Alternative Measures

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The Relation Between Life Satisfaction and the Material

Situation: A Re-Evaluation Using Alternative Measures

Bernhard Christoph

Accepted: 22 November 2009 / Published online: 11 December 2009 Springer Science+Business Media B.V. 2009

Abstract Among the surprising results of research on the relation between a persons

material circumstances and his or her subjective well-being was the finding that this

relationship appears to be rather weak (throughout this paper the terms (general) life

satisfaction, (subjective) satisfaction, happiness and subjective well-being will be

used interchangeably. The same applies to the terms material circumstances, material

conditions, material situation and material well-being). However, more recently

authors began to ask the question, whether this might at least in part be explained by theinsufficiencies of income as an indicator for the material situation. Building on this idea,

they have shown that the inclusion of alternative measures for the respondents material

situationsuch as wealth measures in particularreveals that the relationship between a

persons material well-being and his or her subjective well-being might just be somewhat

stronger than researchers thought before. The paper will follow this lead but will go beyond

current research by first, systematically reviewing the various approaches available for

measuring the material situation and second, by proposing the use of a so-called depri-

vation index, an alternative measure of material well-being, which is frequently used in the

context of poverty research (compare e.g. Townsend in Poverty in the United Kingdom,

Penguin Books, Harmondsworth, 1979; Hallerod in J Eur Soc Policy 5:111129, 1995;

Nolan and Whelan in J Eur Soc Policy 6:225240, 1996). It will be argued, that such a

deprivation based measure will perform better than indicators like income or wealth when

analyzing the relationship between material conditions and subjective well-being. This

hypothesis will be tested using three different German datasets. Based on this data it will

be shown that in all cases deprivation measures perform better in explaining differences in

subjective well-being than the alternatives. However, both types of measures seem to

capture slightly different aspects of the material situation, a result which has also been

found in the poverty literature cited above. Thus using a combination of both seems to be

the best alternative.

Keywords Subjective well-being Standard of living Income

B. Christoph (&)

Institute for Employment Research (IAB), Nuremberg, Germany

e-mail: [email protected]

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Soc Indic Res (2010) 98:475499

DOI 10.1007/s11205-009-9552-4


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1 Income and Happiness: An Introduction

When analyzing the relation between income and subjective well-being, at least two cases

are to be distinguished: first one might look at the correlations found in international

comparisons. This line of research gives an answer to the question whether people in morewealthy nations are, on average, more satisfied with their lives than the populations of

poorer nations are. It has been shown that on this level of analysis there is a positive

relation between the two measures and that it is indeed quite substantial (compare e.g.

Diener and Biswas-Diener 2002). A second line of research focuses on the relation

between income and subjective well-being as it can be found within nations. In contrast to

analyses between countries, which concentrate on the income-satisfaction nexus as it

emerges in aggregate data, this latter strain of research focuses on the relation on the

individual level. It is this second relationship that we shall discuss in the remainder of the

paper.

Prior research indicates that at the individual level, too, a positive correlation between

income and subjective well-being can be found. However, this correlation is rather small,

especially when contrasted by the one found in aggregate data (for an overview compare

Argyle 1999, 2001; Diener et al. 1999; Diener and Biswas-Diener 2002). Moreover it is

much stronger for persons in the lower income brackets than for those with higher incomes

(meaning it is curvilinear in form). Yet while there seems to be general agreement on the

mere facts, i.e. that there is a connection between income and subjective well-being, the

theoretical foundations of this relationship are somewhat disputed.

What seems to be fairly simple to explain is why one would expect the two measures to

correlate in the first place. As for example Argyle (1999: 358) notes, this is mainly becausemore money quite easily translates into a higher standard of living, better housing and the

like. It is two particular aspects of this relationship that are much more difficult to explain:

(1) why the correlations on the individual level are as weak as they are usually reported and

(2) why the relation between both variables is curvilinear in form.

In the literature on well-being there are at least four different theoretical approaches that

are trying to explain this link between income and well-being (for an overview compare

Diener and Biswas-Diener 2002): (1) need theory or livability theory, (2) the relative

standards model, and (3) the cultural approach and (4) specific variants of set-point theory

like Cummins Homeostatic Theory of Subjective Well-being (Cummins2000,2002).

One of the primary advocates of need theory is Veenhoven (e.g. Veenhoven 1991;Veenhoven and Erhard1995). The main postulate of this theory is that there is a direct link

between objective conditions on the one hand side and satisfaction on the other. On a

collective level this means that if objective conditions in a country are more livable (for

example because there is higher wealth or more personal freedom), subjective satisfaction

of the population will be equally higher. In this framework, subjective satisfaction might be

used as an output indicator to evaluate public policies. Likewise, on an individual level this

theory implies that subjects that are better off will show higher satisfaction levels than

those living in poorer objective conditions, since living in more favorable material cir-

cumstances will allow them to meet their basic human needs more effectively. Thisapproach does particularly well at explaining the curvilinear relationship between income

and subjective well-being. The reason for this is that income is assumed to have a

decreasing marginal return: material improvements will make a particular difference for

the poorer parts of society, allowing them to buy very basic goods like sufficient food,

proper housing, clothes and the like, the lack of which should have a large negative effect

on well-being. The same amount of income invested in more fancy or even luxury goods

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will, on the other hand, have a much smaller impact, which is why starting at a certain

threshold, the positive effect of having additional material resources should decrease.

The core idea of the relative standards model is that people might apply various

standards in order to judge their current situation, such as the circumstances they lived in

before or the current situation of relevant others. This is to say that it is not a currently highlevel of income per se, which will make people more satisfied but what is important is the

difference of current income levels to various standards that might be applied. Less for-

mally stated: what makes people really happy is not meeting an absolute standard, but

rather that their current income is higher than it was some time before or that they are

doing better than their neighbors or colleagues do. A good example for this kind of

theoretical approach is provided by Easterlin (2001,2002), who tries to explain the finding,

that a persons happiness (on average) will remain largely unaltered over the life cycle,

even though his or her income will increase during the same period. The reason for this is,

according to Easterlin, that this persons aspirations will equally increase over time, thus

compensating for the material gains. Another example which includes quite a bunch of

different comparison processes is Michalos (1985) Multiple Discrepancies Theory.

Michalos argues that net satisfaction is the result of adding up several discrepancies

experienced as a result of multiple comparison processes, which have before been

described only separately by different theoretical approaches. The main dimensions are

given by contrasting the actual situation, i.e. what one actually has, with (a) ones personal

aspirations, (b) the situation of relevant others, (c) what one deserves, (d) ones basic needs

(a theoretical strain that is not to far from Veenhovens livability theory discussed above),

and (e) intertemporal comparisons as contrasting ones current situation with the past, with

ones past expectations about today or the expectations one has for the future.A core aspect of the relative standards approach is that personal standards might be

changed as ones personal situation improves or deteriorates, i.e. processes of adaptation

are taking place. Thus, increases in the material situation might be answered by raised

standards instead of growing satisfaction. This might be one theoretical explanation why

the correlation between income and well-being is actually so small.

The cultural approach argues that people are socialized to follow (society specific)

social norms and that displaying behaviors that are in accordance with these norms will

increase subjective well-being (cf. Markus and Kitayama 1994; Oishi 2000). In modern

industrial societies, being in paid employment is one of the most important behaviors one

might participate in and therefore having a paid job will increase well-being. Thusaccording to this theoretical approach, having more income appears to be merely a by-

product of following this generally accepted cultural norm. This also implies that people

with a comparably low income might still display high subjective well-being, since despite

the low financial returns they still fulfill the norm by being involved in a productive

activity (compare Diener and Biswas-Diener2002: 151). If this is true and poorer people

show well-being levels that are higher than one would expect given their income, this

could explain why the correlation between income and well-being is rather low.

Set-point theoriy (e.g. Headey and Wearing 1989) in general will not assume a link

between income and subjective well-being. As a specific type of trait theories (for a criticaloverview of trait theories compare e.g. Veenhoven1994), just as the former they are based

on the core idea that happiness is a fixed personality trait, which means it might not easily

be altered by external conditions. Nevertheless, these theories allow actual satisfaction

levels to vary, e.g. due to the influence of specific life-events. Yet ultimately, satisfaction is

assumed to return to its original, person-specific state, the so-called set-point.

The Relation Between Life Satisfaction and the Material Situation 477

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However, specific variants of set-point theory as e.g. Cummins Homeostatic Theory of

Subjective Well-being are, in fact, able to provide explanation for the income-satisfaction-

relation as it is observed. Cummins (2002) assumes that income influences satisfaction

insofar, as it has a positive impact on a persons second order determinants as e.g. his or

her feelings of self-esteem or exerting environmental control. These particular aspects ofpersonality are in turn able to modify individual satisfaction within a specific corridor

defined by the first order determinants, i.e. the persons personality traits in a narrower

sense. This mechanism will be of particular importance for poorer people. Thus Cummins

theory is able to explain the low influence of income on subjective well-being as well as

the relations curvilinear form, but it does so only indirectly, by denying a real influence

of the former on the latter.

So in a nutshell, none of these theories can provide a satisfactory explanation. The first

three theories cannot completely account for the relationship between well-being and the

material situation as it has been observed. What these three theories have in common is that

according to all of them some form of (direct) correlation between subjective satisfaction

and material living conditions is to be expected. Having stated this, there are significant

differences when looking at theoretical expectations in detail: based on livability theory for

example, one would expect diminishing returns of extra income on satisfaction. This is the

case because fulfilling basic needs makes much more of a difference than satisfying more

elaborate ones, which might often be immaterial. On the other hand, in this theoretical

framework it is somewhat unclear, why the overall correlation between the two variables is

as small as it actually is. In contrast to that, the other two approaches will provide no

explanation for the curvilinearity of the income-well-being relationship but give hints, as to

why the relation per se should be a rather weak one. Homeostatic Theory of SubjectiveWell-being on the other hand is able to explain both, the curvilinearity as well as the

weakness of the relationship between income and well-being. This comes, however, at the

price of denying a direct influence of income on well-being altogether, linking it entirely

on personality factors.1

2 Material Situation and Happiness: The Use of Alternative Methods and Indicators

It has been only recently, that researchers have argued that the link between the material

situation and subjective well-being might in fact not be as weak as was previously thought.In essence, there have been two general approaches to make this point: the first is cal-

culating more elaborate models and the second is to apply more appropriate measures for

the material situation.

Researchers trying to apply more elaborate models for most of the part tried to tackle

different types of heterogeneity. One such example is provided by Frijters and his

co-authors (Frijters et al. 2004), who try to control for heterogeneity of individ-

ual characteristics. In their paper they examine the development of life-satisfaction

among East Germans during the decade following German Reunification (19912001).

Applying a fixed-effects ordered logit model for ordinal dependent variables developed1 This appears to be a problem since it is just the fraction of happiness which is not influenced by

personality but by external factors, which is of interest here. This is not to say that personal characteristics

are unimportant for a persons happiness. Quite to the contrary, they should be considered a central

influence-factor (for an overview compare e.g. Diener and Lucas1999). However, using well-being as an

output indicator in analyses on social policies or as proxy for preferences in economic analyses would be

inadequate, should it entirely be determined by personality.

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by Ferrer-i-Carbonell and Frijters (2004), they are able to show that particularly in the

years immediately following unification (i.e. until about the mid 1990s) there is a much

stronger influence of income on life satisfaction than is usually found.

Other researchers put their focus not on individual heterogeneity, but rather try to

model differences in response behavior emerging between different groups of respon-dents or at different stages of the satisfaction scale. An example for this is provided by

Clark et al. (2005). Using a latent class model they can demonstrate that the relation

between well-being and income is different for the four latent groups they identified.2 A

somewhat related idea can be found in two papers by Boes and Winkelmann (2006,

2009). They are applying a particular type of ordered probit model (a so-called gen-

eralized ordered probit model), which allows the effect of income on satisfaction to vary

for each stage of the dependent variable.3 Using this modeling approach they are able to

show that increases in income have stronger effects among respondents that have low

satisfaction scores, i.e. who are rather dissatisfied, than among those whose satisfaction

scores are high.

Another topic that has been approached using more elaborate models is the inter-

relations between the individual and the national level. This was for example an issue in

the above cited paper by Clark et al. (2005), who showed that the latent classes of

respondents identified by their analysis did also differ with regard to their national

composition. A more complex approach to deal with this problem has been provided by

Schyns (2002, 2003). She applied a multilevel model to simultaneously control differ-

ences at the national (GDP) and the individual level. Doing so, she was able to show that

there is an interaction between these two levels and that the relationship between income

and satisfaction is stronger in poor countries than it is in affluent ones, which is mainlydue to the fact that poorer people in less affluent nations score particularly low on well-

being.

Research on providing better measures for the material situation is also relatively

scarce. So it was not without reason that Diener and Biswas-Diener as late as in 2002

called for improving research on better measures for the material situation, which go

beyond mere indicators of income (Diener and Biswas-Diener 2002: 355). An early

attempt to improve on material measures was brought forward by Mullis (1992). In fact

his approach incorporated many new ideas, which have been developed further in recent

years. In his paper, Mullis proposed a composite index for which he used permanent

income, operationlized as average income over several years, and annualized net worth,which set the balance of a persons savings and debts in proportion to his or her

remaining life expectancy. This material measure was divided by a normatively fixed

basic income level, thus resulting in a relative measure for economic well-being. Mullis

could show, that this index was a better predictor of subjective well-being than tradi-

tional measures.

One of the measures that had already been introduced by Mullis but has been used more

frequently in recent years is wealth. The most notable example for this is probably the

2

Due to the lacking information on general life satisfaction in the data used by Clark et al. they can showthis only for the relation between income satisfaction and actual income.3 This means that there is not only a different intercept for each stage of the satisfaction-variable, while

effects of independent variables are assumed to be the same at all stagesas is the case in standard ordered

probit or logit modelsbut coefficients for the independent variables might vary, too. In contrast to a

multinomial model, which would also allow calculating different coefficients for the independent variables

at different stages of the dependent variable, the generalized ordered probit model will consider the ordering

information of the dependent variable when calculating the estimates.


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work of Headey and his collaborators (Headey and Wooden 2004; Headey et al. 2005,

2008). They explicitly argued that income is not the only or necessarily the best indicator

of material standard of living (Headey et al. 2008: 66). In order to provide a better

alternative they supplemented income by measures of wealth and by basic consumption

indicators. Doing so, they were able to show that the overall influence of a personsmaterial situation on his or her subjective well-being is considerably larger than it was

previously assumed.

A further example for the use of wealth indicators is provided by DAmbrosio et al.

(2009). In addition to using a detailed account on wealth they also improved on another

idea brought forward by Mullis, which is using a measure for long-term income (calculated

as average over several years) instead of only relying on current values. They showed that

both, the use of long term income as well as additionally controlling for wealth brought a

significant improvement when analyzing subjective well-being.

Another more complex indicator for material well-being, which is commonly applied in

poverty research and might be useful, when analyzing the relation between subjective well-

being and the material situation is a so-called deprivation index.4 A deprivation index

usually consists of an extended list of items a household may or may not posses, which are

combined into a single measure.5 In the remainder of the paper it will be argued, that it

indeed is a useful tool for analyzing subjective well-being and that applying such an index

will be an improvement compared to using indicators like income or wealth.

Up until now such an instrument has not been commonly used in satisfaction research.

There are some examples where single deprivation items or even a combined index have

been used in descriptive analyses of subjective well-being (e.g. Bohnke and Delhey1999a;

Bohnke2005; Mller 2007). There are also examples where smaller lists of items havebeen used as proxies for wealth (Howell et al. 2005).6 In fact there is one example (Bohnke

and Delhey1999b), where an indicator for deprivation based poverty and income are both

included in a multivariate model explaining satisfaction with the standard of living as well

as satisfaction with some other life domains. However, in this article the deprivation and

the income measure are merely used as control variables by which the authors try to

explain differences in satisfaction between East and West Germany. The differences

between the two predictor variables were not an issue discussed by Bo hnke and Delhey.

Hence, to the knowledge of the author, this is the first time that a systematic comparison of

a detailed deprivation index and income as predictors of subjective well-being is made.

4 It should be noted that the term deprivation in this context does not refer to a subjective concept as it e.g.

does in relative deprivation (Stouffer1949). Since it was designed as a poverty measure it explicitly aims at

identifying those who actually are poor and not those who feel that way (in comparison to some reference

group) but probably are not (compare e.g. Hallerod1995: 115). Instead, the term deprivation refers to living

in material circumstances which are lower than a commonly shared and/or socially agreed upon standard,

i.e. a person is considered to be deprived if he or she does lack a certain amount of items, which are regarded

to be part of this standard. Usually the items that actually make up the standard are identified by either

asking the survey-respondents, which among the items presented to them should be considered a necessity

or by identifying those items, which are held by a majority.5

For a more detailed description compare the paragraph on measures for the material situation below.6 Howell and his collaborators examine the relationship between subjective well-being and material living

conditions for an indigenous ethnic minority in Malaysia. Since many respondents in this population group

have no or only irregular income, collecting adequate income information was not possible. Instead, they

presented heads of households with a list of 13 consumer items, asking which items their households

possessed. However, instead of generating a deprivation index based on these items, they were used as

proxies for monetary wealth by substituting market prices for used goods for each item possessed and

combining the resulting sum with information on savings into a single wealth indicator.

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3 Approaches to Measuring the Material Situation

3.1 General Overview

Before starting the more technical parts of the paper, this section will provide a shortoverview of the approaches available to measure a persons material situation discussed in

the context of poverty research. The most basic distinction made there is the one between

direct and indirect measures of poverty, which was originally introduced by Ringen (1988).

Within this distinction, indirect measures concentrate on the resources available in order to

achieve a particular standard of living. These resources are usually financial in nature and

might include income, savings, assets and the like, income being by far the most popular

measure. Due to this particular focus, the application of indirect measures is also known as

Resources Approach. The core problem connected to it is that financial resources are

merely a proxy for the living standard actually achieved. Income, for example, might be

invested to improve ones standard of living, but it might also be spent otherwise.

Examples for other uses are saving, paying back debts, paying spousal and/or child support

and the like. On the other hand, insufficient income might quite well be supplemented by

additional spending from savings, by home production, by benefits received in kind or from

similar sources. In addition, if one is well endowed with modern appliances and consumer

electronics, a new car and is a homeowner, one might keep up a high standard of living for

quite a while, even on a comparably low income.

In contrast to the former, direct measures of poverty try to capture the essential aspects

of the standard of living without relying on such proxy variables. As might be already

assumed from what has been said above, there are two different strategies to do this: bylooking at actual consumption or by looking at the relevant goods and services that are at

the respondents disposal. The former of the two is known as the Consumption Approach,

the latter as the Standard of Living or Deprivation Approach.

From a theoretical point of view, expenditure or consumption measures7 have many

advantages in comparison to their income-based counterparts and are often considered to

be superior to the latter (Brewer et al.2006,2008; Headey2008; Meyer and Sullivan2003;

Noll and Weick2007). One reason for this is that income might fluctuate over time, in

particular for self-employed persons or people in irregular employment. Yet despite this

irregular stream of income, people try to keep expenditures constant. They do so by

processes of saving and living on those savings or borrowing and paying back their debts.So for these people, while income measures will be fluctuating, consumption measures will

remain largely unaltered. The same will apply in times of material hardship, e.g. after

loosing ones job, where, given sufficient savings, people might keep up their usual standard

of living and consumption for quite a while, notwithstanding their significantly lower

income. In addition, there are some indications that for people with very low income, the

amount of measurement error (underreporting, in particular) for the income variable

7 Measures of expenditure and consumption are related but they are not the same. The main difference is

that measures of expenditure only focus on actual spending, while consumption measures will also accountfor the consumption of durables (like owned housing or cars). They do so by adding an appropriate amount

for their usage/consumption to actual spending (while not fully considering such one-time investments in

case they should fall in the reporting period). Moreover, certain kinds of expenditures like e.g. cash

payments to family members are not considered (for a more detailed description compare for example

Meyer and Sullivan2003, pp. 1188 et seq.). In addition, consumption might also include goods which are

not bought, e.g. benefits in kind (Headey2008), foodstuffs from subsistence farming or other home-made

products (although the latter should not be too common in developed countries).


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exceeds the one found for the consumption variables among respondents with low con-

sumption levels (Meyer and Sullivan2003). The downside of consumption measures is that

they are complex and time consuming to administer in a survey, since they either require

respondents to keep a book of household accounts for a certain period of time8 or to report

(and remember) the amount of various items on a very extensive list they bought duringthis term.

The deprivation approach, on the other hand, is much easier to administer than

expenditure data, but is still somewhat time consuming, when compared to the survey time

required for obtaining income data. Originally developed by Townsend (1979) for his

landmark study on poverty in the United Kingdom, this approach essentially consists in

checking for a selected list of goods and activitieswhich are considered central for an

appropriate standard of livingwhether respondents own these items or participate in

these activities, respectively.

Since the original introduction of the approach, quite a number of problems linked to its

practical implementation have been tackled. The first is the question of consumer pref-

erences: the non-availability of an item or the non-participation in an activity should not

necessarily be considered an indication of deprivation but might merely result from a

particular taste or distaste of the respondent. Should the latter be the case, it surely would

be problematic to consider the fact that this item is lacking to be a sign of material

hardship. For this reason, nowadays items are only considered to indicate deprivation if

they are lacking for financial reasons.

A second problem is how to combine the items on the list to a common index. The most

basic approach is to calculate an unweighted index by simply adding up all items that are

lacking (for financial reasons). However, when looking at a typical list of items used togenerate a deprivation index (compare e.g. Table 7in the Appendix) it is easy to see that

some itemslike for example a home without damp wallsshould be considered more

important for an appropriate standard of living than otherssay like owning a DVD-

Player. For this reason, weighting the items used to build the index seems appropriate.9 In

fact, there are several approaches to achieve this. The three central ones are probably: first,

weighting an item by the percentage of respondents considering it to be important for an

acceptable living standarda procedure which requires to obtain additional information on

this importance during the survey (as done e.g. by Hallerod1994,1995, when constructing

his so-called Proportional Deprivation Index). The second approach is to weight each item

by the percentage of the population that actually owns this item. This latter approach drawson relative deprivations idea, that the lack of a particular item might be considered much

more problematic if it is something which everybody owns and rightfully should own than

if this item is some kind of rarely owned luxury good (compare Desai and Shah1988). An

advantage of the latter approach is that it might be applied without administering any

additional questions. The last one is to select only such items to be part of the index, which

are considered to be highly relevant. This might be achieved for example by selecting only

those items that are owned by at least 50% of the population (as done e.g. by Mack and

Lansley1985). Another method which has been applied by Callan et al. (1993) and Nolan

8 This period is often 1 or 2 weeks. It might, however, be considerably longer. In the German income and

expenditure survey (EVS) for example, respondents have to keep a book of household accounts for 3 months

in total and at least part of them has to keep a detailed list of all expenditure on food, drinks and tobacco for

1 month.9 Even though systematic comparisons between different weighting approaches have shown, that applying

either of these or none at all will only generate minor differences between the resulting indices (Lipsmeier

1999), weighting an index still seems reasonable for conceptual reasons.

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and Whelan (1996) is to use factor analysis to identify the basic items.10 While building the

index on highly relevant items only might not be considered a weighting scheme in the

narrow sense of the word, this procedure should guarantee that only items that are strongly

related to deprivation are taken into account, while others are discarded.

A third problem which has been somewhat more difficult to solve is how to define athreshold for the number of items lacking that is considered necessary, to hold a person to

be poor. A useful differentiation of criteria to define such a poverty line is provided by

Andress and Lipsmeier (2001) who distinguish between statistical and non-statistical cri-

teria. Among the statistical criteria is defining a certain percentage of the mean or median

value to draw a line between the poor and the remainder of the population, just the way it is

done when defining income-poverty. The problem with this is that deprivation indices have

a strongly skewed distribution, often with a modal value of zero, i.e. a majority of

respondents does not lack any of the items on the list. Therefore the core argument of the

aforementioned definition as it is usually applied in the case of incomethat the mean or

median represents an average and thus a normal income levelseems not to be appli-

cable in case of a deprivation index. A second option is to look for the number of items by

which a certain percentage of the population will be classified as poor (e.g. Hallerod1995).

This means for example that we will use a poverty threshold of three if the least well off

10% of the population lack three items or more. The obvious weakness of this approach is

that doing so would be somehow begging the question, since the matter of interesti.e. the

proportion of poor personswould thus be answered by definition instead of being

identified empirically.11 The major non-statistical criterion is to define a particular number

of items, the lack of which is considered to classify a person as poor either by means of an

outside criterion or simply by setting a particular cut-off point (e.g. Mack and Lansley1985). In any case, properly defining deprivation based poverty still remains somewhat of a

problem, which is not solved satisfactorily by any of the approaches described.12

3.2 Income, Consumption or Deprivation: Advantages and Drawbacks

It has sometimes been argued, that deprivation measures (among others) should be best

viewed as attempts to make do in the absence of valid consumption measures (Headey

2008: 26). However, this position seems to be less convincing when considering an

argument made by Andress and Lipsmeier (2001)and Andress et al. (2004) who hold that

direct and indirect measures for the material situation should be understood against thebackground of a simple three stage action-model, in which people (a) hold certain

resources, (b) use these resources in accordance with their preferences in such a way as to

(c) obtain certain, desired results (compare Fig. 1).

Keeping this model in mind it becomes clear, that resource, expenditure/consumption

and deprivation indicators try to measure a persons material situation at the three different

stages of this process: indicators like income or wealth try to measure the resources

available for achieving a proper standard of living, consumption indicators measure the

10 For details of the procedure compare Callan et al. (1993): 150 et seq. and Nolan and Whelan (1996): 228

et seq.11 Even though in our example it is somewhat unlikely that a threshold of three items will identify exactly

ten percent of the population to be poor, the actual percentage value should be close.12 For the current paper, the fact that defining a poverty line based on deprivation measures is a somewhat

difficult task seems to be of minor importance, since it merely focuses on the relationship between well-

being and measures for the material situation but does not explicitly focus on the relation between well-

being and poverty.


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actions taken to attain this goal and deprivation indicators try to measure the material

situation on the level of the results obtained.

Nevertheless, consumption and deprivation indicators have a lot in common, and these

shared characteristics are in fact what would make one assume a much closer relation to

subjective well-being than is found for income or other indirect indicators. The first sharedadvantage is a theoretical one which is based on need theory. At least if this theory should

hold, it should not be the theoretical potential to have ones needs fulfilled (as it is

measured by indirect indicators), which should make one happy, but much more the actual

fulfillment of those needs (which is the focus of direct indicators).

A second shared advantage is that as direct measures for a persons material circum-

stances, both indicators are much less vulnerable to short-term fluctuations than income is.

In many cases, these fluctuations do not happen accidentally but are anticipated, e.g.

among self-employed persons whose income is subject to a certain amount of variation. As

long as the persons concerned take proper measures to compensate for this kind of vari-

ation in such a way as to keep their actual standard of living constant, a measure doing

likewise should much better grasp the connection between their well-being and their

material situation.

Moreover, it has been shown for both indicators that they capture a somewhat different

aspect of the material situation than income does. The most obvious indication of this is

that the groups of persons which are considered to be poor based on income and con-

sumption or income and deprivation measures, respectively are quite distinct (compare e.g.

Brewer et al.2006or Noll and Weick2007for consumption and Hallerod1995or Bohnke

and Delhey1999afor deprivation).13

This should not be considered an advantage per se,

but chances are that the aspects of the material situation captured by those indicators arealso the ones that are more strongly connected to well-being. So in sum, from a conceptual

point of view we could expect both types of direct indicators to be much more strongly

related to subjective well-being than indirect indicators like income and wealth are.

Fig. 1 Overview of different approaches to measuring the material situation. Adapted from: Andress and

Lipsmeier (2001)

13 This is why poverty researchers often argue to combine direct and indirect measures to identify the truly

poor, as e.g. Hallerod(1995) calls the group of persons that is deprived according to both measures.

484 B. Christoph

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In contrast to that, to make a theory-based decision between consumption and depri-

vation indicators seems to be a much more difficult task. As has been shown, both types of

indicators share quite a lot of common advantages that seem to make both of them a good

choice for analyzing the material situation/subjective well-being relation. One difference

between both is that deprivation indicators are usually constructed to capture the avail-ability or non-availability of quite basic items. They usually draw a distinction between

those with a (more or less strongly) restricted standard of living and all the other

respondentsand do so in quite some detail. The downside of this approach is that unlike

consumption indicators they are unable to distinguish between the well off and those that

do even better, because basically all of them can afford all the items on the list.14

This is

why deprivation indices are usually strongly skewed, with sometimes more than 50% of

respondents lacking no item at all. However, what might be a disadvantage for answering

some questions could in fact be useful for answering the current one: one of the most stable

results of previous research is that it is particularly the less well off, for whom material

improvements lead to increased subjective well-being. Thus an indicator focusing more

strongly on the lower end of the material spectrum might in fact do a much better job. Be

that as it may, since in Germany consumption measures are largely unavailable outside

official statistics15 and official data usually do not include subjective measures like well-

being indicators, at this point the only comparison which can be made is that between

indirect measures (income and wealth) and deprivation indicators.16

4 Data and Measures

4.1 Overview of the Datasets Used

In order to prevent that specific properties of a particular dataset might influence results,

three different German datasets comprising information on subjective well-being as well as

on deprivation items have been used in the analyses. Only if all three datasets provide

comparable results, the conclusion seems appropriate that a deprivation index constitutes a

superior indicator of material living conditions to be used when the latters influence on

subjective well-being is analyzed. All three datasets have their particular advantages and

disadvantages, which will be discussed in the following paragraph.

The first dataset used is the 2007 wave of the German Socioeconomic Panel (GSOEP,compare Wagner et al. 2007). In addition to being one of the most commonly applied

German datasets, the 2007 wave has the particular advantage of providing a very detailed

14 The fact that a deprivation indicator is particularly apt to identify people at the lower end of the material

distribution but does not so good a job in distinguishing differences among the better-off has been made e.g.

by Andress et al. (2001).15 The most notable official data being the EVS described above.16 As mentioned above, Headey et al. (2005, 2008) have also examined the influence of consumption

indicators using the British household panel survey (BHPS) and the Hungarian Tarki Panel. At least in thecase of general well-being, results were mixed, showing a strong influence for the Hungarian data but no

significant effects for the BHPS. To what extend these results might be due to the somewhat reduced set of

consumption questions one is restricted to in a multi-topic household survey, i.e. whether more detailed data

would show a stronger effect, might be an interesting question for further research. In any case, since to the

knowledge of the author even a reduced set of consumption data is unavailable in any German dataset

covering subjective well-being as well as deprivation items, an empirical comparison for consumption and

deprivation indicators and their influence on subjective well-being is not possible using German data.


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record of personal wealth and debts, which is not available in the two other datasets. The

downside of the GSOEP is, that it includes only a very restricted set of deprivation items

and moreover, that it does not include any information on the differences in importance,

respondents might assume the items on the list to have (and which could be used as

weighting factors when computing the index).17

The second dataset is the German Welfare Survey 1998 (WS98, compare Thum et al.

1999). At first, the most obvious downside of this dataset seems to be that it is somewhat

aged. However, since we assume the correlation between subjective well-being and

material living conditions not to vary significantly over time, the age of the data should

constitute no problem for the validity of the results presented below. A somewhat more

serious shortcoming of the data is the almost complete lack of information on wealth. An

advantage compared to the GSOEP is the very detailed list of deprivation items and the

availability of information on the relative importance of these items.

The third dataset to be used is the second wave of the Panel Study Labour Market and

Social Security (PASS, compare Trappmann et al. 2009).18 Like the Welfare Survey this

dataset includes a very detailed list of deprivation items and also the corresponding

importance information. In addition, there is at least summary information on wealth,

which is, however, far from reaching the degree of detail the corresponding information in

the GSOEP provides. The downside of this dataset is the comparably low response rate,

particularly in the general population sample. In this sample the response rate on the

household level for the first wave was merely 26.6% in the general population sample and

35.1% in the sample of benefit recipients.19

Summing things up, each of the datasets used has certain advantages and disadvantages.

Table1gives a summary of these. So not all questions of interest might be answered by allof the datasets and results obtained with only one of these datasets might be considered

17 For more details on this problem compare the theoretical discussion of deprivation indices above and/or

the description of index construction below.18 The primary goal of PASS is to provide information on recipients of the reformed unemployment

assistance scheme in Germany, the so-called Arbeitslosengeld II (Unemployment Benefit II). In order to

achieve this goal, the study comprises two different samples, a sample consisting only of households,

receiving this benefit at the date the sample was drawn and a sample of the general population, which might

also include benefit recipients, but only to the degree usually found in the population. As of January 2009this was 10.1% of all applicable persons, i.e. those between 0 and 65 years of age (STBA 2009: 12. This

value equates to 8.1% of the entire population. Note, however, that calculating such a percentage for the

entire population might be somewhat problematic, since this figure does not represent all recipients of means

tested benefits. This is so since people of age 65 and older would have to apply for a different type of benefit,

if they are in need. This benefit will result in quite comparable financial circumstances of the needy person,

but is governed by a different part of the German Social Code, namely social assistance legislation/SGB

XII). For details on the sampling design compare Rudolph and Trappmann (2007). When calculating models

that should provide representative information on the entire population, appropriate weighting factors are

available, which allow to adequately combine the two subsamples.19 This results in an overall response-rate of 30.5%. Response rate within households varied between 84.3

and 85.6% resulting in person-level response rates of 30.0% for the recipient sample, 22.4% for the

population sample and 25.9% for the combined samples (for wave 1 figures compare Christoph et al. 2008).Overall re-response rates for wave two were 62.4% on the household and 53.1% on the person level

(compare Bungeler et al.2009). The welfare survey, which is a cross-sectional survey, had a response rate of

56.1%. Due to the complex sample structure and up to now more than 25 panel waves, it is not possible to

present detailed figures for the GSOEP here. Original response rates on the household level are between

60.6% in sample A (1984) and 40.2% in sample H (2006; compare Haisken-DeNew and Frick2005; von

Rosenbladt et al.2007). For an overview of attrition between panel waves in the GSOEP compare Kroh and

Spiess (2008).

486 B. Christoph

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somewhat weak. If, however, all datasets show comparable results, it seems to be most

likely that these findings are reliable despite the weaknesses of any single dataset.

4.2 Measures and Harmonization

As indicator for subjective well-being, all three surveys used a single measure with

comparable wording. The question-stimulus was How satisfied are you currently with

your life as a whole in general? and responses could be given on an 11-point scale ranging

from 0 (completely dissatisfied) to 10 (completely satisfied).

Forincome, equivalized household income was used. Equivalence weights were applied

according to the modified OECD-Scale (Hagenaars et al. 1994), giving the first person

older than 14 years of age a weight of 1, additional persons over 14 received a weight of

0.5 and persons aged 14 or younger received a weight of 0.3. Since previous research hasshown that the relationship between subjective well-being and income is non-linear and it

has been exemplified that these non-linearities might be properly covered by logging (e.g.

Duncan 1975; Easterlin 2002), logged income has been used. For the Welfare Survey,

income information was still in German Marks (DM). In order to allow for more easy

comparisons of descriptive statistics this was converted to Euro using the official Exchange

rate (1 =1.95583 DM). After that, income was adjusted to 2007 values using the

consumer price index (StaBA 2009).20

Wealth has been operationalized quite differently in the three datasets. As has been

mentioned before, there was no information on wealth available in the Welfare Survey. For

the other two surveys, separate variables were used for savings and debts. The main reason

for this was that separate variables allowed for logging,21 which would be difficult for an

integrated variable, due to the negative values resulting in cases where debts exceeded

savings. The PASS included two items asking respondents for categorized information on

the households savings and debts, the values of which were replaced by the theoretical

category means.22 The 2007 wave of the GSOEP was the first replication of a very detailed

wealth module that had been originally developed for the 2002 wave. This module

included information on each respondents possessions as well as on debts for the fol-

lowing domains: real estate (incl. loans), investments (e.g. stocks) and savings, insurances

Table 1 Overview of the datasets used

Welfare Survey GSOEP PASS

Year 1998 2007 (wave 24) 2007/2008 (wave 2)

N(for analysis) 2,130 11,424 9,408Positive Detailed deprivation

information

Detailed wealth information Detailed deprivation

information

Commonly used reference data Some wealth information

Negative No wealth information Reduced deprivation information Low response rate in

general population sample

20 The consumer price index information standardized to 2005 values in StaBA (2009) was re-standardized

to 1998 values. Prices in the year 2007, which was used as the reference year for price adjustment, were

equal to 114.3% of 1998 prices.21 The actual values logged were savings or debts plus one, in order to be able to calculate a logged value

for respondents with debts or savings of zero.22 For the highest category, indicating 50 000 Euro or more, a value of 75,000 Euro was imputed.


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(e.g. life or private pension insurance), business property, private possessions (e.g. jewelry,

art etc.) and consumer credits.

The integration of the several variables in each of these modules was done following the

general procedures described by Schafer and Schupp (2006) for the 2002 wave. However,

in contrast to the former, no integrated wealth variable was generated, but wealth and debts

(where applicable) from all the components were added up separately. As a result of this

much more detailed account, respondents in the GSOEP have much higher values in the

wealth variables than their PASS counterparts, with maximum values of both, debts and

wealth, exceeding a million Euros (compare descriptive statistics in Table2). A significant

part of this is made up by business property, possessions, or real estate as well as (in case of

debts) housing loans, which are all not accounted for in PASS. For both datasets the debts

and wealth indicator on the household level was transformed into available wealth per

capita before being logged, which means that for the GSOEP the personal information was

aggregated to the household level and for both datasets, household level information was

divided by the number of household members.

Information on deprivation varied strongly between the three surveys. The general

strategy applied here was to take whatever was available in the respective survey.

Therefore the relative effect of the deprivation index is difficult to compare between

surveys, even if problems of scale are accounted for (compare below). This, however,seems not to be too much of a drawback, since the difference that is really of importance in

the current context is that between income and the deprivation index within each survey.

The Welfare Survey included a list of 22 items23 that might be lacking due to financial or

Table 2 Descriptive statistics for subjective well-being, income, wealth and deprivation

Mean Min Max N

Welfare Survey 1998

Satisfaction w. life 7.6 0 10 2,130Household Inc. (new OECD), in , cor. for inflation 1,346 78 21,428 2,130

Deprivation index (wght.) DI items (total, wght./unwght.) 2.2 0 19.4 (19.4/22) 2,130

Depr. index (wght., in % of all items) 11.2 0 100.0 2,130

GSOEP 2007

Satisfaction w. life 6.7 0 10 11,424

Household Inc. (new OECD), in 1,452 156 23,333 11,424

Savings (in 10,000 ) 9.2 0 1,071.5 11,424

Debt (in 10,000 ) 1.5 0 151.9 11,424

Deprivation index (wght.) DI items (total, wght./unwght.) 1.2 0 9.7 (13.7/15) 11,424Depr. index (wght., in % of all items) 8.6 0 71.0 11,424

PASS 2007/2008

Satisfaction w. life 7.1 0 10 9,408

Household Inc. (new OECD), in 1,561 9 66,667 9,408

Savings (in 10,000 ) 1.5 0 7.5 9,408

Debt (in 10,000 ) 0.6 0 7.5 9,408

Deprivation index (wght.) DI items (total, wght./unwght.) 2.0 0 19.9 (23.8/26) 9,408

Depr. index (wght., in % of all items) 8.4 0 83.4 9,408

23 For a detailed list of items available in each survey compare Table 7 in the appendix.

488 B. Christoph

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other reasons as well as the respective information necessary to generate importance

weights. PASS includes a list of 26 items and also covers weighting information.

As mentioned before, the GSOEPs list is considerably smaller and includes only 11

items. Four additional items for amenities (indoor toilet, bathroom/shower, central

heating) and ability to pay the rent were added. For these four items there is noinformation available on whether they are lacking due to financial or for other reasons.

However, since all four items are very basic, chances that they might lack for other

reasons are comparably slim. Therefore it seemed acceptable to assume that if they are

not available this should be due to lack of money. In addition to that, the GSOEP is the

only dataset which does not provide information to generate importance weights. Since

this is so, it was not possible to apply this kind of weights for comparable analyses

across the datasets. Instead, each item in the index was weighted by the percentage of

persons in the respective dataset possessing that itema weighting procedure requiring

no further information. In order to construct the index, in all datasets all (weighted) items

lacking for financialbut not for otherreasons were added up. This number was

transferred to a common scale indicating the percentage of all items in the respective

index that are lacking, in order to allow at least some comparison between effects,

despite the strongly differing number of items in the three indices. As Table2 indicates,

despite the great variance in the number of items considered, the three indices do not

seem to be too different from each other. In fact, for the PASS and the GSOEP the

average percentage of items lacking is almost identical (8.4 and 8.6%, respectively). For

the welfare survey the corresponding percentage is somewhat higher (11.2%), which is

not much of a surprise, however, considering that the list of items in the Welfare Survey

includes quite a number of higher standard items like a newspaper subscription, sup-plemental private health insurance or a private pension plan, which are not covered by

any of the other surveys (compare Table 7 in the appendix for details).

Healthhas been shown to have a significant influence on subjective well-being (Diener

et al.1999), which is in particular true for severe health restrictions or disabilities. Since all

three surveys included somewhat different health indicators, the only comparable infor-

mation available is whether respondents had a disability or not. Accordingly, this infor-

mation has been used as health indicator.

Differences in the way education was administered were rather small. It was easily

possible to transfer educational information of all three surveys into the CASMIN-clas-

sification (cf. Brauns and Steinmann 1999). The three major levels of this classificationwere recoded into dummy variables for low, medium and high education.

Another important predictor of subjective well-being is unemployment (e.g. Clark and

Oswald1994; Gerlach and Stephan1996). Information on employment status was avail-

able in all three surveys and was controlled for accordingly.

Further demographic control-variables included gender, age, marital status (being

married vs. all others) and German citizenship. Since the relation of age and subjective

well-being has been shown to be nonlinear (Frijters et al.2004), the linear age variable was

supplemented by a squared age-effect.

5 Deprivation as a Predictor of Happiness

As a first step of the analysis, income and the deprivation measure will be compared

using simple ordered logit models for predicting life satisfaction. Results for the three

datasets are shown in Table 3. In all of the datasets having more money will result in a


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higher level of satisfaction as will lacking less items contained in the deprivation index.

A second consistent finding is that in all datasets the relation between the deprivation

index and well-being is considerably stronger than that between income and the latter,

which can be seen from the Pseudo R2 and AIC values of the models. The question is,

whether this result will hold, when additional variables are controlled for and whether

both variables, income and deprivation, will contribute a substantial share to improving

the model independent from one another. We will first try to answer this question using

the Welfare Survey data.

In Table4, three different models are compared, the first model including only income

and the control variables, the second the deprivation index and the third takes account of

both these variables. As can be seen from the Pseudo R2 values, the difference between the

models one and two might be somewhat reduced by adding the controls, but it still remains

strong.

Looking at Model 3 we find that combining both indicators in a common model will

improve results only to a minor extent. Nevertheless, income still displays a separate effect

when deprivation is controlled for. However, looking at the standardized coefficients

displayed in the second column of the model table it is obvious, that the contribution made

by income is the smaller of the two.Considering the results for the control variables, they show by and large the figures that

were to be expected. Looking at regional differences, East Germans are significantly less

satisfied than their West German counterparts. A significant gender effect can only be

observed in Models 2 and 3, but age in all models displays the strong curvilinear effect that

could be anticipated based on earlier research. While besides a small effect in Model 1

there is no effect for German citizenship and also no significant education effect, there is a

very strong positive effect of being married, while being unemployed and having serious

health restrictions both have negative effects.

Results for the GSOEP are displayed in Table5. In contrast to the data discussed up to

now, the GSOEP also includes wealth information, which will be controlled for in the

models. Since in terms of the approaches for measuring the material situation discussed

above, income and wealth both are considered indirect measures, while deprivation is a

direct measure for the material situation, the former two are grouped together in the models

discussed below and the combined effect of both is compared to that of the deprivation

index.

Table 3 Basic ordered logit models predicting subjective well-being (0 =completely dissatisfied;

10 =completely satisfied)

WS 1998 GSOEP 2007 PASS 2007/2008

M1 (Inc) M2 (DI) M1 (Inc) M2 (DI) M1 (Inc) M2 (DI)

Deprivation index -0.058*** -0.049*** -0.085***

Ln HH-Inc. (n. OECD) 1.363*** 0.960*** 1.036***

LL (intercept) 3,794.250 -3,794.250 -22,195.709 -22,195.709 -17,953.866 -17,953.866

LL (model) -3,671.930 -3,563.522 -21,797.247 -21,580.564 -17,447.159 -16,742.129

AIC 3.458 3.356 3.818 3.780 3.711 3.561

Pseudo R2 (Mc Fad.) 0.032 0.061 0.018 0.028 0.028 0.068

N 2,130 2,130 11,424 11,424 9,408 9,408

* p\ 0.05; ** p\0.01; *** p\0.001

Note: Intercepts not displayed in the model table

490 B. Christoph

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Results in Table5 mainly provide two additional pieces of information: first, the two

wealth variables (savings and debts) both have a strong significant effect on satisfaction,

which also prevails after income and deprivation have been controlled for. The second

additional result is that, even though the difference is considerably smaller than the one

observed in Table3, the deprivation only model (Model 2) has still more explanatory

power than Model 1, including both income and the two wealth variables. The effects for

the control variables remain largely unaltered, the major difference being that tertiary

education makes some difference in Model 2 and that there is a generally stronger gender

effect.

Table 4 Ordered logit models predicting subjective well-being (0 =completely dissatisfied;

10 = completely satisfied) for the welfare survey 1998

Welfare Survey 1998

M1 M2 M3

Coeff. XY-std.

Coeff.

Coeff. XY-std.

Coeff.

Coeff. XY-std.

Coeff.

Deprivation index -0.051*** -0.381 -0.045*** -0.335

Ln HH-income (new OECD) 1.136*** 0.271 0.522*** 0.119

Region (Ref.: West Germany)

East Germany -0.337*** -0.080 -0.471*** -0.107 -0.376*** -0.085

Nationality (Ref.: other)

German 0.440* 0.042 0.354 0.033 0.294 0.027

Gender (Ref.: male)

Female 0.149 0.037 0.187* 0.045 0.195* 0.046

Age -0.079*** -0.628 -0.075*** -0.570 -0.087*** -0.654

Age (squared) 0.001*** 0.618 0.001*** 0.551 0.001*** 0.626

Marital status (Ref.: other)

Married 0.825*** 0.193 0.626*** 0.141 0.645*** 0.144

Education (Ref.: low)

Medium 0.122 0.030 0.105 0.025 0.048 0.011

High -0.046 -0.008 0.081 0.014 -0.082 -0.014

Health (Ref.: no disability)

Disability -0.885*** -0.096 -0.895*** -0.093 -0.885*** -0.091

Employm. status (Ref.: other)

Unemployed -1.096*** -0.148 -0.828*** -0.108 -0.714*** -0.092

LL (intercept) -3,794.250 -3,794.250 -3,794.250

LL (model) -3,577.344 -3,489.086 -3,477.235

Log rank test (int. vs. full) 433.811*** 610.328*** 634.030***

AIC 3.379 3.296 3.286

PseudoR2 (Mc Fadden) 0.057 0.080 0.084

N 2,130 2,130 2,130Log rank test (M1/M2 vs. M3) 200.218*** 23.702***

* p\0.05; ** p\0.01; *** p\0.001



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These findings are confirmed by the results from the PASS data in Table 6, which

are pretty much the same. In this case the difference between Model 1, including the

indirect indicators and Model 2 containing the deprivation index is more pronounced,

which might be a result of the somewhat more detailed deprivation information

available in PASS. In any case, the general tendency of deprivation to be somewhat

more strongly related to satisfaction than is income alone and even than the indirect

indicators are taken together, remains unaltered. Nevertheless, just as it appeared when

using these instruments for measuring poverty, direct and indirect measures seem to tab


10 = completely satisfied) for the German socio-economic panel (GSOEP) 2007

GSOEP 2007

M1 M2 M3

Coeff. XY-std.

Coeff.

Coeff. XY-std.

Coeff.

Coeff. XY-std.

Coeff.



Ln savings 0.062*** 0.135 0.038*** 0.081

Ln debts -0.051*** -0.110 -0.038*** -0.080


East Germany -0.395*** -0.081 -0.523*** -0.106 -0.431*** -0.086


German -0.057 -0.008 -0.066 -0.009 -0.146 -0.019

Gender (Ref.: male)

Female 0.184** 0.047 0.180** 0.045 0.185** 0.046

Age -0.068*** -0.626 -0.057*** -0.520 -0.063*** -0.567

Age (squared) 0.001*** 0.534 0.000*** 0.425 0.000*** 0.453

Marital Status (Ref.: other)

Married 0.288*** 0.073 0.172** 0.043 0.196** 0.049


Medium -0.077 -0.019 -0.060 -0.015 -0.128 -0.031

High 0.060 0.012 0.203* 0.040 0.033 0.006

Health (Ref.: No disability)

Disability -0.838*** -0.156 -0.796*** -0.147 -0.796*** -0.146


Unemployed -0.675*** -0.101 -0.657*** -0.098 -0.498*** -0.073

LL (Intercept) -22,195.709 -22,195.709 -22,195.709

LL (Model) -21,226.749 -21,132.355 -21,036.626

Log rank test (Int. vs. Full) 1,937.921*** 2,126.708*** 2,318.166***

AIC 3.720 3.703 3.687PseudoR2 (Mc Fadden) 0.044 0.048 0.052

N 11,424 11,424 11,424

Log rank test (M1/M2 vs M3) 380.246*** 191.458***

* p\0.05; ** p\0.01; *** p\0.001


492 B. Christoph

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somewhat different aspects of satisfaction, since both their effects remain significant

when including them in a common model. This result is further supported by the log

rank test displayed at the bottom of the table, which shows that Model 3 constitutes an

improvement over both alternative models, omitting either the deprivation index (M1)

or the indirect indicators (M2). Thus using all indicators for the material situation

discussed jointly instead of considering them alternatives might be the most reasonable

strategy.


10 =completely satisfied) for the panel labour market and social security (PASS)

PASS 2007/2008

M1 M2 M3

Coeff. XY-std.

Coeff.

Coeff. XY-std.

Coeff.

Coeff. XY-std.

Coeff.



Ln savings 0.126*** 0.216 0.051** 0.083

Ln debts -0.043*** -0.079 -0.026** -0.046


East Germany -0.282*** -0.056 -0.352*** -0.067 -0.270** -0.051


German -0.042 -0.006 -0.051 -0.007 -0.155 -0.022

Gender (Ref.: male)

Female 0.154* 0.037 0.238** 0.055 0.231** 0.053

Age -0.119*** -0.718 -0.127*** -0.737 -0.126*** -0.723

Age (squared) 0.001*** 0.584 0.001*** 0.626 0.001*** 0.590

Marital status (Ref.: other)

Married 0.636*** 0.144 0.465*** 0.102 0.466*** 0.101


Medium 0.054 0.026 0.052 0.024 0.022 0.010

High 0.077* 0.044 0.126*** 0.069 0.067 0.036

Health (Ref.: No disability)

Disability -0.674*** -0.099 -0.652*** -0.092 -0.636*** -0.089


Unemployed -0.808*** -0.121 -0.548*** -0.079 -0.435*** -0.062

LL (intercept) -17,953.866 -17,953.866 -17,953.866

LL (model) -16,707.073 -16,406.003 -16,314.704

Log rank test (int. vs. full) 2,493.587*** 3,095.725*** 3,278.324***

AIC 3.557 3.492 3.473PseudoR2 (Mc Fadden) 0.069 0.086 0.091

N 9,408 9,408 9,408

Log rank test (M1/M2 vs. M3) 784.738*** 182.598***

* p\0.05; ** p\0.01; *** p\0.001



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6 Conclusion

This paper started out with the well-known finding, that the relation between a persons

subjective well-being and his or her material situation is surprisingly weak. This finding is

puzzling, in particular since it contradicts popular prima facie expectations about thecauses of happiness. Since this is so, in recent years this relationship has been increasingly

in the focus of research efforts. Categorizing these efforts, two general approaches have

been identified, those trying to improve empirical models and those trying to find more

adequate measures for the material situation than income (alone).

The current paper tried to make a contribution to the latter line of research. Doing so, it

started out with giving an overview of the different concepts for measuring the material

situation. A particular focus was given to the distinction between direct and indirect

measures for the material situation, which is widely known and applied in the context of

poverty research, but which has hitherto not been given much consideration when

examining the material situation/well-being relationship. Breaking with that practice, this

paper has argued that this distinction might be quite useful when applied in well-being

research and that the relation between well-being and direct measures should be stronger

than the one found for indirect measures.

In order to support this hypothesis, three different German datasets have been used. All

these datasets included an income variable as well as information on the possession of

various items, which is necessary to compute a so-called deprivation index, a very

prominent direct measure for the material situation. Two of the datasets included additional

information on wealth. Using this data, it could be shown that the relation between well-

being and the direct measure for the material situation is indeed considerably stronger thanthe one found when using its indirect counterparts. This will not only hold, when con-

sidering the relationship between satisfaction and income but will also remain true, when

supplementing the income variable by additional wealth-information.

This finding fits well with previous research as e.g. the one by Headey and his col-

leagues, which argues that using more advanced material measures than income alone

might quite well lead to a re-evaluation of the influence material circumstances have on

subjective well-being. It adds to the previous argument, by noting that applying direct

measures like a deprivation index will be a significant improvement over analyzing indirect

measures like income and wealth alone. It also gives rise to some additional questions,

which might be the focus of future research. The first would surely be to use other thanGerman data in order to confirm the results and safeguard against the somewhat unlikely

but still possible case that the results reported above represent a German particularity rather

than indicating a generally valid relation. The second would be to start a new attempt to use

detailed consumption data. Even though Headey et al. (2005, 2008) have made a first

attempt to studying the influence of consumption on well-being, in which they found a

small effect for some countries and for others none at all, in the authors opinion this

should not settle the case. This is mainly since Headey and his colleagues used very crude

and strongly aggregated consumption data and it seems not unlikely that their findings are

at least in part a consequence of this crudeness rather than of the general quality ofconsumption data as predictors of subjective well-being. Unfortunately, datasets covering

consumption in great detail usually do not account for subjective variables like well-being

measures. Therefore it might take some time until the relation between detailed con-

sumption measures and satisfaction might be up for analysis, which, however, does not

make the endeavor a less promising one. Nevertheless, until then or until some more

advanced measure will be applied, combining direct measures like a deprivation index with

494 B. Christoph

1 3


21/25

information on income and wealth should be considered the best option to analyze the

relation between subjective well-being and the material situation.

Acknowledgments The author likes to thank Heinz-Herbert Noll, Gerhard Krug, Torsten Lietzmann and

the anonymous reviewer for helpful comments.

Appendix

See Table7.

Table 7 Items used for calculating deprivation indices

Item GSOEP WS98 PASS

1 An apartment with at least as many rooms as persons live there No Yes Yes2 An apartment without damp walls or floors No No Yes

3 An apartment located in a house, which is in a proper state of

repair

Yes No No

4 The house is located in a good neighborhood Yes No No

5 A separate bathroom with bathtub or shower (GSOEP: no

information whether due to financial or other reasons)

Yes No Yes

6 An indoor toilet (GSOEP: no information whether due to

financial or other reasons)

Yes No Yes

7 An indoor toilet and a bathtub or shower No Yes No

8 Central heating, self-contained central heating or districtheating (GSOEP: no information whether due to financial or

other reasons)

Yes No Yes

9 A garden, a balcony or a terrace No Yes Yes

10 To be able to buy new clothing once in a while, even if the old

clothes are not worn-out (WS98: regularly buy new clothes)

No Yes Yes

11 A hot meal at least once per day (WS98: on average; GSOEP: a

hot meal including meat, fish or poultry every two days)

Yes Yes Yes

12 Sufficient winter clothing No No Yes

13 A holiday away from home for at least a 1 week a year Yes Yes Yes

14 To invite friends for dinner at home once a month Yes Yes Yes15 To eat out at a restaurant once a month No Yes Yes

16 Going out to the cinema, a theatre or a concert at least once a

month

No No Yes

17 A newspaper subscription No Yes No

18 A telephone Yes Yes No

19 A car Yes Yes Yes

20 A TV (GSOEP: color TV) Yes Yes Yes

21 A video recorder (PASS: or DVD player) No Yes Yes

22 A computer (PASS: with internet access; GSOEP: internet

access)

Yes Yes Yes

23 A hi-fi system No Yes No

24 A washing machine No Yes Yes

25 A dishwasher No Yes No

26 An upright freezer, a chest freezer or a refrigerator with a

freezer section

No No Yes


1 3


22/25

References

Andress, H.-J., Kruger, A., & Sedlacek, B. K. (2004). Armut und Lebensstandard. Zur Entwicklung desnotwendigen Lebensstandards der Bevolkerung. Koln: Gutachten im Rahmen des Armuts- und

Reichtumsberichts der Bundesregierung.

Andress, H.-J., & Lipsmeier, G. (2001). Armut und Lebensstandard. In Bundesministerium fu r Arbeits und

Sozialordnung (BMAS) (Ed.), Lebenslagen in Deutschland. Der erste Armuts- und Reichtumsbericht

der Bundesregierung. Bonn: BMAS.

Andress, H.-J., Lipsmeier, G., & Lohmann, H. (2001). Income, expenditure and standard of living as poverty

indicatorsdifferent measures, similar results? Schmollers Jahrbuch, 121, 165198.

Argyle, M. (1999). Causes and correlates of happiness. In D. Kahnemann, E. Diener, & N. Schwartz (Eds.),

Well-being. The foundations of hedonic psychology (pp. 353373). New York: Russel Sage.

Argyle, M. (2001). The psychology of happiness (2nd ed.). London: Routledge.

Boes, S., & Winkelmann, R. (2006). The effect of income on positive and negative subjective well-being.

Sozialokonomisches Institut der Universitat Zurich, Working paper no. 0605. Zurich: UniversitatZurich.

Boes, S., & Winkelmann, R, (2009). The effect of income on general life satisfaction and dissatisfaction.

Social Indicators Research, Online First, Retrieved June 2, 2009 fromwww.springerlink.com.

Bohnke, P. (2005).First European quality of life survey: Life satisfaction, happiness and sense of belonging.

Luxembourg: Office for Official Publications of the European Communities.

Bohnke, P., & Delhey, J. (1999a). Lebensstandard und Armut im vereinten Deutschland. WZB working

paper FS III 99-408. Berlin: Social Science Research Center Berlin (WZB).

Table 7 continued

Item GSOEP WS98 PASS

27 To be able to save a fixed amount a month (WS98: DM 100.-) Yes No Yes

28 I have savings for cases of emergency Yes No No

29 To replace worn but still usable furniture with new Yes Yes Yes

30 To be able to pay for unexpected expenses with ones own

money, e.g. to replace a broken washing machine

No No Yes

31 To be able to afford medical treatment which is not fully

covered by ones health insurance, such as dentures or glasses

No No Yes

32 To be able to afford medical treatment and dentures if

necessary, even if it is not fully covered by ones health

insurance

No Yes No

33 Supplemental private health insurance [i.e. in addition to

statutory health insurance]

No Yes No

34 Private pension plan [i.e. in addition to statutory old age

pension]

No Yes No

35 To be able to pay the rent for the apartment and/or the interest

on the house or apartment one lives in always on time

(GSOEP: no information whether due to financial or other

reasons)

Yes No Yes

36 To be able to pay the gas, water, heating and electricity bill

always on time

No No Yes

37 To be able to buy over-the-counter drugssuch as pain-

relievers or medicine against coldif you need them, even if

your health insurance does not cover the costs.

No No Yes

Note: Translation of items in PASS: official translation

All other items: translation by the author

Comments in round brackets denote variations in item wording across surveys

Comments in squared brackets: annotations by the author

496 B. Christoph

1 3
http://www.springerlink.com/http://www.springerlink.com/


23/25

Bohnke, P., & Delhey, J. (1999b). Uber die materielle zur inneren Einheit? Wohlstandslagen und sub-

jektives Wohlbefinden in Ost- und Westdeutschland. WZB working paper FS III 99-412. Berlin: Social

Science Research Center Berlin (WZB).

Brauns, H., & Steinmann, S. (1999). Educational reform in France, West-Germany and the United Kingdom:

Updating the casmin classification. ZUMA-Nachrichten, 44, 745.

Brewer, M., Goodman, A., & Leicester, A. (2006). Household spending in Britain. What can it teach usabout poverty? Bristol: Policy Press.

Brewer, M., Muriel, A., Phillips, D., & Sibieta, L. (2008). Poverty and inequality in the UK: 2008. IFS

Commentary No. 105. London: Institute for Fiscal Studies.

Bungeler, K., Gensicke, M., Hartmann, J., Jackle, R., & Tschersich, N. (2009). IAB-Haushaltspanel im

Niedrigeinkommensbereich. Welle 2 (2007/08). Methoden- und Feldbericht. FDZ Metodenreport Nr. 8/

2009. Nurnberg: Bundesagentur fur Arbeit.

Callan, T., Nolan, B., & Whelan, C. T. (1993). Resources, deprivation and the measurement of poverty.

Journal of Social Policy, 22, 141172.

Christoph, B., Muller, G., Gebhardt, D., Wenzig, C., Trappmann, M., Achatz, J., Tisch, A., & Gayer, C.

(2008).Codebook and documentation of the panel study Labour Market and Social Security (PASS).

Wave 1 (2006/2007). Volume I: Introduction and overview. FDZ Datenreport Nr. 5/2008 . Nurnberg:

Bundesagentur fur Arbeit.Clark, A. E., Etile, F., Postel-Vinay, F., Senik, C., & Van der Straeten, K. (2005). Heterogeneity in reported

well-being: Evidence from twelve European Countries. The Economic Journal, 115, C132C188.

Clark, A. E., & Oswald, A. J. (1994). Unhappiness and unemployment. The Economic Journal, 104, 648

659.

Cummins, R. A. (2000). Personal income and subjective well-being: A review. Journal of Happiness

Studies, 1, 133158.

Cummins, R. A. (2002). Subjective well-being from rich and poor. In W. Glatzer (Ed.), Rich and poor.

Disparities, perceptions, concomitants (pp. 137156). Dordrecht: Kluwer.

DAmbrosio, C., Frick, J. R., & Jantti, M. (2009). Satisfaction with life and economic well-being: Evidence

from Germany. Schmollers Jahrbuch, 129, 283295.

Desai, M., & Shah, A. (1988). An economic approach to the measurement of poverty. Oxford Economic

Papers, 40, 505522.Diener, E., & Biswas-Diener, R. (2002). Will money increase subjective well-being? A literature review and

guide to needed research. Social Indicators Research, 57, 119169.

Diener, E., & Lucas, R. E. (1999). Personality and subjective well-being. In D. Kahnemann, E. Diener, &

N. Schwartz (Eds.), Well-being. The foundations of hedonic psychology (pp. 213229). New York:

Russel Sage.

Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of

progress. Psychological Bulletin, 125, 276302.

Duncan, O. D. (1975). Does money buy satisfaction? Social Indicators Research, 2, 267274.

Easterlin, R. A. (2001). Income and happiness: Towards a unified theory. The Economic Journal, 111, 465

484.

Easterlin, R. A. (2002). The income-happiness relationship. In W. Glatzer (Ed.), Rich and poor. Disparities,

perceptions, concomitants (pp. 151175). Dordrecht: Kluwer.Ferrer-i-Carbonell, A., & Frijters, P. (2004). How important is methodology for the estimates of the

determinants of happiness? The Economic Journal, 114, 641659.

Frijters, P., Haisken-DeNew, J. P., & Shields, M. A. (2004). Money does matter! Evidence from increasing

real income and life satisfaction in East Germany following reunification. The American Economic

Review, 94, 730740.

Gerlach, K., & Stephan, G. (1996). A paper on unhappiness and unemployment in Germany. Economic

Letters, 52, 325330.

Hagenaars, A. J. M., de Voos, K., & Zaidi, M. A. (1994). Poverty statistics in the late 1980s: Research

based on micro-data. Luxembourg: Office for Official Publication of the European Communities.

Haisken-DeNew, J. P., & Frick, J. R. (2005). DTC. Desktop companion to the German socio-economic panel

(SOEP). Version 8.0. Updated to wave 21 (U). Berlin: German Institute for Economic Research (DIW).

Hallerod, B. (1994). A new approach t

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