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3. LAS VOCES SOLITARIAS
In the travel diary survey, several socioeconomic features are collected for each individual and household, which include: the residential registration status, the occupation, the education level, the annual income of the household, the vehicle ownership, the housing condition and the housing property right. Each of these features (except for vehicle ownership) does not seem to be explicitly linked with travel behaviour. However, the overall socioeconomic well-being indicated by some of these features may exert an influence on daily travel, which may be a combined effect of budget, pressure, life style and so on. Therefore, an effort is made in creating an indicator of overall socioeconomic well-being for the households in the data set. The well-being is analysed at the household-level instead of individual-level considering the dependency among household members.
3.3.1 Methods
Latent class analysis (LCA) is applied to stratify the sample households into different levels of socioeconomic well-being. LCA identifies unmeasured class membership from multiple observed characteristics. The number and the sizes of classes are taken as unknown. It is similar to standard cluster analysis techniques in that the goal is to form segments. However, LCA is more preferable for this task for the following reasons. First, LCA assumes the existence of a latent variable that induces spurious relationships among the observed variables rather than just looking for similarities (Hagenaars & McCutcheon, 2002). This corresponds to the notion that there is a latent social class
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membership that links to the differences in various socioeconomic features. Second, LCA is similar to standard cluster analysis techniques in that the allocation of objects to clusters should be optimal according to certain criteria. However, the choice of the criterion is more arbitrary in standard cluster analysis (Hagenaars & McCutcheon, 2002), such as a distance measure that is arbitrarily chosen. Other advantages of LCA include providing a probabilistic estimate of object class membership and being more flexible in terms of the data type. Due to these advantages, LCA is becoming a more popular clustering tool (Hagenaars & McCutcheon, 2002).
When specifying the LCA model, different numbers of latent classes and different combinations of variables are tested and compared. The best fitting model is selected based on performance indicators which include Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood and G-square fit statistics (Linzer & Lewis, 2011). Models with low AIC, BIC, G-square fit and a high log-likelihood are preferred. The interpretability of the identified classes are also considered in the model selection process (Byles et al., 2016; Lanza, Flaherty, & Collins, 2010). The analyses is performed using R package ‘poLCA’ (Linzer & Lewis, 2011).
According to prior studies, factors related to socioeconomic differentiation in urban China include income, occupation, education level, housing condition and so on (Logan, Bian, & Bian, 1999; Wu & Li, 2005). Based on these findings and the data availability of the transport survey, the following variables are considered in the latent class analysis. - Housing condition: Housing property accounts for nearly 70% of the total assets
of Chinese households (Li, Luo, Lu, Deng, & Gan, 2016). The housing-price-to- income ratio was up to 15 in Beijing by 2010 (Tan & Zhao, 2012). Therefore, the socioeconomic well-being of a household is largely determined and reflected by their housing condition. Four variables related to housing condition are considered and tested in the analysis: housing type, housing floor area, housing floor area per capita and the market value of the property. The market value of the property is
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estimated from the housing price data obtained from the largest real estate website in China, Fang.com.
- Car ownership: Despite the fast increase in car ownership in the post-reform era, the rate of car ownership was still only 25% in Beijing by 2010 (Beijing Transportation Research Center, 2011). Therefore, car ownership may also be considered as a representation of a household’s socioeconomic capability.
- Education: Higher education usually indicates a better income and occupation (Bian and Logan, 1996) and higher social status (Wu & Li, 2005). Two relevant variables are considered in the analysis: the average education level of all adult household members and the highest education level among all adult household members.
Table 3-4 Variables considered in LCA
Variables Values
Housing floor area (square meters, sqm)
<50, 50~75, 75~100, 100~150, 150~200, >200
Housing floor area per capita (sqm)
<10, 10~20, 20~30, 30~40, 40~50, 50~60, 60~70, >70
Housing type Old one-floor housing, affordable housing, matchbox housing, commercial housing Housing market value
(million RMB)
<200, 200~300, 300~400, 400~600, >600
Car ownership No car, one car, more than one cars
Highest education in the household No education, primary school, secondary school, high school, technical school, junior college, bachelor, post-graduate
Average education in the household No education, primary school, secondary school, high school, technical school, junior college, bachelor, post-graduate
3.3.2 Results
By testing all possible combinations of these variables and different numbers of clusters, it is found that the model performs best (produces the lowest AIC, BIC, G-square fit statistics and the highest log-likelihood) when the class number is three. The
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combination of variables that produce the best performance are the highest education in the household, car ownership, housing market value and housing floor area. The three classes are labelled as ‘the best-off’ (Stratum 1), ‘the middle class’ (Stratum 2), and ‘the least well-off’ (Stratum 3).
The profiles of the three social strata are as follows. Households in the first stratum usually have at least one member with a bachelor’s degree or higher. More than half of these households own at least one private car. Around seventy percent of these households live in an apartment/house (owned or rented) with a market value of more than four million RMB. Besides, their apartments/houses are all larger than seventy- five sqm. In the second stratum, less than half of the households have a member with a bachelor’s degree or higher. Around thirty percent of them own a private car. About ninety percent of these households live in apartments smaller than seventy-five sqm and with market values of less than four million RMB. In the third stratum, only less than thirty percent of the households have a member with a bachelor’s degree or higher. Less than twenty percent of the households own a private car. Most of these households live in apartments/houses smaller than fifty sqm and with market values of less than two million RMB.
73 Figure 3-6 Socioeconomic characteristics of the three social groups identified by LCA
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