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  • Klemperer follows up and notes that switching costs are quit

    2018-11-13

    Klemperer (1995) follows up and notes that switching costs are quite common and can lead to important consequences, such as welfare losses similar to those observed in an oligopoly and barriers to new entrants. Padilla (1992) develops a model in which ex ante identical firms have ex post asymmetric market shares, while Padilla (1995) shows that in an infinite horizon model with stationary Markovian strategies, the strength of cgrp antagonist is reduced. Although the literature on switching costs is extensive, empirical research encounters difficulties in obtaining information on changes in the suppliers used by each individual, especially in the case of the banking sector. These switching costs are viewed in the corporate finance literature as stemming from an asymmetric information problem between the borrower and the lender, as noted in a review of the relationship banking literature by Boot (2000). Another issue faced in the corporate finance literature that creates switching costs is moral hazard, as noted by Boot et al. (1993). Empirical evidence supporting this channel is provided by Bharath et al. (2007). The scarcity of studies accurately estimating supplier change is due to the mostly customer-specific unobserved opportunity costs. They reflect human capital requirements for changing suppliers, as well as some informational advantage that the incumbent supplier has over its competitors, and represent a utility loss that sometimes cannot be directly calculated from the data, as highlighted by Shy (2002). As noted by Kim et al. (2003), the transition individual-level data required are rarely available to researchers. In this context, both papers develop methods based on equilibrium assumptions about the effect of switching costs on market conduct. Shy (2002) develops an equilibrium concept, called Undercut Proofness, and Kim et al. (2003) start from the assumptions developed in Klemperer (1987c) and proposes some extensions, such as relaxing the assumption of no switching in equilibrium.This advance has some important empirical consequences, since empirical evidence indicates that there is a substantial amount of switching, as also noticed in the Klemperer (1995) review. Kim et al. (2003) start by considering an n firm oligopoly that competes in prices (à la Bertrand) for a non-storable good. Consumers have inelastic demands and maximize their utilities by choosing which firm to buy from given a price vector. It is assumed that consumers have in mind that changing suppliers is costly and add switching costs to the prices charged by the firms from which they did not buy earlier. This behavior produces transition probabilities, which are functions of prices and switching costs, that are in turn aggregated to generate firm demand. The details will be provided in the next section.
    Methodology The methodology used here follows the framework developed by Kim et al. (2003), with a focus on the provision of bank deposit services. The model is formulated to allow the estimation of the structural parameters using aggregate data and is based on the theoretical analysis of customer switching cost effects in the market by Klemperer (1987b), with an additional assumption the customer can change banks in every period. The starting point is the probability that a customer continues purchasing from the same firm:where is the probability that a customer who bought in the previous period from firm i keeps buying from the same firm in next period, and p is the price charged by firm i. Here, is an (n−1) vector of prices offered by rivals other than i, and is a vector of switching costs equal to the scalar s multiplied by a unit vector (n−1): . Thus, Eq. (1) means that the probability that the consumer continues his relationship with bank i depends on the price charged by all banks, taking switching costs into account. Since switching costs are probably different for each customer, s can be interpreted as an average switching cost. Customer-specific deviations from this mean are captured by the slope of the transition probability function, while bank-specific changes in average cost are captured at the level of the function.