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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92960Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 楊子霆 | zh_TW |
| dc.contributor.advisor | Tzu-Ting Yang | en |
| dc.contributor.author | 俞凱翔 | zh_TW |
| dc.contributor.author | Simon Yu | en |
| dc.date.accessioned | 2024-07-09T16:08:02Z | - |
| dc.date.available | 2024-07-10 | - |
| dc.date.copyright | 2024-07-09 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-06-29 | - |
| dc.identifier.citation | Becker, G. S. (2009). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago press.
Becker, G. S. and Chiswick, B. R. (1966). Education and the distribution of earnings. The American Economic Review, 56(1/2):358–369. Black, S. E. (1999). Do better schools matter? parental valuation of elementary education. The quarterly journal of economics, 114(2):577–599. Calonico, S., Cattaneo, M. D., and Titiunik, R. (2015). Rdrobust: an r package for robust nonparametric inference in regression-discontinuity designs. R J., 7(1):38. Cattaneo, M. D., Jansson, M., and Ma, X. (2018). Manipulation testing based on density discontinuity. The Stata Journal, 18(1):234–261. Duncan, G. J. and Murnane, R. J. (2011). Whither opportunity?: Rising inequality, schools, and children’s life chances. Russell Sage Foundation. Fack, G. and Grenet, J. (2010). When do better schools raise housing prices? evidence from paris public and private schools. Journal of public Economics, 94(1-2):59–77. Gelman, A. and Imbens, G. (2019). Why high-order polynomials should not be used in regression discontinuity designs. Journal of Business & Economic Statistics, 37(3):447–456. Gibbons, S., Machin, S., and Silva, O. (2013). Valuing school quality using boundary discontinuities. Journal of Urban Economics, 75:15–28. Hanushek, E. A., Kain, J. F., Markman, J. M., and Rivkin, S. G. (2003). Does peer ability affect student achievement? Journal of applied econometrics, 18(5):527–544. Kane, T. J., Riegg, S. K., and Staiger, D. O. (2006). School quality, neighborhoods, and housing prices. American law and economics review, 8(2):183–212. Kaplan, J. (2020). fastdummies: Fast creation of dummy (binary) columns and rows from categorical variables. R package version, 1(3). Keele, L. J. and Titiunik, R. (2015). Geographic boundaries as regression discontinuities. Political Analysis, 23(1):127–155. Kornrich, S. and Furstenberg, F. (2013). Investing in children: Changes in parental spending on children, 1972–2007. Demography, 50(1):1–23. McCrary, J. (2008). Manipulation of the running variable in the regression discontinuity design: A density test. Journal of econometrics, 142(2):698–714. Owens, A. (2016). Inequality in children's contexts: Income segregation of households with and without children. American Sociological Review, 81(3):549–574. Owens, A. (2019). Building inequality: Housing segregation and income segregation. Sociological Science, 6:497. Owens, A., Reardon, S. F., and Jencks, C. (2016). Income segregation between schools and school districts. American Educational Research Journal, 53(4):1159–1197. Reardon, S. F. (2006). A conceptual framework for measuring segregation and its association with population outcomes. Methods in social epidemiology, 1(169):169–192. Reardon, S. F. and Bischoff, K. (2011). Income inequality and income segregation. American journal of sociology, 116(4):1092–1153. Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of political economy, 82(1):34–55. Tchatoka, F. D. and Varvaris, V. (2021). Neighbourhood, school zoning and the housing market: Evidence from new south wales. Journal of Housing Economics, 54:101790. 林忠樑and 林佳慧(2014). 學校特徵與空間距離對周邊房價之影響分析-以台北市為例. 經濟論文叢刊, 42(2):215–271. 林素菁(2004). 台北市國中小明星學區邊際願意支付之估計. JOURNAL OF HOUSING, 13(1). 簡素錦(2014). 明星國中學區與到校距離對房地產價格影響-以臺北市大安區為例. Master’s thesis, 國立臺灣大學國家發展研究所. https://hdl.handle.net/11296/qn9324. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92960 | - |
| dc.description.abstract | 本研究使用民國101年至109年間臺北市明星國中學區邊界500公尺內的房屋交易資料,包含內政部實價登錄、財政部財政資訊中心的契稅資料、房屋稅資料及個人資料,以斷點迴歸設計(Regression Discontinuity Design)分析臺灣明星國中學區對房屋溢價與收入隔離現象的影響。分析結果顯示,中正國中學區內與學區外的房屋溢價為每坪7.372萬元,並且造成購屋者的年度總收入差距達32.036萬元。相比之下,其他臺北市明星國中學區內與學區外的單坪房屋溢價並不顯著,購屋者的年度總收入差距為17.792萬元,此差距的顯著性並不穩健。在房屋持有人分析中,結果顯示中正國中學區內與學區外的房屋持有人年度總收入差異約為12萬元至20萬元;而在其他明星國中學區內與學區外,房屋持有人的年度總收入並沒有顯著差異。這些結果顯示,明星國中確實會導致房價上漲,並且同時引發顯著的收入隔離現象。 | zh_TW |
| dc.description.abstract | This paper utilizes housing transaction data within 500 meters of the boundary of high-performance junior high school districts in Taipei City from 2012 to 2020, including real price registration data from the Ministry of the Interior and deed tax data, housing tax data, and personal information data from the Fiscal Information Agency, Ministry of Finance. Using regression discontinuity design (RDD), I analyzed the impact of high-performance junior high school districts in Taiwan on housing price premiums and income segregation. The results show that the housing price premium within and outside the Zhongzheng Junior High School district is NT\\$73,720 per ping, and the annual total income gap among house buyers reaches NT\\$320,360. In contrast, the per-ping house price premiums inside and outside the high-performance junior high school districts in Taipei City are not significant. The annual total income gap for home buyers is NT\\$177,920, and the significance of this gap is not robust. The analysis of house owners indicates that the yearly total income difference of house owners within and outside the Zhongzheng Junior High School district ranges from NT\\$120,000 to NT\\$200,000. However, within and outside other star junior high school districts, there is no significant difference in the annual total income of homeowners. These results indicate that high-performance junior high schools indeed lead to an increase in housing prices and simultaneously cause significant income segregation. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-07-09T16:08:02Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-07-09T16:08:02Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 摘要 iii Abstract iv 目次 vi 圖次 viii 表次 ix 第一章前言 1 第二章文獻回顧 5 2.1 教育與貧富不均 5 2.2 學區對房價的影響 6 2.3 相關研究的短缺 7 第三章樣本與資料 9 3.1 資料來源 9 3.2 樣本 10 第四章實證方法 15 4.1 模型架構 15 4.2 學區對房價的影響 15 4.3 學區對收入隔離的影響 17 第五章實證結果19 5.1 主要結果 19 5.2 房屋持有人分析 26 5.3 控制變數的檢驗 28 5.4 樣本密度檢驗 31 第六章結論與未來展望32 6.1 結論 32 6.2 未來展望 33 參考文獻 35 附錄 38 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 學區 | zh_TW |
| dc.subject | 斷點迴歸設計 | zh_TW |
| dc.subject | 貧富不均 | zh_TW |
| dc.subject | 房價 | zh_TW |
| dc.subject | 收入隔離 | zh_TW |
| dc.subject | school district | en |
| dc.subject | income segregation | en |
| dc.subject | income inequality | en |
| dc.subject | regression discontinuity design | en |
| dc.subject | housing prices | en |
| dc.title | 學區、房價與收入隔離- 以臺北市為例 | zh_TW |
| dc.title | School District, Housing Prices, and Income Segregation: Evidence from Taipei City | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 謝志昇 | zh_TW |
| dc.contributor.coadvisor | Chih-Sheng Hsieh | en |
| dc.contributor.oralexamcommittee | 連賢明;朱建達 | zh_TW |
| dc.contributor.oralexamcommittee | Hsien-Ming Lien;Jian-Da Zhu | en |
| dc.subject.keyword | 學區,房價,貧富不均,收入隔離,斷點迴歸設計, | zh_TW |
| dc.subject.keyword | school district,housing prices,income inequality,income segregation,regression discontinuity design, | en |
| dc.relation.page | 51 | - |
| dc.identifier.doi | 10.6342/NTU202401395 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-07-01 | - |
| dc.contributor.author-college | 社會科學院 | - |
| dc.contributor.author-dept | 經濟學系 | - |
| dc.date.embargo-lift | 2029-06-29 | - |
| Appears in Collections: | 經濟學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-112-2.pdf Until 2029-06-29 | 5.97 MB | Adobe PDF |
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