Introduction
Demand estimation is particularly important for players in the automobile sector due to high consumer price sensitivity. For an automobile firm, production output should be pegged on demand forecast for optimal inventory control. The economic models explaining the relationships between the demand for vehicles and household incomes and oil prices implicate the multiplier effects. In this respect, a deep comprehension of the causes and effects of oil price spikes is required for effective management of demand shocks. The focus of this paper is on estimating a demand function showing to what extent fluctuations in the OPEC oil price since 1990 and personal income predict the number of vehicles in the U.S. and personal income.
The real expenditure on automobiles reduces in the aftermath of oil price spikes, indicating a negative correlation between gasoline oil shocks and demand for motor vehicles. Motor vehicle output from countries such as the US is bound to decline during periods of high oil prices due to GDP loss that permeates all economic sectors. Also, monetary responses during spikes in global oil prices can result in economic disruptions that affect demand and household incomes. The number of vehicles to be produced at high or low oil prices can be estimated for effective inventory management.
Demand estimation gives an idea of the demand level for a company’s product over a given period. Demand projections can be used to determine the optimal prices for products to avoid overpricing or under-pricing a product. Corporate decisions to invest in a novel product also require prior demand estimation to ensure that the output matches the existing or projected demand levels. Demand in the motor vehicle industry is a function of personal income and global oil prices.
Literature Review
Registered Vehicles
The number of registered vehicles is a function of multiple independent variables. In particular, personal income and oil prices are the key variables affecting automobile demand. Santini and Poyer demonstrate with a statistical model that spending on cars reduces when gas prices increase using the 1949-2012 historical data (488). The periods also experienced declines in automobile sales, which resulted in a low output of automobiles produced by the leading manufacturers. Therefore, oil shocks have an impact on the number of registered vehicles in a country. An increase in personal disposable income increases spending on luxury goods, including vehicles.
Personal Income
Price sensitivity is higher among lower socioeconomic classes. Personal disposable income has been shown to predict demand for luxury items, such as automobiles, which are considered to have high-income elasticity (Kang and Lee 52). Further, economic prosperity or GDP growth increases per capita income, low unemployment levels, and stable exchange rate and fuel economy (Kang and Lee 50). These factors contribute to higher automobile ownership by the citizens.
OPEC Oil Price
Fluctuations in OPEC-regulated oil prices during the past 40 years have been linked to economic slowdowns, hyperinflation, and labor market changes as the main consequences to the domestic economy (Santini and Poyer 485). Often, a sharp drop in economic growth follows any spike in world oil prices. The automobile industry is a primary economic sector where the effects of oil price spikes are felt. Oil shocks reduce motor vehicle purchases due to a decline in disposable personal incomes.
Specification of the Model
The paper examines factors that influence the demand for vehicles in the United States over time from 1990 to 2015. In this view, the paper focused on critical variables that have considerable influence on the demand for vehicles in the United States. Therefore, the following are two critical variables used in predicting the demand for vehicles in the United States (Q):
Personal income in the United States (Y)
OPEC oil price (O)
As the relationship between the demand for vehicles (dependent variable) and personal income and OPEC oil price are not linear, each variable was linearized using the natural logarithm. Thus, the equation of the linear regression model employed in the study is:
Where β0, β1, and β2 are coefficients that predict the demand for vehicles in the United States, and thus, the regression model will determine them.
Econometrics Results
The study used the data collected in the United States over the past 26 years from 1990 to 2015. The data set comprises a year, the number of vehicles registered (Q), personal income (Y), and OPEC oil price (O). Before analysis, the data were checked for compliance with the assumptions of regression analysis and the expected theoretical basis.
Table 1 below shows the descriptive statistics of the dependent variable (Q) and the independent variables, Y and O were performed. The descriptive statistics show that the number of registered vehicles ranges from 192313 to 263610 (M = 231463.45, SD = 24106.45). Moreover, the descriptive statistics show that personal income ranges from 4906.4 to 15458.5 (M = 9673.47, SD = 3284.03) while OPEC oil price ranges from 12.28 to 109.45 (M = 46.06, SD = 33.5).
Table 1
Table 2 is a regression model showing the extent of relationship and explanation of the demand for vehicles by personal income and OPEC oil price. The regression model has R value of 0.988, which means that there is a very strong relationship between the dependent variable, the number of registered vehicles, and the independent variables, personal income, and OPEC oil price. The adjusted R-square indicates that personal income and OPEC oil price account for 97.38% of the variation in the number of registered vehicles in the United States.
Table 2
The ANOVA table (Table 3) indicates that the regression model is statistically significant in predicting the influence of personal income and OPEC oil price on the demand for vehicles in the United States, F(2,23) = 466.418, p = 0.000.
Table 3
The table of coefficients (Table 4) indicates that personal income (Y) is a statistically significant predictor (p = 0.000) whereas OPEC oil price is a statistically insignificant predictor (p = 0.545).
The equation of the regression model is:
Table 4
Coefficients
As OPEC oil price is an insignificant predictor of the regression model, it was removed from the model and regression analysis performed to create another model.
The second regression model (Table 5) indicates that personal income accounts for 97.79% of the variation in the demand for vehicles in the United States. Moreover, Table 6 indicates that the model is statistically significant in predicting the relationship between the two variables, F(1,24) = 1097.641, p = 000.
Table 5
Table 6
Table 7
Coefficients
Table 7 indicates that personal income is a statistically significant predictor of the number of vehicles registered in the United States (p = 0.000). The regression equation is that:-
Since the coefficient of personal income (Y) in the regression equation is positive (+66385.656), it conforms to the economic theory that elucidates the relationship between income and demand for normal goods. The regression equation implies that a percent increase in personal income results in 66385.656 percent increase in the number of vehicles registered in the United States. These findings are in line with the findings of Kang and Lee showing that income is a statistically significant predictor of the demand for vehicles due to the increase in disposable income among people (49). Thus, the findings imply that an increase in income levels causes an increase in the demand for vehicles in the United States.
Conclusion
Regression analysis indicated that personal income is a statistically significant predictor while OPEC oil price is a statistically insignificant predictor of the demand for vehicles in the United States. In this view, changes in personal income affect the demand for vehicles irrespective of the variations in OPEC oil prices. In the aspect of demand elasticity, the demand for vehicles is very sensitive to the variation in the level of personal income in the United States.
Works Cited
Kang, Hsin-Hong, and Hui-Yen Lee. “The Impact Factors of Chinese Automobile
Demand.” Economics and Finance Review, vol. 3, no.9, 2014, pp. 49-56.
Santini, Danilo and David Poyer. “Gasoline Prices, Vehicle Spending, and National
Employment: Vector Error Correction Estimates Implying a Structurally Adapting, Integrated System, 1949–2011.” Atlantic Economic Journal, vol. 36, no. 1, 2013, pp. 483-491.