Abstract
This study attempts to investigate the pattern of the middle class in the Seoul Metropolitan Region and to explain why such a pattern occurs from spatial perspectives. For this purpose, the middle class are first defined based on four indicators: income, education, occupation and housing. Then, the spatial concentration of the middle class are explored by each indicator for 2010. The explanation of the pattern is pursued based on four dimensions often considered as critical factors in residential location decisions of the middle class: neighborhood, employment, consumption and educational environment. The findings suggest that the spatial distribution of the middle class generally shows a combination of concentric and sector models as we go farther from Seoul. The results of the global and the local regression analyses suggest that employment (producer service jobs) and educational environment (private after-school educational institutes) are important dimensions in explaining the residential location of the middle class in the Seoul Metropolitan Region.
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Notes
Boterman et al. (2010) exploring the spatial pattern of the middle class in Amsterdam is one of a few exceptions.
Ongjin, composed of 113 islands, Yeoncheon, Yangpyeong and Gapyeong do not have information on Jeonse deposit of apartment units, the proxy of income for defining the middle class.
Although use of a more disaggregated spatial unit can improve the reliability of the statistical results, data required for deriving the dependent variables such as education, occupation and housing are available only at the sub-administrative area or place level from the Korean Statistical Information Service, the official provider of the government data. In addition, the existence of the middle class can be underestimated by Jeonse deposit of apartment units, the proxy of income, if we adopt more disaggregated spatial units in that there will be too many zeros in the dataset.
Hong (2005) uses 66 for the owned and 99 square meters for the rented. While no data on house size distinguish between the owned and the rented, we use 66 in this study in order not to exclude home owners with relatively small housing units. This is also comparable with the standard of income in that 90% of the average house size is 67.6 square meters.
Despite the fact that the amount of Jeonse deposit and household income may not have a perfect correlation, the former is the only variable related to the latter that we can extract the proportion of the subjects (people or houses) satisfying the standard in each spatial unit of analysis as the other three indicators.
In addition to the variables listed in Table 2, two sets of employment accessibility measures (for total employment and producer service sectors) as well as the number of cafes (for measuring an aspect of cultural consumption of the middle class) were included in the initial estimation, but later excluded since they all showed variance inflation factors (VIFs) higher than 10 suggesting a significant multicollinearity problem. The variables of the resultant regressions have VIF values between 1.3 and 4.2. A dummy to reflect the difference between Seoul and non-Seoul locations for showing regional transition between spatial units inside and those outside Seoul was also considered, but removed from the final estimation since this variable was not significant (showing confidence level between 0.15 and 0.40).
The optimal bandwidth size obtained in all five GWR models is 70 after four iterations.
The notations of the equation are from Nakata (2014).
Spatial dependence is tested to specify the appropriate model for spatial treatment, if necessary, between spatial lag and error models. However, the test on the OLS residuals does not detect any type of spatial dependence suggesting that OLS will not induce bias in estimating the coefficients.
The study of Kim and Lee (2007) analyzing the factors influencing the housing price in and near Seoul suggests similar results regarding the impacts of public and private after-school educations as in this study.
dongA.com ‘The standards of the new middle class that people perceive are much higher than those established by the government,’ by Dohyeong Kim and Jihyeon Roh May 13 2015 (http://news.donga.com/3/all/20150513/71209263/1) (Accessed 13 May 2015).
Jungdeungurigyoyuk (Our Secondary Education) ‘Special law is required for reducing education gap,’ by Yeongjae Kim May 1 2006.
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Acknowledgements
This work was supported by the Institute for Korean Regional Studies, Seoul National University. The authors thank Cheonghun Lee, a former graduate student in the Department of Geography, SNU, for collecting and compiling the database.
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Appendix Administrative units in SMR
Appendix Administrative units in SMR
id | Regional | Local | Sub-local |
1 | Seoul | Jongro | |
2 | Seoul | Jung | |
3 | Seoul | Yongsan | |
4 | Seoul | Seongdong | |
5 | Seoul | Gwangjin | |
6 | Seoul | Dongdaemun | |
7 | Seoul | Jungrang | |
8 | Seoul | Seongbuk | |
9 | Seoul | Gangbuk | |
10 | Seoul | Dongbong | |
11 | Seoul | Nowon | |
12 | Seoul | Eunpyeong | |
13 | Seoul | Seodaemun | |
14 | Seoul | Mapo | |
15 | Seoul | Yangcheon | |
16 | Seoul | Gangseo | |
17 | Seoul | Guro | |
18 | Seoul | Keumcheon | |
19 | Seoul | Yeongdeungpo | |
20 | Seoul | Dongjak | |
21 | Seoul | Gwanak | |
22 | Seoul | Seocho | |
23 | Seoul | Gangnam | |
24 | Seoul | Songpa | |
25 | Seoul | Gangdong | |
26 | Incheon | Jung | |
27 | Incheon | Dong | |
28 | Incheon | Nam | |
29 | Incheon | Yeonsu | |
30 | Incheon | Namdong | |
31 | Incheon | Bupyeong | |
32 | Incheon | Gyeyang | |
33 | Incheon | Seo | |
34 | Gyeonggi | Ganghwa | |
35 | Gyeonggi | Suwon | Jangan |
36 | Gyeonggi | Suwon | Gwonseon |
37 | Gyeonggi | Suwon | Paldal |
38 | Gyeonggi | Seongnam | Sujeong |
39 | Gyeonggi | Seongnam | Jungwon |
40 | Gyeonggi | Seongnam | Bundang |
41 | Gyeonggi | Uijeongbu | |
42 | Gyeonggi | Anyang | Manan |
43 | Gyeonggi | Anyang | Dongan |
44 | Gyeonggi | Bucheon | Wonmi |
45 | Gyeonggi | Bucheon | Sosa |
46 | Gyeonggi | Bucheon | Ojeong |
47 | Gyeonggi | Gwangmyeong | |
48 | Gyeonggi | Pyeongtaek | |
49 | Gyeonggi | Dongducheon | |
50 | Gyeonggi | Ansan | |
51 | Gyeonggi | Goyang | Deokyang |
52 | Gyeonggi | Goyang | Ilsan |
53 | Gyeonggi | Gwacheon | |
54 | Gyeonggi | Guri | |
55 | Gyeonggi | Namyangju | |
56 | Gyeonggi | Osan | |
57 | Gyeonggi | Siheung | |
58 | Gyeonggi | Gunpo | |
59 | Gyeonggi | Uiwang | |
60 | Gyeonggi | Hanam | |
61 | Gyeonggi | Yongin | |
62 | Gyeonggi | Paju | |
63 | Gyeonggi | Icheon | |
64 | Gyeonggi | Anseong | |
65 | Gyeonggi | Gimpo | |
66 | Gyeonggi | Hwaseong | |
67 | Gyeonggi | Gwangju | |
68 | Gyeonggi | Yangju | |
69 | Gyeonggi | Pocheon | |
70 | Gyeonggi | Yeoju |
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Sohn, J., Oh, S.K. Explaining Spatial Distribution of the Middle Class: A Multiple Indicator Approach with Multiple Explanatory Dimensions. Appl. Spatial Analysis 12, 871–905 (2019). https://doi.org/10.1007/s12061-018-9275-5
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DOI: https://doi.org/10.1007/s12061-018-9275-5