Economic and Political Motivations in Debt Finance in China: Bank Lending and Trade Credit Offering


Using matched datasets constructed from firm-level micro-data and hand-collected bank branch data, this study examines economic and political motivations by banks and supplier firms in bank lending and trade credit offering in China. First, statistical evidence shows that economic motivation is dominant over political motivation. Second, competition among domestic banks and with foreign banks has been found to motivate domestic banks to lend money to private firms by economic motivation. Third, a private firm’s political connections and more easily collateralized assets diminish the economic motivation to find better-performing borrower firms in bank lending to private firms.

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  1. 1.

    Zhao (2013) showed that even though bond financing increases companies’ investment efficiency, political connections decrease firms’ capital allocation efficiency.

  2. 2.

    Comments from anonymous reviewers helped us to make this consideration. We are grateful for this valuable comment.

  3. 3.

    As a result, ΔXit−1 is defined as the change in variable X between periods t−1 and t−2: Xit−1 − Xit−2.

  4. 4.

    Actually, the number of long-term bank loans is quite small compared with short-term bank loans in private firms in China. Besides, we could detect that many missing firm-year observations of long-term bank loans actually mean zero stock values of long-term bank loans.

  5. 5.

    The estimation results for μj, μjt, and μp are not reported due to space limitations. μi cannot be estimated due to the nature of the system generalized method of moments estimation technique used here.

  6. 6.

    Even if ROE or ROS is used as alternative profitability measures instead of ROA, the results obtained remain largely unchanged.

  7. 7.

    Instead of capital productivity, total factor productivity (TFP) could be used as a productivity measure. This was not done here because it seems unnatural for banks and supplier firms to measure customer firms’ TFP, although it is very natural for researchers to do so, and therefore naïve capital productivity can be realistically assumed to serve as a productivity measure by banks and supplier firms. Note that results similar to the important points in this paper can be obtained even when TFP is used instead of capital productivity. Furthermore, also when using another naïve productivity measure, labor productivity, similar results can be obtained.

  8. 8.

    See Roodman (2008) for system GMM estimation.

  9. 9.

    Two-step GMM is used instead of one-step GMM because the former is asymptotically more efficient. Therefore, the Windmeijer (2005) finite sample correction is applied to the two-step covariance matrix to settle the potentially downward-biased two-step standard errors.

  10. 10.

    We use non-listed firms’ data, not including listed private firms. Because non-listed private firms faced and face the most limited access to debt financing in China, it is an important issue which can work better for them to access to debt financing, performing better or having political connections, that is, economic motivation or political motivation by banks and supplier firms.

  11. 11.

    The global financial crisis of 2008 occurred during this sample period. Because of its potential impact on the financing behavior of firms, we split our sample into those before and after the global financial crisis and reran all regressions in this paper using each subsample. This trial found that subsamples produced estimation results largely similar to each other and the whole sample, although, to save space, we have not reported the estimation results. This implies that this paper’s findings had been relevant until around 2013, at least.

  12. 12.

    SOEs are used as a reference.

  13. 13.

    The number of SOEs sampled is 15,804, and their descriptive statistics are reported in Table A1.

  14. 14.

    In the unbalanced panel used here, the number of time periods of available data was only three or four (years) on average. The data constitute a typical small time series and a large cross-section panel. One can safely assume that these panel data tend not to suffer the problem of instrument proliferation by their nature (Roodman 2008).

  15. 15.

    The third column presents estimation results of the specifications using ROA, Sales/Gross assets, and ΔSales−1/Sales−2 altogether in one regression, which seems to be most reliable for calculating the impact of firm performance on debt financing.

  16. 16.

    The Percentage of state ownership−1 does not necessarily take on a value of one (100 percent) even in SOEs because the definition of SOEs is that their largest shareholders are state or state-owned entities, not 100 percent ownership of state or state-owned entities, in China. It should be noted that the first political connections variable, State−1, cannot be included in the models because State−1 necessarily takes on a value of one by the definition of State and SOEs.

  17. 17.

    For example, let us take ROA–1 and HHIbank as a firm performance variable and a domestic banking competition variable, respectively. Because β1 ROA–1 + δ1ROA−1* HHIbank = (β1 + δ1HHIbank) ROA−1, δ1 (the coefficient of interaction terms of firm performance variable and domestic banking competition variable) captures the changing impact of ROA−1 according to the value of HHIbank on ∆Bank loans/Gross assets, namely the economic impact of competition among domestic banks on economic motivation in bank lending. A similar explanation can be applied to the interpretation of the interaction terms of firm performance variables and Foreign banks entry.

  18. 18.

    In the specifications of Table 7, province-specific fixed effect (μp) is dropped due to a simple technical reason. A perfect multicollinearity would arise if stand-alone Foreign banks entry, year dummy variables, and province-specific fixed effect were included in a regression model all at once.


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Authors acknowledge the comments and suggestions from two anonymous referees. All views and errors remain our own. This work was funded by Scientific Research Scientific Research (C) No. 17K03683, Scientific Research Scientific Research (C) No. 19K01633 and Scientific Research (C) No. 20K01630 from the Ministry of Education, Science, Sports, and Culture of Japan (MESSC), the Murata Science Foundation, and the Mitsubishi Foundation No. 30230.

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Correspondence to Go Yano.

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See Table A1.

Table A1 Descriptive statistic: state-owned enterprises (SOEs)

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Yano, G., Shiraishi, M. Economic and Political Motivations in Debt Finance in China: Bank Lending and Trade Credit Offering. Comp Econ Stud (2020).

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  • Debt finance
  • Economic motivation
  • Political motivation
  • Private firms
  • China

JEL Classification

  • G21
  • O16
  • O53
  • P34