We empirically examine some listed Chinese firms’ political connection, ownership, and financing constraints. Politically-connected firms display no financing constraints whereas firms without connection experience significant constraints. Non-connected family-controlled firms bear greater constraints than non-connected state-owned firms.Political connection; investments; financing constraints; Chinese firms

Chan, Kenneth S.

Dang, Vinh Q.T.

Yan, Isabel K.

English

Research Papers in Economics

1 
 
Chinese Firms’ Political Connection, Ownership, and Financing Constraints 
 
 
Kenneth S. Chan
a,b, Vinh Q. T. Dang
c,*, Isabel K.M. Yan
a  
 
a City University of Hong Kong 
b Lingnan College, Sun Yat-Sen University, China 
c University of Macau 
 
November 2011 
 
Abstract 
We empirically examine some listed Chinese firms’ political connection, ownership, and financing constraints. 
Politically-connected firms display no financing constraints whereas firms without connection experience significant 
constraints. Non-connected family-controlled firms bear greater constraints than non-connected state-owned firms. 
 
 
 
 
 
 
JEL classification: E22, G31, G18, O16 
Keywords: Political connection, investments, financing constraints, Chinese firms 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
* Corresponding author: Department of Economics, University of Macau, Taipa, Macau. Tel: (853) 8397-8954; fax: 
(853) 2883-8312; email: dangvinh@umac.mo. 2 
1. Introduction 
 
There have been many studies on the relationship between capital market imperfections and 
investment behaviors of firms in emerging markets.
1 Financial frictions are considered to pose a 
serious impediment to the efficient allocation of credit for corporate investment and therefore 
economic growth (King and Levin, 1993). Some of these studies focus on firms’ characteristics 
like size, age, and business group affiliation that affect their credit access and consequently 
financing constraints. Only a few studies have examined the relationship between firms’ 
financing constraints and investment in transition economies. Konings et al. (2003) find that 
firms in Bulgaria and Romania, the two “slow reformers”, are less sensitive to internal financing 
constraints than firms in Poland and the Czech Republic, the two “fast reformers”. China’s 
capital markets, despite their tremendous economic growth in recent decades, are still under-
developed. Poncet et al. (2010) show that Chinese private firms are credit constrained while 
state-owned firms and foreign-owned firmsare not. In this paper, we focus on the impact of 
political connection on the financing constraints of listed Chinese firms. We also take into 
account ownership information.
2  
Section 2 specifies the empirical model. Section 3 describes data and the results. Section 4 
concludes. 
 
2. Empirical model 
 
In a perfect capital market where financial friction is absent, as assumed by Modigliani and 
Miller (1958), internal and external funds are perfect substitutes and a firm’s investment 
decisions are made independently of its financing choices. Capital markets in reality tend to be 
less than perfect and firms face higher cost for external financing due to asymmetric information 
and agency problems. Therefore, firms are considered financially constrained when their 
investment are sensitive to internal funds. 
We use the following specification for empirical testing: 
01 2 it
it it it
IS C
KK K
          
  
,         ( 1 )  
where Iit is investment, defined as capital expenditure of firm i during period t; St is net sales 
during t; and Ct is defined as cash and equivalent at the beginning of period t. These variables are 
scaled by capital stock Kt, which is measured as the value of property, plant, and equipment at 
the start of period t. Our main interest is α2, the coefficient on the cash-to-capital ratio (cash for 
short). Under the null hypothesis of no financing constraints, α2  ≤ 0. If firms rely on cash 
holdings (our proxy for internal funds) to finance investment, they face financing constraints; 
that is, α2 ≥ 0.  
Firms may hold more cash in anticipation of greater capital investment if expected future 
profit is high. In this case, the coefficient α2 may not reflect financing constraints. An 
“accelerator” term, defined as sales growth (ΔS) over capital stock, is incorporated into (1) to 
account for the effects of future opportunities on investment. Additionally, a firm’s investment 
                                                 
1 See Schiantarelli (1996) for a literature review. 
2 Additionally, compared to Poncet et al. (2010), our study uses a different data source and sample period and 
focuses on publicly-traded firms. 3 
may be affected by its leverage; therefore, we also include a debt-to-capital ratio in the following 
augmented model: 
01 2 2 2 it
it it it it it
IS C S D
KK K K K
  
               
     
     ( 2 )  
To examine the effect of political connection on firms’ financing constraints, we split the 
sample into two groups: firms with and without political ties. To take into account ownership 
structure, we further separate firms into state-owned and family-controlled ones.
3 
 
3. Data and results 
 
To estimate Equations (1) and (2), data for investment, sales, cash balance, capital, and debt 
of Chinese firms listed on Shanghai and Shenzhen stock markets are obtained from Worldscope. 
Financial and service firms are excluded. Outliers for the investment, sales, and cash variables 
are also dropped.  
We examine the biography of the CEO and Chairman of the board of directors of each firm 
using the China Stock Market and Accounting Research (CSMAR) database and Sina Finance 
website.
4 If biographical information or the name of the CEO/Chairman is missing from these 
sources, the firm is excluded from our sample. A firm is considered politically-connected if its 
CEO/Chairman is or was a government official or a military officer or someone with a political 
ranking at the provincial or ministerial level. Out of 1347 firms, 330 are considered to be 
politically connected. Since the CEO/Chairman appointment duration is about three years, we 
combine the information on the profile of CEO/Chairman with three years of financial data taken 
from Worldscope. Information on firm’s ownership type is also obtained from the CSMAR 
database. Our samplecontains up to 3585 firm-year observations of 1347 firms during 2005-
2007. 
Table 1 provides the summary statistics for the major variables. In each cell, the first number 
is the value averaged over 2005-2007 whereas the number in the parentheses is the standard 
deviation. While firms without political ties generate greater sales per unit of capital, they also 
hold more cash relative to politically connected firms. Unconnected firms may hold cash in 
anticipation of higher future investment or as a precaution against liquidity constraints. This 
possibility is examined in the augmented model.  
Table 2 presents the regression results of the baseline model, Equation (1). We apply the 
ordinary least square (OLS), instrument variable (IV), and fixed effects (FE) estimators to the 
two groups of politically-connected and non-connected firms. In Column 1, the investment of 
firms without political ties responds positively to sales (α1 = 0.018) and cash holdings (α2 = 
0.065). The coefficient on cash is statistically significant at 1% level, suggesting that the non-
connected firms face financing constraints. On the other hand, in Column 2, the investment of 
politically connected Chinese firms is not sensitive to internal funds; the coefficient on cash (α2 = 
0.010) is much smaller and is not significant. The connected firms seem to be free from the 
credit constraints endured by their non-connected counterparts.  
OLS estimates may be biased due to the endogeneity of cash holdings. We use two lags of the 
cash variable as its instruments in the IV regressions. The coefficient on cash is not significant 
for the politically-connected firms (-0.008) whereas it is significant for the non-connected firms 
                                                 
3 We thank the anonymous referee for this suggestion. 
4 See http://finance.sina.com.cn. 4 
(0.075). The IV results are consistent with the OLS results. Sargan’s J-statistic for testing the null 
hypothesis of orthogonality of the instruments and the error terms is shown for each IV 
regression. The over-identifying restrictions are accepted for the two IV regressions. We also 
report Cragg-Donald first-stage F-statistic to test the null hypothesis of weak instruments against 
the alternative that they are strong. The reported F-statistics all reject the null hypothesis of weak 
identification. To account for unobserved time-invariant factors, we re-estimate Equation (1) 
using fixed effects regression. The results are similar to those obtained by the other two methods.  
The results based on the augmented model, Equation (2), are shown in Table 3. In Columns 
(1) and (2), investment of firms without political connection responds positively to the amount of 
cash on hand and sales growth. It is not the case with connected firms.  
In Columns (3) and (4), we replace Sales with Tobin’s Q, which is positive and statistically 
significant in the investment equation for both groups. More importantly, non-connected firms 
seem to have financing constraints while connected firms do not.  
With respect to ownership, the results based on IV and FE estimators in Columns (5)-(12) 
suggest that having political connected CEO/Chairman is still important to firms’ investment in 
terms of financing constraint. The coefficient on the cash variable in the regressions for the 
politically-connected state-owned and family-controlled firms is not statistically different from 
zero; on the other hand, it is positive and statistically significant in the regressions for the non-
connected state-owned and family-controlled firms.
5 However, non-connected family-controlled 
firms consistently bear greater constraints than non-connected state-owned firms in both IV 
estimation (estimated coefficient on cash: 0.122 versus 0.085) and FE estimation (0.091 versus 
0.076). 
 
4.  Conclusion 
 
It has been documented that political connection confers significant benefits on firms. For 
example, Faccio et al. (2006) present evidence that politically-connected firms are more likely to 
be bailed out when they face financial difficulties non-connected counterparts. In this study, we 
show that listed Chinese firms with politically connected CEO/Chairman seem to be free from 
financing constraint. These firms probably have easier access to external credit via either the 
personal connection of the firms’ top management, explicit preferential policy treatment, or they 
are perceived by lenders to have implicit government guarantee. The reduction in the financing 
constraint is a benefit to the shareholders of the connected firms and is consistent with the 
literature examining the value of political connection. However, whether this is conducive to an 
economy-wide efficient allocation of capital is a topic for further research. 
 
 
Acknowledgment: We would like to thank an anonymous referee and the Editor for valuable suggestions. We are 
grateful to Hong Zou for his generous help with the political connection data.  Also thanks to Jennifer Lai for her 
excellent research assistance. 
References 
                                                 
5 We also conduct two additional robustness checks. One more year (year 2004) of firms’ financial data is added to 
the analysis. The estimated coefficients in Equations (1) and (2) are very similar and confirm the above results. We 
also divide the firms into four groups according to both the size of their inflation-adjusted assets and the political 
background of their CEO/Chairman. The effect of political connection remains. The investment of politically 
connected firms, whether large or small, is not sensitive to cash holdings whereas the investment of large and small 
non-connected firms is. These results are available on request.  5 
 
Faccio, M., Masulis, R. W., McConnell, J. J., 2006. Political connections and corporate bailouts. Journal of Finance 
61, 2597-2635 
 
King, R. G., Levin, R., 1993. Finance and growth: Schumpeter might be right. Quarterly Journal of Economics 108, 
717-737. 
 
Konings, J., Rizov, M., Vandenbussche, H., 2003. Investment and financial constraints in transition economies: 
Micro evidence from Poland, the Czech Republic, Bulgaria, and Romania. Economics Letters 78, 253-258. 
 
Modigliani, F., Miller, M., 1958. The Cost of capital, corporation finance, and the theory of investment. American 
Economic Review 48, 261-297.  
 
Poncet, S., Steingress, W., Vandenbussche, H., 2010. Financial constraints in China: Firm-level evidence. China 
Economic Review 21, 411-422. 
 
Schiantarelli, F., 1996. Financial constraints and investment: Methodological issues and international evidence. 
Oxford Review of Economic Policy 12, 70-89. 
 
Table 1: Summary statistics (2005-2007) 
 Number  of  firms  Investment  (I/K) Sales  (S/K)  Cash  (C/K) 
All firms  1347  0.229 (0.303)  2.510 (2.127)  0.767 (1.154) 
      
Non-connected firms
a 
   State-owned 
   Family-controlled 
1017 
563 
341 
0.240 (0.312) 
0.214 (0.244) 
0.297 (0.403) 
2.681 (2.165) 
2.679 (2.152) 
2.814 (2.175) 
0.792 (1.124) 
0.714 (1.020) 
0.936 (1.292) 
      
Politically-connected firms
a 
   State-owned 
   Family-controlled 
330 
227 
74 
0.195 (0.272) 
0.204 (0.276) 
0.172 (0.207) 
1.986 (1.913) 
1.967 (1.938) 
2.195 (1.896) 
0.693 (1.271) 
0.628 (1.125) 
0.869 (1.371) 
Note: In each cell, the first number is the value of the variable averaged over 2005-2007 and the number in 
parenthesis is the standard deviation. 
a Firms’ ownership information is missing for some firms. We also drop firms with ambiguous ownership 
classification. Therefore, the sum of sub-groups of state-owned and family-controlled firms in each of the non-
connected and politically-connected groups is not equal to their total.  
 
Table 2: Baseline regression - Dependent variable: I/K 
 
 Non-con 
OLS 
(1) 
Pol-con 
OLS 
(2) 
Non-con 
IV 
(3) 
Pol-con 
IV 
(4) 
Non-con 
FE 
(5) 
Pol-con 
FE 
(6) 
Sales (S/K) 0.018** 
(0.003) 
0.021** 
(0.006) 
0.018** 
(0.029) 
0.023** 
(0.006) 
0.079** 
(0.011) 
0.066** 
(0.020) 
Cash (C/K) 0.065** 
(0.012) 
0.010 
(0.015) 
0.075* 
(0.029) 
-0.008 
(0.019) 
0.078** 
(0.018) 
0.035 
(0.024) 
R-squared  0.090 0.035 0.077 0.044 0.187 0.093 
Observations  2705 880 1705 558 2705 880 
Cragg-Donald
b       458  203     
Sargan statistic 
(p-value) 
   0.749 
(0.387) 
3.282 
(0.070) 
  
Notes: ** and * denote statistical significance at 1% and 5% levels, respectively. Robust standard errors are in 
parentheses. Year dummies are included. “Non-con” and “Pol-con” refer to firms without and with political 
connection, respectively. “IV” denotes instrumental variable regression where cash is instrumented with its first and 
second lags. “FE” denotes regression with time fixed effects. 
a The first-stage F statistic for testing the null hypothesis of weak instruments 6 
Table 3: Augmented regression - Dependent variable: I/K    
 Sales  (S/K) Tobin’s  Q
a State-owned Family-controlled State-owned Family-controlled 
 Non-con 
(1) 
IV
 
Pol-con 
(2) 
IV
 
Non-con 
(3) 
IV
 
Pol-con 
(4) 
IV
 
Non-con 
(5) 
IV 
Pol-con 
(6) 
IV 
Non-con 
(7) 
IV 
Pol-con 
(8) 
IV 
Non-con 
(9) 
FE 
Pol-con 
(10) 
FE 
Non-con 
(11) 
FE 
Pol-con 
(12) 
FE 
Sales (S/K) 0.012* 
(0.006) 
0.023** 
(0.006) 
   0.010 
(0.006) 
0.023** 
(0.007) 
0.015 
(0.013) 
0.021 
(0.011) 
0.080** 
(0.016) 
0.088** 
(0.031) 
0.081** 
(0.020) 
0.064* 
(0.024) 
Tobin’s Q      0.008** 
(0.003) 
0.055** 
(0.020) 
        
Cash (C/K) 0.091** 
(0.031) 
-0.013 
(0.018) 
0.076** 
(0.018) 
0.020 
(0.014) 
0.085* 
(0.037) 
-0.024 
(0.019) 
0.122* 
(0.058) 
0.024 
(0.035) 
0.076** 
(0.031) 
0.039 
(0.057) 
0.091** 
(0.025) 
0.015 
(0.024) 
Leverage 0.020** 
(0.006) 
-0.039 
(0.031) 
-0.041* 
(0.016) 
-0.092** 
(0.031) 
0.050 
(0.047) 
-0.009 
(0.024) 
0.015 
(0.009) 
-0.041 
(0.032) 
-0.138 
(0.114) 
-0.064 
(0.034) 
-0.002 
(0.010) 
-0.014 
(0.055) 
Acceleration 0.028** 
(0.008) 
0.006 
(0.004) 
0.007 
(0.004) 
0.011 
(0.006) 
0.038** 
(0.015) 
0.004* 
(0.002) 
0.021 
(0.013) 
0.013 
(0.011) 
0.013 
(0.013) 
-0.001 
(0.004) 
-0.003 
(0.014) 
-0.018 
(0.021) 
R-squared  0.089 0.054  0.090 0.109  0.096 0.033  0.09  0.165 0.240 0.134 0.172 0.136 
Observations  1697 552  1740  575  979  389  527  115 1528 612  861  184 
Cragg-Donald  416 204  720 345  303 351  97  10         
Sargan statistic 
(p-value) 
1.204 
(0.273) 
2.769 
(0.095) 
0.236 
(0.627) 
0.037 
(0.847) 
1.815 
(0.178) 
2.492 
(0.114) 
1.267 
(0.260) 
0.588 
(0.443) 
    
See notes to Table 2.  
a Here the variable “Sales” is replaced with Tobin’s Q. Tobin’s Q is defined as total debt plus total market value divided by the book value of total assets, all 
calculated at the beginning of period. 
 

Financial constraints and investment: Methodological issues and international evidence.

Financial constraints in China: Firm-level evidence.

Investment and financial constraints in transition economies: Micro evidence from Poland, the Czech Republic, Bulgaria, and Romania.

The Cost of capital, corporation finance, and the theory of investment.

Chinese Firms’ Political Connection, Ownership, and Financing Constraints

Chinese Firms’ Political Connection, Ownership, and Financing Constraints

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