Correlational findings | World Database of Happiness

Study Latif (2011): study CA 1994

Public: 45+ aged adults, Canada, followed 13 years. 1994-2007
Survey name: CA National Population Health Survey: pooled waves
Sample
Respondents: N = 22040
Non Response
Assessment: Interview: Computer Assisted Personal Interview (CAPI)
every 2 years

Correlate

Authors's Label

Income

Our Classification

Remarks

T1:1994-1995, T7:2006-2007 Most respondents retired in this period

Operationalization

3 Highest
2 Upper middle
1 Low middle
0 Low (reference)

Observed Relation with Happiness

Happiness Measure

Statistics

Elaboration / Remarks

O-HL-?-sq-v-5-d

LRCD = +.94 p < .01

REGRESSION coefficient in BINARY (dichotomous) LOGISTIC analysis'

(LOGIT coefficient).

Only the sign of the computed coefficient is informative.

Happiness is a binary or dichotomous variable with Happy =1 and Unhappy=0.

LRCD < 0 indicates that the odds of being happy-to-being unhappy
decreases when

1) the corresponding metric correlate increases
2) the corresponding category of a categorical correlate is compared to the reference category.

LRCD > 0 indicates an increase in the odds for both the above cases.

CURRENT happiness by CURRENT income
Highest (vs Low)

O-HL-?-sq-v-5-d

LRCD = +.55 p < .01

REGRESSION coefficient in BINARY (dichotomous) LOGISTIC analysis'

Upper middle (vs Low)

O-HL-?-sq-v-5-d

LRCD = +.45 p < .01

REGRESSION coefficient in BINARY (dichotomous) LOGISTIC analysis'

Low middle (vs Low)

O-HL-?-sq-v-5-d

b = +.12 p < .01

REGRESSION COEFFICIENT (non-standardized) by LEAST SQUARES (OLS)

Type: test statistic
Measurement level: Correlate: metric, Happiness: metric
Theoretical range: unlimited

Meaning:
b > 0   A higher correlate level corresponds with a higher happiness rating on average.
B < 0   A higher correlate level corresponds with a lower happiness rating on average.
B = 0   Not any correlation with the relevant correlate.

Highest (vs Low)

O-HL-?-sq-v-5-d

b = +.08 p < .01

REGRESSION COEFFICIENT (non-standardized) by LEAST SQUARES (OLS)

Upper middle (vs Low)

O-HL-?-sq-v-5-d

b = +.05 p < .01

REGRESSION COEFFICIENT (non-standardized) by LEAST SQUARES (OLS)

Low middle (vs Low)

O-HL-?-sq-v-5-d

OLRC = +.59 p < .01

REGRESSION coefficient in ORDERED categorical LOGISTIC analysis.

Only the sign of the computed coefficient is informative.

Happiness is an ordered categorical variable. Higher categories correspond to being happier.

OLRC < 0 indicates that the odds of being beyond a chosen happiness category-to- be-ing at or below that category decreases when

1) the corresponding metric correlate increases
2) the corresponding category of a categorical correlate is compared to the reference category.

OLRC > 0 indicates an increase in the odds for both the above cases.

Highest (vs Low)

O-HL-?-sq-v-5-d

OLRC = +.31 p < .01

REGRESSION coefficient in ORDERED categorical LOGISTIC analysis.

Upper middle (vs Low)

O-HL-?-sq-v-5-d

OLRC = +.17 p < .01

REGRESSION coefficient in ORDERED categorical LOGISTIC analysis.

Low middle (vs Low)

O-HL-?-sq-v-5-d

b-fix = +.04 p < .01

REGRESSION COEFICIENT in fixed effects analysis

Type: test statistic
WDH symbol: b-fix
Primary correlate level: metric
Secondary correlate level: nonmetric
Happiness level: metric
Theoretical range: unlimited
Meaning:
Variant of usual (non-standardized) regression coefficient (b), which controls for the secondary variables, by focusing on differences from a fixed level, such as the mean in a category. Aims to reduce the residual variance and to improve the precision of the regression coefficient.

References:
Wikipedia: http://en.wikipedia.org/wiki/Fixed_effects_model
Non-technical text: http://www.jblumenstock.com/files/courses/econ174/FEModels.pdf

T1-T7 CHANGE in happiness by income level:
- Highest (vs Low)

O-HL-?-sq-v-5-d

b-fix = +.02 ns

REGRESSION COEFICIENT in fixed effects analysis

- Upper middle (vs Low)

O-HL-?-sq-v-5-d

b-fix = +.00 ns

REGRESSION COEFICIENT in fixed effects analysis

- Low middle (vs Low)

LRCD, b, OLRC, b-fix controlled for
- gender
- age, age squared
- marital status
- education level
- health status
- province
- lives in an urban area
- home owner

Instrumental Variable used for retirement yields similar results
- discontinuous age specific retirement incentives