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Political Connections and Bank Lines of Credit

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  • Danglun Luo
  • Qianwei Ying

Abstract

We analyze the companies listed on stock exchanges in China from 2004 to 2009 and discover that firms' political connections help them obtain bank lines of credit, especially from state-owned banks. The results also show that political connections have a stronger effect on the acquisition of bank lines of credit for firms that face more financing constraints, are not owned by the state, or are located in regions with intensive government intervention. This paper deepens the field's understanding not only of bank lines of credit but also of the role that political connections play in firms' financing activities.

Suggested Citation

  • Danglun Luo & Qianwei Ying, 2014. "Political Connections and Bank Lines of Credit," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(03), pages 5-21, May.
    • Handle: RePEc:mes:emfitr:v:50:y:2014:i:03:p:5-21
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    1. Martin Feldkircher, 2012. "Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(4), pages 361-376, July.
    2. L Bonga-Bonga, 2009. "Forward Exchange Rate Puzzle: Joining the Missing Pieces in the Rand-US Dollar Exchange Market," Studies in Economics and Econometrics, Taylor & Francis Journals, vol. 33(2), pages 33-48, August.
    3. Grassi, Stefano & Santucci de Magistris, Paolo, 2015. "It's all about volatility of volatility: Evidence from a two-factor stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 62-78.
    4. Koop, Gary & Korobilis, Dimitris, 2011. "UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?," Economic Modelling, Elsevier, vol. 28(5), pages 2307-2318, September.
    5. Simeon Coleman & Juan Carlos Cuestas & Estefanía Mourelle, 2011. "Investigating the oil price-exchange rate nexus: Evidence from Africa," Working Papers 2011015, The University of Sheffield, Department of Economics, revised May 2011.
    6. Jacob Gyntelberg & Mico Loretan & Tientip Subhanij & Eric Chan, 2010. "Private information, stock markets, and exchange rates," BIS Papers chapters, in: Bank for International Settlements (ed.), The international financial crisis and policy challenges in Asia and the Pacific, volume 52, pages 186-210, Bank for International Settlements.
    7. Riané de Bruyn & Rangan Gupta & Lardo Stander, 2013. "Testing the Monetary Model for Exchange Rate Determination in South Africa: Evidence from 101 Years of Data," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 7(1), March.
    8. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    9. Ibrahim Turhan & Erk Hacihasanoglu & Ugur Soytas, 2013. "Oil Prices and Emerging Market Exchange Rates," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S1), pages 21-36, January.
    10. Shaun de Jager, 2012. "Modelling South Africas equilibrium real effective exchange rate A VECM approach," Working Papers 5020, South African Reserve Bank.
    11. Patrick Imam & Camelia Minoiu, 2011. "The Equilibrium Exchange Rate of Mauritius: Evidence from Two Structural Models," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 134-147, November.
    12. Rangan Gupta & Alain Kabundi, 2010. "Forecasting macroeconomic variables in a small open economy: a comparison between small- and large-scale models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 168-185.
    13. Lumengo Bonga-Bonga, 2008. "Modelling the Rand-Dollar Future Spot Rates: The Kalman Filter Approach," The African Finance Journal, Africagrowth Institute, vol. 10(2), pages 60-75.
    14. Hooper, Peter & Morton, John, 1982. "Fluctuations in the dollar: A model of nominal and real exchange rate determination," Journal of International Money and Finance, Elsevier, vol. 1(1), pages 39-56, January.
    15. Pami Dua & Rajiv Ranjan, 2011. "Modelling and Forecasting the Indian Re/US Dollar Exchange Rate," Working papers 197, Centre for Development Economics, Delhi School of Economics.
    16. Christian Dreger & Jarko Fidrmuc, 2011. "Drivers of Exchange Rate Dynamics in Selected CIS Countries: Evidence from a Factor-Augmented Vector Autoregressive (FAVAR) Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(4), pages 49-58, July.
    17. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    18. Balazs Egert, 2012. "Nominal and Real Exchange Rate Models in South Africa: How Robust are they?," CESifo Working Paper Series 3853, CESifo.
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    2. Muhammad Umar & Gang Sun, 2016. "Non-performing loans (NPLs), liquidity creation, and moral hazard: Case of Chinese banks," China Finance and Economic Review, Springer, vol. 4(1), pages 1-23, December.
    3. Jintao Zhang & Zhen Yang & Li Meng & Lu Han, 2022. "Environmental regulations and enterprises innovation performance: the role of R&D investments and political connections," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 4088-4109, March.
    4. McGuinness, Paul B., 2021. "Board member age, stock seasoning and the evolution of capital structure in Chinese firms," International Business Review, Elsevier, vol. 30(3).
    5. Lee, Chien-Chiang & Ning, Shaolin & Hsieh, Meng-Fen & Lee, Chi-Chuan, 2020. "The going-public decision and rent-seeking activities: Evidence from Chinese private companies," Economic Systems, Elsevier, vol. 44(1).
    6. Zhang, Dayong & Cai, Jing & Dickinson, David G. & Kutan, Ali M., 2016. "Non-performing loans, moral hazard and regulation of the Chinese commercial banking system," Journal of Banking & Finance, Elsevier, vol. 63(C), pages 48-60.
    7. Boateng, Agyenim & Liu, Yang & Brahma, Sanjukta, 2019. "Politically connected boards, ownership structure and credit risk: Evidence from Chinese commercial banks," Research in International Business and Finance, Elsevier, vol. 47(C), pages 162-173.
    8. Chan Guo, 2022. "The Impact of Management Succession on Corporate Social Responsibility of Chinese Family Firms: The Moderating Effects of Managerial Economic Motivations," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    9. Ameni Tarchouna & Bilel Jarraya & Abdelfettah Bouri, 2022. "Do board characteristics and ownership structure matter for bank non-performing loans? Empirical evidence from US commercial banks," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 26(2), pages 479-518, June.

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