STABILITY OF HAPPINESS IN A CHANGING SOCIETY

A LATENT GROWTH ANALYSIS ON A ROMANIAN PANEL DATA

Sergiu Bălţătescu

University of Oradea

Fifth ISQOLS Conference

July 20-24.2003 Frankfurt

Recent & challenging issues Recent & challenging issues in quality of life researchin quality of life research

Stability of happiness

Sources of changes in subjective well-being over time

Quality of life in developing and transition countries

Types of stabilityTypes of stability

in societal levels– stability of aggregate levels of happiness

(mean, variance) (Andrews, 1991), (Diener, 1994), (Veenhoven, 1994)

in structure or correlates (Andrews, 1991), (Bryant, 1982).

at the individual level: relative or absolute

Stability at the individual levelStability at the individual level

absolute, that is in measured levels.– This approach (using scores of of change)

was criticized for its low reliability and for influences of mood biases. (Headey & al., 1995).

in rank-orders (or relative stability)– defined in terms of absence of “mobility

along the happiness ladder” (Ehrhardt & al., 2000) (Headey, 1992)

Stability of happiness Stability of happiness (i.e. in rank-order)(i.e. in rank-order)

Two conflicting theories:Happiness is traitlike

Happiness is statelike

Support for traitlike theorySupport for traitlike theory The theory was forwarded by Stones (1995)

(Costa, 1987) Individual differences in happiness are more important in influencing happiness levels and explain greater variance than situational effects.

(Lykken, 1996) Based on the retest of smaller samples of twins after intervals of 4.5 and 10 yrs, it is estimated that the heritability of the stable component of subjective well-being approaches 80%

Support for statelike theorySupport for statelike theory

Veenhoven (1994, 1998) “happiness is quite stable for the short term, but not in the long run, neither relatively nor absolutely”

Implications of the theoriesImplications of the theories

If happiness is a trait (i.e. at a fixed level - relative or absolute), then utilitarian social policies designed to improve well-being cannot meet their end. (Veenhoven, 1994)

Methods of testing stabilityMethods of testing stability

Correlational studies

SEM-based approaches

Correlational studiesCorrelational studies

most frequently used estimation of the correlation of happiness between

two points in time “The reported correlations are typically in the

realm of +.50, which is usually interpreted as evidence for high stability” (Ehrhardt & al, 2000)

“Classical” Pearson P criticized for being too sensible at the change in levels

Tetrachoric correlation recommended.

SEM-based approachesSEM-based approaches

rather recently used First use in QOL research by (Headey & al., 1985) Structural Equations are combinations of

regression, factorial & reliability analysis They use latent variables and take into account the

residual variance (due to errors) (Maruyama, 1998) Recent models: (Ehrhardt & al., 2000) (Saris, 2001)

SEM-based approaches for SEM-based approaches for panel data: crosslaged panel data: crosslaged

modelsmodels (markovian models,

autoregressive crosslaged models)

Critiques:– 1. are fixed effects

models– 2. the mean structure

is ignored– change between 2

points is independent of earlier changes (Curran, 2001)

Hap1 Hap2 Hap3

E1

1

E2

1

E3

1

Pred1 Pred2 Pred3

Ep1 Ep2 Ep3

1 1 1

Critiques of the crosslaged models Critiques of the crosslaged models as applied to happiness dataas applied to happiness data

1. the fixed effects model– it is unreasonable to think that the same determination

mechanism works for all individuals

2. the mean structure is ignoredhappiness data are organized in trends. the slope may be ascending/descending, steep or smooth,

different from one group to another

3. the change between 2 points is independent of earlier or latter changesthe effects of predictors for happiness changes are additive

and on a rather “longue duree”

SEM studies SEM studies Ehrhardt, Saris Ehrhardt, Saris & Veenhoven& Veenhoven, 2000, 2000

Data: German Socio-Economic Panel (11 yearly waves, 1984-1994, people aged 18 years, 5483 respondents).

Year-to-year correlation started at +.45 and increased gradually to +.54.

The correlation between the first and later reports declined through the years: the correlation between the first and the third report varied between .+.38 and +.53

SEM studies SEM studies Ehrhardt, Saris Ehrhardt, Saris & Veenhoven& Veenhoven, 2000, 2000

Two models: 1. life-satisfaction of a person depends mainly on

turns in life 2. life-satisfaction depends also on the person's

stable stocks (body, personality and social position)

Using a SEM model, authors fitted both models.

SEM-based approaches for panel SEM-based approaches for panel data: Latent Growth Curvesdata: Latent Growth Curves

alternative model for the analysis of change Introduced by Meredith & Tisak (1990) They started analyzing trajectories of individual

change over time. The SEM model they introduced was designed to

smooth (they call it “tuckerize”) curves, to estimate the continuos trajectory that gave rise to the time specific observed measures.

Latent Growth Curves Latent Growth Curves (The SEM model)(The SEM model)

Two latent factors: The stable component (the intercept).

– its value is the initial mean value of the level of the studied variable

The changing component (the slope).– its value is the mean value of the slope of the trajectory of

the studied variable

Intercept and slope – may or not be correlated– have different predictors

Latent Growth Curves Latent Growth Curves (The SEM model specification)(The SEM model specification)

The regression coefficients of intercept to observed time variables is set to a constant (say 1)

The regression coefficients of the slope factor to observed variables is linearly increased (0,1,2,3…)

If the slope is supposed to be non-linear, then the regression coefficients are set a non-linear trend (ex: 0, 1, 2, 4, 8) or remain unspecified (0, a, b, c, 1)

ICEPT SLOPE

Y1

1 0

Y2

1 1

Y3

1 2

e

E1

1

e

E2

1

e

E3

1

Latent Growth Curves Latent Growth Curves (Applications)(Applications)

In general psychology developmental processes

– reading skills (Aunola & al., 2002)– self-concept, self-efficacy (Duncan & al., 1993)– antisocial behaviour (Duncan, 2002) (Li & al., 2000)– adolescent substance use (Duncan & al., 1994)

experimantal designs (Muthen & Curran, 1997).In psychological well-being studies changes in positive/negative affect (Charles & al., 2001) stressor-strain relationship (Garst & al., 2000) enhancing psychological well-being of elderly (Li & al.,

2001)

Latent growth curvesLatent growth curvesin well-being modelsin well-being models

Benefits of the method identifies the stable and the changing components

of well-being evaluates the correlations between those two. identifies the predictors of the stable component

(psychological factors like personality) and the predictors of the change (life events)

permit evaluations of different groups (sex, age, clinical, non-clinical, social status, etc) intercept and slope.

A latent growth curves model A latent growth curves model for the German data for the German data

Data were reanalyzed from the covariance matrices and mean included in (Ehrhardt & al., 2000). We used 6 waves (1984-1989).

Data shows a slight decreasing trend.In a variant of the model, I set only the first and the last

regression coefficients from the slope to observed variables, letting the SEM program to estimate the others, as recommended, among others, by Curran (2001).

We used AMOS 4.0 (Arbuckle, 1999)A more complicated design, recommended by (Anderson,

2000)

ICEPT SLOPE

0

y1

1b1

0

y2

1b2

0

y3

1

b3

0

y4

1

b4

0

y5

1

b5

0

y6

1

b6

0; 1

E1

ev1

0; 1

E2

ev2

0; 1

E3

ev3

0; 1

E4

ev4

0; 1

E5

ev5

0; 1

E6

ev6

0; 1

Dev ICEPTSD_ICEPT

0; 1

Dev SLOPESD_SLOPE

Cov_Icept_Slope

Latent growth modelGerman life satisfaction (1984-1989)

Two models:Two models:Linear decreaseLinear decrease

Full LGMFull LGM

7,51

ICEPT

-,40

SLOPE

0

y1

1,00,00

0

y2

1,00,27

0

y3

1,00

,53

0

y4

1,00

,80

0

y5

1,00

1,02

0

y6

1,00

1,00

0; 1,00

E1

1,52

0; 1,00

E2

1,49

0; 1,00

E3

1,38

0; 1,00

E4

1,35

0; 1,00

E5

1,27

0; 1,00

E6

1,33

0; 1,00

Dev ICEPT1,42

0; 1,00

Dev SLOPE-1,12

,44

Latent growth modelGerman life satisfaction (1984-1989)

Fit indexes:Linear

decrease Full LGMChi-square169,29 124,374P 0 0Normed fit index 0,998 0,999Relative fit index 0,998 0,998Incremental fit index 0,998 0,999Tucker-Lewis index 0,998 0,998Comparative fit index 0,998 0,999

Intercept mean: 7,51Intercept SD: 1,42Slope Mean: -0,401Slope SD: 1,114

Correlation SLOPE-INTERCEPT = 0,44

A latent growth curves model A latent growth curves model for the German data for the German data

Mean happines in West Germany (1984-1989)

7,54

7,33 7,34

7,18

7,11 7,10

7,51

7,40

7,30

7,19

7,10 7,11

7,0

7,1

7,2

7,3

7,4

7,5

7,6

1984 1985 1986 1987 1988 1989

Observed Predicted (latent growth with free parameters)

Happiness levels in transition Happiness levels in transition and developing countriesand developing countries

levels are lower giving the poor socio-economic conditions.

Happiness structure in Happiness structure in transition and developing transition and developing

countriescountries

income levels are higher determinants than in other countries.

bottom-up and top-down effects included

Other socio-economic Other socio-economic determinantsdeterminants

macroeconomic instability

concern for unemployment(Graham, 2001)

frustrated achievers (Graham, 2002)

all included, it is suggested that a higher instability of happiness will be found in rapid changing societies

Case study: Romania Case study: Romania november 1996-1999november 1996-1999

1997 was the beginning of a new electoral cycle

dissapointing new government actions

economic and social mood indicators dropped.

economic & social indicatorseconomic & social indicatorsFigure 3. Real GDP (1989=100)

Source (RIQL database)

0

20

40

60

80

100

120

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

economic & social indicatorseconomic & social indicatorsFigure 8. Poverty rate in Romania (1995-2000)

(Source: Zamfir,C.(coord.), 2001)

0

5

10

15

20

25

30

35

40

45

50

1995 1996 1997 1998 1999 2000

Poverty in Romania

The life satisfaction scaleThe life satisfaction scale

Used in ‘Diagnosis of quality of life’ survey program (ICCV, 1990-1999)

Sample: national, random, around 1200 cases

A 5-point simple life satisfaction scale: “Considering the whole situation, how satisfied are you about your daily life?a. Very unsatisfiedb.Unsatisfiedc.Neither unsatisfied, nor satisfiedd.Satisfiede.Very satisfied”

See: Mărginean (1991), Zamfir (1992)See also: www.iccv.ro

Life satisfaction in RomaniaLife satisfaction in Romania(1990-1999)(1990-1999)

Source: Diagnosis of Quality of life

Data were liniarly transformed to 0-10 scale

After 1990, the mean is under the median line

0

1

2

3

4

5

6

Quality of life panel 1996-1998Quality of life panel 1996-1998

representative sample of 700 subjectsanalysis of dropouts shows no significant

differences with the panel.

Quality of life panel 1996-1998Quality of life panel 1996-1998Trends in life satisfaction answers

Romanian Quality of Life Panel 1996-1998

0

5

10

15

20

25

30

35

40

1996 1997 1998

very unsatisfied

unsatisfied

nor... nor...

satisfied

very satisfied

Nonresponse

Correlation tableCorrelation table

LS96 LS97 LS98LS96 Pearson Correlation 1 0,315 0,346LS97 Pearson Correlation 1 0,30LS98 Pearson Correlation 1

N 694 694 694Mean 2,84 2,80 2,70

LGM MODELLGM MODEL

Additional constrains has to be imposed to fit the model:

- equality of the variances of the residual terms

It was used the AMOS option to fit the non-positive covariance matrices

A poor fit resultedThere are no additional constraints

to be imposed to increase the fit of the model

,31

ICEPT

,00

SLOPE

LS96

1,00 ,00

LS97

1,00 1,00

LS98

1,00 2,00

,63

E1

,63

E2

1

,63

E3

1

,00

1

DiscussionDiscussionCorrelational studyCorrelational study

Atypical correlation

They not decrease over time as predicted by Erhardt & al.

Has lower levels than in Germany (and also in other advanced countries)

Correlational studyCorrelational studyPossible causes:Possible causes:

Methodological explanations:– Coarse (ordinal level) measurement used.– It is a less reliable measurement– The coarse correlation coefficients are usually

lower

Correlational studyCorrelational studyPossible causes:Possible causes:

Theoretical :– Macroeconomic instability produce large

variations in the happiness levels.– “Frustrated achievers”, as Graham (2001)

found in other developing countries

Limitations of the methodLimitations of the method

Minimal conditions Semi-interval scales

(over 4 points) Three or more waves in

the panel For 3 vawes, restrictions

to be imposed are often unacceptable from the theoretical point of view

Optimal conditions

Semi-interval scales (over 11-200 points)

More than three waves in the panel (applicable for German Socio-Economic Panel, Russian Panel, etc.)

ReferencesReferences

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Anderson, E. 2000. "Fitting a latent growth curve model." AMOS FAQ 4. Statistical Services FAQ http://www.utexas.edu/cc/faqs/stat/index.html.

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Arbuckle, James L. 1999. "Amos." Chicago: SmallWaters Corp. Argyle, Michael and Benoit Giroux. 1997. "The effect of

environmental variables on happiness/L'effet des variables environnementales sur le bonheur." Revue Quebecoise de Psychologie 18:75-98.

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