Harnessing Local Aspirations for Green and Inclusive Livelihoods:
Establishing the Impact of Rural Enterprise Clusters in India

MONTH & YEAR OF PUBLISHING
January 2024
PUBLISHER
The Society for Technology and Action for Rural Advancement (TARA)
B-32, Tara Crescent,Qutub Institutional Area
New Delhi - 110 016, India
Tel: +91 11 2654 4100, 2654 4200
Email: tara@devalt.org
Website: www.tara.in
AUTHORS
Saundharaya Khanna, Deena Hari Krishna, Shubham Singh, Mohak Gupta, Sudhir Sah andAparajita M Matthew
DEVELOPED UNDER GUIDANCE FROMDr Arun Kumar, Zeenat Niazi and Dr Swayam Prabha Das
RESEARCH SUPPORTED BY
Indian Micro Enterprise Development Foundation (IMEDF)
Website: www.imedf.in
COVER PHOTO BY
The Society for Technology and Action for Rural Advancement
DESIGNED BY
Binu K George
DISCLAIMER
This assessment report is intended for use by policymakers, academia, enterprise support groups
and other non-governmental organisations for guidance on matters of interest only. The decisionand responsibility to use the information contained in this report lies solely with the reader. Content
may be used/quoted with due acknowledgement to the authors.
ACKNOWLEDGEMENTS
This report has benefited from the goodwill and support of the Child and Social Welfare Society(CSWS) in West Bengal, DESI Trust in Karnataka, Treat Foundation in Uttar Pradesh and Social
Organisation of Various Aspects (SOOVA) in Odisha for assessment of their respective clusters. This
study has been funded by the Indian Micro Enterprises Development Foundation (IMEDF).

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

TABLE OF

CONTENTS
Executive Summary

51.

Introduction

9

The Study

10

Methodological Approach

11

Clusters’ Profile & Analysis

17

Cluster Snapshots

17

Respondents’ Profile

26

Analysis of Data

28

Sustainability of the Clusters

80

Pathways to an Enabling Ecosystem

85

Pathway

85

Conclusion

88

2.

Woman artisan printing the fabric using
handblocks at the Naturally Dyed
Handloom Cluster in Shivamogga,
Karnataka. The cluster uses raw materials
like pomegranate skin to create naturaldyes and color their fabric. This dyed
fabric then undergoes techniques likehandblock printing for enhancing theaesthetic value of the product.

3.

Annexures

89

List of Figures

90

List of Tables

91

Sustainability Assessment Framework

92

Questionnaire for Implementation Agency

97

Questionnaire for SPV Members

102

Scoring Mechanism for Clusters’ Sustainability

104

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

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Women artisans working in the Mat Craft Cluster in West Medinipur, West Bengal

4Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

EXECUTIVE
SUMMARY
Rural Enterprise Clusters as Engines of Change
Rural India is an important part of the country’s green and inclusive growth trajectory. It houses
65% of the country’s population and thereby, represents majority. It also contributes 46% to
the national income, which makes it unneglectable economically. This is recognized in the
Economic Survey of India 2022-23 and the Union Budget 2023-241 that offers purview to aslew of government programmes for promoting rural livelihoods. In order to develop Rural
India holistically, however, environmental sustainability, social inclusion and equity will also
have to be kept in mind while designing and implementing such solutions. In this context, therural enterprise clusters, driven by local aspirations and based out of the local community, canbecome engines of change.
Rural enterprise clusters strengthen the micro economies by generating local employment,
demanding locally produced raw materials and distributing the profits among the local
shareholders. Socially, they empower women, youth and other disadvantaged groups byenabling equal and productive participation in the decision-making and value-creation of
the enterprise. By shifting to renewable energy, sustainably consuming water and efficiently
managing their waste, these clusters also promote sustainable consumption and production
practices.

The Study
This report is based on a detailed investigation of four rural enterprise clusters supported by
the Indian Micro Enterprises Development Foundation (IMEDF) as a Nodal Agency under the
SFURTI Scheme. It takes a stock of the clusters’ economic, environmental and social impact – inthe process, building a case for investing into these enterprises towards a green and inclusive
economy. In order to do so, this report adopts a three-pronged methodological framework asgiven below: 1.

Methodology for Selection of the Clusters: There are seven broad categories of clusters
under the SFURTI Scheme. In consultation with IMEDF, one textile cluster, one handicraft
and two agro clusters were selected based on a minimum operational maturity of 2 years.
These were the Natural Dyed Handloom Cluster at Shivamogga, Karnataka, the Mat Craft
Cluster at West Medinipur, West Bengal, the Medicinal Plant Cluster at Barauliya, Uttar
Pradesh and the Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha respectively.

1

Union Budget 2023-24. Accessed at https://www.indiabudget.gov.in/doc/bspeech/bs2023_24.pdf
Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

5

2.

Methodology for Finalization of the Sustainability Assessment Framework (SAF):
A thorough review was conducted of the Global Reporting Initiative (GRI), the Global
Impact Investing Network’s IRIS+, and other sustainability to draw an initial set of impact
indicators. These were refined through piloting with the rural enterprise clusters. The final
framework had 15 indicators spread across environmental sustainability, local economic
distribution and social inclusion and equity.

3.

Methodology for Data Collection and Analysis: Two semi-structured questionnaires
were designed – for the implementation agency and for sampling with the SPV members(artisans and farmers) -- as per indicators finalized under the sustainability assessment
framework. A mix of inducive and deductive approach was undertaken to collect data.
For the survey with SPV members, a sample size of 30% of the total members was taken,
leading to a total of 264 surveys with artisans and farmers across the four rural enterprise
clusters.

Key Findings
The sustainability assessment of the rural enterprise clusters establishes reliable evidence of
their environmental, economic and social impact.
•

Environmental Sustainability of these clusters were assessed based on efficiency of
consumption, circularity of production and end of pipeline for energy, waste and water.
-

About 72% of the total energy consumption averaged across the best three cases

among the assessed clusters were found to be from solar panels.
•

Local Economic Distribution was assessed on attributes capturing the percentage
increase in income of artisans and farmers before and after SFURTI as well as SFURTI visà-vis other alternative livelihoods. Other attributes were localized demand for raw material
and employment generation as well as net additional job created per crore of investment
under SFURTI. Taking a best three of the four clusters assessed,-

Livelihoods were generated for 889 artisans and farmers and 160 employees, whowere now earning 182% more than what they would earn before2.

-

Moreover, with 64% of the raw materials (except machinery) sourced from thelocal market, and 99% of total wages/pay-outs being to local artisans, farmers andemployees, these clusters were positively feeding back into the local rural economy3.

62

Averaged across the three clusters.

3

Defining a local market or area can be difficult, considering that most of the rural enterprise clusters are based in the outskirts of towns/cities, andthe closest local market may actually be far off. Based on the interviews with the Cluster Executive Officers (CEOs), ‘local’ in the context of thisinvestigation is defined as a market/resource within a radius of 100 kilometres from the Common Facility Centre/processing unit.

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

•Social Inclusion and Equity was assessed through finding the representation ofvulnerable groups by gender, caste and age among SPV and board members of thecluster. The pay parity was also assessed for the profiles where both vulnerable andnon-vulnerable groups were engaged. The assessment results were encouraging,demonstrating: -As many as 85% women and 91% belonging to SC/ST/OBC categories among the
SPV members.

-

An established pay parity between gender and caste categories.

-

A further scope of enhancing scope of enhancing participation of youth (aged 18
years to 29 years) through employment and in enterprise governance.

The findings of the report establish a strong case for similar assessments to generate morerobust evidence on the sustainability impacts of such clusters through expanding the samplingbase to include additional sectors and geographies. This would, over a period of time, enablebenchmarking to compare cost-effectiveness of investment and money worth of impact.

Key Recommendations
Through adequate policy, financial, marketing and capacity support, processes of theremaining 355 SFURTI and many other non-SFURTI clusters in India could be optimized
to amplify their economic, environmental and social impact. This necessitates creating an
enabling ecosystem for the mainstreaming of the rural enterprise clusters. Policymakers, private
investors, civic society network, academia and other actors have a key role to play in this. An
enabling ecosystem for mainstreaming of the rural enterprise clusters can be created through,Enabling policies including: -1.
a.

Convergence of Central Sector, Centrally Sponsored and State Schemes
dedicated to setting up and upgradation of rural enterprise clusters.

b.

Setting up of a common platform to enable information exchange, licensing(including Udyam and GeM registration) and benefit transfer to rural enterprise
clusters.

c.

Leveraging alignments to the regional specialization under One District One
Product (ODOP) along with geo-tagging and government certification (such as
India Brand Handloom) for the cluster products.

d.

Provisioning of tax credits and subsidies, fee redemptions and other fiscal and nonfiscal incentives under public finance.

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

7

Fostering Financial Innovation through: -2.
a.

Provisioning for the “mature” rural enterprise clusters to raise capital through themechanisms such as the Social Stock Exchange.

b.

Enabling Credit Guarantee Schemes for Micro and Small Enterprises (CGTMSE)
with differential interest rates for local and green rural enterprise clusters.

c.

Developing Credit Rating Model based on the sustainability assessment of theclusters for green financing, with linkages to geo-spatial data analytics for digitalprocesses.

d.

In a longer run, linking these clusters as projects under Green Credit Programme(GCP), notified in October 2023 as an innovative market-based mechanism designed
to incentivize voluntary environmental action across industries and companies.4

Strengthening Business and Market Linkages by: -3.
a.

Establishing rigorous benchmarking, monitoring and quality regulation
mechanisms.

b.

Developing Product Certification and Standards or linking the cluster products toexisting standards like Fairtrade and EarthSeer.

c.

Setting up Enterprise Ranking and Award System, on same lines as the ranking
frameworks based on the ESG ratings but customized to the model of rural enterprise
clusters.

Facilitating Partnerships and Collaboration through: -4.
a.

Fostering Public-Private Partnership for technical and product innovation.

b.

Setting up an industry-academia-civic society consortium for mentorship anddedicated consultations.

Building local ecosystems led by communities can address interconnected grassroot
challenges, reduce negative impact on the environment while ensuring next generation
rural youth engage and scale these in a sustainable manner. This can be maximised throughinformed decisions, data driven tools, relevant solutions and business models.

4

8

Ministry of Environment, Forest and Climate Change, “Notification issued for Green Credit Program (GCP) and Ecomark Scheme under LiFE
Initiative to promote sustainable lifestyle and environmental conservation,” October 2023. Accessed at https://pib.gov.in/PressReleaseIframePage.
aspx?PRID=1967476#:~:text=These%20initiatives%2C%20the%20Green%20Credit,the%20ideas%20of%20LiFE%20concept.

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

1. INTRODUCTION

India has registered an average population growth rate of 1-1.9% every year. About 53%
of the country’s current population is under 30 years of age, with estimates of another101 million people getting added in the working age population between 2020-30, 61
million in 2030-40 and 21 million in 2040-50 respectively. This offers India a massive
demographic dividend that can boost the GDP growth from the current US$ 3 trillion to
US$ 9 trillion by 2030, and US$ 40 trillion by 2047. Capitalizing on this potential, however,requires that the country not only generates enough jobs to be able to productivity andmeaningfully tap into the youth potential but also generate these in a manner that it reaches
the economically and socially excluded strata, which have so far been invisible from thecountry’s growth story. It would also have to be mindful of the pressure on the natural
resources, considering that the country is already consuming more than the ecosystems
within its boundaries can provide.5 Thus, making sustainable production a pre-requisite tomeaningful job creation. Rural enterprise clusters, driven by local aspirations and feedingback into the local economy, can become engines of a radical change in this context.
Rural enterprise clusters can take the form of a Farmer Producer Organization, a SelfHelp Group or a Society, among others. As collectives based out of the community,
these institutions strengthen the micro economies by generating local employment,
demanding locally produced raw materials and distributing the profits among the local
shareholders. Socially, they empower women, youth and other disadvantaged groups byenabling equal and productive participation in the value-creation and decision-makingof the enterprise. Alongside these economic and social benefits, by shifting to renewableenergy, sustainably consuming water and efficiently managing their waste, the rural
enterprise clusters can promote sustainable consumption and production practices.
Rural enterprise clusters in India are supported under schemes by the Ministry of Micro,
Small and Medium Enterprises (MoMSME), Ministry of Agriculture and Farmers’ Welfare(MoA&FW), the Small Farmers’ Agribusiness Consortium (SFAC), the National Bank ofAgriculture and Rural Development (NABARD) at the Central level and non-farm cluster
development initiatives by State Governments like that of Karnataka6. Non-governmental
initiatives anchored by the civil society network and supported by the Corporate Social
Responsibility (CSR) have also proven paramount to facilitating local livelihoods andclimate change resilience through setting up or upgradation of rural enterprise clusters.
Mainstreaming these clusters for harnessing the local aspirations towards green andinclusive livelihoods requires much more in terms of financing, capacity building, policy
and market support. In this context, there is an increasing need for documenting these5

Confederation of Indian Industries (CII), “India’s Ecological Footprint: A Business Perspective,” 2008. Accessed at http://www.indiaenvironmentportal.
org.in/files/IndiaEcological_Footprint_Report08.pdf

6Non-Farm Cluster Development Programme, Government of Karnataka. Accessed at https://ksrlps.karnataka.gov.in/new-page/Non-farm%20
Livelihoods%20-%20-%20Cluster%20Development/en
Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

9clusters for their operating models and impact, the best practices and scope of optimization to
be able to generate transparent and credible business case for them. This investigation follows
along these lines.

1.1

THE STUDYThis report is based on a detailed study of four rural enterprise clusters supported by the Indian
Micro Enterprises Development Foundation (IMEDF), which is a Nodal Agency under the
SFURTI Scheme. It takes a stock of the clusters’ economic, environmental and social impact – inthe process, building a case for investing into these enterprises towards a green and inclusive
economy.

ABOUT THE SCHEME
Launched in 2005 by the Ministry of Micro, Small and Medium
Enterprises (MoMSME) and revised hence, the Schemeof Funds for Regeneration of the Traditional Industries(SFURTI) focuses on organising the traditional industries andartisans into collectives to make them competitive, ensuresustainable employment for the traditional industry artisans/producers and enhance the marketability of products.
As of January 2024, as many as 359 clusters have been set
up across 28 States in India. Categorised under agriculture,bamboo, coir, handicraft, honey, khadi and textile, theseclusters provide direct livelihoods to about two lakh artisans.
The scope of these enterprises to support climate action
through adopting greener production practices, especiallyin energy, water, and waste management, has foundcognizance under the scheme.

The assessment is undertaken based on a sustainability assessment framework, designed
through a socio-technical approach (i.e., involving direct participation of the end-users in thedesigning processes). It enlists enlists critical social, economic and environmental parameters
and their related attributes to assess the clusters. The overall methodological framework for theinvestigation is provided below.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

1.2 METHODOLOGICAL APPROACH
The methodology for this investigation adopts three distinct yet interconnected approacheseach for selection of the sample, creating a Sustainable Assessment Framework, as well as
piloting it with the selected rural enterprise clusters. This is explained below.
Figure 1: Methodological Framework for Sustainability Assessment of Rural Enterprise Clusters

1.2.1 SELECTION OF CLUSTERS
There are 359 functional SFURTI rural enterprise clusters, spread across seven sectors namely
agro, bamboo, coir, handicraft, honey, khadi and textile. These clusters are supervised by 15
national institutions with sectoral expertise serving as the nodal agencies. The Indian MicroEnterprises Development Foundation (IMEDF) is one of these agencies with a portfolio of 29
SFURTI clusters across 11 States.
In consultation with IMEDF, one textile cluster, one handicraft and two agro clusters wereselected based on a minimum operational maturity of 2 years. These were the Natural Dyed
Handloom Cluster at Shivamogga, Karnataka, the Mat Craft Cluster at West Medinipur, West
Bengal, the Medicinal Plant Cluster at Barauliya, Uttar Pradesh and the Lemongrass andPalmarosa Cluster at Mayurbhanj, Odisha. This selection was supplemented with a review of
their Detailed Project Reports, annual reports and other documents.

1.2.2 DESIGNING OF SUSTAINABILITY ASSESSMENT
FRAMEWORK (SAF)
A thorough review of the sustainability frameworks and standards, including but not limited
to the Global Reporting Initiative (GRI) and the Global Impact Investing Network’s IRIS+, wasconducted to identify the key environment, social, economic and governance attributes for
impact assessment. Acknowledging that these frameworks are not adjusted to the scale andoperating model of micro and small enterprises, including the rural enterprise clusters, a review

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

11Figure 2: Selected IMEDF Clusters for Assessmentof existing literature on the social, environmental and economic impact of the clusters wasundertaken to refine the attributes. This was supplemented by consultations with IMEDF.
The resultant framework was piloted with the rural enterprise clusters to check for its robustness
and impact coverage. Based on the piloting, the framework was restructured and refined.

SUSTAINABILITY PARAMETERS AND ATTRIBUTESThe investigation considers three overarching parameters of cluster sustainability:
Environmental Sustainability, Economic Distribution, and Social Equity & Inclusion. These
parameters are further broken down into attributes.

A. Environmental Sustainability – Efficiency and Circularity
The parameter of environmental sustainability focuses on efficiency and circularity for energy,
water and waste. First, the efficiency of resource use is examined, scrutinizing clusters’ efforts
to optimize energy, water, and raw material consumption while minimizing waste. Second,
circularity practices are evaluated, gauging the extent to which clusters embrace recycling,
reusing, and extending material lifecycles to reduce reliance on new resources.

B. Economic Distribution – Income Enhancement, Localization and Job
Creation
In economic distribution, the evaluation encompasses three fundamental attributes. It assesses
income enhancement strategies, determining clusters impact on economic prosperity ofworkers and local communities. Additionally, it examines localization efforts, gauging how
clusters contribute to local economies by sourcing materials locally and nurturing nearby
industries. Lastly, the attribute of job creation is scrutinized, exploring the diversity and qualityof employment opportunities generated by clusters, thus impacting overall economic stability.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

C. Social Equity & Inclusion – Inclusive Employment, Equitable Pay andInclusive Decision-Making
Within the social equity and inclusion parameter, the analysis concentrates on three pivotal
attributes of inclusive employment, equitable pay and inclusive decision-making. Inclusive
employment is assessed, considering cluster’s endeavors to provide job opportunities
that embrace diverse demographics, including marginalized groups and individuals withdisabilities. Equitable pay practices are examined, ensuring fairness in compensation
irrespective of gender, ethnicity, or other factors. Lastly, the clusters’ engagement of vulnerable
groups in decision making processes is also examined, fostering transparency, collaboration,and collective ownership.

FRAMEWORK DEVELOPMENT
From the identified parameters and attributes, a total of 15 indicators are formulated. Eachindicator meticulously contextualised to the model of the rural enterprise clusters, and theirquantification methods are aligned with the research goals. The complete framework withparameters, attributes and indicators is provided in Annexure 3.

1.2.3 DATA COLLECTION AND ANALYSIS
Based on the framework, the data was collected in a systematic manner, leveraging a
combination of strategies. For quantitative data, surveys were administered to cluster
representatives at the implementation agency to gather information on predefined indicators.
Qualitative insights were obtained through semi-structured interviews with the artisans/farmers,
while archival documents were also analyzed to provide context.

DESIGNING OF SURVEY QUESTIONNAIRE
Data collection for the assessment of sustainability within these clusters involved the use of
two types of questionnaires. The first questionnaire was designed for the implementation

Survey team interacting with the SPV members in the Naturally Dyed Handloom
Cluster in Shivamogga, Karnataka.

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

13

agency (IA) responsible for overseeing SFURTI interventions. This questionnaire relied onvoluntary disclosures from the IA and aimed to gather information on the clusters’ practices
and initiatives related to sustainability. The voluntary nature of the disclosures ensured that the
IA had the flexibility to provide detailed insights into their sustainability efforts. The second typeof questionnaire was designed for the artisans and farmers engaged in the cluster as membersof the Special Purpose Vehicle (SPV). These interviews were conducted to gather data on
various aspects of income, occupation, and social dimension.

QUESTIONNAIRE PILOTING
A multidimensional approach, incorporating both inductive and deductive modes, wasadopted for collection of data on both questionnaires based on the Sustainability Assessment
Framework. The research strategies applied included Voluntary Disclosure, Empirical, Archival,and Analytical. These are explained below.

A. Inductive and Deductive Approach
The research design was rooted in an inductive approach, seeking to derive insights andpatterns from specific cases of industrial clusters. Additionally, a deductive approach wasutilized to apply existing theoretical frameworks to the Indian context under the SFURTI
scheme. This dual approach facilitated a holistic analysis of the sustainability factors at playwithin the clusters.

B. Main Research Strategies
i. Voluntary Disclosure
It involved the collection of self-reported sustainability practices from the rural enterprise
clusters. Information on their environmental, economic, and social initiatives was voluntarily
provided by the clusters, offering firsthand insights into their sustainability efforts.
ii. Empirical Strategy
It encompassed the collection of primary data through survey samples, interviews, and on-site
observations. A structured questionnaire was designed to assess the sustainability performanceof the clusters based on predetermined indicators. In-depth interviews were conducted withthe artisans to gather qualitative insights into their practices and challenges.
iii. Archival Strategy
In addition to primary data collection, this strategy involved a thorough review of documents,
reports, and records related to the operational strategies and policies of the selected industrial
clusters. This approach provided insights into the clusters’ past and present initiatives, offeringvaluable context for understanding their alignment with sustainable development goals andthe SFURTI scheme.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

iv. Analytical Strategy
It encompassed the application of statistical and qualitative analysis techniques to the collected
data. Quantitative data were subjected to descriptive statistics in the form of graphs and charts
to identify relationships between sustainability indicators. Qualitative data from interviews andvoluntary disclosures underwent thematic analysis to extract meaningful patterns and insights.

SAMPLING OF ARTISANS/FARMERS
Figure 3: Methodology for data collection on questionnaires

To ensure the statistical validity and feasibility of the analysis for inferential purposes, a targeted
approach was adopted for the selection of participants in the second questionnaire. Specifically,a sample size equivalent to 30% of the all artisans and farmers in the respective cluster wasdeemed appropriate. The selection of this sample was based on the type of cluster processes
and/or the CFC they were associated with. By considering the type of cluster processes and
CFCs, the sample composition was designed to capture a representative cross-section of the
population under investigation. The sample size collected from piloting of each cluster is given
in Table 1.

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

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Table 1: Sample size of each cluster
Cluster Name

Sample SizeWest Medinipur Mat Craft Cluster

100

Barauliya Medicinal Plant Cluster

30

Shivamogga Natural Dyed Handloom Cluster

70

Mayurbhanj Palmarosa and Lemongrass Cluster

64

Total

264

METHOD FOR DATA ANALYSIS
The data collected through the surveys and interviews underwent thematic analysis using
statistical instruments. The quantification logic used for calculating the clusters’ impact on theparameters, attributes and indicators of the sustainability assessment framework are provided
under Annexure 6.
The subsequent section presents the findings and insights derived from the application of thismethodology.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

2. CLUSTERS’ PROFILE &ANALYSIS
2.1

CLUSTER SNAPSHOTS

CLUSTER 1

CLUSTER 2

Mat Craft Cluster
West Medinipur, West Bengal

Natural Dyed Handloom Cluster
Shivamogga, Karnataka

CLUSTER 3

CLUSTER 4

Medicinal Plant Cluster
Barauliya, Uttar Pradesh

Palmarosa and Lemongrass Cluster
Mayurbhanj, Odisha

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

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CLUSTER 1

Mat Craft Cluster
West Medinipur, West Bengal
CLUSTER SNAPSHOTNatureBabui Grass Mats and Utility Items

Implementing Agency (IA)Child and Social Welfare Society (CSWS)

Cluster

Mat Craft Cluster

Launch of the Cluster

1993

Coverage under SFURTI

2020

Investment under SFURTI

Rs. 2.25 Crore

Figure 4: Tailoring Unit of the Cluster

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

A. Background of the Cluster
The Mat Craft Cluster was started by the Child and Social Welfare Society in 1993 and employed asmall group of women from the village to weave products out of the Madhurkathi grass. It registeredunder SFURTI Scheme seven years later, in 2020, to avail financing for purchasing machines andscaling operations. Currently, it employs more than 500 local artisans, mostly women, from villages
in and around Mankandachak in West Medinipur.
Figure 5: IMEDF-SAF team in the field with the Resource Person from the Implementation
AgencyB. Products
The Mat Craft Cluster utilizes locally grown Madurkathi
grass, crafting over 200 eco-friendly products thecategories of home, fashion and office utility. The
Mat Craft Cluster was started by the Child and Social
Welfare Society in 1993 and employed a small groupof women from the village to weave products out of
the Madhurkathi grass. It registered under SFURTI
Scheme seven years later, in 2020, to avail financing
for purchasing machines and scaling operations. The
cluster has introduced diversified mat weaving looms and sewing machines. While traditional items
cater to local markets, non-traditional products are available on Tribes India (TRIFED’s e-commercesite) and are exhibited at various national fairs, showcasing their biodegradable and eco-friendly
nature.

C. Processes
The Mat Craft Cluster sources Madurkathi from farmers via intermediaries and other inputs fromnearby cities. They use natural dyes to color the grass. The water left from the dyeing process
is treated and reused in the same process multiple times before finally discarding. The cluster
is powered by on-grid solar energy, soon switching to off-grid. They involve 300+ households,
registered as units under Udyam, where trained women weave independently. They receive raw
materials through vouchers and are paid per unit, earning Rs.5000 to Rs.12000 monthly. The
cluster had a partnership with Fabindia earlier. Now, it sells the products through direct markets in
Kolkata and otherwise on Tribes India. They are also part of the Fair-Trade Forum.

D. Way Ahead
Connectivity is one of the key challenges faced by the cluster – affecting its marketing prospects.
Due to the remoteness of the village, the cluster finds it difficult to register and onboard themselves
on e-commerce platforms such as Amazon and Flipkart, as the pickup of the individual products
poses an issue. Their engagement with Tribes India has been possible only because the products
are picked up in bulk, not requiring much to and fro.
References•

Dash, M., & Mishra, B. B. (2021). Problems of handicraft artisans: An overview. International Journal ofManagerial Studies and Research, 9(5), 29–38. https://doi.org/10.20431/2349-0349.0905004

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

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CLUSTER 2Natural Dyed Handloom Cluster
Shivamogga, Karnataka
CLUSTER SNAPSHOTNature of Programme

Artisanal Products (Cotton wears)

Implementation Agency (IA)DESI Trust

Launch of the Cluster

4 October, 1996

Coverage under SFURTI from2019

Investment under SFURTI

Rs. 1.84 Crore

Figure 6: Printing unit of the handloom cluster

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

A. Background of the Cluster
Charaka, a sustainable handloom industry in Sagara taluk, Western Ghats, promotes decentralized
employment. They train individuals in handloom processes, encouraging home-based units. This
enables weavers, dyers, and tailors, especially women, balancing work and home life.
Numerous handloom units operate in Sagara taluk, along with outsourced weaving. The fabricis processed at a Common Facility Centre (CFC), then sold in wholesale or used for garment
production. Quality control is rigorous, and they engage in natural dye block printing.
Charaka’s marketing arm - the DESI Stores, has its retail outlets across Karnataka. It acts as a bridge
between the rural and urban communities through making available traditionally made products.
The cooperative society, mostly women, uses a decentralized stitching and centralized cloth
cutting model. It operates in 100 villages, producing 25,000 to 30,000 meters of cloth monthly.
Profits are shared among weavers and artisans. Through grant under the SFURTI Scheme, thecluster set up additional infrastructure to expand the production.
Figure 7: Weaving unit set-up for women artisans in their homes

B. Process
Charaka uses mechanically spun local cotton from the
National Handloom Development Corporation Ltd (NHDC),
which then undergoes heat scouring for natural dye
receptivity. They focus on a limited, six-color palette fromnatural dyes like indigo, pomegranate skins, and Indianmadder, making the process safer for people and theenvironment. Yarn preparation and loom setup take about aday for simple designs. UNNATI program offers training for
aspiring weavers.
After weaving, fabric undergoes washing, drying, block printing, or other dyeing methods, followedby quality inspection. In addition to the women from Shivamogga, the cluster also employs artisanal
women from North Karnataka. Fabrics are sent to them and the final products are received with due
payment for their services. Sarees are stocked on-campus; yardage goes for tailoring or sale. The
campus has a design unit for new ideas. All scraps from the cutting processes are used for making
book jackets and other products. Those that are too small to be used for it, are used for filling pillows
or patched together for making quilts.

C. Way Ahead
The natural dye cluster grapples with adapting to price fluctuations and changing market demandfor product. This leads to unsold stock. The artisans engaged in the village do also sometimes resist
shifting to production of a different item when the item they were trained on goes out of demand.
Overcoming these hurdles necessitates increased technical agency involvement for efficiency,
addressing working capital issues, and promoting artisan diversification through training andsupport.

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21

CLUSTER 3

Medicinal Plant Cluster
Barauliya, Uttar Pradesh
CLUSTER SNAPSHOTNatureAgriculture-based Products

Implementation Agency

Treat Foundation

Launch of the Cluster

January 2018

Coverage under SFURTI from2020

Investment under SFURTI

Rs. 2.25 Crore

Figure 8: Entrance of the Common Facility Centre

22

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

A. Background of the Cluster
The Treat Foundation’s medicinal plant cluster, operating in Barauliya and Hariharpur Village,
specializes in producing up to 25 medicinal products. Its goal is to establish a sustainableagricultural entrepreneurship ecosystem, increase farmers’ income, and ensure high-quality
medicinal products. The cluster employs modern packaging and state-of-the-art processing
infrastructure for product quality and hygiene.
It holds several certifications, including the state-level UPSOCA organic certification, FSSAI
certification, and product verification by the Regional Food Research and Analysis Centre.
Additionally, the cluster is GST registered, has a PAN, and secures capital from both the SFURTI
scheme and individual investors.
Figure 9: Chilli Powder Packaging Machine in Operation

B. Products
The medicine cluster includes two brands: Raw & Kind, known fororganic products like essential oils, turmeric latte, and green teas, andPavni, which offers spices like chilli, coriander, and turmeric powder.
Turmeric powder sales are significant for the cluster. Raw & Kind iscurrently pursuing a patent for their star product, Turmeric Latte. Pavni,
launched about 6-7 months ago, has a strong customer retention rate,with 80% of sales from repeat customers.

C. Processes
Cluster farmers cultivate medicinal plants like Turmeric, Ginger, Gilloy, Mint, and Basil but facechallenges with Vetiver due to soil concerns and waterlogging. The cluster provides training
to farmers and staff to enhance skills and efficiency, along with insurance coverage for staff,
machines, and stock.
Turmeric is sourced from Sitapur, Uttar Pradesh, packaging from Lucknow, and machineryfrom Hyderabad. Marketing is done via Facebook, Instagram, and Twitter, with partnerships like
Potli and Alaska Agroventures Organic. Products are sold locally or on e-commerce platforms.
Raw & Kind products are shipped to Mumbai, Hong Kong, or sold on Amazon and Indialane.
Pavni products are sold within a 30-40 km radius of the Common Facility Centre (CFC). The
cluster faces competition from 7-8 nearby masala enterprises, currently holding a 25% market
share.

D. Way Ahead
The brand is actively pursuing partnerships with restaurant chains to source their products, witha focus on Raw &’Kind’s Turmeric Latte. ’avni’s products primarily sell through local community
demand. The agency is actively exploring various avenues, both physical and digital, to expand
their business reach. They aspire to increase exports but require additional resources andtrained professionals to facilitate this growth.

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23

CLUSTER 4

Palmarosa and Lemongrass Cluster
Mayurbhanj, Odisha
CLUSTER SNAPSHOTNatureAgriculture-based Products

Implementing Agency (IA)Social Organization of Various Aspects (SOOVA)

Launch of the Cluster

2020

Coverage under SFURTI from

September, 2020

Investment under SFURTI

Rs. 3.83 Crore

Figure 10: Entrance of Common Facility Centre 1 under Palmarosa and Lemongrass cluster

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

A. Background of the Cluster
The Mayurbhanj PLemongrass and Palmarosa Cluster comprises 1250+ farmers from tribal andlandless families in Udala. These families were originally paddy cultivators. Due to water issues,
crop pest and diseases, the yield would often fluctuate. SOOVA, the implementation agency,
supported these families in shifting to essential oil production from lemongrass and palmarosa
instead. Managed by Hingula Agri Producer Company Limited, this cluster spans 400+ acres.
It established a Farmer Producer Company (FPC) for commercial success and social impact,
distinguishing it from NGO-run clusters. These grasses require minimal maintenance and yield
three cuts annually. Ownership is 75% tribal and 30-40% female, with farmers earning Rs. 6,000
to 7,000 monthly on average.
Figure 11: Filtration of Oil from oil-water mix

B. Product and Production
The cluster leases land from farmers, provides them training andinputs (seeds and other raw material) for growing lemongrass
and palmarosa, and procures the produced grass for oil
production. The grass is manually or mechanically cut, and theoil is extracted via steam distillation. The residual grass is used
as fuel for steamers.
The oil is sold in bulk to PurePetal Alliance Limited, which uses
it to create its own products. Farmers are compensated throughland lease payments, payment for cutting grass, and dividends
from shareholding. They are also encouraged to engage in
additional activities like beekeeping and fruit cultivation for
additional income.
Unlike other clusters, it has four CFCs and one main office spread
across different locations within the tribal area to streamline
processing, reducing transportation costs and inconvenience. Thus, the FPC operations in the
district are decentralized yet clustered. The small metal box shown in the lower end of Figure
11 is where the filtration of oil from the oil-water mix takes place.

C. Way Ahead
The cluster plans to use the aromatic waste water which is separated from oil in the filtration
process, for producing phenyl. In addition, Hingula has installed a machine to package the oil
in-house, this is yet at a nascent stage.

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25

2.2

RESPONDENTS’ PROFILE

Two surveys were designed and conducted under this study.
The first was for the Implementation Agency to document cluster level data (including
financials, number and composition of the SPV members, resource consumption in the cluster,end of pipeline etc). The Cluster Executive Officer acted as the main respondent for this survey,
and were all male, based locally and aged between 40-55 years.
Graph 1: Composition of Sample of SPV Members

The second survey was designed the SPV members i.e., artisans and farmers engaged in thecluster to understand the cluster’s impact on their livelihoods among other factors. A random
sample of 30% of all SPV members was taken for each of the four clusters. A total sample of 264
artisans and farmers were surveyed through this, of which 23% were youth (aged between 18
to 29 years), 82% were women and 90% belonged to Scheduled Caste, Scheduled Tribe andOther Backward Classes.
The cluster had generated new employment as 62% of women and 58% of those marginalized(belonging to SC/ST/OBC category) were unemployed with zero personal income before.
The cluster had also increased income of those who were already working before joining
the cluster. About 31% of those sampled – who were already working as artisan – experienced
an average of 64% increase in monthly income. Another 14% were working as agriculturallabourers before and witnessed an average of 10% increase in monthly income.

26

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Graph 2: Composition of Sample

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27

2.3

ANALYSIS OF DATA

The data was analyzed across three broad parameters of Environmental Sustainability, Local
Economic Distribution and Social Equity and Inclusion.

ENVIRONMENTAL SUSTAINABILITY
There are a total of six indicators within environmental sustainability, spread across efficiency
and circularity in use of energy, water and waste. These are detailed below.

Indicator 1- Gross Energy Consumption per crore of Revenue
This indicator serves as a valuable tool to assess the clusters’ energy intensity, identify
opportunities for cost reduction by optimizing energy consumption, and adopting clean
energy practices wherever applicable.

A. Formula for Calculation and the Data Points

7

28

The DG set is run only as per the need, thus, utilizing 100% of the generated electricity.

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Table 2: Data Points and Sources for Indicator 1 under Environmental Sustainability
S.No

Data Points

Source1Electricity consumption from external source (State grid)

Voluntary disclosure

2

Capacity of solar panels

Voluntary disclosure3

Direct sunlight hours

Secondary research4

Capacity of DG sets

Voluntary disclosure

5

Operational hours of the DG set

Voluntary disclosure

6

No. of working days of cluster

Voluntary disclosure

7

Annual revenue

Financial statements

B. Performance of the Clusters

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29

Figure 12: Performance of the clusters for Indicator 1 under Environmental Sustainability

C. Interpretation & Findings
The clusters’ performance on this indicator can struggle due to two factors: - a high or inefficientenergy consumption and/or a low annual revenue.
ANALYSIS: The Natural Dyed Handloom Cluster at Shivamogga, Karnataka stands out as themost efficient with a gross energy consumption per crore of revenue of 4,484 kWh/Crore. Thisis followed by the Medicinal Plant Cluster at Barauliya, Uttar Pradesh and Mat Craft Cluster at
West Medinipur, performing at 18,306 kWh/Crore and 33,186 kWh/Crore respectively. The
Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha ranks low on the indicator with thehighest gross energy consumption per crore of revenue at 36,446 kWh/Crore, driven by thesolar energy consumption across its four CFCs where oil is distilled8 .
OBSERVATION: While important for establishing a business case through efficiency inresource consumption, the indicator of Gross Energy Consumption per Crore of Revenue
suffers from market volatilities in terms of pricing and demand. This is evident in case of Mat
Craft Cluster at West Medinipur, West Bengal that suffers from the issue of a high unsold stock
pulling its annual revenue and thereby net electricity consumption per crore of revenue down.
These volatilities can be avoided by calculating net energy consumption against the cost of
production instead of revenue. This is attempted in Indicator 1.1. In both cases though, thegross energy consumption per crore of revenue is driven by considerable unspent solar energy
in the Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha.

830

The cluster also has an administrative building, which at the time of this study (2022-23), was used for non-SFURTI value addition, record keeping
and other activities. The energy consumption for this building has not been covered under the analysis.

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Indicator 1.1 Gross Energy Consumption per Crore Cost of Production
This indicator assesses the energy intensity in the production process against the money
worth of goods produced. It provides insights into the sustainability and cost-effectiveness ofenergy use in rural enterprises, permitting stakeholders to evaluate the economic viability andenvironmental impact of the production activities within the cluster. A lower value net energy
consumption per crore cost of production indicates a more efficient use of energy resources inrelation to the overall production costs.

A.

Formula for Calculation and the Data Points

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31

Table 3: Data Points and Sources for the Indicator
S.No

Data Points

Source1Electricity consumption from external source (State grid)

Voluntary disclosure

2

Capacity of Solar Panels

Voluntary disclosure3

Direct sunlight hours

Secondary research4

Capacity of DG sets

Voluntary disclosure

5

Operational hours of the DG set

Voluntary disclosure

6

No. of working days of cluster

Voluntary disclosure

7

Cost of production

Financial statements

B.

32

Performance of the Clusters

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Figure 13: Performance of the clusters

The Natural Dyed Handloom Cluster in Shivamogga, Karnataka exhibits a gross energy
consumption of 8,986 kWh per crore of cost of production, followed by the Mat Craft Cluster
at West Medinipur, West Bengal at 41,350 kWh, the Medicinal Plant Cluster at Barauliya, Uttar
Pradesh at 53,013 kWh and lastly, the Lemongrass and Palmarosa Cluster at Mayurbhanj,
Odisha at 69,524 kWh per crore cost of production. In comparison to the gross energy
consumption per crore of revenue, indicator 1.2 adjusts for market volatilities as isevident from the revised positioning of the Mat Craft Cluster at West Medinipur, West
Bengal against the other three clusters.
RECOMMENDATION: Corrective measures both in terms of improving energy efficiency andoptimizing the cost of production are required. The underutilization of solar energy could be
mitigated by channelizing the unused solar power for by-product manufacturing. Regularenergy audits are necessary for the clusters to keep a close check on the energy usage
and identify areas of inefficiency/energy leaks for correction. These audits currently are notregularized in practice amongst the clusters.

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Indicator 2- Net Water Consumption per Crore of Revenue
This indicator gauges how efficiently water resources are utilized by the clusters in the
production process. By monitoring and improving water efficiency, the rural enterprise clusters
can not only exhibit environmental stewardship by becoming responsible consumers ofnatural resources but financially, can optimize cost and become more viable.

A. Formula for Calculation and the Data Points

Table 4: Data Points and Sources for Indicator 2 under Environmental Sustainability
S.No

34

Data Points

Source1

Water source

Voluntary disclosure2

Quantity of water consumption

Voluntary disclosure

3

Quantity of water recycled

Voluntary disclosure

4

No. of working days of cluster

Voluntary disclosure

5

Annual revenue

Financial statements

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

B. Performance of the Clusters

Figure 14: Performance of the clusters for Indicator 2 under Environmental Sustainability

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35

C. Interpretation & Findings
Similar to the energy consumption, the clusters’ performance on net water consumption percrore of revenue can be negatively affected by high water consumption and/or low annual
revenue of the cluster.
ANALYSIS: The Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha registers thehighest net water consumption at 3,375 kl per crore of revenue. This is followed by the Mat
Craft Cluster at West Medinipur, West Bengal at 748 kl per crore of revenue, despite treating
and reusing 17% of water, and the Natural Dyed Handloom Cluster at Shivamogga, Karnataka
at 588 kl per crore of revenue. The Medicinal Plant Cluster at Barauliya, UP, by nature of its
operation, registers the lowest water consumption at 52 kl per crore of revenue.
OBSERVATION: The water-intensity of the production process has a considerable impact
on the clusters’ performance on this indicator. The Lemongrass and Palmarosa Cluster atMayurbhanj, Odisha exhibits high water consumption due to the water intensiveness of theoil distillation processes and cannot be compared against the performance of the otherthree clusters. Water consumption at the Mat Craft Cluster at West Medinipur, West Bengal
and the Natural Dyed Handloom Cluster at Shivamogga, Karnataka is driven by the mat/cloth
washing and dyeing processes as much as the rest of the production. The Medicinal Plant
Cluster at Barauliya, UP uses water for washing the farm produce, but otherwise runs a dry
processing for masala manufacturing. This water intensiveness, or lack thereof, of operations
reflects when the water consumption is pegged against the output. This is attempted in
Indicator 2.1.

36

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Indicator 2.1 - Net Water Consumption per ton of Output
This indicator indicator pegs efficiency of water usage against the total production output in
metric tons. A high value on this indicator depicts high water intensiveness of operations.

A. Formula for Calculation and the Data Points

Table 5: Data points and sources for the Indicator
S.No

Data Points

Source1

Water source

Voluntary disclosure2

Quantity of water consumption

Voluntary disclosure

3

Quantity of water recycled

Voluntary disclosure

4

Total Annual Output

Voluntary disclosure

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37

B. Performance of the Clusters

Figure 15: Performance of the Clusters

The Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha registers the highest net water
consumption per tons of output at 258 kl. This is followed by the Natural Dyed Handloom Cluster
at Shivamogga, Karnataka at 58 kl per tons of output, the Mat Craft Cluster at West Medinipur,
West Bengal at 17kl per tons of output and finally, the Medicinal Plant Cluster at Barauliya, UP
at 3 kl per tons of output. When compared against the results of the net water consumed
per crore unit of revenue, the West Medinipur and Shivamogga clusters can be foundto have switched ranks. This is due to the high annual revenue of the Shivamogga
cluster, potentially driven by market pricing of the kind of product produced, pullingthe net water consumption per crore unit of revenue figures down.
RECOMMENDATION: Water consumption for all clusters should be optimized – both interms of reducing the consumption of the first-use water and increasing reapplication of thetreated water in the production processes. The Shivamogga cluster treats the water but uses
it for watering and cleaning purposes instead. This should be fed back into the manufacturing
process while the water treated and reused for multiple cycles can still be used for other purposes
as and when it becomes unfit for the manufacturing process, as long as it is safe for disposal.

38

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Indicator 3- Net quantity of waste generated per crore of revenue
This indicator quantifies waste generation relative in money worth terms against the clusters’revenue. By monitoring and reducing waste disposal per unit of revenue, the clusters can saveon cost by recycling the raw materials while contributing to environmental sustainability.

A. Formula for Calculation and the Data Points

Table 6: Data points and sources for Indicator 3 under Environmental Sustainability
S.No

Data Points

Source1

Quantity of waste disposed

Voluntary disclosure2

Quantity of waste recycled/reused

Voluntary disclosure

3

Quantity of waste composted

Voluntary disclosure

4

Annual revenue

Financial statements

Total waste utilized refers to the waste diverted from landfill (recycled/composted/reused)
Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

39

B. Performance of the Clusters

Figure 16: Performance of the clusters for Indicator 3 under Environmental Sustainability

C. Interpretation & Findings
ANALYSIS: The Mat Craft Cluster at West Medinipur, West Bengal registers the highest net
waste generation at 29,928 kgs per crore of revenue. The Medicinal Plant Cluster at Barauliya,
UP and the Natural Dyed Handloom Cluster at Shivamogga, Karnataka demonstrate low net
waste generation per crore of revenue at 260 kgs and 20 kgs respectively. The Lemongrass
and Palmarosa Cluster at Mayurbhanj, Odisha does not generate any net waste.
OBSERVATION: Similar to the previous indicator, the net waste generated per crore ofrevenue is heavily dependent can be masked by the revenue figures and the material
intensity of the production process, though the performance can be improved withrecycling and reusage. This can be measured through material efficiency as given under
Indicator 3.1

40

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Indicator 3.1 - Material efficiency as a % of output to inputThis indicator evaluates how much a cluster gets out of its raw materials to promote resource
optimization and subsequent cost reductions. Given that the material efficiency depends upon
the nature of the production process, the clusters are divided into categories of extractive(extracting from the raw material such as in case of the Mayurbhanj and Barauliya clusters)and value added (non-extractive processes, such as in case of the Shivamogga and West
Medinipur clusters) for analysis. For value-added clusters, highest the material efficiency, betterthe performance. For extractive clusters, performance can only be checked by comparing itwith that of similar production processes. A benchmark for masala manufacturing and oil
distillery is taken for comparison with the material efficiency of Barauliya and Mayurbhanj
clusters respectively.

A. Formula for Calculation and the Data Points

Table 14: Data points and sources for Indicator 3.3 under Environmental Sustainability
S.No

Data Points

Source1

Quantity of finished products

Product Register

2

Quantity of raw materials

Process matrix

B. Performance of the Clusters

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41

Figure 18: Material Efficiency of the clusters – Value addition based

Figure 19: Material Efficiency of the clusters – Extraction based

The Mat Craft cluster at West Medinipur, West Bengal and the Natural Dyed Handloom Cluster
at Shivamogga, Karnataka register material efficiency of 73% and 57% respectively. This could
be improved for the latter. In the case of the extractive clusters, the material efficiencies arecloser to the averages – even though the Mayurbhanj cluster performs slightly better andBarauliya cluster lags.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Indicator 4- Renewable energy (solar) as a % of the total energy
consumption
This indicator indicates the proportion of clean, renewable energy sources like solar powerin the overall energy mix. It serves as a crucial metric for the clusters looking to transition togreener energy sources for reducing carbon emissions and increasing energy independence.
By monitoring and increasing the percentage of solar energy, these rural enterprise clusters
can make significant strides towards a more sustainable and environmentally friendly energy
portfolio.

A. Formula for Calculation and the Data Points

Table 8: Data points and Sources for Indicator 4 under Environmental Sustainability
S.No

Data Points

Source1

Actual Consumption from solar panels

Voluntary disclosure2

Direct sunlight hours

Secondary research3

No. of working days of cluster

Voluntary disclosure

4

Annual revenue

Financial statements

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43

B. Performance of the Clusters

Table 9: Source of energy for the clusters
Piloted Clusters (Annual

External Sources

Energy Consumption)(State Grid)

West Medinipur (23,065 kWh)Solar Panel

DG Set

35%

51%

14%

Shivamogga (22,785 kWh)

26%

74%

0%

Barauliya (9,900 kWh)

36%

0%

64%

Mayurbhanj (34,493 kWh)

0%

100%

0%

C. Interpretation & Findings
ANALYSIS: The Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha relies entirely
on solar power to run its oil distillation units. This is followed by the Natural Dyed Handloom
Cluster in Shivamogga, Karnataka, that sources 74% of its annual energy consumption from thesolar panels and the Mat Craft Cluster in West Medinipur, West Bengal at 51% of energy fromsolar panels. The Medicinal Plant Cluster in Barauliya, Uttar Pradesh, doesn’t use solar panels
due to cost and capacity issues.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

OBSERVATION: There are two extreme cases of the Medicinal Plant Cluster at Barauliya, Uttar
Pradesh and the Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha. The former is the
only one with no amount of power being sourced through solar panels due to issues related
to costing and capacity. The latter, on the other hand, by nature of operations requires very
little electricity since the boilers for oil distilling are run on dried grass. All of this is sourced fromsolar panels. This also demonstrates a massive unspent solar energy, which could be directed
towards alternatives uses, such as for running machines for bottling and packaging the oil inhouse.
RECOMMENDATIONS: The Lemongrass and Palmarosa Cluster should assess surplus solar
energy for potential diversification. Continuous optimization of solar energy and exploration ofalternative sources is recommended for all clusters.

Solar panels set up in the Common Facility Centre of Naturally Dyed Handloom Cluster in Shivamogga, Karnataka

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

45

Indicator 5- Percentage of water recycled/reused viz total consumption
This indicator showcases the effectiveness of water conservation and recycling efforts. It
offers valuable insights for clusters to optimize their water management practices, reduce
environmental impact, and ensure long-term water resource sustainability. By monitoringand increasing the percentage of recycled/reused water, these clusters can make significant
progress towards responsible water stewardship and resilience in the face of water scarcity
challenges.

A. Formula for Calculation and the Data Points

Table 10: Data points and Sources for Indicator 5 under Environmental Sustainability
S.No

Data Points

Source1

Quantity of water consumption

Voluntary disclosure2

Quantity of water recycled

Voluntary disclosure

B. Performance of the Clusters

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Table 11: Water consumption by the clusters
Daily consumption in kl

Percentage of water

Piloted Clusters(Net Water Consumption =Annual Consumption

getting recycled(Source: Groundwater)

Total Water Consumption -(in kl)against annual
consumption

Water Reused)
West Medinipur

3 - 0.5 = 2.5

520

17%

Shivamogga

10 - 0 = 10

3000

0%

Barauliya

0.1 - 0 = 0.1

280%Mayurbhanj

5-0=5

1825

0%

C. Interpretation and Findings
ANALYSIS: The Mat Craft Cluster in West Medinipur, West Bengal, is the only cluster which
actively recycles and reuses water at 17%. The other three clusters are not recycling water at
the moment.
OBSERVATION: About 17% of the annual water consumption, primarily at the mat dyeing
stage of the operations, is filtered for removing the natural dyes and chemical catalysts andis reused in the same process a number of times after which, it is filtered one last time anddisposed in the field. The Natural Dyed Handloom Cluster at Shivamogga, Karnataka treats
the water used in the cloth dyeing processes but instead of reusing it in operations, directs it
towards watering and cleaning purposes. This offers potential for the cluster to cut on the total
annual water consumption and save on cost by redirecting the treated wastewater, partially or
wholly, back in the manufacturing process.
The Medicinal Plant Cluster at Barauliya, Uttar Pradesh does not have any mechanism fortreating and reusing the wastewater, all of which currently gets disposed into the field. The
case of Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha is the same. Interview with
the implementation agency however revealed the interest in reusing the wastewater from thedistillation processes for creating by-products. The water that gets separated from the oil at the
final stage of the distillation retains the smell and properties of lemongrass and palmarosa andcan be used for producing phenyl.
RECOMMENDATIONS: The amount of water treated and reused in the manufacturing
process needs to be regularly monitored and the water registers maintained. Concrete dataover a continued period will enable identifying trends of water usage and areas of optimization.

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Indicator 6- Amount of waste utilized (reused & composted) per tonof output
This indicator demonstrates the effectiveness of waste reduction and recycling initiatives inrelation to production output. Increasing waste diversion is not only environmentally friendly
but also cost effective as it reduces the total consumption of raw materials due to recycling.

A. Formula for Calculation and the Data Points

Table 12: Data points and sources for Indicator 6 under Environmental Sustainability
S.No

48

Data Points

Source1

Quantity of waste disposed

Voluntary disclosure2

Quantity of waste recycled/reused

Voluntary disclosure

3

Quantity of waste composted

Voluntary disclosure

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

B. Performance of the Clusters

Table 13: Waste composition of the clusters
Piloted Clusters

Disposed

ReusedComposted

West Medinipur (27040 kg)

77%

0%

23%

Shivamogga (500 kg)

20%

80%

0%

Barauliya (1040 kg)

25%

25%

50%

Mayurbhanj (905 kg)

0%

100%

0%

C. Interpretation and Findings
ANALYSIS: The Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha uses the driedgrass as fuel for running boilers, thus, achieving a 100% waste recycling rate. This is followedby the Natural Dyed Handloom Cluster at Shivamogga, Karnataka at 80% of recycling and the
Medicinal Plant Cluster at Barauliya, UP at 25% of recycling. The Mat Craft Cluster lags at 0%
waste recycled/reused, even though it composts 23% of its waste.
OBSERVATIONS: The Mat Craft Cluster at West Medinipur, West composts 23% of its total
waste but doesn’t reuse or recycle any. The case of the Lemongrass and Palmarosa Cluster
at Mayurbhaj, Odisha is a unique case, disposing no waste per crore of revenue. All grass after
extraction of oil is dried and used as a fuel to run boilers.

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RECOMMENDATIONS: Considering that the waste is in the form of dyed mat and cloth
cuttings, Mat Craft Cluster at West Medinipur, West could use it for producing briquettes. Crossexchange of recycling ideas from Shivamogga cluster may help. In case of the Lemongrass
and Palmarosa Cluster, a follow-up assessment on the carbon emissions due to burning of
the dried grass and the trade off with benefits of waste management may be commissioned
to get a clear visibility of its impact on this indicator. In case the practice of burning grass forrunning steamers cannot be stopped, the dried grass could be converted to low-carbon fuelfor reducing emissions.

Quilts made from the cloth-cuttings at the Naturally Dyed Handloom Cluster in
Shivamogga, Karnataka.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

LOCAL ECONOMIC DISTRIBUTION
There are a total of six indicators capturing local economic distribution for the rural enterprise
clusters across the attributes of income enhancement, localization and job creation. These are
explained under:-Indicator 1- Percentage increase in monthly average income of
SPV members before and after SFURTIThis indicator captures the extent to which the income of artisans and farmers engaged in theassessed rural enterprise clusters has increased vis-à-vis their income levels before joining thecluster. In this sense, this indicator is a direct factor of the livelihood impact.

A. Formula for Calculation and the Data Points

Table 15: Data Points and Sources for Indicator 1 under Local Economic Distribution
S.No

Data Points

Source1Currently Monthly Income

Field Survey

2Monthly Income before SFURTI

Field Survey

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B. Performance of the Clusters

Figure 20: Performance of the clusters for Indicator 1 under Local Economic Distribution

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

C. Interpretation & Findings
ANALYSIS: The West Medinipur Cluster demonstrates the highest change in monthly average
income before and after SFURTI at 386%. Mayurbhanj registers the second highest increase at
82%, followed by Shivamogga at ~77% and Barauliya at ~36%.
OBSERVATIONS: In the Mat Craft Cluster at West Medinipur, West Bengal, about 86% of
the women artisans were unemployed and had no income before. The remaining 14% were
earning a marginal amount through menial and seasonal employment. About 58% of women
engaged in the Natural Dyed Handloom Cluster at Shivamogga, Karnataka had no earnings
before. In contrast, majority of those engaged in the agro-based clusters of Medicinal Plant
at Barauliya, Uttar Pradesh and Lemongrass and Palmarosa at Mayurbhanj, Odisha wereexisting farmers. Across all four clusters, about 31% of the sample worked as artisans before
and witnessed an average of 64% increase in income after engaging with the cluster. About
29% were housewives, and 14% were agricultural laborers. The other occupations that foundmention in the surveys were daily wage and factory labourer, fishermen, school helper etc.

Woman artisan with the loom machine provided by the Mat Craft Cluster in West Medinipur,
West Bengal for weaving products out of dyed babui grass. The cluster provides training to
the women artisans on these machines at the Common Facility Centre (CFC). Those whocomplete the training are provided with looms at their homes to be able to engage in value
addition at their own pace and comfort.

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Indicator 2- Percentage difference in monthly average income of
SPV members under SFURTI vis-à-vis alternative livelihoods
This indicator compares the income generation of the artisans and farmers under SFURTI
clusters with other livelihood options they may have availed had they not been engaged withthe cluster. A positive difference showcases a comparative advantage of engagement with
SFURTI clusters for income enhancement. A negative difference implies that the artisans andfarmers would be better off undertaking alternative livelihood options. This indicator is critical
for the enterprise clusters and in effect, for the policymakers and investors, to identify theefficacy of setting up these clusters as livelihood engines.

A. Formula for Calculation and the Data Points

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Table 15: Data Points and Sources for Indicator 1 under Local Economic Distribution
S.No

Data Points

Source1Currently Monthly Income of Artisans & Farmers

Field Survey

2Monthly Average Income from Alternative Livelihoods

Secondary Research

B. Performance of the Clusters
Benchmarking: Since the wage rates differ by occupation, type of area (rural/urban) and State,
for statistical coherence, this investigation adopts for comparison the average wage earning (inRs.) of casual workers in works other than public work, as defined for rural area (April to June2022) for respective states. This is calculated for 20 days of work a month. Similar inference canbe drawn by comparing SFURTI earnings by artisans and farmers by different indices such as
Living Wage, Living Income, Natural Rural Poverty Line, World Bank Poverty Line etc.
*For casual workers in work other than public works, the before data pertains to year 2020.

Cluster 1 - West Medinipur Mat Craft Cluster:

Income Comparison

SFURTI

Casual Workers in work otherthan Public Works

Monthly Average Income before~ Rs. 858

~ Rs. 4,280

Monthly Average Income after~ Rs. 4,170~ Rs. 5,610% Growth in Income

386%

~ 31%

Percentage Difference in Income of SPV Members viz Alternative Livelihood ~ -26%

Cluster 2 - Shivamogga Naturally Dyed Handloom Cluster:
Income Comparison

SFURTI

Casual Workers in work otherthan Public Works

Monthly Average Income before~ Rs. 4,008~ Rs. 4,750

Monthly Average Income after~ Rs. 7,079~ Rs. 7,000% Growth in Income

~ 77%

~ 47%

Percentage Difference in Income of SPV Members viz Alternative Livelihood ~ 1%

Cluster 3 - Barauliya Medicinal Plant Cluster:
Income Comparison

SFURTI

Casual Workers in work otherthan Public Works

Monthly Average Income before~ Rs. 2,761~ Rs. 5,510

Monthly Average Income after~ Rs. 3,746~ Rs. 6,410% Growth in Income

~ 36%

~ 16%

Percentage Difference in Income of SPV Members viz Alternative Livelihood ~ -42%
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55

Cluster 4 - Mayurbhanj Lemongrass & Palmarosa Cluster:
Income Comparison

SFURTI

Casual Workers in work otherthan Public Works

Monthly Average Income before~ Rs. 3,602~ Rs. 5,610

Monthly Average Income after~ Rs. 6,555~ Rs. 5,700% Growth in Income

~ 82%

~ 2%

Percentage Difference in Income of SPV Members viz Alternative Livelihood ~ 15%

Figure 21: Income and Income Growth Comparison of SFURTI viz Alternative Livelihoods for Mat Craft
Cluster in West Bengal

Figure 22: Income and Income Growth Comparison of SFURTI viz Alternative Livelihoods for Natural Dyed
Handloom Cluster at Shivamogga, Karnataka

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Figure 23: Income and Income Growth Comparison of SFURTI viz Alternative Livelihoods for Medicinal
Plant Cluster at Barauliya, Uttar Pradesh

Figure 24: Income and Income Growth Comparison of SFURTI viz Alternative Livelihoods for Lemongrass
and Palmarosa Cluster at Mayurbhanj, Odisha

C. Interpretation and Findings
ANALYSIS: Farmers engaged in the Lemongrass and Palmarosa Cluster at Mayurbhanj,
Odisha are earning 15% more per month under SFURTI than the benchmarked earning
under alternative livelihood. This moderates out for the Natural Dyed Handloom Cluster at
Shivamogga, Karnataka at 1%. The Mat Craft Cluster at West Medinipur, West Bengal andthe Medinical Plant Cluster, on the other hand, demonstrates a negative of 26% and 42%
respectively in the income comparison.
OBSERVATIONS: In the case of all clusters, the income growth before and after SFURTI
outpaces the growth in defined average earnings of casual workers. This is 386% against 31%
in case of West Medinipur, 77% against 47% in Shivamogga, 36% against 16% in Barauliya and
82% against 2% in Mayurbhanj. This implies that while the income under SFURTI clusters may
yet not have reached the level of defined average earnings of casual workers in some cases, itshows a progressive trend and could soon compare to the benchmark.
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Indicator 3 – Local Procurement of Inputs (except machinery and wages)
This indicator is one of the three critical indicators on local contribution to the economy.
It captures the extent to which the cluster augments local demand of raw materials, except
machinery and human resources, and thus, feeds into the local economy. A higher contribution
to the local economy is paramount to creating resilient micro economies that sustain livelihoods.

A. Formula for Calculation and the Data Points

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Table 17: Data Points and Sources for Indicator 3 under Local Economic Distribution

S.No

Data Points

Source1

Total quantity (kg) of raw material purchased per item

Voluntary Disclosure2

Per unit (kg) price of raw material per item

Voluntary Disclosure

3

Distance of the location of procurement from the CFC

Voluntary Disclosure

B. Performance of the Clusters

Figure 25: Performance of the clusters for Indicator 3 under Local Economic Distribution

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59

C. Interpretation and Findings
ANALYSIS: The Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha and Medicinal
Plant Cluster at Barauliya, Uttar Pradesh have a 100% local procurement rate, followed by the
Mat Craft Cluster at West Medinipur, West Bengal at 87%. The Natural Dyed Handloom Cluster
at Shivamogga, Karnataka has the lowest local procurement at 6%.
OBSERVATION: Agro-based clusters are handholding farmers to grow the grass/agricultural
produce locally, thereby their 100% local procurement rate. The Mat Craft Cluster at West
Medinipur, West Bengal sources the Madurkathi grass – the main raw material – locally.
Other supplies, about 13% of the procurement, are sourced from markets in Kolkata locatedat a distance of 105 km from the cluster. In contrast, 94% of the raw material of the Natural
Dyed Handloom Cluster at Shivamogga, Karnataka comes from Bangalore with one or two
products (dyes) sourced from Chennai. These raw materials are at present not locally available.
Depending upon the feasibility, local farmers could be encouraged to grow plants to retrievedyes being currently sourced from outside.

Women artisan working in Natural Dyed Handloom Cluster, Shivamogga, Karnataka

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Indicator 4 –Wage Expenditure on Local Artisans and Farmers
This indicator captures the percentage of the total wages spent on local artisans and farmers
vis-à-vis migrants and becomes highly crucial in context of the rural enterprise clusters being
perceived as agents of meaningfully employing local communities into productive occupations.
Each cluster should strive to achieve a high performance on this indicator as a measure of theirimpact on augmenting local livelihoods.

A. Formula for Calculation and the Data Points

Table 18: Data Points and Sources for Indicator 4 under Local Economic Distribution
S.No

Data Points

Source1

Total number of artisans and farmers at cluster

Voluntary Disclosure2

Total number of migrants

Voluntary Disclosure

Wage rate per worker

Voluntary Disclosure

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B. Performance of the Clusters

Figure 26: Performance of the clusters for Indicator 4 under Local Economic Distribution

C. Interpretation and Findings
Almost all artisans and farmers engaged across the four SFURTI clusters belong to the local
community – supporting livelihood generation and resilience building of the local economy.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Indicator 5 – Local Retention of Profits
As rural enterprise clusters, their profits should be distributed among the SPV membersi.e., artisans and farmers as dividends on a proportionate basis. This indicator demonstrates
whether and to what extent this distribution is happening at the cluster level.

A. Formula for Calculation and the Data Points

Table 19: Data Points and Sources for Indicator 5 under Local Economic Distribution

S.No

Data Points

Source1

Annual Profits

Profit and Loss Statements

2

Total amount of dividends paid

Voluntary Disclosure, verified byannual financial report of the cluster

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B. Performance of the Clusters

Figure 27: Performance of the clusters for Indicator 5 under Local Economic Distribution

C. Interpretation and Findings
ANALYSIS: The Natural Dyed Handloom Cluster at Shivamogga, Karnataka is the only cluster
distributing dividends among the artisans and farmers at 19%. This remains nil for the otherclusters assessed under this study.
OBSERVATIONS: Against the Shivamogga cluster that has attained “maturity” and is attaining
an annual profit of Rs. 2,97,70,416, the Mat Craft Cluster at West Medinipur, West Bengal, theMedicinal Plant Cluster at Barauliya, Uttar Pradesh and the Lemongrass and Palmarosa Cluster
at Mayurbhanj, Odisha have lower profit ranges and are currently feeding it back into operations
for infrastructural and technological upgradation of the cluster.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Indicator 6 – Total Jobs Facilitated and Net Additional Job Createdper Crore of SFURTI Investment
This indicator comprises two separate indices, one that captures the cumulative number ofartisans and farmers facilitated with a job by the SFURTI cluster and the second that captures
the number of additional artisans and farmers the cluster supported/supports apart fromthose initially mobilized and accounted for in the Detailed Project Report – both percrore of SFURTI investment. These indices shall aid capturing impact of the cluster on livelihood
generation.

A. Formula for Calculation and the Data Points

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65

Table 20: Data Points and Sources for Indicator 6 under Local Economic Distribution
S.No
1

Data Points
Base Number of Artisans and Farmers

SourceDetailed Project Report
(DPR)

2

Number of Artisans and Farmers facilitated each year

Voluntary Disclosure

Number of Dropouts each year

Voluntary Disclosure

Amount of Investment under SFURTICluster Documents

B. Performance of the Clusters

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Figure 28: Performance of the clusters for Indicator 6 under Local Economic Distribution

C. Interpretation and Findings
ANALYSIS: The Mat Craft Cluster at West Medinipur, West Bengal has facilitated a total of 364
jobs per crore unit of investment in the three years’ span, followed by the Medicinal Plant Cluster
at Barauliya, Uttar Pradesh, the Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha andthe Natural Dyed Handloom Cluster at Shivamogga, Karnataka with a total job facilitation percrore unit of investment of 331, 326 and 295 jobs respectively.
OBSERVATIONS: The Barauliya cluster has added the most artisans and farmers on the roll
(excluding dropouts), in addition to those already accounted for in the DPR. The Mayurbhanj
cluster is the only one with neither any additional job creation nor any dropouts – the numberof artisans and farmers engaged in the cluster has remained the same at 1250.

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67

SOCIAL EQUITY AND INCLUSION
The parameter of social equity and inclusion accounts for equal opportunity to vulnerable
groups by age, caste and gender not only in terms of employment generation but alsodecision-making. Three attributes namely, Inclusive Employment, Inclusive Decision Making
and Equitable Pay are identified under this parameter with the respective indicators ofrepresentation of vulnerable groups in SPV members, representation of vulnerable groups in
Board members and pay parity/disparity by gender and caste for same profile of work in thecluster. These are explained below.

Indicator 1- Percentage of SPV members belonging to the vulnerable
section of society
This indicator demonstrates the inclusivity of the cluster in its engagement of artisans andfarmers by gender, caste and age. 10Rural enterprises, especially the enterprise clusters offer
opportunity for women, youth and those belonging to the Scheduled Caste, Scheduled Tribe,and Other Backward Classes to tap into their energies and aspirations for earning livelihoods.
This indicator, in that sense, is paramount to the social impact. The value on this indicator isindividually represented in percentage terms.

A. Formula for Calculation and the Data Points11

10 As per the National Youth Policy of India, youth is an individual between 15 years and 29 years of age. This investigation adopts a range of 18 years to
29 years, barring for those underaged legally to work.
11 The investigation acknowledges that the vulnerability by age, caste and gender is not exclusive i.e., an SPV member could be a woman and also
recognized herself as a scheduled caste or youth. For scoring purposes, the report leverages survey data to identify those SPV members who arevulnerable in some way or the other (by sex, social category or age).

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Table 21: Data Points and Sources for Indicator 1 under Social Equity & Inclusion
S.No
1

Data Points
Total Number of SPV Members

SourceVoluntary Disclosure, verified with
Annual Report2

Number of SPV members that recognized

Voluntary Disclosure

as women, youth or Scheduled Caste/
Scheduled Tribe/Other Backward Classes

B. Performance of the Clusters

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69

Figure 29: Performance of the clusters for Indicator 1 under Social Equity & Inclusion

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

C. Interpretation and Findings
Women Representation: The Mat Craft Cluster at West Medinipur, West Bengal leads in caseof women representation among the SPV members at 98%, closely followed by the Natural
Dyed Handloom Cluster at Shivamogga, Karnataka at 79% and the Lemongrass and Palmarosa
Cluster at Mayurbhanj, Odisha at 78%. The Medicinal Plant Cluster at Barauliya is the only
cluster with a low women participation ratio at 15%. The male artisans in case of Shivamogga
are engaged in dyeing processes and in case of Mayurbhanj, in running the machines for oil
distillation in addition to male farmers. These clusters fare extremely well in comparison to thewomen participation ratio of 15% under the Micro and Small Enterprises Cluster Development
Programme (MSE-CDP), another initiative focused on rural enterprise clusters by the Ministryof MSME.
Representation by Caste: The Lemongrass and Palmarosa Cluster at Mayurbhanj, Odisha
and the Mat Craft Cluster at West Medinipur, West Bengal fare well in terms of representationof SC/ST/OBC among SPV members at 99% and 80% respectively. This is because these arelocated at a tribal dominated area and the fishermen community belonging to Scheduled
Caste respectively. The Natural Dyed Handloom Cluster at Shivamogga, Karnataka also
demonstrates 96% inclusion by caste. The Medicinal Plant Cluster at Barauliya, Uttar Pradesh isagain an outlier with the inclusion rate of only 26%.
In comparison to the caste inclusion under MSE-CDP, all clusters except Barauliya performs at
the higher end on the indice.
Representation by Age: All four clusters demonstrate potential in terms of representationof youth among the SPV members with inclusion rate of 17% each for the Mayurbhanj andBarauliya clusters, 14% for Shivamogga cluster and 7% for West Medinipur. Further probing
during field surveys revealed youth migration from these areas to nearby cities and towns andthe need of specific skills for some profiles as some factors contributing to the low inclusion.
Through youth engagement activities, these clusters can generate impact visibility for the local
communities, and thus, a sense of belonging among the youth. This could help expand thebasket of those available and willing to engage with these clusters.

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71

Indicator 2- Percentage of Vulnerable Groups among Board Members
This indicator is a factor of enterprise governance and runs along the same line as theprevious indicator while capturing inclusivity in decision-making by gender, caste and age. The
value on this indicator is individually represented in percentage terms.

A. Formula for Calculation and the Data Points12

Table 22: Data Points and Sources for Indicator 2 under Social Equity & Inclusion

S.No
1

Data Points
Total Number of Board Members

SourceVoluntary Disclosure, verified with
Annual Report2

Number of Board members that recognized

Voluntary Disclosure

as either women, youth or Scheduled
Caste/Scheduled Tribe/Other Backward
Classes72

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

B. Performance of the Clusters

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India73

Figure 30: Performance of the clusters for Indicator 2 under Social Equity & Inclusion

12 The investigation acknowledges that the vulnerability by age, caste and gender is not exclusive i.e., an SPV member could be a woman and also
recognized herself as a scheduled caste or youth. To account for this, the percentages are calculated separately.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

C. Interpretation & Findings
Women Representation: The Natural Dyed Handloom Cluster at Shivamogga, Karnataka
boasts of 100% inclusivity with all 12 Board Members being women. This moderates out for
the Mat Craft Cluster at West Medinipur, West Bengal at 52%, dropping further to 25% for the
Medicinal Plant Cluster at Barauliya, Uttar Pradesh and 20% for the Lemongrass and Palmarosa
Cluster at Mayurbhanj, Odisha.
Representation by Caste: The Natural Dyed Handloom Cluster at Shivamogga, Karnataka
performs high on caste inclusivity among board members at 100%. Given its location, theLemongrass and Palmarosa Cluster at Mayurbhanj, Odisha demonstrates 100% as well. The
Mat Craft Cluster at West Medinipur, West Bengal shows a moderate caste inclusivity rateof 48% among Board members. This is even when the cluster is in a fishermen community
dominated by SCs. The Medicinal Plant Cluster at Barauliya, Uttar Pradesh ranks low with 12%
caste inclusivity rate.
Representation by Age: Like the case of youth representation among SPV members, theinclusivity rate among Board Members shows a non-zero figure only for the Natural Dyed
Handloom Cluster at Shivamogga, Karnataka.
Overall, the clusters show remarkable trend in women inclusivity in employment andgovernance. The performance is positive in case of caste-based inclusivity as well. There is,however, a considerable scope of enabling more youth to become a part of the enterpriseprocesses both as artisans/farmers and in enterprise governance role.

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Indicator 3- Pay Parity/Disparity by Gender and Caste among
SPV Members
This indicator captures the cluster’s progress on social equity through assessing women’s
wage as % of men’s and that of reserved category artisans/farmers as % of those belonging
to reserved category. This is calculated as an average of only those profiles that employ bothends of gender and caste categories for comparison of wages. The closer the value to 100%,
the better the performance on the indicator. In cases where women earn more than men, or
reserved category artisans and farmers earn more than non-reserved category, the value could
also exceed 100%.
Please note that this indicator does not account for social equity by age, since wages could
rationally increase with an increase in the number of years of experience in a particular tradeor work.

A. Formula for Calculation and the Data Points13

13 The investigation acknowledges that the vulnerability by age, caste and gender is not exclusive i.e., an SPV member could be a woman and also
recognized herself as a scheduled caste or youth. To account for this, the percentages are calculated separately.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Table 23: Data Points and Sources for Indicator 3 under Social Equity & Inclusion

S.No
1

Data Points
Total Number of Profiles with both men

SourceVoluntary Disclosure

and women or ST/ST/OBCs and Generalcategory artisans and farmers employed2

Monthly Wage of Women per Profile where

Voluntary Disclosure

men are also engaged
3

Monthly Wage of Men for Same Profiles

Voluntary Disclosure

4

Monthly Wage of SC/ST/OBCs per Profile

Voluntary Disclosure

where general category artisans andfarmers are also engaged
5

Monthly Wage of General Category

Voluntary Disclosure

Artisans and Farmers for Same Profiles

B. Performance of the Clusters

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77

Figure 31: Pay Parity between Men and Women (Graph 1 & 2) and Pay Parity between General andReserved Categories (Graph 3 & 4)

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

C. Interpretation & Findings
Women Representation: The Mat Craft Cluster at West Medinipur, West Bengal does nothave any profile in which both men and women are employed, making the comparison nonapplicable. But on a broader level, all other clusters demonstrate pay parity among men andwomen. This is significantly higher than the pay parity prevailing in the average wages of men
and women casual workers other than those engaged in public works for rural areas, calculated
for 20 working days a month.
Representation by Caste: All four clusters characterize of equal pay to artisans and farmers
belonging to SC/ST/OBC categories vis-à-vis general category for profiles with both employed.
The Natural Dyed Handloom Cluster at Shivamogga, Karnataka goes beyond equal pay wherefor profiles like dyeing and block printing, SC/ST/OBCs are being paid marginally more. This
seems unintentional since the trend reappears but for the general category artisans and farmers
in case of weaving.

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79

2.4

SUSTAINABILITY OF THE CLUSTERS

The overall sustainability of these clusters can be measured through the below given spiderwebs, which score the four clusters across 14 indicators14, capturing their environmental,
economic and social impact out of 10. This score is drawn from mechanism as explained under
Annexure 6. The greater the area and the total average score of the enterprise cluster, more its
sustainability.

14 Local retention of profit has not been plotted due to 3 out of 4 clusters not having started distribution of dividends.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Indicator

IND

Energy Intensity: Cost of Energy as a % of Cost of Production

E1

Water Intensity: Net Water Consumption per Kg of Output

E2

Renewable energy (solar) as a % of the total energy consumption

E3

Percentage of water recycled/reused viz total consumption

E4

Amount of waste utilized per ton of output

E5

Material efficiency per kg of input

E6

Increase in income of farmers and/or artisans (before & after SFURTI)

Ec1

Percentage Difference in Income of SPV Members viz Alternative Livelihood

Ec2

Local procurement of inputs (except machinery and wages)

Ec3

Wage Expenditure on Local SPV Members

Ec4

Total Jobs Facilitated Created per Crore of Investment

Ec5

Representation of vulnerable group among SPV members

S1a

Representation of vulnerable group among Board Members

S2a

Equitable pay for vulnerables (by sex and social category) for n profile

S3a

Shivamogga, KA

West Medinipur,
WB

Barauliya, UP

Mayurbhanj, OD

Total Avg. Score

66.43

62.5

50.43

69.14

Environment

52.67

32.83

32.33

39

Economic

63

81.8

66

87

99.7

89.67

60.67

100

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81

OVERALL GAPS AND RECOMMENDATIONSIn addition to the cluster wise analysis provided indicator wise earlier in this section,
the following are suggested overall to improve the environmental sustainability, local
economic contribution and social inclusion among these clusters.

Environmental Sustainability•

The solar power remains underutilized at the rural enterprise clusters. Considering thatthese clusters also yield waste like grass or cloth cuttings in case of Mat Craft Cluster ordried lemongrass at Mayurbhanj Cluster, the unutilized solar power could be used to run
machines for generating by-products from the waste – reducing waste disposal while
yielding economic returns.

•

None of the clusters assessed under this study had a ready record of water quality – whichhindered analyzing whether the wastewater being disposed from these clusters was safeor toxic. A regular monitoring is proposed with maintenance of water registers to be able toanalyze the water quality at these rural enterprise clusters.

Local Economic Distribution•

The income levels of artisans and farmers at the West Medinipur and Barauliya Clusters
lag behind both the benchmark of average wage earnings (in Rs.) of casual workers inworks other than public works, as defined for rural area (April to June 2022) for respective
States, and National Rural Poverty Line15 of Rs. 4,428/- per worker per month. While thesehave improved overtime, the profit margins of all clusters except Shivamogga are low, due
to which they are unable to pay dividends to the artisans and farmers. The clusters needto improve their profitability, of which waste-to-wealth (as suggested above) as well as
market aggregation could prove useful.

•

About 94% of the raw material of the Natural Dyed Handloom Cluster at Shivamogga,
Karnataka comes from Bangalore with one or two products (dyes) sourced from Chennai.
These raw materials are at present not locally available. Depending upon the feasibility,
local farmers could be encouraged to grow plants within the ecological scope to retrievedyes being currently sourced from outside. In this way, the local economy can be further
strengthened.

Social Equity & Inclusion
Except for the Medicinal Plant Cluster at Barauliya, Uttar Pradesh, where the women
representation needs to be corrected, other three clusters have a good representation of
women in the SPV and Board. There is a need for engaging more youth into the cluster activities
though, which currently lags behind. Through collaboration with local academic institutions,more youth can be exposed to the operations of the rural enterprise clusters and encouraged
to participate in the local economy through them.

15 Calculated as per Rangarajan method of (per person monthly rate of Rs.1645.3*family of 4.5)/1.672 workers per family. This yields the income valueof Rs. 4,428/- per month.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

A woman artisan weaving mats from babui grass in the Mat Craft Cluster in West Medinipur, West Bengal
Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

83

A woman artisan working on the loom in the Naturally Dyed Handloom Cluster in Shivamogga, Karnataka.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

3. PATHWAYS TO ANENABLING ECOSYSTEM
3. PATHWAYS
The desk research, individual consultations and detailed surveys with both the Implementation
Agency and the SPV members (artisans and farmers) demonstrate reliable evidence of localized
impact towards greener, socially inclusive and thriving micro rural economies. The clusters
assessed under the study were all located in the outskirts of the townships, meaningfully
employing local artisans and farmers most of whom were otherwise either unemployed
or engaged under MGNREGA/were undertaking menial/seasonal jobs. Majority of thesewere also women and/or those belonging to Scheduled Caste, Scheduled Tribes and Other
Backward Caste categories. On the environmental front, the rural enterprise clusters showed
an increasing trend of adopting solar panels as a means of sourcing electricity. These clusters
were also cognizant of the environmental and cost-saving potential of adopting circular
practices, especially reusing the waste for creating by-products.
Amplifying impact of the rural enterprise cluster necessitates mainstreaming it, which further
requires setting up of an enabling ecosystem for these clusters to thrive in. Policymakers,
private investors, civic society network and academia – all have a critical role to play in it. A few
ways in which this can be done are: -PATHWAYS TO SETTING AN ENABLING ECOSYSTEM
1. Enabling Policies
In addition to SFURTI, setting up and upgradation of rural enterprise clusters in India aresupported under schemes by the Ministry of Micro, Small and Medium Enterprises, Ministryof Agriculture and Farmers’ Welfare, the Small Farmers’ Agribusiness Consortium (SFAC), theNational Bank of Agriculture and Rural Development at the Central level and non-farm cluster
development initiatives by State Governments like that of Karnataka16. These schemes andprogrammes can be leveraged through: a.

Convergence of scheme infrastructure and resources wherever applicable andsetting up of a common platform to enable information exchange, licensing(including Udyam and GeM registration) and benefit transfer to rural enterprise
clusters can enable a coordinated effort on the front.

16 Non-Farm Cluster Development Programme, Government of Karnataka. Accessed at https://ksrlps.karnataka.gov.in/new-page/Non-farm%20
Livelihoods%20-%20-%20Cluster%20Development/en
Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

85

b.

There is also an opportunity of creating new rural enterprise clusters, potentially aligned
to the regional specialization recognized under Government initiatives of One District
One Product (ODOP)/One District Two Products (ODTP) among others. This can besupplemented with geo-tagging of the products and registrations with government
certifications like India Brand Handloom.

a.

At the level of public finance, fiscal and non-fiscal incentives in the form of tax credits
and subsidies, start-up incentives, fee redemptions etc can provide a much-needed
support to these clusters to grow.

2. Fostering Financial Innovation
Public financing, though essential, needs to be supplemented with commitment by the private
sector for not only creating more enterprise clusters but also providing refinancing support toexisting ones for adopting greener production practices. In this regard, the social and impact
investors could play a critical role by establishing a lending case for these enterprises throughtheir investments in short and medium teams. This is expected to encourage investment in public
and private financial institutions over a period. At the same time, innovation and integration inregard to the existing avenues available is critical to foster adequate and affordable financial
flows towards these enterprise clusters. This can be achieved through: a.

Provisioning for the “mature” rural enterprise clusters to raise capital the constituted Social
Stock Exchange.

b.

Enabling Credit Guarantee Schemes for Micro and Small Enterprises (CGTMSE) withdifferential interest rates for local and green rural enterprise clusters. The Scheme currently
provides guarantee coverage of up to INR 5 crore for manufacturing or service enterprises.

c.

Developing Sustainability Assessment-based Credit Rating Model to support green
financing into these enterprises.17 Geo-spatial data analytics can be plugged into themodel for verification of the processes digitally, cutting down the transaction cost andimproving the turnaround on the loan disbursement to these enterprises.

d.

Linking these clusters as projects with Green Credit Programme (GCP), notifiedin October 2023 as an innovative market-based mechanism designed to incentivize
voluntary environmental action across industries and companies.18

3. Strengthening Business and Market Linkagesa.

Establishing rigorous benchmarking, monitoring and quality regulation mechanisms
in place. This could be done through adopting or building upon the sustainability
assessment framework developed under this investigation for this purpose.

17 The TransUnion CIBIL in collaboration with SatSure, launched a similar product called ‘CIBIL Credit and Farm Report’ to provide crop loans to farmers
in 2022.
18 Ministry of Environment, Forest and Climate Change, “Notification issued for Green Credit Program (GCP) and Ecomark Scheme under LiFE
Initiative to promote sustainable lifestyle and environmental conservation,” October 2023. Accessed at https://pib.gov.in/PressReleaseIframePage.
aspx?PRID=1967476#:~:text=These%20initiatives%2C%20the%20Green%20Credit,the%20ideas%20of%20LiFE%20concept.

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b.

Developing Product Certification and Standards on the lines of data-based
certifications and standards, including Fairtrade and EarthSeer, which provide social
validation to the products and incentivize greener and socially inclusive practices througha demand-pull factor.

c.

Setting up Enterprise Ranking and Award System, similar to the ranking frameworks
based on the ESG ratings. This ranking and award system for the rural enterprise clusters
may be based on the sustainability assessment data captured quarterly/half-yearly andhoused under the Ministry of Micro, Small and Medium Enterprises.

4. Facilitating Partnerships and Collaborationa.

Fostering Public-Private-Partnership for dedicated research and development toaugment technical and product innovation, including shared product testing and research.

b.

Setting up an industry-academia-civic society consortium for mentorship and dedicatedconsultations to address their business, technological, capacity building and market
needs.

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

87

CONCLUSIONThe SFURTI Scheme, together with MSE-CDP and other Central and State Government
Schemes that support setting up or upgradation of rural enterprise clusters, represent theGovernment’s commitment to bolstering local economic development through livelihood
promotion. These enterprise clusters, however, deliver much more in terms of empowering
youth, women and other vulnerable groups to tap into their aspirations and energies to engage
themselves meaningfully into entrepreneurial ecosystems, from which they are often excluded.
These clusters demonstrate sustainable consumption and production practices throughadoption of renewable energy and conversion of waste into by-products, and links with theChennai HLP on LiFE - lifestyle for sustainable development.
True measurement of the clusters’ triple bottom line shows evidence of impact on the people
and planet. The existing ESG and other sustainability frameworks, however, may not be attuned
to the model of rural enterprise clusters or give due weightage to attributes of local economic
contribution, representation of vulnerable groups in enterprise governance etc – pointing toa need to redesign frameworks and impact assessments based on a holistic approach. The
Sustainability Assessment Framework designed under this study supported by the Indian
Micro Enterprises Development Foundation is first step towards that direction. It assesses theclusters on attributes such as efficiency and circularity of production, contribution to local
economy through local job creation and localized procurement of raw materials, as well as
equal representation of vulnerable groups in employment and enterprise governance, withpay parity for profiles where both ends of the spectrum (vulnerable and non-vulnerable) are
employed.
Replication of the assessment with other rural enterprise clusters, both SFURTI and nonSFURTI, will serve to defining benchmarks which would be critical for policymakers, private
investors and other actors alike to understand sustainability performance of enterprises andthus in a long run, optimize policy processes, plug financing into these through traditional andinnovative mechanisms and design other non-financial support systems for mainstreaming
these clusters in India.

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Annexures

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89

Annexure 1

List of Figures

90

Figure 1: Methodological Framework for Sustainability Assessment of Rural Enterprise Clusters

10

Figure 2: Selected IMEDF Clusters for Assessment

12

Figure 3: Methodology for data collection on questionnaires

15

Figure 4: Tailoring Unit of the Cluster

18

Figure 5: IMEDF-SAF team in the field with the Resource Person from the Implementation Agency

19

Figure 6: Printing unit of the handloom cluster

20

Figure 7: Weaving unit set-up for Women artisans in their homes

21

Figure 8: Entrance of the Common Facility Centre

22

Figure 9: Chilli Powder Packaging Machine in Operation

23

Figure 10: Entrance of Common Facility Centre 1 under Palmarosa and Lemongrass cluster

24

Figure 11: Filtration of Oil from oil-water mix

25

Figure 12: Performance of the clusters for Indicator 1 under Environmental Sustainability

30

Figure 13: Performance of the clusters for Indicator 2 under Environmental Sustainability

33

Figure 14: Performance of the clusters for Indicator 3 under Environmental Sustainability

35

Figure 15: Material Inefficiency of the clusters

38

Figure 16: Performance of the clusters for Indicator 1 under Local Economic Distribution

40

Figure 17: Income and Income Growth Comparison of SFURTI viz Alternative Livelihoods for
Mat Craft Cluster in West Bengal

41

Figure 18: Income and Income Growth Comparison of SFURTI viz Alternative Livelihoods for
Natural Dyed Handloom Cluster at Shivamogga, Karnataka

42

Figure 19: Income and Income Growth Comparison of SFURTI viz Alternative Livelihoods for
Medicinal Plant Cluster at Barauliya, Uttar Pradesh

42

Figure 20: Income and Income Growth Comparison of SFURTI viz Alternative Livelihoods forLemongrass and Palmarosa Cluster at Mayurbhanj, Odisha

52

Figure 21: Performance of the clusters for Indicator 3 under Local Economic Distribution

56

Figure 22: Performance of the clusters for Indicator 4 under Local Economic Distribution

56

Figure 23: Performance of the clusters for Indicator 5 under Local Economic Distribution

57

Figure 24: Performance of the clusters for Indicator 6 under Local Economic Distribution

57

Figure 25: Performance of the clusters for Indicator 1 under Social Equity & Inclusion

59

Figure 26: Performance of the clusters for Indicator 2 under Social Equity & Inclusion

62

Figure 27: Pay Parity between Men and Women (Graph 1 & 2) and Pay Parity between
General and Reserved Categories (Graph 3 & 4)

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Annexure 2

List of Tables
Table 1: Sample size of each cluster

16

Table 2: Data Points and Sources for Indicator 1 under Environmental Sustainability

29

Table 3: Data Points and Sources for Indicator 2 under Environmental Sustainability

32

Table 4: Data points and Sources for Indicator 3 under Environmental Sustainability

34

Table 5: Data points and Sources for Indicator 4 under Environmental Sustainability

37

Table 6: Source of energy for the clusters

39

Table 7: Data points and Sources for Indicator 5 under Environmental Sustainability

38

Table 8: Water consumption by the clusters

43

Table 9: Data points and sources for Indicator 6 under Environmental Sustainability

44

Table 10: Waste composition of the clusters

46

Table 11: Data points and Sources for Indicator 8 under Environmental Sustainability

47

Table 12: Data Points and Sources for Indicator 1 under Local Economic Distribution

48

Table 13: Data Points and Sources for Indicator 2 under Local Economic Distribution

49

Table 14: Data Points and Sources for Indicator 3 under Local Economic Distribution

41

Table 15: Data Points and Sources for Indicator 4 under Local Economic Distribution

55

Table 16: Data Points and Sources for Indicator 5 under Local Economic Distribution

52

Table 17: Data Points and Sources for Indicator 6 under Local Economic Distribution

59

Table 18: Total Jobs facilitated by each cluster

61

Table 19: Data Points and Sources for Indicator 1 under Social Equity & Inclusion

63

Table 20: Data Points and Sources for Indicator 2 under Social Equity & Inclusion

66

Table 21: Data Points and Sources for Indicator 3 under Social Equity & Inclusion

69

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Annexure 3
Sustainability Assessment Framework
P. = Parameter, A. = AttributeP.

A.

Indicator

Calculation

Unit

Gross energy

Formula:[kWh/crore

consumption

Annual electricity consumption in kWh / Annual revenue in crores.

rupees]per crore ofrevenue

Where,•

Annual electricity consumption =Annualelectricity

consumption from external sources (state grid) + Annual energy
consumption from solar panels + Annual energy consumption
from DG sets.
•

Annual electricity consumption from solar panels = Actual
Consumption from solar panels x Average hours of direct
sunlight in a day x No of working days in a year.

•

Annual electricity consumption from DG sets = Capacity of
DG set x Operational hours per day x No of working days in a

Efficiency

Environmental Sustainability

year.
Sub-Indicator

Formula:-[kWh/cost of

Gross Energy

Annual energy consumption in kWh / cost of production in croresproduction]

Consumption

Where,

per Crore Cost•

Annual energy consumption = Annual electricity consumption
from external sources (state grid) + Annual energy consumptionof Production

from solar panels + Annual energy consumption from DG sets.
•

Annual energy consumption from solar panels = Capacityfrom solar panels x Average hours of direct sunlight in a day x No
of working days in a year.

•

Annual electricity consumption from DG sets = Capacity of
DG set x Operational hours per day x No of working days in a
year.

Net water

Formula:[kl/crore

consumption

Annual water consumption in kl / Annual revenue in crores

rupees]per crore ofrevenue

Where,•

Annual water consumption = Total annual groundwater
consumption + Total annual water consumption from othersources - Total water recycled•

Total annual groundwater consumption = Daily consumptionof groundwater x number of working days of cluster.

•

Total annual water consumption from other sources = Totalannual water consumption from municipal line + Total annual
surface water consumption (from ponds & other water bodies).

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P.

A.

Indicator

Calculation

Unit

Sub-Indicator

Formula:[kl/ton of

Net Water

Annual water consumption in kl / Total Annual Output in ton

output]Consumption

Where,

per ton ofAnnual water consumption = Total annual groundwater

Outputconsumption + Total annual water consumption from other sources

Efficiency- Total water recycled
Net quantity

Formula:[kg/croreof waste

Total annual waste disposed in kg / Total annual revenue in crores.

rupees]disposed
per crore of

Where,

revenue•

Net annual waste disposed = Total waste produced - Total
waste utilized•

Total waste utilized = Waste recycled and reused + Waste

Environmental Sustainability

composed
Sub-Indicator

Formula:Material

Total output of finished products in kg / Total input of raw materials

efficiency

in kg

Renewable

Formula:Percentage

Percentage

energy (solar) (Total annual energy consumption from solar panel in kWh /Total
as a % of theannual energy consumption by the cluster in kWh) x 100

total energy
consumption

Where,
Annual energy consumption from solar panels =Actual Consumption from solar panels x Average hours of direct

Circularity

sunlight in a day x No of working days in a year.
Percentage

Formula:of water(Total quantity of water recycled or reused in a year (kl) / Total

recycled/

annual water consumption in kl) x 100

Percentage

reusedviz total
consumption
Amount of

Formula:[kg/

waste utilized(Total annual waste utilized in kg / Total waste generated annually inthousandper ton ofkg) x100.

units]

output
Where,
Total annual waste utilized = Total waste composted + Total
waste reused

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93

P.

A.

Indicator

Calculation

Increase in

Formula:income of[(Current Monthly Average Income under SFURTI – Monthly

Unit
Percentage

SPV Members Average Income Before SFURTI)/Monthly Average Income Before
before & after

SFURTI] x 100

SFURTI
Where,•

Current Monthly Average Income = Monthly wages anddividends currently earned at the SFURTI cluster averaged over
12 months for sampled artisans/farmers.

Income enhancement•

Monthly Average Income before SFURTI = Monthly wages
and dividends (if any) the artisan and/or farmer used to earn
before joining the SFURTI cluster averaged over 12 months forthe same artisans/farmers.

% Difference

Formula:in monthly[(Current Monthly Average Income under SFURTI –Monthly Average

income ofIncome from Alternative Livelihoods)/ Monthly Average Income

Percentage

SPV members from Alternative Livelihoods] x 100
under SFURTI
viz alternative Where,

Economic distribution

livelihoods•

Current Monthly Average Income = Wages and dividends
currently earned at the SFURTI cluster averaged over a year for
sampled artisans/farmers.
Monthly Average Income from Alternative Livelihoods= Wages from other job profiles, such as MGNREGA andminimum wages set at the state/national level for theconcerned industry.

Formula:Localprocurement of [Monthly expenditure (Rs.) on procurement from local vendors /inputs (except

Total monthly expenditure on procurement of raw materials (Rs.)] x

machinery

100

20

and wages)
Where,
Localization•“local” is defined as an area within 100 kilometers from thecluster.

•Monthly Expenditure on Procurement from Local
Vendors = (Total quantity of item 1 x Per unit price of item 1) +(Total quantity of item 2 x Per unit price of item 2) + _ _ _ _ _ _ __ + (Total quantity of item n x Per unit price of item n) averaged
monthly for items procured within 100 sq kms•

Total Monthly Expenditure on Procurement of Raw
Material = (Total quantity of item 1 x Per unit price of item 1) +(Total quantity of item 2 x Per unit price of item 2) + _ _ _ _ _ _ __ + (Total quantity of item n x Per unit price of item n) averaged
monthly for all item

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Percentage

P.

A.

Indicator

Calculation

Wage

Formula:Expenditure[(Monthly Wage Expenditure (Rs.) on local artisans and farmers) /on Local

Total monthly wage expenditure (Rs.)] x 100

Unit
Percentage

Artisans andFarmers

Where,
Total monthly wage expenditure of a cluster = Total
number of artisans and farmers x Average monthly wage rate

Localization

Economic distribution••

Monthly wage on local artisans and farmers = (Total
number of artisans and farmers – Total number of migrants) x
Average monthly wage rate

Local

Formula:retention of(Total annual dividends / Total annual profit) x 100

profits
Where,•

Annual profits (Rs.) = Financial surplus earned over a courseof one year, after deducting for costs and wages•

Annual dividends (Rs.) = Share of profits distributed among

Job Creation by Investment

Total Jobs

Formula:Facilitated andTotal Number of Jobs Facilitated in the 3 Years / Total Investment

Net Additional

under SFURTI Scheme

Percentage

Jobs Createdper Crore of

Net Additional Jobs Created / Total Investment under SFURTI

Investment

Schemeunder SFURTIWhere,•Net Additional Jobs Created = (Number of Artisans andFarmers Facilitated in Year 1 apart from those mentioned in
DPR – Number of Dropouts in Year 1) + (Number of Artisans andFarmers Employed in Year 2 – Number of Dropouts in Year 2) +(Number of Artisans and Farmers Employed in Year 3 – Number
of Dropouts in Year 3)

Representa-

Formula for SPV members belonging to vulnerable groups

Percentage

tion of vulner- [(Number of SPV Members that recognized as either women, youth
Inclusive Employment

Social equity & inclusion

the members/shareholders of the SPV

able groupor Scheduled Caste/Scheduled Tribe/Other Backward Classes) /by gender,

Total Number of SPV Members] x 100

caste andage among

Where,

artisans and/•or farmers

Number of SPV Members that recognized as either
women, youth or Scheduled Caste/Scheduled Tribe/
Other Backward Classes is not exclusive of one another i.e.,an SPV member could be a woman and recognize herself as ascheduled caste or youth.

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95

P.

A.

Indicator

Calculation

Equitable pay Formula for Women’s Wage as % of Men’s:
for women(Monthly Average Wage of Women / Monthly Average Wage of Men

and SC/ST/

for n number of profiles with both employed) x 100

OBCs for n

Formula for SC/ST/OBCs Wage as % of General Category’s:

profile(Monthly Average Wage of Artisans and Farmers belonging to SC/

Unit
Percentage

ST/OBC Category / Monthly Average Wage of Artisans and Farmers

Equitable Pay

belonging to General Category profiles with both employed) x 100
Where,•

Monthly Average Wage of Women for n number of
profiles = [(Monthly Average Wage of Women for Profile 1)

Social equity & inclusion+ (Monthly Average Wage of Women for Profile 2) + _ _ _ +(Monthly Average Wage of Women for Profile n)] / n•Monthly Average SC/ST/OBCs Wage for n number of
profiles = [(Monthly Average Wage of SC/ST/OBCs for Profile
1) + (Monthly Average Wage of SC/ST/OBCs for Profile 2) + _ __ + (Monthly Average Wage of SC/ST/OBCs for Profile n)] / n•‘n’ being the number of profiles with both ends of the spectrum
employed.

Representa-

Formula for Percentage of Vulnerable Groups among Board

nclusive decision-making

tion of vulner- Members:
able groups[(Number of Board Members that recognized as either women,

by gender,

youth or Scheduled Caste/Scheduled Tribe/Other Backward

caste & age

Classes) / Total Number of Board Members] x 100

among board
members

Where,•

Number of SPV Members that recognized as either
women, youth or Scheduled Caste/Scheduled Tribe/
Other Backward Classes is not exclusive of one another i.e.,an SPV member could be a woman and also recognized herself
as a scheduled caste or youth.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Percentage

Annexure 4
Questionnaire for Implementation Agency
PITCH
Development Alternatives is conducting a triple bottom line (economic-social-ecological) impact assessment ofrural enterprise clusters under a research initiative. In consultation with the Indian Micro Enterprise Development
Foundation (IMEDF), we have shortlisted your cluster as one of the four to be assesses and would like to ask you afew questions in that regards.
Answering these questions is voluntary. The information collated as a part of this interaction will only be used for the
purpose as explained above. We will not use the responses for any other purpose.
Shall we begin? Yes/No
Date of the Survey

Month, Day, YearSECTION 1: CLUSTER PROFILE
1

Name of the Cluster

Single Selection
Natural Dye Handloom Cluster

Mat Craft Cluster

Lemongrass and Palmarosa Medicinal Plant Cluster
Cluster2

3

Location of the Cluster

Sector of the Cluster

Single Selection
Shivamogga, Karnataka

West Medinipur, West Bengal

Mayurbhanj, Odisha

Barauliya, Uttar Pradesh

Single Selection
Agro based

4

Name of the Implementation Agency (IA)Text Box

5

Name of Technical Agency

Text Box

6

In which year was the IA established?

Month, Day, Year

7

In which year did the cluster started Month, Day, Year

8

Since when is the cluster operating under Month, Day, Year9

How much grant was received under the Text Box

Handicraft / Handloom

operating?
SFURTI Scheme?
SFURTI Scheme, excluding the margin
money component?
Description: Format in Crore Rs.
The margin money component is 10% of the project cost that the IA had put in as investment.

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

97

10

Have you availed financing for the cluster Multiple Selection
from any other source, apart from the Self-Funding
grant amount?

Funding from Corporate /Impact Investors (CSR Grants)

Financing under Central / State Private Formal Financing (fromGovernment Scheme other bank or authorized lender)
than SFURTI
Private informal financing from Not Applicablefamily / moneylender
Others, please specify ___________________
11

Elaborate the funding received by each Text Box
selected source.
Description: Format in Crore Rs.
Write <Amount> <Source>; <Amount 2> <Source 2> For example, in case of OD of Rs.25000 and self-fundingof Rs.500000, write Rs. 25000 (Private Formal Financing); Rs. 500000 (Self-Funding)In case of values in lakh and crore, write full numeric value (500000 instead of 5lakh)
SECTION 2: ENVIRONMENTAL SUSTAINABILITY

1

For how many days in a year does the Text Box
cluster operate?

2

How much electricity is consumed from Text Box
external sources annually?
Description: Format in kWh
Please upload the electricity bills of the past 12 months at the end of this form.

3

What is the total Actual Consumption from Text Box
solar panels?
Description: Format in kW
If the Cluster uses Solar Energy, else mention NA
In case of multiple solar panels, mention the total capacity of all of them.
Please upload the Purchase Order/Bills of currently installed Solar Panels at the end of this form.

4

What is the total Actual Consumption from Text Box
solar panels?
Description: Format in kW
If the Cluster uses a Diesel Generator, else mention NA
In case of multiple DG Set, mention the total capacity of all of them.
Please upload the Purchase Order/Bills of currently installed DG Sets at the end of this form.

5

For how many hours does the DG sets run Text Box
in a day?
Description:
If the Cluster uses a Diesel Generator, else mention NA
In case of seasonal variation, please write <Average Daily Operational Hours> <Season>
Please upload the Diesel Log Sheet for the year 2022 at the end of this form.

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

6

From where does the cluster source water Multiple Selection
for SFURTI operations?
Ground Water (Tubewell)Surface

Water

Municipal Line

(Lake/Pond/ Private Water Tank

River etc)
Others, please specify ___________________
7

How much total water does the cluster Text Box
consume for SFURTI operations in a day?
Description:
Please mention in kiloliters (kl), with split up by the source
Please upload the Water Balance Chart of the Process water at the end of this form.

8

Do you have a permit for either

Single Selection

groundwater or surface water extraction?
Yes

No

Not Applicable
9What is the capacity of the water storage Text Box
tank?
Description:
Please mention in kiloliters (kl), with split up by the source.
In case of multiple water storage tanks, please mention the cumulative/total capacity.

10

How many times in a week do you refill the Text Box

11What is the capacity of the pump used for Text Box

water storage tank?
water storage tank(s)?
Description:
Please mention in Horsepower (hp)12

What is the capacity of the water recycling/ Text Box
treatment system?
Description:
Please mention in kilolitres (kl)per day
In case you don’t recycle/treat water in the cluster, mention NA or Not Applicable.

13

Have you ever tested the quality of the Single Selection
disposed water?

Yes, we got the water tested by Yes, we tested the disposed
an NABL accredited lab

water qualitatively using awater testing kit

Never
14

In case of water test by an NABL Provision to Upload Pdf
accredited lab, please attach the test
reports or results.

15

In case of qualitative water testing using Text Box
a water testing kit, what were the results?

16

How much waste (in kg) is disposed of by Text Box
the cluster on a weekly basis?

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

99

Description:
This is excluding the waste that is recycled or composted.
Please upload Waste Audit/Assessment Report/Waste Generation Register if any, at the end of this survey
form.
17

How much waste (in kg) is recycled/ Text Box
reused in the SFURTI operations by thecluster on a weekly basis?
Description:
Write ‘Not Applicable’ if waste is not recycled.
This is excluding the composed or disposed off waste.

18

How much waste (in kg) is composted by Text Box
the cluster on a weekly basis?
Description:
Write ‘Not Applicable’ if waste is not composted.
This is excluding the recycled or disposed off waste.
SECTION 3: GOVERNANCE & SOCIAL EQUITY

1

What is the number of board members in Text Box

2

How many of them are male?

Text Box

3

How many of them are female?

Text Box

4

How many board members are from the Text Box

your cluster?

SC/ST/OBC social category?
Description:
Mention separately for SC, ST and OBC in this way:- 3 SCs, 10 STs and 0 OBC
5How many board members are 18 to 29 Text Box
years old?

6

How many employees work at the CFC?

Text Box

Description:
Employees are the people engaged in cluster/CFC operations such as Administration/Finance or Machine
Operator.
Write 0 in case of no employees.
7

How many of these employees are Text Box
artisans/farmers

associated

with

the

cluster?
SECTION 4: LOCAL ECONOMIC DISTRIBUTION
1

Do the members of the Special Purpose Single Selection
Vehicle (SPV) receive dividends?

Yes

No

Only to some members
2

How much dividends do the members of Text Box
the SPV receive per financial year?

100

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

3

How

many

artisans/farmers

joined Text Box

the cluster in the first year of SFURTI
operations?
Description:
This is over and above those mentioned in the DPR.
4

Of the total artisans/farmers engaged Text Box(mentioned

in

DPR

and

additionengagements), how many left the clusterin the first year of the SFURTI operations?
5

How many artisans/farmers joined the Text Box
cluster in the second year of SFURTI
operations?
Description:
This is over and above the artisans/farmers engaged in Year 1

6

How many artisans/farmers left the Text Box
cluster in the second year of the SFURTI
operations?

7

How

many

artisans/farmers

joined Text Box

the cluster in the third year of SFURTI
operations?
Description:
This is over and above the artisans/farmers engaged in Year 1 and Year 2.
8

How many artisans/farmers left the cluster Text Box
in the third year of the SFURTI operations?
SECTION 5: PROCESS MATRIX

1Provision to upload the completed pdf ofthe Process Matrix
SECTION 6: ADDITIONAL DOCUMENTS TO BE UPLOADED
Kindly gather the aforementioned documents (all that applies) and consolidate them into a compressed zip
file, subsequently proceeding to upload said file in this section

1

Electricity Bills for all 12 months of the year2022

2

Purchase Order/Bills of currently installed
Solar Panels

3

Purchase Order/Bills of currently installed
DG Sets

4

Water Extraction Permit for Ground Water/
Surface Water/Other if any

5Water Balance Chart of the Process water

6

Waste Audit/Assessment Report/Waste
Generation Register if any

7

Production Report for Year 2022

8

Diesel Log Sheet for the year 2022

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

101

Annexure 5
Questionnaire for SPV Members
PITCH
Hi, I am __________ from the Development Alternatives. We are conducting a sustainability analysis for the clusters
supported by Indian Micro Enterprises Development Foundation (IMEDF), as a part of which, we are here to pilot yourcluster. The information captured from this survey will solely be used for the purpose of the study, and not be sharedelsewhere.
Shall we proceed?
<Important to take consent> (Yes/No) <Proceed if the answer is ‘Yes’)
Date of the Pilot

Month, Day, Year

Interviewer

Single Selection
Subham Singh

Deena Hari Krishna P

Saundharaya Khanna

Mohak Gupta

Sudhir Shah
1

What is your name?

Text Box

2

What is your age?

Text Box

3

What is your sex?

Text Box

4

What social category do you belong to?

Single Selection
GeneralMarginalized/SC

Marginalized/ST

Marginalized/OBC

Others, please specify ___________________
5

What is the name of the cluster?

Single Selection
Natural Dye Handloom Cluster

Mat Craft Cluster

Medicinal Plant Cluster

Palmarosa and Lemongrass
Cluster

6

In what cluster process are you currently Text Box
engaged in?

7

Which type do you belong to?

Single Selection
I come at the CFC every day to I take the work back to my
work

home

Others, please specify ___________________
Description:
Only applicable to Handloom/Handicraft-based Clusters
8

What is your daily wage rate from the Text Box

9What is your current earning from SFURTI Text Box

SFURTI cluster?
per month?

102

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

10

Since when have you been engaged with Text Box
the cluster? (Year)

11

What was your past occupation before Single Selection
SFURTI?

Cluster/Farmer

Cluster/Artisan

Others, please specify ___________________
12

How much were you earning before Text Box
SFURTI per month?

13

Are you a migrant?

Text Box

14

Which state have you migrated from?

Text Box

Description:
Format: District, State

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

103

Annexure 6
Scoring Mechanism for Clusters’ Sustainability
Indicator
EnergyIND
E1

Scoring Mechanism
Benchmark/Assessed ValueIntensity:

Calculation
Step 1: Find the

KA

WB

UP

OD

161400349831.4

199800138758.4

25455661

5577944

1867475

2835000

1%

6%

11%

5%

Total Cost of

Cost of

Annual Energy

Energy as a

Where, Assessed Value is Total% of Cost of

Cost of Annual Energy Consump-

Productiontion*/Total Cost of Production

Consumption)**Solar Price Per Unit = 7.04** DG Set Electricity Price
Per Unit = 28

*Total Cost of Annual Energy
Consumption = (Annual Electricity

Step 2: Find the Total
Cost of Production

Consumption x Price of Electricity

Step 3: Find the

Per Unit) + (Annual Solar Energy

Assessed Value

Consumption x Per Unit Price) +(Annual Energy Consumption fromDG Set x Per Unit Price)

Step 4: Find the

The best case value is retained as the

Benchmarkbenchmark. Since the indicator has an inverse
relationship with sustainability, the lowest
assessed value of 1% becomes the benchmark.

Step 5: Find the

1%

1%

1%

1%

10.00

1.67

0.91

2.00

3000000

52000028000

1825000

51300

30000

10000

7079.63

58.48

17.33

2.80

257.78Score
Water Intensi- E2

Benchmark/Assessed Value

Step 1: Find thety: Net Water

Total Annual Water

Consump-

Consumption

tion per Kg of

Where, Assessed Value is

Output

Net Water Consumption(in litres) per Kgs of Output(converted to l/kg)(1kl = 1000 l/kg)
Step 2: Find the Total
Output (in kgs)
Step 3: Find theAssessed Value
Step 4: Find the

The best case value is retained as the

Benchmarkbenchmark. Since the indicator has an inverse
relationship with sustainability, the lowest
assessed value of 2.80 becomes the benchmark.

Step 5: Find the

2.8

2.8

2.8

2.8

0.48

1.62

10.00

0.11

Score

104

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

Indicator

IND

Renewable

E3

energy (solar)

Scoring Mechanism

Direct Plotting of %, normalized to a Step 1: Find thescore of 10

as a % of the

KA

WB

UP

OD

74%

51%

0%

100%

7.4

5.1

0

10

0%

17%

0%

0%

0

1.7

0

0

80%

23%

75%

100%

8

2.3

7.5

10

57%

73%

64%

1%

0

0

64.30%

0.80%

5.70

7.30

1.00

1.25

77%

386%

36%

82%

7.70

10.00

3.60

8.20

101%

74%

58%

115%

5.1

2.4

0.8

6.5

Assessed Value
Step 2: Find thetotal energy

Score

consumption
Percentage

Calculation

E4of waterDirect Plotting of %, normalized to a Step 1: Find thescore of 10

Assessed Value

recycled/reusedStep 2: Find theviz total

Score

consumption
Amount ofE5

waste utilized

Direct Plotting of %, normalized to a Step 1: Find thescore of 10

Assessed Value
Step 2: Find theper ton ofScore

outputFor Value Addition Clusters

Step 1: Find theefficiency per(KA & WB): Direct Plotting of %,Material Efficiency

kg of input

normalized to a score of 10

Material

E6

For Extractive Clusters (UP &Increase in

Ec1

income offarmers and/

OD): Assessed Value/Benchmark

Step 2: Find theValue

Benchmark

Where, Assessed Value is the

Step 3: Find theMaterial Efficiency factor

Score

Direct Plotting of %, normalized to a

Step 1: Find thescore of 10*

Assessed Value*Best case scenario is that the income has doubled

or artisansor more, in which case a score of 10 shall be

Step 2: Find theallocated.

Score

Plotting of %, normalized to a score

Step 1: Find theDifferenceof 10 through a range of 50-150%

Assessed Valuein Incomeincome change (income underof SPVSFURTI could be lesser than theMembers viz

bechmark but must be at least half

Step 2: Find theAlternativeof it)

Score(before &after SFURTI)
Percentage

Livelihood

Ec250-60% (0-1), 60-70% (1-2), 70-80%(2-3) so on till 140-150% (9-10)

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

105

Indicator
Local

IND
Ec3

procurement

Scoring Mechanism

Calculation

Direct Plotting of %, normalized to a Step 1: Find thescore of 10

KA

WB

UP

OD

6%

86.24%

100%

100%

0.6

8.6

10

10

100%

99%

95%

98%

10

9.9

9.5

9.8

295.00364.00331.00326.00Assessed Valueof inputs(except

Step 2: Find themachinery

Score

and wages)
Wage

Ec4

Expenditure

Direct Plotting of %, normalized to a Step 1: Find thescore of 11

Assessed Valueon Local SPV

Step 2: Find theMembers

Score

Total Jobs

Ec5

Assessed Value / Benchmark Value Step 1: Find the

Facilitated

Assessed Value

Createdper Crore of
Investment

Step 2: Find the

The best case value is retained as the

Benchmarkbenchmark. Since the indicator has a positive
relationship with sustainability, the highest
assessed value of 364 becomes the benchmark.

Step 3: Find the

364.0

364.0

364.0

364.0

8.1

10.0

9.1

9.0

99%

98%

57%

100%

9.9

9.8

5.7

10

100%

71%

25%

100%

10

7.1

2.5

10

100%

100%

100%

100%

10

10

10

10

Score
Representa-

S1aPlotting of Assessed Value (%) from Step 1: Find thetion of vulner-

surveys, normalized to a score ofAssessed Value

able group10

Step 2: Find the
Score

among SPV
members
Representa-

S2aPlotting of Assessed Value (%) from Step 1: Find thetion of vulner-

surveys, normalized to a score ofAssessed Value

able group10

Step 2: Find the
Score

among Board
Members
Equitable

S3aPlotting of Assessed Value (%) from Step 1: Find thepay forsurveys, normalized to a score ofvulnerables

10(by sexand social
category) for

Assessed Value

Step 2: Find theScore

n profile
GENERAL ASSUMPTIONSFormula for indictors with inverse relationship i.e., where a lower value is ideal is (Benchmark Value/Assessed Value)
Formula for indicators with positive relationship i.e. where a higher value is ideal is (Assessed Value/Benchmark Value)
For % higher than 100%, a maximum of 10 score is taken.
Costing for energy and water are based on price for commercial purposes.

106

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

About TARAThe Society for Technology & Action for Rural Advancement (TARA)
is a social enterprise of the Development Alternatives Group, set up in year
1985 at New Delhi, India. TARA’s vision is to create sustainable livelihoods ona large scale, in pursuit of which, it develops and promotes “scalable solutions
for people and the planet”. As an ‘incubation engine’, TARA’s organisational
objectives have been defined around impact in the areas of Employability,
Entrepreneurship, Clean Technology, Basic Needs, Natural Resource
Management and Institutional Strengthening. A mix of projects and revenue
based activities deliver a range of replicable enterprise based business models,
community development packages and capacity building solutions in fivesectors - Renewable Energy, Water Sanitation and Hygiene, Affordable Housing,Livelihood Support and Recycling.
TARA has been incubating scalable business models in the Bundelkhand
region of Central India. TARA has its presence in other parts of central andnorthern India through projects, products and services. It provides consultancy
and project management services across the country.

About IMEDF
Indian Micro Enterprises Development Foundation (IMEDF) is the social
enterprise development vehicle of the Development Alternatives Group andregistered as a Nodal Agency by the Ministry of MSME, Govt. Of India. It is set
up with an aim to boost an ecosystem for green and inclusive entrepreneurshipthat promotes and supports enterprise models through:

IMEDF

•

Transfer and adaptation of innovative technology based business models•

Provision of credit and finance linkages for entrepreneurs•

Capacity building through technical skill building for business planningand operations•

Integration and mainstreaming of product and services into local and highvalue markets•

Collaborate with institutions to create an ecosystem promoting cluster
development

Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India

107

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Harnessing Local Aspirations for Green and Inclusive Livelihoods: Establishing a Case for Rural Enterprise Clusters in India