A Cost-Benefit Analysis of an Improved Public Transportation Program in Laguna Beach, California
This chapter will discuss the following subtopics, data and sources, techniques of data collection, research design, and data analysis.
This research has used quantitative methods to analyze the data and form an objective view of the economic benefits of the trolley in Laguna Beach, California. According to Babbie (2010), in an analytical research generalization and assessment of numerical data can be done using quantitative analysis to find the relationship between various variables. The assessment of the economic impact of a transport system entails various analytical methods, which are determine the relationship between expenses used in transport such as fleet maintenance. They also determine the financial incomes generated by the transport system such as transit funds income and transit funds net revenue in assessing the overall tax collected. The regression test and analysis of variance (ANOVA) have being found to be effective in determining the existence of any relationships on tax collection, income from transit lines, expenses on fleet maintenance, and revenue from transit lines.
This project was done on a multiple regression analysis using excel to find whether the tax collected in Laguna Beach depends on income from transit lines, expenses on fleet maintenance, and revenue from transit lines. In this research, the Pearson correlation tests showed whether there exists any relationship between the independent variables and the dependent variable. The ANOVA test was used in the determination of the accuracy of the hypothesis. The data used in this analysis was the actual expenses, incomes, and taxes collected in Laguna Beach from 2004 to 2015.
The research used the following hypothesis:
H0: The development of the trolley systems increases trade in Laguna County, which results in higher tax collection.
H1: The development of the trolley system does not increase trade in Laguna County, as a result, there is no change in tax collected.
The following model was developed for the research:
LAGtax= K + X1FM + X2IF + e
LAGtax = Tax collected in Laguna County
K = Constant at the Y-intercept
X1 = Variable for fleet maintenance
FM = Fleet maintenance
X2 = Variable for Federal Bank Interest Rate
IF = Federal Bank Interest Rate
Data and Sources
Statistical data was accessed from the City of Laguna Beach official website and the Wordl Bank Website. The data was from 2004 until 2015. The year 2015 data was not used in the analysis since it did not show the actual expenses incurred in fleet maintenance. Simply, if this data had been used it would have resulted in the development of a misleading model.
Techniques of Data Collection
Primary data was used in this analysis. The data was collected from the City of Laguna Beach official website. Secondary data on the US Federal Bank Interest Rate was accessed from the World Bank website. Since only primary data was used, the paper minimized the occurrence of any error in the development and analysis of the model. After extraction and classification of relevant data into appropriate groups, a regression test was conducted to evaluate the accuracy of the hypothesis in the research model.
The collected data was analyzed based on Keynesian economic theories. Specifically, increased government spending has the impact of improving and accelerating economic development (Harcout, 2008). The impact of the Federal government monetary policies was also assessed by using the Federal Reserve interest rate. In particular, an increase in interest rate has the effect of reducing inflation, as well as increase cost of borrowing, which in turn slows investments. This policy is a contractionary policy. On the contrary, a decrease in interest rate has the effect of increasing inflation levels and investments. It is an expansionary monetary policy. Accordingly, the increased spending by the government of Laguna Beach County through the establishment of a trolley system was expected to spur economic growth in the area by increasing the disposable income of individuals directly employed by the trolley company. In addition, the trolley network was expected to result in increased trade in the area through increased tourism. There was also expected to be an increase in the value of property in areas where the trolleys passed. In general, the increased trade activities and the value of property were expected to increase the county’s tax. Simply, there would be increased property tax, when individuals sold houses and income tax from business profits.
Findings and Discussions, Conclusion
Using a multiple regression analysis, the following regression model was developed for Laguna Beach County.
LAGtax= 9,407,891.93 + 75.911 FM -125078891 IF
LAGtax = Tax collected in Laguna County
K = 9,407,891.93
X1 = 75.911
X2 = -125,978,891
Regression Analysis of the Model
|Adjusted R Square||0.792834341|
|Coefficients||Standard Error||t Stat||P-value||Lower 95%||Upper 95%||Lower 95.0%||Upper 95.0%|
|Federal Bank Interest Rates||-125078891||26368578.88||-4.743482447||0.002099355||-187430672.1||-6.3E+07||-1.9E+08||-6.3E+07|
This model illustrated that there is a positive relationship between the tax collected in the General Fund in Laguna Beach County Tax and Fleet maintenance. An increase in fleet maintenance by one unit has an impact of 75.911 times on the Laguna Tax collection in the General Fund. There is a negative relationship between Federal Bank Interest Rates and the General Fund in Laguna. A one percent increase in the Federal Bank Interest Rates has a
-125,078,891 on Laguna Beach General Fund.
In the ANOVA analysis, the F-critical was 0.002778. Therefore, it was significant since it was less than 5%. Fleet maintenance and Federal Bank Interest variables had a p-value that was less than 5%. Accordingly, we accept the null hypothesis that an increase in trolley system in Laguna Beach increases the net tax collected in Laguna Beach County. In particular, the model illustrated that an increase in Fleet maintenance, which resulted in an increase in tax collected. Since tax is a percentage of revenue from sales or from personal incomes, then fleet maintenance resulted in more sales for the suppliers of spare parts, more jobs for mechanics, and ultimately more sources of taxable income. The negative relationship between the interest rates and tax is because tax is a disincentive, which discouraged trading activities. In turn, this led to less income and accordingly, less tax collection.
Tax Collected in Laguna County
The tax collected in Laguna County acted as a measure of economic performance in the region. When there are a lot of economic activities, there is always more income for businesses and similarly higher salaries. Accordingly, the increase in tax collected is a representative of low economic performance, while high tax collections illustrate more economic performance in a region. Noteworthy, this model assumed that the tax collection was at optimal levels in the represented periods and there was no change in taxation policies.
The final analysis showed that fleet maintenance had a positive relationship with an increase in tax for Laguna Beach County. Notably, the county tax collection in this model acted as a proxy for measuring the increase in the economic performance of the county. Fleet maintenance results in an increase in disposable income in the county. Specifically, the workers, such as mechanics, employed to carry out this work have more disposable income, which they spend in the county. The increase in incomes also leads to more demand for the town’s property. The demand for property in Laguna increases the prices for houses, which leads to more property tax. On a similar breath, a significant percentage of resources are sourced from Laguna County when fleets are maintained. Therefore, local businesses thrive by supplying spare parts and other consumables for fleet maintenance. Further, there are indirect beneficiaries from fleet maintenance such as hotels, solons, lodgings, and shopping malls where direct beneficiaries from fleet maintenance spend their income. In turn, these funds promote the growth of businesses in Laguna, which results in higher income and in turn more income tax. In light of this, the positive correlation variable for the fleet maintenance was economically realistic. To emphasize, the Keynesian economics note that an increase in government spending and investments (in this case the Laguna Beach Trolley) results in more economic growth.
Federal Bank Interest Rate
Interest rate plays an important role, since monetary policies have a direct effect on economic performance. Whereas the Trolley system aims at improving and accelerating economic growth and development, for its success, there must be a friendly economic environment. Worth remembering, the Trolley system is free, therefore, it does not directly earn income for the County. Nonetheless, it acts as an appropriate incentive for economic growth by creating an enabling business environment. In light of this, the income tax represented by the General Fund is not only affected by the Trolley system, it is also influenced prevailing economic conditions in the county, such as the interest rates. Therefore, the use of the interest rate in the model was able to show the relationship it had with the model and its overall impact to the tax collected.
American Public Transportation Association (2007). A Profile of Public Transportation Passenger Demographics and Travel Characteristics Reported in On-Board Surveys, APTA, Washington, DC.
Babbie, R. (2010). The practice of social research. 12th Ed. Belmont, CA: Wadsworth Cengage.
Barget, E., & Gouguet, J. J. (2007). The total economic value of sporting events: Theory and practice. Journal of Sports Economics, 8, 165–182.
Canadian Urban Transit Association (2003), “Transit Means Business: The Economic Case for Public Transit in Canada,” Issue Paper #54m, Canadian Urban Transit Association, Toronto, Ont. Retrieved from http://www.cutaactu.ca/sites/cutaactu.ca/files/issue5.pdf
Crain et al. (1999). “Using public transportation to reduce the economic, social, and human costs of personal immobility.” Transit Cooperative Research Program Report 49, Transportation Research Board, Washington, DC. Retrieved from http://onlinepubs.trb.org/Onlinepubs/tcrp/tcrp_rpt_49.pdf
Daniel, G. (2005). “Transport investment, agglomeration and urban productivity.” World Bank Symposium Papers. Retrieved from http://www.worldbank.org/urban/symposium2005/papers/Daniel.pdf
Dwyer, L., Forsyth, P., & Spurr, R. (2006). Economic impact of sport events: A reassessment. Tourism Review International, 10, 1–10.
Harcourt, G. (2008). The structure of post-Keynesian economics: The core contributions of the pioneers (1st Ed.). London, UK: Cambridge University Press.
Todd, L. (2008). “Evaluating public transit benefits and costs.” Best Practices Guidebook 10,” Victoria Transport Policy Institute. Retrieved from http://www.vtpi.org/tranben.pdf
Tom, T., & Jones, A. (2007). The Economic impact of the Metropolitan Atlanta Rapid Transit Authority, GEMS: Georgia Economic Modeling System, Carl Vinson Institute of Government, Georgia State University. Retrieved from http://www.cviog.uga.edu/publications/free/marta.pdf
Weimer, D. (2009). Cost-benefit analysis and public policy. Journal of Policy Analysis and Management Classics Series, 11(2), 402-412
Weisbrod, Glen (1997). “Assessing the economic impact of transportation projects: How to choose the appropriate technique for your project.” Transportation Research Circular #477, Transportation Research Board, Washington, DC. http://onlinepubs.trb.org/onlinepubs/circulars/circular477.pdf