Executive’s former banking experience, entertainment expenditures and bank

lending decisions: Evidence from China’s non-SOE firms

Gary Tian

Chinese Commerce Research Centre

School of Accounting and Finance

University of Wollongong

Coauthor Xiaofei Pan1

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Motivation

Heavily regulated credit market provides increasing opportunities for exchanging rents with bribes (Pei, 2008; Ngo, 2008; Cai et al., 2011)

thus firms seek rents by establishing connections with government which can mitigate any financial constraints and help them to access bank loans and reduce the cost of borrowing (Cull and Xu, 2003; Brandt and Li, 2003; Li et al., 2008; Faccio, 2010).

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Motivation

but rent seeking literature provides mixed results: Beck et al. (2006) argue that corrupt bank official is an

obstacle to firms raising external finance Cai et al. (2011) find that bribery reduces firm performance,

while this effect is less pronounced in regions with severe government intervention.

Chen et al. (2013) find corruption can improve the efficiency of bank lending

using expenditure for entertainment and travelling purposes (ETCs) as the measure of corruption and a proxy for rent seeking activities is problematic.

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Motivation

we propose a new measure to examine bank lending decision making – firms’ social network connections with banks serving to reduce information asymmetry and monitoring costs.

Adverse selection and moral hazard are the main obstacle to access external capital (Leland and Pyle, 1977; Sufi, 2007)

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Social networks

Social network can reduce information asymmetry and agency problem by lowering the costs of acquiring necessary information about borrowers and increase the availability of finances to them (Peterson and Rajan, 1994; Beaver, 2002) Recent evidence by Engelberg et al. (2012) find that

pre-existing personal relationship can alleviate information asymmetry and reduce firms’ borrowing costs

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Social network in China

In the Asian context, social network can be referred to as Guanxi network which is defined as a special relationship between two parties for a continued exchange of favours based on their mutual benefits and interests (Alston, 1989; Chen and Chen, 2004).

Guanxi requires the experience of interaction through, for example, studying, working or living together (Tsang, 1998)

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Social network in this paper

We explore a specific type of social network with banks through executives’ previous work experience in banks.

Through these work experience, executives have built personal relationship with bank managers and could extend these relationships in other banks through the networks they have built.

Trust established, then executives who posses private information know how to communicate more effectively and securely with banks.

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Why China? Chinese non-SOEs face discrimination of bank loans

and obstacle in accessing external finance Emergence and vibrant growth of private sector in

China during the last decade. How does private sector access external capital? Cross-sectional variations of regional development

enables to examine the different roles and effects of relationship

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Why China ? (2)

Relationship-based business is prevalent in China. About 23% of firms have bank connections, through

Chairman, CEO, CFO and other executives and directors. The mechanism through which social networks help

firms to obtain external finance and secure bank loans. Expenditure on entertainment through offering gifts

and dinner with bankers are essential requirements (Fan 2002) to reinforce the trust/bond relationship.

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Why China? (3)

Collateral is used as the proxy for borrowing cost First, collateral is a key ingredient used to enforce loan contracts

as a response to information asymmetry (the source of adverse selection and moral hazard) (Besanko and Thakor, 1987; Boot et al., 1991; Jimenez et al., 2006; Menkhoff et al., 2012).

Socend, since the recent global financial crisis of 2007, creditors have expanded collateral requirements for their fund lending, and this observed tendency has again attracted considerable attention from academics and practitioners (Harrington, 2009).

Third, the interest rates charged on bank loans are relatively regulated, so less endogeneity issue of collateral

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Contribution

We propose explanations to the coexistence of underdeveloped financial system and fast private sector growth Relationship-based economy (Allen et al., 2005). We

depart from political connection literature, by using social network with banks which is more direct and influential and suffer less endogeneity concern.

Entertainment as corruption (Cai et al., 2011; Chen et al., 2013). Their measurement is problematic and underestimate corruption. We depart from them using entertainment

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Contribution (Cont)

We add additional evidence to the literature of borrower-lender relationship and its financial implication (Engelberg et al., 2012) Social networks can alleviate information asymmetry and

monitoring costs Personal relationship between firms and banks can facilitate

bank loan access, reduce collateral requirement and enhance bank lending efficiency

Non-SOEs use their existing social networks with banks to be favoured by the institutional environment, as well as justify their existence.

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Hypothesis (1)

banks are more likely to obtain private information and become less concerned about any risk of defaulting and require less collateral.

Social network facilitates firm-specific information flow to banks and reduce the information asymmetry

H1a: Firms with social networks with banks have better access to bank loans

H1b: Firms with social networks with banks have lower collateral requirement

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Hypothesis (2)

Banks become more informed and take advantageous positions by obtaining inside information;

facilitating banks more efficient scrutiny of loan applications and help to better evaluate firm’s future earnings.

H2: Bank lending is more efficient for firms with social networks with banks

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Hypothesis (3)

Social relationship can be reinforced by investing in entertainment expenditures in eating, drinking, gifts, Karaoke and sports club membership,

social network facilitates entertainment by providing a channel through which entertainment expenditure can be paid to maintain a better relationship

H3: Social networks with banks facilitate entertainment spending, which strengthens the effect of social networks on firms’ access to bank loans and reduces collateral requirements.

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Hypothesis (4)

Cross sectional variations across administrative regions in China. Different levels of government intervention results in different levels of information asymmetry (Rajan and Zingales, 1998)

H4: The effect of entertainment through social networks on access to bank loans and reducing collateral requirement is more pronounced in regions with more government intervention.

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Sample selection

All of non-SOEs listed firms in China from 2003 to 2010 from CSMAR.

Excludes: Firms with ST and ST* Financial industry Firms with missing information

Final sample of 647 non-SOEs and 3302 firm-year observations

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Variable definition

Bank lending decision Loan_size: Firm’s bank loan / Total debt Long_size: Firm’s long-term loan / Total debt Access_to_loan: dummy variable equal to 1 if Loan_size

is greater than 10% Collateral: Collateralized loans / Total loans

Social network with banks If at least one person in the top management team

(including the Chairman, CEO, CFO and other executive and directors) was a former officer of a bank

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Variable definition (Cont)

Entertainment spending In the annual reports of China’s listed firms there is a

particular item recorded in the Statement of Cash Flow called “Other Cash Payment for the Expenses Related to Operating Activities”.

Two possible items related to entertainment that was provided to cultivate and maintain social relationships.

Business entertainment expenses, business promotion expenses

In the empirical analysis: ETCs / Sales

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Control variablesReturn on sales (ROS) Net income / Total sales

Return on assets (ROA) Net income / Total assets

Firm size (Size) Natural log of firm total assets

Cash-flow volatility The volatility of cash flows for previous three years

Board size (Board) Natural log of total number of directors on the board

Independent director (Indep) Ratio of independent directors to total directors

Leverage (Lev) Total debts / Total assets

Tangibility (Tang) Net property, plant and equipment / Total assets

Sales growth (Sales) Growth rate of sales for each year

Prime rate (Prime) Prime lending rate set by the People’s Bank of China

Debt structure (Structure) Long-term bank loans / Total bank loans

Cost of debt financing (Interest expenses + capitalized interest) / Total debts

Guarantee Guaranteed loans / Total debts

Age Natural log of years since firm established

Employee Natural log of number of employees

Duality A dummy variable equal to1 if the CEO is also the Chairman

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Distribution of bank and political connections

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Table 2 Distribution of bank and political connections Total

sample number

Firms with bank connection

% of the total sample

Firms with political connection

% of the total sample

Firms with both bank connection and political connection

% of the total sample

Panel A: By year 2003 189 52 27.51% 86 45.50% 35 18.52% 2004 246 68 27.64% 108 43.90% 37 15.04% 2005 303 71 23.43% 91 30.03% 42 13.86% 2006 375 88 23.47% 98 26.13% 46 12.27% 2007 445 95 21.35% 103 23.15% 55 12.36% 2008 510 100 19.61% 129 25.29% 57 11.18% 2009 597 134 22.45% 169 28.31% 59 9.88% 2010 637 152 23.86% 132 20.72% 56 8.79%

Difference tests between firms with and without bank connections

  With Networks Without networks

Difference tests

ETC 1.55% 1.32% 0.23%(2.97)***Loan_size 44.58% 38.72% 5.86%(5.77)***Long_size 9.56% 8.41% 1.15%(2.18)**Collateral 34.95% 38.17% -3.22% (-2.02)**Firm size (million) 2,470 4,430 -1,960(-2.64)**CF volatility 8.15% 8.12% 0.03%(0.35)Return on assets 4.69% 2.94% 1.75% (5.97)***Return on sales 8.53% 5.85% 2.68%(4.09)***Board size 9.02 8.76 0.26(3.26)***Independent director 3.17 3.15 0.02 (1.00)Leverage 52.66% 43.27% 9.39% (5.69)***Tangibility (million) 1,440 1,280 160(1.46)Sales growth 121% 121% 0%(0.06)Debt structure 9.53% 8.41% 1.12% (1.82)*Cost of debt 8.35% 8.36% -0.01% (-0.61)Guarantee 27.61% 25.86% 1.75% (0.91)Age 7.18 7.08 0.10(1.13)Employee 2,477 2,670 -193(-0.83)Duality 17.56% 25.77% -8.21%(-4.82)***

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Regression of social network, entertainment on loan access and bank lending efficiency (ROS)

Dependent variable Access_to_loan Loan_size ROS 0.64***(6.20) 0.47***(6.44) 0.48***(5.51) 0.28***(5.90) Network 0.69**(2.19) 0.46**(2.26) 0.07***(2.99) 0.05**(2.00) ETC 0.16(1.53) 0.15(0.25) 0.03(0.66) 0.07(1.56) Network*ETC 0.68**(2.48) 0.32**(2.27) 0.11**(2.21) 0.06***(2.68) Network*ROS 0.69***(2.86) 0.33**(2.32) ETC*ROS 0.23(1.50) 0.75(1.47) Network*ETC*ROS 0.70**(2.16) 0.60**(2.27) Sum tests 2.23** a 2.02** b 2.45** a 2.96*** b Adjusted R2 0.18 0.23 0.25 0.27 Observations 3,302 3,302 3,302 3,302

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H1a

H3H2

H2/3

Regression of effect of social network and entertainment on collateral requirement

Dependent variable is collateral Network -0.05***(-2.62) -0.05**(-2.56) -0.04**(-1.98) ETC -0.06**(-2.28) -0.03(-1.09) Network*ETC -0.09***(-2.92) Sum test a 3.11*** Adjusted R2 0.33 0.34 0.34 Observations 1,815 1,815 1,815

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H1b

H3

Regression of effect of social network and entertainment on long-term bank loan:

evidence of reduction of monitoring costs. Dependent variable Access_to_long Long_loan ROS 0.24**(2.57) 0.09**(2.13) 0.03**(1.97) 0.02**(2.41) Network 0.16*(1.80) 0.10**(2.05) 0.03**(2.46) 0.02**(2.17) ETC 0.72(1.56) 0.72(1.62) 0.05(0.78) 0.05(0.81) Network*ETC 0.52***(2.65) 0.35**(2.45) 0.10**(2.06) 0.10**(2.05) Network*ROS 0.19**(2.45) 0.11**(2.01) ETC*ROS 0.09(1.05) 0.06(0.49) Network*ETC*ROS 0.63***(2.77) 0.06**(2.23) Sum tests 2.42** a 2.59*** b 2.26** a 2.04** b Adjusted R2 0.18 0.18 0.19 0.19 Observations 3,302 3,302 3,302 3,302

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Regression: effect of social network and entertainment spending on bank loan and collateral across regions (H4)

Dependent Variable

Access_to_loan Loan_size Collateral

Good Poor Good Poor Good Poor Network 0.16

(1.02) 0.92** (2.16)

0.03* (1.86)

0.06** (2.01)

-0.02* (-1.83)

-0.06** (-2.39)

ETC 0.27 (1.10)

0.11 (1.55)

0.16 (1.04)

0.01 (0.02)

-0.01 (-0.13)

-0.08** (-2.46)

Network*ETC 0.20 (0.65)

0.35** (1.98)

0.02 (0.12)

0.10*** (2.79)

-0.25 (-1.29)

-0.13** (-2.17)

ROS 0.38*** (4.50)

0.57*** (4.01)

0.26*** (5.23)

0.33*** (3.44)

Network*ROS 0.67 (0.56)

0.70** (1.96)

0.37 (0.96)

0.29** (2.11)

ETC*ROS 0.23 (1.10)

0.16 (1.29)

0.50 (1.08)

0.77 (1.26)

Network*ETC*ROS 0.68 (0.23)

0.71** (2.23)

0.23 (0.31)

0.76*** (2.85)

Each regression includes other control variables in our equation (1) and (2), such as firm age, firm size, employee numbers, CEO duality, firm tangible assets, debt structure, cost of debt, cash flow volatility, ROA, board size, independent director ratio, guaranteed loan, prime rate and year and firm fixed effects. Sum test 1.38 a 2.98*** a 1.47 a 2.10*** a 0.99 b 2.71*** b Pseudo R2 0.17 0.19 0.08 0.15 0.37 0.33 Observations 1665 1637 1665 1637 919 896

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Table 10. Effects of bank connection and political connection across industries Full sample Support industry Non-support industry Panel A: Dependent variable is the access to bank loan Network 0.51**(2.10) 0.45*(1.81) 0.57***(3.73) Political 0.17***(3.19) 0.09(1.26) 0.23***(2.59) Network*Political 0.59(0.70) 0.26(0.22) 0.12**(2.04) Sum test a 1.87* 1.55 2.48** Adjusted R2 0.10 0.08 0.13 Observations 3,302 1,809 1,493 Panel B: Dependent variable is the bank loan ratio Network 0.08***(4.60) 0.05(1.47) 0.10***(4.58) Political 0.05**(2.37) 0.04(1.56) 0.04**(2.55) Network*Political 0.02(0.45) 0.01(0.04) 0.02**(2.33) Sum test b 1.71* 1.31 2.17** Adjusted R2 0.06 0.05 0.09 Observations 3,302 1,809 1,493 Panel C: Dependent variable is the collateral requirement Network -0.08**(-2.05) -0.04**(-2.00) -0.10**(-2.55) Political 0.01(0.68) 0.05(0.67) -0.01(-0.50) Network*Political -0.02(-0.44) -0.03(-0.45) -0.02(-1.42) Sum test c 1.03 1.00 1.60 Adjusted R2 0.34 0.36 0.34 Observations 1,815 1,137 678

Endogeneity issue

Endogeneity issue Bank officials resigned their original positions and

acquired posts in better performing private firms for their monetary and reputational concerns

Top executives in firms being discriminated against with access to bank loans have more incentive to appoint an executive with a social network with banks in order to maintain a good relationship with banks and help overcome the market barriers

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Endogeneity issue

Change regression Examine the effect of change in bank connection status on

change in bank loan finance, with a specific focus on change accounts for time-invariant common unobservable or omitted firm-specific characteristics that might affect the social networks with banks and firm’s bank loan finance.

We create two variables NBCBC, equal to 1 if the firm’s status changes from non-

bank connection to bank connection BCNBC, equal to 1 if the firm’s status changes from bank

connection to non-bank connection.

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Change regressionDependent variable ∆Loan_size ∆Long_size ∆Collateral NBCBC 0.06***

(2.75) 0.04*

(1.69) -0.03**

(-2.27)

BCNBC -0.07*** (-2.75)

-0.02* (-1.92)

0.01 (1.48)

∆ETC 0.03 (0.95)

0.04 (1.12)

0.02 (0.73)

0.03 (1.31)

0.08 (0.24)

0.02 (0.44)

NBCBC*∆ETC 0.30** (2.05)

0.02 (1.33)

-0.07 (-1.26)

BCNBC*∆ETC 0.05 (0.70)

0.06 (1.26)

0.19 (1.32)

∆ROS 0.02 (1.63)

0.05 (1.36)

0.05* (1.93)

0.03 (1.44)

NBCBC*∆ROS 0.02** (2.34)

0.03** (2.73)

BCNBC*∆ROS 0.13** (2.15)

0.03 (0.82)

Each regression includes the change of other control variables in our equation (1) and (2) Sum test 2.00** a

2.12** b 1.49 a 2.64*** b

1.11 a 2.53** b

1.58 a 1.67* b

1.15 a 1.57 a

Pseudo R2 0.18 0.23 0.25 0.16 0.12 0.11 Observations 483 483 483 483 401 401

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Endogeneity issue

We also apply the propensity-matching method to address the endogeneity issue.

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Further tests

Whether the effect of social network can be more pronounced if bank connected executives come from the lending banks Redefining Network variable as equal to 2 for

Lender_network, 1 for Nonlender_network, and 0 for no network. The results are broadly similar.

We divide top management into executives and independent directors social networks The coefficients on independent social network is

smaller, indicating incremental contribution.

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Further tests (Cont)

We consider the number of bank connected members We create one variable, Strength, equal to the number of

executives and directors who have the connections with banks.

We repeat our analysis using political connection Results show the effect of political connection is less

significant than bank connection We also repeat our regression by dividing total

sample into small firms and large firms34

Findings

Social network with banks can: lead to better access to bank loans; Reduce collateral requirement; Enhance bank lending efficiency.

Entertainment spending help to maintain social network and further strengthen social network’s effect

The above results are more pronounced after the economic stimulus package and in the regions with severe government intervetioin.

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Findings

Social network and entertainment spending help to explain the coexistence between a weak institutional framework and vibrant private sector growth in China.

Overall, social network with banks can be a substitute for legal protection and help reduce information asymmetry and creditor concerns and monitoring costs, at least in the context of China.

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Thank you and your comments and suggestions are welcome.

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