Title: Analysis on Singapore Taxi Industry for selecting appropriate tax decision imposed by the Ministry of Transport
In Singapore, the Ministry of Transport proposes two types of tax systems where the first proposal states about imposition of $2.40 as tax on per trip of taxi while the second proposal states about imposition of an everyday lump-sum tax of $50000. In the following sections, an industry analysis is conducted for understanding that between these two tax systems, the Ministry of Singapore can choose the tax imposed for per taxi trip because of its higher revenue compare to others.
Principle recommendation:
The government can adopt the first approach of proposed tax system because of its high return of revenue compare to other
The Ministry of Transport can set a maximum level of price for each trip of taxi so that the incidence of tax cannot hamper passenger’s interest
In this report, the Singapore taxi industry will be analysed based on the proposed imposition of tax by the Ministry of Transport. In this context, the ministry has suggested two systems, which two major taxi companies have denied to follow. The chief reason for disapproving this system is that these two companies realise that the tax burden can increase the price of each taxi trip further and this consequently can decrease their number of passengers in future (Dube 2018). This is because the ultimate of tax will be carried out by passengers. In this context, the following industry analysis is conducted to help the Singapore government by stating that which one of the given two tax approaches will be better of it (Tremblay and Tremblay 2017). The analysis represents tax structure of the government through states that the first approach will create more revenue compare to the second one in the context of Cournot’s duopoly market (Yang, Wu and Wang 2017). The chief motive of the government for imposing this tax system on taxi industry is to invest the money for maintaining and developing conditions of roads and bridges across the city. However, taxi industry opposes this approach to protect their business.
According to the first approach, the government decides to impose $2.40 for per trip of taxi and it is observed that these companies can complete 34300 trips, based on this price. Hence, the amount of revenue that can be generated from this taxi trip will be $82320. On the contrary, the outcomes represent that after imposition of a lump-sum tax, the government can earn revenue worth $70000 every day irrespective of the number of taxis these two taxi companies provide in a particular day. In this situation, every taxi company needs to pay $35000 as tax. Thus, it can be stated that first approach can provide comparatively more revenue to the government.
To discuss the entire situation, the industry obtains its inverse demand function of taxi along with costs of these two associated with their service. These components of costs are wage and benefits that drivers receive, price of fuels, cost for wear-and-tear, vehicle cost along with corporate overheads. Each cost for these two companies are same except the wear and tear one. The cost for driver’s wage and benefits accounts for $8 per trip where fuel cost accounts for $1.50 each trip. Moreover, the corporate overheads accounts for $100000 per day. However, the cost for wear and tear for Gold Trip Taxis is $3 for every trip while that for Dark Grey Cabs is $4.50. In this situation, corporate overheads act as fixed cost while others are considered as variable costs. By summing up these costs, the industry can obtain its total cost for every company from which the report can draw its marginal cost equation. From the given equation of inverse demand function, this report can obtain its total revenue from which marginal revenue of these two companies can be obtained. By equating marginal revenue and marginal cost equations, this company can obtain its equilibrium amount of price and the number of trips that each taxi company should provides to its passengers (Wang et al. 2018). The overall calculation will follow Cournot’s model under duopoly. This type of oligopoly market states that firms select the number of trips simultaneously so that each company can decide the number of trips they can provide every day (Yan, Yang, Yin and Wan 2018). This model of competition tries to work efficiently for the given industry under which two companies can take their decision regarding trips in advance of the market.
Thus, with the help of financial analysis, the report can represent numeric outcomes of these two approaches based on the given data. Through this discussion, the government can select its appropriate tax structure for improving the condition of road and bridges all over Singapore (Stiglitz and Rosengard 2015). This analysis has the importance to provide correct information to the government, based on costs and demands and proper mathematical calculation, based on the process of Cournot’s model.
In taxi industry of Singapore follows all features of an oligopoly market and consequently it can be stated Cournot competition exist in the market and consequently this method will be used to determine the equilibrium price and corresponding number of trip for every taxi company (Klastorin, Mamani and Zhou 2016). The following industry analysis will help the Singapore government to decide that which one between these two can help it to earn comparatively more revenue for tax (Silva, Hassani and Heravi 2018). The following analysis section has two parts: notation and analysis.
Q: total number of taxi trips provided by two companies every day into the market
P: Taxi fare for each trip charged by two companies
Total fixed Cost (TFC) = the cost that cannot be altered during short-run. In this situation, corporate overhead represents this type of cost for both industries
Total variable costs (TVC) = this type of costs can be changed based on the production process, where no production means no variable costs. In this situation, driver wages and benefits, fuel and vehicle wear-and-tear show this type of costs for these two companies of taxi
Total revenue (TR) = total number of taxi for each company available a day * price for each trip of taxi
Marginal revenue (MR) = change in total revenue of every taxi company for each unit of extra trip
Total cost (TC) = TFC + TVC, which means total production consists with total fixed cost and total variable cost
Marginal cost (MC) = for each company, this cost implies change in total cost for an extra unit of trip
Let, to represent Gold Top Taxis, subscript G is used after each notation while for Dark Grey Cabs, D is used as notation.
The following list provides all notations for first company:
QG = Total number of taxis available for one day
TRG = Total revenue of Gold Top Taxis
MRG = Marginal revenue of Gold Top Taxis
TCG = Total cost of Gold Top Taxis
TFCG = Total fixed Cost of Gold Top Taxis
TVCG = Total variable costs of Gold Top Taxis
MCG= Marginal cost of Gold Top Taxis
For second company:
QD = Total number taxi provided service for one day by Dark Grey Cabs
TRD = Total revenue of Dark Grey Cabs
MRD = Marginal revenue of Dark Grey Cabs
TCD = Total cost of Dark Grey Cabs
TFCD = Total fixed Cost of Dark Grey Cabs
TVCD = Total variable costs of Dark Grey Cabs
MCD = Marginal cost of Dark Grey Cabs
In Singapore, the taxi industry has the following Inverse demand function:
Equating marginal revenue with marginal cost, the following outcome will be obtained:
Equating marginal revenue with marginal costs following outcome can be obtained:
The first approach states to impose $2.40 tax on every trip of taxi (Adachi and Fabinger 2017). As a result, total amount of revenue generated by this method will be $($34300 * 2.4) = $82320
Moreover, the price for each taxi trip will become:
P = $(25.5 + 2.4) = $27.9
Figure : Increase in price after imposition of first approach
The second approach states that government will impose a lump-sum tax worth 000 every day without considering the number of total taxi.
In this context, total revenue of the government in every day will be:
$(35000*2) = $70000
Therefore, based on above two outcomes it can be stated that the Ministry of Transport can select first approach, as the amount of revenue in this context is high compare to the second one (Muñoz et al. 2017).
References:
Adachi, T. and Fabinger, M., 2017. Multi-Dimensional Pass-Through, Incidence, and the Welfare Burden of Taxation in Oligopoly.
Dube, G., 2018. The design and implementation of minibus taxi presumptive taxes. The Service Industries Journal, pp.1-19.
Klastorin, T., Mamani, H. and Zhou, Y.P., 2016. To Preannounce or Not: New Product Development in a Competitive Duopoly Market. Production and Operations Management, 25(12), pp.2051-2064.
Muñoz, R.G., Shehab, E., Weinitzke, M., Fowler, C. and Baguley, P., 2017. Operational Software Maturity: An Aerospace Industry Analysis. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 11(9), pp.973-982.
Silva, E.S., Hassani, H. and Heravi, S., 2018. Modeling European industrial production with multivariate singular spectrum analysis: A cross?industry analysis. Journal of Forecasting, 37(3), pp.371-384.
Stiglitz, J.E. and Rosengard, J.K., 2015. Economics of the public sector: Fourth international student edition. WW Norton & Company.
Tremblay, M.J. and Tremblay, V.J., 2017. Tax Incidence and Demand Convexity in Cournot, Bertrand, and Cournot–Bertrand Models. Public Finance Review, 45(6), pp.748-770.
Wang, H., Zhang, K., Chen, J., Wang, Z., Li, G. and Yang, Y., 2018. System dynamics model of taxi management in metropolises: Economic and environmental implications for Beijing. Journal of environmental management, 213, pp.555-565.
Yan, Q., Yang, L., Yin, J. and Wan, Y., 2018. Optimal Licensing in a Stackelberg Duopoly Market under Asymmetric Information of the Marginal Cost. International Journal of Performability Engineering, 14(2), p.341.
Yang, T., Wu, J. and Wang, J., 2017. Duopoly Competition Between Chauffeured Car and Taxi: An Analysis of Pricing and Market Segmentation. Journal of Systems Science and Information, 5(6), pp.511-523.
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