Edoardo Marcucci, Università di Roma Tre Amanda Blomberg Stathopoulos, Università di Trieste

1. 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 Tre Amanda Blomberg Stathopoulos, Università di Trieste

2. Outline • Study Context • Research questions • Related literature • Methodology & Data description • Econometric results • Conclusions & Future research

3. 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

4. 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.

5. 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 to 1) finding the appropriate level of analysis to understand household behaviour, 2) explore data collection methods, 3) quantify power of influence and 4) 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).

6. 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.

7. 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.

8. 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

9. Attributes

10. Data description • Sample: 53 Italian families (53 adolescents, 53 mothers, 53 fathers & 53 joint interviews)

11. Estimation Results

12. Econometric results: MNL • All var.s for each model have expected signs and are highly significant

13. Econometric results: NL (cont.d) • Test for scale differences among membertype-models, • Scale corrected with nested logit “trick”

14. Econometric results: MMNL (cont.d) • Rent & Noise non random variables • SQ, Access, Air all random variables, normal dist & significant variance • Significant improvement compared to MNL specification

15. 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?)

16. Test of representative member model (pooled vs. segment) LR pooled vs. segment – 2  [ LL (pooled) -  LL(single) ] ≥ 2df pooled - single Pooled model ≠ ∑single models

17. Individual heterogeneity? - MMNL Kernels

18. Kernel densities for βs & WTP: Family 16 (Beta SQ) only 16  0; 85% >0; This prevail for all member types! (Beta Air & Acc) are all  0; 100 % < 0;

19. Kernel densities for βs & WTP: Son (Beta ACC) 50  0; of which 100% <0; WTP (ACC) extremely high (Beta Air) 39  0; of which 98% <0;

20. (Beta SQ) lowest among all (Beta ACC) 25  0; 100% <0 Kernel densities for βs & WTP: Mother

21. (Beta SQ) worst among all, (WTP SQ) 2  0 & > 0 (!) Kernel densities for βs & WTP: Father   

22. Individual vs. Group: Polarization Analysis

23. Polarization: Status Quo

24. Concentration: Rent

25. Polarization: Accessibility

26. Concentration: Air Pollution

27. No Difference: Noise Unitary model would produce unbiased estimates only for this attribute (!)

28. Polarization & Concentration: Overview Rent: Concentrated towards mother Access: Polarized towards son SQ: Polarized towards son Air: Concentrated towards mother

29. CONCLUSIONS • At 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)

30. 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.

31. FINE Grazie per la vostra attenzione! Domande?

32. 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.

33. 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

34. Test of representative member model (Mixed vs. Multinominal) LR of MMNL vs. MNL – 2  [ LL (r) -  LL(u) ] ≥ 2df u - re ML improves MNL for all members