Edoardo Marcucci, Università di Roma Tre Amanda Blomberg Stathopoulos, Università di Trieste
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Transcript of Edoardo Marcucci, Università di Roma Tre Amanda Blomberg Stathopoulos, Università di Trieste
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XI Riunione Scientifica Annuale - Società Italiana di Economia dei Trasporti e della Logistica “Trasporti, logistica e reti di imprese: competitività del sistema e ricadute sui territori
locali”, Trieste, 15-18 giugno 2009
Individual and triadic preferences in a choice experiment on
housing location: preference heterogeneity and relative power
Edoardo Marcucci, Università di Roma TreAmanda Blomberg Stathopoulos, Università di
Trieste
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Outline
Study Context Research questions Related literature Methodology & Data description Econometric results Conclusions & Future research
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Study context
“Standard welfare and demand theory is based on individual preferences, and
modern theoretical analysis of household behaviour is based on the rejection of the
notion that households may be regarded as unitary decision makers rather than groups
of individuals (Becker).”
Quiggin. J., (1998) “Individual and Household Willingness to Pay for Public Goods”, American Journal of Agricultural Economics, Vol. 80, No. 1, pp. 58-63
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Research questions Given that household location choices are
taken jointly we control for:
attribute-specific preference heterogeneity among three members
(if relevant heterogeneity exists) who influences the family choices the most (at the attribute level)
potential polarization in collective choices This leads us to estimate the potential bias
compared to using the conventional (unitary) approach.
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Related literature Since the 1980s, the shortcomings of a “black box” approach
where the household is the basic unit of analysis have been exposed.
Joint and Individual preferences fail to “coincide” in numerous empirical tests regarding risk avversion, financial allocation, environmental WTP, labour choices, consumption of durables (car, vacation, housing) and activity patterns*.
A growing body of research is dedicated to1) finding the appropriate level of analysis to understand household behaviour,2) explore data collection methods,3) quantify power of influence and4) consider preference and IPS heterogeneity between members of a decision making unit.
* Arora & Allenby 1999, Corfman 1991, Dalleart 1998, Bateman & Munro 2003, Dosman & Adamowicz 2006, Hensher et al 2008, Beharry-Borg et al 2009, Marcucci et al (in press).
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Main contributions of current study
Adopting a triadic approach as opposed to the universally used dyadic one (i.e. couple based analysis),
Considering the child/adolescent as a decision maker in the household choice,
Focusing on hypotheses testing rather than a definition of a GUF,
Concentrating on attribute level influence patterns,
Controlling for polarization in household choice of residential location.
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Methodology We study household interaction via stated
choice experiments (single vs. joint interviews), Katz (1997), Manski (2000).
Household members were first asked to perform the choice experiments singularly and were stimulated to choose according to their personal preferences
Subsequently, after grouping together three family members, encouraging them to discuss and, then, choose a collectively acceptable housing alternative.
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Methodology (cont.d) Stated choice experiments:
Two stage Conjoint
Design: 4 attributes (31 * 42 * 51) Orthogonal Full profile Fractional factorial (240 sets = 16 rept. 15 blocks) 4 holdout questions (2 monotonicity / 2 stability)
Model specifications MNL, MMNL, Individual-specific MMNL
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AttributesAttributes Levels Description of discrete level
Rent Level 1 20 % lower than current Level 2 10 % lower than current Level 3 Same as current Level 4 10 % higher than current Level 5 20 % higher than current
Noise Level 1 Quiet house Level 2 Low level of noise Level 3 Quite noisy Level 4 Very noisy
Air emissions Level 1 Very low level of emissions Level 2 Acceptable level of emissions Level 3 Quite high emissions Level 4 Very high emissions
Accessibility Level 1 50% Less time to reach work/school Level 2 Same distance as currently Level 3 50 % more time to reach work/school
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Data description Sample: 53 Italian families (53 adolescents,
53 mothers, 53 fathers & 53 joint interviews)Variables Unit of measurement Sample value
Age µ years Mother (50), Father (54), Adolescent (22)
Family size µ (min-max) 3,6 (3-6)
Travel by car % full sample 50%
Travel time µ time in minutes Mother (19), Father (23), Adolescent (20)
Sex % female 48%
Income % in income bracket €30.000 - €60.000 59%
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Estimation Results
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Econometric results: MNL JOINT INDIVIDUAL PREFERENCES POOLED FAMILY SON MOTHER FATHER Beta t-ratio Beta t-ratio Beta t-ratio Beta t-ratio Beta t-ratio SQ 0,943 14,44 1,096 8,94 1,153 9,65 0,818 7,15 0,893 8,09 Rent -0,008 -15,18 -0,009 -9,44 -0,008 -8,96 -0,009 -9,62 -0,006 -7,77 Acc -0,073 -13,18 -0,104 -9,21 -0,107 -9,68 -0,066 -6,42 -0,056 -6,82 Air -0,748 -15,50 -0,839 -9,03 -0,573 -7,29 -0,886 -9,80 -0,852 -9,73 Noise -0,476 -9,17 -0,535 -5,29 -0,513 -5,59 -0,534 -5,65 -0,396 -4,49 Summary statistics Obs 1908 646 646 646 646 LL* -1156,2 -350,3 -386,1 -362,7 -389,3 LL(c) -1743,5 -573,7 -575,4 -593,1 -574,2
2 0,337 0,389 0,329 0,388 0,322 2 adj 0,336 0,387 0,326 0,386 0,319
• All var.s for each model have expected signs and are highly significant
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Econometric results: NL (cont.d)
Scale corrected estimates FAMILY SON MOTHER FATHER Beta t-stat Beta t-stat Beta t-stat Beta t-stat
SQ 0.7172 7.2 0.5421 8.2 0.4571 5.1 0.4068 6.0 Rent -0.0069 -9.6 -0.004 -8.9 -0.0066 -9.9 -0.0036 -7.6 Acc -0.0688 -8.3 -0.0506 -9.2 -0.0387 -5.3 -0.0276 -5.9 Air -0.9471 -12.1 -0.4588 -10.3 -0.958 -13.1 -0.722 -13.1
Noise -0.2417 -3.1 -0.1871 -3.9 -0.2249 -3.3 -0.1522 -2.9 Scale 1 0,676 0,925 0,724 Summary statistics LL* -2314.894 LL(c) -3726.493 McFadden Pseudo R-squared
.725
• Test for scale differences among membertype-models,• Scale corrected with nested logit “trick”
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Econometric results: MMNL (cont.d) FAMILY SON MOTHER FATHER var beta t-ratio beta2 t-ratio2 beta3 t-ratio3 beta4 t-ratio4 Rent (non r) -0,014 -8,48 -0,009 -8,11 -0,014 -8,76 -0,009 -7,32 Noise(non r) -0,983 -5,97 -0,771 -6,21 -1,155 -6,85 -0,745 -5,41 SQ (r.n) 1,427 5,04 1,150 5,35 1,179 3,79 1,140 4,42 SQ (st dev) 1,610 5,89 1,096 4,38 1,940 6,27 1,554 5,77 ACC (r.n) -0,163 -6,58 -0,151 -6,15 -0,122 -5,02 -0,111 -5,74 ACC(st dev) 0,051 1,95 0,065 2,97 0,070 2,66 0,056 3,1 Air (r.n) -1,957 -7,68 -1,131 -6,38 -1,693 -8,39 -1,674 -8,09 Air (st dev) 0,777 4,34 0,698 4,29 0,449 1,84 0,668 4,15 Summary statistics LL* -287,505 -348,225 -300,531 -325,775 LL ( c) -698,717 -698,717 -698,717 -698,717 Rho2 0,467 0,367 0,468 0,414 Rho2 adj 0,463 0,363 0,464 0,410
• Rent & Noise non random variables• SQ, Access, Air all random variables, normal dist & significant variance• Significant improvement compared to MNL specification
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Econometric results: daily WTP & WTA (cont.d)
• Similarity in results between model specifications• Coefficients have expected signs• Extremely high WTP for accessibility for the son (walking mode?)
MNL scale corr FAMILY SON MOTHER FATHER SQ (Ū/level) -3,46 -4,52 -2,31 -3,77
Accessibility (Ū/hour) 19,94 25,30 11,73 15,33 Air pollution (Ū/level) 4,58 3,82 4,84 6,69
Noise (Ū/level) 1,17 1,56 1,14 1,41
MMNL FAMILY SON MOTHER FATHER SQ (Ū/level) -3,33 -4,08 -2,79 -4,17
Accessibility (Ū/hour) 22,74 32,15 17,23 24,37 Air pollution (Ū/level) 4,56 4,01 4,00 6,13
Noise (Ū/level) 2,29 2,73 2,73 2,73
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Test of representative member model (pooled vs. segment)LR pooled vs. segment – 2 [ LL (pooled) - LL(single) ] ≥ 2
df pooled - single
Var iables Pooled Son Mother Father LL* -1156,226 -386,083 -362,749 -389,316
Test Statistic LR=-2* [(LL(pooled)-Sum LL(single)] 36,16
Number of Restrictions 15 5 5 5
Critical Chi-Squared Va lue at 95 % Confidence 25,00 11,07 11,07 11,07
H0 rejected? Yes
Pooled model ≠ ∑single models
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Individual heterogeneity?- MMNL Kernels
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Kernel densities for βs & WTP: Family
Kernel density per E [b_S Q|*, normale]
BSQN1
.052
.104
.157
.209
.261
.0000 2 4 6-2
Kernel dens ity es timate for BSQN1
Dens
ity
Kernel density per E [b_ACCAS S |*, normale]
BACAN1
2.65
5.30
7.96
10.61
13.26
.00-.2250 -.2000 -.1750 -.1500 -.1250 -.1000 -.0750 -.0500 -.0250-.2500
Kernel dens ity es timate for BACAN1
Den
sity
K ernel density per E [b_INQ_ATM|*, normale]
BIAN1
.15
.30
.45
.59
.74
.00-3.00 -2.50 -2.00 -1.50 -1.00 -.50 .00-3.50
Kernel dens ity es timate for BIAN1
Dens
ity
Kernel density per E [WTP _ACAF1|*, normale]
ACAF1
.038
.076
.115
.153
.191
.0004 6 8 10 12 14 162
Kernel dens ity es timate for ACAF1
Dens
ity
K ernel density per E [WTP _IATAF1|*, normale]
IATAF1
.0021
.0043
.0064
.0086
.0107
.000025 50 75 100 125 150 175 200 2250
Kernel dens ity es timate for IATAF1
Dens
ity
(Beta Air & Acc) are all 0;100 % < 0;
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(Beta SQ) only 16 0; 85% >0;This prevail for all member types!
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Kernel densities for βs & WTP: Son
K ernel density per E [b_S QF|*, normale]
BSQN2
.089
.179
.268
.358
.447
.000-.50 .00 .50 1.00 1.50 2.00 2.50 3.00 3.50-1.00
Kernel dens ity es timate for BSQN2
Dens
ity
Kernel density per E [b_ACCASS F|*, normale]
BACAN2
1.89
3.78
5.67
7.55
9.44
.00-.250 -.200 -.150 -.100 -.050 .000-.300
Kernel dens ity es timate for BACAN2
Dens
ity
Kernel density per E [b_INQ_ATMF|*, normale]
BIAN2
.17
.33
.50
.66
.83
.00-2.00 -1.50 -1.00 -.50 .00 .50-2.50
Kernel dens ity es timate for BIAN2
Dens
ity
Kernel density per E [WTP _ACAF2|*, normale]
ACAF2
.018
.036
.054
.072
.090
.0005 10 15 20 25 300
Kernel dens ity es timate for ACAF2
Dens
ity
K ernel density per E [WTP _IATAF2|*, normale]
IATAF2
.001691
.003275
.004859
.006443
.008027
.000107-50 0 50 100 150 200 250-100
Kernel dens ity es timate for IATAF2
Den
sity
(Beta ACC) 50 0; of which 100% <0; WTP (ACC) extremely high
(Beta Air) 39 0;of which 98% <0;
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Kernel densities for βs & WTP: Mother
K ernel density per E [b_S QM|*, normale]
BSQN3
.046
.091
.137
.183
.229
.000-2 0 2 4 6 8-4
Kernel dens ity es timate for BSQN3
Dens
ity
K ernel density per E [b_ACCASS M|*, normale]
BACAN3
1.93
3.86
5.79
7.72
9.66
.00-.2250 -.2000 -.1750 -.1500 -.1250 -.1000 -.0750 -.0500 -.0250-.2500
Kernel dens ity es timate for BACAN3
Dens
ity
Kernel density per E [b_INQ_ATMM|*, normale]
BIAN3
.22
.43
.65
.86
1.08
.00-2.500 -2.250 -2.000 -1.750 -1.500 -1.250 -1.000 -.750-2.750
Kernel dens ity es timate for BIAN3
Dens
ity
Kernel density per E [WTP _ACAF3|*, normale]
ACAF3
.027
.054
.082
.109
.136
.0002 4 6 8 10 12 14 160
Kernel dens ity es timate for ACAF3
Dens
ity
Kernel density per E [WTP _IATAF3|*, normale]
IATAF3
.0030
.0061
.0091
.0122
.0152
.000075 100 125 150 175 20050
Kernel dens ity es timate for IATAF3
Dens
ity
(Beta ACC) 25 0; 100% <0
(Beta SQ) lowest among all
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Kernel densities for βs & WTP: Father
Kernel density per E [b_ACCAS S P |*, normale]
BACAN4
2.25
4.50
6.75
9.00
11.25
.00-.200 -.150 -.100 -.050 .000 .050-.250
Kernel dens ity es timate for BACAN4
Den
sity
K ernel density per E [b_INQ_ATMP |*, normale]
BIAN4
.15
.30
.45
.60
.75
.00-2.50 -2.00 -1.50 -1.00 -.50 .00 .50-3.00
Kernel dens ity es timate for BIAN4
Dens
ity
K ernel density per E [WTP _ACAF4|*, normale]
ACAF4
.021
.042
.063
.084
.105
.0000 5 10 15 20 25-5
Kernel dens ity es timate for ACAF4
Dens
ity
K ernel density per E [WTP _IATAF4|*, normale]
IATAF4
.001452
.002872
.004293
.005714
.007135
.0000310 50 100 150 200 250 300 350-50
Kernel dens ity es timate for IATAF4
Dens
ity
K ernel density per E [b_S QP |*, normale]
BSQN4
.049
.098
.147
.196
.245
.000-2 -1 0 1 2 3 4 5-3
Kernel dens ity es timate for BSQN4
Den
sity
(Beta SQ) worst among all,(WTP SQ) 2 0 & > 0 (!)
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Individual vs. Group:Polarization Analysis
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Level of individual and joint preferences for SQ
0,3
0,4
0,5
0,6
0,7
0,8
0,9
Individual Joint
SQ
Child Mother Father Family
Polarization: Status Quo
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Concentration: RentIndividual and joint preferences for Rent
-0,008
-0,007
-0,006
-0,005
-0,004
-0,003
-0,002Individual Joint
Rent
(€)
Child Mother Father Family
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Polarization: AccessibilityIndividual and joint preferences for Accessibility
-0,08
-0,07
-0,06
-0,05
-0,04
-0,03
-0,02
-0,01Individual Joint
Acce
ss ti
me
to w
ork/
scho
ol (m
in)
Child Mother Father Family
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Concentration: Air PollutionIndividual and joint preferences for Air pollution
-1,1
-1
-0,9
-0,8
-0,7
-0,6
-0,5
-0,4
-0,3
Individual Joint
Leve
l of A
ir po
llutio
n
Child Mother Father Family
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No Difference: NoiseIndividual and joint preferences for Noise
-0,35
-0,3
-0,25
-0,2
-0,15
-0,1
-0,05
Individual Joint
Leve
l of N
oise
Child Mother Father Family
Unitary model would
produce unbiased estimates only for
this attribute (!)
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Polarization & Concentration: Overview
Individual and joint preferences for Rent
-0,008
-0,007
-0,006
-0,005
-0,004
-0,003
-0,002Individual Joint
Rent
(€)
Child Mother Father Family
Individual and joint preferences for Accessibility
-0,08
-0,07
-0,06
-0,05
-0,04
-0,03
-0,02
-0,01
Individual Joint
Acce
ss ti
me
to w
ork/
scho
ol (m
in)
Child Mother Father Family
Individual and joint preferences for Air pollution
-1,1
-1
-0,9
-0,8
-0,7
-0,6
-0,5
-0,4
-0,3
Individual Joint
Leve
l of A
ir po
llutio
n
Child Mother Father Family
Level of individual and joint preferences for SQ
0,3
0,4
0,5
0,6
0,7
0,8
0,9
Individual Joint
SQ
Child Mother Father Family
Access: Polarizedtowards son
Air: Concentratedtowards mother
Rent: Concentratedtowards mother
SQ: Polarizedtowards son
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CONCLUSIONSAt the individual level:
• We have detected relevant attribute-specific heterogeneity among members thus casting doubt on the representative member hypothesis (e.g. air pollution is considered differently by all members).
Comparing individual to household choices:• We have shown that different members have varying degree of influence in joint decisions for housing, (e.g. mother heavily influences for rent; son dominates accessibility)• we have discovered statistically significant polarization in collective choices (Status quo and accessibility)
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FUTURE RESEARCH will focus on:
Capturing heterogeneity in its various forms through advanced model specifications, such as: ML with heteroschedasticity in the variance of the parameters; Error components creating correlations among utilities of different alternatives,
The decision making process including different strategies for information processing (IPS) among members/groups,
Comparing the relative explanatory power of continuous (MMNL) or discrete (LC) mixing functions to discover latent groups once choice invariant variables (eg. Socio-economics and IPS) are introduced in group based models,
Explore cost-efficient and simplified data-collection methods to study group choices and test their robustness.
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FINE
Grazie per la vostra attenzione!
Domande?
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Research question (general) Is there empirical evidence to
question the unitary decision model? If so, what can we do to avoid biased
estimates? How can we model interaction within
groups? Especially, how do we measure
relative power among members.
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Methodology (cont.d) Discrete choice models RUM framework Different model specification:
MNL MMNL Individual-specific MMNL
Estimates produced Attribute coefficients and WTP Individual specific attribute coefficients and
WTP
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Test of representative member model (Mixed vs. Multinominal)LR of MMNL vs. MNL – 2 [ LL (r) - LL(u) ] ≥ 2
df u - re
ML improves MNL for all members
Variables Family Son Mother Father
MNL LL* -350,285 -386,083 -362,749 -389,316
MMNL LL* -287,505 -348,225 -300,531 -325,775
Number of Restrictions MNL 5 5 5 5
Number of Restrictions MMNL 8 8 8 8
Test Statistic LR=-2* [(LLr - LLu)] 125,56 75,72 124,44 127,08
Critical Chi-Squared Value at 95 % Confidence 7,81 7,81 7,81 7,81
H0 rejected? Yes Yes Yes Yes