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Fare-Free Buses: A Comparison

 by Jeff Sinclair   DP_SinclairJeff

            Chapel Hill Transit (CHT) and Durham Area Transit Authority (DATA) both operate in the Research Triangle area of North Carolina, and both share important similarities. They both serve collegiate and healthcare areas, as well as commuters and residential areas. However, they share important differences. DATA serves a larger area both in terms of geography and population. DATA serves a city with mixed development; CHT serves a primarily residential community. However, the biggest difference between the two is CHT’s use of a fare-free system (with one or two exceptions). When implemented, Chapel Hill Transit believed that this fare-free system would substantially increase ridership. However, the alternative hypothesis is that this is not true. In order to explore this question, it is important to figure out the change in the characteristics of each system since the implementation of CHT’s free fare and then to hypothesize the potential factors that influence these changes. In comparing the changes and applying theory, the goal is to roughly determine the effect of a free-fare system on transit.

Chapel Hill implemented its fare free system in 2002. In the year before, the population of Chapel Hill was 48,902 and Durham’s was 192,397 By 2002, when the fare was implemented, the population of Chapel Hill increased by 3.35% to 50,540 and the population of Durham increased to 196,432, a 2.10% change. At the same time, annual passenger miles increased on CHT from 4,394,609 to 10,111,508—a change of 130%. This growth trend has continued, with far less magnitude, in every year since except for a slight decline in the years 2008 through 2010. Annual passenger miles on DATA went from 15,236,582 to 13,821,392—a decline of 10.2%. Durham’s change has been more variable, with declines occurring between 2001 and 2004 followed by a large jump in 2005 and sustained increases until 2009, followed by a slight decline in 2010.

Before continuing further, it is worth noting why annual passenger miles are being used as a measure of consumption. Because their routes vary in length, a good aggregate measure of production for transit companies is miles of transit route. Passenger miles is the aggregate number of miles all passengers have traveled—which is dependent on the miles of transit produced. Alternate measures such as number of boardings are inadequate because they do not account for the additional cost of longer routes, nor for their benefits to those who demand them.             Service increases are also important in the growth of public transit. Between 2001 and 2002, CHT also increased the service it provided (as measured by annual vehicle revenue miles) from 1,633,050 to 1,843,567, an increase of 12.9%. DATA went from 3,018,752 to 2,819,226—a decrease of 7.10%. The annual vehicle revenue miles hovered around this figure until 2008, when they increased by 513,755. Since then they have continued to increase modestly.

With a population increase of 3.35% and a service increase of 12.9%, it is to be expected that CHT would experience increased ridership between 2001-2002. However, ridership more than doubled, far exceeding the modest increase that the population and service increases would suggest. Furthermore, it seems unlikely that the 12.9% increase in service, even if intensively used, would account for the entire 130% increase, or even a large part of it. A more finite analysis would have to include route-by route information. Since then, however, CHT has only experienced moderate growth, and in a few years a slight decline. DATA’s experience in the same years was the reverse—despite posting a population increase of 2.10%, service declined by 7.10% and ridership by 10.2%. The decrease in ridership here is more easily accounted for by the service decline (with the difference potentially made up by the loss of intensively-used routes—though again that requires route-by route data), although in light of the population increase, the fact that the ridership decline is greater than the service decline is interesting.

DATA, unlike CHT, has seem major shocks to its system since then. Between 2007 and 2008, it increased the annual vehicle revenue miles by 17.3%, but only saw a ridership increase of 3.01%. This suggests that an increase in service does not necessarily bring a corresponding increase in ridership, or that the new services took time to catch on. DATA also experienced the opposite between 2004 and 2005, when service was increased by 1.26% but ridership jumped by 52.3%. It has been difficult to find information on what caused those shocks, although possible factors that could have had an influence include any fare changes, gas prices, operational changes, or employer incentives.

Revenue and expenses for these providers also changed between the years 2001-2002 and since then. CHT saw revenue go from $1,751,597 to $1,012,907—a decrease of 42.2%. Before continuing, it is helpful to explain that when the fare-free system was implemented, revenue did not go to zero because UNC-Chapel Hill and the Town of Carrboro both began paying CHT yearly for the free services. These payments essentially replaced the fare and are captured in the “Fare Revenue” section of the NTD profiles. After this, fare revenue went up to $4,117,415, down to $309,722, and back up to $5,357,852. Since then, it has gradually increased every year except for during the recession. There is no clear reason for the decrease to $309,722, which appears to be an outlier, so it will not be strongly considered in the evidence for the cost effectiveness of the program. Overall, the trend for CHT since implementing free fare has been increased “fare” revenue from payments by UNC-CH and the Town of Carrboro. Given the size of the payments by each, it is not surprising that such revenue has increased.

DATA’s fare revenues during 2001-2002 decreased by 7.90% from $2,050,664 to $1,888,826. In the next several years, however, DATA would see a steady increase in revenue marred only by a slight decline during the recession. It is also worth noting that in most years, CHT earned more than DATA in terms of “fare revenue”. This would suggest that the decision by the Town of Chapel Hill to depend on fixed yearly payments UNC-CH and Carrboro turned out to be beneficial for revenue. According to one report, fares collected on buses accounted for only 8% of revenue before the implementation of the free-fare system, which is not major compared to the 42% the fixed payments now account for. By changing from a variable pricing scheme that depended on individuals and trips to a system that relies on two fixed payments from major stakeholders that represent a large number of individuals, CHT achieved important revenue gains.

In light of the data, the implications of fare free service can be considered in two respects: ridership and revenue. First, it makes sense to consider who is using the bus service. According to the 2011 Community Survey put together by Chapel Hill, 50.7% of users ride the bus to get to work. The next largest segment at 27.9% is constituted by people who use the bus to get to school. Following this are social activities, shopping, and medical appointments in that order. There are concerns with this survey, namely that the sample size is very small (353) and that the percentages’ sum is greater than 100%, but it is nonetheless a valuable tool for getting a rough idea of why people ride the bus. Zero car households are also important in driving transit demand, since that is one of their primary means of transit. Zero car households account for 50% or more of the households in most census tracts in Chapel Hill (COA task memorandum). This is certainly true for the areas in which UNC students are housed. Durham also has a significant number of zero car households clustered around downtown, though interestingly enough, Duke’s campus does not appear to have a significant amount of zero-car households. Further information on the demographics of DATA ridership is hard to find, but some inferences can be made based on the availability of other information. First and foremost, by simply looking at a map, it is clear that most bust routes are not convenient to West and Central campuses, and furthermore, the zero-car problem is not very acute, suggesting many have their own cars (Duke has far more parking than UNC, so it’s easier to keep a car on campus). Additionally, Duke mandates that students live on campus for their first three years of school. This diminishes the incentive to use the Durham bus system since all their classes as well as an abundance of food and social options are already on campus. Again referring to maps, DATA seems more oriented towards commuters, with several routes reaching far out into the suburbs, and a high density of routes in neighborhoods with low income and a high number of zero-car households.

Given these data and assumptions about the ridership base, microeconomic theory can be applied to come up with a rough projection of what ridership should look like after a fare change. Utility curves can be assumed to be convex, so as the price of bus transit goes to zero, the quantity demanded should rapidly increase. This assumption is realized in the existing data on CHT when they implemented the free fare system. However, it seems reasonable that there are smaller effects that may mask one another. For example, the demand for transit amongst bus-captive commuters (i.e. zero-car households) will be inelastic—it doesn’t matter if the fare is a dollar or free, they still need to get to work. For non-captive commuters the curve appears to be highly cross-price elastic. Even in the latest community survey, 53.4% out of 607 respondents said they never use Chapel Hill transit—meaning they prefer to walk, bike or drive. But the primary reason for not using transit according to the same survey is that individuals “just prefer to drive” (the survey, of course, having the same faults as earlier but still being a useful barometer). Even when free, transit still suffers at the hands of those who prefer to drive, suggesting that very dramatic cost decreases are needed in order to get these “hardcore drivers” to substitute—decreases that would have to be measured in time and convenience, since the fare is already free. Own price elasticity is more difficult to determine. However, due to the tremendous increase in bus ridership, it can be assumed that this too is elastic. It is harder to find data on students, but it can be assumed that they have an elastic demand curve as well, since so many live within walking or biking distance of UNC, food choices, and social options, with only a few residing further out (grad students). Around 8,900, or almost half, of UNC’s students live on campus, and those that choose to move off generally stay close, as evidenced by density patterns. Because they generally don’t need to travel that far, biking or walking are often substituted for a bus trip, since these are more flexible and very low cost. On the other hand, their curve is not as elastic as that of commuters. Driving is generally not a realistic option, and some do live far enough away from the university that they essentially become bus-captive. Even some who live closer to campus find the bus attractive depending on their destination and schedule, especially if journey is strenuous or they want to travel further off campus to access things such as grocery stores or specialty shops.

Since annual passenger miles, or the quantity demanded, doubled, the change in price is necessary to infer elasticity. Before the implementation of the free-fare system, fares were $0.75 system-wide. With the implementation of free fare, the monetary cost went to zero, but there remained time and convenience costs. Since these are hard to quantify and usually constant, they will not be included. Given these constraints, the price saw a 100% reduction, and a 130% increase in “quantity” consumed. This would suggest that overall demand is in fact elastic. However, this likely masks the fact that demand is relatively inelastic for many in the Chapel Hill community—such as the low income population.

This provides an important example for Durham: Chapel Hill, which has a median income of over $100,000, is far wealthier than Durham, whose median income is about half of that. Chapel Hill has two major factors that allow its demand to be so elastic—a large number of  individuals who both own and prefer to drive their cars (which means they have the economic means to do so) and a large student population that uses the bus system. Durham, on the other hand, has a bus system with a large number of routes in low-income areas and a poorer population over all. Furthermore, student population does not use the bus system as much as that of Chapel Hill. If it is hypothesized that demand for transit in Durham is inelastic because of these factors, then it seems that implementing a free fare system would not significantly increase ridership.

However, there are other reasons that would lend themselves to a fare-free system in Durham. Since the population is lower income, and their transportation costs take up a larger proportion of their budget, a free fare will have a greater impact. A minimum-wage worker will feel the impact of the savings more so than a well-off individual, since he or she makes less money and pays the same fare. This would be one reason for considering a fare-free system in Durham that is not based on estimates of increasing ridership. It is also worth examining where the high-income areas of the county and city are, and how well they are served. Their demand curves will be more elastic, and if they are served well, could generate an increase in ridership.

The implementation of a fare-free system in Chapel Hill had dramatic effects on CHT. Ridership shot upwards even after controlling for population and service growth—and has continued to grow modestly in the last decade. DATA has experienced aggregate growth as well, but in not nearly as astounding proportions and certainly nothing like the jump seen between 2001 and 2002 in Chapel Hill (although there were a few unexplained, moderate increases). Revenue also grew dramatically after the switch to a fare-free system while Durham’s revenue grew much less dramatically in the past decade. Simply looking at this data, it would appear that a free-fare system is a good way to dramatically increase ridership. But using rough data and assumptions about the habits and characteristics of different groups of riders, it is not clear that a fare-free system would drastically increase ridership. This does not mean with any certainty that it would not, but just that there is no reason for believing so based on this data. There could be other reasons for Durham to implement a free-fare system, including social justice concerns, or it may be that the elasticity of demand in Durham is greater than previously asserted due to the growth of the suburbs or a miscalculation in the characteristics of DATA’s ridership. Pursuing this question further would be worthwhile; unfortunately data is difficult to find and often incomplete (one very desirable missing piece is a way to determine how much ridership consists of students). The fare-free system and the elasticity of demand also has important consequences for Chapel Hill, and UNC-CH. Since UNC is the largest provider of funding for CHT, it has to charge students an  $113.50 transit fee (admittedly, this also goes to non-CHT operators). In order to justify such a fee, UNC has to demonstrate that the students get a value from CHT (and the other operators) that meets or exceeds the cost of the transit fee, which is very much like a back-door fare. If most students walk, bike, or drive, then they’re not getting the full value of the fee and UNC would be better off not charging students as much and paying the transit companies less. On the other hand, depending on just how elastic their demand curves are, students will consume a lot more transit at a lower price, and this would seem to justify the decision to charge a fee. The fare free system has also posed additional challenges to CHT, as the Carrboro/UNC funding still hasn’t proven enough to cover costs, and service reductions as well as parking lot fees are being considered.

Both Durham and Chapel Hill have questions to answer regarding fare-free transit. As such transit comes more and more into vogue nationwide, Chapel Hill in particular will be scrutinized. As a neighbor with another major research university, Durham can scarcely afford to ignore the example. And any city that takes such a measure into account must very carefully consider not only others’ experience, but also the characteristics of their own ridership base, which ultimately lend themselves to either the success or failure of such a system.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Bibliography

CNN. (2013, 04 03). CNN Money. Retrieved from CNN.com: http://money.cnn.com/magazines/moneymag/bplive/2010/snapshots/PL3711800.html

Federal Transit Administration. (2013, 04 03). Retrieved from National Transit Database: http://www.ntdprogram.gov/ntdprogram/

Federal Transit Administration. (2013, 03 31). Fare Policies, Structures, and Technologies: Update. Retrieved from Transportation Research Board: www.trb.org/publications/tcrp/tcrp_rpt_94.pdf

idcide.com. (2013, 04 02). Durham, NC Profile. Retrieved from idicde.com: http://www.idcide.com/citydata/nc/durham.htm

The Transit Cooperative Research Program. (2013, 04 02). TCRP Publications Shopping Cart. Retrieved from tcrponline.org: http://www.tcrponline.org/bin/publications.pl?mode=abstract&cat_id=23&pub_id=1590

Town of Chapel Hill. (2013, 04 01). Retrieved from The Town of Chapel Hill: http://www.townofchapelhill.org/index.aspx?page=1390

Town of Chapel Hill. (2013, 04 02). Retrieved from Town of Chapel Hill: http://www.townofchapelhill.org/Modules/ShowDocument.aspx?documentid=15766

Town of Chapel Hill. (2013, 04 01). Retrieved from The Town of Chapel HIll: townofchapelhill.org/Modules/ShowDocument.aspx?documentid=2

United States Census Bureau. (2013, 03 31). State and County Quickfacts . Retrieved from census.gov: http://quickfacts.census.gov/qfd/states/37/37063.html

 

 

 


3 Comments

  1. Jeff,

    After previewing that your paper would analyze the economic and welfare effects of a transit system’s conversion to a fare-free system, I was excited, having also written my Durham paper on transit systems. However, I now am left itching for more information regarding some of your assertions.

    One concern that I had relates to causality. You use Chapel Hill Transit (CHT)’s conversion to a fare-free system as a case study, but you never do distill the causal effects of the conversion. The conversion increased annual passenger miles by 130%, but population and bus service also expanded (as you acknowledge). Further, simultaneous causality could have been at work here: perhaps the conversion was a reaction to exogenous increases in ridership. Without a regression analysis, or at least a quantitative difference-in-differences comparison (e.g. comparing CHT’s growth with DATA’s before and after the conversion), the effect of free fares remains unclear.

    Second, the policy question driving your investigation seems to be whether a municipality’s social surplus is maximized through a free-fare transit system. While we see that Chapel Hill increased ridership after its fare decreased, how much surplus was captured? Assuming these new riders value bus rides at somewhere below $0.75 (the old fare) and $0 (the new fare), then it seems like at least an upper bound estimate could be captured. Likewise, what has been the cost to Chapel Hill? While a comprehensive analysis might need to include secondary effects (e.g. if the fare-free system attracted shoppers from outside Chapel Hill), a more superficial analysis with just the first-order costs and benefits would have been a good first step at answering your policy question.

    Finally, much of your qualitative analysis centers on comparing the transportation mode tendencies of Duke and UNC students. UNC 30,000 students admittedly make up the core of Chapel Hill’s total population of less than 60,000, but Duke’s 15,000 students make up around 6% of Durham’s population of 233,000. Duke students therefore do not drive the behavior of the Durham population, and in turn, DATA does not seem to plans its operations around Duke at all either.

    Overall, I think your paper topic is intriguing. With a bit more focus on causality and economic theory, your research questions promise to yield some very interesting findings and implications.

  2. Hello Jeff,

    I enjoyed your explanation of some of the differences between Durham and Chapel Hill busing systems. I was particularly surprised by the massive jump in CHT passenger miles when the fare-free system was implemented back in 2002, and you did a good job laying out several of the possible explanations for such a jump. I’d like to add a little to your considerations.

    I think you’re right that the modest annual population and busing service increases cannot fully explain the tremendous 130% jump in passenger miles. I would extremely interested in seeing a bit more about density of ridership on given routes, since its clear that ridership necessarily increased substantially on pre-existing routes. One possibility that I would note, especially given the little increase in DATA route ridership, would be the location of natural amenities throughout the community given increased ridership. You note that commuters that rely on public transportation to get to and from would have necessarily inelastic demand for transportation. Conversely, those go to and from work without relying on public transportation already have established ways of getting to their destinations. Therefore, when the shift occurred in Chapel Hill to a fare-free system, it would be unlikely that those who already had ways of getting to their place of work would change their method of transportation because of a $0.75 decrease in commuting costs.

    Therefore, there must be another group with more elastic demand that changed their public transportation consumption patterns. As you note, there is a group of individuals that rely on public transportation to get to places of interest (shopping centers, restaurants, outdoor public spaces, etc.). In Chapel Hill, these locations are far more concentrated than in Durham. While Durham is a bigger city, and likely has more natural amenities, they are still spread out surrounding the central business district. Thus, a fare decrease in Durham would need to be coupled with an expansion of service area around these spread out amenities to realize a large change in utilization. Chapel Hill, on the other hand, has few yet significant hubs of commerce and leisure opportunities. Therefore, as fares fell to zero, consumers likely had a greater incentive to respond according to their elastic demand for access to these hubs. I would suspect, therefore, that ridership increased dramatically along routes that included access to places of interest.

    Further, it may also be the case that there was not simply an increase in the number of riders, but rather the distance and frequency each rider relied on the buses. For those that already utilized the bus for work (and are likely familiar with the bus and its routes), a fare decrease would incentivize them to increase their frequency of ridership towards places of leisure consumption. For Durham, this may also be the case if they choose to implement a fare-free system. This, if true, could be a huge boom for businesses in Durham located around busing routes and stimulate spending downtown. To confirm these suspicions however, it would be necessary (as you mentioned) to conduct a more detailed survey regarding utilization patterns of public transportation.

    Another interesting application would be more on the part of city planning. If data such as yours is expanded in the future, and combined with the location of hubs of commerce, the city could survey individuals to find out more accurately areas they would want to go, but that are so far not serviced. This could be an interesting way to potentially stimulate ridership.

    On the whole, I enjoyed your analysis of the fare-free system in CHT as well as DATA’s system. Hopefully research like this can be expanded in the future!

  3. Dear Jeff,

    I thought that overall you have done a great job analyzing the two bus systems. I thought your comparison of revenues vs. ridership was really interesting and I found the data on who is using the two bus systems really intriguing. Your topic is something I had not thought about prior and I was intrigued. I found your paper insightful and I liked the fact that you preformed a microeconomic analysis of demand. 🙂

    I feel that there are some very good insights already provided to you by the previous comments so I will not touch upon the subjects already covered. I know that you have not taken econometrics or any statistical classes so I think that while overall valuable, the a lot of the insight provided on this topic are things that you will have the opportunity to do later in your academic career. I am also aware of the lack of information you faced and how frustrating you found that so applaud you for finding ways to go around it and creating a great paper despite that!

    There are a few suggestions that I can make with regards to laying out the information you found. I found it a bit confusing to keep track of how revenue and population have been changing through the years and I thought summarizing your findings in a table would have been a great way to visualize the information. Another thing I found a bit confusing was when you talked about CHT in the second paragraph and you said “Annual passenger miles on DATA went from 15,236,582 to 13,821,392—a decline of 10.2%” without introducing the DATA system and when it was started. Maybe it was me, but I found this part a bit hard to follow. That being said, I think you have done a great job overall and I am looking forward to seeing your final project on railroads!

    Best,

    Katerina

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