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An Examination of the Effects of Consumption Expenditures on Life Satisfaction in Australia

Abstract

This paper studies how consumption expenditures, especially spending on certain types of goods and services, affect people’s life satisfaction. The results demonstrate that conspicuous (i.e., visible and positional) spending increases life satisfaction. The analysis suggests that it is one’s conspicuous consumption expenditures relative to those of other households in one’s reference group that really matter. In contrast, savings and spending on basic goods and services, the less visible components of income, do not contribute to life satisfaction. This paper also evaluates the often-discussed relationship between income and life satisfaction, finding that the relationship is largely driven by conspicuous spending. Evidence of relationship heterogeneity across income groups is presented: conspicuous consumption expenditures have a positive influence on life satisfaction for individuals in all income groups; basic consumption expenditures, however, have a negative influence for people in the lowest income quartile. The findings of this paper underscore the importance of social comparisons to people’s well-being and imply that interdependence should be modeled in utility functions. The results are based on the Arellano–Bond generalized method of moments estimation, which controls for bias from unobserved individual heterogeneity and endogenous variables. The data come from the Household, Income and Labour Dynamics in Australia Survey (2006–2010).

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Fig. 1

Notes

  1. Appendix 1 briefly introduces the definition of subjective well-being, its measures, the development of the field as well as the literature on the determinants of life satisfaction or happiness.

  2. The terms life satisfaction and happiness are used interchangeably in this paper and refer to satisfaction with life as a whole.

  3. Perez-Truglia (2013) develops a signaling model of conspicuous consumption, and the model on how utility depends on conspicuous consumption is a predication from that model.

  4. Happiness has been considered as a proxy for utility (Rayo and Becker 2007).

  5. With the theories related to consumption’s impact on utility in mind, savings will only be studied as a component of income in one regression to be compared with expenditures. Additional theories regarding savings’ independent effects on utility could not be adequately addressed here without greatly extending the scope of the paper.

  6. It is important to note that the expenditures of individuals or households on a good or service over a certain period of time frequently differ from their actual consumption for various reasons. For example, there is a high possibility that furniture and household appliances that have been purchased are never consumed in the strict sense, but only be possessed for either short or long periods of time. A few empirical studies (e.g. Noll and Weick 2015), however, have argued that consumption expenditures might be the best proxy for consumption.

  7. This idea is consistent with Maslow (1943)’s theory on hierarchy of needs, which argued that basic, security, and social needs need to be satisfied in order for people to seek social status.

  8. The only durable expenditure included is the housing expenditure.

  9. Following the practice of Household Expenditure Survey by Australian Bureau of Statistics, no imputed rent has been taken into account in calculating the housing expenditures of home owners.

  10. HILDA does not have a separate spending category on personal care. Personal care products are included in “groceries” category.

  11. Friehe and Mechtel (2014) have “meals eaten out” as “food and drinks in restaurant”. “Meals eaten out” in this study, based on expenditure information from the HILDA, includes food but does not include alcohol.

  12. The visibility index by Heffetz (2011) ranks the visibility of alcohol consumed at home to be slightly higher than that of alcohol consumed with meals eaten out and that of holidays, and these two items are considered to be conspicuous in Friehe and Mechtel (2014).

  13. Heffetz (2011) conducted a survey with the main survey question being “Imagine that you meet a new person who lives in a household similar to yours. Imagine that their household is not different from other similar households, except that they like to, and do, spend more than average on [X]. Would you notice this about them, and if so, for how long would you have to have known them, to notice it? Would you notice it almost immediately upon meeting them for the first time, a short while after, a while after, only a long while after, or never?” for 31 different expenditure category titles. Based on the results of the survey, he develops an index of expenditure visibility.

  14. Expenditure or income elasticity, though more discussed by traditional economist, is not directly related to visibility of conspicuousness. For example, insurance can be highly elastic but it is not visible at all.

  15. In addition, the empirical analysis by Heffetz (2011) suggests that tobacco products seem to be inferior goods for a large interval of the income scale.

  16. According to Marx (1849) and Frank (1999), housing can be positional.

  17. A negative value of savings means that a household spent more than its income in that period.

  18. In order to reduce the potential reporting bias relating to financial measures, I use imputed derived variables contained in the HILDA survey. The details of the imputation method can be found in Hayes and Watson (2009) and Sun (2010). To minimize the influence of outliers, I exclude observations with household expenditures or income in the top and bottom 0.1% of the respective distribution. This treatment leaves a few observations with negative income values, and, consequently, these observations (about 0.20% of observations) are omitted from the analysis. The results of my analysis turn out to be similar without eliminating extreme values of income and consumption expenditure as well as omitting negative values of income.

  19. All financial variables constructed so far were household level measures instead of per-capita measures. In order to explore whether this is problematic, I also constructed equivalized measures (i.e., adjusting the level of income, savings, and expenditure variables by dividing over the OECD-modified equivalence scale). The regression results are virtually the same.

  20. Since there are also a small number of observations (around 0.2 percent of the sample) have zero income, I approximate logarithm with inverse hyperbolic sine. In the case of negative savings, I take the absolute value of savings before taking logarithm and add a negative sign after that.

  21. This is the finest geographical information I am able to obtain from HILDA.

  22. Examples of components of hedonic capital are marital status and health.

  23. Region dummies can be endogenous potentially. However, the control of individual fixed effects (\(\alpha_{i}\)) largely reduce the endogeneity of region dummies since over 90% of individuals do not change region of residence during the sample period. I try to treat region dummies as endogenous as well, and the estimation results appear to be similar.

  24. To be conservative, I follow Roodman’s (2009) suggestion to reduce the number of instruments by combining instruments through their addition into sets. Calderón et al. (2002), Beck and Levine (2004), Carkovic and Levine (2005), and Powdthavee (2009) have used this technique. The results turn out to be similar.

  25. I also tried regressing income on past income and expenditures using the same estimation method and found none of the expenditure variables have significant effects on income. This is a way to confirm the causal story (i.e., it is life satisfaction rather than income that is driven by conspicuous consumption).

  26. Readers might also be interested in knowing what the result will be if I classify expenditures according to whether they are on nondurable goods, durable goods, and services. Table 14 in appendix shows the result. It seems that expenditures on nondurable goods, durable goods, and services all have insignificant influences. This implies that this classic classification by economists cannot really tell us what kinds of consumption expenditures are most associated with life satisfaction.

  27. Further discussions on the types of SWB questions and their reliability and validity can be found in Sachs et al. (2013) and OECD (2013).

  28. Less frequent and often large expenditures were collected on a “recall” basis.

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Funding was provided by University of Southern California (US) (Grant No. 1).

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Appendices

Appendix 1: What is Subjective Well-Being

Subjective well-being is the general expression used to cover a variety of individual self-reports of quality of life (Sachs et al. 2013). There are, in general, two types of measures of SWB: cognitive life evaluations and emotional reports. Emotional reports, such as positive affect (a range of positive emotions) and negative affect (a range of negative emotions), capture feelings at one point in time. The focus of this study is on life evaluations, represented by questions asking how happy or satisfied people are with their lives as a whole. Such measures are considered plausible measures of well-being since they are closely related to life circumstances, consistent over a short period of time, and strongly correlated with both objective and subjective measures of well-being.Footnote 27

The study of SWB was the domain of psychology for a long time (Bradburn 1969; Andrews and Withey 1974; Campbell et al. 1976; Diener 1984, 1994, 2000; Diener et al. 1985; Frey and Stutzer 2002). The groundbreaking contribution by Easterlin (1974) linked this sychological research to economics, but there were few followers at that time. Since the late 1990s, the literature on empirical analyses of the determinants of life satisfaction or happiness in different regions and periods has started to grow. The important determinants include, but are not limited to, age, gender, unemployment, education, marriage, health, and material living conditions (e.g., Blanchflower and Oswald 2004a; Blanchflower 2009; Clark and Oswald 1994; Easterlin 1995, 2001; Di Tella et al. 2001; Knight et al., 2009; Frey and Stutzer 2010).

Appendix 2: The Quality of Household Expenditure Data from the HILDA

The HILDA survey asks retrospective questions on various expenditures in the past financial year. This is comparatively simple compared with what most official surveys do. Household Expenditure Survey (HES), which is conducted by Australian Bureau of Statistics every 6 years, collects information on over 600 categories of consumption expenditures by making use of a diary method over a period of two weeks.Footnote 28 A comparison between the HILDA 2010 and HES 2009–2010 on weekly household expenditures in Table 9 in appendix shows considerable consistency in the relative importance of each type of consumption between these two sources. The consistency suggests that the consumption expenditure data from the HILDA can be of good quality.

Appendix 3: Measurement Error in Life Satisfaction

Consider the following dynamic model with unobserved individual fixed effects.

$$LS_{it}^{*} = \beta_{0} LS_{it - 1}^{*} + \beta_{1}^{'} M_{it} + \beta_{2}^{'} X_{1,it} + \beta_{3}^{'} X_{2,it} + \beta_{4}^{'} Z_{i} + \alpha_{i} + \varepsilon_{it}$$
(6)

\(LS_{it}^{*}\) is true self-rated life satisfaction. However, we cannot observe \(LS_{it}^{*}\). What we can observe is a proxy \(LS_{it}\), which is reported by an individual. Individuals may have a systematic tendency to misreport their true life satisfaction scores. For example, more extroverted individuals may always overrate their life satisfaction when asked. Following Powdthavee (2009), I assume that there is a systematic error to how life satisfaction is measured and that this measurement error is driven by a fixed effect and a time-varying component:

$$LS_{it} = LS_{it}^{*} + \theta_{i} + \mu_{it}$$
(7)

Assume the error \(\mu_{it}\) is not serially correlated and is uncorrelated with \(LS_{it}^{*}\) as well as \(M_{it}\), \(X_{1, it}\), and \(X_{2, it}\). Substituting (7) into (6)

$$LS_{it} = \beta_{0} LS_{it - 1} + \beta_{1}^{'} M_{it} + \beta_{2}^{'} X_{1,it} + \beta_{3}^{'} X_{2,it} + \beta_{4}^{'} Z_{i} + \left[ {\alpha_{i} + \left( {1 - \beta_{0} } \right)\theta_{i} } \right] + \left( {\varepsilon_{it} + \mu_{it} - \beta_{0} \mu_{it - 1} } \right)$$
(8)

The presence of time-varying measurement error would make the regression error follow a first order moving average process. Therefore, the error is serially correlated, and I cannot use the second lag of life satisfaction as an instrument in the first-difference equation. What I can do is to use higher order lags (see Tables 8, 9, 10, 11, 12, 13, 14).

Table 8 Consumption categories
Table 9 Weekly expenditures: household expenditure survey (HES) (2009–2010) and HILDA (2010)
Table 10 Life satisfaction, income, total household consumption expenditures, savings, conspicuous consumption expenditures, and basic consumption expenditures (Blundell and Bond GMM estimation results)
Table 11 Life satisfaction and total consumption expenditures, and savings (OLS and FE results)
Table 12 Life satisfaction, conspicuous consumption expenditures and basic consumption expenditures (OLS and FE results)
Table 13 Robustness check: alternative definitions of conspicuous and basic consumption expenditures
Table 14 Life satisfaction and expenditure on durable goods, nondurable goods, and services

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Wu, F. An Examination of the Effects of Consumption Expenditures on Life Satisfaction in Australia. J Happiness Stud 21, 2735–2771 (2020). https://doi.org/10.1007/s10902-019-00161-3

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Keywords

  • Life satisfaction
  • Happiness
  • Subjective well-being
  • Conspicuous consumption
  • Social comparison