Abstract
Pervasive media censorship in China is often seen as a strictly political issue. Although in past years reporters have had leeway to report on economic issues, the Chinese Party/state has moved to tamp down economic journalism, even arresting those who report on bad economic news. This shift brings to the fore an issue long ignored by social scientists – economic censorship. Economic censorship takes place when state-owned enterprises (SOEs) or large private companies pressure the state to censor negative reports or directly pay off media companies to quash such reports in favor of more positive ones. Such economic censorship distorts markets and shifts investor money away from new market entrants and towards large, well-resourced and well-connected SOEs. Using a database of Chinese newspaper articles from 2004 to 2006 and a separate database of newspaper articles, blog posts and micro-blog posts from 2010, and supplemented by secondary sources, this paper examines how media coverage is distorted by censorship and corruption to the benefit of China’s entrenched interests. In particular, I find that private and provincially owned companies receive much more press coverage than do their central government (SASAC) owned equivalents, controlling for a number of factors.
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Notes
SASAC’s official name in Chinese is the 国务院国有资产监督管理委员会or 国资委for short. Note that the number of companies owned by SASAC changes over time. When this research began in late 2015, SASAC officially owned 108 companies, but by late 2016 was down to 103, mostly through mergers. This consolidation has been accelerating over time, and as of September, 2017 had fewer than 100 companies under management, with numbers even lower today (though these mergers should not affect the results shown in this paper). The official SASAC list at http://www.sasac.gov.cn/n86114/n86137/index.html (last accessed Nov. 10, 2016) has been pulled from the web, but is available elsewhere.
These include ICBC, Bank of China, Agricultural Bank of China, China Construction Bank, China Everbright Bank, Orient Securities, and Cinda Asset Management, all of which were examined in the dataset.
Including PetroChina, Inner Mongolia Baotou Steel, and China Gezhouba (a dam holding company), all of which were examined in the dataset.
In particular, the major competitors that might benefit from China Mobile’s woes (China Telecom & China Unicom) are also SASAC SOEs, and so presumably would have an interest in seeing the story get out.
For more detailed information about how these samples were collected, see [10].
http://www.forbes.com/global2000/, last accessed Nov. 10, 2016.
Summary statistics indicate that the non-SASAC sample actually contains larger companies measured by sales, but this difference is not significant at the p < .05 level, and in any case is controlled for in the model.
Most Chinese companies report sales in Chinese RMB, the local currency, but most international sources like Forbes use USD, as I do here for consistency. I use the natural log transformation for sales data to reduce skewness.
A zero-inflated negative binomial model that accounts for “excess” zero counts in the data provides worse fit than a negative binomial model that excludes zero counts. An insignificant Vuong statistic further suggests that the negative binomial is more appropriate than a zero-inflated model.
In a 2014 survey using a stratified random sample 705 Sina Weibo users, around 5% said they used Twitter despite the Chinese government ban on access. This and other data suggest there are millions of Chinese netizens active on Twitter.
Note that the Communist Party’s Politburo Standing Committee in practice outranks the State Council, residing on the (more powerful) Party side of the Party/state system. The Chinese political system is complicated, and legally the National People’s Congress is the highest “state organ,” but in practice true administrative power resides not in the NPC but in the quasi-executive State Council, which generally shares membership with the Party Politburo.
As of 4/1/2016, the Shanghai Exchange had 1090 listed companies and the Shenzhen Exchange has 1761. Not all of the 108 SASAC-owned companies (down to 103 in late 2016 and even fewer since then) are exchange listed.
Indeed, many analysts have noted the weak link between Chinese underlying economic performance and stock market fluctuations.
This policy also probably hurts consumption, as bank savers get very little investment return, money that might otherwise be used for purchases.
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Acknowledgements
A previous version of this paper was presented at the annual Midwest Political Science Association conference in 2016. Special thanks to David Andersen, Tessa Ditonto, Indridi Indridason, Peter Lorentzen, Amy Erica Smith, Rachel E. Stern and Robert Urbatsch for their comments. All errors are my own. Funding was generously provided by the Lucken Faculty Fellowship at Iowa State University and by a Fulbright-Hays grant at the University of California, Berkeley for some of the original data collection. I have no conflicts of interest to declare with respect to this funding or data collection.
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Appendices
Appendix 1: Descriptive Statistics & Correlation Table
Correlation table:
SASAC | Blogs 2010 | News. 2010 | News. 2004–6 | Weibo 2010 | SOE | Central SOE | Sales | Energy Sector | Finance sector | Real estate sector | Tech sector | Sales(ln) | Sentiment | Negative Sentiment | Positive Sentiment | Overall coverage | Negativity | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SASAC | 1 | |||||||||||||||||
Blogs 2010 | −0.1021 | 1 | ||||||||||||||||
News. 2010 | −0.0291 | 0.3515 | 1 | |||||||||||||||
News. 2004–6 | −0.135 | 0.2052 | 0.4263 | 1 | ||||||||||||||
Weibo 2010 | −0.0967 | 0.7232 | 0.3785 | 0.1027 | 1 | |||||||||||||
SOE | 0.5388 | −0.2071 | −0.05 | −0.06 | −0.1709 | 1 | ||||||||||||
Central SOE | 0.8634 | −0.1116 | 0.1213 | 0.051 | −0.0898 | 0.6241 | 1 | |||||||||||
Sales | 0.1615 | −0.0119 | 0.4116 | 0.1632 | −0.0003 | 0.1506 | 0.283 | 1 | ||||||||||
Energy Sector | 0.2437 | −0.0531 | 0.0426 | −0.0683 | −0.0601 | 0.2636 | 0.2603 | 0.3208 | 1 | |||||||||
Finance sector | −0.4105 | −0.0816 | 0.0465 | 0.0918 | −0.047 | −0.2745 | −0.2456 | −0.0066 | −0.2349 | 1 | ||||||||
Real estate sector | −0.0679 | 0.0229 | −0.1068 | −0.0984 | −0.0257 | −0.1524 | −0.1011 | −0.0743 | −0.0987 | −0.1281 | 1 | |||||||
Tech sector | −0.2166 | 0.5387 | 0.2384 | 0.1133 | 0.4451 | −0.402 | −0.2509 | −0.0834 | −0.106 | −0.1375 | −0.0578 | 1 | ||||||
Sales(ln) | 0.1815 | −0.0225 | 0.2804 | 0.2186 | 0.0289 | 0.0964 | 0.2569 | 0.6489 | 0.1762 | −0.0278 | −0.0141 | −0.1628 | 1 | |||||
Sentiment | −0.1219 | 0.0367 | 0.0487 | 0.0216 | 0.0498 | −0.1713 | −0.1204 | −0.0074 | 0.0317 | 0.04 | −0.0917 | 0.0894 | 0.0145 | 1 | ||||
Negative Sentiment | −0.0946 | 0.2253 | 0.193 | 0.0993 | 0.2351 | −0.1964 | −0.118 | 0.0299 | −0.1317 | −0.0593 | 0.1165 | 0.2568 | −0.0356 | 0.2157 | 1 | |||
Positive Sentiment | −0.034 | −0.0677 | −0.0173 | −0.0071 | −0.0659 | 0.0232 | −0.0058 | −0.0313 | 0.1577 | −0.0634 | −0.1729 | −0.0099 | −0.0191 | 0.5498 | −0.2848 | 1 | ||
Overall coverage | −0.1439 | 0.7886 | 0.6129 | 0.7478 | 0.5822 | −0.1704 | −0.0227 | 0.1466 | −0.0654 | 0.008 | −0.0585 | 0.436 | 0.1524 | 0.0432 | 0.2274 | −0.0492 | 1 | |
Negativity | −0.0054 | 0.182 | 0.1568 | 0.0781 | 0.1834 | −0.0866 | −0.0375 | −0.0023 | −0.1552 | −0.0126 | 0.0917 | 0.1813 | −0.0908 | −0.1078 | 0.8108 | −0.5385 | 0.1824 | 1 |
Appendix 2: Alternative Model Specifications
Negative Binomial Regression Models
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
SASAC | −1.059* | −0.94* | 0.052 | −1.011* | −0.209 | −0.713* |
(Std. err.) | 0.49 | 0.473 | 0.302 | 0.406 | 0.275 | 0.335 |
SOE | −0.55 | −0.148 | ||||
(std. err.) | 0.427 | 0.395 | ||||
Central SOE | 1.39** | 0.975* | 0.923* | |||
(std. err.) | 0.481 | 0.415 | 0.434 | |||
Sales (ln) | 0.339*** | 0.348*** | 0.405*** | 0.348*** | 0.401*** | 0.324*** |
(std. err.) | 0.081 | 0.082 | 0.079 | 0.083 | 0.076 | 0.074 |
Energy sector | 0.408 | −0.404 | −0.301 | |||
(std. err.) | 0.444 | 0.454 | 0.453 | |||
Finance sector | −0.135 | −0.004 | 0.269 | |||
(std. err.) | 0.341 | 0.335 | 0.321 | |||
Real estate sector | −0.388 | −0.35 | −0.308 | |||
(Std. err.) | 0.799 | 0.782 | 0.811 | |||
Tech sector | 1.83** | 2.075*** | 2*** | 2.1*** | 2*** | |
(std. err.) | 0.555 | 0.501 | 0.55 | 0.465 | 0.445 | |
Intercept | 3.192*** | 2.902*** | 2.885*** | 2.885*** | 2.903*** | 3.629*** |
(std. err.) | 0.462 | 0.34 | 0.429 | 0.286 | 0.284 | 0.353 |
Alpha (dispersion parameter) | 3.333 | 3.36 | 3.396 | 3.38 | 3.422 | 3.838 |
(std. err.) | 0.354 | 0.361 | 0.355 | 0.358 | 0.353 | 0.392 |
Pseudo R2 | 0.025 | 0.025 | 0.023 | 0.024 | 0.022 | 0.011 |
Mahalanobis Distance Matching Model Balance Tables
For all models
Raw | Matched | |
|---|---|---|
Number of obs = | 205 | 410 |
Treated obs = | 102 | 205 |
Control obs = | 103 | 205 |
Model 1 – Covariate Balance Summary
Variable | Standardized Differences (SASAC vs. Non-SASAC) | Variance Ratio (SASAC vs. Non-SASAC) | ||
|---|---|---|---|---|
Raw | Matched | Raw | Matched | |
SOE | 1.448937 | 0.8784695 | 0 | 0 |
Central SOE | 4.069997 | 1.343204 | 0 | 0 |
Energy sector | 0.4706778 | 0 | 2.600495 | 1 |
Finance sector | −0.8686627 | −0.0128982 | 0.0891598 | 0.9780093 |
Real estate sector | −0.3890833 | 0 | 0.1886073 | 1 |
Tech sector | −0.2778535 | 0 | 0.3288368 | 1 |
ln(sales) | 0.1634128 | 0.3320067 | 1.685315 | 1.545139 |
Model 2 – Covariate Balance Summary
Variable | Standardized Differences (SASAC vs. Non-SASAC) | Variance Ratio (SASAC vs. Non-SASAC) | ||
|---|---|---|---|---|
Raw | Matched | Raw | Matched | |
Central SOE | 4.069997 | 1.28554 | 0 | 0 |
Energy Sector | 0.4650813 | 0 | 2.583051 | 1 |
ln(sales) | 0.1522055 | 0.1040366 | 1.685789 | 1.047357 |
Model 3 – Covariate Balance Summary
Variable | Standardized Differences (SASAC vs. Non-SASAC) | Variance Ratio (SASAC vs. Non-SASAC) | ||
|---|---|---|---|---|
Raw | Matched | Raw | Matched | |
Central SOE | 4.069997 | 1.311048 | 0 | 0 |
Finance sector | −0.8695692 | 0 | 0.0882945 | 1 |
ln(sales) | 0.1522055 | 0.1324289 | 1.685789 | 0.9875275 |
Model 4 – Covariate Balance Summary
Variable | Standardized Differences (SASAC vs. Non-SASAC) | Variance Ratio (SASAC vs. Non-SASAC) | ||
|---|---|---|---|---|
Raw | Matched | Raw | Matched | |
Central SOE | 4.069997 | 1.311048 | 0 | 0 |
Real estate sector | −0.3901649 | 0 | 0.1867769 | 1 |
ln(sales) | 0.1522055 | 0.1268187 | 1.685789 | 1.013392 |
Model 5 – Covariate Balance Summary
Variable | Standardized Differences (SASAC vs. Non-SASAC) | Variance Ratio (SASAC vs. Non-SASAC) | ||
|---|---|---|---|---|
Raw | Matched | Raw | Matched | |
Central SOE | 4.069997 | 1.317032 | 0 | 0 |
Tech sector | −0.2778535 | 0 | 0.3288368 | 1 |
ln(sales) | 0.1634128 | 0.1276886 | 1.685315 | 0.9966126 |
Model 6 – Covariate Balance Summary
Variable | Standardized Differences (SASAC vs. Non-SASAC) | Variance Ratio (SASAC vs. Non-SASAC) | ||
|---|---|---|---|---|
Raw | Matched | Raw | Matched | |
SOE | 1.448937 | 0.8677829 | 0 | 0 |
Tech sector | −0.2778535 | 0 | 0.3288368 | 1 |
ln(sales) | 0.1634128 | 0.0326312 | 1.685315 | 1.07079 |
Model 7 – Covariate Balance Summary
Variable | Standardized Differences (SASAC vs. Non-SASAC) | Variance Ratio (SASAC vs. Non-SASAC) | ||
|---|---|---|---|---|
Raw | Matched | Raw | Matched | |
SOE | 1.448937 | 0.8330466 | 0 | 0 |
Real estate sector | −0.3901649 | 0 | 0.1867769 | 1 |
ln(sales) | 0.1522055 | 0.0256053 | 1.685789 | 1.13225 |
Model 8 – Covariate Balance Summary
Variable | Standardized Differences (SASAC vs. Non-SASAC) | Variance Ratio (SASAC vs. Non-SASAC) | ||
|---|---|---|---|---|
Raw | Matched | Raw | Matched | |
SOE | 1.448937 | 0.8436485 | 0 | 0 |
Finance sector | −0.8695692 | 0 | 0.0882945 | 1 |
ln(sales) | 0.1522055 | 0.020228 | 1.685789 | 1.119453 |
Model 9 – Covariate Balance Summary
Variable | Standardized Differences (SASAC vs. Non-SASAC) | Variance Ratio (SASAC vs. Non-SASAC) | ||
|---|---|---|---|---|
Raw | Matched | Raw | Matched | |
SOE | 1.448937 | 0.8330466 | 0 | 0 |
Energy sector | 0.4650813 | 0 | 2.583051 | 1 |
ln(sales) | 0.1522055 | 0.0101339 | 1.685789 | 1.186453 |
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Hassid, J. Censorship, the Media, and the Market in China. J OF CHIN POLIT SCI 25, 285–309 (2020). https://doi.org/10.1007/s11366-020-09660-0
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DOI: https://doi.org/10.1007/s11366-020-09660-0