {A}\,=\,1\) and a threshold equal to \(-\)0.46, the following values are obtained: \(\upsigma =0.72\), \(\hbox {z}\,=\,1.08\), P\(_{0}=0.575\), \(\uptau _{1}=0.0134\) and \(\upmu _{0}=1.78\). Finally m\(_{0} = 1\) while the following equality has been imposed: \(\upgamma _{0}\,=\,\hbox {cred}_{0}\,=\,\hbox {L}_{0}=20\).

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Ferri, P., Cristini, A. & Variato, A.M. Endogenous fluctuations, markups, capacity and credit constraints. J Econ Interact Coord 11, 273–292 (2016). https://doi.org/10.1007/s11403-015-0154-8

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