thesis submitted for the fulfilment of the degree of...

COMMON PROPERTY RESOURCES, POVERTY AND ENVIRONMENTAL DEGRADATION:

A CASE STUDY IN WEST BENGAL THESIS SUBMITTED FOR THE FULFILMENT OF THE

DEGREE OF PHILOSOPHY IN ARTS (ECONOMICS) AT THE UNIVERSITY OF BURDWAN, WEST BENGAL

By Soma Saha

Department of Economics The University of Burdwan

February, 2014

Pravat Kumar Kuri Phone: 0342-2556566 Ext 438 Associate Professor Email: Department of Economics [email protected] University of Burwan Golapbag, Burdwan Date: West Bengal-713104

TO WHOM IT MAY CONCERN This is to certify that Ms. Soma Saha has duly completed her research work for the thesis entitled “Common Property Resources, Poverty and Environment Degradation: A Case Study in West Bengal” under my supervision. I have approved the thesis and permitted her to submit it for the Ph.D. degree in Economics to the University of Burdwan. Further, I certify that neither this dissertation nor any part thereof was submitted to this or any other University in this country or abroad for Ph.D. or any other degree. It may also be noted that Ms. Soma Saha had delivered two seminar lectures on this research work on 30th October, 2009 and 22nd November, 2011, at the Department of Economics, Burdwan University, in partial fulfilment of the requirement for the submission of the Ph.D. thesis. She has also complied with other relevant conditions specified in the regulations of the University of Burdwan including the residential requirements.

Pravat Kumar Kuri

i

Acknowledgement

I have accumulated many debts of gratitude in the course of my research studies.

Foremost, I would like to express my humble gratitude to my mentor and supervisor

Prof. Pravat Kumar Kuri for his scholarly guidance and constructive supervision. His

invaluable advice, critical revision, perpetual inspiration and consistent

encouragement were necessary for me to complete my dissertation in time. Heartfelt

thanks to him for his invaluable comments and suggestions. In addition, I express my

sincere gratitude to Prof. Arup Chattopadhyay, Head of the Department, Economics

and all the teachers of the Department of Economics, Burdwan University for their

kind support at various phases of my work. I remain thankful to my Principal, Dr.

Amal Kanta Hati and all my colleagues of Tarakeswar Degree College for their

support and good wishes.

Since empirical study is solely dependent on the access to the data, I would like to

acknowledge the help that I had received during my fieldwork from the forest officers

of Bankura and Purulia districts; panchayat office bearers and Head Masters of our

surveyed villages. I am also grateful to all the village respondents of the study area

who took out time to answer the lengthy questionnaires. Without their active

participation and support, the research would never have taken this shape. My special

appreciation to Mr. Suvendu Chel, part-time professor of Bankura Women’s College

and local students for their jovial assistance while I was working at the forest villages.

No research is possible without the library, the centre of learning resources. For the

secondary data sources of my research study, I have consulted many libraries viz.

Central Library of Burdwan University; National Library; Central library of Indian

Statistical Institute, Kolkata. I am indebted to the authorities and staffs of these

institutions for their active cooperation and services. I am very much privileged to

have learned effective use of several econometric software packages from the

Workshop conducted on Research Methodology, by Department of Economics,

University of Burdwan. In this matter my guide, Prof. Pravat Kumar Kuri was a great

help to me in various ways.

I owe a lot to my parents, Sri Rebati Mohan Saha and Smt Anjali Saha; my in-laws,

Sri. J. K. Saha and Smt. Dipali Saha who always encouraged and helped me at every

stage of my personal and academic life and longed to see this achievement come true.

ii

Most gratefully, I acknowledge my immense debt to my dear husband, Jayanta, who

gave me whole hearted support during the research work. In spite of his busy official

schedule, he gave me his helping hand whenever I was in need. I am also grateful to

my daughter Sukrita and son Soumil for the love and encouragement they offered me

while carrying out my studies. I also acknowledge the well wishes of my elder sister,

Supta Manna; sister-in-law, Jayashree Poddar and my dear friend, Suparna Pal.

Finally, I beg to be apologised for any shortcomings.

Soma Saha Department of Economics

The University of Burdwan

Contents

iii

Contents Page Number Acknowledgement i-ii List of Contents iii-vii List of Tables viii-xi List of Figures & Maps xii Chapter 1: Introduction 1-7 Chapter 2: Review of Literature 8-34 2.1: Pioneering Works on Common Property Resources 8 2.2: CPR, Poverty and Environmental Degradation 10 2.3: Agricultural Risk and CPR 17 2.4: Common Forest and Participatory Management 20 2.5: Common Property Resources and Gender 30 Chapter 3: Objectives, Data Source and Methodology 35-51 3.1: Objective of the Study 35 3.2: Data Source 36 3.3: Methodology 40

3.3.1: Conceptual Framework 40 3.3.2: Econometric and Statistical Specification 46

3.4: Hypothesis Tested 51

Contents

iv

Page Number

Chapter 4: Common Property Forest Resources: 52-93 Contribution and Crisis 4.1: Introduction 52 4.2: Data and Methodology 55 4.2.1: Conceptual Framework 55 4.2.2: The Empirical Model Specification 60 4.2.2.A: Determinants of CPR: Multiple Regression Model 60 4.2.2.B: Determinants of Poverty: Poverty Environment 62 Nexus 4.3: Results and Discussion 64 4.3.1: Nature of Dependency on CPRs 64 4.3.2: CPRs and the Contribution to the Household 68 Income 4.3.3: CPRs and the Contribution to the Consumption 71 Expenditure 4.3.4: CPRs and the Contribution to the Employment 73 Generation 4.3.5: Household Energy Consumption and the Extent 74 of Dependency on CPRs 4.3.6: CPRs and Animal Grazing 76 4.4: CPRs and Rural Poverty 78 4.5: Determinants of CPR Extraction 81 4.6: Poverty-Environment Nexus- Logit Model 85 4.6.1: Determinants of Poverty 87 4.7: Conclusion 92

Contents

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Page Number Chapter 5: Agricultural Risk and Common Property 94-119 Resources 5.1: Introduction 94 5.2: Data and Methodology 97 5.2.1: Conceptual Framework 98 5.2.2: The Empirical Model Specification 102 5.3: Results and Discussion 105 5.3.1: Nature of Agriculture in Bankura & Purulia 105 District 5.3.2: Agricultural Productivity 108 5.3.3: Labour Allocation in CPR Collection 110 5.3.4: Agricultural Risk and CPR 111 5.3.5: Association between CPR Extraction and 116 Agricultural Risk: Count Data Regression Model 5.4: Conclusion 119 Chapter 6: Common Forest and Participatory 120-150 Management 6.1: Introduction 120 6.2: Data and Methodology 121 6.3: Forest Cover and its Management in India: An 122 Interstate Analysis 6.3.1: Forest Cover in India 122 6.3.2: Introduction of Joint Forest Management in India 126 6.4: Forest Cover and Joint Forest Management in 128 West Bengal 6.4.1: Forest Cover in West Bengal 128

Contents

vi

Page Number 6.4.2: Joint Forest Management in West Bengal 131 6.5: Forest Management in the Study Area 133 6.6: Collective Action in Joint forest Management 138 6.7: Collective Action and Forest Conservation 145 6.8: Conclusion 150 Chapter 7: Women’s Participation in CPR 151-170 Management 7.1: Introduction 151 7.2: Role of Women in CPR Collection in the Study Area 154 7.3: Women’s Participation in Forest Resource Management 160 7.4: Women’s Participation in JFM and Sustainability in 163 Forest Resources 7.4.1: Empirical Model Specification 163 7.4.2: Results and Discussions 166 7.5: Conclusion 170 Chapter 8: Summary, Conclusion and Policy 171-187

Suggestions 8.1: Summary 171 8.2: Conclusion 182 8.3: Suggestions and Policy Implications 184

Contents

vii

Page Number Bibliography 188-204 Appendices 205-230 Appendix- I: Village Characteristics of Study Area 205 1.1: Characteristics of the Population in the 210 Study Area in Bankura and Purulia 1.1.1: Family Size and Literacy Rate 210 1.1.2: Caste Composition 211 1.1.3: Occupation 211 Appendix -II: Price List of CPRs 212 Appendix-III: Summary Statistics 213 Appendix-IV: Land Ownership Pattern 215 Appendix-V: Property Rights and ‘The Tragedy of 218 the Commons’ Appendix-VI: Joint Forest Management in India 223

List of Tables

viii

List of Tables Page Number Chapter 4: Common Property Forest Resources: Contribution and Crisis Table 4.1: Description of Variables of Multiple Regression 61 Model Table 4.2: Household Collection of Common Property 65 Resources in Last 1 year (Rs) Table 4.3: Annual Income from Various Sources (Rs) 68 Table 4.4: CPRs and its Contribution to Total Consumption 72 Expenditure (Rs) Table 4.5: Employment Provided by CPR Based Activity 73 (in last 1 year) Table 4.6: Household Energy Consumption and the Extent 75 of Dependency on CPR Table 4.7: Dependency on CPRs for Animal Grazing 77 (in last 1 month) Table 4.8A: Distribution of Monthly per Capita Income of 78 the Sample Households in Bankura District (including income derived from CPRs) Table 4.8B: Distribution of Monthly per Capita Income of 79 the Sample Households in Purulia District (including income derived from CPRs) Table 4.9A: Distribution of Monthly per Capita Income of 80 the Sample Households in Bankura District (excluding income derived from CPRs)

List of Tables

ix

Page Number Table 4.9B: Distribution of Monthly per Capita Income of the Sample Households in Purulia District 80 (excluding income derived from CPRs) Table 4.10A: Determinants of CPR Extraction-Bankura District 81 Table 4.10B: Variance Inflation Factors (Bankura District) 82 Table 4.11A: Determinants of CPR Extraction-Purulia District 82 Table 4.11B: Variance Inflation Factors (Purulia District) 83 Table 4.12: Extent of Depletion of Common Property Resources 86 (during 1990-2010) Table 4.13: Description of Variables of Logit Regression Model 88 Table 4.14A: Determinants of Poverty-Bankura District 89 Table 4.14B: Determinants of Poverty-Purulia District 90 Chapter 5: Agricultural Risk and Common Property Resources Table 5.1: Description of Variables in Count Data Model 104 Table 5.2: Crop Productivity 108 Table 5.3: Agricultural Implements Used 109 Table 5.4: Agricultural Labour 110 Table 5.5: Labour Allocation in CPR Collection 110 Table 5.6: Agricultural Production and CPR Collection 112 Table 5.7: Agricultural Shortfall and CPR Collection 114 Table 5.8: Forest Collection as a Function of Agricultural 116 Risk (Bankura District)

List of Tables

x

Page Number Table 5.9: Forest Collection as a Function of 117 Agricultural Risk (Purulia district) Chapter 6: Common Forest and Participatory Management Table 6.1: Forest Density Classification 122 Table 6.2: Forest Cover in India 124 Table 6.3: Forest Cover in States/UT of India 125 Table 6.4: Status of JFM in Different States in India 127 Table 6.5: District Wise Forest Cover in West Bengal 129 Table 6.6: Degraded Notified Forest Land in West Bengal 130 Table 6.7: Forest Protection Committees in West Bengal 131 Table 6.8: Status of JFM Committees in West Bengal 132 Table 6.9: JFM Participation in the Study Area of Bankura 136 and Purulia district Table 6.10: Forest Management and Enforcement of Forest 137 Protection Scheme Table 6.11: Description of Variables in Censored Tobit Model 141 Table 6.12: Determinants of Collective Action-Bankura 142 District Table 6.13: Determinants of Collective Action-Purulia District 143 Table 6.14: Description and Hypothesis in Logit Regression 146 Model Table 6.15: Determinants of Forest Degradation –Bankura 147 District Table 6.16: Determinants of Forest Degradation –Purulia 148 District

List of Tables

xi

Page Number Chapter 7: Women’s Participation in CPR Management Table 7.1: Women Headed Household and CPR Collection 157 Table 7.2: Women and CPR Collection in Last One Month 158 of the Date of Survey

Table 7.3: Participation of Household Members of Study 161 Area in JFM Table 7.4: Description of Variables in Binary Probit Model 165 Table 7.5: Determinants of Forest Sustainability-Bankura 166 Table 7.6: Determinants of Forest Sustainability- Purulia 167 Appendices Appendix-I: Village Characteristics of Study Area Table A1.1: Village Wise Characteristics 210 Table A1.2: Village Wise Occupation Composition of 211 the Surveyed Population Appendix-II: Price List of CPRs Table A2.1: Price List of Common Property Resources 212 in the Surveyed Villages Appendix-III: Summary Statistics Table A3.1: Descriptive Statistics of Quantitative and 213 Dummy Variables for Bankura District Table A3.2: Descriptive Statistics of Quantitative and 213 Dummy Variables for Purulia District Appendix-IV: Land Ownership Pattern Table A4.1: Land Ownership Pattern 215 Table A4.2: Land Ownership Pattern (Own land) 216 Table A4.3: Land Ownership Pattern (Operated land) 217 Appendix-V: Property Rights and ‘The Tragedy of the Commons’ Table A5.1: Prisoner’s Dilemma 222

List of Figures & Maps

xii

List of Figures & Maps Page Number Chapter 3: Objectives, Data Source and Methodology Photo 1: Interview with the household head at Panjhoria 39 Photo 2: Interaction with a villager at Dulaltora 39 Chapter 4: Common Property Forest Resources: Contribution and Crisis Photo 3: Villagers collecting leaves from common forest area in Seolibona 67 Figure 4.1: Percentage contribution to household income by different 69 income generation activities in the study area of Bankura and Purulia districts Figure 4.2: Percentage contribution to household income by different 70 income generation activities in Bankura district Figure 4.3: Percentage contribution to household income by different 70 income generation activities in Purulia district Photo 4: Villagers at Jiyathole using fuelwood 71 Photo 5: Common forest area in Jiyathole 71 Chapter 5: Agricultural Risk and Common Property Resources Figure 5.1: Index number of agricultural production (Cereals) 107 Photo 6 &7: CPR collection by rural households at Ramjibanpur 111 Figure 5.2: Agricultural production & CPR collection in survey area 113 of Bankura district Figure 5.3: Agricultural production & CPR collection in survey area 113 of Purulia district Figure 5.4: Agricultural shortfall and CPR collection in Bankura district 115 Figure 5.5: Agricultural shortfall and CPR collection in Purulia district 115 Chapter 6: Common Forest and Participatory Management Map 6.1: Forest cover map of India 123 Map 6.2: Forest cover map of West Bengal 128 Chapter 7: Women’s Participation in CPR Management Photo 8: Interaction with rural women in Jiyathole 156 Photo 9: Women collecting cowdung in Baldanga 156 Appendix-I: Village Characteristics of Study area Map A1.1: Map of West Bengal- Bankura 206 Map A1.2: District map of Bankura 206 Map A1.3: Map of West Bengal- Purulia 209 Map A1.4: District map of Purulia 209 Appendix-V: Property Rights and ‘The Tragedy of the Commons’ Figure A5.1: Relationship among effort, cost and revenue 220

Introduction

1

CHAPTER 1

INTRODUCTION

The concept of Property rights has an important implication to the use of natural

resources, rural poverty, degradation and conservations. Bromley (1990) describes

property, not as a natural resource but as a benefit stream that arises from that

resource. With property, comes the right to use or access, which can be defined as

one’s claim to a benefit stream. Based on the different access regime and the rights

and duties governing them, the different resource regimes are i) State property ii)

Private property iii) Common property and iv) Open access resource (Bromley and

Cochrane, 1994). In the state property regime, the state has full ownership and control

over the property, while in private property regime it is privately used and controlled

by individuals. In the case of common property, individuals within a group have

access, rights and duties and all others are excluded from its use and decision making

(Ciriacy-Wantrup and Bishop, 1975). Here the group of people who have the right to

its collective use is well defined, and the rules that govern their use of it are set out

clearly and followed universally. In an open access regime, nobody owns as there is

no property right and hence everybody has access to it.

Ostrom (1990) uses the term "common pool resources" to denote natural resources

used by many individuals in common, such as fisheries, groundwater basins and

irrigation systems. She sets a ‘design principles’ which includes clearly defined

boundaries, monitors who are either resource users or accountable to them, graduated

sanctions, and mechanisms dominated by the users themselves to resolve conflicts and

to alter the rules. Ostrom observes that the biggest challenge in a common property

regime is to foster contingent self-commitment among the members.

It is now well established that Common Property Resources (CPRs) are the natural

resources belonging to every community that each member could access purposefully

with specific obligations since no one could exercise their own right exclusively over

them monopolising them as their own property (Jodha, 1986). An identifiable

community alone holds the power to access and manage these resources collectively

Introduction

2

and to which no individual has exclusive property rights. In rural India, the commonly

seen resources endowed by nature such as the abundant lands in the form of village

pastures and grazing grounds, common forest areas in the form of village forests,

protected and un-classed forests, ponds, rivers, rivulets and waste lands used for

agricultural practices form the first and foremost property of the rural common man.

The Common Property Resources are the singular source of human sustenance in the

households that constitute a large section of rural India. CPRs are integral part of the

social and institutional arrangements made to meet the day to day requirements of the

rural poor. The rural poor, especially the landless, are highly dependent on the CPRs

for their subsistence. Earlier studies have also suggested that both the poor and not so

poor also depend on the CPRs for their livelihood. CPRs not only act as a buffer

during the economic crisis arising due to crop failure but also act as an additional

source of income during normal times. Forests have provided ample resource in the

form of Non Timber Forest Products (NTFPs) for the subsistence of the rural poor.

The rural poor collect several NTFPs in the form of fuel wood, shrubs, dry leaves

which are used by them for cooking and heating. The bamboo and cane are used for

construction of house, while the wild grasses and shrubs are used as animal fodder.

The forest is also a rich source for several medicinal plants used for curing diseases.

Fruits, vegetables and roots are collected by the rural poor for consumption and sale.

The critical role of natural resources in the sustenance of the rural livelihood can be

traced to time immemorial.

However, efficient use of the natural resources and a critical balance between stock

and flow of resources is essential. Indiscriminate use of natural resources leads to over

exploitation and then scarcity. The concept of over exploitation of common natural

resources was first published by Hardin (1968) in the article titled ‘The Tragedy of the

Commons’. The parable demonstrates that free access and unrestricted demand for a

finite resource ultimately results in the depletion of the resource through over-

exploitation. Here the author advocates that individuals with a group, acting

independently and rationally according to one’s own interest and with no regard for

others leads to depletion of the shared natural resources, despite their understanding

Introduction

3

that the depletion of the common natural resource is contrary to the group’s long term

best interest.

Hardin introduces a hypothetical example of a pasture shared by local herdsmen. Each

herdsman will try to maximize his yield and will therefore increase the size of his

herd whenever possible. The utility of each additional animal has both a positive and

negative component; Positive: the herdsman receives all of the proceeds from each

additional animal; Negative: the pasture is slightly degraded by each additional

animal. The division of these costs and benefits is unequal: the individual herdsman

gains all of the advantage, but the disadvantage is shared among all herdsmen using

the pasture. An individual herdsman therefore continues to add additional animals to

his herd. Since all herdsmen reach the same rational conclusion, overgrazing and

degradation of the pasture is its long-term fate. Since this sequence of events follows

predictably from the behaviour of the individuals concerned, the author describes it as

a ‘Tragedy’. The metaphor illustrates the argument that free access and unrestricted

demand for a finite resource ultimately dooms the resource through over-exploitation.

This occurs because the benefits of exploitation accrue to individuals or groups, each

of whom is motivated to maximize use of the resource to the point in which they

become dependent on it, while the costs of the over exploitation are borne by all those

to whom the resource is available. This, in turn, causes demand for the resource to

increase, which causes the problem to snowball to the point that the resource is

exhausted. The rate at which exhaustion of the resource is realized depends primarily

on three factors: the number of users wanting to consume the commons, the

consumptiveness of their uses, and the relative robustness of the commons. The

author also addresses potential management solutions to the problems of the

commons through resource management solution like privatization, polluter pays, and

regulation. The author argues against relying on conscience as a means of policing the

commons, suggesting that this favours selfish individuals – often known as free riders

– over those who are more selfless.

However, Hardin’s theory of ‘The Tragedy of the Commons’ has been severely

criticised by social scientists for his failure to recognise that the local commons were

most often CPRs and not open-access. Further the decline of the commons system

http://en.wikipedia.org/wiki/Pasture

http://en.wikipedia.org/wiki/Herder

http://en.wikipedia.org/wiki/Overgrazing

http://en.wikipedia.org/wiki/Management

http://en.wikipedia.org/wiki/Privatization

http://en.wikipedia.org/wiki/Polluter_Pays

http://en.wikipedia.org/wiki/Regulation

http://en.wikipedia.org/wiki/Conscience

http://en.wikipedia.org/wiki/Free_rider_problem

Introduction

4

was the result of a variety of factors like abuse of the rules governing the commons,

land ‘reforms’, improved agricultural techniques, and the effects of the industrial

revolution, all these having little to do with the system's inherent worth (Cox, 1985).

Dasgupta (1982) had postulated that in a dynamic model, the open access renewal

resources like the tropical rain forest, fishes in the open sea, etc. could not be ruined if

the cost of extraction was large relative to the value of the resource itself.

In developing countries, the role of common property resources is very widely spread

especially in the rural areas, where there is coexistence of both community ownership

of the natural resources and private property rights. The collection of common

property resources not only helps to sustain their livelihood, but also helps to generate

additional income. Collection of common property resources by the rural poor have

therefore helped to mitigate poverty to large extent. The rural poor in Southern

Zimbabwe in African continent depend heavily on the collection of natural resources

for their subsistence and the income generated from the collection of the natural

resources contribute about 35 percent of the household income (Cavendish, 1999). In

South Eastern Nigeria, a 10 percent increase in income from forest collections has

helped in about 4.9 percent decline in the number of households living in poverty

(Fonta et al. 2010).

In India, the extent of dependency of CPRs ranges from 15 percent to 29 percent

(Chopra, Kadekodi and Murty, 1989; Jodha, 1986; Singh et al., 1996). The survey

data from the National Sample Survey (NSS) 54th round, suggest that 48 percent of

the rural household collect CPRs. Further, studies on poverty with relation to CPR

collection from forest indicate that poverty increases by as much as 28 percent, when

income from forest is set to zero in poverty calculations (Reddy and Chakravarty,

1999). Based on the study of agro-ecological zones of West Bengal, Beck & Ghosh

(2000) postulated that CPRs constitute about 12 percent of the total income of the

poor household. The field surveys in the state of Himachal Pradesh by Dasgupta

(2006) suggest that the rural poor collect Non Timber Forest Products from the

common forests both for sale as well as for self-consumption. Indiscriminate use of

common property resources leads to decline in CPR land. Further, rampant

deforestation has led to ecological degradation in the forest areas as well. Field survey

Introduction

5

of dry regions in India has shown a decline of 31 percent in the CPR land during the

period 1950-52 to 1982-84 (Jodha, 1986). There has been 30 percent decline of the

total CPR area in Haryana during the period from 1970-71 and 1986-87 (Chopra et

al., 1989). Based on the findings of field survey in Karnataka, Pasha (1992),

concludes that the total land available and used for CPRs showed a reduction of 1.9

percent primarily due to encroachment by the rural rich households, CPR land taken

up for development by the Government and CPRs distributed to the poor for crop

cultivation, housing, etc. as part of Anti-Poverty programme initiated by the

Government.

Collection of Common Property Resources in the form of NTFPs has played a critical

role to mitigate the agricultural risks of the rural poor (Pattanayak & Sills, 2001). The

CPRs act as a safety net and provide the desirable consumption insurance to the rural

poor (Baland & Francois, 2004). Investigating the safety net function of collection of

forest products during crop risks, Delacote (2009) suggest that risk aversion is

positively correlated to forest cover and hence risk reduction policies should be

combined with environmental and forest management policies. Kochar (1999)

suggests that the rural poor in order to smooth their income during agricultural shocks

increase their market hours of work. Sustainable agricultural policies of the

government can help to increase the agricultural income and employment

opportunities for the rural poor, thus reducing their dependence on the CPRs. For

sustainable development of the rural livelihood, management of the common property

resource is crucial. An institutional approach to the study of self-organisation and

self-governance in CPR situations was put forth by Ostrom (1999). According to the

author, active participation in collective management by the rural poor depends on

expected benefits, expected costs, internal norms and discount rates. Berkes (2006)

examining the coastal resource management in the marine observes that there is need

to deal with multiple levels of governance and external drivers of change. The success

of Common Property Resource Management depends on the size of the group,

homogeneity among the group members, effective enforcement mechanisms and past

experiences of cooperation (Baland & Platteau, 1996).

Introduction

6

The National Sample Survey Organisation (NSSO) report (Report number 452:

Common Property Resources in India, Jan–June 1998, NSS 54th Round) gives an

estimate of the Inter-State variation in availability of CPR land. It is observed that the

average area of CPR land available to a household varies over a wide range from 0.01

ha (in Tripura) to 4.37 ha (in Mizoram). The north eastern states apart, CPR land per

household was the highest in Rajasthan (2.04 ha), followed by Madhya Pradesh (0.74

ha) and Gujarat (0.72 ha). The percentage of CPR land to geographical area,

observed, varies from 1 percent (in Tripura) to 32 percent (in Rajasthan) across the

states. West Bengal does not have the relatively large areas of common land as

compared to other regions of India. In West Bengal, CPR land per household is 0.03

ha and the percentage of CPR land to geographical area is 2. It is further observed that

the CPRs in West Bengal are declining at an alarming rate mainly due to agricultural

intensification, commercialisation of CPRs, environmental degradation and

population growth. CPRs are largely the work of women and girl. However, where

CPRs are commercialised, it is observed that men always take greater control over

managing the CPRs. Further studies on CPRs reveal that since women are largely

dependent on CPRs for their livelihood and accessing CPRs adds to the women status

within the household, loss of control over CPRs may lead to reduction in the status.

This affects them both economically and socially.

The study of common property resources is an emerging area of research in West

Bengal. In the economically backward region of the state a significant proportion of

the population is highly dependent on common property resources specially the

common property forest resources. This dependency on common property forest

resources is much higher among the scheduled castes and scheduled tribes population

of the state. Accordingly, the extraction of common property resources has serious

implications to rural household income, employment, economic inequalities, poverty

and more so to natural environment. The property rights to resources have an

important bearing on productivity. Participation in forest management plays a critical

role in resource utilisation and conservation. Further gender equity in participation in

forest management impacts upon sustainable governance of the forest resources.

However to our knowledge, no comprehensive study has yet been conducted to deal

Introduction

7

with these complex issues of poverty, environmental degradation and collective action

in common property resource management in West Bengal.

Based on a primary survey in Bankura and Purulia district of West Bengal, our study

fills the gap of knowledge in this area of research.

There are several dimensions to the study of CPRs. This study is primarily intended to

focus on the following dimensions of common property resources in an integrated

manner:

i) Dependency of rural poor on Common Property Resources (specially

forest resources), Rural Poverty and the Environment

ii) Agricultural Risk and Common Property Resources

iii) Common Forest and Participatory Management

iv) Gender discrimination of CPR dependency and their role in CPR

management

More specifically, this study intends to examine the nature and extent of CPR, the

extent of dependency of rural poor households on CPR with a clear focus on its

gender dimension, its impact on poverty and environmental degradation and the role

of Common Property Forest Management in the State of West Bengal.

Review of Literature

8

CHAPTER 2

REVIEW OF LITERATURE

The subject on Common Property Resources (CPRs) has received considerable

attention both in theoretical as well as in empirical research. Various studies by

distinguished scholars have broadened the understanding of the subject. In accordance

with the dimensions of our study we have reviewed different existing studies by

broadly classifying them into five major sub-themes: i) Pioneering works on Common

Property Resources; ii) CPR, poverty and environmental degradation; iii) CPRs and

its role in mitigating agricultural risk; iv) Common Forest and Participatory

Management and v) Gender dimensions of CPR use and management. Attempts have

been made to make an extensive review of the existing literature on each subject. For

convenience of our understanding, existing literature on each sub-themes have been

arranged in two dimensions: works on the theme outside India and works in India.

This helps to identify new ways to interpret and shed light on any gaps in previous

research, resolve conflicts amongst apparently contradictory previous studies and

suggest the way forward for further research. In conformity with the above sequence

the review of existing literature is given below:

2.1 Pioneering works on Common Property Resources Gordon (1954) was among the first to deal on the economic theory of optimum

utilisation of natural resources. He believed in the conservative dictum that

everybody’s property is nobody’s property and therefore the common natural

resources were free goods for the individual but scarce for the society in large. He

advocated that regulation of the natural resources is possible only through conversion

of the common property into private property or public (Government) property.

The concept of ownership of property rights was first examined by Demsetz (1967).

He advocated that there are three ideal types of ownership: i) Communal ownership

where all members of community can exercise this right; neither citizen nor state can

interfere; ii) Private (owner can exclude others) and iii) State (state can exclude

anyone from using the property).


9

Hardin (1968) was the first scholar to publish an article on the concept of over

exploitation of common natural resources, titled ‘The Tragedy of the Commons’.

Here the author focuses on the depletion of a shared natural resource by individuals in

a group who acts independently and rationally according to each one’s self-interest, in

spite of the fact that they understand that the depletion of the resource is contrary to

the group’s long term best interests. As the access to the natural resource is shared by

all, the benefit of using it goes to the individual user while the consequence of misuse

of the natural resource gets disbursed to the entire community. The reason for this

over exploitation of the natural resource, according to the author, is free access and

unrestricted demand of the finite resource. The author asserts that this problem can be

resolved either through privatisation, polluter pays or regulation.

The argument of Hardin on the common natural resources was criticised by several

authors. The scholars emphasised that Hardin had confused between common

property and open access and had failed to distinguish between ‘collective property’

and ‘no property’ (Ciriacy-Wantrup and Bishop, 1975). The scholars were of the view

that the example cited by Hardin was more appropriate for national rangelands and

parks. The decline of the traditional common system was not due to any inherent

flawed land use policy but was primarily due to variety of other reasons like abuse of

rules governing the commons, improved agricultural techniques and effects of

industrial revolution (Cox, 1985).

Breaking the myth of the ‘Tragedy of the Commons’, Berkes (1989), defines

Common Property Resources as a ‘class of resources for which exclusion is difficult

and joint use involves subtractability’. The common property regime would be

effective only if it has efficiency, stability, resiliency and equitability. Thus a

decentralised collective management of the common property resources by their users

would help to mitigate their depletion. The authority system may be centralised and

diffused to varying degree so as to provide the common property resource users

assurance about the expected behaviour of other users and thereby enable

coordination and minimise ‘free riding’(Runge,1986).


10

2.2 CPR, Poverty and Environmental Degradation Forests constitute a large part of CPRs and the existing literature on CPR dependence

is essentially centred on forest dependence. Forest resources play a crucial role in the

income and subsistence of the rural poor. Cavendish (1999) had analysed the

collection of natural resources by the rural poor in Africa. The author conducted

surveys in Shindi Ward in Southern Zimbabwe using a random sample of 197

households in 29 villages. Majority of the households depend heavily on common

natural resources. The resource dependence varies systematically with income. The

most important finding of the study was that the natural resources contribute

significantly to the income of the rural poor with about 35 percent of the household

income coming from collection of the natural resources.

Based on empirical results of the survey of 313 households in 8 villages in Pahang,

peninsular Malaysia, Schwade et al. (2006) have highlighted that the collection of

Non Timber Forest products (NTFPs) is an important activity for the rural

households. 65 percent of collectors in the study area sold NTFPs for income. The

empirical results also suggest that the households those are poor or farther away from

the market are more dependent on the collections of NTFPs for income or subsistence

as compared to the wealthier households. It is further noted that the diversity in NTFP

collection helps to lower the risk in income generation.

Sapkota & Odén (2008) have analysed the household characteristics and the high

dependence of the rural households on community forest in the Terai region of Nepal.

The study was mainly based on the primary data through survey of 52 households in

Rupandehi district in western region of Nepal popularly known as ‘Terai’. The

empirical results suggest that there is high socio-economic heterogeneity among the

rural households. According to the authors, the forest collection by the households

depends on their wealth, proximity to the common forest area, landholding size and

labour allocation. The authors conclude that in order to prevent over exploitation of

the common forest, the poor households should involve in other income generating

activities like cultivation of Non Timber Forest Products inside the common forest

area.


11

The dependence of the rural poor on the community forest in South Eastern Nigeria

and the impact of forest income to the total household income were studied by Fonta

et al. (2010) based on the report of empirical findings on the survey of 1457 heads of

the households from 18 communities. According to the authors, a 10 percent increase

in income from forest collections has helped to decline in the number of households in

poverty by about 4.9 percent. The study advocates the need for change in the policy in

order to ensure reduction in income inequalities for households who are heavily

dependent on the forest.

Several works have been carried out in India to explore the nature and pattern of

CPRs and its associated linkages to poverty, environmental conservation and

sustainability (Jodha 1985a, 1985b, 1986, 1990; Pasha, 1992; Singh et al., (1996);

Iyengar and Shukla, 1999; Beck and Ghosh, 2000). The empirical findings of Jodha

(1986) were based on field survey of 82 villages covering 21 arid and semi-arid

districts in 7 states of India viz. Andhra Pradesh, Gujarat, Karnataka, Madhya

Pradesh, Maharashtra, Rajasthan and Tamil Nadu. As per the findings, 84-100 percent

of the poor households gather food, fuel, and fodder from the CPRs. The rural poor

use the common pastures for grazing of their herds. CPRs also greatly contribute to

employment and income of the rural people. In almost all the villages in the study

area, the income from CPRs account for 15-23 percent of the total income of the

households.

The important role of forest in the livelihood of the rural poor was highlighted by

Conroy (1991). Based on the primary survey of Panchmahals district of Eastern

Gujarat state, the author observes that the local tribal are highly dependent on the

forest for fuel wood, house construction and manufacturing agricultural implements.

The social forestry programme initiated by the government encouraged the local poor

to grow eucalyptus trees, which made them self-sufficient in fuel wood. Moreover, it

also saved a lot of time of the women and children who gathered fire wood from

distant places. The plantation helped the poor to make agricultural implements and

also earn additional income through sale of timber in a restricted manner.


12

Based on a field survey of 15 villages in Gujarat, Iyengar & Shukla (1999) asserts that

CPRs make up 0.1 percent to 11 percent of the consumption expenditure for farm

households and 1 percent to 22 percent for non-farm households. A study on

utilisation and development of CPRs in the Kandi area of Punjab was undertaken by

Singh et al. (1996). Based on field survey of eight villages in Dasuya-Langerpur

watershed, Hoshiarpur district, Punjab during the period from 1990-91 to 1992-93,

the authors suggest that the rural poor in the study area are highly dependent on CPRs

for their subsistence and it is also a very important source of income contributing 23

percent of their total income. More than 60 percent of the rural households use CPR

land for grazing their livestock and 90 percent collect fuel wood from the common

forest.

On the basis of field survey of Sirmour district in Himachal Pradesh lying in the outer

western Himalayan range, Bon (2000) observed that the communal forest not only

provides timber for households and agricultural implements but also fodder, grass,

food and medicines. The common pastures and wastelands and river beds are used by

the rural poor for grazing. Privatisation and nationalisation of the CPRs have greatly

affected the lives of the poor. The author feels that collective action through social

bindings could be an efficient alternative to privatisation and nationalisations of the

CPRs.

Beck & Ghosh (2000) have studied the common property resources based on a village

survey carried out in agro-ecological zones of West Bengal. The field work was

carried out in seven villages in the district of Purulia, Bardhaman, Birbhum,

Jalpaiguri, North 24 Pargana and South 24 Pargana. The empirical findings suggest

that the CPRs constitute about 12 percent of the total income of the poor household.

Women and girls were primarily involved in the collection of the CPRs with the

proportion of women’s share ranging from 70-78 percent of the total collection.

Agricultural intensification and commoditisation of the CPRs have resulted in the

poor being denied access to the common property resource area.

The importance of common property resources in different agro-climatic zones of

India was analysed by Menon & Vadivelu (2006). The study was based on the


13

secondary data of National Sample Survey (NSS) 54th round on CPRs. According to

the survey data, 48 percent of the household collect CPRs. In the Eastern Plateau and

Hills, the collection of CPRs by the households is highest (71 percent) whereas in the

western dry regions it is the lowest (13 percent). The average annual household value

of CPR collections at the all-India level is Rs 693. It is highest in the western

Himalayas (Rs 1939) and lowest in Trans Gangetic plain (Rs 230). At all India level,

around 58 percent of the household collect firewood and 25 percent collect fodder

from the common forest.

The literatures reviewed above have shown the high dependence of the rural poor on

CPRs. However, there are many studies where the non-poor also benefit from CPRs.

In fact, while the poor benefit more in relative terms, the rich benefit more in absolute

terms (Nadkarni et al. 1989; Pasha 1992; Singh et al. 1996). Further, there is also

evidence that CPRs are often extracted by the rich or allocated in ways that privilege

the needs of the rich (Karanth 1992).

The study of Chopra & Dasgupta (2003) is primarily centred on the dependence of

forest for rural households and seeks to understand whether this dependence is a

consequence of absence of alternative options or a choice made in presence of it.

Based on secondary data samples of approximately 78,000 households in four states,

Bihar, Karnataka, Madhya Pradesh and Maharashtra from NSS Report (54th Round),

the authors conclude that the non-poor households collect NTFPs for sale, provided

they have access and property rights. Collection of NTFPs has been a source of

income generation for the rural households. There has been market driven over-

exploitation of the CPRs.

Based on the field survey covering 447 households of 19 villages from 2 districts of

Arunachal Pradesh; Kuri (2005) opines that 24.89 percent of the total consumption

expenditure of the non-poor households is supported by the CPRs as compared to

24.18 percent for the poor households, which signifies that CPRs play a crucial role in

the livelihood for both poor and non-poor households. The role of common property

resources as a sustainable source of income for the rural household was investigated

by Dasgupta (2006). The field survey was conducted in 15 villages in the Changar


14

belt of Kangra district in the state of Himachal Pradesh. The empirical findings

suggest that rural households collect Non Timber Forest Products from the common

forests both for sale as well as for self-consumption.

A micro level analysis of CPRs and the rural poor was conducted by Pasha (1992).

The author examines the extent to which the rural household have access to the

Common Property Resources on the basis of survey findings of 14 villages in the state

of Karnataka. According to the author, in absolute terms, the contribution from CPRs

to the gross income of the rural non-poor is much more than in the case of poor

families. But in relative terms whatever the poor get from CPRs is very important and

crucial in their household economies.

Based on the study of a single village Rajapura in Magadi ‘taluk’ of Bangalore (rural)

district in Karnataka, Karanth (1992), opines that CPR lands have been privatised in

order to bring land under cultivation for maximum revenue. According to the author,

even when specific schemes were meant to benefit the rural poor, the net benefit was

actually accrued by the non-poor households. Further, encroachment of the common

property was a common phenomenon which benefited the rich and dominant

households of the village.

The dependence of the rural poor on the common natural resources has however led to

its over-exploitation in many cases [Jodha (1986); Singh et al. (1996); Chopra and

Dasgupta (2003)]. Jodha (1986) observed that the CPR land declined by 31 percent in

some states to 55 percent in others during the period from 1950-52 to 1982-84. This

decline in the area and quality of CPR land was primarily due to indiscriminate CPR

collection and changes in the institutional arrangements, including the legal status,

underlying these resources. In another study Chopra et al. (1989) established that the

size of CPRs (including forests) had reduced by 4 percent in the state of Maharashtra

and by 30 percent of the total CPR area in Haryana in the period from 1970-71 to

1986-87.


15

Poverty is often associated with environmental degradation. The rural poor are the

major victims of environment degradation because of their heavy dependence on

natural resources and less alternate source of income. Various studies undertaken by

eminent scholars have broadened the relationship between poverty and environment.

The different environmental factors that affect the poverty of the rural poor have been

put forth by Bucknall et al. (2000). Examining the poverty-environment links, the

authors, conclude that environmental degradation leads to decline in opportunity for

the poor as they are unable to extract CPRs for their subsistence; lowering of capacity

due to ill health and finally loss of security due to natural calamities.

Brocklesby & Hinshelwood (2001) explored the linkages between poverty and

environment by analysing the findings of 23 Participatory Poverty Assessments (PPA)

covering 14 countries in Asia, Europe, Africa and Latin America. Due to long term

environmental trends like deforestation, pollution, soil infertility, salination and

erosion, etc. poor people are more dependent on common property resources for their

livelihood. Poor people, while trying to manage their livelihood, largely resort to

adapting, mitigating and coping strategies like reduced food consumption, substitution

with less nutritious food and use of Common Property Resources like wild food, etc.

The impact of Common Property Resources in the poor people’s livelihood due to its

deteriorating condition was analysed by Biswas (2006). The findings were based on

field survey conducted in two villages: Rakshachwok and Santipur in the district of

Purba Medinipore of West Bengal. Both the villages are adversely affected by the fly

ash disposal of the Kolaghat Thermal Power Plant which is located in their vicinity.

There has been considerable shrinkage of the CPRs and other lands due to dumping of

fly ash in the fallow land, waste land, marshy land, pools, ditches, pits, ponds. Thus

the quality and quantity of fishes and vegetables have drastically reduced, resulting in

the adverse effect on the subsistence of the rural poor. Mukherjee et al. (2009)

examined the connection between tribal household and environmental degradation in

a study on the backward tribal communities of Purulia district in West Bengal in

terms of their physical environment, society, economy and culture. Due to limited

natural resources, the poor tribal communities are under severe pressure to support

their livelihood. High level of poverty has led to environmental degradation. The


16

authors stresses on the need for education as the key for fostering the well beings of

the tribal.

Saha & Kuri (2012, 2013) analysed the critical role of CPRs in sustainable

development of the rural poor. Based on the field surveys of 300 households in the

districts of Bankura and Purulia, the authors conclude that the rural poor, especially

the landless, are heavily dependent on the CPRs for their subsistence. The quantum of

extraction of CPRs for a household depends on several factors relating to household

and the village level characteristics which have far reaching implications to rural

poverty and environmental degradation.

The relationship between poverty and environment is an emerging area of research in

India in recent times. There is contradictory view on the nexus between poverty and

environment. While Dasgupta and Mäler (1994) assert that the rural poor are most

vulnerable to environmental degradation; Duraiappah (1996) infers that poverty is the

main cause of environmental degradation. However, poverty is not the sole cause of

environmental degradation. Increased pressure on CPRs due to population growth,

low income and ignorance of future benefits of CPR has led to environmental

degradation (Jodha, 1990; Somanathan, 1991; Beck and Ghosh, 2000). There are

several literatures which indicate the positive association between wealth and

extraction of CPRs. Singh et al. (1996) observed that the extraction of CPRs in Punjab

was directly proportional to the ownership of private resources.

Similar study on the size, status and use of Common Property Land Resources was

conducted by Iyengar (1989). The author surveyed 25 villages located in five different

geo-physical regions in Gujarat and observed that the CPR land has reduced

substantially over the last 25-30 years (1960 to 1985-86) in the study villages

primarily due to privatisation through encroachment. Further the CPR land has also

deteriorated due to over use and over exploitation and this degrades the environment.

Pasha (1992) attributes the reduction in CPRs lands primarily to encroachment by the

rural rich households, common land taken up for development under social forestry

programme initiated by the Government and CPRs distributed to the poor for crop

cultivation, housing, etc. by the Government as part of Anti-Poverty programme.


17

2.3 Agricultural Risk and CPR Extraction of forest products helps the rural poor to mitigate income shocks. Tropical

forest helps the local rural households by providing ‘natural insurance’. Pattanayak &

Sills (2001) have examined the critical role of collection of NTFPs to mitigate the

agricultural risks of the rural poor by systematically measuring the forest collection

trips. Based on the survey of rural households living on the margin of Tapajós

National Forest in the Brazilian Amazon, the authors observed that agriculture is

always subject to infinite risks due to weather, crop disease, pests, fire, seasonal

flooding, unpredictable and variable soil quality, variation in precipitation pattern, etc.

The forest collection trips of the rural poor increase with agricultural shocks or

expected agricultural risks, thus suggesting that the rural households rely on the forest

to mitigate agricultural risks.

On similar lines, based on a study of the empirical data from the survey of three

villages in Southern Malawi, Fisher & Shively (2003) observed that the rural

households were heavily dependent on the common forest for collection of firewood,

timber and bamboo. The authors conclude that during income shocks due to

unpredictable agricultural production, the households save out of transitory income by

accumulating physical assets in order to reduce their dependence on the forest. Fisher

(2004) advocates that the forest income reduces income inequality i.e. inequality

increases 12 percent when income from forest is not considered.

Based on the field survey of 300 riverine rural households in eight villages in Pacaya-

Samiria National Reserve (PSNR) in north-eastern Peru, Takasaki et al. (2002)

analysed their asset holdings during covariate flood and major health shocks.

Gathering of forest products, hunting and fishing are the primary risk coping

strategies adopted by the households. The other coping strategies are precautionary

savings in the form of food stocks or asset disposition and informal insurance in the

form of borrowing, remittance and mutual insurance. According to Baland & Francois

(2004), although the private properties are efficient, the rural poor tend to favour the

common property resources as they provide the informal consumption insurance to

them during agricultural risk. In spite of its low efficiency gain, the commons are

more likely to influence rural livelihood.


18

The extraction of natural resources as a coping strategy is shaped by local

environmental endowments. Based on the survey of 116 rural households of

Tawahka communities in Eastern Honduras, McSweeny (2004) observes that the rural

poor sell forest products not only to smooth their income but also to meet sudden cash

requirements during any medical emergency. The use of forest resources for

subsistence acts as a ‘safety net’ for the poor and any earnings from the sale of forest

products helps to mitigate the loss of income due to agricultural crisis.

According to Dercon (2002), risk-management has an important role to play in

mitigating income risks during any agricultural shortfall or illness since it is extremely

difficult to implement intra-village credit or any other type of insurance system for the

rural households for coping with the strategies. If the need for insurance is quite large

due to economic crisis, then the less skilled households will devote most of their time

in NTFP collection, thus depriving them of any other development opportunities and

in turn leading to deforestation (Delacote, 2009). The rural households could also use

the livestock as a risk management strategy. Agricultural risk and safety net due to

extraction of forest products have severe impact on deforestation, poverty trap and

environmental degradation of the common property resources. Based on the study in

Honduras, Godoy et al. (2002) argued that in spite of collection of NTFP that has a

low annual value, it plays a critical role to provide insurance in times of economic

crisis.

In the Indian context also, several literatures have highlighted the role of CPRs during

agricultural shocks. Jodha (1978) had attempted to examine the effectiveness of

different adjustment mechanism adopted by the rural household during natural

calamities like drought. Based on the data on rural households in different drought

prone regions in India, the author indicates that the different adjustment mechanism

adopted by the households are reduction in consumption levels, asset depletion &

replenishment, periodic out migration and traditional informal cooperation. However,

maintaining minimum consumption and retaining minimum production potential are

not only inadequate to overcome drought risk but also very expensive. Thus they

depend heavily on CPRs as a source of consumption and income during agricultural

risks.


19

Agarwal (1990) examined the rural poor households in India so as to determine how

they coped up with food insecurity during agricultural shortfall and calamities like

drought and famine. According to the author, in order to overcome seasonal variation

in the crop cycle, the rural poor adopt to diversify their income source, collect

common property resources, borrow money from kin, adjust their consumption

pattern or mortgage their assets. The landless and poor households’ attempts to

diversify their income by seeking available alternate employment, multiple cropping

and intercropping, keeping variety of livestock and poultry, trading and seasonal

immigration of either individuals or families.

The hours of work responses of the rural households to agricultural shocks was

examined by Kochar (1999). Based on the household data of three villages in central

India: Aurepalle (Karnataka), Shirapur & Kanzara (Maharashtra) made available from

International Crop Research Institute of the Semi-arid Tropics (ICRISAT), the author

opines that the rural poor in order to smooth their income during agricultural shocks

increase their market hours of work. The author suggests that interventions in the

labour market through public works program can help the households to improve their

economic security during any economic crisis.

Chakrabarthy (2001) studied the household characteristics of 18 villages from the

districts of Birbhum and Burdwan in West Bengal and suggests that any agrarian

development adversely affects the supply of CPRs, which in turn affects the

livelihood of the rural poor who are largely dependent on them for their subsistence.

On the flip side, these developments also results in increase of income and job

opportunities for the rural poor, which in turn helps to reduce their dependence on

CPRs and thus prevent its over exploitation. The author concludes that sustainable

agricultural policies of the government should help to increase the agricultural income

and employment opportunities for the rural poor, thus reducing their dependence on

the CPRs. Chopra & Dasgupta (2002) analysed the critical role played by common

property resources as a supplement to rural livelihood by acting as the safety net

provider in times of agricultural crisis. Based on the secondary data of NSS 54th

round, the authors opines that CPRs not only play an important role for sustainable

livelihood of the rural poor but also acts as a safety net during agricultural crisis.


20

2.4 Common Forest and Participatory Management Since the publication of the article ‘Tragedy of the Commons’ there has been a

noticeable proliferation of literature on property rights and common property

resources and environmental degradation. The primary goal of managing natural

resources is to maximize the long-term economic benefit for the sustainable

development of the rural poor. Broadly three different schools of thought have

emerged on the property rights. The school of property rights advocates that the

problem of over-exploitation and degradation of common property resources (CPRs)

can be resolved only by creating and enforcing private property rights (Demsetz,

1967; Johnson, 1972; Smith, 1981; Cheung, 1970). According to them, private

property is the most efficient way to overcome over-exploitation of the natural

resources. The second school of thought believes that ‘state property’ regime can

effectively help to reduce the over-exploitation of CPRs (Hardin, 1968). The third

school of thought recommends that community based institution can manage the

common resources in a most effective manner and prevent the 'tragedy of commons'

(Berkes,1989; Wade, 1987; Jodha, 1986; Chopra et al., 1989, Ostrom, 1990).

Defining Private and Common Property Rights, Ostrom (1990) asserts that private

property rights depend upon the existence and enforcement of a set of rules that define

as to who has the right to undertake which activity. Rules are required to establish,

monitor and enforce a property system. The importance of private property rights was

exemplified by Smith (1981), Bromley (1992) and Berkes (1996). According to Smith

(1981), for common property resource, the private property rights are far superior to

state or public property rights. The private property owners not only have

unambiguous exclusivity but also there is a direct and immediate incentive for them to

manage their property well. In case of ‘Private property’ where an individual or

corporation has the right to exclude others, Bromley (1992) maintains that private

property appear to be stable and adaptive because they have the social and legal

sanction to exclude excess population, and effectively to resist unwanted intrusions.

Berkes (1996) affirms that state property regimes do not necessarily guarantee a

sustainable use of resources because their decision-makers do not share the same time

horizon or values of nature as resource users would, whereas private property regime


21

provides the institutional arrangement for a successful exclusion because it is more

effective in making the government enforce their rights.

The property rights school has been criticised by several scholars. The most important

criticism has come from the Marxian theory, which advocates that the institution of

the property rights is established by the state (Bromley 1989). In case of ‘State

property’ regimes where the resources are held entirely by the state, it can control the

inputs and outputs of the natural environment in various ways through taxes or

subsidies to the natural resource use (Ciriacy-Wantrup and Bishop, 1975). According

to Wade (1987), Common Property Resources are to be understood as a subset of

public goods. CPRs are public goods in the sense that they are open to everyone, but

unlike public goods, CPRs have finite benefits, which may lead to their overuse,

depletion, or degradation. The author argues that both private and state property

regimes are expensive to make effective.

Participatory resource management is viewed as a solution to a number of problems

linked to state management of natural resources. Management of the common

property resources are now becoming an integral part of sustainable development.

Ostrom (1990) advocates that the success of the community based management

depends on the well-defined boundaries, congruence between appropriation and

provision rules, graduated sanctions, efficient conflict-resolution mechanisms and

effective monitoring. The author elaborated on an institutional approach to the study

of self-organisation and self-governance in CPR situations. The study further reveals

that individuals always tend to discount future benefits. These discount rates however

are affected by the levels of physical and economic security faced by the rural poor.

There are four internal variables- expected benefits, expected costs, internal norms

and discount rates that affect an individual’s choice of strategies.

Randall (1981) asserts that property rights specify the relationships among people

with respect to the use of things and the penalties for violation of those relationships.

So, there must be an institution to enforce the claims and decide which claim is valid.

Bromley (1992) also clarifies the traditional confusion between open access resources

and common property resources and stresses the need to use the term ‘common


22

property regimes’ in place of ‘common property resources’. All the institutional

arrangements are man-made and hence the natural resources can either be private

property, common property or public (state) property. He further emphasised that the

property regime is basically an authority system which defines the rule for use of the

natural resources.

Bromley & Cernea (1989) observes that the common property resources in the

developing countries are degrading primarily due to dissolution of local community

management and non-establishment of more effective institutions and the success of

any resource management institution depends on the efforts it puts to address the

social arrangement among the rural poor using the natural resources rather than on the

commodities. Further the institution needs to formulate and implement the system of

incentives and sanctions for influencing the behaviour of the rural poor. Richerson et

al. (2001) describes the evolution theory of management of the commons. According

to the authors, there exist a parallel between the sophisticated bounded rationality

models necessary to account for the behaviour of people towards commons and dual

inheritance or gene-culture co-evolutionary theory. In a social institution, there exists

a complex cultural tradition of social behaviour, wherein the disposition to cooperate

varies considerably from person to person, society to society and time to time. Berkes

(2006) examining the coastal resource management in the marine observed that the

commons management may be considered as a management of complex systems with

emphasis of scale, self-organisation, uncertainty and resilience. The common resource

management therefore needs to deal with multiple levels of governance and external

drivers of change.

The factors that contribute to the effectiveness of local organisations as resource

managers were examined by Hobley & Shah (1996). Based on the evidence from

Nepal, the authors are of the view that effectiveness of the community management

depends on the divisibility of the natural resources and its short terms and long term

benefits. It is further important for the community to adapt to opportunities in both

production and marketing.


23

Balland & Platteau (1999) examined the impact of inequality on the ability of the

rural households to undertake a successful collective action by analysing the

individual incentive to contribute. The contribution of the rural households is

dependent on the benefits and the costs involved in the participation. The authors

argue that either privatisation of the common property resources or regulation by state

authority tends to eliminate the implicit entitlements enjoyed by the rural households.

Richards et al. (1999) studied four Forest User Groups (FUGs) in Dhankuta and

Terhathum Districts in the Koshi Hills of the Eastern region of Nepal in order to

examine the ways to improve their equity and to understand its socio-economic

impact. Based on the result findings, the authors developed a replicable participatory

economic methodology by which FUG stakeholder groups could calculate the returns

to community forestry.

Bardhan & Dayton-Johnson (2001) examined the relationship between socio-

economic heterogeneity and distribution implications of common property resource

management. The authors observe that a U-shaped relationship exists between

inequality and commons management, with very low and very high levels of

inequality associated with better performance, while mid-levels of inequality

associated with poor performance.

Poteete & Ostrom (2002) emphasises the need for having policies that enhances the

capabilities of the rural poor to form local management communities or institutions

that have strong legal bindings and that they ensure proper monitoring, sanctioning

and conflict resolution. Baland et al. (2010) reiterates that inequality among the rural

communities discourages the poorer individuals to participate in collective action and

thus reduces its efficiency.

Agrawal & Gibson (1999) asserts that the success of the natural resource conservation

can be ascertained by examining its development and conservation by focussing on

the different interests and actors within communities, their decision making and the

internal and external institutions that shape the decision making process. Tiwari

(2004) makes an attempt to analyse the policy, institutional and management designs


24

of community forest for a sustainable forest management in South and Southeast

Asian countries. The author concludes that forest management is a useful structure for

managing the forests with the help of local villagers.

According to Moretto & Rosato (2002), there has been substantial reduction in the use

of common property resources, primarily due to privatization. However, in those

regions where the local community has been firmly rooted and the privatization has

not been that rampant, the use of CPRs has survived. The success of the communities

of common property users can be attributed to the adoption of appropriate rules for

the users and the ability to enforce the same.

Springate- Baginski et al. (2003) examines 14 sites in the Koshi hills in Nepal and

concludes that the local people can become effective managers for protection of the

forest; robust and efficient planning and decision making makes the community

effective; Community forestry has a generally beneficial impact on household

livelihoods. However, conflict resolution, collective decision making and limited

capacity of the forest department are limitation for its continued existence.

The success and sustainability of the community based natural resource management

(CBNRM) in Botswana was examined by Mbaiwa (2004). The study based on

empirical findings from the survey of 124 household representatives of Okavango

Delta located in north-western Botswana, in south-central Africa concludes that

CBNRM has helped the community to develop a positive approach towards

conservation of the high valued natural resources which has led to several socio-

economic benefits like participation in decision-making, employment and income

generation. Based on the findings of the household survey conducted in 31 villages

adjacent to Chimaliro and Liwonde forest reserves in Malawi in southern central

Africa, Jumbe & Angelsen (2006) observe that forest participation is high in those

locations where the forest dependency of the rural poor is high and it acts as safety net

during economic crisis. However, with increased commercialization of the forest and

a more heterogeneous social context, the forest participation has reduced. The authors

conclude that in addition to forest co-management programme there has to be


25

provision for supplementary income sources for the participants and also increase in

the incentive for participation.

Adhikari (2004) investigated the relevance of transaction costs in determining the

performance of the common property forest management. Based on the survey

analysis of 309 households from the Middle-Hills of Nepal, the author concludes that

transaction costs are higher for poorer households (14 percent) than those for Middle-

wealth (12 percent) or rich households (9 percent). The author suggests that the

institutional structures for different forest regimes and their associated transaction

costs are important determinants for the success of the resource management.

Shrestha & McManus (2008) advocates that in spite of the fact that community forest

management in Nepal has helped to reduce the forest degradation to a large extent; it

has not produced any significant conservation of bio diversity. Since the benefits from

the community forest management remain very limited and unfairly distributed

among the users dependent on the forest products for their subsistence, there is need

for greater understanding of the political ecology of the community forestry.

From the Indian forest history, it is evident that forest resources have influenced the

livelihood of the forest communities (Cavendish, 1999). During the colonial period,

there was need for restriction on the collection of forest products as it was used both

for consumption and commercial purposes. Even after independence, the situation

livelihood did not change very much. In order to restrain the rampant extraction of

forest products, the Government of India, enforced several rules and regulations

(Saxena, 2003). From the late 1970s and early 1990s it was realised that the reversal

in forest degradation was not possible without the active involvement of the local

forest communities. Under this situation, the Joint Forest Management in India

evolved (Joshi, 1983; Kumar, 2002). Jodha & Bhatia (1998) discusses that there is

need for rebuilding community stakes in CPRs; control of the CPRs given to the local

communities and use of local perspective and knowledge to ensure sustainable use of

the natural resources.

Based on the study of participatory institutions in Sukhomajri, a village in the

foothills of the Siwalik range of Himalayas, Chopra et al. (1989) observe that there


26

has been considerable reduction in soil erosion, forest denudation and declining land

productivity due to efficient resource management. Further several sustainable

development projects were initiated through people’s participation. Although the

Sukhomajri model has been a great success, the authors conclude that the latent

conflict and interference from the local panchayat and the urbanisation / consumerism

threatens the institution of participation.

Poffenberger (1993) surveyed households in the Chandana and Harinakuri villages in

the state of West Bengal and Mahapada village and Budhikamari in Orissa and opines

that wherever the state forest departments are supportive, community forest

management groups often are able to sustain protection effectively, even under

pressure from other communities and the private sector. The local government body

could play a vital role in conflict resolution among communities or between

communities and the forest department. Based on the study of common lands

belonging to 293 villages bordering the Aravali Hills in the districts of Faridabad,

Gurgaon, Mohindergarh, Rewari and Bhiwani in Haryana, Babu, P.V.S.C and S.

Chandra (1998) using a mathematical programing model, concludes that clear benefits

have been derived from common property regimes, which have been used to examine

institutional development at the village level.

Prasad (1999) observed that the objectives of the JFM to ensure access to the forest,

payment of fair wages, elimination of intermediaries, prevent exploitation of the

NTFP collectors, ensure better socio-economic conditions, maintenance of benefit

sharing and gender equity, sustainable harvesting, forest protection, natural

regeneration, etc. are not fully met. The author concludes that state control of the

NTFPs have often resulted in restricted access and non- remunerative returns to the

collectors and this may in future jeopardise the achievements of sustainable forest

management. Based on the household data on socio-economic variables obtained

through primary survey of 385 household in 32 villages in three states of India (127 in

Haryana, 123 in Bihar, 135 in Uttar Pradesh), Lise (2000) observes that participation

in JFM increases when the condition of forest is good or the poor are highly

dependent on the forest for their subsistence. Educational level of the family has a

negative correlation with people’s participation in JFM. There is need for healthy


27

interaction between the forest officials and the rural households in order to bring in

transparency for the overall success of the JFM.

Conroy (2001) analysed the constraints to the adaptation of participatory Forest

management in semi-arid India as negative attitudes of the state forest bureaucracy

with a rigidity approach and the strained relationship between forest officials and

villagers; motivation and benefits of the stakeholders; access rights of the villagers to

the forest and its products. The author concludes that there is need for greater

devolution of power to the communities; shift to multi-purpose forest management;

greater benefits for women and poor; and extending participatory forest management

beyond degraded forests. Conroy et al. (2002) examined the self-initiated community

forest management in Orissa. Based on the survey of 43 forest dependent

communities in Orissa, the authors observed that Community forest management has

immensely contributed to the regeneration and sustainable management of the forests

and argue that the formal balance of control of forests be shifted further towards

communities. The authors conclude that a customised approach in place of a

standardised approach is critical for the sustained growth of JFM and recommends

several reforms.

According to Balooni (2002), the need for participatory community forest

management was felt by the policy makers because there was continuous

deforestation and the degradation of forests leading to a huge decline in forest cover

in India, primarily due to misdirected forest policies. The author concludes that for the

success of JFM, we need to ensure equity in representation and participation of the

poor and the women, equitable benefit sharing among the forest department and the

village communities. Singh (2002) probed the status of utilisation and resource

management in Chenani watershed of Jammu & Kashmir. Based on the primary data

of 300 households in 37 villages falling under 30 micro-watersheds and secondary

data, the author is of the view that common forest lands are being converted into

agricultural lands leaving very little scope for their rehabilitation as agricultural lands

are more prone to soil erosion problems. The author concludes that the natural

resources in the study area is badly affected and hence conservation, regeneration,


28

awareness, use and benefit sharing of the resources and effective management can

help to improve its status and conditions in the long run.

The study of Geevan et al. (2003) broadly supports the views of Singh (2002). The

study was carried out in Banni and Naliya in district Kachchh, Gujarat where there

was large scale degradation of the grassland. The authors conclude that there is need

for restructuring of the property and resource management regimes in order to bring

greater efficiency in conservation of the natural resources. The authors also suggest

review of the biodiversity conservation strategy in order to make both the forest

department officials and the local communities fully responsible for achieving its

biodiversity conservation goals. On similar lines, Kuri (2005) studied 447 households

of 19 villages from two districts namely Papumpare and Upper Siang of Arunachal

Pradesh and opined that JFM seems to succeed in only those areas where the level of

deforestation is very acute. The high level of deforestation has almost threatened the

survival of the rural poor living in the forest fringes and thus they have an incentive to

cooperate with the Government in order to bring about afforestation and actively

participate in the Joint Forest Management.

Based on the study of Adimaly, Neriamangalam, Munnar & Marayur Forest Ranges

in South Western Ghats in Idukki district of Kerala State in South India,

Aravindakshan (2011) observes that the Joint Forest Management committee, now

known as Village Forest Council and also termed as Vana Samrakshana Samithis

(VSS) in Southern states was established to bridge the gap between people and the

forest department and have paid dividends with respect to long term conservation of

the forest and development of the local communities. However ambiguity with respect

to recognition and legal status of VSS could hamper the effectiveness of sustainable

utilization of natural resources.

Based on evidence from Western Midnapore division of West Bengal, Sarker & Das

(2006) discusses the role of Forest Protection Committees (FPCs) in long term

sustainability of the JFM system. Based on secondary data on four FPCs (Ambisole,

Bansiasole, Kasia and Kadokata) in West Midnapore district, the authors observe that

the NTFPs play a critical role in the sustenance of the poor. The authors conclude that


29

there is need for restructuring of the present JFM models in order to ensure

optimisation of the NTFPs and livelihood benefits for the poor.

Joshi (1999) examined the role played by different stakeholders responsible for the

success of JFM. Based on the study of factors that influenced the emergence and

success of JFM in southwest Bengal, the author observes that although the leadership

of few senior forest officials and the community initiatives were important, the

supportive role provided by the association of front-line workers of the forest

department was critical and thus that there was need for internalisation of the JFM

concept within state bureaucracies for its sustainability.

Roy et al. (2001) illustrated the negotiation systems and design of incentives in JFM

in West Bengal. The study is based on survey of three districts of West Bengal viz.

Midnapore, Bankura and Jalpaiguri to critically examine the performance of JFM as

an institutional system. The authors suggest that there is need for redefining the role

of the Forest Department as a facilitator and introduce equity in sharing of the rights

and responsibilities amongst all sections of the socio-economic groups.

An analysis of the environment- economic interface pertaining to CPR institutions in

West Bengal was carried out by Bhattacharya (2002). The findings of the survey of

forest resources in Belemath in the district of Burdwan and Matha in the district of

Purulia and water bodies in Hazamdihi in the district of Bankura and two fishermen’s

co-operative in Bon Hooghly and Charcharia in Calcutta, suggest that that the

intervention of the state could be reshaped to institutionalize collaboration between

state administration and local resource users.

Basu (2010) examined the deforestation and people’s participation in JFM in the

district of Bankura. Based on the empirical findings of the survey of 65 households in

villages of Kalaberia and Kalyanpur in the district of Bankura, West Bengal, the

author observes that the control of the forestry by the community through JFM has

helped to reduce illegal felling of trees and encroachment. The FPCs under JFM have

helped to protect and manage the common forest area for sustainable forest

management by restricting over extraction of firewood and NTFPs.


30

2.5 Common Property Resources and Gender The impact of gender difference in property rights in the functioning of the natural

resource management was studied by Meinzen-Dick et.al. (1997). The report of

IUCN-‘The World Conservation Union’ on gender difference in sustainable forest

management suggests that there is need for gender-responsive forest policies by

taking into account the opinions, needs and interests of both men and women.

Bridging the gender inequalities ensures formation of effective sustainable resource

management policies and increased food security, employment opportunities and

household income. It can further avoid conflicts among men and women and thereby

promote equal access of women to land ownership and other resources required for

effective socio-economic participation.

Dealing with the potentials of Common Property Resources in improving the status of

the rural women in Nigerian rainforest ecosystem, Oyerinde (2008) observes that rural

women face unequal access to productive resources and services. Collection of NTFP

by the women is specialised occupation requiring thorough understanding of the

ecology of the forests. The author concludes that Government policies should be

based on social institutions with full participation of the rural women.

The issue of forest tenure and forest governance in Indonesia was dealt by Siscawati

& Mahaningtyas (2012). According to the authors, gender justice is possible through

increasing the voices of women in decision making, adopting gender justice principles

in community organising processes and systematic capacity building on gender justice

in forest tenure and governance.

Jamisolamin (2012) has put forth his views on the reforms in the women led natural

resource management in Philippines. According to the author, existing conflicts from

the management and conservation of natural resources significantly and differently

affect the rural women and give rise to different set of challenges. Women contribute

to development and peace processes as they are able to negotiate and mediate in

conflict situation that leads to resolution. The author concludes that if women are

given consistent recognition in the community and their voices are heard in decision


31

making process then they acts as key players in leading environmental protection and

conservation efforts.

Giri (2012) dealt on the topic of gender in forest tenure by highlighting the pre-

requisite for sustainable forest management in Nepal. The author observes that

women’s right to common forest resource not only depends on their access to and

benefits accrued from the resources but also the right and authority to take decisions

in the forest management. The author concludes that enacting gender equity through

forest tenure is a multifaceted social and political process that also involves

progressive sensitization and education for the women.

The WWF-UK report (2012) on forest management and gender acknowledges that

ignoring gender issues in forest management leads to improper management and

potential loss of essential ecosystems which has different impact on women and men.

There is need for including women’s voice for decision making in forest management

as it will benefit from their knowledge and skills.

Forest plays an important role in livelihood support system for the rural poor women

in India. The role of women’s participation in the decentralised governance of the

community forest in India was studied by several scholars. Based on the data obtained

from the survey of 641 JFM forest protection committees in the state of Madhya

Pradesh, India, Agrawal et al. (2006) are of the view that participation of women in

forest management has significant effects both on the resource-related outcomes and

at the level of institutional effectiveness and therefore the local community should be

designed to ensure women’s participation in forest governance and protection. The

authors conclude that greater involvement of women in forest management in local

decision-making related to forest governance and protection not only leads to greater

control of illicit harvesting of forest products but also improves regeneration in the

forest.

The survival strategies of the tribal women and their role in forest management were

illustrated by Yadama et.al. (1997). Based on the field study of tribal households

conducted in the high altitude area zone of the Eastern Ghats region of Andhra


32

Pradesh in the Vishakhapatnam district, the authors conclude that women can play a

greater role in co-management of forests by increasing their collective bargaining

power. Moreover the state needs to play a critical role in bringing out forest

management policies ensuring that the interests of women are protected in the long

run.

Women’s participation in forest management decisions in upper Kullu valley in

Manali in Himachal Pradesh was illustrated by Bingeman (2003). The author observes

that the Mahila Mandals in the Manali region have established a tradition for

women’s participation in forest management and have played an active role in

involving them in actively monitoring, protecting and managing the common forest

area.

Based on the survey of 240 female respondents in 24 villages in Tamil Nadu,

Murugesan & Namasivayam (2012) advocates that the economic factors viz.

occupation, female income to total household income, earner-dependent ratio, female

wealth to total household wealth and female CPR income to total household income

have greater influence on different levels of women empowerment as compared to the

personal factors viz. age, religion, caste, type of family, size of the family and

education in the study area. The authors conclude that it is important for the

Government to use women Self-Help Groups (SHGs) to maintain and protect the

common forest at the village level, which would not only help to generate income but

also empower the women.

Despite the fact that women are the major users of CPRs, their involvement in

participatory management is negligible (Singh, 2010, Patricia Jeffery, et al., 1998,

Agarwal, 2002). Based on the studies of four states in India, Gujarat, Orissa, Madhya

Pradesh and Andhra Pradesh, Patricia Jeffery, et al.,(1998) asserts that women have

been marginal in the establishment of Joint Management Agreements and village

level forest protection committees. Although the forest department made all efforts to

ensure women’s active participation, their involvement has been insignificant. Singh

(2010) surveyed 200 women respondents in 20 villages in two hilly regions (Shivalik

and Aravalli hills) of Haryana State in order to understand the level of women


33

participation in forest restoration. According to the author, the role of women in forest

management is very important as the collection of forest products to meet subsistence

need is mainly the responsibility of women. Gender equity in benefit sharing should

be ensured so as to improve women’s participation in forest management.

On similar lines, Godbole (2002), asserts that social and cultural constrains have led

to the low participation of women in Joint Forest Management programmes. Women

have little ownership or control over resources such as land and property, education,

technical skills and market information. Hence women are always dependent and

disempowered position as compared to men and therefore run a greater risk of

being excluded from their homes and livelihoods.

Das & Sarker (2008) examines the impact of social capital in a gender sensitive

planning on JFM in West Bengal. The social capital is measured in terms of

productivity, equity and sustainability along with World Bank’s six common

dimensions (groups and networks, trust and solidarity, collective action and

cooperation, information and communication, social cohesion and inclusion, and

empowerment and political action). Based on the survey of Forest Protection

Committees (FPCs) in West Bengal, the authors opine that communal solidarity,

mutual trust and coordinated actions exits in JFM villages, thereby increasing the

social capital. The study further reveals that in female FPC villages there is higher

level of social capital as compared to FPC villages in general. This implies that in

those FPCs where social cohesion and community solidarity is weak, effective

leadership and local support can bring about in improving the social capital.

According to Agarwal (2002), there is gender bias during membership into Forest

Protection Committee in JFM in West Bengal. The General Body (GB) membership

is predominantly for the male household head. However, the woman automatically

becomes a member by virtue of her husband being a member; it is he who is seen as

the primary member. Sarin (1998) studied 20 Community Forest Groups in West

Bengal and observed that 60 percent had no women, and only 8 percent of the 180

Executive Committee members were women. In West Bengal’s Bankura district, the

District Forest Officer had issued a circular stipulating that there should be a


34

minimum of 30 percent women in the general body, which had resulted in raising the

female membership in several villages (Viegas & Menon, 1993).

Based on the survey of FPCs in a remote Block of South-West Midnapore in West

Bengal, viz., Nayagram, Dasgupta et al. (2006) observe that women participation in

JFM can be enhanced by imparting a simple and low-cost technology based training

programme. The constraints observed in women participation can be attributed to lack

of security, lack of confidence, social and cultural restrictions, etc. which can be

overcome through a model that tackles these gender related issues.

The study of common property resources is an important area of research world over.

In rural India, there is high dependency on common property resources, specially the

common property forest resources. This dependency on common property forest

resources is mainly among the forest fringe dwellers. In fact, the extraction of

common property resources affects the rural household income, employment,

economic inequalities, poverty and environment. As the rural poor are the major users

of the common forest resources, they greatly value the forest conservation and

participate in forest management. Further gender equity in participation in forest

management enhances sustainability of the forest resources. As per our knowledge, no

systematic study on common property resources dealing on all these issues have been

carried out in West Bengal. Further from the survey of a good number of existing

studies on common property resources, we observe that the gender aspect of CPR has

also been neglected. Our study fills-in the gap of knowledge in this area of research.

Objectives, Data Source and Methodology

35

CHAPTER 3

OBJECTIVES, DATA SOURCE AND METHODOLOGY

3.1 Objectives of the Study

In the backdrop of the existing literature survey, we have observed that Common

Property Resources (CPRs) play a critical role not only in the livelihood of the rural

poor but also in overall rural development. The rural poor, mainly women, are highly

dependent on the collection of the CPRs from the common forest for their subsistence.

Rural households gather forest products as a supplement to cultivation. Rural

households collect CPRs from the forest to smooth their income and consumption and

this helps to alleviate their poverty. CPRs not only provide a source of income and

employment to the rural poor but also act as a safety net during agricultural shocks.

However indiscriminate gathering of CPRs leads to forest degradation. Rural poor are

now conscious about long term consequences of forest degradation and they

participate in effective forest management to achieve sustainable development.

This study examines how Common Property Resources affect the livelihood pattern

of the rural poor in the study area and its linkage with poverty, gender, environmental

degradation and forest conservation. It specifically addresses the following research

questions;

1) What is the extent of dependency of rural poor on CPRs in West Bengal?

2) To what extent CPRs provide employment and income to rural poor?

3) What is the implication of CPR extraction on rural poverty?

4) What are the factors responsible for CPR extraction?

5) Does the extraction of CPRs lead to environmental degradation?

6) To what extent agricultural risk affects the collection of common property

resources?

7) Is there any relationship between forest dependency and participation in

forest management?

8) What is the impact of participation of forest management on alleviating

environmental degradation?

9) How the women’s active participation affect the sustainability of forest

resources?


36

Consistent to the above research questions, the specific objectives of this study are the

following:

i) To assess the extent of dependency of rural poor on CPRs and to examine

the impact of CPR collections on income, employment and poverty of the

rural poor in the state of West Bengal.

ii) To examine the impact of CPR extraction on environmental degradation.

iii) To examine the interrelationship among agricultural risk, non-timber forest

collection and the extent of rural poverty.

iv) To assess the nature of participation in forest management and examine

the relationship between forest dependency and participation in forest

management.

v) To investigate the impact of participation of forest management on

alleviating the environmental degradation in the study area.

vi) To assess the role of women in CPR collection and to examine the

performance of women in forest management committee.

vii) To investigate empirically the effect of women’s active participation in

management on forest resource conservation.

3.2 Data Source

The study is based mainly on a primary survey on Common Property Resources in

West Bengal conducted in 2011. Multistage sampling technique has been used for the

collection of primary data. We have purposively chosen two economically backward

districts of West Bengal viz. Bankura and Purulia since both of them are covered by

vast forest area and the rural poor are highly dependent on Common Property

Resources for their subsistence.

Bankura is a district of West Bengal in India, located in the western part of the state.

It is part of Bardhaman Division of the state and is included in the area known as

‘Rarh’ in Bengal. As per the Census 2011, the geographical area of the district is

625900 hectare. It has a population of 3,596,292 and occupies 13th position in terms

of population of the state. Bankura is one of the least urbanised districts of the State

having less than 8.33 percent of its total population in urban areas. The literacy rate

of district is 70.95 percent which is much lower than the state average of 77.08


37

percent (Census 2011). Based on the physiographic terrain, the Bankura district can

be broadly divided into 3 regions i) hilly areas in the west, ii) connecting undulating

track in the middle and iii) level alluvial plains to the east. The climate of Bankura is

tropical, dry and sub humid. Bankura district is well known as a drought prone district

of the State.

The economy of Bankura district is primarily based on agriculture with nearly 62

percent of the main workers of the district engaged in the agricultural sector. The

district has 150,465.3 hectares of land under forest. Out of the total forest area

131,203.19 hectares of land is under protected forest and 3889.07 hectares of land is

under reserved forest. Paddy is the main agricultural crop and is produced in 90

percent of the total cultivable land of the district. The district is characterized by low

industrialization with high dependence on agriculture. The cottage and small scale

industry contribute to the economy of the district. In many parts of Bankura District,

the rural poor are largely dependent on the common property resources for their

subsistence as compared to the non-poor. The high dependence of the rural poor on

the common property resources is primarily due to uncertainty in agriculture and low

cost of wage labour.

Purulia is the westernmost district of West Bengal under the Burdwan division. The

geographical area of the district is 625900 hectare. It has a population of 2,927,965

and population density of 486 per sq. km as per the Census 2011. It is inhabited by a

large number of tribal people. Some of the major tribes of Purulia district are Santals,

Bhumij, Kheria, Shabar and others. Among them Santals are the highest population

concentrations in the district. Purulia conjures up a vision of hills, plain lands,

rivulets, forests and wild birds. The south west monsoon is the main source of rainfall

with an average annual rainfall varying between 1100 mm to 1500 mm. The district is

covered mostly by residual soil formed by weathering of bed rocks.

The agriculture is the fundamental occupation in the district though the agricultural

activity faces a tough hindrance due to the irregularity and paucity of rainfall, infertile

red lateritic soil etc. Paddy is the main crop of the district. The crop is mainly

cultivated under rain fed condition. Purulia district is covered by vast forest area. The


38

forest is covered by an area of 115226 hectare. The natural forests of the district are

mostly of mixed nature and restricted to northwest part of the district.

In both Bankura and Purulia districts, forest species like Sal, Palash, Kusum, Mahua,

Neem, Kend are major source for timber, pole, small wood, NTFP, firewood,

medicinal plant to rural poor. Medicinal plants like Aswagandha, Satmuli, and

Vrigoraj are available in these forests. Haritaki, Amla, Bahera, Karanj, Neem, Sal

seeds, Bamboo, Kendu leaves are major Non-Timber Forest Produce found in this

district. A large number of rural poor living in and around forest directly or indirectly

depends on this forest for fodder, fuel wood, small timber and other tangible or

intangible benefits. The common land is used by the households for grazing of their

livestock. The households also collect fodder from the common forest area for the

livestock. The household hunts animals to some extent in the common forest area and

also fish in the common ponds. They collect small fishes viz. gorou, chunamach, etc.

from the common property resources like ponds and lakes. The households also

collect wild mushrooms (called karan chatu in local dialect) from the common forest

area. The detailed village characteristics of the study area is described in Appendix-I.

In order to address the research problem, we have chosen two blocks viz. Saltora in

Bankura district and Santuri in Purulia district. The blocks have been selected taking

into consideration the dominance of CPR based economic activities. In Bankura

district, from Saltora block, we have chosen six villages viz. Panjhoria, Ramjibanpur

(Bandhghat), Seolibona, Baldanga, Dulaltora and Tantirdanga. Three villages viz.

Jiyathole, Marbediya and Ambari have been selected from Santuri block in Purulia

district. The primary survey was carried out in 300 households in 9 villages of

Bankura and Purulia districts. These villages were in close proximity to the forest

area and the people residing in these villages are highly dependent on forest resources

for their livelihood.

In the surveyed villages, we have collected information from households through

complete enumeration method. In the field work carried out in the above mentioned

villages of Bankura and Purulia districts, two types of structured questionnaire were

used for survey of both village level and household level. The village schedule was


39

administered to Panchayat office bearers, Head Masters and other educated persons in

the village. The village schedule covered the type of forest land available in the

villages, forest management, customary rules of CPR management, extent of

environmental degradation in terms of forest area, common village area and grazing

land, etc. The information about the reasons and remedies for CPR depletion and

forest degradation were sought from the respondents. For the household survey,

information on caste/religion, demography, education, occupation, land holding,

annual income, asset ownership, collection of CPRs, monthly expenditure, etc. was

collected from all the households in each village and the importance of CPRs have

been examined in detail. We have collected information by interviewing mainly

household head, senior female members and persons knowledgeable about the forests

in the study villages. The information collected through field surveys has been

presented in tabular form to quantify the various objectives. We have also utilised the

available secondary data sources of NSS report on Common property resources,

Census of India, Government reports viz. India State Forest Report, Forest Survey of

India Report and various Statistical Abstracts of the Government of West Bengal.

PHOTO 1 PHOTO 2

Interview with a household head at Panjhoria Interaction with a villager at Dulaltora

The collection of primary data, especially the quantitative data, in rural West Bengal

is a complex one. Most of the household heads are illiterate and are not acquainted

with the modern quantitative measurements. They measure the agricultural outputs

using traditional methods; not in terms of weight but in terms of volume. For

example, paddy is measured in terms of the number of sacks (1 sack ~ 80 Kg). Land

is measured in terms of bighas (1 bigha = 0.1338 hectare or 0.3306 acre or 1333 m2).


40

The collection of fodder and fuel wood is measured in terms of bojha (1 bojha ~5 Kg

for fodder and ~10 Kg for fuel wood). Most of the households do not keep record of

their age, income, wealth, consumptions, expenditure, savings, etc. and hence the

information gathered about household characteristics is prone to error. Although, we

have covered almost all the households in our surveyed villages, few people refused

to take part in the survey. Generally, we have interviewed the household head or any

senior male / female member of the household. We have not questioned all the

members of the family and their views may be different from that of the respondents.

In few cases, the households did not give accurate information about CPR collections.

In order to overcome few of the problems mentioned above, we have taken the help of

local students residing near the study villages. The problems of quantification during

the survey have been resolved to some extent through participatory approach and

cross verifications of the relevant socio-economic variables.

3.3 Methodology

In our study, we have applied the empirical methodology for statistical estimation and

econometric analysis. We have summarised the methodology as follows:

3.3.1 Conceptual Framework:

I) Common Property Resource Dependency: A Household utility

maximization framework The study has conceptualised the problems of Common Property Resource extraction

under the maximising behaviour of the household where the household maximises the

utility subject to a number of resource constraints. The conceptualisation of the

problem has been done in the framework of Bardhan et al. (2002). Household derive

utility by means of commodity consumption including consumption of goods

collected from the common land and forest, consumption of produced goods, size and

composition of the household and the leisure time.

The utility function ( HKU ) of the household H in village K can be shown as:

( , , , )HK HK HK HK HKU u C A L S=


41

Where HKC = Consumption of goods collected from the CPR

HKA = Consumption of produced goods

HKL = Leisure

HKS = Family size and composition

Household maximises utility subject to two constraints: i) Budget Constraints and ii)

Time Constraints. The optimisation of utility function subject to the budget and time

constraints would give us implicitly the determinants of the collection of the CPR

goods ( HKC ) by the households.

This can be approximated as

( , , , )HK HK HK HK HK HKC C Y S t v=

where HKY =given level of expenditure

HKS =family size and composition

HKt = time taken to collect one unit of CPR and

HKv = village characteristics, i.e. land holding pattern, livestock,

education, distance between house and common forest, sex / gender

The comparative static outcome of the model enables us to identify the factors

determining the extraction of Common Property Resources. The extraction of

products from the common forests is dependent on a host of factors relating to

household and village characteristics. Some factors relating to demographic features

like size of the household, sex composition, availability of working population, etc.;

some are economic e.g. individual land holding pattern, livestock unit, occupational

structure, the existence of market; some are social like education, male headed /

female headed households, etc.; some are geographical like the size of the village,

distance from the residence to CPR field, distance to the nearest market, etc.

We have used a multiple regression model to examine the determinants of CPR

extraction:

0 1 2 3 4 5( )C HKP C FSIZE FEMPER AVRAGE AVRSCH OWNLANDα α α α α α⋅ = + + + + +

6 7 8 9LIVESTOCK FORESTDIST POVR DISMα α α α+ + + + +∈

(Details discussed in Chapter 4 in Section 4.2.1 and 4.2.2.A)


42

II) Linkage between Poverty and Environment The main arguments found in the literature indicate that poverty leads to

environmental degradation; while other studies argue that environmental degradation

leads to low productivity and therefore increase poverty. The rural poor in their

strenuous effort to live are driven to environmental degradation which has long term

influences on rural livelihood. Again environmental degradation lowers the rural

poor’s ability to generate income. Thus poverty and environment has a two-way

relationship. To examine the determinants of poverty, an empirical model especially

the qualitative response model (logit model) has been employed in the study.

Following Deininger & Minten (1999), we have hypothesised that poverty is a

function of environmental degradation alongside other socio-economic variable which

capable us to indicate the rural poverty conditions as well as the extent of

environmental degradation in our surveyed villages.

The probability that a household will be poor can be specified as:

( ) ( )1

1 ii i XP F Xe α βα β− +

= + =+

……………………….(i)

Where iP is the probability that thi household will be poor given iX , where X is a

vector of explanatory variables and e is the natural logarithm. Equation (i) can be

written as:

( )1 1iXiP e α β− + + =

where 1

1

log1i

PxP

α β

+ = − and 1

11P

P− is the odds ratio, whose log gives the odds

that a household is poor.

The model to be estimated is specified as

1 2 3 4log1

ii i i i

i

P FSIZE AVRAGE AVRSCH OWNLANDP

α β β β β

= + + + + −

5 6 7i i iLIVESTOCK FORSTDIST ENVDGRβ β β+ + + +∈

(Details discussed in Chapter 4 in Section 4.2.2.B)


43

III) Agricultural Risk and Common Property Resources In this section, we have made an effort to build up a household model to analyse the

impact of expected agricultural risk and unexpected agricultural shortfall on the

collection of common property resources following the principles of ‘new home

economies’ (Barnum and Squire 1979; Pattanayak and Sills 2001). In our surveyed

villages the main occupation of the households is farming. They primarily depend on

agriculture. However, in time of agricultural crop failures they are bound to depend

on forest products. In poor agrarian economy the natural forest resources act as

insurance as they smoothen household’s income and consumption. In good harvesting

period, they gather surplus forest products which play the role of natural insurance to

mitigate agricultural risk.

Household maximises utility ( )u subject to the production constraint, time constraints

and budget constraints. We have used lagrangian method to solve the maximising

problem and identify the household forest collection in a reduced form of labour

demand equation.

( ), , ,dF N CN n P X H Fξ=

where NP = Opportunity cost of time as measured by off-farm wages in a

complete market

ξ = Agricultural risks

X = Exogenous income

CH = Household characteristics

F = Forest quality

We have hypothesised a positive relationship between agricultural risk and forest

collection labour.

We have considered the Count Data Model to focus on the relationship between CPR

dependency and agricultural risk. Our specified model is

0 1 2 3 4 5FCL AGEHEAD SQAGEH FAMSIZE AVRSCH LIVESTOCKα α α α α α= + + + + + 6 7 8 9FORESTDIST AGRSHLFALL AGRIRISK WAGEα α α α+ + + + +∈

(Detail discussed in Chapter 5 in Section 5.2.1 and 5.2.2)


44

IV) Property Rights and Common Property Forest Management Most of the rural poor in our surveyed villages of Bankura and Purulia districts are

very much dependent on Common Property Resources, mainly forest resources. The

shadow value of these forest resources is zero because there is no barrier to

accessibility of the resources and their availability is also abundant. However,

continuous over-exploitation of the forest products leads to scarcity of the resources.

Therefore, forest product turns to have an economical value and shadow value

becomes greater than zero. Rural poor collects the forest products as long as the value

of the products equal to cost of collection. To protect the forest from massive

collection of trees and forest patches, it is necessary to patrol and monitor the

common forest. The local people do this job in a better way than the forest department

officials. Therefore in order to conserve the forest resources local rural community

should join the Joint Forest Management (JFM), otherwise they have to travel a

longer distance to collect their necessary forest products. Though agriculture is their

main occupation, it is a seasonal activity. Therefore household allocate their

empowered labour between agricultural activities and forest protection activities.

Now, the proper management of forest resources depend on collective action which is

measured by the amount of labour devoted by the households for managing the forest

resources. The extent of collective action of the household depends on variety of

socio-economic factors. We estimate the following empirical model to examine the

determinants of collective action:

12

i

n

M i ij ijj

L a b Y=

= + +∈∑

Here ML is the dependent variable defined as the amount of labour household

contributions to local management, 1ia denotes community dummies, ijY is set of

explanatory variables including the index of organisational intensity, individual

organisational experience, hectares planted, livelihood activities, socio-cultural

heterogeneity, age, gender, household size, wealth, forest condition and ∈ is the error

term.

We have considered censored Tobit Model to examine the impact of forest

dependency on collective action in JFM.


45

Our specified model is

0 1 2 3 4 5iML a b FSIZE b FEMPER b AVRAGE b AVRSCH b PERAGRIN= + + + + +

6 7 8b PERCPRIN b PERCPRCSM b WEALTH+ + + +∈

(Details discussed in Chapter 6 under Section 6.6)

V) Nexus between Forest Management and Forest Degradation Active Forest management lowers the degree of over-exploitation of forest resources

and hence help to reduce the forest degradation. To examine the impact of better

forest management on forest degradation, we have used logit regression model.

1 2 3 4log( )1

i

i

P a b AVRAGE b AVRSCH b FSIZE b OWNLANDP

= + + + +−

5 6 7b LIVESTOCK b POVR b FMACT+ + +

(Details discussed in Chapter 6 in Section 6.7)

VI) Women’s participation in CPR Management The surveyed households in Bankura and Purulia districts are basically poor.

Moreover these villages are very near to forest area and hence collection of CPRs is

very high in these villages. We have observed from our survey that rural women are

mainly responsible for collection of forest products in order to meet their subsistence

needs. Hence they are very much aware of the forest conservation programme. As

women are major users of forest products, sustainability of forest resources depend on

their active participation in Joint Forest Management.

In this section we have identified the factors within rural household and villages

determining the sustainability of forest resources ( SFOREST ) which can be written

as:

SFOREST = f (Family size, Sex ratio, Active participation of women in JFM,

Gender gap in education, Number of male household head, market pressures)


46

We have used a Binary Probit regression model to examine the determinants of

sustainability of forest resources to justify the role of women’s participation in CPR

management.

Our specified model is:

0 1 2 3 4 5SFOREST SEXR FHEAD FLIT DEPR PCATTLEα α α α α α= + + + + +

6 7 8WACTPM DISM PUNSHMα α α+ + + +∈

(Details discussed in Chapter 7 in the Section 7.4.1)

3.3.2 Econometric and Statistical Specification Statistical analyses are applied to represent a clear idea of our hypothesis of the study.

We have used both Statistical and Econometric techniques to analyse our specific

objectives. The several Econometric and Statistical specification applied are as

follows:

I) Multiple Linear Regression Analysis We have used Multiple Linear Regression Model in our study to examine the

determinants of Common Property Resource extraction. The general form of the

multiple regression models is

1 2( , ,...., )ny f x x x= +∈

1 1 2 2 ..... n nx x xβ β β= + + + +∈

where y is the dependent or explained variable and 1 2, ,..., nx x x are the independent

or explanatory variables. ∈ is the disturbance term which arises mainly to capture the

influence of omitted factors on an economic variable. In our study the dependent

variable is household income from common forest and few village and household

characteristics i.e. family size, female percentage in household, age, education, own

land, livestock, poverty, distance to the nearest forest and market are the explanatory

variables. We have explained the relationship between dependent and explanatory

variables through Multiple Regression Model. We have used R-squared to confirm the

goodness of fit of the model and statistical significance can be checked by an F-test of

the overall fit, followed by t-test of individual parameters.


47

II) Logit Regression Model In statistics logistic regression is a type of regression analysis which is used for

predicting the consequence of a categorical dependent variable based on one or more

predictor variables i.e. it is used in estimating empirical values of the parameters in a

qualitative response model. In binary logistic regression, the outcome generally coded

as ‘0’ or ‘1’. If a particular mentioned outcome for the dependent variable is the

notable possible outcome it is usually coded as ‘1’ and contrary outcome as ‘0’.

Logistic regression is employed to anticipate the odds of being a case based on the

values of the explanatory variables. The odds are defined as the probability that a

particular outcome is a case divided by the probability that it is a non-case. In our

study we have applied binary logistic distribution to examine the determinant of

poverty.


( ) ( )11

1 ii i Xi

P E Y F XX e α βα β− +

= = = + = +

Where iP is the probability that thi household will be poor given iX , where X is a

vector of explanatory variables and e is the natural logarithm.

Since the actual estimation of the logit model is done using Maximum Likelihood

Estimation (MLE), conventional R2 is inadequate measure of goodness of fit; we have

used McFadden R-squared measure

2

int

ln ( )1

ln ( )

full

ercept

L MR

L M

∧

∧= −

where fullM = Model with predictors

int erceptM = Model without predictors

III) Count Data Model In statistics, count data is a type of statistical data in which the observations can take

only the non-negative integer values (0, 1,2,3,.…) where these integers originate from

counting rather than ranking. We have used count data model to capture the impact of

agricultural risk on CPR collection. Here the dependent variable is forest collection


48

labour which is measured by the number of major forest collection trips. We have

considered household and village characteristics with agricultural risk and shortfall as

explanatory variables. The Poisson Regression Model has been used to study count

data.

Assuming that the annual number of NTFP collection trips ( in ) follows a Poisson

distribution, with observed frequencies iN , the probability density function of the

Poisson is described by equation (A1), with parameter iλ (Greene, 2003)

( )!

i ini

i ii

eP n NN

λ λ−

= = ………………………………….….…….[A1]

In the Poisson Count Data model, the characteristic of probability density function is

equidispersion with single parameter iλ equal to both mean and variance of the trip.

An adjustment to the variance covariance matrix, where k = number of observations

and l =number of parameters, is used to determine the Statistical significance of the

coefficients in the Poisson model. 1 1

2' ( )1' 'var( ) i

i i

nx xk li i

x x x xλβ λλ

λ λ− −

− = −

∑ ∑ ∑ ∑ ……….[A2]

As the count model very often detect over dispersion or variance greater than the

mean, it is easier to estimate the parameter with maximum likelihood techniques.

The common alternative of Poisson regression model is negative binomial. As the

distribution of forest collection trips has a large concentration of households taking

zero trips as well as large number of trips, it is better to use Negative-Binomial model

which is a mixture distribution of the Poisson with gamma heterogeneity. For the

Negative Binomial model, the densities for positive and zero trips are given in

equation [A3] and [A4] (Green, 2003).


49

'

( )( )( ) ( 1)

in

iNB i i

i

n iP n Nn

i i

θ

θθ

λθθ λ θ λ

−

Γ += = Γ Γ +

+ +

…………………...[A3]

( 0)NB iP n

i

θ

θθ λ

= = +

…………………………………………………….[A4]

As assumed for a negative binomial model, our response variable is a count variable

and the variance of the response variable is greater than the mean of the response

variable. In statistic a zero-inflated model is a statistical model based on a zero-

inflated probability distribution i.e. a distribution that allows frequent zero value

observation. In our study we have applied Zero Inflated Negative Binomial Model

(ZINB) because the number of households taking zero trips and more than zero trips

can be explained in a better way through this model.

The expected count is conveyed as a combination of the two processes i.e.

household’s CPR collection trip against no trip.

E (to take a trip) = prob (not take any trip) * 0 + prob (take any trips) * (E y x=

take any trip)

IV) Censored Tobit Model The Tobit model is a statistical model proposed by James Tobin to describe the

relationship between a non-negative dependent variable and an independent variable.

The Tobit model is also named Censored Tobit Regression Model because few

observations on the dependent variable are censored. In Censored Data model, when

the dependent variable is censored, values in a certain range are all transformed to a

single value (Greene, 2003). We have applied censored Tobit model to explain the

relationship between forest dependency and active forest management. Here the

dependent variable is the number of man days per year a household involves in forest

management activities.


50

From the surveyed data, we observe that about 25 percent of the total respondent in

Bankura district and 15 percent in Purulia district allocated zero man-days to JFM

activities. Since the dependant variable is censored from below, we observe some

estimation problems. Censoring implies expected value of ( )ijME L is non-linear in Y

and constant partial derivatives and the expected value of the dependent variable for

most combinations of parameter estimates and explanatory variable may be negative.

Here we have applied maximum likelihood estimation to estimate a censored Tobit

model. The regression is obtained by making the mean in the preceding correspond to

a classical regression model.

The general formulation is usually given in terms of an index function.

iM iL Yb= +∈

0iML = if 0

iML ≤

*i iM ML L= if 0

iML >

( ) exp( )i viVar vλ∈ =

V) Binary Probit Model In statistics, a Binary Probit Model is a type of regression where the dependent

variable can take only two values and is used to estimate the probability that an

observation with particular characteristics will fall into a specific one of the

categories.

The functional form of the Probit Model is 1 2 2

212

ixz

P e dzβ β

π

+−

−∞

= ∫

We used this model to analysis the determinants of sustainability of forest resources.

Here the dependent variable is sustainability of forest resources where we coded ‘Yes’

response as ‘1’ and ‘No’ response as ‘0’.


51

3.4 Hypothesis Tested

In order to examine the crucial role of Common Property Resources in rural

livelihood, we have tested the following hypothesis in our study:

H1: Common Property Resources (CPRs) have a positive impact on income and employment of rural poor in Bankura and Purulia districts of West Bengal.

H2: CPRs have an immense role in alleviating rural poverty in the study

villages of Bankura and Purulia districts. H3: High extraction of CPRs in the surveyed villages has resulted in

degradation of the environment which further aggravates poverty. H4: CPRs acts as a safety net especially in times of agricultural crisis. H5: There is a strong relationship between forest dependency and

participation in forest management. H6: Active participation in forest management plays a positive role in

alleviating the environmental degradation. H7: Women’s participation in forest management improves the

sustainability of forest resources.

Common Property Forest Resources: Contribution and Crisis

52

CHAPTER 4

COMMON PROPERTY FOREST RESOURCES: CONTRIBUTION AND CRISIS

4.1 Introduction

Common Property Resources (CPRs) can be defined as a ‘community’s natural

resources, where every member has access and usage facility with specified

obligations, without having an exclusive property right over them’(Jodha, 1985b).

Common Property Resources are those resources that are accessible to and

collectively owned or held or managed by an identifiable community and on which no

individual has exclusive property rights. Accessibility to this resource is determined

either by legal status or by convention.

The earliest literature on Common Property Resources (CPR) can be credited to

Hardin for his celebrated work ‘Tragedy of the Commons’ (Hardin, 1968). It

demonstrates that free access and unrestricted demand for a finite resource ultimately

results in the depletion of the resource through over-exploitation. Cox (1985) in the

literature “No Tragedy on the Commons” questions the Hardin’s theory and states that

the decline of the commons system was not due to an inherent flawed land-use policy

but a widespread abuse of the rules governing them, land reforms, improved

agricultural techniques and the effect of industrial revolution.

CPRs are integral part of the social and institutional arrangements for the user

community. The rural poor, especially the landless, are highly dependent on the CPRs

for their subsistence. Several empirical studies have been carried out in India dealing

with the subject of poverty, inequality and dependency of rural households on CPRs

(Jodha, 1986, 1990, 1995; Beck, 1994, 1998; Singh et al., 1996, Iyengar and Shukla,

1999). These studies have postulated that rural poor depend heavily on the CPRs and

these resources provide a source of consumption and income (Ostrom, 1990; Brara

1987, Dasgupta 2006). In fact, in rural areas there are complementarities among

livelihood opportunities like agricultural and livestock incomes and protection of


53

upper catchments for fodder collection and common water resources for irrigation

(Chopra, Kadekodi and Murty, 1989).

The rural poor are highly dependent on the forest for their subsistence. In India,

studies on poverty with relation to CPR collection from forest indicate that when

income from forest is set to zero in poverty calculations, poverty increases by as much

as 28 percent (Reddy and Chakravarty, 1999). Forests contribute a large part of CPRs

especially with the collection of Non Timber Forest Products (Chopra and Gulati,

2001). CPRs supplement rural livelihood and act as safety net for the poor especially

in the time of agricultural crises. Beck and Ghosh (2000) carried out field survey of

seven villages across the agro-ecological zones of West Bengal in India and based on

their findings they estimated that CPR adds about US $ 5 billion a year to the income

of the rural poor or about 12 percent of the household income of the rural poor.

Poverty is often associated with environmental degradation. Roughly half of the

world’s poor live in highly degraded environment. The World Commission on

Environment and Development (Brundtland Commission) wrote (1987): ‘Poverty is a

major cause and effect of global environmental problems. It is therefore futile to

attempt to deal with environmental problems without a broader perspective that

encompasses the factors underlying world poverty’. In recent times, there has been

increasing recognition that the relationship between poverty and environment is

complex and is strongly influenced by economic, social, local demographic,

institutional and cultural factors.

Rural poor are heavily dependent on forest. The household labour allocation decisions

and extraction of forest products are dictated by various socio-economic and

demographic variables. Environmental degradation deepens today’s poverty, whereas

today’s poverty makes it extremely difficult to care for or restore the agricultural base,

to find alternates to deforestation and control soil erosion. Forest degradation refers to

reduction in optimum capability or productivity of the forest. Grazing of domestic

cattle, goat, sheep, etc. causes major problem in forest areas. Over grazing also

adversely affects the soil properties. The adverse effects on soil cause formation of

gullies, loss of top soil and reduction of porosity.


54

The environment matters a lot to the rural poor. The well-being of the poor is strongly

related to the environment in terms of their health, security and earning capacity. The

environment not only provides the sources of livelihood to the rural poor but also

affects their health and influences their vulnerability. Poverty also affects

environment by forcing the rural poor to degrade the environment. Environmental

degradation largely affects the livelihood of the poor. The rural poor are most

vulnerable to environmental degradation because they depend heavily on natural

resources, have less alternative resource, and most often exposed to environmental

hazards, and are least capable of coping to environmental risks (Dasgupta and Mäler,

1994; Lopez Roman, 1997). There is a widely held view that poverty is the main

cause of environmental degradation, because the rural poor are not in a position to use

the natural resources available to them in a sustainable manner (Duraiappah 1996).

This degradation further leads to aggravate the rural poverty.

Under this backdrop, this chapter attempts to explore the nature and pattern of CPR in

Bankura and Purulia districts of West Bengal and the implication of CPR extractions

on rural poverty and environmental degradation.

Specifically, this chapter attempts to explore the

i) nature of dependency of the rural poor on CPRs

ii) determinants of CPR income

iii) role of CPR in poverty reduction

iv) environmental impact of poverty by analysing the relationship among CPR

extraction, rural poverty and environmental degradation


55

4.2 Data and Methodology

The evidence presented in this chapter is based on the primary data collected from

two districts of West Bengal in 2011. The field survey was undertaken in 6 villages in

the district of Bankura in West Bengal, India; viz. Panjhoria, Ramjibanpur

(Bandhghat), Seolibona, Baldanga, Dulaltora and Tantirdanga and 3 villages in the

district of Purulia in West Bengal, viz. Jiyathole, Marbediya and Ambari. Total 300

households were surveyed. These villages were selected for the survey because they

were economically highly backward and the households residing in these villages are

highly dependent on forest resources for their livelihood.

Two types of questionnaire were used in the survey: household schedule for sample

households and village schedule which was administered to Panchayat office bearers,

Head Masters and other educated persons in the village for obtaining village specific

information. For the household survey, information on caste/religion, demography,

education, occupation, land holding, annual income, asset ownership, collection of

CPRs, monthly expenditure, etc. was collected from all the households in each

village. We have examined the importance of CPR in all the study villages in detail.

We have classified the households into two groups; viz. ‘poor’ if household belong to

BPL (Below Poverty Line) and ‘non-poor’ if the household belongs to APL (Above

Poverty Line). Out of the 300 surveyed households, 240 households are considered as

poor and 60 are non-poor. An attempt has been made to quantify the level of

dependency of the rural poor households on common forest resources through tabular

method. The extraction of common property products has serious implications to

poverty. The determinants of CPR extraction have been analysed using multiple

regression technique while the association between poverty and environmental

degradation has been estimated using logit regression method.

4.2.1 Conceptual Framework: In this section attempts have been made to conceptualise the problem of association

among rural poverty and extraction of CPR. The conceptualisation of the problem has

been done following the framework of Bardhan et al. (2002). Households derive

utility by means of commodity consumption. However consumption bundle depends


56

on a variety of goods and services including the consumption of goods collected from

the common land and forests, consumption of produced / market goods, size and

composition of the households and the leisure time. Usually the villagers use the CPR

for their own consumption, but in few cases they sell it in the market or to their

neighbours.

The utility function of the household H in village K can be shown as:

( ), , ,HK HK HK HK HKU u C A L S= ……………………………………………….(1)

where HKC = Consumption of goods collected from the CPR

HKA = Consumption of produced goods

HKL = Leisure

HKS = Family size and composition

It is to be noted that the consumption of goods collected from the common forest

depends on the time spent in collection and sale activities.

Thus,

( ),HK HK HKC F R T= ………………………….……………………..(1a)

where HK CHK SHKT T T= +

= Total time spent in collection activities,

i.e. for Consumption purpose and for the purpose of sale

CHKHK

HK

TRT

= = Proportion of time spent in collecting CPRs for self-

consumption out of the total time spent in collection activities.

It is often found that the use of CPR is guided by the well-defined rules and

regularities of the community. If any individual member overuses the village

commons, he/she is punished according to the provision of the customary rule.

Certainly, the provision of punishment affects the household’s utility. To incorporate

this in our utility function, we have assumed that in normal circumstances an

individual can extract CPR to the level maximum up to the village average extraction


57

rate. If he/she violates the village norms, he/she will be punished. The severity of

punishment depends on the gap between actual rate of collection HKC and the village

average K

K

CN

.

Thus the household utility function can be written as

( ), , ,HK HK HK HK HK EU u C A L S Sα= − ⋅ …………………..…………………(2)

where ES = Quantity of over extraction from CPR. The magnitude of ES is

determined by the village norms.

α = a positive constant exogenously determined by the management

(cost of over-extraction).

In our model, it is assumed that the prices of produced goods ( )KP , collected goods

( )CP and CPR consumption of the rest of the village are fixed and given. The indirect

utility function for the household family size and composition HKS can be represented

as:

( ), , , ,HKHK HK K HK HK EV v C L P Y S Sα= − ⋅ ……………..……………(3)

where (.)v is obtained by maximising (.)u subject to K HK HKP A Y⋅ ≤

The cost of using the CPR depends on the time it takes to collect and the opportunity

cost of this time for the rural household. The opportunity cost of time to collect CPR

depends on the household’s asset, employment opportunities tasks.

Household maximise utility (.)v subject to two constraints:

i) Budget constraints and

ii) Time constraints

The opportunity cost of time to collect CPR depends on the household’s alternate uses

of time. The income of household is earned by allocating the family labour into

different occupations. The different occupations denoted by 1,2,3,4,5i = respectively

are (i) Self-employment in agriculture 1( )HKS , ii) Wage labour in agriculture 2( )HKS ,

(iii) Wage labour in non-agriculture 3( )HKS , (iv) Self-employment in non-agriculture

4( )HKS and (v) Self-employment in livestock grazing 5( )HKS . Besides the above


58

occupational activities, time is allocated to CPR collection for consumption ( )CHKT

and sale ( )SHKT and Leisure ( )HKL .

Now total time taken for CPR collection ( )HKT can be shown as:

HK HK HK CHK HK SHK HKT t C t C t C= ⋅ = ⋅ + ⋅ ……………………………(4)

were HKt = time taken to collect one unit of CPR. The labour allocated to occupation i

is denoted by iHKS .

Now the time constraint is 5

1

iHK HK HK HK

iT S S L T

=

= = + +∑ …………………….………(5)

It is to be noted that total time available for different activities depends on the family

size and composition ( )HKS .

The budget constraints show that the households’ expenditure must be less than the

sum of net income ( )HKY from different sources. In equality form we can write:

Net Income = Expenditure = Income from agriculture 1( )β + Income from non-farm

business 4( )β + Income from livestock activity 5( )β + Income from CPR products

( )C HKP C⋅ + Wage income from agriculture 2( )w + Wage income from non-agriculture

3( )w . We can thus rewrite the equation as follows:

1 1 1 2 2 3 3 4 4 4( ; , , , ) ( , , , , )HK HK HK HK K K K HK K HK HK HK HK K KY S v d P I W S W S S v d P Iβ β= + + +

5 5 5( ; , , )HK HK K K C HKS v P I P Cβ+ + ⋅ ……………………………..(6)

where 1β =Returns to agriculture which depends on labour allocated in agriculture1( )HKS , land 1( )HKv , education ( )HKd , price of non-collected goods ( )KP , village

infrastructure ( )KI

2 2K HKW S =Income of wage labour engaged in agricultural activities where wage

rate is 2KW


59

3 3K HKW S =Income of wage labour engaged in non-agricultural activities where

wage rate is 3KW

4β = Returns to non-farm business which depends on labour allocated in non-

farm business 4( )HKS , non-farm business assets 4( )HKv , education ( )HKd , price of

non-collected goods ( )KP and village infrastructure ( )KI

5β = Returns to livestock activity which depends on labour engaged in

livestock activity 5( )HKS , livestock owned by the household 5( )HKv , price of non-

collected goods ( )KP and village infrastructure ( )KI

C HKP C⋅ =Income from CPR products ( )HKC by selling at the price rate CP .

Now the problem of the household is to select CPR ( )HKC and labour allocation HKL ,

, 1, 2,..5,iHKS i = to maximise its utility (2) subject to constraints (5) and (6).

The optimization of utility function subject to the budget and time constraints would

give us implicitly the determinants of the collection of the CPR goods ( )HKC by the

households. This can be approximated as

( , , , )HK HK HK HK HK HKC C Y S t v=

where HKY =given level of expenditure

HKS =family size and composition

HKt = time taken to collect one unit of CPR and

HKv = village level characteristics, i.e. land holding pattern, livestock,

education, distance between house and common forest, sex / gender, etc.

In this chapter, we have tested the following hypothesis:

H1: Common Property Resources (CPRs) have a positive impact on income and employment of rural poor in Bankura and Purulia districts of West Bengal.

H2: CPRs have an immense role in alleviating rural poverty in the study

villages of Bankura and Purulia districts. H3: High extraction of CPRs in the surveyed villages has resulted in

degradation of the environment which further aggravates poverty.


60

4.2.2 The Empirical Model Specification CPRs are the life support system in rural Bengal. CPRs act as not only a regular

source of income and employment but also a safety net in periods of drought and

other natural calamities. The extractions of CPRs are invariably linked with the

characteristics of household under a specific socio-economic condition. In this section

an attempt has been made to identify the factors determining the extent of forest

product collection. The extractions of products from the common forest depend on

the factors relating to household and village characteristics, i.e., family size and

composition, household labour, land holding pattern, livestock unit, occupational

structure, opportunity cost, economic status, the existence of the market, technology,

education, age, distance between forest and house and institutional factors like

management rule, civil law, etc.

4.2.2.A Determinants of CPR: Multiple Regression Model A multiple regression model has been used to examine the determinants of CPR

extraction.

0 1 2 3 4 5( )C HKP C FSIZE FEMPER AVRAGE AVRSCH OWNLANDα α α α α α⋅ = + + + + +

6 7 8 9LIVESTOCK FORESTDIST POVR DISMα α α α+ + + + +∈

Here dependent variable is C HKP C⋅ i.e. household income from community forest.

The explanatory variables are:

FSIZE = Family size of the household

FEMPER = Percentage of female members in the household

AVRAGE = Experience in collecting CPR product

AVRSCH = Average years (number of years) of schooling of household

OWNLAND = Total land owned (in hectare)

LIVESTOCK = Total number of livestock condensed into animal units (numbers)

FORESTDIST = Distance between house and common forest (in km)

POVR = Poverty of the household

DISM = Distance to the nearest market


61

Here 0α = constant;

iα ( 1, 2,...,8i = ) are the coefficients associated with the explanatory

variables and ∈ is the random disturbance term.

In the following table 4.1, we have presented dependent and explanatory variables,

their expected sign and description.

TABLE 4.1

Description of Variables of Multiple Regression Model Variables Expected

Sign

Variable description

Dependent variable

C HKP C⋅

Household Income from community forest

i.e. CPR income

Explanatory Variables

FSIZE + Average number of population of the

household (size of the family)

FEMPER + Percentage of female members in the

household

AVRAGE + Average age of household, i.e. experience in

collecting CPR product

AVRSCH _ Average years (number of years) of schooling

of household

OWNLAND _ Total land owned by the household (in hectare)

LIVESTOCK + Total livestock of the households converted

into animal units

FORESTDIST _ Distance of the CPR field from the residence of

the households (km)

POVR + Poverty =1, if Household belongs to BPL

Poverty = 0, if household belongs to APL

DISM _ Distance to the nearest market (in km)


62

4.2.2.B Determinants of Poverty: Poverty Environment Nexus The rural poor in their strenuous effort to survive are driven to environmental

degradation which has long term influences on rural livelihood. Due to environmental

degradation, the rural poor’s ability to generate income gradually diminishes. Thus

poverty and environment has a two way relationship. Following Deininger and

Minten (1999), a qualitative response model like logit model is proposed in this study

to examine the determinants of poverty. We consider a range of socio-economic as

well as environmental variable which enable us to express the rural poverty conditions

as well as the extent of environmental degradation in the region.


( ) ( )11

1 ii i Xi

P E Y F XX e α βα β− +

= = = + = +

……….………….(i)

where iP is the probability that thi household will be poor given iX , where X is a

vector of explanatory variables and e is the natural logarithm.

Equation (i) can be written as:

( )1 1iXiP e α β− + + = ……………………………….………………..(ii)

From equation (i), we can write

11i ZP

e−=+

where iZ Xα β= +

( ) 11

Z Z

ZZ Z

e eee e−

= =++

Therefore 1

Z

i Z

ePe

=+

……………………………………….…………….(iii)

If iP is the probability that the thi household will be poor, then ( )1 iP− is the

probability that the thi household will not be poor.

1 11 11 1 1

Z Z Z

i Z Z Z

e e ePe e e

+ −− = − = =

+ + +

Therefore, we can write,

( )1111

i

ZZ XZi

i Z

eP e e eP

e

α β++= = =−

+


63

This can be approximated as

log1

ii

i

P XP

α β

= + −

Now 1

i

i

PP−

is simply the odd ratio whose log gives the odds that a household is poor.

We have used logit regression model to analyse the relationship between poverty and

environmental degradation.

The model to be estimated is specified as

1 2 3 4log1

ii i i i

i

P FSIZE AVRAGE AVRSCH OWNLANDP

α β β β β

= + + + + −

5 6 7i i iLIVESTOCK FORSTDIST ENVDGRβ β β+ + + +∈

where

FSIZE = Family size of the household

AVRAGE = Experience in collecting CPR product

AVRSCH = Average years (number of years) of schooling of household

OWNLAND = Total land owned by the household

LIVESTOCK = Total number of livestock condensed into animal units (numbers)

FORESTDIST = Distance between house and common forest (in km)

ENVDGR = Dummy for extent of environmental degradation

ENVDGR =1; if environment is more degraded

ENVDGR =0; if environment is less degraded

α = constant;

iβ ( 1, 2,...,8i = ) are coefficients associated with the explanatory variable and ∈ is the

random disturbance term.


64

4.3 RESULTS AND DISCUSSION 4.3.1 Nature of Dependency on CPRs

The rural poor in the study villages are largely dependent on the common property

resources for their subsistence as compared to the non-poor. The poor households

depend on the CPRs collected from the common forest area, rivers, ponds and the

common grazing lands. It is observed that the not so poor households also collect

CPRs in various degrees. In order to get a better insight into the CPRs collections

amongst the households, all the surveyed households have been classified into two

groups: BPL (Below Poverty Line) and APL (Above Poverty Line). Since we intend

to examine the relative role of common property resources in the extent of poverty,

we have estimated the income of the households by including as well as excluding the

income from common property resources.

In the study area, we have surveyed 300 households in 6 villages in the district of

Bankura and 3 villages in the district of Purulia. Common Property Resources act like

a life support for all the households in all the villages in the study area. The rural poor

have access to various common property resources like fuel wood, dry leaves, shrubs,

dung cakes, etc. which are mainly used for cooking and heating; bamboos, canes, logs

from trees, dry leaves are used for construction of houses; shrubs and grasses are used

as fodder for the animals; fruits, vegetables, fishes, root, meat from hunted birds and

animals are used for consumptions as well as for sale. Few plants and roots are also

used for medicinal purposes for curing several ailments. These common property

resources are means of subsistence for all the households in the study villages.

Common property resources, mainly forest resources have a critical role in rural

livelihood in our study villages. Forest communities are very much dependent on the

collection of forest products for their domestic as well as commercial needs. The rural

households in the surveyed villages collect fodder/grasses, fuel wood, cow dung,

herbal medicine, bamboo, timber, fruits, honey, vegetables, fish, birds and broom.

The household collection of CPRs in the last 1 year in the surveyed villages is shown

in Table 4.2 below.


65

TAB

LE 4

.2

Dis

tric

t B

lock

Nam

e of

vi

llage

Val

ue o

f Fo

dder

co

llect

ed fr

om

Com

mon

P

rope

rty

in la

st

1 ye

ar

Val

ue o

f Fu

elw

ood

colle

cted

fr

om

Com

mon

P

rope

rty

in

last

1 y

ear

Val

ue o

f C

owdu

ng

colle

cted

fr

om

com

mon

pr

oper

ty in

la

st 1

yea

r

Val

ue o

f H

erba

l M

edic

ine

colle

cted

fr

om

Com

mon

P

rope

rty

in

last

1 y

ear

Val

ue o

f B

ambo

o / S

al

& o

ther

leav

es

colle

cted

from

C

omm

on

Pro

pert

y in

last

1

year

Val

ue o

f Ti

mbe

r co

llect

ed fr

om

Com

mon

P

rope

rty

in la

st

1 ye

ar

Val

ue o

f Fr

uits

/F

low

ers

/Hon

ey

colle

cted

fr

om

Com

mon

P

rope

rty

in

last

1 y

ear

Val

ue o

f H

unte

d bi

rds/

an

imal

s/

snai

ls in

last

1

year

Val

ue o

f V

eget

able

s co

llect

ed

from

C

omm

on

Pro

pert

y in

la

st 1

yea

r

Val

ue o

f Fi

sh

colle

cted

fr

om

Com

mon

P

rope

rty

in

last

1 y

ear

Val

ue o

f br

oom

co

llect

ed

from

co

mm

on

prop

erty

in

last

1 y

ear

Tota

l val

ue

colle

cted

from

C

PR

in la

st 1

ye

ar

BP

L22

3050

875

651

1104

011

760

2234

3300

4464

2260

039

550

1763

243

2022

3059

AP

L4

3000

7500

300

940

100

076

850

026

5022

4091

018

908

Tota

l26

3350

883

151

1134

012

700

2334

3300

5232

2310

042

200

1987

252

3024

1967

BP

L15

1820

048

975

8520

8940

3240

1200

6560

1910

028

000

1392

036

0016

0255

AP

L5

2905

9250

560

1580

00

960

700

250

780

480

1746

5To

tal

2021

105

5822

590

8010

520

3240

1200

7520

1980

028

250

1470

040

8017

7720

BP

L47

8132

014

7850

2844

028

680

1118

024

0014

174

5540

096

095

4200

013

160

5206

99A

PL

714

2885

6394

065

00

092

541

027

5064

00

1630

6To

tal

5482

748

1564

1329

380

2933

011

180

2400

1509

955

810

9884

542

640

1316

053

7005

BP

L7

6240

1900

057

0033

1029

400

1520

7300

6300

3600

1400

5731

0A

PL

00

00

00

00

00

00

0To

tal

762

4019

000

5700

3310

2940

015

2073

0063

0036

0014

0057

310

BP

L18

1784

047

000

1950

091

5055

400

4720

2100

032

900

1344

070

0017

8090

AP

L0

00

00

00

00

00

00

Tota

l18

1784

047

000

1950

091

5055

400

4720

2100

032

900

1344

070

0017

8090

BP

L24

3135

070

759

1244

012

090

6780

022

5420

500

3095

010

480

2350

1999

53A

PL

10

2500

022

00

00

00

050

032

20To

tal

2531

350

7325

912

440

1231

067

800

2254

2050

030

950

1048

028

5020

3173

BP

L65

8021

019

2400

1728

023

600

1074

00

1139

068

400

6797

038

086

9455

5195

31A

PL

1632

300

4350

021

2517

4013

200

1780

1800

4965

2980

1560

9407

0To

tal

8111

2510

2359

0019

405

2534

012

060

013

170

7020

072

935

4106

611

015

6136

01B

PL

1527

000

5750

077

4053

1024

000

2390

1500

015

300

9360

3015

1450

15A

PL

1011

730

2675

062

585

020

00

360

2400

2650

1240

1600

4840

5To

tal

2538

730

8425

083

6561

6026

000

2750

1740

017

950

1060

046

1519

3420

BP

L27

4129

596

250

6720

7430

6360

045

9023

600

2150

013

040

4150

2249

35A

PL

1726

293

5400

052

011

5090

00

016

0027

8796

010

2589

235

Tota

l44

6758

815

0250

7240

8580

7260

045

9025

200

2428

714

000

5175

3141

70B

PL

133

1854

5840

9235

8564

073

930

3191

469

0033

692

1459

0023

3795

1010

7231

830

1339

366

AP

L17

7333

2781

318

0033

9010

00

2653

1610

5650

3660

1890

5589

9To

tal

150

1927

9143

7048

8744

077

320

3201

469

0036

345

1475

1023

9445

1047

3233

720

1395

265

BP

L10

714

8505

3461

5031

740

3634

019

500

018

370

1070

0010

4770

6048

616

620

8894

81A

PL

4370

323

1242

5032

7037

4024

200

2140

5800

1040

251

8041

8523

1710

Tota

l15

021

8828

4704

0035

010

4008

021

920

020

510

1128

0011

5172

6566

620

805

1121

191

BP

L24

033

3963

7553

8511

7380

1102

7051

414

6900

5206

225

2900

3385

6516

1558

4845

022

2884

7A

PL

6077

656

1520

6350

7071

3025

200

4793

7410

1605

288

4060

7528

7609

Tota

l30

041

1619

9074

4812

2450

1174

0053

934

6900

5685

526

0310

3546

1717

0398

5452

525

1645

6S

ourc

e: F

ield

Sur

vey,

201

1

Pur

ulia

Tot

al

Gra

nd T

otalM

arbe

diya

Am

bari

Ban

kura

Tot

al

Pur

ulia

San

turi

Jiya

thol

e

Hou

seho

ld c

olle

ctio

n of

Com

mon

Pro

pert

y R

esou

rces

in la

st 1

yea

r (R

s)

Tant

irdan

ga

Ram

jiban

pur

Bal

dang

a

Hou

seho

ld

cate

gory

(no.

of H

H)

Pan

jhor

ia

Ban

kura

Sal

tora

Seo

libon

a

Dul

alto

ra


66

The economic importance of Non Timber Forest Products in the livelihood of the

rural poor can be analysed in two different dimensions: i) The rural poor collect

NTFPs for their own consumption which they get it free of cost, but have to pay

money if they purchase those products from the market; ii) The rural households

collect NTFPs for commercial purposes, which is a source of income for them utilised

for various purposes. The price of individual items collected as part of CPR is shown

in the Appendix-II. Almost all the households in the surveyed villages collect fuel

wood annually of about 180 bojha (1 bojha ~10 Kg) from the CPRs. As per Table 4.2,

the value of fuel wood collected in last 1 year amounts to Rs 907448 in the study

villages. The fuel wood is used primarily for cooking purposes. It is also used for

drying the paddy. The households collect fodder in the form of grass or leaves from

the common forest areas in order to feed their livestock. We observe from the Table

4.2, that the total collection of fodder in last 1 year in the study area is valued Rs

411619. The household collect a special type of grass called Surgunda from the forest

areas which is used for making of the brooms. The households also collect several

medicinal plants from the CPRs. The common medicinal plants collected are neem

leaves, basak leaves, kalmegh, etc.

Our field survey data reveals that in Bankura and Purulia districts the forest

communities use the forest products for their own consumption as well as for

commercial purposes.

The rural poor of Jiyathole, Bablu Mudikora, a villager of Santuri block in Purulia

district had the following to say:

“We collect forest products like fodder, firewood, sal and kendu leaves,

mohua flower & seeds, etc. on a regular basis. Whenever there is any need for

something which is available in the forest, we rush to collect the product. As

we reside near the forest fringes, at least one member of every household goes

to the forest for collection of forest products every day. The collection is

mainly for our consumption, but occasionally we go to the market to sell the

products.” (Dated 18th December, 2011, medium of language was Santhali and

an interpreter was used)


67

Most of the households hunt birds or animals to some extent although hunting is

prohibited. They collect snails very often. The households collect small fishes viz.

gorou, chunamach, etc. from the common property resources like ponds and lakes.

The households collect wild mushrooms (called karan chatu in local dialect) from the

common forest area. The market price of this mushroom is Rs 100 per Kg. The

households collect leafy vegetables (shak) from the CPRs. The leafy vegetables

mainly collected are Shushni shak and Kulakhara shak. The yearly collection of these

leafy vegetables by the household is about 365 tara (1 tara ≈ 250gms).

Forest is not only an important source of income and consumption; it also plays a vital

role in the social, cultural and religious life of the rural poor. The use of forest

products is evident in the forest communities from their birth to death.

PHOTO 3

Villagers collecting leaves from common forest area in Seolibona

The villagers of Dulaltora of Saltora block in Bankura district, Ram Mandi and Ravi

Murmu, have explained their high dependence on the forest product in their cultural

life:

“We use neem, sal, mohua leaves and flowers for our ceremonies like

marriage, festivals or birth of a new born. We use the mahua flowers to

prepare liquor, which we all drink during these occasions” (Dated 4th July,

2011, Medium of language was Bengali / Santhali)


68

4.3.2 CPRs and the contribution to the Household income The objective of this study is to examine and estimate the contribution of CPRs to the

income of the household in the study villages. The classification of households

according to their economic activities reveals that there are five sources of income of

the households in our study area; agricultural income, wage labour income, Business

income, Service income and income derived from CPRs. The income from CPRs as

percentage of the total income gives a fair estimate of the dependency of the rural

households on CPRs. Table 4.3 below depicts the annual income from various

sources.

TABLE 4. 3

District Block Name of village

Agricultural income

Wage Labour income

Business Income

Service income

Income from CPRs

Total Income

(Including CPR

income)

Percentage of Income

from CPRs to theTotal income

BPL 22 100900 378000 0 0 223059 701959 31.78APL 4 18300 36000 0 540000 18908 613208 3.08Total 26 119200 414000 0 540000 241967 1315167 18.40BPL 15 92400 249600 0 0 160255 502255 31.91APL 5 36600 126000 0 700000 17465 880065 1.98Total 20 129000 375600 0 700000 177720 1382320 12.86BPL 47 309600 1077000 24000 20000 520699 1951299 26.68APL 7 31900 36000 0 774000 16306 858206 1.90Total 54 341500 1113000 24000 794000 537005 2809505 19.11BPL 7 25500 135000 0 22400 57310 240210 23.86APL 0 0 0 0 0 0 0 0Total 7 25500 135000 0 22400 57310 240210 23.86BPL 18 120900 357000 0 6000 178090 661990 26.90APL 0 0 0 0 0 0 0 0Total 18 120900 357000 0 6000 178090 661990 26.90BPL 24 124560 512000 0 16000 199953 852513 23.45APL 1 4200 24000 0 36000 3220 67420 4.78Total 25 128760 536000 0 52000 203173 919933 22.09BPL 65 352800 1025700 0 0 519531 1898031 27.37APL 16 85500 280000 0 696000 94070 1155570 8.14Total 81 438300 1305700 0 696000 613601 3053601 20.09BPL 15 67500 250800 0 12000 145015 475315 30.51APL 10 76500 290400 0 36000 48405 451305 10.73Total 25 144000 541200 0 48000 193420 926620 20.87BPL 27 137250 422800 0 28000 224935 812985 27.67APL 17 118125 474000 0 716000 89235 1397360 6.39Total 44 255375 896800 0 744000 314170 2210345 14.21BPL 133 773860 2708600 24000 64400 1339366 4910226 27.28APL 17 91000 222000 0 2050000 55899 2418899 2.31Total 150 864860 2930600 24000 2114400 1395265 7329125 19.04BPL 107 557550 1699300 0 40000 889481 3186331 27.92APL 43 280125 1044400 0 1448000 231710 3004235 7.71Total 150 837675 2743700 0 1488000 1121191 6190566 18.11BPL 240 1331410 4407900 24000 104400 2228847 8096557 27.53APL 60 371125 1266400 0 3498000 287609 5423134 5.30Total 300 1702535 5674300 24000 3602400 2516456 13519691 18.61

Source: Field Survey, 2011

Annual Income from Various Sources (Rs)

Household category

(no. of HH)

Panjhoria

Ramjibanpur

Saltora

Seolibona

Baldanga

Dulaltora

Tantirdanga

Bankura

Grand Total

Marbediya

Ambari

Bankura Total

Purulia Total

Purulia Santuri

Jiyathole


69

In the surveyed 150 villages in Bankura district, the average percentage of income

from CPRs to the total income is 19.04 percent whereas for the 150 villages surveyed

in Purulia district it is 18.11 percent (Table 4.4). The average percentage of CPR

income to total income in Bankura and Purulia district for BPL is 27.28 percent and

27.92 percent respectively whereas for APL households it is 2.31 percent and 7.71

percent respectively. In total, the percentage of income from CPR to the total income

is 18.61 percent. This implies that the BPL households enjoy a greater proportion of

income from CPRs, both in relative as well as absolute terms. The high percentage of

income from CPRs can be primarily attributed to the extent of poverty in the said

households.

Figure 4.1 below shows the percentage contribution to household income by different

income generation activities weighted by number of household engaged in the activity

in the study areas of Bankura and Purulia districts.

FIGURE 4.1


70

FIGURE 4.2

FIGURE 4.3


71

From the above figures, it is evident that CPR contributes a major portion of

household income in our study area of West Bengal. It is further observed that the

percentage of income from CPRs to total income is almost similar in both the districts

of Bankura and Purulia.

4.3.3 CPRs and the contribution to the Consumption

expenditure: Due to high dependency of CPRs for subsistence in the surveyed villages, attempt has

been made to estimate the proportion of CPR product consumed in the total

consumption expenditure. The CPR products consumed by the rural poor households

consists of roots, leafy vegetables, mushrooms, fish, hunted birds and animals. Fuel

wood is collected from the common forest for cooking purposes.

PHOTO 5 PHOTO 6

Villagers at Jiyathole using fuelwood Common forest area in Jiyathole

Due to high dependency of CPRs for subsistence in the surveyed villages, attempt has

been made to estimate the proportion of CPR product consumed in the total

consumption expenditure as depicted in Table 4.4 below.


72

The CPR products consumed by the rural poor households consists of roots, leafy

vegetables, mushrooms, fish, hunted birds and animals. Analysis of the data from the

surveyed villages of Bankura and Purulia districts shows that percentage of the value

of CPR consumed to the total monthly consumption expenditure is 22.29 percent and

20.17 percent respectively with an average percentage of 21.28 percent (Table 4.4).

Thus the household in the surveyed villages of both the districts are highly dependent

on CPRs for consumption.

TABLE 4.4


Value of CPR product

consumed (Monthly)

Total consumption expenditure

(Monthly)

Percentage of value of CPRs consumed

to the total consumption expenditure

BPL 22 15078.08 58489.58 25.78APL 4 1425.12 13326.33 10.69Total 26 16503.20 71815.91 22.98BPL 15 10756.25 41266.08 26.07APL 5 1270.45 29107.50 4.36Total 20 12026.70 70373.58 17.09BPL 47 36163.25 139815.00 25.87APL 7 1277.29 23735.88 5.38Total 54 37440.54 163550.88 22.89BPL 7 4035.00 17611.00 22.91APL 0 0 0 0Total 7 4035.00 17611.00 22.91BPL 18 12380.00 47097.67 26.29APL 0 0 0 0Total 18 12380 47097.67 26.29BPL 24 15781.17 66945.50 23.57APL 1 235.19 4142.00 5.68Total 25 16016.36 71087.50 22.53BPL 65 39627.17 146883.83 26.98APL 16 6675.23 52605.67 12.69Total 81 46302.4 199489.50 23.21BPL 15 8940.83 37092.67 24.10APL 10 3401.24 31116.50 10.93Total 25 12342.07 68209.17 18.09BPL 27 14641.67 66949.67 21.87APL 17 6781.33 62349.67 10.88Total 44 21423.00 129299.34 16.57BPL 133 94193.75 371224.83 25.37APL 17 4208.05 70311.71 5.98Total 150 98401.8 441536.54 22.29BPL 107 63209.67 250926.17 25.19APL 43 16857.8 146071.84 11.54Total 150 80067.47 396998.01 20.17BPL 240 157403.42 622151 25.30APL 60 21065.85 216383.55 9.74Total 300 178469.27 838534.55 21.28


Jiyathole

Dulaltora

Tantirdanga

Bankura Saltora

Purulia Santuri

Seolibona

Purulia Total

Grand Total

Marbediya

Ambari

Bankura Total

Baldanga

Ramjibanpur

CPRs and its Contribution to Total consumption Expenditure (Rs)

Household category

(no. of HH)

Panjhoria


73

4.3.4 CPRs and the contribution to the Employment

generation: From the results of the surveyed villages it is observed that CPRs has played an

important role in employment generation. Due to economic backwardness of the

villages surveyed, high level of poverty and illiteracy and non-development in the

secondary and tertiary sectors there is no scope of providing gainful employment to

the surplus rural population engaged in agriculture. Moreover the rural poor lack the

requisite technical skills to get employment in other non-agricultural sectors.

Therefore the economic activity through the collection of CPRs is an important source

of employment to the rural poor.

In order to estimate the contribution of CPRs in employment generation, we have

calculated the number of employment man days generated from the various CPR

based activities (Table 4.5).

The employment man days have been generated by analysing the average time spent

by the households in collection of CPR products in the last 1 year. From the survey

data, it is observed that the time spent on collection of CPR products not only varies

between the members of the household but also between households. The time spent

in the collection of the CPRs depends upon the distance of the forest area from the

residence. Moreover the number of household members also influences the decision

TABLE 4.5

BPL APL Total BPL APL Total BPL APL TotalPanjhoria 22 4 26 1026 981 1019 128 123 127Ramjibanpur 15 5 20 1373 390 1127 172 49 141Seolibona 47 7 54 890 744 871 111 93 109Baldanga 7 0 7 985 0 985 123 - 123Dulaltora 18 0 18 922 0 922 115 - 115Tantirdanga 24 1 25 819 540 808 102 68 101Jiyathole 65 16 81 868 741 843 108 93 105Marbediya 15 10 25 665 730 691 83 91 86Ambari 27 17 44 658 647 654 82 81 82

133 17 150 963 684 932 120 85 116107 43 150 786 701 762 98 88 95240 60 300 884 696 847 111 87 106


Purulia TotalGrand Total

Bankura Total

Employment provided by CPR based activity (in last 1 year)

Estimated total employment mandays

Household category (no. of HH)District Block Name of

village

Average no. of hours spent on CPR collection per

household (Hours)

Bankura Saltora

Purulia Santuri


74

on the allotment of labour time for collection of CPRs. Children play a critical in

collection of CPRs. We have assumed that two hours of labour by the children is

equivalent to 1 hour of labour by the adult. Thus the average employment man days of

the households in last one year have been estimated by assuming eight hours of work

as an employment man day. It is observed that the estimated average employment

man days in Bankura and Purulia districts are 116 and 95 respectively in last one year.

Further, Ramjibanpur in Bankura district and Jiyathole in Purulia district have higher

employment man days as compared to other villages. An average BPL household in

the study areas derived 111 man days of employment annually as compared to 87 man

days of employment by an average APL household. Hence we can conclude that the

BPL household spend more time in CPR based activities as compared to the APL

household.

4.3.5 Household Energy consumption and the extent of

dependency on CPRs: In the study villages, the households have very little access to commercial fuels like

coal, kerosene, electricity and cooking gas. The commercial fuels are very costly and

beyond the reach of the poor households. So the poor households rely mainly on fuel

wood gathered from the CPRs for cooking. The fuel wood is the major source of

household energy. Almost all the households in the study area collect fuel wood from

the common forest area. There is no restriction in collection of fire wood from the

common forest area. Further it is also observed that both the BPL and APL

households use the fuel wood gathered from CPRs for the purpose of cooking, leading

to total exploitation of the common forest. In the surveyed villages, the households

gather fuel wood, dung cake for household energy from the CPRs.

The household energy consumption from various sources in the last 1 month and the

dependency of CPR products for the Household energy is shown in Table 4.6 below:


75

TA

BL

E 4

.6

Dis

tric

t B

lock

Na

me

of

vil

lag

e

Va

lue

of

fue

lwo

od

co

nsu

me

d

fro

m C

PR

in

la

st 1

m

on

th

(Rs)

Va

lue

of

Du

ng

ca

ke

co

nsu

me

d

in t

he

la

st 1

m

on

th (

Rs)

Ex

pe

nd

itu

re

on

pu

rch

ase

o

f K

ero

sen

e

in l

ast

1

mo

nth

(R

s)

Ex

pe

nd

itu

re

on

ele

ctr

ic

ch

arg

es

in t

he

la

st 1

mo

nth

(R

s)

Ex

pe

nd

itu

re

on

p

urc

ha

se

of

co

al

in t

he

la

st 1

mo

nth

(R

s)

To

tal

En

erg

y

ex

pe

nd

itu

re

(Rs)

Va

lue

of

CP

R

pro

du

cts

u

sed

as

HH

e

ne

rgy (

Rs)

Pe

rce

nta

ge

o

f th

e v

alu

e

of

CP

R t

o t

he

to

tal

en

erg

y

ex

pe

nd

itu

re

BP

L22

6,3

04

920

1568

00

8792

7224

82.1

7A

PL

4625

25

364

0270

1014

650

64.1

0T

ota

l26

6929

945

1932

0270

9806

7874

80.3

0B

PL

15

4,0

81

710

672

382

05845

4791

81.9

7A

PL

5771

47

252

660

660

1730

818

47.2

7T

ota

l20

4852

757

924

1042

660

7575

5609

74.0

5B

PL

47

12,3

21

2,3

70

2116

1088

017895

14691

82.1

0A

PL

7714

78

288

840

425

1920

792

41.2

5T

ota

l54

13034

2448

2404

1928

425

19815

15483

78.1

4B

PL

71,5

83

475

256

00

2314

2058

88.9

4A

PL

0-

-

0

00

00

0T

ota

l7

1583

475

256

00

2314

2058

88.9

4B

PL

18

3,9

17

1,6

25

736

025

6278

5542

88.2

8A

PL

0-

-

0

00

00

0T

ota

l18

3917

1625

736

025

6278

5542

88.2

8B

PL

24

5,8

97

1,0

37

1060

958

50

8951

6933

77.4

6A

PL

1208

-

32

100

0340

208

61.2

1T

ota

l25

6105

1037

1092

1058

50

9292

7142

76.8

6B

PL

65

16,0

33

1,4

40

3216

2781

023470

17473

74.4

5A

PL

16

3,6

25

177

928

598

05328

3802

71.3

6T

ota

l81

19658

1617

4144

3379

028798

21275

73.8

8B

PL

15

4,7

92

645

816

340

06593

5437

82.4

7A

PL

10

2,2

29

52

736

288

20

3305

2281

69.0

2T

ota

l25

7021

697

1552

628

20

9898

7718

77.9

8B

PL

27

8,0

21

560

1232

422

010235

8581

83.8

4A

PL

17

4,5

00

43

928

450

05921

4543

76.7

3T

ota

l44

12521

603

2160

872

016156

13124

81.2

3B

PL

133

34103

7137

6408

2428

75

50076

41240

82.3

5A

PL

17

2318

150

936

1600

1355

5004

2468

49.3

2T

ota

l150

36421

7287

7344

4028

1430

55079

43707

79.3

5B

PL

107

28846

2645

5264

3543

040298

31491

78.1

5A

PL

43

10354

272.5

2592

1336

20

14555

10627

73.0

1T

ota

l150

39200

2918

7856

4879

20

54853

42118

76.7

8B

PL

240

62949

9782

11672

5971

75

90373

72730

80.4

8A

PL

60

12672

423

3528

2936

1375

19558

13094

66.9

5T

ota

l300

75621

10204

15200

8907

1450

109932

85825

78.0

7S

ou

rce

: F

ield

Su

rve

y,

2011

Gra

nd

To

talA

mbari

Pu

rulia

To

tal

Ba

nk

ura

To

tal

Pu

ruli

a

Sa

ntu

ri

Jiyath

ole

Marb

ediy

a

Ho

us

eh

old

en

erg

y c

on

su

mp

tio

n a

nd

th

e e

xte

nt

of

de

pe

nd

en

cy

on

CP

R

Ho

use

ho

ld

ca

teg

ory

(no

. o

f H

H)

Panjh

oria

Ram

jibanpur

Tantird

anga

Seolib

ona

Bald

anga

Dula

ltora

Ba

nku

ra

Sa

lto

ra


76

In the surveyed villages, it is observed that the households use different sources of

household energy: Fuel wood, Dung cake, Kerosene, Electricity and Coal. Out of

these sources of household energy, the households gather the fuel wood, dung cake

from the CPRs. It is observed that in Bankura and Purulia district on an average 79.35

percent & 76.78 percent respectively (Table 4.6) of the total monthly household

energy consumption were met by the CPR products collected from the common forest

area. In total, an average of 78.07 percent of the total monthly household energy

consumption was met by the CPR products. It is further observed that the gender of

the household member plays an important role in collection of the fuel wood. The

female household members and the children collect fuel wood from the forest area as

compared to adult male household member.

4.3.6 CPRs and Animal Grazing All the households use the common forest area for grazing of the animals. The total

grazing time per household depends on the number of livestock owned by the

household. The common livestock owned by the household in the surveyed villages

are: Cattle, Bullocks, Goats, Pigs, Hens, Chicken, Ducks, Pigeon, etc. The Bullock is

used for ploughing in the agricultural field. The cattle are used for giving milk. The

chicken and ducks are used by the households for laying eggs, while the pigs are

domesticated for meat.

The dependency of CPRs for animal grazing is shown in Table 4.7 below. It is evident

that the APL households have larger number of livestock as compared to the BPL

households. In order to estimate the extent of animal grazing on the CPRs, attempt has

been made to calculate the average number of animal unit grazing days per household

on CPRs. A grazing day implies that an animal has remained in the grazing field of

the CPR at least for 5 hours in a day and the animal has not been fed separately.

Animal grazing days (number of animals x number of grazing days) has been

calculated by changing the number of animals into animal units as per the basis as

shown below:

1 animal unit=1 bullock /cow or 4 goats or 4 pigs or 100 chicken/hens/ducks.


77

From the Table 4.7 it is apparent that in the study villages of Bankura and Purulia

district an average of 88 and 114 animal unit grazing days per household respectively

were observed in the last 1 month. This data signifies the high dependency of the rural

poor on the CPRs for animal grazing.

TABLE 4.7


Number of cows

Number of

Bullocks

Number of goats

Number of pigs

Number of hens/

chickens/ducks

Average number of

animal unit grazing days

per household on

CPR #

BPL 22 32 32 23 12 90 100APL 4 6 8 5 1 24 118Total 26 38 40 28 13 114 103BPL 15 22 22 32 0 62 109APL 5 7 6 8 0 12 91Total 20 29 28 40 0 74 105BPL 47 63 86 72 2 209 108APL 7 1 4 2 0 13 24Total 54 64 90 74 2 222 98BPL 7 2 10 5 1 32 68APL 0 0 0 0 0 0 0Total 7 2 10 5 1 32 68BPL 18 8 24 14 1 38 64APL 0 0 0 0 0 0 0Total 18 8 24 14 1 38 64BPL 24 10 36 21 0 54 64APL 1 0 2 0 0 4 61Total 25 10 38 21 0 58 64BPL 65 39 112 92 0 181 80APL 16 11 24 27 0 59 77Total 81 50 136 119 0 240 80BPL 15 2 18 6 1 22 40APL 10 4 6 4 0 58 59Total 25 6 24 10 1 80 47BPL 27 5 40 19 2 143 57APL 17 22 30 19 3 95 103Total 44 27 70 38 5 238 75BPL 133 137 210 167 16 485 91APL 17 14 20 15 1 53 68Total 150 151 230 182 17 538 88BPL 107 46 170 117 3 346 69APL 43 37 60 50 3 212 83Total 150 83 230 167 6 558 114BPL 240 183 380 284 19 831 81APL 60 51 80 65 4 265 79Total 300 234 460 349 23 1096 81


Note:# A grazing day implies that an animal has remained in the grazing field of the CPR at least for 5 hours in a dayand the animal has not been fed separately. Animal grazing days (number of animals x number of grazing days)has been calculated by changing the no. of animals into animal units as per the basis as shown below:1 animal unit=1 bullock /cow or 4 goats or 4 pigs or 100 chicken/hens/ducks

Dependency on CPRs for Animal Grazing (in last 1 month)

Household category

(no. of HH)

Panjhoria

Dulaltora

Tantirdanga

Bankura Saltora

Seolibona

Baldanga

Ramjibanpur

Grand Total

Marbediya

Ambari

Bankura Total

Purulia Santuri

Jiyathole

Purulia Total


78

4.4 CPRs and Rural Poverty: The objective of this study is to analyse the extent of Poverty in the study villages and

to examine if the income derived from CPRs had any role to play to alleviate the

poverty. The monthly per capita income including the income from CPRs for the 150

households in the study villages have been arranged in the ascending order and then

grouped into 10 classes. As per the Tendulkar Committee report published in

November 2009 and now approved by the planning commission, the revised poverty

line (rural) with the base year 2004-2005 for West Bengal is Rs 445.38. The estimated

monthly per capita income of the households in the study area has been compared

with the revised poverty line for the state. In order to analyse the extent of poverty

and to quantify the role of income from extraction of CPR products in poverty

mitigation, we have used two sets of data i) per capita income of the households

including the income derived from common property resources and ii) per capita

income of the households excluding the income derived from the common property

resources.

Table 4.8A & 4.8B shows the distribution of monthly per capita income including the

income derived from CPRs for Bankura and Purulia district.

TABLE 4.8 A

Sl No. Range of Income per capita per

month

Frequency (No. of Household)

Percentage of (3) to the total

household

Cummulative frequency

Monthly Per Capita

Income (Rs)

Percentage of (6) to the

total

Cummulative total

1 2 3 4 5 6 7 81 0- 400 5 3.33 3.33 1827 1.30 1.302 400-500 38 25.33 28.67 17857 12.73 14.033 500-600 14 9.33 38.00 9461 6.74 20.774 600-700 23 15.33 53.33 15023 10.71 31.485 700-800 20 13.33 66.67 14833 10.57 42.056 800-900 15 10.00 76.67 12666 9.03 51.077 900-1000 8 5.33 82.00 7537 5.37 56.448 1000-1500 12 8.00 90.00 14152 10.09 66.539 1500-2000 6 4.00 94.00 10399 7.41 73.94

10 2000 and above 9 6.00 100.00 36568 26.06 100.00Total 150 100.00 140323 100.00

Source: Field Survey 2011

Distribution of monthly per capita income of the sample households in Bankura district( including income derived from CPRs)


79

If we consider the planning commission estimates of poverty line for West Bengal,

then 28.67 percent in Bankura district and 40 percent in Purulia district are lying

below the poverty line (Table 4.8A & 4.8B). Moreover there is substantial inequality

in the distribution of income.

In the case of Bankura district, the bottom 38 percent of the household has only 20.77

percent of the total income whereas the upper class 6 percent of the household has

26.06 percent of the total income (Table 4.8A). However in the case of Purulia

district, 24.03 percent the total income is generated by 57.33 percent of the total

household whereas the upper class 14.67 percent of the household generates 55.10

percent of the total income (Table 4.8B).

We now exclude the income derived from CPRs from the per capita monthly income.

Table 4.9 shows the distribution of monthly per capita income of the sample

households excluding the income generated through collection of CPR products in

Bankura and Purulia district.

TABLE 4.8 B


month



household


Monthly Per Capita

Income (Rs)

Percentage of (6) to the

total

Cummulative total

1 2 3 4 5 6 7 81 0- 400 20 13.33 13.33 6873 4.25 4.252 400-500 40 26.67 40.00 17885 11.05 15.293 500-600 26 17.33 57.33 14139 8.73 24.034 600-700 20 13.33 70.67 12981 8.02 32.045 700-800 10 6.67 77.33 7343 4.54 36.586 800-900 1 0.67 78.00 879 0.54 37.127 900-1000 4 2.67 80.67 3773 2.33 39.458 1000-1500 6 4.00 84.67 6946 4.29 43.749 1500-2000 1 0.67 85.33 1878 1.16 44.90

10 2000 and above 22 14.67 100.00 89198 55.10 100.00Total 150 100.00 161895 100.00

Source: Field Survey 2011

Distribution of monthly per capita income of the sample households in Purulia district( including income derived from CPRs)


80

As we exclude the income derived from extraction of common property resources

from the data of the surveyed villages, we observed that the extent of poverty in both

the districts have increased by about 26 percent [Poverty level in Bankura district has

increased from 28.67 percent to 55.33 percent (Table 4.8A & 4.9A), whereas in

Purulia district it has increased from 40 percent to 66.67 percent (Table 4.8B &

4.9B)]. This data clearly shows the importance of common property resources in the

subsistence of the poor household and signifies the immense role played by the CPRs

in alleviating the poverty in the study villages.

TABLE 4.9 A


month


Percentage of (3) to the total household


Monthly per capita

Income (Rs)


Cummulative total

1 2 3 4 5 6 7 81 0- 400 46 30.67 30.67 15927 14.28 14.282 400-500 37 24.67 55.33 16934 15.18 29.463 500-600 24 16.00 71.33 13168 11.80 41.264 600-700 13 8.67 80.00 8586 7.70 48.965 700-800 6 4.00 84.00 4562 4.09 53.056 800-900 5 3.33 87.33 4288 3.84 56.907 900-1000 6 4.00 91.33 5900 5.29 62.198 1000-1500 3 2.00 93.33 4075 3.65 65.849 1500-2000 1 0.67 94.00 1933 1.73 67.5710 2000 and above 9 6.00 100.00 36173 32.43 100.00

Total 150 100.00 111546 100.00Source: Field Survey 2011

Distribution of monthly per capita income of the sample households in Bankura district( excluding income derived from CPRs)

TABLE 4.9 B


month


Percentage of (3) to the total household


Monthly per capita

Income (Rs)


Cummulative total

1 2 3 4 5 6 7 81 0- 400 71 47.33 47.33 21784 21.23 21.232 400-500 29 19.33 66.67 13144 12.81 34.033 500-600 10 6.67 73.33 5435 5.30 39.334 600-700 8 5.33 78.67 5010 4.88 44.215 700-800 3 2.00 80.67 2289 2.23 46.446 800-900 2 1.33 82.00 1669 1.63 48.077 900-1000 1 0.67 82.67 938 0.91 48.988 1000-1500 12 8.00 90.67 14869 14.49 63.479 1500-2000 7 4.67 95.33 12616 12.29 75.7610 2000 and above 7 4.67 100.00 24873 24.24 100.00

Total 150 100.00 102627 100.00Source: Field Survey 2011

Distribution of monthly per capita income of the sample households in Purulia district( excluding income derived from CPRs)


81

4.5 Determinants of CPR Extraction We have assumed that collection of CPR is determined by socio-economic and

demographic variables. We have explained the determinants of CPR extraction

through econometric analysis to give an understanding of the relationship between

forest dependency (measured by the household income from the community forest)

and socio-economic variables.

Regression Results and Discussion

The regression model has been tested using the household level data through field

survey in Bankura and Purulia district. Multiple regression analysis has been made

using EViews 7 computer package. The result for the determinants of the household

income from the community forest for Bankura and Purulia district is given in the

Table 4.10 & 4.11 below:

TABLE 4.10 A

Determinants of CPR extraction-Bankura District

Source: Estimated by EViews7 computer software using field survey data of 2011 Note: 1.*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level 2. Summary statistics has been presented in Table A3.1 in Appendix III

Dependent Variable: CPRIN Method: Least Squares Sample: 1 150; Incl obsn: 150

Variable Coefficient Std. Error t-Statistic Prob. C 7282.105 1355.591 5.371904* 0.0000

FSIZE 639.8347 102.3828 6.249433* 0.0000 FEMPER 26.95335 9.081700 2.967874* 0.0035 AVRAGE -10.22113 15.76346 -0.648407 0.5178 AVRSCH -544.4105 97.37766 -5.590712* 0.0000 OWNLAND -299.6904 74.47889 -4.023829* 0.0001 LIVESTOCK 269.3362 133.2933 2.020628** 0.0452 FORESTDIST -459.9032 208.8591 -2.201979** 0.0293 POVR 2372.728 594.3089 3.992415* 0.0001 DISM -106.2043 184.3037 -0.576246 0.5654

R-squared 0.651121 Mean dependent var 11047.09

Adjusted R-squared 0.628694 S.D. dependent var 3309.353 S.E. of regression 2016.551 Akaike info criterion 18.12051 Sum squared resid 5.69E+08 Schwarz criterion 18.32121 Log likelihood -1349.038 Hannan-Quinn criter. 18.20205 F-statistic 29.03176 Durbin-Watson stat 1.685457 Prob(F-statistic) 0.000000


82

TABLE 4.10 B Variance Inflation Factors Sample: 1 150; Included observations: 150

Coefficient Uncentered Centered

Variable Variance VIF VIF C 1837627. 67.78446 NA

FSIZE 10482.25 9.950002 1.432938 FEMPER 82.47728 8.324282 1.057154 AVRAGE 248.4866 8.778397 1.178498 AVRSCH 9482.409 3.107897 1.337570 OWNLAND 5547.106 3.409295 1.411520 LIVESTOCK 17767.11 7.490170 1.795150 FORESTDIST 43622.10 6.080978 1.555698 POVR 353203.1 11.55201 1.309228 DISMK 33967.85 6.750837 1.062677

Source: Estimated by EViews7 computer software using field survey data of 2011

TABLE 4.11 A Determinants of CPR extraction-Purulia District Dependent Variable: CPRIN; Method: Least Squares Sample: 1 150; Included observations: 149

Variable Coefficient Std. Error t-Statistic Prob. C 9326.052 813.2588 11.46751* 0.0000

FSIZE 150.9987 57.41028 2.630167* 0.0095 FEMPER 7.078460 6.320566 1.119909 0.2647 AVRAGE 23.96721 8.194546 2.924776* 0.0040 AVRSCH -214.1267 58.26268 -3.675194* 0.0003 OWNLAND 133.9483 35.06343 3.8201718* 0.0002 LIVESTOCK 56.54811 68.55396 0.824870 0.4109 FORESTDIS -665.4332 187.7843 -3.543605* 0.0005 POVR 911.0631 291.9778 3.120317* 0.0022 DISM -225.5848 130.6185 -1.727051*** 0.0864

R-squared 0.689904 Mean dependent var 9558.933

Adjusted R-squared 0.669825 S.D. dependent var 2125.682 S.E. of regression 1221.434 Akaike info criterion 17.11819 Sum squared resid 2.07E+08 Schwarz criterion 17.31980 Log likelihood -1265.306 Hannan-Quinn criter. 17.20010 F-statistic 34.36086 Durbin-Watson stat 1.566364 Prob(F-statistic) 0.000000

Source: Estimated by EViews 7 computer software using field survey data of 2011 Note: 1.*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level 2. Summary statistics has been presented in Table A3.2 in Appendix III


83

TABLE- 4.11 B Variance Inflation Factors Sample: 1 150 Included observations: 149

Coefficient Uncentered Centered

Variable Variance VIF VIF C 621752.8 61.21651 NA

FSIZE 3344.084 8.191516 1.983340 FEMPER 40.54354 9.305583 1.092243 AVRAGE 68.26892 7.520626 1.079849 AVRSCH 3408.983 11.19958 2.693264 OWNLAND 1248.145 3.836449 1.245577 LIVESTOCK 4759.068 4.052396 1.503749 FORESTDIS 35407.74 16.18120 2.638796 POVR 85116.27 5.174459 1.979491 DISM 47466.12 3.167887 1.020527

Source: Estimated by EViews 7 computer software using field survey data of 2011 The R-square is as high as 65 percent in Bankura district and 68 percent in Purulia

district. The F-statistics for overall goodness of fit of the model is highly significant in

both the districts. Tables 4.10B & 4.11B represents Variance Inflation Factor (VIF)

for explanatory variable. We have observed from our results that VIF is very low i.e.

around 1 in all the cases. Hence we conclude that we do not have multicollinearity

problem. It is evident from the analysis that most of the important variables are

significant with the expected sign. The CPR collection is a labour intensive activity.

Household members collect a lot of CPR products viz. fuel wood, fodder, vegetables,

bamboo, etc. from the forest. They also hunt animals and catch fishes from the

community forest area. All these activities take a lot of time. The larger the family

size, the higher is the labour time available for the collection of community forest

products. In both the districts of Bankura and Purulia, the family size ( FSIZE ) has a

positive impact on community forest income. In Bankura and Purulia districts, the p-

value indicates that the association is significant.

From the regression analysis, we observed that there is a positive relationship between

average age of the household ( AVRAGE ) i.e. experience in collecting CPR products

and CPR income. It is easier for an experienced household member to collect more


84

CPR and thus smooth their consumption and livelihood. In line with our expectation,

the result is positive and significant in Purulia district. However the result is negative

and insignificant in Bankura district. Further it is observed that household with larger

size of livestock ( LIVESTOCK ) collect more fodder to feed the animals. They also

require higher quantity of fuel wood to prepare concentrated food for the animals. So

there is a positive relation between the CPR income and the size of the livestock. The

impact of the number of livestock on CPR income is significant in Bankura district,

but insignificant in Purulia district.

We have hypothesised a positive relationship between female percentage in the

household ( FEMPER ) and CPR income. From our study area in Bankura district, we

observe that female members are mainly involved in collection of CPRs. So the

households with higher percentage of female members collect more CPR products

and hence earn higher income as compared to households with lower percentage of

female members. As per the result, though the percentage of female members in the

household is found to be positively associated with the household level income from

the community forest, the p-value indicates that the association is statistically

significant only in Bankura district.

Education ( AVRSCH ) i.e. average years of schooling of the household has a negative

impact on the income from CPR. Further, the p-value for Bankura and Purulia

districts indicates that it has a significant impact on the income from CPR. Household

members who are better educated get better job opportunities and therefore are less

interested in collection of CPR products. We have hypothesised a negative

relationship between land ownership ( OWNLAND ) and CPR income as household

who own more agricultural land spend more time in cultivation and less time in CPR

collection. In line with our expectation we have observed coefficient of OWNLAND is

negative and significant in Bankura district. However, we observe contradictory

results in case of Purulia district. Here the households with larger ownership of

cultivated land ( OWNLAND ) collect more fuel wood. This is mainly due to the fact

that land in Purulia district is mostly infertile and so mere ownership of land fails to

reduce the dependency on CPR. It is also observed that the forest distance

( FORESTDIST ) i.e. distance between the residence and the common forest area has a


85

negative impact on the CPR income. From the survey, it indicates that households

nearest to the forest area extract more CPR and get more income from the forest

products as compared to the households far away from the forest area. In both the

districts the impact is significant. In the case of distance to the nearest market

( DISM ), the motivation for extraction of forest products is greater when the market

is nearer. This is due to the fact that the rural poor are able to sell the CPR products in

the nearest market and generate income easily. As expected, DISM has a negative

impact on the extraction of CPR product in both the districts. However, the result is

significant only in Purulia district.

The most important finding of this study is impact of poverty on CPR extraction. The

coefficient of dummy variable ( POVR ) is positive and highly significant in both the

districts, which implies that BPL household are more dependent on CPR as compared

to the APL household. Household with low income extract more community forest

product for their consumption purposes and also sell it to increase their income.

Hence in the case of the poor, the income from community forest has a higher

percentage as compared to that of the ‘not so poor’.

4.6 Poverty-Environment Nexus-Logit Model Several literatures have discussed the inter-linkage between poverty and

environmental degradation. While some authors argue that poverty leads to

environmental degradation, others argue that environmental degradation leads to low

productivity which results in increase in poverty. Several concepts have been put forth

by authors to illustrate the over-exploitation in fisheries, forests, over grazing, air and

water pollution, abuse of public land and problems of resource misallocation

(Stevenson, 1991). The inter-linkage between poverty and environmental degradation

has been defined as a ‘vicious circle’ by Dasgupta and Maler (1994). In their study,

the authors argue that the rural poor pushed by increase in population and poverty,

extend their cropping onto fragile marginal lands resulting in further degradation. This

results in reduced yield and this further impoverishes them. It can also be stated that

the environmental degradation adversely affect poverty, because the poor are the most

vulnerable to environmental degradation, due to their heavy dependence on natural-

resource base, and limited resource to cope with adverse environmental effect.


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We have made an attempt to quantify the diminution of common property resources

and the extent of degradation of common forest in the study villages. We have

gathered information from Panchayat Pradhan, Head Master, Public leaders and

household head about the relative position of forest resources comparing present state

with that of twenty years back.

We have shown the extent of depletion of common property resources during 1990-

2010 in our surveyed villages in the following Table 4.12 below:

From our field survey report we have observed that the forest area had declined as

compared to earlier times of 1990s in both the districts. However, the extent of

depletion of forest area is high in Bankura district as compared to Purulia district.

Severe deforestation was detected in three villages viz. Ramjibanpur, Seolibona and

Baldanaga of Bankura district and Marbediya of Purulia district. Village Ramjibanpur

TABLE 4.12

Common Forest area (in Sq. Km)

Common Village land (in Sq. Km)

Common grazing land (in Sq. Km)


Bankura Saltora

Purulia Santuri

Bankura Total

Baldanga

Panjhoria

Ramjibanpur

Purulia Total

Grand Total

Jiyathole

Marbediya

Extent of Depletion of Common Property Resources (during 1990-2010)

13.25 12.81 10.75

Ambari

Dulaltora

Tantirdanga

Seolibona

Percentage Decline District Block Name of

village

17.13

16.87

9.24

8.18

13.25

13.75

10.53

6.25

12.99

10.18

12.05

8.28 7.53

8.78 7.05

10.69 8.93

8.31 7.65

14.35

11.76 9.63

11.72

14.05 10.86

8.57 8.64

7.62 4.78

10.43 8.85

9.8811.64


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of Bankura district witnessed the highest depletion of common forest resources.

Interestingly, no noticeable deforestation was found in Jiyathole village of Purulia

district. The common forest area declined around 12.05 percent in the study area

during 1990-2010.

The villagers of Ramjibanpur of Saltora block in Bankura district, Haren Kora and

Anil Mudikora, have raised their concern on depletion of the common forest area and

have shared their view:

“We now have to walk 2 – 2.5 km more as compared to earlier period (1990s)

in order to gather fire wood and fodder and graze our livestock. This has

resulted in lot of time and energy being spent for reaching the forest area.

Also, several medicinal herbs have become almost rare at the common forest

area” (Dated 28th July, 2011, Medium of language was Bengali / Santhali).

The depletion of common forest resources viz., the forest resources in both the

districts are mainly responsible for absence of any alternative income or employment

opportunities, growing commercialisation of CPR products, emergence of market, and

presence of middle man for market sale. We have also found an enormous pressure on

common forest in our study villages because of the illicit collection of fuel wood,

fodder and small timber by the households. The problem has been tackled by the

formation of Forest Protection Committee under the purview of Joint Forest

Management in our study area of both the districts.

4.6.1 Determinants of Poverty To examine the determinants of poverty a qualitative response model like logit model

is proposed in this study. We consider a range of socio-economic as well as

environmental variable which enable us to express the rural poverty conditions as well

as the extent of environmental degradation in the region. In our study we have

considered forest degradation as a measurement of environmental degradation. The

logit regression model has been tested using the household level data through field

survey in Bankura and Purulia districts. Specification of the regression model has

been presented in the following table where the expected sign and description of the

dependent and explanatory variables are explained.


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TABLE 4.13

Description of Variables of Logit Regression Model Variables Expected

Sign

Variable description

Dependent variable

POVR

Poverty of the household

POVR =1; if the household belongs to BPL

POVR =0; if the household belongs to APL


FSIZE + Average number of population of the household

(size of the family)

AVRAGE _ Average age of household, i.e. experience in

collecting CPR product

AVRSCH _ Average years (number of years) of schooling of

household

OWNLAND _ Total land owned by the household

LIVESTOCK _ Total livestock of the households converted into

animal units

FORESTDIST + Distance of the CPR field from the residence of

the households (km)

*ENVDGR +

Dummy for extent of environmental degradation

ENVDGR =1; if environment is more degraded

ENVDGR =0; if environment is less degraded

*Measuring Environmental degradation

In this study, we have considered forest degradation as a measurement of

environmental degradation. Forest Degradation is measured on the basis of the data

collected in the village survey from three different variables:

i) Extent of forest damage visually seen (FD)

ii) Condition of the forest informed by the respondents as compared to that of

earlier times (FC)

iii) Forest use penetration i.e. the depth into the forest from the village

boundary where use pressure was evident (FP)


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For the purpose of regression analysis we have constructed a composite measure

called Forest Degradation Index (FDI) which is sum of the above three variables i.e.

FDI=FD+FC+FP. All these three variables (FD, FC, FP) are coded (using four point

scale) so that increasing values shows more forest degradation. Each variable has a

substantive impact on the index and they are positively correlated.

The regression result is given in the following Table 4.14A and 4.14B below.

TABLE 4.14 A

Determinants of Poverty-Bankura District Dependent Variable: POVR Method: ML - Binary Logit (Quadratic hill climbing) Sample: 1 150 Covariance matrix computed using second derivatives

Variable Coefficient Std. Error z-Statistic Prob.

C 12.39520 7.864797 1.576035 0.1150 FSIZE 1.568055 0.886717 1.768382*** 0.0770 AVRAGE -0.412058 0.253940 -1.622658 0.1047 AVRSCH 0.817163 0.649429 1.258280 0.2083 OWNLAND -2.371136 1.274703 -1.860148*** 0.0629 LIVESTOCK -2.836798 1.566963 -1.810380*** 0.0702 FORESTDIST 2.721305 1.708830 1.592496 0.1113 ENVDGR 20.12960 10.71125 1.879296*** 0.0602

McFadden R-squared 0.910470 Mean dependent var 0.766667

S.D. dependent var 0.424370 S.E. of regression 0.134763 Akaike info criterion 0.203945 Sum squared resid 2.578856 Schwarz criterion 0.364512 Log likelihood -7.295869 Hannan-Quinn criter. 0.269178 Deviance 14.59174 Restr. deviance 162.9818 Restr. log likelihood -81.49092 LR statistic 148.3901 Avg. log likelihood -0.048639 Prob(LR statistic) 0.000000

Obs with Dep=0 17 Total obs 150

Obs with Dep=1 133 *Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level

Source: Estimated by EViews 7 computer software using field survey data of 2011


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TABLE 4.14 B

Determinants of Poverty-Purulia District Dependent Variable: POVR Method: ML - Binary Logit (Quadratic hill climbing) Sample: 1 150 Included observations: 150 Covariance matrix computed using second derivatives

Variable Coefficient Std. Error z-Statistic Prob. C 4.426351 2.256543 1.961563** 0.0498

FSIZE 0.026525 0.201712 0.131501 0.8954 AVRAGE -0.000489 0.030479 -0.016039 0.9872 AVRSCH -0.053825 0.200660 -0.268240 0.7885 OWNLAND -0.586099 0.172234 -3.402931* 0.0007 LIVESTOCK -0.260669 0.186535 -1.397428 0.1623 FORESTDIST 1.014146 0.532792 1.903457*** 0.0570 ENVDGR 2.466093 1.176092 2.096854** 0.0360





Source: Estimated by EViews 7 computer software using field survey data of 2011 From the above Tables 4.14A & 4.14B we observe that in most of the cases, the

results are consistent with our hypothesis. As expected, i.e. experience in collecting

CPR products ( AVRAGE ) has been found to be a negative influence on poverty

( POVR ) in both the districts. The experienced elder members of the household can

earn more income as compared to the younger one thus helping to reduce poverty.

However, the result is insignificant in both the districts. In line with our expectation

households with more own land ( OWNLAND ) and larger number of livestock

( LIVESTOCK ) have a negative impact on poverty. This indicates that wealthier

households have several options to earn money and therefore the probability of the


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incidence of poverty is low. In Bankura district, both have significant impact on

poverty; however in Purulia district we have observed significant result only in the

case of OWNLAND . We have hypothesized that educated people ( AVRSCH ) get

better job opportunities and thus help to reduce the probability of incidence of

poverty. In Purulia district although the result is consistent with our hypothesis, the

impact is insignificant. In Bankura district, the result is contradictory but insignificant

and therefore can be ignored. It is obvious that households residing closer to the forest

collect more CPR and thus reduce their poverty level. As expected, Forest distance

( FORESTDIST ) has positive impact on poverty ( POVR ) in both districts. However,

the result is significant only in Purulia district. The households with larger family size

( FSIZE ) the probability of incidence of poverty is high. In fact large family size puts

additional financial burden on the earning member which in turn increases their

poverty level. The result is consistent with our hypothesis in both the district.

However the impact is significant only in Bankura district.

The coefficient of environmental degradation is positive and significant which imply

that with more environmental degradation the probability of the incidence of poverty

increases. In fact, households in the study villages live under acute poverty and hence

they are heavily dependent on the natural resources for their subsistence. Under the

circumstances, the level of extraction of the CPRs is higher than their regenerating

process and thus the environment gets further degraded. However, poverty is not the

sole cause for degradation. There are several other factors like rising population,

growing commercialisation of agricultural and forest products and emergence of

market, etc. which leads to forest degradation.


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4.7 Conclusion

This study investigates the dependency of the rural poor on CPRs and the relationship

between rural poor and environmental degradation. The result of the survey of 300

households in Bankura and Purulia districts indicates a very high dependency of the

rural poor on CPR products. The field survey data shows that the average percentage

of CPR income to total income in Bankura and Purulia district is 19.04 percent and

18.11 percent respectively. For BPL households, 27.28 percent in Bankura district and

27.92 percent in Purulia district of the total household income comes from CPR based

activities whereas in case of APL households it is 2.31 percent and 7.71 percent

respectively. Hence poor households enjoy a greater proportion of income from CPRs

both in relative as well as absolute terms. The survey data further reveals that 21.28

percent of the total consumption expenditure of household is supported by CPRs,

which indicates high dependency of the rural households on CPRs for their

consumption.

CPR has also played an important role in employment generation. As per the survey

data, in an average, rural household generate around 116 and 95 employment man

days per annum from CPR based activities in Bankura and Purulia district

respectively. The households collect fuel wood and cow dung from the common

forest area for the purpose of cooking. As per the survey report, an average of 79.35

percent and 76.78 percent of the total household energy consumption were met by the

CPR products collected from the common forest area in Bankura and Purulia districts

respectively. Here we observed that both the poor and not so poor households use fuel

wood gathered from CPRs to cater to their household energy needs. Our survey data

confirms a high dependency of the rural households on the CPRs for animal grazing

with an average of 88 and 114 animal unit grazing days per household in Bankura and

Purulia districts respectively in the last 1 month.

The survey data also shows the immense role of CPRs in alleviating the poverty in the

study villages. As we exclude the income derived from CPRs from the total income of

the households, we observe that the extent of poverty in both the districts have

increased by around 26 percent.


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The most important finding of our multiple regression model is the impact of poverty

on CPR extraction and the result indicate that the coefficient of dummy variable

( POVR ) is positive and significant in both the districts. Therefore we can conclude

that BPL households are more dependent on CPR as compared to the APL

households.

The high extraction of CPRs in the surveyed villages has resulted in degradation of

the environment. Environmental degradation has adversely affected poverty because

of high dependence of the rural poor on the natural resources and this further

impoverishes them. We have used logit regression model to analyse the relationship

between poverty and environmental degradation. The important finding of this

analysis is that the coefficient of environmental degradation is positive and significant

in both the districts which show a strong positive relationship between poverty and

environmental degradation. It is thus desirable to ensure conservation of CPRs in

order to maintain the sustainable livelihood of the rural poor.

We have hypothesised that CPRs have a positive impact on income and employment

of rural poor and have an immense role in alleviating rural poverty. We further

hypothesised that high extraction of CPR has resulted in degradation of the

environment which further aggravates rural poverty. Our survey results are consistent

with our hypothesis. Further, our findings on the high dependency of the rural poor on

CPRs and the subsequent degradation of the environment are in conformity with the

results of similar studies carried out in other Indian states viz. Rajasthan, Karnataka,

(Jodha, 1986); Gujarat (Conroy, 1991); etc. However, our empirical results goes

against the findings of Chopra and Dasgupta (2003) and Kuri (2005) who postulated

that non-poor are equally benefited by extraction of CPRs and are also responsible for

forest degradation.

We can conclude that dependence of the rural poor on Common Property Resources

is a basic necessity and cannot be denied.

Agricultural Risk and Common Property Resources

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CHAPTER 5

AGRICUTURAL RISK AND COMMON PROPERTY RESOURCES

5.1 Introduction

Agriculture is the predominant economic activity in West Bengal. However there is

wide fluctuation in the agricultural productivity in many parts of West Bengal. The

problem of fluctuating crop yield can be attributed to vagaries of the weather and land

degradation in the form of soil erosion, salinity, water logging, etc. The yield is also

affected by outbreak of diseases, pests and other hazards like flood, droughts and fire.

In our study villages in the districts of Bankura and Purulia in West Bengal, the rural

households are heavily dependent on agriculture. These districts have the problem of

‘dry land’ which have low moisture retaining capacity in the soil, thereby leading to

fluctuating crop productivity. Water storage facilities which are the ‘lifeline’ of

irrigation are also lacking in many parts of both the districts. Crop failure also occurs

due to low rainfall. The instability in the crop production raises the agricultural risk

which adversely affects the livelihood and income of the rural households and in turn

also effects their decision to use high technology in farming, thereby impeding the

development process. The fluctuating crop production also affects the price stability.

The rural households have to incur costs for smoothening consumption across income

shocks. The households therefore look for safety net to mitigate the income shocks.

In our study villages of Bankura and Purulia, most of the households live below the

poverty line. The main occupation of the household is agriculture, although they also

engage in off farm wage labour. There are several risks associated with the harvesting

of crops such as weather, seasonal flooding, unpredictable soil quality, crop diseases,

price shocks and forest pests. Under these circumstances, forest acts as a security

especially against crop failure. Households having limited credit and insurance

facility, extract Non Timber Forest Products (NTFP) which not only reduce their

agricultural risk but also help to smooth out their consumption. Hence NTFP has a

supporting role in the wellbeing of the rural poor in the form of ‘natural insurance’.


95

By collecting NTFP, rural households smooth their income as well as consumption in

the period of agricultural shortfall.

The collection of forest products is a common phenomenon to support the livelihood

of the rural poor. In rural area, Common Property Resources (CPRs) are critical

resources for the poor households. CPRs are vital resources for the poor primarily

because the cost of using the CPRs are low and these involve only human labour as

the input. CPRs supplement the rural livelihood and act as safety net for the poor

seasonally or during the agricultural crisis.

Several literatures have discussed the importance of Common Property Resources as

insurance. Based on the survey of rural households living on the margin of Tapajós

National Forest in the Brazilian Amazon, Pattanayak and Sills (2001) had put forth a

positive correlation between collection of NTFP, shortfall in agriculture and the

expected agricultural risk. The advantage of common property resources arises

because of its superior insurance properties which tend to provide income

maintenance to the rural poor (Baland and Francois, 2004). The study on the potential

impact of extraction of NTFP on land use choice was carried out by Delacote (2009).

The study revealed that the rural poor collect forest products in order to reduce the

agricultural risk. If the agricultural risk is reduced, then the households reduce the size

of the safety activity thereby being less dependent on the forest products as compared

to agricultural activities.

Informal insurance arrangements are affected by sustainability constraints, often

excluding the poor from these arrangements (Dercon 2002). Both ‘forest dependent’

and ‘forest related’ people depend on forest for a supplementary source of income and

there is a varied nature of relationships of people to forest and forest products (Byron

and Arnold, 1999). In Sierra Leone, a country in West Africa, the farmers in order to

cope agricultural shocks arising due to irregular rainfall often resort to mortgaging or

pledging cocoa or coffee trees or selling timber to buy food (Leach 1990). Tropical

forest resources are known to reduce the vulnerability of the rural poor to income

shocks. Based on the field study of rural households in Eastern Honduras in Central

America, McSweeney (2004) emphasised that the nature and intensity of the calamity


96

experienced by the rural households as well as the household attributes in the form of

capital and land, affects the extent to which the forest resources are used by them to

insure against the risk.

In a study in Honduras, Godoy et al. (2002) emphasised that in spite of the fact that

extraction of NTFP brings low annual earnings, it still plays an important role in

mitigating the unexpected loss due to agricultural risks. There is increased recognition

to the concept of ‘Natural Insurance’ where even a small amount of earnings from the

forest help to bridge the income gap and thereby acts as a safety net. Hence several

initiatives are taken by the forest communities to promote sustainable use of forest

resources. Takasaki et al. (2002) examined the vulnerability and responses to

covariate flood shock among peasant households in the Amazonian tropical forests.

The study reveals that forest extraction acts as a coping strategy against shocks. The

study also discussed that conservation and development initiatives can help to

mitigate shocks by targeting and deploying contingent support.

Several Indian literatures have also supported the view that common forest products

provide security or insurance against contingencies. Based on survey of semi-arid

regions in India, Jodha (1978) observes that the rural households adopt different

adjustment mechanism like reduction in consumption levels, asset depletion &

replenishment, periodic out migration and traditional informal cooperation. However,

the shortfall in agricultural production is mitigated by collection of common forest

products. Dasgupta and Mäler (1994) had emphasised that the common property

resources provide the rural poor with partial protection in time of unusual economic

stress. In the study of tribal groups of Bihar, Agarwal, (1991) revealed that

communally held forests provided the only means of subsistence during income

shocks. This natural insurance brings an important twist to the discussion by

connecting rural poverty in risky environments with environmental degradation

(Dasgupta, 1993; Duraiappah, 1996). During a localised drought in eastern Gujarat,

majority of the population who experienced acute shortage of food sold trees to buy

food and meet their subsistence (Conroy 1991). The smoothing of the income of the

households arising due to agricultural shocks is done by the labour markets allowing

the households to shift labour from farm to off-farm employment (Kochar 1999).


97

The role of CPRs in rural livelihood is very critical and it act as safety nets especially

in times of agricultural crisis. In our study area of Bankura and Purulia districts of

West Bengal, agricultural activities are subjected to low fertility of soil, scarcity of

water and high dependence on weather. This results in wide variability in production

and productivity in agriculture. Due to the non-availability of alternate income

opportunities, the farmers in the study area fall back on Common Property Resources

to mitigate the agricultural risk.

Under this circumstance, the objective of our study is to determine how and to what

extent agricultural shortfall affects the collection of common forest resources.

Another important objective is to examine the inter relationship among agricultural

risk, non-timber forest collection and the extent of rural poverty.

5.2 Data and Methodology The study is based on primary data collected from field survey on Common Property

Resources conducted in Bankura and Purulia district in 2011. The field survey was

undertaken in 6 villages in the district of Bankura in West Bengal, India; viz.

Panjhoria, Ramjibanpur (Bandhghat), Seolibona, Baldanga, Dulaltora and

Tantirdanga and 3 villages in the district of Purulia in West Bengal, India; viz.

Jiyathole, Marbediya and Ambari. Total 300 households were surveyed.

Majority of the household respondents have listed agriculture as one of their primary

sources of income. Paddy is their main crop. Almost all the households use forest

products. Most of the households (80 percent) made major collection trips to the

forest in the surveyed year. On an average, the distance between common forest and

village community is 2-3 km and the households spend around 2.5 hours to collect the

forest products. During the survey, the households have indicated that they are very

much dependent on nature for all agricultural activities. The households face

agricultural risks which are primarily due to weather risk i.e. rising temperature,

erratic rainfall pattern and increase in severity of drought, flood and cyclones. The

rural households therefore depend on forest products to reduce the risk inherent to

subsistence agriculture. They are therefore dependent on the forest for food or


98

medicine which they cannot produce or purchase. They also sell forest products such

as fuel wood, honey, fish and fruits in the market. The rural households also resort to

wage labour as an alternate source of income. However this is done only for a short

period of time. Moreover the rural households do not get the facilities of insurance

and credit. Therefore the rural households having limited credit and insurance

facilities have to depend on common property resources at the time of agricultural

crisis. These characteristic of the surveyed area makes it an ideal setting for testing

the hypothesis on natural insurance.

We have measured agricultural production in terms of rice equivalent production. We

have collected the data on agricultural production for the current year and last four

consecutive years to determine the agricultural shortfall and risk. We have also

collected the data on different types of CPR products which are collected by the rural

households and the total time spent for the collection of the forest products by each

family unit (monthly basis on an average) for the same period. We have used a tabular

method to quantify the pattern of agriculture and the relationship between agricultural

risk and common property resources in the surveyed area. The importance of CPR as

a safety net during agricultural risk has been analysed using count data model

technique.

5.2.1 Conceptual Framework

Based on the stylized features of the backward rural agrarian economy of Bankura

and Purulia district of West Bengal, an attempt has been made in this section to

develop a conceptual framework to examine the inter relationship between

agricultural crop failures and the collection of common property forest products

following the principles of ‘new home economies’ (Barnum and Squire 1979;

Pattanayak and Sills 2001). In other words, it attempts to examine the effect of

unexpected agricultural shortfall and the expected agricultural risks on the collection

of Non timber Forest Products. Efforts have also been made in this section to examine

the insurance strategy which causes over exploitation of the resources and finally

leads to a poverty trap.


99

In poor agrarian economy the objective of the household is the maximization of utility

subject to full income constraint. Full income includes income from agricultural

production, off-farm wage income, collection of forest products and the opportunity

cost of leisure time (Pattanayak and Sills (2001). The households primarily depend on

agriculture (A). The off-farm wage income (W) and the collections of common forest

products (F) act as a supportive secondary livelihood activities to the rural poor.

The utility function of the household depends on the consumption of agricultural

products ( AC ), consumption of forest products ( FC ), leisure ( LC ) and a numeraire

( NC ) that represents all other commodities and the household characteristics affecting

preferences ( CH ).

The utility function is given by

( ), , ,A F L N Cu f C C C C H= ………………………………………………….(1)

Household maximize utility ( u ) subject to three constraints.

(i) Production function which assume that Agriculture and Forest products

depend on labour input ( AN and FN ), Capital input ( AK and FK ) and

household characteristics ( CH ) such as age, education, experience in

agricultural activities and collection of forest products. Hence the

agricultural and forest production function takes the form

( ), ,A A CA f N K H= …………………………..………………………..(2)

( ), ,F F CF f N K H= ………………………………………………..….(3)

Since the rural poor are very much dependent on nature for agricultural

activities, they are very often subjected to agricultural risk in the form of

drought, flood or attack of pests.

Hence our agricultural production function at equation (2) can be rewritten

as

( ), ,A A CA f N K Hξ= ………………………………………..………...(4)

where ξ measures agricultural risk


100

(ii) The rural poor engage in different household activities in the form of

agriculture, forest collection, off-farm wage labour and leisure or other

household activities. A time constraint implies that sum of households’

allocation of labour into agriculture ( AN ), forestry ( FN ), off-farm wages

( WN ) and leisure or other household activities ( LN ) cannot exceed the

household endowment ( N ).

A F W LN N N N N+ + + ≤ ………………………………………….…….(5)

(iii) The budget constraint which shows the households’ expenditure measured

using existing prices must be less than the sum of net income (Y ) from

agriculture, forest collection, leisure, off-farm wage income, exogenous

income ( X ) and savings ( S ).

A A F F L NC P C P C C Y⋅ + ⋅ + + ≤ ……………………………………..…..(6)

A F L L W WA P F P N P N P X S Y⇒ ⋅ + ⋅ + ⋅ + ⋅ + + ≤ ……………………..(6a)

In equality form, we can rewrite the equation (5a) as follows:

( ) ,, , ( , )A A C A F F C F L L W WY A N K H P F N K H P N P N P X Sξ= ⋅ + ⋅ + ⋅ + ⋅ + +……………………………………………………………………….….(7)

( )1

1

t

t i Ai A Fi F Li Nii

S y C P C P C C X−

=

= − ⋅ − ⋅ − − −∑ …………….……….….(7a)

where y = net income

( ), ,A A C AA N K H Pξ ⋅ = income from agriculture

,( , )F F C FF N K H P⋅ = income from forest

W WN P⋅ = off-farm wage income

L LN P⋅ = income from leisure

X = exogenous income

S = Savings of the previous period

The poor inhabitants collect NTFPs not only to reduce the impact of future

agricultural shocks but also to smooth the income by adding it to current year’s

savings. In poor forest economy, forest collection appears to be a shock absorption

mechanism especially under the situation of crop failures. The poor inhabitants move


101

to the forest for the collection of NTFP. However, in the good harvesting period, they

intend to generate surplus (savings) which act as an insurance for mitigating the future

agricultural risk.

Household maximises utility ( )u subject to the production constraints, time constraints

and budget constraints. Solving this maximising problem using the lagrangian

method, we can describe household forest collection in a reduced form of labour

demand equation.

( ), , ,dF N CN n P X H Fξ= …...………………………………….…(8)

where NP = Opportunity cost of time as measured by off-farm wages in a

complete market

ξ = Agricultural risks

X = Exogenous income

CH = Household characteristics

F = Forest quality

Demand is downward sloping in NP which reflects the opportunity cost of time and

the response to off-farm opportunities will be conditional upon , , CX Hξ and F .

In this chapter, we have tested the following hypothesis:

H1: CPRs act as a safety net especially in times of agricultural crisis, i.e.

there is a positive correlation between forest collection labour and

agricultural risk


102

5.2.2 The Empirical Model Specification

CPRs, mainly forest products, play a vital role in the livelihood of the rural poor

residing in forest fringe areas. The extraction of forest products depend on the factors

relating to household and village characteristics i.e. family size, age, education, own

land, livestock unit, distance between forest and houses, etc. as well as agricultural

risk and shortfall of that year.

In the previous chapter, the reduced form of labour demand equation

( ), , ,dF N CN n P X H Fξ= allows us to test the signs and significance of the

coefficients. Our intention is to examine the impact of agricultural risk on the

collection of forest products.

To capture the impact of agricultural risk on CPR collection, we have considered the

Count Data model (for details see Chapter 3, Section 3.3.2). The Forest Collection

Labour is measured by the number of major forest collection trips during the survey

year. As the number of trips to collect forest products is a nonnegative, integer valued

variable, we have applied Count Data Model using STATA computer software

package. As the count model very often detect over dispersion or variance greater

than the mean, it is easier to estimate the parameter with maximum likelihood

techniques. First we have applied Poisson Regression model. The common alternative

of Poisson regression model is negative binomial which is a mixer distribution of the

Poisson with gamma heterogeneity.

In our survey area, majority of the households collect forest products. However in the

case of wealthier households, they are not themselves involved in forest collection

trips i.e. they take zero trips to the forest. Thus the number of zeros may be inflated

and the number of household taking zero trips cannot be explained in the same

manner as the number of households taking more than zero trips. A Standard Negative

Binomial model would not distinguish between these two processes, but a zero

inflated model allows for and accommodates this complication. To analyse such type

of data set, a Zero Inflated Negative Binomial model should be considered. A zero

Inflated Negative Binomial model assumes that zero outcomes is due to two different


103

processes. In our case the two processes are that the household taking the trip to

collect CPR versus not taking any trip. If not taking any trip, the only possible

outcome is zero. If taking trips to collect CPR, it is then a count process. In zero

inflated Negative Binomial model we use the logistic distribution for the first stage

and Binomial distribution for the second stage. Here we have explained whether a

household takes any trips, as well as additional variables likely to explain whether a

household correctly reports whether it takes any trips. The expected count is

expressed as a combination of the two process.

E (to take a trip) = prob (not take any trip)*0 + prob (take any trips)* (E y x= take

any trip)

The Count Data regression model is specified as follows:

0 1 2 3 4 5FCL AGEHEAD SQAGEH FAMSIZE AVRSCH LIVESTOCKα α α α α α= + + + + + 6 7 8 9FORESTDIST AGRSHLFALL AGRIRISK WAGEα α α α+ + + + +∈

where FCL (Forest Collection Labour) is dependent Variable which is measured by

the number of major forest collection trips during the survey year. Here 0α is the

constant, iα are coefficients associated with the explanatory variables and ∈ is the

random disturbance term.

Explanatory variables, their description and expected correlations of the dependent

and independent variables are given in Table 5.1 below:


104

TABLE 5.1

Description of Variables in Count Data Model Variable Name Variable Description Expected

sign

Dependent

Variable

1. FCL

Forest Collection labour which is measured by the

total number of major forest collection trips during

the survey year

Explanatory

Variables

1. AGEHEAD

Age of household head (in years) +

2. SQAGEH Square age of household head which is a measure

of experience in collecting CPR +

3. FAMSIZE Average number of population of the household

(Size of the family) +

4. AVRSCH Education which is measured by the average years

of schooling (number of years) of household -

5. LIVESTOCK Number of livestock owned by the household

converted into animal units

-

6. FORESTDIST Distance of the CPR field from the residence of

the household (km) as measure of forest quality -

7. AGRSHTFALL

Agricultural shortfall which we measure from the

actual agricultural production in terms of rice (Rs)

in the survey year and Mean agricultural

production in one normal year, i.e.

+

8. AGRIRISK Coefficient of variation of agricultural prod. over

the last 5 years is a measure of agricultural risk

+

9. WAGE Annual Wage income of the household (in Rs) -


105

5.3 Results and Discussion

5.3.1 Nature of Agriculture in Bankura & Purulia district Bankura district is part of the Burdwan Division in the state of West Bengal with an

area of 688200 hectare. It has primarily two Agro Climatic Zones, viz. undulating red

& lateritic zone and Vindhyan Alluvial Zone. The red & lateritic zone has a tropical

dry sub-humid climate with rainfall ranging from 1100 mm to 1400 mm. Here the

soils are well drained and susceptible to soil erosion. Agriculture in this zone is

mainly dependent on rain. This region has undulating moulds interspersed with rocky

hillocks, with much of the rural area covered with scrub jungles and sal woods. In the

Vindhyan Alluvial zone, the soil is deep, texturally medium fine and moderately well

drained. In this region more than one crop is harvested by utilizing canal irrigation

and ground water. The seasons in Bankura are generally distributed as ‘hot summer’

(April-May), ‘monsoon’ (June-September) and ‘cold season’ (November-

February). The humidity is usually medium to high throughout the year and the

rainfall, though not heavy, is usually well distributed. The rainy months are generally

July and August.

Agriculture accounts for almost 70 percent of the district's income. Most of the

farmers are small & marginal. A vast area of Bankura district is not cultivable due to

undulation of land. However, some land is fertile and due to availability of sufficient

water from canal or deep tube wells, cultivation is done. Several small artificial water

reservoirs (barrage) are also available. Bankura district has a net cultivable land of

around 4.3 lakhs hectare and around 4.47 lakhs cultivators. About 46 percent of the

net cropped area is covered under irrigation. The principal crop of Bankura district is

paddy, wheat, oil seeds and vegetables. The different varieties of paddy cultivated are

Aus, Aman and Boro. Aman is the largest variety of paddy cultivated in an area of

311403 hectare. Most of the pre-kharif and kharif rice are grown in rain fed condition.

Wheat is second most important cereal crop in the district and it is cultivated in

limited irrigated areas. Rape seed, mustard and sesame are important oil seeds grown

in this district. Sesame is cultivated in 3 seasons while Rape & Mustard is cultivated

during Rabi season. Potato is also cultivated in large parts of the district. Agriculture

is largely dependent on the vagaries of monsoon.


106

Drought constitutes a major hazard in the district. Intermittent gaps of precipitation

and moisture stress during the monsoon gives rise to serious setback in production

during the kharif, which is the main stay of agriculture in the district.

Purulia district has all India significance because of its tropical location and funnel

shape which funnels the tropical monsoon current from the Bay to the sub-tropical

parts of North West India. Agriculture is the main source of livelihood in the district

of Purulia. About 70 percent of the total agricultural holdings belong to small and

marginal farmers. Most rural households practice subsistence farming under adverse

and risky environmental conditions. The natural resource base can be characterized as

poorly suited to agriculture due to climatic, water resource, and soil conditions. Due

to the topography of the districts, the rivers Kanshabati, Damodar and Dwarakeshwar

flowing provide little irrigation facilities.

Soil erosion and erratic and scanty rainfall are the major stumbling block in successful

irrigation in the district. Irrigation is mainly done through with the help of tanks and

bundhs, which are embankments of accumulated run-off rain water. Cultivation is

predominantly done on a single crop. Paddy is the main crop of the district. Around

50 percent of the total land is under net-cropped area. Almost 77 percent of the net-

cropped area is under the cultivation of Aman paddy. The other varieties of rice

grown are Aus and Boro. Besides paddy, maize, sugarcane, groundnut and pulses are

other important crops grown in this district. The growth in production of cereals for

the period 1980-1987 was 42 percent. In the production of pulses, there has been a

growth of 12 percent in the same period. However, the high potentiality of pulse is

marred by its poor yield (http://www.puruliazp.in).

The agricultural production in Bankura and Purulia districts vis a vis the state of West

Bengal for the year 2008-09, 2009-10 and 2010-11 is illustrated in Figure 5.1 below:


107

FIGURE 5.1

Index Number of Agricultural Production (Cereals) (Base: Triennium ending crop year 1981-82 =100)

Source: Bureau of Applied Economics and Statistics, Government of West Bengal

The agricultural production (cereals) have been depicted in terms of Index number

with a base of Triennium ending crop year 1981-82=100. As is evident from the

figure above, the production of cereals has shown a downward trend for the years

2008-09, 2009-10 and 2010-11 in the districts of Bankura and Purulia. Similar trend

in production of cereals is also observed in the state of West Bengal. The drastic

lowering of production of cereals during the period 2010-11 in the districts of

Bankura and Purulia can be attributed to meager rainfall during the said period. It is

further observed that during the year 2010-11, the production of cereals in Bankura

and Purulia is much lower as compared to that for the State average. This is primarily

due to the fact that compared to other districts; Bankura and Purulia are dependent

entirely on rainfall for cultivation.

2008-09 2009-10 2010-11Bankura 229.81 225.69 115.49Purulia 303.31 244.43 110.11West Bengal 234.40 225.40 211.50

229.81 225.69

115.49

303.31

244.43

110.11

234.40

225.40 211.50

0

50

100

150

200

250

300

350In

dex

No.

of A

gric

ultu

ral P

rodu

ctio

n (C

erea

ls)

Year

Bankura

Purulia

West Bengal


108

Land ownership pattern of the surveyed villages and the levels of inequality in the

distribution of ownland and operated land have been shown in Appendix –IV.

5.3.2 Agricultural Productivity Paddy is the main crop cultivated in the study area. Besides paddy, oil seeds and few

vegetables like potato are also grown. The surveyed villages are prone to drought;

their lands are infertile; and they lack proper irrigation facilities. Hence the villagers

cultivate only single crop annually. The agricultural productivity in the surveyed

villages in the district of Bankura and Purulia is illustrated in Table 5.2 below:

In agriculture, crop yield (Y), also known as "agricultural output", refers to the

measure of the yield of a crop per unit area of land cultivation. The unit by which the

yield of a crop is measured is kilogram per hectare.

From the Table 5.2, we observe that in the 6 villages of Bankura the yield ranges from

2037 to 2213 kg/hectare. The average yield in the surveyed villages of Bankura is

2144 kg/hectare. In contrast, in the 3 villages of Purulia the yield ranges from 1254-

TABLE 5.2


Total area of operated land (L)

(Hectare)

Total Agricultural Production in 2010 (P)

in terms of Paddy (Kg)

Crop Yield (Y) = P / L

(Kg/Hectare)

Panjhoria 7.06 14,610 2,069Ramjibanpur 5.74 12,000 2,091Seolibona 15.59 34,440 2,209Baldanga 1.60 3,500 2,188Dulaltora 6.20 13,720 2,213Tantirdanga 6.48 13,200 2,037Jiyathole 20.09 34,680 1,726Marbediya 8.83 11,074 1,254Ambari 12.72 20,606 1,620

42.67 91,470 2,14441.64 66,360 1,59484.31 157,830 1,872


Bankura TotalPurulia TotalGrand Total

2544150150

182581

Crop Productivity

Household category

(no. of HH)

2620547

Bankura

Purulia

Saltora

Santuri

http://en.wikipedia.org/wiki/Agriculture

http://en.wikipedia.org/wiki/Tillage


109

1726 kg/hectare. The average yield in the surveyed villages of Purulia is 1594

kg/hectare. From the Table we can infer that the average yield of Purulia is lower than

that of Bankura. The average yield of the 9 surveyed villages is 1872 kg/hectare,

which is significantly low as compared to the state average of 2708 kg/hectare in

2010-11.

The various agricultural implements used by the households in the surveyed villages

of Bankura and Purulia are shown in Table 5.3 shown below:

From the Table 5.3, we can infer that most of the households in the villages surveyed

in the district of Bankura and Purulia own wooden plough and a pair of bullock. The

households that do not own the wooden plough or bullock hire them for use as

agricultural implements. Power tiller and pump set are available with very few

households. Majority of the households have hired the thresher. From the Table we

can conclude that majority of households are so poor that they cannot afford to

purchase mechanised agricultural implements like Power tiller and pump set. The

households are totally dependent on wooden plough and bullocks for cultivation.

The engagement of labour in the process of sowing, weeding and harvesting in the

surveyed villages is tabulated in the Table 5.4 is shown below:

TABLE 5.3

Owned Hired Owned Hired Owned Hired Owned Hired Owned Hired110 29 214 52 0 5 10 5 21 161 2118 24 236 47 0 0 3 1 26 98 1228 53 450 99 0 5 13 6 47 259 3


150150300

BANKURAPURULIA

TOTAL

District Others

Agricultural Implements used

Household category

(no. of HH)

Wooden Plough Bullock Power tiller /

Tractor Pumpset Thresher


110

As is evident from the data on agricultural labour (Table 5.4) in the surveyed villages

in the district of Bankura and Purulia, both men and women participate in agriculture

in process of sowing, weeding and harvesting. Further it is also observed that children

are also involved in agricultural labour in several surveyed villages. It is also seen that

several households hire labour for sowing, weeding and harvesting.

5.3.3 Labour allocation in CPR collection The labour allocation in CPR collection in the surveyed villages of Bankura and

Purulia district is shown in Table 5.5 below:

TABLE 5.4

Male Female Child Hired Male Female Child Hired Male Female Child Hired

Total 150 188 141 21 48 187 129 21 38 187 113 19 39

Total 150 201 156 19 19 201 138 19 16 201 101 19 13

Total 300 389 297 40 67 388 267 40 54 388 214 38 52Source: Field Survey, 2011

Bankura Total

Grand Total

Purulia Total

Agricultural Labour

Household category

(no. of HH)

Labour engaged in Sowing Labour engaged in Weeding Labour engaged in HarvestingDistrict

TABLE 5.5

Male Female Child Male Female Child


1445

180 63.77

46 20722 1862 65.4337087

57.06417

74.33

90 51.84

147 47.98

10521

17406

19681

4425

1545 2460

Purulia Total 150 196 262 12 8996

388 478

Purulia

Grand Total 300

Bankura Total 150 192 216 34 11726

Ambari 44

Marbediya 25 34 43 2

59 73 8 2145

110 75.7620 26 3 1350

170 68.41

2252

5306

1911 2776

Jiyathole 81

Tantirdanga 25

Dulaltora 18

103 146 2

31 35 5

7080 1165 68.79

0 79.710 615Baldanga 7 9 8 740

24 0 1828Ramjibanpur 20

74 84 26 4412Seolibona 54

Santuri

Labour allocation in CPR collection


No. of Households

No. of Household members involved in

CPR collection

Monthly collection time in CPR collection

(in Hours)

Average Monthly CPR collection time

per member (in Hours)

32 39 0 1610Panjhoria 26

Bankura Saltora

2764

4069 0 79.99

0 91.8426


111

From the data on labour allocation in CPR collection (Table 5.5) surveyed villages in

the district of Bankura and Purulia, we observe that both male and female household

members are involved in the CPR collection. It is further observed that the female

members are more involved as compared to the male in CPR collection. In few

households, the child member is also involved in CPR collection. In the year 2010,

the average monthly CPR collection time per household member was 74.33 hours (i.e.

an average of 2.5 hours per day per household member) in Bankura district and 57.06

hours. (i.e. close to 2 hours per day per household member) in Purulia district, which

is higher than normal. Due to low rain fall in 2010, the agricultural production was

less. Hence we can presume that the labour time normally allotted to agriculture was

utilised in CPR collection.

5.3.4 Agricultural Risk and CPR CPR extractions play an important role in agricultural risk management. In order to

justify the relationship between agricultural risk and CPR extraction, we have

collected data on agricultural production for three years 2008, 2009 and 2010 and

measured the agricultural shortfall. The fluctuation in agricultural production is

observed to have an immediate impact on CPR collection.

PHOTO 6 PHOTO 7

CPR collection by villagers of Ramjibanpur in Bankura district

The level of agricultural production and collection of CPR in the study area over the

three years period is shown in Table 5.6 below:


112

The agricultural production is taken in terms of production of rice. The average cost

of rice is taken as Rs 20/- per kg. From the Table 5.6 we can infer that the annual

production of rice in the surveyed villages of Bankura district for the year 2008 is

around Rs 26.3 lakhs, for 2009 it is around Rs 24.9 lakhs and for the year 2010 it is

around Rs 18.2 lakhs. In Purulia district the annual average agricultural production for

the year 2008, 2009 and 2010 is around Rs 18.1 lakhs, 17 lakhs and 13.2 lakhs

respectively. The agricultural production for the year 2010 is drastically lower than

that for the year 2008. This is primarily due to the fact that the rainfall during the year

2010 was much below normal. The percentage of CPRs collection with respect to the

agricultural production in the surveyed villages of Bankura district for the year 2008,

2009 and 2010 is 35 percent, 42 percent and 76 percent respectively and for Purulia it

is 43 percent, 53 percent and 84 percent respectively . We can therefore conclude that

due to low agricultural production in the year 2010, the rural households were

compelled to collect more common from property resources as compared to year 2009

and 2008.

TABLE 5.6

Agricultural Production

(in Rs)

CPR Collection

(in Rs)

Percentage of CPR collection

with Agricultural Production


(in Rs)

CPR Collection

(in Rs)




(in Rs)

CPR Collection

(in Rs)




353733 31

62

316259

264000

693720

500178 143366

86274 62194

904407 332823 37

240000

688800869169 387962 45

109549

85022 52682

167733 47437156

177720

57310

74

537005 78

82

274400

39

2009

1829400

1327320

2516456 80

1395265 76

1121191 84

31567201944138 46

126354 40

178090 65

142693 33

139877

1815071

45

42

1706696 897347

1046791

53

537240 237781 44

4198997

783788 43

Ambari 44

Bankura Total 150

571231 207437

2631296 928278 35 2492301

4446367 1712066

36

150

Purulia Santuri

Ramjibanpur 20

Dulaltora 18

Name of village

No. of Households

Panjhoria 26

Grand Total 300

Purulia Total

Marbediya 25

SaltoraSeolibona 54

Agricultural Production and CPR collection

292200380092 162673 43

2010

83

District Block

241967

2008

350800 147165 42

Bankura

77

72

29482370

358137

29

70000Baldanga 7

613601 88

87

314170 76412120

221480

Tantirdanga 25

Jiyathole 81

193420

861488 520611 60926560 454379 49

317280 121972

203173

307968 13895538


113

The pictorial representation of agricultural production and CPR collection in the study

villages of Bankura and Purulia district is shown in Figure 5.2 & 5.3 respectively

below:

FIGURE 5.2

FIGURE 5.3


114

In order to establish the critical role of CPR during agricultural crisis, we measured

the agricultural shortfall for the year 2008, 2009 and 2010. Agricultural shortfall is the

measure from the actual production (Rs) in terms of rice and the mean agricultural

production (Rs) in one normal year. The level of agricultural shortfall and collection

of CPR in the study area over the three years period is shown in Table 5.7 below:

It is evident from the Table 5.7 that there was no agricultural shortfall in both districts

in the year 2008, since 2008 has been considered as the normal year. However, in the

year 2009 and 2010 the agricultural shortfall has shown an increasing trend. In the

year 2010, the agricultural production variability resulted in agricultural shortfall of

Rs 209032 in Bankura and Rs 164391 in Purulia as compared to the normal year

(2008). The Table shows a positive relationship between agricultural shortfall and

CPR collection in our surveyed villages over the three years period. It is further

observed that even during the period of no agricultural shortfall, household extract

CPRs in order to generate surplus income to mitigate future agricultural risk.

TABLE 5.7

Agricultural Shortfall

(in Rs)

CPR Collection

(in Rs)


(in Rs)

CPR Collection

(in Rs)


(in Rs)

CPR Collection

(in Rs)


241967

Bankura Saltora

Panjhoria 26 147165(7395)

Agricultural Shortfall and CPR collection


No. of Households

2008 2009 2010

177720

Seolibona 54 330 332823 387962

Ramjibanpur 20 (615) 109549 126354

57310

Dulaltora 18 143366

537005

Baldanga 7 (1050) 52682 62194

89910

22119

5314

314170

193420

Ambari 44 207437 237781

Marbediya 25 121972 138955 13246

4507210442

Jiyathole 81 454379

167733 203173

139877 178090

Tantirdanga 25 142693

106073520611

2516456

897347 1121191

Grand Total 300 1712068

1395265

Purulia Total 150 783789

209032

164391

373423

90795

84321

175116

Bankura Total 150 928279 1046791

1944138

Purulia Santuri

162673

613601

Note: The figures in bracket indicate negative shortfall which implies that agricultural production is more than mean.

26009

25129

40549

(2880)

(17925)

909

5619

56351

1515

12415

13986

74071

(191)

(3885)

(2430)

2205

(3870)

(1215)

(15045)


115

The pictorial representation of agricultural shortfall and CPR collection in the study

villages of Bankura and Purulia district is shown in Figure 5.4 & 5.5 respectively

below:

FIGURE 5.4

FIGURE 5.5


116

5.3.5 Association between CPR extraction and

Agricultural Risk: Count Data Regression Model We have assumed that forest collection labour is determined not only by socio-

economic, demographic variable but also by agricultural shock and agricultural risk.

We have explained the determinants of forest collection labour through econometric

analysis to give an understanding of the impact of agricultural production risk on the

extraction of forest products following Pattanayak and Sills (2001). The regression

models outlined earlier have been tested using household level data collected through

field survey in Bankura and Purulia district, West Bengal. We have collected several

information including the variables relating to the determinants of forest labour

collection. We have applied Count Data Model using Stata Computer package to

determine the frequency of forest collection trips. We have considered Poisson,

Negative Binomial and Zero-Inflated Negative Binomial Regression Models to

analyse our surveyed data.

The result for the determinants of forest collection labour is given in the following

Tables 5.8 & 5.9 below:

TABLE 5.8

No. of Obs.=115 No. of Obs.=115 No. of Obs.=115LR chi2(9) = 1794.06 LR chi2(9) = 33.41 Inflamation model=logit Prob > chi2 = 0.0000 Prob > chi2 = 0.0001 LR chi2(9 = 59.27 Prob > chi2 = 0.0000Log likelihood = -2265.9415 Pseudo R2= 0.2836 Log likelihood = -693.68771 Pseudo R2= 0.0235 Log likelihood = -612.0182

VARIABLECoeffi cient

Std. Error Z P>|Z| Coeffi cient


Std. Error Z P>|Z|

AGEHEAD .0004 .0027 0.16 0.876 -.0060 .0220 -0.27 0.786 .0006 .0122 0.05** 0.032SQAGEH -.0000 .0000 -1.64*** 0.101 .0000 .0002 0.03 0.979 -.0000 .0001 -0.44*** 0.104FAMSIZE -.0415 .0058 -7.12* 0.000 -.0542 .0493 -1.10 0.272 .0051 .0279 0.18 0.856AVRSCH -.0340 .0040 -8.38* 0.000 -.0335 .0344 -0.97 0.331 -.0146 .0197 -0.74** 0.016LIVESTOCK .0231 .0057 4.00* 0.000 -.0423 .0473 0.89 0.371 .0133 .0269 0.49 0.622FORESTDIST -.0849 .0089 -9.53* 0.000 -.0731 .0702 -1.04 0.298 -.0203 .0407 -0.50 0.617AGRSHTFALL .0013 .0001 13.81* 0.000 .0016 .0010 1.68*** 0.093 .0026 .0006 4.64* 0.000AGRIRISK .0106 .0008 13.24* 0.000 .0298 .0087 3.48* 0.001 .0070 .0053 1.33** 0.012WAGE -.0000 .0000 -3.55* 0.000 -.0000 .0000 -0.34 0.737 -.0000 .0000 -2.05** 0.041Constant 5.2530 .0757 69.40* 0.000 4.6134 .6477 7.12* 0.000 5.1012 .3689 13.83* 0.000

Likelihood-ratio test of alpha=0: Vuong test of zinb vs. standard negative binomial:

chibar2(01) = 3144.51 Prob>=chibar2 = 0.000 z = 2.28 Pr>z = 0.00112

*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent levelSource: Estimated by Stata 8 Computer Software using Field Survey Data of 2011

Forest Collection as a Function of Agricultural Risk(Bankura District)

POISSON REGRESSION NEGATIVE BINOMIAL ZERO INFLATED NEGATIVE BINOMIAL


117

From the above Tables 5.8 and 5.9, we have observed that the results are consistent

for most of the variables in both the districts. However our choice of the best model is

Zero Inflated Negative Binomial model (ZINB). To compare the negative binomial

and ZINB model, we apply the Vuong statistic. The Vuong test compares the ZINB

model with a standard Negative Binomial model. A significant Z test indicates that

the Zero Inflated Negative Binomial model is better. Hence we have turned to the

estimated results of ZINB model. We have detected that the association between

forest collection trips and age of the household ( AGEHEAD ) is positive and square

age of the household head ( SQAGEH ) is negative. In our analysis of both the

districts, we have observed significant result. The coefficients on age and the square

of age imply that households with older heads normally take more trips on forest

collection except the oldest household. Household’s accumulated knowledge about

the local forest make it easier for them to take more trips and collect huge amount of

forest products. In fact, younger generation are more comfortable with commercial

substitutes of the traditional forest product. Almost all the household members in the

study area collect CPRs. Hence the larger the family size ( FAMSIZE ) the more is the

forest trip for the collection of CPR products. The result is significant in Purulia

district, but insignificant in Bankura district. Education ( AVGSCH ) i.e. the average

TABLE 5.9

No. of Obs.=123 No. of Obs.=123 No. of Obs.=123LR chi2(9) = 5537.03 LR chi2(9) = 103.55 Inflamation model=logit Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 LR chi2(9) = 54.42 Prob > chi2 = 0.0000Log likelihood = -1949.7648 Pseudo R2= 0.5868 Log likelihood = -724.67487 Pseudo R2= 0.0667 Log likelihood = -605.0434

VARIABLECoeffi cient



Std. Error Z P>|Z|

AGEHEAD 0.0071 0.0026 2.71* 0.007 0.0086 0.0219 0.39 0.695 0.0029 0.0105 0.28*** 0.082SQAGEH -0.0001 0.0000 -3.78* 0.000 -0.0002 0.0002 -0.67 0.505 -0.0001 0.0001 -0.44*** 0.059FAMSIZE 0.0362 0.0035 10.44* 0.000 0.0940 0.0346 2.72* 0.007 0.0276 0.0155 1.78*** 0.075AVRSCH -0.0294 0.0042 -7.06* 0.000 -0.0695 0.0345 -2.02** 0.044 -0.0133 0.0174 -0.76 0.446LIVESTOCK -0.0059 0.0035 -1.66* 0.098 -0.0372 0.0333 -1.12 0.265 0.0004 0.0158 0.02 0.981FORESTDIST -0.7177 0.0163 -44.12* 0.000 -1.1101 0.1268 -8.75* 0.000 -0.4932 0.0688 -7.17* 0.000AGRSHTFALL 0.0000 0.0000 0.22 0.825 0.0002 0.0004 0.40 0.690 0.0001 0.0002 0.74*** 0.058AGRIRISK 0.0128 0.0007 17.96* 0.000 0.0360 0.0088 4.08* 0.000 0.0094 0.0036 2.60* 0.009WAGE -0.0000 0.0000 -4.73* 0.000 -0.0000 0.0000 -2.24** 0.025 -0.0000 0.0000 -0.65 0.515Constant 5.9812 0.0633 94.53* 0.000 6.1501 0.5241 11.73* 0.000 5.7847 0.2535 22.82* 0.000

Likelihood-ratio test of alpha=0: Vuong test of zinb vs. standard negative binomial:

chibar2(01) = 2450.18 Prob>=chibar2 = 0.000 z = 3.21 Pr>z = 0.0007

*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent levelSource: Estimated by Stata 8 Computer Software using Field Survey Data of 2011

Forest Collection as a Function of Agricultural Risk(Purulia District)

POISSON REGRESSION NEGATIVE BINOMIAL ZERO INFLATED NEGATIVE BINOMIAL


118

years of schooling of the household has a negative impact on forest collection trips in

both the districts which indicated that households who are better educated get better

job opportunities and therefore are less interested in collecting CPR during

agricultural crisis. The result is significant only in Bankura district.

We have predicted that both wage income and the number of livestock have a

negative impact on forest collection trip to extract NTFPs. In line with our

expectation, wage income (WAGE ) is negatively related with forest collection labour.

The result is statistically significant in Bankura district, but insignificant in Purulia

district. In fact households who have a sufficient wage income are less interested in

forest collection trip. Thus creation of job opportunities in non-farm sectors is

expected to have a significant role in CPR extractions. However, the coefficient of

livestock ( LIVESTOCK ) is observed to be positive, but insignificant. The positive

relation indicates that the household with larger size of livestock take more trips to the

forest in order to gather fodder to feed their farm animals. We have further observed

that forest distance ( FORESTDIST ) i.e. the distance between the residence and the

common forest area have a negative relationship with major forest collection trips.

From our study area, we infer that household who live nearer to the common forest

area extract more CPR and hence generate more income from it and thus help to

mitigate agricultural crisis. Household who live farther away from the forest area are

unable to smooth their income and consumption by collecting CPR products during

agricultural shock. However, the result is significant only in Purulia district.

The key findings of our regression results indicate that the coefficients on agricultural

risk parameters ( AGRIRISK ) and shock parameter ( AGRSHTFALL ) are positive and

significant in both Bankura and Purulia district, which suggest that household with

greater agricultural shortfall and risk are likely to take more forest collection trips.

This result supports our hypothesis that CPR product is used by rural households as a

safety net during the time of agricultural crisis. Thus CPRs help to mitigate

agricultural risk by smoothening the income and consumption of the rural poor.


119

5.4 Conclusion

In this study we investigated the impact of agricultural risk on the collection of

common forest products based on our surveyed villages of Bankura and Purulia. Most

of the households in the surveyed area are very poor. Agriculture is their main

occupation and therefore they depend on nature for any agricultural activities. There

are several agricultural risks associated such as adverse weather, seasonal flooding,

unpredictable soil quality, crop diseases, price shocks, etc. The rural poor have limited

credit and insurance facility and therefore they extract forest products not only to

reduce their agricultural risk but also help to smooth their income. As per the

surveyed data, the percentage of CPR collection with respect to agricultural

production is very high in the year 2010 due to lower agricultural production as

compared to the previous years 2008 and 2009.

Since agricultural practice in the study area is backward in nature and subjected to

weather risk in the form of agricultural shock in times of production shortfall, the

farmers fall back upon CPRs for their survival and also addresses significantly their

agricultural risk. As established from our field data in the year 2010, the agricultural

production variability resulted in agricultural shortfall of Rs 209032 in Bankura and

Rs 164391 in Purulia as compared to the normal year (2008). Interestingly, it is

observed that during this period the extraction of CPR is also high compared to the

normal year. To capture the impact of agricultural risk on CPR collection, we have

considered the Count Data model using STATA computer software package. The key

findings of our regression results indicate that the coefficients of agricultural risk

parameters ( AGRIRISK ) and shock parameters ( AGRSHTFALL ) are positive and

significant in both Bankura and Purulia districts, which suggest that household with

greater agricultural shortfall and risk are likely to take more forest collection trips, i.e.

the result goes with the hypothesis..

Hence we can conclude that CPRs supplement the rural livelihood and act as safety

net for the poor seasonally or during the agricultural crisis.

Common Forest and Participatory Management

120

CHAPTER 6

COMMON FOREST AND PARTICIPATORY MANAGEMENT

6.1 Introduction

A majority of the rural poor in India are largely dependent on common property

resources such as forest resources. Forests contribute extensively to the social and

economic well-being of the rural poor. One fifth of the land area of India is covered

by forest. As per World Bank Report (2006), an estimated 275 million people in rural

areas of India depend largely on forests. According to the report, about half of India’s

89 million tribal people, the most disadvantaged section of the Indian society, live in

forest fringe areas, and they tend to have close cultural and economic links with the

forest. Forest products, including the non-timber forest products (NTFPs) like food,

fruits, flowers, medicines etc., provide the means of subsistence for the rural poor.

Forest dwellers, which also include a high proportion of tribal, are among the poorest

and most vulnerable groups in society.

The incentive involved in common property resource management was first

established by Gordon (1954) and later by Hardin (1968). Hardin formulated that over

exploitation of the common property resources led to depletion of shared limited

resources as several individuals acted independently in their own self-interest. The

concept of decentralized collective management of the common property resources

was postulated by Berkes (1989) and Ostrom (1990). Ostrom (1990) was of the view

that the CPR management would be successful if there were defined boundaries, an

efficient and effective conflict-resolution and monitoring mechanism. Gibbs and

Bromley (1989) have advocated that institutions, through rights and rules, provide

incentives for the group members to take certain actions to achieve a desired outcome.

Common property rights and Hardin’s theory of ‘The Tragedy of the Commons’ has

been discussed in Appendix-V.

Several empirical researches dealing with the dependence of common property

resources of the rural poor were conducted in different regions in India. Noteworthy,


121

amongst them are the Jodha (1986), Iyengar (1997), Beck and Ghosh (2000), whose

theories highlighted the danger of depletion of the common property resources due to

pressure from privatization. The importance of participatory management in resolving

the crisis of CPRs in India was postulated by Chopra et al. (1989). On the study of

historical perspective of Joint Forest Management, Sarker and Das (2006) observed

that resistance movement of the forest communities in Midnapore in West Bengal was

the key to the success of the Joint Forest Management programme. The authors were

also of the view that the immediate survival needs, generating mainly subsistence and

income from non-timber forest products (NTFPs) for the forest protection committee

members were the key to the long term sustainability of Joint Forest Management

system. Balloni (2002), in the study of participatory forest management in India

stresses on the need for equity in the participation and representation of the

marginalised classes (poor and women) with equal benefit between the forest

department and the forest communities. The author also suggests the need for

formulation of new and effective silvicultural practices in order to increase the

productivity of Non-Timber Forest Products (NTFPs).

Highlighting the extent of forest cover and the practice of JFM across the states of

India in general and West Bengal in particular, this chapter assess the nature of

participation in forest management and examines the relationship between forest

dependency and participation in forest management in our study area. The chapter

also focuses the relationship between the intensity of management practice and the

degradation of forest resources.

6.2 Data and Methodology

In our study area we have captured demographic and socio-economic characteristics

of rural households, their dependence on forest products and their participation and

involvement in Joint Forest Management. On the basis of this information, we have

analysed how the socio-economic factors and dependency on forest products affect

the level of participation and collective action in Joint Forest Management. We have

used a tabular method to quantify the Forest Management and enforcement of forest

protection scheme by comparing JFM in 9 villages of these two districts. We have


122

used both statistical and econometric techniques (Censored Tobit Model) to analyse

the determinants of collective action in JFM and to test several hypotheses. Logit

Regression Method was used to analyse the nexus between active forest management

and forest degradation.

In this chapter we have tested the following hypothesis:

H1: There is a strong relationship between forest dependence and active

participation in JFM

H2: Active participation in forest management plays a positive role in

alleviating the environmental degradation.

6.3 Forest Cover and its Management in India: An Interstate

Analysis

6.3.1 Forest Cover in India

The forest resources play an important role in the environmental and ecological

security of India. Indiscriminate and massive (approximately 4.3 million hectare)

diversion of forest land during 1950-1980 for non-forestry purposes, necessitated the

need for conservation and development of the forest resources. This led to the

enactment of the Forest Conservation Act, 1980 whose primary objective was to

provide a higher level of protection to the forests and to regulate diversion of the

forest land in India for non-forestry activities. However there is depletion of the forest

resources in view of increasing population pressure and development activities.

The forest cover as per Forest Survey of India (FSI) denotes all lands which have a

tree canopy density of more than ten percent when projected vertically on the

horizontal ground, with a minimum areal extent of one hectare. The assessment of

forest cover of the entire country is carried out by FSI at an interval of two years by

interpretation of satellite data. The classification scheme adopted in the assessment is

shown in Table 6.1 below:

TABLE 6.1

Very Dense Forest All lands with tree canopy density of 70% and aboveModerately Dense Forest All lands with tree canopy density between 40% and 70% Open Forest All lands with tree canopy density between 10% and 40% Scrub Degraded forest lands with canopy density less than 10%Non-Forest Area not included in any of the aboveSource: ISFR 2011

Forest Density Classification


123

As per the India State Forest Report 2011 (ISFR 2011), the forest cover assessment is

based on the IRS P6 LISS-III satellite data in digital form corresponding to the period

October 2008 to March 2009 procured from National Remote Sensing Centre (NRSC)

at Hyderabad.

The Forest Cover Map of India depicting the forest cover in all the states and Union

Territories is shown below:

MAP 6.1

Forest Cover Map of India

Source: India Forest Report 2011


124

As per the assessment of 2011 (ISFR 2011), the forest cover is shown in Table 6.2

below:

As per the assessment of 2011, the total forest cover is 692,027 km2 which is 21.05

percent of the total geographical area of the country. Based on the density classes, the

area covered by Very Dense Forest is 83,471 km2 (2.54 percent), Moderately Dense

Forest is 320,736 km2 (9.76 percent) and Open forest is 287,820 km2 (8.75 percent).

The forest cover in the different Indian states and Union territories of India is shown

in the Table 6.3 below. As per the FSI assessment of 2011, in terms of area-wise,

Madhya Pradesh has the largest forest cover (77,700 km2) followed by Arunachal

Pradesh (67,410 km2), Chhattisgarh (55,674 km2), Maharashtra (50,646 km2) and

Orissa (48,903 km2). However, in terms of percentage of forest cover to the total

geographical area, the state of Mizoram is the highest with 90.68 percent, followed by

Lakshadweep (84.56 percent), Andaman & Nicobar Islands (81.51 percent),

Arunachal Pradesh (80.50 percent), Nagaland (80.33 percent), Meghalaya (77.02

percent) and Tripura (76.07 percent).

The change in the forest cover state wise of the assessment of 2011 as compared to

the assessment of 2009, indicates that there is positive changes in the states of Punjab

(100 km2), Jharkhand (83 km2), Tamil Nadu (74 km2), Andaman & Nicobar (62 km2),

Rajasthan (51 km2) and Orissa(48 km2). However, it is also observed that has been

negative changes in the states of Andhra Pradesh (281 km2), Manipur (190 km2),

Nagaland (146 km2), Arunachal Pradesh (74 km2), Mizoram (66 km2) and Meghalaya

(46 km2).

TABLE 6.2

Class Area (Sq. Km) Percentage of Geographical AreaForest Covera) Very Dense Forest 83,471 2.54b) Moderately Dense Forest 320,736 9.76c) Open Forest 287,820 8.75Total Forest Cover 692,027 21.05Scrub 42,176 1.28Non-Forest 2,553,060 77.67Total Geographical Area 3,287,263 100.00Source: ISFR 2011

Forest Cover in India


125

On all India level, the very dense and moderately dense forest has shown a net

improvement of 43 km2 and 498 km2 respectively. However, there has been reduction

in the open forest area by 908 km2. The decrease in the forest area in the state of

Andhra Pradesh was mainly due to management interventions like harvesting of short

rotation crops followed by new generation / plantations, forest clearance in some

encroached areas. In Manipur, the decrease in the forest cover was due to shortening

or shifting of the cultivation cycle. The positive change in the state of Punjab can be

attributed to the growth of young plantations carried out under externally aided

project and Agro-forestry activities. In Jharkhand the increase in forest area is

primarily due to effective protection of the forest by the Forest protection committee

and the plantation activities (ISFR 2011).

TABLE 6.3

Forest Cover in States /UT of India (Area in Km2)

State / UT Geogra-

phical Area (GA)

Forest Area as per 2011 assessment

Percentage of Forest

Area to GA

Forest Area as per 2009 assessment

Real Change in Forest

cover from 2009

assessmentAndhra Pradesh 275,069 46,389 16.86 46670 -281Arunchal Pradesh 83,743 67,410 80.50 67484 -74Assam 78,438 27,673 35.28 27692 -19Bihar 94,163 6,845 7.27 6804 41Chhattisgarh 135,191 55,674 41.18 55678 -4Delhi 1,483 176 11.87 177 -1Goa 3,702 2,219 59.94 2212 7Gujrat 196,022 14,619 7.46 14620 -1Haryana 44,212 1,608 3.64 1594 14Himachal Pradesh 55,673 14,679 26.37 14668 11Jammu & Kashmir 222,236 22,539 10.14 22537 2Jharkhand 79,714 22,977 28.82 22894 83Karnataka 191,791 36,194 18.87 36190 4Kerala 38,863 17,300 44.52 17324 -24Madhya Pradesh 308,245 77,700 25.21 77700 0Maharashtra 307,713 50,646 16.46 50650 -4Manipur 22,327 17,090 76.54 17280 -190Meghalaya 22,429 17,275 77.02 17321 -46Mizoram 21,081 19,117 90.68 19183 -66Nagaland 16,579 13,318 80.33 13464 -146Orissa 155,707 48,903 31.41 48855 48Punjab 50,362 1,764 3.50 1664 100Rajasthan 342,239 16,087 4.70 16036 51Sikkim 7,096 3,359 47.34 3359 0Tamil Nadu 130,058 23,625 18.16 23551 74Tripura 10,486 7,977 76.07 7985 -8Uttar Padesh 240,928 14,338 5.95 14341 -3Uttarakhand 53,483 24,496 45.80 24495 1West Bengal 88,752 12,995 14.64 12994 1A&N Islands 8,249 6,724 81.51 6662 62Chandigarh 114 17 14.91 17 0Dadra & Nagar Haveli 491 211 42.97 211 0Daman & Diu 112 6 5.36 6 0Lakshadeep 32 27 84.38 26 1Puducherry 480 50 10.42 50 0Total 3,287,263 692,027 21.05 692,394 -367Source: ISFR 2011


126

6.3.2 Introduction of Joint Forest Management in India More than 200 million people are partially or fully dependent on the forest resources

for their livelihood in India. The importance of this natural resource was felt even in

the ancient times which can be traced to the ancient text of Atharva Veda. India has a

long history of scientific management of the forest resource. During the British

administration, the National Forest Policy of 1894, the first formal forest policy in

India, was administered which put more emphasis on forest resource as a source of

revenue to the states. After Independence, the first Forest Policy of India, 1952 was

enacted which put more emphasis on plantation of high yield commercial timbers like

teak, eucalyptus, which have relatively low exclusion costs. It recognised the

ecological and environmental aspects of forest management. The JFM program was

an outcome of several struggles and social movements by the local communities who

were dependent on the forest for their subsistence. The ‘Arabari’ experiment in the

early 1980’s in West Bengal was a forerunner of the JFM program. This successful

experiment led to the development of a new forest management strategy which was

later named as ‘Joint Forest Management’ in 1990.

The Indian National forest policy (1988) and the government resolution on

participatory forest management on 1st June 1990, made it possible for the State

Forest Departments to put greater emphasis on the people’s participation in the

management of the forest resources. The Joint Forest Management was an outcome

of the realisation that active and willing participation of the forest communities was

critical for the forest regeneration program to succeed. Further the forest communities

should directly benefit and also have authority. Hence, under the Joint Forest

Management, a village committee popularly known as Forest Protection Committee

(FPC) and the state forest department enter into a JFM agreement. As part of this

agreement, the local villagers assist in safeguarding the forest resources from grazing,

fire and illegal harvesting. In return they receive non-timber forest products and also a

share of the revenue from the sale of timber products. JFM augments the forestry

regime with a process that ensures rapid adaptation to changes in what people need,

want and can do.


127

The present status on the number of JFM committees, area under JFM, number of

families of Schedule Tribe involved in JFM and the total number of families involved

in JFM is depicted in Table 6.4 as shown below:

As is evident from data in Table 6.4 above, the Joint Forest Management is very

active in the states of Andhra Pradesh, Chhattisgarh, Jharkhand, Madhya Pradesh,

Maharashtra, Orissa and Uttarakhand and it is elaborated in Appendix-VI.

TABLE 6.4

Sl No. State No. of JFM committee

Area under JFM (in ha.)

No. of Families of Scheduled Tribe involved

in JFM

Total no. of families

involved in JFM

1 Andhra Pradesh 8663 2,290,000 480,000 1,590,000 2 Arunachal Prdesh 347 90,000 23,308 23,308 3 Assam 503 80,000 28,459 57,341 4 Bihar 532 370,000 32,303 205,000 5 Chhattisgarh 7050 2,830,000 270,000 6 Goa 26 10,000 336 7 Gujarat 1734 240,000 140,000 200,000 8 Haryana 875 56,000 165,500 9 Himachal Pradesh 1690 420,000 36,000 265,000 10 Jammu & Kashmir 2697 114,100 11 Jharkhand 10903 2,190,000 510,000 1,280,000 12 Karnataka 3887 320,000 24,705 190,000 13 Kerala 327 170,000 12,255 51,300 14 Madhya Pradesh 14173 6,000,000 800,000 1,700,000 15 Maharashtra 10242 2,500,000 500,000 1,800,000 16 Manipur 280 90,000 22,000 26,000 17 Meghalaya 73 4,000 18 Mizoram 270 20,000 40,000 40,000 19 Nagaland 335 20,000 85,000 85,000 20 Orissa 9778 820,000 700,000 1,700,000 21 Punjab 1224 180,000 91,000 22 Rajasthan 4224 580,000 200,000 400,000 23 Sikkim 155 10,000 17,000 46,000 24 Tamil Nadu 1367 480,000 10,000 240,000 25 Tripura 374 100,000 17,000 33,000 26 Uttar Pradesh 1892 80,000 83,000 800,000 27 Uttarakhand 10107 860,000 15,000 500,000 28 West Bengal 4096 630,000 110,000 480,000

TOTAL 97,824 21,554,100 3,886,030 12,238,785 Source: FSI, SFR 2009

Status of JFM in different states of India


128

6.4 Forest Cover and Joint Forest Management in West Bengal 6.4.1 Forest cover in West Bengal

In the state of West Bengal, the forest cover as per the interpretation of the satellite

data of Nov 2008 – Jan 2009, is 12,995 km2 which is 14.65 percent of the

geographical area of the state (ISFR 2011). West Bengal has 2,984 km2 areas under

dense forests, 4,646 km2 areas under moderately dense forests and 5,365 km2 areas

under open forests. The forest cover of the state is shown in the map below:

MAP 2

Map of Forest Cover in West Bengal

Source: India State Forest Report 2011


129

The district wise forest cover in the state of West Bengal is shown in the Table 6.5

below:

It is evident from the above Table 6.5 above that the percentage of forest cover to the

total geographical area is highest in the district of Darjeeling, followed by Jalpaiguri,

South 24 Pargana, Mednipur, Bankura and Purulia. We have selected Bankura and

Purulia for our field survey since both of them are covered by vast forest area and the

rural poor are highly dependent on the forest resources for their livelihood. However,

we have noticed that the forest land has been degraded in several districts of West

Bengal.

TABLE 6.5

Very Dense Forest

Mod. Dense forest

Open forest

Bankura 6,882 213 510 333 1,056 15.34 Bardhaman 7,024 44 135 82 261 3.72 Birbhum 4,545 0 42 63 105 2.31 Kolkata 185 0 0 0 0 - Coochbehar 3,387 0 15 79 94 2.78 Dakshin Dinajpur 2,219 0 2 13 15 0.68 Darjeeling 3,149 714 663 912 2,289 72.69 Howrah 1,467 0 53 93 146 9.95 Hoogli 3,149 0 9 52 61 1.94 Jalpaiguri 6,227 681 514 1309 2,504 40.21 Malda 3,733 0 113 51 164 4.39 Mednipur 14,081 253 1,171 1172 2,596 18.44 Murshidabad 5,324 0 63 44 107 2.01 Nadia 3,927 2 74 53 129 3.28 North 24 Pargana 4,094 20 18 51 89 2.17 Purulia 6,259 43 373 381 797 12.73 South 24 Pargana 9,960 1,014 889 503 2406 24.16 Uttar Dinajpur 3,140 0 2 174 176 5.61 Grand Total 88,752 2,984 4,646 5,365 12,995 14.64 Source: ISFR 2011

District wise forest cover in West Bengal (Area in Sq. Km)

District Geographical Area (GA)

Assessment 2011 Total forest cover

Percentage of Forest cover to Geographical

Area


130

Table 6.6 below shows the degraded notified forest land in West Bengal:

From the Table 6.6 above, we observe that in the surveyed districts of Bankura and

Purulia, the extent of degraded land is very high as compared to other districts of

West Bengal. Encroachment into the forest for agricultural, settlement and other

purpose along with rampant extraction of forest products are responsible for the

degradation.

TABLE 6.6

Bankura 203.64Bardhaman 20.34Birbhum 39.99Kolkata 0Coochbehar 0.15Dakshin Dinajpur 0Darjeeling 21.82Howrah 0Hoogli 0Jalpaiguri 23.32Malda 0Mednipur 110.7Murshidabad 1.57Nadia 0.25North 24 Pargana 0.21Purulia 114.29South 24 Pargana 0.98Uttar Dinajpur 0Grand Total 537.25Source: Wastelands Atlas of India, 2011Published by Ministry of Rural Development , Deptt. of Land ResourcesGovt. of India and NRSA, Deptt. of Space, Govt. of India

Degraded Notified Forest Land in West Bengal (Sq. Km)District Unit (in Sq. Km)


131

6.4.2 Joint Forest Management in West Bengal West Bengal is the pioneer state for initiating Joint Forest Management in India. The

data on the number of Forest Protection Committees (FPC) and the number of

members participating in them, for different divisions of West Bengal is shown in

Table 6.7 below:

TABLE 6.7

(As on 31.03.2011)

Male Female Total S.C. S.T OthersDarjeeling 73 14,376 3,812 415 4,227 139 1,006 3,082 Kalimpong 64 26,238 3,582 195 3,777 204 875 2,698 Kurseong 46 13,095 2,043 3,068 5,111 431 1,195 3,482 Wildlife I 31 14,905 1,287 1,572 2,859 286 516 1,966 Jalpaiguru 63 20,248 11,431 638 12,069 5,255 3,399 3,415 Baikunthapur 64 12,899 5,978 129 6,107 4,475 628 1,004 Cooch Behar 26 3,904 2,932 209 3,141 1,497 517 491 Wildlife III 26 7,021 4,360 180 4,540 727 2,483 1,330 B.T.R.(E) 17 9,331 3,340 103 3,443 1,548 1,334 461 B.T.R.(W) 33 25,596 4,064 489 4,553 768 2,563 1,173

Raigunj 21 1,163 1,727 74 1,801 864 412 525

Malda 4 210 543 18 561 261 296 4 Midnapur 363 45,956 48,038 2,801 50,839 10,131 9,186 31,522 Jhargram 474 52,179 38,254 2,449 40,703 9,135 14,906 16,662 Kharagpur 254 27,438 18,421 12,281 30,702 6,950 9,504 14,248 Rupnarayan 213 26,398 26,331 1,343 27,674 6,419 7,814 13,441 Bankura (N) 539 43,493 50,437 2,083 52,520 19,819 7,912 24,789 Bankura (S) 620 44,193 53,849 4,422 58,271 13,806 18,427 26,038 Panchet 231 28,383 27,710 1,593 29,303 11,120 4,736 13,447 Purulia 213 30,729 20,741 867 21,608 6,012 6,944 8,652 Kangsabati (N) 244 17,641 23,578 881 24,459 5,677 8,089 10,693 Kangsabati (S) 305 25,169 29,561 569 30,130 4,293 14,944 10,893 Burdwan 73 20,102 16,854 2,928 19,782 7,273 5,771 6,738 Durgapur 23 2,391 1,871 7 1,878 523 699 656 Birbhum 195 10,481 16,332 313 16,645 6,017 5,267 5,401 Howrah 4 479 815 319 1,134 537 238 359 Nadia-Msd. 11 916 957 44 1,001 246 254 603 East Medinipur 19 1,813 4,760 1,097 5,857 1,256 50 4,551 S.T.R 11 12,844 3,958 107 4,065 3,642 254 169 24-Pgs (S) 40 42,534 12,287 12,186 24,473 13,116 517 10,840

4300 582125 439853 53380 493233 142427 130736 219333Source: SDF 2010-11

TOTAL

Forest Protection Committees in West Bengal

Zone Division

Total No. of

FPC

Area Protected

(ha.)

Number of Members

Hilly

Duars- Terai

North Bengal Plains

South Bengal

Estua-rine


132

As on March 2011, there are 4,300 Forest Protection Committee (FPC) in West

Bengal comprising of 4,93,233 members protecting a forest area of about 5,82,125

hectares. The Total number of Eco-Development Committees (EDCs) in the state is

123 comprising of 22,460 members protecting a protected forest area of 87,537

hectare (SFR 2010-11). From the Table 6.6 above, we have observed that the number

of Forest Protection Committee in the Bankura (N), Bankura (S) and Purulia division

were 539, 620 and 213 respectively (WB SFR 2010-11) which is very high as

compare to other districts of West Bengal. There are 52,520 members involved in

JFM activities in Bankura (N), 53,849 members in Bankura (S) and 30,729 members

in Purulia (WB SFR 2010-11).

The Status of JFM Committees in West Bengal during the period 2008-2011 is shown

in Table 6.8 below:

As is evident from the Table 6.8 above, although there has been an increase in the

number of JFM committees in West Bengal from 4,218 in 2008 to 4,300 in 2011, the

total number of members involved in JFM has decreased from 576,078 to 493,538

during the same period. However, there has been increase in area under JFM from

553,409 ha to 582,161 ha during the period 2008 to 2011.

TABLE 6.8

Male Female

1 31-Mar-2011 4,300 582,161 439,853 53,380 493,538 2 31-Mar-2010 4,271 562,527 428,747 58,032 486,769 3 31-Mar-2009 4,253 557,063 438,269 44,484 482,753 4 31-Mar-2008 4,218 553,409 530,936 47,072 576,078

Source: SFR of WB, Govt of WB

Status of JFM Committees in West Bengal Sl No.

Assessment Year

Number of JFM committee

Area under JFM (in ha.)

Number of members Total Number of members

involved in JFM


133

6.5 Forest Management in the Study Area

The field survey was undertaken in 6 villages in the district of Bankura and 3 villages

in the district of Purulia in West Bengal. The villages in the study area are situated in

the forest fringes below the hills. They are highly inaccessible and sparsely populated.

Most of the villagers are extremely poor. They are dependent on farming for their

subsistence. However these villages do not have any provision for irrigation and have

to depend entirely on rain for farming. Several households do not own any land and

hence work as hired labourers to meet their livelihood. The villagers are engaged in

agriculture only for about six months. For the rest of the year they are without any

work. So they involve in non-agricultural activities like fishing, hunting and

collection of common forest products. The villagers collect forest products in the form

of twigs, snails, fodder/grasses, fuel wood, cow dung, herbal medicine, bamboo,

timber, fruits, honey, vegetables, fish, birds and broom. As these forest products have

several uses in the socio-economic lives of the rural poor, they are becoming

concerned about protecting these resources.

The villagers of Jiyathole village in Santuri block of Purulia district, Mangal Kora,

Narain Kora and Pradip Mudikora, have commented in the group discussion:

“We have to protect the important plants like sal, kendu, mahua, piyal at any

cost as we use them both for domestic and commercial purposes.” (Dated 20th

December, 2011, medium of language was Bengali and Santali)

In all the surveyed villages it is observed that the villagers are engaged, amongst other

activities, in Joint Forest Management for the protection of their forests. The Joint

Forest Management not only helps to protect the forest against indiscriminate felling

of trees but also provides the survival needs of the villagers. It also plays a critical

role in conservation of the bio diversity through people’s participation. The Joint

Forest Management was initiated in the study villages in 1992 after the JFM

resolution 1990 of Govt. of West Bengal. The Forest Department of the Government

of West Bengal is actively involved in the Joint Forest Management and along with

the villagers are jointly responsible for protection of the forest and wildlife.


134

A Forest Protection Committee (FPC) in the study areas has been formed whose

prime responsibility is to protect the forest. The FPC was formed after several

discussions of the villagers with the forest staff of the local Beat office. Most of the

village households become member of the Forest Protection Committee. The

household head and spouse of majority of the family becomes member of the General

Body. In the study villages, around 10-15 household members in each of the villages

are part of the Forest Protection Committee. The Forest Protection Committee has an

Executive Committee. As per the by-laws, the FPC is normally headed by a Chairman

of the Panchayat Samiti. The other members include the Gram Panchayat Pradhan,

elected representatives of the villages (maximum 6) and the Beat Officer (as member

secretary). In most of the cases it is observed that the FPC members are persons

normally holding a higher rank among the villages or a leader amongst the villagers.

The Executive Committee plays the critical role of executing the Joint Forest

Management programme. The FPC involves the villagers in plantation of trees and to

take regular care of their plantation. The villagers are entitled to 25 percent of the

usufructs after auctioning of the plantation. They also actively involve the villagers in

protection of the forest resources. The FPC ensures that the villagers are able to meet

their subsistence through collection of forest products. The villagers collect dried

leaves, fire wood, canes, etc. from the forest area. The FPC also involves the local

people in planning, development of the forest and regeneration of the forest through

plantation of trees. The men and women of the villages protect the allotted forest area

through patrolling activities on rotation basis day and night. This has brought down

the illicit felling of trees for timber in the study villages. Every month a General Body

meeting is held by the Forest Protection Committee. The meeting schedule is

announced in every village by beating of drums and all the villagers are requested to

attend the meeting without fail. During the meeting, the villagers also discuss with

the forest officials their need for collection of Non Timber Forest Products from the

forest as they are highly dependent on it.


135

According to the villagers of Panjhoria village of Bankura district, Ravidas Hembram,

Gurupada Husda and Gopinath Hembram:

“We are twenty six households living in this village. Agriculture is our

primary occupation. Sometimes we also work as wage labourers. Besides this

we collect forest products for our own consumption and at times for sale. We

are members of the forest protection committee. The forest department

provide us job for planting and felling of trees and we are paid for it. We want

to protect our forest as we are allowed by forest department to collect

firewood, fodders, leaves, fruits and vegetables from the forest in a restricted

manner without any cost.” (Dated 28th August, 2011, medium of language was

Bengali.)

The FPC plays a vital role in enforcing the guidelines laid down by the JFM schemes.

In case, anyone breaks the rule enforced by the Forest Protection Committee or

engage in illicit felling of the forest trees, then the villagers reprimand him. In case he

continues to illegally cut the forest trees then, when caught, the Forest Protection

Committee take away his cutting implements and impose a penalty ranging from Rs

100/- to Rs 500/- depending upon the seriousness of the crime. In case the offender

refuses to pay the penalty, he is brought to the Beat Officer who along with the

Executive Committee members decides on the quantum of punishment to be enforced.

It has been observed that imposition of heavier penalties is ineffective, since the

villagers are poor and therefore unable to pay the fine. It is further observed that in

most cases imposition of fine of Rs 100/- along with public denouncement has been a

more effective tool in controlling the offence. The FPC also plays a vital role in

controlling inter-village conflicts.

After the formation of Joint Forest Management in the study villages in 1992, it has

been possible to protect the forest resource to a large extent. It has been possible to

prevent the massive extraction of the forest resources due to felling of the trees. The

villagers have now become aware of the importance of conservation of the forest.

Since the villagers are extremely dependent on the forest resources for their

subsistence, hence they are also responsible to protect the forest.


136

The statistics on JFM participation for the surveyed villages of Bankura and Purulia is

shown in Table 6.9 below:

As per Table 6.9 above, 75.33 percent of the surveyed household in Bankura district

and 85.33 percent of the surveyed household in Purulia district participate in JFM.

The percentage of households participating in JFM meetings in the surveyed villages

of Bankura and Purulia districts are 55.33 percent and 78 percent. As per the survey

report, 82 percent of the surveyed villages in Bankura district are either highly

satisfied or satisfied with the performance of JFM as compared to 94.67 percent in

surveyed villages in Purulia district. The survey result also shows that the percentage

of household in the surveyed villages of Bankura district involved in planning and

TABLE 6.9

Number Percentage Number Percentage Number Percentage113 75.33 128 85.33 241 80.33

83 55.33 117 78.00 200 66.67

=>Highly Satisfied 83 55.33 67 44.67 150 50.00 =>Satisfied 40 26.67 75 50.00 115 38.33 =>Neutral 18 12.00 6 4.00 24 8.00 =>Not Satified 9 6.00 2 1.33 11 3.67

=>Planning & decision making 91 60.67 98 65.33 189 63.00 =>Implementation 19 12.67 24 16.00 43 14.33 =>Benefit Sharing 2 1.33 4 2.67 6 2.00 =>Evaluation 1 0.67 2 1.33 3 1.00 =>Not Involved 37 24.67 22 14.67 59 19.67

=>Strong Participation 83 55.33 117 78.00 200 66.67 =>Occasional Participation 16 10.67 8 5.33 24 8.00 =>Not very Often 8 5.33 3 2.00 11 3.67 =>Hardly ever 6 4.00 0 - 6 2.00 => Not involved 37 24.67 22 14.67 59 19.67


JFM Participation in the study area of Bankura and Purulia districts

BANKURA PURULIA

No. of Household Participation in JFM monthly meetings

No. of Household participation in JFM

Note: No. of households surveyed: Bankura=150; Purulia=150;

Performance of Community / Forest Management

Participation of house hold members in organizing activities

Rate of family members participation in Community / Forest management

TOTAL


137

implementation of JFM is 60.67 percent and 12.67 percent respectively as compared

to 65.33 percent and 16 percent in the surveyed villages of Purulia district. Further,

55.33 percent of the surveyed household in the Bankura district strongly participate in

JFM activity as compared to 78 percent in the surveyed villages in Purulia district.

The participation of households and the enforcement of forest protection scheme in

the study villages have been depicted in Table 6.10 below:

From the above Table 6.10, it is observed that the participation of household in Joint

Forest Management activities is low in the villages of Ramjibanpur and Baldanga in

Bankura district. The incidents of cases of violation of rules, number of overuse

caught, warned and freed, and number of cases imposed penalty and refusal of penalty

for the year 2008, 2009 and 2010 have also been shown for the study villages in Table

6.10. From the data, it is observed that the incidents of cases of violation of rules have

shown a declining trend in most of the surveyed villages during the period 2008-2010.

From the above data it is also observed that in the villages of Ramjibanpur, Seolibona

and Baldanga the incidents of cases of rule violation and refusal of penalty imposed

are high as compared to other villages in the study area. It is further observed that in

TABLE 6.10

2008 2009 2010 2008 2009 2010 2008 2009 2010 2008 2009 2010Panjhoria 26 26 18 7 6 4 4 3 2 3 3 2 2 1 1Ramjibanpur 20 10 2 9 7 6 5 4 3 4 3 3 3 3 3Seolibona 54 40 20 13 10 9 8 5 5 5 5 4 3 2 4Baldanga 7 2 0 6 5 5 3 3 2 3 2 3 3 2 3Dulaltora 18 18 15 6 4 3 4 2 2 2 2 1 2 1 0Tantirdanga 25 17 14 8 6 5 5 3 2 3 3 2 2 1 2Jiyathole 81 78 70 7 3 1 3 3 0 4 2 1 1 1 0Marbediya 26 15 8 12 8 6 8 6 3 4 3 3 3 3 2Ambari 43 35 23 9 7 4 5 4 2 4 3 2 2 1 1

300 241 170 77 56 43 45 33 21 32 26 21 21 15 16Source:Survey data 2011

Bankura

Puru

lia

TOTAL

Forest Management and Enforcement of Forest Protection Scheme

District Name of VillageNo. of Households

No. of Households participated

in JFM (2010)

Active JFM participants

(2010)

Incidents of cases of

violation of rules

No. of Overuse caught, warned

and freed

No. of cases imposed penalty

No. of cases refused to pay

penalty


138

the year 2010, the number of incidents of ‘refused to pay penalty ’ is nil in the villages

of Dulaltora in Bankura district and Jiyathole in Purulia district where forest

degradation is least as compared to other villages (Table 4.12 in Chapter 4). This

implies that forest management is more active in these villages as compared to other

villages in the study area.

The members of Forest Protection Committee in Seolibona village of Bankura district,

Rajesh Mudikora, Dilip Mudikora and Jodeswar Mudikora gave their opinion:

“Involvement of community members need to be organised and the position of

job creation for forest protection need to be made compulsory” (Dated 24th

September, 2011, medium of language was Bengali)

We can therefore infer that with the establishment of Joint Forest Management,

enforcement of forest protection has shown a very positive trend. Further the

households have realised that protection of the common forest is in their own interest

and violating the rules has an adverse effect to them. Forest management thus plays

an instrumental role in forest preservation and conservation. More active the

management greater is the possibility of forest conservation and vice versa.

6.6 Collective Action in Joint Forest Management

Collective action refers to concerted actions of people that share a common interest,

perceive that interest and act to achieve it (World Bank, 1998). It is voluntary or

mandatory depending on the type of action being executed and the institutions within

or through which the action is executed (Gregario, et al., 2004). Our primary focus in

this study is to explore how forest dependency influences household’s active

participation in Joint Forest Management. Local communities, whose income from

agriculture or other sources is uncertain, are very much dependent on forest for their

income and consumption and are thus more interested to conserve forest resources.

Initially, the households who were very much dependent on the forest and whose

income from forest resources was high could not afford the cost of restrained forest

use. These households therefore did not show any interest in the conservation of the

forest resources as they stayed out of the JFM programme and collected forest


139

products illegally. Later they realised that if they did not conserve the forest resources

then it was not possible to smoothen their income and consumption during the period

of agricultural uncertainty.

The forest scarcity and degradation is the crucial factor to inspire the villagers to join

JFM. The Joint Forest Management becomes effective only if the local rural

community whole heartedly participates in the managing of the common forest

resources. In order to protect the forest from illegal felling of the trees, the forest

patches has to be guarded and monitored day and night by the villagers as per the

guidelines laid down in the JFM. Further, the collection of common forest products

like fuel wood, fodder, fruits, vegetables, etc. is restricted to only certain specific

areas of the forest as per the guidelines of the JFM and thus the local rural villagers

have to travel a longer distance in order to collect the common forest products. The

villagers are to be involved in plantation and regeneration of the high value forest in

order to generate economic gain through the usufructs in the long run. Since a large

part of their participatory labour is to be involved in JFM activity, there is always a

question of trade off with that of agricultural operations. The villagers are involved in

agriculture primarily for their own consumption. The agricultural activity is seasonal.

This is due to the fact that there is lack of proper irrigation facility in the villages and

the villagers have to depend heavily on the rain for their cultivation. Moreover, the

rural households do not have any work during the non-agricultural seasons. Hence the

villagers have to decide about allocating their endowed labours either in agricultural

activities or in forest protection activities that also produces means for their

subsistence. Now, the forest resources that can be managed effectively by the local

rural communities depend on the strength of the collective action i.e. joint action of

the community to conserve forest resources as well as improve rural livelihood.

We have explained the determinants of collective forest management through

econometric analysis to give an understanding of the relationship between forest

dependency and active forest management. Collective action in forest management is

measured by time involvement in the management of forest resources. The 9 villages

in our study area from the two districts of Bankura and Purulia have been considered

separately and their results have been compared. Attempt has been made to capture


140

the relevant socio-economic variables that influence the strategy of collective action

in forest management in the study area. In this context following Bwalya (2004), we

have specified an econometric model of collective action and discuss the choice and

expected signs of explanatory variables. We estimate the following econometric

model of collective action

12

i

n

M i ij ijj

L a b Y=

= + +∈∑ ………………………………………..….(1)

where 1ia denotes community dummies, ijY is set of explanatory variables including

the index of individual organisational experience, livelihood activities, socio-cultural

heterogeneity, age, gender, household size, wealth, forest condition and ∈ is the error

term. ML is the dependent variable defined as the amount of labour household

contributes to local management and i and j indexes communities and individual

variables respectively. Our intension is to examine the impact of forest dependency on collective action in

JFM. Here the dependent variable is the number of man days per year involves in

JFM activities. Field survey data shows that about 25 percent of the total respondents

in Bankura district and 15 percent in Purulia district allocated zero man days to JFM

activities. Hence we have applied Censored Tobit model as it makes survey data more

convenient to analyse. Since the dependent variable is censored from below, we apply

maximum likelihood estimation to estimate the Censored Tobit model.

Our specified model is

0 1 2 3 4 5iML a b FSIZE b FEMPER b AVRAGE b AVRSCH b PERAGRIN= + + + + +

6 7 8b PERCPRIN b PERCPRCSM b WEALTH+ + + +∈

Here 0a is constant and ( 1,2,...,8)ib i = are the coefficients associated with the

explanatory variables and ∈ is the random disturbance term.

The households devote their time in JFM activities like planning and implementation,

monitoring, silvicultural activities at the cost of other livelihood activities i.e.

agriculture, off farm wage labour, etc. and leisure.


141

In the following Table 6.11, we present the dependent and explanatory variables and

their description, measurement and expected signs.

TABLE 6.11

Description of Variables in Censored Tobit Model Variables Variable Description Expected Sign

Dependent Variable

Collective Action ( ML )

Number of man days i.e. Labour

contribution in monitoring, planning

& implementation and management of

community forest resource

Independent Variables

1. FSIZE Average number of population of the

household (Size of the family) +

2. AVRAGE Average age of the respondent -/+

3. AVRSCH Average year of schooling of

household +

4. PERAGRIN Percentage of Agricultural income to

Total income -

5. PERCPRIN Percentage of income from common

property resources to Total income +

6. PERCPRCSM Percentage of CPR consumption to

Total consumption +

7. WEALTH Total household assets

WEALTH = 1 for ‘Well to do’

household

WEALTH = 0 for poor household

-

The key independent variables are percentage of Agricultural income to total income

( PERAGRIN ) and CPR income to total income ( PERCPRIN ). It was expected that

households whose agricultural income is steady are less interested in active

participation in JFM. On the other hand household whose percentage of CPR income

to Total income are high i.e. more dependent on forest resource are actively involved

in collective action in Joint Forest Management.


142

Empirical estimation of model specified in the above equation is described in the

following Tables 6.12 and 6.13:

TABLE 6.12

Determinants of Collective Action (Bankura District) Dependent Variable: LABJFM (Contribution of labour in JFM) Method: ML - Censored Normal (TOBIT) (Quadratic hill climbing) Sample: 1 150 Included observations: 121 Left censoring (value) at zero Covariance matrix computed using second derivatives

Variable Coefficient Std. Error z-Statistic Prob. C -8.939720 11.55629 -0.773580 0.4392

FSIZE 1.763608 0.756720 2.330597** 0.0198 AVRAGE 0.280050 0.192963 1.451314 0.1467 AVRSCH -0.009263 0.775809 -0.011940 0.9905 PERAGRIN -0.595780 0.184329 -3.232150* 0.0012 PERCPRIN 0.540002 0.233852 2.309159** 0.0209 PERCPRCSM 0.083001 0.365277 0.227227 0.8202 WEALTH -4.858511 3.303721 -1.470618 0.1414

Error Distribution SCALE:C(9) 13.76905 1.044966 13.17655 0.0000 Mean dependent var 15.67769 S.D. dependent var 12.84926

S.E. of regression 11.75636 Akaike info criterion 6.971670 Sum squared resid 15617.94 Schwarz criterion 7.179622 Log likelihood -412.7860 Hannan-Quinn criter. 7.056127 Avg. log likelihood -3.411455

Left censored obs 25 Right censored obs 0

Uncensored obs 96 Total obs 121 *Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level

Source: Estimated by EViews7 computer software using field survey data of 2011


143

TABLE 6.13 Determinants of Collective Action (Purulia District) Dependent Variable: LABJFM (Contribution of labour in JFM) Method: ML - Censored Normal (TOBIT) (Quadratic hill climbing) Sample: 1 150 Included observations: 121 Left censoring (value) at zero Covariance matrix computed using second derivatives

Variable Coefficient Std. Error z-Statistic Prob. C 4.171649 7.946446 0.524970 0.5996

FSIZE 1.553762 0.523497 2.968042* 0.0030 AVRAGE 0.109737 0.132708 0.826905 0.4083 AVRSCH -0.070155 0.533623 -0.131468 0.8954 PERAGRIN -0.910248 0.123300 -7.382397* 0.0000 PERCPRIN 0.550850 0.160520 3.431650* 0.0006 PERCPRCSM 0.212440 0.254061 0.836177 0.4031 WEALTH -7.024702 2.260913 -3.107020* 0.0019

Error Distribution SCALE:C(9) 9.637295 0.656865 14.67165 0.0000 Mean dependent var 18.14876 S.D. dependent var 12.78584

S.E. of regression 9.156199 Akaike info criterion 7.152541 Sum squared resid 9473.466 Schwarz criterion 7.360492 Log likelihood -423.7287 Hannan-Quinn criter. 7.236998 Avg. log likelihood -3.501890

Left censored obs 9 Right censored obs 0 Uncensored obs 112 Total obs 121

*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level Source: Estimated by EViews 7 computer software using field survey data of 2011

We have observed from the determinants of Collective Action in JFM that most of the

parameter estimates for household variables have expected sign and are statistically

significant. Results show that family size ( FSIZE ) and average age ( AVRAGE ) have

a positive effect on collective action in JFM in the surveyed villages of both Bankura

and Purulia district. Family size ( FSIZE ) is positive and significant suggesting that a

larger family size collects more CPR product and thus increases the scarcity of the


144

forest resources. So they are bound to actively participate in the JMF in order to

conserve the forest resources. Positive effect of average age of the respondent

( AVRAGE ) implies that aged people, due to their experience, are more interested to

allocate labour in Joint Forest Management. In the cases of AVRAGE , the result is not

significant. In both the districts we have observed that education ( AVRSCH ) has a

negative effect on collective action in JFM. This implies that educated people are

mostly involved in the service sector or off-farm wage labour and hence they get less

time to devote in monitoring or silvicultural activities in the JFM. However, this result

is insignificant in both the districts. Actually in some cases educated household

understand the importance of preservation of forest resources and are thus more likely

to actively participate in the JFM activity and also motivate other villagers to

participate as well.

It is evident from the analysis that collective action in JFM is negatively related to

percentage of agricultural income to total income of the household ( PERAGRIN ) in

both the district of Purulia and Bankura. In Bankura district, it is significant at 1

percent level of significance and in Purulia district it is highly significant. This

implies that household who has steady income from agriculture does not bother much

about forest resources and are therefore less interested in active participation in JFM.

On the other hand, household who depend on CPR products because of uncertain

agricultural income are more interested in conserving forest resources and give more

labour time in different activities of JFM. In line with our expectation, we observe a

positive relation between percentage of CPR income to total income ( PERCPRIN )

and collective action in JFM in both the districts and the result is significant with

Bankura at 5 percent level and Purulia at 1 percent level. This indicates that the

households participate in the JFM, primarily to gain access to the forest outputs from

the forest resources, so as to overcome the uncertainty and insecurity in their

livelihood.

We have hypothesised a positive relation between percentage of CPR consumption to

total consumption ( PERCPRCSM ) and active participation in JFM. In fact the

households who depend more on CPR for their consumption purposes are very much

concerned about forest resource conservation and hence actively participate in JFM.


145

However, the coefficient is positive in this case in both the districts although the result

is insignificant. The coefficient of wealth (WEALTH ) have the expected negative

signs on collective action management in the two surveyed districts and significant at

1 percent level in Purulia and 10 percent in Bankura. This signifies that wealth

reduces the incentive among the households to actively participate in the JFM. In few

cases ‘well to do’ households participate in the JFM only for social capital i.e.

personal interest, self-esteem, respect, etc. or for their compulsion of strong ties with

the Government officials. The above result indicates a strong relationship between

forest dependence and active participation in JFM.

6.7 Collective Action and Forest Conservation

Forest management has its inherent impact on the conservation of forests. More active

the management practice is, greater is the probability of efficient use of forest

resources. Forest management lowers the degree of over exploitation and hence the

degradation of forest resources. We now examine the role of forest management to

reduce forest degradation by using logit regression model. A range of socioeconomic

and environmental variable has been considered.

The logit-regression model has been fitted as follows:

1 2 3 4log( )1

i

i

P a b AVRAGE b AVRSCH b FSIZE b OWNLANDP

= + + + +−

5 6 7b LIVESTOCK b POVR b FMACT+ + +

Here the dependent variable is forest degradation ( FDGR ) which is a dummy

variable equal to ‘1’ if there is ‘more degradation’ and equal to ‘0’ if there is ‘less

degradation’.

Forest Degradation is measured on the basis of the data collected in the village survey

from three different variables - Extent of forest damage visually seen (FD), Condition

of the forest informed by the respondents as compared to that of earlier times (FC)

and Forest use penetration i.e. the depth into the forest from the village boundary

where use pressure was evident (FP). All these three variables (FD, FC, FP) are coded


146

(using four point scale) so that increasing values shows more forest degradation

(Detail in Chapter 4 in Section 4.6.1).

In the following Table 6.14 we have explained the explanatory variables, their

descriptions and expected signs:

TABLE 6.14

Description and Hypothesis in Logit Regression Model Variables Variable Description Expected

Sign

Dependent Variable

1. FDGR

Forest Degradation

FDGR =1, if there is more degradation

FDGR =0, if there is less degradation


1. FSIZE Average number of population of the

household (Size of the family) +

2. AVRAGE Average age of the respondent _

3. AVRSCH Average year of schooling of household +

4. OWNLAND Total land owned by the household _

5. LIVESTOCK Total number of livestock condensed

into animal units +

6. POVR Poverty of the household

POVR =1; if household belongs to BPL

POVR =0; if household belongs to APL

+

7. FMACT Active Forest Management

FMACT =1 in case of active forest

management

FMACT =0 in case of inactive forest

management

_


147

The logit regression model has been tested using the household level data through

field survey of Bankura and Purulia districts. The result is given in Table 6.15 below:

TABLE 6.15

Determinants of Forest Degradation (Bankura District) Dependent Variable: FRDGR Method: ML - Binary Logit (Quadratic hill climbing) Sample: 1 150 ; Included observations: 150 Covariance matrix computed using second derivatives


AVRAGE 0.067295 0.054709 1.230044 0.2187 AVRSCH -0.963454 0.373627 -2.578651* 0.0099 FSIZE 0.259937 0.384891 0.675353 0.4995 OWNLAND -0.497346 0.395422 -1.257761 0.2085 LIVESTOCK 2.019892 0.943984 2.139753** 0.0324 POVR 6.757563 3.032961 2.228042** 0.0259 FMACT -6.082197 2.162583 -2.812468* 0.0049







148

TABLE 6.16 Determinants of Forest Degradation (Purulia District) Dependent Variable: FRDGR Method: ML - Binary Logit (Quadratic hill climbing) Sample: 1 150 Included observations: 150 Covariance matrix computed using second derivatives


AVRAGE 0.025670 0.060074 0.427301 0.6692 AVRSCH -0.401253 0.360988 -1.111541 0.2663

FSIZE 1.240305 0.551595 2.248578** 0.0245 OWNLAND -0.375603 0.263661 -1.424570 0.1543 LIVESTOCK 0.627338 0.511499 1.226470 0.2200

POVR 3.689960 2.208836 1.670545*** 0.0948 FMACT -5.095440 2.001949 -2.545240** 0.0109

McFadden R-squared 0.897336 Mean dependent var 0.673333 S.D. dependent var 0.470565 S.E. of regression 0.139214 Akaike info criterion 0.236391 Sum squared resid 2.752054 Schwarz criterion 0.396958 Log likelihood -9.729344 Hannan-Quinn criter. 0.301625 Deviance 19.45869 Restr. deviance 189.5379 Restr. log likelihood -94.76893 LR statistic 170.0792 Avg. log likelihood -0.064862 Prob(LR statistic) 0.000000

Obs with Dep=0 49 Total obs 150 Obs with Dep=1 101


From the analysis it can be observed that forest degradation ( FDGR ) is positively

related to average age ( AVRAGE ), family size ( FSIZE ), number of livestock

( LIVESTOCK ) and poverty ( POVR ) whereas negatively related to education

( AVRSCH ), total land owned ( OWNLAND ) and Forest Management activity

( FMACT )in both the districts. In fact every family member of the household engages

themselves in collecting forest products and thus degrades the environment to a large


149

extent. Hence in the case of households with large family size ( FSIZE ), the

probability of the incidence of forest degradation is high. However, the result is

significant only in Purulia district. The positive relation between ( AVRAGE ) and

( FDGR ) suggest that experienced elder members degrade the forest resources

critically by collecting more CPR products from forest. However, the result is

insignificant in both the districts. Again, households with large number of livestock

( LIVESTOCK ) damage the environment badly by accumulating fodder to feed

animals. Hence larger the number of livestock more is the probability of incidence of

forest degradation. The coefficient of poverty ( POVR ) is positive and significant in

both the districts which suggest a strong positive relationship between poverty and

forest degradation.

As expected education ( AVRSCH ) is negatively related to forest degradation.

Educated people have many alternative income opportunities and hence less interested

in collecting forest products. Thus educated people help to reduce the incidence of

forest degradation. However, the result is significant in Bankura district only. In the

case of ( OWNLAND ), the negative relationship indicates that the household who own

large agricultural land get less time to collect CPRs from forest and hence causes less

forest damage. Here the impact is insignificant in both the districts.

The most important finding of this study is the role of active Joint Forest Management

(JFM) in reducing the forest degradation. In line with our hypothesis, Forest

Management activity ( FMACT ) has the expected negative sign i.e. active forest

management is associated with a smaller extent of forest degradation.

This result demonstrates that improved management has a positive role to check

environmental degradation. Again it is observed that the association between Forest

Management activity ( FMACT ) and forest degradation ( FDGR ) is negative and

significant in both the districts. It is observed that most of the rural poor depend on

common property resources for their livelihood. Hence the CPRs should be properly

managed so that the rural poor can get maximum benefit but not at the cost of nature.


150

6.8 Conclusion

The empirical evidence based on the survey of 6 villages in Bankura and 3 villages in

Purulia suggest that the rural households are highly dependent on Common Property

Resources for their subsistence and are therefore very much concerned about the

regular depletion of the forest resources. In order to ensure the availability of the

forest resources in the long run, the rural households have actively participated in the

Joint forest Management activity in the study area. Empirical evidence suggests that

75.33 percent of the surveyed household in Bankura district and 85.33 percent of the

surveyed household in Purulia district participate in JFM. We have explained the

determinants of collective forest management through censored Tobit model to give

an understanding of the relationship between forest dependency and active forest

management. It is evident from the analysis that income from agriculture plays an

important role in the participation of JFM. In those households where the percentage

of the agricultural income to the total income is very low, the participation in JFM is

very active. We further observed that collective action in JFM is positively related to

percentage of CPR income to total income and the impact is significant in both the

districts. Therefore the result is consistent with our hypothesis. In fact, the households

participate in the JFM activity to gain access to the forest resources and thus mitigate

any uncertainty and insecurity in their income. We had hypothesised that active

participation in forest management plays a positive role in alleviating environmental

degradation. The logit regression result confirms that Joint Forest Management has a

critical role in reducing forest degradation. Improved forest management plays a

positive role in sustainable forest ecosystem.

Women’s Participation in CPR Management

151

CHAPTER 7

WOMEN’S PARTICIPATION IN CPR MANAGEMENT

7.1 Introduction

Forests are a vital livelihood support system for the rural poor. Rural women are

highly dependent on forest resources as it plays an important role in the viability and

subsistence of the households. Every day early morning, groups of women set out

towards the forest to collect firewood and several non-timber forest products like

fruits, medicines. The extreme heat, lack of water and difficult terrain make this work

of CPR collection extremely difficult. Degradation of forest leads to forest produce

being available further away from the place of dwelling, thereby increasing the

drudgery of the women. However, the rural women continue to do so since it provides

them with firewood, food and income through sale. A study in Uttar Pradesh, India

showed that rural women obtained 33 to 45 percent of their income from forests as

compared with only 13 percent in the case of men (IUCN-factsheet-gender and

forestry, 2010). Empirical evidence from southern districts of Bihar, suggest that rural

women collect mahua flowers (Madhuca indica), kendu leaves (Diospyros

melanoxylon) used in making indigenous cigarettes, mushrooms and mahua seeds,

tamarind (Tamarindus indica) (Rao, 1996). In West Bengal, tribal women gather sal

(Shorea robusta) leaves for six months of the years (Poffenberger, 1993). Based on

the empirical study of tribal women in Jharkhand, Kelkar and Nathan (1991), assert

that "within the family the income from sale of forest produce tends to be counted as

the income of the individual who gathers and sells the produce. Increasing the income

from forestry will thus also help strengthen the position of women within the family."

Rout et al. (2010) opine that collection of Non Timber Forest Product provides

employment for the tribal women and they have larger potential for generating

employment in future.

However, lack of rights and responsibilities to control and use the common property

resources make them highly vulnerable. Rural women consider the forest as their

source of livelihood and hence are very much concerned about the long term

sustainable governance of the forest resources. Indiscriminate collection of forest


152

resources leads to degradation of forests and the rural women have to travel longer

distance from their home to collect them. Women consider that the environmental

degradation can lead to their reduced supplementary income and community forest

management can help to reduce it. Thus any environmental policy should not only

take into account the economic impact of the women and but also leverage the role of

women in the protection and management of the forest resources (Agarwal, et al.,

2006). Understanding of women’s relationship with the environment needs to

recognise the "relationships of power and authority, negotiation and bargaining and

the wider social relations in which 'decisions' about land and trees are embedded"

(Leach, 1990). Jamisolamin (2012) believes that women can play a key role in

ensuring environmental protection and conservation provided they are allowed to take

decision in the management process. Thus women can play a critical role in the Joint

Forest Management for long term sustainability of the scarce natural resources.

For a sustainable forest management, women should not only have access to the forest

and accrue the benefits from it but also the right and authority to take decisions (Giri,

2012). Securing tenure and access rights to forest resources is a critical step towards

achieving environmental and social justice (Buchy, 2012). The author opines that

there is widespread discrimination against rural women as they face exclusion on

gender and ethnicity grounds and are denied some of the basic rights due to the

unclear, unsecured and unequal tenure rights. Women normally encounter negative

perception not only from the village men but also from the male officials of the forest

department. In West Bengal, for instance, there were many complaints from the rural

women that the male officials discouraged them from coming to the forest office and

also rebuked them if they came in the evening (Narain, 1994). Further, rural women

were also excluded from several other activities like water users associations, village

councils, etc. (Agarwal, 2001).

Active participation of women in forest management involving local decision-making

can not only have a positive impact on the resource related outcomes but also

significantly increase the effectiveness of the institution involved in forest governance

and protection through greater control of illicit harvesting of forest products and

regeneration in the forest (Agarwal, et al., 2006). Women can also play a greater role


153

in co-management of forests by increasing their collective bargaining power (Yadama

et al., 1997). Rural women play an important role in the development and peace

process by mediating during conflict situations. Women’s participation in forest

management through Mahila Mandals has led to active monitoring, protecting and

managing the common forest area (Bingeman, 2003). Women Self-Help Groups

(SGHs) play an important role in maintaining and protecting the common forest

which not only helps to generate income but also to their empowerment (Murugesan

& Namasivayam, 2012).

Participation of the women in forest management differ from one region and culture

to another, by one caste and tribe to another, by class position and by position in the

household and hence the forest department and NGOs should bring forth sensitive and

local specific strategies for them (Patricia Jeffery, et al., 1998). Women tend to avoid

participation in the forest management primarily due to lack of security, lack of

confidence, social and cultural restrictions (Dasgupta, 2006). Low participation of

women in Joint Forest Management programmes can be attributed to social and

cultural constrains (Godbole, 2002). In a study of 20 Community Forest Groups, Sarin

(1998) found that 60 percent had no women, and only 8 percent of the 180 Executive

Committee members were women. According to Dasgupta (2006), women

participation in JFM can be enhanced by imparting a simple and low-cost technology

based training programme.

In Bankura district of West Bengal, the District Forest Officer had issued a circular

stipulating that there should be a minimum of 30 percent women in the general body,

which had resulted in raising the female membership in several villages (Viegas &

Menon, 1993). Das & Sarker (2008) opine that communal solidarity, mutual trust and

coordinated actions exits in JFM villages, thereby increasing the social capital.

According to the authors, those Forest Protection Committees (FPCs) where social

cohesion and community solidarity is weak, effective leadership and local support can

bring about in improving the social capital.

Despite the fact that women are the major users of common property resources, their

involvement in Joint Forest Management is generally marginal in India. To regulate


154

illicit grazing and removal of forest products, direct involvement of women in JFM is

essential.

Under this backdrop, based on our primary data, this chapter attempts to explore the

role of women participation in sustainability of the common property resources in the

study area. In specific terms the objective of the study are as follows:

i) To explore the role of women in CPR collection

ii) To examine the performance of women in JFM committees

iii) To investigate empirically the effect of women’s active participation in

JFM on forest resource preservation and conservation.

In order to study the impact of women’s participation on sustainability of the forest

resources, we have planned to estimate a binary probit model.

In this chapter we have tested the following hypothesis:

H1: Women’s participation in forest management improves the

sustainability of the forest resources

7.2 Role of Women in CPR collection in the study area

Forests contribute critically to the survival of the rural poor and the women who own

little private land. The common forest resources in the study areas of Bankura and

Purulia provide the rural poor with food, medicine, fodder, firewood, etc. The women

collect firewood for domestic as well as for commercial purposes. They collect certain

tree barks and medicinal herbs which are used as industrial raw material as well as for

medicinal purposes. Apart from collection of firewood, the rural women are also

involved in the collection of fodder from the common forest area. Sal and Kendu

leaves used as vegetables are also collected by the rural women in the study areas on a

regular basis almost throughout the year. The Sal leaves are stitched by hand to make

Sal plates, which are then sold in the local market. The rural women also sell their

collected products like firewood, Sal stick (used as tooth brush) and wild vegetables at

the local market. However, due to poor transportation system and lack of organised


155

market in the study areas, they are forced to sell the forest products to mobile agents

or middlemen, who visit their village quite often, even if they receive a lower price.

Generally the men collect forest products only when they are not engaged in

agricultural or other off-farm activities. Thus CPR collection for men is a secondary

job. However, collection of forest products for rural women living in the forest

fringes is always a primary occupation. Thus forest plays a crucial role in their daily

lives.

The women villagers of Panjhoria village of Saltora block in Bankura district, Bani

Mudikora and Durga Hembram, share their views with us:

“We collect fuel wood, fodder, sal and kendu leaves, mahua fruits and flowers

from our nearby forest for domestic as well as commercial purposes without

harming the whole plant. Except during monsoon, we gather forest products

all the year round. When we need money we have to sell the forest products

immediately. Most of the time we do not get the correct price.” (Dated 4th

October, 2011, medium of language was Bengali)

Forest women collect NTFPs as much as possible for commercial purposes. However,

they also gather the same in a restricted way for their own consumption. Although

women are the major collector of NTFPs and have good knowledge about the forest,

they cannot actively participate in JFM which is in general male dominated in our

surveyed area. During the fieldwork a number of women member highlighted the fact

that their opinion was not considered in JFM meeting. When the Forest Department

officers arranges meeting, it is mainly attended by the male villagers. The Beat

officers also meet with male villagers only.

Women members of JFM in village Ambari of Purulia district, Mongala Kora and

Sabita Hansda, have commented in the group discussion:

“We gather fallen seeds, leaves, grass, fruits and flowers so that plants are not

damaged. We collect only those NTFPs which are permitted by the FPC. We

obey them because if Forest Department take any action against us we will

suffer a lot. We are very poor and solely dependent on forest products. Hence

we have to protect the local forest at any cost for our own endurance.” (Dated

28th November, 2011, medium of language was Santali/ Bengali)


156

PHOTO 8 PHOTO 9

Interaction with rural women in Jiyathole Women collecting cow dung in Baldanga

In our study villages in the district of Bankura and Purulia in West Bengal, we have

analysed the average income and average monthly contribution of CPR to household

income for male and female headed households and is shown in Table 7.1 below. As

per the table, the percentage of Female headed households in the villages of Dulaltora

(27.78) in Bankura district and Jiyathole (29.63) and Ambari (25) in Purulia district is

relatively high. We have observed from Table 4.12 in Chapter 4, the extent of

depletion of common property resources during 1990-2010 is less in the above

mentioned 3 villages where percentage of female headed household is high as

compared to the rest of the villages in the study area. Hence, we can infer that female

headed households are more concerned about forest conservation as compared to the

male headed households.

The average income of male headed households and the female headed household

excluding and including CPR income along with their average monthly contribution of

CPR as depicted in Table 7.1 shows a particular trend. In most cases in the female

headed households, the average contribution of CPR to the household income is high as

compared to male headed household.


157

In the village of Panjhoria in Bankura district, the average monthly contribution of CPR

in the household income for a female household is as high as 40 percent as compared to

22 percent for a male headed household. Similarly in the village of Jiyathole in Purulia

district, the average monthly contribution of CPR in the household income for a female

and a male headed household is 55 percent and 42 percent respectively. The result from

our surveyed villages in Bankura and Purulia district reveal that the average monthly

contribution of CPR in the household income for a female and a male headed

household is 44 percent and 26 percent respectively. This data implies that the female

headed households depend more on CPR collections as compared to the male headed

households to supplement their household income.

In Table 7.2 below, we depict the time spent by the household members in CPR

collections and the employment days generated thereby.

TABLE 7.1

(Rs) (%) (Rs) (%)

Panjhoria 23 3 11.54 653 246 841 412 188 22 166 40 Ramjibanpur 20 0 - 1,058 - 1,254 - 196 16 - -

Seolibona 45 9 16.67 912 554 1,089 847 177 16 293 35 Baldanga 6 1 14.29 529 490 693 641 164 24 151 24 Dulaltora 13 5 27.78 546 844 751 1,138 205 27 294 26

Tantirdanga 24 1 4.00 606 700 771 899 165 21 199 22 Jiyathole 57 24 29.63 757 646 1,314 1,439 557 42 793 55

Marbediya 21 4 16.00 468 579 605 678 137 23 99 15 Ambari 33 11 25.00 825 381 959 505 134 14 124 25

131 19 12.67 771 540 954 798 183 19 258 32 111 39 26.00 724 555 1,083 1,069 359 33 514 48 242 58 19.33 749 550 1,014 977 265 26 427 44


Bankura

Purulia

Women Headed Household and CPR collection

Bankura TotalPurulia TotalGrand Total

Percentage of female headed

household to total

household

District Name of village

No. of Male HouseHolds

Head

No. of Female

HouseHolds Head

Average income of Male Head HouseHolds excluding

CPR income (Rs)

Average income of Female

Head HouseHolds excluding

CPR income (Rs)

Average income of Male Head HouseHolds

including CPR income

(Rs)

Average income of Female

Head HouseHolds

including CPR income

(Rs)

Average Monthly Contribution of CPR in HouseHold Income

Male Female


158

TABL

E 7.2

Male

Fem

aleTo

tal

Male

Fem

aleTo

tal

Male

Fem

aleTo

tal

Male

Fem

aleTo

tal

Panj

horia

2632

3466

00

016

1040

6956

796

1511

Ram

jiban

pur

2026

2450

00

018

2827

6445

929

1411

Seol

ibon

a54

7484

158

260

2644

1270

8011

492

711

9Ba

ldan

ga7

98

170

00

615

740

1355

912

10Du

lalto

ra18

2038

583

03

1350

2252

3602

87

8Ta

ntird

anga

2531

3566

50

519

1127

7646

878

109

Jiyat

hole

8110

314

624

92

02

5306

1052

115

827

69

8M

arbe

diya

2535

4479

20

216

2025

6541

856

77

Amba

ri44

5872

130

80

820

7043

2063

904

86

150

192

223

415

340

3411

726

1968

131

407

811

915

019

626

245

812

012

8996

1740

626

402

68

730

038

848

587

346

046

2072

237

087

5780

97

108

Sour

ce: F

ield S

urve

y, 20

11

to 8

hour

s job

) and

then

divid

ing it

again

with

the n

umbe

r of h

ouse

hold

mem

bers

Wom

en an

d CP

R co

llect

ion in

last

one

mon

th o

f the

dat

e of s

urve

y

Note:

The A

vera

ge E

mplo

ymen

t man

days

for m

ale an

d fem

ale P

er m

embe

r Per

mon

th is

calcu

lated

by di

viding

the t

ime s

pent

by th

em in

CPR

colle

ction

by 8

(One

man

day i

s equ

ivalen

t

Bank

ura T

otal

Puru

lia To

tal

Gran

d Tot

al

Dist

rict

Nam

e of v

illage

No. o

f Ho

useH

olds

No. o

f Adu

lts in

volve

d in

CPR

Colle

ction

No. o

f Chid

ren i

nvolv

ed in

CPR

Co

llect

ionTim

e inv

loved

in C

PR C

ollec

tion

in las

t 1 m

onth

(Hou

r)

Aver

age E

mplo

ymen

t man

days

Pe

r mem

ber P

er m

onth

in C

PR

colle

ction

Bank

ura

Puru

lia


159

From Table 7.2, we observe that in the study villages in Bankura and Purulia districts,

out of total 873 adult members involved in CPR collections in the last 1 month, 485

members are female and 388 are male. This figure implies that the female adult

members consider CPR collection as an important household activity as compared to

the male adults in the study area. The time involved in CPR collection in the last 1

month in hours for the male and female members shown in Table 7.2. The data

reveals that female members of the household spent more time in CPR collections as

compared to the male members in all the villages in the study area. The income

generated by collection of CPRs by the household members supplement the total

household income. In order words, collection of CPRs by the household members is a

means of employment. We have depicted the average employment man days

generated in collection of CPRs for both adult male and female. One man day is

equivalent to 8 hours of work. Thus the average employment man days for male and

female is calculated by dividing the time consumed by them in CPR collection by 8

and then dividing it with the number of household members. From the table, we

observe that the average employment man days in CPR collection for male and

female in the study villages of Bankura district is 8 and 11 man days per month

respectively. Similarly, in the study villages in Purulia district, the average

employment man days in CPR collection for male and female are 6 and 8 man days

per month respectively. From the Table 7.2, we notice that the female adult members

are involved in CPR collections to a much larger extent as compared to the male

members. Further, the female members generate higher employment man days

through CPR collections as compared to the male members in the household.

Hence we can conclude that the women play a much larger role in CPR collections as

compared to the men in the study area. Further, the women are able to supplement

the household income through CPR collection. The CPR collections are not only used

for consumption but also act as an additional source of income through sale.


160

7.3 Women’s Participation in Forest Resource Management

Government of India (GoI) had launched the Joint Forest Management programme in

1990 in order to conserve, protect the forest and also rejuvenate the degraded ones.

JFM was initiated for sharing of products, responsibilities, control and decision

making authority over forest lands between the local community and the forest

department. JFM provided the opportunities for the forest dependent people to meet

their subsistence requirement. It provided the users a stake in the forest benefits and a

role in planning and management for sustainable development of the forest. However,

due to social, economic and cultural constrains, participation of women in JFM

program is limited. The lack of participation by women greatly reduced their

opportunity to share information and knowledge. Further it also prevents them to

voice their opinions. Thus several activities could negatively impact on both women

and their use of the forests. Further, women’s concerns weren’t heard at JFM

meetings, mainly due to the fact that men always decided the timing of the meetings.

Even when women were physically present at meetings their views weren’t heard and

only the opinion of the men are taken into account (Patricia, et al., 1998). While, the

women are interested in ensuring increased and sustained availability of NTFPs, the

men are generally interested in maximising monetary returns. The poor involvement

of women also meant that the choice of species for planting in JFM areas was often

decided by men, who chose cash profits over fuel and fodder yields. This reduced

women’s involvement and interest in the JFM program. In several cases information

about the provisions, rules and responsibilities of JFM program are not communicated

to the women folk. The Forest Department staffs also do not make efforts to

understand women’s point of view and push them for active participation in the JFM

program. The views of the women are not considered. For the women, participation in

JFM meeting means loss of wage. No provisions are made for the security of the

women taking active part in the JFM program. Women normally collect firewood

from the forest and the forest department staffs often prohibit such activities in the

protected areas. Since no alternatives are made for women who depend on the

firewood for their income, they easily become resentful of JFM (Pathak, 2000).

In Table 7.3, we have depicted the participation in the Joint Forest Management in the

study villages in 2010.


161

TABL

E 7.3

Male

Fem

ale

Male

Fem

ale

Male

Fem

ale

Male

Fem

ale

Male

Fem

ale

Male

Fem

ale

Male

Fem

ale

Male

Fem

ale

Panjh

oria

255

287

3611

155

72

52

20

72

Ramj

ibanp

ur9

010

212

28

20

00

02

02

0Se

olibo

na36

1042

1251

1823

1210

010

44

04

2Ba

ldang

a3

03

04

03

00

00

00

01

0Du

laltor

a16

821

1025

1412

85

37

20

01

1Ta

ntirda

nga

2411

3012

4220

1512

102

84

20

72

Jiyath

ole91

2310

231

128

4777

3020

715

710

06

3Ma

rbediy

a16

218

522

815

42

14

10

01

2Am

bari

3415

4118

5525

2010

124

128

50

63

113

3413

443

170

6576

3932

730

1210

022

714

140

161

5420

580

112

4434

1231

1615

013

825

474

295

9737

514

518

883

6619

6128

250

3515

Sour

ce: F

ield

Sur

vey,

2011

Parti

cpati

on of

Hou

seho

ld me

mber

s of s

tudy

area

in JF

M

No. o

f non

-ac

tive

mem

bers

in JF

M (20

10)

Distr

ictNa

me

of

villa

ge

No. o

f Hou

seho

ld

mem

bers

parti

cipat

ion

in

JFM

(201

0)

No. o

f mem

bers

invo

lved

in

plan

ning

&

decis

ion

mak

ing

(2010

)

No. o

f mem

bers

invo

lved

in

Impl

emen

tatio

n (20

10)

No. o

f Hou

seho

ld

mem

bers

parti

cipat

ion

in

JFM

(200

8)

No. o

f Hou

seho

ld

mem

bers

parti

cipat

ion

in

JFM

(2009

)

Bank

ura T

otal

Puru

lia T

otal

Gran

d Tot

al

No. o

f mem

bers

invo

lved

in

Bene

fit sh

arin

g (20

10)

No. o

f mem

bers

invo

lved

in

Eval

uatio

n (20

10)

Bank

ura

Purul

ia


162

In Table 7.3, we have shown the breakup of members (male and female) involved in

the different aspects of the JFM i.e. planning and decision making, implementation,

benefit sharing and evaluation. The Non-active members (male and female) have also

been shown separately.

The female villagers of Ambari in Purulia district, Badani Kishu and Neoti Mudikora,

had shared their view on Joint Forest Management:

“We are the major collectors of forest products in our village and also

members of JFM. The Forest Protection Committee members, most of the

time, do not inform us about the meetings ……even if we are present our

opinions are not considered…. we are forced to stay away from planning,

implementation and benefit sharing activities… We feel neglected” (Dated

19th December, 2011. Medium of language- Santali /Bengali)

From Table 7.3, we observe that the participation of the female members in JFM is 65

in Bankura district and 80 in Purulia district which is very low as compared to the

male members. However the participation of the female members in JFM has

increased during the last 3 years in both the districts. Majority of the household

members actively participating in the JFM are involved in the planning & decision

making stage only. The number of members involved in implementation, benefit

sharing and evaluation is very low. It is also observed that there are a sizeable number

of household members who are not active participants in the JFM and therefore they

are not active members. Participation of the female members is restricted to the

planning and decision making stage only.

Thus we can conclude that participation in JFM is mainly dominated by the male

members and therefore the female members have almost no say in the JFM activities

in the study area.


163

7.4 Women’s Participation in JFM and Sustainability in Forest Resources

From our study area, we have observed that women members spent more time in CPR

collection as compared to their male counterpart and female headed household depend

more on CPR collections as compared to the male headed households to supplement

their household income. Hence forest degradation affects the women member badly

and thus they are very much concerned about forest resource sustainability. Therefore

we have assumed that active participation of women member in JFM have a positive

impact on sustainability of forest resources. However, sustainability of forest

resources also depends on some socio-economic variables like sex ratio, female

headed household, female literacy, per capita cattle unit, dependency ratio, market

pressure and imposition of fine on CPR rule breakers.

7.4.1 Empirical Model Specification Rural women are responsible for collecting forest products in order to meet the

subsistence needs of the household. Collection of NTFPs also acts as a supplement to

their income. Women greatly value the forest conservation since degradation of the

forest not only leads to depletion of the forest resources but increases the distance to

be covered in order to collect them. Women who are largely dependent on the forest

for collection of forest resources like firewood and fodder for their subsistence are

also very much aware of the forest restoration program.

As women are major users of forest resources, they should involve themselves in

planning, decision making and implementation of the forest conservation programme.

Participation of women in JFM includes reforestation work and protection of

plantation, freely grazing livestock and illicit removal of forest products. Hence

sustainability of forest resources depends on active participation of women in JFM. In

addition to examining the importance of women’s participation in JFM, we have also

considered some demographic factors such as family size, sex ratio, gender gap in

education, male headed household etc. and market pressures. We have also examined

their influence on forest conservations.


164

In this section we have identified these factors within rural household and villages

determining the sustainability of forest resources through econometric analysis to give

an understanding between active participation of women in JFM and sustainability of

forest resources ( )SFOREST which can be written as:

SFOREST = f (Family size, Sex ratio, Active participation of women in JFM,

Gender gap in education, Number of male household head, market pressures)

We have used a Binary Probit regression model to examine the determinants of


Our specified model is:

0 1 2 3 4 5SFOREST SEXR FHEAD FLIT DEPR PCATTLEα α α α α α= + + + + +

6 7 8WACTPM DISM PUNSHMα α α+ + + +∈ ….……………..(1)

Here the dependent variable is

SFOREST = Sustainability of forest resources

Sustainability is measured on the basis of the data collected in the village survey from

three different variables:

i) Regulate illicit grazing

ii) Control the extensive removal of forest products

iii) Regenerate the allotted forest

All these three variables is a dichotomous variable where we coded ‘Yes’ response as

‘1’ and a ‘No’ response as ‘0’. To construct the sustainability of forest index, we have

created a dummy variable that takes the value of ‘1’ if all the three variables related to

forest sustainability have value’1’ and otherwise have value ‘0’. Thus if the responses

to all the three variables were ‘Yes’, the value of sustainability of forest index would

be ‘1’. If one of the variables were coded as ‘0’, the value would be ‘0’.


165

The Explanatory variables are defined in Table 7.4 below:

TABLE 7.4

Description of Variables in Binary Probit Model Explanatory

variables

Description Expected

Sign

SEXR Sex Ratio i.e. ratio of the female to male in the

household +

FHEAD

Female Headed Household

FHEAD =1, if the household head is female

FHEAD =0, if the household head is male

+

FLIT Female Literacy rate i.e. number of years of schooling

of the female members of the household +

DEPR

Dependency Ratio which indicates the employment

condition of the household i.e. proportion of number of

non-working members to the total number of family

members in the household

_

PCATTLE

Per Capita cattle unit which is the ratio of cattle unit to

the family size i.e. 1 cattle unit= 1 bullock /cow or 4

goats or 4 pigs or 100 chicken /hens/ducks

_

WACTPM

Number of women member actively participating in the

Joint Forest Management. Active participation of

women implies they participate in meetings of the

forest protection committees; they are involved in

planning, decision making and implementation of the

forest conservation programme.

+

DISM Distance to the closest market (in km); i.e. measure of

market pressures by variation in distance from markets +

PUNSHM

Punishment against violation of CPR rules

PUNSHM =1, if the JFM committee impose fines on

members who break CPR rules

PUNSHM =0, if no fine is imposed on members who

violates CPR rules

+


166

Here 0α is constant and iα ( i = 1, 2, …,8) are the coefficients associated with the

explanatory variables and ∈ is the random disturbance term.

7.4.2 Results and Discussions We have tested this regression equation using household level data collected through

field survey in Bankura and Purulia districts in West Bengal. We have estimated the

regression equation by binary probit model using EViews 7 economic software. The

results of our analysis are presented in Table 7.5 and Table 7.6.

TABLE 7.5

Determinants of Forest Sustainability (Bankura District) Dependent Variable: SFOREST Method: ML - Binary Probit (Quadratic hill climbing) Sample: 1 150; Included observations: 150 Covariance matrix computed using second derivatives

Variable Coefficient Std. Error z-Statistic Prob. C -6.426145 2.913384 -2.205732** 0.0274

SEXR 3.186774 1.712963 1.860387*** 0.0628 FHEAD -0.056348 1.350760 -0.041716 0.9667

FLIT 1.043060 0.603126 1.729421*** 0.0837 DEPR -2.701982 2.273579 -1.188427 0.2347

PCATTLE 0.623331 1.037894 0.600572 0.5481 WACTPM 1.062673 0.595308 1.785081*** 0.0742

DISM -0.440801 0.646270 -0.682069 0.4952 PUNSHM 3.724002 1.624603 2.292254** 0.0219







167

TABLE 7.6

Determinants of Forest Sustainability (Purulia District)


From the above tables 7.5 & 7.6, we observe that in most of the cases, the results are

consistent. However, in some cases we have got contradictory results. As expected,

Sex Ratio ( )SEXR has been found to have a positive influence on Sustainability of

Dependent Variable: SFOREST Method: ML - Binary Probit (Quadratic hill climbing) Sample: 1 150 Included observations: 150 Covariance matrix computed using second derivatives


SEXR 0.041166 0.275027 0.149681 0.8810 FHEAD 1.092519 0.528422 2.067511** 0.0387 FLIT 0.404143 0.156220 2.587015* 0.0097 DEPR -4.668060 1.366426 -3.416256* 0.0006 PCATTLE 0.075123 0.565857 0.132760 0.8944 WACTPM 1.162318 0.450463 2.580275* 0.0099 DISM -0.026964 0.276332 -0.097577 0.9223 PUNSHM 1.226608 0.482273 2.543390** 0.0110

McFadden R-

squared 0.792005 Mean dependent var 0.486667 S.D. dependent var 0.501497 S.E. of regression 0.202104 Akaike info criterion 0.408195 Sum squared resid 5.759286 Schwarz criterion 0.588833 Log likelihood -21.61462 Hannan-Quinn criter. 0.481582 Deviance 43.22924 Restr. deviance 207.8375 Restr. log likelihood -103.9187 LR statistic 164.6082 Avg. log likelihood -0.144097 riProb(LR statistic) 0.000000


Obs with Dep=1 73


168

forest resources in Bankura and Purulia district. As the number of female members in

the family increases relative to the male members, the Sex Ratio increases. Since the

women are mainly involved in collection of forest products, the forest degradation

affects them badly and they are more conscious about forest conservation. Therefore

we observe that there is a positive relation between Sex Ratio and sustainability of

forest resources. Our result is significant in Bankura district but insignificant in

Purulia district.

In the case of female headed households ( FHEAD ), we noticed positive relation with

sustainability of forest resources as female members are more interested to improve

forest quality. In line with our expectation, we have observed positive and significant

result in Purulia district. However, the impact is negative and insignificant in Bankura

district.

The regression results shows a positive relationship between female literacy ( FLIT )

and sustainability of forest resources. As expected we observe that the coefficient of

female literacy has positive and significant impact on sustainability of forest resources

in both the districts. In fact educated female members understand the importance of

forest conservation. Hence with increase in female literacy the probability of forest

sustainability also increases.

In line with our expectation, Dependency Ratio ( DEPR ) is found to be negative in

case of forest resource conservation. As dependent member increases in the family,

burden on the forest products increases which ultimately decreases the probability of

forest resource sustainability. The result is significant in Purulia district and

insignificant in Bankura district.

From the regression analysis, we observe that there is a positive relationship between

per capita cattle unit ( PCATTLE ) and sustainability of forest resources. In fact,

household with higher per capita cattle unit collect more fodder to feed animals and

require higher quantity of fuel wood to prepare concentrated food for the animals.

Hence there is a negative relation between the two. However, in contrary to our

expectation, we have observed positive but insignificant results in both the districts. It


169

may be possible that household having large number of cattle become more conscious

about forest conservation for the survival of their livestock.

In the case of distance to the closest market ( )DISM we assume that when markets

are nearer, the motivation for extraction of forest resources is much greater. Contrary

to our expectation, DISM has a negative impact on sustainability of forest resources

in both the districts suggesting that as distance to markets decreases; control of forest

degradation is more prominent. This can be explained by the fact that as distance to

market decreases, the probability of inspection of Government officials to monitor the

forest protection committee increases.

As expected, punishment against CPR rule breakers ( PUNSHM ) has positive and

significant impact on forest resource sustainability in both the districts. In fact, if

forest protection committee imposes fine on rule breakers, illegal extraction of CPR

products is controlled.

The most important finding of this study is impact of active participation of women

member in JFM (WACTPM ) on sustainability of forest resources and the result has

been found to be positive and significant in both the districts. Greater and effective

involvement of women member in planning, decision making and implementation

activities improve the forest quality. Since the women are involved in collection of

NTFPs like firewood and fodder which is their means of subsistence and also a source

of income, they are more likely to prevent illegal harvesting of the forest products. It

is also observed that men who are engaged in patrolling the common forest find it

difficult to apprehend women who involve in indiscriminate use of the forest

resources. Thus the patrol team should consist of both men and women.

The results advocate that gender equity in participation in JFM enhances sustainable

governance of the forest resources.


170

7.5 Conclusion

Our study has investigated the impact of women’s active participation in JFM on

sustainability of forest resources in our surveyed villages of Bankura and Purulia

districts in West Bengal. The result of the survey indicate that female headed

household depend more on CPR collections as compared to the male headed

households to supplement their household income. Again, female members generate

higher employment man days through CPR collection in comparisons with male

members of the household. Hence we can conclude that women play a significant

role in CPR collection. Despite the fact that the women are the major users of the

common property resources, their involvement in Joint Forest Management is

negligible. We have estimated the impact of women’s active participation in JFM on

sustainability of forest resources through binary probit model using EViews 7

economic software. The most important finding of this study is the positive and

significant impact of active participation of women member in JFM on sustainability

of forest resources in both the districts, which is consistent with our hypothesis.

Women greatly value the forest conservation since forest degradation leads to

depletion of forest resources which ultimately increases the distance to be covered in

order to collect forest products. Hence, women are more concerned about the

improvement of regeneration of forest resources. Therefore, women should involve

themselves in planning, decision making and implementation of forest conservation

programme. Further women should participate in patrolling the forest with male

guards and thus prevent illegal extraction of forest resources in a better way.

Therefore we conclude that gender equity in JFM participation improves the


Summary, Conclusion and Policy Suggestions

171

CHAPTER 8

SUMMARY, CONCLUSION AND POLICY SUGGESTIONS 8.1 Summary In the foregoing chapters, the thesis dealt with Common Property Resources (CPRs)

collection and its implications to rural Poverty and Environmental Sustainability.

Attempt has also been made to analyse the association between Agricultural risks and

CPR Collection. The issues of Property Rights, Gender and Resource management

have also been covered in the study with firm field data of Bankura and Purulia

districts of West Bengal. The thesis is made up of eight integrated chapters. However,

the core of the thesis which dealt with major research questions of the study consists

of four chapters (Chapter 4, 5, 6 & 7). The thesis starts with a brief introduction which

deals with the identification of research problem. Chapter 2 highlights an exhaustive

review of the existing literature on the research problem. The objectives, research

questions, sample design and methodology including the conceptual frameworks of

the study have been outlined in Chapter 3. The four main chapters that we have dealt

with in the thesis are Common Forest Resources: Contribution and Crisis (Chapter 4),

Agricultural Risk and Common Property Resources (Chapter 5), Common Forest and

Participatory Management (Chapter 6) and Women’s Participation in CPR

management (Chapter 7). A brief description of conceptual framework has been given

at the beginning of four main chapters along with objectives. We have tested these

objectives on the basis of empirical data and intensive observations from these

villages. The Summary, Conclusion and Policy suggestions of the study appear in

Chapter 8 followed by Bibliography and Appendices.

In Chapter 4 we have explained the nature of dependency of the rural poor on

CPRs and its impact on income, employment and rural poverty. Environmental

impact of poverty has also been explained by analysing the relationship between rural

poverty and environmental degradation. This chapter broadly analyses the

contribution and crisis of CPRs in the study area.


172

The major findings can be summarised below:

i) Common Property Resources and Rural Livelihood Common Property Resources (CPRs) play a vital role in the rural livelihood of

our study villages. Rural poor are very much dependent on Common Property

Resources for their subsistence. They collected CPRs from the common forest

area, rivers, ponds and common grazing lands. A large variety of CPRs are

gathered by the poor and not so poor households for domestic and commercial

purposes. The rural households collect various CPRs like fuel wood, dry leaves,

shrubs, dung cakes, etc. for cooking and heating purposes. Common forest

supply raw materials like bamboo, canes, logs from trees, dry leaves for

constructing their houses. Their cattle graze in the common forests and the

shrubs and grasses are utilised as fodder for the animals. Fruits, vegetables,

fishes, root, meat from hunted birds and animals are procured from CPRs and

are used for consumption as well as for sale. Few plants and roots are used as

medicines to cure their ailments. Therefore, Common Property Resources are

means of subsistence for all the households in the surveyed villages.

ii) CPRs as Life Supporting Resource The field survey data reveals that around 19.04 percent of the household income

in Bankura district and around 18.11 percent in Purulia district is coming from

the CPR based activities. For BPL households, 27.28 percent in Bankura district

and 27.92 percent in Purulia district of the total household income comes from

CPR based activities whereas in case of APL it is 2.31 percent and 7.71 percent

respectively. This implies that BPL households enjoy a greater proportion of

income from CPRs both in relative as well as absolute terms. Further, the

contribution of CPRs to the total monthly consumption expenditure is around

22.29 percent and 20.17 percent in Bankura and Purulia district respectively.

Thus CPRs act as a life supporting resource in our surveyed villages.


173

iii) CPR and Employment Generation CPRs play an important role in employment generation. In our study area, there

is very little scope of employment in non-agricultural sector due to high level of

poverty, illiteracy and lack of technical skills of the rural households. Hence

most of people engage themselves in CPR-based activities. It is interesting to

note that an average household could generate around 116 and 95 employment

man days annually from CPR based activities in the study villages of Bankura

and Purulia district respectively.

iv) CPR as a Source of Energy

Almost all the surveyed households collect fuel wood and cow dung from the

common forest area for the purpose of cooking. The households use very little

commercial fuels like coal, kerosene, electricity and cooking gas as they are

very costly and beyond the reach of the poor households. On an average 79.35

percent and 76.78 percent of the total monthly household energy consumption

were met by the CPR products collected from the common forest area in

Bankura and Purulia districts respectively. It is also observed that both the BPL

and APL households use fuel wood gathered from CPRs for their household

energy needs.

v) CPR and Animal Grazing Most of the households in our surveyed villages utilise common forest area for

animal grazing. The dependency of the households on CPR in our study area for

animal grazing has been estimated in terms of the creation of animal unit

grazing days in common grazing land. As estimated, CPR supported 88 animal

unit grazing days per household in Bankura district in last one month during

survey. The dependency of households on CPR for animal unit grazing in

Purulia district is much higher; an average household gets support of 114 animal

unit grazing days per month, which confirms the heavy dependence of the rural

poor on the CPRs for animal grazing.


174

vi) CPRs and Rural Poverty CPR plays a vital role to alleviate rural poverty. With the exclusion of the

contribution of CPRs to the total household income, the extent of poverty

increases around 26 percent in our study area.

vii) Regression Results: Determinants of CPR extraction The CPR product extraction by rural households in our study area depends on a

host of factors relating to household and village characteristics. The family size,

average age, level of education, female percentage, land holding pattern,

livestock unit, the distance between dwelling place and the common forest,

market distance are some of the socio-economic, demographic and geographical

factors, which determine the extraction of CPR products. We have used multiple

regression model to examine the determinants of CPR extraction. It is evident

from the analysis that most of the important variables are significant with the

expected sign. From the regression analysis we observed that the family size

( FSIZE ), size of livestock ( LIVESTOCK ), female percentage in the household

( FEMPER ) have a positive impact on community forest income in both the

districts. Since CPR extraction is a labour intensive technique, larger the family

size higher is the labour time available for the collection of community forest

products. Again households with larger number of livestock collect more fodder

to feed the animals. We further observe that female members are mainly

involved in CPR collection in our surveyed villages in both the districts.

Education ( AVRSCH ) i.e. average years of schooling of the household, forest

distance ( FORESTDIST ), distance to the nearest market ( DISM ) have

negative impact on CPR income in both the districts. Household members who

are better educated get better job opportunities and therefore are less interested

in CPR collection. Household nearest to the forest area extracts more CPRs than

the household far away from the forest area. In the case of distance to the

nearest market, the motivation for extraction of CPR product is greater when

market is nearer due to greater marketability of the CPR products. We observed

that there is a positive and significant relationship between average age of the

household ( AVRAGE ) i.e. experience in collecting CPR products and CPR

income in Purulia district. It is easier for an experienced household member to


175

collect more CPRs and thus smooth their consumption and livelihood. However,

the result is negative and insignificant in Bankura district. Again, we have

observed that the coefficient of OWNLAND is negative and significant in

Bankura district. However the result is contradictory in Purulia district which

implies that households with large ownership of cultivable land collect more

fuel wood. This is mainly due to the fact that land in Purulia district is mostly

infertile and so mere ownership of land fails to reduce the dependency on CPRs.

Most importantly, CPR acts as a life supporting system to the rural poor. In the

absence of alternate employment opportunities to the rural poor, they are highly

dependent on CPRs. Our regression analysis indicates that there is a positive

relationship between poverty ( POVR ) and the CPR income and the result is

highly significant in both the districts. Hence in the case of the poor, the income

from common forest has a higher percentage as compared to that of the ‘not so

poor’.

viii) Poverty and Environment The environment matters a lot to the rural poor. The wellbeing of the poor is

strongly related to the environment. From our field survey report, we have

observed that the common forest area declined around 12.05 percent in the

study villages during the period 1990-2010. However, the depletion of forest

area is high in Bankura district as compared to Purulia district. To analyse the

relationship between poverty and environmental degradation, we have used

logit regression model. The important finding of our study is that the coefficient

of environmental degradation is positive and significant in both the districts

which imply that with more environmental degradation the probability of the

incidence of poverty increases. In fact, most of the households in our study area

are very poor and are heavily dependent on CPRs for their livelihood. The level

of extraction of CPRs is higher than their regenerating process and thus the

environment gets further degraded. However, poverty cannot be made solely

responsible for environmental degradation. Rise in population, growing

commercialisation of agricultural and CPR products, emergence of market are

other factors responsible for forest degradation.


176

In Chapter 5, the interrelationship among agricultural risk, non-timber forest

collection and the extent of rural poverty has been analysed.

The major findings are:

i) Nature of Agriculture The economy of Bankura and Purulia districts are primarily based on

agriculture. Paddy is the main crop of both the districts. In addition, wheat,

oilseeds, maize, sugarcane, groundnut and pulses are other important crops

grown in the surveyed villages. Since agriculture is largely dependent on the

vagaries of monsoon and drought, there are several risks associated with it in

our study area such as adverse weather, seasonal flooding, unpredictable soil

quality, crop diseases, price shocks, etc. The high dependence of the rural poor

on the CPRs in the form of Non Timber Forest Products (NTFPs) is primarily

due to uncertainty and low productivity in agriculture.

ii) Agricultural Productivity Most of the farmers in our study area belong to small size category and the

nature of agriculture is backward in nature. The average yield of agricultural

productivity in the surveyed villages of Bankura is 2144 kg/hectare whereas in

Purulia district it is 1594 kg/hectare in the year 2010, which is much lower than

the state average (2708 kg/hectare). Most of the households in the villages are

so poor that they cannot afford to purchase mechanised agricultural implements

like power tiller and pump set and therefore used mainly wooden plough and

bullocks for cultivation. From the surveyed data on agricultural labour in the

surveyed villages of both the districts reveal that men and women equally

participate in agriculture in the process of sowing, weeding and harvesting.

Further it is also observed that children are also involved in agricultural labour

in the several surveyed villages.


177

iii) Agricultural Risk and CPR CPR in our study area mitigates a sizeable proportion of agricultural risk. Since

agricultural practice in the study area is backward in nature and subjected to

weather risk in the form of agricultural shock in times of production shortfall,

the farmers fall back upon CPRs for their survival and also addresses

significantly their agricultural risk. Here CPRs act as a safety net during the

period of agricultural risk. As established from our field data in the year 2010,

the agricultural production variability resulted in agricultural shortfall of Rs

209032 in Bankura and Rs 164391 in Purulia as compared to the normal year

(2008). Interestingly, it is observed that during this period the extraction of CPR

is also high compared to the normal year.

iv) Agricultural Risk and Extraction of Forest Products: Count Data

Regression Model We have explained the determinants of forest collection labour through

econometric analysis to give an understanding of the impact of agricultural

product risk on the extraction of forest products. The forest collection labour is

determined not only by social, economic, demographic variable but also by

agricultural shock and agricultural risk. We have applied Count Data Model

using STATA computer software package. As per the Count Data analysis, the

association between forest collection trips and age of the household head

( AGEHEAD ) is positive and significant whereas square of age of the household

head ( SQAGEH ) is negative and significant which imply that households with

older heads normally take more trip on forest collection except the oldest

household member. As almost all the household members in the study area

collect CPRs, the larger the family size ( FAMSIZE ), the more is the forest trips

to collect NTFPs. As expected education ( AVRSCH ) i.e. the average years of

schooling of the household has a negative impact on forest collection trips in

both the districts. On the basis of our results we can infer that households with

large number of livestock ( LIVESTOCK ) are equally responsible for excessive

extraction of forest products as they take more trips to gather fodder to feed

animals. It was observed that forest distance ( FORESTDIST ) i.e. the distance


178

between the residence and the common forest has a negative impact on forest

collection trips. The key findings of our regression results indicate that the

coefficients of agricultural risk parameters ( AGRIRISK ) and shock parameters

( AGRSHTFALL ) are positive and significant in both Bankura and Purulia

districts, which suggest that household with greater agricultural shortfall and

risk are likely to take more forest collection trips. Hence we can conclude that

NTFP has a supporting role in the wellbeing of the rural poor in the form of

‘natural insurance’. By collecting NTFP, rural households smooth their income

as well as consumption in the period of agricultural crisis.

We have examined the nature of participation in forest management and its impact

on resource utilisation and conservation in Chapter 6. The chapter also focused on

the relationship between the intensity of management practice and the degradation of

forest resources.


i) Joint Forest Management (JFM) in the Study Area The surveyed villages in the study area are covered by vast forest area. Most of

the villages are extremely poor and are highly dependent on forest products for

their subsistence. Since the forest communities have more knowledge about the

forest, their active involvement is necessary for the sustainability of the forest

resources. The Joint Forest Management was initiated in the study villages in

1992 after the JFM resolution (1990) of the Govt. of West Bengal. In order to

ensure the availability of the forest resources in the long run, the rural

households have actively participated in Joint Forest Management (JFM). A

Forest Protection Committee (FPC) in the study areas has been formed whose

prime responsibility is to protect the forest. The FPC involves the local people

in planning, development of the forest and regeneration of the forest through

plantation of trees. The FPC plays a vital role in enforcing the guidelines laid

down by the JFM schemes. In case anyone breaks the rule enforced by the

Forest Protection Committee or engage in illicit felling of the forest trees, then

FPC take away his cutting implements and impose a penalty ranging from Rs

100/- to Rs 500/- depending upon the seriousness of the crime.


179

ii) JFM Participation As per the surveyed data, 75.33 percent of the surveyed households in Bankura

district and 85.33 percent in Purulia district participated in JFM. The survey

result also shows that the percentage of household involved in planning and

implementation of JFM in the surveyed villages of Bankura were 60.67 percent

and 12.67 percent respectively as compared to 65.33 percent and 16 percent in

the surveyed villages of Purulia. The data reveals that the incidents of cases of

violation of rules have shown a declining trend in most of the surveyed villages

during the period 2008-2010. It is further observed that the Joint Forest

Management is active in the villages of Dulaltora in Bankura district and

Jiyathole in Purulia district. From our surveyed data we can infer that with the

establishment of JFM, enforcement of Forest protection has shown a positive

trend. Further, the households have realised that protection of the common

forest is in their own interest and violating the rules has an adverse effect on

them.

iii) Determinants of Collective Action in JFM Collective action refers to concerted actions of people that share a common

interest, perceive that interest and act to achieve it (World Bank, 1998). The

forest resources that can be effectively managed by the rural poor depend on the

strength of the collective action i.e. joint action of the community to conserve

forest resources as well as improve rural livelihood. We have explained the

determinants of collective forest management through censored Tobit Model to

give an understanding of the relationship between forest dependency and active

forest management. We have observed from the determinants of collective

action in JFM that family size ( FSIZE ) and average age ( AVRAGE ) have a

positive effect and education ( AVRSCH ) has a negative effect on collective

action in the surveyed villages of both Bankura and Purulia districts. The

coefficient of agricultural income to total income ( PERAGRIN ) is negatively

related to collective action in JFM which implies that households with steady

income from agriculture are less interested in forest product collection and in

active participation in JFM. Again wealth (WEALTH ) has negative and

significant impact on collective action management in the surveyed villages of


180

both the districts. It is evident from the analysis that collective action in JFM is

positively related to percentage of CPR income to total income ( PERCPRIN )

and the impact is significant in both the districts. This indicates that households

whose dependence on forest product is high are more interested in active

participation in JFM to overcome the uncertainty and insecurity in their

livelihood. It is further observed that percentage of CPR consumption to total

consumption ( PERCPRCSM ) has a positive impact on active participation in

JFM which implies that household who depend more on forest products for

their consumption purposes are very much concerned about forest conservation

and hence actively participate in JFM.

iv) Collective Action and Forest Conservation Our field survey data reveals that collective action in forest management has its

inherent impact on the conservation of forests. Active forest management

lowers the degree of over exploitation and hence the degradation of forest

resources. We have explained the role of forest management in the reduction of

forest degradation by using logit regression model. The result demonstrates that

improved management has a positive role to alleviate environmental

degradation. It is observed that the association between Forest Management

activity and forest degradation is significant in both the districts. As most of the

rural poor depend on CPR, especially forest products for their livelihood, CPRs

should be properly managed so that the rural poor can get maximum benefit

maintaining the sustainability of forest resources.

In Chapter 7, we have examined the role of women in CPR collection and their

performance in JFM committees. We further investigated empirically the effect of

women’s active participation in JFM on forest resource conservation.


181


i) Female Headed Household and CPR collection Forest plays an important role in livelihood support system for the rural poor

women in our surveyed villages. Our field survey data reveals that the average

contribution of CPR to the total household income for a female headed

household is 44 percent as compared to 26 percent for male headed household.

This implies that female headed household are heavily dependent on CPR

collections to supplement their household income. Further, female members

generate higher employment man days through CPR collection as compared to

the male members in the household. Thus, women are able to supplement the

household income through CPR collection which is used for consumption as

well as a source of income through sale.

ii) Women’s Participation in JFM Despite the fact that the women are the major users of CPRs, their

involvement in JFM is negligible. As rural women are highly dependent on

forest resources, they greatly value the forest conservation since degradation of

the forest not only leads to depletion of the forest resources but increase the

distance to be covered in order to collect them. We have observed from our field

survey report that the number of adult female participants in JFM in 2010 is 65

and 80 in Bankura and Purulia district, which is extremely low as compared to

170 and 205 of adult male participants in the said district. We further observed

that the female participation in JFM has increased in the year 2010 as compared

to the previous years. However, male domination in JFM still remained in the

study area of both the districts.

iii) Gender Equity in JFM and Forest Resource Sustainability We have estimated the impact of women’s active participation in JFM on

sustainability of forest resources through binary probit model using EViews 7

economic software. We have considered some other socio-economic variables

like sex-ratio, female headed household, female literacy, per capita cattle unit,

dependency ratio, market pressure and imposition of fine on CPR rule breaker

on which sustainability of forest resources also depend. Our regression result


182

indicates that sex-ratio, female headed household and female literacy have a

positive influence on sustainability of forest resources. Again, dependency ratio

( DEPR ) is found to be negative in case of forest resource conservation which

implies that as dependent member increases in the family, burden on forest

products increases which ultimately degrades the forest resources. The per

capita cattle unit ( PCATTL ) also has a positive impact on sustainability of

forest resources. Again, punishment ( PUNSHM ) against CPR rule breaker has

positive and significant impact on forest resource sustainability in both the

districts. However, distance to the closest market ( DISM ) has a negative

impact on sustainability of forest resources. The most important finding of this

study is the positive and significant impact of active participation of women

member in JFM (WACTPM ) on sustainability of forest resources in both the

districts. Active involvement of women in planning, decision making and

implementation prevent illegal harvesting of the forest products and thus

improve forest quality. Hence gender equity in JFM participation enhances

sustainability governance of the forest resources.

8.2 Conclusion

The theme of the research is Common Property Resource (CPR) collection and its

impact on Poverty and Environmental Degradation and Conservation. The use of

CPRs mainly forest resources is manifold in the livelihood of forest fringe people.

Systematic and Sustainable development of CPRs can make better the standard of

living of the rural poor and play a crucial role in the conservation of forest resources.

Our field survey has provided various research findings. Firstly, our survey report

indicates a very high dependency of the rural poor on CPRs. The data reveals that

18.67 percent of household income and 21.28 percent of household expenditure is

derived from CPR extraction. Due to enormous demand, collection of Non Timber

Forest Products (NTFPs) is an important occupation for our surveyed households.

Almost all the households collect fuel wood from the common forest area for the

purpose of cooking. Most of the households utilise CPRs for animal grazing. Thus

CPRs have an immense role in rural livelihood. The data shows that with the access to


183

common property resources the poverty level reduces by around 26 percent, which

signifies that the extent of poverty increases sharply in the absence of CPR income.

However, the high extraction of forest resources along with population growth,

growing commercialisation of agricultural and CPR products and emergence of

market in our surveyed villages have resulted in the decline of forest area of 12.66 sq.

km which further impoverishes the forest livelihood.

In our study we also investigated the impact of agricultural risk on the collection of

common forest products. Agriculture is the main occupation of most of the surveyed

households and therefore they depend on nature for any agricultural activities. During

any natural calamities they have no other alternate livelihood opportunities except to

extract forest products. We have observed from our field data in the year 2010 that

due to high agricultural shortfall, the households have extracted more CPR as

compared to the normal year.

In order to ensure the availability of the forest resources in the long run, rural

households have actively participated in collective forest management. Our field

survey data reveals that active forest management has a crucial role in reducing forest

degradation. The field survey report indicates that, although JFM is still under male

domination, women being the major users of forest resources greatly value the forest

conservation and therefore participate in JFM activities. However, the female

participation in JFM is far from satisfactory.

In view of the present condition of the forest resources and the rural livelihood in our

study area, there is an immediate need to conserve forest resources through collective

action of rural communities and it is expected that gender equity in participatory

management will improve the sustainability of forest ecosystem.


184

8.3 Suggestions and Policy Implications

On the basis of our findings of the study, we now propose some important policy

suggestions for the betterment of the rural livelihood in the context of our surveyed

villages of Bankura and Purulia districts of West Bengal.

The rural poor in our study area are highly dependent on Common Property

resources especially forest resources due to the lack of alternative livelihood

opportunities. The wellbeing of the forest communities is strongly related to the

sustainability of forest resources. However, during our research study we have

observed many situations where this dependence of forest products leads to forest

degradation. Non-availability of alternate income opportunities compel the village

dwellers to depend on CPRs for their livelihood. Thus without offering alternative

income opportunities to the villagers, it is not possible to control excessive

extraction of forest resources. Hence there is an urgent need to expand economic

opportunities especially through the development of non-farm activities so that the

dependence of rural poor on CPRs is reduced drastically.

Under this backdrop, we have the following policy suggestions:

1) Small scale eco-friendly industries including handicrafts should be encouraged

and incentivised by the government.

2) Commercialisation of the agricultural crops should be promoted.

3) Awareness about the real commercial value of the NTFPs at the actual market.

4) NTFPs based industries should be established for the economic development

of forest community.

5) Infrastructure in the form of roads, electricity, drainage system and medical

facilities for the poor should be ensured.

6) Awareness about the importance and benefit of conservation of the forest

resources should be propagated.

7) Unconventional energy sources like solar energy and bio gas needs to be

adopted in order to reduce the dependence on the forest resources.


185

8) Educational opportunities need to be improved in the study area, so that they

realise the economic value of NTFPs as well as the importance and benefit of

conservation of forest resources. Further, education gives better job

opportunities and hence reduces dependency on forest resources.

Our field survey report indicates that CPRs supplement the rural livelihood and

act as safety net during agricultural crisis. Since the villagers in our study area are

dependent on nature for any agricultural activities, there are several agricultural

risks such as adverse weather, seasonal flooding, unpredictable soil quality, crop

disease etc. associated with it. Based on the above research findings we have

proposed few policy suggestions which not only encourage agricultural

development but also reduce rampant extraction of valuable forest products at the

time of agricultural shortfall.

1) Agricultural production needs to be increased by the use of modern

fertilisers and tools.

2) Traditional mode of livelihood of the rural poor should change by

involving them in new agro-economic activities like off-season

vegetables, etc.,

3) Irrigation system needs to be drastically improved including the rain

water harvesting system to cater to agricultural shocks due to drought.

4) ‘Dry land’ farming should be encouraged.

5) Government should take initiative in providing easy credit facilities at

the time of agricultural crisis.

Empirical evidence of our study confirms that collective action of forest

communities in forest management has an important role in forest resource

conservation. Hence there is a need to motivate the local villagers to actively

participate in forest management activities. The suggestions below follow our

research findings of related issues.

1) Motivate the rural households to actively participate in the

conservation and regeneration of the common forest areas, so that they


186

understand the relationship between environment and their

development.

2) JFM should implement such rules so that indiscriminate felling of trees

or over grazing of the cattle should be prohibited.

3) The policies and strategies for the afforestation programmes should be

formulated in such a manner that it leads to high success rate and

encourages whole hearted participation of the rural poor.

4) The forest policies should cater mainly to the overall conservation of

the ecosystem and ensure that the forest degradation is minimised to

the maximum extent.

5) The forest department officials should be properly trained and

groomed to ensure that they mix well with the rural households and

disseminate the information for development of the forest area.

The most important finding of our research is the lack of active participation of

women in JFM despite the fact that they are the prime collectors of forest

products. Since women are greatly concerned about the forest conservation their

active involvement in forest management improves the sustainability of forest

resources. On the basis of our research findings we have suggested few policy

prescriptions:

1) Women’s active involvement in forest management should be ensured

and they should be encouraged to take decisions.

2) Forest management policies should be directed at favouring the women

to avoid discrimination.

3) Women’s organisation in the villages needs to be strengthened, so that

they are not marginalised.

4) Emphasis should be made on female literacy so that they can get

alternative income opportunities.


187

Since our research has been conducted in the economically highly backward villages

of Bankura and Purulia districts of West Bengal, collection of data and information

was not an easy work at all. Keeping in mind all the limitations of the research study

we can conclude that systematic and sustainable use of forest products, creation of

alternative employment opportunities and extensive afforestation programme in our

surveyed area can only save the forest environment. Government should take proper

initiative to generate more awareness among forest communities for the protection,

regeneration and development of Common Property Resources.

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http://www.wwf.org.uk/what_we_do/making_the_links/women_and_conservation/

Appendices

205

APPENDIX-I

VILLAGE CHARACTERISTICS OF STUDY AREA

District-Bankura Bankura district is the fourth largest district in the state of West Bengal and is located

in its western part, which is popularly known as ‘Rarh’ from time immemorial. It is

situated between 22° 38’ and 23° 38’ North latitude and between 86° 36’ and 87° 46’

East longitude. The total geographical area is 6,882 km2. Geographically, on the north

and north-east, the district is bounded by Bardhaman district from which it is

separated mostly by the Damodar River. On the south-east it is bounded by Hooghly

district, on the south by West Medinipur and on the west by Purulia district. In shape,

it resembles an isosceles triangle wedged in between Purulia and Bardhaman. It is the

“connecting link between the plains of Bengal on the East and Chota Nagpur plateau

on the West.”

MAP A1.1 MAP A1.2

Map of West Bengal-Bankura District Map of Bankura

Bankura district comprises of three subdivisions: Bankura Sadar, Khatra and

Bishnupur. The Bankura Sadar subdivision consists of Bankura municipality and

eight Community Development (CD) blocks. The Khatra subdivision consists of eight

CD blocks, whereas, Bishnupur subdivision consists of Bishnupur and Sonamukhi

municipality and six CD blocks. In total there are 22 Community Development

Blocks. The field survey has been conducted in Bankura Sardar sub-division under

Saltora CD Block.

http://en.wikipedia.org/wiki/Bardhaman_district

http://en.wikipedia.org/wiki/Damodar_River

http://en.wikipedia.org/wiki/Hooghly_district

http://en.wikipedia.org/wiki/Hooghly_district

http://en.wikipedia.org/wiki/Purulia_district

http://en.wikipedia.org/wiki/Ganges_Delta

http://en.wikipedia.org/wiki/Chota_Nagpur_plateau

http://www.google.co.in/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=B1KTszh2psMLVM&tbnid=kphG5JWjATlB5M:&ved=0CAUQjRw&url=http://www.veethi.com/places/west-bengal-bankura-district-615.htm&ei=tg3GUq_nJKaSiQeD1YHoDQ&bvm=bv.58187178,d.aGc&psig=AFQjCNEWk29itBUqokwK8d7lOi-aftRD2g&ust=1388797047637983

Appendices

206

As per Census 2011, the population of Bankura district is 3,192,695 of which male

and female were 1,840,504 and 1,755,788 respectively. There was change of 12.64

percent in the population as compared to population as per Census 2001. The areas to

the east and north-east are low lying alluvial plains, whereas to the west, the surface

gradually rises, giving way to undulating tracks and interspersed with rocky hillocks.

The western part of the district has poor, ferruginous soil and hard beds of laterite

with scrub jungles and sal woods. Three major categories of soil are found in Bankura

district: i) red soil ii) alluvial soil and iii) laterite soil. Red soil is found in southern

parts of the district, predominantly in Bishnupur. Bankura has a rich source of forest

with a recorded forest area covering 1482 km2 which is 21.53 percent of the total

geographical area of the state (SFR 2010-11).

The economy of Bankura district is primarily based on agriculture with nearly 62

percent of the main workers of the district engaged in the agricultural sector. The net

area under cultivation is 356904.4 hectare. Paddy is the main agricultural crop and is

produced in 90 percent of the total cultivable land of the district. The cereals

cultivated are Aus, Aman, Boro, Wheat and Maize. The land is primarily of 3 types-

Sali, Suna and Tara or Danga. Sali is suitable for growing of Aman rice, Suna for

various crops like Aus, kharif, sugarcane, cotton, tobacco. The other crops grown in

the district are potato, sugarcane, groundnut and pulses. Livestock also plays an

important role in the economy of Bankura district.

The field survey was undertaken in 6 villages under Saltora Block in the district of

Bankura in West Bengal, India; viz. Panjhoria, Ramjibanpur (Bandhghat), Seolibona,

Baldanga, Dulaltora and Tantirdanga. The village of Ramjibanpur (Bandhghat) and

Seolibona has a total area of 205 hectares. The community is Kora. Ramjibanpur

(Bandhghat) has 54 households and the population is around 250. Out of the total

number of households, 52 are landless. The number of BPL (Below Poverty Line)

card holders in this village is 47. There is no pacca road and the kachcha road present

that can be driven on by a jeep is around 3 km which was constructed in the year

2009-10. The nearest post office and the bank is about 2.5 km and 4 km away

respectively from the village. The market is about 2.5 km away, while the police

station is 12 km away. The high secondary school is at a distance of 10 km. The

http://en.wikipedia.org/wiki/Alluvium

http://en.wikipedia.org/wiki/Iron_oxide

http://en.wikipedia.org/wiki/Laterite

http://en.wikipedia.org/wiki/Shorea_robusta

Appendices

207

primary health centre is 4 km away. The cultivated land is around 18 bighas. The

common forest land is about 4 km. The village has an individual pasture of 1 bigha

and a village common pasture of around 2.5 bighas. There are 2 ponds and 2 canals.

Around 290 acres of the forest land is planted and 60 hectares are unplanted.

The village of Dulaltara lies under the CD Block Saltora of Bankura district. The

community is Santal. The total area is 37.6 hectares. There are 18 households with a

population of 77. All the households in the village are BPL card holders. There is

pacca road of 2.5 km and kachcha road of 1 km. The bus stand, post office, bank,

market, primary school and primary health centre is about 2 - 2.5 km away from the

village. The secondary school and police Station is 9 km and 12 km away respectively

from the village. All the households gather common property resources in the form of

firewood and fodder from the forest. Although hunting of animals and birds is

prohibited, the rural poor indulge in hunting to a small extent. Fishing in the ponds

and lake is restricted to once in a week.

The village of Panjhoria and Tantirdanga has 19 and 25 households respectively. In

Panjhoria 17 households are landless; whereas in Tantirdanga 23 households are

landless. There exists the Forest Protection committee which is responsible for

maintaining the community rule for the access and extraction of common property.

The share of the Forest Protection Committee is equally distributed among the

community members.

The villagers collect bamboo, wood, leaves and canes for the construction of their

houses. Fruits, roots, leafy vegetables, mushroom, etc. are collected form the common

forest area. The leafy vegetables mainly collected are Shushni shak and Kulakhara

shak. The common fruits collected by the households from the common land are

mango and jamun. The households also collect several medicinal plants to take care of

the health of the households. The common medicinal plants collected are neem leaves,

basak leaves, kalmegh, etc. The common land is used by the households for grazing

of their livestock. The households also collect fodder from the common forest area for

the livestock. The household hunts animals to some extent in the common forest area

and also fish in the common ponds. The households collect small fishes viz. gorou,

chunamach, etc. from the common property resources like ponds and lakes. The

households also collect wild mushrooms (called karan chatu in local dialect) from the

common forest area.

Appendices

208

District-Purulia Purulia is the westernmost district of the state of West Bengal. It has all-India

significance because of its tropical location, its shape as well as function like a funnel.

It funnels not only the tropical monsoon current from the Bay of Bengal to the

subtropical parts of north-west India, but also acts as a gateway between the

developed industrial belts of West Bengal and the underdeveloped areas in Orissa,

Jharkhand, Madhya Pradesh and Uttar Pradesh (http://purulia.gov.in). Purulia district

lies between 22°60’and 23°50’ north latitudes and 85°75’ and 86° 65’ east longitudes.

The total geographical area of the district is 6259 km². This district is bordered on the

east by Bankura, Paschim Medinipur districts, on the north by Bardhaman district of

West Bengal state and Dhanbad district of Jharkhand state, on the west by Bokaro and

Ranchi districts of Jharkhand state and on the south by West Singhbhum and East

Singhbhum districts of Jharkhand state.

MAP A1.3 MAP A1.3

Map of West Bengal-Purulia District Map of Purulia

Purulia district consists of three subdivisions: Purulia Sadar East, Purulia Sadar West

and Raghunathpur. Purulia Sadar East comprises of Purulia town and seven

Community development (CD) blocks, Purulia Sadar-West consists of Jhalda

municipality and seven other CD blocks, Raghunathpur subdivision comprises of

Raghunathpur municipality and six community development blocks. Purulia district

has in total 20 Community Development blocks. The field survey has been conducted

in Raghunathpur sub-division under Santuri CD Block.

http://purulia.gov.in/

http://en.wikipedia.org/wiki/Bankura_district

http://en.wikipedia.org/wiki/Paschim_Medinipur_district

http://en.wikipedia.org/wiki/Bardhaman_district

http://en.wikipedia.org/wiki/Dhanbad_district

http://en.wikipedia.org/wiki/Jharkhand

http://en.wikipedia.org/wiki/Bokaro_district

http://en.wikipedia.org/wiki/Ranchi_district

http://en.wikipedia.org/wiki/West_Singhbhum_district

http://en.wikipedia.org/wiki/East_Singhbhum_district

http://en.wikipedia.org/wiki/East_Singhbhum_district

http://www.google.co.in/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=CAD3UwktMJrH5M&tbnid=Y82UFmUg_-ZsHM:&ved=0CAUQjRw&url=http://purulia.gov.in/&ei=ZKLQUrCAFsmTiQf4zICIDA&bvm=bv.59026428,d.aGc&psig=AFQjCNH8UnR9jWXWgLIYyQhwMOW66SsBJw&ust=1389490925390930

Appendices

209

The population of Purulia district as per Census 2011 was 2,930,115 of which male

and female were 1,496,996 and 1,433,119 respectively. In 2001 census, Purulia had a

population of 2,536,516 of which males were 1,298,078 and remaining 1,238,438

were females. There was change of 15.52 percent in the population compared to

population as per Census 2001. As per 2011 census, 87.26 percent population of

Purulia districts lives in rural areas of villages. Purulia is one of the drought prone

districts of West Bengal. It has a sub-tropical climate nature and is characterized by

high evaporation and low precipitation. The average annual rainfall varies between

1100 and 1500 mm. The topography of Puruila district is undulated and thus nearly 50

percent of the rainfall flows away as runoff. The district is covered mostly by residual

soil formed by weathering of bed rocks. The district has rich forest cover. The total

forest area in Purulia district is 876 km² comprising of 112 km² of Reserved Forest,

729 km² of protected forest and 35 km² of unclassed state forest. The forest area is

about 14 percent of the total geographical area of the district (SFR- 2010-11). The

forest consists mainly of Sal and few other species.

Purulia is one of the most economically backward districts of West Bengal. It lags

behind in agricultural arena as compared to other districts of West Bengal because of

unpredictable weather conditions, unfavourable soil and poor irrigation system. The

main cultivated crops are paddy, wheat, sugarcane, mustard and potatoes. A large

number of the rural population works as agricultural wage labourers. Purulia is

famous for lac cultivation. The lac industry is being encouraged by the government as

well as other private organizations. There is very high dependence of the rural poor on

the forest for their subsistence.

The field survey was undertaken in 3 villages under Santuri Block in the district of

Purulia in West Bengal, India; viz. Jiyathole, Marbediya and Ambari. The village of

Jiyathole is about 3 km away from the town of Santuri. It has 81 households with a

population of 445, out of which 65 households are BPL card holders. Agriculture and

wage labour are their main occupation. Marbediya village is situated about 3.5 km

away from Santuri. It has 25 households with 138 household members. Here 15

households are poor. Marbediya has 1 primary school and 1 high school in which

students of Jiyathole and Marbediya study. Ambari village is about 4 km from the

town of Santuri comprising of 44 households and a population of 231. It has one

primary school.

Appendices

210

1.1 Characteristics of the population in the study area in Bankura and

Purulia The socio-economic characteristics of the 300 surveyed households in the districts of

Bankura and Purulia during the survey year 2011 is depicted. Here we have analysed

the family size, the literacy rate, caste composition and employment status of the

households.

1.1.1 Family Size and Literacy rate

The village wise analysis of the family size and literacy rate has been shown in Table

A1.1 below:

The surveyed 300 households in Bankura and Purulia districts had a population of

1522 during the survey year 2011. The average family size was 4.72 and 5.43 for the

two districts respectively. From the above table it is evident that the literacy rate in the

study area of Bankura and Purulia was 45.34 and 44.35 respectively, which implies

that more than half the population are illiterate. Ramjibanpur in Bankura district had

the highest literacy rate of 51.61 percent and Tatirdanga has the lowest (32.46

percent). The low level of education in the study area is very alarming.

TABLE A1.1

Male Female Total

Panjhoria 26 66 63 129 4.96 51.16Ramjibanpur 20 51 43 94 4.70 52.13

Seolibona 54 131 131 262 4.85 49.24Baldanga 7 14 16 30 4.29 36.67Dulaltora 18 37 42 79 4.39 36.71

Tantirdanga 25 63 51 114 4.56 32.46Jiyathole 81 222 223 445 5.49 44.94

Marbediya 25 72 66 138 5.52 36.96Ambari 44 110 121 231 5.25 47.62

150 362 346 708 4.72 45.34150 404 410 814 5.43 44.35300 766 756 1522 5.07 44.81



Bankura Total

Total populationDistrict Block Name of

villageNo. of

Households

Purulia Santuri

Village wise Characteristics

Bankura Saltora

Average Family

Size

Literacy Rate in

Percentage

Appendices

211

1.1.2 Caste Composition: In our study area, majority of the households belong to schedule tribe and the

community is Santhal. The villagers follow the religion of Sahi Dharam.

1.1.3 Occupation: The occupation of the working population in the study area has been classified into

four categories- i) primary sector comprising of agriculture, cottage and related

industries ii) secondary sector comprising of factory and business related and iii)

Tertiary sector comprising of service and Wage labour. Table A1.2 shows the village

wise occupational composition of the surveyed population.

From Table A1.2 it is evident that in Bankura district, out of 447 total working

population, 281 (62.80 percent) are involved in agriculture and 146 (32.64 percent) in

wage labour. In Purulia district, 353 (69.05 percent) household members are engaged

in agriculture and 147 (28.67 percent) in wage labour, out of total working population

of 512. We can thus infer that agriculture is the main occupation of the surveyed

households, followed by wage labour.

TABLE A1.2

Agriculture Business Service Wage Labour

Panjhoria 26 54 3 6 19 82Ramjibanpur 20 37 0 4 16 57

Seolibona 54 107 0 6 54 167Baldanga 7 9 0 0 6 15Dulaltora 18 24 0 0 22 46

Tantirdanga 25 50 0 1 29 80Jiyathole 81 193 1 2 74 270

Marbediya 25 64 0 2 26 92Ambari 44 96 0 7 47 151

150 281 3 17 146 447150 353 1 11 147 512300 634 4 28 293 959


Total working

population

Village wise occupation composition of the surveyed population


Working population engaged inDistrict Block Name of

villageNo. of

Households

Bankura Saltora

Purulia Santuri

Bankura Total

Appendices

212

APPENDIX-II

PRICE LIST OF CPRs The price list of the common property resources in the study area is shown in Table

A2.1 below:

TABLE A2.1

Sl No. Unit Market Price(Rs) GrassShrubDry leaves

2 per kg 2.50 3 per kg 5.00

Kalmegh (leaf) per kg 2.00 Neem (leaf) per kg 15.00 Basak (leaf) per kg 2.00 Bamboo per piece 20.00 Sal (leaf) per 1000 plates 50.00 Kend (leaf) per jhuri 30.00

6 per log 1,200.00 Bel (fruit) per kg 2.50 Amla (fruit) per kg 3.50 Tal (fruit) per piece 3.00 Mango (fruit) per kg 25.00 Kend (fruit) per kg 12.00 Jamun (fruit) per kg 10.00 Mahua (flower) per kg 5.00 Honey per kg 25.00 Meat per kg 80.00 Snail per kg 40.00 Shak (leafy vegetable) per kg 5.00 Kachu per kg 30.00 Oal (Tuber root) per kg 30.00

10 per kg 60.00 11 per piece 5.00

Source : Field Survey 2011Broom

Vegetables9

Fish

Timber

Cow dung (dung cake)

4 Herbal medicine

Fuelwood

5 Bamboo / Sal & other leaves

Hunded birds / animals /snails8

7 Fruits / Flowers / Honey

Price List of Common Property Resources in the Surveyed Villages

Common Property Resource

1 Fodder per kg 2.00

Appendices

213

APPENDIX-III

SUMMARY STATISTICS

Based on our primary data, we present the summary statistics in Table A3.1 & A3.2

below:

TABLE A3.1 Descriptive Statistics of Quantitative and Dummy variables for Bankura District

FSIZ

E FEM PER

AVR AGE

AVR SCH

OWN LAND

LIVE STOCK

FORESTDIST POVR CPRIN DISM

Mean 4.69 48.87 28.79 2.249 3.1246 2.9478 1.67700 0.886 11047 2.130

Median 5.00 50.00 26.98 1.790 2.3000 2.8350 1.00000 1.000 11583 2.000

Maximum 14.0 100.0 75.00 6.670 18.000 7.3100 3.60000 1.000 22915 5.000 Minimum 1.00 0.000 12.67 0.000 0.0000 0.0000 0.50000 0.000 240.0 1.000 Std. Dev. 1.93 18.70 11.37 1.962 2.6352 1.6605 0.98656 0.318 3309 0.924

Skewness 0.85 0.003 1.523 0.472 2.2860 -0.068 0.61780 -2.440 -0.434 0.719

Kurtosis 5.69 3.572 6.158 1.963 9.9368 2.7280 1.69192 6.951 3.939 3.226

Sum 704 7331 4319 337.4 468.70 442.17 251.550 133.0 16570

63. 319.6

Sum Sq. Dev. 555 52122 19286 573.6 1034.7 410.86 145.023 15.07 1.63E

+09 127.2

Observations 150 150 150 150 150 150 150 150 150 150


TABLE A3.2 Descriptive Statistics of Quantitative and Dummy variables for Purulia District

FSIZE FEM PER

AVR AGE

AVR SCH

OWN LAND

LIVE STOCK

FORESTDIST POVR CPRIN DISM

Mean 4.340 45.22 30.93 5.034 4.5776 2.3303 1.96593 0.620 9577 1.813 Median 4.000 48.00 28.16 4.750 4.0000 2.05000 1.75000 1.000 9627 2.000 Maximum 14.00 100.0 80.00 11.00 15.000 10.7600 4.25000 1.000 13950 4.000 Minimum 1.000 0.000 11.41 0.000 0.0000 0.00000 0.45000 0.000 275 1.000 Std. Dev. 2.454 16.62 12.67 2.842 3.1882 1.79158 0.87217 0.487 2130 0.814 Skewness 0.800 0.329 1.219 0.043 0.8079 1.17314 0.47161 -0.494 -0.512 0.802 Kurtosis 4.000 2.987 4.459 1.886 3.0024 5.58171 2.00872 1.244 3.049 3.123

Sum 651.0 6783 4640 750.1 686.65 349.550 294.890 93.00 1436631 272.0

Sum Sq. Dev. 897.6 41181 23947 1195 1514.5 478.256 113.343 35.34 6.76E

+08 98.77

Observations 150 150 150 149 150 150 150 150 150 150


Appendices

214

We have observed that in all the cases in Bankura and Purulia district the mean is

almost equal to the median and the distribution has nearly zero skewness, which

implies normal distribution i.e. data are symmetric about mean. However, as per the

result, Kurtosis of all the variables is not nearly 3, except for female percentage and

CPR income in Bankura district.

Table A3.1 and A3.2 depicts average family size is nearly 4 in both the districts and

percentage of female member in the household is nearly 48 in Bankura district and 45

in Purulia district. The average age of our sample household varies from 12 to 75

years in Bankura district and 11 to 80 years in Purulia district. The Table further

shows that average education is nearly 2 in Bankura district and 5 in Purulia district.

The area of ‘ownland’ varies from 0 to 18 bighas in Bankura district and 0 to 15

bighas in Purulia district. In fact, few of our sample households are landless; some are

small and marginal farmers (less than 2 bighas) whereas some are medium (2-4

bighas) and large (more than 4 bighas) farmers. The average livestock owned by the

household is nearly 2 in both the districts. The forest distance varies from 0.5 to 3.6

km in Bankura district and 0.45 to 4.25 km in Purulia district. The above Tables show

that annual CPR income of our sample household varies from Rs 240 to Rs 22915 in

Bankura district and Rs 2275 to Rs 13950 in Purulia district. The distance to the

closest market varies from 1 km to 5 km in Bankura district whereas 1 km to 4 km in

Purulia district.

Appendices

215

APPENDIX-IV

LAND OWNERSHIP PATTERN Table A4.1 illustrates the land ownership pattern in the surveyed villages in the

district of Bankura and Purulia.

The total area of ‘lease in’ land for the 150 surveyed households of Bankura and

Purulia district is 28.47 hectare and 7.09 hectare respectively (Table A4.1). This is in

sharp contrast to the area of ‘own land’ for the surveyed households of Bankura and

Purulia which is 14.48 hectare and 35.49 hectare respectively. The above data shows

that the households in the surveyed villages of Bankura are poor and therefore they

have very meagre land of their own. Theses households have to ‘lease in’ the land for

cultivation and meet their ends. The households in the surveyed villages of Purulia

seem to be better off as compared to the surveyed villages of Bankura. These

households have comparatively larger ‘own land’ and therefore the area of ‘leased in’

land is less.

The land ownership pattern for the own land in the surveyed villages of Bankura and

Purulia has been illustrated in Table A4.2 below. Here we have considered different

size class based on the area of the own land. The different size class used are i) 0

hectare i.e. landless farmers ii) 0-0.25 hectare iii) 0.25-0.50 hectare iv) 0.50-0.75

hectare v) 0.75-1.0 hectare vi) above 1.0 hectare.

BANKURA PURULIA TOTAL1 No. of Households 150 150 3002 Total area of lease in land (Hectare) 28.47 7.09 35.563 Total area of lease out land (Hectare) 0.28 0.27 0.554 Total area of ownland (Hectare) 14.48 35.49 49.975 Total area of operated land (Hectare) 42.66 42.31 84.98


TABLE A4.1Land Ownership Pattern

Appendices

216

From Table A4.2 above, we can infer that 44 percent of the surveyed households in

the district of Bankura who have no ‘own land’. It can also be observed that 90.7

percent of the surveyed households in the district of Bankura belong to landless and

marginal size own land holders (0-0.25 hectare) and they cater to 68 percent of the

total own land. It is further observed that 9.4 percent of the surveyed households

belong to small and medium land owners (0.25-0.75 hectare) having 32.1 percent of

the total ‘own land’. There are no large size (>1 hectare) own land holders. This

implies that majority of the households have almost equally very small area of own

land which indicates that households are extremely poor and have fragmented land.

The Gini coefficient which shows the degree of inequality in the distribution of ‘own

land’ holding is 0.52. Thus there is high degree of inequality in the ownership

distribution of land owners.

In the case of the surveyed villages in the district of Purulia, it is observed that 6.7

percent of the households are without any own land. Survey results show that 93.3

percent of the surveyed households in the district of Purulia have ‘own land’ in the

range of 0-0.50 hectare which account for 78 percent of the total own land, whereas

only 1.3 percent of the surveyed households belong to large size group (> 1 hectare)

having 6 percent of the total own land. Here the Gini coefficient is 0.33.

TABLE A4.2

BANKURA

Size Class (in hectare)

No. of holdings

Percentage of holding

Cummulative percentage of holdings

Xt

Area of own land (Hectare)

Pecentage of area

Cummulative percentage of

area of own land Yt

Gini Coefficient

0 66 44.0 44.0 0.00 0 0.00 - 0.25 70 46.7 90.7 9.83 68.0 68.00.25 - 0.50 13 8.7 99.3 4.13 28.6 96.50.50 - 0.75 1 0.7 100 0.50 3.5 1000.75- 1.0 0 0 100 0.00 0 100> 1.0 0 0 100 0.00 0 100Total 150 100 14.46 100

PURULIA


No. of holdings



Xt

Area of own land (Hectare)

Pecentage of area


area of own land Yt

Gini Coefficient

0 10 6.7 6.7 0.00 0.0 0.00 - 0.25 89 59.3 66.0 13.91 39.2 39.20.25 - 0.50 41 27.3 93.3 13.80 38.9 78.00.50 - 0.75 5 3.3 96.7 2.88 8.1 86.10.75- 1.0 3 2.0 98.7 2.80 7.9 94.0> 1.0 2 1.3 100 2.12 6.0 100Total 150 100 35.51 100Source: Field Survey, 2011

Land Ownership Pattern (Own Land)

0.527067773

0.330582934

Appendices

217

The land ownership pattern for the ‘operated land’ in the surveyed villages of

Bankura and Purulia has been illustrated in Table A4.3 below:

From Table A4.3 above, we observed that 9.3 percent of the surveyed households in

the district of Bankura have no operated land. Survey results reveal that 93.3 percent

of the surveyed households in the district of Bankura belong to landless, marginal and

small category of farmers and they cater to 82.4 percent of the total operated land. It is

further noted that only 1.3 percent of the surveyed households are large size operated

land holders (>1 hectare) who have 6.4 percent of the total operated land. This implies

that majority of the households have almost equally small area of operated land. The

Gini coefficient is 0.26, which is much lower than the degree of inequality as in the

case of own land.

From the survey results in the district of Purulia, it is observed that 6 percent of the

households are without any operated land. Further, 86.7 percent of the surveyed

households have operated land in the range of 0-0.50 hectare which account for 63.7

percent of the total operated land, whereas only 2 percent of the surveyed households

belong to large size group (> 1 hectare) having 7.7 percent of the total operated land.

Here the Gini coefficient is 0.36.

TABLE A4.3

BANKURA


No. of holdings



Xt

Area of operated

land (Hectare)

Percentage of area


area of operated land

Yt

Gini Coefficient

0 14 9.3 9.3 0.00 0.0 0.00 - 0.25 42 28.0 37.3 8.04 18.8 18.80.25-0.50 84 56.0 93.3 27.13 63.6 82.40.50 - 0.75 7 4.7 98.0 3.99 9.4 91.80.75-1.00 1 0.7 98.7 0.77 1.8 93.6> 1.00 2 1.3 100 2.74 6.4 100Total 150 100 42.67 100

PURULIA


No. of holdings



Xt

Area of operated

land (Hectare)

Percentage of area


area of operated land

Yt

Gini Coefficient

0 9 6.0 6.0 0.00 0.0 0.00 - 0.25 79 52.7 58.7 12.74 30.6 30.60.25-0.50 42 28.0 86.7 13.79 33.1 63.70.50 - 0.75 11 7.3 94.0 6.42 15.4 79.10.75-1.00 6 4.0 98.0 5.50 13.2 92.3> 1.00 3 2.0 100 3.19 7.7 100Total 150 100 41.64 100Source: Field Survey, 2011

0.362992315

0.260695258

Land Ownership Pattern (Operated Land)

Appendices

218

APPENDIX-V PROPERTY RIGHTS AND ‘THE TRAGEDY OF THE COMMON’ The concept of over exploitation of the natural resources in the ‘Commons’ was first

published by Garett Hardin (1968) in his highly debatable article ‘The Tragedy of the

Commons’. The ‘Tragedy of commons’ is a dilemma arising from the situation in

which several individuals, acting independently and rationally consulting their own

self-interest, will ultimately deplete a shared limited resource, even when they are

fully aware that it is not in anyone’s long-term interest for it to happen. The article

highlighted two important aspects about the natural resource, usually referred to as a

common-pool resource to which a large number of people have full access:

i) There is huge demand on the natural resources due to high growth in

human population and therefore there is increased usage of the resources.

ii) Excessive demand of the natural resources leads to unlimited usage by

many users, resulting in over exploitation.

The concept has been used to explain the reason for overgrazing in pastures,

overexploitation of fisheries, air and water pollution, depletion of fuel wood and

ground water, decline in wild life, etc. (Stevenson, 1991). The article describes the

situation in a common pasture where there is no restriction to entry for the herdsmen.

In primitive times, overpopulation of the pasture by herds did not occur because of

natural attrition. However, in modern times, it is not so and the balance will be tipped

as adding one more animal to the grazing land will cause it to be overpopulated. In

spite of this, each rational herdsman wants to maximize his gains by adding one more

animal to the herd. The marginal utility of adding one more animal to the herd has one

positive and one negative component. The positive component is a function of the

increment of one animal. Now that the herdsman receives all the benefits from the

sale of the additional animal, the positive component can be considered to be almost

equal to +1. The negative component is the function of the additional overgrazing

which resulted due to addition of one more animal to the herd. In this particular case it

may be noted that the effect of over grazing due to addition of one more animal to the

herd by a herdsman is shared equally by all the herdsmen. Hence the negative utility

for a herdsman is a fraction of -1. Thus the rational herdsman concludes that it is

Appendices

219

logical to add one more animal to the herd since it maximizes his gain while the cost

is distributed equally amongst all the herdsmen. Since all the herdsmen reach the

same calculation, overgrazing is inevitable. Each herdsman will continue to impose

costs on all of the others, until the pasture is depleted, which is detrimental to all.

Thus the ‘free riding’ leads to the Tragedy. Hardin had recommended that the tragedy

of the commons could be prevented by strict regulation by the government. He also

felt that privatization of the common property could solve the problem.

The term ‘common property’ as described by Hardin (1968) has been highly debated

as he failed to distinguish between ‘common property’ and ‘open access’ resource.

Common Property represents private property for the group (since all others are

excluded from use and decision making), and that individuals have rights (and duties)

in a common property regime (Ciriacy-Wantrup and Bishop, 1975). Open access is a

situation in which there is no property. Since there is no property rights in an open

access situation, “everyone‘s property is nobody’s property”. It can only be said that

“everybody’s access is nobody’s property” (Bromley, 1999). Thus the critics argue

that Hardin’s article is more apt for open access resource instead of common property

resource. Elinor Ostrom (2000) emphasized that the tragedy of the commons may not

be as prevalent or as difficult to solve as Hardin implies, since locals have often come

up with solutions to the commons problem themselves. Contrary to Hardin’s view on

the Tragedies of the commons, the pastures were well looked after for many centuries

before they depleted for reasons unrelated to any flaw in the commons (Cox, 1985).

To avert the tragedy of the commons, different solutions have been put forth by

several Economists. One view is that the overexploitation and the degradation of the

common property resource can be creating and enforcing private property rights

(Demsetz 1967). Private property helps to incorporate the externalities generated from

over exploitation of the common property resource. Another view to overcome the

overexploitation is to entrust the commons to the state regime which would have full

authority over it (Hardin 1968). Of late, a large number of scholars have advocated

that the best solution to overcome the ‘Tragedy of the commons’ is for the users to

form a decentralized collective management of the CPRs. This theory of the use of

Appendices

220

Collective management of the CPRs has been favoured by Jodha (1986), Chopra

et.al., (1989), Berks 1989, Ostrom (1990).

The influencing article of the ‘Tragedy of the Commons’ by Hardin (1968) could also

be described as a dilemma arising from a situation wherein several users act

independently and rationally in their own self-interest, ultimately depleting the shared

limited resource although they are very well aware that it would lead to long term

adverse situation for all. This theory is explained with the help of Figure A5.11 as

shown below:

FIGURE A5.1

Relationship among effort, cost and revenue

*q 0q

Source: Ostrom, E, et.al.(2002); The Drama of the Commons

As per the illustration in Figure A5.1 above, on the X-axis we have depicted the effort

required in extraction of the common property resource (q) and on the Y- axis we

have depicted the Total Cost (TC) / Total Revenue (TR) associated with the

extraction. The effort in extraction of the resource ranges from *q , the social optimum

achieved under private maximization i.e. MR MC= to 0q , the level of extraction

Appendices

221

under open access with AR AC= and profit ( ) 0π = . In order to achieve *q , there has

to be cooperation amongst all the members of the community and they must all agree

to exercise restraint in their effort to extract from the resource. Here the q is above *q in the case of CPR without cooperation, since each member tries to impose on

others a negative externality in extracting from the resource that he does not take into

account. This in turn leads to depletion of the resources.

The dilemma associated with the herdsmen whether to act independently at one’s

own interest or to cooperate and act collectively looking into the long term interest of

the CPRs can be best compared with the Prisoner’s Dilemma (PD). The Prisoner’s

Dilemma is a problem in game theory that demonstrates as to why two people or

group of people will not cooperate even if it is in both their best interest to do so. The

Prisoner’s Dilemma (PD) is the theoretical foundation to explain the tragedy of the

commons or the non-cooperative use of a CPR (Baland and Platteau, 1996).

A classic example of the prisoner's dilemma (PD) is presented as follows:

In an example of forest management, if the forest department and the village

community agree to participate, then both the forest department and village

community may share the forest produce equally at Rs 3 lakhs each. If the village

community alone participates and not the forest department, then the forest

department may collect all the forest produce and auction it and earn a forest revenue

to a tune of Rs 5 lakh (and not a total of 6 lakh) , but the village community gets

nothing and will be the net loser. If the village community does not participate, it may

indulge in illegal cutting of some timber and get its market price worth Rs 5 lakhs and

the forest department gets nothing and is the net loser. Finally, if both the forest

department and the village community do not participate in the forest management,

then the forest department may collect some timber and other forest produce and the

village community may collect some forest produce illegally, thereby each gaining

revenue equal to just Rs 1 lakh (Table A5.1).

Appendices

222

TABLE A5.1

Prisoner’s Dilemma

(Rs Lakhs)

Village

Community

Forest Department

Participate Do not participate

Participate 3 ; 3 0 ; 5

Do not participate 5 ; 0 1 ; 1

In the classic form of this game, cooperating is strictly dominated by defecting, so that

the only possible equilibrium for the game is for all players to defect. No matter what

the other player does, one player will always gain a greater payoff by playing defect.

Since in any situation playing defect is more beneficial than cooperating, all rational

players will play defect, all things being equal. In the above example, both the village

community and the forest department do not participate in the forest management and

settle for a pay-off of Rs 1 lakh each. However the best set of pay-offs could have

been each getting Rs 3 lakhs.

According to Amartya Sen, all it takes to make cooperation individually optimal is

‘assurance’ that others will cooperate. Hence, this is an assurance problem. (Sen

1967). It may be possible to overcome the prisoner’s dilemma by converting the

dilemma into an assurance problem by changing either incentives or personal

motivations.

http://en.wikipedia.org/wiki/Dominance_(game_theory)

http://en.wikipedia.org/wiki/Nash_equilibrium

http://en.wikipedia.org/wiki/Perfect_rationality

Appendices

223

APPENDIX-VI JOINT FOREST MANAGEMENT IN INDIA

The forest cover and the status of Joint Forest Management in different states of India

are discussed in detail below:

Andhra Pradesh is one of the pioneer states to adopt Joint Forest Management in

India. Based on the Satellite (IRS P6 LISS-III 2009) data of Oct 2009 to Mar 2010,

the forest cover in the state is 42,163 km2 which is 15.32 percent of the total

geographical area of the state (AP SFR 2011). In line with the guidelines on JFM

issued by the Government of India in 1990, the Government of Andhra Pradesh issued

formal orders in 1992 for adopting Joint Forest Management (JFM) as a strategy for

rehabilitation of degraded forests. In 1993, detailed guidelines were also issued for

taking up JFM through village level committees called ‘Vana Samrakshana

Samithies’ (VSS).As per the Andhra Pradesh State of Forest Report 2011, there are

7,718 Vana Samrakshana Samities (VSSs) or JFPCs in the State. A forest area

covering 15,199.8 km2 which is 23.8 percent of the total forest area is under the

purview of Community Forest Management (CFM). Total 1,539,000 members are

involved in CFM which includes 465,000 members belonging to Scheduled Tribe

(S.T) and 323,000 members belonging to Scheduled caste (SC).

Arunachal Pradesh is the largest state in the North East region of India. The forest

cover as per satellite data of Nov-Dec 2008 is 67,410 km2, which is 80.50 percent of

the total geographical area of the state (India State Forest Report 2011).

Approximately one third of the state’s land area is managed by the Forest Department;

the remaining two third is largely under the management of the tribal communities. Joint

Forest Management was introduced in the state in 1997. There are 347 JFM

committees which manage about 90,000 hectare of forest area. The total number of

members involved in JFM activity is 23,308 all of which belongs to the Scheduled

Tribes (SFR 2009).

The forest cover in the state of Assam, based on the satellite data of Nov 2008-Jan

2009 is 27,673 km2 which is 35.28 percent of the state’s geographical area (India SFR

2011). With the objective to reduce environmental degradation and rural poverty, the

Govt. of Assam promulgated the 'Assam Joint (people's participation) Forestry

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Management Rules, 1998'. Total 28 Forest Development Agencies (FDAs) were

constituted during 2002-03 with 503 Joint Forest Management Committees (JFMCs)

managing 80,000 hectares of forest area. Government of Assam adopted the 'Assam

Forest Policy, 2004' which emphasized JFM to graduate to Community Forest

Management aiming at sustainable forest management. The total number of families

involved in JFM in the state of Assam is 57,341 including 28,459 families of

Scheduled Tribes (SFR 2009).

The forest cover in the state of Bihar based on the satellite data of Nov 2008-Jan

2009 is 6,845 km2 which is 7.27 percent of the total geographical area of the state

(ISFR 2011). Joint Forest Management was initiated in 1990. There are 532 JFM

committees managing 3.7 lakh hectare of forest area as on March 2005. About 2.05

lakh families are involved in JFM of which 32,303 families belonged to Scheduled

Tribes (SFR 2009).

The State of Chhattisgarh has a forest cover of 55,674 km2 based on the satellite date

of Oct 2008-Jan 2009, which is 41.18 percent of the total geographical area of the

state (India SFR 2011). Joint Forest Management began in the state in 1991.

However, with the creation of the new state a new JFM resolution was issued in 2001.

Out of the total of 19,720 villages in the State, 11,185 villages situated within the 5

km range of the forest areas form the JFM area. There are 7,887 JFM committees in

the state. A total of about 33,190 km2 of forest area is under the purview of JFM,

which is about 55.52 percent of the total forest area of the state. There are more than

2,763,000 families involved in JFM activity consisting of 1,521,000 families of

Scheduled Tribe and 471,000 families of Schedule Caste (Source: Forest Dept., Govt.

of Chhattisgarh).

Goa has a forest cover of 2,219 km2 based on the satellite data of Feb 2009, which is

59.94 percent of the total geographical area of the state (ISFR 2011). Joint Forest

Management was started by the Government of Goa in 2003. Subsequently, three

Forest Development Agencies were constituted, i) North Goa Forest Development

Agency ii) South Goa Forest Development Agency and iii) Forest Development

Agency (Wildlife). This greatly helped in the participation of the local community in

the planning & the implementation of appropriate Afforestation Programme at the

grass root level and help in the decision making process. There are 26 JFM

committees managing 10,000 hectares of forest area with 336 families involved in

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JFM activity. As per JFM notification of Goa, sharing of benefits is not based on

harvested forest produce but on the value addition to the forests being protected by the

JFM committees (Source: Forest Dept., Govt. of Goa and SFR 2009).

On the basis of the satellite data of Oct-Nov 2008, the forest cover in the state of

Gujarat is 14,619 km2, which is 7.46 percent of the total geographical area of the

state (ISFR 2011). Based on the National Forest Policy, 1988 and the guidelines in

June, 1990 regarding the involvement of the local communities and voluntary

agencies in forest protection, management and regeneration to rejuvenate degraded

forest lands, the Joint Forest Management (JFM) programme was launched in Gujarat

vide government resolution in March 1991. As of March, 2012, there are 3,274 JFM

committees managing the forest area of 0.43 million hectare. The working under the

Joint Forest Management has been appreciated by the Government of India as three

of the Van Kalyan Samities namely- Pingot, Bapda, Balethi and Motia have been

awarded Priyadarshani Vriksha Mitra Award in recognition of their services for the

protection and regeneration of the degraded forest (Source: Forest Dept. Govt. of

Gujarat). There are 0.81 million members involved in JFM activities. The total

number of families involved in JFM is 0.2 million, of these 0.14 million families

belong to the Schedule Tribes (SFR 2009).

The state of Haryana is among the pioneers in implementation of JFM which began

in 1976, much before the formulation of the forest policy by the Government of India.

The model developed in Sukho Majri village of Haryana, employing participation of

people in protecting forests who were given rights over water and forest produce in

return, is now world famous. Haryana has a forest cover of 1,608 km2 based on the

satellite data of Oct-Nov 2008, which is 3.64 percent of the total geographical area of

the state (ISFR 2011). Village level forest committees (VFC) have been constituted in

over 817 villages under National Afforestation Programme (NAP). Moreover, 1135

VFCs have also been constituted under Japan International Cooperation Agency

(JICA) funded Integrated Natural Resource Management Project and Haryana

Community Forestry Project (HCFP). The benefits obtained from JFM programme

due to increased production in forest areas as well as water from the water-harvesting

dams are distributed equally among all the households (Source: Dept. of Forest, Govt.

of Haryana). About 165,500 families are involved in JFM covering a forest area of

56,000 hectares (SFR 2009).

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Himachal Pradesh has a forest cover of 14,679 km2 based on the satellite data of

Oct-Dec 2008, which is about 26.37 percent of the total geographical area of the state

(ISFR 2011). The Joint Forest Management was started in Himachal Pradesh in 1993

which has about 11 percent of the forest area under it. Total 1,690 JFM committees

manage a forest area of 0.42 million hectares. Around 2,65,000 families are involved

in JFM activities , of which 36,000 families belong to the Schedule Tribes (SFR

2009).

The forest cover in the state of Jammu and Kashmir based on the satellite data of

Oct-Dec 2008 is 22,539 km2 which is 10.14 percent of the state’s geographical area

(ISFR 2011). JFM started in J&K in 1990 and it has about 11 percent of the forest

area under its purview. There are 2697 JFM committees managing 1,141 km2 of the

forest area. The members are entitled to a share of 25 percent of the net proceeds from

the first major harvest of the plantation and are also entitled to collect grass, fodder,

fallen wood, etc. free of cost (Handbook of Forest Statistics, J&K Forest Department,

2006).

The satellite data of Nov 2008 – January 2009 reveals that Jharkhand has a forest

cover of 22,977 km2 which is 28.82 percent of the state’s geographical area (ISFR

2011). JFM was started in the state in 1990 and it has about 92.80 percent of the forest

area under it. The participatory approach was applied in degraded forests through the

constitution of village forest management and protection committees (VFMPC).

There are 10,903 JFM committees managing 2.19 million hectares of the forest area.

Around 1.28 million families are involved in JFM activities, of which 0.51 million

families belong to Schedule Tribes (SFR 2009).

Karnataka is one of the earliest states to issue a Government order in 1993 on

Participatory Management, by adopting JFPM policy to involve local community in

protection and management of degraded forests having canopy density up to 0.25 and

also provide 50 percent share in forest produce to the VFCs from JFPM areas. As per

the satellite data of Oct 2008-Jan 2009, the state has a forest cover of 36,194 km2

which is 18.87 percent of the total geographical area of the state (ISFR 2011). There

are 5200 VFCs / EDCs managing a forest area of 0.34 million hectares (Source:

Forest Dept., Govt. of Karnataka). In the state of Karnataka, around 0.19 million

families are involved in JFM activity, of which 24,705 families belong to Schedule

Tribes (SFR 2009).

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The forest cover in the state of Kerala based on the interpretation of satellite data of

February 2009 is 17,300 km2, which is 44.52 percent of the total geographical area of

the state (ISFR 2011). In 1998, JFM was started in the state of Kerala, which covers

about 15 percent of the forest area of the state. Several organisations like Vana

Samrakshana Samithi (VSS), Adivasi Vana Samrakshana Samithi (AVSS), Eco-

Development Committee (EDC) are in operation by the state forest department.

There are 327 JFM committees managing about 0.17 million hectare of forest area.

Around 51,300 families are involved in JFM activity of which 12,255 families

belonging to Schedule Tribe (SFR 2009).

Madhya Pradesh is a pioneering state in implementation of JFM programme. The

Government of M.P. issued the first resolution in this regard in 1991. Learning from

experiences, the State Government revised JFM resolution in 1995, 2000 and 2001.

Steps have been taken to integrate the local institutions by involving the Gram Sabha

in the formation and functioning of JFM Committees. The forest cover in the state

based on satellite data of Oct-Dec 2008, is 77,700 km2 which is 25.21 percent of the

total geographical area of the state (ISFR 2011). A total of about 60,000 km2 of forest

area is under JFM, which is about 63 percent of the total forest area of the State. So

far 14,073 JFM committees have been constituted, of which 9035 are VFCs, 4201 are

FPCs and 785 are EDCs, manage 6 million hectare of forest area (Source Forest

Dept., Govt. of Madhya Pradesh). About 1.7 million families are involved in JFM of

which 0.8 million families belong to Schedule Tribes (SFR 2009).

Joint Forest Management started in the state of Maharashtra in 1990. The JFM

programme gathered momentum in the year 1996-97 with the aid of a World Bank

Project. The forest cover, based on the satellite data of Oct-Dec 2008, is 50,646 km2

which is 16.46 percent of the state’s geographical area (ISFR 2011). There are 12,648

JFM committees managing around 2.6 million hectare of forest area which is about 40

percent of the forest area of the state. More than 25 lakh families are involved in JFM,

of which around of which around 6 lakh families belong to the Scheduled Tribes

(Annual Report 2010-11, Forest Dept., Govt. of Maharashtra). Under JFM, the

communities have full entitlement to Non Timber Forest Product (NTFP) species

excluding Tendu and Cashew (SFR 2009).

The State of Manipur has a forest cover of 17,090 km2 based on the satellite data of

Jan-Feb 2009, which is 76.54 percent of the state’s geographical area (ISFR 2011).

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Joint Forest Management started in Manipur in 1990 and has 280 JFM committees

which manages about 90,000 hectare of forest area, which is 5.39 percent of the total

forest area of the state. Around 26,000 families are involved in the JFM programme,

of which 22,000 families belong to the Scheduled Tribes (SFR 2009).

Meghalaya joined the rest of the country in the JFM activity when it notified the

constitution of Forest Development Agency and application of JFM principles on 9th

September, 2003. The Forest & Environment Dept. registered 7 FDAs constituted in 7

(seven) Social Forestry Divisions covering all the districts of the State. The Joint

Forest Management Committees formed by respective FDAs have been registered by

the concerned Conservator of Forests (Social Forestry) (Source: Forest &

Environment Dept., Govt. of Meghalaya). The forest cover in Meghalaya based on

satellite data of Dec 2008-Jan 2009 is 17,275 km2 which is 77.02 percent of the state’s

geographical area (ISFR 2011). There are 73 JFM committees in Meghalaya,

managing around 4000 hectares of forest area. All the families involved in JFM

belong to Schedule Tribes (SFR 2009).

Mizoram has a forest cover of 19,117 km2 based on the satellite data of Jan 2009,

which is 90.68 percent of the total geographical area of the state (ISFR 2011). JFM

was introduced in Mizoram in 1990. Active involvement of the local people through

the mechanism of JFM has significantly helped the Forest Department in its efforts to

enrich and protect the valuable forest wealth of the State. There are 270 JFM

committees managing about 20,000 hectare of forest area. More than 40,000 families

are involved in JFM activity, mainly belonging to the Scheduled Tribes (SFR 2009).

JFM was introduced in the state of Nagaland in 1997. The forest cover of the state as

per the satellite data of Nov 2008 – Feb 2009 is 13,318 km2 which is 80.33 percent of

the total geographical area of the state (ISFR 2011). Nagaland has only 11.7 percent

of Forests under the Government and the remaining 88.3 percent of the Forests belong

to Non-government Communities of the villages. Community forest committees have

been formed in Kohima (8), Mokokchung (33), Tuensang (30), Wokha (34), Doyang

(9), Zunheboto (37), Mon (19), Peren (12), forest divisions and formation of more

number of community forest committees is in progress in different forest divisions

(Source: Forest Dept., Govt. of Nagaland). There are 335 JFM committees managing

20,000 hectares of forest area. Around 85,000 families are involved in JFM, mostly

belonging to the Scheduled Tribes (SFR 2009).

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Orissa is one of the pioneer states to initiate forest management as early as 1950s. It

is the first state to issue the resolution in August 1988, where the villagers were

assigned specific roles in the protection and conservation of Reserve Forests (RF)

adjoining their villages, and in turn, were granted few concessions in meeting their

requirements of fuel wood, fodder, bamboo and small timber. The forest cover based on

the satellite data of Oct 2008-Jan 2009, is 48,903 km2 which is 31.41 percent of the

geographical area of the state (ISFR 2011). There are 9,778 JFM communities managing

0.82 hectare of the forest area, which is about 14 percent of the forest area. About 1.7

million families are involved in JFM, of which around 0.7 million families belong to the

Scheduled Tribes (SFR 2009).

The forest cover in the state of Punjab, based on the satellite data of November 2008

is 1,764 km2, which is 3.50 percent of the geographical area of the state (ISFR 2011).

The JFM programme was started in the state in 1993. There are 1,224 JFM

committees managing about 0.18 million hectares of forest area, which is about 58

percent of the forest area of the state. More than 91,000 families are involved in JFM

activity (SFR 2009).

Rajasthan has a forest cover of 16,087 km2 based on the satellite data of Oct-Dec

2008, which is 4.70 percent of the geographical area of the state (ISFR 2011). The

JFM programme started in the state in the year 1991. There are 4,882 JFM

committees managing about 0.78 hectare of forest area which is about 19 percent of

the forest area of the state (Source: Forest Dept. JFM Report, Govt. of Rajasthan).

More than 0.4 million families are involved in the JFM programme, of which around

half of the families are from Scheduled Tribes (SFR 2009).

The JFM programme in the state of Sikkim started in 1998. Sikkim has a forest cover

of 3,359 km2 based on the satellite data of Dec 2008, which is 47.34 percent of the

total geographical area of the state (ISFR 2011). There are 155 JFM committees

managing a forest area of 10,000 hectares. More than 46,000 families are involved in

the JFM activity, of which 17,000 families belong to Scheduled Tribes (SFR 2009).

The evolution of JFM in Tamil Nadu has been much influenced by the social forestry

movement and practices prevailed in the state. Tamil Nadu has a forest cover of

23,625 km2 based on the satellite data of Oct 2008-May 2009, which is about 18.16

percent of the geographical area of the state (ISFR 2011). JFM started in the state in

1997 which coincided with the launch of Japan Bank for International Cooperation

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(JBIC) project. The JFM concept of Institution building was in the form of

constitution of Village Social Forestry Committees (VSFC) in the Social Forestry

Villages. Out of total 12,612 Village panchayats in Tamil Nadu, Village Social

Forestry Committee (VSFC) have been constituted in 4,343 villages, roughly covering

35 percent of the Village panchayats in the state. More than 0.24 million families are

involved in the JFM programme, of which 10,000 families belong to the Scheduled

Tribes. Water harvesting structure is an important component of the JFM programme

in Tamil Nadu (SFR 2009).

Tripura has a forest cover of 7,977 km2 based on the satellite data of Jan 2009, which

is 76.04 percent of the geographical area of the state (ISFR 2011). There are 472 JFM

committees managing about 0.12 million hectare of forest area which is about 18

percent of the forest area of the state. More than 44,882 families are involved in the

JFM activity, of which 26,891 families belong to Scheduled Tribes (Dept. of Forest,

Govt. of Tripura).

The forest cover in the state of Uttar Pradesh based on the satellite data of Oct 2008-

Jan 2009 is 14,338 km2, which is 5.95 percent of the geographical area of the state

(ISFR 2011). Joint Forest Management started in Uttar Pradesh in 1992. Here VFCs

have been characterized as Forest Officers under the Village Forests, under section 28

of the Indian Forest Act 1927, thus empowering village community with the rights of

the FD over the Reserve Forest. There are 1,892 JFM committees managing about

80,000 hectares of forest area. More than 0.8 million families are involved in JFM

activity, of which 83,000 families belong to Scheduled Tribes (SFR 2011).

Uttarakhand has a forest cover of 24,496 km2 based on the satellite data of Oct-Dec

2008, which is 45.8 percent of the state’s geographical area (ISFR 2011). The JFM

project was implemented in the state of Uttarakhand in 1992, when it was a part of

Uttar Pradesh. Initially, delay in the formation of VFCs and ineffective

implementation of the JFM Project by the Forest Department by not ensuring

adequate and active participation of the village communities especially women

defeated the objective of creating sustainable forest management. There are 10,107

JFM communities managing about 0.86 million hectares of forest area, which is about

25 percent of the forest area of the state. Around 0.5 million families are involved in

JFM activity, of which around 15,000 families belong to the Scheduled Tribes (SFR

2009).

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