Introduction
The price of gasoline fuel in the United States has risen greatly over the past few years. For instance, in 2002, the price increased from $1.148 per gallon in January to $4.114 in July 2008. The rise in the price has caused panic in the fuel industry in the United States. During this time of increase in gasoline prices, the total or aggregate public transit ridership has rapidly grown at increasing rate while the growth of the number of vehicle miles has significantly reduced. The decrease in vehicle miles traveled and the increase of transit ridership is notable since personal cars used in the United States has been on the rise since 1940. According to the National Household Transportation Survey that was conducted in the year 2001, it was revealed that 87.9% of all employees going to work were using their private means compared to public transit which stood at only 4.7%. It is worthwhile to note that private vehicles and public transit are a substitute, an increase in the gasoline prices makes the private vehicles expensive to use as a means of transport and results to an increase in demand of public transit. There are other factors affecting the travel mode choice but the price of gasoline plays a greater role in determining the prices.
This paper comprehensively discusses or analyzes the impact of rising gasoline prices on the United States public transit, aiming to establish the extent to which the gasoline prices have determined the mode of transport and the consumer behavior. In order to accomplish this, the study has come up with research questions and research objectives as outlined below.
Research questions
- What is the impact of rising gasoline prices on United States public transit ridership?
- What are the determinants of the rise in gasoline prices in the United States?
Research objectives
To find out the impact of rising gasoline prices on the United States transit ridership.
To investigate the factors affecting the rise of gasoline prices in the United States.
Quick pause
This essay take you 4 hours to write?
Order yours — we'll do the heavy lifting while you study what makes this one work.
Order an essayThe research questions were appropriate as they enabled the researcher to thoroughly examine and test the cross-price elasticity of the four modes of public transit demand in relation to gasoline prices. Cross-price elasticity is defined as the measure of the percentage change in a good x's demand caused by a change in another good Y's prices. For the sake of this study, the researcher estimated the change in transit demand caused by 1% in gasoline prices. In order to allow for comparison, the study further employed a procedure similar to other previous studies but using a dataset that gives a more precise description than the previous study. For example, one of the importance of the dataset employed in this study is that there a panel data containing monthly observations of demand and supply from more than 100 cities across the nation. This study there brought on board more cities than the previous study. Secondly, the study included observations of the recent rise in gasoline prices enabling the researcher to see how the ridership respond in these unusual circumstances.
Significant of the study
The study is very important in finding the relationship between the prices of gasoline and the United State public transit ridership. To be more precise, understanding of the relationship between the rise of gasoline prices and the public transit will be very useful to the public transit policymakers and the public transit agencies. The public transit agencies require this information so as to determine the cost of fuel as well as the demand for their products which do change with time. For example, it has been established that an increase in the price of gasoline makes the provision of services more expensive especially the buses at the same time increases the demand of the public transit ridership for the transport.
According to Winston and Maheshri (2006), fares only account for 40% of the transit operating cost. This means that service provision during the high increase in gasoline prices cannot be sustainable even if the fare prices are increased. The knowledge of how the rise or fall of the price of gasoline affects demand will be a great asset to the transit authorities as they will be able to respond to the changes of the gasoline prices with an optimal level of fare and services. In addition, policymakers will know how the changes in the price of gasoline affect the mode of transport among customers. Moreover, the policymakers will understand in details the social cost associated with greenhouse gas emission, the vehicle use, congestion, foreign oil dependence and traffic accidents.
This paper will also enable the policymakers to encourage the reduction of vehicle use in the United State roads on the basis of the factors that have led to a reduction of these vehicles use and increase of the public transit ridership. Improving the transport infrastructure will also rely on the data found by the study to assist in designing the best cost-effective and the safest mode of transport in the United States.
Literature review
Over the years, public transits have been researched by various economists, policy researchers and the urban planners. They both used the time-series analysis and the cross-sectional analysis in finding the relationship between the changes in the gasoline prices and the public transit ridership in the various part of the world. The previous study made use of experimental and random sampling as a means of data collection criteria to establish the effects of the rise of gasoline prices on the public transit in various parts of the countries, especially in Europe. Taylor (2002) divided the factors affecting the transit ridership into two that is internal and external. According to Taylor, internal factors are those factors that a can be determined by the system operator such as the level of service, fare, and quality of the service offered. External factors, on the other hand, are mainly exogenous and they include the geographical, economic and demographic. The study wanted to examine the effects of each factor on the extent of the change of demand of public transit ridership, It was found that the external factors were stronger than the internal factors. The study further found that the external factors were at the manager's system and it was affected by the aggregate change of price of other goods, increase in taxation and the consumer income. Further, demographic factors also played a key role in shaping the public transit ridership. Litman (2004) used estimates of 81 studies and found the mean own-the price of elasticity -38 showing that a 1% increase of transit fare decreases the demand of the transit by 38%. Litman further found that the estimates range from -.009 to -1.32. This means that the transit can exhibit both perfectly inelastic and more than unitary elastic demand. On the service delivery, it was found that the service delivery was measured on the revenue miles. Litman found that the service delivery had a significant positive relationship with the ridership.
Holmgren (2011), on his finding, revealed that an average elasticity with respect to miles covered with the vehicle revenue kilometer was .72. This shows that 1% increase in the number of hours in the transit vehicles in operation or service, the ridership goes up by 72%. The external factors affect the transit demand by changing the relative costs and benefits of transit. (Fink, 2003), found that geographical factors such as urban form, topography, area of urbanization and climate all affect the consumer behavior in choosing the mode of transport. Demographic factors were also significant in affecting the mode of transport. For example, the higher percentage of immigrants, college students were found to be positively correlated with public transit demand and people living in poverty and a household with a vehicle had a negative correlation. Economic factors such as unemployment and income were found to have a strong effect on the public transit ridership. They concluded that the internal factors such as the fare, quality of service were significance to affect the taste of consumers.
Measuring the effect of vehicle operating cost on transit ridership
The cost of operating a vehicle is an economic variable that affects the transit demand. Bridge and highway bridge tolls affect the transit demand. From the previous studies, it was found that for every 1% increase in tolls, increases ridership by 37%. Further, increase in parking fees have increased transit demand over the years. According to Taylor and Miller (2008), the cross-sectional studies revealed that gasoline price had an insignificant effect on ridership.
Time-Series Estimates of cross-price elasticity
The study that used the time-series estimates to establish the impact of the rise of gasoline prices on the public transit was tested and the result is represented in the table below
| Study | Short-run | Long-run | Not defined | years | Note |
|---|---|---|---|---|---|
| Agthe and Billings (1978) | 0.08-0.80 | 0.42 | 1973 | Tuscons, AZ | |
| Doi and Allen 1986 | 0.112 | 1978 | Honolulu buses | ||
| Voith 1991 | 1.05 | 2.69 | 1986 | Urban rail | |
| Mattson 2008 | .08-.50 | 2007 | town buses transit |
Source: Mattson (2008)
Agthe and Billings (1978) conducted a study and estimated the cross-price elasticity of .42 using Tuscon and AZ buses. Doi and Allen (1986) used data for Honolulu buses and estimated the cross-price elasticity of .112. Voith (1991) uses a panel data set of rapid transit of 129 stations. He also estimated the cross-price elasticity and found 1.05 in the short run and 2.69 in the long run. The study in the table above is of interest to the current study because of two main reasons. First, all the three studies seek to establish the best model for explaining the impact of the rise in gasoline prices on transit demand. Secondly, the data are recently making the analysis and findings more meaningful. The study, therefore, plays a critical role in explaining the role of gasoline on the recent trends in travel behavior. It was found that allowing 9/11 the Iraq war and Katrina to interact with cross-price elasticity. They, therefore, concluded that there was a positive correlation between the rise of gasoline price and the public transit ridership.
The weakness of the literature
From the papers reviewed, it was evident that the previous study just looked at the aggregate data but ignored the difference that exists between cities. From experience it is known the travel mode choices depends on the level of service offered and the location. However, the study did not explain where and why people ride transit more. Both Billings and Mattson failed to provide more contents to their findings. Billing results are only applicable to 5 cities they sampled while Mattson findings have very little meaning outside the small town he studied.
Overview of the literature
It was found that the increase in gasoline prices increases the cost of private transport and increase the demand for public transit ridership. The study deviated from the previous literature since this study is conducted on the United States public transit as opposed to previous studies that were conducted in various cities in different countries. The study also deviated from the reviewed literature as the literature used the experiment and random sampling as the main method of data collection. This paper made use of panel data as the sole method of data collection and analysis.
Research methodology
Introduction
This chapter introduces the research design, data collection method, and type of sample used and the instruments of data collection. The chapter also explains the sources of data used and the method of data analysis.
Research Design
Research design refers to the strategy that the researcher uses to gather, present, analyze and provide finding on a given study. For instance, the study adopted the panel data research method as the best method for collecting and analyzing the data. Panel data is also known as cross-sectional time-series derived from a number of observations, especially in towns. This research design was chosen since it gives a deeper understanding of the travel behavior of consumers. Panel data allows the researcher to control variables that cannot be measured or be observed such as national policies, business practices across companies and cultural factors.
Type of Sampling
Sampling refers to the process where the predetermined observations are drawn from a small group to represent the entire population. The study employed multistage sampling method to have a vivid and comprehensive data that can give a fair presentation of the findings. Multi-stage sampling is a sampling method where the all the methods of sampling are combined in stages to give a clear observation.
Model specification
It refers to the process of determining the independent variables and dependent variables and their relationship in the regression equation. The model used in this study was based on the theoretical consideration and not empirical. Berenchman (1993), describes a consumer theory of public transport to have a demand function as D=D(P, T, Y, Q, I, V, Z) where D is transit demand in trips, P is fare, T is the vector of travel times is vector of service provision is service qualities, I is vector for population characteristics is substitute prices and Z is the vector for urban characteristics. There was also need for the study to allow the coefficient to represent elasticity, the researcher estimated the log-log demand specification for each mode of transit (Baum-Snow, 2000). The transit ridership was included as dependent variables while the price of gasoline as an explanatory variable. Other factors were also considered to take care of the bias that may have risen. These factors were the transit supply, strike by workers, construction of new transit lines. To be more specific of the occurrences, the study also considered the monthly transit supply and the fixed year effect (Yij) to take care of the unobserved determinant of transit demand that vary yearly may be due to the level of fare, level of service offered and the quality of services. It was observed that both gasoline prices and the transit ridership exhibit strong seasonality hence fixed month effect (Mik) was also incorporated in the model. Therefore, the model specification for the study was
lnRit=α0+ α1lnSit+ α2lnPit+∑βijYj+∑βikMk+εit
Where R is ridership transit supply is regional gasoline price, Y is yearly fixed effect and M is monthly fixed effect, ε is error term, i, t, j, and k shows the city, observation period, year and month respectively.
Predicted results
In the model specification, the coefficient of gasoline prices and transit supply are expected to have positive values. It is also expected that the coefficient of transit supply is greater than the coefficient of gasoline prices. It was also hypothesized that the coefficient of gasoline price will increase over time as related social and print media will cause people to become more aware of their gasoline expenditures.
Data Collection Method
The research study obtained the supply data and the transit ridership from the National transit database (Holmgren, 2007). The federal government of United States requires the all transit agencies to submit transit ridership and supply data as a condition to receive funding from the federal government. This study used the National Transit Database of between the year 2003 and the year 2009 totaling to 100 periods.
The gasoline data was provided by the US Energy Information Administration. These are average prices of gasoline sold in each of the seven regions and reported in nominal dollars. The gasoline prices were adjusted for inflation as was reported as 2000 US dollars.
Research findings, conclusion and recommendations for further studies
Introduction
This chapter introduces the data analysis and, finding and the interpretation of the study. The study used both descriptive statistics and inferential statistics to explain the findings.
Ridership by mode
The table below shows the ridership by mode, the percentages across cities in the United States.
| Ridership by mode | %ridership | cities |
|---|---|---|
| Motorbus | 50 | 200 |
| Commuter rail | 4 | 13 |
| Heavy rail | 30 | 10 |
| Light rail | 16 | 20 |
| Total | 100 | 243 |
Source: Researcher (2018)
From the table above, motorbuses are the majority with 50% ridership and are found in 200 cities in the United States. Commuter rail, on the other hand, is the lowest with 4% ridership. It can be said that there is a general decrease in ridership percentage across cities and with different modes of transport. This may be as a result of different gasoline consumption across the different modes.
The graph below shows the relationship of fuel prices and transit demand across cities in the United States
The graph shows that the demand for transit ridership is negatively related to the changes in fuel prices in various cities. Motor buses are more popular than commuter rails. However, some cities did not be not significantly correlated to fuel prices since all estimates were had 10% significance level. For example, Washington DC, Dallas, and San Francisco increase in fuel price were not significant at some point.
Descriptive statistics
| Statistic | Gas price | Motorbus | Commuter rail |
|---|---|---|---|
| Mean | 2.157778 | 16.44444 | 2.333333 |
| Standard error | 0.219651 | 6.265228 | 0.471405 |
| Median | 2.27 | 9 | 2 |
| Mode | – | – | 2 |
| Standard deviation | 0.658953 | 18.79569 | 1.414214 |
| Sample variance | 0.434219 | 353.2778 | 2 |
| Kurtosis | -1.0449 | 0.100615 | -0.01786 |
| Skewness | 0.266791 | 1.282668 | 0.947018 |
| Range | 1.9 | 49 | 4 |
| Minimum | 1.35 | 1 | 1 |
| Maximum | 3.25 | 50 | 5 |
| Sum | 19.42 | 148 | 21 |
| Count | 9 | 9 | 9 |
From the table above, it can be deduced that the mean for gasoline for the various mode of transport decreases from motorbus to light rail. This shows that the rise in gasoline prices affects negatively the mode of transport in the United States.
Inferential statistics
The dependent variables (price of gasoline) were regressed against the independent variables such as the quality of the service offered, the fare of various modes of transit, demographic factors, and taxation. The aim of regression was to find out the relationship between the rise in the price of gasoline against other factors such as the travel behavior of consumers on various mode of transit, the economic factors and the cost of operating the public and private vehicles or ridership in the United States. The table below shows the result.
Regression table
| Term | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% |
|---|---|---|---|---|---|---|---|---|
| Intercept | 2008.42 | 2.711954 | 740.5803 | 5.43E-09 | 1999.789 | 2017.051 | 1999.789 | 2017.051 |
| gas prices | 1.135501 | 1.135149 | 1.00031 | 0.390874 | -2.47705 | 4.748052 | -2.47705 | 4.748052 |
| Motorbus | -0.03294 | 0.043966 | -0.74912 | 0.508172 | -0.17285 | 0.106983 | -0.17285 | 0.106983 |
| Commuter rail | -0.31416 | 0.272372 | -1.15342 | 0.332292 | -1.18097 | 0.552649 | -1.18097 | 0.552649 |
| Heavy rail | -0.19815 | 0.045636 | -4.34188 | 0.02255 | -0.34338 | -0.05291 | -0.34338 | -0.05291 |
| Light rail | -0.02034 | 0.016652 | -1.2212 | 0.309227 | -0.07333 | 0.032659 | -0.07333 | 0.032659 |
From the table, there is a general decrease in the mode of transit coefficient. There is a negative relationship between the mode of transit and the fuel prices. As the price of gasoline goes up, the mode of transit decreases. This means that rise in gasoline price in the United States has a negative impact on the public transit ridership in the United States. These findings are in agreement with the literature reviewed which came up with a similar finding. It is therefore evident that gasoline prices were significant in determining the transit demand in the United States.
Findings
This study is in agreement with the previous studies that gasoline prices affect the demand for the public transit of ridership. However, the study has revealed that the demand for public transit varies from city to city depending on the demographic and economic factors of those cities. The studies have established that there is a negative correlation between the rise of the price of gasoline and the US public transit ridership (Haire, 2007). As the price of gasoline increases, the demand for private vehicles reduces and the management goes up. It was also observed that with the increase in gasoline price, the transit agencies and operator increased fare and this affects the travel behavior among the customers. The rise of the price of gasoline had a negative effect on the income of the commuters and this determined the mode of transit they choose. For example, when the price of gasoline increases the demand for public transit increases as commuters stop or reduce the use of private vehicles to move from or to work. It has also tax implications in that both the incidence and the impact of tax rest on the consumer which is not desirable.
Conclusion
The study found that there is a positive relationship between the rise of the price of gasoline and the demand for public transit ridership (FitzRoy, 1999). The demand for public transit is affected by both internal factors and external factors. Internal factors included the fare charged by the transit, the level of quality offered and the level of service offered. The external factors were economic, geographic and demographic factors.
Recommendations for further studies
This research study recommends that further study is done with more variables such as population and gender as factors that affect the mode of transit. It further recommends the study to be conducted outside the United States to find out whether the same result can be arrived at. Policymakers should ensure that the rise of gasoline prices is controlled so that the commuters and the transit agencies are not badly affected. The flexible tax policy should be implemented to take into account of these unforeseen contingencies.
References
Baum-Snow, N. and Kahn, M. (2000) "The Effects of New Public Projects to Expand Urban Rail Transit." Journal of Public Economics 77 (2), 241-263.
Bento, A., M. Cropper, A. Mobarak, and K. Vinha. (2005) "The Impact of Urban Spatial Structure on Travel Demand in the United States," The Review of Economics and Statistics.
Flink R. (2003) "Transit pricing research: A review and synthesis," Transportation 117-139
Currie, G., and J. (2007) "Transit Ridership, AutoGas Price, and World Events: New Drivers of Change?" Transportation Research Record: Journal of the Transportation Research Board, 2007, 1992, 3-10.
FitzRoy, F, and I. (1999) "Season Tickets and the Demand for Public Transport," Kyklos, 219-238.
Goodwin, P. (1992) "A Review of New Demand Elasticity with Special Reference to Short and Long Run Effects of Price Change," Journal of Transport Economics and Policy, 155-169.
Haire, A., and R. (2007) "Impact of Rising Fuel Prices on U.S. Transit Ridership," Transportation Research Record: Journal of the Transportation Research Board
Holmgren, J. (2011) "Meta-analysis of public transport demand," Transportation Research Part
Mattson, J. (2008) "Effects of Rising Gas Prices on Bus Ridership for Small Urban and Rural Transit Systems," Working Paper.
Taylor, B., and C. (2002) "The Factors Influencing Transit Ridership: A Review and Analysis of the Ridership Literature," UCLA Department of Urban Planning, 2002, Working Paper.
Taylor, B., D. Miller, H. Iseki, and C. (2008), "Nature and/or nurture? Analyzing the determinants of transit ridership across U.S. urbanized areas," Transportation Research Part.
