Download The Financial Reporting Practices of State

Political Incentives to Suppress Negative Financial
Information: Evidence from China
Joseph D. Piotroski
T.J. Wong
Tianyu Zhang
Conference on Contemporary Issues of Firms and Institutions
Financial Transparency in China
The “common consensus” is that Chinese firms suffer from poor corporate
governance and weak corporate transparency.
For example, China ranked 2nd in terms of corporate opacity among a set of 40
developing economies (Gelos and Wei, 2005).
In terms of market-based measures of the information environment:

China ranked 2nd in terms of stock price synchronicity (Morck, Yeung and Yu,
2000)

Heightened degree of negative skewness in returns, consistent with the
suppression of negative financial information / downside trading frictions.
In terms of financial reporting practices

Low levels of timely loss recognition (Ball, Robin and Wu, 2001).

Prevalence of propping activity among state-controlled firms (Jian and Wong,
2006)
Impact of Primitive Institutions on Financial Reporting Practices
Prior research shows that legal, political, financial, regulatory and cultural
institutions exert strong pressures on economic behavior and outcomes.
Institutions associated with strong investor protections are associated with more
favorable financial reporting practices
• Less earnings management (Leuz, Nanda and Wysocki, 2003)
• Greater corporate transparency (Bushman, Piotroski and Smith, 2004)
• Stronger timely loss recognition practices (Ball, Kothari and Robin, 2000)
• Greater earnings informativeness (Fan and Wong, 2002; DeFond Hung and
Trezevant, 2004)
• Greater use of high quality auditor (Francis, Khurana and Pereira, 2003)
•
Cross-country variation in financial reporting are driven by market demands
and contracting-related forces.
However, other institutions in the economy can produce adverse incentives.
•
One such potential institutional is the State’s control of economic assets.
Adverse Incentives Created by State Ownership of Firms
Economic policies of the State frequently reflect the desire of politicians to
consolidate power and accumulate wealth (e.g., Lindbeck (1976); North (1990);
Olson (1993))
•
State controlled firms are not oriented towards maximizing firm value.
• Politicians use their control to achieve political objectives or to maximize
their private benefits (e.g., Shleifer and Vishny, 1993; LaPorta et al., 2002;
Rajan and Zingales, 2003).
•
These objectives produce weak corporate governance practices and
enormous inefficiencies among state firms (Shleifer and Vishny, 1997;
OECD, 2005; Wurgler, 2000).
• Poor performance vs. non-state-owned firms; Inefficient investment behavior and
asset management.
•
Moreover, serious conflicts can arise between the State (as the controlling
shareholder) and minority shareholders.
Research Question: Do political forces create adverse financial reporting incentives
among publicly-traded state-controlled firms? And do these forces dominate
traditional market demand and contracting-related incentives in certain settings?
Prior research: “Political Costs” and Financial Reporting Practices
Political forces are expected to shape the financial reporting incentives of
non-state-controlled firms
•
Watts and Zimmerman (1986): Accounting choices are influenced by the
expected political costs of a given financial reporting outcome.
•
Political costs include:
•
•
•
•
•
Heightened tax burdens (e.g., windfall profits tax).
Greater regulation.
Threat of greater government intervention into firm’s business activities.
Outright expropriation of productive assets.
And a host of other direct and indirect costs.
Prior cross-country research documents that political forces shape financial
reporting practices among non-state-controlled firms.
•
Bushman, Piotroski and Smith (2004); Bushman and Piotroski (2006); Leuz
and Oberholzer (2007):
•
Basic conclusions: As the likelihood of government intervention increases, firms
have an incentive to reduce transparency and to tilt reported valuations to
minimize these costs.
Prior research: “Political Costs” and Financial Reporting Practices
Minimal evidence on how political forces shape the financial reporting
incentives and practices of state-controlled firms.
•
Wang, Wong and Xia, 2006; Gul, 2006; Guedhami and Pittman, 2007:
Examine the impact of political economy forces on audit choice and audit
practices among newly privatized firms.
•
Jain and Wong (2006) document the use of related party transactions that
are designed to allow the listed firm to meet specific “bright line” profitability
targets in the context of China.
In this paper, we examine the impact that political incentives have on the
financial reporting practices of state-controlled entities.
Focus on one particular dimension: The incentive to release or suppress
negative financial information in a timely manner.
Focus on one reporting environment: The People’s Republic of China
Motivation: Why China?
Vast majority of Chinese listed firms are state-controlled enterprises.
Political forces matter in China; considerable variation across regions.
•
Differences in economic policy and performance (Value max. versus Full Employment).
•
Differences in ownership structure (Decentralized pyramids versus direct control).

Differences in the type of shares issuances (A shares; H shares; Overseas listings).
•
Differences in the level of investor protections, regulation and market development.
•
Differences in level of domestic / foreign investor interest.
Economic Significance: China is the world’s largest “developing” economy
•
•
•
4th largest GDP; Growth in GDP expected to be 10.5% in 2007.
$60 Billion (US) in actual foreign investment in 2006; Commitments 3x.
Recent adoption of IFRS.
Chinese firms suffer from governance conflicts that jointly
(a) create inefficient behavior,
(b) gives rise to demand for information about bad outcomes and
(c) creates incentives to suppress this information.
Incentives to Release Negative Financial Information in China
From the traditional market demand and contracting perspective, greater transparency
will benefit the firm and the State.
•
Limit the consumption of private benefits by controlling shareholders.
•
Increase the ability of both the State and foreign investors to monitor local politicians and
managers
•
Reduce information gathering costs
•
Improve the efficiency of capital allocation and asset management decisions.
•
Lead to greater levels of foreign direct investment (e.g., Gelos and Wei, 2005)
•
Lower the firm’s / country’s cost of capital and enhance market development.
Foreign investors have a demand for greater transparency.
Impact of decentralization through pyramidal ownership arrangements.
•
Information asymmetry between owners and managers creates demand for information
about bad outcomes (for monitoring purposes).
•
Decentralization reduces the likelihood of government reprisal for managers reporting
losses.
Would expect to observe better transparency in regions with stronger investor
protections, pro-market development policies and greater foreign investment.
Incentives to Suppress Negative Financial Information in China
Managers and local politicians incur a personal cost by reporting poor performance.
•
Performance influences political promotions and demotions (Chen, Li and Zhou, 2005; Li
and Zhou, 2005).
Bad news could have material impact on foreign investment activity and perceptions.


Negative news translates into a “loss of face” for local politicians and the State.
Negative news undermines investor confidence, leading to a ‘torpedo’ effect.
In both cases, the cost of reporting poor performance is expected to be greater in regions
with strong expected performance, value max. orientation and strong foreign interest
than in neglected or underperforming regions.
Suppression of bad news allows politicians and politically astute managers to
•
•
•
•
Hide inefficiencies in project selection and asset management.
Hide expropriation-related activities from minority shareholders.
Mask the inefficient allocation of resources to achieve political objectives.
Hide the diversion of resources as a result of political cronyism and corruption.
•
The value of retaining control is expected to be larger in “wealthy” provinces and
provinces with greatest foreign investment.
Empirical Proxies for Financial Reporting Incentives
Strength of provincial-level legal and market-development institutions.
•
Marketization index (Fan and Wang, 2001).
•
Legal environment Index (Fan and Wang, 2001).
•
Property rights protection index (Fan and Wang, 2001).
•
Deregulation index (Demerger et al, 2002).
Provincial conditions and government policy incentives
•
Unemployment rate in the region.
•
Average return on equity for state-owned firms in the region.
•
Average percentage of non-operating assets.
•
R&D spending as a percentage of fiscal expenditures.
Incentives created by heightened foreign investment
•
Provincial and Industry-level measures of foreign investment activity
• FDI / GDP each year.
• Foreign ownership (average percentage of foreign capital to total capital).
• Fixed asset investment: FDI / SOE
Empirical Proxies for Financial Reporting Incentives
Strength of provincial-level legal and market-development institutions.
•
Marketization index (Fan and Wang, 2001).
•
Legal environment Index (Fan and Wang, 2001).
•
Property rights protection index (Fan and Wang, 2001).
•
Deregulation index (Demerger et al, 2002).
Market Demand and
Contracting arguments
Transparency will be
greater in “strong”
regions
Provincial conditions and government policy incentives
•
Unemployment rate in the region.
•
Average return on equity for state-owned firms in the region.
•
Average percentage of non-operating assets.
•
R&D spending as a percentage of fiscal expenditures.
Incentives created by heightened foreign investment
•
Provincial and Industry-level measures of foreign investment activity
• FDI / GDP each year.
• Foreign ownership (average percentage of foreign capital to total capital).
• Fixed asset investment: FDI / SOE
Empirical Proxies for Financial Reporting Incentives
Strength of provincial-level legal and market-development institutions.
•
Marketization index (Fan and Wang, 2001).
•
Legal environment Index (Fan and Wang, 2001).
•
Property rights protection index (Fan and Wang, 2001).
•
Deregulation index (Demerger et al, 2002).
Market Demand and
Contracting Arguments
Transparency will be
greater in “strong”
regions
Provincial conditions and government policy incentives
•
Unemployment rate in the region.
•
Average return on equity for state-owned firms in the region.
•
Average percentage of non-operating assets.
•
R&D spending as a percentage of fiscal expenditures.
Political Incentive
Arguments
Suppression will be
greater in “strong”
regions
Incentives created by heightened foreign investment
•
Provincial and Industry-level measures of foreign investment activity
• FDI / GDP each year.
• Foreign ownership (average percentage of foreign capital to total capital).
• Fixed asset investment: FDI / SOE
Anecdotal Evidence: Suppression of Bad News in China
Environmental / Health Issues:
Notable Examples: SARS virus (2003) and Bird Flu Virus (2005)
“Even in a China that is more capitalist than ever, the instinctive official response to
bad news is to suppress it with all the force available to the nominally communist
state.” Financial Times (July 2007)
Economic and Business Activities:
“Unfavorable news – information that could put local leaders in a bad light in Beijing
– is routinely suppressed by multiple layers of party propaganda officials in
towns, counties, cities and provinces.” The Seattle Times (August 2007)
“Chinese officials often suppress bad news because they fear upsetting the public or
their bosses or to prevent economic losses.” Cox International (August 2007)
“[The] suppression of bad news remains an unedifying habit that dies hard on the
Mainland.” South China Morning Post (June, 2007)
Anecdotal Evidence: Suppression of Bad News in China
Measurement of Economic performance
“[The] Chinese government had systematically falsified its gross domestic product
data to hide an economic downturn that took place in 1998 and 1999.” Thomas
Rawski (2001)
“Many have reluctantly, and to varying degrees, come around to his view. Earlier
this year, for example, Goldman Sachs … endorsed Rawski’s take that the
Chinese economy was caught in a downdraft in the late 1990’s.”
“The government’s handling of the SARS epidemic earlier this year has also
strengthened Rawski’s case. ‘Now everyone knows the Chinese government
suppressed health statistics…The only question now is whether the
government’s suppression of bad news spreads into the economics area as
well.’” Forbes (November 2003)
Question: Do these tendencies spill over into the financial reporting realm?
Empirical Proxies for Suppression of Negative Financial
Information: Stock price “crash” metrics
We interpret the presence of large, negative stock prices “crashes” as a proxy
for the delayed flow of negative information into prices.
•
Following Jin and Myers (2006) and Chen, Hong and Stein (2001).
•
The release of current and previously suppressed negative information at
one time will lead to larger negative returns and greater negative skewness.
As opposed to the alternative return distribution where negative news is
released in a timely sequential manner.
•
Utilized two crash statistics:
NCSKEW = Third moment of each stock’s daily residual return, scaled by the cubed
standard deviation of residual returns, times negative one.
DUVOL = Ratio of the log standard deviation of returns on “down” days to the log of the
standard deviation of returns on “up” days.
Greater skewness, ceteris paribus, is indicative of a greater likelihood that
earlier negative news was withheld.
Empirical Proxies for Suppression of Negative Financial
Information: Stock price “crash” metrics
Prior research supports this interpretation of negative skewness as a
measure of information suppression (or constrained flow of information)
about bad outcomes.
•
Jin and Myers (2006) show that cross-country crash measures are
significantly related to measures of firm disclosure, corporate opacity and
stock return synchronicity.
•
Bris, Goetzmann and Zhu (2007) show that downside volatility is
significantly larger when short-selling is constrained, consistent with a build
up (i.e., artificial suppression) of non-priced negative financial information in
those settings with downside trading frictions.
•
Hutton, Marcus and Tehranian (2007) and Kothari, Shu and Wysocki (2007)
provide U.S. evidence supporting this interpretation of these crash statistics.
Research Design: Stock price “crash” metrics
Our primary research design will test for an association between these crash
statistics and institutional variables designed to proxy for political incentives to
suppress negative financial information.
Estimate pooled cross-sectional model:
NSCKEWi,t or DUVOLi,t =  + 1INSTITUIONSi,t + 2LOGSIZEi,t + 3GROWTHi,t + 4SIGMAi,t
+ 5TURNOVERi,t + 6TURNOVERi,t-1 + 7BETAi,t + 8RETi,t + 9RETi,t-1 + i,t
Control variables drawn from Chen, Hong and Stein (2001).
Table 1: Composition of the Sample
Number of
Observations
Percentage of
Total Sample
Distribution by fiscal year:
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Total
6
37
132
207
223
345
458
515
549
623
657
3,752
0.16%
0.99%
3.52%
5.52%
5.94%
9.20%
12.21%
13.73%
14.63%
16.60%
17.51%
100%
Distribution by number of layers:
1
2
3
Total
557
2,483
712
3,752
14.85%
66.18%
18.97%
100.00%
Table 2: Descriptive Statistics
Mean
Median
Std. Dev.
Minimum
Maximum
N
NCSKEW
DUVOL
-0.71
-0.33
-0.70
-0.35
0.81
0.25
-6.03
-1.08
3.00
0.53
3752
3752
LOG(SIZE)
SIGMA
BETA
TURNOVER
RET
GROWTH
14.60
0.02
1.02
0.08
-0.03
0.09
14.56
0.02
1.04
0.06
-0.06
0.09
0.77
0.01
0.22
0.06
0.31
0.38
12.26
0.00
0.03
0.00
-1.29
-1.46
17.90
0.07
1.67
0.55
1.64
1.59
3752
3752
3752
3752
3752
3752
Table 3: Correlation Matrix
Panel A: Firm characteristics and regional institutions
(Spearman Correlations)
NCSKEW
DUVOL
Log(SIZE)
SIGMA
TURNOVER
RET
GROWTH
BETA
NCSKEW
DUVOL
1.00
0.94
1.00
LOG(SIZE)
SIGMA
DTURNOVER
RET
GROWTH
BETA
-0.20
-0.13
-0.16
-0.07
-0.03
-0.11
-0.21
-0.25
-0.29
-0.08
-0.02
-0.11
1.00
-0.08
0.01
0.20
0.12
-0.22
1.00
0.84
-0.09
-0.13
-0.06
1.00
-0.03
-0.08
0.04
1.00
0.22
-0.10
1.00
-0.07
1.00
Marketization Index
Legal Index
Property Rights Index
Market Deregulation Index
-0.01
-0.07
-0.06
-0.03
-0.02
-0.08
-0.07
-0.04
0.11
0.16
0.09
0.12
0.07
0.10
0.17
0.11
0.10
0.13
0.20
0.14
-0.02
-0.02
-0.01
-0.01
-0.02
0.00
-0.05
-0.02
-0.03
0.00
0.01
-0.01
Average ROE
Non-performing assets
Unemployment rate
R&D/GDP
R&D/Fiscal Expenditure
-0.05
0.05
0.14
-0.04
-0.05
-0.05
0.05
0.24
-0.06
-0.09
0.16
-0.14
-0.05
0.11
0.10
0.07
-0.08
-0.37
0.18
0.15
0.11
-0.12
-0.39
0.17
0.17
0.03
-0.05
0.11
0.01
-0.04
0.01
-0.02
0.04
-0.10
-0.01
-0.05
-0.05
0.06
0.01
-0.03
-0.06
FDI/GDP
FDI Ownership
Fixed assets investment: FDI/SOE
-0.05
-0.06
-0.05
-0.08
-0.07
-0.06
0.11
0.18
0.12
0.18
0.12
0.12
0.20
0.15
0.16
-0.04
-0.01
-0.03
-0.04
-0.03
-0.02
Industry: FDI/GDP
Industry: FDI ownership
Industry fixed assets invest: FDI/SOE
0.07
0.06
-0.03
0.08
0.06
-0.05
-0.10
-0.04
0.05
-0.27
0.04
0.07
-0.22
0.01
0.05
0.08
-0.06
0.01
-0.06
-0.10
-0.01
0.01
-0.03
-0.01
0.22
-0.01
Table 3: Correlation Matrix
Panel B: Correlation among regional institutions
Market
Index
Legal
Index
Property
Rights
Index
Market
Dereg.
Index
Marketization Index
Legal Index
Property Rights Index
Market Deregulation Index
1.00
0.71
0.43
0.84
Average ROE
Non-performing assets
Unemployment rate
R&D/GDP
R&D/Fiscal Expenditure
Aver.
ROE
NonPerforming
Assets
1.00
0.79
0.80
1.00
0.59
1.00
0.79
0.75
0.82
0.85
-0.69
0.68
0.81
0.84
0.87
-0.65
0.39
0.66
0.70
0.69
-0.44
0.67
0.79
0.86
0.85
-0.69
1.00
-0.88
-0.34
0.37
0.42
1.00
0.87
0.91
-0.60
FDI/GDP
FDI Ownership
FA invest: FDI/SOE
-0.17
0.36
0.61
-0.30
0.48
0.61
-0.11
0.53
0.32
-0.16
0.36
0.65
0.59
0.65
0.66
Industry: FDI/GDP
Industry: FDI ownership
Industry FA inv: FDI/SOE
0.79
0.02
0.08
0.68
0.02
0.02
0.39
0.03
0.03
0.67
0.03
0.03
0.02
0.06
0.05
Unempl.
Rate
R&D
/
GDP
R&D/
Fiscal
Expend.
FDI
/
GDP
FDI
Owner.
Fixed
Asset
Invest
1.00
0.91
-0.68
1.00
-0.64
1.00
-0.24
0.53
0.63
-0.10
0.60
0.61
-0.24
0.49
0.64
0.21
-0.42
-0.47
1.00
0.01
-0.26
1.00
0.34
1.00
0.59
0.09
0.04
0.65
0.03
0.05
0.66
0.01
0.05
-0.88
-0.02
-0.02
-0.34
0.16
0.04
0.37
0.04
0.04
0.42
-0.07
-0.02
Industry
FDI/GDP
1.00
0.02
0.06
Industry
FDI
Owner.
1.00
0.03
Table 4: Influence of provincial institutions on the incentives to
suppress negative financial information
NCSKEW (Jin and Myers)
INSTITUTIONSt
LOGSIZEt
GROWTHt
SIGMAt
TURNOVERt
TURNOVERt-1
BETAt
RETt
RETt-1
Constant
Observations
Adjusted R-squared
DUVOL (Chen, Hong and Stein)
Marketization
Index
Legal
Index
Property
Rights
Index
Market
Deregulation
Index
Marketization
Index
Legal
Index
Property
Rights
Index
Market
Deregulation
Index
0.026
(2.31)**
-0.164
(-4.15)***
-0.034
(-1.11)
-24.177
(-2.51)**
2.458
(2.93)***
1.176
(2.18)**
-0.567
(-10.83)***
-0.166
(-4.36)***
0.041
(0.90)
2.144
(2.78)***
0.015
(1.22)
-0.164
(-4.15)***
-0.035
(-1.15)
-23.998
(-2.47)**
2.455
(2.92)***
1.152
(2.12)**
-0.569
(-10.91)***
-0.167
(-4.36)***
0.042
(0.93)
2.221
(2.82)***
0.015
(1.82)*
-0.165
(-4.30)***
-0.035
(-1.16)
-23.981
(-2.47)**
2.445
(2.93)***
1.143
(2.12)**
-0.570
(-10.94)***
-0.166
(-4.38)***
0.042
(0.94)
2.206
(2.84)***
0.038
(2.22)**
-0.166
(-4.17)***
-0.033
(1.08)
-19.299
(-2.31)**
2.062
(2.85)***
1.088
(1.90)*
-0.555
(-10.67)***
-0.176
(-4.92)***
0.051
(1.08)
2.228
(2.82)***
0.009
(2.67)**
-0.064
(-5.44)***
-0.011
(-1.48)
1.302
(0.69)
0.164
(1.01)
0.244
(1.76)*
-0.210
(-16.45)***
-0.116
(-9.65)***
0.020
(1.76)*
0.625
(3.15)***
0.005
(1.68)
-0.063
(-5.60)***
-0.011
(-1.52)
1.365
(0.70)
0.163
(1.00)
0.236
(1.69)
-0.211
(-16.44)***
-0.116
(-9.71)***
0.020
(1.80)*
0.651
(3.16)***
0.005
(2.87)***
-0.064
(-5.87)***
-0.011
(-1.53)
1.371
(0.70)
0.160
(0.98)
0.233
(1.68)
-0.211
(-16.54)***
-0.116
(-9.83)***
0.020
(1.82)*
0.647
(3.19)***
0.012
(2.33)**
-0.064
(-5.44)***
-0.011
(-1.44)
2.057
(1.18)
0.102
(0.69)
0.220
(1.51)
-0.211
(-16.51)***
-0.118
(-10.15)***
0.023
(1.97)*
0.672
(3.19)***
3723
0.16
3723
0.16
3723
0.16
3666
0.16
3723
0.28
3723
0.28
3723
0.28
3666
0.28
Table 5: Influence of regional economic policies and performance
on the incentives to suppress negative financial information
NCSKEW
INSTITUTIONSt
LOGSIZEt
GROWTHt
SIGMAt
TURNOVERt
TURNOVERt-1
BETAt
RETt
RETt-1
Constant
Observations
Adjusted R-squared
DUVOL
Average ROE
Unemployment
Rate
NonPerforming
Assets
R&D /
Fiscal
Expend.
Average ROE
Unemployment
Rate
NonPerforming
Assets
R&D /
Fiscal
Expend.
0.004
(1.89)*
-0.164
(-4.19)***
-0.037
(-1.22)
-23.725
(-2.44)**
2.423
(2.90)***
1.150
(2.14)**
-0.559
(-10.71)***
-0.164
(-4.37)***
0.046
(1.00)
2.291
(2.89)***
0.010
(0.56)
-0.157
(-4.25)***
-0.037
(-1.23)
-19.280
(-2.28)**
2.136
(2.91)***
1.026
(1.75)*
-0.545
(-10.35)***
-0.177
(-5.07)***
0.049
(1.08)
2.146
(2.80)***
-0.001
(-2.42)**
-0.163
(-4.24)***
-0.038
(-1.25)
-23.705
(-2.43)**
2.426
(2.90)***
1.148
(2.14)**
-0.557
(-10.69)***
-0.165
(-4.41)***
0.046
(1.01)
2.323
(2.92)***
0.022
(1.53)
-0.159
(-4.79)***
-0.019
(-0.85)
3.774
(0.30)
2.103
(1.74)*
0.009
(0.02)
-0.495
(-10.18)***
-0.228
(-6.08)***
0.050
(1.13)
1.663
(2.52)**
0.002
(3.51)***
-0.064
(-5.69)***
-0.012
(-1.60)
1.401
(0.72)
0.157
(0.96)
0.237
(1.71)*
-0.209
(-16.80)***
-0.115
(-9.84)***
0.021
(1.88)*
0.682
(3.31)***
0.004
(0.72)
-0.062
(-5.61)***
-0.012
(-1.57)
2.134
(1.17)
0.116
(0.76)
0.204
(1.39)
-0.206
(-16.12)***
-0.118
(-10.21)***
0.022
(1.92)*
0.640
(3.13)***
-0.000
(-3.43)***
-0.063
(-5.72)***
-0.012
(-1.63)
1.422
(0.72)
0.157
(0.96)
0.236
(1.72)*
-0.208
(-16.69)***
-0.116
(-9.86)***
0.021
(1.88)*
0.688
(3.34)***
0.005
(1.21)
-0.064
(-6.05)***
-0.006
(-0.81)
6.277
(2.42)**
0.314
(1.15)
0.028
(0.15)
-0.205
(-16.64)***
-0.138
(-11.45)***
0.018
(1.49)
0.503
(2.62)**
3752
0.16
3744
0.15
3752
0.16
3364
0.11
3752
0.28
3744
0.28
3752
0.28
3364
0.28
Table 6: Influence of provincial-level foreign investment on the
incentive to suppress negative financial information
NCSKEW
INSTITUTIONSt
LOGSIZEt
GROWTHt
SIGMAt
TURNOVERt
TURNOVERt-1
BETAt
RETt
RETt-1
Constant
Observations
Adjusted Rsquared
DUVOL
FDI / GDP
FDI
Ownership
Fixed Asset
Invest (FDI /
SOE)
FDI / GDP
FDI
Ownership
Fixed Asset
Invest (FDI /
SOE)
0.815
(2.12)**
-0.174
(-5.07)***
-0.037
(-1.54)
-4.498
(-0.40)
1.461
(1.50)
0.537
(1.27)
-0.521
(-10.21)***
-0.134
(-4.69)***
0.046
(1.08)
1.411
(2.31)**
0.265
(1.75)*
-0.169
(-4.16)***
-0.035
(-1.18)
-24.055
(-2.46)**
2.450
(2.90)***
1.146
(2.15)**
-0.569
(-10.78)***
-0.163
(-4.30)***
0.043
(0.96)
2.322
(2.91)***
0.156
(2.32)**
-0.168
(-4.23)***
-0.035
(-1.17)
-24.071
(-2.51)**
2.431
(2.92)***
1.164
(2.18)**
-0.568
(-11.07)***
-0.161
(-4.23)***
0.047
(1.02)
2.322
(2.94)***
0.270
(2.63)**
-0.066
(-6.05)***
-0.010
(-1.42)
4.595
(1.98)*
0.030
(0.14)
0.190
(1.32)
-0.206
(-16.01)***
-0.107
(-11.34)***
0.017
(1.44)
0.459
(2.63)**
0.112
(3.37)***
-0.066
(-5.80)***
-0.012
(-1.60)
1.332
(0.68)
0.162
(0.97)
0.234
(1.72)*
-0.211
(-16.50)***
-0.114
(-9.81)***
0.021
(1.87)*
0.694
(3.36)***
0.052
(2.95)***
-0.065
(-5.64)***
-0.012
(-1.55)
1.301
(0.68)
0.158
(0.98)
0.241
(1.75)*
-0.211
(-16.98)***
-0.114
(-9.76)***
0.022
(1.89)*
0.687
(3.33)***
3545
3723
3752
3545
3723
3752
0.14
0.16
0.16
0.29
0.28
0.28
Table 7: Influence of industry-level foreign investment on the
incentive to suppress negative financial information
NCSKEW
INSTITUTIONSt
LOGSIZEt
GROWTHt
SIGMAt
TURNOVERt
TURNOVERt-1
BETAt
RETt
RETt-1
Constant
Observations
Adjusted Rsquared
DUVOL
Industry
FDI / GDP
Industry
FDI
Ownership
Industry
Fixed Asset
Invest (FDI /
SOE)
Industry
FDI / GDP
Industry
FDI
Ownership
Industry
Fixed Asset
Invest (FDI /
SOE)
-1.042
(-2.71)*
-0.178
(-7.24)***
-0.009
(-0.37)
-0.927
(-0.42)
2.377
(8.78)***
-0.140
(-0.35)
-0.498
(-26.86)***
-0.229
(-27.05)***
0.055
(1.39)
1.989
(4.07)**
0.242
(1.87)
-0.195
(-7.20)***
-0.035
(-2.73)*
-20.721
(-3.49)**
1.987
(4.62)***
0.186
(0.41)
-0.594
(-18.77)***
-0.136
(-11.43)***
0.077
(2.75)*
4.343
(2.24)*
-0.078
(-3.48)***
-0.153
(-4.42)***
-0.047
(-3.19)***
-23.298
(-4.94)***
2.299
(4.98)***
1.058
(1.90)*
-0.540
(-16.58)***
-0.167
(-5.97)***
0.040
(1.37)
2.299
(1.40)
-0.317
(-1.48)
-0.070
(-7.83)***
-0.002
(-0.17)
5.428
(1.81)*
0.351
(1.13)
-0.039
(-0.31)
-0.206
(-9.47)***
-0.142
(-8.48)***
0.018
(1.34)
0.570
(3.70)***
0.094
(1.85)*
-0.073
(-9.16)***
-0.011
(0.86)
1.474
(0.53)
0.119
(0.49)
0.018
(0.14)
-0.224
(-9.04)***
-0.111
(-7.16)***
0.026
(1.80)*
1.026
(4.50)***
-0.017
(-1.22)
-0.061
(9.62)***
-0.015
(-1.55)
1.232
(0.59)
0.150
(0.83)
0.200
(1.89)*
-0.202
(-10.15)***
-0.116
(-9.53)***
0.019
(1.74)*
0.679
(3.60)***
2560
2331
3510
2560
2331
3510
0.09
0.15
0.15
0.27
0.27
0.27
Alternative Research Design: Timely Loss Recognition Practices

The preceding results hinge upon our interpretation of crash statistics as proxies for
the extent to which adverse financial news is suppressed.

Examine variation in a financial accounting practice related to the timely incorporation
of negative financial information into earnings: Timely loss recognition.

Following Basu (1997), prior cross-country studies (e.g., Ball, Kothari and Robin,
2000) measure timely loss recognition practices using variations of the following
model:
NIi,j,t = α0 +α1NEGi,j,t + β1RETi,j,t + β2NEGi,j,t*RETi,j,t + εi,j,t,
where NEG=1 if RET < 0.
Incremental
Bad News
Sensitivity
Ball, Robin and Wu (2001) shows that TLR, on average, is low in China.
We search for variation in TLR practices within China conditional on the underlying
regional incentives
Table 8: Timely loss recognition practices of state-controlled firms
Panel A: Provincial institutions
Return*RD*HIGH
Return
RD*HIGH
RD*Return
Return*HIGH
RD
HIGH
Intercept
Observations
R-squared
Legal Index
Property
Rights Index
Market
Deregulation
Index
Nonperforming
assets
Unemployment
rate
-0.037
(-3.62)***
0.007
(4.06)***
0.000
(0.13)
0.060
(8.63)***
0.005
(2.30)**
0.003
(1.71)*
-0.005
(-2.66)***
0.023
(20.91)***
-0.032
(-3.13)***
0.008
(4.79)***
-0.002
(-0.76)
0.058
(8.30)***
0.003
(1.48)
0.005
(2.40)**
-0.002
(-1.13)
0.022
(19.53)***
-0.021
(-2.02)**
0.010
(6.21)***
-0.003
(-0.96)
0.053
(7.59)***
0.000
(0.11)
0.005
(2.63)***
-0.001
(-0.34)
0.021
(18.64)***
-0.038
(-3.66)***
0.007
(3.67)***
0.001
(0.20)
0.061
(8.59)***
0.006
(2.41)**
0.003
(1.58)
-0.005
(-2.88)***
0.024
(20.86)***
0.025
(-2.47)**
0.011
(7.44)***
-0.001
(-0.21)
0.029
(3.87)***
-0.003
(-1.13)
0.004
(1.89)*
0.002
(0.98)
0.020
(16.36)***
4505
0.11
4505
0.10
4505
0.10
4435
0.11
4540
0.10
Marketization
Index
R&D/GDP
R&D/Fiscal
Expenditure
Average
ROE in
state Sector
0.046
(4.46)***
0.011
(7.48)***
0.008
(2.75)***
0.018
(2.33)**
-0.002
(0.64)
0.000
(0.01)
-0.003
(-1.78)*
0.022
(20.31)***
-0.032
(-3.06)***
0.009
(5.66)***
-0.001
(-0.52)
0.057
(8.60)***
0.003
(1.18)
0.004
(2.30)**
-0.004
(-2.38)**
0.023
(20.94)***
-0.005
(-0.50)
0.007
(3.04)***
-0.005
(-1.58)
0.068
(9.75)***
0.001
(0.21)
0.007
(3.72)***
0.004
(2.07)**
0.019
(14.05)***
-0.022
(-2.08)**
0.009
(5.46)***
0.001
(0.44)
0.051
(7.56)***
0.003
(1.27)
0.003
(1.43)
-0.001
(-0.56)
0.021
(19.37)***
4527
0.11
4540
0.10
3926
0.14
4540
0.10
Table 8: Timely loss recognition practices of state-controlled firms
Panel B: Foreign investment activity
Return*RD*HIGH
Return
RD*HIGH
RD*Return
Return*HIGH
RD
HIGH
Intercept
Observations
R-squared
FDI/GDP
FDI Ownership
Fixed assets
investment:
FDI/SOE
-0.003
(-0.30)
0.009
(5.50)***
0.003
(0.94)
0.060
(8.52)***
0.004
(1.59)
0.004
(2.05)**
-0.003
(-1.55)
0.021
(19.03)***
-0.021
(-1.98)**
0.009
(5.74)***
0.001
(0.22)
0.052
(7.63)***
0.002
(1.06)
0.003
(1.78)*
-0.002
(-1.24)
0.022
(20.25)***
-0.020
(-1.97)**
0.009
(5.74)***
0.001
(0.45)
0.052
(7.59)***
0.002
(0.98)
0.003
(1.58)
-0.003
(-1.86)*
0.022
(20.93)***
-0.013
(-0.96)
0.002
(0.75)
0.007
(2.05)**
0.066
(5.82)***
0.028
(7.43)***
0.003
(1.13)
-0.005
(-2.51)**
0.023
(16.70)***
0.007
(0.58)
0.009
(4.14)***
0.004
(1.18)
0.042
(4.93)***
0.005
(1.75)*
0.003
(1.12)
-0.008
(-3.51)***
0.024
(15.99)***
0.004
(0.35)
0.009
(4.97)***
0.002
(0.79)
0.042
(5.04)***
0.002
(0.72)
0.003
(1.32)
-0.005
(-3.07)***
0.024
(19.32)***
4121
0.15
4505
0.10
4540
0.10
3015
0.15
2837
0.12
4256
0.11
Industry: FDI/GDP
Industry: FDI
ownership
Industry fixed assets
investment: FDI/SOE
Influence of decentralization on the reporting incentives of statecontrolled firms
Pyramidal ownership structure in China is one mechanism by which the
State credibly delegates decision rights to managers.
•
Fan, Wong and Zhang (2007) - Pyramids improves labor and investment
efficiency and total factor productivity.
•
In our sample, nearly 85% of these publicly firms are controlled by the state
through a multi-layer pyramid.
•
To the extent that decentralization reduces the likelihood of government
reprisal for poor performance, we would expect the incentive to suppress to
be weaker among firms controlled through a pyramidal arrangement.
Preliminary Analysis: In terms of reporting incentives:
•
We find no evidence that decentralization impacts the suppression or
release of negative financial information using our crash statistics.
•
We do find evidence that the pyramid structure increases the incentives for
TLR.
Summary of Results and Conclusions
We find that the suppression of negative financial information, as proxied by both
stock return crashes and weaker timely loss recognition practices, is stronger in
those regions characterized by:
•
Strong investor protections and pro-market regulation.
•
Strong economic performance / value maximization policy orientation.
•
High levels of foreign investment and ownership.

These patterns are consistent with managers and local politicians responding to
political incentives when releasing information about state-controlled firms.

Thus, in those settings where market demand and contracting arguments would
suggest transparency should be greatest, these alternative incentives appear to
dominate.

Understanding the various financial reporting incentives of politicians and
politically-astute managers is a first-order concern for investors in China
Comparison of Chinese TLR practices versus
Developed Countries
0.400
TImeliness Coefficient(s)
0.350
0.300
0.250
Good News
0.200
Bad News
0.150
0.100
0.050
0.000
China
United
States
United
Kingdom
Australia
Germany
France
Japan
Comparison of Chinese TLR practices versus Other
Asian and Emerging-Market Countries
0.180
Timeliness Coefficients
0.160
0.140
0.120
0.100
Good News
Bad News
0.080
0.060
0.040
0.020
0.000
China
India
Brazil
Singapore
Malaysia
Indonesia