Socio -Economic Impact Assessment of AFPL
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Socio -Economic Impact Assessment of AFPL
Table of Contents
ACKNOWLEDGEMENT ............................................................................................................ 11
EXECUTIVE SUMMARY .......................................................................................................... 12
List of Acronyms .......................................................................................................................... 14
INTRODUCTION ........................................................................................................................ 17
ABOUT THE MICROFINANCE SECTOR ............................................................................ 17
HISTORY OF MICROFINANCE ............................................................................................ 17
PRESENT SCENARIO OF MICRO-CREDIT ........................................................................ 18
...................................................................................................................................................................................
...............................20
MFI and COVID-19 .................................................................................................................. 20
The Model of Microfinance ...................................................................................................... 24
MICRO, SMALL AND MEDIUM ENTERPRISE .................................................................. 26
ABOUT SOCIO-ECONOMIC IMPACT ASSESSMENT....................................................... 27
Redefining the concept of impact assessment .......................................................................... 28
ABOUT THE PROJECT .......................................................................................................... 29
ABOUT THE ORGANIZATION............................................................................................. 29
HISTORY OF AFPL ................................................................................................................ 30
VISION, MISSION AND GOALS OF AFPL .......................................................................... 30
OBJECTIVES OF AFPL .......................................................................................................... 31
PRESENT STATUS OF AFPL ................................................................................................ 31
PRODUCTS OFFERED ........................................................................................................... 32
VARIOUS DEPARTMENTS IN AFPL ................................................................................... 34
WORK STRUCTURE .................................................................................................................. 36
WORK FLOW STRUCTURE ...................................................................................................... 36
General challenges that are faced in impact assessment research ............................................ 37
LITERATURE REVIEW ............................................................................................................. 38
CONCEPT MATRIX................................................................................................................ 38
RBI GUIDELINES FOR NBFC-MFI ...................................................................................... 38
PRUDENTIAL NORMS .......................................................................................................... 39
PRICING OF LOANS .............................................................................................................. 39
Universal client protection: ....................................................................................................... 39
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OTHER CUSTOMER PROTECTION MEASURES .............................................................. 40
RBI GUIDELINES/INSTRUCTIONS FOR LENDING TO MSME SECTOR ...................... 40
Issue of Acknowledgement of Loan Applications to MSME borrowers .............................. 40
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Collateral ............................................................................................................................... 40
Composite loan ..................................................................................................................... 41
Credit Linked Capital Subsidy Scheme (CLSS) ................................................................... 41
LOAN TERMS AND CONDITIONS OF AFPL ..................................................................... 42
MFI CLIENTELE ................................................................................................................. 42
MSME CLIENTELE ............................................................................................................ 43
LEVELS OF IMPACT OF MFI ............................................................................................... 43
ASSESSMENT OF THE IMPACT OF MICROFINANCE INSTITUTES ............................. 44
DOING IMPACT EVALUATION ........................................................................................... 45
Microfinance and the Millennium Development Goals in Pakistan: Impact Assessment Using
Propensity Score Matching (microfinance and financial inclusion in Pakistan) ....................... 46
Impact of Microfinance Institutions in Women Economic Empowerment: A Case of Butwal
Sub- Municipality, Nepal........................................................................................................... 47
Impact Of Microfinance Institutions On Growth And Development Of Small And Medium
Enterprises: A Case Study Of Machakos Town, Kenya ............................................................ 49
PROGRESS OUT OF POVERTY ............................................................................................ 50
Microfinance: An Effective Strategy to Reach the Millennium Development Goals ............... 50
International Labor Organization Framework for Impact Assessment ..................................... 51
Impact Assessment Framework of World Bank ....................................................................... 52
Methods employed in Impact Evaluation designs .................................................................... 53
Methodology and Sampling .......................................................................................................... 54
Research problem...................................................................................................................... 54
Objectives ................................................................................................................................. 54
Research Design........................................................................................................................ 54
Sampling Design ....................................................................................................................... 56
Data collection tool ................................................................................................................... 56
PRESENCE OF AFPL .................................................................................................................. 58
Approach for analysis ................................................................................................................... 59
Difference-in-difference analysis (DID) ................................................................................... 59
Paired t- test .............................................................................................................................. 60
Wilcoxon’s signed rank test ...................................................................................................... 61
Correlation and Regression ....................................................................................................... 62
Concept of brand equity with focus on Cognitive loyalty ........................................................ 63
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Digital Credit ............................................................................................................................ 65
PPI (PROGRESS OUT OF POVERTY INDEX) ..................................................................... 65
Propensity Score Matching (PSM) ........................................................................................... 67
Logistic Regression ................................................................................................................... 67
Adjudging overlap through pictorial inspection: .................................................................. 67
Data Analysis and Findings .......................................................................................................... 69
Economic Empowerment Indicator .......................................................................................... 69
Scoring .................................................................................................................................. 69
Income: ..................................................................................................................................... 73
Per capita income: ..................................................................................................................... 76
Livestock: .................................................................................................................................. 79
Assets ........................................................................................................................................ 81
Fuel ........................................................................................................................................... 83
Water ......................................................................................................................................... 85
Housings ................................................................................................................................... 88
Big emergency .......................................................................................................................... 92
Small emergency....................................................................................................................... 94
Savings ...................................................................................................................................... 96
Economic Empowerment Index ................................................................................................ 98
Social Empowerment .................................................................................................................. 100
Budget Making........................................................................................................................ 101
Decision making ..................................................................................................................... 103
MOBILITY ............................................................................................................................. 107
Social Media ........................................................................................................................... 109
Overall Social Empowerment Index ....................................................................................... 111
Risk Empowerment Index........................................................................................................... 113
General Risk Confidence ........................................................................................................ 113
Entrepreneurial Risk Confidence ............................................................................................ 115
Digital Readiness .................................................................................................................... 117
Risk Empowerment Index........................................................................................................... 120
COVID- SOCIAL FACTORS .................................................................................................... 123
Vaccine safety ......................................................................................................................... 123
PERCENT VACCINATED .................................................................................................... 124
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PER CAPITA EDUCATION DURING COVID ................................................................... 127
Socio -Economic Impact Assessment of AFPL
FOOD SECURITY ................................................................................................................. 129
COVID- ECONOMIC FACTORS ............................................................................................. 132
MORATORIUM ..................................................................................................................... 132
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If affected status: ..................................................................................................................... 134
CUTDOWN COST ................................................................................................................. 136
BUSINESS STATUS.............................................................................................................. 138
PPI ............................................................................................................................................... 142
MSME Client impact assessment: .............................................................................................. 152
Business General (PAN India) ................................................................................................ 152
CLIENT GENERAL (ZONE ) ............................................................................................... 157
ZONE 1 : central Northern: ................................................................................................ 158
Zone 2: Central India ......................................................................................................... 159
Zone 3: Eastern India .......................................................................................................... 160
Zone 4: South-western ........................................................................................................ 161
COVID General (IMPACT) ( PAN) ....................................................................................... 162
BUSINESS STRATEGY (PAN) ............................................................................................ 165
FOCUSED GROUP DISCUSSION (FGD) ANALYSIS ........................................................... 169
Purpose of taking loan ............................................................................................................ 169
Product Awareness.................................................................................................................. 170
Awareness regarding other credit options ............................................................................... 171
Client satisfaction .................................................................................................................. 171
Impact of Annapurna in Financial practices ........................................................................... 172
Entrepreneurial activities ..................................................................................................... 172
Quality of life .......................................................................................................................... 173
Social impact ........................................................................................................................... 174
Feedback ................................................................................................................................. 174
Diminishing interest rate of AFPL .......................................................................................... 175
Impact due to COVID ............................................................................................................. 175
Work status ............................................................................................................................. 177
FGD CONCLUSION .............................................................................................................. 177
Conclusion .................................................................................................................................. 179
STUDY LIMITATIONS ............................................................................................................ 179
Recommendations ....................................................................................................................... 180
Personification of target clients............................................................................................... 180
Persona 1: Sangeeta Devi ................................................................................................... 180
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Persona 2 : Jyoti Himrlka .................................................................................................... 182
Persona 3 : Kiran Bora ........................................................................................................ 183
Persona 4: Parsuram ............................................................................................................ 184
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Suggestions from our end to each persona: ............................................................................ 184
Annexure 1: MFI Questionnaire ................................................................................................. 186
Annexure 2: MSME Questionnaire............................................................................................. 196
Annexure 3: FGD Questionnaire ................................................................................................ 202
References: .................................................................................................................................. 206
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Figure 1:Microfinance Portfolio of MFIs ..................................................................................... 19
Figure 2: Borrower Outreach ........................................................................................................ 20
Figure 3:Measures taken by MFIs to assist borrowers.................................................................. 21
Figure 4:Hard Times of Small Borrowers ..................................................................................... 22
Figure 5:Crisis Management Roadmap......................................................................................... 23
Figure 6:Micro Finance Institution ............................................................................................... 24
Figure 7:Self Help Group Model .................................................................................................. 25
Figure 8: Micro,Small & Medium Enterprises ............................................................................. 26
Figure 9:The Objectives of AFPL ................................................................................................. 31
Figure 10: Operational Highlights ................................................................................................ 32
Figure 11: Annapurna Presence .................................................................................................... 32
Figure 12: The Work Flow Structure ............................................................................................ 37
Figure 13: Literature Review Concept Matrix .............................................................................. 38
Figure 14:Impact of Microfinance ................................................................................................ 44
Figure 15:Impact of Microfinance Institutions in Women Economic Empowerment,
Independent & Dependent Variables............................................................................................. 48
Figure 16:Sample population of SMEs Machakos town, Kenya .................................................. 49
Figure 17: Presence of AFPL ........................................................................................................ 58
Figure 18: Wilcoxon’s signed rank test ........................................................................................ 62
Figure 19:Concept of Brand Equity .............................................................................................. 63
Figure 20: PPI Scoring .................................................................................................................. 66
Figure 21: Scoring of Economic Empowerment Indicator ........................................................... 70
Figure 22: Scoring of Fuel ............................................................................................................ 70
Figure 23: Scoring of Water.......................................................................................................... 71
Figure 24: Scoring of House ......................................................................................................... 71
Figure 25: Scoring of Emergencies ............................................................................................... 71
Figure 26: Average Income Control ............................................................................................. 73
Figure 27: Average Income Experiment ....................................................................................... 73
Figure 28: Per Capita Income of Control Group ........................................................................... 77
Figure 29: Per capita Income of Experiment Group ..................................................................... 78
Figure 30: Livestock of Control Group ......................................................................................... 80
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Figure 31: Livestock of Experiment Group .................................................................................. 80
Figure 32: Average Expenditure in purchasing asset in last two years ......................................... 82
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Figure 33: Types of fuel used by control group clients ................................................................ 84
Figure 34: Types of fuel used by Experiment Group clients ........................................................ 84
Figure 35:source of water of control group clients 2 years before ............................................... 85
Figure 36:Source of water of control group client at present ........................................................ 86
Figure 37:source of water of experiment group clients 2 years before ......................................... 86
Figure 38:Source of water of experiment group at present ........................................................... 87
Figure 39Type of house before Control Group ............................................................................. 89
Figure 40 type of house after for control group ............................................................................ 89
Figure 41type of house before for experiment group.................................................................... 90
Figure 42 type of house after experiment group ........................................................................... 90
Figure 43 house score ................................................................................................................... 91
Figure 44 Big emergencies Experiment group
Figure 45 big emergencies Control group
...................................................................................................................................................................................
...............................93
Figure 46 Small emergencies Experiment group
Figure 47 Small
emergencies control group............................................................................................................. 95
Figure 48 savings experiment group
Figure 49 saving control group ........ 97
Figure 50 EEI ................................................................................................................................ 98
Figure 51 Budget making state wise before and after ..................................................................101
Figure 52 Budget making Control and Experimental group ........................................................102
Figure 53 Decision making control and experiment group ..........................................................103
Figure 54 Decision making categories .........................................................................................104
Figure 55 mobility........................................................................................................................107
Figure 56 mobility category wise.................................................................................................107
Figure 57 social media usage state wise ......................................................................................109
Figure 58 Social media usage ......................................................................................................109
Figure 59 Social empowerment index .........................................................................................111
Figure 60 Confidence index .........................................................................................................113
Figure 61 Entrepreneurial Risk ....................................................................................................115
Figure 62 Digital ready control and experiment group ................................................................117
Figure 63 Risk empowerment index ............................................................................................120
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Figure 64 Vaccine safety among control and experiment group .................................................123
Figure 65 : Average of people vaccinated among control and experiment group .......................126
Figure 66 Average of per capita edu- control, experiment and India ..........................................128
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Figure 67 avg of per capita experiment group
Figure 68 avg of per capita
edu control group
......................................................................................................................................................12
9
Figure 69 Food security control and experiment group ...............................................................130
Figure 70Moratorium Control group ...........................................................................................133
Figure 71 Moratorium experiment group.....................................................................................134
Figure 72 If affected control group ..............................................................................................135
Figure 73 if affected experiment group........................................................................................136
Figure 74Cut down cost control group ........................................................................................138
Figure 75 cut down cost experiment group ..................................................................................138
Figure 76 Business status of control group ..................................................................................139
Figure 77 Business status of experiment group ...........................................................................140
Figure 78: Type of business .........................................................................................................152
Figure 79: Reason for taking loan ................................................................................................153
Figure 80: Raw material purchase ...............................................................................................153
Figure 81 Networking of business ...............................................................................................154
Figure 82Net digi score ................................................................................................................154
Figure 83: Digital rollout .............................................................................................................155
Figure 84: Business Advice sources ............................................................................................156
Figure 85: Type of products .........................................................................................................156
Figure 86: Household assets ........................................................................................................157
Figure 87: Investment initially and at present ..............................................................................157
Figure 88: Difference in savings from initial phase .....................................................................158
Figure 89: Loan rating of AFPl ....................................................................................................158
Figure 90: Education qualification of Clients ..............................................................................159
Figure 91: Type of Business Clients are in ..................................................................................162
Figure 92: Time to recover from COVID lockdown ...................................................................163
Figure 93: Problems faced by Clients during COVID .................................................................163
Figure 94: Acumen to face financial emergency .........................................................................164
Figure 95: Difference in number. of employees ..........................................................................164
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Figure 96: Moratorium status .......................................................................................................165
Figure 97: Why AFPL? ................................................................................................................166
Figure 98: Competitors of AFPL .................................................................................................166
Figure 99: Comparison of loans taken .........................................................................................167
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Figure 100: Cash reserves ........................................................................................................... 167
Figure 101: Safety Index ............................................................................................................. 168
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ACKNOWLEDGEMENT
We would like to express our sincere gratitude to our Reporting officer, Ms. Ananya Pan, Deputy
Vice President of SPM, AFPL for her valuable advice, enthusiastic encouragement, and helpful
assessment, throughout this project. She provided us with a great learning experience and we are
thankful for her advice and support in keeping our development on track. We would also like to
express our heartfelt appreciation to Mr. Gaurav Shome, Mr. Yash Agarwal, and Mr. Priyanshu
Nayak for their insightful suggestions and recommendations for improving our online data
collection and survey. We would also like to thank all the Annapurna Department Heads for their
helpful and constructive ideas and suggestions during the project's planning and development. We
also appreciate the contributions of Unit managers, Area managers, Branch managers, and field
officers in aiding with online data collecting at their various locations. Finally, we would like to
thank Prof. Ujjal Sharma, SI Chairperson, Mr. Aditya Gupta, Placement Officer, Indian Institute
of Forest Management, Bhopal, for giving us such a wonderful opportunity to learn, study and
implement what we learned so far in our project.
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EXECUTIVE SUMMARY
Microfinance is not a new concept in India. It is a key instrument for raising poor people's living
standards. If we check in the history, we can see that the people have saved small amounts from
their earnings and also borrowed small amounts of money from individuals and groups in the
framework of self-help in order to launch new enterprises or new farming endeavors. So, we can
say that Microfinance is a program that helps disadvantaged rural people to pay off their debts
and retain their social and economic position in their communities. Microfinance provides
‘micro' credit with no collateral for reasons such as starting a small business, agricultural
purposes, acquiring assets or animals, and so on. Similarly, over the last five decades in India, the
micro, small, and medium enterprise (MSME) sector has evolved as a very active and brilliant
industry’s serve as auxiliary units to major enterprises and contribute significantly to India's
socio-economic growth. MSMEs not only aid in the development of rural and disadvantaged areas
but also play an important role in creating employment opportunities.
Microfinance has the potential to improve the socio-economic development of vulnerable and
disadvantaged people, particularly women, by establishing a community-based framework built
on mutual assistance and trust. They are more focusing on the rural poor people and aim to
promote financial inclusion by providing financial services to them. Microfinance impact
assessments have been more common in recent years, with programs utilizing them not just to
show the efficacy of microfinance, but also to improve it. As a result, impact assessment
supports community development and empowerment, as well as capacity building and social
capital development. The goal of a Social Economic Impact Assessment is to take a proactive
approach to development and achieve better development outcomes, not only to identify and
mitigate negative or unexpected consequences. It may be more essential to assist communities
and other stakeholders in identifying development goals and ensuring that good results are
maximized than it is to minimize harm from negative consequences. As all know, how Covid had
hit every corner of the world. Similarly, the microfinance sector was also affected by it.
Microfinance lenders have a vast reach throughout the world, but owing to covid 19 lockdowns
and people's incapacity to repay their debts, they now appear to be an enormous liability. The
industry must still find its way through while the epidemic unfolds.
This project's focus is on assessing the socio-economic impact of loans given by Annapurna
Finance to their clients. But due to the Covid-19 pandemic, the researchers were not able to go to
the field to collect and understand the lives of SHG/JLG members of Annapurna. But by
leveraging modern technology, the researchers delve deeper into the lives of SHG/JLG members
and determine the parameters which are significantly influencing the lives of them in social and
economic position. For this project, the study was carried out in 14 states - Assam, Bihar,
Chhattisgarh, Gujarat, Haryana, Jharkhand, Karnataka, Madhya Pradesh, Odisha, Punjab,
Rajasthan, Tamil Nadu, and West Bengal. The research procedure was the same for every state.
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The researchers were planning to propose a new product called digital credit to Annapurna and
also considered the MSME.so to get a better understanding of the digital credit, MSME,
microfinance industry, impact evaluation, and various methodologies that might be utilized to
estimate the socio-economic impact of AFPL on its clients, a thorough review of various
literature was conducted. after getting a clear picture about the impact assessment the researchers
created various methodologies to do the impact assessment. The sampling was done using
proportional sampling and by using mechanical sampling methods. For verifying the cutoff of the
lag period regression discontinuity was used.
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The data were collected virtually i.e., data were collected through telephonic calls and for these
collected data, to analyze them the researchers used Progress out of Poverty Index PPI,
difference-in-difference method DID, Propensity Score Matching PSM, and virtual FGD. To
determine the significance of the data, a two-sample paired t-test was used. It was followed by
recommendations and comments for the organization based on the analysis.
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List of Acronyms
SPM
Social Performance Management
AFPL
Annapurna Finance Private Limited
GIS
Geographic Information System
MFI
Microfinance Institutions
FY
Financial Year
SFB
Small Finance Bank
SHG
Self Help Group
JLG
Joint Liability Group
RBI
Reserve Bank of India
KYC
Know Your Customer
USAID
United States Agency for International Development
ADB
Asian Development Bank
MSDP
Microfinance Sector Development Program
ADBI
Asian Development Bank Institute
BC
Business Correspondent
USD
United States Dollar
SEWA
Self-Employed Women’s Association
SBLD
Self-help group Bank Linkage Programme
PSM
Propensity Score Matching
PPI
Progress Out of Poverty
SEIA
Socio-Economic Impact Assessment
NGO
Non-Governmental Organization
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DID
Difference In Difference Method
SME
Small and Medium Enterprise
RCT
Randomized Control Trial
RD
Regression Discontinuity
ODK
Open Data Kit
LPG
Liquefied Petroleum Gas
FGD
Focused Group Discussion
FCO
Field Collection Officer
BM
Branch Manager
HIL
Home Improvement Loan
HO
Head Office
HR
HR
MSME
Micro, Small and Medium Enterprise
NBFC
Non-Banking Financial Company
SWASTH
Safe Water and Sanitation to Households
CAGR
Compound Annual Growth Rate
MFIN
Microfinance Institutions Network
PMSVANidh PM Street Vendor's AtmaNirbhar Nidhi
i
CoC
Code of Conduct
QAR
Quarterly Adherence Report
PMEGP
Prime Minister Employment Generation Programme
NABARD
National Bank for Agriculture and Rural Development
BRAC
Bangladesh Rural Advancement Committee
KB
Khushhali Bank
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INTRODUCTION
ABOUT THE MICROFINANCE SECTOR
The microfinance sector represents a specialized field within the financial industry that caters
specifically to the financial requirements of individuals and communities with limited access to
conventional banking services. Its primary focus is on serving low-income individuals,
microentrepreneurs, and marginalized populations who lack the collateral and formal
documentation typically demanded by traditional financial institutions.
Central to the microfinance sector are microfinance institutions (MFIs), which serve as the
primary entities responsible for offering a range of financial products and services. These
offerings include microcredit (small loans), microsavings, microinsurance, and money transfers.
The sector's overarching objective is to foster financial inclusion, poverty reduction, and
socioeconomic development by tailoring financial services to individuals who are excluded from
mainstream banking channels.
The microfinance sector operates based on fundamental principles of inclusivity, sustainability,
and responsible lending. Its aim is to empower individuals and communities by providing them
with the necessary financial tools and support to engage in income-generating activities, establish
small businesses, and improve their economic well-being. Additionally, microfinance places
significant emphasis on generating positive social impact alongside financial viability, actively
addressing social issues and advocating for social justice.
Microfinance has gained global recognition for its potential to alleviate poverty, particularly in
developing countries where access to finance is limited. By offering small-scale financial
services, the sector endeavors to create opportunities for income generation, asset accumulation,
and overall economic empowerment. Furthermore, microfinance strives to promote financial
literacy, entrepreneurship, and women's economic participation, thereby contributing to social
and gender equality.
Over time, the microfinance sector has undergone substantial evolution, leveraging technological
advancements to expand its reach and enhance operational efficiency. The integration of digital
financial services, such as mobile banking and digital payments, has enabled MFIs to extend
their services to remote areas while simultaneously reducing transaction costs. This innovation
has opened up new avenues for advancing financial inclusion, amplifying the impact of
microfinance initiatives.
HISTORY OF MICROFINANCE
The history of microfinance can be traced back to the emergence of informal savings and credit
groups in the 18th and 19th centuries, where individuals pooled resources to provide small loans
within their communities. However, it was not until the mid-20th century that microfinance
gained formal recognition as a means to address poverty and financial exclusion.
A significant milestone in microfinance occurred in 1976 with the establishment of Grameen
Bank in Bangladesh by Muhammad Yunus, a Nobel laureate. Grameen Bank introduced the
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concept of microcredit, offering small loans to impoverished individuals, particularly women,
who lacked access to traditional banking services. This groundbreaking approach demonstrated
the creditworthiness of even the poorest individuals and their potential to leverage financial
resources for improving their lives. The history of microfinance can be traced back to the
emergence of informal savings and credit groups in the 18th and 19th centuries, where
individuals pooled resources to provide small loans within their communities. However, it was
not until the mid-20th century that microfinance gained formal recognition as a means to address
poverty and financial exclusion.
A significant milestone in microfinance occurred in 1976 with the establishment of Grameen
Bank in Bangladesh by Muhammad Yunus, a Nobel laureate. Grameen Bank introduced the
concept of microcredit, offering small loans to impoverished individuals, particularly women,
who lacked access to traditional banking services. This groundbreaking approach demonstrated
the creditworthiness of even the poorest individuals and their potential to leverage financial
resources for improving their lives.
The success of Grameen Bank inspired the proliferation of microfinance institutions (MFIs)
worldwide. These institutions combined social missions with sustainable business models,
aiming to empower individuals, encourage entrepreneurship, and promote financial inclusion.
Over time, different microfinance models evolved. The group lending model, popularized by
Grameen Bank, emphasized social collateral and peer support, allowing borrowers to access
loans without conventional forms of collateral. Individual lending models also gained
prominence, enabling more personalized financial services.
In the 1990s, microfinance gained international recognition, receiving endorsements from
organizations like the United Nations and World Bank as an effective strategy for poverty
reduction. The declaration of the International Year of Microcredit in 2005 further highlighted
the importance of microfinance in achieving development goals. This recognition led to
increased investments in microfinance initiatives by financial institutions, governments, and
philanthropic organizations, driving its expansion.
Despite its progress, microfinance encountered challenges such as over-indebtedness, high
interest rates, and aggressive lending practices, which prompted a shift towards responsible and
client-centered approaches. Lessons were learned regarding the significance of financial literacy,
borrower training, and comprehensive development interventions to ensure sustainable impact.
Technological advancements have revolutionized microfinance. The advent of mobile
technology and digital financial services has facilitated reaching underserved populations in
remote areas, lowering transaction costs, and offering a wider array of financial products. These
technological innovations have opened up new opportunities for expanding financial inclusion
and maximizing the impact of microfinance.
The success of Grameen Bank inspired the proliferation of microfinance institutions (MFIs)
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worldwide. These institutions combined social missions with sustainable business models,
aiming to empower individuals, encourage entrepreneurship, and promote financial inclusion.
Over time, different microfinance models evolved. The group lending model, popularized by
Grameen Bank, emphasized social collateral and peer support, allowing borrowers to access
loans without conventional forms of collateral. Individual lending models also gained
prominence, enabling more personalized financial services.
In the 1990s, microfinance gained international recognition, receiving endorsements from
organizations like the United Nations and World Bank as an effective strategy for poverty
reduction. The declaration of the International Year of Microcredit in 2005 further highlighted
the importance of microfinance in achieving development goals. This recognition led to
increased investments in microfinance initiatives by financial institutions, governments, and
philanthropic organizations, driving its expansion.
Despite its progress, microfinance encountered challenges such as over-indebtedness, high
interest rates, and aggressive lending practices, which prompted a shift towards responsible and
client-centered approaches. Lessons were learned regarding the significance of financial literacy,
borrower training, and comprehensive development interventions to ensure sustainable impact.
PRESENT SCENARIO OF MICRO-CREDIT
Currently, Microcredit remains highly relevant in fostering financial inclusion and supporting
marginalized individuals and communities. Its global growth has been substantial, benefiting
millions of borrowers with microloans. As reported by the Microfinance Barometer 2020, over
140 million borrowers worldwide accessed microfinance institutions (MFIs), resulting in a loan
portfolio valued at around $140 billion. Microcredit has made significant strides in regions like
Asia, Africa, and Latin America, with notable impact seen in countries such as Bangladesh,
India, and Peru in terms of reaching underserved populations and reducing poverty.
Technological advancements have played a pivotal role in expanding the accessibility of
microcredit. Through digital platforms and mobile banking, MFIs have been able to extend their
services to remote areas while reducing transaction costs. These innovations have streamlined
loan disbursement and repayment processes, improved operational efficiency and increased
opportunities for borrowers.
Regulatory frameworks for microcredit operations vary across countries, with the Reserve Bank
of India (RBI) serving as an example. In India, the RBI has implemented specific guidelines to
ensure responsible lending practices, consumer protection, and prevention of over-indebtedness.
These measures are intended to create a more sustainable and inclusive microcredit sector. The
social impact of microcredit remains highly positive, encompassing poverty alleviation, women's
empowerment, and the promotion of entrepreneurship. By granting access to financial services
and capital, microcredit enables individuals to establish and expand small businesses, generate
income, and enhance their overall economic well-being.
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Socio -Economic Impact Assessment of AFPL
The Asset Under Management (AUM) of MFIs is Rs 1,31,163 Cr as on 31 March 2023,
including owned portfolio Rs 1,07,232 Cr and managed portfolio (off BS) of Rs 23,931
Cr. The owned portfolio of MFIN members is about 77.5% of the NBFC-MFI universe
portfolio of Rs 1,38,310 Cr
On a YoY basis AUM has increased by 38.7% as compared to 31 March 2022 and by
15.7% in comparison to 31 December 2022
Loan amount of Rs 1,30,563 Cr was disbursed in FY 22-23 through 3.1 Cr accounts,
including disbursement of Owned as well as Managed portfolio. This is 59.3% higher
than the amount disbursed in FY 21-22
Average loan amount disbursed per account during FY 22-23 was Rs 42,010 which is an
increase of around 12.9% in comparison to the last financial year.
As on 31 March 2023, the borrowings O/s were Rs 97,420 Cr. Banks contributed 60.3%
of borrowings O/s followed by 22.2% from non-Bank entity, 9.3% from AIFIs, 4.1%
from other sources and 4.1% from External Commercial Borrowings (ECB).
During FY 22-23, NBFC-MFIs received a total of Rs 74,787 Cr in debt funding, which is
59.2% higher than FY 21-22. Banks contributed 69.2% of the total Borrowing received
followed by non-Bank entities 21.0%, AIFIs 6.7%, ECB 1.8% and Others 1.3%
Total equity increased by 25.4% as compared to end of Q4 FY 21-22 and is at Rs 26,332
Cr as on 31 March 2023. Portfolio at Risk (PAR)>30 days as on 31 March 2023 has
reduced to 4.0% as compared to 9.7% as on 31 March 2022.
MFIs have presence in 27 states and 5 union territories. In terms of regional distribution
of portfolio (GLP), East and North-East accounts for 32% of the total NBFC-MFI
portfolio, South 26%, North 17%, West 15%, and Central contributes 10%, n.d.)
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MODEL OF MICROFINANCE
Figure 6: JLG Micro Finance Models
Microfinance in India encompasses various models that cater to the financial requirements of
underserved individuals and drive inclusive growth. Here are three prominent microfinance
models operating in the Indian context:
Self-Help Group (SHG) Model: The SHG model is widely implemented in India, playing a
pivotal role in empowering marginalized communities and women. SHGs are voluntary groups
comprising 10 to 20 members who pool their savings and contribute to a common fund. These
groups engage in regular meetings, savings mobilization, and collaborative decision-making.
SHGs can access loans from banks based on their collective creditworthiness and the guarantee
provided by their members. This model prioritizes financial literacy, capacity building, and
social empowerment.
An illustration of the SHG model in India is the National Rural Livelihoods Mission (NRLM),
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initiated by the Ministry of Rural Development. NRLM aims to strengthen and establish SHGs
across the country by offering capacity-building support, credit accessibility, and livelihood
opportunities, particularly in rural areas. Through NRLM, SHGs have succeeded in generating
income, improving livelihoods, and uplifting the socio-economic status of their members.
Microfinance Institutions (MFI) Model: MFIs play a crucial role in extending financial
services to underserved individuals, particularly in remote and rural regions of India. Operating
under the individual lending model, MFIs directly provide small loans to borrowers based on
their individual creditworthiness and repayment capacity. These loans are typically utilized for
income-generating activities, such as initiating or expanding small businesses. Leveraging
technology and innovative lending methodologies, MFIs bridge the gap for borrowers who lack
access to formal banking services.
An example of the MFI model in India is SKS Microfinance, which obtained a non-banking
financial company (NBFC) license as one of the pioneering MFIs in the country. SKS
Microfinance primarily focuses on providing microcredit to low-income individuals, particularly
women, in rural and semi-urban areas. Through its network of branches and innovative loan
products, SKS Microfinance has facilitated entrepreneurship, enhanced livelihoods, and
promoted financial inclusion among its clients.
Government-Sponsored Microfinance Programs: The Indian government has introduced several
microfinance programs to address the financial needs of marginalized populations and foster
inclusive development. These programs aim to facilitate credit accessibility, promote
entrepreneurship, and enhance livelihoods. Collaborating with banks, MFIs, and self-help
groups, the government ensures effective implementation of these programs.
An instance is the Pradhan Mantri MUDRA Yojana (PMMY), launched in 2015, which provides
collateral-free loans to micro and small enterprises. The loans offered under PMMY are
categorized into three segments: Shishu (up to INR 50,000), Kishore (from INR 50,000 to INR 5
lakh), and Tarun (from INR 5 lakh to INR 10 lakh). PMMY is implemented through diverse
financial institutions, including public and private sector banks, MFIs, and non-banking financial
companies (NBFCs). The program has facilitated access to finance for micro-entrepreneurs,
empowering them to initiate or expand their businesses.
These microfinance models, encompassing the SHG model, MFI model, and governmentsponsored programs, demonstrate the diverse approaches taken in India to promote financial
inclusion, empower marginalized communities, and foster entrepreneurship. Each model has
made significant contributions to poverty alleviation, livelihood enhancement, and socioeconomic
development
in
various
parts
of
the
country.
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Figure 7: Self Help Group Model
A JLG is a Joint Liability Group, which is a group of five women and is formed by the
members with assistance, guidance and supervision of the Centre Officer (Field Officer). The
members support each other emotionally and financially by guaranteeing the repayment of each
of their loans. The peer pressure and close social ties ensure the timely repayment of loans as well
credit discipline.
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The members jointly stand liable for the repayment of the loan disbursed to each of them. A
guarantee notes for the same is taken when the group is formed; as these microloans are
collateral-free, hence peer pressure is the only way to ensure timely recovery. Like most MFIs
focused on providing loans only to women as it is established that women are prudent borrowers,
as they are better end-users of money and endeavor to uplift their families.
JLG is a proven concept of risk-free lending in India and other parts of the globe. In India, many
MFIs have been successfully implementing this model of lending with more concentration on the
southern and eastern parts of the country.
ABOUT THE MSME SECTOR
The MSME sector, short for Micro, Small, and Medium Enterprises, is an essential part of the
economy in many countries, including India. It encompasses a wide range of businesses that differ
in size, investment, and operational scale. MSMEs are characterized by their relatively small size
and are typically owned and operated by individuals or families.
MSMEs play a critical role in driving economic growth, fostering innovation, creating jobs, and
promoting entrepreneurship. They make significant contributions to a country's GDP and
contribute to inclusive and sustainable development.
The classification of MSMEs may vary, but generally, it is based on criteria such as the number of
employees, annual turnover, and investment in plant and machinery or equipment. In India, for
example, MSMEs are classified based on their investment in these assets.
MSMEs are diverse and operate in various sectors, including manufacturing, services, agriculture,
and trade. They often target specialized markets, offer unique products or services, and contribute
to regional development. MSMEs are known for their agility in adapting to changing market
conditions and have the potential to create jobs, particularly in rural and semi-urban areas.
Despite their significant contributions, MSMEs face challenges such as limited access to finance,
inadequate infrastructure, complex regulations, and skill gaps. Governments and financial
institutions recognize the importance of supporting the MSME sector and have implemented
policies and programs to address these challenges. These initiatives aim to facilitate access to
credit, promote technology upgrades, simplify regulations, provide skill development
opportunities, and improve market connections for MSMEs.
The MSME sector is dynamic and responsive to market changes, making it a crucial component
of any economy. It serves as a breeding ground for innovation, entrepreneurship, and job creation,
fueling economic growth and fostering social inclusivity. Governments and stakeholders continue
to prioritize the development and growth of the MSME sector through targeted interventions and
supportive policies, recognizing its significance.
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Microfinance NBFCs (MFI-NBFCs) focus on serving the micro enterprise population. In 2012,
regulations were made to formally bring MFIs into the purview of NBFC regulations so that RBI
can keep a check on their lending rates and capital inflow. According to RBI regulations, 85
percent of the loan portfolio of an NBFC-MFIs should be to borrowers whose annual income does
not exceed INR 60,000 (USD 923) in rural areas, and INR 120,000 (USD 1846) in semi-urban
areas, with loans being capped at INR 50,000. As there is no restriction on the way an NBFC-MFI
can hold the balance 15 percent of the assets many of these MFIs are looking to scale up their
business by tapping into the credit needs of the micro enterprises.
Currently MFI-NBFCs serve 87 percent of the total MFI customer base, and are at the forefront of
providing innovative loan products with flexible payment plans and micro-insurance, majorly to
the micro enterprise segment.
In terms of Ministry of MSME, Go, Office Memorandum (OM) F. No. 12(4)/2017-SME dated
March 8, 2017, it is clarified that for ascertaining the investment in plant and machinery for
classification of an enterprise as Micro, Small and Medium, the following documents could be
relied upon:
(I) A copy of the invoice of the purchase of plant and machinery; or
(ii) Gross block for investment in plant and machinery as shown in the audited accounts; or
(iii) A certificate issued by a Chartered Accountant regarding purchase price of plant and machinery.
Further, the Ministry has clarified that for the investment in plant and machinery for the purpose
of classification of an enterprise as Micro, Small or Medium, the purchase value of the plant and
machinery is to be reckoned and not the book value (purchase value minus depreciation)
The effective date for the above provision would be from the date MSMED Act, 2006 came into
force and not prospectively. The above provisions would be applicable to section 7 (1) (a) and
section 7 (1) (b) of the MSMED Act, 2006 i.e., enterprises engaged in manufacturing of goods
and rendering of services as well. The detailed instructions in this regard were issued to the
Scheduled Commercial Banks vide our circular FIDD.MSME & NFS. BC. No.
10/06.02.31/2017-18 dated July 13, 2017.
ABOUT SOCIO-ECONOMIC IMPACT ASSESSMENT
Around the world, the provision of microfinance is becoming a mainstream development
intervention for poverty alleviation and empowerment of the poor. Microfinance involves the
provision of thrift, credit and other financial services and products of very small amounts for
enabling the poor to raise their income levels and improving living standards. As a concept, it
emerged in the early 1970s with the recognition
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that the poor need a wide range of financial services including credit, savings, and insurance and
money transfers. Microfinance is operated through small groups. Homogeneity in terms of
socioeconomic conditions and levels of living form the basis for group formation. Experience
around the world reveals that this group-based approach can equip the poor to access financial
services on easy terms and conditions. Microfinance has the capacity to enhance the socioeconomic development of the vulnerable and marginalized group, especially women by creating
a community-based structure that builds mutual support and trust. The goal of impact assessment
is to bring about a more ecologically, socio-culturally and economically sustainable and
equitable environment. Impact assessment, therefore, promotes community development and
empowerment, builds capacity, and develops social capital (social networks and trust).
Social Impact Assessment includes the processes of analyzing, monitoring and managing the
intended and unintended social consequences, both positive and negative, of planned interventions
(policies, programs, plans, projects) and any social change processes invoked by those
interventions. Its primary purpose is to bring about a more sustainable and equitable biophysical
and human environment.
The focus of concern of Social Economic Impact Assessment is a proactive stance to
development and better development outcomes, not just the identification or amelioration of
negative or unintended outcomes. Assisting communities and other stakeholders to identify
development goals, and ensuring that positive outcomes are maximized, can be more important
than minimizing harm from negative impacts.
HISTORY OF MSME
The history of the MSME sector can be traced back to the early stages of industrialization and
economic progress. The emergence of small and medium enterprises can be attributed to the
growth of cottage industries and artisanal businesses during the pre-industrial era. During the
18th and 19th centuries, small-scale enterprises played a vital role in local economies,
contributing to trade, commerce, and employment. These businesses were typically familyowned, specializing in specific crafts or trades, and served as the foundation of local economies,
meeting the needs of the community and sustaining livelihoods. The advent of industrialization
in the late 19th century brought about changes in the business landscape with the rise of factorybased production and mass manufacturing. Despite these changes, small enterprises continued to
thrive by adapting to evolving market conditions and finding their place in specialized industries.
In the mid-20th century, governments worldwide recognized the potential of small-scale
industries for economic development and job creation, leading to the formulation of policies to
support their growth. In India, after gaining independence in 1947, specific attention was given
to promoting small-scale industries. The establishment of institutions like the Small Industries
Development Bank of India (SIDBI) and the National Small Industries Corporation (NSIC)
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aimed to provide financial and technical assistance to small enterprises. The Industrial Policy
Resolution of 1956 further demonstrated the government's commitment to fostering small-scale
industries and encouraging entrepreneurship.
Over time, the MSME sector in India has undergone significant transformations. The
government has introduced various initiatives, including the creation of dedicated industrial
estates, the implementation of financial schemes, and the simplification of regulatory procedures,
all aimed at supporting the growth of small and medium enterprises.
In recent years, the MSME sector has gained increased recognition for its substantial
contributions to economic growth, employment generation, and export earnings. Governments
and policymakers continue to prioritize the development of MSMEs by implementing measures
to enhance their competitiveness, facilitate access to credit, promote technology adoption, and
stimulate innovation.
The history of the MSME sector underscores its resilience and ability to adapt to changing
economic landscapes. From traditional artisanal businesses to modern small enterprises, the
sector has played a pivotal role in the socio-economic development of nations, fostering
entrepreneurship and facilitating inclusive growth.
PRESENT SCENARIO OF MSME
The current state of the MSME sector in India is characterized by its noteworthy contribution to
the economy in terms of employment generation, GDP growth, and export performance. With a
workforce exceeding 110 million individuals, MSMEs play a critical role as one of the largest
providers of employment in the country. The sector encompasses a wide array of enterprises
spanning various industries. In regard to GDP, MSMEs account for approximately 30% of
India's total economic output, emphasizing their significance in driving progress and
advancement. Additionally, MSMEs exhibit substantial potential for exports, bolstering the
nation's export earnings through the provision of diverse products and services. While accessing
finance remains a challenge for MSMEs, initiatives like the Pradhan Mantri Mudra Yojana
(PMMY) strive to mitigate this concern by offering collateral-free loans to micro and small
enterprises. Embracing technology is also pivotal for MSMEs to enhance their competitiveness,
prompting the government to introduce schemes and programs that promote digitalization. Policy
reforms and support, including the implementation of the Goods and Services Tax (GST) and
measures to simplify business operations, further foster an environment conducive to the growth
and development of MSMEs in India.
The micro sector, comprising approximately 630.52 lakh enterprises, constitutes more than 99%
of the total estimated number of MSMEs. The small sector, with around 3.31 lakh enterprises,
accounts for 0.52%, while the medium sector, with approximately 0.05 lakh enterprises,
represents 0.01% of the total estimated MSMEs. Out of the total estimated 633.88 MSMEs,
about 51.25% or 324.88 lakh MSMEs are located in rural areas, while the remaining 48.75% or
309 lakh MSMEs are situated in urban areas.
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Fig
Out of a total of 633.88 MSMEs, a significant majority of 608.41 lakh MSMEs (approximately
95.98%) were proprietary concerns. The ownership of proprietary MSMEs was predominantly
held by males, with men owning 79.63% of enterprises compared to 20.37% owned by females.
This pattern of male dominance in ownership was consistent in both urban and rural areas,
although it was slightly more pronounced in urban areas, where 81.58% of enterprises were
owned by men, compared to 77.76% in rural areas.
MSME MODELS
In India, the MSME sector encompasses diverse models tailored to the specific needs and
characteristics of small and medium enterprises. Here are three prominent models commonly
observed:
Proprietorship Model: The proprietorship model is widely prevalent, especially among smallscale businesses. In this model, a single individual owns and manages the business, assuming
complete responsibility for its operations, profits, and liabilities. Proprietorship offers simplicity
in setup and decision-making, making it a preferred choice for many Indian entrepreneurs and
small businesses. This model allows individuals to initiate ventures with minimal regulatory
requirements and provides flexibility in terms of ownership and control.
Partnership Model: The partnership model is another popular structure for Indian MSMEs. It
involves collaboration between two or more individuals to establish and operate a business
together. Partnerships are governed by agreements outlining the roles, responsibilities, profitsharing arrangements, and decision-making processes among partners. This model facilitates the
pooling of resources, skills, and expertise, enabling partners to leverage their collective strengths.
Partnerships are commonly seen in professional services sectors, such as legal firms, accounting
practices, and consultancies.
Private Limited Company Model: The private limited company model is a well-recognized and
regulated form of business structure in India. It provides limited liability protection to
shareholders and establishes a distinct legal identity for the company. To establish a private
limited company, registration under the Companies Act is mandatory. This model enables
businesses to raise capital by offering shares to investors, fostering growth and expansion
opportunities. Private limited companies in India adhere to company law and follow well-defined
corporate governance practices.
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In addition to these models, India also embraces variations like limited liability partnerships
(LLPs) and cooperatives, which cater to specific types of MSMEs. The choice of the appropriate
model depends on factors such as the business's nature, ownership preferences, liability
considerations, and growth aspirations of entrepreneurs. Recognizing the significance of the
MSME sector, the Indian government has implemented various initiatives and policies to support
its growth and development. These include facilitating access to finance, promoting technology
adoption, simplifying regulations, and facilitating market linkages.
ABOUT THE PROJECT
The topic of the project is the study of the socio-economic impact assessment of PAN India for the
financial year 2020-2021 of both the MFI and MSME loans provided by Annapurna Finance. The
study includes research about the lives of the SHG/JLG, small businesses and enterprise
members which are based on the parameters that affect the social and economic status of the
clientele. This research also guided us to decide the current status (Good or poor) of women
SHG/JLG, business enterprises and other income generating activities under them, if any.
There can be various indicators/Parameters which symbolize the successful functioning of good
SHGs and an equal number of indicators could be found in the case of poor-performing
SHGs/JLGs. Some of these indicators could be tangible, which could be observed, whereas
others could be in intangible form; they are not physically present, but their presence can be
sensed and inferred.
ABOUT THE ORGANIZATION
Annapurna Finance Pvt. Ltd (AFPL) was established in 2009 and is now one of the top ten
NBFC-MFIs in the country. It has its roots as a part of a not-for-profit entity, Peoples Forum, an
NGO which worked for the development and welfare of unserved sections of the society which
was started by Mr. Gobinda Chandra Pattnaik. The microfinance activities started with the
inception of Mission Annapurna Finance under Peoples Forum from the year 2005. The
organization was established to serve economically backward clients by bringing them to the
mainstream, providing need-based financial services at their doorstep. The focus has been clear,
to reach the areas where formal financial institutions find it
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unprofitable to settle in. Its objectives have not only been limited to just reach and serve but also
providing financial and technical support to strengthen entrepreneurial skills for the effective and
efficient undertaking of business activities. Annapurna Finance, over the years, has continued to
innovate in its products and delivery mechanisms, to make the whole product life cycle of microcredit as relevant as possible for its clients. The aim is to offer multiple need-based products,
which can serve specifically all the customer life cycle needs of micro-credit.

Mission ASHRA- An initiative to support the mentally ill and destitute woman.

Ama GHARA- An initiative carved out of ASHRA that provides a home and
rehabilitation center for homeless children.

SAKHI- One-stop center to address all types of issues related to women

Mission ASHA- An initiative to provide primary education to unprivileged children

Aanand Bhandar- An initiative to provide nutritional and healthy midday meals to
school children.

Mission Annapurna- An initiative out of which Annapurna Microfinance was carved
out with a vision to serve the unprivileged population and reduce their dependence on
informal sources of finance.
HISTORY OF AFPL
Annapurna started its operations under mission Annapurna in 2007. In 2009 it acquired NBFC
(Gwalior Fin and Leasing Co), and in 2010 it became AMPL (Annapurna Microfinance Private
Limited). Various products like SWASTH loan, Education loan, Dairy loan, MEL loan, HIL,
JLG micro-credit and SAMARTH loan were started in a period of two years from 2014 to 2016.
In 2017, the "SA" rating for good social performance management was assigned to Annapurna by
MicroFinanza. In 2018 AMPL changed its name to AFPL (Annapurna Finance Private Limited).
VISION, MISSION AND GOALS OF AFPL
Vision-Establishment of a self-sustainable and economically empowered rural, tribal & sub-urban society.
Mission- Empowerment of 20,00,000 poor women and households for their economic security
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by 2020. Bring recognition, legitimacy, respect and opportunity for 5,00,000 micro-entrepreneurs
by 2020.
Goal - Increase availability of a wider range of microfinance services & improve the ability of
20,00,000 poor women for efficient use of such services by the year 2020.
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PRESENT STATUS OF AFPL
At present AFPL is operational in 18 states with a network of 856 branches and an employee base
of 7341, an outstanding portfolio of ₹4560.86 crores with a client base of 1.9mn. (As of February
2021) (Annapurna finance Pvt. Ltd., 2021)
Also, On the day of Diwali, 14th November 2020, Annapurna Finance Pvt. Ltd (AFPL)
inaugurated 101 branches all over the country. Even though there was a pandemic situation in the
country, Annapurna Finance Pvt. Ltd. went ahead with the decision and the inauguration of the
branches was done by a virtual medium keeping the pandemic situation in mind.
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PRODUCTS OFFERED

Group loans - smaller loans for income generation are given to the members of SHGs
and JLG's, for a tenure of 12-24 months. At an interest rate of 21.5% (reducing). The loan
amount varies from Rs 10000 to 80000 in different loan cycles. Insurance provided INR
5.6 in every Rs.1000 of loan for one year. Repayment frequency Weekly/fortnightly/
Monthly as per the borrower’s choice. Processing fees 1% of loan amount +GST.
Mainly caters to:
•
•
•
•
•
Agriculture Crops: Cultivation of different crops & land development
crops like Paddy, sugarcane, mushroom, Vegetable, groundnut etc.
Agri Equipment Finances: Bullock and Bullock cart, threshers, tractor,
Power tiller, Paddy reaper, Water Pump set & lift Irrigation etc.
Agri Allied: Dairy farming, Poultry, goat rearing, Piggery, Fishery etc.
Micro Enterprise: Spice making, dry food processing, Individual business,
Beetle shop, Fruit shop, Fast food stalls, paddy processing unit etc.
Handicrafts and Handlooms: Weaving cotton sarees, Bamboo products, coir
production, Brass work etc.

SWASTH Loan- Safe Water and Sanitation to Households, this loan is provided to
individuals to build facilities like toilets, and purchase water purifiers, which helps in
improving their overall health and hygiene. This loan is provided for a tenure of 12-24
months, at an interest rate of 21.5%(Reducing). Repayment frequency
Weekly/fortnightly/ Monthly as per the borrower’s choice. Processing fees are 1% of loan
amount + Service Tax.

MSME Finance- This loan is provided to micro, small, and medium enterprises to
support their working capital needs. The loan is utilized by customers for upgrading or
renovating existing business infrastructure, replenishment of stock, expanding the
business operations etc. This loan is given at an interest rate of 18-26% (reducing) for a
tenure of 12-120 months. Collateral required for the loan amount above 3 lacs. The
repayment frequency is monthly and processing fees are 2% + service tax.

Home Improvement Loan- This is a short-term loan provided to the customers for
repairs and maintenance of their residences. This loan is provided for the tenure of 12-48
months, at an interest rate of 21.5% (reducing). The processing fee is 1% + GST.
 SAMARTH Loan- This loan is provided to persons with a disability, single mothers,
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Impact
Assessment
unmarried women, widows,
transgender
and
members of
ofAFPL
the leprosy-affected community
(Individual Lending). It is provided for a tenure of 12-36 Months, at an interest rate of
22% (reducing). The main purpose of this loan is to empower the people who are
excluded from the general category of financial inclusion in society. The repayment
frequency is monthly and processing fees are 1% of Loan Amount + service tax.

Housing Finance- This loan is provided to customers with a formal or informal source of
income for home construction, flat purchase, home renovation and balance transfer. It is
provided for a tenure of 12-240 months, at an interest rate of 14-26% (Reducing), loan
amount varies from Rs100,000-300,000 for unsecured loans and Rs 300,000-2500, 000
for secured loans (where the collateral is mandatory). The repayment frequency is
monthly and processing fees are 2% + service tax.

Dairy Development Loan- This loan is provided to the customer for purchasing cross
breed cow/buffalo, cattle shed and other dairy equipment. This loan is given for the
tenure of 12 – 36 months and the amount vary from Rs 40000-150000 at the interest rate
of 23%(reducing). This loan is given to customers who already have experience in the
dairy business. Insurance provided INR 5.6 in every Rs.1000 of loan for one year.
Repayment frequency Weekly/fortnightly/ Monthly as per the borrower’s choice.
Processing fees 2% of loan amount + Service Tax, Cattle Insurance 4% per annum + Tax.

Solar Loan- Solar Loan allows the clients to buy solar lights, which are cost-effective
and eco- friendly. This is a short-term loan that is provided for the tenure of 6-12 months
at an interest rate of 23% (reducing). Processing fees are 1% of loan amount + GST.

Consumer Durable Loans- This loan is provided to support clients to fulfil their needs
and buy durable consumer products with fewer burdens on them. This loan is provided
for a tenure of 6- 12 months, at an interest rate of 22.5% (reducing). Processing fees are
1% of loan amount + GST.

Sandhi Loan- This loan is a special micro-credit facility scheme for providing affordable
loans to street vendors. The scheme enabled the street vendors to resume their livelihoods
that have been adversely affected due to COVID-19 lockdown. This loan is provided for a
tenure of 12 months, at an interest rate of 21.5% (reducing). The repayment frequency is
monthly and processing fees are 1% of loan amount + GST.
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VARIOUS DEPARTMENTS IN AFPL

Audit Department: The audit department has played an important role in setting up State
Audit Committees in the zones where it is operating at present and ensuring that every
deviation and problem is addressed. The audit department is also responsible for finding
out ways in which it can help AFPL to counter the existing problems and the upcoming
possible risks. It is responsible for operational and field level checks as well as H.O.
Audits of all the departments and represents the H.O. functions audit in Board Meetings.

Technology Department: The technology department is responsible for releasing the
regular updates of software and other technologies used to smoothen up the various
processes taking place in the organization to achieve targets and for the organization's
steady growth. The department is driven by three forces of reduction in time, human
efforts and errors.

Credit Department: The credit department is responsible for all the credit-related
decisions that involve checking the creditworthiness of loan applicants. The credit team
comprises employees and Appraisal Officers who are deployed in various states
depending upon the business need and the work gets monitored by the Credit managers of
respective areas.

Human Resources Department: The HR department of Annapurna is responsible for
looking after the recruitment process and training of new employees. It is also responsible
for looking after the work culture of the organization, talent development and employee
engagement related programs within the organization.

Solar Loan- Solar Loan allows the clients to buy solar lights, which are cost-effective
and eco- friendly. This is a short-term loan that is provided for the tenure of 6-12 months
at an interest rate of 23% (reducing). Processing fees are 1% of loan amount + GST.

Consumer Durable Loans- This loan is provided to support clients to fulfil their needs
and buy durable consumer products with fewer burdens on them. This loan is provided
for a tenure of 6- 12 months, at an interest rate of 22.5% (reducing). Processing fees are
1% of loan amount + GST.

Sandhi Loan- This loan is a special micro-credit facility scheme for providing affordable
loans to street vendors. The scheme enabled the street vendors to resume their livelihoods
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that have been adversely affected due to COVID-19 lockdown. This loan is provided for a
tenure of 12 months, at an interest rate of 21.5% (reducing). The repayment frequency is
monthly and processing fees are 1% of loan amount + GST.
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
Product Department: The product department plays a major role in offering products
that can enable the clients to access financial services, which in turn improves their
livelihood opportunities and standard of living. Recently, the department has introduced
privilege loans, at lesser interest rates, to its customers who are loyal and have completed
three loan cycles of the product. Other than product diversification, the product
department is also focusing on catering for the needs of new customer segments with less
formal income sources and documented income proof. In future, it plans to expand the
product line by offering credit and non-credit based products.

GIS Department: GIS department plays a vital role in extracting rich insights from
spatial analysis, which is more accessible to on-field staff and middle to lower-level
managers. The staff is given the training to enable them to view and interpret maps without
supervision, which would help them to make an informed decision on targeting specific
areas combining several interrelated indicators seamlessly. The product and Operations
department has also started using insights gained from the GIS department.

Risk Department: Risk management is an integral part of a financial institution's
strategic decision-making process to ensure that its corporate objectives are consistent
with an appropriate risk-return trade-off. The Risk Department is responsible for
conducting RCSA periodically for which each department identifies the risks arising
from its products and processes which are assessed based on their degree on a scale for
severity and frequency. The Risk Department needs to monitor the key risks indicators
identified by each department and get them captured in dashboards. The dashboard
monitors and visualizes the indicators that get breached above the threshold limit.

Finance and Research Department: The finance and operations department are
responsible for the outreach as well as the impact of Annapurna's services in various
servicing states of the country. Maintaining the portfolio, generating portfolio growth,
increasing the outreach and disbursement of loans are responsibilities of the Finance and
Operations Department.

SPM Department: Apart from providing microcredit loans to the rural community,
Annapurna has also equally invested in improving the conditions and standard of living of
its clients through microfinance plus services. Annapurna religiously follows the
Universal Practices of Social Performance Management, taking into consideration the
relevant guidelines defined by other regulatory bodies. Annapurna periodically tracks the
relevant and necessary social information like demographics, the standard of living and
poverty statistics of the clients. Studies such as Impact assessment, client satisfaction &
customer feedback etc. are carried out by this department only. Annapurna's client base
mostly comprises women from rural areas who are not much aware of financial services
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that they could make use of, due to which they don't take active participation in household
financial decision making. Annapurna provides clients training on financial literacy and
basic hygiene, to enable them to make appropriate choices.
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WORK FLOW STRUCTURE
An assessment of the socio-economic impact of AFPL clients was conducted, and a final report
summarizing the findings and conclusions was presented. The assessment work was divided into
two main phases: desk work and field work. The desk work phase encompassed three distinct
stages, namely orientation and planning, pilot study, and data analysis and report generation. The
orientation and planning stage spanned one week and involved familiarizing ourselves with
AFPL, its operations, and various products offered. Additionally, we conducted a thorough study
of relevant articles, research papers, and journals to gather insights for the project. We also held
discussions regarding the research methodology, sampling technique, sample size determination,
and modes of data collection. Questionnaires for individual interviews, MSMEs, and FGDs were
prepared, reviewed and approved by AFPL mentors, thereby preparing us for the subsequent
field survey and data collection phase.
The pilot study, conducted over a period of two days, aimed to assess the viability of the
questionnaires and identify any potential challenges in data collection. Data was gathered from a
limited number of clients, both from the microfinance institutions (MFIs) and micro, small, and
medium enterprises (MSMEs) branches in three AFPL locations. The analysis of the pilot survey
data allowed us to identify any shortcomings in the questionnaires, which were then revised and
finalized accordingly.
The field survey and data collection phase involved visiting 76 branches of AFPL across 14
states. Data was collected from 2180 MFI sample points and 380 MSME sample points, resulting
in a total sample size of 2560 clients. The states covered during data collection included Bihar,
Chhattisgarh, Gujarat, Haryana, Jharkhand, Karnataka, Madhya Pradesh, Maharashtra, Odisha,
Punjab, Rajasthan, Tamil Nadu, West Bengal, and Assam.
Following the completion of the 40-day fieldwork, all the interns regrouped at the Head Office.
The data collected by each intern was compiled in Google Sheets, undergoes cleaning and
sorting procedures to ensure its quality. Subsequently, the consolidated data were subjected to
analysis. The findings, observations, and recommendations were documented in the report and
presented during a formal presentation. Any necessary adjustments were made based on the
outcomes of the analysis.
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Figure 12: The Work Flow Structure
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COMPREHENSIVE RBI GUIDELINES
Here is an elaborative explanation of the RBI guidelines for Non-Banking Financial Companies
(NBFCs) operating as Microfinance Institutions (MFIs):

Registration: NBFCs intending to operate as MFIs must obtain registration from the
Reserve Bank of India (RBI) under the provisions of the Reserve Bank of India Act,
1934. This registration ensures that the NBFC-MFIs operate within the regulatory
framework defined by the RBI.

Minimum Net Owned Fund (NOF): NBFC-MFIs are required to maintain a minimum
Net Owned Fund of ₹5 crore (Indian Rupees Five Crore) for entities operating in all
areas, except for North Eastern states where the minimum requirement is ₹2 crore (Indian
Rupees Two Crore). The NOF acts as a measure of the financial strength and stability of
the NBFC-MFI.

Borrower Eligibility: NBFC-MFIs should extend loans only to low-income households,
particularly those in rural areas, who have a demonstrated means of repayment. This
criterion ensures that loans are provided to those who genuinely require financial support
and have the ability to repay the borrowed amount.

Loan Amount and Tenure: The aggregate amount of loans extended to a borrower by
NBFC-MFIs should not exceed ₹1,50,000 (Indian Rupees One Lakh Fifty Thousand).
Additionally, the tenure of loans exceeding ₹30,000 (Indian Rupees Thirty Thousand)
should be a minimum of 24 months. These limits help prevent over-indebtedness and
encourage responsible lending practices.

Pricing of Loans: The interest rate charged by NBFC-MFIs should be determined based
on the guidelines provided by the RBI. It should take into account factors such as the cost
of funds, risk factors, and reasonable returns for the NBFC-MFI. The methodology for
calculating the interest rate and processing fee must be transparent and disclosed to the
borrowers.

Group Lending Model: NBFC-MFIs typically follow the group lending model, wherein
loans are extended to self-help groups (SHGs) or Joint Liability Groups (JLGs). These
groups provide collective responsibility for loan repayment, reducing the risk for
individual borrowers and promoting a supportive environment for timely repayment.

Fair Practices: NBFC-MFIs are expected to adhere to fair practices and ensure
transparency in their operations. They should disclose all relevant information to
borrowers regarding the terms and conditions of the loan, including interest rates,
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processing fees, and any other charges. This transparency helps borrowers make
informed decisions and protects them from exploitative practices.

Grievance Redressal: NBFC-MFIs must establish a robust grievance redressal
mechanism to address borrower complaints effectively. The contact details of the
designated officer responsible for grievance redressal should be communicated to the
borrowers. This mechanism ensures that borrowers have a channel to raise concerns or
seek resolution in case of any grievances.

Data Reporting: NBFC-MFIs are required to submit periodic reports to the RBI,
providing information on their financial position, lending activities, and other relevant
data, as specified by the central bank. This reporting helps the RBI monitor the
functioning of NBFC-MFIs, assess their compliance with regulations, and gather insights
for policy formulation and systemic stability.
These comprehensive guidelines aim to ensure responsible lending practices, protect the rights of
borrowers, and promote the stability and development of the microfinance sector in India. By
complying with these guidelines, NBFC-MFIs contribute to the inclusive growth of the economy
while providing vital financial services to low-income households and fostering entrepreneurship
in rural areas.
PRUDENTIAL NORMS
Following prudential norms have been specifically made applicable to NBFC-MFIs:
Capital adequacy ratio: 15 percent of the aggregate risk weighted assets
Asset classification: A loan asset is recognized as a non-performing asset if
interest/principal payment is overdue for 90 days or more.
Provisioning requirements: The loan provisions should be higher of –
• 1 percent of the outstanding loan portfolio, or
• 50 per cent of the aggregate loan instalments which are overdue for
more than 90 days and less than 180 days and 100 per cent of the
aggregate loan instalments which are overdue for 180 days or more.
PRICING OF LOANS
NBFC-MFIs are required to comply with the following norms for pricing of microfinance loans:
(i)
(ii)
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They are permitted to charge only three components viz., interest charge,
processing fees (limit of 1 percent of gross loan amount) and insurance premium on
actual basis.
Interest rate should be lower of –
a. cost of funds plus margin of 10 per cent for NBFC-MFIs with loan portfolio exceeding ₹100
crore and 12 per cent for others;
Socio -Economic Impact Assessment of AFPL
b. 2.75 times of the average base rate of the five largest commercial banks.
c. The average base rate of the five largest commercial banks is announced by RBI at
the end of each quarter which determines the interest rate for the ensuing quarter.
Universal client protection
To ensure responsible lending in an evolving industry, within the broad framework provided by
the RBI Master Directions for NBFC-MFIs, RBI Fair Practices Code, Industry Code of Conduct
(CoC), SRO has framed rules, standards and benchmarks for MFIN member NBFC MFIs.
Adherence to Industry CoC and MFIN Standards is mandatory for the membership of MFIN,
ensuring client protection.
While the Code of Conduct has been in place since years and is revised periodically, it was in
2019 that MFIN, jointly with Sa-Dhan & FIDC, released the Code of Responsible Lending
(CRL). Applicable to all providers in the microcredit space, by adopting the CRL, lenders have
committed themselves to universally accepted principles of customer protection.
CRL is guided and overseen by a Steering Committee representing various peer groups as well as
the Self- regulatory Organization (SROs), MFIN and Sa-Dhan. The Steering Committee is
Chaired by Dr. Harun R Khan (Former Deputy Governor, RBI). The adherence to CRL norms by
Providers is checked through a Quarterly Adherence Report (QAR) based on independent data
from a Credit Bureau. Based on the
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performance by the Provider on QAR, the Provider is certified as a ‘Responsible Lender’ by the Steering
Committee along with an adherence score.
OTHER CUSTOMER PROTECTION MEASURES
Certain other customer protection measures have been specifically made applicable to NBFCMFIs which include the following:
(i)
(ii)
(iii)
(iv)
(v)
(vi)
Not more than two NBFC-MFIs can lend to the same borrower.
No security deposit/ margin shall be collected from the borrower.
There shall be no penalty charged on delayed payment.
All sanctions and disbursement of loans shall be done only at a central location.
Recovery shall normally be made only at a central designated place. Field staff shall
be allowed to make recovery at the place of residence or work of the borrower only if
borrower fails to appear at the central designated place on two or more successive
occasions.
Every NBFC-MFI is required to become a member of at least one self-regulatory
organization (SRO) recognized by RBI and is also required to comply with the code
of conduct prescribed by the SRO.
RBI GUIDELINES/INSTRUCTIONS FOR LENDING TO MSME SECTOR
Issue of Acknowledgement of Loan Applications to MSME borrowers
Banks are advised to mandatorily acknowledge all loan applications, submitted manually or
online, by their MSME borrowers and ensure that a running serial number is recorded on the
application form as well as on the acknowledgement receipt. Banks are further advised to put in
place a system of Central Registration of loan applications, online submission of loan
applications and a system of e-tracking of MSE loan applications.
Collateral
Banks are mandated not to accept collateral security in the case of loans up to Rs.10 lakh
extended to units in the MSE sector. Banks are also advised to extend collateral- free loans up to
Rs. 10 lakhs to all units financed under the Prime Minister Employment Generation Programd
(PMEGP) administered by KVIC.
Banks may, on the basis of good track record and financial position of the MSE units, increase
the limit to dispense with the collateral requirement for loans up to Rs.25 lakh (with the approval
of the appropriate authority).
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Banks are advised to strongly encourage their branch level functionaries to avail of the Credit
Guarantee Scheme cover, including making performance in this regard a criterion in the
evaluation of their field staff.
Composite loan
A composite loan limit of Rs.1 crore can be sanctioned by banks to enable the MSE
entrepreneurs to avail of their working capital and term loan requirement through Single
Window.
Credit Linked Capital Subsidy Scheme (CLSS)
Government of India, Ministry of Micro, Small and Medium Enterprises had launched Credit
Linked Capital Subsidy Scheme (CLSS) for Technology Upgradation of Micro and Small
Enterprises subject to the following terms and conditions:
(i)
Ceiling on the loan under the scheme is Rs.1 crore.
(ii)
The rate of subsidy is 15% for all units of micro and small enterprises up to loan
ceiling at Sr. No. (I) above.
(iii)
Calculation of admissible subsidy will be done with reference to the purchase price
of plant and machinery instead of term loan disbursed to the beneficiary unit.
(iv)
SIDBI and NABARD will continue to be implementing agencies of the scheme.
(i)
Streamlining flow of credit to Micro and Small Enterprises (MSEs) for facilitating timely and
adequate credit flow during their ‘Life Cycle’:
In order to provide timely financial support to Micro and Small enterprises facing financial
difficulties during their ‘Life Cycle’, guidelines were issued to banks vide our circular
FIDD.MSME & NFS.BC.No.60/06.02.31/2015-16 dated August 27, 2015 on the captioned
subject. Banks are advised to review and tune their existing lending policies to the MSE sector
by incorporating therein the following provisions so as to facilitate timely and adequate
availability of credit to viable MSE borrowers especially during the need of funds in unforeseen
circumstances:
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i)
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To extend standby credit facility in case of term loans
Socio -Economic Impact Assessment of AFPL
ii)
Additional working capital to meet with emergent needs of MSE units
iii)
Mid-term review of the regular working capital limits, where banks are
convinced that changes in the demand pattern of MSE borrowers require
increasing the existing credit limits of the MSMEs, every year based on the
actual sales of the previous year.
iv)
Timelines for Credit Decisions
LOAN TERMS AND CONDITIONS OF AFPL
MFI CLIENTELE
The loan will be given to a borrower whose household annual income for rural areas does not
exceed ₹1,25,000 while for urban/sub-urban areas, it does not exceed ₹ 2,00,000.
In the first cycle, the loan amount should not exceed ₹ 75,000, and in subsequent cycles, it should
not exceed ₹ 1,25,000.
Borrower's total indebtedness should not exceed Rs. 1,25,000 excluding loans for
education and medical expenses.
When the loan amount exceeds Rs.30,000, the total loan tenure should be at least 24 months.
Loans should not be granted to customers who have previously obtained loans from any
of the other two MFIs.
When it comes to the final pricing of the loan, there will only be three components: the
interest charge, the processing charge, and the insurance premium (which includes the
administrative charges).
Maximum interest rate charged is fixed at 24% per annum at present on a declining basis.
Borrowers should not be required to pay a security deposit.
Loan processing fee for all qualifying asset products (excluding vehicle loan) is fixed at
1% of the loan amount disbursed exclusive of service tax as applicable.
The disbursement amount for all loans must be in multiples of Rs.1,000 only.
Loans should be made without the use of collateral and there should not be any
prepayment penalty.
The loan amount is repayable in weekly, fortnightly, or monthly instalments, depending
on the borrower's preference.
To fulfil RBI KYC Guidelines and Anti-Money Laundering Standards, the following
process must be followed for KYC compliance:
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Compulsory capture of primary and secondary KYC for all newly formed as well as
second and subsequent loan cycle groups, with the exception of Assam and North Eastern
states, which are on the exception list. Both IDs will be used to conduct a credit bureau
check.
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Clients from all states except Assam and the North Eastern States are required to
obtain an Aadhaar ID.
All states with the exception of Assam and Odisha are required to capture the voter ID
for 90% loans.
A minimum of 80% voter identification is required in Assam and Odisha.
Secondary KYC: Ration Card, MGNREGA Job Card (Annapurna Finance Private Limited, 2018).
MSME CLIENTELE
The loan size ranges from ₹ 50,000 - ₹ 25,00,000
Collateral is required for loans greater than ₹ 3,00,000
The existing loan tenure is 12 - 120 months with monthly repayment frequency.
The rate of interest ranges from 18% to 26% per annum on a declining basis.
Processing fees are capped at 2% plus service tax.
LEVELS OF IMPACT OF MFI
Broadly speaking there are three categories of impact due to microfinance and these categories are:
i)
ii)
iii)
Economic
Socio-cultural
Psychological
The economic category includes accumulation of wealth, changes in income, reducing
vulnerability, outcome of level of enterprises etc., the socio-cultural category includes change in
power relationships (status position), shift of economic decision making from men to women,
social and cultural diversity etc.; and the psychological category includes women empowerment,
psychological strength due to financial strength, political empowerment etc.
The components of impact assessment are: (i) poverty reduction, (ii) gender mainstreaming (i)
social development and (iv) institutional development. Poverty reduction can have
subcomponents like change in income, saving, assets positions, consumptions (food, social
obligations, clothes, education, entertainment etc.). Gender mainstreaming can have subcomponents like women in decision making, participation, business or income generation
network building etc. Social development has the subcomponents like employment generation
education, health, migration etc.
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Figure 14: Impact of Microfinance
Household level · The impact assessment programs should capture the changes in household
level due to microfinance programs. The household economic positions like income,
expenditure, assets position. livelihood portfolios etc. may be changed over time due to the
increasing access of households to microfinance products and services. The socio-psycho chances
can be experienced at the household level i.e., the change in literacy, migration, gender equality,
health, social status etc.
Individual level · In general, effective microfinance programs bring a positive change in
individual level. It develops managerial ability among the beneficiaries and increases status and
position not only in the society but also in the house/family. The increase in capacity
development due to microfinance programs leads to a change in an individual's income level,
expenditure pattern, living condition, literacy position, awareness, accessibility, and equity and
equality to the household and community assets etc.
Enterprise level - Microfinance programs influence microenterprises operations le., change in
profits, scale of operations, diversifications etc.
Though the ambit of socio-economic impact assessment is ever expanding and hopping onto the
trend another dimension of risk was added. Especially in this tough time of covid impact the
assessment through the lens of pandemic was quintessential too. In addition to capturing the
enterprise level impact, this year MSME was brought into the ambit of studying.
ASSESSMENT OF THE IMPACT OF MICROFINANCE INSTITUTES
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There were times when bank loans were unavailable to the people on the base of the pyramid.
And the need for money for any business, emergency or any other cause took them to be
exploited by informal money lenders (Da Silva, 2007). So lately the emergence of an institute
which aims to tackle poverty by providing them loans in needed times has been witnessed.
These institutes known as microfinance
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institutes have made drastic progress in the last few decades. But are these institutes really
helping the class of society? Is the creation of Joint liability groups a way forward? Or is the
outreach of MFIs increasing to the remotest areas? (Hermes, Lensink; 2007)
To find these answers the impact of microfinance is being evaluated and it is not a new concept. It
started with the study by Mahabub Hossain in 1988 where he carried out an extensive MFI
assessment “Credit for the Alleviation of Rural Poverty: The Grameen Bank in Bangladesh”. The
basic and most astonishing results of that study were that the average household income of the
participant group was 43% more than the non-participants. Also, the increment of the total
revenue of households was also highest for the landless members of the beneficiary group.
After that over the years impact assessment of microfinance was one of the most discussed topics
among the researchers and a plethora of literature has been published. Various economists used
different tools to understand the impact of microfinance on the poorest. And most of the research
shows that microfinance has impacted the broad range of people in both social as well as
economical front. Which was for the first time statistically proven by Sahidhur Khandker and
Mark Pitt in 1988. In the paper “Fighting Poverty with Microcredit” & “The Impact of GroupBased Credit Programs on Poor Households in Bangladesh: Does the Gender of Participants
Matter?” Khandker and Pitt assessed the impact of three programs Grameen Bank, Bangladesh
Rural Advancement Committee (BRAC) and RD-12. And that’s how the measurement of impact
assessment started to evolve towards more accurate methods. So, there are two three most
discussed among the researchers, and a combination of which we have used to evaluate the
impact of AFPL.
DOING IMPACT EVALUATION
It is very important to note that Dean Karlan and Nathanael Goldberg in their paper titled
“Impact of Microfinance” says that the definition of “small” and “poor” has a great impact on
what exactly constitutes microfinance. Moreover, if the programs do not provide sufficiently
similar services to a sufficiently similar target group, it is difficult to determine why one program
may perform better than another. In this context, they came up with four reasons to evaluate
different interventions and decide how best to allocate scarce resources. Firstly, an impact
evaluation is akin to good market and client research. As a result, an impact evaluation does not
even need to be considered an activity outside the bounds of good business practices. Profitable
businesses can and should invest in learning how to have the greatest positive impact on their
clients. By increasing customer loyalty and wealth, the institution is more likely to keep the clients
for a longer period of time and to provide them with the services they require, resulting in
increased profitability. Secondly, even financially self-sufficient financial institutions frequently
receive indirect subsidies from donor agencies in the form of soft loans or free technical
assistance. Thirdly, impact evaluations are not simply about measuring whether a given program
is having a positive effect on participants. Impact evaluations provide practitioners and
policymakers with critical information about the types of products and services that work best for
specific types of clients. Lastly, while many microfinance programs aim to be for-profit entities,
not all are. Many are non-profit organizations, and some are government-owned.
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According to Karlan and Goldberg, there are at least nine traditional features of microfinance: -
Small transactions and minimum balances (whether loans, savings, or insurance),
loans for entrepreneurial activity,
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collateral-free loans,
group lending,
target poor clients,
target female clients,
simple application processes,
provision of services in underserved communities,
market-level interest rates.
Following are the impact indicators mentioned by Karlan and Golberg in their journal: -
1.
2.
3.
4.
Enterprise Income
Consumption or Income Levels (Poverty)
Consumption Smoothing
Broader Impacts-children’s education and nutrition, housing stock, empowerment, and
social capital (Karlan & Goldberg, 2007)
Microfinance and the Millennium Development Goals in Pakistan: Impact Assessment Using
Propensity Score Matching (microfinance and financial inclusionin Pakistan)
For Pakistan, Financial inclusion is regarded as a critical way of achieving the aim of inclusive
economic growth. It is estimated that around 6.5 million people require financial inclusion, with
just about 5% supplied by microfinance. To improve the microfinance service and to expand the
accessibility of microfinance services the Khushhali Bank (KB) was founded in August 2000. It
was created with the assistance of the Asian Development Bank as part of the Government of
Pakistan's poverty reduction plan. The major goal of the Khushhali Bank is to provide long-term
microfinance services to the underprivileged to decrease poverty and promote economic growth
through community building and social mobilization. Later it became an essential component in
the Microfinance Sector Development Program (MSDP) in Pakistan.
To identify and measure how much Khushhali Bank contributed, the Asian Development Bank
Institute conducted a KB study in May-June 2005. The study included 2,881 households. Among
them, 1416 households are KB borrowers and the rest 1465 are nonborrowers. For the impact
assessment, ADBI suggested the PSM method. The idea behind this technique is to select a
control group that has observable features comparable to the treated group but did not take part in
the intervention. So, by comparing the difference in outcomes of these groups, the researcher can
identify whether there is any impact or not. Here the control groups are the nonborrowers (1465)
and the treated groups are the KB borrowers (1416). Here they used the Nearest-Neighbor (or
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one-to-one) matching so that they got observations with
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the closest propensity scores. This study aims to estimate the program's average effect on the KB
borrowers.
Impact of Microfinance Institutions in Women Economic Empowerment: A Case of Butwal SubMunicipality, Nepal
The purpose of this study was to evaluate the role of microfinance in Butwal Sub-Municipality,
Rupandehi district.
The overall goal of this research is to examine the impact of microfinance on the socioeconomic
status of rural women in the Rupandehi district.
The study's specific objectives are as follows:
To investigate the relationship between women's economic empowerment and the
functions of MFIs in Butwal-Sub municipality.
To investigate whether there is a difference in women's empowerment conditions across
MFIs in Butwal-Sub municipality.
A conceptual framework has been developed to help understand the role of MFIs. The dependent
and independent variables comprise the conceptual framework. Economic empowerment of
women is regarded as a dependent variable in this study, and this variable is dependent on the
independent variables listed below.
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Figure 15: Impact of Microfinance Institutions in Women Economic Empowerment,
Independent & Dependent Variables
To achieve the study's objectives, descriptive and analytical research designs were used.
According to the NRB, 76 MFIs (51 Microfinance Development Banks and 25 NGOs) were
providing microfinance services in Nepal as of mid-October 2017. Butwal-Sub district has nearly
eleven MFIs.
A five-point Likert Scale questionnaire has been designed to collect primary data on MFI
functions and women's economic empowerment. The collected data have been analyzed by using
the statistical tools with the help of Statistical Package for Social Science (SPSS).
The correlation has been used to determine the connection between the dependent variable and
variables that are unrelated. Regression analysis and the ANOVA test are carried out in this
study. For data dependability, Cronbanch's Alpha is used for testing.
Findings -
According to the findings of the study, respondents have a positive attitude and are
satisfied with the services provided by MFIs.
There is a scarcity of practical training programs offered by microfinance institutions.
The variables, credit facility, income status, saving facility, and insurance facility are all
positively correlated with women empowerment.
Women's empowerment is most dependent on credit facilities.
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Impact Of Microfinance Institutions On Growth And Development Of Small And Medium
Enterprises: A Case Study Of Machakos Town, Kenya
The survey design was used to investigate the impact of microfinance institutions on the growth
and development of SMEs in Machakos town. Because several SMEs were sampled, a
descriptive survey was appropriate for the study.
This study's target population included microfinance institutions as well as small and mediumsized businesses in Machakos. Machakos is a Kenyan town located 64 kilometers southeast of
Nairobi.
Machakos town offers growth opportunities for SMEs due to its rapid expansion and proximity to
Nairobi. Microfinance institutions in the area are likely to have an impact on the growth and
development of SMEs in the sector. The list of SMEs in Machakos Municipal Council was used
to create the sampling frame for SMEs. To select SMEs to participate in the study, stratified
random sampling was used.
Figure 16: Sample population of SMEs Machakos town, Kenya
Data analysis was carried out using quantitative and qualitative techniques. The qualitative data
analysis process entailed explaining information obtained from the empirical literature and
answering open- ended questionnaire questions. Quantitative analysis entailed the use of
numerical measures to determine the scores of responses provided.
A multiple regression analysis was performed to determine the relationship between MFI
financing of SMEs, provision of financial literacy skills, development of management skills
among SMEs, and facilitation of marketing and growth and development of SMEs in Machakos
town.
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Results and Discussion Many SMEs cited the provision of finances and business loans as one of
the products that aided their growth and expansion. The effects of MFI lending to SMEs on the
growth and development of small and medium-sized businesses, as measured by various
assertions, revealed that the majority of MFI respondents are neutral.
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The study also revealed that, when it comes to the rate of expansion based on various parameters,
few respondents can attribute their growth to financial literacy skills provided by MFIs.
Managerial skills in SMEs are critical in preparing SME owners to face changes in the business
environment and plan appropriate technological changes. This is demonstrated by 45.7 percent
of respondents who believe that SMEs owners and managers who are trained in management
skills are better able to grow their businesses.
The facilitation of marketing as a service provided by MFIs received a neutral response from 54.5
percent of respondents.
PROGRESS OUT OF POVERTY
The Progress Out of Poverty Index (PPI) is a tool that enables a microfinance institution (MFI) to
estimate its clients' poverty rate. The PPI is a high-quality poverty measurement tool with the
potential to add significant value to MFIs looking to implement quantitative, evidence-based
strategies for monitoring the poverty outreach objectives outlined in their social mission. The PPI
also enables MFIs to provide quantitative evidence of their poverty outreach to external donors
and lenders, who may use this evidence to make funding decisions. Because it is a collection of
non-financial indicators such as housing type, family size, typical foods eaten by the family, and
the number of children attending school, the PPI can be used as a poverty assessment and
targeting tool. Grameen Foundation (Grameen Foundation, 2008)
Microfinance: An Effective Strategy to Reach the Millennium Development Goals
Eradicating Poverty - Microfinance enables poor people to protect, diversify, and
increase their sources of income, which is a critical step toward escaping poverty and
hunger. The ability to borrow a small sum of money to capitalize on a business
opportunity, pay school fees, or bridge a cash-flow gap can be a first step toward breaking
the cycle of poverty. Microfinance also protects low-income households from eviction.
Loans, savings, and insurance help to smooth out income fluctuations and keep
consumption levels stable even during hard times. The availability of financial services
acts as a buffer for unexpected emergencies, business risks, seasonal slumps, or events that
can push a poor family into destitution, such as a flood or a death in the family.
According to a study of SHARE clients in India, there has been a significant shift in
clients' employment patterns—from irregular, low-paid daily labor to diversified sources
of earnings, increased employment of family members, and a strong reliance on small
business.
Promoting Children’s Education - One of the first things poor people around the world
do with their new microenterprise income is invest in their children's education.
According to studies, children of microfinance clients are more likely to attend school
and stay in school longer. Dropout rates among students are much lower in microfinanceclient households. A longitudinal study in a BRAC area in Bangladesh discovered that
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basic competency in reading, writing, and arithmetic among children 11–14 years old in
member households increased from 12% at the start of the programd in 1992 to 24% in
1995. In 1995, only 14% of children in non-member households passed the education
competency tests.
Improving Health Outcomes for Women and Children - Illness is generally the most
important crisis for poor families. Deaths in the family, taking time off from work when
sick, and health-care
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related expenses can deplete incomes and savings. They can lead to selling assets and
indebtedness. Households of microfinance clients appear to have better nutrition, health
practices, and health outcomes than comparable non-client households. Larger and more
stable incomes generally lead to better nutrition, living conditions, and preventive health
care. Increased earnings and financial management options also allow clients to treat
health problems promptly rather than waiting for conditions to deteriorate.
Empowering Women - Women have historically been the primary clients of
microfinance programs. Women are frequently more financially responsible than men,
with better repayment performance. Perhaps most importantly, access to financial
services can empower women to become more confident, assertive, involved in family
and community decisions, and better able to confront systemic gender inequities.
However, such empowerment is far from automatic— gender-related issues are complex.
Appropriate program design can have a strong, positive impact on women's
empowerment, resulting in women owning more assets, playing a more active role in
family decisions, and investing more in family welfare.
International Labor Organization Framework for Impact Assessment
Step 1 - Prepare for the Impact Evaluation
Clearly define the program objective.
Prepare a results chain.
Set up a monitoring system with appropriate indicators and data-collection mechanisms.
Write down the learning objectives and evaluation questions.
Identification of an array of impact evaluation methods.
Step 2 - Define timeline and budget
Step 3 - Set up an evaluation team
Step 4 - Develop an evaluation plan
1. Creating a sampling strategy –
a. Determine the population of interest.
b. Identify a sampling frame.
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c. Draw the desired number of units from the sampling frame using one of the
available sampling methods:
2. Planning the data collection -
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a. Determine the timing of the data collection.
b. Check for existing versus new data.
c. Selecting the data collection process and techniques.
Step 5 - Develop and pilot a survey instrument
Designing and testing the survey which includes the questionnaire design, internal review of the
questionnaire, piloting and revision to address the issues.
Step 6 - Conduct a baseline survey and analysis
1.
2.
3.
4.
Collecting the baseline data shortly before the program begins.
Validity testing of the data and protection of the data.
Quality data entry.
Analysis and Report Writing
Step 7 - Disseminate the findings
Impact Assessment Framework of World Bank
For the socio-economic impact assessment of MFI, the research is separated into four major steps:
1.
2.
3.
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Pre-evaluation assessment: Before starting the evaluation, the research team should
have a fair idea of the needs for the project and what are we looking for after the
research. To do so team first need to have an understanding of
a.
Characteristics of the intervention like what are the segment of people we are
evaluating.
b.
Objectives of the interventions
c.
Basic outcomes and indicators to evaluate the results
Evaluation design: Since the team has a fair idea of intervention and also of the
impact the team is looking for, this step is to understand the methods to design the
study to complete it in the most efficient manner and to ensure the results are not
biased. Also, the major idea behind the step is to avoid redundancy because from data
collection to data evaluation, these steps require a lot of resources.
a.
So, to optimize the evaluation process, a proper plan for research is mandatory
which requires following steps.
a.
Review what kind of data is available and what kind of data is also needed to
evaluate the impact
b.
Understand the evaluation and find a suitable methodology for the research.
Data collection: After a roadmap of the study, the major part of evaluation is
Socio -Economic Impact Assessment of AFPL
collection of data. To optimize the data collection first thing is to understand the what
kind of data is needed and after that accordingly
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a.
b.
Design a survey which includes all the indicators we are looking for,
Run a pilot questionnaire testing to ensure the validity of questions and also to
understand the practical nuances of the survey.
c.
Conducting field work to collection of data is also a crucial part of research as
it provides a clear understanding of the ground. This step also helps to gather
qualitative information along with quantitative data.
d.
Validating and processing of data is the last step of the data collection process
which gives a clear picture of validity of data and validity of evaluation
process. And processing of the data where the extraction of needed information
is taken care of the raw data which does not account for any information which
makes sense for the evaluation.
Analysis of results: After the data collection part, it is to analyses the results,
comparison of control and treatment group. And most importantly presentation of the
results in a manner that makes sense to the needed people. Which shows the impacts
and gives a comprehensive overview of the project.
4.
Methods employed in Impact Evaluation designs
 Randomized controlled trials - A randomized controlled trial (RCT) is a method of
evaluating the impact of a program or policy intervention in which the population
receiving the program or policy intervention is chosen at random from the eligible
population, and a control group is also chosen at random from the same eligible
population. It assesses the extent to which specific, planned impacts are realized
random assignment of members of the population eligible for treatment to one or more
treatment groups (who receive the intervention1 treatment or variations of it) or to the
control group (who receive either no intervention or the usual intervention, if the
treatment is an innovation to an existing intervention) is what distinguishes an RCT.
After a set period of time, the effects on specific impact areas for the various groups
are compared.

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Difference in Difference - For two time periods, the outcomes of two groups are
monitored. One of the groups receives a treatment in the second period but not in the
first. The second group is not subjected to the treatment during either period. When
the same units within a group are observed in each time period (panel data), the
average gain in the second (control) group is subtracted from the average gain in the
first (treatment) group. This removes biases in second period comparisons between the
treatment and control groups that could be the result of permanent differences
between those groups, as well as biases in treatment group comparisons over time that
could be the result of trends.
Socio -Economic Impact Assessment of AFPL

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Regression Discontinuity - A type of quasi-experimental design in which
participants are assigned to treatment conditions based on a specific threshold value
or cutoff score. Individuals near the threshold value are theoretically comparable and
only differ on the basis of their treatment assignment, allowing a researcher to
estimate treatment effects. The analysis of such a design entails comparing the
regression lines for those who received the treatment (i.e., received a reward) to those
who did not receive the treatment (i.e., no reward).
Socio -Economic Impact Assessment of AFPL

A continuous straight line for the two groups indicates that there is no effect of
reward on performance, whereas any break or jump (discontinuity) in the line across
the groups indicates that there is an effect of treatment.
Propensity Score Matching - Propensity score matching generates treatment and
control group participant sets. A matched set is made up of at least one participant from
the treatment group and one from the control group who have similar propensity
scores. The goal is to approximate a random experiment, thereby avoiding many of
the issues associated with observational data analysis. The basic steps to propensity
score matching are:
o
o
Gather and prepare the data.
Estimate the propensity scores. The true scores are unknown, but they can
be estimated using a variety of techniques such as discriminant analysis,
logistic regression, and random forests. The “best” method is debatable, but
logistic regression is one of the more popular methods.
o Match the participants using the estimated scores.
o Examine the covariates for an even distribution across groups. If the
matching process successfully distributes covariates across the
treated/untreated groups, the scores are good estimates for true propensity
scores (Ho et. al, 2007).
Methodology and Sampling
Research problem
To study the nature and extent of social and economic impact that intervention microfinance
activities of AFPL has on clients during and before COVID.
Objectives
To find how much the AFPL clients are advanced in terms of economic in the last two
year and it is done by monitoring the economic parameters like how much their annual
income is increased, per capita income, savings, assets they have, how they are dealing
with emergencies, livestock, types of houses, cooking fuel, and source of water.
To measure about their social upliftment by tracking down the social parameters like how
frequent they are visiting places i.e., Mobility, use of social media, client participation in
budget making, and the clients decision making ability.
To measure their risk-taking capacity by analyzing how open they are to use digital
system, their entrepreneurial risk and general risk confidence
To identify how covid affected the clients by measuring the social and economic
parameters of covid.
Research Design
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This study employs a cross-sectional design because it is an observational study that
analyses data collected at a specific point in time. Because we cannot use time series analysis
due to time constraints,
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this study is best suited for many assessment studies. The sample data is divided into two groups:
control (before and after) and experiment (before and after). The control group includes clients
who have been with the company (AFPL) for less than six months, while the experiment group
includes clients who have been with AFPL for more than two years. This was done with the idea
that six months is a very short time span for any significant impact on the client's lifestyle and
financial situation. This method was also used because it is impossible to ensure proper
cooperation and contribution from people who are not affiliated with AFPL. By dividing the
sample into these two groups, it was attempted to determine whether or not there is a significant
difference in the socioeconomic conditions of these two groups.
It was attempted in this study to include an equal number of representatives from both groups
from all branches covered. The reasoning behind this is to negate the impact of external factors.
Due to time constraints, we are powerless to intervene. The assumption is that all respondents in a
branch have a similar level of exposure to such external factors.
About the Control Group:
Issues of Cross-sectional studies:



Assumption of similarity among the members of each group
(Income, consumptions)
Assumption of not getting worse off
Not Sorting of groups based on economic background
Reasons to use Control group:
1.
2.
3.
4.
5.
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Comparison of new and old customers based on various factors
such as income, consumption pattern and assets information.
It also easier to compare both groups and less complicated
assumption as both the groups are getting not chosen based on the
economic background
No need to survey the people who are not part of program which
removes the biases of research
And also, no need to follow up a single client on for a longitudinal
study…. Which is more exhausting study?
Elimination of incomplete sample bias (Who left the group are of
Socio -Economic Impact Assessment of AFPL
6.
7.
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positive impact so the overall impact shows a negative trend).
Elimination of attrition bias (Suppose poor left and rich remains then
also only positive impact will be dominant and could lead to biases.
Little to no cost sampling with total random and no biased groups.
Socio -Economic Impact Assessment of AFPL
Sampling Design
The population from which the sample is derived includes the clients of AFPL. The total
population size was 1300000, out of which 1600 clients were surveyed for studying impact
assessment. 14 states have been selected for the survey of the client population and also on the
basis of the mixed ratio of control and experiment clients present in that state. The experimental
group consists of clients who are with Annapurna for more than 6 months and the control group
consists of clients who are with Annapurna for less than or equal to 6 months.
A multi-stage sampling technique has been used for the study.
First stage: Proportional sampling was used in the first stage to select the number of districts per
state. The numbers of Clients from that particular district were selected on the basis of the
proportion of client population in the state and availability of control and experiment clients both.
In order to get data points from each stratum. It was made sure 1/3rd of the data points was
coming from SC/ST, and have a proper share of caste.
Second stage: 300 client data points were given to each Intern for their respective assigned
states. Systematic sampling was used in the second stage. In order to choose 300 data points from
the huge data of respective states with the help of population size and sample size.
k=N/n
k = systematic interval sampling
N = population size
n = sample size
Third stage: Mechanical sampling was done with that based on calling capacity we kept the
buffer (30- 35%) for online glitches.
Fourth Stage: Due to online limitations, we had to perform resampling. It was made sure that the
samples chosen were as similar as possible to the previous one keeping the district, branch,
purpose of the loan and time period in mind.
Data collection tool
The tool used for collecting questionnaire’s data was Kobo Toolbox. It is an application that
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helps in collecting data both online and offline, and the major advantages of Kobo Toolbox are
if a user doesn’t
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have an internet connection still, they can collect the data offline and can upload the same
whenever they get an internet connection, it can be used on any phone, tablets or any browser, it
synchronizes data with SSL and provides strong safeguards against data loss. The best part of the
tool is that it allows the matrix related questions, which makes the collection easy. Once updated
at the main design it will automatically update the form at the surveyor system (PC, Mobile, etc.).
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PRESENCE OF AFPL
Figure 17: Presence of AFPL
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Approach for analysis
Difference-in-difference analysis (DID)
DID is a statistical technique used in quantitative social science research to examine the impact
of an intervention using observational study data, by examining the differential effect of an
intervention on a ‘experiment group' versus a ‘control group.' It computes an intervention's effect
by comparing the average change over time for the experiment group to the average change over
time for the control group. Depending on how the experiment group is chosen, this method is
intended to mitigate the effects of extraneous factors and selection bias.
The Difference in Difference method compares the outcomes over a given time period of a
population enrolled in a program (treatment group) and a population not enrolled in a program
(comparison group). To use the difference-in-difference method, the outcomes in the treatment
and comparison groups must be measured both before and after the time period. If the before and
after outcome variables for the experiment group are A and B, respectively, and the before and
after outcome variables for the control group are C and D, respectively, then the program's
impact is simply calculated as the difference of the two differences, i.e. DD Impact= (B-A)-(DC).
In difference-in-differences, the first difference between the group enrolled in the program's
before and after states controls for all the factors that remain constant over time because we are
comparing the same group over time. To account for extraneous factors over time, we use the
second difference between the before and after states of a non-enrolled group. In this way, we
can obtain an accurate estimate of the program.
To determine the differences in DID method, a two-sample paired t-test was used. The null and
alternative hypotheses for determining whether or not there is a difference are as follows:
Null Hypothesis: - The difference (for any parameter) between the before and after differences
of the control and experimental groups is insignificant or zero, i.e.
DD Impact = (B-A) -(D-C) = 0 or (B-A) = (D-C)
Alternate Hypothesis: - The difference (for any parameter) between before and after differences
of control and experiment groups is significant or greater than zero, i.e.
DD Impact= (B-A) -(D-C) ≠ 0 or (B-A) ≠ (D-C)
Interpretation of output of two sample paired t-test is as follows:
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If P value in output of t test is less than α value considered and if value of t statistic does not lie between –
t critical to + t critical, then the null hypothesis is rejected.
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Rationale behind using DID
DID removes biases from treatment group comparisons over time that could be the
result of trends caused by other causes of the outcome.
When individual-level randomization is not possible, DID can be used.
DID eliminates biases in post-intervention period comparisons of the treatment and
control groups.
DID can be used in observational settings where exchangeability between treatment and
control groups cannot be assumed.
Assumptions in DID
In the absence of treatment, the unobserved differences between the treatment and
control groups remain constant over time.
Treatment and control groups have parallel trends in outcome.
For repeated cross-sectional designs, the composition of the treatment and control groups
remains stable.
No spillover effects.
Limitations in DID
DID necessitate baseline data and a control group.
DID cannot be used if the control group has a different outcome trend than the experiment group.
If intervention allocation is determined by baseline outcome, DID cannot be used.
DID cannot be used if the composition of the control and experiment groups before and
after the change is not stable.
Paired t- test
To understand the mean difference between two groups which are in this case control and
experimental group, is zero or is there any significance difference between the mean of two
groups. In this test each sample is measured twice subjecting it to paired inspection. That is why
it is also called a dependent sample t-test.
The null hypothesis for this test is that there is no significance difference between two groups.
An alternative hypothesis depends on the kind of results we are looking for. For example, if we
just want to understand if there is any impact (Both negative and positive) we use two tailed
paired t-test. But if we want to understand the degree of positive impact or the degree of negative
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impact, one tailed paired t-test is a way to go.
H0: There is no significant difference in the mean of the sample.
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Two tailed H1: True mean difference in both samples is zero.
Upper tailed H1: Difference in true mean is greater than 0.
Lower tailed H1: Difference in true mean is less than 0.
Assumption for paired sample t-test: Though paired t-test is one of the best ways to understand
the degree of deviation from one sample group to another. But being a parametric test, it takes
some assumptions which needs to be checked before the assurance of the validity of the result.
The following are the few assumptions:
1.
The dependent variables must be continuous: As this test is based on the normal
distribution, sample data must be numeric and continuous.
2.
The observations (Sample) must be independent of each other: Though we cannot test
the independence of the sample, it is usually independent as we collected sample data which is
not dependent on each other.
3.
Samples are normally distributed: This assumption is generally can be checked by the
graphs such as histograms as no data in the real world is perfectly normal but symmetry in
graphs can be key to check this assumption. Apart from that assumption can also be checked by
the Shapiro-Wilk test.
4.
No outliers: In the test which includes the mean to find the significance difference,
outliers can influence the result. So, in such a case though the removal can be a solution but this
can cause other kinds of errors to data.
So, in these kinds of situations where assumptions are not met, the way forward is a nonparametric test such as Wilcoxon’s signed rank test which is ordinal by nature.
Wilcoxon’s signed rank test
When the assumptions of outliers and normality are not made, we use this test which can provide
us information about whether two samples are taken from the same group and whether there is
any significant difference in the mean.
This test also runs on the following assumption:


Data is paired and from same population
Samples are independent and random
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Figure 18: Wilcoxon’s signed rank test
Correlation and Regression
Correlation is a statistical measure that indicates how much two or more variables fluctuate in
tandem. A positive correlation indicates the degree to which those variables increase or decrease
in tandem; a negative correlation indicates the degree to which one variable increases as the
others decrease. When the fluctuation of one variable reliably predicts the fluctuation of another,
there is a tendency to believe that the change in one causes the change in the other. Correlation,
however, does not imply causation. There could be an unknown factor influencing both variables
in the same way. In statistics, the correlation coefficient has a value between +1 and -1. When the
correlation coefficient is close to one, the two variables are said to have a perfect degree of
association. As the correlation coefficient value approaches zero, the relationship between the
two variables weakens.
Assumptions of Correlation -
Regression analysis involves identifying and assessing the relationship between a dependent
variable and one or more independent variables, also known as predictor or explanatory
variables. Linear regression investigates relationships that are easily described by straight lines or
that can be generalized to many dimensions. A simple linear regression analysis is performed
when there is a single continuous dependent variable and a single independent variable. This
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of AFPL Multiple regression is a
analysis assumes a linear Socio
relationship
between
the two variables.
technique used to learn more about the relationship between several independent or predictor
variables and a dependent or criterion variable.
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The following assumptions underpin the regression model: 







The relationship between the independent variable and the dependent variable is linear.
The error term is expected to have a value of zero.
The assumption of homoscedasticity states that the variance of the error term is constant
for all values of the independent variable.
There isn't any autocorrelation.
The independent variable has no relationship with the error term.
The error term has a normal distribution.
On average, the difference between the observed (yi) and predicted (yi) values is zero.
On average, the estimated values of errors and the values of independent variables are unrelated.
Concept of brand equity with focus on Cognitive loyalty
Microfinance is a great means to help and support individuals who are poor & financially
excluded to become financially independent and it is a competitive market as well. But does the
social-economic assessment lead to economic value for the organization? What we understood
was that the entire revenue generated could be attributed towards Brand Equity. When we have
strong brand equity, the customers will rely more on the organization. If we talk about Cognitive
Loyalty, the customer will have an Image (Look at the product offered as superior over other
products in the market) of the product that recommends more to the other people, they will be
more loyal. When an organization creates more Brand equity among the people, this leads to
income generation, income augmentation, emotional attachment. So, we have also included this
element in our project.
Figure 19:Concept of Brand Equity
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Brand romance is an introverted subjective state in response to a brand as a stimulant, and it is
identified by the following factors: positive strong sense to a brand, high arousal created by the
brand, and the brand's
tendency
to
dominate
in
the
consumer's cognitive
mind. In Annapurna’s case, the socio-economic
impact assessment has rarely focused on “Brand Romance” i.e., how customers connect to
Annapurna. Basically, it is a commitment from the customer to re-buy or re- patronize a product
or set of products offered by a particular brand. The cognitive level of loyalty is all about having
explicit knowledge about a product or brand.
Brand equity, on the other hand, can be defined as “the value attached to a functional product or
service by associating it with the brand name”. Brand loyalty, brand awareness, perceived
quality, brand associations, and other proprietary brand assets are all part of it.
Cognitive Stage: Brand Awareness, Brand Image and Brand Perceived Quality
Brand awareness - When a consumer recognizes a brand, this is referred to as brand awareness
(Aaker 1991). The idea is that a consumer recognizes a brand because of prior exposure. As a
result of the consumer's awareness of the brand, that brand becomes part of the consumer's
consideration set for an actual purchase.
Brand Image - Brand image is defined as a consumer's mental impression of a brand's overall
personality (Marconi 2000). A simple gesture of even the FCO can help in this regard wherein
he/she can placate the customers in times of crisis rather than only following the conventional
system i.e., to basically adopt an emic perspective instead of an etic perspective. This will help
the customer feel Annapurna as not just an MFI company but someone who can help them during
their crisis moments.
Brand Perceived Quality - Brand perceived quality is defined as a consumer's perception of a
brand's overall quality – which is not always based on knowledge of specifications (Aaker 1991).
According to this definition, consumers do not need to have prior experience with the product in
order to judge its quality. Rather, it is possible to distinguish between perceived and actual
consumer perceptions of a product. This is what Annapurna has to tap because in places where the
competition between the different MFIs is very high, Annapurna has to lure customers from their
rivals by creating a very positive perception in their minds.
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Attitudinal indices of Brand Loyalty:
Cognitive Loyalty = (RR/AR) * {1- (BA- RR- AR)/BA}
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Were
,
RR = The number of brands in the rejection
region. AR = The number of brands in the
acceptance region.
BA = The number of brands of which the consumer is aware.
Cognitive loyalty towards a brand would be ascertained after deducting the brands that fall in the
rejection region from the brands that the consumer is aware of and might use in the future.
Digital Credit
In the digital financial services (DFS) industry, digital credit solutions are quickly gaining
traction as a new, innovative way to get electronic money. Digital credit products differ from
traditional forms of credit, in that the creditor can register, score, approve and lend using smart,
feature phone technologies or online platforms. According to the Consultative Group to Assist the
Poor (CGAP), there are three distinct aspects that distinguish digital credit products from other
DFS or loan services. They identified that, without any brick-and-mortar infrastructure, Digital
credit loans can be remotely applied for, approved, and disbursed. Similarly, digital credit
eliminates the number of transactions and time between loan registration and payout because the
approval of the credit is automatic. The last one is, because of the technology anybody can avail
loan in less than 72 hours or in other words we can say the approval for the loan is instant. In
addition to traditional financial information such as credit scores and bank account information,
digital credit also takes alternative or non-traditional data into account to determine credit
eligibility. However, although these products provide customers with a handy option to obtain
cash for personal or company expenditures, they also pose a danger. Borrowers who do not
comprehend the loan conditions or take out more than they can afford to repay may end up in
debt or with a bad credit rating.
Take a close look into Kenya. Digital credit is in high demand in Kenya as a substitute for both
informal and conventional financial services. In the last decade, Kenya has made significant
progress in terms of financial inclusion because of digital credit. In Kenya, digital credit is
available and comes in a number of formats. It includes mobile phone applications, payroll
lending, mobile money wallets. similarly, a variety of providers like banks, mobile network
operators, and even savings and credit cooperatives. Mobile systems like Kenya's M-Peas may
be a method to improve access to inexpensive credit through digital loans. The impact of MShari, a short-term savings and loan service offered through M-Pesa, on Kenyan families' access
to credit, resilience, and savings was studied using a regression discontinuity methodology. After
the study, it was found that M-Shwari was able to get credit from a variety of sources. And also,
the borrowers reported spending more on education and were better prepared to respond to
negative financial shocks without sacrificing back elsewhere. Also, in the year from 2016 to
2018, the number of digital loans granted has nearly doubled and 86% of the loans were taken
using digital credits. For formal players like the banks, around 50% of their new loans are now
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digital.
Socio -Economic Impact Assessment of AFPL
PPI (PROGRESS OUT OF POVERTY INDEX)
The Poverty Probability Index (PPI) is a tool to measure poverty and is used by businesses and
organizations with a mission to serve the needy and underprivileged. It is reliable, cost effective,
easy to utilize and generates accurate results is country specific and collects data from the
nationally represented surveys which vary from one country to another. The PPI index helps MFIs
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Socio -Economic Impact Assessment of AFPL
to assess and improve their performance among the poor and poorest and. allows them to track
poverty levels over time.
Figure 20: PPI Scoring
.
The PPI is a set of 10 simple questions which can be answered in 5 to 10 minutes by a memberof
the household. The questionnaire incorporates the household’s attributes and asset ownership
which act as the basis for evaluation The responses based on the questionnaire are used to
determine the possibility that the household is living below the poverty line. With the help of
PPI,businesses can integrate poverty data into their assessments and strategic decision-making.
Highlights
R68, MMR: It has been standardised for 31 poverty lines and in addition it can be used to tally
with other indices like health.
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Rangarajan: Unbiased, measurable over years with ability to estimate for crossing to the area
of above poverty line.
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RBI: Focuses on cash rather than consumption, doesn’t factor in inflation, more income oriented and not
per-capita income.
Propensity Score Matching (PSM)
1. Validity of PSM depends on following two conditions:(a) conditional independence and
(b) sizable common support or overlap in propensity scores across the participant and
nonparticipant samples.
2. With matching methods,one tries to develop a counterfactual or control group that is as
similar to the treatment group as possible in terms of observed characteristics.
3. Each participant is matched with an observationally similar nonparticipant,and then the
average difference in outcomes across the two groups is compared to get the program
treatment effect. There are many methods for matching, like radial or nearest
neighbouring matching for the purpose of study nearest neighbour matching without
replacement was employed.
4. CHOOSING THE FACTORS
5. Thus one needs to choose those factors which wouldn’t impact selection but have bearing on the
output/result. Based on understanding we came up with three:
6. Distance from town (Since in R68 line, the MMRP one, RBI suggests likelihood poverty
based on urban or rural setting)
7. Distance from bank (Since financial activities and financial access are closely linked)
8. Financial healthy habit (The deviation from ideal financial management is an indicator
of how one is managing his/her own finance)
Logistic Regression
Logistics regression was done with cutoff of 0.5. Following result was found.
It can be seen that none of the p-values are significant but in case of deviation, Wald test
value>standard errors it has generated.
Adjudging overlap through pictorial inspection:
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Socio -Economic Impact Assessment of AFPL
One can clearly see a very high degree of overlap between the baseline and treatment group,
since both the criteria are met, we can go for PSM.
Benefit?
Doing away with net-underestimation. We didn’t lose numbers linearly but arealy.
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Socio -Economic Impact Assessment of AFPL
Data Analysis and Findings
The questionnaire was divided into social and economic parameters in order to draw conclusions
from the data collected across the states. The parameters added up to demonstrate economic and
social empowerment. The two empowerment indicators, as well as their various parameters, are
listed below. Apart from these two majors, various other parameters such as COVID, Risks are
also added to understand the broader picture of impact of AFPL.
Economic Empowerment Indicator
This indicator is all about the change in economic status of two groups of clients of AFPL to
understand their actual impact on the clients in terms of economy. The two groups are control
which consists consumers less than or equal to 6 months old where the experiment group
consists the older segment of consumers. The extent of difference of the both groups also been
captured before and after two years of both control and experiment group to understand the
impact of AFPL.
Economic empowerment Index: Economic empowerment index is the key to show the economic
impact of the MFI on the client. In this index we compare two groups of client, control and
experiment, on the basis of different key economic aspects such as income, livestock, assets etc.
Parameters used in the calculation of EEI: Parameters used in the calculating the EEI are
based on the basic needs of a decent lifestyle, their preparations to cope with financial emergency
in tough times, for example COVID 19 lockdowns etc. The reason behind using these factors is
to understand the impact of AFPL on each parameter and collectively as a whole index on the
clients.
1.
2.
3.
4.
5.
6.
7.
8.
9.
Income
Per capita income
Livestock
Assets
Fuel
Water
Housings
Big emergency
Small emergency
To combine all the factors in a single index, all factors are converted into same scale of 1 to 10,
where each factor was assigned a score and each data has been given the score from the same.
Based on the scoring later on all the scores were added to devise the final score of economic
empowerment index.
Scoring
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Socio -Economic Impact Assessment of AFPL
Figure 21: Scoring of Economic Empowerment Indicator
Scoring is based on the assumption to remove the outliers’ biases for the study, curve could be
normalized and also all the data points could be on the same level. For example, there is a very few
data points in total income which went above 45000Rs. So, to avoid the skewness of the data
because of outliers, singular bracket of 45000+ has been created which reduces the biased in the
scoring patterns. And similarly, all the indicators were taken into account to take all the scores on
same levels.
Scoring of Fuel: For the scoring of fuel, best source which is electricity was given 10 where the
kerosine was given 2.5. And accordingly, all other scorings were done.
Figure 22: Scoring of Fuel
Scoring of Water: In the scoring of water, best possible choice which is piped water is given 10
where the unstable water supply where people have to depends on others has given zero. And all
others sources are given score according to conveniences.
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Figure 23: Scoring of Water
Scoring of Houses: The scoring of the houses is done is best on the type of the house and the
best option which is pucca house is given 10 and all others are normalized accordingly.
Figure 24: Scoring of House
Scoring of emergencies: Savings are subjected to size of emergency and capacity of consumers.
So, the scoring of emergency is done based on the size where small and big emergency has been
assigned different scores. And scoring of the saving is done based on their suitability. For
example, Savings which is the best option to tackle an emergency is given score of 1 and
normalized score of 10. And so on for all others ways of savings.
Figure 25: Scoring of Emergencies
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Economic empowerment index scoring: All the indicators are added up to provide overall
empowerment index of each individual. The formula is the sum of all indicators score divided by
total maximum possible score which is 90.
Economic Empowerment Index (En) = ΣEi/ ΣEi(max)
Where, Eith Economic Indicator, Ei(max)=Maximum score of ith Economic Indicator = 90
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Income:
The increase in average income for the control group was found to be 11.36% while the
experiment group exhibited 25.03% increase for the same.
The experiment group clientele which has been associated with AFPL for a longer duration has
shown around 13.7% more growth in average income for the same duration in comparison to the
control group. Despite the pandemic, the rural economy was untouched and there was a paradigm
surge in primary activities.
Figure 26: Average Income Control
Figure 27: Average Income Experiment
There is significant difference was found between control group and experiment group. (pvalue=4.03E- 11). Which indicates that the impact of AFPL on the clients in the income is
positive and significant, and also extent of the positivity has increased as mentioned above.
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t-Test: Paired
Two Sample
for Means
Variable 1
Variable 2
Mean
0.038540333
0.085787452
Variance
0.017987097
0.021784924
Observations
781
781
Pearson
0.023173112
Correlatio
n
Hypothesized
0
Mean
Difference
df
780
t Stat
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-6.698532611
Socio -Economic Impact Assessment of AFPL
P(T<=t) onetail
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2.01E-11
Socio -Economic Impact Assessment of AFPL
t Critical one- 1.646809514
tail
P(T<=t) two- 4.03E-11
tail
t Critical two1.963010003
tail
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Per capita income:
There is a significant difference between the control and experiment groups in the average rise in
per capita income (p-value =0). Which indicates that there is an impact of AFPL on the clients in
terms of per capita income. Also, we can see that the mean is increasing in experiment group than
control group which shows the extent of impact is positive.
t-Test: Paired
Two Sample
for Means
Variable 1
Variable 2
Mean
0.054108723
0.102022756
Variance
0.018562211
0.022071853
Observations
791
791
Pearson
-0.03605734
Correlatio
n
Hypothesized
0
Mean
Difference
df
790
t Stat
-6.568136774
P(T<=t) onetail
t Critical onetail
P(T<=t) twotail
t Critical twotail
4.62E-11
1.646784726
9.23E-11
1.962971387
The increase in average per capita income is 14.37% for the control group as compared to
26.44% of the experiment group. This explains the fact that the pace of increase of average per
capita income of control groups is lesser as compared to the experiment group. Which shows the
extent of impact that AFPL has
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Socio -Economic Impact Assessment of AFPL
created
is
quite
positive
Figure 28: Per Capita Income of Control Group
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and
visible.
Socio -Economic Impact Assessment of AFPL
Figure 29: Per capita Income of Experiment Group
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Socio -Economic Impact Assessment of AFPL
Livestock:
In the t test table, there is a significant difference between the control and experiment groups in
the average savings (p-value =0.000195). Which indicates that there is an impact of AFPL on the
clients in terms of livestock’s assets. Also, we can see that the mean is increasing in experiment
group than control group which shows the extent of impact is positive.
t-Test: Paired Two Sample for Means
Control
Experiment
Mean
-0.021
0.035723
Variance
0.05861
0.126427
Observations 781
Pearson
Correlatio
n
Hypothesize
d Mean
Difference
781
0.0333
0
df
780
t Stat
-3.74355
P(T<=t)
one 9.74E-05
- tail
t Critical one1.64681
tail
P(T<=t)
two 0.000195
- tail
t Critical two1.96301
tail
The increase in average livestock worth is 10.42% for the control group and 14.36% of the
experiment group. This explains the fact that the in the covid scenario, both the control group
and the experiment group has majorly focused on agriculture activities due to reverse migration.
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And Leveraging PSM
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Figure 30: Livestock of Control Group
Figure 31: Livestock of Experiment Group
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Assets
There is a significant difference between the control and experiment groups in using different
kinds of assets (p-value = 1.16E-05). Which explains that there is an impact of AFPL on the
clients in terms of different assets cumulation. Also, we can see that the mean is increasing in
experiment group than control group which shows the extent of impact is positive.
t-Test: Paired Two Sample for Means
Variable 1
Variable 2
Mean
0.046338
0.075379
Variance
0.014904
0.019684
Observations 792
Pearson
Correlatio
n
Hypothesize
d Mean
Difference
792
0.008385
0
df
791
t Stat
-4.41278
P(T<=t)
one 5.81E-06
- tail
t Critical one1.646782
tail
P(T<=t)
two 1.16E-05
- tail
t Critical two1.962968
tail
To understand the extent of increment the before after study of assets has been done which
shows the increase in assets is 6.30% for the control group as compared to 10% of the
experiment group. The experiment group has been able to accumulate more worth of assets as
compared to the control group courtesy the support provided by Annapurna. Which is highly
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Socio -Economic Impact Assessment of AFPL
positive impact of AFPL on clients, not only economical but also socially because amount of
assets defines the impact of household’s social status in community as well.
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Socio -Economic Impact Assessment of AFPL
Figure 32: Average Expenditure in purchasing asset in last two years
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Socio -Economic Impact Assessment of AFPL
Fuel
There is a significant difference between the control and experiment groups in using different
kinds of fuel (p-value = 7.21E-08). Which explains that there is an impact of AFPL on the clients
in terms of different assets cumulation. Also, we can see that the mean is increasing in experiment
group than control group which shows the extent of impact is positive.
t-Test: Paired Two Sample for Means
control
experiment
Mean
0.026145
0.073155
Variance
0.024924
0.036361
Observations 786
Pearson
Correlatio
n
Hypothesize
d Mean
Difference
0.042175
0
df
785
t Stat
-5.43769
P(T<=t)
one 3.61E-08
- tail
t Critical one1.646797
tail
P(T<=t)
two 7.21E-08
- tail
t Critical two1.962991
tail
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Socio -Economic Impact Assessment of AFPL
The increase in upgraded fuel type is only 1.90% for the control group as compared to 5.20% of
the experiment group. The experiment group has started using more of reliable sources of fuel
like LPG as compared to the control group.
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Figure 33: Types of fuel used by control group clients
Figure 34: Types of fuel used by Experiment Group clients
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Socio -Economic Impact Assessment of AFPL
Water
There is a significant difference between the control and experiment groups in using different
kinds of water sources for their daily consumption (p-value =5.78E-05). Which indicates that
AFPL had created the impact on clients. Though the assumption of water availability lies but the
control and experiment group analysis balance that assumption and impact of AFPL is crystal
clear.
t-Test: Two-Sample Assuming Unequal Variances
control
experiment
Mean
0.072761665
0.128856
Variance
0.071681379
0.082902
Observations 793
Hypothesize
d Mean
Difference
0
df
1589
t Stat
-4.032466653
804
P(T<=t)
one 2.89E-05
- tail
t Critical one1.645813138
tail
P(T<=t)
two 5.78E-05
- tail
t Critical two1.961458036
tail
The increase in upgraded water source is 11.49% for the control group as compared to 22.34% of
the experiment group. This explains the fact that the pace of increase of control groups towards
acquiring more regular water sources like piped water is lesser as compared to the experiment
group.
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Figure 35:source of water of control
group clients 2 years before
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Socio -Economic Impact Assessment of AFPL
Figure 36:Source of water of control group client at present
Figure 37:source of water of experiment group clients 2 years before
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Figure 38:Source of water of experiment group at present
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Socio -Economic Impact Assessment of AFPL
Housings
There is a significant difference between the control and experiment groups in using different
kinds of house (p-value = 0.003711). Which indicates that AFPL had created the impact on clients
in terms of their living space, kind of facilities etc.
t-Test: Two-Sample Assuming Unequal Variances
control
experiment
Mean
0.080387
0.115849
Variance
0.05083
0.068123
Observations 792
Hypothesize
d Mean
Difference
0
df
1567
t Stat
-2.90609
804
P(T<=t)
one 0.001856
- tail
t Critical one1.645827
tail
P(T<=t)
two 0.003711
- tail
t Critical two1.961479
tail
The increase in upgraded housing type is only 4.80% for the control group as compared to 8.50%
of the experiment group. This explains the fact that the pace of increase of control groups
towards pucca and semi-pucca houses is lesser as compared to the experiment group. Also, the
state wise bifurcation of the type of house also been done, where we can clearly see that how a
state is doing in terms of the housing facility and what is the impact of AFPL in which state.
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Figure 39Type of house before Control Group
Figure 40 type of house after for control group
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Figure 41type of house before for experiment group
Figure 42 type of house after experiment group
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Figure 43 house score
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Socio -Economic Impact Assessment of AFPL
Big emergency
There is a significant difference between the control and experiment groups in facing big
emergencies (p- value = 7.57E-07). Which shows the interest and capacity of control group is
clearly different from experiment group, which also suggests the impact of AFPL is quite evident
in terms of big emergency tackle planning.
t-Test: Paired Two Sample for Means
Variable 1
Variable 2
Mean
0.037518
0.119234
Variance
0.09235
0.119022
Observations 792
Pearson
Correlatio
n
Hypothesize
d Mean
Difference
792
-0.00644
0
df
791
t Stat
-4.98612
P(T<=t)
one 3.79E-07
- tail
t Critical one1.646782
tail
P(T<=t)
two 7.57E-07
- tail
t Critical two1.962968
tail
There is 6.77% decrease in case of control group as compared to 16.95% decrease in case of
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Socio -Economic Impact Assessment of AFPL
experiment groups. This explains that the experiment groups go for MFI/govt support more than
the control group. The experiment group is more into savings when it comes to big emergencies
than the control group.
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Figure 44 Big emergencies Experiment group
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Figure 45 big emergencies Control group
Socio -Economic Impact Assessment of AFPL
Small emergency
There is a significant difference between the control and experiment groups in facing small
emergencies (p-value = 0.010571). Which shows the approach of control group is different from
experiment group to tackle the small emergencies, which also suggests the impact of AFPL is
quite evident in terms of small emergency tackle planning.
t-Test: Paired Two Sample for Means
Control
Experiment
Mean
0.063564
0.018936
Variance
0.146477
0.091632
Observations 792
Pearson
Correlatio
n
Hypothesize
d Mean
Difference
-0.00895
0
df
791
t Stat
2.56267
P(T<=t)
one 0.005286
- tail
t Critical one1.65E+00
tail
P(T<=t)
two 0.010571
- tail
t Critical two1.96E+00
tail
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792
Socio -Economic Impact Assessment of AFPL
And the DID analysis indicates the that there is 11.40%% decrease in case of control group as
compared to 7.20% decrease in case of experiment groups. The experiment group can better
tide over small
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emergencies than the control group. The experiment group is more into savings when it comes
to small emergencies than the control group.
Figure 46 Small emergencies Experiment group
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Figure 47 Small emergencies control group
Socio -Economic Impact Assessment of AFPL
Savings
There is a significant difference between the control and experiment groups in the average
savings (p- value =9.17E-08). Which indicates that the savings of control group is significantly
different from the savings of experimental group.
t-Test: Paired
Two Sample
for Means
<6
>6
Mean
-0.018565941 0.015877081
Variance
0.018103582
0.01636298
Observations
781
781
Pearson
0.075994959
Correlatio
n
Hypothesized
0
Mean
Difference
df
780
t Stat
-5.393464512
P(T<=t) onetail
t Critical onetail
P(T<=t) twotail
t Critical twotail
4.58E-08
1.646809514
9.17E-08
1.963010003
The decrease in average savings is 9.48% for the control group as compared to an increase of
7.14% of the experiment group. This explains the fact that the in the covid scenario, the savings of
the control group has decreased whereas it has increased for the experiment group. This is largely
because of Annapurna’s intervention.
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Figure 48 savings experiment group
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Figure 49 saving control group
Socio -Economic Impact Assessment of AFPL
Economic Empowerment Index:
All the indicators which we have taken into account for the economic empowerment index, has
been summed up to find out the overall impact of AFPL on the clients. This index of economy
suggests that not only on individually indicators, but also on cumulative manner, AFPL has
created a significant impact on the experimental group rather than control group as suggested in
graph. Where the red line which advocates the experimental group is above than the blue line
that is of control group in every parameter. And same is the case for the overall index score.
Figure 50 EEI
There is significant difference in the EEI between the control group and the experiment group (pvalue = 2.51E-21). Which shows the significance of the above results and also provides the stats
that mean of control group is less than of experimental group.
t-Test: Paired Two Sample for Means
Control
Experiment
Mean
0.385825
0.777156
Variance
0.552808
0.695497
Observations 792
Pearson
Correlatio
n
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-0.02004
792
Socio -Economic Impact Assessment of AFPL
Hypothesize
d Mean
Difference
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0
Socio -Economic Impact Assessment of AFPL
df
791
t Stat
-9.76E+00
P(T<=t)
one 1.26E-21
- tail
t Critical one1.65E+00
tail
P(T<=t)
two 2.51E-21
- tail
t Critical two1.96E+00
tail
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Social Empowerment
The social empowerment index shows the social development of the individuals of both the
groups i.e., Experiment and Control.
Social empowerment index was calculated on the basis of following parameters keeping the
COVID situation in scenario: 



Budget Making
Decision Making
Mobility
Social Media.
To determine overall social empowerment index, each indicator was given a normalized score,
and difference of the scores between data from two years (COVID) and present and then
summed up the differences of each indicator and divided it by total maximum score to obtain an
overall social index for each individual which is further used for analysis, and overall social
empowerment index was calculated to determine the social impact.
Below is the Formula used to calculate overall social empowerment index (SEI).
Social Empowerment Index (Sn) = Sy/Sy(max)
Where Si= ith Social Indicator,
Si(max)=Maximum scores ith Social Indicator.
Indicator
1
2
3
4
Decision Making
excluding
others
along
self
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Socio -Economic Impact Assessment of AFPL
Indicator
0
1
Budget Making
no
yes
Mobility
no
yes
Indicator
1
1
2
3
0
Social Media
Whats
A pp
Facebook
Both
Other
None
Budget Making
Figure 51 Budget making state wise before and after
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Figure 52 Budget making Control and Experimental group
There is a significant difference between the control and experiment groups in Budget Making (p-value
= 0.035439). There is 48% increase in the case of the experiment group as compared to in
46.8%increase in the case of the control group.
This explains that the experiment group practices budget-making more than the control group,
which helps them to track their spending. This can benefit the experiment group to ensure that
there is always enough money to pay for food, bills, and other expenses.
There were two questions of decision making in our questionnaire with two options each, so we
Summed up the scores of responses of each question and then normalized it to the scale of four,
in order to apply t-Test.
t-Test: Paired Two Sample for Means
Variable 1
Variable 2
Mean
0.101641
0.126894
Variance
0.060136
0.048669
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Observations
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792
792
Socio -Economic Impact Assessment of AFPL
Pearson Correlation -0.04592
Hypothesized
Mea 0
n Difference
df
791
t Stat
-2.10691
P(T<=t) one-tail
0.01772
t Critical one-tail
1.646782
P(T<=t) two-tail
0.035439
t Critical two-tail
1.962968
Inference- As per the t-test output above, there is significant difference in number of
clients who Improved their participation in decision making. Number of clients taking part
in Budget making
Is more in case of the experiment group as compared to the control group. This is evident in our
t-Test output also as it indicates to reject the null hypothesis. So, we can say that there is a
positive impact of AFPL which helped in improving the participation of the clients in various
decision making
Decision making
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Figure 53 Decision making control and experiment group
There is a significant difference between the control and experiment groups in decision making (p-value
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Socio -Economic Impact Assessment of AFPL
= 0.015877863).
There is a 12.46% increase in the case of the experimental group compared to the 8.49%
increase in the case of control groups.
Figure 54 Decision making categories
The decision making includes decisions related to the use of loans, business/livelihood activities,
children’s future and purchasing/selling of assets.
The number of clients taking part in decision making is more in the case of the experiment
group as compared to the control group.
There were 2 questions of decision making in our questionnaire with 2 options each.
Also There were 4 questions of decision making in our questionnaire with 3 options
each, so we
Summed up the scores of responses of each question and then normalized it to the scale of four,
in order to apply t-Test.
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t-Test: Paired Two Sample for Means
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Variable 1
Variable 2
Mean
0.05396619
0.076723017
Variance
0.031914587
0.040980914
Observations 769
Pearson
Correlatio
n
769
0.065383467
Hypothesized
0
Mean
Difference
df
768
t Stat
-2.417073539
P(T<=t) onetail
0.007938932
t Critical one- 1.646840111
tail
P(T<=t) two- 0.015877863
tail
t Critical two- 1.96305767
tail
Inference- As per the t-test output above, there is significant difference in number of clients who
Improved their participation in decision making. Number of clients taking part in decision
making
is more in the case of the experiment group as compared to the control group. This is evident in
our t-Test output also as it indicates to reject the null hypothesis.
So, we can say that there is a positive impact of AFPL which helped in improving the
participation of the clients in various decision making.
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MOBILITY
Figure 55 mobility
Figure 56 mobility category wise
There is a significant difference between the control and experiment groups in the mobility
condition (p- value = 1.14E-05). There is a 7.83% increase in the case of the experimental group
compared to the 0.78% increase in the case of control groups.
The mobility includes activities such as going to community meetings, market and online
meetings. Most of the clients, who reported improvement in their mobility are from the
experiment group.
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t-Test: Paired Two Sample for Means
Variable 1
Variable 2
Mean
0.038573
0.10101
Variance
0.062679
0.087129
Observations 792
Pearson
Correlatio
n
Hypothesize
d Mean
Difference
792
-0.05707
0
df
791
t Stat
-4.41715
P(T<=t)
one 5.70E-06
- tail
t Critical one1.646782
tail
P(T<=t)
two 1.14E-05
- tail
t Critical two1.962968
tail
The table above indicates to reject the null hypothesis, which suggests that there is a significant
difference between the mobility conditions of both the groups. The mobility conditions
of clients of experiment group is better than that of the control group clients can also be
inferred From the above output.
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Social Media
Figure 57 social media usage state wise
Figure 58 Social media usage
There is a significant difference between the control and experiment groups in the usage of social
media (p-value =0.0000000018). There is a 27.08% increase in the case of the experimental
group compared to the 1.79% decrease in the case of control groups.
This explains that the experiment groups got more associated with social media platforms like
WhatsApp, Facebook in the last 2 years. Being connected via social media shows their social
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connectivity and relationship/wellbeing.
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t-Test: Paired
Two Sample
for Means
Variable 1
Variable 2
Mean
0.0749143406 0.1474422535
Variance
0.0471908219 0.0740975928
Observations 781
Pearson
Correlatio
n
Hypothesize
d Mean
Difference
781
0.0888225713
0
df
780
t Stat
-6.089655988
P(T<=t)
one 0.0000000009
- tail
t Critical one1.646809514
tail
P(T<=t)
two 0.0000000018
- tail
t Critical two1.963010003
tail
Inference- The table above indicates to reject the null hypothesis, which suggests that
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there is a Significant difference between the social media of both the groups. The use of
social media
of clients of experiment group is better than that of the control group clients can also be inferred
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from the above output.
Overall Social Empowerment Index
Overall social empowerment index is cumulative of all the individual social indicators. The
overall social empowerment index is the sum of indexes of individual social indicators divided by
the sum of maximum scores of each indicator.
Figure 59 Social empowerment index
Control
Experiment
Budget Making 0.083
0.125
Decision
Making
Mobility
0.053
0.076
0.038
0.099
Social Media
0.072
0.148
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The above figure depicts the status of the clients from both the groups on each indicator
and overall, with the help of mean values retrieved from t-Test outputs. Overall social
empowerment
index is higher in case of the experiment group as compared to the control group. The reason is
that the increase in individual social parameters is higher in the case of the experiment group as
compared to the control group. Therefore, it can be concluded that AFPL’s intervention has helped
in improving the overall social status of clients associated with AFPL for a longer period of time
at a faster rate as compared to control group clients. AFPL have helped clients in becoming
independent, self-reliant, increased their awareness level, due to which clients have been socially
empowered
t-Test: Paired Two Sample for Means
Control
Experiment
Mean
0.244691
0.448678
Variance
0.184209
0.243341
Observations 792
Pearson
Correlatio
n
Hypothesize
d Mean
Difference
0.0216
0
df
791
t Stat
-8.87E+00
P(T<=t)
one 2.31E-18
- tail
t Critical one1.65E+00
tail
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P(T<=t)
two 4.61E-18
- tail
t Critical two1.962968
tail
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Inference- the above t-Test output indicates to reject the null hypothesis, which reflects that there
is a significant difference between the means of overall social empowerment index of both
control and experimental group. The mean for experiment group is higher so, we can infer that
there is a
positive impact of AFPL on the overall social empowerment of its clients.
Risk Empowerment Index
General Risk Confidence
Confidence during financial crises and courage to face the fear of uncertainty indicate the
individual’s general risk confidence. Scores were assigned as 5 or 10(Normalized on a scale of 01) based on whether the individual had or didn’t have confidence during crises by referring to
their emergency planning and savings.
Figure 60 Confidence index
From the chart above, it can be seen that the control group as well as the experiment group
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showed an increase in general risk confidence over the period of 2 years. However, the increment
in general risk confidence for the experiment group was higher (52.34%) than the control group
(11.39%). This indicates
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that the general risk confidence of clients associated with AFPL for a longer period of time has
increased at a faster rate which indicates that AFPL’s intervention has helped them to do so more
efficiently.
Control
Experiment
Mean
0.055057618 0.247119078
Variance
0.085426311 0.206802587
Observations
781
Pearson
Correlation
0.148291092
Hypothesized
Mea 0
n Difference
df
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780
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t Stat
-10.67504562
P(T<=t) one-tail
3.18E-25
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t Critical one-tail
1.646809514
P(T<=t) two-tail
6.35E-25
t Critical two-tail
1.963010003
From the t-Test output above, we can see that there is significant difference between the control
and experiment group. The test indicates that the null hypothesis should be rejected. We can say
that the intervention of AFPL has made a positive impact and has helped to increase the general
risk confidence of the clientele.
Entrepreneurial Risk Confidence
Confidence to take risky decisions for entrepreneurial ventures leads to space for innovation and
encourages the person to create more opportunities. Scores were assigned based on factors such
as loan taken from other sources (0/1), formation of new business (2), expansion of existing
business (0.5) and first-time business ventures (1). The sum total score was then normalized on a
scale of 0-1.
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Figure 61 Entrepreneurial Risk
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Based on the chart, we can conclude that the clientele in the experiment group were more
confident to take entrepreneurial risk as compared to the control group clientele.30.9% of the
experiment group clientele showed more entrepreneurial risk confidence than the control group
clientele which can be attributed to the positive intervention of AFPL. The clients associated
with AFPL for a longer duration have shown more confidence to take risks for entrepreneurial
activitiies.
Control
Experiment
Mean
0.263128
0.344245526
731
Variance
0.077854
0.086584429
875
Observations
782
0.181789
Pearson Correlation 015
Hypothesized
Mea 0
n Difference
df
781
t Stat
6.18316
2
598
P(T<=t) one-tail
5.06E-10
t Critical one-tail
1.646807
006
P(T<=t) two-tail
1.01E-09
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t Critical two-tail
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1.963006
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From the t-Test output above, we can see that there is significant difference between the control
and the experiment group. The test indicates that the null hypothesis should be rejected. We can
say that the intervention of AFPL has made a positive impact and has helped to increase the
entrepreneurial risk confidence of the clientele.
Digital Readiness
Digital readiness of a person is his/her readiness, openness or enthusiasm towards digital
innovations. The higher the digital readiness of a client, the more likely he/she is to opt for
digital solutions/facilities. Scores were assigned based on the client’s digital credit openness
(0/1/2), availability of smartphone (0/1), usage of social media (0/1), atm cards (0/1) and digital
payment platforms (0/1). The total score was then computed based on the mentioned parameters
and then normalized on a scale of 0-1.
Figure 62 Digital ready control and experiment group
Based on the chart, we can clearly say that the clientele in the experiment group were more
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digitally ready as compared to the control group clientele.30.38% of the experiment group
clientele showed more digital readiness than the control group clientele which can be attributed to
the positive intervention of Aplite clients associated with AFPL for a longer duration are hence
more digitally ready and open to innovation.
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Control
Experiment
Mean
0.421729
0.549287
Variance
0.070367
0.086033
Observations
783
783
Pearson Correlation
0.282307
Hypothesized Mean Difference 0
df
782
t Stat
-10.6431
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P(T<=t) one-tail
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4.25E-25
Socio -Economic Impact Assessment of AFPL
t Critical one-tail
1.646805
P(T<=t) two-tail
8.49E-25
t Critical two-tail
1.963002
From the t-Test output above, we can see that there is significant difference between the control
and the experiment group. The test indicates that the null hypothesis should be rejected. We can
say that the intervention of AFPL has made a positive impact and has helped to increase the
digital readiness of the clientele.
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Risk Empowerment Index
Overall Risk Empowerment Index is cumulative of all the individual risk indicators. The overall
risk empowerment index is the sum of indexes of individual risk indicators divided by the sum of
maximum scores of each indicator.
Figure 63 Risk empowerment index
Control
Experiment
Digital Ready 0.43
0.56
Entrepreneuria 0.26
l
General
0.03
0.35
0.17
The above figure exhibits the status of the clients from both the groups on the basis of each
indicator and overall, with the help of mean values retrieved from t-Test outputs. Overall risk
empowerment index is higher in the case of the experiment group as compared to the control
group. The reason is that the individual risk empowerment parameters are higher in the case of
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the experiment group as compared to the control group. Hence, it can be concluded that AFPL’s
intervention has helped in improving the overall risk taking capacity and digital readiness of the
clients associated with AFPL for a longer period of time as
compared to the control group clients. AFPL has helped the clients in becoming financially confident during financial
crises and in general unforeseen circumstances.
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Control
Experiment
Mean
0.717629
1.078662
Variance
0.163788
0.421781
Observations 792
Pearson
Correlatio
n
Hypothesize
d Mean
Difference
792
#N/A
0
df
791
t Stat
-13.5764
P(T<=t)
one 3.41E-38
- tail
t Critical one1.646782
tail
P(T<=t)
two 6.82E-38
- tail
t Critical two1.962968
tail
Inference- the above t-Test output indicates to reject the null hypothesis, which reflects that
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there is a significant difference between the means of overall risk empowerment index of both
the control and the
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experiment group. The mean for the experiment group is higher and hence we can infer that
there is a positive impact of AFPL on the overall risk empowerment of its clients.
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COVID- SOCIAL FACTORS
Vaccine safety
Vaccine safety index shows the confidence of clients in COVID vaccines. Due to rumors of
vaccine side effects and a few cases of deaths, people have a doubt about vaccine safety.
To determine the confidence level, we simply asked them whether they are confident about
vaccine safety or not and gave a score accordingly.
YES
1
NO
0
Figure 64 Vaccine safety among control and experiment group
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On doing the t test of the collected data, there was no significant difference between control
and experiment group clientele, as p-value = 0.076319.
t-Test: Paired Two Sample for Means
Variable 1
Variable 2
Mean
0.061869
0.066162
Variance
0.002362
0.002242
Observations
792
792
Pearson Correlation -0.00655
Hypothesized Mean
0
Difference
df
791
t Stat
-1.77478
P(T<=t) one-tail
0.03816
t Critical one-tail
1.646782
P(T<=t) two-tail
0.076319
t Critical two-tail
1.962968
Inference- As per the t test output above, there is no significant difference in vaccine safety
among control and experiment group. That little doubt is there in the mind of every individual
irrespective of them being connected to Annapurna or not.
PERCENT VACCINATED
Percent vaccinated index shows the number of clienteles who have taken the vaccine. Here we
asked the total number of members above 18 years in the family and the number of people
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vaccinated, calculated the average and performed the t-test.
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t-Test: Paired Two Sample for Means
Control
Experiment
Mean
0.281688
0.326418
Variance
0.094627
0.112739
Observations 785
Pearson
Correlatio
n
0.070163
Hypothesized
0
Mean
Difference
df
784
t Stat
-2.85362
P(T<=t) onetail
0.002218
t Critical one- 1.6468
tail
P(T<=t) two- 0.004436
tail
t Critical two- 1.962994
tail
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Percent vaccinated reflected a significant difference among control group and experiment group, p-value
=0.043761842. Also, with experimental group clientele being 19.94% more vaccinated than
control group.
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Figure 65 : Average of people vaccinated among control and experiment group
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PER CAPITA EDUCATION DURING COVID
Education has been severely impacted because of the pandemic and lockdowns. But there are
two segments, one which has smartphone and internet facilities to continue their education and
second who is deprived of such luxury.
Hence to get the impact in numbers, we made these codes,
1
Severely impacted
Education completely stopped due to
covid
2
impacted
Education is somehow going on, by
self or home tuitions
3
No impact
Education is not impacted, have all
online mediums
For to get the per capita impact on education, we summed the score given by them to above
question and divided that by the number of children studying in the family.
PER CAPITA EDU= Sum_covid_edu/Total_number_of_children
On calculating the t-test on control and experiment group, we found significant difference of
education quality between them.
t-Test: Paired Two Sample for Means
Variable 1
Variable 2
Mean
0.437355
0.473953
Variance
0.100477
0.121346
Observations 783
Pearson
Correlatio
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-0.02339
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Socio -Economic Impact Assessment of AFPL
n
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Hypothesized
0
Mean
Difference
df
782
t Stat
-2.14952
P(T<=t) onetail
0.01595
t Critical one- 1.646805
tail
P(T<=t) two- 0.031899
tail
t Critical two- 1.963002
tail
Inference - Per capita education has been impacted because of the pandemic, but was
significantly better for the experiment group than control group, p-value= 0.031899. Also reflected
8.37% difference between experiment and control group.
Figure 66 Average of per capita edu- control, experiment and India
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Figure 67 avg of per capita experiment group
Figure 68 avg of per capita edu control group
FOOD SECURITY
Food availability was another major problem faced by people during the lockdowns, hence we
tried to calculate the food security by asking them simple question having three options that gave
them score accordingly.
Indicator
code
score
no problem at all
0
10
at most 1 meal/day for most of the time
2
0
for yearlong food is available for two time
1
5
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Figure 69 Food security control and experiment group
On doing the t-test,
t-Test: Paired Two Sample for Means
Variable 1
Variable 2
Mean
0.675414
0.780255
Variance
0.108974
0.089724
Observations
785
785
Pearson Correlation 0.154907
Hypothesized
Mea 0
n Difference
df
784
t Stat
-7.1652
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P(T<=t) one-tail
8.97E-13
t Critical one-tail
1.6468
P(T<=t) two-tail
1.79E-12
t Critical two-tail
1.962994
Inference- Significant difference was found in food security among control group and experiment group,
p-value = 1.79E-12. Experiment group was 15.63% more confident about their food availability.
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COVID- ECONOMIC FACTORS
MORATORIUM
To know whether they availed the moratorium facility given by Annapurna or not, we asked if
they did, they opted for it or not, and have coding to yes or no for calculation.
indicator
code
score
YES
1
10
NO
0
0
After collecting the data, we performed t-test and found,
t-Test: Paired Two
Sample for Means
Variable 1
Variable 2
Mean
0.027969349 0.044061
Variance
0.002017227 0.002468
Observations
783
Pearson Correlation
0.043017729
Hypothesized
Mea 0
n Difference
df
7.82E+02
t Stat
-6.872294418
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P(T<=t) one-tail
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6.46E-12
Socio -Economic Impact Assessment of AFPL
t Critical one-tail
1.646804505
P(T<=t) two-tail
1.29E-11
t Critical two-tail
1.9630022
Significant difference with P-Value 1.29E-11, Also 57.54% clientele in control group were
more inclined towards moratorium against the experimental group.
Figure 70Moratorium Control group
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Figure 71 Moratorium experiment group
If affected status:
For the business activities of people, we calculated whether they are affected by COVID lockdowns or not.
indicator
code
score
YES
1
10
NO
0
0
After collecting the data, we performed t-test and found,
t-Test: Paired Two
Sample for Means
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Variable 1
Variable 2
Mean
0.03256705
0.021584
Variance
0.002198901 0.001695
Observations
783
Pearson Correlation
0.079239105
783
Hypothesized
Mea 0
n Difference
df
782
t Stat
5.131123866
P(T<=t) one-tail
1.82E-07
t Critical one-tail
1.646804505
P(T<=t) two-tail
3.64E-07
t Critical two-tail
1.9630022
Inference- Here also we got significant difference in business affected by COVID, control group
was more impacted than the experiment group, giving p-value= 3.64E-0.
Figure 72 If affected control group
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Figure 73 if affected experiment group
CUTDOWN COST
Cut down cost was one of the major factors in reflecting the impact of COVID on experiment
and control groups.
indicators
code
score
not affected
0
10
very severe (reduced by more than half)
2
0
severe (nominal cuts)
1
5
t-Test: Paired Two Sample for
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Means
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Variable 1
Variable 2
Mean
0.51087
0.542839
Variance
0.091751
0.118841
Observations
782
782
Pearson Correlation
0.038452
Hypothesized Mean Difference
0
df
781
t Stat
-1.98636
P(T<=t) one-tail
0.023671
t Critical one-tail
1.646807
P(T<=t) two-tail
0.047343
t Critical two-tail
1.963006
Inference- Here also we got significant difference in business affected by COVID, control group
was more impacted than the experiment group, giving p-value= 0.047343.
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Figure 74Cut down cost control group
Figure 75 cut down cost experiment group
BUSINESS STATUS
Businesses were severely affected due to lockdowns, hence to calculate impact on them we
added a question in our questionnaire with following options,
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indicator
code
Score
not affected
0
10
good ongoing
0
10
stopped due to covid.
3
7.5
affected by covid
2
5
stopped before covid
1
2.5
Figure 76 Business status of control group
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Figure 77 Business status of experiment group
On conducting the t-test, we concluded the following
t-Test: Paired
Two Sample
for Means
Variable 1
Variable 2
Mean
0.702134541
0.779107
Variance
0.062148935
0.071043
Observations
773
773
Pearson
0.141615162
Correlatio
n
Hypothesized
0
Mean
Difference
df
772
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t Stat
-6.328015396
P(T<=t) onetail
2.10E-10
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t Critical one- 1.646829806
tail
P(T<=t) two- 4.21E-10
tail
t Critical two- 1.963041616
tail
Inference- As per the t test output above, there is a significant difference in business status
of the experiment group when compared to the control group. And with P-value= 4.21E-10.
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PPI:
The scores for PPI were calculated based on the 10 set of simple questions which incorporate
factors such as assets, family member and the family head. The total sum is obtained by adding
up all the individual scores corresponding to the 10 different questions and gives the total score of
a household. The PPI score of the household is then compared against the national standards to
determine whether the household is living below the poverty line. For this study, we have used 5
national standards(India) for evaluation.
3.8$
4$
100% National
Rangarajan RBI Rural
RBI Urban
Key takeaways
Low cost and quick way to score the probability that the family is living beneath the poverty line.
Essential for developmental organisation to allow better target and segment policies, thus
strategy.
What lines are being used?
R68, MMR. 3.8$, 4$, 100% Rangarajan, RBI urban and rural.
PPI ($4.00) CONTROL GROUP
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From the above graph, we can clearly see that the poverty level of the control group has
decreased. The maximum decrease can be seen in the case of West Bengal. In the state of
Jharkhand, we can see that the decrease in the poverty levels is the lowest.
PPI ($4.00) EXPERIMENT GROUP
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The above graph also clearly highlights that the poverty levels of the experiment group have also
significantly decreased over the period. The maximum decrease can be seen in the case of West
Bengal. However, in the state of Karnataka, we cannot see any fall in the poverty level in the
experiment group.
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PPI ($3.80) CONTROL GROUP
From the above graph, we can clearly see that the poverty level of the control group has
decreased. The maximum decrease can be seen in the case of West Bengal. However, in the case
of Jharkhand, the fall in the poverty level is the lowest.
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PPI ($3.80) EXPERIMENT GROUP
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The above graph also clearly highlights that the poverty levels of the experiment group have also
significantly decreased over the period. The maximum decrease can be seen in the case of West
Bengal. However, in the state of Karnataka, there is no fall in the poverty levels.
PPI (100% National Rangarajan) CONTROL GROUP
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From the above graph, we can clearly see that the poverty level of the control group has
decreased. The maximum decrease can be seen in the case of West Bengal. However, in the case
of Jharkhand, the fall in the poverty level is the lowest.
PPI (100% National Rangarajan) EXPERIMENT GROUP
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The above graph also clearly highlights that the poverty levels of the experiment group have also
significantly decreased over the period. The maximum decrease can be seen in the case of Tamil
Nadu. However, in the state of Karnataka, there is no fall in the poverty levels.
Change_Cont Change_Experim
rol
ent
-8.41%
-12.77%
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PPI (RBI) CONTROL GROUP
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We can clearly see that in most of the states there is a fall in poverty levels between before 2
years and present in both the RBI Rural and RBI Urban categories. However, in states like
Jharkhand,Tamil Nadu and West Bengal, we can see that there is no fall in poverty level in the
RBI Urban category. Similarly, in Tamil Nadu and Jharkhand, the poverty levels have remained
constant for the RBI Rural category.
PPI (RBI) EXPERIMENT GROUP
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We can clearly see that in most of the states there is a fall in poverty levels between before 2
years and present in the RBI Rural category. However, we can see that in most of the states , the
poverty levels have remained constant for the RBI Urban category.
Overall change(CONTROL GROUP)
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India
Change_ Change_100 Change_RBI_ur Change_RBI_r Change_3.
$4
%
ban
ural
8$
5.93% 8.41% 1.54% 1.17% 6.30%
Overall change(EXPERIMENT GROUP)
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Change_ Change_100 Change_RBI_ur Change_RBI_r Change_3.
$4
%
ban
ural
8$
7.87% 12.77% 1.48% 3.40% 8.56%
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MSME Client impact assessment:
As the MSME clients of AFPL have different needs and different importance of loans, we have
created a descriptive analysis of MSME clientele of AFPL where we have taken in to
consideration the following parameters:





Business status of clients
General status of clients
Perception towards various types of service providers
Planning for various aspects of business
Impact of COVID and their preparation for future
Business General (PAN India)
Types of business: Based on the survey we found that around 23.3 percent of the total MSME
clients are engaged with Kirana/grocery/fruit/vegetable/general stores as their business.The
Garments/clothes/tailor/footwear shops and mobile /electronic/ hardware shops,each account for
12.2% of the MSME clientele.11.1% of the clients are involved in the Hotel/food manufacturing
business. Similarly,cosmetics,medical shops,garages,etc account for the rest of the businesses
undertaken by the MSME clientele as seen in the chart.
Figure 78: Type of business
Loan Purpose: The majority of the MSME clients are taking loans from AFPL to purchase stock
for their existing business. 3.3% of MSME clients are taking loans to expand their business and
around 4.2% of MSME clients took loans for home construction.The rest of the clients took the
loan for other purposes excluding these.
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Figure 79: Reason for taking loan
Raw Material Purchase: The Chart describes the areas where the MSME clients purchase their
raw materials from. Most of the clients are purchasing their raw materials locally. Some of the
clients are purchasing their raw materials from other areas across their state(within state) and
some from other states as well(national).
The null group represents the clients who took loans for their personal use and are not involved
in procuring raw materials for business.
Figure 80: Raw material purchase
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Selling Products: Based on the survey we found that the majority of the clients are selling their
products locally followed by clients who are selling their products both locally and within the
state(inter district). Also, we found that few people are selling their products nationally(inter
state).
Figure 81 Networking of business
Net DIGI Score:
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Figure 82Net digi score
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The graph shows the net digi scores of different states. The net digi score is the sum of
Individual scores of parameters like social media usage( WhatsApp, Facebook and others) along
with business platforms.
Digital Rollout: This graph tells about the availability of digital resources, mindset of clients
towards implication of digital credit and other tech based credit resources. This tells us about the
locations where roll out is easier and more penetration is possible at the same time. Bigger the
bubble, better the rollout.
Figure 83: Digital rollout
Sources of Advice: The graph exhibits the various sources of advice which the clients depend
upon for business related queries or issues.Majority of the clients take advice from their family
members and fellow businessmen.They also acquire information and advice from the government
and workshops/seminars .
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Figure 84: Business Advice sources
Types of products: The graph shows the type of product(document) provided by the MSME
clients of Annapurna against which loans were issued.
Figure 85: Type of products
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CLIENT GENERAL (ZONE )
Asset before asset After: The graph shows how much assets the MSME clients in the Central
northern zone had before covid and how much they acquired recently.
Figure 86: Household assets
Part of association:
The Graph shows the clients in the central northern zone who are a part of associations
(1=Yes,0=No)like trade associations, labour associations etc. v/s the fulfilment of their entire
capital requirement through formal sources of lending.
Formal & Informal Sources
The graph shows the details of the percentage of clients in the central northern zone who opted
for formal and informal sources for borrowing at the initial stages of their business and at present.
Figure 87: Investment initially and at present
Saving difference
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The Graph simply shows the difference between the savings of Central northern zone clients of
MSME when they started their business and what their savings are at present.
Figure 88: Difference in savings from initial phase
ZONE 1 : central Northern:
Loan Service rating of Annapurna: The graph shows how the MSME clients in the central
northern zone rated the loan service of Annapurna. Majority of the clients are satisfied with the
loan service.
Figure 89: Loan rating of AFPl
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Education Qualification : The graph shows the education qualification of the MSME clients
in the central northern zone. The majority of the clients are well educated and most of them are
UG.
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Figure 90: Education qualification of Clients
Zone 2: Central India
Loan Service rating of Annapurna
The graph shows how the MSME clients in the Central Zone rated the loan service of
Annapurna.Majority of the clients are satisfied with the loan service.
Education Qualification
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The graph shows the education qualification of the MSME clients in the central zone of India. The
majority of the clients are well educated and most of them are educated till 9th to 10th .
Zone 3: Eastern India
Loan Service rating of Annapurna
The graph shows how the MSME clients in the Eastern Zone of India rated the loan service of
Annapurna.Majority of the clients are satisfied with the loan service.
Education Qualification
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The graph shows the education qualification of the MSME clients in the Eastern zone of India.
The majority of the clients are well educated and most of them are educated till 11th to 12th .
Zone 4: South-western
Loan Service rating of Annapurna
The graph shows how the MSME clients in the South-western Zone of India rated the loan service
of Annapurna.Majority of the clients are satisfied with the loan service.only few people are neutral
Education Qualification
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The graph shows the education qualification of the MSME clients in the South-western zone of
India. The majority of the clients are well educated and most of them are educated till 9th to 10th
.
COVID General (IMPACT) ( PAN):
In this section the graphs are indicating the various impact of COVID 19 on the clients’
businesses as well as their preparedness and back up plans in near future if the conditions do not
get better or some other emergency comes up.
Types of business:
It is the bifurcation of various firm’s sales on the basis of type of business to understand which
industry got the worst hit during the pandemic.
Figure 91: Type of Business Clients are in
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Expected time to recover the business:
Figure 92: Time to recover from COVID lockdown
This graph shows the expected time to recover the business of the MSME clients because of
Covid. The majority of the clients need one month in order to come back to their full potential.
Zone wise division of major problems faced by clients during COVID: The graph below
shows the major problems faced by businesses during COVID such as stock maintenance, regular
expenses, wages of workers etc.
Figure 93: Problems faced by Clients during COVID
The graph below shows how the respective clients of each zone managed their financial problems
during the pandemic and includes the various services which the clients have chosen over the
course of the lock down period.
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Figure 94: Acumen to face financial emergency
Impact of COVID on number of Employees:
Due to the lockdown,businesses were not at their optimum standards and to keep them running,
the owners had to go for cost cutting techniques such as the reduction in the number of
employees and cutting down their wages.The following graph shows the zone wise difference in
the percentage between the number of employees before and after the lockdown.
Figure 95: Difference in number. of employees
Moratorium taken:
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To understand the business confidence and financial status of the clients we asked them if they
had taken the moratorium during the COVID lockdown.The zone wise distribution is shown
in the graph below
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which indicates that mostly people from central india have opt for moratorium which means that
they did not have the financial resources to pay the emi during the COVID lockdown.Other than
that most of the clients opt out of it. Which tells us that AFPL has created a positive impact on
the financial stability of clients.
Figure 96: Moratorium status
BUSINESS STRATEGY (PAN): To understand the business acumen of clients we have
created an indicator exhibiting the business conditions and the business planning of the clients.
Reasons by client:
The following graph highlights the major reasons which influenced the clients to choose AFPL.
As we can see in the graph below, the majority of the clientele chose AFPL due to the low
interest rate provided by AFPL compared to other sources available or known to them.
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Figure 97: Why AFPL?
Competitors:
Following are the major competitors of AFPL. As we can see that IDFC and Bajaj finserv are
one of the biggest competitors in the areas where AFPL is functional.
Figure 98: Competitors of AFPL
From the chart,it can be seen that apart from AFPL, in 3 out of the 4 zones, people preferred
banks over NBFC-MFIs.This indicates that even though people tend to prefer banks over MFIs,
AFPL was still able to penetrate the market through these clients which indicates that AFPL has
been creating a positive impact on the perception of the clients.
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Figure 99: Comparison of loans taken
Cash reserves:
The following graph shows the cash that has been kept in reserve to keep the respective business
functioning during times of need or emergencies. Most clients have financial reserves to run their
businesses for at least a month and upto 3 months.Some clients have enough reserves to run their
businesses for more than 3 months and only a few of the clients have limited reserves and can
run their business for less than a month.
Figure 100: Cash reserves
Safety Index:
Following index is based on the combination of various aspects of business such as cash
reserves, sales and purchase on credit and the recovery time(50% weightage) needed for business
to run again at full potential. This overall shows the condition of business as a whole and also
suggests safety measures taken by the clients.
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Figure 101: Safety Index
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FOCUSED GROUP DISCUSSION (FGD) ANALYSIS
Focused group discussions include people from the same background or experiences and bring
them together to discuss a specific topic of interest. To assess the socio- economic impact of
AFPL better we conducted 13 FGDs at different branches across India. There were a total of 105
members involved in FDGs.
As this was an online internship, FGDs too were conducted virtually through Zoom call, Gmeet
or whatsapp video calling. Field officers played a vital role in successful execution of these
FGDs.
For FGDs, impact assessment was divided thoroughly into different parameters to measure the
impact of different products offered by AFPL.










The major objectives of this study were to find out Purpose of taking loan
Awareness among clients about products offered by Annapurna
Awareness regarding other credit options availability
Client satisfaction
Comparison of annapurna vs other MFIs
Any entrepreneurial activity taken
Quality of life
Social impact
Any feedback
PAN INDIA
Total member under study- 105
Purpose of taking loan
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Figure 102Purpose of taking loan
InferenceThe above bar graph reflects the different purposes for which clients took the loan amount. The
majority of the client base took loans for agriculture and allied sectors followed by business and
income generation activities across all zones.
Product Awareness
Figure 103: Product awareness
InferenceThe AFPL clients are most aware of Dairy loans provided by the company followed by
education. All products by AFPL are great and much needed initiatives ,but lack awareness
because need for them in rural India is not of most importance for the clientbase at present.
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Awareness regarding other credit options
Figure 104: Caption regarding Credit option
InferenceThere is a consistent awareness of the MFI sector in the rural region across all zones. Banks
have also started penetrating in the remote areas reflecting a good percentage of clientele
awareness.
Client satisfaction
To analyse client satisfaction, we gave a score to client’s responses.
LIKE
1
DISLIKE
0
The straight line (in the below fig.) marked at 1.5 is the average, any mark above the line shows
that the clients of that particular zone like the loan amount and installment frequency set by
AFPL.
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Figure 105: Client satisfaction
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InferenceFrom the above graph it can be easily inferred that the clients are very much satisfied with the
services and product offerings by AFPL. According to their feedback the one thing they like the
most is monthly instalment collection which puts less pressure on them.
Impact of Annapurna in Financial practices
To analyse the impact of AFPL in Financial practices, we gave a score to client’s responses.
Yes
2
Yes, but Not Because of
Annapurna
No
1
0
The straight line (in the below fig.) is the average, which is different for different parameters.
Any mark above the line shows that AFPL has made some major impact in that particular zone.
Figure 106: AFPL's Impact in financial practices
InferenceAFPL has a great impact on the lives of its clients, a major shift in their income can be seen
because of Annapurna. Apart from increasing income, AFPL has also given a reason to the
women of the family to have a bank account and also operate it.
Entrepreneurial activities
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Figure 107: Entrepreneurial activities
InferenceAFPL has given rise to many budding entrepreneurs who were lagging behind because of
inadequate financial resources. Many have bought assets in the form of cows and buffalo to start
a dairy, some purchased stock for their ongoing business, renovated their shops, etc.
Quality of life
To analyse the impact of AFPL in Quality of life, we gave a score to client’s responsesRemain
same
Improved
1
Degraded
0
2
The straight line (in the below fig.) is the average, any mark above the line shows that AFPL has
made some major impact in that particular zone.
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Figure 108: Quality of Life
Inference-
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The quality of lifestyle of clients has improved, especially in case of education and emergency
readiness. Based on their feedback, many villages have the potential to grow but are stuck at one
or another parameter due to missing direction and guidance.
Social impact
To analyse the impact of AFPL’s Social impact on client , we gave a score to client’s responses-
YES
1
NO
0
The straight line (in the below fig.) is the average, any mark above the line shows that AFPL has
made some major impact in that particular zone.
Figure 109: Social impact
InferenceAFPL has a great social impact on the lives of its clients. It has helped its clients in starting new
business as well as has increased their association with the society. The most important factor is
that AFPL has increased women's participation in household decision making.
Feedback
Rate of interest compared to other MFIs
To analyse the feedback of clients on AFPL’s ROI compare to other MFIs , we gave a score to client’s
responses-
Lower
2
Higher
0
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Same
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1
Socio -Economic Impact Assessment of AFPL
The straight line (in the below fig.) is the average, any mark above the line shows that AFPL
has made some major impact in that particular zone.
Figure 110: interest rate sentiment
InferenceMajority of the clientele of AFPL said Annapurna’s ROI is lower compared to other MFI’s apart from
central-northern India, their majority of clientele had no idea and few said its higher.
Diminishing interest rate of AFPL
To analyse the feedback of clients on AFPL’s diminishing interest rate policy, we gave a score to client’s
responses-
like
2
No
1
idea
dislike 0
The straight line (in the below fig.) is the average, any mark above the line shows that AFPL
has made some major impact in that particular zone.
Figure 111: Knowledge of diminishing interest rate
InferenceMajority of the clientele of AFPL like its policy of diminishing interest rate apart from centralnorthern India, their majority of clientele had no idea about diminishing interest rate.
Impact due to COVID
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COVID vaccine taken graph and inference
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To analyse how many clients of AFPL have taken vaccine, we gave a score to client’s responses-
YES
1
NO
0
The straight line (in the below fig.) is the average, any mark above the line shows that AFPL
has made some major impact in that particular zone.
Figure 112: Vaccine status
Inference Majority of the clients in all the zones have taken the vaccine.
Figure 113: percent of family member vaccinated
InferenceWhen it comes to the number of families with all the members vaccinated, every zone has done well.
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Work status
Figure 114: Work status
InferenceCOVID lockdowns had a severe impact on everyone, especially on rural people, as many had to
migrate back to their villages, as we all saw in the news and lost their source of income. From
having stable jobs they had to take up any unstable work at hand to fulfill their basic needs.
FGD CONCLUSION
 According to the responses we got from clients, some major highlight from FGD are Clients need big loans for expanding or initiating their businesses
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


In many villages people are still unaware about products offered by AFPL, to make the
information engaging we can provide it in visuals and youtube videos
Also, AFPL can make videos on topics like moratorium , diminishing interest rates, etc
for better understanding
Due to lockdown, education was one of the most impacted factors. AFPL can start
giving technology loans to students for buying smartphones, laptop, etc
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Conclusion
From the research, we understood that AFPL is really influencing and pushing their clients to
achieve financial inclusion. As we did research on various parameters to substantiate this we
found that the experience group clients of AFPL show significant differences in economic,
social, risk, and covid indicators than the control group clients of AFPL. The experience group
clients show significant differences from the control group in the economic indicator like Asset,
livestock, Income, dealing with emergencies, savings, per capita income, housing, fuel, and
water. In each of these, the experienced group clients are advanced than the control group and we
found that the significant difference is p-value= 2.51E21. Coming to the social indicator, the p-value is 4.61E-18 which shows that experiment group
clients are much social than the control group clients and they are making a budget, have high
decision-making ability, mobility and they are using new technologies like social media. Dealing
with the risk the significant difference p-value is 6.82E-38 which shows that the experienced
group is more confident to face risk than the control group. Similarly, in the covid crisis, the
experienced group is less affected by covid in the economy and social as compared to control
groups and the significant difference is 2.46E-10 in social and 8.33E-09 in the economic index.
This clearly shows the positive Impact done by Annapurna on their clients.
STUDY LIMITATIONS:
Though the survey was conducted with all the precautions and a pilot testing was also conducted
to full proof the plan. But as the assessment was a work from home project and there are various
other factors which might have tweaked the results. These factors are following:







Interview environment- Response by clients were influenced due to on;ine data collection
Trust Issues: Sometimes during the data collection, clients were skeptical about the data
they are sharing because the discussion was on call.
Some clients given the responses which are general as they didn’t had the exact information.
The time limit per call was also an issue as clients were also having works which leads
them to hurry during the call.
Sometimes relatives of clients picked up the call and they provided information based
on their knowledge.
Study is based on the before and after situation of household. So sometimes it was hard for
clients to recall all the information.
Clients were having limited awareness about various factors such as financial, or social
which also lead to some general and biased information.
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Recommendations
 At some branches of West Bengal and Odisha, complaints related to FCO were found.
 Proper checking of loan cards should be done before handing them over to clients.
 Clients should be given latest and updated loan cards.
 Addressing consumption-based needs like marriage funds, medicine and hospital fees
should be a priority keeping in mind the changing consumption patterns and living
standards of borrowers.
 Need to start giving big loans (MSME) in the Region of Nashik and Bhandara (Sakoli)
of Maharashtra for business purposes.
 Use of YouTube to disseminate information rather than focusing on video calls.
 General tendency to borrow from multiple sources, other formal sources to make up their
need. Need to keep a tab on that and create parallel information by tying up with those
institutions.
 Quick relay of data from HO to BO, regarding loan and EMIs.
 Keeping a tab on competitors like L&T gave sanitizers to their clients, similar incentives
in such hours of extremity will add value to brand image of Annapurna. With special
focus on technology development.
 FCOs are staying long to recover loans reaching way early or staying way later than usual
to meet the targets.
 Annapurna should ensure that coercive methods of recovery are not used.
 Hand-Holding businesses rather than just giving them loans, this can be free or pay per
basis. For example, tying together the dairy loans for a given small area. (Waterfall
model)
 They are not aware of consumer durable loans like toilet or water purifier. There needs to
be more promotion for example the office spaces can be used to promote specific dummy
products and loan.
 In small emergency case there seems like a threshold hurdle where people have moved on
from money lender to relatives but still far from formal institutions and primary reason is
that the documentation process needs to be simplified. (Align with customer stage and
journey)
 Low risk= high automation, high risk= manual intervention. (Composable banking architecture)
 Triage assessment (a crisis assessment tool that focuses on the affective, behavioural,
and cognitive domains that are influenced by a crisis).
 CDL if can be done with local market sourcing rather than foreign vendor (Attabira
market has expressed discontent).
 In meetings after moratorium was declared gents came to the meet. There were high pitch
discussion (Hinjilicut). Lady staff shouldn’t be sent alone in such anticipatory situations.
 Leveraging of SHGs as agents can be a successful way to enable higher lead generation by
reaching to women borrowers who earlier could not be reached due to lack of awareness
and sociocultural barriers.
Personification of target clients
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To make our analysis more robust, we took the advantage of first-hand survey collection and
found that there are majorly 3 4 types of customers which AFPL should target for lasting impact
and also to find a way to penetrate these segments. So, from the analysis we did, we created 4
personas representing a category of clients of AFPL each.
Persona 1: Sangeeta Devi
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Persona 2 : Jyoti Himrlka
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Persona 3 : Kiran Bora
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Persona 4: Parsuram
Suggestions from our end to each persona:
We have highlighted the need of each persona and accordingly provided some key suggestions
to target each persona which represent a category of target segment of AFPL clients.
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Annexure 1: MFI Questionnaire
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Annexure 2: MSME Questionnaire
Generic Questions
1. When did you start your business?
2. Capital structure:
Initially
Component
Savings
Borrowed from informal sources
Borrowed from banks/MFI
Total
Amount
Today
Component
Savings
Borrowed from informal sources
Borrowed from banks/MFI
Total
3. Gender
Male
Female
Others
4. Highest education level
Uneducated
Primary till 5th
6th and above till 8th
9th to 10th
11th and 12th
UG
PG and above
5. What is your current total savings?
6. What was your savings at the time of loan?
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7. Household asset information?
8. Do you use digital platforms? (Checkbox)
a.
b.
c.
d.
WhatsApp
Facebook
None
Others
e. Business platforms
Product Services
9. Where do you sell your products?
a.
b.
c.
d.
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Local
Within state
National
International
Specify
Specify
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10. Where do you get your raw materials from?
a. Local
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b. Within state
c. National
d. International
Annapurna finance/Environment
11. Have you taken a loan from other sources?
Other MFIs
Banks
Formal Institutions
Money Lenders
No
NBFC-MFI
12. Why did you choose Annapurna?
(Easy loan repayment, Amount offered, Interest rate)
13. Why do you want to continue with
Annapurna? (Easy loan repayment, Amount
offered, Interest rate)
14. Sources of information and advice for business and legal matters?
Govt
Fellow businessmen
Lawyer
Accountant
Family
Other seminars and workshops
Banks
Others
15. Are you part of any association (Sangh like trade associations, labor associations etc.)?
Yes
If yes, Name
No
16. Have you introduced any new machines that can make work and production faster or
lead to reduction in no. of employees?
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a. Yes
b. No
17. How do you rate the loan services of Annapurna finance?
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Good
Neutral
Bad
18. Do you use:
a. ATM CARD
b.
Phonepe/Googl
e/Other digital tools
c. Other specify
Yes/No
Yes/No
Year
19. Would you like to opt for digital credits?
a. Yes
b. Maybe (Will see)
c. Never
Performance
20. What was your firm's total sales in
Rs? a. 2019…...
b. 2020……
21. How would you rate the performance of your business over the last before and after covid?
Before
After
Positive
Neutral
Negative
COVID
22. Was your business operational during COVID?
a. Yes
b. No
23. Number of employees?
Before COVID (2019)
Present (2021)
24. What is the expected time for the company's business recovery?
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a. 1 month
b. 3-6 months
c. More than 6 months
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d. Inability to judge
25. Please choose the most significant financial problems for your company during the outbreak.
a. Staff wages and social
security charges
b. Rent
c. Repayment of loans
d. Payments of invoices
e. Managing stocks and Inventory
f. Other expenses
g. No specific problem
26. How much cash do you keep in reserve to ensure business continuity? (It would be in
months). The more the merrier (Contingency preparedness index)
a. Less than 1 month
b. 1-3 months
c. 3 months or more
27. How did you manage your finances regarding running of business during covid?
(Checkbox). (More the yes, it means the better is the ability/knowhow of businessman to
sail through the tough time/crisis).
a. Loans by commercial banks
b. Loans by MFIs
c. Private individuals
(money lenders)
d. Negotiating with lenders to
avoid withdrawing loans
e. Reduction of operating costs
(e.g., layoffs and salary
reductions)
f. Was it managed through
daily cash flows
g. No problems at all
28. How much of the payment is on credit? (We can link it to the business health given the
upcoming third wave)
Sale
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a. High (more than 75%)
b. Medium (more than 50%)
c. Low (less than 50%)
Procurement
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a. High (more than 75%)
b. Medium (more than 50%)
c. Low (less than 50%)
29. Was moratorium on collection of loan installments useful to your organization?
Opted
Yes
No
If yes,
a. Yes
b. No
c. Don't Know
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Annexure 3: FGD Questionnaire
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ways-microfinance-institutions-are-responding-covid-19
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