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