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on Transport Economics |
| By: | Harrington, Deirdre; Hadjiconstantinou, Michelle |
| Abstract: | The UK Government restrictions on non-essential work in response to the COVID-19 pandemic has meant that millions of working aged-adults were forced into an unplanned change of lifestyle. We aim to present data on changes in planned commuting behaviour of public transport and car commuters and to describe the facilitators and barriers to switching commuting behaviours, with a specific focus on cycling and walking. An online survey queried individuals’ transport mode to/from work before becoming aware of the COVID-19 threat and their transport mode plans once UK Government restrictions are lifted. Free-form text responses were also collected on why they may switch to a sustainable mode of transport (e.g. walk, bicycle or bus) to work in the future and what would help/allow them to achieve this. Quantitative and qualitative data on those who commuted by car (single occupant) and public transport (bus/rail/park & ride) were analysed and presented separately. Overall, 725 car and public transport commuters responded; 72.4% were car commuters and 27.6% were public transport commuters before COVID-19. Of the car commuters, 81.9% plan to continue travelling by car once restrictions are lifted while 3.6% and 6.5% plan to change to walking and cycling, respectively. Of the public transport commuters, 49.0% plan to switch modes. From the free-form text responses three themes were identified: (a) perceived behavioural control towards cycling and walking (infrastructure and safety of roads, distance, weather) (b) key motivators to encourage a switch to cycling and walking (provision to support cycling, personal and environmental benefits, provision to support cycling); (c) the demands of current lifestyle (job requirements, family and lifestyle commitments). These UK data show how the COVID-19 pandemic has been an “external shock” causing some individuals to reassess their commuting mode. This provides an opportunity for theory-based behaviour change interventions tackling motivations, barriers and beliefs towards changing commute mode. |
| Date: | 2020–08–03 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:46hzd&r=all |
| By: | Fulton, Lewis; Compostella, Junia; Kothawala, Alimurtaza |
| Abstract: | UC Davis researchers have developed a cost model of travel choices that individuals make related to urban vehicle travel. These choices can include deciding to own, ride in, and drive a private vehicle or use pooled or solo ridesourcing (e.g., Uber). The model considers both monetary and non-monetary factors that affect travel choice. Monetary factors include the costs of purchasing, maintaining, and fueling different types of privately owned vehicles; and the cost of using ridesourcing services. Non-monetary (or “hedonic”) factors include travel time, parking time/inconvenience, willingness to drive or be a passenger in a driven or automated vehicle, and willingness to travel with strangers. The travel choices affected by these factors impact broader society through traffic congestion, pollution, greenhouse gas emissions, accidents, etc. and thus may be an important focus of policy. This report reviews recent literature, considers factors affecting travel choices, and reports, on a conjoint pilot survey or stated preferences. Finally, it considers approaches to apply time value to factors that are not typically associated with specific trips, such as time spent on vehicle maintenance and parking. The results should enable a deeper understanding of the likelihood that individuals will own and use private vehicles or use shared (solo and pooled) ridesourcing, and how automated vehicle services could affect these choices in the future. The study also highlights additional research needs, such as a large scale stated preference study covering more factors than have been included in previous studies. View the NCST Project Webpage |
| Keywords: | Social and Behavioral Sciences, Travel costs, value of time, mode choice, cost estimating, hedonic costs, non-monetary costs |
| Date: | 2020–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt8tc6v0b5&r=all |
| By: | Ioannou, Petros; Giuliano, Genevieve; Dessouky, Maged; Chen, Pengfei; Dexter, Sue |
| Abstract: | International trade continues to increase, with container trade growing at a 9.5% annual rate worldwide and at a 6% annual rate in the United States. Container ships are also getting bigger to meet this growing demand. As a result, cargo is concentrated into the largest ports, which intensifies bottlenecks on the road networks surrounding these ports. Thus, logistics companies are faced with increasing complexity in their operations and increasing traffic congestion that adds costs, as well as greenhouse gas emissions and local air pollution. The transition to zero emission truck technology could add further complexity, requiring companies to plan for electric trucks’ shorter ranges and longer refueling times. Researchers at the University of Southern California developed a centrally coordinated freight routing system and ran several simulations to minimize the social costs of freight transportation, also accounting for adoption of electric trucks. The researchers also interviewed several individuals with responsibility for trucking operations in the Los Angeles region to better understand the implementation issues of a centrally coordinated freight routing system. This policy brief summarizes the findings from that research and provides policy implications. View the NCST Project Webpage |
| Keywords: | Engineering, Alternate fuels, Electric vehicle charging, Electric vehicles, Fleet management, Freight traffic, Routing, Trucks, Zero emission vehicles |
| Date: | 2020–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt1m62h1dd&r=all |
| By: | Jiachen Ye; Peng Ji; Marc Barthelemy |
| Abstract: | The airline industry was severely hit by the COVID-19 crisis with an average demand decrease of about $64\%$ (IATA, April 2020) which triggered already several bankruptcies of airline companies all over the world. While the robustness of the world airline network (WAN) was mostly studied as an homogeneous network, we introduce a new tool for analyzing the impact of a company failure: the `airline company network' where two airlines are connected if they share at least one route segment. Using this tool, we observe that the failure of companies well connected with others has the largest impact on the connectivity of the WAN. We then explore how the global demand reduction affects airlines differently, and provide an analysis of different scenarios if its stays low and does not come back to its pre-crisis level. Using traffic data from the Official Aviation Guide (OAG) and simple assumptions about customer's airline choice strategies, we find that the local effective demand can be much lower than the average one, especially for companies that are not monopolistic and share their segments with larger companies. Even if the average demand comes back to $60\%$ of the total capacity, we find that between $46\%$ and $59\%$ of the companies could experience a reduction of more than $50\%$ of their traffic, depending on the type of competitive advantage that drives customer's airline choice. These results highlight how the complex competitive structure of the WAN weakens its robustness when facing such a large crisis. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2007.02109&r=all |
| By: | Börjesson, Maria (Swedish National Road & Transport Research Institute (VTI)); Bastian, Anne (City of Stockholm); Eliasson, Jonas (Linköping University) |
| Abstract: | This paper provides two micro-economic models that derive the social cost of a low emission zone (LEZ) for light vehicles. We apply the models to a proposed LEZ for light vehicles in Stockholm, which would prohibit diesel cars of Euro 5 or lower and gasoline cars of Euro 4 or lower in the inner city (25 km2 ). The first model is based on how an increase in user cost impacts traffic volumes in the inner city. This rather conventional user cost calculation of drivers’ loss requires however some strong assumptions. The second model shows that drivers’ losses can be calculated based on price changes observed on the used car market. Our empirical results indicate that the second model yields a twice as large welfare loss as the first. The forecasted benefits of the LEZ consist primarily of air quality improvements leading to health benefits. The empirical results must be interpreted with caution, but we find that the social benefit of air quality improvements is less than a tenth of the social cost. |
| Keywords: | Dieselgate; Low emission zones; Environmental zones; Cost-benefit analysis; Car market |
| JEL: | D61 H54 R41 R48 |
| Date: | 2020–08–28 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:vtiwps:2020_007&r=all |
| By: | Nilsson, Jan-Eric (Research Programme in Transport Economics); Odolinski, Kristofer (Research Programme in Transport Economics) |
| Abstract: | This paper provides empirical evidence on the optimal timing of rail infrastructure renewal. Using an econometric approach on data from the Swedish railway network, we establish a relationship between cumulative tonnes and maintenance costs, as well as between cumulative tonnes and infrastructure failures that cause train delays. Together with average values on delay hours per failure and assumptions on passengers per train, we perform example calculations on the optimal timing for a track renewal. This timing will depend on the case considered, such as whether traffic intensity is high or low. Empirical evidence on the relationship between line capacity utilisation and delay time can provide more robust estimates for the different cases considered by an infrastructure manager. Still, the results in this paper is a significant step towards a usable cost-benefit analysis model for the timing of rail infrastructure renewals. |
| Keywords: | Railway; Infrastructure; Optimization; Renewal; Maintenance; Train Delays |
| JEL: | H54 L92 R49 |
| Date: | 2020–08–28 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:trnspr:2020_004&r=all |
| By: | Isaac, Raphael S |
| Abstract: | The last century brought a shift in rail propulsion from the (typically) coal-powered steam engine to a combination of the diesel-electric locomotive and the electrified locomotive running under electrified overhead lines. While, no doubt, an advance over the earlier technology, the two incumbent technologies are not without their shortcomings. In the current era, rapid technological developments and increased concerns about climate change have also spurred interest away from the internal combustion engine and the use of fossil fuels in various applications. These same technologies hold promise in a rail context, a mode of transportation that relies on a smaller number of more centralized operators. With the tremendous investment of time, cost, and other resources that can go into a pilot experiment of a fuel technology and, often, related regulatory processes, it makes sense to determine the key candidates for such pilots. A major goal of this work is to help industry and government narrow down the key technologies, in terms of cost, viability, and environmental impacts, and simultaneously identify the challenges that may be encountered by a given technology that otherwise appears to hold significant promise. This study focuses on a U.S. context, and on the period between 2022 and 2038. Passenger and freight rail routes and systems were examined, each with different characteristics, via simulations of a single rail trip, A general environmental analysis was also performed on freight switcher locomotive activity. The fuels examined included diesel, natural gas, Fischer-Tropsch diesel, hydrogen, and, in a passenger rail and switcher context, diesel and hydrogen powertrains paired with batteries to take in regenerative braking energy. The study finds cost reductions with both natural gas and (natural gas-derived) Fischer-Tropsch diesel, but with limited environmental benefits. Hydrogen via fuel cell has significant promise to reduce GHG and criteria pollutant emissions. That technology’s costs, both fuel and equipment, are highly uncertain; however, the study finds that, with lower bound projected costs, it could be competitive with diesel-electric costs; in the case of passenger rail, hybridization with batteries is also compelling. Hybridized hydrogen also was found to demonstrate a clear environmental benefit in switcher locomotive applications. |
| Keywords: | Engineering, Physical Sciences and Mathematics, fuel, passenger rail, freight rail, hydrogen |
| Date: | 2020–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt3wt0n8tx&r=all |
| By: | John Rocha (University of the Incarnate Word, USA); Kruti Lehenbauer (University of the Incarnate Word, USA) |
| Abstract: | A transportation revolution occurred forty years ago with the deregulation of the industry, particularly in the United States. With the deregulation complete, the transportation industry has been slow in developing a total customer satisfaction environment particularly in terms of the industry-wide total-customer package. The bulk of the transportation service offered in the U.S. has not exceeded third-party logistics. Since our barriers for entry are low and at times non-existent, what roadblocks hamper U.S. transportation companies from developing services that its global competitors already offer its customers? The end game of supply chain logistics is to augment customer value - Bowersox, Closs, Stank, 2000. This paper aims to identify what obstacles have prevented transportation companies from transforming into fifth party logistics (5PL) providers who can ensure optimum customer service by resolving fundamental logistical problems efficiently. The transformation into 4PL or 5PL can help current 3PL transportation and other companies to provide maximum benefits to customers by resolving complex supply chain issues, improving warehouse technology, increasing efficiency in transit times and creating a seamless process through the use of information technology. |
| Keywords: | Supply Chain Management, Logistics, 3PL, 5PL, Transportation, Evolving logistics systems |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:smo:kpaper:0017jrk&r=all |
| By: | Selod,Harris; Soumahoro,Souleymane |
| Abstract: | This paper reviews the emerging big data literature applied to urban transportation issues from the perspective of economic research. It provides a typology of big data sources relevant to transportation analyses and describes how these data can be used to measure mobility, associated externalities, and welfare impacts. As an application, it showcases the use of daily traffic conditions data in various developed and developing country cities to estimate the causal impact of stay-at-home orders during the Covid-19 pandemic on traffic congestion in Bogotá, New Dehli, New York, and Paris. In light of the advances in big data analytics, the paper concludes with a discussion on policy opportunities and challenges. |
| Date: | 2020–06–30 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wbrwps:9308&r=all |
| By: | Seeley, Brien A. MD.; Rakas, Jasenka PhD |
| Abstract: | UC Berkeley has long been known as the home of important societal movements. In early October 2019, the electric aircraft movement came to UC Berkeley (UCB) courtesy of UCB’s Institute for Transportation Studies (ITS) and the College of Engineering. At what some have called the “Woodstock of Aviation”—the Sustainable Aviation Symposium (SAS) convened leaders of that movement from across the globe for two full days in UC’s Pauley Ballroom to explore how to solve important societal-enviro-economic issues in transportation with breakthroughs and innovations in high-tech physics, chemistry and electrical engineering. Beyond Science, Technology, Engineering, Art and Mathematics (STEAM) topics, the faculty presentations spanned a broad spectrum of UC’s graduate and undergraduate curriculum and included those by prominent UC faculty members, professors from other universities, leaders from NASA as well as several by experts in private industry. SAS 2019 was unique among conferences in focusing on how the future driverless, emission-free sky taxis of urban air mobility (UAM) could affordably transform transportation and neighborhoods at scale in metro regions and beyond. The socio-enviro-economic prospects for that transformation’s potential for regional mass transit by air that could ease surface gridlock, untenable infrastructure costs and climate change, showed why SAS 2019 engaged for the first time the disciplines of urban and environmental planning and civil engineering. SAS 2019 resulted in a growing awareness of the pan-topic relevance of UAM and justified both the continuation of SAS at UC Berkeley as well as further activities of the Aviation Futures Lab at UCB. |
| Keywords: | Engineering |
| Date: | 2020–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsrrp:qt4t76186k&r=all |
| By: | Remi Jedwab; Adam Storeygard |
| Abstract: | Previous work on transportation investments has focused on average impacts in high- and middle-income countries. We estimate average and heterogeneous effects in a poor continent, Africa, using roads and cities data spanning 50 years in 39 countries. Using changes in market access due to distant road construction as a source of exogenous variation, we estimate a 30-year elasticity of city population with respect to market access of about 0.08-0.13. Our results suggest that this elasticity is stronger for small and remote cities, and weaker in politically favored and agriculturally suitable areas. Access to foreign cities besides international ports matters little. Additional evidence points suggestively to rural-urban migration as the primary source of this population increase, though we cannot fully rule out natural increase or reallocation across cities. |
| JEL: | F15 F16 O18 O20 R11 R12 R4 |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27670&r=all |
| By: | Prateek Bansal; Daniel H\"orcher; Daniel J. Graham |
| Abstract: | Crowding valuation of subway riders is an important input to various supply-side decisions of transit operators. The crowding cost perceived by a transit rider is generally estimated by capturing the trade-off that the rider makes between crowding and travel time while choosing a route. However, existing studies rely on static compensatory choice models and fail to account for inertia and the learning behaviour of riders. To address these challenges, we propose a new dynamic latent class model (DLCM) which (i) assigns riders to latent compensatory and inertia/habit classes based on different decision rules, (ii) enables transitions between these classes over time, and (iii) adopts instance-based learning theory to account for the learning behaviour of riders. We use the expectation-maximisation algorithm to estimate DLCM, and the most probable sequence of latent classes for each rider is retrieved using the Viterbi algorithm. The proposed DLCM can be applied in any choice context to capture the dynamics of decision rules used by a decision-maker. We demonstrate its practical advantages in estimating the crowding valuation of an Asian metro's riders. To calibrate the model, we recover the daily route preferences and in-vehicle crowding experiences of regular metro riders using a two-month-long smart card and vehicle location data. The results indicate that the average rider follows the compensatory rule on only 25.5% of route choice occasions. DLCM estimates also show an increase of 47% in metro riders' valuation of travel time under extremely crowded conditions relative to that under uncrowded conditions. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2007.03682&r=all |
| By: | Hercules Haralambides (Dalian Maritime University); Olaf Merk (International Transport Forum) |
| Abstract: | This paper analyses the potential impacts on global trade initiated by the Belt and Road Initiative. The Initiative is examined as a collection of planned transport-corridor developments and discusses their impact on maritime trade flows. |
| Date: | 2020–06–16 |
| URL: | http://d.repec.org/n?u=RePEc:oec:itfaab:2020/02-en&r=all |
| By: | Lubell, Mark; Pia Vantaggiato, Francesca |
| Abstract: | Many of California’s critical transportation infrastructure assets along the coast are vulnerable to sea level rise (Figure 1). Climate adaptation generally and sea level rise adaption specifically entail land-use and transportation decisions that affect multiple jurisdictional levels. These decisions involve many stakeholders, including local, regional, county, state and federal agencies, non-governmental organizations, and individual citizens. Adapting transportation infrastructure to sea level rise requires collaboration among these actors. This is a challenging task given that different agencies and stakeholders have different mandates and priorities, which imply different ways of looking at the common issue of adaptation to sea level rise. Researchers at the University of California, Davis examined four case studies of governance processes formed around transportation assets threatened by sea level rise: a state highway along the San Francisco Bay, a coastal highway and railroad in San Diego County, and the Port of Long Beach. The researchers interviewed stakeholders, consulted policy documents, and organized a workshop with agency stakeholders to identify lessons learned and develop practical suggestions for facilitating collaboration to address sea level rise. |
| Keywords: | Engineering |
| Date: | 2020–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt4sb537gw&r=all |
| By: | Hung-Hao Chang; Chad Meyerhoefer; Feng-An Yang |
| Abstract: | Recent studies demonstrate that air quality improved during the coronavirus pandemic due to the imposition of social lockdowns. We investigate the impact of COVID-19 on air pollution in the two largest cities in Taiwan, which were not subject to economic or mobility restrictions. Using a generalized difference-in-differences approach and real-time data on air quality and transportation, we estimate that levels of sulfur dioxide, nitrogen dioxide and particulate matter increased 5 - 12 percent relative to 2017 - 2019. We demonstrate that this counterintuitive finding is likely due to a shift in preferences for mode of transport away from public transportation and towards personal automobiles. Similar COVID-19 prevention behaviors in regions or countries emerging from lockdowns could likewise result in an increase in air pollution. |
| JEL: | I12 Q53 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27604&r=all |
| By: | Ciro Biderman (Fundação Getúlio Vargas) |
| Abstract: | This paper explores to what extent a road-use charge levied from transport network companies for their ridesharing services can mitigate negative impacts of ridesharing. This approach is being applied in the city of São Paulo in Brazil. |
| Date: | 2020–07–31 |
| URL: | http://d.repec.org/n?u=RePEc:oec:itfaab:2020/06-en&r=all |
| By: | Youssef M. Aboutaleb; Moshe Ben-Akiva; Patrick Jaillet |
| Abstract: | This paper introduces a new data-driven methodology for nested logit structure discovery. Nested logit models allow the modeling of positive correlations between the error terms of the utility specifications of the different alternatives in a discrete choice scenario through the specification of a nesting structure. Current nested logit model estimation practices require an a priori specification of a nesting structure by the modeler. In this we work we optimize over all possible specifications of the nested logit model that are consistent with rational utility maximization. We formulate the problem of learning an optimal nesting structure from the data as a mixed integer nonlinear programming (MINLP) optimization problem and solve it using a variant of the linear outer approximation algorithm. We exploit the tree structure of the problem and utilize the latest advances in integer optimization to bring practical tractability to the optimization problem we introduce. We demonstrate the ability of our algorithm to correctly recover the true nesting structure from synthetic data in a Monte Carlo experiment. In an empirical illustration using a stated preference survey on modes of transportation in the U.S. state of Massachusetts, we use our algorithm to obtain an optimal nesting tree representing the correlations between the unobserved effects of the different travel mode choices. We provide our implementation as a customizable and open-source code base written in the Julia programming language. |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2008.08048&r=all |
| By: | Valerie A. Ramey |
| Abstract: | Can greater investment in infrastructure raise U.S. long-run output? Are infrastructure projects a good short-run stimulus to the economy? This paper uses insights from the macroeconomics literature to address these questions. I begin by analyzing the effects of government investment in both a stylized neoclassical model and a medium-scale New Keynesian model, highlighting the economic mechanisms that govern the strength of the short-run and long-run impacts. The analysis confirms earlier findings that the implementation delays inherent in infrastructure projects reduce short-run multipliers in most cases. In contrast, long-run multipliers can be sizable when government capital is productive. Moreover, these multipliers are greater if the economy starts from a point below the socially optimal amount of public capital. Turning to empirical estimation, I use the theoretical model to explain the econometric challenges to estimating the elasticity of output to public infrastructure. Using both artificial data generated by simulations of the model and extensions of existing empirical work, I demonstrate how both general equilibrium effects and optimal choice of public capital are likely to impart upward biases to output elasticity estimates. Finally, I review and extend some empirical estimates of the short-run effects, focusing on infrastructure spending in the ARRA. |
| JEL: | E62 H41 H54 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27625&r=all |
| By: | Lee, Amy |
| Abstract: | The UC Davis Campus Travel Survey is an annual survey led by Transportation and Parking Services (TAPS) and the National Center for Sustainable Transportation, part of the Institute of Transportation Studies at UC Davis. It collects a rich set of data about travel to the UC Davis campus, demographics, and attitudes toward travel. The 2019- survey collected data from 3,098 people affiliated with UC Davis about their travel to campus during a single week in October 2019. It used a stratified random sampling method with the intent to gather a representative sample of the campus population. About 18 percent of those invited responded to this year’s survey. For the statistics presented throughout this report, we weight the responses by campus role (freshman, sophomore, junior, senior, Master’s, PhD, faculty, and staff) and gender so that the proportion of respondents in each group reflects their proportion in the campus population. |
| Keywords: | Social and Behavioral Sciences |
| Date: | 2020–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt9929r4j1&r=all |
| By: | Jan Wessel (Institute of Transport Economics, Muenster) |
| Abstract: | Although several papers have shown that bike ridership is affected by actual weather conditions, this is the first study to comprehensively investigate the impact of forecasted weather conditions on bike ridership. The results show that both actual and forecasted weather conditions can be used as useful explanatory variables for predicting bicycle usage. Even incorrect weather forecasts can impact on bike ridership, which underlines the importance of weather forecast effects for traffic planners; for example, forecasted rain can reduce bike traffic by 3.6% in periods that turn out to be rain-free. Additionally, a digital image-processing method is used to calculate the darkness of the cloud coverage displayed on weather forecast maps. The results imply that bike ridership is significantly smaller in regions with darker forecasted clouds. It is also shown that weather forecasts have a stronger impact on recreational bike traffic than on utilitarian traffic. Furthermore, various lagging and leading effects of rain forecasts are outlined. Morning rain forecasts can, for example, reduce bike ridership in midday and afternoon hours that were predicted to be rain-free. To derive these results, hourly bicycle counts from 188 automated counting stations in Germany are collected for the years 2017 and 2018. They are linked to actual weather data from Germany's National Meteorological Service and with historical weather forecasts that are deduced from weather maps of Germany's most-watched television news program. Log-linear and negative binomial regression models are then used to estimate the weather forecast effects. |
| Keywords: | Cycling, bike ridership, automated counting stations, weather conditions, weather forecasts, image processing |
| JEL: | R49 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:mut:wpaper:32&r=all |
| By: | Stephanie Assad; Robert Clark (Queen's University); Daniel Ershov; Lei Xu |
| Abstract: | Economic theory provides ambiguous and conflicting predictions about the association between algorithmic pricing and competition. In this paper we provide the first empirical analysis of this relationship. We study Germany's retail gasoline market where algorithmic-pricing software became widely available by mid-2017, and for which we have access to comprehensive, high-frequency price data. Because adoption dates are unknown, we identify gas stations that adopt algorithmic-pricing software by testing for structural breaks in markers associated with algorithmic pricing. We nd a large number of station-level structural breaks around the suspected time of large-scale adoption. Using this information we investigate the impact of adoption on outcomes linked to competition. Because station-level adoption is endogenous, we use brand headquarter-level adoption decisions as instruments. Our IV results show that adoption increases margins by 9%, but only in non-monopoly markets. Restricting attention to duopoly markets, we find that market-level margins do not change when only one of the two stations adopts, but increase by 28% in markets where both do. These results suggest that AI adoption has a significant effect on competition. |
| Keywords: | Artificial Intelligence, Pricing-Algorithms, Collusion, Retail Gasoline |
| JEL: | L41 L13 D43 D83 L71 |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:qed:wpaper:1438&r=all |