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Categorical dependent variables:Application using WVS data from selected Arab countries
Irina Vartanova
Institute for Futures Studies, Stockholm
ERF Workshop – May 11, 2015
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Example: Income and Happiness
• Positive but diminishing association between income andhappiness (see Clark, et al., 2007 for a review)
• The association can be partially explained by reversecausation and by unobserved individual characteristics, suchas personality traits.
• Relative income is more important than actual income, besidescomparisons of relative position are made across nations.
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Variables
Taking all things together, would you say you are
• Very happy
• Rather happy
• Not very happy
• Not at all happy
1
0
On this card is an income scale on which 1 indicates the lowestincome group and 10 the highest income group in your country.We would like to know in what group your household is.
1 - Lowest group 10 - Highest group
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Data
• WVS, 6th wave
• 12 MENA countries: Algeria, Egypt, Iraq, Jordan, Kuwait,Lebanon, Libya, Morocco, Palestine, Qatar, Yemen
• Bahrain excluded
• Pool sample: 9928 after listwize deletion of missing cases
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Model Summary
Model 1 Model 2
s.income 0.267∗∗∗ (0.010) 0.252∗∗∗ (0.011)Palestine −0.308∗∗∗ (0.106)Iraq −0.786∗∗∗ (0.100)Jordan 0.367∗∗∗ (0.113)Kuwait 0.825∗∗∗ (0.135)Lebanon −0.353∗∗∗ (0.105)Libya 0.507∗∗∗ (0.103)Morocco 0.014 (0.106)Qatar 2.084∗∗∗ (0.231)Tunisia 0.011 (0.106)Egypt −2.482∗∗∗ (0.097)Yemen −0.132 (0.106)Constant −0.102∗∗ (0.047) 0.260∗∗∗ (0.089)N 14617 14617Log Likelihood −7558.220 −6399.145
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(Relatively) full model of happinessBased on the extensive review of existing factors of subjectivewell-being (Dolan et. all, 2008), we control for:
• Gender - women tend to report higher happiness.• Age squared - younger and older generations are happier.• Marital status, being married is associated with the highest
happiness and being divorced with the lowest.• Having children, the effect is mixed. Positive effect on life
satisfaction, but not on happiness. Negative consequences ofadditional children, also culturally dependant.• Health.• Education has positive effect, especially in low income
countries.• Unemployment is detrimental for happiness especially among
men.• Religiosity have positive effect on happiness.• General trust positively associated with happiness.
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(Relatively) full model of happiness - 2
Happiness
s.income 0.198∗∗∗ (0.013)female 0.307∗∗∗ (0.054)poly(age, 2)1 5.221 (3.915)poly(age, 2)2 21.006∗∗∗ (3.252)education Middle −0.057 (0.060)education High −0.055 (0.081)marital.st Divorced −0.661∗∗∗ (0.148)marital.st Widowed −0.396∗∗∗ (0.125)marital.st Single −0.366∗∗∗ (0.109)children 1 child 0.278∗∗ (0.125)children 2 or more 0.113 (0.100)to be continued
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(Relatively) full model of happiness - 3
s.health Good −0.649∗∗∗ (0.069)s.health Fair −1.765∗∗∗ (0.076)s.health Poor −2.767∗∗∗ (0.112)imp.religion Rather important −0.474∗∗∗ (0.089)imp.religion Not very important −0.895∗∗∗ (0.166)imp.religion Not at all important −1.164∗∗∗ (0.214)general.trust 0.325∗∗∗ (0.065)unemployed −0.494∗∗∗ (0.103)Female:unemployed 0.158 (0.175)Constant 1.712∗∗∗ (0.155)N 13750Log Likelihood −5302.329
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Country Effect: All Pairwise Comparisons
Pal
estin
e
Iraq
Jord
an
Kuw
ait
Leba
non
Liby
a
Mor
occo
Qat
ar
Tuni
sia
Egy
pt
Yem
en
Egypt
Tunisia
Qatar
Morocco
Libya
Lebanon
Kuwait
Jordan
Iraq
Palestine
Algeria 0.570.12
0.930.11
0.060.12
−0.600.16
0.040.13
−0.070.12
0.260.12
−1.660.24
0.140.12
2.850.11
0.540.12
0.360.11
−0.510.12
−1.160.16
−0.530.13
−0.640.12
−0.310.12
−2.230.24
−0.420.12
2.280.11
−0.020.12
−0.870.12
−1.520.15
−0.890.12
−1.000.11
−0.670.11
−2.590.24
−0.780.11
1.920.10
−0.390.11
−0.660.16
−0.020.13
−0.130.12
0.200.12
−1.720.25
0.080.12
2.790.11
0.480.13
0.630.16
0.530.15
0.850.16
−1.070.26
0.740.16
3.440.15
1.140.16
−0.110.13
0.220.13
−1.700.25
0.110.13
2.810.12
0.510.14
0.330.12
−1.590.24
0.210.11
2.920.10
0.610.12
−1.920.24
−0.110.12
2.590.11
0.290.12
1.810.24
4.510.24
2.210.25
2.700.11
0.400.12
−2.300.11
Significantly < 0Not SignificantSignificantly > 0
bold = brow − bcol
ital = SE(brow − bcol)13 / 25
Multiplicity Correction
• Since we test 66hypothesissimultaneously,around 3 of themcould be significantby chance
• There are severalways to correct formultiple testing. HereI use the Holmcorrection which setsthe α for the entireset of tests equal toαn .
Pal
estin
e
Iraq
Jord
an
Kuw
ait
Leba
non
Liby
a
Mor
occo
Qat
ar
Tuni
sia
Egy
pt
Yem
en
Egypt
Tunisia
Qatar
Morocco
Libya
Lebanon
Kuwait
Jordan
Iraq
Palestine
Algeria 0.570.12
0.930.11
0.060.12
−0.600.16
0.040.13
−0.070.12
0.260.12
−1.660.24
0.140.12
2.850.11
0.540.12
0.360.11
−0.510.12
−1.160.16
−0.530.13
−0.640.12
−0.310.12
−2.230.24
−0.420.12
2.280.11
−0.020.12
−0.870.12
−1.520.15
−0.890.12
−1.000.11
−0.670.11
−2.590.24
−0.780.11
1.920.10
−0.390.11
−0.660.16
−0.020.13
−0.130.12
0.200.12
−1.720.25
0.080.12
2.790.11
0.480.13
0.630.16
0.530.15
0.850.16
−1.070.26
0.740.16
3.440.15
1.140.16
−0.110.13
0.220.13
−1.700.25
0.110.13
2.810.12
0.510.14
0.330.12
−1.590.24
0.210.11
2.920.10
0.610.12
−1.920.24
−0.110.12
2.590.11
0.290.12
1.810.24
4.510.24
2.210.25
2.700.11
0.400.12
−2.300.11
Significantly < 0Not SignificantSignificantly > 0
bold = brow − bcol
ital = SE(brow − bcol)
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Interpretation: Odds Ratios
• Odds ratios describe the factor by which odds change as onevariable changes holding other constant. They are easilycalculated:
ebs.income = 1.22
• Interpretation: For all of the people who live in the sameMENA country, have the same gender, age, education and soon and with 5 score on subjective income - for every 100 ofthem we would expect to be happy, we would expect 122 ofthe same types of people, but who had 6 score on subjectiveincome, be happy.
• Odds ratios remain only relative comparisons due tounobserved heterogeneity (Mood, 2010).
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Alternatives: Types of Marginal Effects
• Average Marginal Effects - takes the average of the marginaleffect across all cases used to estimate the model.
• Marginal Effect at the Mean - takes the marginal effect ofeach variable holding all other variables constant at theirmean values.
• Marginal Effect at Representative values - take the marginaleffect of each variable holding all others constant atsubstantively/theoretically interesting values
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Predicted Probabilities
s.income effect plot
s.income
happ
y
0.70
0.75
0.80
0.85
0.90
2 4 6 8 10
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Model Fit, Evaluation and Comparison
• Specification tests: Wald test, Likelihood ratio test
• Pseudo-R2
• Information criteria: AIC, BIC
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Testing: Wald and Likelihood-Ratio Test
• A Wald test is base on the assumption that B N(β,V (B))and tests whether β = 0
• A likelihood-ratio test compares two models, a full one (MF )with coefficients BF and a nested model (MR) which places qlinear restrictions on the coefficients in BF :
LLR = −2(LL(MR)− (LL(MF ) χ2q
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Hypothesis Test for Subjective Income
Wald test
Res.Df Df χ2 Pr(> χ2)1 137182 13718 1 229.48 < 2.2e − 16 ∗ ∗∗
LR test
Df LogLik Df χ2 Pr(> χ2)1 32 -5302.32 31 -5420.2 -1 235.69 < 2.2e − 16 ∗ ∗∗
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Pseudo-R2
• Pseudo-R2 rely on analogous to the linear model, but none ofthem can be interpreted as the proportion of variation in thedependent variable explained by the independent variables.
• Many different types, each of them produce different results
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Several Pseudo-R2 for the Happiness Model
OLS R2 = 1− SSr esidualSStotal
Efron’s R2 = 1−∑
N(yi−πi )2∑
N(yi−y)2.308
McFadden’s R2 = 1− logL(MFull )
logL(MNull ).291
Cox & Snell’s R2 = 1−
logL(MNull)
logL(MFull)
2N
.273
Count R2 = CorrectCount
.824
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AIC and BIC
Akaike’s Information Criterion
AIC = −2log(L(Θ‖data)) + 2K
Bayesian Information Criterion
BIC = −2log(L) + Klog(n)
• Whether to use AIC or BIC depends on how much one wantsto penalize additional model parameters.
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