Elasticity of demand and behaviour-based price discrimination

Esteves, Rosa Branca; Reggiani, Carlo

doi:10.1016/j.ijindorg.2013.10.010

Cited by 42 publications

(36 citation statements)

References 20 publications

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“…A large theoretical literature studies price discrimination based on purchase status, surveyed in Fudenberg et al (2006) and Esteves (2009). As in Thisse and Vives (1988), these models typically show that price discrimination leads firms to compete more fiercely over each marginal consumer; thus, price discrimination is pro-competitive and benefits consumers, although social welfare costs of discrimination are well known (e.g.…”

Section: Introductionmentioning

confidence: 99%

Price Discrimination in Asymmetric Industries: Implications for Competition and Welfare

Gnutzmann

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractPrice discrimination by consumer's purchase history is widely used in regulated industries, such as communication or utilities, both by incumbents and entrants. I show that such discrimination can have surprisingly negative welfare effects -even though prices and industry profits fall, so does consumer surplus. Earlier studies that did not allow entrants to discriminate or assumed symmetric firms yielded sharply different results, the pro-competitive effect of price discrimination are stronger in these settings. Imposing a pricing constraint on incumbent's discrimination leads the entrant to discriminate more heavily, but still improves both consumer and producer welfare.

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Section: Introductionmentioning

confidence: 99%

Price Discrimination in Asymmetric Industries: Implications for Competition and Welfare

Gnutzmann

2014

SSRN Journal

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“…Thisse and Vives (1988) compares competition based on completely individualized prices with competition based on uniform prices within the framework of a 1 Taylor (2003), Esteves (2010), Gabrielsen (2004), Chen andZhang (2009), Gehrig andStenbacka (2004), and Gehrig, Shy, and Stenbacka (2011) are examples of subsequent studies that have applied related approaches for analyzing history-based pricing. Fudenberg and Villas-Boas (2007) and Esteves (2009) present extensive literature surveys on behavior-based price discrimination.…”

Section: Introductionmentioning

confidence: 99%

Investment in customer recognition and information exchange

Shy

Stenbacka

2013

Information Economics and Policy

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We investigate how costly acquisition and exchange of customer-specific information affects industry profit and consumer welfare. Consumers differ in their preferences for competing brands and in their switching costs between brands. Brand-producing firms use their acquired knowledge of customer-specific preferences to differentiate prices. We show that consumers are worse off when firms acquire information about their preferences and that information sharing between firms further magnifies their losses. No information sharing supports a subgame perfect equilibrium that is also efficient. Finally, equilibrium investments in customer information may be excessive if firms bear low costs of acquiring customer-specific information. Terms of use: Documents in EconStor may

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“…Price elasticity of demand (PED) is an economic concept that features the elasticity of the quantity required of certain goods, in relation to changes in their price. () In mathematical terms, it is the ratio between the change in demand and the percentage of the change in price, given by

P E D = ((), \frac{Q_{x} - Q_{v}}{Q_{x} + Q_{v}}) false/ (/), ((, \frac{P_{x} - P_{v}}{P_{x} + P_{v}})),

where Q x is the initial quantity and Q v the final quantity.…”

Section: Price Elasticity Of Demand For Electricitymentioning

confidence: 99%

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Miranda

Abaide

Sperandio

et al. 2018

Int Trans Electr Energ Syst

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Summary Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.

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Elasticity of demand and behaviour-based price discrimination

Cited by 42 publications

References 20 publications

Price Discrimination in Asymmetric Industries: Implications for Competition and Welfare

Price Discrimination in Asymmetric Industries: Implications for Competition and Welfare

Investment in customer recognition and information exchange

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

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