Explore the vast range of titles available.

The South African taxi industry has a negative image that is associated with violence and disrespect for its clients. This has led to it losing many of its clients to other industry players leading to more violence as taxi owners lose profits. The issue of communication as a possible solution to the challenges the taxi industry experiences, from the perspectives of the taxi drivers, has not been explored. Hence, this paper sought to gather the views of drivers on the role of improved communication to bringing about peace and good relations with passengers. A qualitative approach was adopted as a form of data collection in which 12 participants were interviewed in the eThekwini Metropolitan Municipality in South Africa. The participants were selected among the taxi drivers who were asked to share their communication challenges and strategies. The findings revealed that poor communication skills and language can be a barrier to communication between drivers and passengers and between taxi associations. Effective communication strategies can contribute to economic freedom and peace. A further finding from the study is that taxi drivers, acting on their own agency, can tackle communication challenges in the eThekwini taxi industry and contribute to change.

Technology has facilitated new, nontraditional work arrangements, including the ride-sharing company Uber. Uber drivers provide rides anytime they choose. Using data on hourly earnings and driving, we document driver utilization of this real-time flexibility. We propose that the value of flexibility can be measured as deriving from time variation in the drivers’ reservation wage. Measuring time variation in drivers’ reservation wages allows us to estimate the surplus and labor supply implications of Uber relative to alternative, less-flexible work arrangements. Despite other drawbacks to the Uber arrangement, we estimate that Uber drivers earnmore than twice the surplus they would in less-flexible arrangements.

In most cities, the taxi industry is highly regulated and has restricted entry. Ride sharing services, such as Uber and Lyft, which use mobile internet technology to connect passengers and drivers, have begun to compete with traditional taxis. This paper examines the efficiency of ride sharing services vis-a-vis taxis. In most cities with data available, UberX drivers spend a significantly higher fraction of their time, and drive a substantially higher share of miles, with a passenger in their car than do taxi drivers. Reasons for this efficiency advantage are explored.

Uber, the ride-sharing company launched in 2010, has grown at an exponential rate. Using both survey and administrative data, the authors provide the first comprehensive analysis of the labor market for Uber’s driver-partners. Drivers appear to be attracted to the Uber platform largely because of the flexibility it offers, the level of compensation, and the fact that earnings per hour do not vary much based on the number of hours worked. Uber’s driver-partners are more similar in terms of their age and education to the general workforce than to taxi drivers and chauffeurs. Most of Uber’s driver-partners had full- or part-time employment before joining Uber, and many continue in those positions after starting to drive with the Uber platform, which makes the flexibility to set their own hours especially valuable. Drivers often cite the desire to smooth fluctuations in their income as one of their reasons for partnering with Uber.

This paper provides field evidence on how reference points adjust, a degree of freedom in reference-dependence models. Examining this in the context of cabdrivers’ daily labor-supply behavior, we ask how the within-day timing of earnings affects decisions. Drivers work less in response to higher accumulated income, with a strong effect for recent earnings that gradually diminishes for earlier earnings. We estimate a structural model in which drivers work toward a reference point that adjusts to deviations from expected earnings with a lag. This dynamic view of reference dependence reconciles conflicting “neoclassical” and “behavioral” interpretations of evidence on daily labor-supply decisions.

I replicate and extend the seminal work of Camerer et al. (“Labor Supply of New York City Cabdrivers: One Day at a Time,” Quarterly Journal of Economics, 112 [1997], 407–441), who find that the wage elasticity of daily hours of work for New York City taxi drivers is negative and conclude that their labor supply behavior is consistent with reference dependence. In contrast, my analysis of the complete record of all trips taken in NYC taxi cabs from 2009 to 2013 shows that drivers tend to respond positively to unanticipated as well as anticipated increases in earnings opportunities. Additionally, using a discrete choice stopping model, the probability of a shift ending is strongly positively related to hours worked but at best weakly related to income earned. I find substantial heterogeneity across drivers in their elasticities, but the estimated elasticities are generally positive and rarely substantially negative. I find that new drivers with smaller elasticities are more likely to exit the industry, whereas drivers who remain quickly learn to be better optimizers (have positive labor supply elasticities that grow with experience). These results are consistent with the neoclassical optimizing model of labor supply and suggest that consideration of gain-loss utility and income reference dependence is not an important factor in the daily labor supply decisions of taxi drivers.

This paper presents a dynamic equilibrium model of a taxi market. The model is estimated using data from New York City yellow cabs. Two salient features by which most taxi markets deviate from the efficient market ideal are, first, matching frictions created by the need for both market sides to physically search for trading partners, and second, regulatory limitations to entry. To assess the importance of these features, we use the model to simulate the effect of changes in entry, alternative matching technologies, and different market density. We use the geographical features of the matching process to back out unobserved demand through a matching simulation. The matching function exhibits increasing returns to scale, which is important to understand the impact of changes in this market and has welfare implications. For instance, although alternative dispatch platforms can be more efficient than street-hailing, platform competition is harmful because it reduces effective density.

This article analyses the dynamic spatial equilibrium of taxicabs and shows how common taxi regulations lead to substantial inefficiencies as a result of search frictions and mis-allocation. To analyse the role of regulation on frictions and efficiency, I pose a dynamic model of spatial search and matching between taxis and passengers. Using a comprehensive dataset of New York City yellow medallion taxis, I use this model to compute the equilibrium spatial distribution of vacant taxis and estimate intraday demand given price and medallion regulations. My estimates show that the weekday New York market achieves about $5.7 million in daily welfare or about $27 per trip, but an additional 53 thousand customers fail to find cabs due to search frictions. Counterfactual analysis shows that implementing simple tariff pricing changes can enhance allocative efficiency and expand the market, offering daily consumer surplus gains of up to $227 thousand and up to 49 thousand additional daily taxi-passenger matches, a similar magnitude to the gain in matches generated by adopting a perfect static matching technology.

Rideshare drivers pay a proportion of their fares to a ride-hailing platform operator, a commission-based compensation model used by many service providers. To Uber drivers, this commission is known as the Uber fee. By contrast, traditional taxi drivers in most US cities make a fixed payment independent of their earnings, usually a weekly or daily medallion lease, keeping every fare dollar net of lease costs and other expenses. We assess these compensation models using an experiment that offered random samples of Boston Uber drivers opportunities to lease a virtual taxi medallion that eliminates the Uber fee. Some drivers were offered a negative fee. Drivers’ labor supply response to our offers reveals a large intertemporal substitution elasticity, on the order of 1.2, and higher for those who accept lease contracts. At the same time, our virtual lease program was undersubscribed: many drivers who would have benefited from buying an inexpensive lease chose to sit out. We use these results to compute the average compensation required to make drivers indifferent between rideshare and taxi-style compensation contracts. The results suggest that rideshare drivers gain considerably from the opportunity to drive without leasing.