ANTECEDENTS OF BANK MANAGERS’ RISK PERCEPTIONS IN LENDING TO MSME
A Thesis Submitted to Goa University for the award of the degree of
DOCTOR OF PHILOSOPHY
HARSHA SWAPNIL TALAULIKAR GOA UNIVERSITY,
TALEIGAO- GOA 403206
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I, Ms. Harsha Swapnil Talaulikar, do hereby declare that this dissertation entitled “Antecedents of bank managers’
risk perceptions in lending to MSME” is bonafide record of research work done by me under the guidance of Dr.
(Ms) Purva G. Hegde Desai, Professor, Department of Management Studies, Goa University.
I also declare that this dissertation or part thereof, has not been submitted by me for the award of any degree, Diploma, Title or Recognition before.
Harsha Swapnil Talaulikar
Place: Goa University
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This is to certify that the Ph.D. thesis titled “Antecedents of Bank Managers’ Risk perceptions in lending to MSME” is an original work carried out by Ms. Harsha Swapnil Talaulikar under my guidance, at the Department of Management Studies, Goa University.
This dissertation or a part thereof, has not formed the basis for the award of any Degree, Diploma, Title or Recognition before.
Dr. (Ms.) PURVA G. HEGDE DESAI Professor,
Department of Management Studies, Goa University.
Place: Goa University
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TABLE OF CONTENTS
Title Page No.
1 Introduction 1-20
2 Literature Review 21-57 3 Research Methodology and
58-84 4 Descriptive Analysis 85-105 5 Results and Analysis 106-161 6 Theoretical Contributions,
Managerial Implications and Future Research Issues
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LIST OF TABLES
1.1 1 Definition of Small and Medium Enterprises with European Union
1.2 1 Definition of Small and Medium Enterprises by World Bank Standards
3 1.3 1 Present ceilings on investment for
4 1.4 1 Present ceilings on investment for
4 1.5 1 Categorisation of MSME based on
5 2.1 2 Performance of MSME Sector in India 22 2.2 2 Bank Credit to Micro and Small
24 2.3 2 Composition of Priority Sector 25
2.4 2 Principles of lending 29
2.5 2 Credit Evaluation Factors 30
2.6 2 Risk Assessment Criteria of banks in the state of Goa
34 2.7 2 Three Pillars of institutions (Scott
42 3.1 3 Parameters for MSME lending
identified in Content Analysis
60 3.2 3 Result of Inter-Rater Reliability for the
73 3.3 3 Result of Fleiss Kappa Calculation 74 3.4 3 Benchmark Scale for Fleiss Kappa 76 3.5 3 Results of the test Cronbach’s Alpha 79 3.6 3 Cronbach’s Alpha calculation for Risk
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3.7 3 Population of Public Sector Banks in the state of Goa
81 3.8 3 Sample Size determination of finite
83 4.1 4 Demographic Characteristics of the
85 4.2 4 Demographic Characteristics of the
86 4.3 4 Demographic Characteristics of the
sample- Educational Qualification
87 4.4 4 Demographic Characteristics of the
87 4.5 4 Demographic Characteristics of the
87 4.6 4 Cross Tabulation of Overall Risk
Perception and Gender
88 4.7 4 Cross Tabulation of Overall Risk
Perception and Designation
88 4.8 4 Cross Tabulation of Overall Risk
Perception and Qualification
88 4.9 4 Cross Tabulation of Overall Risk
Perception and Age
89 4.10 4 Cross Tabulation of Overall Risk
Perception and Experience
89 4.11 4 Cross Tabulation of Risk Perception of
Adverse Selection and Gender
90 4.12 4 Cross Tabulation of Risk Perception of
Adverse Selection and Designation
90 4.13 4 Cross Tabulation of Risk Perception of
Adverse Selection and Educational Qualification
4.14 4 Cross Tabulation of Risk Perception of Adverse Selection and Age
91 4.15 4 Cross Tabulation of Risk Perception of
Adverse Selection and Experience
91 4.16 4 Cross Tabulation of Risk Perception of
Moral Hazard and Gender
92 4.17 4 Cross Tabulation of Risk Perception of
Moral Hazard and Designation
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4.18 4 Cross Tabulation of Risk Perception of Moral Hazard and Educational
4.19 4 Cross Tabulation of Risk Perception of Moral Hazard and Age
93 4.20 4 Cross Tabulation of Risk Perception of
Moral Hazard and Experience
94 4.21 4 Chi-Square Test Results for association
between Overall Risk Perception and Designation
4.22 4 Chi-Square Test Results for association between Overall Risk Perception and
4.23 4 Chi-Square Test Results for association between Risk Perception of Adverse Selection and Educational Qualification
4.24 4 Chi-Square Test Results for association between Risk Perception of Adverse
Selection and Age
4.25 4 Chi-Square Test Results for association between Risk Perception of Adverse
Selection and Experience
4.26 4 Summary of Chi-Square Test Results for association between Risk Perception and its types and Bank
Managers’ demographic factors.
4.27 4 Chi-Square Test Results for association between Perceived Information Asymmetry and Bank Managers’
4.28 4 Chi-Square Test Results for association between Perceived Information Asymmetry and Bank Managers’
5.1 5 Result of Test for Normality of Overall Risk Perception
107 5.2 5 Result of Test for Normality of
Perceived Information Asymmetry
108 5.3 5 Result of Test for Normality of
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5.4 5 Result of Test for Normality of Risk Attitude
110 5.5 5 Result of Test for Normality of
110 5.6 5 Result of Test for Normality of
111 5.7 5 Test Results of Linearity between Total
Risk Perception and Information Asymmetry.
5.8 5 Test Results of Linearity between Total Risk Perception and Intuition.
113 5.9 5 Test Results of Linearity between Total
Risk Perception and Risk Attitude.
114 5.10 5 Test Results of Linearity between Total
Risk Perception and Normative Behaviour.
5.11 5 Test Results of Linearity between Total Risk Perception and Trust.
115 5.12 5 Test results of Multicollinearity
between Overall Risk Perception and other Independent Variables
5.13 5 Results of Hypothesis Testing H1
120 5.14 5 Results of Hypothesis Testing H2
122 5.15 5 Results of Hypothesis Testing H3
123 5.16 5 Results of Hypothesis Testing H4
125 5.17 5 Results of Hypothesis Testing H5
126 5.18 5 Factors leading to Overall Risk
127 5.19 5 Conceptual Model for Overall Risk
129 5.20 5 Results of Hypothesis Testing H6
133 5.21 5 Results of Hypothesis Testing H7
135 5.22 5 Results of Hypothesis Testing H8
136 5.23 5 Results of Hypothesis Testing H9
138 5.24 5 Results of Hypothesis Testing H10
139 5.25 5 Conceptual Model for Risk Perception
of Adverse Selection
5.26 5 Results of Hypothesis Testing H11
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5.27 5 Results of Hypothesis Testing H12
145 5.28 5 Results of Hypothesis Testing H13
146 5.29 5 Results of Hypothesis Testing H14
148 5.30 5 Results of Hypothesis Testing H15
150 5.31 5 Conceptual Model for Risk Perception
of Moral Hazard
151 5.32 5 Summary of test results of the three
types of risk perceptions
153 5.33 5 Test results of Effect of Moderation by
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TABLE OF FIGURES
No. Chapter Title
Initial Proposed Conceptual Model for antecedents of Risk Perception in MSME Lending
Refined Proposed Conceptual Model of antecedents of Risk
Perception in MSME lending 69 Figure
Revised Model for Bank Managers’ Overall Risk
Perception in MSME Lending 159
Revised Model for Bank Managers’ Risk Perception of Adverse Selection in MSME
Revised Model for Bank Managers’ Risk Perception of
Moral Hazard in MSME Lending 161
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TABLE OF ANNEXURES
No. Title Page Nos.
A Measurement Scales 183-190
B Tables of Content Validity Test 191-197 C Descriptive Statistics tables of
Normality Tests 198-208
Descriptive Statistics tables and Graphical representation of Linearity Tests
209-211 E Results of Moderation Tests 212-223 F Questionnaire for Exploratory
Interviews with the Bank Manager 224-226
G Content Validity Test 227-232
H Inter Rater Reliability for
Normative Behavioural Constructs 233 I Questionnaire to test the proposed
model for Risk Perception 234-238
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I would like to thank ALMIGHTY for providing me with the inspiration and granting me the strength, knowledge and will power to undertake this research study and complete it to best of my abilities.
Apart from my own efforts, the success of this thesis depends largely on the encouragement and guidelines of many others. I take this opportunity to express my gratitude to the people who have been instrumental in the successful completion of this research. I would like to show my greatest appreciation to my research Guide, Dr. Purva G. Hegde Desai, Professor, Department of Management Studies, Goa University, for giving me this opportunity to do research work and providing valuable suggestions throughout this study. Her knowledge, vision, dedication and motivation have deeply inspired me in making this exhaustive research work fun and a truly learning experience. She has been like a lighthouse to steer me through all the phases of this research work and always encouraged me to present the research findings with clarity and ease. It was a great privilege and honour to work and study under her guidance.
I wish to express my sincere gratitude to Professor K. B. Subhash, Dean
of the Department of Commerce & Management Studies, Goa University,
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without his encouragement and guidance this project would not have materialized.
My special appreciation and thanks to Dr. B. A. Gomes for periodically reviewing my progress, as an expert on the Faculty Research Committee.
His advice on both research as well as on my career have been invaluable.
I am indebted to Dr. Nandakumar Mekoth, Professor, Department of Management Studies, Goa University, for his expert opinions on the subject specific as well as statistical issues involved in the course of the study. His valuable guidance and inputs have enriched my research.
It gives me great pleasure to acknowledge the frank advice and feedback received from, the faculties of the department Dr. M. S. Dayanand and Dr. Nirmala R., during the course of my Ph.D. study. Their valuable insights on the topic helped me keep focussing in the right direction.
I owe my deep sense of gratitude to Dr. Nilesh Borde, the faculty of the Department of Management Studies, for offering a helping hand and in deciding the right approach towards selecting the statistical tools for my data analysis. His experience and thoughtful guidance, timely decision and critical comments have been of immense help to me during my Ph.D.
I am grateful to all the fellow researchers, in the Department of
Management studies, Goa University for their support and
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I am very thankful to the principal of our college Sridora Caculo College of Commerce & Management studies, Mapusa, Dr. Santosh Patkar for his continuous encouragement and my colleagues at BBA Department, Mr.
Abhishek Karmali, Course Coordinator and other faculty members, Mr.
Rajeev Narvekar, Mr. Sushant Chari and Ms. Pooja Lawande Karmali for their valuable opinions and moral support throughout my research. Our discussions in the staff room on the research issues and the process of research helped to boost my morale.
My special thanks and appreciations also go to my colleague Mr. Sushant Chari for giving a patient hearing to my uncounted doubts in developing the project and and willingly helping me out with his abilities.
I am also thankful to the administrative staff of the department Mr. Vivek Borkar and Ms. Suchita Joshi for their cooperation and help.
I would like to offer special thanks to my father Mr. Ashok D. Bhembre and my sister in law Dr. Smruti Ajit Kamat who went out of their way to help me in collecting data from bank managers. A big thanks to my ex- students and students of BBA batch 2015-18 who assisted me in data collection.
Also, I like to thank the participants in my survey, the bank managers who
have shared their precious time during the process of data collection in
spite of their busy schedules. Special mention of Mr. Amlesh Tripathi,
Former Branch Manager, UCO Bank, Mapusa and Mr. Jones Fernandes,
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Former Manager of Bank of India, Baroda Branch for guiding me and offering me their valuable knowledge and advice on the topic of my study.
I would like to thank my loved ones, who have supported me throughout the entire process of the research. My in laws Mr. Mohan Talaulikar and Mrs. Malan Talaulikar for their understanding & endless love and my parents Mr. Ashok Bhembre and Mrs. Rajani Bhembre, for their support throughout the duration of my studies and will be grateful forever.
I am very much thankful to the bundle of Joy - my son Mast. Yash Talaulikar for rigorous follow up and my husband Mr. Swapnil Talaulikar for their understanding, continual support, perseverance to complete this research work. Also I express my thanks to my sister, Ms. Shama Kaustubh Sanzgiri and her family for their blessings and valuable prayers.
I would like to thank the administrative staff of Department of Management Studies, Goa University without whose help it wouldn’t have been possible to complete my research.
Finally I would like to thank all those who knowingly or unknowingly
contributed to my research work in some way or the other.
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Antecedents of Bank managers’ risk perceptions in lending to Micro, Small & Medium Enterprises (MSME)
By: Harsha Swapnil Talaulikar
Research Guide: Dr. Purva G. Hegde Desai, Professor, Department of Management studies, Goa University
Despite being a catalyst to economic development, the Micro, Small and Medium Enterprises (MSME) sector faces problems in financial access. Banks are the main source of external finance for MSMEs across countries. However, banks perceive MSME market as risky, costly, and difficult to serve. The bottleneck as perceived by banks seems to be information asymmetry, which refers to the lack of transparency regarding the financial conditions.
The present study uses ‘Institutional Theory’ and tries to identify the antecedents of risk perceptions of bank managers towards MSME lending, in the situation of information asymmetry.
Objectives of the study:
The following are the objectives of the study:
To identify the antecedents of perceived risk among the bank managers towards lending to Micro, Small & Medium Enterprises (MSME).
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To find out the impact of these factors on the bank managers’risk perceptions in MSME lending.
To identify the factors affecting the 2-type of risk perceptions identified in the literature -Risk of adverse selection and Risk of moral hazard.
To find out if these factors moderate the relationship between information asymmetry and risk perception.
To identify the demographics of bank managers that lead to different levels of risk perceptions in MSME lending.
The design of this research includes three stages namely content analysis of banks’ application forms and checklists of documents required for MSME loans, qualitative and quantitative methodology to achieve the objectives of the research.
In the first stage, content analysis of banks’ application forms and checklists of required documentation for MSME start-up loans was done by collecting these from the different types of Banks. The second stage, which is an exploratory research that included interactions with the bank managers of different banks.
Third stage describes the quantitative methodology used for testing of hypothesis based on the proposed conceptual model.
The study was conducted in the state of Goa in India. The sample selected was the bank managers of public sector banks in the state of Goa. The data was collected by personally administering the questionnaire to 250 branches of public sector banks in North and South Goa. The data was collected from 218 bank managers.
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This study proposed and tested a conceptual model based on literature review and exploratory and quantitative study.According to this study, the factors which significantly influenced the risk perceptions of bank managers are perceived information asymmetry, risk attitude, normative behavior and perceived trust. Intuition, which is one of the intrinsic factors of bank managers, was found as not influencing bank managers’ the risk perceptions.
The relationship between information asymmetry and risk perception is moderated only by bank managers’ risk attitude. This concludes that the impact of information asymmetry is reduced if the bank manager has a favourable risk attitude.
Limitation of the study
The limitation of the study is that this study considers sample of only Public Sector bank managers. As lending to MSME in the state has been found to be limited in the cases of private and cooperative banks. And these were not included in the study. This can be as a limitation.
Sociological authors, Luhmann (1993) and Baecker (1991) assert that, “Risk is not an ‘objective’ fact in the business environment which can be assessed through probability calculus but is continuously created by bankers themselves when they make decisions.” This research studies the subjective aspect of risk perceived by bankers in MSME lending by considering the soft factors in MSME lending along with the hard financial data.
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This research further studies the moderation impact of bank managers’
cognitive factors like risk attitude, intuition and perceived trustworthiness on all the three types of risk perception. The study concludes that risk attitude moderates the relationship between information asymmetry and the risk perceptions. Thus, it reiterates that the risk perception have a strong influence of ‘soft’ factors like personality trait of risk attitude.
This study has used risk classification categories thereby classifying the two types of risk perceptions that managers have, namely risk of adverse selection and risk of moral hazard based on the literature and exploratory study.
The measurement scale was put through different refining techniques of factor analysis along with validity and reliability testing to get a comprehensive scale for testing different aspects of risk perception. Hence the scale could be used in other studies to assess the risk perceptions and the factors influencing the risk perceptions
MSMEs are often more informational opaque. This information asymmetry makes the financing of MSMEs challenging.
The results of this research prove that information asymmetry is significantly important when it comes to bank managers’ all the three types of risk perception. This means that the MSME units should focus on minimizing information asymmetry and ensure that they have all the criteria pertaining to information while applying for the loan.
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The factors other than information asymmetry like bank managers’ risk attitude and perceived trust have individually shown significant impact on bank managers’risk perception. This research will thereby develop an understanding of the decision-making criteria used by bankers to increase the chance of getting MSME loan request approved by fulfilling the required criteria adequately.
The moderating relationship shows that if the bank manager has a favourable attitude towards MSME sector, then MSME lending can be improved even if there are flaws in information provided by the MSME.
Trustworthiness of MSME client can be favourably perceived by the bank manager by interacting with the MSME owner. It is also identified in the literature by Ferrary (2003) that the greater the density of the interpersonal relationships the greater is the access to information. The researcher further states that some information is inaccessible in the framework of strict professional relationships. This will help the manager to perceive clients’
professional as well as his personal nature. This positive aspect of developing interpersonal relationships can also be incorporated while designing the training related policy to the bank managers.
Risk perception; Information asymmetry; lending to MSMEs; Intuition; Risk attitude; Trust
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CHAPTER –1 INTRODUCTION
1.1.1 MSMEs in the world
Worldwide, the Micro, Small and Medium Enterprises (MSMEs) sector have been accepted as an engine of economic growth. The MSMEs constitute over 90% of the total enterprises in most of the economies and are credited with generating highest employment growth rates.
They account for a major share of industrial production and exports. (NSE-SIDBI-N-TREES Report, 2011)
Small and Medium Enterprises (SMEs) play a major role in most economies, particularly in the developing countries.
A World Bank Group study, 2010 suggests that there are 365-445 million MSMEs in emerging markets. Out of these 25-30 million are formal SMEs, 55-70 million are formal micro enterprises and 285-345 million are informal SMEs.
1.1.2 MSME Nomenclature
Micro, small and medium enterprises are also referred to as Small and medium enterprises or SMEs, and small and medium-sized businesses or SMBs in some countries.
The abbreviation SME occurs commonly in the European Union and in international organizations, such as the World Bank, the United Nations and the World Trade Organization. The term small and medium-sized businesses or SMBs is predominantly used in the United States of America (USA).
In South Africa the term SMME for Small, Medium and Micro Enterprises is used. Elsewhere in Africa, they use MSME for Micro, Small and Medium Enterprises.
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1.1.3 Global Definition of MSME
While there is general agreement that the SME market is significant in size and importance, there is considerable variation in their definition around the world.
Pobobsky (1992) cites a study of the International Labour Organization, which identifies over 50 definitions in 75 countries.
A common definition of SMEs includes registered businesses with less than 250 employees.
This places the vast majority of all firms in the SME sector. Of the 132 economies covered in World Bank Report 2010, 46 economies define MSMEs as those enterprises having up to 250 employees. SMEs are estimated to account for at least 95 percent of registered firms worldwide; in Europe, for example, this number is well over 99 percent. To narrow this category, SMEs are sometimes distinguished from microenterprises as having a minimum number of employees, such as 5 or 10. They can be further divided into small enterprises (SEs) and medium enterprises (MEs).
European Commission through a guide determines the criteria for defining enterprises:
number of employees, annual turnover and annual balance sheet (European Commission:
2005). It is determined that meeting the criteria of the number of employees is mandatory, while filling another from the two financial criteria is a choice of the enterprise. The definition of SMEs that came into effect from 1 January 2005 is shown in the following table.
Table 1.1 Definition of Small and Medium Enterprises with European Union standards.
Category of Enterprise
Number of people working
Annual turnover of the Enterprise
Medium Sized <250 <= €50 million
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Small Sized < 50 <= € 10 million
Micro Sized <10 <= € 2 million
Source: European Commission (2005)
Table 1.2 Definition of Small and Medium Enterprises by World Bank Standards Category of
Number of Employees Total Annual Sales
Medium Sized >50; <=300 <= $ 3000000-15000000 Small Sized >10; < =50 <= $100000 - 3000000
Micro Sized <10 <= $ 100000
Source: Independent Evaluation Group (2008)
Alternative criteria for defining this sector includes annual sales, assets, and size of loan or investment, annual turnover or by industry.
While the appropriate definition of the sector ultimately depends on the local context, the most used SME classification at the World Bank provides an illustration of criteria similar to many used around the world. To qualify as a micro, small, or medium enterprise (MSME) under this World Bank classification, a firm must meet two of three maximum requirements for employees, assets, or annual sales. For client reporting purposes, IFC’s Global Financial Markets Department uses loan size as a proxy, since some banks are unable to report according to SME firm size.
Many banks currently serving SMEs do in fact use annual sales figures, and average bank- reported maximum thresholds ($16 million) are remarkably similar to the World Bank classifications ($15 million) (IFC Report 2010).
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1.1.4 MSME Definition in India
In India, micro, small and medium enterprises as per MSMED Act, 2006 are defined based on their investment in plant and machinery (for manufacturing enterprise) and on equipment for enterprises providing or rendering services. The present ceilings on investment for enterprises to be classified as MSMEs are as follows:
Table 1.3 Present ceilings on investment for Manufacturing Enterprises Manufacturing Enterprises – Investment in Plant & Machinery
Micro Enterprises up to Rs. 25 Lakh
Small Enterprises above Rs. 25 Lakh & up to Rs. 5 Crore
Medium Enterprises above Rs. 5 Crore & up to Rs. 10 Crore
Source: Micro, Small & Medium Enterprises Development (MSMED) Act, 2006
Table 1.4 Present ceilings on investment for Service Enterprises Service Enterprises – Investment limit in equipment
Micro Enterprises Rs. 10 lakh
Small Enterprises above Rs. 10 Lakh & up to Rs. 2 Crore
Medium Enterprises above Rs. 2 Crore & up to Rs. 5 Crore Source: Micro, Small & Medium Enterprises Development (MSMED) Act, 2006
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1.1.5 Contribution of MSME
The SME sector is important to national economies because it contributes significantly to employment and GDP. In many countries, the majority of jobs are provided by SMEs. As per the recent study published by IFC and McKinsey & Company in 2010, in the 30 high-income countries of the Organization for Economic Cooperation and Development (OECD), SMEs represent over two-thirds of formal employment and in low-income countries, this figure tends to be smaller, especially where the informal sector is large; but it is still significant.
According to World Bank data report, 2010, in the emerging economies, the formal SMEs contribute up to 60% of total employment and up to 40% of national income (GDP). Further, according to estimates of World Bank, to absorb the growing global workforce there will be 600 million jobs needed in the next 15 years in the continents of Asia and Sub-Saharan Africa.
In emerging markets, most formal jobs are generated by SMEs.
In the high-income economies, it is not so. MSMEs are not only denser in the business structure, but also employ a higher percentage of the workforce. In half of the high-income economies covered, formal MSMEs employed at least 45 percent of the workforce, compared to only 27 percent in low-income economies.(World Bank Enterprise Survey, 2010)
These indicators highlight the importance of MSMEs to economic development and job creation. Formal MSMEs employ more than one-third of the global population, contributing around 33 percent of employment in developing economies. From a regional perspective, East Asia and the Pacific have the highest ratio of MSME employment to total employment (Kushnir, Mirmulstein, and Ramalho, 2010).
SMEs are an important source of export revenues in some developing economies. SME shares of manufactured exports in selected East Asia and African developing economies and OECD countries have been very high. An interesting observation is that SMEs contribute a larger
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share of manufactured exports in more industrialized East Asian economies (56% in Chinese Taipei, more than 40% in China) and in India (31.5%) than the less industrialized African economies (<1% in Tanzania and Malawi).
In India MSME sector has emerged as highly vibrant and dynamic sector of the Indian economy. MSMEs not only play crucial role in providing large employment opportunities at comparatively lower capital cost than large industries but also help in industrialization of rural & backward areas, thereby, reducing regional imbalances, assuring more equitable distribution of national income and wealth. MSMEs are complementary to large industries as ancillary units.
The MSME sector contributes in a significant way to the growth of the Indian economy. This sector has a huge network of over 32 million units that create an employment of about 70 million. The sector manufactures more than 6000 products thus contributing directly and indirectly, about 45% to manufacturing output and about 40% of exports. (CII-PWC Report, 2013). The growth in this sector has in fact acknowledged the fact that this sector will be able to achieve the target of National Manufacturing Policy of having 25% share in GDP from its current level of 16% by the end of 2022.
1.1.6 Key obstacles for MSME worldwide
The World Bank Enterprise Surveys, 2010 identified the obstacles for the firms of all sizes worldwide. It was found that among the list of 15 potential obstacles, electricity and access to finance were the two most-cited by businesses in developing countries. Firms of different sizes ranked the obstacles differently. In the survey, more small businesses listed access to finance as their biggest obstacle than do medium enterprises, and fewer large firms.
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This World Bank data also confirms the point that SMEs are less likely to be able to obtain bank loans than large firms. SMEs instead rely on internal funds, or cash from friends and family, to launch and initially run their enterprises. About half of formal SMEs don’t have access to formal credit. The research proves that the financing gap is even larger when micro and informal enterprises are taken into account. As per the research, approximately 70% of all micro, small and medium-sized enterprises (MSMEs) in emerging markets lack access to credit. While the gap varies considerably region to region, it’s particularly wide in Africa and Asia. The current credit gap for formal SMEs is estimated to be US$1.2 trillion; the total credit gap for both formal and informal SMEs is as high as US$2.6 trillion.
Big Corporates have access to the domestic primary capital market both through equity and other instruments. Small firms are vulnerable because of their dependency on financial institutions for funding. These firms simply do not have access to public capital markets.
Banks are the main source of external finance for them across countries. (Beck, Demirguc- Kunt, and Maskimovic, 2008).
Researchers have identified a persistent gap in the funding markets in the case of small firms.
The following researchers Macmillan Committee (1931), Wilson Committee (1971), Cruickshank (2000) all identified funding gaps towards small firms in one form or another.
Ross (1996) suggests that there is a general reluctance from the banks to evaluate projects because they view small firms as self-employed individuals.
First Biz-Greyhound Knowledge Group SME Survey 2014 with a sample of 540 MSME highlights the key challenges faced by SMEs in India. Across all categories, access to financial and credit instruments is observed as the most critical challenge (79%), compared to inflexible labour laws that was stated as the least critical challenge (70%) by SMEs in India.
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Over the years, this segment has been fuelling economic growth and has been a catalyst to industrialization of rural and backward areas. This sector has been labour intensive and its contribution to employment generation in India has been significant. Despite being a catalyst to country’s economic development, the sector faces problems in access to finance. Hence this study looks for the plausible reasons as to why is there difficulty in availing MSME finance in India.
1.2 BANKING INDUSTRY
Worldwide commercial banks undertake a wide variety of activities, which play a critical role in the economy of every country. They pool and absorb risks for depositors and provide a stable source of investment and working capital funds to various sectors of the economy.
Keynes (1930) and Pesek and Saving (1967) quotes that the commercial banks are central to a macro economy as they act as financial intermediaries between savers and investors. In addition, they provide a smooth functioning payment system that allows financial and real resources of every country to flow relatively freely. The banking industry also backs up source of liquidity for any sector in the economy. Banks are a particularly important source of funds for small borrowers who often have limited access to other sources of external finance. The three main interrelated functions of commercial banks are holding of deposits;
creating credit through lending and investment activities; and providing a mechanism for payments and transfers of funds for various productive activities. The extension of credit or lending is, thus, the principal activity of a commercial banks.
Ever since India opted for development plans for its growth, the role and importance of banking and other financial institutions have gained in importance. Research has also confirmed the inter-linkage between finance and growth in India. The Indian growth process
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has essentially been ‘finance-led’. The expansion in the Indian financial sector played an enabling role in promoting capital accumulation. (Reddy, 2006).
1.2.1 Indian Banking Industry
In India, prior to initiation of financial sector reforms in the early 1990s, the lending operations of the banking sector were highly regulated. These were related to an administered structure of interest rates, high levels of pre-emptions in the form of reserve requirements, and credit allocation to certain sectors. There were requirements of prior approval from the Reserve Bank for sanction of credit beyond a threshold.
Over the past years, Indian banking Sector has evolved from socialist, regulated business to liberalized, modernized and technologically oriented one. The Banking Sector has played an important role in the modern economy by providing credit to the different segments. In order to stimulate the economy and support the growth of banking Sector in India, Reserve Bank of India (RBI) has proactively adopted several policy measures from time to time.
1.2.2 Sources of finance for MSME
Finance is a crucial ingredient for economic growth. The modes of financing industrial development have been changing over the years (Levine, 1997). Financial systems tap savings and then channelize the funds to a wide spectrum of industrial activities. The mode of the provision of industrial finance is as important for fostering industrial growth as is the quantum of funds.
a. Different sources of finance
MSMEs require capital infusion right from its inception. This capital infusion is always through term loans and working capital loans.
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As per the study report of CII-PWC, 2013, MSMEs have been relying on following sources for financing their needs:
• Company’s retained earnings, funding through sale of assets
• Inherited capital, personal savings, loans from relatives, friends and from unregulated market
• Institutional financing from scheduled commercial banks
• Venture capital funds/ seed funds among the formal financial institutions
Other significant forms of SME financing include leasing and hire purchase, trade credit and fiscal incentives in the form of tax breaks.
Lekhanya and Mason, 2014 state that “ ‘Bank’ and ‘own funds’ were the most important sources of funds mentioned by the majority of respondents, but only ‘Banks’ was statistically significant in differentiating between more and less successful SMEs. This finding is consistent with the importance of bank finance consistently mentioned in the literature (Liedholm and Mead, 1999; Van Auken, 2001;Ishengoma and Kappel, 2008; Romanian Commercial Bank, 2008), and confirms that access to bank finance does play a role in the success or otherwise of rural SMEs.” Thus, for MSMES, the role of banks still remains dominant for their financing needs.
Few other researcher Nguyen and Ramachandran (2006) studied the determinants influencing the capital structure of small and medium-sized enterprises (SMEs) in Vietnam and empirically proved that short-term liabilities are prominantly used to finance their operations.
b. Bank as a source of finance
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Brunsa and Fletcherb (2008) also quote that like other Western countries, borrowings from banks are the most common source of external ﬁnancing for small business in Sweden (Winborg and Landstro¨ m 2001; Berggren 2002).
Berger and Udell (2003) report that debt represents 50% of the capital structure of small ﬁrms in the US. Fifty-two per cent of ﬁrms less than two years old have debt as their major source of funding with commercial bank loans the most common source of external ﬁnancing.
The MSMEs look towards banks for their credit needs as commercial banks are the primary source of finance for them (Petersen & Rajan, 1994; Cole et al., 1996; Berger & Udell, 2002;
Ghosh, 2007; Ruis et al., 2009).
Previous research has also found that the bank loans are the most important external source for ﬁnancing the capital requirements of Swedish small and medium sized enterprises (SMEs). (Barton and Matthew 1989; Meyer 1993; Winborg and Landstro¨ m 2001), but that these SMEs generally have diﬃculties obtaining such loans (Walker 1989; Binks, Ennew, and Reed 1992).
The literature shows high dependence of MSME sector worldwide on the banking institutions for the provision of credit.
c. Finance in India
In India, commercial banks constitute the largest source of financial assistance for the MSME sector among the formal source of finance. SME Rating Agency of India Limited (SMERA) is a third party rating agency started by Government of India that gives ratings on the basis of creditworthiness of micro, small and medium enterprises in India for ratings on creditworthiness.
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These ratings enable MSME units to raise bank loans at competitive rates of interest.
SMERA’s MSME rating scale consists of two parts, the first part gives an evaluation of condition and the second part gives an evaluation of the size. SMERA rating categorises MSMEs based on size, so as to enable fair evaluation of each MSME amongst its peers.
Table 1.5 Categorisation of MSME based on SMERA Rating MSME 1 Highest Rated
MSME 2 High Rated
MSME 3 Above average Rated MSME 4 Average Rated
MSME 5 Below average Rated MSME 6 Inadequate Rated MSME 7 Low Rated MSME 8 Lowest Rated
Alternate Financing options suggested are venture capital funds, supply chain financing and factoring. (PWC, India, 2014)
1.2.3 MSME lending-A concern
In India the overall demand for finance in the MSME sector is estimated to be INR 32.5 trillion ($650 billion). The majority of finance demand from these enterprises is in the form of debt, estimated at approximately INR 26 trillion ($520 billion). (International Finance Corporation, 2012)
Banks in India have traditionally been the main source of credit for various sectors of the economy. They have also funded borrowing requirements of the central and state
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governments by investing in their securities. Lending and investment operations of banks in India have evolved in response to the changing needs of the economy. In India, like many other emerging market economies, commercial banks remain the most important source of credit supply to the Industries.
The institutionalized financial sector has a high risk perception of the MSME sector, owing to their vulnerability to economic trends and risk of elongation of working capital cycles. The SME market has been perceived by banks as risky, costly, and difficult to serve. (IFC Report, 2010)
It is observed that, the problem faced by Indian banking system is that there is no transparency regarding the financial conditions of MSMEs. These enterprises mostly transact in cash and have little incentive to maintain proper financial records as bookkeeping increases the cost of operations. In some cases the owners of these enterprise themselves may not grasp the financial conditions well. This situation in the literature is termed as Information Asymmetry there by leading to heightening the risk perceptions of the bankers’ resulting in hesitation from banks to give loan to MSME units.
Hence this study focusses on information asymmetry as the important cause for the bank managers’ risk perception.
The traditional methods of credit appraisal used by banks and financial institutions are based on evaluation of financial information and documentation which may be unreliable or simply not available in the case of MSMEs. Estimates have in fact indicated that more than 90% of MSMEs do not have adequate financial statements, account books and history of tax returns, effectively excluding them from access to formal financing. The vast number and dispersed nature of MSMEs (with estimates of the number of MSMEs ranging from 4.5 crore to 6.0
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crore) makes physical outreach through traditional banking channels challenging both in terms of scaling up and viability (Ministry of Finance, 2015).
It is for the above reasons this study will be very significant for MSMEs in understanding the importance of maintaining financial Information which is one of the important parameters in the access to finance.
1.3 SIGNIFICANCE OF THE STUDY
In spite of the fact that MSMEs are beneficial to the economy of the country in more than one ways, they still face numerous hindrances that are obstructing the growth of the segment.
As financial institutions are constrained by the lack of readily available financial information on SMEs, they consider financial viability very much critical for risk assessment. The poorly documented financial information compels them to either reject the loan applications or sanction lower than the required credit limits.
Some of the important criteria for MSME loan assessment were identified by researchers in the literature.
This study considers information asymmetry as the principle factor. However there are many other factors researched in the literature pertaining to MSME lending. Profitability, financial stability and liquidity of the MSME unit are identified by Berry, et al. 1993a in his study, whereas loan characteristics, financial profitability, collateral backup, entrepreneurial characteristics, margin money, earlier track record, entrepreneurial skills and purpose of loan were identified as important criteria that banks consider in MSME loan assessment by Bhalla
& Kaur 2012. Other researcher like Fertuck (1982) mentioned about Security, financial strength, business ability and honesty and Deakins and Hussain (1995) and Fletcher (1995)
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mentioned trading experience, equity stake, gearing and profitability to be the criteria that banks look for, in assessing MSME loan proposals.
The study looks at the factors related to the risk perception and will also throw light on the financing issues.
1.3.1 Managerial Implication
Bankers face a situation of information asymmetry when assessing lending applications. The information required to assess the competence and commitment of the entrepreneurs, and the prospects of the business is either unavailable, uneconomic to obtain or difficult to interpret.
(Binks and Ennew, 1996, 1997). This results in bank managers facing a great amount of risk as quoted by Deakins, 1999.
MSMEs are often more informational opaque. They either do not have the required information or the amount of information supplied by MSME is not enough. This makes the financing of MSMEs challenging. This concept of information asymmetry creates problems for lenders. As a result of these problems, these firms may be credit rationed (Stiglitz and Weiss 1981), means that they do not get as much credit as they want although they are willing to meet the conditions set by the lender.
This study will be of great help to MSME units to minimise information asymmetry and understand and comprehend the factors that bank managers look for in the presence of informational asymmetry to sanction the required loan amount. Further it will develop an understanding of the decision-making criteria used by bankers to increase the chance of getting their loan request approved by fulfilling the required criteria adequately.
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This study is beneficial to MSME sector as it will make them understand the importance of various the credit assessment tools used by financial sector in evaluations of MSME loan proposals thereby easing out one of the most important challenges faced by MSME sector.
1.4 THE SCOPE OF THE DISSERTATION
The overall objective of this study is to ascertain the factors that lead to the risk perception among the bank managers towards MSME lending in presence of information asymmetry.
The research also tried to determine the contribution of each of the factor to the overall risk perception.
This research has been limited to the public sector banks in the state of Goa. The data on various factors has been collected from the managers’ of the public sector banks only. It has been noticed from the data collected from lead bank that among the three sectors of banks which set target under Annual Credit Plan (ACP), least number of private banks offer credit to the industries, followed by cooperative banks. Maximum MSME lending is done only by public sector banks in the state of Goa.
Banking statistics in the state of Goa as per the lead bank records 2015.
Number of bank branches operating in the state: Scheduled Commercial Banks (524) + Cooperative Banks (140) = 670
Rural Banks (312) + Semi Urban (358) = 670 No RRB (Regional Rural Bank) in the state
Nationalized Banks (24) Private Sector Banks (17) Cooperative Banks (13)
As per the data collected from Lead bank, Goa state as on the 15.12.15, the Goa state has a total of 497 public sector branches. An adequate sample has been taken from this population of public sector banks.
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1.5 OBJECTIVES OF THE STUDY
The following are the objectives of the study:
To identify the antecedents of perceived risk among the bank managers towards lending to Micro, Small & Medium Enterprises (MSME).
To find out the impact of these factors on the bank managers’ risk perceptions in MSME lending.
To identify the factors affecting the 2-type of risk perceptions identified in the literature-risk of adverse selection and risk of moral hazard.
To find out if these factors moderate the relationship between information asymmetry and risk perception.
To identify the demographics of bank managers that lead to different levels of risk perceptions in MSME lending.
1.6 STATEMENT OF THE PROBLEM
The purpose of the study is to identify the factors that lead to risk perception in the bank managers in MSME lending. The impact that each factor has on risk perception has also been found out. The study further tries to identify those factors which moderates the relationship between information asymmetry and risk perception. The effect of the demographic factors like age, gender, qualification and designation of bank managers on the level of risk perception was also studied.
1.7 OVERVIEW OF METHODOLOGY
The research covers identification of factors that lead to risk perceptions in the bank managers while evaluating MSME loan proposals in the presence of information asymmetry.
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In the first level a detailed literature review was done to shortlist all the factors which affects MSME lending.
In-depth exploratory interviews were conducted with the three bank managers. The two bank managers selected for the interview were from public sector banks -Corporation Bank and UCO Bank and the third manager selected was from Bicholim Urban Cooperative Private Limited. The selection of bank managers was done with an intention to understand the MSME lending procedures in each of the types of the banks in the state of Goa.
The result of the in-depth interviews revealed firstly about the difference in the procedures used by the two types of banks. In addition, it was also noted that the quantum of MSME loans handled by the cooperative and the private sector banks together is much lower as compared to scheduled commercial banks in the state of Goa. The detailed hypotheses for the research leading to a proposed model of research were derived from the first stage of research.
In the second stage a pretested structured questionnaire was administered to the Bank Managers or senior loan officers from the branches of various public sector banks (including specialized SME branches) in the state of Goa.
Quantitative testing of the hypotheses is done in second stage of the research and final conclusions were drawn.
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1.8 ORGANISATION OF THESIS
The thesis consists of six chapters. The following is the outline of its contents.
The first chapter provides background for the study with introduction and highlights the significance of the study. The statement of the problem and the research objectives guiding this research are presented. The scope of the study is also presented.
The second chapter consists of literature review, of numerous studies carried out by researchers in banking, risk perceptions and MSME lending decisions literature. Theory of informational asymmetry, Institutional theory of three pillars of decision making and research studies in risk perception and perceived risk are also elaborated, providing theoretical background to this study and drawing of hypotheses.
The third chapter explains the research methodology adopted in this study and formulation of additional hypotheses. This chapter also explains scale development including content validity and reliability test leading to the development of the final questionnaires.
The fourth chapter reports the descriptive statistical results and analysis. The interpretation of the results then follows.
The fifth chapter present the results of the quantitative study based on statistical tests followed by the interpretations of results.
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The sixth chapter provides the contribution of the study to the MSME lending research. The results of this research are validated with the help of existing literature and presented in this chapter. The limitations of the study and directions for future research are provided and the chapter ends by stating the managerial implications of the study.
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The main objective of this research is to identify the the antecedents of perceived risk among the Bank Managers towards lending to Micro, Small & Medium Enterprises (MSME) and the impact that these factors have on diverse types of risk perception identified therein. In order to study this topic, existing literature on MSME lending, the decision-making criteria for lending, risk perceptions and existing theories and gaps are identified in this chapter.
The chapter begins with the scenario of Global and Indian Micro, Small and Medium Enterprises Sector. Lending practices and decision making to this sector, theoretical background followed by presentation of gaps, operational definitions, proposed model and formulation of hypothesis.
2.2 Global MSME Sector
Micro, Small and medium enterprises (MSMEs) play a significant role in economic development in every country. This sector contributes around 60 percent of total formal employment in the manufacturing sector in both advanced economies and in developing countries (Ayyagari et al, 2007).
2.3 MSME Sector in India
MSME sector has emerged as a highly vibrant and dynamic sector of the Indian economy over the last six decades. MSMEs not only play crucial role in providing large employment opportunities at comparatively lower capital cost than large industries but also help in
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industrialization of rural & backward areas, thereby, reducing regional imbalances, assuring more equitable distribution of national income and wealth. MSMEs are complementary to large industries as ancillary units and this sector contributes enormously to the socio- economic development of the country.
As per the results of fourth all India census of MSME, the sector contributes significantly to the number of enterprises, employment and output of the country. Based on the data sets of fourth all India census of MSME, augmented with data sets of Economic Census, 2005 and growth rate observed during fourth (1998) and fifth (2005) Economic Census, the performance of MSME sector is summarised as below.
Table 2.1 Performance of MSME Sector in India
Total working Enterprises (In lakhs)
Employment (In Lakhs)
Share of MSME
Manufacturing output in the total manufacturing output (%)
MSME in total GDP (%)
2006-07 361.76 805.23 42.02 35.13
2007-08 377.36 842 41.98 35.41
2008-09 393.7 880.84 40.79 36.12
2009-10 410.8 921.79 39.63 36.05
2010-11 428.73 965.15 38.5 36.69
2011-12 447.64 1011.69 37.47 37.97
2012-13 447.54 1061.4 37.33 37.54
2013-14 488.46 1114.29 NA NA
2014-15 510.57 1171.31 NA NA
23 | P a g e Source: Compiled from Annual Report 2014-15 of Ministry of Micro, Small, Medium Enterprises
2.4 Lending to MSME Sector
Adequate and timely availability of the credit are the two most important criteria for the growth and development of any sector in the economy. Despite being a catalyst to the economic development and being a driving force to the nation’s development, there are many challenges faced by MSME sector. This is prominently visible from the literature and the past economic data.
Access to institutional finance is one of the major constraints faced by MSMEs which not only hinders their own growth prospects, but also negatively affects the entire economy.
The banking system in India has been actively involved in financing various sectors of the economy, of which, financing of industry in general and financing of MSMEs in particular, has been of high significance. But despite the growth in other avenues of raising resources by the industry, there is a lack of adequate and timely provision of credit to the Industry (EPW Research Foundation 2009).
The statistics compiled in the Fourth Census of MSME sector September 2009 revealed that only 5.18% of the units (both registered and unregistered) had availed of finance through institutional sources, 2.05% had finance from non-institutional sources. Most units i.e.
92.77% had no finance or dependant on self-finance.
Credit flow to the SSI sector is not found to be adequate. This is evident from the Reserve Bank of India publications. It is also observed that bank credit flow to the sector is just 18.11
% of the production value, which is below the Nayak Committee norm i.e. minimum bank finance should be 20 per cent of projected production value.
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Many studies using firm-level survey data have shown that MSMEs not only perceive access to finance and the cost of credit to be greater obstacles than large firms, but these factors constrain MSME’s performance more than the large firms (Schiffer and Weder, 2001; Beck, Demirgüç-Kunt, and Maksimovic, 2005; and Beck, Demirgüç-Kunt, Laeven, and Maksimovic, 2006).
Banks have limited their exposure to the MSME sector as seen by the secondary data analysis of Lead Bank. As per this Secondary data, it is quite visible that the percentage growth in MSME credit is going down.
Table 2.2 Bank Credit to Micro and Small Enterprises OUTSTANDING BANK CREDIT TO MICRO AND SMALL ENTERPRISES (Rs
Outstanding credit of banks under Public Sector
Outstanding credit of banks under Private Sector
Outstanding credit of foreign banks
Outstanding credit of all scheduled Commercial Banks
2005 678 86 69 834
2006 824 (21.6) 104 (21.3) 84 (22.1) 1012 (21.3)
2007 1026 (24.4) 131 (26.1) 116 (38.0) 1273 (25.7) 2008 1511 (47.5) 469 (257.1) 155 (33.1) 2135 (67.7)
2009 1914 (26.6) 467 (0.0) 181 (16.6) 2561 (19.9)
2010 2784 (45.4) 645 (38.3) 211 (16.6) 3640 (42.1)
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2011 3766 (35.3) 879 (36.1) 215 (2.2) 4860 (33.5)
2012 3963 (5.2) 1105 (25.8) 218 (1.1) 5286 (8.8)
2013 5025 (26.8) 1547 (40.0) 300 (37.9) 6872 (30.0) 2014 6160 (23.4) 2001 (29.8) 300 (-1.76) 8504 (23.8) 2015 7015 (13.1) 2321 (15.6) 308 (4.6) 9645 (13.4) 2016 6669 (-4.94) 2456 (5.81) 247 (-19.8) 9373 (-2.8)
Source: Compiled from the Basic Statistical Returns of Scheduled commercial banks in India, Various Issues, Reserve bank of India.
Considering the importance of MSME in generation of employment, it has been given the priority status in lending by banks. Under the current norms, Schedule commercial banks are required to deploy 40% of their net bank credit in priority sector which includes agriculture, SSI and others. If banks do not satisfy the priority sector target, they are required to lend money to specific government agencies at very low rates of interest.
While there is a quota earmarked for agriculture, there is none for SSI. The % of the total credit given to SSI as a part of priority sector is decreasing over the years as visible from the table below.
Table 2.3 Composition of Priority Sector Priority Sector (Amount in Rupee Billion)
Total of which % composition
Year Agriculture SSI Other Agriculture SME Other
1990-91 429.15 167.50 171.81 89.84 39% 40% 21%
1991-92 454.25 181.57 181.50 91.18 40% 40% 20%
1992-93 498.32 199.63 200.26 98.43 40% 40% 20%
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1993-94 538.80 212.08 226.17 100.55 39% 42% 19%
1994-95 641.61 239.83 276.38 125.40 37% 43% 20%
1995-96 733.29 270.44 318.84 144.01 37% 43% 20%
1996-97 848.80 314.42 359.44 174.94 37% 42% 21%
1997-98 995.07 348.69 435.08 211.30 35% 44% 21%
1998-99 1146.11 396.34 484.83 264.94 35% 42% 23%
1999-00 1318.27 443.81 528.14 346.32 34% 40% 26%
2000-01 1544.14 519.22 560.02 464.90 34% 36% 30%
2001-02 1752.59 607.61 571.99 572.99 35% 33% 33%
2002-03 2116.09 735.18 603.94 776.97 35% 29% 37%
2003-04 2638.34 905.41 658.55 1074.38 34% 25% 41%
2004-05 3814.76 1252.50 745.88 1816.38 33% 20% 48%
2005-06 5107.38 1739.72 912.12 2455.54 34% 18% 48%
2006-07 6359.66 2303.77 1179.10 2876.79 36% 19% 45%
2007-08 7480.73 2753.43 1326.98 3400.32 37% 18% 45%
2008-09 9324.59 3386.56 1689.97 4248.06 36% 18% 46%
2009-10 10921.79 4161.33 2064.01 4696.45 38% 19% 43%
2010-11 12393.86 4603.33 2291.01 5499.52 37% 18% 44%
2011-12 13991.00 5226.23 2591.91 6172.86 37% 19% 44%
2012-13 15397.96 5899.14 5622.96 3875.86 38% 37% 25%
2013-14 18297.24 6659.79 7078.13 4559.32 36% 39% 25%
2014-15 20103.24 7658.8 8003.43 4441.01 38% 40% 22%
2015-16 22259.07 8825.9 8475.87 4957.30 40% 38% 22%
Source: Compiled from various issues of Handbook of statistics on Indian Economy.
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2.4.1 Non-Performing Assets (NPAs) associated with Priority Sector and its effect on Lending
Ever since the introduction of financial sector reforms in India, the NPA (non-performing assets) of the banking system have been getting attention. According to the literature the banks cannot book income on such accounts at the same time they are required to charge the funding cost and provision requirements to their profits (Patidar & Kataria, 2012). Bhaumik and Piesse, 2006 also points out that NPA provisioning regulation of the RBI discourages credit disbursal by banks.
Although this is true, Basu 2004 points out that bankers’ risk perception towards SMEs is heightened by the poor historical performance of SME loan portfolios, particularly loans made by the public-sector banks, which account for more than 90% of all lending to SMEs.
The study reports that non-performing loans (NPLs) of more than 15% of their total SSI loan portfolio. This is the reason for being risk averse to expanding their SSI portfolios, which conforms the findings of Talaulikar, Hegde Desai & Borde, 2014.
2.5 Decision making pertaining to bank lending
The bank loans are the most important external source for ﬁnancing the capital requirements of MSMEs (Barton and Matthew 1989; Meyer 1993; Winborg and Landstrom 2001), but Previous research have also found that MSMEs generally have diﬃculties obtaining such loans (Walker 1989; Binks, Ennew, and Reed 1992). The problem of inaccessible and inadequate finance is attributed to the fact that majority of MSMEs are privately held and owner managed entities. As quoted by Bruns & Fletcher, 2008, owner managed ﬁrms are less transparent and therefore these ﬁrms have an information advantage as compared to the external ﬁnancers. This is called information asymmetry (Binks & Ennew, 1996, 1997) and