The Value of Time in the United States: Estimates from Nationwide Natural Field Experiments

Abstract

The value of time determines relative prices of goods and services, investments, productivity, economic growth, and measurements of income inequality. Economists in the 1960s began to focus on the value of non-work time, pioneering a deep literature exploring the optimal allocation and value of time. By leveraging key features of these classic time allocation theories, we use a novel approach to estimate the value of time (VOT) via two large-scale natural field experiments with the ridesharing company Lyft. We use random variation in both wait times and prices to estimate a consumer's VOT with a data set of more than 14 million observations across consumers in U.S. cities. We find that the VOT is roughly $19 per hour (or 75% (100%) of the after-tax mean (median) wage rate) and varies predictably with choice circumstances correlated with the opportunity cost of wait time. Our VOT estimate is larger than what is currently used by the U.S. Government, suggesting that society is under-valuing time improvements and subsequently under-investing public resources in time-saving infrastructure projects and technologies.

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

“…due to investments into time-saving infrastructure). Our estimate is similar to Goldszmidt et al (2020) who in field experiments experimentally manipulate bundles of waiting times and prices offered to users of the Lyft ride-sharing app, estimating the V OT for reductions in waiting time during commuting to be $19 in the US in 2020. This suggests that using hedonic experiences leads to similar (though not identical) results as observed behaviour.…”

“…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.…”

“…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. studies that experimentally manipulate bundles of waiting times and prices offered to users of ride-sharing apps (Goldszmidt et al, 2020). While stated-preference studies typically estimate V OT s to be less than 25% of the mean wage rate, observed behaviour often reveals much higher values, typically in excess of 75%.…”

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

“…Our paper also builds on a growing set of economic studies of the impacts of ridehailing markets (Goldszmidt et al, 2020, Alvarez and Argente, 2020, Leard and Xing, 2020, Young and Farber, 2019, Castillo, 2019, Moskatel and Slusky, 2019, Hall et al, 2018, Cohen et al, 2016. Thus far, the ride-hailing literature has relied heavily upon observational or stated-preferences methods.…”