Do fewer people mean fewer cars? Population decline and car ownership in Germany

Ritter, Nolan; Vance, Colin

doi:10.1016/j.tra.2013.01.035

Cited by 28 publications

(28 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, a smaller population and household size in Japan will lead to more car ownership. This result is similar to those of Ritter and Vance (2013) and Whelan (2007), which project that car ownership will increase in Germany and Great Britain, respectively, for the next two decades. In addition, the ratio of elderly people has various effects depending on car types.…”

Section: According To the Statistics Bureau Ministry Of Internal Affsupporting

confidence: 88%

“…de Jong et al (2004) and Ortúzar and Willumsen (2011) note that models depend highly on the data characteristics and are roughly divided into two types: the aggregate data model (e.g., Dargay, 1999Dargay, , 2002Ingram and Liu, 1999) and the disaggregated data model (e.g., Matas and Raymond, 2008;Ritter and Vance, 2013;Sun et al, 2014;Whelan, 2007).…”

Section: Model Classification From the Previous Studiesmentioning

confidence: 99%

“…The demographic factors of car ownership have been empirically investigated, especially at the disaggregate level, in the recent studies such as Matas and Raymond (2008), Ritter andVance (2013), Whelan (2007), and Sun et al (2014). Ritter and Vance (2013) and Whelan (2007) In Spain, according to the Spanish Household Budget Survey (Matas and Raymond, 2008), household car ownership was 51.2% in 1980; that number increased to 63.…”

Section: Determinants Of Car Ownershipmentioning

confidence: 99%

“…Ritter and Vance (2013) and Whelan (2007) In Spain, according to the Spanish Household Budget Survey (Matas and Raymond, 2008), household car ownership was 51.2% in 1980; that number increased to 63. 1% and 72.6% in 1990 and 2000, respectively.…”

Section: Determinants Of Car Ownershipmentioning

confidence: 99%

“…The population is projected to be 116 and 87 million people in 2030 (8.9% decrease) and 2060 (32.3% decrease), respectively. 1 Regarding the estimation of car ownership in Japan, an important question becomes whether fewer people lead to less car ownership, as is discussed in Ritter and Vance (2013).…”

Section: Population Measuresmentioning

confidence: 99%

See 4 more Smart Citations

Demographic determinants of car ownership in Japan

Yagi

Managi

2016

Transport Policy

View full text Add to dashboard Cite

This study empirically examines the demographic determinants of car ownership in Japan between 1980 and 2009. Unique car cohort data, composed of the car age and 11 car types, at the prefectural level, is analyzed. The primary reason for examining the demographic determinants of car ownership in Japan is because Japan is projected to face radical demographic changes in the next few decades.These projected changes include depopulation and an aging population with diminishing household size. This study will be the first empirical study of the car cohort model with large countrywide observations in the recent literature. This study classifies the demographic determinants into five categories: (I) longitudinal factors, (II) economic factors, (III) natural factors, (IV) social factors, and (V) other transports. Although some tendencies vary among car types, this study finds the following tendencies of ordinary car ownership (compact four-wheel drive trucks and regular and compact passenger cars). Regarding the longitudinal factors, the long-run effect is much higher than average in the recent literature, whereas the semi elasticity of car age is approximately −7%. Regarding the economic factors, the elasticities of income and fuel price on car ownership tend to be less intense than in earlier studies. Regarding the natural factors of population increase, the elasticities of population and average household size on car ownership tend to be negative. This indicates that a decrease in population and household size in Japan will accelerate car ownership. In addition, the ratio of elderly people has various effects depending on car types. Regarding the social factors of population increase, car ownership tends to be encouraged by the concentration of population within prefecture, and increased and decreased for relatively new (aged 2-11) and old (aged 12+) cars, respectively, by the concentration of population across prefectures. The former is probably due to a composite effect in urban and rural areas, whereas the latter may be a quick update cycle due to an effect of urbanization. Regarding other transports, the degrees of train and bus use tend to be negatively associated with ordinary car ownership. However, these effects are considerably small and often insignificant as in the literature.

show abstract

Section: According To the Statistics Bureau Ministry Of Internal Affsupporting

confidence: 88%

Section: Model Classification From the Previous Studiesmentioning

confidence: 99%

Section: Determinants Of Car Ownershipmentioning

confidence: 99%

Section: Determinants Of Car Ownershipmentioning

confidence: 99%

Section: Population Measuresmentioning

confidence: 99%

See 3 more Smart Citations

Demographic determinants of car ownership in Japan

Yagi

Managi

2016

Transport Policy

View full text Add to dashboard Cite

show abstract

Household car-ownership in a world of constant change: The continued influence of traditional variables and the rising influence of emerging mobility scenarios

Narayanan,

Álvarez-Ossorio Martinez,

Antoniou

2024

Transportation

View full text Add to dashboard Cite

Emerging modes (e.g., cargo bikes), mobility solutions (e.g., shared mobility services) and policy measures (e.g., the reduction of public parking spaces) are envisioned to reduce private car-ownership. However, the development of disaggregate car-ownership models dealing with them, which can be integrated with transport simulation systems, is still missing. Therefore, this study aims to fill this gap through the development of household car-ownership models, focusing on the cities of Madrid, Regensburg and Leuven. These cities correspond to distinct urban contexts in Europe. Models based on a representative individual as well as based only on household variables are explored, to check whether the latter shows congruence with the former and can achieve good summary statistics with lower data requirements. Furthermore, such models with only household-level variables are also found to be useful in ascertaining the impact of small-scale shared services. The estimation results show the influence of socio-demographic characteristics, mobility patterns, mobility tool ownership, transport supply and urban characteristics. Discussions are included on behavioural and policy insights. For example, cargo bike ownership and the presence of shared services support in car-ownership reduction. Furthermore, public parking spaces have a significant impact on single car-ownership in Madrid, but not on multiple car-ownership. Besides, the contrasting effects found for the mobility rates in Madrid and Regensburg convey the importance of urban design contexts. The contributions from this study enable to estimate the household car-ownership with the consideration of emerging mobility scenarios, and to devise policies to reduce private car-ownership and promote sustainable urban mobility.

show abstract

Spatial patterns of car sales and their socio-economic attributes in China

Liu

Song

et al. 2017

Chin. Geogr. Sci.

View full text Add to dashboard Cite

Using data from the Economic Advisory Center of the State Information Center (SIC), we examined the spatial patterns of car sales in China at the prefectural level in 2012. We first analyzed the spatial distributions of car sales of different kinds of automakers (foreign automakers, Sino-foreign joint automakers, and Chinese automakers), and then identified spatial clusters using the local Moran's indexes. Location quotient analysis was applied to examine the relative advantage of each type of automaker in the local markets. To explain the variations of car sales across cities, we collected several socioeconomic variables and conducted regression analyses. Further, factor analysis was used to extract independent variables to avoid the problem of multicollinearity. By incorporating a spatial lag or spatial error in the models, we calibrated our spatial regression models to address the spatial dependence problem. The analytical results show that car sales varied significantly across cities in China, and most of the cities with higher car sales were the developed cities. Different automakers exhibit diverse spatial patterns in terms of car sales volume, spatial clusters, and location quotients. The scale and incomes factor were extracted and verified as the two most significant and positive factors that shape the spatial distributions of car sales, and together with the spatial effect, explained most of the variations of car sales across cities.

show abstract

Do fewer people mean fewer cars? Population decline and car ownership in Germany

Cited by 28 publications

References 27 publications

Demographic determinants of car ownership in Japan

Demographic determinants of car ownership in Japan

Household car-ownership in a world of constant change: The continued influence of traditional variables and the rising influence of emerging mobility scenarios

Spatial patterns of car sales and their socio-economic attributes in China

Contact Info

Product

Resources

About