nep-tre New Economics Papers
on Transport Economics
Issue of 2016‒09‒04
five papers chosen by
Erik Teodoor Verhoef
Vrije Universiteit Amsterdam

  1. Cooperative liner shipping network design by means of a combinatorial auction By Buer, Tobias; Haass, Rasmus
  2. The Gothenburg congestion charges: CBA and equity By West, Jens; Börjesson, Maria
  3. The Young and the Carless? The Demographics of New Vehicle Purchases By Christopher J. Kurz; Geng Li; Daniel J. Vine
  4. The Regularity and Irregularity of Travel: an Analysis of the Consistency of Travel Times Associated with Subsistence, Maintenance and Discretionary Activities By Thomas Longden
  5. Competition in Swedish passenger railway : entry in an open-access market By Vigren, Andreas

  1. By: Buer, Tobias; Haass, Rasmus
    Abstract: Cooperation in the ocean liner shipping industry has always been important to improve liner shipping networks (LSN's). As tight cooperations like alliances are challenged by antitrust laws, looser forms of cooperation among liner carriers might become a reasonable way to increase efficiency of LSN's. Our goal is to facilitate a loose form of cooperation among liner carriers. Therefore, we introduce a coordination mechanism for designing a collaborative LSN based on a multi round combinatorial auction. Via the auction, carriers exchange demand triplets, i.e. orders which describe the transport of containers between ports. A standard network design problem which includes ship scheduling and cargo routing decisions is used as isolated network design problem of an individual carrier. A carrier has to solve this isolated problem repeatedly during the auction so that the carrier is able to decide which demand triplets to sell, on which demand triplets to bid, and what prices to charge. To solve these problems we propose a variable neighborhood search based matheuristic. The matheuristic addresses the isolated planning problem in four phases (construct ship cycles, modify cycles, determine container flow, and reallocate ships to cycles). Our computational experiments on a set of 56 synthetic test instances suggest that the introduced combinatorial auction increases profits on average compared to isolated planning significantly by four percent. The more diverse the original assignment of demand triplets and ships to carriers is, the higher the potential for collaboration; for 18 diverse instances, the profits increase on average by ten percent.
    Keywords: liner shipping,network design,combinatorial auction,bundle bidding,collaborative planning,variable neighborhood search
    Date: 2016
    URL: http://d.repec.org/n?u=RePEc:zbw:bclgwp:6&r=tre
  2. By: West, Jens (KTH); Börjesson, Maria (KTH)
    Abstract: This paper performs an ex-post cost- benefit and equity analysis of the Gothenburg congestion charges introduced in 2013. We base the analysis on observed effects transport model that is able to predict the effect of the charges on travel times and traffic volumes with high accuracy. We find that the net social benefit of the charge is positive. However, we also show that the system is regressive. Low income citizens pay a larger share of their income for three reasons. First, all income classes are highly car dependent in Gothenburg, due to the relatively low public transport share. Second, workers in the highest income class have considerably higher access to company cars, and are therefore either exempt from paying the charge, or can deduce the charge from their income tax. Third, high income individuals have higher values of time. Moreover, the revenue is spent mainly on a rail tunnel, which primarily benefits commuters residing far out in the region.
    Keywords: Congestion charges; Cost-benefit analysis; Welfare analysis; Equity; Transport policy; Decision support
    JEL: R41 R42 R48
    Date: 2016–08–29
    URL: http://d.repec.org/n?u=RePEc:hhs:ctswps:2016_017&r=tre
  3. By: Christopher J. Kurz; Geng Li; Daniel J. Vine
    Abstract: FEDS Notes Print{{p}}June 24, 2016{{p}}The Young and the Carless? The Demographics of New Vehicle Purchases{{p}}Christopher Kurz, Geng Li, and Daniel Vine{{p}}U.S. sales of new light vehicles have rebounded strongly since the end of the 2007-09 recession and are considered one of the bright{{p}}spots of the recovery. Indeed, sales totaled 17.4 million units in 2015, about the same rate as the all-time record set in 2000 (thin black{{p}}line in figure 1). Personal vehicle sales, which exclude sales to businesses and governments, have also rebounded strongly since the end{{p}}of the recession (thick blue line in figure 1).{{p}}As{{p}}sales{{p}}have{{p}}rebounded, some analysts have noticed a shift in the age composition of new light vehicle buyers. Indeed, a number of recent studies and{{p}}press articles have documented a dramatic decline in young adults' willingness to own vehicles, particularly in the years since the 2007-09{{p}}recession. For example, Fortune recently cited the decline in the fraction of new vehicles purchased by young adults--defined as 18 to 34{{p}}year olds--as evidence that financial constraints for that age group had increased and their interest in driving had decreased.1 As quoted{{p}}in the article, young adults "just don't think driving is cool--or even necessary--anymore." Similar stories abound and often attribute these{{p}}changes to the rising popularity of social media, which reduces the need to travel, and alternative means of transportation, such as ride-sharing,{{p}}public transportation, and biking, which reduce the need of owning a vehicle.2{{p}}Much of this analysis was published shortly after the 2008 financial crisis and the 2007-09 recession, when many of the so-called{{p}}millennial generation were entering adulthood. Because the financial crisis had severe and lingering effects on many household decisions,{{p}}distinguishing its effects on vehicle purchases from the effects of cultural and technological changes can be quite difficult. For example,{{p}}The Atlantic notes that while today's younger buyers do have some unique characteristics, they have begun looking increasingly like their{{p}}older cohorts as their employment and income prospects have improved.3{{p}}In this note, we use data on new vehicle purchases from the Consumer Expenditure Survey (CE) and J.D. Power & Associates to{{p}}examine the changes in new vehicle-buying demographics over time. We show that the average age of new vehicle buyers has risen{{p}}since 2000 and that these increases were biggest during the 2007-09 recession. Although young buyers have been purchasing new{{p}}vehicles at lower rates in recent years, the two most important factors that contributed to the rise in the average age of new vehicle buyers{{p}}seem to be (1) the aging of the Baby Boomers--a large group that continued to purchase new vehicles at a solid rate during and after the{{p}}2007-09 recession; and (2) the decline in the new vehicle purchase rate for 35 to 50 year olds over the past 10 years.4 Moreover, we{{p}}show with a probability model that vehicle purchase rates declined for all age groups after 2007, but these declines are roughly the same{{p}}among the age groups once economic factors such as employment and income are taken into account.{{p}}Changes in the Age Distribution of New Vehicle Sales{{p}}The average age of new vehicle buyers has increased notably over the past 15 years, as shown by the two solid lines in figure 2.{{p}}According to J.D. Power and Associates, the average age of new vehicle buyers rose from 43-1/2 years in 2000 to more than 49 years in{{p}}Note. Personal sales exclude sales to businesses and governments. Data are seasonally adjusted. Shaded area indicates NBER recession.{{p}}Source. Light vehicle sales from Ward's Automotive Group, Ward's Communications. Ward's AutoInfoBank. http://wardsauto.com/miscellaneous/wards-autoinfobank.{{p}}Personal sales from IHS Automotive, driven by Polk.{{p}}Accessible version{{p}}Figure 1. Sales of New Light Vehicles, 1999:Q1 to 2015:Q4{{p}} FRB: FEDS Notes: The Young and the Carless? The Demographics of New Vehicle Purc... Page 1 of 6{{p}} https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/the-young-and-the-carless-the... 6/24/2016{{p}}2009 (thick blue line). Average age stepped up most sharply in 2009, the first year after the financial crisis, and it has moved sideways{{p}}since the end of the 2007-09 recession.5 Similarly, the average age of the heads of households that reported buying a new vehicle in the{{p}}CE survey rose more than 5 years between 2000 to 2014 (thin black line), also with a more notable increase during the 2008 to 2009{{p}}period than at other times.6{{p}}Some--but not all--of the increases in the average age of new vehicle buyers reflects the aging of the overall U.S. population. According to{{p}}the U.S. Census Bureau, the median age of U.S. residents increased 2 years between 2000 and 2015 (red dashed line in figure 2).{{p}}Similarly, the average age of heads of households in the CE survey increased about 3 years (not shown). The rise in the average age of{{p}}new vehicle buyers during this period was roughly twice as large as the increase in the age of the overall population.{{p}}Looking at the age composition of new vehicle buyers in more detail, table 1 shows the share of new vehicles purchased by people in four{{p}}age groups in the years 2000, 2005, 2010, and 2015. The share of new vehicles bought by 16 to 34 year olds declined by about 6{{p}}percentage points between 2000 and 2015, consistent with the anecdotes of younger buyers' declining interest in buying vehicles.{{p}}However, the share of new vehicles bought by 35 to 49 year olds fell by an even-larger 9 percentage points. And the share of new{{p}}vehicles purchased by people 55 years and older--the only age group to register a real increase--rose by a dramatic 15 percentage points.{{p}}Interestingly, the most pronounced changes between 2000 and 2015 in the age distribution of new vehicle buyers are the decline in the{{p}}share of new vehicles bought by the 35 to 49 age group and the rise in the share bought by the 55 and over age group.{{p}}Changes in{{p}}the age profile{{p}}of the overall{{p}}U.S.{{p}}population{{p}}likely explain{{p}}some of the{{p}}changes{{p}}shown in{{p}}table 1, but{{p}}the rates at{{p}}which people{{p}}in each age{{p}}group purchase new vehicles have also shifted over time. We explore this idea further by decomposing the rate of car purchases for each{{p}}age group in table 2, which shows the number of new vehicles purchased per 100 people in each age group in each year. The age groups{{p}}that purchase new vehicles at the highest rates--on average almost 7 out of 100 people per year--are the 35 to 49 and the 50 to 54 year{{p}}olds. The average buying rate of these groups fell about 40 percent between 2005 and 2010, a period that included the 2007-09{{p}}recession, and then recouped about 90 percent of that decline between 2010 and 2015.{{p}}The age{{p}}group that{{p}}buys new{{p}}vehicles at{{p}}the lowest{{p}}rate--on{{p}}Note. Shaded area indicates NBER recession.{{p}}Source. Consumer Expenditure Survey, Bureau of Labor Statistics; Power Information Network – PIN, a business division of J.D. Power and Associates; and{{p}}United States Census Bureau.{{p}}Accessible version{{p}}Figure 2. Average Age of New Vehicle Buyers and Median Age of the U.S. Population, 1996 to 2015{{p}}Source. Power Information Network – PIN, a business division of J.D. Power and Associates.{{p}}Table 1. Share of New Light Vehicles Purchased by Age Group{{p}}(Percent){{p}}Year Age group: 16 - 34 years Age group: 35 - 49 years Age group: 50 - 54 years Age group: 55+ years{{p}}2000 28.6 39.2 11.1 21.2{{p}}2005 24.3 36.6 11.5 27.4{{p}}2010 19.8 31.4 12.2 36.5{{p}}2015 22.6 29.9 11.2 36.4{{p}}Table 2. New Vehicles Purchased per 100 People per Year by Age Group{{p}}Year Age group: 16 - 34 years Age group: 35 - 49 years Age group: 50 - 54 years Age group: 55+ years{{p}}2000 5 8.3 8.7 4.9{{p}} FRB: FEDS Notes: The Young and the Carless? The Demographics of New Vehicle Purc... Page 2 of 6{{p}} https://m-pubtest.frb.gov/econresdata/notes/feds-notes/2016/the-young-and-the-carless-the... 6/24/2016{{p}}average about 3-1/2 out of 100 people per year--is the 16 to 34 year olds. The new vehicle buying rate for this young group fell roughly 50{{p}}percent from 2005 to 2010--a bigger decline than was observed for the 35 to 54 year olds--but it also recovered after 2010 and returned to{{p}}about 90 percent of its pre-recession level by 2015.{{p}}The pattern in new vehicle buying for the 55 years and over age group is somewhat different than for the others. The buying rate for this{{p}}group, which averages 5 out of 100 people per year, fell only 20 percent from 2005 to 2010, and a robust recovery after 2010 pushed it up{{p}}to 5.7 in 2015, well above its pre-recession level.{{p}}In summary, the average age of new vehicle buyers increased by almost 7 years between 2000 and 2015. Some of that increase reflected{{p}}the aging of the overall population, but some of it reflected changes in buying patterns among people of different age groups. The most{{p}}relevant changes in new vehicle-buying demographics over this period were a decline in the per-capita rate of new vehicle purchases for{{p}}35 to 54 year olds and an increase in the per-capita purchase rate for people over 55. The per-capita purchase rate among younger{{p}}buyers also declined over this period, but the contribution of this decline to the rise in the average age of new vehicle buyers was not{{p}}disproportionately large.{{p}}In the next section, we estimate a model of new vehicle purchases that includes economic and demographic factors, and we test more{{p}}formally whether age-specific new vehicle buying patterns changed after 2007.{{p}}Model of Household New Vehicle Purchase Likelihood{{p}}Consider the probability model of whether household buys a new vehicle shown in the equation below:{{p}}where{{p}}g = 1 if age is less than 35{{p}}g = 2 if age is between 35 & 49{{p}}g = 3 if age is between 50 & 54{{p}}g = 4 if age is greater than 54{{p}}POST = 1 if year > 2007.{{p}}The indicator variable new equals 1 if the household purchased a new vehicle during their survey year and 0 otherwise, and bing indicates{{p}}that the head of household is in one of four age group bins. The indicator POST equals 1 if the household was interviewed after 2007 and{{p}}0 otherwise. The vector X includes household demographics (race, education, having children, and marriage status) and economic{{p}}variables (employment status, school enrollment, and combined household income).7{{p}}We estimate this standard probability (probit) model on roughly 80,000 household responses to the CE survey collected between 1996{{p}}and 2014, which included about 6,300 new vehicle purchases. Table 3 presents the estimates from the model of the average marginal{{p}}effect of each variable listed in the table on the probability that a household purchased a new vehicle in the past year. The column labeled{{p}}"Baseline Model" presents estimates from a baseline specification, which tests for differences among the age groups in the average{{p}}propensity to purchase new vehicles and whether those propensities changed after 2007. The column labeled "Model with Controls"{{p}}presents estimates from the full model, which also includes demographic and economic variables. The marginal probabilities estimated on{{p}}the demographic and economic variables seem sensible and suggest that households are more likely to buy a new vehicle if they are{{p}}white, married, have more education,8 and have a higher income.{{p}}Note. Average purchase rate is calculated over the four years listed in the table.{{p}}Source. Authors' calculations based on data from the Power Information Network – PIN, a business division of J.D. Power and Associates; Ward's Automotive{{p}}Group, Ward’s Communications (Ward's AutoInfoBank. http://wardsauto.com/miscellaneous/wards-autoinfobank); and United States Census Bureau.{{p}}2005 3.8 7.1 7.3 5.2{{p}}2010 2 4.3 4.8 4.1{{p}}2015 3.5 6.6 6.7 5.7{{p}} Memo: Average 3.6 6.6 6.9 5{{p}}Table 3. Marginal Effects from Probit Regression: Propensity to Purchase a New Vehicle{{p}}(Significance Indicators: *** is p 55) is{{p}}equal to the coefficients for both the youngest (age
    Date: 2016–06–24
    URL: http://d.repec.org/n?u=RePEc:fip:fedgfn:2016-06-24&r=tre
  4. By: Thomas Longden (Centre for Health Economics Research and Evaluation (CHERE), University of Technology Sydney)
    Abstract: Regular and irregular travel patterns coincide with different underlying purposes of travel and days of the week. Within this paper, it is shown that the balance between subsistence (i.e. work) and discretionary (i.e. leisure) activities is related to differences in travel patterns and explains consistency across years. Using eight years of time use diary entries this paper finds that travel time related to subsistence activities tends to be regular and stable. In contrast, travel time associated with discretionary activities tends to be more unpredictable and varies greatly between discretionary and non-discretionary days. These findings have consequences for the travel time budget literature as consistency of average travel time is found to be driven by work days, which are frequent and have stable travel times. This is offset by discretionary days as they tend to have longer travel times with greater variability but are fewer in number.
    Keywords: Travel Time Stability, Time Allocation, Discretionary Activities, Switching Model
    JEL: R4 R41
    Date: 2016–07
    URL: http://d.repec.org/n?u=RePEc:fem:femwpa:2016.49&r=tre
  5. By: Vigren, Andreas (VTI)
    Abstract: The Swedish market for passenger railway services has been open to competition since the year 2010. Although minor entries have been made since this date, the incumbent SJ only faced substantial competition when MTR Express entered the Stockholm-Gothenburg line in March 2015. Using unique Sweden ticket price data from operators' websites, this paper investigates what effects this entry has had on market prices. The results show that the incumbent's prices decreased by 12.8 percent on average between March 2015 and June 2016. The price level of the competitor is well below the average price that was offered on the railway market in the pre-entry period. Further, the largest price reduction, in percentage terms, was found on tickets booked 10 days before the departure date. Finally, the decrease in the average price of the incumbent seems to be an ongoing process, and a further drop in price would not be unexpected.
    Keywords: Railway; Entry; Open access; Competition; Prices; Web crawler
    JEL: C10 L19 L92 R40
    Date: 2016–08–29
    URL: http://d.repec.org/n?u=RePEc:hhs:ctswps:2016_018&r=tre

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