Search Frictions and Efficiency in Decentralized Transportation Markets

Brancaccio, Giulia; Kalouptsidi, Myrto; Papageorgiou, Theodore; Rosaia, Nicola

doi:10.3386/w27300

“…The empirical literature on the spatial match between supply and demand is new and small. To our knowledge, Buchholz (2018); Brancaccio et al (2019c) are the only papers directly examining this issue, and papers such as Frechette et al (2019); Brancaccio et al (2019a,b) look at related problems. They extend the empirical techniques in the matching literature (see Petrongolo and Pissarides (2001) for a survey) in order to structurally infer the size of unobserved demand (e.g., passengers searching for rides) in different locations of a decentralized-matching market, when only the size of supply (e.g., available drivers) and the number of demand-supply matches (e.g., realized rides) are observed.…”

Section: Related Literaturementioning

confidence: 99%

“…2018) focuses on how drivers' decisions are impacted by the inter-temporal, intra-daily, externalities from rides given by other drivers Brancaccio et al (2019c). study the inefficiency arising from transportation of goods/passengers to locations from which the car/ship would likely need to return vacant.…”

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confidence: 99%

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Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ride Sharing

Ghili

¹

,

Kumar

²

2020

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This paper develops a strategy with simple implementation and limited data requirements to identify spatial distortion of supply from demand-or, equivalently, unequal access to supply among regions-in transportation markets. We apply our method to ride-level, multi-platform data from New York City (NYC) and show that for smaller rideshare platforms, supply tends to be disproportionately concentrated in more densely populated areas. We also develop a theoretical model to argue that a smaller platform size, all else being equal, distorts the supply of drivers toward more densely populated areas due to network effects. Motivated by this, we estimate a minimum required platform size to avoid geographical supply distortions, which informs the current policy debate in NYC around whether ridesharing platforms should be downsized. We find the minimum required size to be approximately 3.5M rides/month for NYC, implying that downsizing Lyft or Via-but not Uber-can increase geographical inequity.

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“…Our empirical application using US data from 2001 through 2017 finds that aggregate search effort has 6 Hosios (1990) focuses on markets with homogeneous agents. However the same condition is necessary (but not sufficient) in the case of (one-sided) heterogeneity (see Brancaccio et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

Beyond Cobb-Douglas: Flexibly Estimating Matching Functions with Unobserved Matching Efficiency

Lange

¹

,

Papageorgiou

²

2020

Self Cite

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Exploiting results from the literature on non-parametric identification, we make three methodological contributions to the empirical literature estimating the matching function, commonly used to map unemployment and vacancies into hires. First, we show how to non-parametrically identify the matching function. Second, we estimate the matching function allowing for unobserved matching efficacy, without imposing the usual independence assumption between matching efficiency and search on either side of the labor market. Third, we allow for multiple types of jobseekers and consider an "augmented" Beveridge curve that includes them. Our estimated elasticity of hires with respect to vacancies is procyclical and varies between 0.15 and 0.3. This is substantially lower than common estimates suggesting that a significant bias stems from the commonly-used independence assumption. Moreover, variation in match efficiency accounts for much of the decline in hires during the Great Recession.

show abstract

“…Our empirical application using US data from 2001 through 2017 finds that aggregate search effort has 6 Hosios (1990) focuses on markets with homogeneous agents. However the same condition is necessary (but not sufficient) in the case of (one-sided) heterogeneity (see Brancaccio et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

Beyond Cobb-Douglas: Flexibly Estimating Matching Functions with Unobserved Matching Efficiency

Lange

¹

,

Papageorgiou

²

2020

Self Cite

View full text Add to dashboard Cite

Exploiting results from the literature on non-parametric identification, we make three methodological contributions to the empirical literature estimating the matching function, commonly used to map unemployment and vacancies into hires. First, we show how to non-parametrically identify the matching function. Second, we estimate the matching function allowing for unobserved matching efficacy, without imposing the usual independence assumption between matching efficiency and search on either side of the labor market. Third, we allow for multiple types of jobseekers and consider an "augmented" Beveridge curve that includes them. Our estimated elasticity of hires with respect to vacancies is procyclical and varies between 0.15 and 0.3. This is substantially lower than common estimates suggesting that a significant bias stems from the commonly-used independence assumption. Moreover, variation in match efficiency accounts for much of the decline in hires during the Great Recession.

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Search Frictions and Efficiency in Decentralized Transportation Markets

Abstract: NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Cited by 13 publications

References 6 publications

Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ride Sharing

Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ride Sharing

Beyond Cobb-Douglas: Flexibly Estimating Matching Functions with Unobserved Matching Efficiency

Beyond Cobb-Douglas: Flexibly Estimating Matching Functions with Unobserved Matching Efficiency

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