Provincial economic performance and underpricing of IPOs: Evidence from political interventions in China

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Provincial economic performance and underpricing of IPOs: Evidence from political interventions in China☆ Jianlei Liu a, Konari Uchida b, Yuan Li a, * a b

School of Finance, Capital University of Economics and Business, China Faculty of Economics, Kyushu University, Japan

A R T I C L E I N F O

A B S T R A C T

JEL Classification: G32 O16

We examine the underpricing of 2,131 Chinese initial public offerings (IPOs) between 2005 and 2017. The results indicate that state-owned enterprises (SOEs) controlled by the local government (local SOEs) offer significantly higher underpricing when they go public than SOEs controlled by the central government and non-SOEs do. This phenomenon is evident for local SOEs from less developed provinces, after controlling for the direct effect of regional economic performance. These results suggest local government officials underprice initial public offerings to make regional companies successfully go public to promote the regional economy.

Keywords: Chinese IPOs Underpricing Provincial economic performance State-owned enterprises Local government officials

1. Introduction It is well documented that firms leave money on the table in the form of underpricing when they go public (Ibbotson, 1975; Ritter, 1984, 1991; Kim et al., 1993; Su and Fleisher, 1999; Beckman et al., 2001; Ekkayokkaya and Pengniti, 2012). Previous studies have viewed underpricing as an effective way to complete initial public offerings (IPOs), which are generally subject to uncertainty and information asymmetry (Ritter, 1984; Rock, 1986; Beatty and Ritter, 1986). While the underpricing phenomenon is commonly observed around the world, China is one country that offers extremely high underpricing in IPOs. Tian (2005) demonstrates that between 1991 and 2000, the average underpricing of Chinese IPOs was 267 percent, which is much higher than the average of US IPOs (18.4 percent between 1980 and 2001) (Ritter, 2003). This paper examines whether underpricing is offered in China to boost regional economic growth. IPOs have various favorable impacts on the regional economy by increasing the financing abilities and bargaining power of local companies (Caporale et al., 2004; Bernstein, 2015). Furthermore, IPOs promote the creation of new business, increase job opportunities, and attract investor attention (Pagano et al., 1998; Arestis et al., 2001). IPOs also enhance the reputation and economic agglomeration of the area, which further enhances regional economic growth (Rousseau and Wachtel, 2000; Li and Zhou, 2015). It is noteworthy that

regional economic performance is an important promotion standard for local Chinese politicians (Li and Zhou, 2005; Chen et al., 2005; Piotroskia and Zhang, 2014; Bao et al., 2016). In the tournament-like competition, career-minded local officials attempt to take advantage of regional economic developments to climb the political ladder (Piotroskia and Zhang, 2014). Accordingly, provincial government officers have a strong incentive to encourage local enterprises to go public (Li and Zhou, 2005; Piotroskia and Zhang, 2014). Meanwhile, IPOs are generally susceptible to uncertainty and information asymmetry, and government officials need a way to ensure successful stock issuances. Importantly, the government still closely regulates the process of listing (Li and Zhou, 2005; Piotroskia and Zhang, 2014; Bao et al., 2016; Chen et al., 2018). For instance, the China Securities Regulatory Commission (CSRC) will not review IPO applications that have not been nominated by the local government. The regulatory system provides local government officials room to intervene in the IPO process. We test the novel Hypothesis that IPO underpricing serves as local governments’ leverage to make regional firms go public smoothly, by using a sample of 2,131 Chinese IPOs between 2005 and 2017. Heterogeneity should exist among companies in terms of the influence of local governments. State-owned enterprises (SOEs) are controlled directly by politicians, while non-state-owned enterprises (non-SOEs) are only regulated through soft measures (e.g., licenses, regulations, political

☆ The Project is financially supported by the National Social Science Fund of China (16CJY040) and JSPS KAKENHI Grant Number JP19H01507. * Corresponding author. Faculty of Economics, Kyushu University, 744 Motooka Nishi-ku, Fukuoka 819-0395, Japan. E-mail addresses: [email protected] (J. Liu), [email protected] (K. Uchida), [email protected] (Y. Li).

https://doi.org/10.1016/j.econmod.2019.09.055 Received 9 July 2019; Received in revised form 29 September 2019; Accepted 29 September 2019 Available online xxxx 0264-9993/© 2019 Published by Elsevier B.V.

Please cite this article as: Liu, J. et al., Provincial economic performance and underpricing of IPOs: Evidence from political interventions in China, Economic Modelling, https://doi.org/10.1016/j.econmod.2019.09.055

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This paper makes important contributes to the literature. First, our study adds novel evidence that regional governments have an incentive to underprice IPO stocks. Numerous studies have attempted to uncover the motives for underpricing based on information asymmetry and uncertainty-based theories (e.g., Ritter, 1984; Rock, 1986; Beatty and Ritter, 1986; Brennan and Franks, 1997). Previous Chinese studies have pointed out several factors associated with large underpricing, such as information asymmetry (Yu and Tse, 2006), ownership structure (Chan et al., 2004; Chi and Padgett, 2005), market conditions (Ma and Faff, 2007), allocation mechanisms (Chiou et al., 2010; Liu et al., 2014b), regulatory changes (Cheung et al., 2009; Kao et al., 2009; Tian, 2011), institutional environment (Liu et al., 2014b), auditor quality (Ma and Faff, 2007) and underwriters’ reputations (Carter and Manaster, 1990; Su and Bangassa, 2011). Our findings suggest local government motives for economic growth can serve as a factor associated with the extremely large underpricing in Chinese IPOs. This finding provides an important policy implication not only for China, but also for many emerging markets. Second, this paper extends the literature on local government economic policies. Previous studies argue that Chinese local governments are more like revenue-maximizing agents. In particular, the fiscal decentralization policy motivated them to promote economic development (Arestis and Demetriades, 1997; Jin et al., 2005; Johansson et al., 2017). Competing for outside capital is reckon as one of the most important factors in economic growth, and local governments usually establish investment promotion bureaus and offer favorable policies (e.g., preferential tax policies and land support) to attract investments (Maskin et al., 2000; Li and Zhou, 2005; Liu et al., 2012; Cull et al., 2015). This paper shows evidence that IPOs serve as an economic stimulating measure for local governments. The rest of this paper is organized as follows. Section 2 presents the Hypothesis development. Section 3 explains the data and variables. The empirical analysis and results are presented in Section 4. The final section sums up the paper.

Table 1 Variables and definitions. Variables

Definitions

ADIR

Market-adjusted initial return on the first trading day of the IPO stock. Natural logarithm of the mean values of local Gross Domestic Product (GDP) for 3 years before IPO year. Natural logarithm of the mean values of local GDP Per Capita for 3 years before IPO year. Dummy variable that equals to one for firms controlled by the local governments directly and zero otherwise. Dummy variable that equals to one for all firms controlled by the local governments (LSOEs and LSSOEs) and zero otherwise. Dummy variable that equals to one for all firms controlled by the central government (CSOEs and CSSOEs) and zero otherwise. The odds of winning the lottery to show the chance of winning the IPO lottery. The market return between offering and listing day of an IPO company. Dummy variable equals to one for the top ten underwriters in China and zero for others. Dummy variable takes a value of one if the firm issues seasoned equity offerings in the subsequent 2 years and zero for others. Natural logarithm of the number of shares offered. The percentage shares held by top management before the IPO year. Lagged days from offering day to listing day. Price to earnings ratio. Years of the firm’s operation at the IPO date. Natural logarithm of a firm’s total assets. A firm’s total liabilities divided by total assets.

LNGDP LNPCGDP LSOED Local SOED Central SOED LOTTERY M_RETURN UNDERWRITER SEO LNOFFERNUM MANAGEROWN LAGDAY P/E AGE LNASSET LEVERAGE

Table 2 Distribution of sample. This table shows the sample distribution. Our sample consists of 2,131 firms that went public on the SHSE and SZSE between 2005 and 2017. We divide our sample into five sub-samples: SOEs are controlled by the central government directly (CSOE); SSOEs are controlled by the central government (CSSOE); SOEs are controlled by the local governments directly (LSOE); SSOEs are controlled by the local governments (LSSOE); and the Non-SOEs. IPO year

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total

Number of IPOs

2. Hypothesis

(%)

CSOE

CSSOE

LSOE

LSSOE

Non-SOE

3 3 6 2 7 5 1 4 0 2 1 1 1 36

2 5 9 6 2 10 3 2 0 1 5 3 5 53

2 7 18 4 5 24 8 9 1 6 8 8 18 118

1 13 11 7 3 10 6 5 0 3 6 11 3 79

7 34 69 56 78 295 261 134 1 112 198 194 406 1845

Since the 1978 economic reform, the Chinese government has promoted decentralization by devolving financial and administrative authorities to provincial governments. Provincial officials have assumed authority and responsibility for supporting their provincial economic development (e.g., constructing infrastructures and attracting investments) (Li and Zhou, 2005). The central government has formulated a series of reward and punishment systems to align the interests of local governments and their officials with provincial economic performance (Jin et al., 2005; Li and Zhou, 2005). Previous studies show that provincial leaders’ promotion is significantly linked to their region’s economic performance (Chen et al., 2005; Li and Zhou, 2005). By using data from 1979 to 2002, Li and Zhou (2005) find that provincial leaders are more likely to be re-elected or promoted when the local economy shows higher growth rate. Chen et al. (2005) examined data from 1979 to 2002 to highlight the influence of regional economic growth on provincial leaders’ career path. Both central and local governments are encouraging enterprises to use the capital market for direct financing. For example, the Jiangsu Provincial Department of Housing and Urban Rural Development and the Jiangsu Securities Regulatory Bureau jointly convened a conference on March 25–26, 2019, to promote the listing of enterprises in the local construction industry.1 Similarly, Guizhou Province, an underdeveloped western region, is actively promoting listing of local enterprises on the SSE STAR Market (Science and Technology innovation board).2 These

0.70 2.91 5.31 3.52 4.46 16.14 13.09 7.23 0.09 5.82 10.23 10.18 20.32 100

networks) (Piotroskia and Zhang, 2014). Non-SOEs should have a weak incentive to follow local government’s requests, since they will not receive sufficient benefits from regional economic growth, while underpricing directly decreases their proceeds. There also exists variety among SOEs in terms of the influence of the local government. Some SOEs are directly controlled by the central government (CSOEs), which may not care much about regional economic growth. Meanwhile, some SOEs are controlled by the local government (LSOE) and are likely to be its policy target. Our sample includes 118 LSOEs, as well as 79 IPOs controlled by LSOEs. Our results show that these local SOEs offer significantly higher underpricing than the other IPOs. The high underpricing is evident for local SOEs from less developed provinces, after controlling for the direct effect of regional economic development. These results support the view that local government officials use underpricing to make regional companies go public.

1

See http://www.jiangsu.gov.cn/art/2019/3/28/art_60095_8287736.html, the website of the People’s Government of Jiangsu Province. 2 See http://www.guizhou.gov.cn/xwdt/dt_22/bm/201902/t20190215_226 1083.html, the website of the People’s Government of Guizhou Province. The SSE STAR Market is an independent new board, which started on June 13, 2019. 2

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Table 3 Descriptive Statistics for underpricing. This table shows the mean (median) values of initial returns (underpricing). Panel A indicates the initial returns by IPO years; Panel B shows the initial returns for different types of SOEs: SSOEs controlled by the central government (CSSOE); all SOEs controlled by the central government (Central SOE); SSOEs controlled by the local governments (LSSOE); all SOEs controlled by the local governments (local SOE), and Non-SOEs. The results of mean (median) different test are presented. See Table 1 for definitions of variables. year

Mean (%)

t-stat.

Panel A: ADIR by calendar year 2005 50.51 5.94*** 2006 82.24 10.67*** 2007 191.97 18.91** 2008 123.80 11.65*** 2009 73.23 18.30*** 2010 41.47 19.23*** 2011 21.47 12.45*** 2012 26.35 5.73*** 2013 27.69 0.84 2014 38.47 79.52*** 2015 39.71 47.70*** 2016 43.63 148.23*** 2017 43.80 214.72*** Total 51.60 42.02 *** Panel B: Initial return by different types of SOEs CSSOE 85.14 6.90*** Central SOE 71.44 8.89*** LSSOE 86.02 8.99*** Local SOE 75.14 13.03*** Non-SOE 48.13 40.15***

Median (%)

z-stat.

Min (%)

Std. Dev. (%)

Max (%)

N

48.65 69.08 167.31 90.08 69.03 32.74 15.14 15.73 27.69 38.14 37.77 43.19 43.46 41.98

3.41*** 6.83*** 9.23*** 7.53*** 8.46*** 15.84*** 11.30*** 8.26*** 0.45 9.66*** 12.80*** 12.77*** 18.03*** 39.37 ***

4.48
1.23 34.69 25.00
0.49
10.32
18.64
20.94
5.21 11.87 0.00 28.99 0.00
20.94

32.91 60.72 107.91 92.02 39.00 40.00 28.82 57.04 46.52 5.39 12.29 4.34 4.24 56.68

129.21 352.76 562.51 441.15 205.44 267.13 185.97 608.02 60.58 52.87 80.38 58.37 55.46 608.02

15 62 113 75 95 344 279 154 2 124 218 217 433 2131

64.84 49.80 43.08 44.06 41.74

6.17*** 8.00*** 7.69*** 12.07*** 36.61***


9.10
9.10
7.08
7.08
20.94

89.89 75.79 85.09 80.93 51.49

507.97 507.97 434.75 434.75 608.02

53 89 79 197 1845

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Table 4 Descriptive Statistics for other variables. This table indicates descriptive statistics for independent variables. Panel A shows descriptive statistics of continuousvariables. Panel B presents information for dummy variables. See Table 1 for definitions of the variables. Panel A: Independent variables

GDP (billion RMB) GDP Per Capita (thousand RMB) LOTTERY M_RETURN Number of offering share (million) MANAGEROWN LAGDAY P/E AGE Total assets LEVERAGE

Mean

Std. Dev.

Min

Median

Max

N

3073.642 51.521 0.009 0.007 812.708 0.194 11.660 45.054 2.309 20.089 0.478

2026.647 25.027 0.022 0.050 3842.684 0.253 4.203 32.076 0.596 1.271 0.182

34.235 3.657 0.001
0.230 86.700 0.000 0.000 0.000 0.000 15.251 0.034

2562.327 46.188 0.004 0.008 280.000 0.042 11.000 32.61 2.398 19.986 0.488

7382.577 109.415 0.613 0.229 120,000.00 0.997 107.000 369.920 3.584 27.069 0.982

2131 2131 2116 2131 2131 2131 2131 2131 2131 2026 2026

Panel B: Dummy variables LSOED Local SOED Central SOED SEO UNDERWRITER

N (value of one)

% (value of one)

118 197 89 392 985

5.54 9.24 4.18 18.40 46.22

facts illustrate that Chinese provincial politicians recognize local firms’ listing as an important measure for stimulating the local economy (Chi and Padgett, 2005; Li and Zhou, 2005). The link between their career path and the local economy gives them an incentive to increase IPOs, which can spur local economic growth (Li and Zhou, 2005; Du and Xu, 2009). Such an incentive is likely strong for politicians of less developed provinces, since IPOs will have only a marginal impact on the economic growth of developed provinces. IPOs are generally subject to uncertainty, and previous studies have viewed underpricing as an effective way to complete IPOs successfully (Ritter, 1984; Rock, 1986; Beatty and Ritter, 1986). When adverse selection makes uninformed investors wary of the quality of IPO companies, underpricing occurs in equilibrium to attract them to the market. Brennan and Franks (1997) demonstrate that issuers are motivated to underprice their new shares for successful public offerings and wide distributions. Importantly, over 90 percent of investors are individuals in

China, most of whom are likely uninformed (Teoh et al., 1998; DuCharme et al., 2001). Hence, underpricing is crucial for completing IPOs that provide external capital to companies and improve the reputation of the firms and local government (Mok and Hui, 1998). Underpricing is costly for issuers who are unwilling to discount their shares to spur the regional economy. However, local government officials can influence IPO companies, since the IPO process is still closely regulated by the Chinese government (Li and Zhou, 2005, 2015; Piotroskia and Zhang, 2014; An et al., 2016; Bao et al., 2016; Chen et al., 2018). The central government controls IPOs as a scarce resource, and then distributes the issuance resources to provincial or municipal levels. The China Securities Regulatory Commission (CSRC) will not review IPO applications that have not been nominated by the local government. CSRC officials need to ask the provincial government about the proposed issue during the examination process. Then, they submit the IPO application to the Stock Issuance Examination and Verification Committee 3

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Table 5 Univariate analyses. This table indicates the mean of the market-adjusted initial return (ADIR) for subsamples upon average GDP (the left side of Table 5) or GDP Per Capita (the right side of Table 5) for 3 years before IPO year. Firstly, we equally divide the firms into two groups upon average GDP or GDP Per Capita for 3 years before IPO year (the low and the high group). The low groups include the IPOs from the undeveloped provinces with lower GDP or GDP Per Capita. The high groups include the IPOs from the developed provinces (higher GDP or GDP Per Capita). Panel A shows the results between the low economic performance group and the high group for CSOEs. Panel B to Panel G show the results for CSSOE, Central SOE, LSOE, LSSOE, Local SOE and Non-SOE, respectively. In Panel H, we compare the ADIR between Central SOEs and Local SOEs under the low economic performance groups and the high groups, respectively. In Panel I, we compare the ADIR between Central SOEs and Local SOEs under the low economic performance groups and the high groups, respectively. See Table 1 for definitions of variables. Upon average GDP for 3 years before IPO year Panel A: Sub-sample of CSOE Low ADIR (%) 51.265 N 36 Panel B: Sub-sample of CSSOE Low ADIR (%) 93.724 N 46 Panel C: Sub-sample of Central SOE Low ADIR (%) 75.084 N 82 Panel D: Sub-sample of LSOE ADIR (%) Low

High /

High 28.758 7 High 28.758 7 High

78.211 N 83 Panel E: Sub-sample of LSSOE Low

43.310 35

ADIR (%) 100.813 N 59 Panel F: Sub-sample of Local SOE Low

42.369 20

ADIR (%) 87.602 N 142 Panel G: Sub-sample of Non-SOE Low ADIR (%) 57.724 N 842 Panel H: Central SOEs vs. Local SOEs ADIR (%) Low Central SOEs 75.084 Local SOEs 87.602 Difference
12.518 t-stat.
1.05 Panel I: Local SOEs vs. Non-SOEs ADIR (%) Low Local SOEs 87.602 Non-SOEs 57.724 Difference 29.878*** t-stat. 4.78

High

High 42.968 55 High 40.080 1003

Upon average GDP Per Capita for 3 years before IPO year High vs. low Difference /

t-stat. /

High vs. Low Difference
64.966*

t-stat.
1.82

High vs. Low Difference
46.326

t-stat.
1.57

High vs. Low Difference
34.901**

t-stat.
2.27

ADIR (%) N

ADIR (%) N

ADIR (%) N

ADIR (%) N

High vs. Low Difference t-stat.
58.444***
2.77

High vs. Low Difference
44.634***

t-stat.
3.57

High vs. Low Difference
17.644***

t-stat.
7.44

Low

High

57.881 17

45.345 19

Low

High

112.734 31

46.266 22

Low

High

93.307 48

45.839 41

Low

High

78.007 89

36.714 29

Low ADIR (%) N

High

99.912 59

45.025 20

Low

High

89.740 148

40.106 49

Low

High

ADIR (%) N

55.921 870

41.182 975

ADIR (%) N

High vs. Low Difference
12.536

t-stat.
0.90

High vs. Low Difference
66.468***

t-stat.
2.82

High vs. Low Difference
47.468***

t-stat.
3.08

High vs. Low Difference
41.293**

t-stat.
2.55

High vs. Low Difference t-stat.
54.887**
2.58

High vs. Low Difference
46.633***

t-stat.
3.60

High vs. Low Difference
14.739***

t-stat.
6.20

N 82 142

High 28.758 42.968
14.210
1.03

N 7 55

ADIR (%) Central SOEs Local SOEs Difference t-stat.

Low 93.307 86.740 6.567 0.43

N 48 148

High 45.839 40.106 5.733 1.08

N 41 49

N 142 842

High 42.968 40.080 2.888 0.60

N 55 1003

ADIR (%) Local SOEs Non-SOEs Difference t-stat.

Low 86.740 55.921 30.819*** 4.83

N 148 870

High 40.106 41.182
1.076
0.27

N 49 975

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Based on the type of controlling shareholder, Chinese firms can be categorized as CSOEs (SOEs owned by the central government), LSOEs (SOEs owned by the local government), SSOEs (SOEs owned by another SOE), and Non-SOEs (non-state-owned enterprises). CSOEs are generally nationwide large-scale companies occupying a dominant position in a key industry, and are strictly monitored by the central government. LSOEs are controlled directly by the local government and operate mainly in their province as a pillar of the local economy (Chen et al., 2015). The development of LSOEs contributes significantly to local economic growth and improvements in people’s living standards; LSOEs also bring abundant tax revenues. Local governments keep close contact and communicate with LSOEs and offer maximum support to them in the IPO process (Li et al., 2011; Liu et al., 2014a). Therefore, local governments can exert much stronger power over LSOEs than CSOEs and Non-SOEs can. SSOEs are not controlled directly by the government, but

(SIEVC) for further examination and verification. The committee will also communicate with the provincial government regarding the issuer.3 Thus, the approval and support of local governments are crucial for companies in the IPO process. Furthermore, firms need local government support on various issues in the IPO process, such as taxation, land, and choice and coordination of intermediary agents. Previous studies suggest that political connections provide Chinese companies with various benefits, such as approval of new equity issues (Li et al., 2011; Piotroski and Zhang, 2014; Bao et al., 2016; Chen et al., 2017). Taken all together, Chinese IPO firms have an incentive to follow the local government’s requests for regional economic growth. Meanwhile, heterogeneity may exist among IPO companies in influence of the local government and willingness to accept underpricing.

3

See “Economic Laws,” published by the China CPA Association 4

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Table 6 Correlation matrix.

1. LNGDP 2. LNPCGDP 3. SOE 4. LOTTERY 5. M_RETURN 6. UNDERWRITER 7. SEO 8. LNOFFERNUM 9. MANAGEROWN 10. LAGDAY 11. PE 12. AGE 13. LNASSET 14. LEVERAGE

1

2

3

4

5

6

7

8

9

10

11

12

13

14

1.000 0.602
0.299
0.077
0.033 0.051
0.021
0.157 0.187
0.090
0.327 0.334 0.089
0.182

1.000
0.246
0.110
0.000 0.042
0.002
0.059 0.187
0.102
0.365 0.389 0.174
0.263

1.000 0.020
0.002 0.007
0.051 0.436
0.239 0.059 0.065
0.143 0.311 0.112

1.000
0.025 0.003 0.053 0.141
0.050
0.045
0.043
0.107 0.055 0.143

1.000
0.021 0.113
0.018 0.046 0.092 0.004 0.029 0.010
0.089

1.000
0.006 0.057 0.004
0.009 0.000 0.027 0.050 0.035

1.000
0.062 0.076
0.092
0.066
0.015
0.001 0.045

1.000
0.190 0.024
0.095
0.163 0.760 0.200

1.000
0.015
0.115 0.133
0.046
0.113

1.000 0.118
0.118
0.045 0.028

1.000
0.325
0.391 0.159

1.000 0.133
0.205

1.000 0.217

1.000

government (CSSOEs), and Non-SOEs will not receive sufficient benefits by discounting their offering prices. Meanwhile, controlling shareholders of local SOEs may have an incentive to pursue regional economic growth in exchange for underpricing. Local government officials will have such a strong incentive when their provinces are underdeveloped. These discussions give rise to the following Hypothesis 4

Table 7 Regression results for the entire sample. This table shows regression results of ADIR for the whole sample. Model 1 and 3 employ OLS estimations; Model 2 and 4 adopt province-fixed effects model estimations. We compute t-statistics for OLS coefficients by using province-clustering standard errors. In each regression, the observation is deleted from the analysis when necessary independent variables are not available. See Table 1 for definitions of variables. Independent variable

LNGDP LSOED

Model 1

Model 2

Model 3

Model 4

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)


0.019 (-1.50) 0.109*** (2.85)


0.183 (-1.14) 0.096** (2.69)


0.018 (-1.41)


0.172 (-1.11)

0.083*** (2.71)
1.358*** (-3.34)
0.678*** (-3.99)
0.001 (-0.06) 0.076*** (3.34)
0.118*** (-5.93) 0.002 (0.05)
0.000 (-0.06) 0.955*** (25.2) 0.008 (0.46) 0.083*** (5.41)
0.112** (-2.00) Yes Yes 0.135 (0.54) 0.617 2012

0.076*** (3.13)
1.336** (-2.23)
0.676** (-2.23) 0.000 (0.04) 0.079 (1.58)
0.111*** (-4.68) 0.004 (0.15)
0.000 (-0.12) 0.953*** (9.00) 0.008 (0.56) 0.081*** (2.97)
0.129 (-1.58) Yes Yes 1.482 (1.07) 0.588 2012

Local SOED LOTTERY M_RETURN UNDERWRITER SEO LNOFFERNUM MANAGEROWN LAGDAY PE AGE LNASSET LEVERAGE INDUSTRY YEAR Constant Adj. R2 N


1.370*** (-3.37)
0.671*** (-3.95) 0.000 (0.01) 0.076*** (3.35)
0.118*** (-5.91)
0.001 (-0.03)
0.000 (-0.13) 0.962*** (25.3) 0.009 (0.55) 0.083*** (5.40)
0.110** (-1.97) Yes Yes 0.150 (0.60) 0.618 2012


1.351** (-2.24)
0.668** (-2.19) 0.001 (0.11) 0.079 (1.58)
0.110*** (-4.67) 0.002 (0.06)
0.000 (-0.17) 0.958*** (9.02) 0.010 (0.66) 0.081*** (2.93)
0.127 (-1.55) Yes Yes 1.580 (1.12) 0.585 2012

Hypothesis. Local SOEs offer high underpricing especially when their regional economy is underdeveloped. 3. Data and variables 3.1. Sample selection and data We include all Chinese A-share firms that went public on the Shanghai Stock Exchange (SHSE) and the Shenzhen Stock Exchange (SZSE) between 2005 and 2017. We extract IPO data and macroeconomic data from the CSMAR database. Our sample period starts in 2005 when the book-building system was introduced to avoid the change in offering price determination method affecting our results (Tian, 2011). Importantly, the book-building system increases opportunities for political factors to affect IPO underpricing (Liu et al., 2014b). We exclude financial companies and B-share IPOs. Our final sample of 2,131 IPO firms (563 firms on the SHSE; 1,568 firms on the SZSE) is more comprehensive than those of previous Chinese papers (Chan et al., 2004; Chen et al., 2004; Cheung et al., 2009; Chi and Padgett, 2005; Guo and Brooks, 2008; Mok and Hui, 1998; Su and Bangassa, 2011; Su and Brookfield, 2013; Wang, 2005; Yu and Tse, 2006). Table 1 presents variable definitions. Table 2 presents the sample distribution by year and ownership type. IPO markets were active especially in 2010, 2011, and 2017. Local SOEs comprise a significant portion of SOEs, while more than 85 percent of the entire sample are non-SOEs. 3.2. Measures for underpricing We adopt the market-adjusted initial return as a measure of IPO underpricing, which is calculated as the percentage difference between the offering price and the closing price for an IPO on the first trading day relative to simultaneous market return (Chan et al., 2004; Guo and Brooks, 2008): ADIRi ¼ ðPi1
Pi0 Þ=Pi0
ðPm1
Pm0 Þ=Pm0

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

are owned by another state-owned enterprise. The local government can effectively influence SSOEs, especially when the controlling shareholder is a LSOE (LSSOEs). Hereafter, LSOEs and LSSOEs are referred to as Local SOEs. Likewise, CSOEs and SSOEs controlled by the central government are referred to as Central SOEs. Controlling shareholders of CSOEs, SSOEs controlled by the central

4

One can criticize that politicians/governments have less knowledge to value such firms. Meanwhile, CEOs and directors are likely to have certain business experience to evaluate the value of their operations. Concentrated ownership structures may make these insiders care about interests of the controlling shareholder (underpricing for successful IPO). 5

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Table 8 Regression results for undeveloped and developed provinces. This table shows regression results of ADIR for undeveloped provinces (Model 1 to Model 4) and developed provinces (Model 5 to Model 8) by GDP. Model 1, 3, 5, and 7 employ OLS estimations; Model 2, 4, 6, and 8 adopt province-fixed effects model estimations. We compute t-statistics for OLS coefficients by using province-clustering standard errors. In each regression, the observation is deleted from the analysis when necessary independent variables are not available. See Table 1 for definitions of variables. Undeveloped provinces Independent variable

LNGDP LSOED

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)


0.004 (-0.13) 0.149** (2.43)


0.259 (-1.34) 0.130** (2.34)

0.000 (0.01)


0.237 (-1.25)


0.032 (-1.19) 0.059 (0.93)


0.515 (-1.58) 0.065 (1.69)


0.031 (-1.16)


0.523 (-1.61)

0.119** (2.32)
1.882*** (-2.79)
0.718** (-2.34)
0.011 (-0.41) 0.019 (0.54)
0.152*** (-4.91)
0.008 (-0.15)
0.002 (-0.45) 1.161*** (9.99)
0.013 (-0.52) 0.103*** (3.91)
0.032 (-0.36) Yes Yes 0.021 (0.04) 0.688 1018

0.102*** (3.17)
1.785*** (-3.28)
0.654 (-1.43)
0.012 (-0.46) 0.029 (0.56)
0.138*** (-4.37) 0.002 (0.03)
0.002 (-0.46) 1.159*** (6.17)
0.005 (-0.17) 0.099*** (3.56)
0.052 (-0.61) Yes Yes 1.882 (1.18) 0.640 1018

0.026 (0.62)
1.183* (-1.93)
0.571** (-2.23) 0.000 (0.00) 0.139*** (2.79)
0.068 (-1.33) 0.017 (0.36) 0.000 (0.10) 0.598*** (2.72) 0.035 (1.34) 0.078 (1.61)
0.261* (-1.87) Yes Yes 0.822 (1.58) 0.361 994

0.038 (1.77)
1.229 (-1.78)
0.549*** (-4.26) 0.003 (0.27) 0.142*** (3.95)
0.072 (-1.38) 0.020 (0.53) 0.000 (0.18) 0.609*** (9.65) 0.032 (1.56) 0.079 (1.75)
0.284** (-2.46) Yes Yes 5.770 (1.66) 0.247 994

Local SOED LOTTERY M_RETURN UNDERWRITER SEO LNOFFERNUM MANAGEROWN LAGDAY PE AGE LNASSET LEVERAGE INDUSTRY YEAR Constant Adj. R2 N

Developed provinces

Model 1


1.933*** (-2.86)
0.707** (-2.29)
0.008 (-0.31) 0.020 (0.58)
0.151*** (-4.91)
0.012 (-0.23)
0.002 (-0.60) 1.173*** (10.04)
0.010 (-0.41) 0.103*** (3.92)
0.031 (-0.34) Yes Yes 0.021 (0.04) 0.688 1018


1.844*** (-3.32)
0.640 (-1.38)
0.009 (-0.38) 0.030 (0.58)
0.135*** (-4.40)
0.001 (-0.02)
0.002 (-0.54) 1.167*** (6.18)
0.002 (-0.07) 0.098*** (3.60)
0.050 (-0.59) Yes Yes 2.058 (1.27) 0.632 1018


1.176* (-1.93)
0.565** (-2.20) 0.000 (0.02) 0.139*** (2.79)
0.069 (-1.34) 0.017 (0.38) 0.000 (0.10) 0.598*** (2.72) 0.035 (1.36) 0.077 (1.58)
0.259* (-1.85) Yes Yes 0.858 (1.64) 0.362 994


1.223 (-1.77)
0.543*** (-4.15) 0.004 (0.30) 0.142*** (3.92)
0.073 (-1.37) 0.019 (0.52) 0.000 (0.17) 0.609*** (9.63) 0.033 (1.63) 0.078 (1.74)
0.281** (-2.42) Yes Yes 5.712 (1.65) 0.250 994

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

the top 10 underwriters (UNDERWRITER), and a dummy variable that takes a value of one for companies conducting seasoned equity offerings (SEO) in the two years after the IPO. Panel A of Table 4 presents that the average LOTTERY is 0.9 percent in our sample. Panel B shows that 985 IPO companies (about 46 percent of the sample firms) chose a top ten underwriter. This panel also indicates that only 392 (18.40 percent) IPO firms issued seasoned equity after the IPO. Consistent with previous studies (Su and Fleisher, 1999; Chi and Padgett, 2005; Lin and Tian, 2012; Su and Brookfield, 2013), we include the state-ownership structure (SOE); the percentage of shares of managers’ holdings (MANAGEROWN); the natural logarithm of the number of offering shares (LNOFFERNUM); the P/E ratio (PE); the lag time (LAGDAY); and common financial variables, such as the size of assets (LNASSET), firm age (AGE) at the point of IPO, and the asset-liability ratio (LEVERAGE) as control variables.

where ADIRi is the market-adjusted initial return of stock i; Pi1 (Pm1) is the closing price of stock i (SHSE or SZSE A-Share Index) on the first trading day of stock i; Pi0 is the offering price of stock i; Pm0 is the closing price on the SHSE or the SZSE A-Share Index on the offering date of stock i. Table 3 shows the level of underpricing (ADIR) by year and controlling shareholder type. The average ADIR is 51.60 percent, suggesting that significant underpricing is offered in Chinese IPOs. Meanwhile, the level of underpricing declines significantly over time. The mean ADIR is 80–120 percent between 2006 and 2008, while IPOs in the last three years show approximately 40 percent ADIR. With regard to controlling shareholder type, LSOEs and LSSOEs discount the offering price more than CSOEs and Non-SOEs do. The result is consistent with our view that provincial governments offer underpricing when LSOEs and LSSOEs go public to boost the regional economy. 3.3. Determinants of underpricing

3.4. Economic performance measures Previous studies mainly examine three asymmetric information theories to explain the underpricing of IPOs: the winner’s curse Hypothesis, the ex-ante uncertainty hypothesis, and the signaling hypothesis. Following previous studies (Allen and Faulhaber, 1989; Beatty and Ritter, 1986; Carter and Manaster, 1990; Grinblatt and Hwang, 1989; Rock, 1986; Welch, 1989; Yu and Tse, 2006), we adopt the following variables as a proxy for these three theories: the lottery rate for new shares (LOTTERY), the market return between the offering and listing days of IPOs (M_RETURN), a dummy variable indicating IPOs underwritten by

Our Hypothesis predicts that local SOEs (LSOEs and LSSOEs) offer significant underpricing, especially when they are located in a less developed province. Following previous studies (Piotroski and Zhang, 2014), we adopt two measures of provincial economic performance: local Gross Domestic Product (GDP) and local GDP per capita, both of which are measured by the average value for three years prior to the IPO. Panel A of Table 4 shows summary statistics of provincial economic performance. The mean local GDP and GDP per capita are 3,073.63 billion RMB 6

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Table 9 Regression results: Province classification by GDP Per Capita. This table shows regression results of ADIR for undeveloped (Model 1 to Model 4) and developed provinces (Model 5 to Model 8) by GDP Per Capita. Model 1, 3, 5, and 7 employ OLS estimations; Model 2, 4, 6, and 8 adopt province-fixed effects model estimations. We compute t-statistics for OLS coefficients by using province-clustering standard errors. See Table 1 for definitions of variables. Undeveloped provinces Independent variable

LNPCGDP LSOED

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)


0.075 (-1.63) 0.126** (2.14)


0.054 (-0.18) 0.138** (2.59)


0.069 (-1.53)


0.066 (-0.23)


0.030 (-0.93)
0.008 (-0.30)


0.009 (-0.04) 0.001 (0.03)


0.029 (-0.92)


0.011 (-0.05)

0.081* (1.69)
0.916* (-1.90)
0.738** (-2.41)
0.006 (-0.22) 0.111*** (2.67)
0.135*** (-3.34)
0.045 (-0.79)
0.003 (-0.82) 1.113*** (6.15)
0.002 (-0.08) 0.093*** (2.64)
0.131 (-1.42) Yes Yes 0.546 (0.86) 0.670 1030

0.098** (2.29)
0.920* (-1.73)
0.713* (-1.86)
0.001 (-0.04) 0.125* (1.85)
0.126*** (-4.00)
0.038 (-0.82)
0.004 (-1.20) 1.113*** (8.02)
0.002 (-0.07) 0.084*** (2.78)
0.156** (-2.19) Yes Yes 0.542 (0.21) 0.669 1030

0.016 (0.64)
3.477*** (-2.59)
0.806*** (-3.89) 0.006 (0.44) 0.036 (1.13)
0.086* (-1.87) 0.045 (0.95) 0.002 (1.13) 0.338*** (3.16) 0.028** (1.96) 0.070 (1.47)
0.146 (-1.07) Yes Yes 0.665 (1.54) 0.344 982

0.025** (2.16)
3.353*** (-3.44)
0.792*** (-3.46) 0.007 (0.31) 0.035 (0.71)
0.088** (-2.44) 0.048 (1.25) 0.002 (1.22) 0.340*** (4.09) 0.026 (1.32) 0.072* (1.88)
0.157 (-1.35) Yes Yes 0.461 (0.21) 0.344 982

Local SOED LOTTERY M_RETURN UNDERWRITER SEO LNOFFERNUM MANAGEROWN LAGDAY PE AGE LNASSET LEVERAGE INDUSTRY YEAR Constant Adj. R2 N

Developed provinces


0.918* (-1.89)
0.731** (-2.38)
0.004 (-0.14) 0.111*** (2.67)
0.134*** (-3.31)
0.045 (-0.80)
0.004 (-1.00) 1.119*** (6.17) 0.000 (0.00) 0.092*** (2.63)
0.133 (-1.44) Yes Yes 0.613 (0.96) 0.671 1030


0.935* (-1.77)
0.703* (-1.82) 0.001 (0.03) 0.125* (1.82)
0.123*** (-4.02)
0.039 (-0.81)
0.004 (-1.38) 1.119*** (7.98) 0.002 (0.10) 0.083*** (2.71)
0.158** (-2.23) Yes Yes 0.433 (0.17) 0.670 1030


3.491*** (-2.60)
0.804*** (-3.89) 0.006 (0.44) 0.036 (1.12)
0.086* (-1.86) 0.044 (0.92) 0.002 (1.12) 0.339*** (3.18) 0.029** (2.02) 0.070 (1.48)
0.147 (-1.07) Yes Yes 0.661 (1.54) 0.362 982


3.371*** (-3.45)
0.789*** (-3.44) 0.007 (0.32) 0.034 (0.71)
0.087** (-2.42) 0.046 (1.20) 0.002 (1.22) 0.341*** (4.10) 0.027 (1.37) 0.072* (1.88)
0.157 (-1.34) Yes Yes 0.429 (0.19) 0.343 982

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

into two groups based on an economic performance measure. Given that the Chinese economy has grown continuously during the sample period, this identification makes many early period observations belong to undeveloped provinces. We adopt an unbalanced sample structure, since the number of provinces conducting strategic underpricing may have decreased over time. In the early years, many provinces may have adopted strategic underpricing due to their small economic size, while many provinces already have developed in recent years and lost the incentive to offer underpricing to spur their regional economy. In the later part of the paper, we also classify sample provinces annually and conduct robustness tests. Panel D and Panel E of Table 5 indicate that local SOEs (LSOEs and LSSOEs) offer a significant discount for their IPO stocks, especially when their province is underdeveloped (78.21 and 100.81 percent, respectively, in low GDP provinces). We find a significant difference in ADIR of these companies between low and high GDP provinces. Central SOEs and Non-SOEs also offer high underpricing when the province is underdeveloped. However, the difference in ADIR between developed and undeveloped provinces is not as large as the difference for Local SOEs. We also find that local SOEs provide significantly larger underpricing than Central SOEs and Non-SOEs do, when they are from an undeveloped province. These findings are consistent with the Hypothesis that local governments provide underpricing to stimulate a regional economy when regional companies go public.

(about 439 billion USD) and 51,520 RMB (about 7,360 USD), respectively. The Chinese economy experienced astonishing growth after the introduction of the economic reforms in 1978. In 2005, China’s GDP was worth 18,308.48 billion RMB (about 2,285.97 billion USD). At the end of 2017, the GDP value increased to 82,712.17 billion RMB (about 12, 146.15 billion USD). Between 2005 and 2017, the value of GDP per capita in China increased from 14,040 RMB (about 1,753 USD) to 59,660 RMB (about 8,759 USD). Large variation exists in macroeconomic performance across province and time. Taking IPOs in 2017 as an example, Tibet recorded the lowest GDP (103.29 billion RMB; approximately 15 billion USD), while the largest province (Guangdong) produced 7,382.58 billion RMB (approximately 1,054 billion USD). With regard to GDP per capita, Gansu province has the lowest average per capita GDP (26,747 RMB; approximately 3,821 USD), while the city of Tianjin has the highest GDP per capita – 109,414 RMB (about 15,630 USD). These figures suggest that undeveloped provinces will have a strong incentive to stimulate their economies. 4. Empirical results 4.1. Univariate analyses In this section, we investigate whether local SOEs offer high underpricing, especially when they are from an undeveloped province. To identify undeveloped provinces, we equally divide our sample companies 7

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Table 10 Regression results for the early period. This table shows regression results of ADIR for undeveloped (Model 1 to Model 4) and developed provinces (Model 5 to Model 8) between 2005 and 2010. Model 1, 3, 5, and 7 employ OLS estimations; Model 2, 4, 6, and 8 adopt province-fixed effects model estimations. We compute t-statistics for OLS coefficients by using province-clustering standard errors. See Table 1 for definitions of variables. Undeveloped provinces

Developed provinces

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Independent variable

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

LNGDP


0.031 (-0.66) 0.210** (2.31)


1.056 (-1.23) 0.233*** (3.90)


0.020 (-0.43)


0.898 (-1.10)


0.861** (-2.51)
0.006 (-0.30)

15.92*** (27.63) 0.066 (0.99)


0.864** (-2.56)

16.70*** (36.0)

0.179** (2.37)
3.244 (-1.48)
0.834* (-1.93) 0.015 (0.324) 0.023 (0.34)
0.186*** (-3.97) 0.011 (0.10)
0.003 (-0.72) 1.373*** (9.385)
0.029 (-0.74) 0.114*** (2.93) 0.065 (0.44) Yes Yes
0.050 (-0.07) 0.685 502

0.197*** (3.79)
2.973 (-1.41)
0.725 (-1.16)
0.003 (-0.06) 0.046 (0.53)
0.166*** (-3.81) 0.017 (0.12)
0.005 (-1.10) 1.360*** (6.06)
0.012 (-0.32) 0.107** (2.34) 0.089 (0.57) Yes Yes 7.512 (1.11) 0.646 502

0.059 (0.39)
13.99** (-2.47)
0.204 (-0.27)
0.043 (-0.66) 0.308** (2.32)
0.001 (-0.00) 0.004 (0.02) 0.004 (0.47) 0.638* (1.92)
0.011 (-0.17) 0.040 (0.46)
0.377 (-1.41) Yes Yes 9.496** (2.34) 0.521 178

0.121 (1.53)
10.91*** (-7.37)
0.142 (-0.58)
0.045** (-2.46) 0.333 (1.55) 0.012 (0.12) 0.074** (2.34) 0.002 (0.17) 0.637*** (5.81)
0.001 (-0.02) 0.016 (0.43)
0.314*** (-2.87) Yes Yes
168.1*** (-33.7) 0.529 178

LSOED Local SOED LOTTERY M_RETURN UNDERWRITER SEO LNOFFERNUM MANAGEROWN LAGDAY PE AGE LNASSET LEVERAGE INDUSTRY YEAR Constant Adj. R2 N


3.829* (-1.69)
0.809* (-1.85) 0.019 (0.40) 0.022 (0.33)
0.185*** (-3.96)
0.003 (-0.02)
0.004 (-0.95) 1.381*** (9.43)
0.022 (-0.55) 0.114*** (2.91) 0.065 (0.44) Yes Yes 0.078 (0.11) 0.684 502


3.641 (
1.620)
0.687 (-1.08) 0.001 (0.01) 0.042 (0.485)
0.162*** (-3.78) 0.007 (0.05)
0.006 (-1.30) 1.363*** (6.00)
0.003 (-0.09) 0.106** (2.29) 0.084 (0.54) Yes Yes 8.905 (1.24) 0.646 502


14.05** (-2.58)
0.225 (-0.30)
0.042 (0.65) 0.303** (2.29)
0.000 (-0.00)
0.004 (-0.03) 0.004 (0.48) 0.640* (1.93)
0.013 (-0.19) 0.043 (0.49)
0.379 (-1.43) Yes Yes 9.456** (2.31) 0.521 178


10.72*** (-7.75)
0.165 (-0.67)
0.044* (-1.97) 0.329 (1.53) 0.015 (0.14) 0.060** (2.29) 0.002 (0.17) 0.641*** (6.04)
0.001 (-0.04) 0.019 (0.48)
0.314*** (-2.92) Yes Yes
160.2*** (-27.9) 0.527 178

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

and αYear ). Table 6 presents the correlation among independent variables. Naturally, there is a high correlation between LNGDP and LNPCGDP. No other variables have serious correlations. Regression results for the entire sample are presented in Table 7. Models 1 and 2 adopt LSOED as a key independent variable, while Models 3 and 4 use Local SOED. In China, significant heterogeneity exists in institutional developments, ethnicity, culture, and so on across provinces, which potentially affect underpricing. Therefore, Models 2 and 4 implement province-fixed effects model estimation to control for timeinvarying province characteristics while Models 1 and 3 adopt OLS. Consistent with the univariate analysis results, LSOED and Local SOED have a positive and significant coefficient, suggesting that Local SOEs discount their IPO shares significantly more than Central SOEs and NonSOEs do. The estimated coefficients indicate that Local SOEs offer 8–11 percent larger underpricing, which is economically significant given that the unconditional mean ADIR is 51.6 percent. Table 7 provides a negative coefficient to LNGDP, suggesting that IPO companies need to sell their shares for a lower price when regional economic conditions deteriorate. With respect to the control variables, Table 7 shows that all coefficients of LOTTERY are significantly negative, which supports the winner’s curse Hypothesis (Guo and Brooks, 2008). However, for the ex-ante uncertainty hypothesis, we only find significant coefficients on M_RETURN. The positive coefficients of SEO are consistent with the signaling theory, although the statistical significance disappears in province-fixed effects models. Table 7 also shows there is a negative and significant relationship between LNOFFERNUM and IPO

4.2. Regression results We estimate the following equation to examine our hypotheses with controlling for variable factors. ADIRi;t ¼ α þ β1 ProvinceGDPP;t
3t
1 þ β2 LocalSOEDummyi;t þ γControlsi;t
1 þ αInd þ αYear ð þ ηP Þ þ uit Subscripts i, p, and t indicate firm, province, and year, respectively. The key independent variable is Local SOEDummy, which is a dummy variable indicating local government control: LSOED takes a value of one for LSOEs, and zero for others, while Local SOED takes a value of one for LSOEs and LSSORs, and zero for others. These dummy variables are predicted to have a positive coefficient, especially in undeveloped provinces. Regional economic performance is likely to affect underpricing directly, since investors may perceive uncertainty when a region performs poorly. This direct effect is controlled by including Province GDP: the natural logarithm of provincial GDP (LNGDP) or GDP per capita (LNPCGDP). Controls include LOTTERY, M_RETURN, UNDERWRITER, and SEO to control the effects of information asymmetry. Firm characteristic variables, such as SOE, MANAGEROWN, LNOFFERNUM, PE, LAGDAY, AGE LNASSET, and LEVERAGE, are also added to the independent variable. Firms are removed from the analysis when the necessary independent variables are unavailable. We estimate the equation by using ordinary least squares (OLS) method or province-fixed effects model (add ηP ). All estimations include industry and year dummies (αInd 8

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Table 11 Regression results: Province classification by year. This table shows regression results of ADIR. We divide our sample into undeveloped (Model 1 to Model 4) and developed provinces (Model 5 to Model 8) each year. Model 1, 3, 5, and 7 employ OLS estimations; Model 2, 4, 6, and 8 adopt province-fixed effects model estimations. We compute t-statistics for OLS coefficients by using province-clustering standard errors. See Table 1 for definitions of variables. Undeveloped provinces Independent variable

LNGDP LSOED

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)


0.039 (-1.64) 0.128* (1.95)


0.300 (-1.69) 0.104 (1.67)


0.036 (-1.54)


0.279 (-1.59)


0.104 (-1.33) 0.078 (1.15)


0.037 (-0.16) 0.082*** (7.33)


0.105 (-1.33)


0.031 (-0.13)

0.113** (2.22)
1.777*** (-2.79)
0.670** (-2.59)
0.021 (-1.02)
0.001 (-0.05)
0.123*** (-4.41)
0.035 (-0.97)
0.003 (-0.82) 0.907*** (8.66) 0.009 (0.37) 0.075*** (3.13)
0.011 (-0.13) Yes Yes 0.451 (1.12) 0.670 1002

0.098*** (2.91)
1.670*** (-3.12)
0.702* (-1.88)
0.017 (-0.81) 0.001 (0.03)
0.105*** (-3.32)
0.033 (-1.08) 0.000 (0.05) 0.903*** (6.95) 0.011 (0.43) 0.066** (2.51)
0.039 (-0.66) Yes Yes 2.452 (1.54) 0.617 1002

0.043 (0.78)
1.183* (-1.89)
0.658* (-1.86) 0.007 (0.28) 0.159*** (3.22)
0.118** (-2.14) 0.028 (0.48)
0.002 (-0.58) 1.024*** (3.86) 0.008 (0.29) 0.104** (2.04)
0.249 (-1.61) Yes Yes 0.669 (0.77) 0.596 1010

0.046 (1.43)
1.178 (-1.69)
0.653 (-1.49) 0.005 (0.30) 0.163** (2.17)
0.121** (-2.33) 0.029 (0.83)
0.002 (-0.93) 1.021*** (5.57) 0.009 (0.41) 0.106 (1.82)
0.247 (-1.26) Yes Yes 0.054 (0.03) 0.595 1010

Local SOED LOTTERY M_RETURN UNDERWRITER SEO LNOFFERNUM MANAGEROWN LAGDAY PE AGE LNASSET LEVERAGE INDUSTRY YEAR Constant Adj. R2 N

Developed provinces


1.813*** (-2.85)
0.654** (-2.49)
0.019 (-0.90)
0.001 (-0.03)
0.123*** (-4.40)
0.042 (-1.18) 0.000 (0.02) 0.918*** (8.66) 0.010 (0.41) 0.075*** (3.13)
0.005 (-0.06) Yes Yes 0.473 (1.17) 0.662 1002


1.724*** (-3.23)
0.686* (-1.80)
0.015 (-0.71) 0.001 (0.02)
0.103*** (-3.28)
0.038 (-1.31) 0.000 (0.00) 0.909*** (6.96) 0.013 (0.53) 0.065** (2.54)
0.034 (-0.58) Yes Yes 2.621 (1.63) 0.610 1002


1.178* (-1.88)
0.651* (-1.85) 0.007 (0.31) 0.159*** (3.22)
0.118** (-2.14) 0.029 (0.50)
0.002 (-0.58) 1.026*** (3.87) 0.009 (0.35) 0.103** (2.03)
0.249 (-1.61) Yes Yes 0.693 (0.81) 0.596 1010


1.172 (-1.68)
0.647 (-1.49) 0.005 (0.34) 0.163** (2.20)
0.121** (-2.32) 0.030 (0.86)
0.002 (-0.91) 1.023*** (5.51) 0.010 (0.48) 0.105 (1.75)
0.246 (-1.25) Yes Yes 0.054 (0.03) 0.595 1010

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

their shares for a low price to stimulate the regional economy. All the models in Table 8 carry an insignificant coefficient on LNGDP, probably because of the small variation in the level of economic development within the subsample. Meanwhile, some variables, such as LOTTERY, PE, and LNOFFERNUM, have different coefficients in absolute value between Low and High GDP provinces. These findings support the presumption that regional economic performance affects uncertainty and the information asymmetry of IPOcompanies, thereby influencing the marginal impacts of various factors on underpricing. We also use GDP per capita to identify undeveloped provinces. The regression results are presented in Table 9, where the natural logarithm of GDP per capita (LNPCGDP) is used as an independent variable. Again, Models 1 through 4 suggest that Local SOEs offer a significantly greater underpricing than Central SOEs and Non-SOEs do when they are from an undeveloped province. In contrast, Models 5 through 7 find no significant difference in underpricing between Local SOEs and others from developed provinces. Model 8 carries a positive and significant coefficient on Local SOED, but the estimated coefficient suggests that Local SOEs provide only 2.5 percent greater underpricing than Central SOEs and NonSOEs do. Overall, our results support the Hypothesis that local governments use underpricing to make regional companies go public smoothly for the purpose of regional economic stimulation.

underpricing. The result is consistent with Chi and Padgett’s (2005) findings. To establish whether the large underpricing of Local SOEs is motivated by a desire to stimulate the economy, we run separate estimations for Low and High GDP provinces. Given that regional economic developments may affect uncertainty and the information asymmetry of IPO companies, we should allow all the independent variables to have different coefficients between Low and High GDP provinces. Models 1 through 4 of Table 8 show regression results for Low GDP provinces, all of which engender a positive and significant coefficient to LSOED and Local SOED. The coefficient becomes larger from Table 7, suggesting that Local SOEs located in an undeveloped province offer economically significant underpricing when they go public. The negative coefficient of LNGDP in Table 7 suggests that IPOs in the Low GDP sample generally offer high underpricing. One might argue that the significant coefficient of LSOED and LSSOED in Table 7 is attributable to the fact that many Local SOEs went public in the early period, during which the economic performance of many provinces was relatively weak. However, the significantly greater underpricing by Local SOEs shown in Table 8 indicates that these SOEs have an incentive to offer underpricing beyond the level explained by regional economic conditions. Central SOEs and Non-SOEs do not have such an incentive, which should be associated with local government officials’ motive. In marked contrast, Models 5 to 8 indicate that Local SOEs discount IPO shares only as much as Central SOEs and Non-SOEs in developed provinces. These results support our Hypothesis that local governments encourage SOEs to sell

4.3. Additional analyses One can suggest that our results arise from the unbalanced sample 9

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Table 12 Regression results: Add Central SOE Dummy. This table shows regression results of ADIR for undeveloped (Model 1 to Model 4) and developed provinces (Model 5 to Model 8). Model 1, 3, 5, and 7 employ OLS estimations; Model 2, 4, 6, and 8 adopt province-fixed effects model estimations. We compute t-statistics for OLS coefficients by using province-clustering standard errors. See Table 1 for definitions of variables. Undeveloped provinces Independent variable

LNGDP LSOED

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)

Entire sample (OLS)

Entire sample (Province effects)


0.004 (-0.13) 0.150** (2.42)


0.252 (-1.32) 0.135** (2.22)

0.001 (0.02)


0.227 (-1.21)


0.034 (-1.28) 0.062 (0.96)


0.521 (-1.62) 0.068 (1.79)


0.033 (-1.23)


0.529 (-1.65)

0.124** (2.40) 0.023 (0.30)
1.880*** (-2.78)
0.712** (-2.35)
0.011 (-0.41) 0.019 (0.55)
0.153*** (-4.52)
0.005 (-0.09)
0.001 (-0.42) 1.159*** (10.02)
0.012 (-0.48) 0.101*** (3.85)
0.028 (-0.32) Yes Yes 0.028 (0.06) 0.685 1020

0.113*** (3.37) 0.050 (0.73)
1.770*** (-3.20)
0.641 (-1.43)
0.012 (-0.45) 0.031 (0.58)
0.141*** (-4.20) 0.007 (0.15)
0.002 (-0.42) 1.156*** (6.19)
0.003 (-0.12) 0.096*** (3.51)
0.047 (-0.54) Yes Yes 1.856 (1.19) 0.642 1020

0.027 (0.64)
0.002 (-0.03)
1.182* (-1.93)
0.568** (-2.21) 0.000 (0.03) 0.139*** (2.79)
0.068 (-1.31) 0.016 (0.34) 0.000 (0.09) 0.598*** (2.72) 0.035 (1.33) 0.077 (1.60)
0.263* (-1.87) Yes Yes 0.846 (1.62) 0.361 992

0.040* (1.83) 0.036 (0.58)
1.225 (-1.78)
0.545*** (-4.17) 0.003 (0.26) 0.143*** (3.95)
0.072 (-1.34) 0.020 (0.54) 0.000 (0.14) 0.609*** (9.59) 0.032 (1.54) 0.078 (1.72)
0.285** (-2.47) Yes Yes 5.846 (1.71) 0.247 992

Local SOED Central SOED LOTTERY M_RETURN UNDERWRITER SEO LNOFFERNUM MANAGEROWN LAGDAY PE AGE LNASSET LEVERAGE INDUSTRY YEAR Constant Adj. R2 N

Developed provinces

0.009 (0.11)
1.934*** (-2.87)
0.704** (-2.32)
0.008 (-0.32) 0.020 (0.58)
0.151*** (-4.81)
0.010 (-0.19)
0.002 (-0.56) 1.172*** (10.09)
0.010 (-0.39) 0.102*** (3.89)
0.029 (0.32) Yes Yes 0.043 (0.09) 0.688 1020

0.034 (0.48)
1.839*** (-3.30)
0.630 (-1.40)
0.009 (-0.37) 0.031 (0.59)
0.138*** (-4.19) 0.003 (0.06)
0.002 (-0.51) 1.166*** (6.19)
0.001 (-0.02) 0.097*** (3.58)
0.046 (0.53) Yes Yes 2.043 (1.29) 0.635 1020


0.001 (-0.02)
1.174* (-1.93)
0.561** (-2.18) 0.001 (0.05) 0.139*** (2.79)
0.069 (-1.32) 0.016 (0.36) 0.000 (0.08) 0.598*** (2.72) 0.035 (1.36) 0.078 (1.57)
0.260* (-1.85) Yes Yes 0.887 (1.69) 0.362 992

0.035 (0.57)
1.219 (-1.76)
0.538*** (-4.05) 0.004 (0.28) 0.142*** (3.92)
0.072 (-1.34) 0.020 (0.54) 0.000 (0.12) 0.609*** (9.58) 0.033 (1.62) 0.077 (1.71)
0.283** (-2.43) Yes Yes 5.786 (1.69) 0.250 992

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

classification. Models 1 through 4 implement the regression for undeveloped provinces, most of which provide a positive and significant coefficient to LSOED and Local SOED. Overall, the evidence rules out the possibility that our results arise from local governments commonly discounting IPO stocks during the early period. Models 5, 7, and 8 suggest that Local SOEs do not offer significantly greater underpricing in developed provinces than other types of companies do. LSOED has a positive and significant coefficient in Model 6, potentially because some provinces from the early years are classified as developed provinces, even though their GDP is still not large enough. Nevertheless, Model 6 generates a smaller coefficient on LSOED than Model 2 does. Overall, our results support the Hypothesis that local government officials sell IPO stocks for a low price to boost the local economy. So far, we have treated Central SOEs and Non-SOEs as a single group, since local governments cannot control these companies. Meanwhile, the level of underpricing may differ between these companies. Previous studies suggest SOEs can receive preferential treatment in business opportunities, bank loan availability, and so on. This special status may reduce underpricing due to decreased uncertainty and information asymmetry. We address the issue by adding the Central SOE dummy (one for Central SOEs and zero for others) to the independent variable. Table 12 presents regression results. After including Central SOE dummy in our regressions, we still find a positive and significant coefficient for LSOED and Local SOED from Model 1 to 4 (undeveloped provinces), while Models 5 through 7 provide an insignificant coefficient

structure, in which undeveloped observations are concentrated in the early years of our sample period. This bias occurs if Local SOEs commonly offer large underpricing, irrespective of economic conditions during the early period. To address this concern, we run a regression for undeveloped and developed provisions during the early period (year 2005–2010). If this concern is the case, we should find a significantly greater underpricing for Local SOEs both for undeveloped and developed provinces. The regression results are presented in Table 10. Models 1 through 4 conduct regressions for undeveloped provinces, all of which engender a positive and significant coefficient on LSOED and Local SOED. Meanwhile, the regressions for developed provinces (Models 5 through 8) provide an insignificant coefficient to these variables, suggesting that local governments do not provide large underpricing even in the early period, to the degree that they achieve economic development. We also implement the same analysis for the late period (2011–2017). The untabulated results carry an insignificant coefficient to LSOED and Local SOED, irrespective of the level of provincial GDP. The result is consistent with our Hypothesis, given that many provinces have generated large GDP in recent years while IPO underpricing has become less effective as a measure of economic stimulation. To further address the concern, we also divide sample companies equally into two groups every year by provincial GDP. This classification will distribute the IPOs of undeveloped provinces proportionally among the early and late years. Table 11 shows the regression results under this 10

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to these variables (developed provinces). All models carry an insignificant coefficient for Central SOE dummy, suggesting that Central SOEs are indifferent with Non-SOEs in terms of the level of underpricing. Local SOEs have a distinctive incentive to discount IPO shares.

Chen, Y., Li, H., Zhou, L., 2005. Relative performance evaluation and turnover of provincial leaders in China. Econ. Lett. 88 (3), 421–424. Chen, J., Ke, B., Wu, D., Yang, Z., 2018. The consequences of shifting the IPO offer pricing power from securities regulators to market participants in weak institutional environments: evidence from China. J. Corp. Financ. 50, 349–370. Chen, Y.,S., Wang, S.S., Li, W., 2015. Institutional environment, firm ownership, and IPO first-day returns: evidence from China. J. Corp. Financ. 32, 150–168. Chen, D., Guan, Y., Zhang, T., Zhao, G., 2017. Political connection of financial intermediaries: evidence from China’s IPO market. J. Bank. Financ. 76, 15–31. Cheung, Y., Ouyang, Z., Tan, W., 2009. How regulatory changes affect IPO underpricing in China. China Econ. Rev. 20, 692–702. Chi, J., Padgett, C., 2005. Short-run underpricing and its characteristics in Chinese initial public offering (IPO) market. Res. Int. Bus. Financ. 19, 71–93. Chiou, J., Li, M., Cheng, L., 2010. Pricing and allocation mechanisms in underpricing of Chinese IPOs. China Econ. Rev. 43, 93–108. Cull, R., Xu, L.C., Yang, X., 2015. Market facilitation by local government and firm efficiency: evidence from China. J. Corp. Financ. 42, 460–480. Du, J., Xu, C., 2009. Which firms went public in China? A study of financial market regulation. World Dev. 37, 812–824. DuCharme, L.L., Malatesta, P.H., Sefcik, S.E., 2001. Earnings management: IPO valuation and subsequent performance. J. Account. Audit Financ. 16, 369–396. Ekkayokkaya, M., Pengniti, T., 2012. Governance reform and IPO underpricing. J. Corp. Financ. 18, 238–253. Grinblatt, M., Hwang, C.Y., 1989. Signaling and the pricing of new issues. J. Financ. 44, 393–420. Guo, H., Brooks, R., 2008. Underpricing of Chinese A-share IPOs and short-run underperformance under the approval system from 2001 to 2005. Int. Rev. Financ. Anal. 17, 984–997. Ibbotson, R.G., 1975. Price performance of common stock new issues. J. Financ. Econ. 2, 235–272. Johansson, A.C., Luo, D., Rickne, J., Zheng, W., 2017. Government intervention in the capital allocation process: excess employment as an IPO selection rule in China. China Econ. Rev. 44, 271–281. Jin, H., Qian, Y., Weingast, B., 2005. Regional decentralization and fiscal incentives: federalism, Chinese style. J. Public Econ. 89, 1719–1742. Kao, J.L., Wu, D.H., Yang, Z.F., 2009. Regulations, earnings management, and post-IPO performance: the Chinese evidence. J. Bank. Financ. 33, 63–76. Kim, J.B., Krinsky, I., Lee, J., 1993. Motives for going public and underpricing: new findings from Korea. J. Bus. Financ. Account. 20, 306–686. Kirkulak, B., Davis, C., 2005. Underwriter reputation and underpricing: evidence from the Japanese IPO market. Pac. Basin Financ. J. 13, 451–470. Li, H., Zhou, L., 2005. Political turnover and economic performance: the incentive role of personnel control in China. J. Public Econ. 89, 1743–1762. Li, G., Zhou, H., 2015. Political connections and access to IPO markets in China. China Econ. Rev. 33, 76–93. Li, K., Wang, T., Cheung, Y., Jiang, P., 2011. Privatization and risk sharing: evidence from the split share structure reform in China. Rev. Financ. Stud. 24 (7), 2499–2525. Liu, J., Uchida, K., Gao, R., 2012. Political connections and the long-term stock performance of Chinese IPOs. J. Int. Financ. Mark. Inst. Money 22, 814–833. Liu, J., Uchida, K., Gao, R., 2014a. Earnings management of initial public offering firms: evidence from regulation changes in China. Account. Finance 54, 505–537. Liu, J., Uchida, K., Gao, R., 2014b. Legal protection and underpricing of IPOs: evidence from China. Pac. Basin Financ. J. 27, 163–187. Lin, Z.J., Tian, Z., 2012. Accounting conservatism and IPO underpricing: China evidence. J. Int. Account. Audit. Tax. 21, 127–144. Loughran, T., Ritter, J.R., Rydqvist, K., 1994. Initial public offerings: international insights. Pac. Basin Financ. J. 2, 165–199. Ma, S., Faff, R., 2007. Market conditions and the optimal IPO allocation mechanism in China. Pac. Basin Financ. J. 15, 121–139. Maskin, E., Qian, Y., Xu, C., 2000. Incentives, scale economies, and organization forms. Rev. Econ. Stud. 67, 359–378. Mok, H., Hui, Y., 1998. Underpricing and aftermarket performance of IPOs in Shanghai, China. Pac. Basin Financ. J. 6, 453–474. Pagano, M., Panetta, F., Zingales, L., 1998. Why do companies go public? An empirical analysis. J. Financ. 53 (1), 27–64. Piotroskia, J.D., Zhang, T., 2014. Politicians and the IPO decision: the impact of impending political promotions on IPO activity in China. J. Financ. Econ. 111 (1), 111–136. Ritter, J.R., 1984. The “hot issue” market of 1980. J. Bus. 57, 215–240. Ritter, J.R., 1991. The long-run performance of initial public offerings. J. Financ. 46, 3–27. Ritter, J.R., 2003. Difference between European and American IPO markets. Eur. Financ. Manag. 9, 421–434. Rock, K., 1986. Why new issues are underpriced. J. Financ. Econ. 15, 153–186. Rousseau, P.L., Wachtel, P., 2000. Equity markets and growth: cross-country evidence on timing and outcomes, 1980-1995. J. Bank. Financ. 24, 1933–1957. Su, D., Fleisher, B.M., 1999. An empirical investigation of underpricing in Chinese IPOs. Pac. Basin Financ. J. 7, 173–202. Su, C., Bangassa, K., 2011. The impact of underwriter reputation on initial returns and long-run performance of Chinese IPOs. J. Int. Financ. Mark. Inst. Money 21, 760–791. Su, C., Brookfield, D., 2013. An evaluation of the impact of stock market reforms on IPO under-pricing in China: the certification role of underwriters. Int. Rev. Financ. Anal. 28, 20–33. Tian, L., 2005. Financial Regulations, Investment Risks, and Determinants of Chinese IPO Underpricing. Working paper. Peking University Management School and London Business School.

5. Conclusions Numerous studies (Ritter, 1991; Loughran et al., 1994; Beckman et al., 2001; Kirkulak and Davis, 2005; Ekkayokkaya and Pengniti, 2012; Liu et al., 2014b) have found IPO underpricing around the world, which remains a puzzle in the research on finance. Among potential factors, we stress that local government officials’ concerns about their regional economies affect underpricing. Under the Chinese regulatory decentralization system, local government officials’ promotion is based on their economic performance. IPOs will spur regional firms’ growth by providing them with alternative sources of external funding. Accordingly, local government officials have a strong motivation to make regional firms go public to stimulate economic development. It is well documented that underpricing can raise the possibility of full subscriptions of IPO, while it is costly for issuers (or their shareholders). Since shareholders of SOEs controlled by the local government can receive benefits from underpricing (increased IPOs), we predict that these SOEs offer high underpricing, especially when they are from an undeveloped province. We test the Hypothesis by using 2,131 Chinese IPOs during the period 2005 to 2017. We identify SOEs directly controlled by the local government (LSOEs) and SOEs controlled by LSOE (LSSOE) as SOEs controlled by the local government. We found that these SOEs from less developed provinces underprice more than other types of companies. However, such a phenomenon is not evident for SOEs controlled by a local government in a developed province. These results suggest that local government officials have an incentive to underprice IPOs to ensure more companies go public successfully, thus improving their economic development and political promotion. This paper contributes to the literature by showing novel evidence that regional governments have an incentive to underprice IPO stocks. The local government incentive will serve as a significant factor associated with extremely high underpricing of Chinese IPOs. This paper also extends the literature on local government economic policies by showing that IPOs can serve as an economic stimulation measure. Meanwhile, this research does not examine post-IPO performance of the firms and regional economy. These issues are left as an important task of future research. References An, H., Chen, Y., Luo, D., Zhang, T., 2016. Political uncertainty and corporate investment: evidence from China. J. Corp. Financ. 36, 174–189. Allen, F., Faulhaber, G.R., 1989. Signaling by underpricing in the IPO market. J. Financ. Econ. 23, 303–323. Arestis, P., Demetriades, P., 1997. Financial development and economic growth: assessing the evidence. Econ. J. 442, 783–799. Arestis, P., Demetriades, P., Luintel, K., 2001. Financial development and economic growth: the role of stock markets. J. Money Credit Bank. 33 (1), 16–41. Brennan, M.J., Franks, J., 1997. Underpricing, ownership and control in initial public offerings of equity securities in the UK. J. Financ. Econ. 45, 391–413. Bao, X., Johan, S., Kutsuna, K., 2016. Do political connections matter in accessing capital markets? Evidence from China. Emerg. Mark. Rev. 29, 24–41. Beatty, R., Ritter, J.R., 1986. Investment banking, reputation, and the underpricing of initial public offerings. J. Financ. Econ. 15, 213–232. Beckman, J., Garner, J., Marshall, B., Okamura, H., 2001. The influence of underwriter reputation, keiretsu affiliation, and financial health on the underpricing of Japanese IPOs. Pac. Basin Financ. J. 9, 513–534. Bernstein, S., 2015. Does going public affect innovation? J. Financ. 70 (4), 1365–1403. Caporale, G.M., Howells, P.G., Soliman, A.M., 2004. Stock market development and economic growth: the causal linkage. J. Econ. Dev. 29, 33–50. Carter, R.B., Manaster, S., 1990. Initial public offerings and underwriter reputation. J. Financ. 45, 1045–1067. Chan, K., Wang, J., Wei, K.C., 2004. Underpricing and long-term performance of IPOs in China. J. Corp. Financ. 10, 409–430. Chen, G., Firth, M., Kim, J., 2004. IPO underpricing in China’s new stock markets. J. Multinatl. Financ. Manag. 14, 283–302. 11

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Economic Modelling xxx (xxxx) xxx Welch, I., 1989. Seasoned offerings, imitation costs, and the underpricing of initial public offerings. J. Financ. 44, 421–449. Yu, T., Tse, Y.K., 2006. An empirical examination of IPO underpricing in the Chinese Ashare market. China Econ. Rev. 17, 363–382.

Tian, L., 2011. Regulatory underpricing: determinants of Chinese extreme IPO returns. J. Empir. Financ. 18, 78–90. Teoh, S.H., Welch, I., Wong, T.J., 1998. Earnings management and the long-run market performance of initial public offerings. J. Financ. 53, 1935–1974. Wang, C., 2005. Ownership and operating performance of Chinese IPOs. J. Bank. Financ. 29, 1835–1856.

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