How many cents on the dollar? Women and men in product markets

Kricheli‐Katz, Tamar; Regev, Tali

doi:10.1126/sciadv.1500599

Cited by 34 publications

(16 citation statements)

References 15 publications

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“…On Airbnb, black hosts charged less than non-black hosts for the equivalent rental [10]. On eBay, women sellers received a smaller number of bids and lower prices for the same product [19]. On TaskRabbit, workers' gender and race were correlated with their evaluations such as reviews and ratings [15].…”

Section: Analysis Of Service Providersmentioning

confidence: 99%

“…We include four variables characterizing the demographic features of a CT. They are: (1) total population (popu), which accounts for the effect that populous CTs tend to have more hosts by pure chance, (2) fraction of residents whose age is between 20 and 34 (younд), (3) fraction of non-white residents (nonwhite), which is included based on previous results showing discrimination in online markets [10,15,19], and (4) fraction of foreign-born residents (f oreiдn).…”

Section: Independent Variablesmentioning

confidence: 99%

“…Understanding service providers in the sharing economy has interested researchers across many disciplines. A great effort has been put into the analysis of demographics, such as age, gender, and race [14,18], as well as the demonstration of the existence of a gender gap [19] and of gender and racial bias and discrimination in online marketplaces [10,15]. Despite these efforts, our understanding is still limited by the lack of empirical and large-scale analysis on the socialeconomic status aspect.…”

Section: Introductionmentioning

confidence: 99%

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Service Providers of the Sharing Economy

2017

Proc. ACM Hum.-Comput. Interact.

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Many "sharing economy" platforms, such as Uber and Airbnb, have become increasingly popular, providing consumers with more choices and suppliers a chance to make profit. They, however, have also brought about emerging issues regarding regulation, tax obligation, and impact on urban environment, and have generated heated debates from various interest groups. Empirical studies regarding these issues are limited, partly due to the unavailability of relevant data. Here we aim to understand service providers of the sharing economy, investigating who joins and who benefits, using the Airbnb market in the United States as a case study. We link more than 211 thousand Airbnb listings owned by 188 thousand hosts with demographic, socio-economic status (SES), housing, and tourism characteristics. We show that income and education are consistently the two most influential factors that are linked to the joining of Airbnb, regardless of the form of participation or year. Areas with lower median household income, or higher fraction of residents who have Bachelor's and higher degrees, tend to have more hosts. However, when considering the performance of listings, as measured by number of newly received reviews, we find that income has a positive effect for entire-home listings; listings located in areas with higher median household income tend to have more new reviews. Our findings demonstrate empirically that the disadvantage of SES-disadvantaged areas and the advantage of SES-advantaged areas may be present in the sharing economy.

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Section: Analysis Of Service Providersmentioning

confidence: 99%

Section: Independent Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Service Providers of the Sharing Economy

2017

Proc. ACM Hum.-Comput. Interact.

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“…В литературе известны и другие примеры различий в ценах в зависимости от пола продавца или покупателя. Так, женщины, выставлявшие на продажу товары на аук-ционе eBay в США в 2009-2012 гг., по-лучали меньше запросов и менее низкую окончательную цену, чем мужчины, кото-рые продавали точно такой же товар [Kricheli-Katz, Regev, 2016]. Подобные раз-личия авторы исследования объясня ют половой дискриминацией: товары, про да-ваемые женщинами, психологически вос-принимаются как менее ценные, чем то-вары, продаваемые мужчинами.…”

Section: результаты регрессионного анализаunclassified

Price Discrimination In New Apartments Market In Russia

Storchevoy¹,

Kholodilin²,

Sorokin³

et al. 2016

RMJ

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“…The SIMEX correction increases the size of main effects and interaction effects of E(IRR). 20 For Model (2), the gender difference in the importance of E(IRR) is so strong that the coefficient of women's E(IRR) is only marginally significant and vanishes if measurement error controls are omitted. In other words, the application's predictable quality is a much better indicator of the funding probability for men than for women.…”

Section: Hypothesis 1 -Level Effectmentioning

confidence: 99%

Measuring Applicant Quality to Detect Discrimination in Peer-to-Peer Lending

Weizsäcker

Zankiewicz

2017

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. ❆❜str❛❝t Terms of use: Documents in EconStor mayWe measure the quality of applications for online peer-to-peer lending in Germany and relate it to gender discrimination. The data context allows summarizing application quality as a single numeric measure, the expected internal rate of return. The measure serves as a control variable and is interacted with the applicants' gender. We find that women enjoy higher funding rates than men, mainly because they are less punished when they offer a low application quality. The evidence is consistent with the hypothesis that the predominantly male lenders have a less precise understanding of women's applications than of men's applications.JEL-Classification: D14, D84

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How many cents on the dollar? Women and men in product markets

Abstract: Women sellers receive about 80 cents for every dollar a man receives when selling identical new products on eBay.

Cited by 34 publications

References 15 publications

Service Providers of the Sharing Economy

Service Providers of the Sharing Economy

Price Discrimination In New Apartments Market In Russia

Measuring Applicant Quality to Detect Discrimination in Peer-to-Peer Lending

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