What drives markups? Evolutionary pricing in an agent-based, stock-flow consistent, macroeconomic model
Pascal Seppecher1 , Isabelle Salle2 , Marc Lavoie1 1
Université de Paris 13, CEPN, 2 Utrecht University School of Economics
Congrès de l’Association Française d’Economie Politique Thursday, July 6th, 2017
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
AFEP Rennes 2017
1 / 29
En guise d’introduction
How markups move, in response to what, and why, is however nearly terra incognita for macro. . . we are a long way from having either a clear picture or convincing theories, and this is clearly an area where research is urgently needed. Blanchard (2008) The State of the Macro
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
AFEP Rennes 2017
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Contents
1
Model A stock-flow consistent agent-based model A model of collective adaptation
2
Simulations Baseline Technological shock Behavioral shock
3
Conclusion
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
AFEP Rennes 2017
3 / 29
Model
1
Model A stock-flow consistent agent-based model A model of collective adaptation
2
Simulations
3
Conclusion
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
AFEP Rennes 2017
4 / 29
Model
A stock-flow consistent agent-based model
Jamel: an agent-based post-Keynesian model Agent-based: multiple agents (hundreds of firms, thousands of households — but only one bank), heterogenous agents, endogenous heterogeneity, radical decentralisation: no planner, no auctionneer, no access to any macro-information, all interactions are direct and individual. Post-Keynesian: procedural rationality, fundamental uncertainty, endogenous money, stock-flow consistency.
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
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Model
A stock-flow consistent agent-based model
Structure of real flows labor
consumption goods Capitalists
Sector 2 (consumption goods) labor Sector 1 (intermediate goods)
intermediary goods consumption goods Workers investment goods investment goods
Sector 3 (investment goods)
labor
investment goods P. Seppecher, I. Salle, M. Lavoie
What drives markups?
AFEP Rennes 2017
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Model
A model of collective adaptation
Endogenous heterogeneity of behaviors Dynamic, endogenous heterogeneity, resulting from the action of two simultaneous opposing forces: Differentiation, by innovations and errors, Homogenization, by selection and imitation.
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
AFEP Rennes 2017
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Model
A model of collective adaptation
Collective (non-intentional) adaptation Three mechanisms of collective adaptation: Short-run: heterogeneity. If the diversity of behaviors is large enough, the set contains the adapted behavior to new conditions. Medium-run: self-reinforcement. The firms with the adapted behaviors grow faster, thus they gain and play a heavier role in the resulting macro behavior. Long-run: selection and imitation. Competitive pressures force firms that have an inadequate behavior to adopt observed successful behaviors or to disappear.
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
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Model
A model of collective adaptation
Endogeneisation of the markups ϕ Heterogenous markups (each firm i has its own markup ϕi ), The markup of each firm ϕi changes continuously following a random walk (small random mutations), There are two motives of bankruptcy: I I
If the firm becomes insolvent (ie if liabilities > assets), It the firm loses all its fixed capital.
If a firm i goes bankrupt: I I I
The bank refunds the firm, The firm gives up its markup, It adopts a new markup copied on the one of a surviving firm j
ϕi = ϕj
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
AFEP Rennes 2017
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Model
A model of collective adaptation
Resolution of the high-margins/market-shares trade-off
⇒ For each firm, there is a trade-off between high margins and market shares;
⇒ This trade-off will be solved collectively, ie by endogenously
eliminating the markups incompatible with market conditions.
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
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Simulations
1
Model
2
Simulations Baseline Technological shock Behavioral shock
3
Conclusion
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
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Simulations
Baseline
Baseline Simulation: an Emergent Structure of Markups
(b) 100 replications 0.6
(a) Baseline simulation 0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
200
400
600
800
S1
P. Seppecher, I. Salle, M. Lavoie
1,000 1,200 1,400 1,600 1,800 2,000
S2
S3
mark-up (S1)
0.0
0
0
500
mark-up (S2) 1000
mark-up (S3) 1500
2000
period
What drives markups?
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Simulations
Baseline
Markups Selection & Endogenous Heterogeneity
−0.2
0
0.2
0.4
0.6 0.8 Markup
1
1.2
0.5
0
−0.5
1.4
1
Return On Assets
0
−0.2
0
0.2
0.4
0.6 0.8 Markup
1
1.2
0.5
0
−0.5
1.4
0.2
0.2
0.15
0.15
0.15
0.1
0.05
0
−0.2
0
0.2
0.4
0.6 0.8 Markup
1
1.2
0.1
0.05
0
1.4
Market shares (%)
0.2
Market shares (%)
−0.2
0
0.2
0.4
0.6 0.8 Markup
1
1.2
0.2
0.2
0.15
0.15
0.15
0.1
0.1
0.05
0.05
0
0
−0.2
0
0.2
0.4
0.6 0.8 Markup
P. Seppecher, I. Salle, M. Lavoie
1
1.2
1.4
0
0.2
0.4
0.6 0.8 Markup
1
1.2
1.4
−0.2
0
0.2
0.4
0.6 0.8 Markup
1
1.2
1.4
−0.2
0
0.2
0.4
0.6 0.8 Markup
1
1.2
1.4
0.1
0
1.4
−0.2
0.05
0.2
Inventories (%)
Market shares (%)
Return On Assets
0.5
−0.5
Inventories (%)
1
Inventories (%)
Return On Assets
1
0.1
0.05
−0.2
0
0.2
0.4
0.6 0.8 Markup
1
What drives markups?
1.2
1.4
0
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Simulations
Baseline
Stability of Relative Prices (a) Sector 1 to Sector 2
(b) Sector 2 to Sector 3 2.5
0.5 2 0.4 1.5
0.3 0.2
1
0.1
0.5
0
0 0
200
400
600
800
1,000 1,200 1,400 1,600 1,800 2,000
0
500
Relative price
1,000
1,500
2,000
Relative price
(c) Sector 3 to Sector 1 1 0.8 0.6 0.4 0.2 0 0
500
1,000
1,500
2,000
Relative price
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
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Simulations
Baseline
Amounts of Labor and ‘Natural Prices’ We calculate l1 ; l2 ; l3 , which are the amounts of labor, direct, indirect and hyper-indirect, required for the production of one unit of good in each sector S1 ; S2 ; S3 . l1 =
l2 =
1
q1
1+
1+
q2 l3 =
P. Seppecher, I. Salle, M. Lavoie
1
k1 l3
(1)
u1 d k
k2 l3
u2 d k
+ j2 l1
(2)
u3 d k
(3)
q3 u3 d k − k3
What drives markups?
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Simulations
Baseline
Relative Prices and Natural Prices (a) Sector 1 to Sector 2
(b) Sector 2 to Sector 3 2.5
0.5 2 0.4 1.5
0.3 0.2
1
0.1
0.5
0
0 0
500
1,000
Relative price
1,500
2,000
0
500
Natural price
1,000
Relative price
1,500
2,000
Natural price
(c) Sector 3 to Sector 1 1 0.8 0.6 0.4 0.2 0 0
500
1,000
Relative price
P. Seppecher, I. Salle, M. Lavoie
1,500
2,000
Natural price
What drives markups?
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Simulations
Technological shock
Technological Shock on Sector 2
What: simulation of a dramatic, exogenous, technological shock; Where: Sector 2 (consumption goods); How: productivity goes from 100 to 200; When: t = 1000.
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
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Simulations
Technological shock
Macro-Consequences (a) Labor Market 200
(b) Labor Market 14,000
Shock
Shock
12,000 150
10,000 8,000
100 6,000 4,000
50
2,000 0
0 0
500
1,000
S1
S2
1,500
2,000
0
S3
500
Capacity
(c) Workforce Distribution 0.7
1,000
Labor demand
1,500
Labor Supply
2,000
Employed
(d) Ponzi firms 1
Shock
0.6
Shock
0.8
0.5
0.6
0.4 0.4 0.3 0.2
0.2
0 0
200
400
600
800
S1
P. Seppecher, I. Salle, M. Lavoie
1,000 1,200 1,400 1,600 1,800 2,000
S2
0
200
S3
400
600
800
S1
What drives markups?
1,000 1,200 1,400 1,600 1,800 2,000
S2
S3
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Simulations
Technological shock
Relative Prices and Natural Prices (a) Sector 1 to Sector 2 0.7
(b) Sector 2 to Sector 3 2.5
Shock
0.6
Shock
2
0.5 1.5
0.4 0.3
1
0.2 0.5 0.1 0
0 0
500
1,000
Relative price
1,500
2,000
0
500
Natural price
1,000
Relative price
1,500
2,000
Natural price
(c) Sector 3 to Sector 1 Shock
1 0.8 0.6 0.4 0.2 0 0
500
1,000
Relative price
P. Seppecher, I. Salle, M. Lavoie
1,500
2,000
Natural price
What drives markups?
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Simulations
Technological shock
Adaptation of markups
(b) 100 replications
(a) Shock on Baseline
shock on productivity in S2
0.6
Shock
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
200
400
600
800
S1
1,000 1,200 1,400 1,600 1,800 2,000
S2
P. Seppecher, I. Salle, M. Lavoie
S3
mark-up (S1)
0.0
0
0
500
mark-up (S2) 1000
mark-up (S3) 1500
2000
period
What drives markups?
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Simulations
Behavioral shock
Behavioral Shock on Sector 2
What: simulation of a dramatic, exogenous, behavioral shock; Where: Sector 2 (consumption goods); How: markup goes from approx. 0.2 (on average) to 0.6; When: t = 1000.
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
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Simulations
Behavioral shock
Adaptation of markups
(a) Average markups (weighted by market shares)
(b) Average markups (arithmetic)
Shock
Shock
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0 0
200
400
600
800
S1
1,000 1,200 1,400 1,600 1,800 2,000
S2
P. Seppecher, I. Salle, M. Lavoie
0
200
S3
400
600
800
S1
What drives markups?
1,000 1,200 1,400 1,600 1,800 2,000
S2
S3
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Simulations
Behavioral shock
Relative Prices and Values (a) Sector 1 to Sector 2
(b) Sector 2 to Sector 3
Shock
0.5
Shock
2.5
0.4
2
0.3
1.5
0.2
1
0.1
0.5
0
0 0
500
1,000
Relative price
1,500
2,000
0
500
Natural price
1,000
Relative price
1,500
2,000
Natural price
(c) Sector 3 to Sector 1 Shock
1 0.8 0.6 0.4 0.2 0 0
500
1,000
Relative price
P. Seppecher, I. Salle, M. Lavoie
1,500
2,000
Natural price
What drives markups?
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Simulations
Behavioral shock
Deformation of the production structure (a) Relative capacities of production 0.8
(S1+S2)/S3
0.7 0.6 0.5 0.4 1.1
1.2
1.3
1.4
1.5
S2/S1
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
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Conclusion
1
Model
2
Simulations
3
Conclusion
P. Seppecher, I. Salle, M. Lavoie
What drives markups?
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Conclusion
Conclusion (1/4)
Our model is a complex system, it includes several interdependent sectors. These interdependencies are both real (labor and commodities) and monetary (money and debts). Thanks to the radical decentralization principle of ABM, and guided by the observations of Alchian (1950), we simulate the endogenous evolution of individual and aggregate pricing behaviors under the pressure of competition.
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Conclusion
Conclusion (2/4)
As the model includes three industrial sectors, we can observe the emergence of a structure of relative prices. Relative prices appear to “gravitate” around their “natural prices”, that is, around the ratio of the quantities of labor required for the production of the merchandises. Thus, the system of the “natural prices” dominates the evolution of the relative prices (and thus the evolution of the markups, since at the microeconomic level, markups determine prices).
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Conclusion
Conclusion (3/4) Last but not least, it is worth noting that our model is definitively eclectic, featuring ingredients from different and sometimes competing schools of thought. The model includes the post-Keynesian theory of endogenous money and its stock-flow consistent approach; it includes the concerns of Leontief for industrial interdependence; it is consistent with the classical idea that industrial prices gravitate towards values that are roughly proportional with the sum of the direct and indirect quantities of necessary labor, as can be found in Sraffa, Pasinetti and Lee; it also relies on Simon’s procedural rationality and on Alchian’s evolutionary behavior.
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Conclusion
Conclusion (4/4)
Yet, our model is not a chimera. Every ingredient is used because it plays a judicious role in the construction of the model, and results in a coherent synthesis that goes beyond the theoretical borders that fragment economics. This type of models has then the strong advantage of (re)activating the dialog and the exchanges between parallel and competing schools of thoughts in order to contribute to the emergence of a new, alternative paradigm in (macro)economics.
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