Social capital and cost of debt in Vietnamese household businesses

Abstract

The paper provides the impact of social capital on the cost of debt of rural household businesses in Vietnam by using the panel data sample of 4,874 household businesses. Social capital is considered as the connections with people in the society including connections with people in the same business sector, people in the different business sector, bank officials and politicians. Besides, social capital is also measured as the assistance from government, state-owned commercial banks, state-owned enterprises, private commercial banks, friends & relatives and business association. The results show that the connections with politicians, the assistance from government, state-owned enterprises, private commercial banks, friends & relatives and business associations can help the household businesses in reducing the cost of using debts. The paper draws up a broader picture about the use of informal resources like social capital in household businesses. Accordingly, this highlights the importance of social capital in a network-oriented country like Vietnam.

Keywords:

• social capital
• social networks
• household
• household businesses

Subjects:

• H31
• J24
• L25
• L14
• O53

1. Introduction

Many scholars have investigated on the association between social capital and access to finance that stronger connection with social networks may partially help firms relax their financial constraints with lower cost of debt (Ahlstrom & Bruton, 2006; Le et al., 2006; Talavera et al., 2012). The social networks especially get more important in network-oriented countries like Vietnam (Pham & Talavera, 2018). However, most of the studies focus on the listed firms or big-sized enterprises, so that the research on household businesses/micro-enterprises is still the gap of the literature in this field.

The paper aims to fill out the gaps by figuring out the influence of social capital on the cost of debt of rural household businesses in Vietnam. We focus on Vietnamese rural household businesses for several reasons. First, although social networks have been an familiar topic in much recent development literature, until now they have not been widely studied on the development and socioeconomic change in rural of Vietnam. Second, according to the World Bank’s Enterprise Surveys for Vietnam (World Bank, 2015), financial constraint is one of the biggest business obstacles for firms in Vietnam. Therefore, with stronger social networks, firms are more likely to have advantage in access to financial resources especially for loans with lower cost. Third, Vietnam is a network-oriented economy (Pham & Talavera, 2018), where social networks plays an important role in doing business (Meyer & Nguyen, 2005). Fourth, social capital is getting more important in household businesses than its in large enterprises because household businesses are usually more dependent on their social relationship like relatives, immediate neighbors, friends, etc., for credit and support (Turner & Nguyen, 2005), or further to improve innovation and share knowledge with the aim of improving profits, productivity and their market share (Woolcock & Narayan, 2000).

The most important finding in our study is that social capital has a negative impact on the cost of debt. With the help of social networks, firms may get the financial supports, in which helps those businesses in reducing the cost of using debt.

The paper is organized as follows. Section 2 reviews the literature. Section 3 sets up the methodology. Section 4 presents the empirical results and section 5 summarizes the findings and some discussions.

2. Literature reviews

2.1. Social capital

Social capital is described by the presence of a certain set of informal values, norms, and networks fostering cooperation and facilitating collective action (Fukuyama, 1997; Woolcock, 2010). Social capital focuses on the resources embedded in one’s social network and how access to and use of such resources benefit the individual’s actions (Lin, 2001).

Social capital is the advantages and benefits resulting from the owner’s relationships, interactions and networks. According to Putnam (1993), social capital is produced by the extension of family-based or community-based relationships. This leads to the fact that participation in social networks benefits individuals (Aldrich & Zimmer, 1986; Johannisson, 1988). Based on Poon et al. (2012), social capital in entrepreneurs can be seen as micro level for human capital of family/household and macro levels for the support of social associations.

2.2. How social capital helps Vietnamese rural household businesses in reducing cost of debt?

Many small and medium enterprises report that financial constraint is one of the biggest obstacles for firms (World Bank, 2016) and they need to seek external capital such as formal loans or informal credit from different resources (Pham & Talavera, 2018). However, since the Vietnamese banking sector is heavily regulated, therefore, informal credit is also an option for firms. This is especially for rural household businesses since such enterprises are very small and its operation effectiveness is not so high as of bigger-sized enterprises, hence, accessing to formal financial resourses is getting more and more difficult. Besides formal credit, other informal credit sources widely used in Vietnamese rural household businesses include: (1) loans from family and friends; (2) loans from private lenders; and (3) trade credit from suppliers and customers (Pham & Talavera, 2018). Hence, with a larger and stronger social capital, rural household businesses tend to be easier in accessing to financial resources.

The support of social capital is more important in rural household businesses than bigger-sized enterprises because of the characteristic of rural household businesses. Rural household businesses are known, as household’s enterprises, so doing business in such firms is not very formal. Santarelli and Tran (2013) mention that informality of business environment of young entrepreneurs in Vietnam highlights the role of close interactions with family, relatives, and friends in stimulating entrepreneurial activities rather the formality of business environment.

Social capital may help not only in accessing informal financial resources but also for accessing formal loans with lower interest rate (Ha & Frommel, 2019). The role of social capital is known as different types of networks of firm: networks with government and financial institutions, business-related networks and networks with family members or relatives. Different types of networks can help firms in different ways (Pham & Talavera, 2018). For example, with the relationship with government officials, firms can be easier to access to the formal loans from banks or other state-owned financial institutions with even better loan terms (Le & Nguyen, 2009; Tenev et al., 2003;O’Connor, 2000). This can be explained by the considerable power and influence of government officials in project approval and resource allocation (Meyer & Nguyen, 2005). Moreover, membership of a business association or political party is one way of spreading knowledge about a firm’s existence, as well as being an indicator of reputation (Coleman, 1988). And this may also help firms increase the probability to access to credit. For instance, Talavera et al. (2012) have shown that in China, membership of Communist Party can help the onwer of firm in getting loans from state-owned banks or with membership of business associations, firms have advantages for commercial bank loan applications.

3. Methodology

3.1. Models and variable measurement

In order to figure out the influence of social capital on the cost of debt, the below model is built.

$\mathit{\text{Cost}} \mathit{\text{of}} \mathit{\text{deb}}{t}_{\mathit{\text{it}}}= {\beta }_0+ {\beta }_1\ast \mathit{\text{Social}} \mathit{\text{Capita}}{l}_{\mathit{\text{it}}}+ {\beta }_2\ast \mathit{\text{Control}} \mathit{\text{Variable}}{s}_{\mathit{\text{it}}}+ e_i$

Cost of debt is measured by total interest expense over total liabilities. Social capital is measured by different perspectives including same sector business people (SSP), different sector business people (DSP), bank officials (BOF), politicians (POL), government assistance (GOV), state-owned commercial bank assistance (SCB), private commercial bank assistance (PCB), state-owned enterprise assistance (SEA), friend, relative family assistance (FRA), business association assistance (BAA). Owner’s gender (OGE), firm age (FAG), owner’s education background (EDU) and firm size (FSI) are used as the control variables in this study. Table 1 shows the variable measurement in detail.

Table 1. Variable measurement

Variable Name		Measure
Cost of debt	COD	Total interest expense over total liability
Owner Gender	OGE	The gender of firm’s owner 0 for male, 1 for female.
Firm Age	FAG	The age of firm.
Education	EDU	1 if the owner holds college degree or above; 0 otherwise.
Firm Size	FSI	The logarism of total asset.
Leverage	LEV	Total liabilities over total owner equity
Same sector business people	SSP	The total number of same sector business people the firm has connected with
Different sector business people	DSP	The total number of different sector business people the firm has connected with
Bank officials	BOF	The total number of bank officials the firm has connected with
Politicians	POL	The total number of politicians the firm has connected with
Government assistance	GOV	1 if the household gets the assistance from the government; 0 otherwise
State-owned commercial bank assistance	SCB	1 if the household gets the assistance from State-owned commercial banks; 0 otherwise
State-owned enterprise assistance	SEA	1 if the household gets the assistance from state-owned enterprises; 0 otherwise
Private commercial bank assistance	PCB	1 if the household gets the assistance from private commercial bank; 0 otherwise
Friend, relative family assistance	FRA	1 if the household gets the assistance from friends, relative family; 0 otherwise
Business association assistance	BAA	1 if the household gets the assistance from business associations; 0 otherwise

3.2. Data

We use the data from survey carried out in collaboration between the Institute of Labour Studies and Social Affairs (ILSSA) in the Ministry of Labour, Invalids and Social Affairs (MOLISA) and Department of Economics, University of Copenhagen with funding from DAN-IDA in 2011, 2013 and 2015. The data comprises the information of 4,874 observations including 1617, 1593, 1664 observations in 2011, 2013, 2015, respectively. Those household businesses have registration in nine provinces including Hanoi, Ho Chi Minh City, Haiphong, Khanhhoa, Lamdong, Longan, Nghean, Phutho and Quangnam. The detail information of observations by provinces and years is shown in Table 2.

Table 2. Observations by provinces and years

Province	2011	2013	2015
Ha Noi	381	394	427
Hai Phong	106	84	107
HCMC	291	290	288
Khanh Hoa	62	55	58
Lam Dong	63	64	65
Long An	90	104	99
Nghe An	275	251	267
Phu Tho	219	226	223
Quang Nam	130	125	130
Total	1617	1593	1664

In the total of 4,874 observations, 65.7% of the owners of the household businesses are female, the oldest household business is 61 years old, only 6% of the owners hold college degree or above. In average, the household businesses have more than 27 social connections, 13 connections with same sector business people, 9 connections with different sector business people, 11 connections with bank officials and more than 2 connections with politicians. The detail descriptive statistics of the data are shown in the Table 3.

Table 3. Descriptive statistics

Variable Name	Obs	Mean	Min	Max
Cost of debt	4,874	0.019	0	0.198
Owner Gender	4,874	0.657	0	1
Firm Age	4,874	15.2	1	61
Education	4,874	0.06	0	1
Firm Size (million VND)	4,874	1,579.3	10	18,623
Same sector business people	4,874	13.13	3	63
Different sector business people	4,874	9.08	2	64
Bank officials	4,874	11.82	2	63
Politicians	4,874	2.45	0	20
Government assistance	4,874	0.36	0	1
State-owned commercial bank assistance	4,874	0.05	0	1
State-owned enterprise assistance	4,874	0.23	0	1
Private commercial bank assistance	4,874	0.03	0	1
Friend, relative family assistance	4,874	0.39	0	1
Business association assistance	4,874	0.40	0	1

4. Data analysis results

The table below shows the OLS regression results. The values of R-square from the OLS regression results range from 18.23% to 26.1%. The most interesting result in the table is that social network has a strong effect on the cost of debt of household businesses.

From the analysis results, TSN has a negative effect on cost of debt. It means that if the businesses have bigger connection with the society, they can enjoy some benefits in reducing the cost of debt. In order to figure out the impacts of different types of social network, the results reveal that SSP, DSP, POL have negatively impacts on the cost of debt of household businesses. These can be explained that the businesses can get the financial support from both people in same sector and in different sectors, which helps to reduce the cost of using liabilities. Besides, the connections with politicians may help the firm in accessing financial resources, which also benefit those household businesses with lower cost of debt.

It can be figured from the results that GOV, SEA, PCB, FRA, BAA have negative influences on the cost of debt of household businesses in Vietnam. These are consistent with the explanation that with the support and assistance from the government, state-owned enterprises, private commercial banks, friends & relative family and business associations, Vietnamese household businesses may enjoy the benefit of reducing the cost of debt. The results also show that BOF and SCB have no impact on the cost of debt of household business in Vietnam (Table 4).

Table 4. OLS regression results

Variable	Coef.	p-value	Coef.	p-value	Coef.	p-value	Coef.	p-value		Coef.	p-value	Coef.	p-value
OGE	0.0010	0.396	0.0008	0.511	0.0009	0.452	0.0009	0.439		0.0015	0.169	0.0009	0.429
FAG	−0.0003***	0.000	−0.0003***	0.000	−0.0003***	0.000	−0.0002***	0.000		−0.0003***	0.000	−0.0003***	0.000
FSI	0.0157***	0.000	0.0157***	0.000	0.0157***	0.000	0.0158***	0.000		0.0142***	0.000	0.0157***	0.000
EDU	0.0000	0.992	0.0000	0.990	0.0000	0.985	0.0001	0.978		0.0010	0.651	0.0000	0.988
LEV	0.0375***	0.000	0.0377***	0.000	0.0377***	0.000	0.0368***	0.000		0.0348***	0.000	0.0377***	0.000
SSP	−0.0002***	0.000
DSP			−0.0002***	0.002
BOF					0.0000	0.249
POL							−0.0023***	0.000
GOV										−0.0229***	0.000
SCB												−0.0008	0.761
Obs	4874		4874		4874		4874			4874		4874
F	30.08***		29.22***		28.82***		39.39***			56.71***		28.64***
Prob>F	0.000		0.000		0.000		0.000			0.000		0.000
R square	0.1857		0.1836		0.1824		0.1998			0.2451		0.1823
Variable	Coef.		p-value		Coef.	p-value	Coef.		p-value	Coef.		p-value
OGE	0.0010		0.378		0.0009	0.433	0.0012		0.269	0.0011		0.298
FAG	−0.0002***		0.001		−0.0003***	0.000	−0.0003		0.000	−0.0002		0.000
FSI	0.0154***		0.000		0.0156***	0.000	0.0142		0.000	0.0137		0.000
EDU	0.0001		0.969		0.0002	0.945	−0.0001		0.977	−0.0001		0.971
LEV	0.0355***		0.000		0.0377***	0.000	0.0345		0.000	0.0339		0.000
SEA	−0.0275***		0.000
PCB					−0.0079***	0.002
FRA							−0.0270***		0.000
BAA										−0.0262***		0.000
Obs	4874				4874				4874			4874
F	48.1***				28.96***				54.78***			60.08***
Prob>F	0.000				0.000				0.000			0.000
R square	0.2376				0.1833				0.2614			0.261

Notes: *** p < 0.01, ** p < 0.05, * p < 0.1

As the data contains a panel component, problems can occur with regard to cross-sectional characteristics as heteroscedasticity or time-series characteristics as autocorrelation and omitted variables. With those problems, fixed effects model and random effects model are the most usually estimations to address (Becchetti et al., 2015; Baird et al., 2012). We use Breusch-Pagan test to choose between OLS and random effects model. The test indicates that random effects model is more suitable. We continue to use Hausman test to find whether fixed or random effects model is chosen and the result shows that the fixed effects model is more suitable.

The Table 5 shows the regression result of fixed effects model. The results show that POL, GOV, SEA, PCB, FRA, BAA have negatively influence on the cost of using liabilities of household businesses in Vietnam. These re-confirm that with the connections with politicians, the support of government, state-owned enterprises, friend & relative family and business associations, Vietnamese household businesses may enjoy the advantage of using financial resources with lower cost.

Table 5. Fixed effects model regression results

Variable	Coef.	p-value	Coef.	p-value	Coef.	p-value	Coef.	p-value	Coef.		p-value	Coef.	p-value
OGE	−0.0016	0.488	−0.0015	0.509	−0.0017	0.470	−0.0022	0.336	−0.0013		0.572	−0.0017	0.468
FAG	−0.0009***	0.000	−0.0009***	0.000	−0.0008***	0.000	−0.0007***	0.000	−0.0010***		0.000	−0.0009***	0.000
FSI	0.0074***	0.001	0.0075***	0.001	0.0073***	0.001	0.0071***	0.002	0.0075***		0.001	0.0075***	0.001
EDU	0.0025	0.538	0.0023	0.566	0.0027	0.505	0.0022	0.586	0.0023		0.558	0.0026	0.524
LEV	0.0199***	0.000	0.0200***	0.000	0.0199***	0.000	0.0198***	0.000	0.0190***		0.000	0.0199***	0.000
SSP	0.0000	0.753
DSP			−0.0001	0.128
BOF					−0.0001	0.131
POL							−0.0017***	0.000
GOV									−0.0198***		0.000
SCB												−0.0039	0.240
Obs	4874		4874		4874		4874		4874			4874
F	17.33***		17.71***		17.7***		24.5***		48.8***			17.55***
Prob>F	0.000		0.000		0.000		0.000		0.000			0.000
Variable	Coef.		p-value		Coef.	p-value	Coef.	p-value		Coef.			p-value
OGE	−0.0004		0.848		−0.0017	0.467	−0.0009	0.677		−0.0011			0.628
FAG	−0.0014***		0.000		−0.0009***	0.000	−0.0013***	0.000		−0.0012***			0.000
FSI	0.0082***		0.000		0.0074***	0.001	0.0083***	0.000		0.0063***			0.004
EDU	0.0012		0.762		0.0028	0.491	0.0028	0.474		0.0021			0.590
LEV	0.0192***		0.000		0.0200***	0.000	0.0185***	0.000		0.0169***			0.000
SEA	−0.0167***		0.000
PCB					−0.0095**	0.023
FRA							−0.0190***	0.000
BAA										−0.0203***			0.000
Obs	4874				4874		4874			4874
F	34.13***				18.21***		50.37***			54.09***
Prob>F	0.000				0.000		0.000			0.000

5. Conclusion

The results of the paper have pointed out that social capital has a negative impact on the cost of debt of Vietnamese household business. With the support of social network, the household businesses may get some advantages, which help those businesses in reducing the cost of debt. In details, people in the same & different sectors, bank officials and politicians can give some financial support to the firms; besides, the support of government, state-owned enterprises, private commercial banks, friends & relative family and business associations may help the household businesses in accessing into different financial resources with lower cost.

The results of the paper are also consistent with the characteristics of household businesses. Different with the bigger-sized enterprises, the way of doing business of the households are much more flexible. The managers of the businesses may rely more on their own connections, unpaid labor as family members or friends, etc. Besides, Vietnam is known as a network-oriented country, the role of social network seems to be stronger.

The results of this study can be the reference for Vietnamese Government in their business environment improvement process when it gives a broader view of the effect of social capital on the cost of debt of household businesses in Vietnam. Generally, this study brings a broad view and provide policymakers with information and references on the role of social capital. We do believe that this is not only the case of Vietnam, but also it can be the lesson of other developing countries (are also known as emerging markets).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is funded by Funds for Science and Technology Development of the University of Danang under project number [B2020-DN04-42].