THE MANIPULATION OF FINANCIAL DATA AMONG CHINESE ENTREPRENEURS?

Yanbo Wang

Boston University

wyanbo@bu.edu

August 5th, 2012

2

STARTING POINT

Established wisdom: formal institutions matter in economic development and firm growth

Law and finance

The China puzzle

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ALTERNATIVE EXPLANATIONS

Alternative explanations The role of informal institution

Contract enforcement (Zhou et al. 2003) & financing (Allen, Qian and Qian 2004; Batjargal & Liu, 2004)

Informal, local-level govt accountability (Tsai 2011) The role of developmental state

Local state corporatism (Oi 1992 )

But, these factors could also lead to corruption, crony capitalism & incompetent firms (Fisman 2001; Kang 2002; Johnson 2003)

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ANECDOTAL EVIDENCES: STRONG NATION, POWERFUL ENTREPRENEURS & (MANY) SCANDALOUS FIRMS

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RESEARCH AGENDA

Examine financial data manipulation among Chinese entrepreneurs

With Toby Stuart Document the prevalence of fraud in China Identify antecedents of fraud

With Jizhen LI Identify (potential) agents of positive changes Explore the strategic implications of fraudulent

behaviors Do cheating & non-cheating entrepreneurs use (state-

granted) resources differently? Do they adopt different strategies in org. building and tech

innovation? Explore how data manipulation hinders knowledge

development in academia

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CHALLENGES IN THE STUDY OF FRAUD

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RESEARCH DESIGN: ONE FIRM TWO BOOKS

Book for grant application Ministry of Science &

Technology (MOST) US SBIR-like grant

Each firm: Up to 1 m RMB + local matched funds

> 700m US$ allocated in 2011

Book for business license renewal Local Bureau of Industry

& Commerce (BIC) Main local regulatory

agency Collaborate with taxation

bureau

No collaborations btw: “Arbitrage” Opportunities

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ACCOUNTING LAW OF THE PEOPLE’S REPUBLIC OF CHINA Article 42 where the

provisions of this Law is violated by committing one of the following acts …

(5) the measures for accounting arrangement are arbitrarily changed;

(6) the basis for preparing financial and accounting reports provided to different users of accounting documents is inconsistent

Where one of the acts as stipulated in the preceding paragraph is committed and therefore a crime is constituted, criminal liabilities shall be investigated according to law

Article 43 where accounting vouchers or account books are forged or altered, or false financial and accounting reports are prepared, and therefore an crime is constituted, criminal liability shall be investigated in accordance with law

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TAIDE COMPRESSOR CORP: AN EXAMPLE

A prominent dean, a national award and a fraud

Net profit 2001 2002 2003

BIC book -1.48m

-3.07m -3.84m

Grant book

1.25m 2.55m 14.7m

“A place not where business ethics are lacking in particular cases, but where business ethics are lacking altogether” !

Not without risk Nanjing 2003: 87/88 real

estate firms were fined for 759m RMB collectively

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OVERALL PICTURE Revenue Discrepancy

Profit Discrepancy

Asset Discrepancy

-50

510

Prof

it, re

porte

d to

MO

ST

-5 0 5 10Profit, reported to BIC

Profit comparison across two books

050

100

150

Rev

enue

, rep

orte

d to

MO

ST

0 50 100 150 200Revenue, reported to BIC

Revenue comparison across two books

050

100

150

Ass

et, r

epor

ted

to M

OS

T

0 50 100 150Asset, reported to BIC

Asset comparison across two books

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COMPARISON OF KEY FINANCIALS ACROSS TWO ACCOUNTING BOOKS

Unit: million yuan Profit Revenue

Asset

Data reported to MOST 1.255 11.207 13.114

Data reported to BIC 0.182 10.271 16.087

Gap between the two books 1.073 0.930 -2.973No connection

Political connection

Difference

Profit gap, 1000 yuan 611.28 1501.25 -889.97***

Profit consistency 0.37 0.11 0.26***

Revenue gap, 1000 yuan

933.64 3347.42 -2413.78*

Revenue consistency .51 .23 .28***

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REGRESSION: PROFIT MANIPULATION OLS: magnitude of profit gap

Logit: no discrepancy

OLS: profit gap /registered capital

OLS: profit gap/ labor size

Political connection 709.27** -3.26** 4.86*** 13.88**

(261.3) (1.07) (1.3) (4.28)

Ranked college 86.3 -0.77 2.59+ 4.2(165.22) (0.53) (1.5) (4.82)

No chairperson 312.22 -0.06 1.37 8.55*

(203.77) (0.66) (0.95) (4.19)

Local Organizational equity holder (OEH) 205.94 1.68 0.42 3.09

(233.12) (1.16) (1.4) (3.67)

Nonlocal - OEH-1434.34** 5.12*** (2.97 -19.61**

(448.27) (1.48) (2.07) (7.11)

Auditor with power 521.68+ 0.9 2.22 7.39

(281.19) (0.71) (1.4) (4.71)

CPA number -7.66** 0.03** -0.03 -0.16**

(2.78) (0.01) (0.02) (0.06)

Adjusted R^2 (chi^2) 0.3 (61.06) 0.34 0.08

Industry-, region- and year- fixed effects are included Other controls: entrepreneur’s education and age, firm age, IP, TMT science & engineering background; K, L,Number of owners, and corporate governance variables

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REGRESSION: GRANT

  Political connection only

Profit manipulation only

Logit regression, full model with profit manipulation

Linear probability regression, full model with profit manipulation

  Model 1 Model 2 Model 3 Model 4

Political connection 0.83*   1.21* 0.21*

  (0.41)   (0.58) (0.10)Profit consistency   -0.96* -1.43** -0.23*

    (0.42) (0.54) (0.11)Returnee founder     1.32 0.28

      (0.97) (0.17)chi^2 17.97 22.53 85.26  R-Squares       .0759618

Industry-, region- and year- fixed effects are included in all models; all the other controls are included in models 3 & 4

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VC AS AN AGENT OF POSITIVE CHANGE?

Tentative results OLS: Tax gap

b = -220.8 (148.9) OLS Profit gap

b = -2.91*** (.89) Controls:

1) # investors; 2) corp. investor; 3) firms age; 4) founder age; 5) industry; 6) resource endowment; 7) outside investor shares .

Robust S.E.

Cross-section data; cannot rule out selection effect

Next steps: Differences across

VCs State vs. non-state Local vs. outsider Active vs. passive

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SUMMARY: Fraud is depressingly prevalent Politically connected firms

Very bold in data manipulation, particularly on profit

Social outsider: owners from different provinces Much more cautious

Auditors Powerful ones facilitate cheating Bigger firms seems to care their reputation

Fraud seems to pay Positive correlation between profit discrepancy and the receipting of

state grant

VCs seem to be an agent of positive change Data manipulation = headaches

Policy makers Investors Researchers!