IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v177y2022ics0040162522000464.html
   My bibliography  Save this article

Asymmetric effects of urbanization on shadow economy both in short-run and long-run:New evidence from dynamic panel threshold model

Author

Listed:
  • Pang, Jingru
  • Li, Nan
  • Mu, Hailin
  • Jin, Xin
  • Zhang, Ming

Abstract

Urbanization and shadow economy are both common social problems in developing countries, yet rare studies have explored their potential relationships. Based on the provincial panel data in China, this paper estimates both the short-run and long-run non-linear relationship between urbanization and shadow economy combining with GMM (Generalized method of moments) and threshold analysis specifications, for symmetric analysis and asymmetric analysis, respectively. This paper also estimates if shadow economy is consistent with the EKC (Environment Kuznets Curve) assumption, and including other explanatory variables such as energy consumption, FDI (Foreign Direct Investment), tertiary industry and environmental regulation. Results show that urbanization does have an inversed U-shaped effect on shadow economy, so does economic growth, for both symmetric and asymmetric analyses. Tertiary industry can redouble the scale of shadow economy, which is consistent with all four analysis aspects. Other explanatory variables show slight differences in either symmetric/ asymmetric or short-run/ long-run results.

Suggested Citation

  • Pang, Jingru & Li, Nan & Mu, Hailin & Jin, Xin & Zhang, Ming, 2022. "Asymmetric effects of urbanization on shadow economy both in short-run and long-run:New evidence from dynamic panel threshold model," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:tefoso:v:177:y:2022:i:c:s0040162522000464
    DOI: 10.1016/j.techfore.2022.121514
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162522000464
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2022.121514?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. Lv, Yulan & Chen, Wei & Cheng, Jianquan, 2020. "Effects of urbanization on energy efficiency in China: New evidence from short run and long run efficiency models," Energy Policy, Elsevier, vol. 147(C).
    2. James E. Rauch, 1993. "Economic Development, Urban Underemployment, and Income Inequality," Canadian Journal of Economics, Canadian Economics Association, vol. 26(4), pages 901-918, November.
    3. Ummad Mazhar & Ceyhun Elgin, 2013. "Environmental Regulation, Pollution and the Informal Economy," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 9, pages 62-81.
    4. Soham Baksi & Pinaki Bose, 2010. "Environmental Regulation in the Presence of an Informal Sector," Departmental Working Papers 2010-03, The University of Winnipeg, Department of Economics.
    5. Lazăr, Dorina & Minea, Alexandru & Purcel, Alexandra-Anca, 2019. "Pollution and economic growth: Evidence from Central and Eastern European countries," Energy Economics, Elsevier, vol. 81(C), pages 1121-1131.
    6. Frey, Bruno S. & Weck-Hanneman, Hannelore, 1984. "The hidden economy as an 'unobserved' variable," European Economic Review, Elsevier, vol. 26(1-2), pages 33-53.
    7. Ceyhun Elgin & Oğuz Öztunalı, 2014. "Environmental Kuznets Curve for the Informal Sector of Turkey (1950-2009)," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 61(4), pages 471-485, September.
    8. Myung Hwan Seo & Sueyoul Kim & Young-Joo Kim, 2019. "Estimation of dynamic panel threshold model using Stata," Stata Journal, StataCorp LP, vol. 19(3), pages 685-697, September.
    9. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    10. Kookana, R. S. & Drechsel, Pay & Jamwal, P. & Vanderzalm, J., 2020. "Urbanisation and emerging economies: issues and potential solutions for water and food security," Papers published in Journals (Open Access), International Water Management Institute, pages 732:139057..
    11. Ting Xu & Zhike Lv & Luoqi Xie, 2018. "Does Country Risk Promote the Informal Economy? A Cross-National Panel Data Estimation," Global Economic Review, Taylor & Francis Journals, vol. 47(3), pages 289-310, July.
    12. Wu, Haitao & Hao, Yu & Weng, Jia-Hsi, 2019. "How does energy consumption affect China's urbanization? New evidence from dynamic threshold panel models," Energy Policy, Elsevier, vol. 127(C), pages 24-38.
    13. 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.
    14. Friedrich Schneider & Andreas Buehn & Claudio Montenegro, 2010. "New Estimates for the Shadow Economies all over the World," International Economic Journal, Taylor & Francis Journals, vol. 24(4), pages 443-461.
    15. Friedrich Schneider & Andreas Buehn & Claudio E. Montenegro, 2011. "Shadow Economies All Over the World: New Estimates for 162 Countries from 1999 to 2007," Chapters, in: Friedrich Schneider (ed.), Handbook on the Shadow Economy, chapter 1, Edward Elgar Publishing.
    16. Zhao, Jingfeng & Tang, Jianmin, 2018. "Industrial structure change and economic growth: A China-Russia comparison," China Economic Review, Elsevier, vol. 47(C), pages 219-233.
    17. Goel, Rajeev K. & Nelson, Michael A., 2016. "Shining a light on the shadows: Identifying robust determinants of the shadow economy," Economic Modelling, Elsevier, vol. 58(C), pages 351-364.
    18. Elgin, Ceyhun & Oyvat, Cem, 2013. "Lurking in the cities: Urbanization and the informal economy," Structural Change and Economic Dynamics, Elsevier, vol. 27(C), pages 36-47.
    19. Dang, Viet Anh & Kim, Minjoo & Shin, Yongcheol, 2012. "Asymmetric capital structure adjustments: New evidence from dynamic panel threshold models," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 465-482.
    20. Cong Minh Huynh, 2020. "Shadow economy and air pollution in developing Asia: what is the role of fiscal policy?," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 22(3), pages 357-381, July.
    21. Zhao, Jingjing & Chai, Lihe, 2015. "A novel approach for urbanization level evaluation based on information entropy principle: A case of Beijing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 114-125.
    22. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    23. Fischer, Edwin O & Heinkel, Robert & Zechner, Josef, 1989. " Dynamic Capital Structure Choice: Theory and Tests," Journal of Finance, American Finance Association, vol. 44(1), pages 19-40, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aysel Amir & Korhan Gökmenoğlu, 2023. "Analyzing the Drivers of the Shadow Economy for the Case of the CESEE Region," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 71(2), pages 155-181, February.
    2. Wang, Xueyang & Sun, Xiumei & Ahmad, Mahmood & Zhang, Haotian, 2023. "Does low carbon energy transition impede air pollution? Evidence from China's coal-to-gas policy," Resources Policy, Elsevier, vol. 83(C).

    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. Dang, Viet Anh & Kim, Minjoo & Shin, Yongcheol, 2014. "Asymmetric adjustment toward optimal capital structure: Evidence from a crisis," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 226-242.
    2. Wu, Haitao & Xia, Yufeng & Yang, Xiaodong & Hao, Yu & Ren, Siyu, 2021. "Does environmental pollution promote China's crime rate? A new perspective through government official corruption," Structural Change and Economic Dynamics, Elsevier, vol. 57(C), pages 292-307.
    3. Seo, Myung Hwan & Shin, Yongcheol, 2016. "Dynamic panels with threshold effect and endogeneity," Journal of Econometrics, Elsevier, vol. 195(2), pages 169-186.
    4. Piotr Dybka & Bartosz Olesiński & Marek Rozkrut & Andrzej Torój, 2020. "Measuring the uncertainty of shadow economy estimates using Bayesian and frequentist model averaging," KAE Working Papers 2020-046, Warsaw School of Economics, Collegium of Economic Analysis.
    5. Wu, Haitao & Hao, Yu & Weng, Jia-Hsi, 2019. "How does energy consumption affect China's urbanization? New evidence from dynamic threshold panel models," Energy Policy, Elsevier, vol. 127(C), pages 24-38.
    6. Shi Wang & Yizhou Yuan & Hua Wang, 2019. "Corruption, Hidden Economy and Environmental Pollution: A Spatial Econometric Analysis Based on China’s Provincial Panel Data," IJERPH, MDPI, vol. 16(16), pages 1-23, August.
    7. Giorgio Canarella & Stephen M. Miller, 2019. "Determinants of Optimal Capital Structure and Speed of Adjustment: Evidence from the U.S. ICT Sector," Working papers 2019-06, University of Connecticut, Department of Economics.
    8. Dang, Viet Anh & Kim, Minjoo & Shin, Yongcheol, 2012. "Asymmetric capital structure adjustments: New evidence from dynamic panel threshold models," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 465-482.
    9. Wu, Haitao & Xu, Lina & Ren, Siyu & Hao, Yu & Yan, Guoyao, 2020. "How do energy consumption and environmental regulation affect carbon emissions in China? New evidence from a dynamic threshold panel model," Resources Policy, Elsevier, vol. 67(C).
    10. A. P. Kireenko & E. N. Nevzorova, 2019. "Shadow Economy in the Countryside of Russian Regions," Regional Research of Russia, Springer, vol. 9(1), pages 66-77, January.
    11. Péter Benczúr & Virmantas Kvedaras, 2021. "Nonlinear impact of financial deepening on income inequality," Empirical Economics, Springer, vol. 60(4), pages 1939-1967, April.
    12. Phoebe W. Ishak & Mohammad Reza Farzanegan, 2022. "Oil price shocks, protest, and the shadow economy: Is there a mitigation effect?," Economics and Politics, Wiley Blackwell, vol. 34(2), pages 298-321, July.
    13. Elgin, Ceyhun & Oyvat, Cem, 2013. "Lurking in the cities: Urbanization and the informal economy," Structural Change and Economic Dynamics, Elsevier, vol. 27(C), pages 36-47.
    14. Jinhua Shao & Brayan Tillaguango & Rafael Alvarado & Santiago Ochoa-Moreno & Johanna Alvarado-Espejo, 2021. "Environmental Impact of the Shadow Economy, Globalisation, Trade and Market Size: Evidence Using Linear and Non-Linear Methods," Sustainability, MDPI, vol. 13(12), pages 1-20, June.
    15. Natalia Szomko, 2017. "The Importance of Estimation Method Choice for the Analysis of the Determinants of Capital Structure– An Example of Poland," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 3(1), pages 3-20, June.
    16. Luan, Bingjiang & Zou, Hong & Chen, Shuxing & Huang, Junbing, 2021. "The effect of industrial structure adjustment on China’s energy intensity: Evidence from linear and nonlinear analysis," Energy, Elsevier, vol. 218(C).
    17. Goel, Rajeev K. & Saunoris, James W. & Schneider, Friedrich, 2019. "Drivers of the underground economy for over a century: A long term look for the United States," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 95-106.
    18. Andrea Nocera & M. Hashem Pesaran, 2022. "Causal Effects of the Fed's Large-Scale Asset Purchases on Firms' Capital Structure," CESifo Working Paper Series 9695, CESifo.
    19. Durand, Robert B. & Greene, William H. & Harris, Mark N. & Khoo, Joye, 2022. "Heterogeneity in speed of adjustment using finite mixture models," Economic Modelling, Elsevier, vol. 107(C).
    20. Hao, Yu & Gai, Zhiqiang & Wu, Haitao, 2020. "How do resource misallocation and government corruption affect green total factor energy efficiency? Evidence from China," Energy Policy, Elsevier, vol. 143(C).

    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:tefoso:v:177:y:2022:i:c:s0040162522000464. 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.sciencedirect.com/science/journal/00401625 .

    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.