IDEAS home Printed from https://ideas.repec.org/a/eee/chieco/v32y2015icp57-67.html
   My bibliography  Save this article

Empirical study of the local government deficit, land finance and real estate markets in China

Author

Listed:
  • Pan, Jiun-Nan
  • Huang, Jr-Tsung
  • Chiang, Tsun-Feng

Abstract

The purpose of this study is to explore an important issue concerning the relationship among the local government deficit, land finance and real estate markets in China. This study uses a panel data of 30 provinces in China during 1999–2010 to estimate the panel smooth transition regression model. Since the estimated transition threshold value is 14.62, provinces with the ratio of fiscal deficits to GDP of greater than 14.62% are categorized as in the high fiscal difficulty (HFD) regime. Otherwise they are in the low fiscal difficulty (LFD) regime. The primary finding of this study is that the land leasing fee has a significantly positive influence on the total value of commodity buildings sold in the LFD regime. In addition, the local fiscal deficit has a significantly positive impact on the real estate market in the LFD regime, but this impact turns to be negative in the HFD regime.

Suggested Citation

  • Pan, Jiun-Nan & Huang, Jr-Tsung & Chiang, Tsun-Feng, 2015. "Empirical study of the local government deficit, land finance and real estate markets in China," China Economic Review, Elsevier, vol. 32(C), pages 57-67.
  • Handle: RePEc:eee:chieco:v:32:y:2015:i:c:p:57-67
    DOI: 10.1016/j.chieco.2014.11.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1043951X14001424
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chieco.2014.11.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Nan-Kuang, 2001. "Bank net worth, asset prices and economic activity," Journal of Monetary Economics, Elsevier, vol. 48(2), pages 415-436, October.
    2. Li, Lixing & Wu, Xiaoyu, 2014. "Housing price and entrepreneurship in China," Journal of Comparative Economics, Elsevier, vol. 42(2), pages 436-449.
    3. González, Andrés & Teräsvirta, Timo & van Dijk, Dick & Yang, Yukai, 2005. "Panel Smooth Transition Regression Models," SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics, revised 11 Oct 2017.
    4. Gerlach, Stefan & Peng, Wensheng, 2005. "Bank lending and property prices in Hong Kong," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 461-481, February.
    5. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    6. Fouquau, Julien & Hurlin, Christophe & Rabaud, Isabelle, 2008. "The Feldstein-Horioka puzzle: A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 25(2), pages 284-299, March.
    7. Tan, Rong & Qu, Futian & Heerink, Nico & Mettepenningen, Evy, 2011. "Rural to urban land conversion in China — How large is the over-conversion and what are its welfare implications?," China Economic Review, Elsevier, vol. 22(4), pages 474-484.
    8. Cheng Hsiao, 2007. "Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 1-22, May.
    9. Wang, Songtao & Yang, Zan & Liu, Hongyu, 2011. "Impact of urban economic openness on real estate prices: Evidence from thirty-five cities in China," China Economic Review, Elsevier, vol. 22(1), pages 42-54, March.
    10. Wu, Jing & Gyourko, Joseph & Deng, Yongheng, 2012. "Evaluating conditions in major Chinese housing markets," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 531-543.
    11. Jansen, Eilev S & Terasvirta, Timo, 1996. "Testing Parameter Constancy and Super Exogeneity in Econometric Equations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 735-763, November.
    12. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    13. Mr. Ashvin Ahuja & Lillian Cheung & Gaofeng Han & Mr. Nathan Porter & Wenlang Zhang, 2010. "Are House Prices Rising Too Fast in China?," IMF Working Papers 2010/274, International Monetary Fund.
    14. Zheng, Siqi & Kahn, Matthew E., 2008. "Land and residential property markets in a booming economy: New evidence from Beijing," Journal of Urban Economics, Elsevier, vol. 63(2), pages 743-757, March.
    15. Maddala, G S & Wu, Shaowen, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 631-652, Special I.
    16. Mr. Paul Louis Ceriel Hilbers & Ms. Lisbeth S Zacho & Mr. Qin Lei, 2001. "Real Estate Market Developments and Financal Sector Soundness," IMF Working Papers 2001/129, International Monetary Fund.
    17. Hung-Gay Fung & Alan Guoming Huang & Qingfeng Wilson Liu & Maggie Xiaoqin Shen, 2006. "The Development of the Real Estate Industry in China," Chinese Economy, Taylor & Francis Journals, vol. 39(1), pages 84-102, February.
    18. Ashvin Ahuja & Lillian Cheung & Gaofeng Han & Nathan Porter & Wenlang Zhang, 2010. "Are House Prices Rising Too Fast in China?," Working Papers 1008, Hong Kong Monetary Authority.
    19. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207, Decembrie.
    20. Richard J. Herring & Susan Wachter, 1999. "Real Estate Booms and Banking Busts: An International Perspective," Center for Financial Institutions Working Papers 99-27, Wharton School Center for Financial Institutions, University of Pennsylvania.
    21. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    22. Cheng Hsiao, 2007. "Rejoinder on: Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 56-57, May.
    23. Eloisa T. Glindro & Tientip Subhanij & Jessica Szeto & Haibin Zhu, 2011. "Determinants of House Prices in Nine Asia-Pacific Economies," International Journal of Central Banking, International Journal of Central Banking, vol. 7(3), pages 163-204, September.
    24. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lena Dr䧥r & Jan-Oliver Menz & Ulrich Fritsche, 2014. "Perceived inflation under loss aversion," Applied Economics, Taylor & Francis Journals, vol. 46(3), pages 282-293, January.
    2. Wu, Po-Chin & Liu, Shiao-Yen & Zhai, Rui-Xiang, 2018. "Nonlinear impacts of operating risk and demand management policy on banks’ performance: The role of leading indicator," Economic Analysis and Policy, Elsevier, vol. 59(C), pages 40-53.
    3. Lee, Chien-Chiang & Chiu, Yi-Bin, 2013. "Modeling OECD energy demand: An international panel smooth transition error-correction model," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 372-383.
    4. Seleteng, Monaheng & Bittencourt, Manoel & van Eyden, Reneé, 2013. "Non-linearities in inflation–growth nexus in the SADC region: A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 30(C), pages 149-156.
    5. Pei-Tha Gan, 2019. "Economic uncertainty, precautionary motive and the augmented form of money demand function," Evolutionary and Institutional Economics Review, Springer, vol. 16(2), pages 397-423, December.
    6. Galimberti, Jaqueson K., 2009. "Conditioned Export-Led Growth Hypothesis: A Panel Threshold Regressions Approach," MPRA Paper 13417, University Library of Munich, Germany.
    7. Natalya Ketenci, 2015. "Capital mobility in the panel GMM framework: Evidence from EU members," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 12(1), pages 3-19, July.
    8. Ewing, Bradley T. & Payne, James E. & Caporin, Massimilano, 2022. "The Asymmetric Impact of Oil Prices and Production on Drilling Rig Trajectory: A correction," Resources Policy, Elsevier, vol. 79(C).
    9. John Nana Francois & Andrew Keinsley, 2023. "Intratemporal elasticity of substitution between private and public consumption: new evidence and implications," Empirical Economics, Springer, vol. 65(4), pages 1655-1692, October.
    10. Itchoko motande Mondjeli mwa ndjokou, 2015. "Too much public expenditures, less economic growt," Economics Bulletin, AccessEcon, vol. 35(3), pages 1985-1991.
    11. Olivier Damette & Mathilde Maurel & Michael A. Stemmer, 2016. "What does it take to grow out of recession? An error-correction approach towards growth convergence of European and transition countries," Post-Print halshs-01318131, HAL.
    12. Salisu, Afees A. & Adediran, Idris A. & Oloko, Tirimisiyu O. & Ohemeng, William, 2020. "The heterogeneous behaviour of the inflation hedging property of cocoa," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    13. Lee, Chien-Chiang & Chiu, Yi-Bin, 2011. "Electricity demand elasticities and temperature: Evidence from panel smooth transition regression with instrumental variable approach," Energy Economics, Elsevier, vol. 33(5), pages 896-902, September.
    14. Yi-Bin Chiu, 2012. "Deforestation and the Environmental Kuznets Curve in Developing Countries: A Panel Smooth Transition Regression Approach," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 60(2), pages 177-194, June.
    15. Solarin, Sakiru Adebola & Ozturk, Ilhan, 2016. "The relationship between natural gas consumption and economic growth in OPEC members," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1348-1356.
    16. Alessio Ciarlone, 2015. "House price cycles in emerging economies," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(1), pages 17-52, March.
    17. Kholodilin Konstantin A. & Menz Jan-Oliver & Siliverstovs Boriss, 2010. "What Drives Housing Prices Down? Evidence from an International Panel," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(1), pages 59-76, February.
    18. Afi Etonam Adetou & Komlan Fiodendji, 2019. "Finance, Institutions, Remittances and Economic growth: New Evidence from a Dynamic Panel Threshold Analysis," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 9(2), pages 1-4.
    19. Chen, Chaoyi & Pinar, Mehmet & Stengos, Thanasis, 2021. "Determinants of renewable energy consumption: Importance of democratic institutions," Renewable Energy, Elsevier, vol. 179(C), pages 75-83.
    20. Bian, Timothy Yang & Gete, Pedro, 2015. "What drives housing dynamics in China? A sign restrictions VAR approach," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 96-112.

    More about this item

    Keywords

    Local government deficit; Land finance; Real estate; China; Panel smooth transition regression model;
    All these keywords.

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • H71 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Taxation, Subsidies, and Revenue
    • H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chieco:v:32:y:2015:i:c:p:57-67. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/chieco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.