nep-ind New Economics Papers
on Industrial Organization
Issue of 2024‒02‒05
six papers chosen by



  1. Cournot oligopoly: a discrete time sticky-prices paradox By Pierre Bernhard; Marc Deschamps
  2. Measuring the Deviations from Perfect Competition: International Evidence By Razzak, Weshah
  3. Strategic Responses to Algorithmic Recommendations: Evidence from Hotel Pricing By Daniel Garcia; Juha Tolvanen; Alexander K. Wagner
  4. Testing Collusion and Cooperation in Binary Choice Games By Erhao Xie
  5. Patient Costs and Physicians' Information By Michael J. Dickstein; Jihye Jeon; Eduardo Morales
  6. Intellectual Property Rights and the Efficiency of International Production Networks: Evidence from the Automotive Industry By Giuseppe Cavaliere; Graziano Moramarco; Alireza Naghavi

  1. By: Pierre Bernhard (MACBES Team, INRIA, Université de Côte d'Azur, France); Marc Deschamps (Université de Franche-Comté, CRESE, UR3190, F-25000 Besançon, France)
    Keywords: Sticky price, Cournot oligopoly, Dynamic Game, Discrete time
    JEL: C61 C72
    Date: 2024–01
    URL: http://d.repec.org/n?u=RePEc:crb:wpaper:2024-01&r=ind
  2. By: Razzak, Weshah
    Abstract: We use aggregated macroeconomic data for 43 countries to test the microeconomic condition for Perfect Competition, whereby the price level is equal to the marginal cost in the long run. We postulate two forms of Perfect Competition in the macro data: a weaker-form and a stronger-form. The former exists if the price level and the marginal cost share a common long-run trend; i.e., cointegrated. The latter exists if the market price and the marginal cost are equal in the long run.
    Keywords: Perfect Competition, price level, marginal cost, time series, cointgration, nonparametric
    JEL: C12 C13 C22 D01 D41
    Date: 2023–12–29
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:119605&r=ind
  3. By: Daniel Garcia; Juha Tolvanen; Alexander K. Wagner
    Abstract: We study the interaction between algorithmic advice and human decisions using high-resolution hotel-room pricing data. We document that price setting frictions, arising from adjustment costs of human decision makers, induce a conflict of interest with the algorithmic advisor. A model of advice with costly price adjustments shows that, in equilibrium, algorithmic price recommendations are strategically biased and lead to suboptimal pricing by human decision makers. We quantify the losses from the strategic bias in recommendations using as structural model and estimate the potential benefits that would result from a shift to fully automated algorithmic pricing.
    Keywords: advice, algorithmic recommendations, human decisions, adjustment cost, delegation
    JEL: D22 D83 L13
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_10849&r=ind
  4. By: Erhao Xie
    Abstract: This paper studies the testable implication of players’ collusive or cooperative behaviours in a binary choice game with complete information. In this paper, these behaviours are defined as players coordinating their actions to maximize the weighted sum of their payoffs. I show that this collusive model is observationally equivalent to an equilibrium model that imposes two restrictions. The first restriction is on each player’s strategic effect and the second one requires a particular equilibrium selection mechanism. Under the equilibrium condition, these joint restrictions are simple to test using tools in the literature on empirical games. This test, as suggested by the observational equivalence result, is the same as testing collusive and cooperative behaviours. I illustrate the implementation of this test by revisiting the entry game between Walmart and Kmart studied by Jia (2008). Under the equilibrium condition, Jia’s original estimates are consistent with the first restriction on the strategic effects, serving as a warning sign of potential collusion. This paper tests and rejects the second restriction on the equilibrium selection mechanism. Thus, the empirical evidence suggests that Walmart and Kmart did not collude on their entry decisions.
    Keywords: Econometric and statistical methods; Market structure and pricing
    JEL: C57 L13
    Date: 2023–11
    URL: http://d.repec.org/n?u=RePEc:bca:bocawp:23-58&r=ind
  5. By: Michael J. Dickstein; Jihye Jeon; Eduardo Morales
    Abstract: Health insurance plans in the U.S. increasingly use price mechanisms to steer demand for prescription drugs. The effectiveness of these incentives, however, depends both on physicians' price sensitivity and their knowledge of patient prices. We develop a moment inequality model that allows researchers to identify agents' preferences without fully specifying their information. Applying this model to diabetes care, we find that physicians lack detailed price information and are more price-elastic than full-information models imply. We predict that providing physicians detailed information on prices at the point of prescribing can save patients 12-23% of their out-of-pocket costs for diabetes treatment.
    JEL: I11 I13 L0 L15
    Date: 2024–01
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:32014&r=ind
  6. By: Giuseppe Cavaliere; Graziano Moramarco; Alireza Naghavi
    Abstract: This paper investigates the potential benefits of intellectual property rights (IPR) institutions for international production networks. Using unique data on manufacturer-supplier linkages in the automotive industry, we establish a positive empirical relationship between the productivity and efficiency of manufacturing firms and IPR protection in their suppliers’ locations. Notably, IPRs do not have the same impact on ownership networks, and protection of physical property rights does not generate any improvement in performance. We confirm that the results are not driven by other firm-level characteristics and address potential endogeneity concerns by employing a novel gravity-based IV approach, followed by a GMM analysis.
    Keywords: International production networks, Intellectual property rights, Ownership, Internalization, Automotive industry, Knowledge dissipation, Firm efficiency
    JEL: F21 F23 L14 L25 L62 O34 G32
    Date: 2024–01–16
    URL: http://d.repec.org/n?u=RePEc:csl:devewp:492&r=ind

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