1 Introduction

1.1 Income–SWB Relationship

The paradox between income growth and a corresponding rise in reported subjective well-being (SWB) was identified nearly half a century ago in the seminal work of Easterlin (1974). Since then, there has been much debate about the income–SWB relationship. In his more recent evaluation of the topic, Easterlin (2001) concludes that people with higher incomes, on average, are happier than those with lower incomes; although there is a point of diminishing returns, he suggests that as income rises, people increase their material aspirations, which in turn undermines the potential gains of SWB.

As Clark et al.’s (2008) literature review suggests, the general consensus of previous studies is in line with Easterlin’s conclusion that income indeed raises happiness, with diminishing returns to income. Some of those studies include analyses using cross-sectional survey data from one country with or without standard demographic controls (e.g. Graham and Pettinato 2002; Blanchflower and Oswald 2007). Other studies use panel data from one country to control for unobserved individual fixed effects (e.g., Winkelmann and Winkelmann 1998; Ferrer-i-Carbonell 2005; Gardner and Oswald 2007). Yet other studies employ macroeconomic variables, including GDP, to control for country fixed effects (e.g. Di Tella et al. 2003; Helliwell 2003; Leigh and Wolfers 2006).

However, the findings in the literature seem to be highly dependent on what a given study actually measures for SWB (see, e.g. Deaton 2008; Stevenson and Wolfers 2008; Diener et al. 2010; Kahneman and Deaton 2010). According to the Organisation for Economic Co-operation and Development (OECD 2013), there are three main types of indexes that measure SWB: those related to “life evaluation,” in which an individual evaluates his or her own life experiences or life as a whole (e.g., life satisfaction and the Cantril ladder); those related to “emotional well-being” (affect balance), which measure an individual’s feelings or psychological state at or during a certain point or period in time; and those related to the concept of “eudaimonia,” which capture an individual’s goal orientation or sense of life purpose. It is important to note that there is some debate about whether these two types of life evaluation indexes—life satisfaction and the Cantril ladder—represent the same thing (Helliwell et al. 2012). For example, the Cantril ladder produces a wider distribution of responses (Office for National Statistics 2011) and is more strongly correlated with income than other life evaluation indexes. Diener et al. (2010) show that income seems to be positively correlated with cognitive measures of SWB but not with affective measures. Similarly, Kahneman and Deaton (2010) demonstrate that in the United States, there might be an income threshold for emotional well-being of around US$75,000, after which emotional well-being does not increase, while there might not be such an income threshold for life evaluation. Jebb et al. (2018) statistically identify points of income satiation for other countries. The authors apply spline regression models to global data from the Gallup World Poll and, controlling for demographic factors, find that satiation occurs at US$95,000 for life evaluation and US$60,000 to US$75,000 for emotional well-being. In addition, they find substantial variation across world regions, with satiation occurring later in wealthier regions; for some countries, incomes beyond satiation are associated with lower life evaluations. Thus, there is clear evidence that the income–SWB relationship depends on the SWB measure, but the reasons are not yet fully clear. Given that most income is used for consumption, a main reason may be related to consumption. The current study is the first to examine directly whether the consumption–SWB relationship differs between affective, cognitive, and eudaimonic SWB measures.

1.2 Definition of SWB

The broader concept of SWB has drawn much attention (Benjamin et al. 2012); however, as mentioned, our interest lies in the difference between affective, cognitive, and eudaimonic SWB measures. In this subsection, to contextualize the affective, cognitive, and eudaimonic SWB measures adopted in our study, we review the big picture of the evolving massive body of literature, chiefly drawing on a book-length treatment by Sirgy (2012).

Many researchers distinguish between cognitive and affective aspects of SWB (Campbell 1976; McKennell 1978; Andrews and McKennell 1980; McKennell and Andrews 1980; Organ and Near 1985; Brief and Roberson 1989; Crooker and Near 1995; Kahneman 2011). How do affective and cognitive measures of SWB relate to each other? Most studies regard life satisfaction as self-evaluation of happiness (Diener 1984; Veenhoven 1984a, 1984b; Veenhoven and Coworkers 1994). In other words, life satisfaction is viewed as a “cognitive” conceptualization of happiness or SWB and may involve judgments of fulfillment of an individual’s needs, goals, and wishes (Cantril 1965; Andrews and Withey 1976; Campbell 1976; Michalos 1980; Diener 1984). Diener et al. (1985) define life satisfaction as a cognitive judgmental process dependent upon a comparison of one’s circumstances with what is thought to be an appropriate standard. One of the most well-known cognitive measures is the Cantril ladder (Cantril 1965), comprised of steps 0 at the bottom to 10 at the top, in which respondents are asked which step of the ladder represents where each feels that he or she stands at the present time. Meanwhile, positive and negative emotions are referred to as affective measures (Ahuvia and Friedman 1998).

However, that is not the end of the story, as a large body of the literature emphasizes the relative nature of SWB. In the cognitive evaluation of happiness, an individual must surely assess his or her situation relative to some benchmark or reference, such as his or her own life in the past, the lives of surrounding people, a global standard, or a shifting “best possible life for you” standard that moves upward with rising living standards (Deaton, 2008).Footnote 1 In addition, Ahuvia and Friedman (1998) suggest that SWB includes both affective and cognitive components and that the cognitive and affective elements of SWB tend to be correlated. They state that “the cognitive judgement that one’s life is a failure is likely to result in negative affect and the reverse for more flattering cognitions.”

Another question arises: can eudaimonic well-being be treated equally with affective and cognitive measures of SWB? While Kesebir and Diener (2009) consider that eudaimonic well-being and other SWB measures are sufficiently close, Vitterso et al. (2010) argue that they are theoretically distinct, as the cybernetic principles underlying them are different. Goal attainment in hedonic well-being reflects homeostatic balance, which reflects a state of happiness. By contrast, lack of goal attainment reflects a state of disequilibrium that induces feelings of interest, curiosity, challenge, and task absorption. The latter may be reflective of eudaimonic well-being.

This points to the need to treat eudaimonic well-being separately from the dichotomy between affective and cognitive measures. Peterson et al. (2005) develop a measure based on Seligman’s theory of authentic happiness, which seems to capture the three major dimensions of happiness: hedonic well-being, life satisfaction, and eudaimonia (Sirgy 2012).Footnote 2

Thus, in the current study, we adopt the following four SWB indexes: life satisfaction (cognitive), the Cantril ladder (cognitive), affect balance (affective), and eudaimonia.Footnote 3 Affect balance can be classified with hedonic happiness (well-being) and affective measures, and short-term well-being (Kozma et al. 1992; Parducci 1995; Sumner 1996; Kahneman 1999). By contrast, life satisfaction, the Cantril ladder, and eudaimonia can be classified with eudaimonic well-being (happiness), cognitive measures (Phillips 2006; Kesebir and Diener 2009; Vitterso et al. 2010), and long-term well-being.Footnote 4

1.3 Consumption–SWB Relationship

In studies on SWB, in the tradition of the Easterlin paradox, income is often utilized as the yardstick (Kahneman and Deaton 2010). At a macro level, national income and GDP are frequently discussed with the implicit assumption that they are relevant to welfare. However, microeconomic theory starts with the assumption that individuals maximize utility derived from consumption, not from income. Income and consumption have equivalent impact on welfare only in a static, one-good setting in which individuals do not save at all. Moreover, recent studies stress other contributors to individual well-being, such as social capital, leisure, and inequality (e.g., Stiglitz et al. 2010; Benjamin et al. 2014; Tsurumi and Managi 2017).

This point rationalizes our focus in the current study: we consider the association of SWB with consumption instead of income. The literature on the total consumption–SWB relationship is huge, including studies by Guillen-Royo (2008), Headey et al. (2008), Zimmermann (2014), and Noll and Weick (2015). Headey et al. (2008) show somewhat contradictory results: the total consumption–life satisfaction relationship is positive for the United Kingdom but negative for Hungary. It is possible that people may become dissatisfied and insecure once their consumption level rises for a given level of income and wealth. There are other potential channels that make this relationship seem inconsistent. For example, Guillen-Royo (2008) suggests that in the Peruvian corridor, consumption has a meaning beyond mere satisfaction of basic needs, such as status concerns regarding the reference group, the pleasure of consuming, providing for household basics, and the expectation of escaping social marginalization. Likewise, Zimmermann (2014) shows total consumption in the United States is significantly associated with SWB, but only via certain consumption goods, such as experiential and conspicuous expenses.

Moreover, we treat both total consumption and different subtypes of consumption, as the latter may yield different marginal utilities. One category worth studying is relational consumption, through which people feel connected to others and which often entails both leisure and social capital investment.Footnote 5 Examples include consumption involving interactions with family members, friends, and acquaintances, such as sending a gift, having dinner, or traveling in social groups. DeLeire and Kalil (2010), Noll and Weick (2015), and Zhang and Xiong (2015) study the effect of relational consumption of leisure on SWB. However, relational consumption has a deeper meaning than that of a mere leisure activity. Like economic conditions, social capital exerts a significantly positive influence on SWB relative to other factors (Frey and Stutzer 2002; Bjørnskov 2003; Di Tella et al. 2003; Becchetti et al. 2008; Bruni and Stanca 2008). Relatedly, Kasser and Sheldon (2002) examine consumption practices during Christmas and consumer well-being. They show that consumers report higher levels of happiness when they consider family and religious experiences as important but lower levels of happiness when they consider spending money and buying gifts as important. In addition, by utilizing social capital indicators that measure trust in others and citizen participation, Bjørnskov (2003) shows that social capital affects happiness to a greater degree than income, at least in developed countries. Although our concept of relational consumption in this study does not necessarily entail social capital investment, we can conjecture that relational consumption may exert a correspondingly powerful influence on SWB.Footnote 6

A related but separate class of consumption worth clarifying here is experiential consumption, such as vacations and concert tickets, as opposed to material consumption or the possession of goods (Van Boven and Gilovich 2003; Dunn and Weidman 2015; Gilovich et al. 2015). We focus on consumption involving interactions with other people and not experiential consumption per se. Relational and experiential consumption share common traits: dining out with family, for example, is an activity that falls under both categories. Eating out alone, by contrast, is an example of experiential consumption but not relational consumption.Footnote 7

Based on literature review, Dolan et al. (2008) suggest that the effect of material well-being on SWB is not linear but rather diminishes with higher levels of material well-being; this is similar to the economic concept of diminishing marginal utility. Diminishing marginal effects are also revealed in empirical studies. For example, Gokdemir (2015) reports that, in Turkey, only the consumption of durable goods is correlated with life satisfaction. DeLeire and Kalil (2010) analyze US data for nine consumption categories and show that, of all the categories measured, only one, leisure, is positively correlated with SWB. Zhang and Xiong (2015) employ 77 consumption categories and 13 SWB indicators to examine the relationship between consumption and SWB in Japan. A notable finding of their study is the particularly strong correlation between relational consumption and SWB. Dumludag (2015) analyzes countries at different stages of development in Europe and shows that the relationship between life satisfaction and each consumption category varies in accordance with the development stage. Relatedly, Pandelaere (2016) reviews studies on experiential versus material purchases and suggests that even though many studies find an advantage for experiential purchases, this effect does not occur for materialists. He states that materialists do not benefit more from material than from experiential consumption owing to unrealistic expectations.

The literature outlined in this section has not clarified how the choice of SWB indexes affects the consumption–SWB relationship, which is not sufficiently discussed in the extant literature. In addition, on the consumption side, we consider both material and relational consumption.

The rest of the paper is organized as follows. Section 2 describes the theoretical model; Sect. 3 presents the empirical models and data; Sect. 4 describes the estimation results; and Sect. 5 discusses the findings, presents the conclusion, and indicates opportunities for further research.

2 Theoretical Model

We first simplify the argument by equating utility with SWB. Although some previous works stress the distinction between the two (e.g., Glaeser et al. 2016), for the purpose of comparison with the literature using SWB indexes, we adopt SWB as the proxy of utility in the current study. Kahneman and Krueger (2006) mention that acceptance of self-reported measures of well-being, subject to the many caveats required by subjective measurement, could have a profound impact on economics and that the currently available results suggest that those interested in maximizing social welfare should shift their attention from increasing consumption opportunities to increasing social contacts. Given our literature review in subsection 1.3, we start from individual utility as a function of material consumption and relational consumption. We assume that a representative agent’s instantaneous utility at \(t\) depends on material consumption (\({C}_{1}\)) and relational consumption (\({C}_{2}\)):

$$U\left( {C_{1} \left( t \right),C_{2} \left( t \right)} \right)$$

where in theory, marginal utilities are positive with regard to both types of consumption, namely, \({U}_{1}\hspace{0.17em}>\hspace{0.17em}0\) and \({U}_{2}\hspace{0.17em}>\hspace{0.17em}0\).

Our literature review in subsection 1.2 suggests that there are broadly three types of well-being: affective, cognitive, and eudaimonic. Some studies also suggest there are two types of psychological well-being: short- and long-term. Kozma et al. (1992) theorize that happiness is a direct function of two psychological states, one short term and the other long term. The short-term state is an affective state that involves positive and negative affects mostly influenced by environmental factors. The long-term state is also an affective state involving both positive and negative affects but is dispositional and less affected by environmental factors than the short-term state. Parducci (1995) and Kahneman (1999) argue that affective measures of SWB of any time period are a conceptual summation of these separate hedonic values, positive and negative, divided by the duration of that period. By contrast, global assessments that people make when asked to rate their overall happiness are cognitive measures of SWB, because they are based on an individual’s thoughts and judgments about major elements of his or her life. Sumner (1996) argues that well-being must be based on global evaluations of life (i.e., a long-term construct).

One way to incorporate all of these types of well-being into the model would be to hypothesize that affective or emotional well-being may capture the effects of spur-of-the-moment consumption or short-term consumption, while cognitive well-being or the evaluation of life may better reflect consumption in the long term or for future ends. Thus, we interpret affective versus cognitive well-being as corresponding to the maximization of instantaneous versus intertemporal utility. The latter can be written as

$$V\left( t \right) = \mathop \int \limits_{t}^{\infty } U\left( {C_{1} \left( \tau \right),C_{2} \left( \tau \right)} \right)e^{{ - \delta \left( {\tau - t} \right)}} d\tau ,$$

where \(\delta >0\) is the pure rate of time preference. It follows that the marginal effects of consumption of type \(j\in (\mathrm{1, 2})\) on SWB would correspond to marginal utility (\(\partial U/\partial {C}_{j}\)) versus marginal intertemporal utility (\(\partial V/\partial {C}_{j}\)). For the present purpose, it is sufficient to note that in general, they should be expected to differ.Footnote 8 As mentioned in subsection 1.2, there is a question of whether eudaimonic well-being can be treated equally with affective and cognitive measures of SWB.

From the literature on the relationship between income and SWB, which we show in subsection 1.1, we expect cognitive measures and eudaimonia to be more correlated with consumption than affective measures. However, the relationship between income and SWB may depend on how income is used. The literature review in subsection 1.3 suggests that, concerning relational consumption and material consumption, relational consumption is expected to be more correlated with SWB than material consumption. However, regarding the relationship between total consumption and SWB, we cannot expect correlation between total consumption and SWB, because it may depend on the share of relational consumption and material consumption in total consumption; this would then become an empirical issue.

3 Estimation Strategy

3.1 Empirical Model

As we attempt to examine the consumption–SWB relationship, we are essentially interested in the partial derivatives of instantaneous or intertemporal utility. For this empirical purpose, a reduced form of \(U\) or \(V\) from the prior section would be as follows:

$$SWB_{i}^{k} = \gamma_{1} + f\left( {consumption_{i} } \right) + \mathop \sum \limits_{j} \alpha_{j} X_{ij} + \epsilon_{1}$$

where i represents the individual and \(SWB_{i}^{k}\) represents the SWB indicator of type k, with k being any of the following four types: the Cantril Ladder (cognitive SWB); life satisfaction (cognitive SWB); affect balance (affective SWB); and eudaimonia. \({\gamma }_{1}\) is a constant. The main independent variable, \({consumption}_{i}\), represents the monthly total consumption of individual i. \(X_{ij}\) are control variables that have been used in prior studies of SWB (i.e., age, gender, level of subjective health, education, marriage, having children, having grandchildren, and personality).Footnote 9,Footnote 10 Furthermore, since consumption expenditure is reported per household, we also control for the number of people in the household.Footnote 11 Finally, \(\alpha_{j}\) are coefficients and \({\epsilon }_{1}\) is the uncorrelated error term.

Because we are concerned about the different effects of material and relational consumption, our empirical model is extended as follows:

$$SWB_{i}^{k} = \gamma_{2} + g_{1} \left( {C_{1i} } \right) + g_{2} \left( {C_{2i} } \right) + \mathop \sum \limits_{j} \beta_{j} Y_{ij} + \epsilon_{2}$$

where material consumption (\({C}_{1}\)) and relational consumption (\({C}_{2}\)) are included on the right-hand side. Like in Eq. (1), \({\gamma }_{1}\) is a constant, \({\epsilon }_{2}\) is an uncorrelated error term, and \({Y}_{ij}\) stands for other control variables with \({\beta }_{j}\) as the coefficient. To ensure that residual consumption does not affect our results, we also control for other consumption \(\left( {consumption_{i} - \left( {C_{1i} + C_{2i} } \right)} \right)\).

Using a reduced form of our theoretical construct, this study aims to clearly explore the different functional forms of consumption. Thus, we utilize nonparametric functions for consumption. Regarding the other explanatory variables, we account for parametric functions and apply semiparametric regression, as in prior studies. Previous studies of SWB using nonparametric methods include Blanchflower and Oswald (2008), Ferreira and Moro (2010), and Stevenson and Wolfers (2013).

There are two obstacles to the usual nonparametric multiple regressions. First, as the number of explanatory variables increases, the sparseness of the data inflates the variance in the estimates, and massive amounts of data are required for accurate estimation. Second, because a nonparametric regression does not provide an equation relating the average response to the independent variables, we need to display the response surface graphically by slicing the surface. When the number of explanatory variables exceeds three, the shape of the sliced surface becomes difficult to interpret (see Hastie and Tibshirani 1990).

These problems have spurred the development of additive regression models (Stone 1985). In this study, we use generalized additive models. We use cubic spline smoothingFootnote 12 iteratively to minimize the partial residuals that remain after removing the influence of the other variables in the model. In this model, a Bayesian approach is employed to derive standard errors and confidence intervals. Our estimation technique follows Wood (2004, 2008).

3.2 Data

This study relies on data from 9,635 subjects who submitted valid responses from a total of 13,690 respondents to our original Internet survey conducted in March 2017 across Japan.Footnote 13 The main dependent variables for SWB are summarized in Table 1, while Table 2 shows the control variables.

Table 1 Survey questions for each measure of SWB
Table 2 Definition of control variables

The survey selected respondents based on prefectural demographics, such as population, gender, and age ratios, to ensure that the sample represented Japan’s population. The survey was conducted as follows. First, an invitation was emailed from the internet survey company to relevant cohorts from their 1 million-plus registered monitors in Japan. To obtain a random sample, e-mails were sent to selected monitors based on prefectural demographics, such as population, gender, and age, and basic attributes, such as income level, and education. About 67% of the individuals who were contacted completed the survey.

Consumption is of utmost interest among the independent variables. In particular, we ask the questions listed in Table 3 to differentiate material consumption from relational consumption. Material consumption represents the consumption of goods, both consumer durables and others, but not services. Our measure of relational consumption pertains to consumption involving interactions with family, relatives, friends, and acquaintances. While monthly household consumption expenditure includes expenditures for housing, cars, and bikes, both material consumption and relational consumption do not. In addition, monthly household consumption expenditure includes medical expenses, insurance, and educational expenses, which are not included in either material or relational consumption.

Table 3 Questions regarding types of consumption

In Table 4, we show the descriptive statistics of both the dependent and explanatory variables, while the frequencies of each type of consumption expenditure are shown in Fig. 1. Among SWB indexes, we find that affect balance and the Cantril ladder have the smallest standard deviations and that subjective happiness has the highest mean.

Table 4 Basic statistical values of variables in the analysis
Fig. 1
figure 1

Frequency of each type of consumption expenditure per month (US$)

4 Estimation Results

The estimation results for the entire sample are summarized in Figs. 2, 3a, and b. In each figure, the horizontal axis corresponds to total monthly household consumption expenditure, material consumption, and relational consumption, respectively, while the SWB indicator is shown on the vertical axis. The solid line is the estimated consumption function curve, while the dotted lines represent the 95% confidence intervals. “0” on the vertical axis denotes the sample’s average SWB. Table 5 shows the saturation points (if any) and model fit statistics. We find that the smooth term of Eq. (1) is statistically significant.

Fig. 2
figure 2

Relationship between Consumption and SWB Indicators by Type. Notes The vertical axis represents the effects on each type of SWB indicator, while the horizontal axis represents the monthly household consumption expenditure (US$). The solid lines constitute the nonparametric function curves, while the dotted lines denote the 95% confidence intervals

Fig. 3
figure 3

a Relationship between monthly material consumption expenditure and SWB. b Relationship between monthly relational consumption expenditure and SWB. Note Because of the wide confidence intervals, the results for consumption expenditure in the top 5% range are omitted

Table 5 Satiation point and model fit statistics (Total consumption)

Figure 2 can be divided into two groups: the upper panels show the effects on the Cantril ladder, life satisfaction, and eudaimonia (Group 1), while the lower panels show the effects on affect balance (Group 2). We find that there might not be satiation for Group 1 in the upper panels of Fig. 2. The sluggish growth of SWB is even more evident for Group 2 and seems to reach satiation at approximately US$2,000, as shown in the lower panels of Fig. 2. In addition, we find a smaller marginal effect of consumption on the Cantril ladder, eudaimonia, and affect balance than on life satisfaction from US$0 to approximately US$1,500.

The somewhat ambiguous results shown in Fig. 2 lead us to examine the relationship between the SWB indicators and the consumption categories in Eq. (2). Figures 3a and b depict the estimation results for material consumption expenditure and relational consumption expenditure, respectively. Tables 6 and 7 show the saturation point (if any) and model fit statistics.

Table 6 Satiation point and model fit statistics (Material consumption)
Table 7 Satiation point and model fit statistics (Relational consumption)

We find that the smooth terms of Eq. (2) is statistically significant, as shown in Fig. 3a and b. In the figures, we find saturation points for material consumption in the case of both Group 1 and Group 2, while no such saturation state exists for relational consumption. Unlike total consumption, Fig. 3a and b show that subjective happiness has a similar trend to that of the Cantril ladder, life satisfaction, and eudaimonia, with a smaller marginal effect of consumption on affect balance than on the other SWB indexes. Furthermore, the marginal effect of material consumption tends to be lower than that of relational consumption for all SWB indices.

5 Discussion and Conclusion

A saturation point can be hypothesized such that SWB does not grow even if consumption or income grows, as the well-known Easterlin (1974) paradox suggests. Previous studies have mainly addressed the effect of income, but the breakdown of income into different consumption types should be more relevant for SWB, partly because a nontrivial portion of income is saved.

Our empirical results are discussed as follows. First, the consumption satiation effect in terms of SWB is not strongly evident. Compared with previous studies, we note similarities to Kahneman and Deaton (2010) in that total consumption contributes to the Cantril ladder but no more than a certain threshold with regard to Kahneman and Deaton’s (2010) definition of emotional indicators (i.e., affective measures) of SWB. Changing the explanatory variable from income to total consumption does not change Kahneman and Deaton’s (2010) conclusion. This observation can be explained in several ways. One is a situation in which an individual allocates income to consumption and savings in an optimal fashion so that the effects of income and consumption on SWB are equalized on the margin. Another explanation is more applicable to countries where the household savings rate is near zero, such as the case of Portugal and Greece over the past several years (OECD 2018), so that the effects of income and total consumption are trivially on par with each other. In light of Seligman’s (2002) theory of authentic happiness and Haybron (2000), our results suggest that total consumption contributes to life satisfaction (prudential happiness) and eudaimonia (perfectionist happiness) but no more than a certain threshold for hedonic well-being (psychological happiness).

Furthermore, our theoretical formulation uniquely attempts to differentiate the SWB indices by explaining instantaneous versus intertemporal utility maximization by individuals. This argument is consistent with Kozma et al. (1992), who theorize that happiness is a direct function of two psychological states, one short term and the other long term. The underlying assumption is that short-term, instantaneous utility yields an affective measure of SWB, while long-term intertemporal utility leads to a cognitive measure of SWB.

Our empirical results suggest that total consumption contributes to cognitive measures of SWB (Cantril ladder, life satisfaction) and eudaimonia, while there is a certain threshold for affective measures (affect balance). This finding is consistent with the literature on the income–SWB relationship.

However, once material versus relational consumption is extracted from the total consumption data, we find that there are saturation points for all SWB indexes for material consumption. In other words, the classic Easterlin paradox applies to the consumption of material goods and services. The literature suggests that materialistic people tend to experience financial problems, such as getting into debt, engaging in compulsive shopping, and declaring bankruptcy (Roverts 2011), and show that materialism is negatively related to life satisfaction. Our results are consistent with those of Kasser and Sheldon (2002), because the marginal effect of material consumption tends to be lower than that of relational consumption for all SWB indexes.

Finally, our results show that SWB grows without clear limits for relational consumption for all SWB indexes. This implies that relational consumption is expected to be key in improving both short-term and long-term well-being, as defined in our theoretical formulation. In other words, setting aside expenditures for relational consumption always enables an individual to achieve “sustainable” well-being.Footnote 14

Among the literature on the consumption expenditure–SWB relationship, Perez-Truglia (2013) and Wu (2019) demonstrate that conspicuous spending increases life satisfaction. For example, Wu (2019) suggests that it is an individual’s conspicuous consumption expenditure relative to that of reference group households that really matters. In this regard, the current study’s limitation is that we cannot fully consider the detailed breakdown of relational consumption, which includes both conspicuous and inconspicuous consumption. In other words, if people include some of their conspicuous consumption in relational consumption, our estimation results partly reflect an individual’s conspicuous consumption expenditure relative to that of other households in his or her reference group. Moreover, if some relational consumption goods are conspicuous while others are not, they have different impacts on short- and long-term well-being (Arrow and Dasgupta, 2009). Therefore, further studies that fully distinguish conspicuous consumption and relational consumption are needed.

Another challenge is to compare the effects of consumption versus income explicitly. Conventional price theory suggests that under utility maximization, indirect utility can be written solely as a function of relative prices (of general goods and relational goods in our context) and income as consumption variables are substituted away. To compare the marginal effects of consumption and income on SWB, therefore, we need to scale direct and indirect utility functions, which poses an empirical challenge.

Finally, many studies have been conducted using SWB indicators to elucidate the importance of social capital and leisure (e.g., Rodríguez-Pose and von Berlepsch 2014; Wang and Wong 2014; Rotondi et al. 2017). It is assumed that relational consumption is deeply connected with a plenitude of social capital and leisure. As we show in the dynamic model in Appendix, digging more deeply into the interaction between relational consumption and social capital is an important direction for future research.