Car Consumer Demand Sensitivity to Income

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Introduction

Macroeconomic research has historically identified the relationship between consumer income and consumption, which is largely attributable to changes in macroeconomic policy. According to demand theory, higher level of income increases the demand of the product, and therefore consumption. Moreover, price theory of demand posits that an increase in price will reduce the demand of the product and vice versa, taking a cetirus paribus assumption. This paper tries to understand the consumer demand sensitivity to income of the consumer for automobiles.

Previous research has been conducted to understand the consumer demand for automobiles (Wetzel & Hoffer, 1982; Berry, Levinsohn, & Pakes, 2004). Cramer (1973) found that the consumer demand for a product is sensitive to changes with different income levels, and that price elasticity of the product changes at different income levels and vice versa.

The study conducted by Wetzel & Hoffer (1982) has shown that the demand and price elasticity of automobiles differ significantly due to change in styling, gasoline prices, and demographic changes. Berry, Levinsohn, & Pakes (2004) also demonstrate how preference and demand for products change with change in the attributes in household data.

Here automobiles consider only new cars. Therefore, the research question that is being addressed is whether consumer demands income sensitive. Further, the paper will also develop the demand function for new cars in the US. The paper will first address the data that is being used for the research. Then a discussion on the methodology applied for the research is talked about. After this, the paper presents the analysis of the data using regression analysis, and presents the research findings. The following section describes the data collection, description and methodology used for the research.

Methodology

The data that is analysed for the study is presented is extracted from the U.S. Bureau of Labor Statistics (2010) database. The data that is being used for the study are consumer purchase of new car from 1990 to 2008 (Table 1). Further, the purchase of cars figure is also categorized for the consumer level of income.

Table 1: Consumer purchase of new cars and income before tax from 1990 to 2008.

New Car Purchase based on Income
Consumer Purchase of New Cars Less than $5,000 $5,000 to $9,999 $10,000 to $14,999 $15,000 to 19,999 $20,000 to $29,999 $30,000 to $39,999 $40,000 to $49,999 $50,000 to $69,999 $70,000 and more Cars Price Index Disposable Income
1990 1159 210 238 445 915 826 1262 1531 0 0 118.3 28937
1991 1078 344 138 419 634 823 1033 1712 0 0 124.1 30729
1992 1131 204 231 421 216 680 1196 1834 1835 3546 126.9 30786
1993 1216 394 226 268 718 790 1320 1561 2050 3643 129.8 31890
1994 1391 360 272 451 681 1162 1329 2452 1955 2948 133.9 33098
1995 1198 155 282 611 816 755 1085 1432 2014 2899 134.1 33864
1996 1209 585 368 218 562 858 1278 1482 1941 3153 135.4 34864
1997 1229 60 224 544 408 976 1579 1682 1915 2865 133.6 36684
1998 1383 421 208 692 648 691 953 1195 2150 3550 131.9 38358
1999 1628 419 479 799 961 996 1237 1769 1781 3773 131.3 40652
2000 1605 57 491 588 750 1168 1453 1896 1986 3434 132.8 41532
2001 1685 749 396 678 688 972 1502 1876 2097 3572 132 44587
2002 1753 422 347 687 1141 762 1446 2054 2264 3433 129.5 46934
2003 2052 510 288 750 518 1236 1470 1425 2654 4832 129.5 48596
2004 1748 279 294 512 450 913 1237 1337 2254 3477 131.7 52287
2005 1931 445 213 96 382 1213 937 1339 2317 4012 131.8 56304
2006 1798 798 72 526 339 701 984 927 1761 3827 128 58101
2007 1572 255 181 533 731 619 788 901 1508 3199 126.2 60858
2008 1305 0 239 201 537 610 727 909 1228 2567 128.9 61774

Therefore, we get the number of cars purchased by consumers based on their income. The income groups that are used are ‘Less than $5,000’, ‘$5,000 to $9,999’, ‘$10,000 to $14,999’, ‘$15,000 to 19,999’, ‘$20,000 to $29,999’, ‘$30,000 to $39,999’. ‘$40,000 to $49,999’, ‘$50,000 to $69,999’, and ‘$70,000 and more’. Then data on commodity price index for cars are quarried from the U.S.

Bureau of Labor Statistics (2010) database. Data on disposable income i.e. income after tax for individuals has been derived from U.S. Bureau of Labor Statistics website for 1990 through 2008. Table 2 presents the descriptive statistics of the data. The table shows that the mean purchase of cars in the period 1990 to 2008 for different income groups.

Table 2: Descriptive Statistics.

New Car Purchase based on Income
Consumer Purchase of New Cars Less than $5,000 $5,000 to $9,999 $10,000 to $14,999 $15,000 to 19,999 $20,000 to $29,999 $30,000 to $39,999 $40,000 to $49,999 $50,000 to $69,999 $70,000 and more Cars Price Index Disposable Income
Mean 1477.42 350.89 273.00 496.79 636.58 881.63 1200.84 1542.84 1774.21 3091.05 129.98 42675.53
Standard Error 68.10 49.92 24.42 44.70 52.69 46.05 56.39 92.65 159.60 274.29 0.93 2505.98
Standard Deviation 296.84 217.59 106.46 194.84 229.66 200.74 245.79 403.85 695.69 1195.61 4.08 10923.33
Kurtosis -1.11 -0.07 0.34 -0.40 0.07 -0.90 -0.74 0.17 3.30 3.77 2.57 -1.10
Skewness 0.36 0.38 0.52 -0.49 0.28 0.50 -0.38 0.18 -1.87 -1.88 -1.38 0.51
Count 19 19 19 19 19 19 19 19 19 19 19 19
Confidence Level(95.0%) 143.07 104.88 51.31 93.91 110.69 96.75 118.47 194.65 335.31 576.26 1.96 5264.88

The standard deviation for the data is high, and the skewness is mostly positive only for some income groups it is negative. The car price index has low relatively low standard deviation.

Berry, Levinsohn, & Pakes (2004) have shown in their research that the demand function for a product may alter considerably due to the introduction of a another variable into the demand function. In this paper, disposable income along with annual prices index of cars is used as the variables to determine the demand for the cars, which are differentiated, based on the income of the individual.

The data is analysed using regressions analysis. The main purpose of the paper is to see how the demand for new car purchase is affected by changes in income of individual and price. Apart from this, the paper also identifies how demand is affected by changes in price based on the income group the consumer belongs to. The analysis of the data is presented in the following section.

Analysis

First, a regression analysis is done on the overall purchase of new cars and how it changes with car price index and disposable income is done.

Table 3: Regression analysis of new car purchase and car price index and individual disposable income.

Regression Statistics
Multiple R 0.705
R Square 0.497
Adjusted R Square 0.434
Standard Error 223.348
Observations 19
ANOVA
df SS MS F Significance F
Regression 2 787865.101 393932.550 7.897 0.004
Residual 16 798151.531 49884.471
Total 18 1586016.632
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept -838.99 1684.79 -0.50 0.63 -4410.58 2732.60
Cars Price Index 11.77 12.93 0.91 0.38 -15.65 39.18
Disposable Income 0.02 0.00 3.82 0.00 0.01 0.03

The regression analysis shows that the demand for the automobiles demand for new cars variance in the analysis is 49%. The data analyzed is statically significant at 0.004 (<0.05) at 95% level of significance. The demand function that can be derived from the regression analysis is:

  • New Car demand = -838.99 + 11.77*Car Price Index +0.02 Disposable Income (1)

Therefore, the demand function derived for cars has a positive intercept for car prices, which refutes the demand function. The relationship between car purchase and disposable income is found to be positive with intercept being 0.02.

Figure1 shows that individuals belonging to higher income groups have purchased greater number of cars on an average over the period 1990 through 2008. Therefore, the figure makes it clear that there is a difference in demand for cars with changes in income of individual. In order to understand how the demand function will vary with changes in income and price regression analysis is done for purchase of cars for different income groups and car price index and disposable income.

As there are 9 groups, the analysis may become cumbersome. In order to make it simpler, the data for the different groups are clubbed into 3 groups i.e. less than 20,000, 20,000 to 70,000, and more than equal 70,000.

Mean purchase of new cars for different income levels
Figure 1: Mean purchase of new cars for different income levels

The regression analysis for the income group below $20,000, the r-square is found to be 2.1% (Table 4). Further the analysis is not found to be statistically significant as the F value is 0.83 (>0.05) at 95% significance level. The analysis shows that in this income group, consumer demand for new cars is positively related to car price index and negatively related to disposable income.

Table 4: Regression analysis for income group Less than 20,000.

Regression Statistics
Multiple R 0.147633012
R Square 0.021795506
Adjusted R Square -0.100480056
Standard Error 501.7297228
Observations 19
ANOVA
df SS MS F Significance F
Regression 2 89742.24859 44871.1243 0.178249078 0.838372895
Residual 16 4027723.436 251732.7147
Total 18 4117465.684
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 211.1148898 3784.707268 0.055781035 0.956206874 -7812.106051 8234.335831
Cars Price Index 13.32963982 29.05242588 0.458813315 0.652539357 -48.25875134 74.91803099
Disposable Income -0.004370056 0.010838219 -0.403207947 0.692132101 -0.027346054 0.018605942

The second group is income of $20,000 to $70,000 (Table 5). The analysis gives a r-square of 48% indicating greater reliability. F-value is 0.005 which is statistically significant at 95%. The analysis can be utilized to find the estimated demand function for new cars for individuals belonging to this particular income group.

Table 5: Regression analysis for income group 20,000 to 70,000.

Regression Statistics
Multiple R 0.695248283
R Square 0.483370174
Adjusted R Square 0.418791446
Standard Error 859.31571
Observations 19
ANOVA
df SS MS F Significance F
Regression 2 11054162.9 5527081.452 7.484975127 0.005074997
Residual 16 11814775.83 738423.4895
Total 18 22868938.74
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept -18088.64718 6482.092381 -2.790556831 0.013093884 -31830.06907 -4347.225278
Cars Price Index 187.2502566 49.7582759 3.763198247 0.001699741 81.76742456 292.7330887
Disposable Income -0.019950622 0.018562687 -1.074770138 0.298419003 -0.05930176 0.019400517

The demand function, thus, found is:

  • New Car Demand = -18088 + 187.25 *Car Price Index – 0.019 Disposable Income (2)

The demand function shows that the demand for new cars is highly price sensitive and is positively related to price index. Further, it is negatively related to disposable income and is less sensitive to changes in disposable income.

For this income group, i.e. income more than 70,000, gives an r-square of 54% (Table 6). The analysis explains 54% of the variance in demand of new cars in the regression. The regression is statistically significant at 95% level (F-value is 0.001<0.05).

Table 6: Regression analysis for income group More than 70,000.

Regression Statistics
Multiple R 0.740203882
R Square 0.547901787
Adjusted R Square 0.491389511
Standard Error 852.6700272
Observations 19
ANOVA
df SS MS F Significance F
Regression 2 14097818.14 7048909.072 9.695270138 0.001745269
Residual 16 11632738.8 727046.1752
Total 18 25730556.95
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept -21847.21407 6431.961877 -3.396664111 0.003686021 -35482.36405 -8212.064094
Cars Price Index 177.5202613 49.37346072 3.595459153 0.00242234 72.85320096 282.1873216
Disposable Income 0.043665207 0.018419129 2.3706445 0.030656094 0.004618398 0.082712016

Therefore, the regression gives the statistically significant estimated function for future prediction, which is actually the demand curve for this particular income group. The estimated demand function is as follows:

  • New car demand = -21847.21 + 177.52*Car Price Index + 0.04*Disposable Income (3)

Equation (3) shows the demand for new cars for people belonging to an income groups above $70,000. The demand function shows that demand is price sensitive and is positively related to price. Further, the demand has a positive relation with disposable income, however, it has a low sensitivity to income.

Conclusion

The above analysis shows that the demand for new cars in US is positively related to price index of cars from 1990 to 2008. As the result is statistically significant, the relation can be said to hold for all time-period for the US consumer demand for new cars.

Further, this relation between new car demand and price is found to be consistent across the three income groups studied in the research, as all have positive relation. The demand for new cars all over US is found to be positively related to disposable income. This finding, however, varies across the three income groups. The positive relation is found to hold only in case of the income group ‘More than 70,000’.

Therefore, it indicates that at lower income levels, demand for new cars has a negative income elasticity. This research therefore, shows that the demand for new cars in the US is sensitive to changes in price and income positively. However, variances in the demand and income, price relation occurs due to changes in income level of the consumers.

References

Berry, S., Levinsohn, J., & Pakes, A. (2004). Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market. Journal of Political Economy, vol. 112, no. 1 , 68-105.

Bureau of Labor Statistics. (2010). Databases, Tables & Calculators by Subject. Web.

Cramer, J. S. (1973). Interaction of Income and Price in Consumer Demand. International Economic Review, Vol. 14, No. 2 , 351-363.

Wetzel, J., & Hoffer, G. (1982). Consumer Demand for Automobiles: A Disaggregated Market Approach. Journal of Consumer Research, Vol. 9 , 195-199.

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