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Measuring market power when firms price discriminate

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Abstract

We propose conduct parameter-based market power measures within a model of price discrimination, extending work by Hazledine (Econ Lett 93:413–420, 2006) and Kutlu (Econ Lett 117:540–543, 2012) to certain forms of second-degree price discrimination. We use our model to estimate the market power of US airlines in a price discrimination environment. We find that a slightly modified version of our original theoretical measure is positively related to market concentration. Moreover, on average, market power for high-end segment is greater than that of low-end segment.

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Notes

  1. See Perloff et al. (2007) and Stole (2007) for extensive surveys on market power and price discrimination, respectively.

  2. Dai et al. (2014) study price dispersion both theoretically and empirically.

  3. The HHI is a measure of market concentration, with implications for market power under certain circumstances.

  4. Price dispersion may happen for reasons other than cost differences and market power. For example, in a framework with identical firms (same marginal costs), Kutlu (2015) and Baris and Kutlu (2015) show that if the consumers have limited memories even when each firm sets a single price, price dispersion may exist.

  5. For a discussion of the etymology of price discrimination and its degrees, see Hazledine (2015).

  6. The equilibrium of this conduct parameter model is derived in Kutlu (2016).

  7. Varian (1985, Section 1), Formby and Millner (1989), Hazledine (2006, 2010), Kumar and Kutlu (2016), and Kutlu (2009, 2012, 2016) use this demand structure based on the valuations of consumers.

  8. Recall that \(P_{1}=P\left( Q_{1}\right) \) is the high-end price, \(P_{2}=P\left( Q_{1}+Q_{2}\right) \) is the low-end price, and \(Q=Q_{1}+Q_{2}\) is the total quantity.

  9. Note that the reason for measurement error is not due to a measurement mistake that is done by the researcher. It is rather due to using an incorrect model so that the variable used in the model differs from the one that should be used in the estimations.

  10. For the single-price setting Puller (2009) argues that if the firms play a dynamic game, including time dummies can handle potential estimation problems that lead to inconsistent parameter estimation.

  11. To be more precise this is the absolute value of the price elasticity of demand, which we use throughout.

  12. Here, by optimal \(P_{1}\) for a given \(Q_{2}\), we mean the equilibrium for the conduct parameter game when \(Q_{2}\) is treated as given.

  13. For more details about identification in the framework of conduct parameter approach, see Bresnahan (1982), Lau (1982), Perloff et al. (2007), and Perloff and Shen (2012).

  14. For the log–lin demand form, we assumed zero marginal cost for the sake of getting a closed-form solution for the equilibrium. Similarly, for the lin–lin demand functional form we assume that marginal costs are constant.

  15. For more information about the financial situations of the US airlines, see Borenstein (2011) and Duygun et al. (2016).

  16. Borenstein and Rose (1994) divide the round-trip price by 2.

  17. Note that since prices are calculated based on the percentile prices, the number of tickets with price above \(P_{1}\) and \(P_{2}\) would satisfy the constant fraction assumption.

  18. The Benchmark estimates are based on smaller number of observations than we present in the descriptive statistics table. This is due to the lagged instruments that we use in the estimations. The Keep Outlier PD estimates are based on more observations as this data set keeps the outlier values.

  19. See Goolsbee and Syverson (2008) for a study that is using these weights.

  20. The 5th, 50th, and 95th percentiles of \(\theta \) estimates for the data set that keeps outliers for PD are 0.45, 0.99, and 1.72.

  21. We would still get \(\theta _{1}>\theta _{2}\) at any conventional significance level if we proxy \(\hbox {MC}\) by the average of all prices below 5th percentile of prices. For this scenario, the median and mean for \(\theta _{2}\) estimates are 0.66 and 0.71, respectively.

  22. See Corts (1999) for a criticism of static conduct parameter models.

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Authors and Affiliations

  1. Department of Economics, Antalya International University, Antalya, Turkey

    Levent Kutlu

  2. School of Economics, Georgia Institute of Technology, Atlanta, GA, 30332-0615, USA

    Levent Kutlu

  3. Department of Economics, Rice University, Houston, TX, USA

    Robin C. Sickles

Authors
  1. Levent Kutlu
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  2. Robin C. Sickles
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Correspondence to Levent Kutlu.

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Kutlu, L., Sickles, R.C. Measuring market power when firms price discriminate. Empir Econ 53, 287–305 (2017). https://doi.org/10.1007/s00181-017-1251-4

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