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How does credit market innovation affect the fiscal policy of state governments?

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Abstract

We investigate whether the introduction of state credit default swaps (CDS) affects the fiscal spending of state governments. A difference-in-differences analysis indicates that CDS-referenced states show a significant decline in government spending compared to non-CDS-referenced states when state CDS issuance introduces a more transparent fiscal information environment. We also find an improvement in state media coverage post-CDS-initiation. The results suggest that CDS initiation can provide valuable information for the fiscal conditions of state governments, which in turn leads to spending cuts. The evidence indicates the important role that credit market innovation activities play in the fiscal decisions of state governments.

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Notes

  1. Bankruptcy does not apply to U.S. states’ general obligations, while non-state municipal entities can enter Chapter 9 of the U.S. Bankruptcy Code similar to Chapter 11 for corporations.

  2. Das et al. (2014), on the other hand, argue that financial innovations could weaken market efficiency if the benefits generated by innovations accrue to only a few market participants and have a negative effect on the rest of the market.

  3. To rule out the possibility that the introduction of CDS may be “simultaneous” to a worsening of the deficit or financial conditions of the state government, we also use the current and lagged state deficit and debt as the control variables in the regression. The estimated results remain unchanged. Short-term movements in population are associated with state government spending (Nakamura and Steinsson 2014), and the allocation of federal funds at the state level often depends on state population levels (Suárez Serrato and Wingender 2016). We also use the government spending per capita as the dependent variables instead of controlling the state population and obtain similar results.

  4. We also use robustness standard errors that clustered at the state level for Eqs. (1) through (3) and obtain similar results.

  5. Our conclusion remains unchanged if we use the period 20,012,013 to estimate the model parameters or exclude post-initiation years of CDS-referenced states from the estimation (as in Subrahmanyam et al. (2014)).

  6. The results are similar if (1) we use two non-CDS-referenced states with the closest propensity score to the CDS-referenced state or (2) we require that the maximum difference between the propensity score of the non-CDS-referenced state and the propensity score of the CDS-referenced state should not exceed 10% in absolute value.

  7. We thank an anonymous reviewer for providing this explanation.

  8. NewsLibrary.com covers 6780 newspapers and other news sources for news research. We lose 1068 observations because of data availability.

  9. Following Gentzkow et al. (2006) and Campante and Do (2014), we use Ln(Monday) to control for newspaper size.

  10. The U.S. Census Bureau terminated the collection of data for the Statistical Compendia program effective October 1, 2011. Because of this data limitation, we can collect circulation data from the Census website (https://www.census.gov/library/publications/time-series/statistical_abstracts.html) only from 2001 to 2009.

  11. The characteristics of newspapers (e.g., specialization, nationalization, or circulation) may lead to heterogeneous effects on state fiscal spending, which is an important direction for future research. We are grateful to the reviewer for providing this idea.

  12. We do not include CDS_Trading and Liquidity because of multicollinearity.

  13. The conclusion remains unchanged if the crisis period is defined as 20,072,009 (Mishkin 2011) or 20,062,008 (Longstaff 2010).

  14. When we use the general expenditure or direct expenditure as the dependent variable, the main results remain unchanged.

  15. The results are robust to selection with replacement. There are 25 observations (non-CDS-referenced states) for each CDS-referenced state, due to the true CDS-referenced states are excluded. The number of observations is 3,750 (= 25 observations × 25 CDS-referenced states × 6 years).

  16. We obtain similar results for state governments’ general expenditure and direct expenditure.

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Funding

Ministry of Science and Technology, Taiwan, MOST 107-2410-H-004-026-MY3, Sheng-Syan Chen, MOST 110-2410-H-468-006-MY2, Hsien-Yi Chen.

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  1. Department of Finance, College of Management, Providence University, Taichung City, Taiwan

    Hsien-Yi Chen

  2. Department of Finance, College of Management, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, Taiwan

    Sheng-Syan Chen

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Appendix A

Appendix A

See Fig. 

Fig. 2
figure 2

Markit’s state CDS sample distribution by calendar year

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2, Tables

Table 13 Variable definitions and data sources
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13 and

Table 14 Logistic regression estimates
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14.

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Chen, HY., Chen, SS. How does credit market innovation affect the fiscal policy of state governments?. Rev Quant Finan Acc 62, 389–420 (2024). https://doi.org/10.1007/s11156-023-01207-7

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