Political connection status decisions and benefits for firms experiencing financial difficulties in emerging markets

  • Received December 6, 2021;
    Accepted July 26, 2022;
    Published August 4, 2022
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  • Article Info
    Volume 20 2022, Issue #3, pp. 164-177
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    1 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

There is relatively little research exploring the benefits of political connection status decisions for firms experiencing financial difficulties in emerging markets. This paper investigates financially distressed firms that benefit from their political connection status in Indonesia. This study uses three measurements of financial distress as the dependent variables: Altman Z-score, negative working capital, and interest coverage ratio. Firm size, profitability, liquidity, leverage, and operating cash flow are independent variables. Quarterly data for the period from 2012 to 2018 from 327 non-financial companies were obtained from the Indonesia Stock Exchange. To analyze the relationship between financially distressed companies and decisions on the status of political connections as supporters or opponents, the random effects probit model (REPM) was used. The results show that firms with political status as opposition to the government have a strong positive correlation with experiencing financial difficulties. Meanwhile, companies with political connections as government supporters have a strong negative correlation. Companies with politically connected status as opposition experience financial difficulties in terms of negative working capital and interest coverage ratios. Then, debt financing was not found to be a significant problem for financially distressed companies with a political support status of the government. There are indications that they have benefited from political connections, such as more accessible debt financing.

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    • Table 1. Descriptive statistics
    • Table 2. Model 1, random effects probit model, dependent variable: Altman Z-Score (ALTMAN)
    • Table 3. Model 2, random effects probit model, dependent variable: negative networking capital (NWC)
    • Table 4. Model 3, random effects probit model, dependent variable: interest coverage ratio (NICR)
    • Conceptualization
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    • Data curation
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    • Formal Analysis
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    • Funding acquisition
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    • Investigation
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    • Methodology
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    • Project administration
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    • Resources
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    • Software
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    • Supervision
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    • Validation
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    • Visualization
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    • Writing – original draft
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    • Writing – review & editing
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