Principles Of Transportation Economics
- Pages: 4
- Word count: 946
- Category: Economics Sports Transportation
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What is an economic model and ideally what characteristics would we like our economic models to satisfy?
Trevor Jones (2005) wrote that economics “identifies various market failures that make the community worse off (p. 38). In this line of thinking, he said that “[economic] models of the market assume that private and social costs and benefits coincide” (Jones, 2005, p. 38).
More specifically, an economic model is the representation of certain economic activities or processes of a specific economic entity. This model might be a diagrammatic, mathematical, written or verbal representation. Let’s consider the economic model of a business. Hirschey (2002) created a mathematical model of the primary goal of a business – that is, profit maximization in the short run and expected value maximization in the long run (pp. 5-6). The author clarified that “[the] value of the firm is the present value of the firm’s expected future net cash flow” (pp. 5-6).
Figure 1 shows the foregoing economic model of a for-profit organization.
Source: Hirschey, 2002, p. 6
Where π1, π2, … , πn represent the expected profits in each year, t, and i is the appropriate interest or discount rate for that business.
The characteristics that I would want my economic model to satisfy are:
- First, it should be able to provide the information that the users of the model needs in decision making.
- Second, it must be able to compare alternatives.
- Third, it must also take into account several economic information that are difficult to quantify.
- The economic model should be parsimonious (Skeels, 2006, p. 257).
- The model can be used by a different economic entity within the same setting (Saatcioglu, Korap & Volkan, 2007, p. 51).
Question 2
Suppose that you have a simple economic model that shipper demands for transportation services depend upon the transportation rate charged.
(a) Identify the cause and effect relationship in this model.
The economic model for this situation is mathematical, and it would appear like this:
Dt = f(tx)
Wherein Dt represents the shipper demands for transportation services, and tx is the transportation rate charged.
The above mathematical representation of the model means that the amount of transportation services shipper demands is a function of the transportation rate. In other words, as the transportation rates charged by shipping companies the amount of transportation a shipper demands goes down. Consequently, as the transportation rate goes down, then the transportation demanded by a shipper increases. This type of relationship between two or more variables is called inverse relationship.
(b) Why is this model a qualitative model of shipper demands?
The economic model of the shipper demands for transportation services which depend upon the transportation rate charged is qualitative because:
- the model tries to test the hypothesis that the shipper demands for transportation services depend on the transportation rate charged,
- in general, qualitative model “[describes] systematic structures and processes” (Heise & Durig, 1999). In this scenario, the model is describing the structure governing the shipper demands for transportation services; and
- although, the model is numerical, it is still considered an example of a qualitative model because it is defined by a constraint – the transportation rate charged.
(c) How would you transform this economic model into an econometric model of shipper demands?
As Jones said “[the] traditional economic approach to estimating a demand function is for the firm to use statistical methods, using data collected either by the firm or other outside sources, such as industry expectations or government agencies” (2005, p. 106). As such, to transform the economic model into an econometric model I will need to collect data for the key independent variable affecting the shipper demands.
After this data are collected, then the second step is to obtain or calculate the best fir or equation that best explains the quantity demanded. This is done by using statistical tools such as linear regression.
(d) In your econometric model, how would you test the hypothesis that demand depends upon transportation rate charged?
There is a need, however, to perform an economic verification of the economic models that were derived using statistical tools. Although, statistics might have generated an apparent strong relationship between the quantity demanded and transportation rates, that relationship might be spurious. As such, the economist must first check the signs and magnitudes of the estimated variables – both dependent and independent which in this case are the transportation demanded and rates charged, respectively.
Jones (2005) suggested that for “normal goods the own price elasticity of demand should have a negative sign, the cross-price elasticity of demand should have a negative sign and the income elasticity of demand should be positive” (pp. 111-112). Otherwise, the hypothesis derived from statistical analysis with regard to the relationship of transportation demanded and the rate charged needs to be checked again.
An economist can also perform several statistical verifications in testing the hypothesis. Correlation coefficient, F-test, t-test, and standard error estimate can be utilized. Economic verifications techniques such as multi-collinearity, and autocorrelation can also be used.
References
Heise, D. & Durig, A. (1999). Qualitative models. Accessed on June 19, 2007, Available from http://www.indiana.edu/~socpsy/papers/QualEncyclo.htm.
Hirschey, M. (2002). Fundamentals of managerial economics. New York: Thompson.
Jones, T. (2005). Business economics and managerial decision making. New Jersey: John Wiley & Sons.
Skeels, C. (2006). Simplicity, inference and modeling: Keeping it sophisticatedly simple. Economic Record, 82 (257), 226-227.
Saatcioglu, C, Korap, H. & Volkan, A. (2007). Modeling purchasing power parity using co-integration: Evidence from Turkey. Journal of American Academy of Business, 11 (2), 51-57.