The choice of formal or informal finance: Evidence from Chengdu, China

China Economic Review 19 (2008) 659–678

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China Economic Review

The choice of formal or informal finance: Evidence from Chengdu, China Guibin ZHANG ⁎ School of Economics, Faculty of Commerce, University of Wollongong, Wollongong, NSW 2522, Australia Monash Asia Institute, Monash University, Melbourne VIC 3000, Australia

a r t i c l e

i n f o

Article history: Received 22 October 2007 Received in revised form 6 September 2008 Accepted 10 September 2008 JEL classification: C42 E26 O17 R51

a b s t r a c t China's economic development since 1978 has been fuelled largely by a new private sector which is supported by both formal and informal financing channels. Although there is still a debate on the role of alternative financing mechanisms, the existence of such practices in China is accepted in the literature. This paper looks into the socio-economic patterns of private entrepreneurs regarding the choice of formal or informal finance, using survey data from Chengdu, China. Both the logit and ordered logit model are employed to examine the hypothesised factors. The results show that reputation and relationships play important roles in formal financing of the small private firms in China. © 2008 Elsevier Inc. All rights reserved.

Keywords: Informal economy Formal and informal sectors Private businesses in China Informal finance

1. Introduction As one of the fastest growing emerging markets in the world, China is experiencing a transition from a planned, socialist economy to a market or mixed economy. The emergence of a significant private sector is one of the most important developments of the market-oriented reforms in China over the last quarter of a century. Nevertheless, the rapid growth of the private sector remains contentious. To begin with, how could an entire private sector develop in the virtual absence of private capital? Many have argued that the private sector has been locked out of access to formal channels of capital (Tsai, 2002; Vicziany & Zhang, 2005c; Zhang, 1999, etc.). This automatically makes the private sector's dependence on informal funding very important (Allen, Qian, & Qian, 2005). This paper will explore the factors affecting private entrepreneurs' choice between formal and informal financing practices. By doing so, the paper will also examine the role of reputation and relationship in private sector financing. In particular, this paper analyses the results of data collected via questionnaires during fieldwork in Chengdu, one of China's most important inland cities. During the Maoist period, Chengdu had formed the very core of China's ‘Third Front Construction’ strategy, designed as part of the Chinese Communist Party's response to Western and Soviet hostility. With the end of Maoism, economic development focussed on the eastern seaboard—to the detriment of the central and western regions of China. However, private entrepreneurship was not to be held back in Chengdu and quickly rose up to meet the challenges offered by Deng Xiaoping's reform policies. In this way, this paper seeks to redress the current bias in favour of research about the economic reform process on the eastern seaboard.

⁎ School of Economics, Faculty of Commerce, University of Wollongong, Wollongong, NSW 2522, Australia. E-mail address: [email protected]. 1043-951X/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.chieco.2008.09.001

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Many have argued the core-identifying characteristic of informal financing mechanisms is that they are based on reputation and relationship rather than relying on anonymous interaction between a client and a formal financial institution (e.g. Allen et al., 2005; Ayyagari, Demirguc-Kunt, & Maksimovic, 2007; Tsai 2002). The term “informal” implies that the proper functioning of the institution relies at least as much on the establishment of reputation and relationship as on formal rules and procedures. Greif (1989, 1993) emphasises the important role of reputation and relationship in the context of certain traders' organizations in the eleventh century, where institutions based on reputation and implicit contractual relations were developed to overcome the problems of lack of legal and contract enforcement mechanisms. Following this, Allen et al. (2005) attribute the growth of the Private sector to the reliance on alternative financing channels and corporate governance mechanisms based on reputation and relationships. However, Ayyagari et al. (2007) suggest that the role of reputation and relationship based financing and governance mechanisms in supporting the growth of private sector firms is likely to be overestimated and unlikely to substitute for formal mechanisms. Despite the debates on the relative importance of formal or informal financing mechanism in the rise of the private sector in China, all these literature implies that reputation and relationship play crucial roles in informal financing. In this paper, we will show that the reputation and relationship factors are actually very important for formal financing of the small private firms in China. The rest of the paper is organised as follows. First, the research design of this paper is explained. Section 3 then introduces the survey data collected from private entrepreneurs in Chengdu. Sections 4 and 5 detail how the key variables were measured and present the descriptive statistics for the relevant indicators. This is followed by an account of the results of the regression models and a conclusion. 2. Research design: The hypotheses The purpose of this paper is to empirically measure the factors that influence the financing practices of private entrepreneurs in Chengdu. The financing patterns of the private sector in western China are determined by socio-political considerations as much as economics. Tsai (2002) suggests a number of factors which affect the institutionalisation of private entrepreneurs' financing, including: strength of political ties, residential origin, years in business gender and gross annual income. Based on her research, this section reviews competing explanations regarding the private entrepreneur's choice of financing practices, derives testable hypotheses, and proposes a systematic set of independent variables as explanatory factors. First, as suggested by Rona-Tas (1994), political power is converted into economic advantage when planned economies begin to transform themselves into market systems. His research found that the ex-communist cadres (party bureaucrats) maintain their advantageous position when they become entrepreneurs and do extremely well in the dynamic business sector. Wank (1995, 1999) elaborated in detail on the political dependency of private entrepreneurs in China. Choi and Zhou (2001) have also argued that the importance of political connections for Chinese entrepreneurs increases as the scale of businesses grows. In particular, state patronage was needed to help Chinese firms overcome the uncertainty that frequently surrounded their legal status. Case studies of private enterprises in western China by Vicziany and Zhang (2004, 2005a) again show that political connections have been helpful for the most successful private entrepreneurs, especially if they begin business as green-field private enterprises. Basing their work on three case studies, including two of Xinjiang's leading millionaire entrepreneurs, they show that party connections help in developing local markets, provide clients for fledgling businesses and facilitate access to credit. Research by Choi and Zhou (2001) is amongst the best in this field as they have analysed 1993 nationwide survey data collected by the All China Federation of Industry and Commerce on 1440 sample entrepreneurs throughout China. They demonstrate empirically that having being a cadre member has a positive effect on profits. In particular, Khwaja and Mian (2005) have argued that banks do favour politically connected firms in less developed economies. Based on these studies, it is reasonable to suppose that access to political and bureaucratic networks may be reflected in the private entrepreneurs' choice of financing strategies. The following hypothesis is based on this assumption: H1. Private entrepreneurs with close political or bureaucratic ties are more likely to employ more formal financing practices than those with fewer political connections. If party connections determine access to formal funds, then interpersonal ties and local reputation will influence access to informal financial sources. Having local networks outside the party system depends on the household registration status of the entrepreneurs and their length of residence in particular cities. Hence it is important to differentiate between private entrepreneurs who are indigenous to a place and those who are migrants from other places, including other cities in Sichuan province. Discrimination against migrants in China is reasonably well documented. Some scholars (Day & Ma, 1994; Solinger, 1999) have examined the nature of this discrimination, with respect to the social networks of outsiders, as well as their employment prospects and access to political support. These studies both conclude that migrant entrepreneurs face more problems compared with their native counterparts (Solinger, 1999). In addition to this, the rural people who have flocked to China's cities in recent years are generally viewed with derision. They suffer from discrimination in the job and home rental markets, and their children even have difficulty accessing local schools (Nielsen, Smyth, & Zhang, 2006). It is therefore a reasonable assumption that the raw entrepreneurs amongst the migrant population will also suffer from relative marginalisation (Chen, 2004). This in turn may be reflected in the choice they make about financing strategies, as new migrants are probably more likely to be excluded from community-based financial institutions. This is not to say that well-established migrant enclaves in urban centres are incapable of developing their own financing institutions. In fact, migrant networks play an important role in the survival of new migrants (Zhao, 2003). Zhao's study shows that

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experienced migrants have a positive and significant effect on subsequent migration. In some cases, the migration of people is driven by entrepreneurial ambition, as in the case of the minority Uygur who have migrated from Xinjiang to Shanghai and Beijing (Vicziany & Zhang, 2005b). Despite this, even migrants who are able to develop their own financial responses to capital scarcity can expect to face more political, bureaucratic and community obstacles in accessing local sources of credit beyond their immediate family, relatives and migrant community. These issues have given rise to the second hypothesis: H2. Private entrepreneurs indigenous to Chengdu are more likely to employ formal financing practices than migrants. Given that private entrepreneurs are more able to create and manage viable enterprises with increased human capital (Astebro & Bernhardt, 2003), it is reasonable to include human capital indicators in the following analysis. There are numerous definitions of human capital cited in the literature, which cover a broad contextual spectrum. There are, however, several recurring factors that are cited as relevant determinants of human capital. These include, for example, educational level, skill level, personal aptitude, experience, attitude and behaviour (Davenport, 1999). Of these, the factor that serves as the best indicator of human capital is the level of education. Boshoff and Schutte (1998) argued that education level is one of the main predictors of entrepreneurial success in a developing country. Van-de-Ven, Hudson, and Schroeder (1984) and Jo and Lee (1996), found a direct and linear relationship between educational levels and entrepreneurial performance. Moreover, Bates (1990), using an analysis of a nationwide random sample of 7743 small firms in the US, showed that entrepreneurs with higher educational qualifications are likely to raise funds from capital markets more easily, and survive longer in the market, than those without such qualifications. Access to formal financial sources requires more than just literacy for a number of specific tasks: maintaining and analysing accounts, understanding the rules and procedures for bank credit and the capacity to manage the accounts and books of a wide variety of financial sources in order to diversify the range of access to business capital. Even in the case of micro-credit, it has been found that minimal educational competence is essential for reading bank passbooks, understanding the length and conditions of loans and being able to monitor loan repayments in order to prevent fraud by the lending agency (Li, Rozelle, & Zhang, 2004). These arguments suggest a third hypothesis: H3. Private entrepreneurs with a good education are more likely to choose more formal financing practices than less educated private entrepreneurs. There is a vast literature on the effect of business experience on entrepreneurial performance. Financial growth cycle theory suggests that raw entrepreneurs often started as individual entrepreneurs who could not get access to formal financial channels because their credit status was very low (Berger & Udell, 1998). As these small, private firms became better established, their creditworthiness and reputation improved and they developed characteristics that enabled formal institutional funding bodies to better monitor loans in a manner not possible when the companies were founded. More specific research has also been done to establish the relationship between business experience and entrepreneurial performance. Stuart and Abetti (1990) found a strong positive correlation, and other studies have found similar results (e.g. Sandberg & Hofer, 1987; Vesper, 1980). Based on these findings, the fourth hypothesis considered is: H4. Private entrepreneurs with well-established business are more likely to choose more formal financing practices than less established private entrepreneurs. It is worth noting that private enterprises in China have gradually resorted to credit reporting and rating agencies to have their creditworthiness evaluated for the benefit to potential lenders. This has happened as Chinese capitalism has grown and matured and the competition for scarce capital has compelled firms to develop ways to ration the available credit. The result has been that the credit rating status of private firms has become an important indicator of their reputation and ability to access formal or informal financial sources. Many studies have examined the effect of credit scoring on small business lending. In their study of large US banking organisations, Frame, Srinivasan, and Woosley (2001) found that credit scoring was associated with an 8.4% increase in the amount of credit extended to small businesses. Frame, Padhi, and Woosley (2004) added two more dimensions to their study of the characteristics of credit-scored loan recipients: income levels and the location of a firm. Their results suggest that regardless of income levels and location, credit scoring increases the average size of loans. Approaching the question from a different angle, Berger, Espinosa-Vega, Frame, and Miller (2004) examined how credit scoring reduced uncertainty about borrowers (calling it informational opacity) and made it possible for lenders to receive loans with longer repayment periods (i.e. extended loan maturity). The authors found that debt maturity is significantly longer for borrowers when they borrow from banks that use credit scoring methods. Despite these studies, no research has been conducted to ascertain how credit rating systems affect the choice between formal and informal financing practices. Since China is establishing a national credit reporting and rating system, this study will test the following hypothesis: H5. Private entrepreneurs who have their business credit status rated are more likely to access formal financing practices, while private entrepreneurs without credit rating experience are more likely to use informal financing practices. In summary, the strength of the entrepreneur's political or bureaucratic ties (POL), their residential origin (NAT), educational level (EDU), the length of business experience (BEX), and the firm's credit rating (RAT) are all good indicators of whether the firm is

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likely to resort to formal or informal financing practices (FIN). Choosing between these financing practices can, therefore, be summarised by the following empirical specification: FIN ¼ f ðPOL; NAT; EDU; BEX; RATÞ Regression modelling is used to capture the overall impact of the independent variables on the dependent variable, holding each of the independent variables constant. A detailed explanation of both the dependent variable and independent variables, together with the rationale behind the selection of this model, are given later in this paper. In order to see whether there are differences between getihu (private firms with less than 8 employees) and siying qiye (private firms with more than 8 employees), comparisons are made throughout this paper between these two categories of data. The process by which private entrepreneurs in Chengdu choose formal or informal financing mechanisms is, of course, more complex than can be ascertained from estimated coefficients. Statistical modelling and regression analysis, however, enables this study to assess, rather than merely assert, that the choice of financing mechanisms is determined by measurable factors. 3. The data The data for this paper is taken from the results of a survey that the author undertook in Chengdu from August 2004 to February 2005; 272 questionnaires were distributed and in-depth interviews were conducted. The survey yielded 172 valid questionnaires that had been randomly distributed through three channels as described in Table 1: (1) by the author; (2) through the Chengdu SME Management Bureau; (3) through the SME Division of the Sichuan Economic and Trade Commission (hereafter SETC). All the surveys with getihu (micro-entrepreneurs, or individual business owners) were conducted in the bazaars and free markets of Chengdu by the author (n = 102). Note that many private entrepreneurs are not officially registered with the Chengdu Administration of Industry and Commerce (CAIC), the government agency in charge of regulating private businesses. As a result, the CAIC could not be relied on for the selection of survey participants. Instead, much of this data collection involved the author walking in the streets and alleys of Chengdu to locate the small entrepreneurs from bazaars. In the process of locating various markets, the need to find micro-entrepreneurs representing various commercial trades was taken into consideration (e.g. restaurants, shops, blacksmiths, etc.). To some degree this resulted in a stratified sample. The author approached local residents and micro-entrepreneurs through informal conversations, and then a number of individuals in the marketplaces were selected to participate in the survey. Potential respondents were given an explanatory statement about the research and informed that the survey was only intended for academic purposes and that they were guaranteed confidentiality. Consent forms were signed when a local business person agreed to collaborate. Throughout this period of fieldwork the author was deeply aware of the risks of collecting insufficient data, given that much of the informal financial sector straddles the divide between the legitimate and illegitimate economy in China. At the same time, the imperatives of this research had to be reconciled with a respect for the respondents and in particular their rights to privacy and confidentiality. Some questionnaires were completed by the entrepreneurs themselves, who felt it would be faster for them to fill in the forms without any assistance. However, others preferred not to do so. In these instances the survey questions were delivered orally and the responses were written down by the author on the questionnaire. In the course of the survey process, an informal friendly tone was adopted to facilitate a more natural conversation. This approach helped gather not merely more information but also franker responses to the questions. Sometimes the business owner was so happy with the encounter that completing the questionnaire was interspersed with a tour of the production facilities of the firm. Such site visits provided invaluable experiences. Surveys with the owners of siying qiyejia (private entrepreneurs who owned businesses employing more than eight people) were much more difficult to conduct on my own. First, the siying qiyejia had to be identified, which was impossible without assistance from an appropriate organisation. Second, because of their size, the owners of siying qiyejia demanded a more formal approach, such as insisting that an appointment be made. Typically, this author had to rely on government organisations, Non-

Table 1 Summary of the 272 distributed questionnaires Type of business surveyed

No.

Valid

102 All survey questionnaires were distributed in the bazaars and ‘free markets’ of Chengdu by the author 70 14 survey questionnaires were distributed and collected by the Chengdu SME management Bureau; 34 survey questionnaires were distributed and collected by the SME Division of the SETC; 22 survey questionnaires were distributed and collected by the author through interviews. 172 73 27 100 These questionnaires were incomplete and therefore invalid. They were not used.

Getihu (individual businesses) Siyingqiye (private enterprises)

Total Rejected Getihu (individual businesses) Siyingqiye (private enterprises) Total

Description

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Government Organisations (NGOs), or quasi-NGOs to assist with providing introductions and assuring the respondents that the survey was voluntary and totally confidential. Fortunately many of the most useful organisations had been identified prior to fieldwork in Chengdu. The first task was to contact these key organisations to seek their support and assistance: (1) (2) (3) (4) (5)

Chengdu Administration of Industry and Commerce (CAIC); SME Division of the SETC; Chengdu SME Management Bureau; Chengdu Chamber of Commerce (CCC); and Chengdu City Private Entrepreneurs' Association.

Letters of support were obtained from many of these organisations after copies of the explanatory statement and consent form were given, as per the requirements of the Monash University Standing Committee on Ethics in Research involving Humans (SCERH). As Table 1 shows, a total of 72 questionnaires from the larger business enterprises were collected. Sometimes the local association itself distributed the questionnaire on my behalf, which occurred in the case of the Chengdu SME Management Bureau and the SME division of the SETC. In order to get a list of the names and contact details of siying qiyejia, assistance had to be sought from the appropriate organisation. The request to the Chengdu City Private Entrepreneurs' Association was problematic in this regard. Although this group appeared to be a NGO representing the interests of the private entrepreneurs, it was actually a government agency affiliated with the Chengdu Administration of Industry and Commerce. Moreover, 90% of its members were owners of getihu, not siying qiyejia. Instead, the author had to rely on information and permission provided by the Chengdu Chamber of Commerce (CCC), all of whose members were siying qiyejia. It was not possible to get the CCC itself to mail out the questionnaire. But with their assistance, potential survey participants were randomly selected from their membership list. After an initial appointment with these siying qiyejia, usually with the owners or CEOs of siying qiye, the questionnaire was administered. This was often followed by a more indepth discussion. Altogether 22 completed questionnaires were collected in this manner. 4. Operationalisation of the dependent variable Before proceeding, it is worth clarifying in brief the operationalisation of the dependent variable. The dependent variable was the entrepreneurs' choice of a financing mechanism. The choice depended on the degree of formality in the financing practices employed, which could be measured in the following two ways. 4.1. Dichotomous dependent variable FIN The simplest way is to represent the dependent variable as a dummy variable FIN (choice of financing mechanism), coded either 1 (if the entrepreneur chooses formal finance) or 0 (if the entrepreneur chooses informal finance). As the dependent variable is dichotomous, conventional regression methods are inappropriate. Even the linear probability model is heteroskedastic and may predict probability values beyond the (0, 1) range; thus the logistic regression model is a better choice to estimate the model in this paper. When measuring FIN, the data is taken from Survey Questions 18 and 19. Question 18 asks the participating entrepreneur whether his or her business has ever borrowed money from an officially recognised financial institution, which includes all types of banks and rural credit cooperatives. If the answer is “yes”, the financing practices employed are regarded as a case of ‘formal finance’ and coded 1. If the respondent's answer is “no” to Question 18, then the answer to Question 19 was examined to see whether informal financing was used. Question 19 asks the entrepreneur whether his or her firm has ever borrowed money from informal sources such as a friend, relative, or pawn shop. If the answer to Question 18 is “no”, and the answer to Question 19 is “yes”, then informal financing was employed as the private entrepreneur could only borrow money from informal sources. Therefore FIN is coded 0. There were only nine cases when respondents responded to both questions with a “no”, indicating that no financing activities were involved at all. The descriptive statistics of FIN are shown in Table 2. Table 2 shows that nearly 50% of the private firms have only ever borrowed money from formal banks and rural cooperatives, with the relevant proportions being 53.7% and 46.9% for siying qiye and getihu respectively. These figures seem a little surprising when compared with previous studies. The survey results from the IFC (2000) showed that 91.5% of the private firms in Chengdu

Table 2 Private entrepreneurs' choice of financing practices (FIN) FIN

Code

getihu %⁎ (n)

siying qiye %⁎ (n)

Total %⁎ (n)

Informal finance Formal finance Total

0 1

53.1 (51) 46.9 (45) 100.0 (96)

46.3 (31) 53.7 (36) 100.0 (67)

50.3 (82) 49.7 (81) 100.0 (163)

Note: ⁎ Percentage of valid responses.

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Table 3 Degree of formality of the financing practices employed DFF

Code

getihu % (n)

siying qiye % (n)

Total % (n)

Most informal Less informal Moderately formal Less formal Most formal Total

1 2 3 4 5

55.9 (57) 8.8 (9) 3.9 (4) 16.7 (17) 14.7 (15) 100.0 (102)

48.6 (34) 2.9 (2) 3.9 (4) 11.4 (8) 24.3 (17) 100.0 (70)

52.9 6.4 7.6 14.5 18.6 100.0

(91) (11) (13) (25) (32) (172)

relied on self-finance capital to begin their business while only 2.8% got their initial capital from bank loans. In the case of financing after start-up, the sample firms continued to depend on internal sources, with only 25.5% of loans coming from banks and credit unions (IFC, 2000). The main reason of the difference between the survey results is that the IFC study was based on capital financing, either start-up capital financing or additional investment for expansion; it excluded short-term bank loans to improve cash-flow. In the present study, if the private firm had ever received at least one bank loan, no matter for what purpose, it was counted as a case of that private entrepreneur having access to formal finance. In itself, this would not have been a problem for this paper but for the fact that the respondents did not answer the other questions that accompanied this one. These other questions would have provided a much more finely grained explanation of the nature of state funding including the size of the loans, the length of the loans and the frequency with which firms accessed bank credit. Given all these considerations, this paper that reports on the aggregate results of this survey cannot be taken as providing strong evidence for access to formal banking. 4.2. Ordinal dependent variable DFF Another means to measure the dependent variable is to consider the degrees of formality of financing practices employed by private entrepreneurs in Chengdu. The dependent variable is then denoted DFF and coded ordinally from 1 to 5, where 1 stands for the most informal financing mechanism and 5 for the most formal. It is assumed that among the formal financial institutions there is a difference in the degree of formality. The rationale behind this is that the largest four state banks could be regarded as the most formal of the financial institutions (coded 5), the joint stock commercial banks slightly less formal than the large state-owned banks (coded 4), the Chengdu City Commercial Bank is moderately formal (coded 3) and the rural cooperatives are the most informal of the officially recognised institutions (coded 2). The curb market credit activities (including borrowing from friends, loan sharks and informal credit associations) would be considered the most informal of all financing practices (coded 1). Table 3 provides a brief summary of the descriptive statistics. For the ordinal dependent variable DFF, the most suitable regression would be an ordinal regression, i.e. ordered logit model analysis. This was the method adopted below. As Table 3 shows, the getihu are utilising more informal financing sources than siying qiye, but the difference is not as large as expected. Again, only 53% of the total private firms resort to the most informal curb market; others still depend on various

Table 4 Summary of proposed independent variables Variable name

Description

Definition

POL

Political or bureaucratic connections

NAT EDU

Whether or not a native of Chengdu The level to which an entrepreneur was educated

RAT BEX AGE

Credit rating status Business experience Age categories of the respondents

SOF

Size of the firm

0 = No state-related work experience 1 = Ordinary staff in a state unit 2 = Section chief/Manager in a state unit 3 = Chief leader/CEO in a state unit A binary variable set to 1 if the respondent is a native of Chengdu 1 = Primary school and below 2 = Junior middle school 3 = High school 4 = Vocational training 5 = College 6 = Graduate school and above A binary variable set to 1 if the private business has a credit rating Number of years the private business has been in operation 1 = Under 20 years old 2 = 20–29 years old 3 = 30–39 years old 4 = 40–49 years old 5 = Over 50 years old A binary variable set to 1 if the private firm is a siying qiye, which employs more than 8 people.

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Table 5 Private entrepreneurs' political/bureaucratic background (1) State employment history

Code

getihu % (n)

siying qiye % (n)

Total % (n)

No state-related work experience State-related work experience Total

0 1

61.8 (63) 38.2 (39) 100.0 (102)

51.4 (36) 48.6 (34) 100.0 (70)

57.6 (99) 42.4 (73) 100.0 (172)

Table 6 Private entrepreneurs' political/bureaucratic background (2) Previous work position (POL)

Code

getihu % (n)

siying qiye %⁎ (n)

Total % ⁎ (n)

No state-related work experience Ordinary staff Section chief/manager Chief leader/CEO

0 1 2 3

61.8 15.7 16.7 5.9

52.2 (36) 20.3 (14) 21.7 (15) 5.8 (4)

57.9 (99) 17.5 (30) 18.7 (32) 5.8 (10)

(63) (16) (17) (6)

⁎Percentage of valid responses.

recognised financial institutions ranging from rural cooperatives to the state-owned banks. It seems that informal finance is not as pronounced in Chengdu as Tsai (2002) found to be the case in other locales in China. According to Tsai (2002), informal finance was very popular in Zhejiang, Fujian and Henan. 5. Measurement of the independent variables As previously mentioned, this paper analyses the role of a number of independent variables in explaining the choice of financial practices employed by private entrepreneurs in Chengdu. Table 4 summarises the independent variables, and a more detailed explanation of each of the key variables is presented below, together with an analysis of the survey responses to each question. 5.1. POL (Political or bureaucratic connections) As noted at the start of the paper, there are good reasons to believe that successful businesses in China have good connections to political and bureaucratic networks. In a survey of the kind administered for the present study, however, it is inappropriate to directly ask whether the private entrepreneur has any political or bureaucratic connections. The survey thus asks whether the owner of the business or any other senior management personnel have ever worked in a danwei (a government working unit, which could either be a government bureau/department/committee/institution or state-owned enterprise). This question serves as a proxy for government or bureaucratic connections. It is assumed that people who have had such experience would probably retain their official networks even if they were no longer employed by the state sector. Such political connections would be very helpful when accessing formal finance. The survey results are presented in Table 5. Table 5 shows that there were more private entrepreneurs without any state-related work experience (57.6%) than those with such experience. The situation is particularly true of getihu; only 38.2% of the getihu had any previous association with state employment. The results suggest that private entrepreneurs in larger siying qiye are more likely to have political/bureaucratic ties than getihu. At the same time some 51.4% of larger firms appeared to lack any government ties. It is important to remember that all these firms had grown from being fully private or green-field entrepreneurial firms into middle and large organisations. The questionnaire also asked those respondents who had worked in the government or state-owned sector what level they had held, as shown in Table 6. POL is an ordered variable showing the private entrepreneur's background ranging from 0 (no state-related work experience) to 3 (chief leader/CEO in a public or state-related organisation). Thus, three levels of employment for those with state-related work

Table 7 Residential origin of private entrepreneurs NAT

Code

getihu % (n)

siying qiye % (n)

Total % (n)

Outsider Native Total

0 1

47.1 (48) 52.9 (54) 100.0 (102)

48.6 (34) 51.4 (36) 100.0 (70)

47.7 (82) 52.3 (90) 100.0 (172)

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Table 8 Private entrepreneurs' education level EDU

Code

getihu % (n)

siying qiye % (n)

Total % (n)

Primary school and below Junior middle school High school Vocational training College Graduate school and above Total

1 2 3 4 5 6

17.6 (18) 24.5 (25) 19.6 (20) 20.6 (21) 14.7 (15) 2.9 (3) 100.0 (102)

0 8.6 17.1 4.3 54.3 15.7 100.0

10.5 18.0 18.6 14.0 30.8 8.1 100.0

(0) (6) (12) (3) (38) (11) (70)

(18) (31) (32) (24) (53) (14) (172)

experience have been defined: ordinary staff, section chief/manager and chief leader/CEO. The classification was made with the expectation that the more senior the previous work position, the stronger the political ties and the easier it would be to access formal financing. Many small street vendors might be laid-off workers from SOEs or workers who have retired early from SOEs and these people would not necessarily have any political connections. Such people will be quite different to cadre entrepreneurs and would appear in the category of ordinary staff in Table 6. 5.2. NAT (whether or not a native of Chengdu) The independent variable NAT is operationalised in a more straightforward way. The survey asked explicitly whether the participant was a native of Chengdu or not. It is interesting to note that identity with birthplace remains a strong factor in Chinese life. For example, some private entrepreneurs confided that they had shifted their entire families to Chengdu but they still regarded themselves as outsiders. Certainly, these families are perceived to be outsiders by native-born residents of Chengdu. The distribution of Chengdu natives and outsiders are presented below (Table 7), where outsiders are coded as 0 and natives as 1. Table 7 shows that a high proportion (47.7%) of private entrepreneurs, but not the majority, came from outside Chengdu. Some of these migrant entrepreneurs came to Chengdu precisely because of the business opportunities offered. The fact that Chengdu has attracted a large number of migrants reflects its status as the most important business centre in western China, which from time to time has been designated as the alternative national capital to Beijing (Kennedy, 2003: 12). 5.3. EDU (education level) The EDU value have been coded ordinally as follows: primary school and below (1), junior middle school (2), high school (3), vocational training (4), college (5), and graduate school and above (6). Table 8 shows that the private entrepreneurs from siying qiye seem to be better educated than those from getihu, with 70% of siying qiyejia having a college-level education or above, while this figure was just 17.6% for getihu. This finding is reasonable in the sense that those with better education are more likely to be willing and capable of expanding their businesses into larger private enterprises (see earlier discussion in this paper). By contrast, 17.6% of the getihu had only primary schooling or less, relative to no siying qiye in this category. Amongst the getihu the most common education level was junior middle school, representing some 24.5% of respondents; in the case of the siying qiye the most common educational level was college, which accounted for some 54% of respondents. These descriptive statistics suggest a strong positive relationship between the level of formal education and the size of businesses in Chengdu. In another study of entrepreneurship in Xinjiang, Vicziany and Zhang found that illiteracy and low levels of education hampered the capacity of a small firm to expand (Vicziany & Zhang, 2004). 5.4. BEX (length of business experience) This independent variable is the only continuous variable in the dataset. It denotes the number of years that the entrepreneurs had been in business at the time of data collection in 2005. The business experience and age of a firm were taken to be synonymous. Given that China's private sector has only recently emerged, the oldest private business surveyed was 24 years old. Table 9, however, suggests that almost all the private firms in Chengdu are quite young, having an average age of only twelve years. No major difference emerged between small and larger firms.

Table 9 Descriptive statistics for business experience BEX

N

Minimum (years)

Maximum (years)

Mean

Std. Deviation

Getihu Siying qiye Total

102 70 172

1 3 1

23 24 24

11.7 12.6 12.1

6.4 5.8 6.2

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Table 10 Private entrepreneurs' credit rating status RAT

Code

getihu % (n)

siying qiye % (n)

Total % (n)

No credit rating With credit rating Total

0 1

Nil Nil Nil

60.0 (42) 40.0 (28) 100.0 (70)

83.7 (144) 16.3 (28) 100.0 (172)

5.5. RAT (credit rating status) The survey also asked explicitly whether the firm had been given a credit rating. The independent variable RAT was coded as a dummy variable: with credit rating experience set at 1, and no credit rating experience at 0. It is worth noting that the credit rating services at the present time are only provided for larger enterprises (including siying qiye). None of the getihu can obtain a credit rating, even if they wanted one. This is one of the reasons why the regression analysis of this research deals with getihu and siying qiye separately. Table 10 shows that amongst the siying qiye some 40% of firms have already been given a credit rating, even though the credit rating system has only been introduced on a pilot basis. This is still only a minority of medium and large companies but it signifies that the use of the credit rating system is increasing, and likely to become even more significant in the future. 5.6. Independent variables for controls Two independent variables AGE and SOF are included as control variables. AGE is the age of the participating private entrepreneurs, and it is recorded within the ranges shown in Table 11. An older age does not necessarily mean longer business experience, but it might have some impact on greater human capital accumulation. Table 11 suggests an interesting trend that the bigger siying qiye firms have older owners, which might be explained by the fact that entrepreneurs generally need more experience to run larger firms. Alternatively, it is possible that perhaps the smaller firms have managers who retire earlier and allow their children to take over the business. Sun (1999) suggests that this is more difficult to do in larger firms, and thus less common. Another control variable is SOF, which denotes the size of the private firms surveyed. Given the fact that there were more getihu (53.1%) than siying qiye (46.3%) that resorted to informal financing practices (see Table 2), it was worth testing whether the size of the firm played a role in choosing different financing practices. A simple rationale behind this is that smaller businesses are less able to provide appropriate information (e.g. financial statements) when applying for credit through formal financial institutions (IFC, 2000). Moreover, an emerging credit reporting industry is helping the larger siying qiye to cope with problems of asymmetrical information. Smaller getihu, however, have no access to any credit ratings at all. Table 12 shows the proportion of the 172 firms that are getihu and siying qiye. This variable acts as proxy for firm size. 5.7. Econometric methodology Before proceeding to the regression analysis, we take into account some potential problems with the following employed econometric methodology. A Spearman Rank correlation matrix of all independent variables is presented in Table 13.1. As a

Table 11 Age of private entrepreneurs AGE

Code

getihu % (n)

Under 20 20–29 30–39 40–49 Over 50 Total

1 2 3 4 5

3.9 16.7 48.0 26.5 4.9 100.0

(4) (17) (49) (27) (5) (102)

siying qiye % (n)

Total % (n)

0.0 (0) 1.4 (1) 38.6 (27) 34.3 (24) 25.7 (18) 100.0 (70)

2.3 10.5 44.2 29.7 13.4 100.0

(4) (18) (76) (51) (23) (172)

Table 12 Size of the firms SOF

Code

Total % (n)

Less than 8 employees (getihu) More than 8 employees (siying qiye) Total

0 1

59.3 (102) 40.7 (70) 100.0 (172)

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Table 13.1 Spearman rank correlation matrix

POL (Political connections) NAT (Residential origin) EDU (Education level) BEX (Business experience) RAT (Credit rating status) AGE (Age) SOF (Size of the firm)

POL

NAT

EDU

BEX

RAT

AGE

SOF

1.00 .19 .28 .27 .39 .27 .09

1.00 .001 .24 .09 .16 −.11

1.00 .03 .41 .18 .50

1.00 .19 .56 .07

1.00 .34 .53

1.00 37

1.00

rule of thumb, a correlation coefficient of 0.6 or more suggests potential multicollinearity. As shown in Table 13.1, the correlation coefficients between each pair of variables are less than 0.6. Thus, there should not be any potential multicollinearity between the independent variables. Therefore, all the independent variables discussed above are included in the following empirical models. In addition, the variable RAT (credit rating status) can be endogenous as firms are usually assigned a rating only when they apply for a bank credit and be informed of the rating if the loan application is approved. Thus, Hausman's specification error test is employed to examine whether the variable RAT is endogenous in the proposed logit model. The test is reported in Table 13.2, where model (b) refers to the logit model without the RAT variable and model (B) refers to the logit model with the RAT variable. Our diagnostic testing shows that the difference in coefficients between models (b) and (B) fits the asymmetric assumptions, indicating that the coefficients in the logit model with the RAT variable are consistent and efficient. Therefore, credit rating status is not an endogenous variable in the logit model. 6. Results of the regression analysis This section reports the results for both a simple logit model analysis and ordered logit model analysis, depending on how the dependent variable is defined. 6.1. Binary logistic regression analysis Binary logistic regression is used where the dependent variable is operationalised as FIN. The same methodology applies to the whole dataset (with a valid sample size of 163), the data for getihu (with a valid sample size of 96), and the data for siying qiye (with a valid sample size of 67). The results of the binary logistic regression are presented in Tables 14–16, respectively. The estimation results from Table 14 are for the whole dataset including both getihu and siying qiye. A test of the full model against a constant-only model was statistically significant (χ2 = 152.937), indicating that the model reliably distinguished between entrepreneurs who choose formal financing and those who choose informal financing. The model was able to correctly classify 90.1% of cases. The results show that age (AGE) and business experience (BEX) are statistically insignificant predictors of the dependent variable. However, the signs on the coefficients of the statistically significant independent variables are as predicted, which shows that there is strong positive relationship between these independent variables and the dependent variable. The odds ratio (the probability of the event divided by the probability of the non-event) for POL, NAT, EDU, RAT are quite large, which reflects

Table 13.2 Hausman's specification test for credit rating status (RAT) Coefficients (b) partial POL (Political Connections) NAT (Residential Origin) EDU (Education level) BEX (Business experience) AGE (Age) SOF (Size of the firm) Test

Result

(B) All

3.45 3.34 1.57 1.61 .99 .93 − .02 − .02 .66 .56 − 1.76 −2.74 Ho: difference in coefficients not systematic chi2(3) = (b − B)'[(V_b − V_B)^(−1)](b − B) = 29.14 Prob N chi2 = 0.97 Do not reject Ho

Notes: b = consistent under Ho and Ha; obtained from logit. B = inconsistent under Ha, efficient under Ho; obtained from logit.

(b − B) Difference

sqrt(diag(V_b − V_B)) s.e.

.11 − .04 .07 − .00 .10 .97

.13 .29 .09 .02 .18 .50

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Table 14 Binary logistic regression results on data from both getihu and siying qiye Variable

Estimated coefficient

Wald

Odds ratio

Constant POL (Political connections) NAT (Residential origin) EDU (Education level) BEX (Business experience) RAT (Credit rating status) AGE (Age) SOF (Size of the firm) Model chi-square [df] % Correct predictions Nagelkerke R square

−7.22⁎⁎⁎ 3.34⁎⁎⁎ 1.61⁎⁎ 0.93⁎⁎⁎ −0.02 3.93⁎⁎⁎ 0.56 −2.74⁎⁎⁎

1.85 31.10 5.20 12.12 0.10 7.42 1.12 8.36 152.94 [7] 90.10 0.82

0.00 28.09 5.00 2.53 0.98 50.91 1.74 0.07

Dependent variable = FIN, sample size = 163. Note: ⁎⁎⁎ statistically significant at the 1% level; ⁎⁎ statistically significant at the 5% level; ⁎ statistically significant at the 10% level.

the strong positive effects of these independent variables on accessing formal finance. Even the smallest odds ratio for EDU in Model 1 is 2.526, suggesting a one unit positive change in EDU would make resorting to formal finance more than twice as likely to occur. In short, political/bureaucratic connections (POL), residential origin (NAT), education level (EDU) and credit rating status (RAT) have a statistically significant impact on the entrepreneurs' choice of financing practices. The control variable SOF (size of firm) is statistically significant but it has an unexpected negative sign. The reason might be that the actual finance pattern for getihu and siying qiye is quite different, and the manner in which the hypothesised predictors affect the dependent variable is also different. Thus the dataset needs to be split in order to deal with the different-size firms separately. The results of this exercise are reported in Tables 15 and 16. Table 15 presents the results based on the getihu data. The independent variable RAT is omitted in this model since no getihu have credit ratings. The overall model is statistically significant according to the Chi-Square statistic (χ2 = 89.538). The coefficients on POL, NAT and EDU are statistically significant with positive signs consistent with Table 14, which support the hypotheses described previously. The coefficient on BEX is statistically insignificant.

Table 15 Binary logistic regression results on the getihu data Variable

Estimated coefficient

Wald

Odds ratio

Constant POL (Political connections) NAT (Residential origin) EDU (Education level) BEX (Business experience) AGE (Age) Model chi-square [df] % Correct predictions Nagelkerke R square

−7.17⁎⁎⁎ 3.63⁎⁎⁎ 2.50⁎⁎⁎ 0.80⁎⁎⁎ −0.04 0.70

7.75 19.09 6.99 6.65 0.27 0.94 89.54[5] 89.60 0.81

0.00 35.57 12.15 2.22 0.96 2.02

Dependent variable = FIN, sample size = 96. Note: ⁎⁎⁎ statistically significant at the 1% level; ⁎⁎ statistically significant at the 5% level; ⁎ statistically significant at the 10% level. Table 16 Binary logistic regression results on the siying qiye data Variable

Estimated coefficient

Wald

Odds ratio

Constant POL (Political connections) NAT (Residential origin) EDU (Education level) BEX (Business experience) RAT (Credit rating status) AGE (Age) Model chi-square [df] % Correct predictions Nagelkerke R square

−19.04⁎⁎ 3.97⁎⁎ 1.64 2.39⁎⁎ −0.18 4.70⁎⁎ 1.80

5 5 1 5 0 5 1 72.85 [6] 93.90 0.89

0.00 53.82 5.16 10.89 0.84 109.37 6.07

Dependent variable = FIN, sample size = 67. Note: ⁎⁎⁎ statistically significant at the 1% level; ⁎⁎ statistically significant at the 5% level; ⁎ statistically significant at the 10% level.

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Table 17 Ordinal regression results Variable

POL (Political connections) NAT (Residential Origin) EDU (Education Level) BEX (Business experience) RAT (Credit rating Status) AGE (Age) Model chi-square [df] Nagelkerke R square

Entire dataset

getihu data

Sample size = 172

Sample size = 102

siying qiye data Sample size = 70

Estimate

Wald

Estimate

Wald

Estimate

Wald

1.64⁎⁎⁎ 1.16⁎⁎ 0.29⁎⁎ 0.21⁎⁎⁎ 2.42⁎⁎⁎ −0.97⁎⁎⁎ 156.98[6] 0.65

51.99 8.38 4.37 27.03 18.00 12.16

1.74⁎⁎⁎ 1.63⁎⁎⁎ 0.28⁎ 0.20⁎⁎⁎

32.44 9.65 2.56 16.18

−0.96⁎⁎ 83.91[5] 0.61

6.41

1.09⁎⁎⁎ 1.05 0.63⁎ 0.24⁎⁎⁎ 3.40⁎⁎⁎ −0.05 83.64 [6] 0.76

7.50 2.36 3.30 10.00 17.95 1.24

Dependent variable = DFF. Note: ⁎⁎⁎ statistically significant at the 1% level; ⁎⁎ statistically significant at the 5% level; ⁎ statistically significant at the 10% level.

The regression results for the siying qiye data are presented in Table 16. A test of the full model against a constant-only model was statistically significant (χ2 = 72.848), which indicates that the model reliably distinguished between entrepreneurs who choose formal financing and those who choose informal financing. The model was able to correctly classify 93.9% of cases. The coefficient on the independent variable NAT (residential origin) is statistically insignificant, which indicates that the private entrepreneurs native to Chengdu in siying qiye do not necessarily employ more formal financing practices. The reason for this might be that there are more factors in the larger siying qiye affecting financing choice so that residential origin does not matter so much, unlike the case for the smaller getihu entrepreneur. However, the coefficients on POL, EDU and RAT are statistically significant, and the odds ratio in each case is high, which implies that siying qiye are more likely to employ formal financing practices if they have a credit rating, have better educated owners or have owners with better political connections. 6.2. Ordered logit model analysis In this section, ordinal regression is adapted where the dependent variable is operationalised as DFF. The ordered logit model analysis is based on the assumption that the dependent variable can be ordered in terms of the degree of formality of the financing practices employed. The estimation results of the model reflect the probability that a given independent variable has an effect on each ordered degree of the dependent variable. The ordered logit model is used for three sets of data: the whole dataset (with a valid sample size of 172), the data for the getihu (with a valid sample size of 102), and the data for siying qiye (with a valid sample size of 70). The ordinal regression results are presented in Table 17. The coefficient on POL is statistically significant at 1% in all three cases, which supports the hypothesis that private entrepreneurs who have close political or bureaucratic ties are more likely to employ more formal financing practices. The independent variable NAT is statistically significant with the expected sign for the entire dataset and the getihu. But as in the binary logistic regression model, it has a statistically insignificant coefficient in the model using the siying qiye data. EDU is statistically significant in all three cases. The results of the independent variable RAT are similar to the results of the binary logistic regression. It is worth noting that the business experience coefficient is statistically significant at 1% in the ordinal regression analysis in all three cases. This result suggests that private entrepreneurs who run businesses with a long history are more likely to employ formal financing practices. 7. Conclusion In this paper, both the logit model and ordered logit model were specified to empirically test the hypotheses stated at the beginning. The results from binary logistic regression support all four hypotheses except for that relating to business experience. The ordinal regression results support all five hypotheses. In short, private entrepreneurs with strong political connections, who are natives of Chengdu, who have better formal education, who possess credit rating experience and who run businesses with a long history are more likely to employ formal financing practices. However, given less representativeness of the firm nature due to small size of the sample firms, the results documented above are regarded as association rather than causality. A revisit of the explanatory variables reveals that they are actually good indicators of so called “reputation and relationships” in the informal financing literature. Political connections, residential origin, and even education serve to measure the owner's social relations and to some degree reputation; credit rating is certainly a scale for reputation of creditworthiness; business experience can be also considered as a measure of reputation. The empirical results show that all these factors have significant impact on formal financing of the small private firms. In other words, it is concluded that relationship and reputation also play important roles in formal financing in China although they are conventionally labelled as main characteristics of informal mechanisms. Acknowledgments This author is grateful to Prof. Russell Smyth, Prof Marika Vicziany and the anonymous referee for their comments on earlier versions of the paper. Financial support provided by Monash Postgraduate Publication Award is gratefully acknowledged.

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Appendix A. Survey questionnaire

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