Can real-effort investments inhibit the convergence of experimental markets?

Cason, Timothy N.; Gangadharan, Lata; Nikiforakis, Nikos

doi:10.1016/j.ijindorg.2010.01.004

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…Bartling et al (2014) examined markets where the buyer had the market power, and found that sellers were less willing to engage in costly punishment following a low price when the price was chosen in an apparently procedurally fair way (via competitive auction) than otherwise (dictated by the buyer). Still other studies have found that fairness can matter in markets with moral hazard (Fehr et al 1993), adverse selection (Renner and Tyran 2004), and where seller costs are based on performance on a real-effort task (Cason et al 2011).…”

Section: Relevant Literaturementioning

confidence: 99%

Pricing in Competitive Search Markets: The Roles of Price Information and Fairness Perceptions

Anbarcı

Feltovich

2018

Management Science

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We use a competitive search (price-posting) framework to experimentally examine how buyer information and fairness perceptions affect market behavior. We observe that moving from 0 to 1 uninformed buyers leads to higher prices in both 2(seller)x2(buyer) and 2x3 markets: the former as predicted under standard preferences, the latter the opposite of the theoretical prediction. Perceptions of fair priceselicited in the experiment-are a powerful driver of behavior. For buyers, fair prices correlate with priceresponsiveness, which varies systematically across treatments and impacts sellers' pricing incentives. For sellers, fair prices correlate with underpricing , which also varies systematically across treatments.

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Section: Relevant Literaturementioning

confidence: 99%

Pricing in Competitive Search Markets: The Roles of Price Information and Fairness Perceptions

Anbarcı

Feltovich

2018

Management Science

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“…Each buyer's valuation of the good is v (= 21.40 EUR in our experiment), each seller's cost is 0. Buyers and sellers may differ in the extent to which they (1) dislike an unequal distribution of the surplus from trade (see Franciosi et al 1995;Borck et al 2002;Cason et al 2011) and (2) internalize the externalities they generate by trading with each other. Suppose each buyer and seller has a type t distributed with full support on [0, 1] according to a cumulative distribution function G(t).…”

Section: Model and Hypothesesmentioning

confidence: 99%

Where to look for the morals in markets?

et al. 2019

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There is a heated debate on whether markets erode social responsibility and moral behavior. However, it is a challenging task to identify and measure moral behavior in markets. Based on a theoretical model, we examine in an experiment the relation between trading volume, prices and moral behavior by setting up markets that either impose a negative externality on third parties or not. We find that moral behavior reveals itself in lower trading volume in markets with a negative externality, while prices mostly depend on the market structure. We further investigate individual characteristics that explain trading behavior in markets with negative externalities.

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“…The Encryption task involves matching elements from one set to elements in another set according to a matching rule. This real-effort task is widely adopted in the literature (Cason, Gangadharan, and Nikiforakis, 2011;Charness, Masclet, and Villeval, 2014;Erkal, Gangadharan, and Koh, 2018).…”

Section: Manual Modementioning

confidence: 99%

The Economics of Babysitting a Robot

Alekseev

2020

SSRN Journal

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I study the welfare effect of automation on workers in a setting where technology is complementary but imperfect. Using a modified task-based framework, I argue that imperfect complementary automation can impose non-pecuniary costs on workers via a behavioral channel. The theoretical model suggests that a critical factor determining the welfare effect of imperfect complementary automation is the automatability of the production process. I confirm the model's predictions in an experiment that elicits subjects' revealed preference for automation. Increasing automatability leads to a significant increase in the demand for automation. I explore additional drivers of the demand for automation using machine learning analysis and textual analysis of choice reasons. The analysis reveals that task enjoyment, performance, and cognitive flexibility are the most important predictors of subjects' choices. There is significant heterogeneity in how subjects evaluate imperfect complementary automation. I discuss the implications of my results for workers' welfare, technology adoption, and inequality.

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Can real-effort investments inhibit the convergence of experimental markets?

Cited by 8 publications

References 26 publications

Pricing in Competitive Search Markets: The Roles of Price Information and Fairness Perceptions

Pricing in Competitive Search Markets: The Roles of Price Information and Fairness Perceptions

Where to look for the morals in markets?

The Economics of Babysitting a Robot

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