Introduction
The market of car in US remains fiercely competitive from the beginning in the late 1890s until now. Beginning in the 1970s, a combination of high oil prices and increased competition from foreign auto manufacturers severely affected the car companies in US. Therefore, it is necessary to investigate the car industry in the period of time in 1970s to understand not only the car market but also the market operation as a whole. In this research we want to investigate the six variables which seem to have impact on the number of car in US from 1975 to 1990. This result can contribute to the judgement on the car industry in US. Moreover, it helps to strength the theory of the relationship between macroeconomic and microeconomic factors and the quality of product sold.
The research has use the quantitative method and has the following structure:
Part 1: Data description
Part 2: Econometrics model
Part 3: Robustness check
Part 4: Result table
I. Abstract
This research investigates the relationship between microeconomic, macroeconomic variables and number of cars sold in US. The main objective is to determine the factors that affecting the number of car sold in US. This research covers the time period from 1975 to 1990. The analysis methods that have been applied in this study include descriptive statistics, linear regression and correlation analysis. The findings show that price, income have positive relationship with the number of car sales in US, while the prime interest rate and population have negative relationship with the number of car sales in US. The income has the most influence on the quantity of car sold while the population has unreliable effect on it. However, the gap in impact on number of cars sold among four factors is not huge. The findings were consistent with the previous findings done by other researcher.
II. Literature Review
There are many researches that investigated the relationship between quantity of car sold and its determined factors all around the world. Our research focuses on the relation between number of car sold in US and six variables including Price index, Prime interest rate, Income, Unemployment rate, Stock, Population. In the research process, there are some studies which share the same common with objects to our studies’. We present them here below.
In 2010, Faculty of Mechanical Engineering, Industrial Engineering and Computer Sciences in School of Engineering and Natural Sciences University of Iceland performed a study called The Effects of Changes in Prices and Income on Car and Fuel Demand in Iceland. It examined the elasticities of demand for fuel and cars in Iceland will be examined, both with a common classical reversible demand model and also with an irreversible model, in order to examine asymmetric effects from variables influencing the demands.
It constructed both reversible and reversible models for the demand of new cars and then used regression analysis on these models. The econometrics results showed that income has a great impact on the demand for new cars in Iceland. Increase in income has much more effect on the purchase of new cars than the size of the car fleet, which means that more new cars come into the fleet and more old ones go out when income increases. So that the car fleet changes with increasing income and consists more of newer and better cars that use less energy and are better for the environment.
In 2012, Education University of Sultan Idris Malaysia did a research on Automobile Sales and Macroeconomic Variables: A Pooled Mean Group Analysis for Asean Countries. This paper analysed the impact of economic variables on automobile sales in five ASEAN countries involving Malaysia, Singapore, Thailand, Philippines and Thailand collecting annual data from 1996 to 2010. The long term and short term correlation between these variables are implemented using the panel error-correction model. Two methods are implemented specifically the Mean Group (MG) and Pooled Mean Group (PMG). These two methods were introduced by Pesaran dan Smith (1995) and Pesaran et al. (1999). Result from the test shows that gross domestic product (GDP), inflation (CPI), unemployment rate (UNEMP) and loan rate (LR) have significant long term correlation with automobile sales in these ASEAN countries. The GDP variable is found to have positive relationship with car sales. This proves that national income level is an important determinant for the automotive industry. In contrast, spikes of inflation, unemployment rate and interest rate are found to inflict negative impact on car sales. Besides, each country is influenced by different variables in the short term period.
In 2013 Joseph Chisasa and Winnie Dlamini from University of South Africa, South Africa did a research on An Empirical Analysis Of The Interest Rate-Vehicle Purchase Decision Nexus In South Africa. This paper empirically examines the link between interest rates and the borrowers’ decision to purchase a passenger vehicle in South Africa.
They used monthly time series data of passenger vehicles purchased, household income, fuel prices, prime interest rates and producer price index for manufacturers from January 1995 to December 2011. With passenger vehicle unit purchases as the dependent variable, they obtained OLS estimates of the passenger vehicle purchase function. Results show that there is a negative, but insignificant, relationship between interest rates and passenger vehicle purchases in South Africa. Holding other factors constant, a 1% increase in interest rate results in a 0.9% decrease in passenger vehicle purchases. Household income, fuel price and producer price index are observed to have a positive and insignificant impact on the decision to purchase a passenger vehicle.
In 2014, Vaal University of Technology University of KwaZulu did a research on The Impact of Inflation on the Automobile Sales in South Africa. This paper analysed the relationship between inflation (INF) and Automobile sales in South Africa by using the co-integration and causality tests. The analysis has been conducted using monthly data over the period 1960:1 through 2013:9. The empirical results show that there is a long-run relationship between new vehicle sales and inflation over the sample period of 1969 to 2013.
Other factors that have been analysed were income level, interest rate, financial aggregate and unemployment rate. These include in the research by Shahabudin (2009) on domestic and foreign cars sales. In this research, it was discovered that all variables could significantly influence car sales. However, this regression model suffered from heteroscedasticity that affected the efficiency to gauge domestic and foreign car sales. In this research, it is proven that all variables could significantly influence car sales. However, the problem of heteroscedasticity had impaired the efficiency of the model as a whole.
Dargay (2001) using Family Expenditure Survey from 1970 t0 1995, it was found out that the statistics of vehicle ownership recorded a positive upward trend with income increase. However, there is a negative correlation when there is an income reduction. This is associated with the personal habit of individual consumers as vehicle is seen as an important necessity in the present context of everyday life.
IV. Theoretical background
In many countries car is one of the most expensive goods and is considered as a luxury good. However, in this research we want to examine the number of cars sold in US generally, which means that car is considered as a normal good. The theory we based on is the theory of principle of microeconomics and macroeconomics formulated by N. Gregory Mankiw. The detail application of this theory will be presented in order of the relationship between the dependent variable and four independent variables in our research.
Price index
A price index (also known as "price indices" or "price indexes") is a normalized average (typically a weighted average) of price relatives for a given class of goods or services in a given region, during a given interval of time. It is a statistic designed to help to compare how these price relatives, taken as a whole, differ between time periods or geographical locations.
In the research, we will analyze the effect of consumer price index (CPI) on the quantity of goods sold. The CPI is the measure of overall cost of the goods and services bought by a typical consumer. It is also a helpful means to measure the inflation rate.
Because the CPI indicates prices changes—both up and down—for the average consumer, the index is used as a way to adjust income payments for certain groups of people. For instance, more than 2 million U.S. workers are covered by collective bargaining agreements, which tie wages to the CPI. If the CPI goes up, so do their wages. The CPI also affects many of those on Social Security—47.8 million Social Security beneficiaries receive adjusted increases in income tied to the CPI. And when their incomes increase, the demand for goods and services also increases, which raises the quantity of goods sold, in our case is quantity of new cars sold.
Income
According to the theory of market forces of supply and demand in microeconomics of Mankiw, income is one of the main factors that shifts the demand curve, which contributes to the change in the number of product sold.
When being considered as a normal good, the income and the price goes in the same direction, which means an increase in income leads to an increase in demand. In the model, the demand curve shifts to the right. As a result, when the demand rises, it raises the quantity of car sold.
Prime interest rate
The prime rate is the interest rate that commercial banks charge their most creditworthy corporate customers. ese are the businesses and individuals with the highest credit ratings. They received this rate because they are the least likely to default. Banks have little risk with these loans The prime interest rate, or prime lending rate, is largely determined by the federal funds rate, which is the overnight rate that banks use to lend to one another. Prime forms the basis of or starting point for most other interest rates—including rates for mortgages, small business loans, or personal loans—even though prime might not be specifically cited as a component of the rate ultimately charged.
Banks base most interest rates on prime. That includes adjustable-rate loans, interest-only mortgages, and credit card rates. Their rates are a little higher than prime to cover banks' bigger risk of default. They've got to cover their losses for the loans that never get repaid. The riskiest loans are credit cards. That's why those rates are so much higher than prime. The prime rate affects household when it rises. When that happens, the monthly payments increase along with the prime rate.
The prime rate also affects liquidity in the financial markets. A low rate increases liquidity by making loans less expensive and easier to get. When prime lending rates are low, businesses expand and so does the economy. Similarly, when rates are high, liquidity dries up, and the economy slows down.
In sum, the prime rate considered as a factor affecting the quantity of product sold has the same role and effect as interest rate. It influences the quantity in two sides: the household which affects the consumption and the firms which affects the investment or production. According to the theory of aggregate demand of Mankiw, the interest rate has the power to shift the aggregate demand curve.
Changes in interest rates can affect several components of the AD equation. The most immediate effect is usually on capital investment. When interest rates rise, the increased cost of borrowing tends to reduce capital investment, and as a result, total aggregate demand decreases. Conversely, lower rates tend to stimulate capital investment and increase aggregate demand.
On the household side, lower interest rate encourages them to hold money in hands. Consumer confidence about the economy and future income prospects also affect how much consumers are willing to extend themselves in spending and financing obligations. As a result, it increases the consumption. An increase in interest rates may lead consumers to increase savings since they can receive higher rates of return. A corresponding increase in inflation often accompanies a decrease in interest rates, so consumers may be influenced to spend less if they believe the purchasing power of their dollars will be eroded by inflation.
Unemployment rate
The unemployment rate is defined as the percentage of unemployed workers in the total labor force.
One of the main factors influencing demand for consumer goods is the level of unemployment, which is measured by the unemployment rate. The more people there are receiving a steady income and expecting to continue receiving one, the more people there are to make discretionary spending purchases. That also means when the unemployment rate increases, the demand for a good decreases, which leads to the decrease in the quantity sold of a product. Therefore, the monthly unemployment rate report is one economic leading indicator that gives clues to demand for consumer goods.
Stock
The stock represents for the number of cars on the road. This number of cars in the time series data shows the trend in consumption of cars. In other words, it tells the demand direction of people. If the number increase time after time, the demand increases, therefore, the quantity of car sold and the stock go the same direction. In contrast, when the demand for car decreases, the stock has a negative impact on number of car sold.
Population
According to Microeconomics knowledge developed by Mankiw, the change in population will shift the demand curve. As the population increases, the demand for goods increase because each member of the population has needs to be filled. That leads to the increase in the quantity of goods sold.
However, these needs changes overtime as the segments of the population age and their needs and wants change. So that there is nothing sure about the increase in the quantity of a specific goods sold if the population increase in real-life situation.
V. Data description
1. Variables table
2. Data description
VI. Econometrics model
1. Population regression function (PRE)
2. Sample of regression function (SRF)
VII. Robustness check
1. Multi-collinearity
2.1 Qualitative analysis
Figure 7.3: Residual plot against QNC (Source: Gretl)
The research has shown the relationship between the six economic variables and the quantity of car sold in US in the period of 1975 to 1990. From the analysis results, it can see that only four variables including prime interest rate, income, population and price has relationship with number of new cars sold. The unemployment and number of cars on the road do not hold effect. The income has the most impact on the number of car sold in a positive way. Together with the price, income variable has the positive relationship with the dependent variable. In contrast, the prime interest rate and the population has a negative relationship with the number of car sold.
However, when applying the result into reality, we found that population variable does has impact on the number of new cars sold but the scale impact did not as much as the result numbers told. This could come from the drawback of our observations. The number of observations is small, the time is restricted in fifteen years, the origin of the observations is not clear enough. All these things could lead to some imprecise in our research result.
XI. Appendix