ADITIVOS DEL CEMENTO Y SU FUNCIÓN

It has previously been stressed that individual data file is merged into household data file, by selecting ‘key variable’ from each dataset. The sample size of the merged data file is 8438, which is equivalent to that of a household data file. Under further analysis, it is necessary to organise a working file for empirical investigation by obtaining useful observations and reducing unnecessary components. Linking the research objectives of this thesis with the dataset, results in data pertaining to housing consumption being regarded as an important variable when organising the working file. More specifically, household tenure choice and mortgage borrowing are crucial as mechanisms to guarantee useful observations. The steps taken when organising a working data file are as presented below:

Firstly, the variable ‘current tenure choice’ in the merged data file was selected as the key variable for filtering unnecessary observations. Originally, three types of tenures were involved in this variable: owner-occupied (7097 households), renting (992 households) and

160

living-for-free (346 households) (i.e. they are living with parents or relatives, or living with somebody else with no charge) separately. Considering the research objectives, we only targeted households renting or owning a house with a given amount of housing expenditure. Therefore, those samples do not have housing expenditure on housing were excluded, including groups of ‘living for free’ and those ‘owning inherited houses’, as housing affordability was not expected to be a problem for those groups. More specifically, 346 of ‘living-for-free’ households were excluded from the working file, accounting for 4.1 per cent of the 8438 households. Moreover, 61 renters were not charged rents, and so were removed from the dataset. Regarding information detailing rental payment and frequency, this was the most problematic proportion of the dataset, since it contains many missing values and potentially erroneous data. Where typing mistakes were evident, i.e. when erroneous figures and extreme values appeared in the data set, it was removed.

Secondly, considering the influence of housing expenditure on housing borrowing, households without a mortgage were excluded, including those groups owning an inherited house, those purchasing public houses via the housing reform, and those who had have paid off their mortgages. Based on this consideration, the variable ‘whether have housing borrowings’was selected as the second key variable when forming the working file. According to the descriptive statistics, 6961 observations were removed, because the households did not have to meet mortgage payment. In addition, linked to the research questions, this current thesis interested in housing affordability for households with mortgages, therefore, the research data sample were further filtered based on type of mortgages. The key variable, ‘what type of housing borrowings you have’ was employed to filter irrelevant sample data. After making these adjustments, the data sample was reduced to 426, comprising households with either a mortgage, HPF debt, or a mortgage in conjunction with HPF debt. Those with housing borrowings from commercial debt or via non-regulated channels were excluded from the data sample, because these types of borrowing generate higher costs and offer relevant shorter terms.

Next, the working file was organised by cleaning those observations containing erroneous income data, and deleting records with missing values, extreme values and erroneous data.

161

The criteria for organising the working sample size was that, if the income at the household level was between a given range of national income, then the data could be retained in the data file; otherwise it should be classified as unusual data and dropped. The national level income data for both urban and rural households in 2010 were collected, and were used the data as a reference to clear the erroneous income data in the CHFS 2011 dataset. Income data were collected for the low-income and high-income groups. According to the national statistics, the 1st _{quintile represented the low-income group, having an average annual} income2 _{of 13970 yuan in 2010; while the 5}th _{quintile represented the high-income group,} having an average annual income of 45344 yuan. As this figure was measured per capita, it is necessary to obtain the total household income by multiplying the average number of working people in the household. According to the statistics, the average number of working people in a household was 1.5 in 2010 (NBS, 2010). Accordingly, the total household income for the 1st _{quintile of urban household was 20685 yuan, and for the 5}th _{quintile was 68016} yuan. Therefore, observations in the income ranged between 20685 yuan and 68016 yuan were retained in the working file, and all others were dropped. Following the same procedures, income data at the national level for rural households were observed. The statistics show that for the 1st _{quintile the average annual income was 3566 yuan, while for} the 5th _{quintile it was 18327 yuan. However, the average number of working people in a rural} household was 2.85, because the birth control policy was not implemented in rural area. The average household income for rural households were computed, the 1st _{quintile was 10163} yuan, whereas that for the 5th _{quintile was 53231 yuan. Therefore, the observations at the} income range of 10163 yuan and 53231 yuan were kept, all the other observations were dropped. The following step is moved on to clarifying the housing expenditure to income ratios.

This thesis employs the ratio approach to obtain the housing expenditure to income ratio. To achieve this, household level data were used to calculate the housing expenditure to income ratio for both renters and homeowners. According to the definition of the ratio approach, if housing expenditure exceeds 30 per cent of total household income, this should be classified

162

as a housing affordability issue (Hulchanski, 1995). With regard to the mortgage lending criteria in China, the China Banking Regulatory Commission (CBRC) stated that the mortgage payment to income ratio should not exceed 50 per cent of total household income, otherwise mortgagors should be regarded as having severe difficulties in meeting their mortgage payments. After reviewing the descriptive statistics in our dataset, it became apparent that the mortgage payment to income ratio exceeded 50 per cent; and so those observations with extreme values affecting the housing expenditure to income ratio were removed. Finally, considering the effects of age across the lifecycle path, data pertaining to individuals aged over 20 years old were kept. After taking the steps detailed, the final sample size for the working file was 675, comprising 301 homeowners and 374 renters.

In addition, in relation to the existing studies concerning tenure choice in China and lifecycle theory, this current thesis aimed at examining the variations in terms of housing affordability and tenure choice among different social groups. A number of existing studies have applied similar considerations. Wang and Li (2004), Deng et al. (2005), and Tang and Coulson (2017) investigated variations in homeownership among different age cohorts by splitting the data sample for those aged under and over 40. In addition, Chen (2016) examined the heterogeneity of tenure choice by focusing on the different social groups in China’s urban population. Chen and Yang (2017) captured the likelihood of achieving homeownership by introducing different levels of educational attainment to their model. Accordingly, based on these approaches, this current model examines differences in housing affordability and tenure choice by introducing different social groups, including: (1) age groups (households aged under or over 40); (2) ‘hukou’ location (Households have urban or rural ‘hukou’); (3) income groups (income less or greater than the average annual household income); and (4) education groups, including households with high educational achievement (i.e. college or above) or basic educational achievement (i.e. A-level or below).