Abstract
We propose that local corruption distorts the allocation of government-controlled resources and impairs the contract environment, thereby reducing firms’ use or suppliers’ provision of trade credit. We use a sample of Chinese-listed firms from 2007 to 2020 to examine the role of local corruption in firms’ access to trade credit and find that the level of local corruption is negatively related to firms’ trade credit use. This effect is more pronounced in firms with weak (vs. strong) internal governance, slack (tight) external monitoring and high (low) supplier concentration. The results of path analysis show that local corruption extends short-term bank loans as well as government subsidies and impairs firms’ accounting quality, thereby inhibiting firms’ demand for or suppliers’ provision of trade credit. Moreover, the post-2012 anti-corruption campaign in China plays a significant role in correcting the misallocation of trade credit caused by corruption. The results of this study illuminate the negative external effects of local corruption on trade credit.
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Notes
For example, El Ghoul and Zheng (2016) show that trade credit comprised 10.84–32.37% of firms’ total sales in their sample of more than 27,000 firms across 49 countries from 1993 to 2013. Meanwhile, firms in Asian countries, such as China and India, tended to extend more trade credit.
The Chinese central government initiated a new round of anti-corruption campaigning by announcing the ‘Eight-point Policy’, a policy directive ordering cadres to forego conspicuous perks and other clear signs of favouritism. To enforce the anti-corruption initiatives, the Central Commission for Discipline Inspection began to dispatch central inspection teams to various provinces from December 2012. Therefore, we regard 2013 as the starting year of the anti-corruption campaign.
The level of local corruption in this study is comparable to that in research in Chinese settings (Xu and Yano, 2017; Zhang et al., 2019b). For example, Zhang et al. (2019b) measured local corruption by the number of corrupt officials per 10,000 civil servants and found an average level of local corruption equal to 34.589 between 1998 and 2012 in China. The average level of corrupt official cases per 100 officials in our sample period is 0.273 (Table 1).
In contrast, private banks in the United States are more independent and more vigilant against political corruption; therefore, local corruption hinders their lending activities (Bermpei et al., 2021).
The sample period begins in 2007 because China’s listed firms only just reformed the split-share structure, which refers to the situation in which some shares of listed firms are listed and circulated, while other shares are temporarily not listed for circulation. The reform of the split-share structure eliminates differences between non-tradable and tradable shares, clarifies the nature of firm equity and is conducive to introducing market-oriented incentives and restraint. This reform creates healthy self-restraint and effective external monitoring mechanisms and further improves corporate governance. Our sample period ends in 2020 because the Procuratorial Yearbook of China covering 2021 local corruption data is not yet available.
Since April 22, 1998, the Shanghai and Shenzhen Stock Exchanges have conducted special treatments on the stocks of listed firms with abnormal financial or other conditions and appended ‘ST’ before their designations. Thus, such types of stocks are called ST stocks.
As mentioned in earlier studies, such as Huang et al. (2017), Ellis et al. (2020) and Huang and Yuan (2021), as well as by an anonymous reviewer, there is inevitably measurement error in the quantification of local corruption. For example, a small number of corrupt officials have cross-regional employment experience, resulting in conflicts between places of political corruption and places of filing cases. To mitigate the impact of such measurement bias, we employ alternative measures of local corruption and exclude the samples from some specific regions in the robustness tests. See the ‘Empirical Results’ section for more detail.
As Ellis et al. (2020) and Huang and Yuan (2021) suggest, this is especially important because the primary regressor of interest, local corruption, is measured at the province level and is therefore perfectly correlated across firms within the same province. Note that our results are also robust to clustering at the firm level.
As shown in Table 1, the standard deviation of trade credit is 0.163. Since the independent variable (CORRUPTION) has a significant coefficient of − 0.044 in Column 3 of Table 2, a one-unit increase in the independent variable could explain 0.044 / 0.163 100% = 26.99% of the standard deviation of the dependent variable (TRADE_CREDIT).
From 2007 to 2009, the Tianjin municipality government cracked down on corruption in a series of corruption cases involving several senior local officials, including the then Chief Procurator, the former Director of the Police Bureau and the then Secretary of the Politics and Law Committee. In 2010, the Tianjin Municipal Commission for Discipline Inspection launched the ‘433 clean government project’ to conduct continuous anti-corruption education for local officials to build a clean and honest civil service. Therefore, the degree of corruption in Tianjin declined after 2010.
In contrast to the subsequent dynamic classification, we use the static degree of corruption from 31 provinces in the cross-section to distinguish between high- and low-corruption regions. When the static degree of corruption in a region (i.e., the average value of 11 observations in the time series consistent with Fig. 1) is lower than the national median, we define it as a static low-corruption region; otherwise, it is a static high-corruption region. During our sample period, we observe that 158 firms moved their headquarters, of which 12 (61) firms migrated from a static high-corruption (low-corruption) region to a static low-corruption (high-corruption) region and 85 firms changed headquarters within static high-corruption regions or low-corruption regions. Therefore, we mainly focus on the static transition of corruption culture resulting from relocating headquarters from static low-corruption regions to static high-corruption regions.
In China’s political system, only officials at or above the provincial and ministerial level are appointed across provinces by the central government, while most public officials are employed within a province. We manually collect the publicly available information about these provincial and ministerial officials’ positions from the website of the CPC Central Commission for Discipline Inspection (https://www.ccdi.gov.cn). We define corrupt officials at or above the provincial and ministerial level whose corruption occurred in their previous posting in another province as ‘non-locally corrupt’ officials.
Note that among more than 680,000 corrupt officials during 2007 and 2020 in China, only 104 corrupt officials at or above the provincial and ministerial level had multi-provincial experience. This finding is consistent with the view in The Development History of China’s Anti-corruption Campaign (published by the Party School Press of the CPC Central Committee in 2021. More information may be available at the website: http://www.zgffclfzs.cn) that local officials are more prone to corruption than non-local officials because the former could establish complex social networks and interest chains through their long-term local experience. In further robustness tests, we also add non-locally corrupt officials to the provinces in which they previously worked or remove the non-locally corrupt officials from the provinces where the corruption cases were filed and add them to the provinces in which they previously worked. We find that the negative relationship between local corruption and trade credit remains. We do not report these results to save space; however, the data are available upon request.
The 2005 World Bank Enterprise Survey of China did not cover Xizang firms; hence, 88 observations were lost from the full sample.
Chinese provincial governments at all levels subscribe to the provincial CPC newspapers, which aim to publicise the party’s programmes, principles and policies. The provincial CPC newspapers play a key role as the party’s mouthpiece; therefore, its condemnations of and reports on corrupt officials reflect well the determination of provincial leaders and their democratic development organisations to actively respond to the anti-corruption policies and directives of the CPC Central Committee.
We follow Smith (2016) in employing the instrumental variables estimation and propensity score-matching approaches to support the robustness of our main findings. We do not report the results to save space, but they are available upon request.
After Xizang, Beijing has the lowest degree of local corruption (Panel B, Fig. 1).
The total number of observations in the two groups is smaller than that of the full sample because individual firm observations that appeared only once in a group were omitted when we controlled for firm fixed effects in our regressions. A similar phenomenon exists in all the group regressions in this study.
In the Chinese stock market, it is mandatory for listed firms to disclose information about the total procurements from their top five suppliers. For business confidentiality, most listed firms often disclose their supplier information anonymously (e.g., ‘the largest supplier’ or ‘supplier No. 1’). In contrast, listed firms can voluntarily disclose other detailed information about a single supplier, such as supplier name, location and individual procurement share. This opacity not only restricts our investigation of supplier characteristics but also leads to the loss of large numbers of observations when examining the impacts of the purchase proportion from the largest supplier (TOP1SUPPLIER) and suppliers’ Herfindahl–Hirschman Index values (SHHI).
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Acknowledgements
Xiaofeng Quan appreciates the support by the Research Funds of Research Funds of Major Project of National Social Science Foundation of China (Project No 17ZDA087). Any errors that remain are our own.
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Appendices
Appendix A: Variable Definitions
Variable | Definition |
|---|---|
TRADE_CREDIT | The percentage ratio of accounts payable to total liabilities |
CORRUPTION | Local corruption is measured by the provincial number of cases involving corrupt officials filed per 100 local officials |
HIGH_CORRUPT | High-corruption region dummy that equals 1 if the degree of local corruption is higher than the national median in year t and 0 otherwise |
LOGSIZE | The natural logarithm of total assets |
BOOK-TO-MARKET | The book-to-market ratio is the book value of equity divided by the market value of equity |
ROE | Firm profitability as the return on equity |
GROWTH | Firm growth; that is, the growth rate of primary business income |
LOSS | Earnings loss is a dummy variable equal to 1 if the firms’ earnings are negative and 0 otherwise |
TANGIBLE | The proportion of tangible assets, including property, plant and equipment, divided by total assets |
LOGAGE | The natural logarithm of 1 plus the firms’ listed age |
SOE | State-owned enterprise dummy that equals 1 if the firm is a state-owned enterprise and 0 otherwise |
RISK | Stock return volatility is the standard deviation of the daily stock return of the measurement year |
TOP5SUPPLIER | Percentage of shares purchased from all top five suppliers |
GDP | Regional per capital GDP, that is, the natural logarithm of local per capital GDP |
EDUCATION | Regional education degree; that is, the proportion of the local population with an associate college education or higher |
POPULATION | Regional population, the natural logarithm of the total local population measured in 10,000 s |
HEAD_CHANGE | Dummy variable equal to 1 if firms moved their headquarters and 0 otherwise |
POSTa | Dummy variable equal to 1 for observations after firms move their headquarters from a static low-corruption region to a static high-corruption region and 0 otherwise |
CORRUPT_JUMP | Dummy variable equal to 1 if regions where firms are headquartered experienced an effective dynamic jump in corruption culture and 0 otherwise |
POSTb | Dummy variable equal to 1 for observations after an effective dynamic jump from low to high corruption in the corruption culture in regions where firms are headquartered and 0 for changes before the effective dynamic jump in corruption culture |
SC_TRADE_CREDIT | An alternative measurement of trade credit, namely the percentage ratio of total accounts payable divided by total sales costs |
A_TRADE_CREDIT | An alternative measurement of trade credit, namely the percentage ratio of total accounts payable divided by total assets |
T_TRADE_CREDIT | An alternative measurement of trade credit, namely the sum of accounts payable, bills payable and prepayments divided by total assets |
CORRUPTION_NEW | An alternative measurement of local corruption using the provincial number of cases filed against corrupt officials minus the number of corrupt provincial and ministerial officials with multi-provincial experience, deflated by the total number of local officials |
ENTERTAINMENT | An alternative measurement of local corruption, following Huang et al. (2017), namely the average ratio of firms’ entertainment and travel costs to sales in a province using data from the 2005 World Bank Enterprise Survey of China |
ANTI_CORRUPTION | Anti-corruption efforts by local CPC newspapers, namely the ratio of the total number of reports on corrupt officials divided by the total number of news articles in provincial CPC newspapers |
LOG_CORRUPTIONa | An alternative measurement of local corruption, namely, the natural logarithm of 1 plus CORRUPTION, minus the natural logarithm of 1 plus ANTI_CORRUPTION |
LOG_CORRUPTIONb | An alternative measurement of local corruption, namely CORRUPTION divided by ANTI_CORRUPTION |
SHORT_LOAN | Short-term loan ratio, which is banks’ short-term loans divided by total assets |
SUBSIDY | Government subsidies, that is, firms’ access to government subsidies divided by total sales |
ACCRUALS | Discretionary accruals, calculated using the modified Jones model developed by Dechow et al. (1995) |
QUALITY | Quality of financial reporting measured following Appendix C in Chen et al. (2020) |
ΔSHORT_LOAN | The change in short-term loan ratios, which is the SHORT_LOAN of the current year minus the SHORT_LOAN of the previous year |
ΔSUBSIDY | The change in government subsidies, which is the SUBSIDY of the current year minus the SUBSIDY of the previous year |
NOTES_RATIO | Notes payable ratio, namely, the percentage of notes payable among all credit extensions, including both accounts payable and notes payable |
LAG_ROE | Past firm profitability, which is the return on equity in the previous year |
POLITICAL | Political connection dummy that equals 1 if the chairperson or CEO of a firm has government experience and 0 otherwise |
LAG_ACCRUALS | Discretionary accruals in the previous year |
LAG_QUALITY | Quality of financial reporting in the previous year |
Appendix B: Correlation Analysis
Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(1) TRADE_CREDIT | 1 | −0.032 | −0.242 | −0.205 | 0.069 | 0.060 | −0.057 | −0.044 | −0.250 | −0.128 | 0.028 | −0.151 | 0.114 | 0.064 | 0.082 |
(2) CORRUPTION | −0.044 | 1 | −0.046 | 0.017 | −0.046 | −0.024 | 0.012 | 0.126 | 0.022 | 0.008 | 0.044 | 0.029 | −0.471 | −0.506 | 0.323 |
(3) LOGSIZE | −0.246 | −0.083 | 1 | 0.592 | 0.150 | 0.040 | 0.001 | −0.040 | 0.373 | 0.291 | −0.284 | −0.197 | 0.123 | 0.142 | −0.060 |
(4) BOOK-TO-MARKET | −0.206 | −0.003 | 0.602 | 1 | −0.186 | −0.104 | 0.023 | 0.037 | 0.297 | 0.270 | −0.374 | −0.109 | 0.050 | 0.053 | −0.049 |
(5) ROE | 0.073 | −0.018 | 0.115 | −0.094 | 1 | 0.337 | −0.159 | −0.105 | −0.105 | −0.031 | −0.096 | −0.080 | 0.014 | −0.010 | 0.021 |
(6) GROWTH | 0.009 | −0.011 | 0.034 | −0.080 | 0.223 | 1 | −0.101 | −0.043 | −0.150 | −0.081 | −0.037 | −0.014 | −0.008 | −0.036 | 0.044 |
(7) LOSS | −0.054 | 0.009 | 0.001 | 0.024 | −0.302 | −0.077 | 1 | 0.054 | 0.039 | 0.039 | 0.039 | 0.049 | −0.029 | −0.010 | −0.031 |
(8) TANGIBLE | −0.113 | 0.119 | 0.010 | 0.088 | −0.059 | −0.053 | 0.071 | 1 | −0.018 | 0.118 | −0.005 | 0.060 | −0.226 | −0.196 | 0.035 |
(9) LOGAGE | −0.257 | 0.045 | 0.340 | 0.303 | −0.095 | −0.071 | 0.041 | 0.042 | 1 | 0.405 | −0.146 | −0.007 | −0.007 | 0.063 | −0.133 |
(10) SOE | −0.123 | 0.008 | 0.303 | 0.278 | −0.002 | −0.061 | 0.039 | 0.166 | 0.397 | 1 | −0.067 | −0.038 | −0.170 | −0.048 | −0.203 |
(11) RISK | 0.007 | 0.056 | −0.273 | −0.357 | −0.101 | 0.009 | 0.041 | −0.014 | −0.112 | −0.051 | 1 | 0.062 | −0.147 | −0.100 | −0.001 |
(12) TOP5SUPPLIER | −0.136 | 0.034 | −0.177 | −0.088 | −0.061 | 0.010 | 0.056 | 0.055 | 0.019 | −0.013 | 0.050 | 1 | −0.029 | −0.026 | −0.004 |
(13) GDP | 0.111 | −0.410 | 0.153 | 0.070 | −0.010 | −0.009 | −0.027 | −0.227 | −0.008 | −0.182 | −0.193 | −0.032 | 1 | 0.832 | −0.088 |
(14) EDUCATION | 0.031 | −0.577 | 0.186 | 0.068 | −0.011 | −0.013 | −0.004 | −0.183 | 0.051 | 0.050 | −0.084 | −0.025 | 0.683 | 1 | −0.404 |
(15) POPULATION | 0.085 | 0.271 | −0.085 | −0.042 | 0.024 | 0.016 | −0.031 | 0.031 | −0.141 | −0.194 | −0.007 | −0.030 | −0.075 | −0.513 | 1 |
Appendix C: Changes in Use of Trade Credit Caused by Local Corruption
Panel A: Annual trends in local corruption in Shanghai vs. Henan.
Panel B: Annual changes in FMG’s use of trade credit from 2007 to 2020 (FMG moved its headquarters in 2014).
Panel C: Annual trends of local corruption in Tianjin.
Panel D: Tianjin firms’ use of trade credit (2007–2009 vs. 2010–2020).
2007–2009 | 2010–2020 | Difference tests | ||||||
|---|---|---|---|---|---|---|---|---|
(High-corruption period) | (Low-corruption period) | (High corruption—Low corruption) | ||||||
Obs | Mean | Median | Obs | Mean | Median | Difference in mean value | Difference in median value | |
TRADE_CREDIT | 79 | 0.119 | 0.149 | 360 | 0.194 | 0.226 | −0.075*** | −0.077*** |
(−3.91) | (−4.36) | |||||||
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Cai, W., Quan, X. & Tian, G.G. Local Corruption and Trade Credit: Evidence from an Emerging Market. J Bus Ethics 185, 563–594 (2023). https://doi.org/10.1007/s10551-022-05215-w
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DOI: https://doi.org/10.1007/s10551-022-05215-w