Blockchain Information Disclosure and Trade Credit

ABSTRACT

Blockchain technology is an important part of the infrastructure of the digital economy. We investigate the relationship between blockchain information disclosure and trade credit. Using Python to extract the information from MD&A, the results show that blockchain information disclosure significantly increases an enterprise’s trade credit. This effect is stronger for enterprises with weak bargaining power and those located in areas with high social trust. The mechanism of the effect is improved corporate governance and reduced information asymmetry, which reduce default risk. The results are consistent when we consider heterogeneity issues. Our research clarifies the significant role of blockchain technology in the real economy.

KEYWORDS:

• Blockchain technology
• trade credit
• information disclosure
• textual analysis

JEL:

• D22
• G32
• M21

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1. April 20, 2020, National Development and Reform Commission press conference. http://www.scio.gov.cn/xwfbh/gbwxwfbh/xwfbh/fzggw/Document/1677563/1677563.htm.

2. Source: Announcement by the State Internet Information Office on the issuance of the fourth batch of domestic blockchain information service record numbers. http://www.cac.gov.cn/2020–10/28/c_1605447893747716.htm.

3. Source: April 9, 2020, “Opinions of CPC Central Committee and State Council on Building a More Complete System and Mechanism for Market-oriented Allocation of Factors.” http://www.gov.cn/zhengce/2020–04/09/content_5500622.htm.

4. Source: Announcement of the State Internet Information Office on the issuance of the fourth batch of domestic blockchain information service record numbers. http://www.cac.gov.cn/2020–10/28/c_1605447893747716.htm.

5. The interval for the sample of regional financial technology companies constructed by Song, Zhou, and Si (2021) is 2011–2018, so the sample interval of the instrumental variables in this paper is also 2011–2018.

6. The control variables are degree of marketization (Market), degree of industry competition (HHI), proportion of independent directors (Independent), corporate value (TobinQ), whether the company violates regulations (Violation), the natural logarithm of executive compensation (Salary), economic policy uncertainty (EPU), GDP growth rate (GDPGR) and inflation (CPI). CV is the same as in the main model.

7. Huang, Teoh, and Zhang (2014) and Bushee, Gow, and Taylor (2018) use regression by year to obtain fitted values and residual errors mainly because more variables are used for fitting in the regression, and a higher degree of freedom is required to make a reasonable estimation. Although there are many variables in the by year and by industry regressions, the sample size for each group of regressions is relatively small, and it is difficult to obtain an appropriate estimation result. Model (2) has many variables and the same problems as the referenced research. Therefore, the industry effect is controlled in the regression, and regression by year is used for fitting.

8. From 2001 to 2010, listed companies were rated excellent, good, qualified or unqualified; since 2011, A, B, C and D have been used instead. Companies rated excellent or good (or A or B) have high-quality information disclosure relative to their peers.

Additional information

Funding

The work was supported by the National Science Foundation of China [NSFC:72202079; 72002043; 62002068]; General Project of Philosophy and Social Sciences of Guangdong Province (GD22CGL42); Innovation Team Project of Guangdong Provincial Department of Education (2022WCXTD020).