credit risk factors and access to finance: evidence …...tacneng(2015) in their study on assessing...

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Credit Risk Factors and Access to Finance:

Evidence from the CBMS Philippine Entrepreneurship

Dr. Junette A. Perez

Mr. Denmark C. Alarcon

Mr. Mar Andriel S. Umali

Abstract Access to debt finance can be explained by the capability to pay of the borrower. The idea is that

the better the capability to pay of a borrower, the wider his option to access debt finance or

source bank capital. As a derivative function of the individual and business characteristics of the

borrower, it includes the housing type, demography, education, share of income, total sales and

total expenses, business and unemployment skills and economic skills. Using the Community

Based Monitoring(CBMS), a three stage methodology has been implemented. Regression

results show that having business capital is positively affected by the total sales and total

expenses of the household entrepreneur. As business’ total sales and total expenses affect the

capability to pay of the borrower and the cash flow condition of a business, having more than

adequate total sales to cover total expenses would guarantee repayment of debt on the normal

course of business. Single housing type has been found negatively significant to business

capital as cash flow from sales may be redirected for house repairs and maintenance or for rent

payment. Meanwhile, the occurrence of bank loans is anchored on the collateral of the

borrower, such that if the borrower has appliances, has business assets and shows capability to

pay through rent payment his chances to source bank capital is better as compared to those

without. Access to debt finance has also improved total sales performance, total family income

and business assets of the entrepreneurs. As such, the paper calls for examining alternative

sources of collateral and guarantees for micro and small medium enterprises (MSME)’s debt

financing; empowering resource stewardship and risk management skills at the household level

and championing a need for a credible source of information through a credit exchange bureau or

comprehensive database center solely for MSME’s.

Chapter 1: Introduction

The ADB Asia SME Finance Report(2014) cites poor access to finance as one of the critical factors

impeding the development of the small and medium-sized enterprises (SME). Consistent with the ADB

report, Hermes and Lensink(2007), the GEM Philippine Report(2013) emphasizes financing as one of

the problems for entrepreneurs. Similar assessments have been forwarded by Khor, Jacildo and

Tacneng(2015) in their study on Assessing Mandated Credit Programs: Case Study of the Magna Carta

in the Philippines. While access to formal sector financing is key to affecting firms’ dynamism, the

mandated credit program known as the MSME Magna Carta (Magna Carta) has been designed to improve

financial access of MSME’s. Currently the MSME’s account for 99.6% of total firms and 61% of total

employment in the Philippines. Unfortunately, loans allocated to MSME’s declined drastically from a

peak of 30% of total loans in 2002 to 16.4% in 2010( Khor et al, 2013). Reasons include noncompliance

among universal and commercial banks to the requirements of the magna carta and a decline in loans

granted by rural and cooperative banks to MSME’s. There has also been a manifested growth of bank

lending portfolio diverted to non-MSME clients.

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1.1 Research problems and objectives:

The paper presents a different perspective on the issue. By looking at the cause of the situation, access to

debt finance may likely be attributed to the inherent characteristics of MSME borrower as individual or

as a group. Perez(2013) shows that the demographic and economic attributes as education, civil status,

income, gender, number of dependents among others; shape the capability to pay of a borrower. By

identifying risk factors associated with the borrower’s capability to pay, decisions can be designed

towards minimizing and managing such risks.

The paper proposes two major problems:

What credit risk factors affect access to debt finance?

Is access to debt finance significant to total sales performance of entrepreneurs?

The objectives of the paper cover:

1. Profiling selected households using the credit risk factors as grouping categories.

2. Determining the risk factors that affect the access to debt financing through multiple linear

regression analysis;

3. Estimating the propensity scores of the borrowers, which is the probability of being able to be

granted loan by the bank, using logit model; and

4a. Establishing the significance of accessing debt financing in entrepreneurial activities by applying

simple linear regression, and

4b. Verifying the impact of levels of credit scores in accessing debt finance by performing propensity

score matching.

5. Recommending policy and future research directions.

The paper examines three hypotheses:

Ho1: Access to debt finance is not a function of the individual and business characteristics of the

borrower.

Ho2: The occurrence of bank loans is not a function of the individual and business characteristics of the

borrower.

Ho3: Access to debt finance is not significant to total sales performance of entrepreneurs.

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Chapter 2. Review of Literature

2.1 Micro Small Medium Enterprises(MSME)’s in the Philippines

Defined in two ways; through employment levels as used by the National Statistics Office (NSO) and

through asset values by the Bangko Sentral ng Pilipinas(BSP). While the firms’ need for additional

capital is typically addressed by banks, bonds market, equities market, non-bank lending institutions like

quasi banks and investment houses, pawnshops, financing cooperatives, savings and loans associations,

insurance companies, venture capitalists, and specialized government lending corporations; a significant

portion of financing comes from informal sector players, such as family members, friends, and

unaccredited retail lenders (Khor et al, 2013). Conceivably, MSME’s limited access to debt financing

affected its ability to source capital from the equities and bonds market and practically shutting off the

rest. Low profitability and absence of acceptable collateral propel MSME’s to rally personal assets

for business. Early on, the study of Chigunta(2002) has expressed a generally lack of consistent,

updated and systematic data on youth and women entrepreneurs in general. This condition is further

aggravated by a lack of mechanism for a credible source of credit information to facilitate bias free

credit assessments(Khor et al, 2013).

Table 1: SMEs' Sources of Funding (% of current funding) Source: SERDEF-UP ISSI WBES ICPS-ADB PEP-IFC WBES

1992 2000 2004 2006 2009a

Own resources 78 52 60 69 76.4 Bank loans 15 21 11 19 10.2

Non-bank finl inst. 0.9

Informal creditb

7 27 29 12 12.4

Total 100 100 100 100 100

ICPS-ADB = Investment Climate and Productivity Study, Asian Development Bank, PEP-IFC = Private Enterprise Partnership

for the Philippines (PEP-Philippines) SME Financing Survey, International Finance Corporation, SERDEF-UP ISSI = Small

Enterprise Research and Development Foundation-University of the Philippines Institute for Small Scale Industries; SME = small

and medium enterprise, WBES = World Bank Enterprise Survey.

a Shares in the firms' working capital.

b Purchases on credit from suppliers/advances from customers + loans from moneylenders, friends, and relatives.

Sources: Nangia and Villancourt 2007; WBES 2009 as cited by Khor et al(2013)

Presently, the banking industry holds 80% of the approximately P10 trillion of total domestic financial

assets (Khor et al, 2013). Unfortunately, the magnitude of this asset portfolio minimally translates to

MSME lending despite the Magna Carta for MSME’s. As shown in Table 1, data demonstrates a

significant source of funding from internal resources of the household entrepreneur.

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2.2 Risk Factors, Effectiveness and Access to Finance in Entrepreneurship

The development of entrepreneurship begins with the vocabularies of owner entrepreneurs(Demetry,

2014). Using a theoretical framework where vocabularies marked the stages in firm creation, nascent

entrepreneurs identify its business as labor of love and changes once the firm stabilizes. Entrepreneurs

call themselves as business owners.

While the provision of cheap credit to micro finance institutions are used by many governments and

institutions alike to fill financing gaps in the financial system, such subsidize credit has also led to

synthetic profitability for MFI's's with minimal impact to total lending(Garmaise and Natividad, 2013).

This level of distinctions of access to finance can also be deduced from studies among developed and

developing economies.

Wagner(2014)states that access to external finance is usually a salient obstacle for entrepreneurs and

SME’s. Colla, Ippolito, and Li (2013) examine debt specialization of public U.S. firms and find that 85%

borrow predominantly with one type of debt such that the bigger the firm the more diverse the mix of

debt financing. These firms are more likely run by white males, with college degree, with patents and R

and D. They are also more likely to survive than firms which rely mainly on credit cards, trade credit, or

friends and family. Such condition often characterizes entrepreneurs operating within developing and

struggling economies.

The levels of financial sophistication is also pronounced among advanced economies amidst patterns of

race, gender and other demographic characteristics. Colla, Ippolito, and Li (2013) and Abraham(2014)

highlight that women generate less value from using social ties in contrast to men. Women tend to

converge in less resourceful networks, networks that are poorer in social and economic resources such

that clients, friends or family members prefer men over women in terms of exchange resource contacts.

Moreover the paper of Thebuad(2014) suggests that while all start-ups encounter greater difficulty

securing investment after the financial crisis, investors are significantly more likely to rely on the gender

of a start-up owner(s) and somewhat more likely to rely on the race of its owner(s), as a basis of

investment decisions during the recession. In contrast, investors are only marginally more likely to rely

on the firm’s profitability and not at all, more likely to rely on its credit risk or the owner(s) industry and

entrepreneurship experience during the recession period. In this regard, Mijid(2015)in his paper prompts

the need for studies on patterns of women accessing finance in small businesses. This result is further

validated by Mogulious and Kydors(2011) stating that women have higher rates of funding denial among

businesses in the US and Canada.

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Personality, human capital and educational attainment similarly create a link to the growth and stability of

an entrepreneurship. Hegde and Tumlinson(2014) assess that ability and not pedigree (e.g., educational

qualifications) matters for productivity. The paper implies that entrepreneurs have higher ability than

employees of the same pedigree, employees have better pedigree than entrepreneurs of the same ability

and entrepreneurs earn more, on average, than employees of the same pedigree. Consistent with Lanahan

and Fieldman(2014) different forms of entrepreneurial human capital; educational attainment, years of

experience working in the same industry as the start-up, prior start-up experience, and the size of the start-

up team demonstrate contrasting results along stages of entrepreneurship growth. Renko(2014) on one

hand highlights that nascent entrepreneurs show better alignment between entrepreneur's financial

motivation and revenue expectations from a business opportunity; with a higher likelihood of a

successful startup. While Yang(2014) suggests that knowledge and information about entrepreneurship

are more salient and relevant to creating new business and learning on the job, Ruef and Ziebarth(2014)

identify patterns of inverse relationship between levels of development and self-employment rates and

the tendency for lower skilled individuals to crowd into business ownership as entrepreneurship, thereby

driving down the productivity of these sectors.

Presently, the trend for entrepreneurship is moving towards opportunity identification, valuation and

maximization. Minniti(2014) presents a paradigm shift in the entrepreneurship process from

opportunity discovery to exploitation and setting the foundation.

Chapter 3: Theoretical, Conceptual and Operational Frameworks

3.1 Theoretical Framework:

Harry Markowitz’s(1952) theory of risk and return has withstood the test of time and events years past

and present. Many developments raise fundamental questions and yet the basic theory holds, the higher

the risk the higher the return expected from any enterprise activity in all forms.

3.2 Conceptual Framework:

To work out the idea, risk management is applied at the base household level. The identification of risk,

measurement and valuation and management and control are conducted where two categories of risk are

grouped into non controllable(market) and controllable(unique). The idea is the better the capability to

pay of the borrower the wider the opportunities available to access debt finance borrowers. Access to

credit can come from subsidies, grants to open credit accommodations from commercial banks and

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other financial institutions. In evaluating the capability to pay of borrower and his chances to access debt,

credit risk factors considered unique to individual borrowers are critical. Formal financial institutions

operate for profit such that decisions to extend credit require rigorous and systematic evaluation on the

capability of borrower to pay. Weins et al (2015), Mwangi et al (2012), Boating et al(2014) and Sharma et

al(2014).

3.3 Operational Framework:

A three(3) phase methodology will be implemented in the paper. Applying Multiple Regression Analysis

to Stage 1, business capital proxies as access to debt finance. It serves as dependent variable while the

independent variables include individual and business attributes unique to the household borrower.

These risk factors are housing characteristics, demographic factors, education, business, share of income,

unemployment skills and economic skills among others. CBMS questionnaire does not include the

specified amount of bank loan instead a checklist of sources of capital has been provided. The nature

and types of business assets are also not included in the survey data.

Three equations are formulated for the objectives:

Equation 1: Determines the risk factors that affect the access to debt financing

The multiple regression model is given below:

Business Capital = f(housing characteristics, demography, education, business, share of income)

Equation 2: Estimates the propensity scores of borrowers to be granted bank loan

The logit model using risk factors in Equation 1is given below:

Occurrence of Loan from Banks = f(housing characteristics, demography, education, business,

share of income)

Equation 3a: Establishes the significance of accessing debt financing in entrepreneurial activities(total

sales performance)

The regression model is given below:

Ln(Total Sales) = f(predicted probabilities of getting an approved loan or the propensity score)

3b: Verifies the impact of levels of propensity scores in accessing finance

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Chapter 4: Methodology

4.1 Research Design

For this study, purposive sampling is employed in gathering the sample. The data comes from the

Community Based Monitoring Survey conducted among households across the Philippines.

To determine the significance of risk factors in accessing debt financing for entrepreneurial activities and

the impact of debt financing to entrepreneurship, this paper uses exploratory and probabilistic approaches.

Aside from the descriptive analysis of data, the methodology is composed of three main stages:

Stage 1:

1) determining the risk factors that affect the access to debt financing through multiple linear

regression analysis;

Stage 2:

2) estimating the propensity scores of the borrowers, which is the probability of getting an

approved loan, using logit model; and

Stage 3:

3a) establishing the significance of accessing debt financing in entrepreneurial activities by

applying simple linear regression, and

3b) verifying the impact of levels of credit scores in accessing finance by performing propensity

score matching.

4.2 Data Description and Collection Method

Since the purpose of this study engages mainly on the effect of micro financing to business, the

researchers select data coming from respondents of the survey who have businesses. The choice of

business capital as proxy for access to debt finance has been made due to lack of exact data on amount of

loan borrowed from the bank. Note however that business capital coded (1) are those with business

capital sourced from banks otherwise, coded(0) for business capital from non bank sources.

4.3 Method of Data Analysis

Descriptive analysis of data is done to describe the characteristics of sample chosen for the study. These

statistics include but are not limited to the measures of central tendency and variability. After establishing

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the descriptive statistics of the sample, the results of statistical models have to be analyzed. As mentioned,

there are three stages used in this part of the study.

Stage 1: Determining the risk factors that affect the access to debt financing

To determine the risk factors affecting access to financing, a multiple linear regression model is

employed. The variables to be used are the following:

a) Dependent variable - business capital as proxy for access to debt financing.

b) Independent variables – respondent’s house type (Single, Duplex, Commercial, and Others),

house ownership (Owned, Rent, and Others), respondent’s gender, educational attainment

(College graduate and Others), and business insurance as dummy variables; number of family

members who are working abroad, pregnant, single parent, disabled; respondent’s age, number of

jobs, housing rent, and number of appliances owned; business’ number of years, business asset,

total sales, and total expenses; and total income of the family. The variables are identified based

on the unique risk factors affecting the capability to pay of the borrower.

The multiple regression model is given below:

Business Capital = f(housing characteristics, demography, education, business, share of income)

Variance Inflation Factor (VIF) is computed to detect multi-collinearity among variables. In the presence

of multi-collinearity, highly correlated independent variables have to be dropped to correct the model and

avoid bias in the results of regression. Moreover, Breusch-Pagan/Cook-Weisberg test for

heteroscedasticity is utilized in detecting unequal variances in the model. Robust standard errors in the

multiple regression is used in case heteroscedasticity persists.

Stage 2: Estimating the propensity scores of borrowers

Logit model is applied to estimate the probability of borrowers in getting an approved bank loan. This

model estimates the probability that a respondent will be able to source bank loan based on the risk

factors identified in Stage 1. The computed probabilities will be the propensity scores.

For this model, the variables to be used are the following:

a) Dependent variable – occurrence of bank loan where it is either 1 if business capital is obtained

through bank loan or 0 otherwise;

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b) Independent variables – respondent’s house type (Single, Duplex, Commercial, and Others),

house ownership (Owned, Rent, and Others), respondent’s sex, educational attainment (College

graduate and Others), and business insurance as dummy variables; number of family members

who are working abroad, pregnant, single parent, disabled; respondent’s age, number of jobs,

housing rent, and number of appliances owned; business’ number of years, business asset, and

total expenses; and total income of the family. The number of independent variables may be

omitted due to collinearity.

The logit model is given below:

Occurrence of Loan from Banks = f(housing characteristics, demography, education, business,

share of income)

Stage 3a: Establishing the significance of accessing debt financing in entrepreneurial activities

A simple linear regression model is used in determining the significance of access to debt financing to

business. This model has only two variables:

a) Dependent variable – The dependent variable is the logarithmic Total Sales of the business.

The data for Total Sales is transformed in order to minimize variation due to potential large

differences in total sales of the businesses. As an implication of data transformation, the impact of

independent variable, provided that it is significant, will be measured as the relative change in

Total Sales on the average. Total sales is used as an indicator of the performance of the business

instead of net income. This is to exclude the effect of other variables inherent in the business such

as administrative costs and other factors such as interest and tax payments that also affect

business performance.

b) Independent variable – The probability of borrowers in getting an approved loan is the

independent variable. From Stage 2, predicted probabilities are computed to estimate the

propensity scores of the borrowers.

The regression model is given below:

Ln(Total Sales) = f(predicted probabilities of getting an approved loan or the propensity score)

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Stage 3b: Verifying the impact of levels of propensity scores in accessing debt finance

In this research, propensity score matching is used to estimate the effect of being granted loan on access

to debt financing. Capacity to be granted bank loan is represented by the propensity scores obtained from

Stage 2 while business capital is again used as proxy for debt financing. A covariate that is related to both

propensity scores and business capital will be used.

The respondents will be grouped into 5 groups. These groups will be identified with regard to the range of

propensity scores as follows:

(1). from 0.00 (inclusive) to 0.20 (exclusive);




(5). from 0.80 (inclusive) to 1.00 (inclusive).

The propensity score matching will then be performed to determine how one person belonging to a

particular group has, on the average, a different business capital requirement needed as compared to

others who do not belong to his group. By performing this statistical matching test, it is possible to

estimate the effect of borrower’s capacity to be granted bank loan.

Chapter 5: Results and Discussion

Following the procedures indicated in the Methodology, the results are as follows:

5.1 Descriptive Analysis

The characteristics of the selected sample have to be described in order to have a better understanding of

the sample and to see if the descriptive data is in line with the results of the statistical models used.

Table 2: Frequency of location

Location Frequency Percentage

Manila (Barangays near DLSU and DLS-CSB) 74 9.6%

Bago City, Negros Occidental 193 25.1%

Cavite (Dasmarinas and Maragondon) 16 2.1%

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Lipa, Batangas 24 3.1%

Marikina 462 60.1%

Total 769 100%

Majority of the respondents included in the sample comes from Marikina with 60.1% of the sample size,

followed by Bago City in Negros Occidental, Manila (barangays near DLS-CSB and DLSU), Lipa and

Cavite; respectively. Among the given locations, Marikina has the most number of respondents with

businesses and thus it has the biggest part of the sample.

Table 3: Frequency of respondents’ gender

Gender Frequency Percentage

Male 417 54.2%

Female 352 45.7%

Total 346 100%

Male respondents outnumber female respondents, posting 54.2% of the sample size. This suggests that

there are more male than female entrepreneurs in the locations where surveys were performed.

Table 4: Frequency of the respondents’ age

Age Frequency Percentage

18-27 33 4.3%

28-37 143 18.6%

38-47 191 24.8%

48-57 209 27.2%

58-67 135 17.6%

68-77 50 6.5%

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78-above 8 1.0%

Total 769 100%

About 70.6% of the total respondents have ages ranging from 28 – 57 years. This shows that a big chunk

of entrepreneurs are in their late twenties to late fifties. Only 4.3% of the entrepreneurs can be considered

young, belonging to 18 – 27 age bracket. Using the GEM Youth Entrepreneurship definition,

approximately only 19-20% of the entrepreneurs within 18 – 34 age bracket are represented in the

sample. Xavier et al(2014) reported that although access to finance is a perennial problem for all

businesses, the youth are particularly vulnerable. They have no credit history to validate capacity to pay,

have inadequate collateral to secure loans and with no established careers to demonstrate support or

capacity to sustain financial obligations .

Table 5: House Type

House type Frequency Percentage

Single 595 77.4%

Duplex 38 4.9%

Multi 128 16.7%

Commercial 8 1.0%

Total 769 100%

With regard to type of housing the respondents have, 77.4% lives in a single unit type of housing while

16.7% has multi-unit type of housing. These two types of housing alone comprise the 94.1% of the

sample size. The housing type often affects level of access to bank financing.

Table 6: Ownership of House

Ownership Frequency Percentage

Homeowner 462 60.1%

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Renter 129 16.8%

Others 178 23.1%

With regard to house ownership, 60.1% of the respondents owned the house they live in while 16.8% of

them only rent. The 23.1% of the respondents either do not own the house of their residence but instead

they are using the house either with consent or without consent from the real owner.

Table 6: OFW Family Members, Single Parents, College Graduates, and Insurance to Business

Variables Frequency Percentage Average

Family with OFW members 50 6.5% 1.3

Single Parent 109 14.2% N/A

Reached College or College

Graduate

260 33.8% N/A

Business Insurance 67 8.7% N/A

About 6.5% of the sample have family members that are working overseas. The number of OFW in the

identified family ranges from 1 to 5 with an average of 1.3 members. On the other hand, 14.2% are

considered single parents while 33.8% of the respondents have reached college or are college graduates.

Only 8.7% of the respondents have business insurance.

Table 7: Sources of Business Capital

Source Frequency Percentage

No Capital Needed 65 8.5%

Own Savings 561 73.0 %

Family Savings 115 15.0%

Loan from Family, Friends or Relatives 55 7.2%

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Loan from Bank or Commercial Institution 18 2.3%

Loan from Private Money Lender 36 4.7%

Loan Assistance from Government Institutions 6 0.8%

Loan Assistance from NGO 6 0.8%

Credit from Customers 10 1.3%

Other Sources 57 7.4%

The most popular source of business capital is own saving which comprises 73% of the sample followed

by family savings and contribution at 15.0% of the sample. Business capital from bank loans shows only

a small percentage, only 2.3% of the sample. This result is consistent with the pattern of sources of capital

reported in the Philippine GEM Report(2013) and the ADB Report of Khor et. al(2014). Schott et

al(2014) on Future Potential: A GEM Perspective on youth entrepreneurship 2015 indicate that half of

the respondents, regardless of age category, relied on personal savings to fund entrepreneurial ventures.

Table 8: Other variables

Variables Minimum Maximum Mean Std. Deviation

Total Family Income 0.00 40,600,000.00 406,164.32 1,557,660.85

Total Business Sales 0.00 30,000,000.00 135,199.07 1,129,325.81

Total Business Asset 0.00 100,000,000.00 301,805.60 4,181,456.28

Business Capital 1.00 10,000,000.00 54,625.22 442,317.15

The total family income of the respondents has an average of Php406,164.32 with a minimum of Php0.00

and a maximum of Php40,600,000.00. With regard to business, total business sales has a mean of

Php135,199.07 with a high of Php30,000,000.00 and a low of Php0.00 while total business asset averaged

Php301,805.60 with a high of Php100,000,000.00 and a low of Php0.00. As for the business capital, it

has an average of Php54,625.22 with a maximum of Php10,000,000.00 and a minimum of Php1.00. It is

important to note that all the variables recorded high standard deviations that suggest large variability in

the amounts given by the respondents.

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To further examine the sample, descriptive data are provided for observations that have bank loans and no

bank loan. A biserial correlation is also conducted to test the correlation between business capital coming

from bank loans and from other sources of financing and between total sales of respondents with bank

loans and without bank loans. Moreover, descriptive data are also provided to observations that are

grouped according to propensity scores as discussed in Methodology Stage 3b. However, just two groups,

[0.00,0.20) and [0.20,1.00], according to propensity scores are presented for descriptive analysis since no

observation falls within [0.40,0.60) and [0.60,0.80) intervals, and only one observation falls within

[0.80,1.00] interval. This result confirms the nature of high risk micro finance borrowers.

Table 9: Descriptive Statistics of Observations Without Bank Loan and With Bank Loans


Wit

h B

ank L

oan

Total Family Income 92,300.00 1,140,000.00 573,247.61 257,577.94

Total Business Sales 0.00 30,000,000.00 1,792,166.67 7,041,713.23

Total Business Asset 0.00 50,000,000.00 3,115,555.56 11,712,920.20

Business Capital 1,000.00 10,000,000.00 777,277.78 2,322,544.34

Wit

ho

ut

Ban

k L

oan

s

Total Family Income 0.00 40,600,000.00 402,406.79 1,574,512.41

Total Business Sales 0.00 6,000,000.00 95,533.36 338,259.68


Business Capital 10,000.00 6,000,000.00 37,587.49 255,363.59

Table 9 validates the positive impact of bank loans with respect to family income, business sales and

assets in contrast to those without bank loan.

From the sample, 751 observations are identified to have no bank loans while only 18 observations have.

There are notable differences in the values of variables in both groups, especially the mean and standard

deviation. The group with bank loans has Php170,840.82 higher average total family income with a

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notably lower variability as compared to the group without bank loan. With respect to total business sales

and total business assets, the group with bank loans posts Php1,696,633.31 and Php2,881,368.72 higher

average amounts than the group without bank loan; respectively, but with higher variability in total

business sales and in total business assets. These suggest that respondents with bank loans have better

source of cash coming from both family income and business sales and have accumulated more business

assets than can be used in generating more sales. Moreover, group with bank loans require higher

business capital on the average which is Php739,690.29 higher or 20.7 times bigger than the mean

business capital of the group with no bank loan.

Profiling of the respondents who have the highest business capital that came from bank loan and from

other sources of financing is as follows:

a) The respondent with the highest business capital, among the ones who had source of

financing from bank loan is a 67-year old male who resides in Brgy. San Roque, Marikina,

living and owning a single-detached house. The respondent has business type under

Engineering Firm, with a business capital amounting to Php 10,000,000 and an income of

Php 470,400. Total sales was reported to be Php30,000,000 and with business assets

disclosed as Php50,000,000 in the past year.

b) The respondent with the highest business capital, among the ones who did not have source of

financing from banks, is a 25-year old male who resides in Bago City, Negros Occidental. He

is lives and owns a single-unit residential house. He is single, college graduate and self-

employed individual, earning Php 1,050,000 a year. The respondent’s business type is

Commercial Buildings for Rent, where the source of financing is under personal and family

savings. The total income is Php 1,146,000, total sales of Php 6,000,000 and a business asset

of Php 30,000,000.

Table 10: Biserial Correlation

rpb t-score Degrees of freedom

Busi

nes

s C

apit

al +0.25 +7.25 767

P one-tailed < 0.0001

two-tailed < 0.0001

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To

tal

Sal

es

+0.23 +6.46 767

P one-tailed < 0.0001

two-tailed < 0.0001

The results of biserial correlation indicate that the total business capital is positively correlated to the

occurrence of loan. Albeit weakly correlated, the positive correlation is significant at 95% level of

confidence in either one-tailed or two-tailed test. This means that respondents who incurred loans have

higher total business capital. Similarly, a weak positive correlation is established by the biserial

correlation test between total sales and the occurrence of loan.

Table 11: Descriptive Statistics of Observations Grouped According to Propensity Scores


[0.0

0,0

.20)

Total Family Income 0 41,000,000.00 402,963.57 1,629,158.75

Total Business Sales 0 6,000,000.00 95,524.97 337,265.95

Total Business Asset 0 30,000,000.00 111,260.84 1,181,514.64

Business Capital 1 6,000,000.00 41,680.10 267,724.78

Propensity Score 0 0.19 0.02 0.03

[0.2

0,1

.00

]

Total Family Income 134,000.00 1,300,000.00 649,840.00 367,426.47

Total Business Sales 0.00 30,000,000.00 3,124,400.00 9,444,182.53


Business Capital 10,000.00 10,000,000.00 1,127,600.00 3,121,569.20

Propensity Score 0.20 0.99 0.33 0.23

18

Grouping the sample according to propensity scores obtained from Stage 2, 709 observations belongs to

group with propensity scores from 0.00 to 0.20 exclusive while only 10 observations have propensity

scores between 0.20 to 1.00 inclusive. The total family income, total business sales, total business asset

and total business capital recorded are all higher in the second group, on the average. The second group

posts total family income, total business sales, total business asset and total business capital higher

average business asset, approximately 1.6, 32.7, 1.6 and 27.1 times larger on the average; respectively.

Further, the profiles of respondents with lowest and highest probabilities of getting a bank loan are the

following:

a) The respondent that has the lowest probability of getting a bank loan (1.12x10-12

) which is very

close to zero is a 53-year old male that resides in Marikina and lives in a duplex housing where

the lot is rented. The respondent is an elementary graduate, food vendor and with total family

income of 506,400 in a year. His business has a capital of Php1,500. The total sales of his

business in a year is Php139,400 while business asset is Php6,000.

b) The respondent that has the highest probability of getting a bank loan (0.98529) is a 67-year old

male that resides in Marikina and lives in a single detached housing in that he owns. The

respondent is a college graduate, has an engineering firm and has a total income of Php470,400 in

a year. His engineering firm has a capital of Php10,000,000 and a total business asset amounting

to Php50,000,000. The business, has a total sales of Php30,000,000 in the past year.

V.2 Statistical Models Analyses

As mentioned, there are three stages in the methodology that involves statistical inferencing. The results

are provided below.

Stage 1: Determining the risk factors that affect the access to debt financing

The independent variables of the multiple linear regression model shows no multicollinearity as

seen in Figure 1 through the use of Variance Inflation Factor test, thus there is no need to drop any of the

independent variables for this particular reason. However, Breusch-Pagan/Cook-Weisberg test as shown

in Figure 2 indicates that the problem of heteroscedasticity is present in the model. To address this

problem, multiple linear regression model is run using robust standard errors.

19

Figure 1 Figure 2

The results of multiple linear regression with robust standard errors indicate the following (please

refer to Table 12):

A. Significant variables at 95% level of confidence:

1. The housing types (single) is significantly related to business capital. In fact, it showed

negative relationships to business capital as cash flow from sales may be redirected for

house repairs and maintenance or for rent payment.

2. The total amount of expenses of a business has a positive relationship with business

capital. This is understandable since higher business expenses must require higher

business capital to cover the expenditures.

3. The total amount of business sales has a positive relationship with business capital.

Increasing sales will result to an increase in the business capital. This is possible since

increase in sales generates higher cash earnings which can be infused to business as capital

during expansion.

B. Overall test of significance at 95% level of confidence

20

The p-value of F-statistic (0.0000) shows that all risk factors used as independent variables in

the regression model are jointly significant in predicting the business capital, on the average.

C. Coefficient of determination

The R-square (0.8556) indicates that the variation in risk factors used as independent

variables explains about 85.56% of variation in the business capital, on the average.

Table 12: Multiple Linear Regression with Robust Standard Errors

Stage 2: Estimating the propensity scores of borrowers in capacity being granted bank loan

Logit model is applied to calculate the probability that a borrower will get a bank loan. This model

estimates the probability that a respondent will be able to acquire bank loan based on the risk factors

identified in Stage 1. The results of the logit regression indicate the following (please refer to Table 13):

A. Significant variables at 95% level of confidence:

1. The number of appliances owned by a family is positively related to propensity score. The

more appliances owned by a family, the higher the propensity score obtained. The

21

appliances considered in the survey are computer, refrigerator, television sets,

microwave, and washing machine among others.

B. Significant variables at 90% level of confidence:

1. Ownership of house and lot has a positive relationship to propensity score. In particular,

family that rents either house or lot has higher propensity score.

2. Business asset has a positive relationship to the propensity score. This implies that the

higher the total business assets, the higher the chance of being granted loans from banks.

C. Overall test of significance at 95% level of confidence

The p-value of Chi square-statistic (0.0006) shows that all risk factors used as

independent variables in the logit model are jointly significant in estimating the

probability that a borrower will be granted the loan, on the average.

Table 13: Logit Model Results

Stage 3a: Establishing the effectiveness of accessing debt financing in entrepreneurial

activities

A simple linear regression model is used in determining the effectiveness of access to debt financing to

business’ total sales performance. This model has only two variables:

22

a) Dependent variable – The dependent variable is the logarithmic Total Sales of the business.

b) The propensity scores, from Stage 2 logit regression. This represents the probability that a

borrower will be granted a bank loan.

Test for heteroscedasticity as seen in Figure 3 shows that problem does not persist in the model. With

this, the simple linear regression is performed. The result of linear regression (please refer to Table 14)

indicate that the propensity scores of the borrowers (probability of the borrowers to be granted a bank

loan) has a positive significant effect to the logarithmic Total Sales of the business. This means that a 0.01

increase in the propensity to pay will translate to a 0.061% increase in the total sales of the business. This

further suggests that the higher the probability that a borrower will get the bank loan based on the risk

factors identified in Stage 1, the higher the total sales.

Figure 3

Table 14: Linear Regression

Stage 3b: Verifying the impact of levels of propensity scores in accessing finance

Due to low estimated values from the logit model, only three groups are identified to have elements.

These groups have range of propensity scores as follows – Group 1 from 0.00 (inclusive) to 0.20

(exclusive); Group 2 from 0.20 (inclusive) to 0.40 (exclusive); and Group 3 from 0.80 (inclusive) to 1.00

(inclusive). The other two groups, from 0.40 (inclusive) to 0.60 (exclusive) and from 0.60 (inclusive) to

0.80 (inclusive), have no elements. This indicates that respondents have generally low probability of

23

getting an approved bank loan which is understandable since most of the businesses are only small scale

businesses in need of micro-financing.

The propensity score matching is performed by treating a particular group to be in the same level of

capacity to pay and borrow debt capital versus the other groups and then estimating the impact of being in

a particular group on the amount of business capital, on the average, that needs to be financed as

compared to other groups. Further, Total Sales is used in the matching technique as a covariate that is

related to both propensity scores and business capital. However, even if three groups are identified to

have elements, the propensity score matching only produces results for Group 1 versus other groups and

Group 2 versus other groups mainly because Group 3 has only one element which hinders the matching

test to proceed. Due to this, the Group 3 is dropped from the propensity score matching procedure.

The results of performing the propensity score matching shows that the group having propensity scores

from Group 1 0.00 to 0.20 (exclusive) shows an amount of business capital that is 5,219.93 pesos lower

than Group 2 (please refer to Table 15), on the average. This clearly indicates that in the group where the

probability of getting bank loans is very low, the business capital needed is also lower, on the average. It

is possibly because people belonging in this group have limited access to other sources of financing and

thus they rely greatly on debt financing.

Table 15: Propensity Score Matching (Group 1 versus Group 2)

Chapter 6: Conclusions and Policy Recommendations:

Results demonstrate that total sales, total expenses and housing type are credit risk factors influencing the

capability to pay or the ability to access debt financing of household entrepreneurs. The capability to be

granted bank loan (probability to access debt finance) is significantly influenced by the number of

24

appliances, business assets and an ability to pay rent. These results confirm the need for a measurable

collateral in sourcing bank capital. Furthermore, the study validates the positive impact of bank loans or

access to debt finance to total family income, total business sales and business assets of entrepreneurs in

contrast to those without bank loans(without access to debt finance).

As such, the paper recommends the following:

1. Exhaust and examine all possible recourse for collateral and guarantees for MSME’s access to debt

financing and simultaneously empower resource stewardship and risk management skills among

household entrepreneurs. Risk management and technical skills enhance and strengthen the capability to

pay of the entrepreneur through risk identification, measurement and valuation and management of risk

factors.

2. Champion and support the creation of a credit information exchange, or a credit bureau, or a database

center solely for MSME’s. The bureau as a bastion for improved and better information of credit

evaluation and understanding of the MSME’s houses information, conducts studies and surveys on the

nature of assets, liabilities and risk including the demographic, economic and social profile of the

MSME’s in the Philippines and the ASEAN.

3. Channel or link household entrepreneurs to diversified pathways of financing through the internet for

breakthrough financing and repayment schemes as crowd funding and angel financing and prompts

innovation in products and services at the barangay level as well.

4. Engage experts, practitioners and MSME’s in meaningful lectures, brown bag discussions and

educational tours to develop appreciation of the stock and bond markets. Open invitations and

membership to annual stockholders meeting of select blue chip companies and other industry immersion

tours and simulation exercises.

5. Institute a sustainable feedback mechanism of government and privately run programs and

interventions through constant dialogues, town halls, research cycles and conferences for continuous

improvement and monitoring, synergy and collaboration among sectors.

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