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Access to Finance and Innovation in Small and Medium Enterprises

Danh Vinh Le

Ton Duc Thang University

[email protected]

Huong (Anne) Le

Northeastern Illinois University

[email protected]

Thanh Tien Pham

Ton Duc Thang University

[email protected]

Lai Van Vo*

Western Connecticut State University

[email protected]

February 22, 2019

Abstract

This paper examines the relation between a firm’s capacity to access external capital and its

innovation among SMEs in Vietnam. We find that SMEs with high debt ratio tend to be more

innovative. After controlling for several firm characteristics, long term debt remains significantly

positively correlated with innovation. We further show that both bank loans and loans from family

and friends help these firms innovate, especially in developing new products and/or technology.

Overall, our paper suggests that external financing plays an important role in enhancing innovation

in small and medium enterprises.

JEL classification: G31, G32, O32

Key words: access to finance, external capital, financing, innovation, SMEs

__________________________

*Corresponding author

mailto:[email protected]




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1. Introduction

While literature on finance and innovation for large public firms in developed countries such

as the United State is widely documented (Brown et al. 2009 and Hall et al. 2009), access to finance

for innovation in small and medium enterprises (SMEs) is still unexplored (Ayyagari, Demirgüç-

Kunt, and Maksimovic 2011). Research on this topic for firms in developing countries is even more

scare, even though in these countries, SMEs account for over 90% of all companies outside the

agricultural sector and generate a major source of employment, remarkable domestic and export

earnings, and make a considerable contribution to the overall added value (Ayyagari et al. 2011, and

Love and Roper 2015).

One reason for the limited literature on financing innovative activities of small firms in

emerging markets is the lack of data. One of the most popular data source on SME innovation is the

World Bank Enterprise Surveys that cover over 19,000 firms in 47 developing countries. Using this

database, Ayyagari et al.’s (2011) preliminary results suggest that external financing is positively

correlated to firm innovation. However, as mentioned in their paper, the limited data does not allow

them to address many fundamental issues to confirm the positive relation, such as the causality

between financing and innovation, which types of external financing are important, and how formal

and informal financial applications affect SMEs’ innovation. These issues are also common in other

studies on SMEs (Love and Roper 2015).

In this paper we use a large, newly published, data set of SMEs in Vietnam. The data were

obtained through surveys conducted under the collaboration and administration of the Development

Economics Research Group (DERG) at the University of Copenhagen (UCPH) - Denmark, the

United Nations University's World Institute for Development Economics Research (UNU-WIDER),

http://www.econ.ku.dk/derg/


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the Vietnamese Central Institute for Economic Management (CIEM), and the Institute of Labor

Science and Social Affairs (ILSSA). The data cover around 2,500 non-state private manufacturing

SMEs in 9 cities and provinces of Vietnam. The dataset contains information that had previously

rarely been available, such as formal and/or informal loan applications, the need of external loans,

the proportion of investments financed by family and friends, and the number of bank officials with

whom SMEs usually contact. More importantly, these data allow us to address the above mentioned

issues that previous studies on SMEs were unable to.

In addition to the richer data, study on Vietnamese SMEs is important because even though

these SMEs are very small in their sizes, they account for about 98% of all enterprises, 40% of GDP,

and 50% of employment in Vietnam in 2016.1 The average total assets of these firms in our sample

is 5.667 billion VND (about 272,000 USD) and the total revenue is 7.057 billion VND (about

337,000 USD). Firms of this size are, in general, excluded in the previous studies on financing and

innovation. Further, similar to SMEs over the world, these firms face many obstacles to growth such

as access to finance, access to product markets, technology update, and competition from foreign

firms.

In this paper, we investigate the impact of firms’ capacity to access external finance on their

innovation by answering the following questions: whether external financing helps innovation

overall, what types of financing resources are used to fund innovation in Vietnamese SMEs, which

external financing sources are more important for firm innovation, whether the effect of external

financing is the same for all types of innovation, whether firms need external capital to innovate,

1 https://www.vietnam-briefing.com/news/facilitating-sme-growth-vietnam.html/

https://www.vietnam-briefing.com/news/facilitating-sme-growth-vietnam.html/

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whether the informal financing source plays any role in enhancing innovation, and whether

investment financing components affect firm innovation.

Following Ayyagari et al. (2011) and Lee et al. (2015), we define innovation as one of the

three core innovative activities: (1) introduction of new products, (2) improvement of existing

products, and (3) introduction of new technology. These innovations include both original

innovation as well as new-to-firm innovation which are commonly used in developing countries.

We first examine the overall impact of external financing resources on innovation. We show

that debt is significantly positively correlated with firm innovation. To further examine which source

of debt is more important for innovation, we look at the impact of long term debt and short term debt

on innovation separately. After controlling for firm characteristics, our results show that long term

debt remains to have a strong impact on innovation while short term debt does not. This is reasonable

because innovation is a risky long term investment, which is less likely to be financed by short term

debt. Furthermore, different from large public firms, SMEs are less likely to use market-based

securities instruments to raise capital. This evidence demonstrates that small firms are different from

large public firms that usually finance their innovation by cash flow or equity issuance (Brown et

al. 2009 and Hall and Lerner 2009).

To further examine the reletion between long term debt and firm innovation, we run

regressions of each of the three types of innovation on long term debt and other firm characteristics.

The results show that long term debt is significantly related to all three types of innovation.

Next, we test whether firms reveal their need for external finance to support their innovation.

To measure the need for external financing we use loan need, formal loan applications, or informal

loan applications. While loan need captures a firm’s current need of external finance to fulfill

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innovation investment, formal and informal loan application measure both the need of external

finance to start and accomplish this investment. Our result shows that firms need external capital to

both start and fulfill innovation projects.

Finally, we look at the impact of the different components of external financing resources on

firm innovation. Different from large public firms in developed countries where the capital markets

are developed, financing resources for SME in Vietnam consist of bank loans, loans from friends

and family, and others. Among these sources, loans from friends and family play a significant role

in financing SMEs (Beck and Demirguc-Kunt 2006). Therefore, adding financial resources from

family/friends and others into consideration shows us a more dynamic picture of the relation between

access to finance and firm innovation.

As expected, bank loans are an important source for SMEs to be innovative. These loans help

firms not only improve their existing products but also invest in new technology. We also document

that loans from family and friends enhance firm innovation, especially in introducing new

technology. This finding is consistent with findings by Beck and Demirguc-Kunt (2006), who show

the important role of loans from family and friends in firm growth.

An important empirical issue for this research topic is the endogeneity issue that there may

exist unobservable factors that have impacts on both innovation and external financing. Due to data

limitation, this issue is largely not addressed in previous studies on SMEs. In this paper, we robustly

test our results using a 2SLS with instrument variables approach. We choose a firm’s social network

with bank officials as an external instrumental variable for external financing. This is because social

network with bank officials can help firms secure external financing resources easier but is unlikely

to directly affect firm innovation. We measure this network by the number of bank officers a firm

regularly contacts with. Our results are robust under this 2SLS setting.

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Our paper is related to Ayyagari et al. (2011), who show a preliminary positive relation

between proportion of external loans to investment and firm innovation. We extend their study by

investigating the dynamic picture of access to finance and firm innovation. Our paper is also related

to the literature on SMEs financing (Ayyagari et al. 2007, and Beck et al. 2006), and the literature

on SMEs innovation (Segerstrom, 1991 and Lee et al. 2015). However, while these studies focus on

the obstacles faced by SMEs in obtaining external financing, we examine the impact of access to

finance on firm innovation.

To the best of our knowledge, this is the first empirical paper investigating the impact of

several dimensions of access to finance on innovation in SMEs. Moreover, this is also the first paper

examining such impact in Vietnam, a developing country with very high speed growth. Overall, our

paper demonstrates the important role of access to external finance in firm innovation.

The remainder of this article is structured as follows. Section 2 reviews the related literature.

Section 3 describes sample selection, discusses variables measurement, and provides sample

descriptive statistics. Section 4 presents the empirical results and discussions. Section 5 provides

robustness tests and section 6 concludes.

2. Literature Review

2.1. Innovation and Innovation Measurement in SMEs

Research on innovation usually employs patenting and/or research and development (R&D)

activities to measure firm innovation (Brown et al. 2009, and Pham et al. 2018). Even though these

measures reflect innovation in large companies, especially in developed countries such as the United

States, they are not useful to capture innovation in SMEs. First, the number of SMEs engaging in

patenting activities is very small. For example, in the United Kingdom, only 4 percent of innovation-

active firms actually engage in patenting (Hall et al. 2013). Second, patenting and R&D are highly

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sector-biased, which is not suitable to measure innovation outside of the high-tech industries where

SMEs account for a large proportion.

Innovation in SMEs is normally considered in a broader sense. While fundamental inventions

normally take place in large firms or research institutions, SMEs play the role in near-to-market

development and initial market diffusion. The Organization for Economic Co-Operation and

Development (OECD) documents such innovation as “new-to-firm innovations” which include

improvement or new introduction of a product or process/technology, a new marketing method, or

a new organizational method (Mortensen and Bloch 2005, and OECD 2018). These concepts reveal

an ‘open innovation’ paradigm that has reduced the cost of innovation investments, making

innovation more accessible to SMEs (OECD 2018). Similarly, the US Advisory Committee on

Measuring Innovation (2008) also defines innovation as “the design, invention, development and/or

implementation” of new products, processes, systems, services, organizational structures or business

models for the purpose of creating new value for firms and customers.2

Literature on SMEs measures innovation based on these open concepts and classifications.

For example, Love and Roper (2015) employ the innovation concepts provided by the US Advisory

Committee on Measuring Innovation. Ahn et al. (2015) classify ‘open innovation’ into three groups:

technology-related, market-related and organization-related innovations and examine their effects

on firm performance. In contrast, Lee et al. (2015) measure innovation by whether SMEs have

introduced a new product in the previous 12 months. Ayyagari et al. (2011) use new product/process

2 Advisory Committee on Measuring Innovation in the 21st Century Economy (2008) ‘Innovation Measurement -

Tracking the state of innovation in the US economy’, A report to the Secretary of Commerce.

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related activities or opening of a new plant or new joint venture with foreign partner to capture

innovation.

Innovation is generally spilled over from the developed countries to the developing ones. As

discussed in Ayyagari et al. (2011), firms in general and SMEs in particular in developing economies

are far from the technology frontier and have different approaches to innovation. Therefore, in

addition to original and fundamental inventions, SMEs in developing countries also adopt such

innovations as new products, improved products, new production process or technology and new

organization structure that have been widely applied in the developed economies. While both

discovering new products and copying products of other firms can foster economic growth and

technological progress, imitation is more common in less-developed economies (Segerstrom 1991).

Innovation in SMEs is at the core of growth strategies: more innovative firms tend to have

high productivity. Ahn et al. (2015) show that SMEs tend to perform better when they are engaged

in innovation. Further, they document that innovative SMEs benefit from working with non-

competing partners such as customers, consultancy and public research institutes. Love and Roper

(2015) show the positive relation between innovation in SMEs and exporting and growth. In

addition, innovative SMEs can pay better wages and offer better working conditions, helping reduce

inequalities in the economy. Using the World Business Environment Survey (WBES) in 1999 and

2000, Beck et al. (2005) show that these firms constitutes over 60% of total employment in

manufacturing industries in many countries such as the U.S., Japan, Denmark, and Brazil. Moreover,

they are considered the engine of growth and industrialization process in many developing countries

(Beck and Demirguc-Kunt 2006).

2.2. Access to Finance and Innovation in SMEs

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Although SMEs are important to economic development, they face more obstacles to grow

than large firms do. Using firm-level survey data on the business environment across 80 countries,

Ayyagari et al. (2007) examine several factors that potentially affect firm growth and show that

financing resource, political instability and crime are three main impediments to firm growth.

Among these factors, access to finance is the obstacle faced most commonly by SMEs (Freel 2000,

and Lee et al. 2015). Analyzing the same data, Beck et al. (2006) point out that 39% of small firms

consider financing as a major obstacle while this figure for large firms is 32%. Similarly, they also

find that, in general, small firms have less access to finance than large firms in both developed and

developing countries.

Different from large public firms, SMEs are less likely to issue stock or debt in financial

markets to finance their innovative activities. First, SMEs tend to have more asymmetric

information, making them difficult to obtain public capital (Berger and Udell 1995). Second, because

almost all of these firms are private, they could not issue stocks unless they become public

companies. Therefore, they are highly dependent on loans from financial institutions. Cull et al.

(2006) analyze the financial channels available to SMEs in the core North Atlantic economies during

the 19th and early 20th centuries and demonstrate that banks are important for firm growth.

In addition to size, innovative SMEs face more obstacles to obtain external capital because

innovation itself is a risky activity and its outcomes are uncertain (Pham et al. 2018). Moreover,

Guariglia and Liu (2014) state that innovation activities are characterized by high adjustment costs

due to spending on research personnel (i.e. skilled workers, scientist, engineers and specialists). In

addition, innovation projects have large proportion of intangible assets, which is less likely used as

collateral to borrow external capital. This makes it difficult for SMEs to raise capital to finance their

innovation projects.

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Given SME’s less capacity to obtain capital, access to finance plays an important role in

enhancing their innovation. Using data derived from firms in the UK, Canepa and Stoneman (2007)

find that financial factors have significant effect on innovation activities and the effect is more

significant for smaller firms and firms in the high tech sectors. Similarly, Oakey (1997) argues that

all concerns related to innovation are directly or indirectly affected by the lack of capital. Hottenrott

and Peters (2012) demonstrate that financial constraints due to imperfections in capital market

reduce investments in innovative activities to below the desired level. Using large panel data from

Chinese firms, Girma et al. (2008) also document that financial constraints prevent firms from

innovation capability, and access to finance is positively and significantly associated with innovation

activities. More specifically, privately and collectively owned firms with domestic bank loans tend

to be more innovative than other firms, including state-owned firms.

Access to finance for SMEs depends on the infrastructure that supports financial transactions,

such as the development of financial markets and institutions, legal systems, and information

environment. In countries with high development of financial environments and more adaptable

legal systems, firms tend to face fewer obstacles to obtain external capital. In contrast, firms in

developing countries where the legal systems are less developed and information environment is

more asymmetric tend to pay higher costs to raise capital. In such countries, finance from informal

sources such as friends and family plays a significant role (Beck and Demirguc-Kunt 2006).

Even though there have been some studies on the relationship between financial constraints

and innovation, overall the literature on this issue is limited, especially in the developing countries.

This paper aims to fill this gap by examining the effect of access to finance on the adoption of

innovation in SMEs in Vietnam – a developing and transition economy with high speed growth.

3. Data, Variables Measurement, and Descriptive Statistics

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3.1. Data

We use data from the surveys on SMEs in Vietnam conducted by the Development

Economics Research Group (DERG) at the University of Copenhagen (UCPH), the United Nations

University's World Institute for Development Economics Research (UNU-WIDER), Central

Institute for Economic Management (CIEM), and Institute of Labor Science and Social Affairs

(ILSSA). The surveys collected data from approximately 2,500 non-state manufacturing SMEs in 9

cities and provinces of Vietnam.3 The SMEs can be registered (formal) or non-registered (informal)

household firms. The inclusion of firms without a business registration license or tax code and

registration with the authorities is an important contribution and a unique trait in Vietnam.4

This survey is the only data set available in Vietnam that contains innovation and financing

information for SMEs. Its questionnaire includes, beside other items, various types of innovation

activities and financing sources. This survey distinguishes between whether the SMEs introduced

new products, improved existing products, or introduced new production process/ technology; and

which sources were used to finance investment activities. This set of information is used to construct

the variables of interest in our paper.5

We use data from the surveys in 2011 and 2013.6 We delete any firm-year observations with

missing values for the variables used in our analyses. To reduce the effects of outliers, we winsorize

3 They are Hai Phong, Hanoi (including Ha Tay), Phu Tho, Nghe An, Quang Nam, Lam Dong, Khanh Hoa, Ho Chi

Minh City, and Long An. 4 The SMEs in this survey are defined based on the World Bank classification; that is, micro-enterprises have up to 10

employees, small-sized enterprises up to 50 employees, medium-sized enterprises up to 300 employees, and large

enterprises more than 300 employees. 5 The surveys’ method, questionnaires, and data are available at https://www.wider.unu.edu/database/viet-nam-sme-

database 6 The surveys were conducted since 2005 with data available for download for surveys in 2011, 2013, and 2015.

However, the 2015 survey does not contain some important questions related to access to finance such as formal and

informal short-term and long-term debts. Thus, our paper only uses the data from the 2011 and 2013 surveys.



https://www.wider.unu.edu/database/viet-nam-sme-database


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all variables used in our paper at 1% and 99% percentiles. Our final sample has 4,968 observations

from 2,484 firms.

3.2. Variables Measurement

3.2.1. Measures of innovation

We follow previous studies on innovation in SMEs (e.g. Ayyagari et al. 2011, and Lee et al. 2015)

as well as a widely-used manual by OECD to define innovation as one of the three activities: (1)

introduction of new product groups, (2) improvement of existing products (or change specification),

and (3) introduction of new technology (or product process). Based on these categories, we create

three corresponding proxies for innovation. If firms introduced new product groups, INNO1 is

defined as 1, otherwise INNO1 is 0. Similarly, INNO2 and INNO3 are assigned to be 1 if firms make

any improvements of existing products and introduce new technology, respectively, and 0 otherwise.

Finally, if firms involve in any one of the three activities, we assign the value of 1 to the general

innovation (INNO) dummy variable. To quantify the level of innovation, in our robustness test, we

use aggregate innovation (AINNO) as the sum of three types of innovation.

3.2.2. Measures of capacity to access external financing resources

Literature on financing and innovation in large public firms in developed countries usually

examine the effect of debt-equity structure on firm innovation (Atanassov et al. 2007, and Hall and

Lerner, 2009), while studies on this topics in SMEs usually use the proportion of investment financed

by debt to proxy for firm’s capacity to access external financing resources (Ayyagari et al. 2011 and

Lee et al. 2015). In this paper, we employ several measures to proxy for SMEs’ capacity to access

external financing. There measures include debt structure, fund need, loan applications, and

investment financing components. Specifically, we first use debt structure measured by long term

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debt ratio (LDEBT), short term debt ratio (SDEBT), or total debt ratio (TDEBT) to proxy for firms’

capacity to raise external capital. These ratios are respectively computed as the proportion of long

term debt, short term debt and total debt to total assets.

To measure the need of external financing, we use loan need, formal loan applications and

informal loan applications. Loan need (LOANNEED), formal loan applications (FLOANAP) and

informal loan applications (ILOANAP) are dummy variables which are equal to 1 if firms still need

additional loans or firms have submitted loan applications formally and informally, respectively, and

0 otherwise. We use all three variables to capture the need of external financing because while loan

need reflects the current need of loans by SMEs, it may not be perfectly correlated with the debt

ratios. In addition, loan applications can be used to capture loan need in both the past 2 years or

present.

Finally, we follow Ayyagari et al. (2011) and Lee et al. (2015) to use the proportion of

investments financed by bank/credit institutions (BANKF), family/relatives (FAMILYF) and other

sources (OTHERF) to proxy for firms’ access to finance. These investment financing variables are

measured by their percentage of total investment.

We include in the Appendix the list of all variables, including the other control variables,

along with the description on their measurement.

3.3. Descriptive Statistics

3.3.1. Descriptive statistics for the whole sample

Table 1 provides the descriptive statistics for all variables used in our paper. From the Panel

A, 32.1% of firms in our sample engaged in at least one type of innovation (INNO). However, most

of the innovation activities belong to the second category (INNO2). Specifically, 27.5% of firms

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reported that they made some improvements to the existing products. On the other hand, only 2.4%

of SMEs introduced new products within two years of the time of surveys (INNO1) and 9.8% of

firms introduced new technology (INNO3). These results show that innovation in Vietnamese SMEs

is mainly “new-to-firm” innovations rather than original ones. This evidence is consistent with

previous findings in other countries (Ayyagari et al. 2007).

***** insert table 1 here*****

Panel A of Table 1 also shows that firms on average have a debt ratio of 7.7%. Of the debt

components, long term debt is less than short term debt. For firms in our sample, long term debt ratio

has an average of 2.2% while short term debt ratio 5.3%. The debt ratios suggest that SMEs in

Vietnam borrow significantly less debt than the large public firms in Vietnam do (Vo et al., 2019).

This result is consistent with previous studies on SMEs (e.g. Beck et al. 2006). Regarding loan need,

27.8% of firms in our sample have submitted their formal loan applications, while 64.1% have

submitted their informal loan applications over the previous two years. At the same time, 15.4% of

firms indicated that they still need additional loans for their growth. Therefore, while the actual debt

ratios are very low, there is a large proportion of firms in need of external financing, signifying the

obstacles to obtain external financing for SMEs.

The table also shows that on average, about 20% of investment capital is financed by banks

and other credit institutions. This figure for borrowing from family and friends is 5.5% and for

borrowing from other resources is 4.6%. These results demonstrate that bank loans are still the most

important external financing source for small firms’ investments.

Regarding firm characteristics, the average firm in our sample has sales of 7.057 billion VND

(around 337,000 USD) and total assets of 5.667 billion VND (272,000 USD.) With this size, firms

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in our sample are very small. These firms are, however, not young with the average age of 15.5

years. They hold 9.8% cash to total assets and have a tangible assets ratio of 76.8% on average.

Panel B of table 1 presents the correlation between the innovation variables and financing

variables. The Panel shows that innovation measures are positively correlated with total debt, long

term debt and short term debt ratios, all statistically significant at 1%. These results are consistent

with the previous findings that SMEs is more likely to finance innovation by debt (e.g. Ayyagari et

al. 2011) but opposite to the studies for large public firms in developed countries that firms are

reluctant to finance their innovation by debt (Hall and Lerner, 2009). Panel B also presents that firms

with innovation tend to need more external loans. The correlations between innovation and loan

need, formal loan applications and informal loan applications are significantly positive. These

positive correlation coefficients between external financing and innovation provide the initial

evidence that for SMEs in Vietnam, high capacity to access external financing is associated with

more innovation.

3.3.2. Innovation Statistics by Industry

Firm innovation depends on the industries in which they operate. Therefore, to further

understand firm innovation in our sample, we calculate the innovation for firm in each industry. The

results are reported in table 2.


Table 2 shows that firms in all industries are engaged in three types of innovation. Among

these types of innovation, firms in our sample mainly focus on the second type, which is

improvements in existing products. This type of innovation is also the most popular innovation in

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SMEs over the world (Ayyagari et al. 2011). Interestingly, except agriculture, other industries have

witnessed new technology adopted by SMEs.

In general, the industry with the most innovation is leather. Around 47.50% of firms in this

section introduce at least one type of innovation. However, almost all innovation in the leather

industry is improvement in existing products. Only 9.99% of firms introduce new technology and

2% of them introduce new product groups over last 2 years. The next some industries with high

proportion of firms involving innovation are jewelry, music equipment, and medical equipment,

paper and electricity and computer. In contrast, firms in agriculture, food and beverage, and refined

petroleum industries are less likely to adopt innovation. This finding is reasonable because not much

innovation occurs in these industries over last several decades.

4. Empirical Results

4.1. Base-line Regression Specification

As discussed in Section 3.2, the dependent variables (innovations) used for empirical analysis

in this paper are dichotomous. Specifically, these variables are equal to 1 if a firm innovates and 0

otherwise. Therefore, we employ a maximum likelihood estimation (MLE) using probit model to

investigate the impact of access to finance on firm innovation. Our base-line regression model is as

follows:

P(INNOVATIONi =1|X) =Φ(α +β1FINi + β2FIRM-CHARi + β3IND-DUM +εi) (1)

where INNOVATION is either one of the innovation variables (INNO, INNO1, INNO2, or INNO3),

Φ is cumulative distribution function, FIN is a financing variable which is realized external financing

(total debt ratio, long term debt ratio, or short term debt ratio) or needed external financing (loan

need, formal loan applications, informal loan applications, or investment financing loan

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proportions), FIRM-CHAR is a vector of control variables, and IND-DUM is industry dummy

variables.

As documented in previous studies (Atanassov et al. 2007), firm size significantly affect

firm innovation. Therefore, we control for firm size measured by total revenues. We also control for

cash holding and profitability of the firms because these variables are significantly correlated with

innovation (Hall and Lerner, 2009). Further, we include the tangible assets and proportion of

investment financed by firm’s owned capital because these factors might affect firms’ investment

policy. We use age to control for the effect of the firm’s life cycle on the relation between external

financing and innovation. We also control for industry fixed effects because the propensity to

innovate might be different for different industries.

Our coefficient of interest is β1. If external financing helps innovation, the coefficient β1 will

be positive.

4.2. Financing Sources and Firm Innovation

To examine the relation between actual external financing and firm innovation we run

regression model (1) using the debt ratios as the financing variables. The results of the regressions

are reported in table 3.


Table 3 shows that total debt ratio is positively related to firm innovation. However, the

coefficient is not statistically significant. When we break total debt into long-term debt and short-

term debt, the long-term debt ratio is significantly positively related to firm innovation. The

coefficient of long term debt is 0.676 (p-value 0.008), associating with a marginal effects of 0.224

(keeping all other variables at their means.) This effect of long term debt on innovation is statistically

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significant and economically meaningful. In contrast, columns 3 and 4 show the insignificant

relation between short-term debt and firm innovation. These results imply that these two types of

debt play different roles in explaining firm innovation. While firms can be charged higher interest

rate for long-term debt, they may not face the roll-over risk as they do when borrowing short-term

debt. In addition, innovation is a long-term investment whose maturity matches with long-term debt.

Table 3 also shows that firm size, cash holding, and capital expenditure are positively related

to firm innovation. Moreover, firms using large proportion of their owned capital to finance their

investments tend to be more innovative. This finding is consistent with the previous finding that

firms usually finance their innovation by their owned capital (Beck et al. 2006, and Hall and Lerner,

2009).

To further examine the role of long-term debt in innovation investments, we run regression

model (1) of three other innovation measures on long term debt and other control variables. The

results are reported in table 4.


Consistent with the results in table 3, table 4 shows that long-term debt ratio is significantly

positively related to all types of innovation, confirming that firms usually use long term debt to

finance their innovation investments.

4.3. Loan Need and Firm Innovation

In the previous section we examine the effect of debt financing on innovation. In this section

we study the impact of loan needs on innovation. While the amount of debt can be used to capture

firm’s capacity to successfully obtain external capital, loan needs measure the need of external

capital that may or may not be realized. Table 5 presents the results.

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Table 5 shows that the coefficient on LOANNEED is positive and statistically significant at

1% level, suggesting that the need of loan is significantly positively related to firm innovation.

Similarly, both formal and informal loan applications are positively correlated with firm innovation.

However, in terms of magnitude, the coefficient of formal loan applications is larger than that of

informal loan applications. These results imply that formal loans are more important determinants

of firm innovation than informal loans.

For the three types of innovation analyzed separately, the coefficients on formal loan

applications are positive and statistically significant in the regression of improvement in existing

products (INNO2) and of new technology (INNO3). The coefficients on informal loan application

are also positive but are statistically significant in the regression of introduction of new products

(INNO1) and of new technology (INNO3). The coefficient of formal loan application is much higher

than that of informal loan application as shown in column 6. This implies that formal loans are

important for firms to improve their existing products or introduce new technology while informal

loans are an important determinant of introduction of new products (INNO1).

4.4. Investment Financing and Firm Innovation

In this section, we focus on the amount firms borrowed to directly finance their investments.

Specifically, we use the proportion of each type of capital used to finance firms’ investments:

proportion of investments financed by banks or credit institutions, by family and friends, and by

other sources. Similar to debt ratios, these proportions measure the amount of capital firms already

obtained. However, while debt ratios directly show firms’ borrowing capacity, these proportions

mainly focus on the components of debt used by firms to directly finance their investments. Some

previous studies employ this approach to measure firm’s capacity to raise capital. For example,

20

Ayyagari et al. (2011) combine all types of external finance contributed to investment to measure

this capacity. Different from them, we investigate the impact of each types of capital used to finance

firms’ investments on firm innovation to explore more detailed information about this impact. We

then use the model (1) to run regression of firm innovation on each proportion and other firm

characteristics. The results are reported in table 6.


On average, the proportions of investment financed by banks and credit institutions and by

family and friends are significantly positively correlated with firm innovation while the proportion

of investments financed by other sources is not significantly related to firm innovation. This

evidence suggests that loans from banks or credit institutions and from family and friends are very

important for firms to invest in innovation.

As shown in table 1, the average proportion of investments financed by banks and credit

institutions are much higher than that ratio by family and friends. However, the results in table 6

show that the coefficients of proportion of investments financed by family and friends on INNO1

(introduction of new products) an INNO3 (introduction of new technology) are higher than that

proportion financed by banks and credit institutions. We further investigate the impact of these

investment-financing proportions on each type of innovation by computing their marginal effects.

The results (not reported) show that the marginal effects of funds from family and friends are the

highest on both INNO1 and INNO3, implying that SMEs typically use funds from family and friends

to invest in riskier projects such as innovation. This finding is reasonable because innovation

investments are riskier and have less embedded collaterals.

21

Consistent with the risk and collateral hypothesis, table 6 shows that the positive relation

between bank loans and firm innovation mainly comes from the relation between these loans and

firms’ improvement in existing products. The proportion of investments financed by banks and credit

institutions is negatively related to firms’ introduction of new products and technology, although

this relation is not significant. This evidence suggests that banks are still reluctant to finance “core”

innovation projects.

In contrast, the proportion of investments financed by family and friend is positively related

to all types of innovation, meaning that loans from family and friend are very important for SMEs

to invest in all types of innovation projects.

5. Robustness Test

5.1. Addressing Endogeneity Issue

In model (1) we control for several firm characteristics when we examine the relation

between firms’ external financing and innovation. However, there may exist some unobservable

factors which may affect both firms’ debt capacity and innovation. To deal with this endogeneity

problem, we employ a 2SLS regression method with instrument. We use a firm’s network with bank

officials as an external instrumental variable for external financing. We measure this network by the

logarithm of one plus the number of bank officials a firm regularly contacts with. This variable is

also available through the survey data.

We choose social network as an instrument for external financing because it should be highly

related to firm’s debt capacity and it does not have a direct impact on firm innovation. To formally

test for the validity of the instrument, we run regressions of debt ratios on social network and other

22

firm characteristics. The results in the panel A of table 7 show that this network is positively related

to firm’s debt capacity and statistically significant at 1% level. We then use the Cragg-Donald Wald

F-statistic and Stock and Yogo test to test for week instruments. The results (not reported) show that

the null hypothesis of week instrument is rejected, meaning that social network is a valid instrument

for firms’ debt capacity.


The results from the second stage of 2SLS regression method are reported in panel B of table

7. Consistent with the results in table 3 and 4, long-term debt ratio is significantly positively

correlated with firm innovation. Moreover, both total debt and short-term debt ratios are positively

related to firm innovation, although weaker. These results support the hypothesis that external

financing resources are important determinants of firm innovation.7

5.2. Alternative Measure of Innovation

In the previous sections, we define innovation as dummy variables and use probit model (1)

to estimate the impact of access to finance on firm innovation. In this section, we robustly test the

relation between capacity to access external finance and firm innovation by employing a different

measure of innovation and a different regression model. Specifically, we follow Ayyagari et al.

(2011) to define aggregate innovation as sum of the three dummy innovation variables. We then use

the logarithm of this variable plus one as a measure of aggregate innovation. Because this aggregate

innovation is not dichotomous, we employ OLS regression method to examine the impact of access

to finance on innovation. Our regression model is as follows:

7 We also robustly test the results in Sections 4.3 and 4.4 using the instrumental variable approach and get qualitatively

the same results. These regressions with IV are available from the authors upon request.

23

AINNOi = α + β1FINi + β2FIRM-CHARi + β3IND-DUMj + ԑi (2)

where AINNO is the logarithm of one plus aggregate innovation, FIN is one of the debt ratios which

is total debt ratio, long-term debt ratio or short-term debt ratio, FIRM-CHAR is a set of firm

characteristics, IND-DUM is industry dummy variables, and i denotes firm i. We report the

regression results in Table 8.


Consistent with the findings in the table 3, table 8 shows that both total debt and long term

debt are positively correlated with firm’s aggregate innovation. This result confirms our finding

reported in Section 4 that firms with high debt tend to be more innovative.

6. Conclusion

This paper investigates the relation between firms’ capacity to access external capital and

firm innovation. Using the data of SMEs in Vietnam, we show that both long – term and short – term

debts are positively correlated with firm innovation. After controlling for firm characteristics, long

– term debt remains significantly related to firm innovation. This evidence is consistent with

previous studies on SMEs but inconsistent with studies on large public firms in developed countries.

Moreover, we present that long-term debt is positively related to all types of innovation, which

include the introduction of new products, improvement in existing products or introduction of new

technology. These results are qualitatively the same when we employ the 2SLS method with external

instrumental variables.

We further use several proxies to measure firms’ loan needs as well as firms’ capacity to

access external capital and examine their impacts on firm innovation. We find that innovation

24

investment requires firms to acquire more external capital. Specifically, loan need, formal loan

applications and informal loan application are positively related to firm innovation.

Finally, we show that both proportion of investment financed by banks and credit institutions

and by family and friends are important resources for firm innovation. However, bank loans mainly

support firms to improve their existing products while loans from family and friends help firms

improve their existing products as well as introduce new technology.

Overall, our paper shows that access to finance is an important determinant of innovation for

Vietnam’s SMEs. Firms with strong capacity to borrow external capital tend to be more innovative.

Further, we show that in addition to formal loans, informal loans also play an important role in

enhancing innovation in SMEs. Our paper also connects access to finance and firm growth

documented in previous studies by documenting the important role of external finance on innovation.

25

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27

Appendix: Variable Definition

This Appendix provides definitions of all variables used in the paper. Items in parentheses indicate the

corresponding questions from the surveys’ questionnaires.

Variable Definition

Innovation INNO Dummy variable which is equal to1 if firm has at least one

innovation activities and 0 otherwise.

INNO1 Dummy variable which is equal to 1 if firm introduces any new

product groups and 0 otherwise (q122).

INNO2 Dummy variable which is equal to1 if firm makes any

improvements of existing products and 0 otherwise (q123).

INNO3 Dummy variable which is equal to 1 if firm introduces any new

production processes/ new technology and 0 otherwise (q124).

AINNO The sum of INNO1, INNO2 and INNO3.

Access to Finance TDEBT The ratio of total debt to total assets (q75/q73c)

LDEBT The ratio of long-term debt to total assets ((q75c+ q75d)/ q73c)

SDEBT The ratio of short-term debt to total assets ((q75a+ q75b)/ q73c)

LOANNEED Dummy variable which is equal to 1 if firm still needs additional

loans and 0 otherwise (q86).

FLOANAP Dummy variable which is equal to1 if firm has any formal loan

applications and 0 otherwise (q80). Formal loans are loans granted

by banks or from targeted programs.

ILOANAP Dummy variable which is equal to 1 if firm has any informal loan

applications and 0 otherwise (q88). Informal loans include those

from private moneylenders, relative and friends to owner,

enterprises, and others.

BANKF The percentage of investment financed by bank loans (q71b2)

FAMILYF The percentage of investment financed by family and relatives

(q71b4+ q71b5)

OTHERF The percentage of investment financed by other sources (q71b3)

Firm Characteristics LSALE The natural logarithm of total revenues (q1a)

TANG The ratio of total fixed assets to total assets (q73a)

CASH The ratio of total cash and deposits to total assets (q73ba)

LAGE The natural logarithm of firm age (2013- q6a)

PM The ratio of total gross profit to total revenues (q1i/ q1a)

CEAT The ratio of investment in physical capitals to total assets ((q70aa +

q70ab+ q70ac+ (q70ag)/ q73a)

OWNF The ratio of investment financed by firm own capital (q71b1)

28

Table 1: Descriptive Statistics

Panel A of this table reports the summary statistics for the variables and Panel B reports the correlations

between innovation variables and financing variables. All variables are defined in the Appendix. Sample

consists of Vietnamese small and medium enterprises in surveys conducted in 2011 and 2013 by the

University of Copenhagen and its partners. N is the number of observations. All continuous variables are

winsorized at 1st and 99th percentiles. *, ** and *** denote statistical significance at 10%, 5% and 1% levels,

respectively.

Panel A: Summary Statistics

Variable MAX MIN MEAN MEDIAN STD 25th

Pctl

75th

Pctl

N

Innovation

INNO 1 0 0.321 0 0.467 0 1 4,968

INNO1 1 0 0.024 0 0.154 0 0 4,968

INNO2 1 0 0.275 0 0.447 0 1 4,968

INNO3 1 0 0.098 0 0.298 0 0 4,968

AINNO 1.386 0 0.253 0 0.382 0 0.693 4,968

Access to Finance

TDEBT 0.903 0 0.077 0.002 0.159 0 0.079 4,968

LTDEBT 0.489 0 0.022 0 0.076 0 0.000 4,968

SDEBT 0.797 0 0.053 0 0.127 0 0.040 4,968

LOANNEED 1 0 0.154 0 0.361 0 0 4,968

FLOANAP 1 0 0.278 0 0.448 0 1 4,968

ILOANAP 1 0 0.641 1 0.480 0 1 4,968

BANKF 100 0 20.031 0 37.059 0 4.000 4,968

OTHERF 100 0 4.583 0 18.481 0 0 4,968

FAMILYF 100 0 5.530 0 20.526 0 0 4,968

Firm Characteristics

LSALE 11.239 3.484 6.979 6.802 1.597 5.889 7.950 4,968

TANG 0.996 0.112 0.768 0.842 0.218 0.662 0.936 4,968

CASH 0.588 0.001 0.098 0.055 0.117 0.021 0.125 4,968

LAGE 4.344 1.099 2.654 2.639 0.544 2.303 3.045 4,968

PM 0.597 0.018 0.210 0.200 0.101 0.138 0.275 4,968

CEAT 0.950 0.000 0.085 0.002 0.170 0.000 0.089 4,968

OWNF 100 0 20.538 0.000 37.121 0.000 20.000 4,968

29

Panel B: Correlation matrix between Financing and Innovation Variables

INNO INNO1 INNO2 INNO3 TDEBT LDEBT SDEBT LOANNEED FLOANAP ILOANAP BANKF OTHERF

INNO1 0.23***

INNO2 0.90*** 0.10***

INNO3 0.48*** 0.09*** 0.26***

TDEBT 0.11*** 0.02 0.08*** 0.10***

LDEBT 0.08*** 0.03** 0.07*** 0.09*** 0.57***

SDEBT 0.08*** 0.00 0.06*** 0.07*** 0.83*** 0.03***

LOANNEED 0.11*** 0.04*** 0.10*** 0.11*** 0.33*** 0.18*** 0.29***

FLOANAP 0.15*** 0.03*** 0.13*** 0.14*** 0.45*** 0.29*** 0.37*** 0.69***

ILOANAP 0.08*** 0.03** 0.07*** 0.09*** 0.19*** 0.10*** 0.17*** 0.13*** 0.16***

BANKF 0.12*** 0.01 0.11*** 0.10*** 0.43*** 0.25*** 0.37*** 0.53*** 0.85*** 0.09***

OTHERF 0.00 0.02 0.00 0.00 0.11*** 0.09*** 0.08*** 0.06*** 0.02 0.18*** -0.07***

FAMILYF 0.03** 0.03* 0.02 0.04** 0.03** 0.03* 0.03* -0.02 -0.06*** 0.20*** -0.10*** -0.06***

30

Table 2: Innovation by Industry

This table presents the descriptive statistics of firm innovation (INNO, INNO1, INNO2, INNO3) by

industries. All variables are defined in the Appendix. Sample consists of Vietnamese small and medium

enterprises in surveys conducted in 2011 and 2013 by the University of Copenhagen and its partners. N is

the number of observations.

INDUSTRY N INNO INNO1 INNO2 INNO3

Leather 101 0.475 0.020 0.465 0.099

Jewelry, music equipment, and

medical equipment

401 0.464 0.040 0.409 0.110

Paper 142 0.444 0.021 0.359 0.197

Electronic machinery, computers,

radio

139 0.432 0.065 0.388 0.173

Apparel 242 0.409 0.029 0.372 0.091

Services 32 0.406 0.281 0.156 0.125

Recycling 10 0.400 0.200 0.200 0.000

Rubber 252 0.381 0.020 0.357 0.087

Motor vehicles 29 0.379 0.103 0.379 0.207

Textiles 206 0.369 0.019 0.340 0.097

Fabricated metal products 859 0.354 0.031 0.319 0.077

Publishing and printing 124 0.331 0.008 0.266 0.137

Non-metallic mineral products 222 0.311 0.032 0.252 0.113

Other transport equipment 17 0.294 0.118 0.235 0.059

Wood 503 0.278 0.014 0.251 0.060

Basic metals 63 0.270 0.016 0.238 0.095

Chemical products 92 0.239 0.000 0.217 0.130

Food and beverages 1514 0.224 0.011 0.166 0.100

Agriculture 5 0.200 0.000 0.200 0.000

Refined petroleum 15 0.067 0.000 0.067 0.067

31

Table 3: Financing Sources and Firm Innovation

This table reports the results from the probit regression model:

P(INNOi =1|X) =Φ(α +β1FINi + β2FIRM-CHARi + β3IND-DUMMY +εi) (1)

where INNO is an innovation indicator for firms that have at least one innovation activity, Φ is cumulative

distribution function, FIN is a financing variable which is total debt ratio, long term debt ratio, or short term

debt ratio, FIRM-CHAR is a vector of control variables, and IND-DUMMY is industry dummy variables.

All variables are defined in the Appendix. Sample consists of Vietnamese small and medium enterprises in

surveys conducted in 2011 and 2013 by the University of Copenhagen and its partners. N is the number of

observations. All variables are winsorized at 1st and 99th percentiles. P-values are reported in parentheses

and *, ** and *** denote statistical significance at 10%, 5% and 1% levels, respectively.

(1) (2) (3) (4)

INNO INNO INNO INNO

TDEBT 0.245

(0.108)

SDEBT 0.040 0.152

(0.822) (0.406)

LDEBT 0.676*** 0.725***

(0.008) (0.006)

TANG 0.142 0.105 0.086 0.139

(0.311) (0.439) (0.539) (0.325)

LSALE 0.135*** 0.136*** 0.136*** 0.135***

(0.000) (0.000) (0.000) (0.000)

CASH 0.446* 0.412* 0.379 0.451*

(0.061) (0.078) (0.109) (0.058)

LAGE 0.024 0.025 0.023 0.025

(0.525) (0.500) (0.527) (0.500)

PM -0.143 -0.152 -0.146 -0.148

(0.507) (0.479) (0.497) (0.490)

CEAT 0.506*** 0.531*** 0.610*** 0.477***

(0.000) (0.000) (0.000) (0.000)

OWNF 0.004*** 0.004*** 0.004*** 0.004***

(0.000) (0.000) (0.000) (0.000)

Intercept -2.246*** -2.261*** -2.205*** -2.280***

(0.001) (0.001) (0.001) (0.001)

Ind. FE Yes Yes Yes Yes

N 4,968 4,968 4,968 4,968

Pseudo R2 0.0680 0.0687 0.0676 0.0688

32

Table 4: Long-term Debt and Firm Innovation


P(INNOi =1|X) =Φ(α +β1FINi + β2FIRM-CHARi + β3IND-DUMMY +εi) (1)

where INNO is either INNO1, INNO2, or INNO3; Φ is cumulative distribution fuction; FIN is financing

variables which is long term debt ratio and short term debt ratio; FIRM-CHAR is a vector of control

variables, and IND-DUMMY is industry dummy variables. All variables are defined in the Appendix.

Sample consists of Vietnamese small and medium enterprises in surveys conducted in 2011 and 2013 by

the University of Copenhagen and its partners. N is the number of observations. All variables are winsorized

at 1st and 99th percentiles. P-values are reported in parentheses and *, ** and *** denote statistical

significance at 10%, 5% and 1% levels, respectively.

(1) (2) (3)

INNO1 INNO2 INNO3

SDEBT -0.021 0.205 -0.150

(0.955) (0.269) (0.492)

LDEBT 0.897* 0.704*** 0.674**

(0.059) (0.008) (0.030)

TANG -0.119 0.194 -0.024

(0.686) (0.178) (0.893)

LSALE 0.008 0.143*** 0.203***

(0.810) (0.000) (0.000)

CASH -0.393 0.609** -0.204

(0.458) (0.012) (0.515)

LAGE 0.260*** -0.020 -0.054

(0.002) (0.601) (0.292)

PM -0.949** 0.012 0.072

(0.049) (0.956) (0.807)

CEAT 0.027 0.059 0.826***

(0.921) (0.672) (0.000)

OWNF 0.003*** 0.003*** 0.006***

(0.004) (0.000) (0.000)

Intercept -5.488 -2.209*** -7.039

(0.960) (0.001) (0.957)

Ind. FE Yes Yes Yes

N 4,861 4,968 4,968

Pseudo R2 0.0912 0.0647 0.1143

33

Table 5: Financial Need and Firm Innovation


P(INNOi =1|X) =Φ(α +β1LOANNEEDi + β2FIRM-CHARi + β3IND-DUMMY +εi) (1)

where INNO is one of the innovation variables (INNO, INNO1, INNO2, and INNO3), Φ is cumulative

distribution fuction, LOANNEED is a financing variable which is loan need (LOANNEED), formal loan

applications (FLOANAP), and informal loan applications (ILOANAP); FIRM-CHAR is a vector of control

variables; and IND-DUMMY is industry dummy variables. All variables are defined in the Appendix.





(1) (2) (3) (4) (5) (6)

INNO INNO INNO INNO1 INNO2 INNO3

LOANNEED 0.161*** 0.160 -0.029 -0.068

(0.004) (0.267) (0.689) (0.428)

FLOANAP 0.293*** 0.105 0.254*** 0.352***

(0.000) (0.436) (0.000) (0.000)

ILOANAP 0.089** 0.160* 0.056 0.170***

(0.034) (0.093) (0.195) (0.005)

TANG 0.098 0.112 0.114 -0.072 0.170 0.073

(0.467) (0.408) (0.400) (0.797) (0.220) (0.678)

LSALE 0.128*** 0.117*** 0.134*** -0.011 0.128*** 0.179***

(0.000) (0.000) (0.000) (0.750) (0.000) (0.000)

CASH 0.413* 0.478** 0.420* -0.301 0.632*** -0.016

(0.077) (0.041) (0.073) (0.565) (0.008) (0.959)

LAGE 0.024 0.025 0.027 0.267*** -0.019 -0.053

(0.517) (0.503) (0.475) (0.001) (0.619) (0.307)

PM -0.162 -0.152 -0.106 -0.886* 0.037 0.180

(0.452) (0.481) (0.624) (0.068) (0.867) (0.542)

CEAT 0.528*** 0.305** 0.605*** -0.083 -0.049 0.558***

(0.000) (0.015) (0.000) (0.754) (0.708) (0.000)

OWNF 0.004*** 0.005*** 0.004*** 0.003*** 0.003*** 0.007***

(0.000) (0.000) (0.000) (0.003) (0.000) (0.000)

Intercept -2.175*** -2.170*** -2.300*** -1.151** -2.162*** -2.960***

(0.001) (0.001) (0.000) (0.035) (0.001) (0.000)

Ind. FE Yes Yes Yes Yes Yes Yes

N 4,968 4,968 4,968 4,856 4,968 4,953

Pseudo R2 0.0689 0.0731 0.0683 0.0954 0.0676 0.1228

34

Table 6: Investment Financing and Firm Innovation


P(INNOi =1|X) =Φ(α +β1FINi + β2FIRM-CHARi + β3IND-DUMMY +εi ) (1)

where INNO is one of the innovation variables (INNO, INNO1, INNO2, and INNO3), Φ is cumulative

distribution function, FIN is a financing variable which is a proportion of investment financed by banks,

family/friends, or others, FIRM-CHAR is a vector of control variables, and IND-DUMMY is industry

dummy variables. All variables are defined in the Appendix. Sample consists of Vietnamese small and

medium enterprises in surveys conducted in 2011 and 2013 by the University of Copenhagen and its

partners. N is the number of observations. All variables are winsorized at 1st and 99th percentiles. P-values

are reported in parentheses and *, ** and *** denote statistical significance at 10%, 5% and 1% levels,

respectively.

(1) (2) (3) (4)

INNO INNO1 INNO2 INNO3

BANKF 0.003*** 0.001 0.003*** 0.004***

(0.000) (0.288) (0.000) (0.000)

OTHERF 0.000 0.003 -0.000 0.001

(0.739) (0.214) (0.991) (0.640)

FAMILYF 0.003*** 0.004** 0.002* 0.006***

(0.001) (0.026) (0.073) (0.000)

TANG 0.108 -0.128 0.141 0.038

(0.426) (0.644) (0.306) (0.825)

LSALE 0.121*** 0.006 0.133*** 0.190***

(0.000) (0.855) (0.000) (0.000)

CASH 0.469** -0.393 0.589** -0.071

(0.045) (0.447) (0.014) (0.820)

LAGE 0.026 0.255*** -0.020 -0.057

(0.478) (0.002) (0.601) (0.276)

PM -0.134 -0.845* 0.017 0.129

(0.534) (0.080) (0.939) (0.661)

CEAT 0.308** -0.028 -0.018 0.560***

(0.016) (0.916) (0.893) (0.000)

OWNF 0.005*** 0.004*** 0.003*** 0.008***

(0.000) (0.002) (0.000) (0.000)

Intercept -2.176*** -5.435 -2.094*** -7.168

(0.001) (0.961) (0.002) (0.966)


N 4,968 4,861 4,968 4,958

Pseudo R2 0.0732 0.0930 0.0668 0.1233

35

Table 7: Financing and Firm Innovation - 2SLS Approach

This table reports the results from the first and second stages of the 2SLS regression of firm innovation on

financing variables and other firm characteristics. Instrumental variable is a firm’s bank network which is

the logarithm of one plus the total bank officers with whom a firm regularly contacts with. All variables are

defined in the Appendix. Sample consists of Vietnamese small and medium enterprises in surveys

conducted in 2011 and 2013 by the University of Copenhagen and its partners. All variables are winsorized

at 1st and 99th percentiles. Panel A reports the results of first-stage of the regressions and panel B reports

the results of second-stage of the regressions. P-values are reported in parentheses and *, ** and *** denote

statistical significance at 10%, 5% and 1% levels, respectively.

Panel A: The First-stage of 2SLS Regression

(1) (2) (3)

TDEBTi LDEBTi SDEBTi

NETWORKi 0.032*** 0.014*** 0.018***

(0.000) (0.000) (0.000)

TANGi -0.251*** -0.035*** -0.206***

(0.000) (0.000) (0.000)

LSALEi 0.004*** 0.000 0.004***

(0.003) (0.745) (0.001)

CASHi -0.281*** -0.051*** -0.219***

(0.000) (0.000) (0.000)

LAGEi 0.001 -0.001 0.001

(0.804) (0.532) (0.734)

PMi -0.003 0.015 -0.018

(0.877) (0.197) (0.277)

CEATi 0.446*** 0.126*** 0.292***

(0.000) (0.000) (0.000)

OWNFi -0.000*** -0.000*** -0.000***

(0.000) (0.000) (0.000)

Intercept 0.157*** 0.078** 0.077

(0.005) (0.020) (0.117)

Ind. FE Yes Yes Yes

Method OLS OLS OLS

N 4,968 4,968 4,968

Adj. (Pseudo) R2 0.4391 0.1365 0.3351

36

Panel B: The Second-stage of 2SLS

(1) (2) (3) (4)

INNO INNO INNO INNO

TDEBT 1.886*

(0.062)

SDEBT 3.485* 2.323

(0.068) (0.116)

LDEBT 4.228* 1.397***

(0.064) (0.008)

TANG 0.571* 0.813* 0.246 0.624*

(0.054) (0.056) (0.135) (0.081)

LSALE 0.123*** 0.116*** 0.130*** 0.121***

(0.000) (0.000) (0.000) (0.000)

CASH 0.943** 1.175** 0.629** 0.994**

(0.015) (0.019) (0.022) (0.024)

LAGE 0.025 0.023 0.032 0.026

(0.513) (0.554) (0.408) (0.493)

PM -0.124 -0.069 -0.191 -0.107

(0.568) (0.761) (0.387) (0.625)

CEAT -0.272 -0.450 0.037 -0.285

(0.580) (0.452) (0.912) (0.592)

OWNF 0.005*** 0.005*** 0.005*** 0.005***

(0.000) (0.000) (0.000) (0.000)

Intercept -2.524*** -2.493*** -2.559*** -2.517***

(0.000) (0.000) (0.000) (0.000)


N 4,968 4,968 4,968 4,968

37

Table 8: External Financing and Aggregate Innovation

This table reports the results from the OLS regression model:

AINNOi = β0 + β1FINi + β2FIRM-CHARi + β3IND-DUMMYj + ԑi (2)

where AINNO is the logarithm of one plus aggregate innovation, FIN one measure of debt ratios which is

total debt ratio, long-term debt ratio or short-term debt ratio, FIRM-CHAR is a set of firm characteristics,

IND-DUMMY is industry dummy variables, and i denotes firm i. All variables are defined in the Appendix.





(1) (2) (3) (4)

AINNO AINNO AINNO AINNO

TDEBT 0.067

(0.118)

SDEBT -0.011 0.026

(0.827) (0.610)

LDEBT 0.246*** 0.254***

(0.001) (0.001)

TANG 0.028 0.008 0.020 0.026

(0.477) (0.838) (0.594) (0.510)

LSALE 0.045*** 0.046*** 0.045*** 0.045***

(0.000) (0.000) (0.000) (0.000)

CASH 0.076 0.053 0.071 0.077

(0.250) (0.420) (0.276) (0.243)

LAGE 0.004 0.004 0.005 0.005

(0.665) (0.667) (0.636) (0.635)

PM 0.002 0.001 -0.002 -0.001

(0.978) (0.991) (0.975) (0.981)

CEAT 0.142*** 0.177*** 0.140*** 0.130***

(0.000) (0.000) (0.000) (0.001)

OWNF 0.001*** 0.001*** 0.001*** 0.001***

(0.000) (0.000) (0.000) (0.000)

Intercept -0.299* -0.287* -0.308* -0.311*

(0.084) (0.097) (0.074) (0.072)


N 4,968 4,968 4,968 4,968

Adj. R2 0.0862 0.0857 0.0878 0.0877

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