The Value of Time: Evidence from Auctioned Cab Rides

Buchholz, Nicholas; Doval, Laura; Kastl, Jakub; Matějka, Filip; Salz, Tobias

doi:10.2139/ssrn.3592164

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Our estimates are larger than recent private travel elasticities from the United States gasoline market, which are larger than had been found in prior studies with aggregate data and cross-sectional designs Levin et al (2017). They are also larger than those found in the United States taxi market (Rose and Hensher, 2014) However, they are consistent with recent estimates from ride-hail services in Prague (Buchholz et al, 2020). Our estimates may differ with the earlier literature for several potential reasons: (1) Prior studies have typically examined the effects short-run price changes; (2) Whereas prior studies have typically focused on transport markets with higher-quality substitutes, this study specifically focuses on a transit-constrained city; (3) The large price changes examined in this study may induce significant substitution from lower quality substitutes.…”

Section: Price Elasticity Of Demandsupporting

confidence: 82%

The Demand for Mobility: Evidence from an Experiment with Uber Riders

Christensen

Osman

2023

SSRN Journal

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Section: Price Elasticity Of Demandsupporting

confidence: 82%

The Demand for Mobility: Evidence from an Experiment with Uber Riders

Christensen

Osman

2023

SSRN Journal

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“…Our paper contributes to the literature that attempts to estimate the V OT , which mostly looks at reductions in travel or waiting time during commuting, and which, so far, relies exclusively on stated or revealed preferences (Deacon and Sonstelie, 1985;Calfee and Winston, 1998;Lam and Small, 2001;Small et al, 2005;Fezzi et al, 2014;Wolff, 2014;Buchholz et al, 2020;Goldszmidt et al, 2020). Our paper adds an alternative method, which also allows us to move beyond existing studies that lack data on what people are actually doing beyond a narrow domain such as commuting.…”

Section: Introductionmentioning

confidence: 99%

“…The second relies on revealed preferences and consists of observational studies, e.g. observing road choices with different travel times and tolls, or choices of potential ride shares with different waiting times and prices (Lam and Small, 2001;Small et al, 2005;Fezzi et al, 2014;Buchholz et al, 2020); quasinatural experiments, e.g. studies that exploit exogenous variations in gas prices across geographical areas and record the willingness to queue longer for a lower price, or that look at speeding (Deacon and Sonstelie, 1985;Wolff, 2014); or natural field experiments, e.g.…”

Section: Introductionmentioning

confidence: 99%

Back to Edgeworth? Estimating the Value of Time Using Hedonic Experiences

Krekel

MacKerron

2023

SSRN Journal

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“…Ride-hailing platforms are the matching place for passengers (demand side) and drivers (supply side). Part of the literature focuses on the demand side of the market and studies issues such as demand elasticity and consumer surplus (Cohen et al, 2016;Moradi et al, 2020) or characterizing and forecasting short-term demand (Saadi et al, 2017;Ke et al, 2017;Buchholz et al, 2020). In contrast, another part of the literature pertains to the supply side of the market.…”

Section: Introductionmentioning

confidence: 99%

Accept or reject a ride? This is the problem

Pour

Madanizadeh

Joshaghani

2023

JES

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PurposeOnline ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.Design/methodology/approachThe authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.FindingsThe authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.Originality/valueTo answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

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The Value of Time: Evidence from Auctioned Cab Rides

Cited by 6 publications

References 18 publications

The Demand for Mobility: Evidence from an Experiment with Uber Riders

The Demand for Mobility: Evidence from an Experiment with Uber Riders

Back to Edgeworth? Estimating the Value of Time Using Hedonic Experiences

Accept or reject a ride? This is the problem

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