Economics Assignment on UBER and TAXI
Task 1: Industry Structure
If the route of M1 and M2 is selected (the most preferable choice on Google Maps and maps on Uber website) to travel to Melbourne Tullamarine Airport from Deakin University, Burwood Campus, then the distance is 40.1Km. If UberX is selected for ride, the fare will be around $61-79 excluding the chances of traffic and fees of tolls. Travelling through other taxi services in Melbourne, Victoria, the estimated cost is shown to be $94 with a distance of 42.94Km and $7.30 as extra toll fees. Comparing both of the situations, I will select UberX for this ride because of competitive prices, timely services and considering their rate of per Km of $1.00 and per minute of $0.32.
The long-run equilibrium of the taxi industry in Victoria is shown in figure 1, whereas, the diagram of individual monopolistic competitive taxi companies before and after the introduction is shown in figure 2.
Figure 1: Long-run equilibrium (self-made)
Figure 2: Individual monopolistic competition (self-made)
In the long-run equilibrium, the taxi companies in the market are making normal profits, whereas, the equilibrium point is never reached – new products (taxi services) enters and exist the market, whereas, some do better than the others. In the monopolistic competition and after the introduction of UberX, the products are differentiated and the concentration ratio of the industry is low. It is believed by customers that many non-price differences exist among the products which is because of product diffrentiation ultimately resulting into a strong competition between all the taxi industries and a plenty of customer switch from one taxi to another. This situation is valid in both the contexts i.e. before and after the introduction of UberX. The monopolistic model of competition is accurately representing the taxi market in Victoria, whereas, the monopoly model does reflect the taxi market in Victoria but is not accurate as the monopolistic competition model. Previously, the taxi industry was not monopolistic in Australia and Victoria but the introduction of UberX increased the competition in Australia.
In all three parts of the question, it can be explained through the supply and demand analysis – supply of the taxi rides will increase in part (i) and (ii), whereas, the price of taxi rides will decrease, thereby, decreasing the demand of taxi rides in Victoria (figure 3). The fare regulation will significantly impact the price of a taxi ride and the number of taxi rides in Victoria because it will increase the demand, thus, increasing the price and lowering the supply of overall taxi rides as shown in figure 4.
Figure 3: Supply and demand analysis for Part (i) and (ii) (self-made)
Figure 4: Supply and demand analysis for part (iii) (self-made)
As UberX is not regulated in Victoria, it has a significant impact on competition in the taxi and ride-sharing industry of industry. The drivers of UberX cars does not have to deal with any regulation, license and fare regulation policies due to which the company have more money in-hand to spend in boosting the performance of the company. This might be one of the reason behind such competitive prices of UberX taxis. On the other hand, those who find taxi expensive, they can move for buses and trains as they are the best suitable option in Victoria after taxis and UberX.
The time series graph of the price of a Victorian Taxi License for years 2004-2014 is shown in figure 5.
Figure 5: Time series graph
There are mainly two goals of constructing a time series graph, (a) recognising the nature of the phenomenon illustrated by the sequence of observations, and (b) to identify/forecast trends for future (Dell, 2015). The graph in figure XX is representing that from 2004 the price of Victorian Taxi License increased non-linearly and a decrease was observed after 2010 till 2013, however, in 2014 an increase of $5,000 is observed. In the current scenario, predicting the future price of a Victorian Taxi License is a bit difficult, however, it can be said that in future the price of a Victorian Taxi License will slightly increase than that of 2014 but will be less than the maximum price of $500,000 in 2010.
Observing the time series graph, it is evident that the introduction of Uber in 2012 significantly affected the price of taxi license in Victoria as a major decline is observed in the price of the year 2011 ($480,000) and 2012 ($380,000). Further, the price continued to decrease in the year 2013 ($280,000) which are the signs of Uber affecting the taxi industry of Victoria. According to O’Sullivan (2015), other factors which contributed towards the decrease in Victorian taxi license prices are partly because of the issuance of more licenses and the total number of licensed taxis released onto the road. However, O’Sullivan also believes that the introduction of Uber is the main contributor towards the decrease in the license prices.
Section (b): Fares regulation
In metropolitan Melbourne, the lowest fares of a taxi are in the day-time, a little more in the overnight and peak fares after 10pm on Friday and Saturday nights. According to an official article published on the Victorian News (The Age) by Carey (2014) that this variable price is established to handle two chronic problems. Firstly, to manage the shortage of taxis on the nights of Friday and Saturday – increasing the fares will reduce the number of people calling for taxis, therefore, the shortage of taxis on overnight, Friday and Saturday nights can be handled. Secondly to manage the short fare refusal by most of the taxi drivers – most of the drivers refuse to offer their services overnight and especially on Friday and Saturday night after 10 PM and to handle this issue the fares are increased in such timings.
In point of economics, the surge pricing by Uber is referred to as the most basic application of supply and demand – the price will rise once the need of service increases. Simply, if a sudden price surge occurs, the supply of Uber will rise while decreasing the demand (Griswold, 2015). Figure 6 is used to illustrate this concept.
Figure 6: Effects of Surge Pricing after (S1, D1) and before (S, D) – Supply increases and demand decreases, thus, the equilibrium point also shifts and a new price is set (Self-made)
Dube and Vargas (2013) argued that an important determinant of conflict in Colombia is the value of coffee production calculated through the price of coffee multiplied by the area of land under coffee cultivation. In order to validate this argument, a relationship test, using scatter plot diagrams and trend line equation has been carried out which will examine the relationship between a number of guerilla attacks and value of coffee production. The scatter plot diagram is shown in figure 7, also displaying the regression equation and coefficient of R2.
Figure 7: Scatter plot diagram and regression equation
According to Anderson and Swe (2002), in order to demonstrate a correlation between two quantitative variables, scatter plot diagrams are most commonly used and most of the times the relationship is linear. The author further stated that the correlation is usually classified into three basic types, namely, linear, non-linear and no correlation. The scatter plot diagram of guerilla attacks versus the value of coffee production is demonstrating a non-linear negative relationship i.e. if one variable increases, the other variable will decrease in value. However, it is important to note that this relation is also observed in a linear negative relationship but a linear model can be defined by a straight line (trend line), whereas, the relationship observed in figure 7 can be best described through a curve. An example of a non-linear negative relationship is also shown in figure 8.
Figure 8: Example image of a non-linear negative relationship (Anderson and Swe, 2002)
Further, the regression equation and R2 coefficient can also be used to explain the relationship between the two variables. Assuming the number of guerilla attacks on two particular situations was 0 and 1. The value of coffee production can be calculated from the regression equation as follows:
For guerilla attacks = 0;
For guerilla attacks = 1;
It is evident from the example that as the number of guerilla attacks are increasing, the value of coffee production decreases, hence, indicating a negative relationship among both of the variables.
The coefficient of determination is known as the R2 which indicates a proportion and is a value between 0 and 1. If the value of R2 is close to 0 then the estimated model is not accurate and illustrates a poor relationship, whereas, if the value if R2 is 1 then the estimated model is perfect in explaining the variation among variables (Friedman et al. 2009). In this case, the value of R2 comes out to be 0.002 from which it can be concluded that only 0.2% of the variability in the response is explained by the estimated model (scatter plot diagram). It, therefore, implies that,
99.8% of the response is left unexplained by the scatter plot diagram, therefore, it can be said that the estimated model in figure 1 is more towards uncertainty and scatter plot diagram does not accurately predict the relationship between these two variables.
The concept of opportunity costs can also be used to explain the relationship between a number of guerilla attacks and the value of coffee production. Mankiw (2014) defined opportunity cost of an item refers to all those things which must be forgone to acquire a particular item. Opportunity costs are used as an important concept in business decision making and are of particular importance to economists. Considering this case in which a negative relationship exists between the number of guerilla attacks and the value of coffee production, it can be said that the opportunity costs are those costs which Colombia could have earned by decreasing the number of guerilla attacks, hence, increasing the value of coffee production which ultimately increases the total revenues. Simply, if guerilla attacks are happening, it is decreasing the value of coffee production (as depicted in the scatter plot diagram shown in figure 7) and hence the revenue which could have been generated by the value of coffee production is forgone and can be termed as the opportunity costs.
The scatter plot diagram of the relationship between a number of causalities and value of coffee production is shown in figure 9.
Figure 9: Scatter plot diagram and the regression equation
According to figure 9, a very weak non-linear negative relationship exists between these two variables – a total number of causalities and value of coffee production. The trend line is also illustrating a downwards shift and the value of R2 is significantly close to 0 which depicts that this model does not accurately explain the relationship among these variables. The regression equation is also showing that when the number of capsulitis will increase, a decrease will occur in the value of coffee production. However, it is important to note that the relationship is not linear and the scatter plot diagram is not explaining 99.79% of the variation in the response.
In both of the situations, it was observed that the value of coffee production was decreasing by an increase in the number of causalities and guerilla attacks. Referring to the concept presented by Sutton (2006) if the value of coffee production is affecting the prices of coffee, i.e. unstable prices, then it may have political consequences because most of the policymakers are interested in the effects of periodic food scarcity which may result in serious malnutrition and starvation. The author further mentioned that a stabilisation mechanism must be used to stabilise the prices which can address the scarcity of food and price support. In this case, the Colombian government will need to stabilise the price of coffee so to avoid inflation as well as deflation. In order to stabilise the prices of a particular item, various strategies are used by the government and the commonly used are to monitor the business cycle and adjusting the benchmark interest rates so that aggregate demand in the economy can be controlled. Though these strategies are helpful in stabilising the prices, each comes with a cost of implementing it. For e.g. if the Colombian government decides to increase the interest rate, it will make the coffee expensive to borrow and investment and spending will be discouraged. The concept of opportunity costs also applies in this situation – if the government increases the interest rates, the spending and investments will lessen which, therefore, can be defined as the opportunity costs the government could have earned if they have decreased the interest rates.
The concept of opportunity costs is already explained in the latter paragraphs and relating this concept to the two ceasefire conditions, namely, land reform and the production of drug (cocaine) to be replaced with alternate crops, important conclusions can be derived. Land reform refers to the social and economic development of rural areas and the provision of land to poor farmers. If this ceasefire condition is implemented, the government of Colombia have to forget the prices they were previously acquiring from the rural areas and that can be referred as the opportunity costs. Further, an article published by BBC News (2015) believes that land reform will not be a cheap alternative for the government, therefore, the costs to implement this ceasefire condition will also be termed as the opportunity costs which the Colombian government have to forget in order for the land reform to occur. Secondly, replacing the production of drugs with the production of alternative crops can also be explained through the concept of opportunity costs. The government will have to forget the contribution of drug production to their GNP and GDP which can be referred as the opportunity costs. The implementation of this condition is also associated with an opportunity costs the government will have to invest to replace the production of drugs with that of alternative crops.
Leinwand (2007) mentioned that if the prices of drugs increases, it does not necessarily indicate the success of the government in eliminating the drugs because when the drug prices will increase, drug cartels will make more money and a significant amount of people will enter the market. If drug prices increase, the government of Colombia will lose a significant amount of opportunity costs as Gootenberg (2013) mentioned that around 5.5% of Colombia’s GDP is because of illegal drug trade across various countries.
The simple model of multiple regression constructed on MS Excel is shown in figure 10. The dependent variable is the number of causalities and the independent variables are years (time trend), value of coffee production, value of oil production and natural logarithm of population (population). The data-set consists of 17,964 values, whereas, for regression only 5,000 values were selected as the sample size from the complete data-set because of the limitations of the software used, especially the LINEST function of MS Excel.
Figure 10: Simple model of multiple regression (Self-made)
The value of coffee production is the X Variable 2 in the regression model and while observing the P-value it is clear that the changes in the predictor (the value of coffee production) are not associated with the changes in the response (the number of causalities). It is commonly believed that if the P-value is lower than the alpha value of 0.005 then the values are statistically significant, whereas, in this case this value is 0.37, higher than the alpha value, hence, indicating an insignificant relationship. The other coefficient of the regression analysis are also indicating the same results.
The value of oil production is the X Variable 2 in the regression model and analysing the P-value of this independent variable which is 2.18E-05, or 0.000218, it is evident that changes in the predictor (value of oil production) are significant and statistically associated with the response (number of causalities). The closer the P-value to 0, a more strong significant relationship is formed. Apart of that, if the P-value of the intercept (number of causalities) is observed, which is
0.004, it also indicates that the dependent variable has strong association with some of the variables and the value of oil production is one of them.
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