Dynamics of vehicle ownership in Singapore

Dynamics of Vehicle Ownership in Singapore By

Yunke Xiang

B.E in Urban Planning B.A in Economics Peking University (2011)

Submitted to the Department of Urban Studies and Planning in partial fulfillment of the requirements for the degree of

Master in City Planning and

Submitted to the Department of Civil and Environmental Engineering

in partial fulfillment of the requirements for the degree of

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The author here by grants to MIT the permission to reproduce and to distribute publicly paper and electronic copies of the thesis document in whole or in part in any medium now known or

hereafter created. Author

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Dynamics of Vehicle Ownership in Singapore

By Yunke Xiang

Submitted to the Department of Urban Studies and Planning on Jan 24, 2014 in partial fulfillment of the requirements for the degree of Master in City Planning for the degree of Master of Science in Transportation.

ABSTRACT

Cities around the world are trying out a wide range of transportation policy and investment alternatives to reduce car-induced externalities. However, without a solid understanding of how people behave within the constraints from these policies, it is hard to tell which of these policies are really doing the job and which may be inducing unintended problems. The focus of this paper is the determinants of vehicle ownership in the motorized city-state context of Singapore.

Using survey data from 1997 to 2008, a discrete choice model of vehicle ownership suggests that income dominates the household vehicle ownership decision. Further modeling, attempting to detect preference change over the years, suggests that the dynamics of income's influence on vehicle ownership is changing, perhaps reflecting a combination of the nation's increasingly high ownership costs and expanding transit system. All income groups have become less likely to own cars over time, with

households in the lowest income groups apparently being affected the most. For 2008, the distance to rail transit stations had a discernible relationship with households'

likelihood of owning more than one car, and accessibility and relative travel costs also influenced vehicle ownership. Including these variables, however, had very modest influence on improving model fit.

Thesis Supervisor: Associate Professor P. Christopher Zegras

Title: Associate Professor of Department of Urban Studies and Planning and Department of Civil and Environmental Engineering

Acknowledgments

This research was supported by the Singapore National Research Foundation under the SMART Future Urban Mobility Research Group. The views expressed herein are those of the author and are not necessarily those of the Singapore National Research Foundation. I thank also Singapore Land Transport Agency for sharing with me the Travel Surveys and the help and support in developing this project.

I also offer my heartfelt gratitude to:

Chris Zegras, whose dedication for land use and transportation issues has guided and shaped this project at every stage. I feel motivated and assured after seeing your effort in research and advocating the innovative solutions for problems in transportation practice. I feel grateful for your countless comments and tireless commitment to editing and revising my drafts. As my professor and advisor, you have been an inspirational and motivating force and have profoundly shaped my understanding of urban planning and transportation.

Joseph Ferreira, who has supported me with his insights and encouragement.

Yi Zhu, Jingsi Xu, Weixuan Li,who have offered generous help when I need it the most. Shan Jiang, Miguel Andres Paredes, Diao Mi, Xiaosu Ma and all the members in the long term group and SMART center for their help and support.

My friends, for brightening my life at MIT.

Mom, dad, and Yichuan for your continuous love and support that have brought me where I am.

Contents:

ABSTRACT ... 2

CHAPTER 1: INTRODUCTION ... 7

M otivation ... 7

Thesis Structure... 8

CHAPTER 2: EMPIRICAL SETTING AND RESEARCH QUESTIONS ... 9

Private Vehicle Ow nership Managem ent... 10

Additional Registration Fee ... 11

Private Vehicle Usage M anagem ent ... 13

The Off-peak Car (OPC) scheme ... 13

Public Transit Im provem ents... 15

Research Questions... 19

CHAPTER 3: M ETHODS ... 20

Prior Research... 20

Direct Precedents... 22

M ultinom ial Logit M odel ... 24

Preference Variation Test... 25

CHAPTER 4: DATA AND MODEL STRUCTURE ... 28

Data sources... 28

Basic M odel Structure ... 28

Dependent Variables ... 29

Explanatory Variables... 31

CHAPTER 5: MULTINOMIAL LOGIT MODELS... 37

1997,2004,2008 Evolution of Vehicle Ow nership... 37

Interpretation of vehicle Ownership Models of 1997,2004, and 2008... 38

2008 Model with Locational Variables and Transportation-/Accessibility Related Variables ... 46

Interpretation of 2008 Model... 49

Discussion and Implication... 51

Limitations and Further Research... 53

Conclusion... 54

Appendix:...56

References ... 59

Tables: Table 1 OPC restricted hours ... 14

Table 2 Public transport ridership ... 15

Table 3 Transit fares per ride ... 18

Table 4 Vehicle availability by ownership type... 31

Table 5 Family types definition... 33

Table 6 Variables in 1997 2004 2008 models ... 36

Table 7 Model A: Separate models for 1997, 2004 and 2008... 41

Table 8 Pooled models with different scale parameter (Model B) and same scale param eter (M odel C) ... 4 2 Table 9 Results for 1997-2004... 43

Table 10 Results for 2004-2008 ... 44

Table 11 Relative ratios with residence in HDB parameter ... 45

Table 12 Base 2008 Motor Vehicle Ownership Model with only social economic v a riab les ... 4 8 Table 13 Final model with accessibility index... 48

Figures:

Figure 1 Gini coefficient based on different methods (household income from work

including employer CPF contributions)...9

Figure 2 Vehicle fleet growth... 10

Figure 3 Mass Rapid Transit System Expansion Plan for 2030 ... 16

Figure 4 MNL Model Structure ... 29

Figure 5 Income coefficient comparison ... 45

CHAPTER 1: INTRODUCTION Motivation

Singapore is long known as a place where motor vehicle ownership and use has been tightly controlled and highly priced. As a result it has relatively low motorization rates (defined as vehicles per 1000 persons) and low growth rates in the motor vehicle fleet. Singapore is often held up as a model for others to follow. Versions of its vehicle restraint approach have even made it to China's megacities (e.g., Shanghai, Beijing).

In the past fifty years, Singapore has established a three-pillar transportation management system which is among the strictest in the world and has been regarded as effective and exemplary in addressing the land use and transportation problem: The country implemented a vehicle ownership control system in 1972, adopted congestion pricing in 1974, and has been expanding its public transit system since 1987.

The three-pillar system has curbed the rapid growth of the car fleet. From 1980 to 2004, Singapore's motorization rate grew from 63 to 100 cars per thousand people compared to Taipei's motorization rate's growth from 55 to 245 cars per thousand people in the same time period (AchARyA et al, 2007; Singapore Department of Statistics, 1980, 2004). As for public transit, the results of the 2012 Household

Interview Travel Survey (HITS) showed the percentage share of public transport trips during the morning and evening peak hours reached about 63 per cent (Land Transport Authority, 2013).

However, criticism of the current transportation policy also exists. Cheong and Toh (2010) found that Singapore's vehicle kilometers travelled per capita was still high while public transport modal share barely improved given the country's firm

transportation demand management policies. One reason that explains such results, as Barter (2013) argues, is that the objective of Singapore's congestion pricing system is not to limit traffic, rather, is to optimize speed in order to maximize traffic flow at congested times. Under such a system, users have the incentive to shift their departure

time of automobile trips and the routes they are taking, rather than shifting travel to public transportation, per se.

To better understand the behavioral impacts of Singapore's vehicle ownership and usage costs and restrictions, this thesis explores the vehicle ownership behavior among Singaporean households, addressing the question: How have vehicle ownership

patterns changed over time and have the underlying preferences towards ownership changed for different types of households. Finally, I examine the implications of these results for urban system modeling.

Thesis Structure

This thesis follows the following structure:

* Chapter 2 presents the empirical setting of the study by reviewing the transportation management policies in Singapore, including private vehicle ownership management, private vehicle usage management, and public transit improvements. It also presents the research questions.

* Chapter 3 reviews the existing vehicle ownership models for Singapore and beyond. It also presents the methods adopted in this thesis.

" Chapter 4 describes the data and the model structures. It also outlines the variables in the models.

* Chapter 5 models the determinants of household vehicle ownership decisions and the evolution of these determinants over time.

* Finally, Chapter 6 concludes with a summary of the findings, recommendations, limitations and areas for further work.

CHAPTER 2: EMPIRICAL SETTING AND RESEARCH QUESTIONS

Singapore is one of the wealthiest countries in Asia with a total population of 5.3 million and a GDP per capita of nearly US$51,000 in 2012. Measured in 1990 dollars, the average household monthly income rose from S$3080 (US$2425) in 1990 to S$4170 (US$3280) in 2000 at an average annual rate of 2.8% (Appendix Table 13). Despite its development, Singapore has among the highest income inequality in the world; its Gini Coefficient of income inequality increased from 0.454 to 0.478 from 2002 to 2012 (Key Household Income Trends, 2012) This income inequality may also induce lower vehicle ownership than would otherwise be predicted by average income levels (Gakenheimer, 1999).

-Based on Per Ilousehoki Member

Based on Modified OECD S&ale

"Based on Squate Root Sale

0.482 0.478 04544 0.473 0.47 0.471 0.472 . 0.465 0.460 0.454 0.457 0.456 0.457 0.452 0.452 .460.449 0.44g 0.4240 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Figure 1 Gini coefficient based on different methods (household income from work including employer CPF contributions)

Source: Singapore Department of Statistics

Devoted to solving housing and employment problems when the nation was founded, Singapore placed less relative emphasis on transportation system

major concern by the early 1970s. The number of private motorcars in the country doubled from 70,000 in 1961 to around 143,000 in 1974 (Appendix Table 13).

700000 140 600000 120

500000

=*=Private motor cars

Persons per prvate motor car (Vehicle/ thousand persons) Figure 2 Vehicle fleet growth

Data Source: Singapore Department of Statistics

Singapore has taken an integrated approach to manage its vehicle fleet growth, including strict control over private vehicle ownership as well as usage. Both measures were initiated in the seventies and progressively increased since to alleviate congestion. In the eighties, the country also started to develop travel options to encourage people to shift to public transit options. The following part of this chapter will introduce the three-pillars: ownership management, usage management, and provision of other travel option.

Private Vehicle Ownership Management

Singapore's vehicle ownership management has two main components: the

Registration Fee is a revenue-raising scheme, which also limits fleet growth, and the Quota System aims to control the total number of cars that can hit the street every year.

Additional Registration Fee

In 1972, the Singapore government introduced the Additional Registration Fee (ARF) as 15 percent of the Open Market Value (OMV) of the vehicle. The ARF increased to 150 percent in February 1980, and 175 percent in October 1983 (Phang et al, 1990). At least partly as a result, people tended to buy smaller cars with lower OMV to mitigate

the price effects (Tan et al, 2009). A tiered ARF rate was introduced in 2013. In the tiered scheme, a car with an OMV of up to $20,000 will be taxed at the rate of 100 per cent. The next $30,000 will be taxed at 140 per cent, and any OMV above $50,000 at 180 per cent.1 In addition to the ARF scheme, Singapore introduced the Vehicle Quota System (VQS) in 1990 (Tan.L.H, 2001).

The VQS with its auctions of Certificates of Entitlement (COEs) aims to keep the annual car fleet growth below a certain level, historically 3 percent. The quota is

determined by the actual number of vehicles taken off the roads (i.e. number of vehicles de-registered), the allowable growth in the vehicle fleet, the unused quota from last period, and adjustments arising from temporary certificates that have expired or were cancelled.2 A prospective car owner has to bid for a Certificate of Entitlement (COE) under the VQS system, which allows the vehicle to be used for 10 years.

When the VQS was first introduced, the quota licenses were transferable which gave rise to serious speculation (Tan.L.H, 2001). Since Oct. 1991, the resale of quota licenses in all categories except "goods vehicles and buses" and "open"3 was prohibited (Tan.L.H, 2001). However, once a COE is used to purchase a vehicle, it can technically be

transferred together with the vehicle, subject to restrictions.

1_{http://www.lta.gov.sg/content/ltaweb/en/roads-and-motoring/owning-a-vehicle/costs-of-owning}

-a-vehicle/tax-structure-for- cars.html

2_{http://www.lta.gov.sg/content/ltaweb/en/roads-and-motoring/owning-a-vehicle/vehicle-quota-s} ystem/overview-of-vehicle-quota-system.html.

The Vehicle Quota System has been effective in the sense that it has controlled the overall number of vehicles that have been added to the road. For example, in 2011 only around 8500 new private and company cars were added to the fleet (As shown in Table 13 in Appendix). However, it has basically made car ownership a luxury good in

Singapore.

The price of a COE is determined in a first seal bid process that happens twice a month. The price has been ever-increasing since the start of the quota system, and has boomed with the increasingly stringent cap on fleet growth in recent years. Since May

2009, when the Land and Transport Agency (LTA) lowered the vehicle growth rate from 3% to 1.5% as one of the measures to further control traffic congestion, the price of COE for type A (Cars with engine capacity 1,600cc and below) and type B (Cars with engine

capacity 1,601cc and above) vehicles has risen rapidly, from S$5000(US$4040) to more than S$80,000(US$64700). In August 2012, the vehicle growth rate was lowered to 1% and further lowered to 0.5% in February 2013. By 2013, the COE price reached above S$90,000 (US$71,208). Apart from the COE and the ARF, a registration fee of S$140

(US$110), an excise duty of 20% of the OMV and a 7% Goods and Services Tax has to be paid. All these taxes and fees easily represent 80% of the full cost to the consumer of a car. For example, Toyota Camry with an OMV of S$25,021 (US$19,700) costs

S$176,988(US$140,032) in 2013 after accounting for all the taxes and fees.4

The only fiscal measure that has lowered the price of the COE is the financing restriction that the Monetary Authority of Singapore imposed in 2013 March. The restriction limits both the loan amount and tenure. It includes a 60% maximum

loan-to-value (LTV) for vehicles with an open market value (OMV) that does not exceed $20,000 (including relevant taxes and the price of the Certificate of Entitlement). For a motor vehicle with an OMV of more than $20,000, the maximum LTV is 50%. The tenure of a motor vehicle loan is capped at 5 years. The rules took effect from 26 February

4 From the document "Cost (S$) For Cars Registered in Nov 2013" available at:

2013 and a drop of nearly S$30,000(US$24,000) in COE prices was observed for February and March.

Private Vehicle Usage Management

The vehicle usage management and pricing system in Singapore evolved in three phases: an original restriction on entering the restricted zone of the CBD; the restriction of using expressways, and the incorporation of the two systems into a single one with automated pricing measures. The ultimate goal behind levying the usage cost is to make road users aware of the externality cost of driving and incorporating those costs into the decision about whether or not to drive, the period during which to make a trip, and the route to take.

In 1975, the Area Licensing Scheme (ALS) was introduced in Singapore as the first urban traffic congestion pricing scheme to be successfully implemented in the world. As the charge for drivers entering downtown went into place, the initial drop in traffic into the CBD was 45% (Phang at el, 1990). The drop was not sustained over time, however, due to increased employment in the CBD (Phang at el, 1990). In 1995, the government implemented the Road Pricing Scheme (RPS) as a way to reduce the bottlenecks at congested expressways and arterials outside the CBD. The initial drop in traffic volumes along RPS-monitored expressways dropped by 41% from 12,400 to 7,300 vehicles while public transportation travel speeds increased by 16% (Lam and Toan, 2006). The Electronic Road Pricing (ERP) scheme, introduced in 1998, integrated the ALS and RPS systems in an automated manner.

The Off-peak Car (OPC) scheme

The off-peak car (OPC) scheme was introduced to allow more affordable vehicles for those owners willing to limit usage to off-peak periods. The scheme was first implemented on October 1994 as a substitute for the Weekend Car (WEC) scheme that was introduced in May 1991. The restricted hours for weekdays are 7.00am to 7.00pm ( OPC restricted hours). The user has the option to buy an Electronic Day License (e-Day

The OPC offers the option for people who are interested in using the car to save on registration fee at the cost of not being able to use the car in peak hours. For newly registered cars under the OPC scheme, there is an upfront rebate of

S$17,000(US$13385) off the COE and ARF in exchange for the reduced usage. For a Toyota Camry 2.0 that costs around US$140,032 in 2013 after accounting for all the taxes and fees, the rebate of OPC is almost 10% of the total cost.5 There is also an S$800 (US$629) discount per year from the normal car road tax of around S$1200 (US$945).

Table 1 OPC restricted hours

OPC scheme OPC scheme (after

2010 revision)

Weekdays (except public holidays) 7.00am to 7.00pm 7.00am to 7.00 pm Saturdays (except public holidays) 7.00am to 3.00pm No restriction Eve of New Year, Lunar New Year, Hari 7.00am to 3.00pm No restriction Raya Puasa, Deepavali and Christmas

Sundays and public holidays No restriction No restriction Source: www.lta.gov.sg

However, in practice, the OPC has had limited reach. According to the 2008

Household Interview Survey, only 1% of all households, approximately 12,000, have access to off-peak cars. Apparently few households have interest in owning cars with restrictions on time of use, suggesting most households highly value the flexibility of being able to use a car when they would like to.

To make the scheme more attractive, LTA revised it such that, from January 2010 onwards, existing cars converted to, or new cars registered under, the revised OPC scheme can enjoy unrestricted use on Saturdays public holidays ().

Public Transit Improvements

Along with the strict controls over private vehicle ownership and use, the

government has aimed to expand public transportation options including the bus, Mass Rapid Transit (MRT), and Light Rail Transit (LRT). Table 1 shows the daily passenger trips travelled on these modes. About 63 percent of the total trips made in Singapore during the AM peak hour are on public transport (Land Transport Authority, 2013). However, in its 2030 vision the Singapore government aims to have 75 percent of all journeys in peak hours undertaken on public transport (Land Transport Authority,

2013).

Table 1 Public transport ridership

Mode Average Daily Ridership ('000 passenger-trips)

MRT 2525

LRT 124

Buses 3481

Thomson Line

-~n

Region Line Line

NorthEast Line Extension Donun nLine TuisWest Extension Downtonn Line Extension Eastern Region North-South Line

D~wnwnn irwExtension -- Existing Fadines

Cinde line --. Land Tranmport Vasbwr Plan 2=0 RAI Lines

Stage 6 ---- Land Tranport Master Plan 2013 Ru Lines

Nom: LTMP 2006 Rail lines include Thomson Line. Eastern Region Line, Tuns West Extension, and North-South Line Extension.

Figure 3 Mass Rapid Transit System Expansion Plan for 2030

Buses enjoy the highest ridership in Singapore among all public transit options. There are two main bus operators in Singapore, namely, SBS Transit Ltd (the previous Singapore Bus Services) and Trans-Island Bus Services Ltd (TIBS) which took over the private bus companies that provided services in the early sixties and seventies (Phang at el, 1990). Singapore Bus Services was renamed SBS Transit in 2001, which reflects SBS's move from being just a bus operator to a provider of both bus and train services (Faishal, 2003).

The Singapore MRT system is another major component of the public transport system. In August 1987, the government created a quasi-private company,

Singapore Mass Rapid Transit (SMRT) Limited, which is owned by the Mass Rapid Transit Corporation, and which had responsibility for running the MRT system. Part of the MRT system began operation in November 1987, and the initial system, comprising 41 stations and a route length of some 66 km was completed by 1989. It has since grown rapidly into the backbone of the public transport system in

Singapore, with an average daily ridership of 2.406 million in 2011, approximately 71% of the bus network's 3.385 million. Currently, SMRT operates the North-South MRT line, East-West line (including the Changi branch), and Circle Line. SBS Transit operates the North East MRT line, Downtown MRT Line, Sengkang LRT and Punggol LRT lines (Figure 3).

MRT stations are reasonable indicators of easy public transport access to destinations within the city. The routes are designed in such a way that the placement of MRT stations provides the greatest accessibility to public

housing-the Housing Development Board (HDB) estates, which represent nearly 80% of the total housing in the country (SingStat 2013). The LTA's Land Transport Master Plan for 2013 proposes the vision of having 8 in 10 households living within a 10-minute walk from a train station.

Even though different companies run the various public transport services and modes, the fares have been integrated via the TransitLink fare card. The price of bus/MRT fare is calculated based on the distance between the origin and

destination stops/stations. Typical fares ranges from US$0.54 to US$1.54 per ride (Table 2).

Transit Link was a private company set up in November 1987 to oversee the integration of the MRT with existing bus services through the use of a common bus-rail ticket. With integration, the fares for multi-modal trips involving both bus and MRT receive discounts, giving the commuter rebates at each transaction. The EZLink card, introduced in 2002, is a contactless smart card that speeds up payment. The EZLink card quickly became the primary method of payment,

replacing the fare card (Lam and Toan, 2006). Table 2 outlines the average cost of a public transport trip. Under the distance-based fare structure, people travelling the same distance will pay the same fare for the same type of service, whether they travel directly or make transfers.

Table 2 Transit fares per ride

(Numbers within parenthesis are fares on air-conditioned buses)

Transit Service EZ Link fare (US dollars)

Maximum Average

Trunk bus service 1.35 1.07

(1.54) (1.25)

Feeder bus service 0.54

--(0.57)

Express 2.02 1.73

North-East Line and 1.74 1.40

Circle Line

North-South and 1.54 1.25

East-West Lines, and LRT

Research Questions

Singapore has certainly been at the forefront of charging, quite highly, for vehicle ownership and use in an attempt to manage motorization. Yet, as the country continues its economic growth and its residents become wealthier, private ownership and use remains in reasonably high demand. Despite the high

ownership and usage costs and extensive public transit system, car ownership and use continues increasing. For example, even though the numbers of public

transport trips has increased from 4.33 million in 2000 to 5.37 million in 2010, the current level of mode share of public transport has not increased. The mode share for public transport dropped from 63% in 1998 to 56% in 2008 but has reportedly gone back up to 63% in 2012 (Choi and Toh, 2010; Land Transport Authority, 2013).

Clearly vehicle ownership and use remain highly prized among Singaporeans. A perverse effect may be partly at work here; since the up-front cost of purchasing a car is so high, sensitivity to road pricing may be lowered due to its relatively low cost vis-A-vis the "sunk" costs in ownership. People who get a car tend to use the car extensively. To further understand the results of Singapore's evolving

transportation and motorization management policies, I aim to examine whether and how household vehicle ownership preferences have changed over time. I expect that preferences have particularly changed for lower income groups. In addition, I am to assess, given the nation's strong motorization management and high densities and extensive public transport system, the built environment and transportation levels of service factors relate to vehicle ownership. The next Chapter introduces the methods I will use in this analysis.

CHAPTER 3: METHODS

Prior Research

There is a vast body of literature available on various types of auto ownership modeling. Some literature on car ownership has focused on examining car

ownership at an aggregated level (Holtzclaw et al., 2002; Clark, 2007). These studies analyze ownership decision at a regional or zonal level, but cannot readily capture detailed household-level characteristics. On the other hand, disaggregated vehicle ownership/availability models provide much more details and policy relevant findings by examining car ownership decisions at the household level (Train, 1986; Bhat and Pulugurta, 1998; Dargay and Gately, 1999). De Jong et al. (2004) provide a comprehensive review of nine types of vehicle ownership models: aggregate time series models, aggregate cohort models, aggregate car market models, heuristic simulation models, static disaggregate ownership models,

indirect utility models of car ownership and use, static disaggregate car-type choice models, panel models and pseudo-panel models and dynamic car transactions models with models for the duration until replacement, acquisition or disposal, and with conditional type choice. In this thesis, given my objective of understanding factors related to vehicle ownership, and due to data limitation regarding vehicle prices, types and transactions, I will use disaggregate models of household vehicle ownership.

There are at least three kinds of disaggregated model structures that have been used in modeling car ownership: Multinomial Logit Model (MNL), Ordered

Response Logit (ORL) and Nested Logit (NL) models. An MNL model of vehicle ownership assumes that a household makes a one-time choice of the number of vehicles to have. An ORL applies when the choices can be assumed as incrementally taken; that is, that a household decides, first, whether to have zero or one or more vehicles. If the one-or-more alternative is selected, the household decides again to have one or two-or-more vehicles. This process continues until the households have considered all alternatives. The NL model assumes that each household considers all alternatives simultaneously, but groups some alternatives as being

more similar than others (e.g., electronic bike and motorcycle). Potoglou and Susilo (2008) have tested the different assumptions about the behavioral choice

mechanism with two different modeling structures: the ordered and unordered. The outcome showed that MNL models performed the best among all the models they tested.

Household income and demographic characteristics are the oft-identified dominant determinants of car ownership (Schimek, 1996; Kim and Kim, 2004; Zegras, 2010; Potoglou and Kanaroglou, 2008; Salon, 2009). To further understand the role of income, Nolan (2010) proposed a binary random effects model to

analyze the car ownership decision of Irish households for the period 1995 to 2001. The paper reported that fixed income exerts greater influence on the ownership level decision than current income.

As for the role of urban form and accessibility to public transportation or jobs, some clear patterns emerge from the studies. Zegras (2010), using a multinomial logit model with data for Santiago (Chile) in 2001, finds a number of transport level-of-service, relative location, and built environment variables to be related to vehicle ownership. A homogenous picture emerges in relation to public transport supply. Schimek (1996), Kim and Kim (2004), Bento et al.(2005), Potoglou and Kanaroglou(2008), Matas and Raymond(2008) and Salon (2009) provide evidence that having higher accessibility to public transit is negatively associated with the number of cars per household. Gao et al. (2008) and Chen et al. (2008) both showed that having higher accessibility to employment reduces dependence on personal vehicles. Matas and Raymond (2009) captured the effect of urban structure through a measure of job accessibility to employment by public transport and found that spatial variables play a significant role in explaining the probability of car ownership. Guo (2013) investigated the impact of residential parking supply on private car ownership with a nested logit model and found that, in a parking-scarce place like New York City, parking supply can significantly determine household car ownership decisions. The influence even exceeds the role of household income and demographic characteristics. Due to limitations of parking data availability in

Singapore, this thesis will not include a variable capturing parking supply. However, attention should be paid in the role of parking in the future studies.

One major problem of most studies is endogeneity, which in the vehicle ownership case could include simultaneity bias, whereby residential location and vehicle ownership decisions influence each other; and, omitted variable bias, whereby unobserved variables-such as attitudes-produce the ownership outcome, but these attitudes also correlate with other characteristics that may produce incorrect associations (Zegras, 2010). Cao (2013) applied the statistical control approach in a quasi-longitudinal research design and found that the Light Rail Transit (LRT) in the Minneapolis-St.Paul metropolitan does not have an independent/direct impact on auto ownership. His study suggests that the observed impact of the LRT on auto ownership is a result of residential

self-selection, meaning that those who prefer to own fewer vehicles choose to live near the LRT to better enable their vehicle ownership preferences.

Few studies have examined changing vehicle ownership behaviors over time in a disaggregate way. Zegras and Hannan (2012) examine changes in household vehicle ownership preferences from 1991 to 2001 in Santiago de Chile, using a MNL models, finding that preferences changed from 1991 to 2001, suggesting that as incomes rise and vehicle ownership becomes increasingly affordable, demographic and land-use and other contextual variables change in their influence. Most notably, the relationship between vehicle ownership and land use mix appears to weaken over time, while the distance to CBD effect strengthens, and the residential density effect varies in the apparent direction of change, depending on the vehicle

ownership category.

Direct Precedents

Few studies have focused on private car ownership in Singapore using disaggregate models. Van Eggermond, M. et al. (2012) estimated a household vehicle ownership model for Singapore using an MNL model with data from the

(roughly equivalent to the building level) in the survey. Among their findings: income increases the utility of owning a car; lower income groups tend to prefer a car more if children are in a younger age group than in a higher age group; and the number of driving licenses among household members increases the utility of owning a vehicle. Including driver license possession in the model could, however, introduce simultaneity bias in the model. They use an entropy index proposed by Cervero and Kockelman (1997) and Chu (2002) to account for neighborhood heterogeneity:

Es5 0 0 = - p

(1) In(k)

where pi is the proportion of developed land-use category in category k. In total 5 land use types were considered: business, community area and education, residential, commercial and parks within 500 meters radius of the residential postal code. Values of El vary between 0 and 1, with one indicating even

distribution among all land-use types and zero implying single type of land-use within radius of 500 meters (Van Eggermond, M. et al, 2012). Values of the entropy index close to one imply ease of access to activities and therefore the parameter of El is expected to be negative (Potoglou and Kanaroglou, 2008).

They found a higher measure of heterogeneity decreases the utility of car ownership. Apart from the household characteristics and land use characteristics, they also examined the influence of measurable costs, which may influence car owernship-car parks and Electronic Road Pricing gantries. The variable capturing whether there is a multi-story car park within 500 meters of the dwelling and distance to nearest ERP gantry proved to be insignificant. By contrast, an MRT stop within 500 meters to the dwelling and an MRT stop within 500 meters to the work location (based on the furthest distance work location among workers in the household)6 both decrease the household's utility of owning a vehicle.

6 _{A household can contain multiple working members; therefore either a choice or aggregation} has to be made on work-side points of interest. The maximum distance within a household from the work location to a MRT station was chosen.

In this thesis, I test the hypothesis of household vehicle ownership preference changes using datasets from 1997, 2004 and 2008. The models include some location-specific characteristics (e.g., distance to MRT stations) as explanatory, exogenous, variables, although in practice they may be endogenous (e.g., households may jointly choose their residence and vehicle ownership). Multinomial Logit Model

In the MNL model, decision-makers choose from a set of alternatives in the discrete choice framework. Each choice can be characterized by a number of attributes. Each attribute contributes to the utility of the choice. The overall utility of a choice i to individual n is defined by a deterministic and a stochastic part:

Uin = Vin + ein (2)

Vin is the systematic indirect utility expressed as a function of observable variables with a vector fik of taste parameters and the vector Xinof attributes of

the choice,

in =1 f W ink (3)

and Ein as a random utility component.

An individual n will select the choice with the highest utility Uin from among the options in the choice set

Pn(i) = Pr(Uin > Un, V] E Cn, * i) = _{Pr(Vin + Ein Vn + Ein, V] E Cn, ,] i)} = Pr(§cn Vin -Vn + Ein, V] E Cn, J i)

=Pr(Ein ! Vin - Vn + Ein, V E CJ # i) (4)

Letting f(Ein, E2n, ---,

'jcn)

terms and considering alternative i to be the first alternative in the choice set, then:

Pa(1)

f

2n, Ejn) d Ejn dEjnIn de1 n

f~1n=-oo fE2n=-oo inn=--~

(5)

The MNL is the most commonly used discrete choice model due to its ease of estimation and simple mathematical structure (McFadden, 1974). It is based on the assumption that the random terms, often-called error terms or disturbances, are identically and independently (i.i.d.) Gumbel distributed. The choice probabilities equal to:

pj)= eVinV (6)

ZjECn e 6

Assuming Uin = Vin + Ein, for all i E Cn, and that the disturbances Cin are

i.i.d. Gumbel-distributed with a parameter and a scale parameter i > 0, then

P,(j)= el v (7) EjECn e Avj

Preference Variation Test

As discussed in the previous chapter, my interest is in comparing vehicle ownership preferences over time: we want to compare models from three different time periods, 1997, 2004 and 2008. However, given that the models are estimated

on samples and the particular form of the MNL model, care must be taken in

comparing models across time to ensure that the observed differences from model estimation do not come from inherent variances in the underlying data (i.e., surveys). When comparing parameters from models in different times, one must account for the possible differences in error component variances. As noted by Swait and Louviere (1993), failure to do so confounds differences in scales (or magnitudes) of model parameters due to error variance differences with real differences in model parameters. To be more specific, the scale parameter y, in Equation 7, cannot be identified and is arbitrarily assumed to be one. This

assumption will not have an effect on the utility order within one dataset. Between datasets, however, the values of the scale parameters may differ significantly, implying variability across the datasets. Even though i cannot be identified in a

As an example Arnold et al (1983) reported variation in choice model parameters over time and space in retailing, without considering the variation in scale parameters. Severin et al (2001) point out that Arnold et al's (1983) result was problematic because the retail choice model parameters for two time periods (say 1997 and 2008) would appear to differ if their variance scale ratio was not equal to 1; that is, p1997 = 02008, p1997 * 12008. Variance scale ratio differences

suggest that differences in research contexts over time and space drive differences in the effects of unobserved factors on choices, not the differences in effects of common observed factors (Severin et al, 2001).

In our case, we want to test whether underlying preferences have changed over time, utilizing models estimated on a sample taken at different points in time. For example, for the 1997 and 2008 time periods, we want to know whether the model estimation parameters are different, which would indicate underlying vehicle ownership behavioral changes from 1997 to 2008, as reflected in the different magnitude/significance of the same socio-economic and policy variables in the models.

Following the methods suggested by Severin et al (2001) and taking the years 1997 and 2008 as examples, I will firstly estimate unrestricted MNL models to allow different parameters in each data set as Model A. I then restrict the

parameters to be identical (W1997 = 02008) but allow different variance-scale ratio parameters for each data set (14997 * 112008) and estimate Model B. Models A and B

will be compared using the likelihood ratio test statistic: -2[LN g=1 LN_(g)],

the distribution of which is chi square. The degrees of freedom equal the difference in the number of parameters between Models A and B.

If the likelihood ratio test statistic is not significant, it leads to the conclusion that the model allowing different parameters (unrestricted) has less explanatory power than the restricted model (Model B). In such a case, we could not reject the assumption that 01997 = P2008. The scale parameter ratio we get from estimating Model B can be close to 1, which indicates a similar dataset context. It can also be different from 1, which may be due to the unobserved factors that lead to more

variance in one dataset. If, after taking differences in random component variances between the two data sources into account, the common parameters in the indirect utility function do not differ significantly then the underlying choice process revealed is stable across the time periods.

If the likelihood ratio test statistic is significant, it leads to the conclusion that the unrestricted model, allowing different parameters, has more explanatory power than the restricted model. Therefore, we can reject the assumption that P1997 =

P2008. The scale parameter ratio we get from estimating Model B won't indicate the real ratio because P1997 # 12008. In this case, we can further explore the reasons for the statistically significant differences across the two dataset by comparing individual coefficients between the two-year segments. Since there is only one scale parameter for all the coefficients in each data set, if we observe that the coefficients from 2008 are not proportionally different from the coefficients for 1997, we should know the real mechanism has changed across years. For example, if

1distance,1997

sincome,1997

cannot know the real number of y in this case, we still can know that the underlying choice mechanism is not stable across the time periods because the coefficients' change are not proportional.

CHAPTER 4: DATA AND MODEL STRUCTURE

The data I use come from several sources. The Household Interview Travel Survey 2008 (HITS) and the Household Travel Surveys from 1997 and 2004 are used as the foundation. The survey represents some 1% of the total households of Singapore. In these surveys, carried out for LTA, randomly selected households are questioned on their travel behavior for a single workday. The 2008 survey contains 10,641 households, the 2004 survey contains 9500 households, and the 1997 survey contains 7019 households.

The limitation of the available travel surveys is that they do not indicate whether the household actually owns the vehicle or whether it is just available to them temporarily; we also do not know when they purchased the vehicle nor any details of the vehicle (e.g., make, model, etc.). I cannot, therefore, include aspects such as the COE price into the model. Additional data for model estimation include: network travel times and costs from the origin destination matrices provided from a model run by the Singapore Land Transport Authority (LTA) and land use data from the Urban Redevelopment Authority (URA).

Basic Model Structure

Household Attributes

Locational Variables for the

Accessibility

Figure 4 MNL Model Structure

Dependent Variables

Instead of just looking at whether a household has a car or not, I examined various alternatives of vehicle holding, The different combinations of household vehicle portfolios, based on 2008 HITS, 2004 and 1997 HIS, can be found in Table 5. The number of vehicle holding alternatives to be included in the vehicle ownership models is dependent on the numbers of households choosing each reported vehicle availability level. Eighteen different combinations of ownership are included in the table. Due to the constraints of the survey data, only eight of the combinations have consistent data from 1997 to 2008. I consider the following four ownership

categories for the model estimation: " No motorized vehicle;

* One normal car; " Multiple normal cars;

" Other vehicles (off-peak cars, light -good etc.)

Vehicle

The survey data suggest little change in the share of households with no motor vehicle available in the three years studied, with 49% in 1997 and 2008 and 52% in 2004. Similarly, the share of one- and two-car owning households has varied little over the 20 years, a relatively astounding stasis in the international context in the face of Singapore's economic growth and clearly a product of the motorization management path the country has taken. Motorcycle ownership has similarly remained relatively static.

Another thing worth noticing is the rate of off-peak car access. In 2008, 1% households had access to an Off-peak car. Among the Off-peak car owning households, 8% of them have another normal car.

Table 3 Vehicle availability by ownership type

1997 2004 2008

Type of Vehicle Available No Vehicle

1 Normal car (only) Motorcycle (only) 2+ Normal cars

Heavy/Light Goods Vehicle Taxi

Normal car+ Motorcycle Off Peak car

Normal car+ Heavy/light Goods Vehicle (2004 van) Off Peak car + Motorcycle Normal car+ Taxi

Heavy/Light Goods Vehicle+ Motorcycle (2004 van) Taxi +Motorcycle

Normal car + Off Peak car Rental

Off Peak car+ Taxi

Normal car+Off Peak car+ Motorcycle

Other Sub Total of

All Motorized Vehicles The Expanded Total

49 31.8 6.4 4.3 4.5 1.4 1 0.9 0 0.1 0.2 52 29.3 6.6 4.7 4.6 1.1 1.1 0.3 0 49.4 30.8 5 4.6 3.2 1.6 1.1 1 0.6 0.3 0.3 0.1 0.1 0.1 0.1 0 0 0.4 50.6 7020 0.3 47.7 1.7 48.9 1004132 1143718

1, Source: 1997 HIS, 2004 HIS, 2008 HITS.

2, 2004,2008 data are expanded with the household expansion factors in each survey. 1997 data is unexpanded.

Explanatory Variables

The following variables were considered in the estimation data set for the vehicle ownership model. Each variable was tested for inclusion in the final model based on how well each explains the observed household choice behavior, and based on statistical considerations.

Household Variables

values, using CPI published by Singapore Department of Statistics .7 In the surveys, income was reported as a categorical variable; to allow for the comparison between years, nine categories for monthly household income (in 2008 Singapore dollars) are defined: Not reported, SG$0, SG$0-1486, SG$1486-3308, SG$3309-4128, SG$4129-5749, SG$5750-7540,

SG$7541-10446, and above SG$10446.

* The number of children under 14 years old in the household measures the potential need for the purchase of vehicles to take children to school or other activities. A vehicle offers the possibility of dropping and picking up children at school and otherwise economizing on family trip-making.

* The number of workers in the household measures the potential need for purchase of vehicles for home-based work trips.

e Family "type" aims to capture the life cycle of the household and its potential implications for the purchase of vehicles. Eight family types have been defined incorporating information about number of children under 14 years old, number of workers, and number of members older than 60: Childless multi-worker family, Childless single-working family, Nuclear family, Extended family, Worker and retired family, Grandparents family, Retired family, and others. It is expected that worker and retired family, retired family may have higher utility of a vehicle because private vehicles offer better travel experience for the senior members of the families (Table 4).

Table 4 Family types definition

Family types Description

Childless multi-worker family More than 1 worker, no kids, no retired members Childless single-worker One worker, no kids, no retired members

Nuclear family Kids below 14 and workers, no retired members

Extended Family Three generation family

Worker and retired family Workers and retired members

Grandparent family Kids below 14 years and family member older than 60, but no workers

Retired family Only retired members

Others Others

Locational Variables for the Residence

* Dwelling unit type (Whether HDB) measures the effect of dwelling type on ownership. HDB residential complexes are designed with food courts and shopping and services, therefore the people residing there may have less need to drive to malls or restaurants.

Travel Time and Cost Variables

* The ratio of travel time using private vehicle versus public transit measures the relative attractiveness of using a car for home to work trips versus using public transit. The values are derived using the TAZ to TAZ travel time estimates in the morning rush hours (7:30-9:30) from a network model simulation result provided by LTA and using the household's residential location and each working family member's reported work location.

* The ratio of travel cost using private vehicle versus public transit measures the relative out-of-pocket costs of using car for home to work trips versus using public transit. The private car usage cost is the ERP charge for

home-based work trips in the morning for all working family members using highway. The total cost for public transit is from the simulated assignment of the best public transit paths, taking into account both MRT/LRT and buses. Again, both values are measured at the TAZ-to-TAZ level and are provided by

Accessibility Variables

* Household proximity to an MRT station approximates the local

accessibility/convenience of public transport. Two types of measurements have been used: the distance to the nearest MRT station and dummy variables indicating distance less than 200 meters, 200-400 meters, 400-600 meters, 600-800 meters, 800-1000 meters, and above 1000 meters.

* A gravity-based measure of automobile accessibility from the home location represents the potential ease of accessing opportunities across the island. In total, six types of opportunities were used in the calculation:

Manufacturing, office, retail, hotel, port& airport and education institution. A high accessibility value implies ease of access to these opportunities and therefore the parameter estimates for the accessibility indicator are expected to be negative (i.e., more accessible places, all else equal, are associated with reduced vehicle ownership). Singapore's Urban Redevelopment Authority (URA) provided the data. I use Hansen's (1959) gravity-based measure of accessibility.

Aipv - 0 1 (8)

f(Cijpv)

Where

Aipv is accessibility at the home zone i to jobs at zone j using private

vehicle

Oj is the number of jobs at zone

j

f(C1pv) The impedance or cost function to travel between zone i and

zone

j

for 8 < ci; < 35, f(Cijpv) = 287 - 287(Cijpv - 168)/(360 - 168) (10)

for 35 < cij < 168, f(Cijpv) = 0.04040723(Cijpv - 6)4

-3 2

3.4860416(Cjjv - 6) + 117.3 2876(Ci jp - 6)

-2026.192(Cijpv - 6) + 19874.2 (11)

for ci > 168, f(Cijpv) = 0.00002639(Cijpv 32)4

-0.01031252(Cijpv - 32)3 + 1.516426(Cijpv - 32)2 - 106.161(ijy

Table 5 Variables in 1997 2004 2008 models

1997 2004 2008

No Vehicle 53.0 56.4 49.8

Type of Vehicle 1 Normal car (Any) 35.5 33.3 36.9

Available for

Housbefod 2+ Normal car 4.6 4.1 5.4

,Household Other Vehicle 6.9 6.2 7.9 income=O 9.1 8.1 0.0 Income <1496 15.0 11.9 7.2 1486-3305 14.6 21.9 19.6 Household 3309-4128 17.8 11.0 11.3 Income 4129-5749 6.2 8.5 14.4 Category 5750-7540 8.9 11.2 12.8 7541-10446 7.8 3.5 9.6 >10446 7.8 8.6 8.9 No response 12.7 15.4 16.2 0 Worker 18.7 17.1 6.5 #Worker 1 Worker 45.4 41.3 49.1 2 Worker 27.6 29.1 36.5 >3 Worker 8.3 12.5 7.9 0 Child below 14 68.7 59.9 63.7

N Children 1 Child below 14 18.6 21.8 19.8

Below 14 years 2 Child below 14 10.1 15.0 13.1

>3 Child below 14 2.6 3.3 3.4

Retired family 5.0 5.6 2.0

Extended Family 1.7 5.6 6.0

Grandparent family 6.3 6.5 2.2

Childless multi-worker family 16.8 19.0 20.4

Type of Family Childless single-worker 24.0 15.0 19.9

Worker and retired family 15.4 14.9 19.5

Nuclear family 23.3 27.9 28.1

Other 7.5 5.6 2.3

Housing Household Living in HDB 83.0 89.0 78.0

Mean (Standard Deviation) 0.59

Travel Cost Ratio of total travel time of private vehicle versus transit -- - (0.18)

Ratio

0.46

Ratio of total travel cost of private vehicle versus transit -- -- (0.73)

754 710

Distance to the nearest MRT _{- (484) (7)}

3696

Gravity accessibility 3696

T o t s

7 2 7 ( 1 2 8 7

CHAPTER 5: MULTINOMIAL LOGIT MODELS

This section presents the model estimation results. First, I examine how preferences may have evolved from 1997 to 2008. Then I present the 2008 models with additional details on households' residential location, which may influence

ownership tendencies. Such models could not be estimated for the earlier years because of data limitations, specifically the lack of land use data and transportation system performance measures.

1997, 2004, 2008 Evolution of Vehicle Ownership

To understand whether household preferences for vehicle ownership have changed, under the combination of increasing ownership costs and expanding supply of alternatives, I first estimated unrestricted MNL models to allow different parameters in each data set (Table 6) of the three years. Then, I restricted the parameters to be identical but allowed for different variance-scale parameters for the data from the three different years (Model B, in Table 7 Pooled models with different scale parameter (Model B) and same scale parameter (Model C)

). We can compare Models A (unrestricted) and Model B (restricted) using the likelihood ratio test, as described above. The result is that the unrestricted model

differs significantly from the restricted models at greater than 99% confidence level; the vehicle ownership choice processes are not stable across the three years.

Model C (in Table 7 Pooled models with different scale parameter (Model B) and same scale parameter (Model C)

) allows us to test whether we can combine the three data sets into a single model. A test of the likelihood ratio of the pooled and scaled models (B and C) of three years shows that the difference is significant at the 95% confidence level, so the pooled model differs from rescaled models, suggesting that we can reject the hypothesis of equal variance scale ratios of three years.

Table 9). In both cases, the unrestricted models outperform the restricted models at the 95% confidence level, with 2004 to 2008 models having more significant improvement. This indicates that the choice preference changes happen both in the

1997-2004 period, as well as in 2004-2008 periods. But the latter period witnessed bigger preference change. The model Cs (Table 8, Table 9)allow us to test the hypothesis of equal variance scale ratios. It turns out that for the 1997-2004 model, the pooled model doesn't differ from rescaled models, suggesting we cannot reject the hypothesis of equal variance scale ratios from 1997 to 2004, however in 2004-2008, such hypothesis can be rejected.

To sum up, compared using the likelihood ratio test, for both 1997-2004 and 2004 -2008 periods, the models with different choice parameters for each year (Model A), outperforms the models which restricts the parameter estimates to be the same across years (Model B); hence the choice preferences have changed over time, after allowing for variance-scale differences.

To compare the differences across the relevant components of the various alternatives utility function across time, I choose a common significant variable and divide each parameter in the respective utility functions by the chosen variable's parameter estimate. Doing so provides us a relative ratio that is comparable given the different scale parameters (Table 1011). Ideally this variable would be for a continuous variable so that that would demonstrate a tradeoff, but in our case, we don't have such a variable. I have chosen the residence in HDB as the variable and divide each parameter by the absolute value of it here. Even though it is not theoretically meaningful, it allows us to keep all the income estimates and thus be able to compare them. The results of the comparison can be found in the following section.

Interpretation of vehicle Ownership Models of 1997,2004, and 2008

A closer look at the data reveals that the socio-demographics and dwelling type variables are statistically significant and have the expected signs across three years.

Income

The effect of increase in income is consistent with other studies. Looking at model result from 2008, households with less than S$5750 income are less likely to own any normal cars at all. Households earning more than S$7541 are more likely to own a car or equally possible to own two cars, households in income group S$10446 have the largest probability to have two or more cars.

From an evolution perspective, as shown with the comparable ratios in Table 10, almost all income groups have lower utility of having any car in 2008 as compared to 1997 and 2004. Also, as can be seen in Figure 5, the 2008 curves for one car and two or more cars are steeper than the curves from 1997 and 2004, in other words, the gaps in low income groups are larger than the gaps in high income groups. This indicates that, while the likelihood of vehicle ownership for all income groups has been declining, the lower income groups are becoming even less likely to own a car from 2004 to 2008. Such effect was not as clear in 1997 to 2004. This result confirms with our conclusion that the mechanism of decision-making is changing from 1997, 2004 to 2008 because otherwise we should observe a proportional shift of coefficients due to the variance-scale difference or no shift at all.

Family types

As for family types, the likelihood of having a car increases as the family moves to the later stage of the life cycle chain: Childless families are less likely to own vehicles, while grandparent families and retired families are more likely to own a vehicle or even two. This finding contrasts Kim and Kim's (2004) study of the States. They find that coupled households are likely to own more automobiles than both senior household and single household. Children in a household are found not to be a significant indicator of household automobile ownership.

A closer look at the evolution of the effect of family types shows that in 1997, grandparents families also are more likely to own a vehicle, however the retired

families, without the presence of the children are less likely to own a vehicle. The 2004 model shows similar results.

The presence of children is a significant indicator of automobile ownership in Singapore. The presence of children increases the likelihood of owning a vehicle for all types of families in three years. For example, if we compare the effect of nuclear family versus childless multi-worker family, we can see that across the three years, the presence of children in the nuclear family makes it more likely to own a vehicle than the childless multi-worker family or single-worker family.

Dwelling

Households residing in HDB estates have lower utility of owning a vehicle as compared to household residing in condominiums or landed property across the three years. Dwelling type here may provide an indication of amenities in the

residential area. Households residing in HDB may have better access to amenities than households residing in the private apartment or landed property because of the mixed-use development.

Distance to MRT

The farther away from the nearest MRT station, the higher the utility to the household of owning one or more cars in 2004. However, as the MRT network becomes more ubiquitous across the island, the strength of the relationship

between household auto ownership and distance to MRT is apparently weakening. In 2008, distance to the nearest MRT station only significantly relates to the household's utility of owning a second car.

Table 6 Model A: Separate models for 1997, 2004 and 2008 Variables Constant Income <1486 1486-3308 3309-4128 4129-5749 5750-7540 7541-10446 10446 Household residing In HDB Etended Family Grandparent family

Childless mufti-worker family

Retired family

Childless single-worker

Worker and retired family Nuclear family Distance to MRT * of estimated parameters. Number of observations Final log-Bkelhooda Adjusted rho-square 1997 2004

1 Car 2+Cars Other 1 Car 2+Cars Other

Beta t-test 0.461 3.55 -1.59 -11.29 -0.859 -7.03 0.405 4.16 0.578 4.25 0.933 7.82 L46 11.3 1.76 11.11 -1.14 -12.73 0.375 1.38 0.504 3.22 -0.381 -2.79 -0.965 -5.05 -0.058 -0A4 -0.604 -4.22 0.304 2.37 Beta -0.89 -1.96 -1.74 -0.367 0.279 0.163 119 2.28 -2.A 1.13 0.59 -0.217 -0.701 0.071 0.034 -0.023 48 6465 -5560 0.374

t-test Beta t-test Beta t-test Beta t-test Beta

-3.34 -2.37 -8.65 0.264 1.62 -1.36 -4.38 -2.97 4.14 -0.364 -2.06 -1.65 -13AS -3.31 -4.58 0.408 -3.56 0.158 0.95 -0.732 -8.78 -1.78 -5.96 0.538 1.44 0.038 -0.22 -0.206 -2.13 -0.976 -3.19 0.662 0.8 0.19 0.8 0.162 L62 -0.513 -1.82 0.376 0.58 0.092 0.39 0.751 8.01 0.329 156 0.268 4.44 0.103 0.37 1.13 7.58 0.967 3.49 0.138 9.78 0.229 -0.52 L49 12.69 1.95 10.48 -0.153 -14.97 1.1 3.63 -1.3 -12.58 -2.35 -16.08 0.592 2.49 -0.532 -0.97 0.563 3.42 0.87 2.39 -0.035 1.71 0.7 3.04 0.752 4.69 0.762 2.09 0.126 -0.2 -0.386 1.69 -0.065 -0.46 0.062 0.19 -0.192 1A3 42 -3.86 -0.927 -5.17 -1.2 -2.58 -1.17 0.24 -0A1 -1.97 0.314 2.15 0.437 1.23 -0A4 0.11 -0.299 1.37 0.053 0.37 0.271 0.8 -0.579 -0.08 0.095 0.47 0.58 424 0.383 118 0.041 0.256 4.53 0.648 6.5 0.048 t-test -7.33 2.4 3.39 3.75 1.82 1.23 0.35 -0.41 1.68 -0.13 0.51 -0.86 -3A4 -1.94 -2A9 0.2 0.46 51 8657 -7417 0.378 2000

1 Car 2+Cars Other

Beta t-test Beta t-test Beta

1.73 7.69 -0.008 -0.02 -1.14 -3.5 -14.43 -10A -62.52 -14A2 -1.3 -17.76 -3.53 -6.37 -0.078 -0.89 -8.71 -2.23 -5.57 0.092 -0.253 -2.74 -1.15 4.71 0.052 0.09 0.95 -0.211 -1.08 0.003 0.52 4.84 0.436 2.37 -0.417 1.3 9.46 1.77 9.64 0.363 -1.06 -13.28 -2.23 -16.01 .0.164 -0.649 -2.79 -0.305 -0.75 -0A23 0.499 166 0.733 1.54 0.269 -1.08 -5.04 -0.951 -2.58 -0.53 0.189 0.62 0.668 137 0.427 -1.06 4.94 -1.34 -3A8 -0.871 -0.904 -4.2 -0.401 1.09 -0.338 -0.541 -2.56 -0.607 -1.67 0.2 0.119 1.88 0.562 5.63 0.158 51 7971 -7015 0.361 t-test -3.26 -5.37 -0.52 0.57 0.32 0.02 -1.73 137 -1.07 1.21 0.58 1.68 0.86 -2.75 -2.64 -0.65 159