1

Conspicuous Consumption and Satisfaction over the Life Cycle☻

(Draft not to be quoted)

Manoj K. Pandey*

Australian National University

Abstract

Numerous studies have observed that people compare their relative positions in the

consumption distribution within their reference group and signal their wealth to others by

consuming highly observable goods. These in turn affect their well-being. One of the basic

predictions of the signaling model of conspicuous-consumption is that an individual‟s well-

being or satisfaction should increase with an increase in his or her household‟s ranking in the

distribution of highly observable consumption within its reference group but should not be

affected by increase in its ranking of highly unobservable consumption. While there is some

empirical evidence in favour of this prediction for aggregate data, no attempt has yet been

made to test this hypothesis over the individual‟s life cycle. This study attempts to fill this

gap in the literature. The analysis is based on the panel data from Household, Income and

Labour Dynamics in Australia (HILDA) surveys for seven waves (2005-2011). Our study

does not find conclusive evidence in support of the predictions of the signaling model. We

find that the predictions differ across individuals‟ life cycle and are sensitive to the choice of

the estimation methods. For the people in the middle age (45-59 years), the prediction is

consistent with the hypothesised surmise but for other stages of life, the findings are not

robust.

☻I wish to acknowledge Prof. Raghbendra Jha for his excellent supervision, valuable guidance and extensive discussions throughout the process of this work. I am also grateful to my panel members Dr. Robert Sparrow and Dr. Creina Day for their very helpful comments on earlier versions of this paper. I am also thankful to Dr. Nitin Gupta for some of his critical

suggestions. This paper uses individual level (unit record) data from Household, Income and Labour Dynamics in Australia (HILDA) survey. The HILDA Project was initiated, and is funded, by the Australian Government Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views expressed in this paper, however, are those of the author. I am solely responsible for any error in the paper and should not be attributed to either FaHCSIA or the Melbourne Institute or the institution to which I am affiliated.

*Author‟s address: Manoj K. Pandey, PhD Candidate, Arndt-Corden Department of Economics, Crawford School of Public Policy, College of Asia and the Pacific, Australian National University, Canberra ACT 2600, AUSTRALIA. Phone: +61 6125 5537, Email: manoj.pandey@anu.edu.au.

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1. Introduction

Numerous studies have observed that people compare their relative positions in the

consumption distribution within their reference group and signal their wealth to others by

consuming highly observable goods. These in turn affect well-being1 of others in the

reference group. One of the basic predictions of the signaling model of conspicuous-

consumption is that an individual‟s well-being or satisfaction should increase with an

increase in his or her household‟s ranking in the distribution of highly observable

consumption within its reference group but should not be affected by increase in its ranking

of highly unobservable consumption. There is some empirical evidence in favour of this

prediction for aggregate data (e.g., Perez-Truglia, 2013, Winkelmann (2012). There is also

evidence which suggests that consumer behaviour changes strikingly over the life cycle

(Gourinchas and Parker, 2002). Going by this, we can assume that individuals consumption

priorities and signaling behaviour might depend on their positioning at the different stages of

life cycle. Therefore, evidence at the aggregate level might not sufficiently reflect upon

prediction of the model over the life cycle.

To the best of our knowledge, no attempt has yet been made to test the signaling model of

conspicuous consumption over the individual‟s life cycle. This study attempts to fill this gap

in the literature and makes three contributions to the existing empirical literature. First, while

few recent studies have made an attempt to test the signaling model of conspicuous

consumption using subjective well-being data, none of them have tested the hypothesis across

various stages of individual‟s life cycle. This study is the first to address this concern. In

particular, following approaches similar to Charles et al. (2009) and Perez-Truglia (2013),

this paper attempts to provide an answer to the question „are the predictions of signaling

model of conspicuous consumption consistent over life cycle?‟. Second, even at the aggregate

level, there appears to have been no attempt to test the signaling model of conspicuous

consumption for the Australian population. Using Australian panel data, this study attempts

to perform this task. Third, this study uses conditional fixed effect ordered logit model based

on BUC estimators (BUCFEOL) due to Baetschmann et al. (2011). To check the sensitivity,

we also employ other approaches, namely, pooled cross-sectional ordered logit (PCSOL), and

random effect ordered probit model with Mundlak transformation (REOPROBMT) to show

how the results are sensitive to the use of the methodology.

1 In the literature, well-being, subjective well-being, happiness, utility, life-satisfaction and welfare are used as

synonyms. In this paper, we also use these terms interchangeably.

3

In this paper, we employ Household, Income and Labour Dynamics in Australia (HILDA)

panel data for years 2005-2011. We investigate the effect of household‟s household ranking

in the consumption of observable and unobservable goods within its reference group on

subjective well-being of consumers in different age groups. The study utilised expenditure

panel data on household expenditures on groceries (highly unobservable goods) and cloth and

footwear (highly observable goods) and individual level life satisfaction scores to test the

prediction. In the model specifications, we take into account age, consumer‟s own, peer

group income and other individual, household, community, state level characteristics and

time. Based on the analysis, the study does not find conclusive evidence in support of the

predictions of the signaling model. We find that the predictions differ across individuals‟ life

cycle and are sensitive to the choice of estimation method. For the middle aged (45-59), the

prediction is consistent with the hypothesised surmise that the satisfaction levels should

increase with an increase in the household‟s ranking in the consumption of highly observable

goods but is not affected by their rankings in the consumption of highly unobservable goods.

For other stages of life the study does not find consistent evidence in support of the prediction

theory of the signaling model.

The remainder of the paper is organized as follows. The next section reviews the related

literature on subjective well-being, the signaling model of conspicuous consumption and

describes its various predictions. The details of the model and econometric specifications are

discussed in Section 3. Section 4 introduces the data, construction and definitions of variables

used in the paper. In section 5, the empirical results are reported and sensitivity analysis is

performed. Section 6 concludes.

2. Related literature and model predictions

2.1 Subjective well-being, measurement and its determinants

Subjective well-being has always been an important subject for psychologists2, economists

3

and social-scientists4. In welfare economics, consumer‟s well-being is conceptualised by the

2A large body of literature exists on various aspects of this subject. Some of the earlier works include Van Praag

et al.(1973), Campbel at el. (1976), Morawetz etc. al. (1977), Shin (1980), Larsen et al. (1984), Diener (1984),

Andrews (1991), Fox and Kahneman (1992), Mullis (1992), Veenhoven(1991, 1993). 3For detailed literature review see Frey and Stutzer (2001, 2002), Clark and Oswald (2002), Easterlin (1974,

2001), Graham and Pettinato (2002), Borghesi and Vercelli (2012) 4 Frey and Schneider (1978), Inglehart (1990) and Gallie et al. (1998)

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satisfaction of consumer‟s preferences5 for goods and services, and the usual proxy to

measure this satisfaction level has been the consumer‟s income or purchasing power. Given

the limitations of gross domestic product (GDP) as a measure of well-being, various

subjective well-being measures have been employed to provide new insights in policy

making and to capture a number of difficult-to-measure phenomena, such as the trade-off

between inflation and unemployment, the costs of air pollution, or the values attached to

environmental amenities (Deaton, 2013).

Psychological research has provided subjective well-being as another proxy for such

satisfaction of consumer‟s preferences and has therefore, provided a common platform for

both economists and psychologists. Individual‟s well-being can be measured in various ways

but majority of the recent literature measures this by asking a similar question as ‟Are you

satisfied with your life as a whole?‟. Individual judges the overall quality of his or her life

along a certain scale (say, 0-10 where 0=not satisfied, to 10=fully satisfied) and measure

degree of well-being.

Some of the key correlates of happiness studied in the existing theoretical and empirical body

of literature are individual‟s age (e.g., Clark and Oswald,1994; Frijters and Beatton, 2012

among others), absolute and relative income (e.g., Frank, 1985; Easterlin, 1974, 2001; Frey

and Stutzer, 2002; Gerdthman and Johannesson, 2001; Deaton, 2008 among others), health

and nutritional status (e.g., Graham, 2008; Frey and Stutzer, 2002 among others), marital

status (e.g., Lucas and Clark, 2006; Gardner and Oswald, 2006 among others), and

employment status (e.g., Boyce, 2010; Bonsang and Klein, 2012 among others) among other

socio-economic variables. However, the association between well-being and these variables

are not consistently established across studies.

2.2 Dynamics of Life Satisfaction over Life Cycle

In the economic and psychology literature, dynamics of life satisfaction over life cycle or the

age-happiness relationship is discussed at length. However, until the end of the last century,

the opinion was divided about how happiness of individuals varies with their age. Earlier

studies found different relationships between age and happiness making the dynamics

inconclusive. For instance, studies found relationships as U-shaped pattern (e.g. Clark and

5In the literature, subjective well-being, happiness, utility, well-being, life-satisfaction and welfare are used as

synonyms. In this paper, we also use these terms interchangeably.

5

Oswald, 1994) to almost flat (e.g. Easterlin et al., 1993) to a negative relationship (e.g.

Winkelmann and Winkelmann, 1998) to even an inverted U-shaped (Alesina et al., 2004; van

Praag et al., 2000). However, majority of the literature after 2000 and across the countries,

has found that the relationship is U-shaped (see Gerdtham and Johannesson, 2001;

Blanchflower and Oswald, 2001, 2004, 2008; Seifert, 2003; Hayo and Seifert, 2003; Clark,

2006). Some of the recent studies again contradict the findings that well-being is U-shaped in

age. Among those, Frijters and Beatton (2012) and Kassenboehmer and DeNew (2012) are

the most recent ones.

Based on three panel data sets, the German Socio-economic Panel (GSOEP), the British

Household Panel Survey (BHPS) and the Household Income Labour Dynamics Australia

(HILDA), Frijters and Beatton (2012) found that for the 20-60 age range, the relationship

between happiness and age weakly looks U-shaped and concluded that after controlling for

fixed effect, the happiness increase around the age of 60 followed by a major decline after 75,

with the U-shape in middle age disappearing such that there is almost no change in happiness

between the ages of 20 and 50. Kassenboehmer and DeNew (2012) also found evidence

against the U-shaped relationship and concluded that pooled OLS may provide a different

shape from that with fixed effect regressions.

2.3 Signaling Model of Conspicuous Consumption

A growing body of theoretical and empirical literature has observed that households and

individuals (i) care not only about their own levels of income, consumption and well-being,

but also compare to those who belong to their reference group6 and, (ii) individuals signal

others about their relatively higher wealth by consuming highly observable goods

(Duesenbery, 1949; Leibernstein, 1950; Clark and Oswald, 1996; McBride, 2001; Easterlin,

2001; Ferrer-i-Carbonell, 2005; Luttmer, 2005; Dynan and Ravina, 2007; Clark et al., 2008).

Latter is the key supposition of the conspicuous consumption model. This model

hypothesizes that people consume highly observable goods to signal that they possess higher

ranked status than others in their reference group. In the literature, this hypothesis is tested

using methodologies based on stated preferences (Carlsson et al., 2007), laboratory

experiments (Fennis, 2008) and consumption expenditure data (Charles et al, 2009; Kaus,

2013).

6 Also known as „Veblen effect‟ in the literature as evidence goes way back to Veblen (1899, 1925). This effect

is tested in some of the recent studies (e.g., Luttmer, 2005)

6

Numerous studies have attempted to investigate the role of income as a signal of status and

tested the hypothesis that people derive utility from their rank in a reference group. On this,

Brown et al. (2008) claimed that while relative income and income rank are related concepts,

the concerns are distinct. Frijters and Leigh (2008) found that the non-migrants react to the

arrival of migrants by working more hours per week. They argued that when population

turnover is high, the leisure activities of non-migrants become less observable and individuals

are made worse off, since the visibility of conspicuous leisure then decreases and the status

race must be played out primarily via conspicuous consumption.

Glazer and Konrad (1996) studied the signaling value of donations to universities in the US

and their findings support the prediction of the conspicuous consumption model. Using a

matched employer–employee panel data, Clark et al. (2009) examined whether job

satisfaction of a worker is higher when other workers in the same establishment are better-

paid and found favourable empirical evidence. They argued that the difference in the earnings

hinges on the nature of the reference group and earnings not only induce jealousy but also

provide a signal about the worker‟s own future earnings. They established that the positive

future earnings signal outweighs any negative status effect and this phenomenon is stronger

for men and in the private sector but weaker for those nearer retirements. Boes et al. (2010)

hypothesised that individual life satisfaction depends on a comparison of own rank and rank

of one‟s parents. Based on analysis using German Socio-Economic Panel data, they found

evidence in favour of relative rank hypothesis. However, increase in consumption today

comes at the cost of leisure and people need to work hard. Arrow and Dasgupta (2009)

argued that although higher current period consumption increase relative status of

consumption now but results in a lower relative future consumption.

In a more recent study, based on nationally representative data for United States on

consumption for racial groups, Charles et al. (2009) demonstrated that declining visible

consumption in reference group income is a key prediction of the status-signaling model.

They showed that Black and Hispanic households spend their larger budget shares in

consumption of visible goods than other households in the United States. Following the same

approach for South Africa, Kaus (2013) examined whether the differences in visible

expenditures can be explained with a signaling model of status seeking and found that, in

contrast to the findings of Charles et al. (2009), the model failed to explain the differences

within the group of White South Africans. In another study, Kuhn et al. (2011) established

the link between reference-group income and conspicuous consumption. They used data from

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Netherlands on a special lottery that awards prizes to every ticket holder in a weekly selected

random postal code and found that when a number of households win the lottery in the same

postal code, the non-winning households in that neighbourhood change their consumption of

goods that are highly observable. Consistent with the conspicuous consumption model,

Heffetz (2011) found that income elasticity is endogenously predicted to be higher if a

household spend larger shares of their budgets on some (but not all) visible goods and lower

if it is not.

On the economic study of conspicuous consumption effect, individuals with higher ranking

status are found to be relatively happier (Di Tell, Haisken-De New, & MacCulloh, 2007),

healthier (Wilkinson, 2000; Marmot, 2003) and live longer (Oswald & Rablen, 2008). More

recently, few studies have also tested the relationship between conspicuous consumption and

individual well-being. Among those, Winkelmann (2012) and Perez-Truglia (2013) are the

most recent. Using Swiss household panel data and combining relevant information from

other sources, Winkelmann (2012) found that the prevalence of luxury cars (a highly visible

good) in the municipality of residence significantly reduces own income satisfaction. Using

panel data on household expenditure and individual well-being, in a more recent study,

Perez-Truglia (2013) tested the conspicuous consumption model using subjective well-being

data. The empirical results showed that signaling model of conspicuous consumption predicts

that a consumer‟s well-being increases with increase in his or her household‟s rankings of

observable consumption but does not get affected by such rankings of unobservable

consumption within its reference group. These results were consistent with the predictions of

the conspicuous-consumption model7.

3. Data, Definitions and Variable Construction

3.1 The Data

We used a representative panel data from the Household Income and Labour Dynamics in

Australia (HILDA) survey8, covering the period 2005-2011 inclusive

9. The survey contains

personal and household level information through well-designed questionnaires and asks

7 While majority of the empirical studies verify the accuracy of conspicuous consumption model, overall

evidence is inconclusive. For extensive discussion, see Heffetz (2011), Herffetz and Frank (2011) and Perez-Truglia (2013). 8 The HILDA Project was initiated, and is funded, by the Australian Government Department of Families,

Housing, Community Services and Indigenous Affairs (FaHCSIA) and is managed by the Melbourne Institute

of Applied Economic and Social Research (Melbourne Institute). 9 While this survey begins in year 2001, we opted to use the data only for waves 2005-2011 as complete data on

expenditure was not available for first 4 waves.

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detailed questions on individual‟s annual economic and subjective well-being, as well as

labour market and family dynamics. The HILDA also collects data on individual income

from multiple sources during the preceding financial year. The income sources include wages

and salaries, income from business and rent, share dividends, private superannuation income,

private transfers (e.g. child support payments) and public transfers (pensions, unemployment

benefits etc.). All income and expenditure variables are converted to real values expressed in

terms of Australian Dollars in 2001 using consumer price indices available from the

Australian Bureau of Statistics (ABS, 2012). To prevent income values from being treated as

missing data, $1 is added to all incomes before taking the log values, except for the cases

where information about income is not provided.

The HILDA panel data has been exploited in the past in numerous studies (e.g. Booth and

Van Ours, 2008, 2009; Frijters and Beatton, 2012; Paul and Guilbert, 2013, among others).

For the analysis purposes, we use balanced panel data by restricting dataset to those

respondents only for which information is recorded and followed in all the seven available

HILDA surveys since 2005. Consequently, final samples used for the analysis includes 63931

observations for 9133 individuals. However, in the regression analysis, because of the

missing values in some of the variables under study, the number of observations shown in

different models might vary.

Apart from the variables used from HILDA surveys, we also constructed few relevant

demographic and economic variables at the state level. Demographic variables include

gender-wise unemployment, life-expectancy at the age 65, and standardised death rates.

Economic variables were established house prices and per capita GDP expressed in real

values of Australian Dollars in 2001. These data were collected from the websites of Reserve

Bank of Australia (RBA) and Australian Bureau of Statistics (ABS)10

.

3.2 Variable Construction

3.2.1 Determination of Life Cycle Stages using Satisfaction Data

The variables used in the estimation are as follows. The measure of life-satisfaction

(subjective well-being or happiness) is the standard life satisfaction question in individual

10 Can be accessed from Reserve Bank of Australia website( http://www.rba.gov.au/) and Australian Bureau of

Statistics website (http://www.abs.gov.au/)

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questionnaire: All things considered, how satisfied are you with your life?11

. The possible

responses are measured on a scale numbered from 0 (not at all satisfied) to 10 (fully

satisfied).

Figure 1 depicts the relationship between average happiness and average age of person, male

and female in Australia over 2005-2011. It shows that while average happiness score reduces

in the age group 15-45, it begins to increase after that until the age of 75 from where again a

diminishing trend is observed. Further, there appears to have a sharp decline in the age range

15-30 as compared to 30-45. Similarly, in the age group 45-75, the increasing trend is sharper

in the range 45-60 as compared to 60-75. Based on this, individual‟s life cycle is divided into

5 stages, namely, 15-29 (younger young), 30-44(young), 45-59 (middle-aged), 60-74(old)

and 75 and above (older old)12

.

Figure 1. Relationship between happiness and age.

11There has been a considerable debate on the usefulness and validity of self-reported measures of well-being and health. However, recent economic and psychological literature suggests that these measures are quite

reliable and reflect important information (see Layard, 2005; Gilbert, 2006; Schimmack, 2006). While we do not

find any study that suggests validity of the self-reported life-satisfaction in HILDA data, like other similar

studies, we also assume that ordinal measure of subjective well-being used in the study is valid and reliable. 12 We also used other age-groups for the analysis, however, results were found robust. Thus, while the selection

of age-groups for various life stages seems arbitrary, it is not.

7.5

88

.59

Happin

ess

(M

ean)

10 20 30 40 50 60 70 80 90 100

Age (years)

Person Male

Female

Source: Author's Computation using HILDA data

Australia: 2005-2011

Happiness and Age

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3.2.2 Reference Group

Following Charles et al. (2009) and Perez-Truglia (2013), the reference group is required to

be defined to compute and construct the variables on relative expenditure for both highly

observable and highly unobservable consumptions. Charles et al (2009) argued that quality of

clothing can serve to signal wealth in random interactions with strangers, who are more likely

to live and work near the place of residence or work. Perez-Truglia (2013) mentioned that the

other signallers competing for the same non-market goods are also likely to live and work in

the same geographical area. Thus, use of geographical area of residence as the definition of a

household‟s reference group is justified. This is widely used in the literature on conspicuous

consumption. For instance, Charles et al. (2009) recommended using social affiliation and

regional proximity and used state of residence as the reference group. Winkelmann (2012)

used municipality, the smallest administrative and political unit in Switzerland, and canton of

residence, whilst Kaus (2013) defined reference groups at the provincial level. Perez-Truglia

(2013) used three different definitions of reference groups based on Primary Sampling Unit

(PSU) for the year of survey or over 5-year window and also by taking into account some

household characteristics.

In accordance with the literature, we use reference group defined as similar households living

in a closest possible geographical area covered in HILDA sampling and year of survey.

HILDA sampling covers all the 8 Australian States/Territories: New South Wales (NSW),

Victoria (VIC), Queensland (QLD), South Australia (SA), Western Australia (WA),

Tasmania (TAS), Northern Territory (NT) and Australian Capital Territory (ACT). These 8

states are further divided into 4 sections: Major, Other, Bound and Rural. We define

geographical area in which an individual lives as the combination of section of state of

residence in a year. Each of these geographical areas13

is combined with some of the

household characteristics such as household size, average age of the household members and

whether household average member has attained university degree or not. Thus, a reference

group consists of households with similar characteristics living in the same geographical

areas in a particular year.14

13 We do recognise that some of the geographical areas have few households only. However, this does not seem

to affect our analysis and hence we do not make any attempt to discard those sample units. 14 We also tried with other combinations of state, major statistical regions, major sections and household

characteristics for the robustness check.

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3.2.3 Rankings of Households

We utilized annual expenditure data available in the survey as proxy for consumption. As in

Perez-Truglia (2013), we require at least one consumption category that is highly observable

(visible) and another that is highly unobservable (invisible). The classification of visible and

invisible consumption is an empirical task. Charles et al. (2009) and Heffetz (2011)

conducted surveys to classify visible goods and they found that clothing is one of the visible

goods. Unfortunately, for Australian population such a survey is not available. Therefore, in

line with Charles et al. (2009), Heffetz (2011) and Perez-Truglia (2013), we also used

annualised expenditure in clothing and footwear (combined)15

as the observable goods.

Groceries that includes food, cleaning products, pet food, and personal care products is an

obvious candidate for invisible consumption as we consume it within the boundary of our

residences.16

Therefore, annualised expenditure on groceries consumed at home as the

unobservable goods.

Now, each of the households is ranked based on relative consumption of groceries and cloth

+ footwear in the reference group separately. For computation of ranks, procedure used in

Perez-Truglia (2013) is followed. A rank of a household is defined as the share of households

in the same reference group with less or equal expenditure in the corresponding categories.

Each of the ranking variables takes a value from 0 and 1.17

3.2.4 Peer Groups

In the literature of subjective well-being, peer group is defined in terms of age (McBride,

2001), region (Stutzer, 2004), neighbours (Luttmer, 2005), a combination of age, education,

employment status (van de Stadt et al., 1985), a combination of age, education and region

(Ferrer-i-Carbonell, 2005), a combination of gender, age, education, and region (Jorgensen

and Herby, 2004). Recently, Paul and Guilbert (2013) defined peer groups by age and

education, whereby all those who are within 15% of the individual‟s age and have attend

same level of education. We define peer group in a slightly different manner.

15 In our survey, the expenditure is not separately available for clothing and foot-wear. 16

In our survey, the expenditure on groceries does not separately give expenditures on food, cleaning products,

pet food, and personal care products. Also, expenditure on groceries does not include expenditures on alcohol or tobacco, which might be used outside the home. 17 A household is ranked 0 if none of the household in the corresponding reference group has consumption

expenditure less than or equal to that household and 1 if all the households have consumption expenditure less

than or equal to that household in the corresponding category. Similarly, a ranking value of 0.6 in the groceries

expenditure corresponding to a household indicates that 60% of the households in the reference group have

groceries expenditure less than or equal to that household.

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Table 1.

Sample Characteristics at Different Stages of Life: 2005-2011

Characteristics Younge

r

Young

adults

Youn

g

adults

Middle-

aged

adults

Older

adults

Older

Old

adults

All

adults

% Male 49.16 48.42 48.59 48.54 43.81 48.31

Average Age (mean, in years) 23.23 37.27 51.80 66.04 80.51 47.13

% Married 16.10 62.83 70.35 72.11 52.56 57.60

% Employed 77.53 82.24 77.50 27.65 3.05 64.73

% with Bachelor Degree 15.69 18.27 11.56 7.51 4.25 12.96

% Australia born 85.30 77.83 70.10 66.82 69.04 74.34

Average Happiness (mean) 7.87 7.67 7.73 8.13 8.38 7.86

Average Annual Income (mean) 25,690 45,135

42,396 26,451

16,161 35,490

Average Household Annual Income (mean) 18,745 18,78

9

18,405 11,97

3

5364 16,51

8

Average Peer Group Income (mean) 26,274 45,52

4

43,140 26,84

3

16,535 36,01

7

Average Annual Groceries

Expenditure (in AUD)

8053 8365 8533 6737 5778 7886

Average Annual Cloth+FootWear

Expenditure (in AUD)

1600 1770 1557 1022 699 1469

% Annual Share of Groceries

Expenditure in Total Income

31.35 18.53 20.13 25.47 35.75 22.22

% Annual Share of Cloth+FootWear in

Total Income

6.23 9.42 8.46 8.54 13.04 8.89

% Annual Share of Groceries

+ Cloth+FootWear Expenditure in Total Income

37.58 22.45 23.80 29.33 40.08 26.36

Average Rank Groceries Expenditure (mean) 0.52 0.54 0.53 0.53 0.55 0.53

Average Rank for Cloth + FootWear

Expenditure (mean)

0.52 0.54 0.52 0.53 0.53 0.53

Correlation coefficients between ranks in the groceries

and cloth+footwear expenditure Expenditure

0.31 0.34 0.35 0.30 0.26 0.32

% with Long Term Health Condition

(Disability)

12.90 18.47 29.55 47.73 63.91 29.09

Average Household Size

(mean)

2.67 2.10 2.51 2.02 1.69 2.28

Source: Author‟s Computation from HILDA panel data for years 2005-2011. Figures are weighted across 7

waves surveyed annually between 2005 and 2011.

In this paper, all the individuals of the same gender, same education attainment group

(belongs to group 1 if degree bachelor and above and group 2 if degree lower than bachelor),

whose year of birth fall within a range of plus minus 5 years of the individual‟s year of birth

and those who live in the same sections of the major statistical regions of the state of

13

residence in the year of survey form a peer group.18

Based on our definition of peer group, we

compute mean income of the peer group and call it peer-group income.

3.3 Descriptive Statistics

Table 1 and Table 2 display some of the sample characteristics of individuals and the

distribution of happiness across different stages of life cycle. Younger young individuals are

characterized by lowest proportion of married and the largest group born in Australia, with

lowest average annual income in the working age population (15-59), living in households

with highest average family size but with lowest incidences of disability and second highest

average happiness score after older olds.

On the other hand, older old adults are characterized by a stage in the life cycle when

individuals possess lowest level of employment, less educated, live in relatively small

households with least income and expenditure, with highest level of disability but also with

highest level of happiness. The proportion of individuals being married, born outside

Australia, share of being disabled, and happiness increases, whilst educational attainment,

employment, average annual income and household size gets smaller and smaller with each

later stage of life. We also observe that more than 93 precent of individuals has life

satisfaction higher than average score of 5. The average income of Australians over a period

of 2005-2011 was AUD 35490 annually, slightly higher than their previous year income but

less than the peer group. Over the same period, the average household spent AUD 7886 on

groceries that includes food, cleaning products, pet food and personal care products and AUD

1469 on clothing and footwear. Thus, groceries, clothing and footwear expenditure comprised

about 26 precent of the average household income.

Table 2 and Figure 2 display how happiness and income change over life cycle. It is evident

that while income (own and peer-group) first increases from younger young generation to

young then it begins declining first with slow pace and then with rapid speed. It is interesting

to note that happiness and income move in opposite directions. Figure 3 reflects on how

happiness and household‟s rank in groceries and clothing and footwear expenditure changes

over life cycle.

18 We also follow other definitions of peer group as suggested in the literature and use those for the sensitivity

check.

14

Table 2.

Life Satisfaction at Different Stages of Life: 2005-2011 (%)

Life Satisfaction

Responses

Younger

Young

adults

Young

adults

Middle-

aged adults

Older

adults

Older Old

adults

All adults

0 0.07 0.07 0.09 0.15 0.09 0.09

1 0.05 0.21 0.26 0.24 0.06 0.19

2 0.26 0.37 0.5 0.58 0.24 0.42

3 0.28 0.75 0.89 0.52 0.3 0.63

4 0.8 1.16 1.25 0.84 0.6 1.03

5 3.13 4.49 5.3 4.17 3.58 4.36

6 6.59 7.45 6.23 5.27 3.32 6.27

7 23.26 23.32 21.52 12.4 11.84 20.03

8 34.9 37.01 34.59 32.28 31.01 34.67

9 21.3 18.94 20.81 26.43 24.53 21.64

10 9.35 6.23 8.56 17.14 24.41 10.68

>5 95.4 92.95 91.71 93.52 95.11 93.29

>8 30.65 25.17 29.37 43.57 48.94 32.32

N 11467 17507 18540 11626 4368 63508

Mean 7.87 7.67 7.73 8.13 8.38 7.86

CV 0.17 0.18 0.19 0.19 0.17 0.18

Min 0 0 0 0 0 0

Max 10 10 10 10 10 10

Source: Author‟s Computation from HILDA panel data for years 2005-2011. Figures are weighted across 7

waves 2005-2011. Life-satisfaction responses range from 0 (not at all satisfied) to 10 (fully satisfied).

15

Figure 2. Relationships between happiness and income (own and peer-group) for younger young (15-29), young

(30-44), middle-aged (45-59), old (60-74), and older old (75 and above) adults

Figure 3. Relationships between happiness and household rankings in the groceries and clothing and footwear expenditure

for younger young (15-29), young (30-44), middle-aged (45-59), old(60-74), and older old (75 and above) adults. Vertical

lines are drawn at the age of 15, 29, 44, 59 and 74.

1020

3040

50

Inco

me

(in T

hous

and

AU

D)

7.6

7.8

88.

28.

4

Hap

pine

ss(m

ean)

15-29 30-44 45-59 60-74 75 & above all agesAge Groups (in years)

Happiness Income

Previous Year Income Peer Group Income

Source: Based on HILDA, 2005-2011

Australia: 2005-2011

Happiness and Income by age groups

.45

.5.5

5.6

Household

's r

ank in E

xpenditure

7.6

7.8

88.2

8.4

Happin

ess(m

ean)

20 40 60 80 100Age (in years)

Happiness Groceries

Cloth and Footwear

Source: Based on HILDA, 2005-2011

Australia: 2005-2011

Happiness and Household's Ranks in Exp. on Groceries and Cloth+Footwear by individual's age

16

We find the relationship almost similar to the relationship between happiness and income.

However, on average rankings of household‟s clothing and footwear expenditure is almost

same as that of groceries expenditure in all the age groups. Also, even if rankings of

household‟s clothing and footwear expenditure and that of groceries expenditure move in the

same direction, the correlation coefficients-that measures linear relationship between them

are low (0.26 to 0.35.

4. Econometric Model Specification

4.1 Model Specification

In this paper, we closely follow theoretical model discussed in Perez-Truglia (2013). For

empirical specification, let us assume that its is a latent unobserved ordered measure of

reported life satisfaction or subjective well-being for individual i at any year t . Let ivhitc be

the consumption of invisible good (i.e. groceries) in the household of individual i in the year

t and ( )ivghitR c be the rank of the household in the consumption of invisible good in its

reference group at time t . Similarly, let v ghitc and ( )vghitR c , respectively, be the consumption

and the household ranking in the consumption of visible good (i.e. cloth and footwear) in its

reference group at time t . Let us also assume that it is a vector of individual, household,

community and state level covariates. Then the empirical model is given by

0 2 3 4 5 6

i i

ln( ) ( ) ln( ) ( ) * ( ) ..(1)

1,2,....., ; 1,2,....., ; 1,2,.....,T

it ivhit ivhit vhit vhit vhit it X i t its c R c c R c Age R c

i N h H t

where ( 0,...6)j j are the parameters and X is a vector of parameters.

i denotes the

individual-specific time-invariant component (fixed effects), t denotes the time effect and

it

is the individual and time-varying disturbance term.

Now, as our objective is to examine the relationship of life satisfaction with ( )ivhitR c and

( )vhitR c over the life cycle, for the estimation purposes, we would estimate following

specifications.

First, we would use age, age-square and interaction of age with the two key variables

( )ivhitR c and ( )vhitR c in the full sample. Accordingly, the model can be called full sample-

specification 1 and is respecified as follows.

17

0 1 2 3 4 5

6 7 8

i i

ln( ) ( ) * ( )

ln( ) ( ) * ( ) ,.........................(2)

1,2,....., ; 1,2,....., ; 1,2,.....,T

it ivhit ivhit ivhit

vhit vhit vhit it X i t it

s Age AgeSquare c R c Age R c

c R c Age R c

i N h H t

Second, as decided in section 3, we have 5 age groups representing 5 stages of the life cycle.

Going by this, we use 5 age dummies (for age group:15-29, 30-44, 45-59, 60-74, 75 and

above) based on 5 select stages of life in the full sample and rank variables interacted are

with these dummies. We call this as full sample-specification 2 and model is rewritten as

follows.

5

0 6 7 8 9

2

5 5

2 2

i

ln( ) ( ) ln( ) ( )

* ( ) * ( ) ,..................(3)

1,2,....., ; 1,2,....., ; 1,2,...

it k k ivhit ivhit vhit vhit

k

k k ivhit k k vhit it X i t it

k k

s AgeDummy c R c c R c

AgeDummy R c AgeDummy R c

i N h H t

i..,T

Where ( 1,2,3,4,5)kAgeDummy k are the 5 age dummies. For k=1, dummy for age group

15-29 is the reference group and therefore, excluded from the model. , , are the

parameters.

Third, we divide the full sample into 5 subsamples and use model specified in equation 1 for

each of the subsamples. For any age group, we call these specifications as subsample-

specification 3. The same model is applied to full sample and 5 sub-samples corresponding

to 5 stages of life cycle.

Thus, life-satisfaction variable is dependent variable and household‟s rankings in the

consumption expenditure of groceries (highly unobservable) and the ranking of clothing and

footwear expenditure (highly observable) are the two key explanatory variables for our

interest. We are interested mainly in how the signs and significances of the coefficients of

( )ivhitR c and ( )vhitR c change over the life cycle after controlling for other variables.

We

include household income in the model as income could be a natural candidate for proxy of

own consumption and a natural monetary comparison scale in order to gauge the magnitude

of the consumption externality (Winkelmann, 2012). As opposed to Winkelmann (2012), we

use household income net of the individual income to avoid any kind of possible

18

endogeneity.19

We also control for peer group income (net of individual) and income

inequality variables. Other controls are the usual socio-demographic variables such as gender,

educational attainment, employment status, marital status, number of kids, long term health

condition (disability), whether born in Australia, whether live in urban or remote areas, and

household size, family‟s average health status net of own health status. Apart from these

explanatory variables, we also control for some of the state level variables disaggregated by

gender such as unemployment rate, life expectancy at age 65, standardised death rate, and

aggregated real house price for established houses, and log of real per capita GDP. Time

dummies are also included.

4.2 Estimation Strategies

Self-reported life satisfaction or happiness is the dependent variable that takes values between

0, 1, 2…and 10. After analysing studies dealing with happiness, Ferrer-i-Cabonell and

Frijters (2004) noted that pyscologists and sociologist usually interpret these responses as

cardinal and comparable across individuals, and accordingly, run OLS based regressions on

happiness. Economists, on the other hand, assume these responses as ordinal and use ordered

latent response mo1dels. They tested the changes in the results due to choice of assumptions

and found that while the cardinality or ordinality does not qualitatively change the results, the

treatment of the unobserved time-invariant effects does affect the results. By considering the

distribution of the responses (skewed towards higher extreme) and given the ordinal nature of

survey responses, we assume that satisfaction responses are ordinal and all the individuals do

share the same interpretation of each possible response20

. Accordingly, we prefer to comment

on the empirical results based on the ordinal assumption.

Our use of panel data enables us to control for unobserved individual characteristics while

estimating individuals‟ reported well-being. Lykken and Tellegen (1996) have estimated that

between one-half to four-fifth of the variation in individuals‟ reported well-being results from

genes and upbringing, underlining the importance of controlling for individual-specific fixed

effects. However, controlling for an individual effect on an ordinal (categorical) scale

variable is problematic.

19 As inequality in the income was not significant in any of the model, later we drop it. 20

In section 5, we also perform sensitivity analysis by assuming cardinality.

19

A common empirical approach to fixed-effect ordinal estimation is to convert ordinal variable

into a dichotomous dependent variable. For this, usually an arbitrary common cut-off point is

chosen to reduce categorical variable into a (0,1) scale. This allows the introduction of fixed

effect in a binomial logit/probit model setup and the estimation of the parameters using

Chamberlain‟s technique. However, while doing so, we lose a great deal of information

which may lead to measurement errors.

To overcome such problems, FF-type estimation model proposed by Ferrer-i-Carbonell and

Frijters (2004) is used in some of the recent empirical literature (e.g. Frijters et al. (2004),

Booth and Van Ours (2008, 2009), Knabe and Ratzel (2011), Kassenboehmer and Haisken-

DeNew (2009), Clark et al (2009), Jones and Schurer (2011)). In this model, individual fixed

effects can be introduced along with the individual specific thresholds

' '

ik i,k 1Pr( ) ( ) ( )it i it i its k x x , where k represents a life-satisfaction

response category, 0,1,....10k . Ferrer-i-Carbonell and Frijters (2004) showed that instead

of a common cut-off point, individual specific cut-off points can be chosen by selecting

specific threshold level for each individual. In this way, the fixed effects ordered logit

specification can be transformed as a fixed-effects binomial logit that allows Chamberlin‟s

method to be used. This arrangement removes the individual specific effects i as well as the

individual specific thresholds ik from the likelihood specification. However, Baetschmann

et al. (2011) showed that since the choice of cut-off is endogenous and substantially biased in

some cases, FF-type estimators are in general inconsistent. Moreover, this approach does not

perform well when some of the response categories have small number of observations.21

Because of these concerns, instead of following these procedures, we use following three

approaches in an ordinal scale setting.

First, we begin with pooled cross-section ordered logit model (PCSOL). This model does not

control for individual specific heterogeneity. In this model, we have

' '

k 1Pr( ) ( ) ( ),it k it its k x x with 0 1 10, 0, . , is the logistic

cumulative distribution function and other variables and parameters are as defined above.

'k s are unknown parameters to be jointly estimated with the 's . Thus, for a latent variable

21This is an issue in HILDA data where less than 10 percentage of total observations are attributed to first 6

response categories (0-5) and rest 5 response categories contain more than 90 percentage of observations (see

Table 1).

20

*

its , *Pr( ) Pr(k 1 )it its k s k . This method is used as a baseline model in the happiness

literature (see for instance, Booth and Van Ours (2008, 2009)).

Second, we use conditional (fixed effects) ordered logit model with „Blow-Up and Cluster

(BUC)‟ estimators (BUCFEOL) proposed by Baetschmann et al. (2011). In this method,

every sample observation is replaced by (k-1) copies, k being the total number of response

categories (they call it blow up sample). Then each of these copies is dichotomized at the

different cut-off points. Using this entire sample, parameters are estimated through

conditional maximum likelihood (CML) logit. Baetschmann et al. (2011) emphasised that

the standard errors are clustered at the individual level. One advantage of using this method is

that it does not suffer from the potential problems associated with some cut-offs resulting in

small sample sizes and is preferred when the number of observation corresponding to some of

the response categories are very few.

Third, instead of using random-effects method22

, we use a random effect ordered probit

model with Mundlak transformation (REOPROBMT). One advantage of using this method is

that this preserves the ordinal nature of the dependent variable, without any need for

dichotomising, and also dispenses with the orthogonality requirements of random-effect

estimation. In this way, this approach overcomes the inefficiency problems associated with

the fixed-effects model but still maintains number of key fixed effect assumptions. In this

approach, we parameterise the individual effect as 0 1 ii i

. The term i denotes the

mean of an individual specific variable for individual i over all time points t and included

as an application of Mundlak (1978) method. In this way, the individual effect i is

decomposed into a random effect 0i , which is uncorrelated with the right-hand side variables

and the time-mean values of some of the (time-varying) regressors that allowed to be

correlated with the random effects. We use the mean values of the individual-specific

characteristics over all waves as additional explanatory variables to address the possible

correlation between unobservable personal traits and a subset of explanatory variables. This

approach is utilised by Clark et al. (2009), Boyce (2010), Ferrer-i-Carbonell (2005), and Paul

and Guilbert(2013) among others.

22 As random-effect (RE) estimation is based on the assumption that individual heterogeneity is uncorrelated

with explanatory variables of interest. In practice, this assumption is very strong and once violated, it is likely

to produce biased estimates.

21

These approaches are used in recent studies focusing on the estimation of life-satisfaction in

various contexts. However, such empirical strategies are not free from caveats. PCSOL

model represents a mere association and does not control for unobservable factors correlated

with life-satisfaction and some of the other explanatory variables. Thus, PCSOL estimates

may provide biased coefficients. In estimating happiness equations, we would expect that

unobserved individual heterogeneity could be important since some of these may be

correlated both with the propensity to report satisfaction responses and some of the key

explanatory variables included in the model.

Both the BUCFEOL and REOPROBMT appropriately control for individual heterogeneity.

Also, fixed effect estimates from BUCFEOL model could be interpreted as producing causal

effects. However, fixed-effect models do not allow time-invariant characteristics such as

gender, residence, education etc. to be included in the model and focus solely on explaining

the within-person variation. REOPROBMT model circumvents some of the problems in

BUCFEOL model by including the time-mean values of the observable characteristics

assumed to be correlated with the unobservable heterogeneity. However, choice of such

observable characteristics is arbitrary, which may affect the estimates.

The discussions above clearly suggest that while BUCFEOL and REOPROBMT might be

better choice than the PCSOL, but we do not have a clear idea which one of these two

approaches is better. Therefore, in this paper, we have opted for a strategy to estimate and

report BUCFEOL econometric results23

and then compare these results with REOPROBMT

and PCSOL. Also, we can compare all the three approaches based on assumption of

cardinality to check for robustness in the estimates.

5. Empirical investigation

In this section, we present estimation results based on econometric specification in

section 4.

23 We do not know any statistical test to test whether BUCFEOL is more efficient than REOPROBMT.

However, Hausman test is used to compare fixed with random effects under cardinal assumption (linearity) with

generalised least squares (GLS). Hausman test rejects the null hypothesis that the difference in coefficients is

not systematic and hence it is safe to use fixed effect. We used results from the Hausman tests for full sample:

specification 1, full model: specification 2, and five sub-samples as an indirect hint to choose BUCFEOL model

over the REOPROBMT.

22

5.1 Econometric Results

Table 3 displays BUCFEOL regression estimates24

for life-satisfaction. The first column

corresponds to full-sample-specification1 in the section 4, second column to the full-sample-

specification2 and the columns 3-7 onwards refers to sub-samples corresponding to younger-

young (15-29), younger (30-44), middle aged (45-59), older (60-74) and older-old (75 and

above) adults, respectively.

Estimates from the first column of Table 3 suggest that an increase in the ranking of cloth and

footwear expenditure (highly observable) as well as the ranking of groceries expenditure

(highly unobservable) in the reference group significantly increase life satisfaction.25

Moreover, coefficients of both the rankings are not statistically different from each other.

This indicates that the prediction from the model is not fully consistent with the prediction of

conspicuous-consumption model.

Ceteris paribus, life satisfaction does not change significantly with the ranking of highly

unobservable consumption expenditure at the higher age. But at the higher ages, an increase

in the ranking of highly unobservable consumption expenditure significantly reduces life

satisfaction. At the mean value of sample age, while the magnitude of the marginal26

increases in the life satisfaction due to an increase in the ranking of cloth and footwear

expenditure (0.08) is higher than that of the increases due to ranking of groceries expenditure

(0.02) but the difference is not statistically significant. The results indicate that the prediction

of the conspicuous consumption model is not consistent over the life cycle.

Second column of Table 3 documents estimates corresponding to full sample-specification 2,

as described in section 4. None of the rankings in the reference group significantly change the

life satisfaction on their own.27

24For estimation purposes, we use STATA codes provided in Baetschmann et al. (2011). 25 A similar result is obtained when we drop absolute levels of expenditure from the full sample-specification1

(Table A.1). However, ranking in the cloth and footwear expenditure was not significant when we exclude log

of average household income net of person income from the full sample-specification1. Other estimates remain

the same (see Table A.2) 26Based on the coefficient estimates of column 1 in Table 3, the marginal effect is defined as the partial

derivatives

( )

it

ivhit

s

R c

and

( )

it

vhit

s

R c

and computed at the mean value of age.

27 Results remain the same when we drop log of average household income net of person income from the full

sample-specification2 (Table A.2). However, household ranking in the consumption expenditure of groceries

become positive and significant when we exclude absolute levels of expenditure from the full sample-

specification2 (Table A.1).

23

Table 3. Backup and Cluster Ordered Logit Satisfaction Coefficient Estimates

Variables Full Sample: Model 1

Full Sample: Model 2

Sub-Samples: Model3 Younger

Young

Young Middle-

aged

Older Older-

old

Age Dummy: 30-44 0.051 (0.117)

Age Dummy: 45-59 0.253* (0.141)

Age Dummy: 60-74 0.334* (0.172)

Age Dummy: 75 and above 0.203 (0.228)

HH rankings in Groceries Exp. 0.264** 0.143 0.187 0.176 -0.119 -0.090 -0.280 (0.132) (0.100) (0.115) (0.116) (0.099) (0.132) (0.205)

HH rankings in Cloth Footwear Exp. 0.221* 0.108 0.008 0.132 0.238** -0.103 -0.184 (0.127) (0.099) (0.116) (0.116) (0.109) (0.151) (0.231)

Age Dummy: 30-44 × HH rankings in Groceries Exp. -0.016 (0.132)

Age Dummy: 45-59 × HH rankings in Groceries Exp. -0.216* (0.129)

Age Dummy: 60-74 × HH rankings in Groceries Exp. -0.174 (0.146)

Age Dummy: 75 and above × HH rankings in Groceries Exp.

-0.324* (0.188)

Age Dummy: 30-44 × HH rankings in Cloth Footwear Exp.

0.022 (0.126)

Age Dummy: 45-59 × HH rankings in Cloth Footwear Exp.

-0.010 (0.127)

Age Dummy: 60-74 × HH rankings in Cloth Footwear Exp.

-0.100 (0.142)

Age Dummy: 75 and above × HH rankings in Cloth Footwear Exp.

-0.095 (0.185)

Log of HH Groceries Exp. 0.024 0.024 -0.014 -0.033 0.019 0.059 0.110* (0.019) (0.019) (0.041) (0.042) (0.032) (0.038) (0.064)

Log of HH Cloth+Footwear Exp. 0.007 0.007 0.012 0.011 -0.009 0.013 0.039 (0.008) (0.008) (0.016) (0.018) (0.016) (0.019) (0.027)

Log of average HH income net of person income 0.038*** 0.038*** 0.005 0.035* 0.038*** 0.075*** -0.002 (0.008) (0.008) (0.018) (0.019) (0.014) (0.025) (0.044)

Log of peer-group income -0.009 -0.009 -0.029** 0.016 -0.010 0.004 0.013 (0.007) (0.007) (0.012) (0.012) (0.018) (0.023) (0.031)

Age 0.127 (0.111)

Age × Age -0.000 (0.000)

Age × HH rankings in Groceries Exp. -0.005** (0.003)

Age × HH rankings in Cloth Footwear Exp. -0.003 (0.003)

Number of observations 116,014 116,014 18,609 28,001 30,114 19,678 7,356 Chi-Square Statistics 312.7*** 317.9*** 239.8*** 150.7*** 80.09*** 269.8*** 48.23*** Number of clusters 8401 8401 1624 2641 2770 1757 641 R-Squared 0.00923 0.00942 0.0153 0.0174 0.00876 0.00923 0.0217 Chi-square stats to test equality of coefficients 0.05 0.05 1.03 0.06 4.87** 0.00 0.08

Notes. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.#these observations are created by the model through the

process of „Backup and Cluster‟ and are different from actual number of observations. The results are reported only for key variables of

interest. Individual specific included control variables are gender, employment status, marital status (3 dummies), log of hhsize, number of

kids (4 dummies), own disability (dummy), hh mean health status net of person, whether born in Australia (dummy), educational attainment

(4 dummies), and residence location (2 dummies). Included state level control variables are unemployment rate (by gender), life-expectancy

at 65 (by gender), standardised death rate (by gender), real house prices (establishedhhs), log of real PCGDP, and state dummies. Time

control variable includes 6 year dummies. Results also include ten estimated cut-off points. The ancillary parameters and estimates for the

included control variables are not reported to save space. Full set of results, however, can be obtained from the author.

24

When interacted with age dummies, while increase in the ranking of highly unobservable

consumption expenditure significantly reduces life satisfaction of individuals in the age group

45-59 years and 75 years and above; increase in the ranking of highly observable

consumption expenditure does not significantly change the life satisfaction in any of the age

groups.28

This again indicates that the prediction of our model is not consistent with the

prediction of the conspicuous consumption model over the life cycle.

Columns 3-7 of Table 3 display estimates corresponding to subsamples-specification 3 in

section 4. As predicted by the conspicuous-consumption model, life satisfaction of

individuals in the middle age group increases with the ranking of clothing expenditure

(highly observable) in the reference group, but it does not change with the ranking of

groceries expenditure (highly unobservable). Moreover, the coefficients of both the ranking

variables are statistically different from each other. This suggests that the coefficient effect of

ranking of highly observable consumption on life satisfaction is higher than the effect of

ranking of highly unobservable consumption. However, increasing the absolute level of

clothing and footwear expenditure does not increase life satisfaction over and above the

effect of relative clothing and footwear expenditure in the reference group. This result may be

attributed to the fact that people in the middle age (45-59) spend money on clothing and

footwear almost exclusively for its signaling value-or at least for other reasons of positional

nature (Truglia, 2013). For younger-young, young29

, older and older-old adults, none of these

rankings significantly affect their life-satisfaction. Thus, while overall evidence is

inconclusive, the prediction of the conspicuous-consumption model is consistent only for the

people in the age group 45-59 (middle age stage of the life cycle).

28 Corresponding to coefficient estimates in the second column of Table 3, the marginal effect is defined as the

partial derivatives

( )

it

ivhit

s

R c

and

( )

it

vhit

s

R c

and computed corresponding to all the age groups. For HH

rankings in Groceries Exp., the effects are computed as 0.16, 0.20, -0.04 and 0.15, respectively for age group: 30-44, age group: 45-59, age group: 60-74, age group: 75 and above, respectively. For HH rankings in Cloth

Footwear., the effects are computed as 0.12, -0.01, -0.12 and -0.08, respectively for age group: 30-44, age

group: 45-59, age group: 60-74, age group: 75 and above, respectively. 29 When we exclude absolute levels of expenditure from the sub sample-specification3 (results not provided

here, but can be obtained upon request), younger young sample also predicts similar to the middle aged.

However, contrary to the results for middle aged, the coefficients of the two ranks are not statistically different.

25

5.2 Sensitivity analysis

5.2.1 Comparing estimates in PCSOL, BUCFEOL and REOPROBMT models

In this section, we compare results obtained from three different approaches. As all other

explanatory variables are the same, we can roughly compare the effects of the key variables.

Estimates of the REOPROBMT and PCSOL models are displayed in appendix Tables A.1.1

and Table A.2.1, respectively. We observe that coefficient estimates of rankings in the

consumption of groceries and rankings in the consumption of clothing and footwear obtained

from BUCFEOL and REOPROBMT models30

are similar for the full sample-specification 1

(column 1 in each of the tables Table 3 and Table A.1.1). However, when we compare

BUCFEOL with PCSOL, we find that all the estimates are similar except that in PCSOL,

interaction of age and ranking of cloth and footwear expenditure is now negative and

significant.

In the full sample-specification 2, the results from all the three approaches differ from each

other. While none of the ranks are significant on their own in BUCFEOL, ranking of highly

observable expenditure is significant and positive in case of REOPROBMT. Ranking of both

highly observable and highly unobservable expenditure are significant and positive in case of

PCSOL. This means that while estimates from REOPROBMT model fully supports the

prediction of conspicuous-consumption model, PCSOL supports it partially and BUCFEOL

does not at all.

When we compare these approaches for sub-samples-specification 3, we observe that for the

middle aged, predictions of all the three models are consistent with the basic prediction of

conspicuous-consumption model. REOPROBMT and PCSOL models also predict this for

young adults. For younger young the prediction is supported only by PCSOL model. For

older and older-old adults, the estimates from PCSOL are similar to that of the BUCFEOL.

However, REOPROBMT estimates suggest that for older and older-old adults, ranking of the

cloth and footwear expenditure do not significantly changes the life satisfaction, but, life

satisfaction reduces with ranking of the groceries expenditure.

30 Table A.2.1 and Table 3 have heteroskedasticity-robust standard errors closeted at the individual level. Table

A.1.1 reports standard errors as program for computation of robust standard error is not available in STATA

11.2.

26

5.2.2 Cardinality vs ordinality

As discussed in section 4, numerous studies by researchers are carried out on the cardinality

assumption of satisfaction responses when performing regressions (see Ferrer-i-Cabonell and

Frijters, 2004; Perez-Truglia, 2013; Boyce, 2010). To ascertain influence of this assumption

on our empirical results, we repeated all estimations assuming that the satisfaction responses

on a scale of 0-10 are not ordinal but cardinal measures of satisfaction. The results from fixed

effect, random effect with Mundlak transformation and pooled cross-sectional regression

estimates for various specifications are documented in Appendix Tables (see Tables A.3.3 for

fixed effect; Tables A.1.4 for random effect with Mundlak transformation; and Tables A.2.4

for pooled cross-section). After comparing empirical results, we found that while majority of

the results do not change, results based on these two assumptions (cardinal and ordinal) are

not identical. For instance, for full sample –specification 1 and subsample-specification 3 for

younger young adults, while BUCFEOL estimates suggests that household ranking of cloth

and footwear expenditure do not significantly change the life satisfaction, with the same

specification, fixed effect GLS estimate found that ranking of cloth and footwear expenditure

has positive and significant effect on the life satisfaction (See Table A.3.3). Nonetheless,

these results are also contrary to the prediction of the conspicuous-consumption model.

Similar differences in the results are also found when random effect (with Mundlak

Transformation) and pooled cross-section results are compared under both the assumptions

(Table A.1.4 and Table A.2.4, respectively).

5.2.3 Change in reference group

As stated earlier, definitions of reference groups are quite arbitrary, and the empirical results

can be sensitive to the manner in which reference groups are defined. To check the

sensitivity, we also excluded some of the household characteristics from the reference group

as defined in section 3 and added some of the household head characteristics such as gender,

age and average educational attainment of household head into the geographical areas and

year of surveys. While new empirical results were quite robust, we found that after adding

more household characteristics, the number of households in each of the reference groups

was quite small and as a consequence of this the rank of households are concentrated on

extreme values (0 or 1). However, estimates were changed when we exclude all the

27

household characteristics from the reference group as now the reference group is much bigger

and diverse. Thus, we prefer the reference group as defined in the data section.

5.2.4 Change in the highly observable consumption goods

We also tried with annual expenditure on vehicle that includes spending on buying brand new

and used vehicles, and fuel cost as another proxy for highly observable consumption.

BUCFEOL estimates changed dramatically. These results are reported in Table A.3. It may

be attributed to the fact that vehicle expenditure is more associated with the quality of

vehicles. Also, not all people buy cars at the regular basis and therefore, annual expenditure

has lot of zero reporting.

5.2.5 Change in specifications

We suspect that sign and significance of key ranking variables might be sensitive to the

choice of model specification-in particular-income and expenditure related variables. To test

this, we re-specified life-satisfaction equations by changing income variables in each of the

models used in the analysis. In one of the specifications, we removed log household income

net of individual income and use only log of the expenditures in the visible and invisible

consumption goods. In other one, we only kept log household income net of individual

income and not the log of the expenditures in the visible and invisible consumption goods31

.

While we find mostly the similar results, they are not identical. However, for the aggregate

sample, our results were quite similar to the results in Perez-Truglia (2013) in some of the

models32

.

We also checked for robustness by adding inequalities in the distribution of income (Gini

variable) among peer groups in all the models, but finally, we decided to drop it as it was

insignificant in all the models and was not affecting key results in anyway. We also tried a

specification by excluding peer group income variable and again estimation result was found

quite robust for the key variables under study.

31 The estimation results are not reported here due to space constraint. However, full set of results can be

obtained upon request. 32 These results may not be entirely attributed to the conspicuous consumption but there could be some possible

confounding factors-positional as well as non-positional behind as well. For a more extensive discussion on

confounding factors, see section 5 and 6 from Perez-Truglia(2013).

28

6. Conclusions

For many years, subjective well-being has been an important subject for scholars from

multiple disciplines that include psychology, economics and other arena of social-sciences.

However, the opinion is still divided about the exact nature of happiness and age relationship

over the life cycle. Literature suggests that people consume highly observable goods to signal

there wealth to others and this is the key of the conspicuous- consumption theory. One of the

basic predictions of the signaling model of conspicuous-consumption is that the satisfaction

level of an individual should increase with his or her rankings in the consumption of highly

observable goods but should not be affected by his or her rankings in the consumption of

highly unobservable goods. There is some empirical evidence in favour of the prediction at

the aggregate level. However, there is no such empirical evidence available over different

stages of individual‟s life cycle. This study attempted to fill this gap in the literature by

testing the signaling model of conspicuous-consumption model.

Our study does not find conclusive evidence in support of the predictions of the signaling

model. We find that the predictions differ across individuals‟ life cycle and are sensitive to

the choice of estimation method. For the middle aged (45-59), the prediction is consistent

with the hypothesised surmise that the satisfaction levels should increase with an increase in

the household‟s ranking in the consumption of highly observable goods but is not affected by

their rankings in the consumption of highly unobservable goods. For other stages of life the

study does not find consistent evidence in support of the prediction theory of the signaling

model.

Negative externality imposed on others by consumption conspicuous goods has many policy

implications, including a tax policy on the conspicuous goods to correct the imposed

distortion (Frank, 1985 as in Truglia, 2013). But tax on conspicuous goods might not be a

good political decision-especially when voters of certain age group are the main consumers

of the conspicuous goods. For instance, our study suggests that the consumers in the middle

age receive a positive utility by consuming cloth and footwear (conspicuous goods) relative

to their reference group but impose negative externality on others. Imposing a tax on such

goods to correct the distortion imposed by negative externality might be a good economic

option but the decision may not go well with the voters and if voters of the middle age group

are in majority it might have interesting political consequences.

29

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34

Appendix Tables

35

Table A.1.1. Random-effect Ordered Probit (with Mundlak Transformation) Satisfaction Coefficient Estimates

Variables Full Sample

: Model 1

Full Sample

: Model 2

Sub-Samples: Model3

Younger Young Young Middle-aged Older Older-old

Age Dummy: 30-44

-0.036

(0.049)

Age Dummy: 45-59

0.098*

(0.054)

Age Dummy: 60-74

0.355***

(0.063)

Age Dummy: 75 and above

0.610***

(0.084)

HH rankings in Groceries Exp. 0.166*** 0.077 0.057 0.082 -0.057 -0.131* -0.201*

(0.063) (0.050) (0.066) (0.059) (0.055) (0.069) (0.111)

HH rankings in Cloth Footwear

Exp.

0.159** 0.090* 0.031 0.125** 0.116* -0.044 0.002

(0.063) (0.052) (0.072) (0.062) (0.060) (0.079) (0.130)

Age Dummy: 30-44 × HH

rankings in Groceries Exp.

-0.028

(0.061)

Age Dummy: 45-59 × HH

rankings in Groceries Exp.

-0.110*

(0.060)

Age Dummy: 60-74 × HH

rankings in Groceries Exp.

-0.151**

(0.067)

Age Dummy: 75 and above × HH

rankings in Groceries Exp.

-0.236**

(0.092)

Age Dummy: 30-44 × HH

rankings in Cloth Footwear Exp.

0.024

(0.060)

Age Dummy: 45-59 × HH

rankings in Cloth Footwear Exp.

-0.015

(0.059)

Age Dummy: 60-74 × HH

rankings in Cloth Footwear Exp.

-0.042

(0.067)

Age Dummy: 75 and above × HH

rankings in Cloth Footwear Exp.

-0.081

(0.090)

Log of HH Groceries Exp. 0.013 0.013 0.013 0.007 0.022 0.022 0.033

(0.010) (0.010) (0.023) (0.022) (0.019) (0.023) (0.033)

Log of HH Cloth+Footwear Exp. -0.002 -0.002 0.007 0.008 -0.005 0.000 0.010

(0.004) (0.004) (0.010) (0.009) (0.008) (0.010) (0.015)

Mean(HH rankings in Groceries

Exp.) 0.078* 0.078* 0.141 0.040 0.091 0.172* 0.118

(0.044) (0.044) (0.105) (0.087) (0.085) (0.102) (0.169)

Mean(HH rankings in Cloth

Footwear Exp.) -0.060 -0.059 -0.022 -0.109 0.028 0.056 -0.347*

(0.049) (0.049) (0.116) (0.096) (0.093) (0.117) (0.186)

(0.004) (0.004) (0.011) (0.009) (0.008) (0.011) (0.016)

Mean(Log of HH Groceries Exp.) -0.002 -0.002 -0.012 -0.036 -0.011 -0.019 0.061

(0.016) (0.016) (0.037) (0.031) (0.032) (0.035) (0.059)

Mean(Log of HH Cloth+Footwear

Exp.) 0.011* 0.010* 0.006 0.013 -0.002 0.008 0.014

(0.006) (0.006) (0.015) (0.013) (0.012) (0.015) (0.022)

Age -0.028***

(0.004)

Age×Age 0.000***

(0.000)

Age× HH rankings in Groceries

Exp. -0.004***

(0.001)

Age× HH rankings in Cloth

Footwear Exp. -0.002

(0.001)

Number of observations 57,547 57,547 9,883 15,798 17,004 10,897 3,971

Chi-Square Statistics 2091*** 1986*** 364.2*** 649.0*** 658.1*** 365.9*** 163.5***

Notes: Standard errors in parentheses,***<0.01,** p<0.05, * p<0.1. Apart from the included controlled variables means of appropriate

variables are also included in the model as suggested by Mundlak (1978). The results are reported only for key variables of interest.

Individual specific included control variables are gender, employment status, marital status (3 dummies), log of hhsize, number of kids (4

dummies), own disability (dummy), hh mean health status net of person, whether born in Australia (dummy), educational attainment (4

dummies), and residence location (2 dummies). Included state level control variables are unemployment rate (by gender), life-expectancy at

65 (by gender), standardised death rate (by gender), real house prices (establishedhhs), log of real PCGDP, and state dummies. Time control

variable includes 6 year dummies. Results also include ten estimated cut-off points. The ancillary parameters and estimates for the included

control variables are not reported to save space. Full set of results, however, can be obtained from the author.

36

Table A.2.1. Pooled Cross-section Ordered Logit Satisfaction Coefficient Estimates

Variables Full

Sample:

Model 1

Full

Sample:

Model 2

Sub-Samples: Model3

Younger

Young

Young Middle-

aged

Older Older-

old

Age Dummy: 30-44 -0.153**

(0.060)

Age Dummy: 45-59 -0.041

(0.063)

Age Dummy: 60-74 0.658***

(0.075)

Age Dummy: 75 and above 1.444***

(0.108)

HH rankings in Groceries Exp. 0.293*** 0.154** 0.104 0.096 0.040 -0.000 -0.141

(0.087) (0.070) (0.079) (0.071) (0.064) (0.081) (0.132)

HH rankings in Cloth Footwear Exp. 0.485*** 0.254*** 0.179** 0.166** 0.304*** 0.097 -0.243

(0.087) (0.072) (0.089) (0.073) (0.070) (0.092) (0.160)

Age Dummy: 30-44 × HH rankings in Groceries Exp. -0.045

(0.086)

Age Dummy: 45-59 × HH rankings in Groceries Exp. -0.071

(0.085)

Age Dummy: 60-74 × HH rankings in Groceries Exp. -0.235**

(0.099)

Age Dummy: 75 and above × HH rankings in Groceries

Exp.

-0.311**

(0.139)

Age Dummy: 30-44 × HH rankings in Cloth Footwear

Exp.

0.045

(0.085)

Age Dummy: 45-59 × HH rankings in Cloth Footwear

Exp.

-0.061

(0.085)

Age Dummy: 60-74 × HH rankings in Cloth Footwear

Exp.

-0.142

(0.098)

Age Dummy: 75 and above × HH rankings in Cloth

Footwear Exp.

-

0.439***

(0.141)

Log of HH Groceries Exp. 0.020 0.020 0.058** 0.006 0.039 -0.041 0.041

(0.014) (0.014) (0.027) (0.030) (0.026) (0.032) (0.037)

Log of average HH income net of person income -0.010* -0.010 0.010 0.023* -0.037*** -0.011 -0.010

(0.006) (0.006) (0.013) (0.013) (0.011) (0.013) (0.019)

Log of average HH income net of person income 0.022*** 0.020*** 0.014 0.005 0.036*** 0.026** 0.026

(0.004) (0.004) (0.011) (0.009) (0.007) (0.011) (0.026)

Log of peer-group income -0.001 -0.005** -0.018*** 0.014*** -0.010* -0.009 0.010

(0.003) (0.003) (0.006) (0.005) (0.005) (0.006) (0.009)

Age -

0.059***

(0.004)

Age×Age 0.001***

(0.000)

Age×HH rankings in Groceries Exp. -

0.005***

(0.002)

Age× HH rankings in Cloth Footwear Exp. -

0.006***

(0.002)

Number of observations 54,429 54,429 9,129 15,233 16,357 10,071 3,639

Chi-Square Statistics 5056*** 4949*** 4226*** 1065*** 1334*** 709.1*** 801.6***

R-Squared 0.0311 0.0300 0.0187 0.0249 0.0263 0.0220 0.0242

Notes: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. The results are reported only for key variables of interest.

Individual level included control variables are gender, employment status, marital status (3 dummies), log of hhsize, number of kids (4

dummies), own disability (dummy), hh mean health status net of person, whether born in Australia (dummy), educational attainment (4

dummies), and residence location (2 dummies). Included state level control variables are unemployment rate (by gender), life-expectancy at

65 (by gender), standardised death rate (by gender), real house prices (establishedhhs), log of real PCGDP, and state dummies. Time control

variable includes 6 year dummies. Results also include ten estimated cut-off points. The ancillary parameters and estimates for the

included control variables are not reported to save space. Full set of results, however, can be obtained from the author

37

Table A.3.3. Fixed-effect Regression Satisfaction Coefficient Estimates

Variables Full

Sample:

Model 1

Full

Sample:

Model 2

Sub-Samples: Model3

Younger

Young

Young Middle-

aged

Older Older-

old

Age Dummy: 30-44 0.023

(0.048)

Age Dummy: 45-59 0.115**

(0.057)

Age Dummy: 60-74 0.152**

(0.069)

Age Dummy: 75 and above 0.104

(0.090)

HH rankings in Groceries Exp. 0.115** 0.064 0.082* 0.076 -0.043 -0.048 -0.105

(0.056) (0.043) (0.049) (0.047) (0.042) (0.053) (0.090)

HH rankings in Cloth Footwear Exp. 0.087 0.040 0.007 0.045 0.093** -0.042 -0.079

(0.055) (0.044) (0.052) (0.047) (0.045) (0.061) (0.101)

Age Dummy: 30-44 × HH rankings in Groceries Exp. -0.008

(0.056)

Age Dummy: 45-59 × HH rankings in Groceries Exp. -0.095*

(0.055)

Age Dummy: 60-74 × HH rankings in Groceries Exp. -0.080

(0.060)

Age Dummy: 75 and above × HH rankings in Groceries

Exp.

-0.142*

(0.082)

Age Dummy: 30-44 × HH rankings in Cloth Footwear Exp. 0.012

(0.054)

Age Dummy: 45-59 × HH rankings in Cloth Footwear Exp. -0.005

(0.054)

Age Dummy: 60-74 × HH rankings in Cloth Footwear Exp. -0.041

(0.060)

Age Dummy: 75 and above × HH rankings in Cloth

Footwear Exp.

-0.040

(0.080)

Log of HH Groceries Exp. 0.011 0.010 -0.005 -0.017 0.008 0.029* 0.045*

(0.007) (0.007) (0.017) (0.017) (0.014) (0.016) (0.025)

Log of HH Cloth+Footwear Exp. 0.005 0.005 0.006 0.008 -0.002 0.006 0.019

(0.003) (0.003) (0.007) (0.007) (0.006) (0.008) (0.012)

Log of average HH income net of person income 0.016*** 0.016*** 0.001 0.012* 0.017*** 0.033*** 0.000

(0.003) (0.003) (0.007) (0.007) (0.006) (0.009) (0.020)

Log of peer-group income -0.004 -0.004 -0.012** 0.007 -0.005 0.001 0.004

(0.003) (0.003) (0.005) (0.005) (0.006) (0.008) (0.013)

Age 0.059

(0.054)

Age×Age -0.000

(0.000)

Age× HH rankings in Groceries Exp. -0.002**

(0.001)

Age× HH rankings in Cloth Footwear Exp. -0.001

(0.001)

Number of observations 57,563 57,563 9,883 15,800 17,009 10,906 3,971

R-squared 0.010 0.010 0.016 0.018 0.009 0.008 0.019

Number of individuals 9,084 9,084 2,010 3,436 3,614 2,300 857

F-statistics 12.95*** 10.77*** 3.870*** 7.092*** 3.769*** 2.193*** 2.801***

F test that all u_i=0 7.502*** 7.547*** 5.136*** 5.496*** 6.572*** 6.893*** 5.292***

Notes. Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The results are reported only for key variables of interest. Individual

level included control variables are gender, employment status, marital status (3 dummies), log of hhsize, number of kids (4 dummies), own

disability (dummy), hh mean health status net of person, whether born in Australia (dummy), educational attainment (4 dummies), and

residence location (2 dummies). Included state level control variables are unemployment rate (by gender), life-expectancy at 65 (by gender),

standardised death rate (by gender), real house prices (establishedhhs), log of real PCGDP, and state dummies. Time control variable

includes 6 year dummies. Estimates for the included control variables are not reported to save space. Full set of results, however, can be

obtained from the author.

38

Table A.2.4. Pooled Cross-section Robust Regression Satisfaction Coefficient Estimates

Variables Full

Sample:

Model 1

Full

Sample:

Model 2

Sub-Samples: Model3

Younger

Young

Young Middle-

aged

Older Older-

old

Age Dummy: 30-44 -

0.106***

(0.040)

Age Dummy: 45-59 -0.019

(0.041)

Age Dummy: 60-74 0.434***

(0.047)

Age Dummy: 75 and above 0.889***

(0.063)

HH rankings in Groceries Exp. 0.194*** 0.106** 0.063 0.063 0.019 0.022 -0.074

(0.056) (0.047) (0.052) (0.044) (0.042) (0.054) (0.086)

HH rankings in Cloth Footwear Exp. 0.331*** 0.175*** 0.117** 0.100** 0.213*** 0.064 -0.168

(0.057) (0.048) (0.057) (0.046) (0.047) (0.063) (0.108)

Age Dummy: 30-44 × HH rankings in Groceries Exp. -0.023

(0.059)

Age Dummy: 45-59 × HH rankings in Groceries Exp. -0.053

(0.057)

Age Dummy: 60-74 × HH rankings in Groceries Exp. -0.147**

(0.063)

Age Dummy: 75 and above × HH rankings in Groceries

Exp.

-0.192**

(0.083)

Age Dummy: 30-44 × HH rankings in Cloth Footwear Exp. 0.034

(0.058)

Age Dummy: 45-59 × HH rankings in Cloth Footwear Exp. -0.048

(0.057)

Age Dummy: 60-74 × HH rankings in Cloth Footwear Exp. -0.113*

(0.063)

Age Dummy: 75 and above × HH rankings in Cloth

Footwear Exp.

-

0.271***

(0.084)

Log of HH Groceries Exp. 0.014* 0.014* 0.041** 0.008 0.028* -0.034* 0.024

(0.008) (0.008) (0.018) (0.018) (0.015) (0.018) (0.024)

Log of average HH income net of person income -

0.011***

-

0.011***

0.003 0.014* -

0.032***

-0.013 -0.010

(0.003) (0.003) (0.008) (0.007) (0.006) (0.008) (0.012)

Log of average HH income net of person income 0.014*** 0.012*** 0.009 0.001 0.023*** 0.015** 0.016

(0.003) (0.003) (0.007) (0.006) (0.005) (0.007) (0.018)

Log of peer-group income -0.000 -0.003* -0.011** 0.009*** -0.006 -0.005 0.008

(0.002) (0.002) (0.004) (0.003) (0.003) (0.004) (0.006)

Age -

0.036***

(0.002)

Age×Age 0.001***

(0.000)

Age×HH rankings in Groceries Exp. -

0.003***

(0.001)

Age× HH rankings in Cloth Footwear Exp. -

0.004***

(0.001)

Number of observations 54,429 54,429 9,129 15,233 16,357 10,071 3,639

Adjusted R-squared 0.0918 0.0887 0.0519 0.0752 0.0759 0.0543 0.0598

F-statistics 115.7*** 95.61*** 12.35*** 29.15*** 31.53*** 14.13*** 6.511***

Notes: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. The results are reported only for key variables of interest. Individual

level included control variables are gender, employment status, marital status (3 dummies), log of hhsize, number of kids (4 dummies), own

disability (dummy), hh mean health status net of person, whether born in Australia (dummy), educational attainment (4 dummies), and

residence location (2 dummies). Included state level control variables are unemployment rate (by gender), life-expectancy at 65 (by gender),

standardised death rate (by gender), real house prices (establishedhhs), log of real PCGDP, and state dummies. Time control variable

includes 6 year dummies. Estimates for the included control variables are not reported to save space. Full set of results, however, can be

obtained from the author

39

Table A.1.4. Random-effect with Mundlak Transformation Regression Satisfaction Coefficient Estimates

Variables Full

Sample:

Model 1

Full

Sample:

Model 2

Sub-Samples: Model3

Younger

Young

Young Middle-

aged

Older Older-

old Age Dummy: 30-44 -0.070

(0.045)

Age Dummy: 45-59 0.037

(0.051)

Age Dummy: 60-74 0.256***

(0.060)

Age Dummy: 75 and above 0.488***

(0.079)

HH rankings in Groceries Exp. 0.117** 0.043 0.033 0.069 -0.041 -0.109* -0.160*

(0.057) (0.044) (0.055) (0.053) (0.047) (0.060) (0.092)

HH rankings in Cloth Footwear Exp. 0.149*** 0.076* 0.044 0.105* 0.127** -0.030 -0.004

(0.057) (0.046) (0.062) (0.055) (0.052) (0.071) (0.105)

Age Dummy: 30-44 × HH rankings in Groceries Exp. 0.004

(0.056)

Age Dummy: 45-59 × HH rankings in Groceries Exp. -0.070

(0.054)

Age Dummy: 60-74 × HH rankings in Groceries Exp. -0.098

(0.061)

Age Dummy: 75 and above × HH rankings in Groceries

Exp.

-0.166**

(0.081)

Age Dummy: 30-44 × HH rankings in Cloth Footwear Exp. 0.039

(0.053)

Age Dummy: 45-59 × HH rankings in Cloth Footwear Exp. 0.015

(0.053)

Age Dummy: 60-74 × HH rankings in Cloth Footwear Exp. -0.034

(0.060)

Age Dummy: 75 and above × HH rankings in Cloth

Footwear Exp.

-0.089

(0.080)

Log of HH Groceries Exp. 0.013 0.012 0.010 0.001 0.018 0.024 0.030

(0.009) (0.009) (0.018) (0.021) (0.017) (0.019) (0.032)

Log of HH Cloth+Footwear Exp. 0.001 0.001 0.005 0.009 -0.002 0.006 0.012

(0.004) (0.004) (0.009) (0.010) (0.008) (0.010) (0.013)

Log of average HH income net of person income 0.017*** 0.017*** 0.016* 0.014* 0.018*** 0.031*** 0.001

(0.004) (0.004) (0.009) (0.008) (0.006) (0.010) (0.017)

Log of peer-group income -0.002 -0.004 -0.013*** 0.010** -0.005 -0.004 0.012

(0.003) (0.003) (0.005) (0.005) (0.006) (0.007) (0.009)

Mean(HH rankings in Groceries Exp.) 0.069* 0.068* 0.136 0.045 0.058 0.145 0.078

(0.039) (0.039) (0.089) (0.071) (0.078) (0.092) (0.142)

Mean(HH rankings in Cloth Footwear Exp.) -0.051 -0.049 -0.033 -0.083 0.009 0.067 -0.295*

(0.043) (0.043) (0.101) (0.078) (0.082) (0.099) (0.162)

Mean(Log of HH Groceries Exp.) -0.003 -0.003 -0.025 -0.025 -0.005 -0.007 0.061

(0.015) (0.015) (0.036) (0.027) (0.030) (0.037) (0.055)

Mean(Log of HH Cloth+Footwear Exp.) 0.009 0.009 0.008 0.010 -0.000 0.002 0.012

(0.006) (0.006) (0.014) (0.011) (0.011) (0.014) (0.020)

Age -

0.028***

(0.004)

Age×Age 0.000***

(0.000)

Age× HH rankings in Groceries Exp. -0.003**

(0.001)

Age× HH rankings in Cloth Footwear Exp. -0.001

(0.001)

Number of observations 57,547 57,547 9,883 15,798 17,004 10,897 3,965

Number of individuals 9,082 9,082 2,010 3,436 3,613 2,298 857

Chi-Square Statistics 1757*** 1653*** . 531.4*** 563.9*** 361.5*** .

Notes: Robust Standard errors in parentheses,***<0.01,** p<0.05, * p<0.1. Apart from the included variables means of all the individual

specific variables are also included in the model as suggested by Mundlak (1978). The results are reported only for key variables of interest.

Individual specific included control variables are gender, employment status, marital status (3 dummies), log of hhsize, number of kids (4

dummies), own disability (dummy), hh mean health status net of person, whether born in Australia (dummy), educational attainment (4

dummies), and residence location (2 dummies). Included state level control variables are unemployment rate (by gender), life-expectancy at

65 (by gender), standardised death rate (by gender), real house prices (establishedhhs), log of real PCGDP, and state dummies. Time control

variable includes 6 year dummies. Estimates for the included control variables are not reported to save space. Full set of results, however,

can be obtained from the author.

40

Table A.3. Backup and Cluster Ordered Logit Satisfaction Coefficient Estimates

Variables Full

Sample:

Spec 1

Full

Sample:

Spec 2

Sub-Samples: Spec 3

Younger

Young

Young Middle-

aged

Older Older-

old

Age Dummy: 30-44 0.098

(0.119)

Age Dummy: 45-59 0.272*

(0.142)

Age Dummy: 60-74 0.275

(0.174)

Age Dummy: 75 and above 0.163

(0.237)

HH rankings in Groceries Exp. 0.319** 0.158 0.188* 0.195* -0.049 -0.117 -0.284

(0.130) (0.098) (0.113) (0.114) (0.097) (0.133) (0.203)

HH rankings in Vehicle Exp. -0.007 0.075 0.022 0.119 -0.155 0.129 -0.153

(0.126) (0.097) (0.114) (0.107) (0.104) (0.149) (0.236)

Age Dummy: 30-44 × HH rankings in Groceries Exp. 0.003

(0.129)

Age Dummy: 45-59 × HH rankings in Groceries Exp. -0.211*

(0.127)

Age Dummy: 60-74 × HH rankings in Groceries Exp. -0.208

(0.143)

Age Dummy: 75 and above × HH rankings in Groceries

Exp.

-0.346*

(0.189)

Age Dummy: 30-44 × HH rankings in Vehicle Exp. -0.089

(0.119)

Age Dummy: 45-59 × HH rankings in Vehicle Exp. -0.049

(0.121)

Age Dummy: 60-74 × HH rankings in Vehicle Exp. 0.048

(0.137)

Age Dummy: 75 and above × HH rankings in Vehicle

Exp.

-0.007

(0.202)

Log of HH Groceries Exp. 0.025 0.025 -0.010 -0.027 0.014 0.062 0.109*

(0.019) (0.019) (0.040) (0.042) (0.032) (0.038) (0.064)

Log of HH Vehicle Exp. 0.008 0.007 0.005 -0.012 0.027 -0.010 0.049

(0.010) (0.010) (0.019) (0.021) (0.020) (0.031) (0.035)

Log of average HH income net of person income 0.037*** 0.038*** 0.005 0.035* 0.035** 0.077*** -0.002

(0.008) (0.008) (0.018) (0.019) (0.014) (0.025) (0.046)

Log of peer-group income -0.009 -0.009 -0.029** 0.016 -0.012 0.005 0.013

(0.007) (0.007) (0.012) (0.012) (0.018) (0.023) (0.031)

Age 0.124

(0.112)

Age×Age 0.000

(0.000)

Age× HH rankings in Groceries Exp. -0.006**

(0.003)

Age× HH rankings in Vehicle Exp. 0.001

(0.003)

Number of observations 116,014 116,014 18,609 28,001 30,114 19,678 7,356

Chi-Square Statistics 309.8*** 316.1*** 238.9*** 147.3*** 77.00*** 276.7*** 48.66***

Number of clusters 8401 8401 1624 2641 2770 1757 641

R-Squared 0.00910 0.00933 0.0152 0.0172 0.00850 0.00926 0.0217

Notes. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.#these observations are created by the model through the

process of „Backup and Cluster‟ and are different from actual number of observations. The results are reported only for key variables of

interest. Individual specific included control variables are gender, employment status, marital status (3 dummies), log of hhsize, number of

kids (4 dummies), own disability (dummy), hh mean health status net of person, whether born in Australia (dummy), educational attainment

(4 dummies), and residence location (2 dummies). Included state level control variables are unemployment rate (by gender), life-expectancy

at 65 (by gender), standardised death rate (by gender), real house prices (establishedhhs), log of real PCGDP, and state dummies. Time

control variable includes 6 year dummies. Results also include ten estimated cut-off points. The ancillary parameters and estimates for the

included control variables are not reported to save space. Full set of results, however, can be obtained from the author.