{"title":"汽车均价对城市私家车保有量的影响——基于面板数据分析的研究","authors":"Hongtai Yang, Jingying Wang, Yinan Lin, Guocong Zhai, Xiaohan Liu, Siyu Tao","doi":"10.1109/ICTIS.2019.8883721","DOIUrl":null,"url":null,"abstract":"In order to study the effect of average car price on city-level private car ownership in China, a panel data of 204 target cities in China for the period of 2006 to 2015 is collected and investigated. Nine variables including average car price to average income ratio (ACP/AI), economic characteristics, urban characteristics, and transportation characteristics of cities are selected as potential explanatory variables. Pooled regression model, fixed effect model and random effect model are adopted and compared by fitting the panel data. The results of the Hausman test indicate that the fixed effect model fits the data better. Gross domestic product per capita, population density, highway density, per capita area of urban road, number of taxis per 10,000 population and number of buses per 10,000 population have significantly positive effects on private car ownership while ACP and has significantly negative effects on private car ownership. Among them, ACP/AI which reflect the proportion of car prices in people’s purchasing power has the highest coefficient. When ACP/AI decreases by 1%, the city-level private car ownership increases by 0.590%, while other variables are controlled. This finding could provide reference for policy makers to make a better balance between controlling for the car ownership in a city by adjusting the car price and ensuring GDP and tax collection from vehicle industry as a certain level.","PeriodicalId":325712,"journal":{"name":"2019 5th International Conference on Transportation Information and Safety (ICTIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Effect of Average Car Price On City-Level Private Car Ownership: A Study Based On Panel Data Analysis\",\"authors\":\"Hongtai Yang, Jingying Wang, Yinan Lin, Guocong Zhai, Xiaohan Liu, Siyu Tao\",\"doi\":\"10.1109/ICTIS.2019.8883721\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to study the effect of average car price on city-level private car ownership in China, a panel data of 204 target cities in China for the period of 2006 to 2015 is collected and investigated. Nine variables including average car price to average income ratio (ACP/AI), economic characteristics, urban characteristics, and transportation characteristics of cities are selected as potential explanatory variables. Pooled regression model, fixed effect model and random effect model are adopted and compared by fitting the panel data. The results of the Hausman test indicate that the fixed effect model fits the data better. Gross domestic product per capita, population density, highway density, per capita area of urban road, number of taxis per 10,000 population and number of buses per 10,000 population have significantly positive effects on private car ownership while ACP and has significantly negative effects on private car ownership. Among them, ACP/AI which reflect the proportion of car prices in people’s purchasing power has the highest coefficient. When ACP/AI decreases by 1%, the city-level private car ownership increases by 0.590%, while other variables are controlled. This finding could provide reference for policy makers to make a better balance between controlling for the car ownership in a city by adjusting the car price and ensuring GDP and tax collection from vehicle industry as a certain level.\",\"PeriodicalId\":325712,\"journal\":{\"name\":\"2019 5th International Conference on Transportation Information and Safety (ICTIS)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 5th International Conference on Transportation Information and Safety (ICTIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTIS.2019.8883721\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 5th International Conference on Transportation Information and Safety (ICTIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTIS.2019.8883721","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Effect of Average Car Price On City-Level Private Car Ownership: A Study Based On Panel Data Analysis
In order to study the effect of average car price on city-level private car ownership in China, a panel data of 204 target cities in China for the period of 2006 to 2015 is collected and investigated. Nine variables including average car price to average income ratio (ACP/AI), economic characteristics, urban characteristics, and transportation characteristics of cities are selected as potential explanatory variables. Pooled regression model, fixed effect model and random effect model are adopted and compared by fitting the panel data. The results of the Hausman test indicate that the fixed effect model fits the data better. Gross domestic product per capita, population density, highway density, per capita area of urban road, number of taxis per 10,000 population and number of buses per 10,000 population have significantly positive effects on private car ownership while ACP and has significantly negative effects on private car ownership. Among them, ACP/AI which reflect the proportion of car prices in people’s purchasing power has the highest coefficient. When ACP/AI decreases by 1%, the city-level private car ownership increases by 0.590%, while other variables are controlled. This finding could provide reference for policy makers to make a better balance between controlling for the car ownership in a city by adjusting the car price and ensuring GDP and tax collection from vehicle industry as a certain level.