







































Ecology, Economy and Society–the INSEE Journal 6(2): 7-31, July 2023 

 

RESEARCH PAPER 

Does Assignment of Individual Property Rights 
Improve Forest Conservation Outcomes? Empirical 
Evidence from West Bengal, India 

Sandip Chand and Bhagirath Behera  

Abstract: The past few decades have seen significant changes in the governance of 
forests in India. The Scheduled Tribes and Other Traditional Forest Dwellers 
(Recognition of Rights on Forest) Act (FRA), 2006, was a landmark act passed in 
the Indian Parliament to assign individual property rights over forest resources that 
have been de facto used by local communities. This paper examines whether the 
assignment of individual property rights results in positive outcomes for forest 
conservation using village-level forest patta (forest land title) and census data from 
Bankura district in West Bengal. Vegetation Continuous Fields data has been used 
to measure the change in forest cover from 2006 to 2012. The results show that the 
percentage of forest patta land in the village, distance to markets, the existence of 
pucca roads, and the presence of forest protection committees (FPCs) are 
negatively and significantly related to forest degradation, implying improvement in 
forest conservation outcomes. The presence of tribal people, a larger population, 
and higher literacy rate are positively associated with forest degradation, meaning 
that they have an undesirable impact on forest conservation outcomes. The 
findings of this study strongly suggest that the assignment of individual property 
rights to both the Scheduled Tribes (STs) living in the forest and Other Traditional 
Forest Dwellers (OTFDs) under the FRA, 2006, tends to improve forest 
conservation outcomes. Hence, it is suggested that the distribution of individual 
forest rights (IFRs) under the FRA may further improve forest conservation 
outcomes. 

Keywords: Forest Rights Act, Individual Forest Rights, Forest Governance, Forest 
Conservation, West Bengal. 

 

 
 Research Scholar, Department of  Humanities and Social Sciences, Indian Institute of  
Technology, Kharagpur, India. chandsandip12@gmail.com  

 Professor, Department of  Humanities and Social Sciences, Indian Institute of  
Technology, Kharagpur, India. bhagirath9@gmail.com  

Copyright © Chand and Behera 2023. Released under Creative Commons Attribution © 

NonCommercial 4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of  Economic 
Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581–6152 (print); 2581–6101 (web).  

DOI: https://doi.org/10.37773/ees.v6i2.709  

mailto:chandsandip12@gmail.com
mailto:bhagirath9@gmail.com
https://doi.org/10.37773/ees.v6i2.709


Ecology, Economy and Society–the INSEE Journal [8] 

 

 

1. INTRODUCTION 

According to property rights theory, a person who holds exclusive rights to 
a property has an incentive to protect it because he or she will keep getting 
services from it. It further suggests that there are different classes of 
property rights holders, who have varying rights with respect to incentives, 
the types of activities undertaken, and the outcomes achieved (Schlager and 
Ostrom 1992; table 1). The bundles of property rights may be de jure or de 
facto. De jure rights are those that can be enforced legally with formal 
institutions, whereas de facto rights originate among and are enforced by 
resource users themselves (Schlager and Ostrom 1992). The right of 
exclusion provides proprietors considerable incentives to undertake 
required investments in natural resources because they can be reasonably 
confident of earning a return on their investment (Posner 2014). Therefore, 
exclusion rights incentivize resource owners to make long-term investments 
in the resource that he or she owns and make it more productive (Schlager 
and Ostrom 1992). 

In light of the above arguments in favour of individual property rights, it 
can be seen why nearly every developing country in the world is currently 
experimenting with some kind of change in natural resource management 
by devolving some rights to the community and individual users to use and 
manage natural resources (Edmonds 2002; Persha et al. 2010). For several 
decades, India has also devolved a significant degree of property rights 
concerning the management of forest resources to local communities. The 
first step in this regard was taken in the 1988 National Forest Policy, which 
accepted the role of local people in forest resources management, followed 
by the adoption of the Joint Forest Management Programme in 1990. 
However, in 2006, a landmark forest act, called the Scheduled Tribes and 
Other Traditional Forest Dwellers (Recognition of Rights on Forest) Act, 
was passed in the Indian Parliament, which assigned individual property 
rights to forest dwellers over forest resources that have been de facto used 
by them. However, it is important to note that individual property rights 
granted to forest dwellers under the FRA are not well defined in contrast to 
private property rights. For instance, under the FRA, forest dwellers do not 
have the right to transfer/trade forest land for which the patta (documented 
legal right/title over forest land) has been provided, and hence, they lack 
exclusive ownership rights over the land (Table 1), which violates the 
fundamental conditions needed to ensure an efficient structure of property 
rights with respect to a private property regime (Behera and Engel 2006). 
Table 2 summarizes the evolution of major forest policies, acts, and 
programmes in India since Independence, which clearly shows that over the 



[9] Chand and Behera 

 

 
 

years, India has devolved several critical rights over forest resources to local 
forest dwellers. 

Table 1: Bundles of Property Rights in FRA 

Bundles of 
Rights  

Position Patta Land Community 
Forest Resources 

Access and 
withdrawal 

Authorized user ✓ ✓ 

Management Claimant ✓ ✓ 

Exclusion Proprietor ✓ ✓ 

Alienation Owner   

Source: Authors  

Section 3(1) of the FRA provides de jure access, withdrawal, management, 
and exclusion rights in the case of IFRs, locally known as forest patta (see 
Table 1). As per Section 4(4) of the FRA, the rights recognized and vested 
will be heritable but not transferable or alienable, and in the absence of a 
direct offspring, the rights will be passed on to the patta holder’s closest 
relatives. Since forest dwellers cannot sell or lease their rights of 
management and exclusion over patta land under the FRA, the patta 
holders are the de jure proprietors of that land. The official statistics of the 
Ministry of Tribal Affairs (Government of India 2021) indicate that 
1,973,349 pieces of IFR land have been distributed, which cover 1.72 
million hectares of forest land. 

Given the above-mentioned strong arguments in favour of devolving 
individual property rights to achieve effective natural resources 
management and conservation outcomes, it is imperative to examine 
whether assignment of IFRs to forest dwellers under the FRA in India have 
improved forest conservation outcomes. Limited studies have examined the 
effectiveness of the FRA in improving these outcomes. This paper attempts 
to bridge this knowledge gap. We identify factors that are likely to influence 
forest conservation outcomes through an extensive review of the related 
literature and using our own understanding from field visits and analyse 
these factors using appropriate regression techniques. These factors include 
the assignment of individual property rights over forests along with 
physical, socio-economic, and demographic factors that may influence 
effective forest resource management and conservation outcomes after the 
implementation of the FRA. 

 



Ecology, Economy and Society–the INSEE Journal [10] 

 

 

Table 2: Evolution of Forest Policies in India since Independence 

Major Forest Policies, Acts, 
and Programmes 

Main Features 

Indian Forest Policy, 1952  

Introduced a functional classification of forest 
land. It aimed to bring 33% of the total 
geographical area of the country under forest 
cover 

National Commission on 
Agriculture, 1976 

Emphasis on meeting the requirements of 
forest-based industries through commercial 
forestry following a scientific approach to 
growing trees 

Forest Conservation Act, 
1980 

Minimize deforestation and conserve 
biodiversity and wildlife 

Indian Forest Policy, 1988 
Recognition of the participation of local forest 
inhabitants in the protection of forest resources 

Joint Forest Management 
Programme, 1990 

Adoption of forest department and community-
based joint forest management 

Forest Rights Act, 2006 
Devolution of forest rights to ST forest peoples 
and other traditional forest dwellers 

Source: Authors 

There is a large body of literature on the variables that might explicate the 
differential outcomes seen in forest conservation. It is observed that forest 
degradation is multifaceted, context-dependent, and caused by several 
socio-economic and demographic processes. A significant number of 
deforestation drivers have been reported globally, although the causes of 
deforestation are complex and region-specific (Ullah et al. 2022). Geist and 
Lambin (2001) arrived at a multi-causal structure of the determinants of 
deforestation, which describes the various interlinked proximate and 
underlying factors that contribute to deforestation. The proximate factors 
are human interventions that directly affect forest conservation or 
degradation outcomes—for example, wood extraction, expansion of 
agriculture, and extension of infrastructure. The underlying factors are vital 
factors that reinforce the proximate factors, which include demographic, 
socio-economic, technological, and policy and institutional factors (Geist 
and Lambin 2001). 

Demographic factors significantly influence forest conservation outcomes. 
Human population pressure has been shown to be an important factor 
contributing to forest degradation (Wibowo and Byron 1999). Population 
size can have an impact on deforestation through the number of rural 
households requiring lands for farming, firewood, and timber (Kaimowitz 
and Angelsen 1998; López 2022). 



[11] Chand and Behera 

 

 
 

Socio-economic factors play an important role in forest conservation and 
degradation. Households associated with forest degradation have low 
incomes, are less educated, and own less land (Mena, Bilsborrow, and 
McClain 2006; Yanai et al. 2020). Moreover, marginal farmers and labourers 
are more likely to be involved in deforestation due to insufficient physical 
and financial capital (Angelsen et al. 2014; Nerfa, Rhemtulla, and Zerriffi 
2020). Consequently, families with relatively lower socio-economic status 
are more likely to contribute to deforestation (Specht et al. 2019). In 
addition, forest degradation is also found to be linked to closer proximity to 
markets and towns, shorter distance from the main roads and waterways, 
and greater road density, all of which assist in resource extraction activities 
and fuel an increase in demand for forest produce (López 2022; Haq et al. 
2022; Li et al. 2015). 

Cultural factors (e.g., beliefs and individual or household behaviours) are 
also important drivers of forest conservation outcomes. The indigenous and 
traditional cultures of India hold the religious view that plants and groves in 
the natural world are sacred (Sukumaran et al. 2008; Ray and Ramachandra 
2010; Singh et al. 2010). Since ancient times, such socio-cultural and 
religious beliefs among indigenous groups have helped in conserving forest 
areas (Kandari et al. 2014). 

Policy and institutional factors are crucial in forest conservation. As 
mentioned above, property rights regimes, titling, legalization, and 
consolidation (of individual titles) may influence conservation outcomes. In 
developing countries, co-management or joint forest management policies 
have been implemented, which have had mixed outcomes with regard to 
forest management and protection (Datta and Sarkar 2012; Behera 2009). 

Very few empirical studies exist that examine the impact of the FRA on 
forest conservation outcomes even 14 years after the implementation of the 
Act. Khosla and Bhattacharya (2020) emphasize the recognition of IFRs, 
which have a substantial effect on forest conservation outcomes. Guntuka 
and Kukrety (2019) studied changes in forests using geospatial tools and 
recorded the net impact of the implementation of the FRA on forest 
conservation. Their results show that the forest areas awarded to tribal 
households under the FRA seem to have been adversely affected in terms 
of forest conservation outcomes, and the continued use of forest areas for 
agriculture under the FRA as well as encroachment may further adversely 
influence the ecosystem (Guntuka and Kukrety 2019). Some wildlife 
activists have opposed the FRA as being anti-conservation. However, 
others indicate that under the Act, local communities can be roped in to 
promote biodiversity conservation by blocking the use of forest land for 



Ecology, Economy and Society–the INSEE Journal [12] 

 

 

large-scale construction projects and by applying local knowledge and 
values to promote conservation (Broome, Rai, and Tatpati 2017; Sarangi 
2017). 

The remainder of this paper is organized as follows. Different provisions 
under the FRA and a description of its organizational structure are 
presented in Section 2. Section 3 provides a description of the study area, 
the sources of data used in the study, and the methods applied to analyse 
the data. The results and discussion are presented in Section 4. Section 5 
concludes with significant policy implications. 

2. PROVISIONS UNDER THE FRA AND ITS 
ORGANIZATIONAL STRUCTURE 

As indicated in Section 1, the FRA empowers local communities by giving 
them some property rights over forests, which is a radical departure from 
previous forest policies, including provisions under the Joint Forest 
Management Programme. The primary goal of the FRA is to “recognize 
and vest the forest rights over forest land in forest-dwelling STs (FDSTs) 
and OTFDs who have been residing in forests for generations but whose 
rights could not be recorded.” The Act grants forest dwellers rights over the 
sustainable use of forest resources, biodiversity conservation, and 
maintenance of ecological balance (Ministry of Tribal Affairs 2006). 
Therefore, it decentralizes forest management and devolves responsibility 
for it to forest dwellers and local village-level institutions in place of the 
state (Lee and Wolf 2018). The Act applies to tribal people and OTFDs 
who have been residing for three generations—which means for over 75 
years before 2005—and who rely on nearby forest resources for their 
livelihood requirements (Ministry of Tribal Affairs 2006). 

There are three categories of forest rights that the eligible parties can claim: 
(1) individual rights to forest land for self-cultivation and habitation; (2) 
community rights of ownership, collection, and use of traditionally collected 
non-timber forest produce as well as other customary community rights; 
and (3) community forest resource (CFR) rights, which establish legitimate 
community-based forest governance (Ministry of Tribal Affairs 2006; Lee 
and Wolf 2018). The Act recognizes 13 specific rights of forest dwellers 
which previously existed in all types of forest lands, even in protected areas 
(CFR–LA 2016; Ministry of Tribal Affairs 2006). Section 5 of the FRA 
empowers forest patta holders and the gram sabha to act to conserve and 
protect forests, wildlife, and biodiversity as well as their adjacent catchment 
areas (Ministry of Tribal Affairs 2006). Table 3 reports the main rights and 
responsibilities enshrined in the FRA, and Figure 1 presents the 



[13] Chand and Behera 

 

 
 

organizational structure of the FRA, indicating various activities and the 
corresponding institutional levels. 

Table 3: Rights and Responsibilities of Forest-Dwelling Scheduled Tribes (FDSTs) 
and Other Traditional Forest Dwellers (OTFDs) under the FRA 

Types Rights and Responsibilities 

Individual 
forest rights 

(1) Rights to inhabit and cultivate forest land for livelihood 
needs 
(2) Rights over disputed lands and rights for the conversion of 
pattas or leases or grants issued by any local authority or any 
state government on forest land to titles 
(3) Rights to in-situ rehabilitation and getting alternative land in 
case of illegal eviction 

Community 
rights 

(1) Rights to own, collect, and use minor forest products 
(2) Community rights of forest-dependent people, for instance, 
nistar1 
(3) Other community rights such as fishing, grazing, etc. 
(4) Rights to have access to biodiversity and intellectual property 
rights over biodiversity 
(5) Habitation rights for primitive tribal groups 
(6) Any other customarily enjoyed traditional rights excluding 
hunting 

Rights to 
community 
forest 
resources 

Protection and management rights over those community forest 
resources that they have been taking care of so far for 
sustainable use 

Duties of the 
forest rights 
holder and 
gram sabha 

(1) Protect wildlife, forests, and biodiversity 
(2) Protect adjoining catchment areas and other ecologically 
sensitive areas 
(3) Confirm that the regulation decision of community forest 
resources is in accordance with the aim of protecting wild 
animals, the forest, and the biodiversity 

Source: Authors 

 
1 Nistar refers to the permission given to forest dwellers to extract small trees from forest 

areas at predetermined rates, along with certain forest products meant for personal and 

legitimate domestic consumption. 



Ecology, Economy and Society–the INSEE Journal [14] 

 

 

Figure 1: Organizational Structure of the FRA (Activities at Various Institutional 
Levels) 
 

 

 
 
 

Source: Authors 



[15] Chand and Behera 

 

 
 

3. STUDY AREA, SOURCES OF DATA, AND METHODS 

This section presents a detailed description of the study area, including 
geographical features and forest characteristics. In addition, a detailed 
discussion of the data sources and methods used in this study is reported. 

3.1. Description of the Study Area 

Forests in the state of West Bengal in India are found in three major 
regions: in the south-west, in the north, and in the Sundarbans region. This 
study was carried out using remotely sensed forest cover data in Bankura 
district in West Bengal. Bankura district is situated in the south-western part 

of the state between 22 38 and 23 38 north latitude and 86 36 and 87 

46 east longitude.  

Figure 2: Map Showing the Location of the Study Area 

 
Source: Authors 



Ecology, Economy and Society–the INSEE Journal [16] 

 

 

This district has three subdivisions—Bankura, Bishnupur, and Khatra. 
Khatra subdivision, shown in Figure 2, is our study area. Most of the tribal 
population who are directly dependent on the forest for their livelihoods 
have been residing in Khatra subdivision for centuries. Geologically, it is a 
plateau fringe area. Sal (Shorea robusta) is the most common species in this 
forest; other significant species include shimul (Bombax ceiba), palash (Butea 
monosperma), mahua (Madhuca longifolia), gamar (Gmelina arborea), teak (Tectona 
grandis), shirish (Albizia lebbeck), arjun (Terminalia arjuna), and bamboo. Table 4 
shows the biannual status of forest cover in Bankura district from 1991 to 
2021. The data shows an increasing trend in total forest cover from 1991 to 
2019. However, a negative change can be seen in 2021. 

Table 4: Year-Wise Forest Cover in Bankura District 

Year 

Geogr
aphical 
Area 
(GA)  

Very 
Dense 
Forest 

Moderat
ely 
Dense 
Forest 

Open 
Forest 

Total 
Forest 
Cover 

Percen
tage of 
GA 

Change with 
Respect to 
Previous 
Assessment 

1991 

6,882 

153 600 753 10.94 — 

1993 160 660 820 11.92 11.92 

1995 197 653 850 12.35 30 

1997 226 641 867 12.60 17 

1999 233 636 869 12.63 2 

2001 453 482 935 13.59 26 

2003 101 295 584 980 14.24 45 

2005 100 315 612 1,027 14.92 2 

2009 214 510 332 1,056 15.34 2 

2011 213 510 333 1,056 15.34 0 

2013 222 365 657 1,244 18.08 188 

2015 212 379 673 1,264 18.37 20 

2017 220 388 662 1,270 18.45 8 

2019 222.33 395.27 
667.9

8 
1,285.

58 18.68 15.58 

2021 226.34 411.67 
641.3

6 
1,279.

37 18.59 −6.21 

Source: The India State of Forest Reports from 1991 to 2021 (Forest Survey of India 
2021). 

Note: Area is given in sq km. 

 

 



[17] Chand and Behera 

 

 
 

Table 5: Block-Wise Distribution of IFR Titles in Bankura District 

Serial 
No. 

Subdivision Block 

Total No. of 
Beneficiaries 

under the FRA 
2006 (as 

Provided by the 
PO cum DWO 

of BCW2) 

Total No. of 
Beneficiaries 

under the 
FRA 2006 

(According to 
BL & LROs3) 

1 

Khatra 

Taldangra 1,081 906 

2 Hirbandh 718 602 

3 Raipur 350 281 

4 Khatra 182 255 

5 Simlapal 792 727 

6 Indpur 531 310 

7 Ranibandh 630 511 

8 Sarenga 374 268 

9 

Bankura 

Bankura-I 0 0 

10 Bankura-II 19 39 

11 Barjora 184 159 

12 Saltora 129 118 

13 Onda 853 829 

14 Chhatna 176 172 

15 Mejia 6 6 

16 Gangajalghati 273 273 

17 

Bishnupur 

Bishnupur 505 505 

18 Joypur 97 78 

19 Kotulpur 0 0 

20 Sonamukhi 233 233 

21 Patrasayer 324 268 

22 Indus 0 0 

  Total 7,457 6,540 

Source: District Land and Land Reforms Office (DL&LRO), Bankura (2020). 

Table 5 reports the block-wise distribution of IFR titles in Bankura district. 
According to the project officer cum district welfare officer of the 
Backward Classes Welfare and Tribal Development Department, 

 
2 PO cum DWO of BCW refers to the project officer-cum-district welfare officer, Backward 
Classes Welfare Department.  
3 BL & LROs refers to the block land & land reforms officers. 



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Government of West Bengal, about 7,457 beneficiaries have received patta 
under the FRA here. Khatra subdivision has the largest number of 
beneficiaries as compared to the other two subdivisions—namely, Bankura 
and Bishnupur. 

3.2. Sources of Data 

There are about 1,400 villages in Khatra subdivision; among them, 89 are 
uninhabited and are therefore excluded from data collection. In addition, it 
is also observed that only 569 villages include forest areas, and these are 
considered in the model; 218 villages have obtained IFR titles (patta). 
Village-level forest patta data for these 218 villages have been collected 
from the Divisional Forest Office, Bankura. Socio-economic and 
demographic data have been sourced from the Village and Town Directory, 
Census 2011. The remotely sensed forest cover data obtained from the 
“Socioeconomic High-resolution Rural-Urban Geographic” (SHRUG) data 
set has been used for measuring the forest growth rate from 2006 to 2012 
(SHRUG n.d.). Since the dependent variable is continuous, the ordinary 
least-squares (OLS) regression model has been used to analyse the data. A 
similar regression model has been used by other researchers (Dash and 
Behera 2015). 

3.3. Variable Descriptions and Hypothesized Effects 

3.3.1. Dependent Variable 

Forest conservation outcomes are measured using a number of parameters: 
change in area under forest cover, change in canopy density, land 
degradation and soil erosion, reduction in wildlife numbers, and so on 
(Basu and Nayak 2011; Dash and Behera 2013). The change in percentage 
of forest cover from 2006 to 2012 has been measured using SHRUG data, 
as indicated above. It is important to note that the forest cover data 
available in the SHRUG data set are obtained from Vegetation Continuous 
Fields, which is a product of the Moderate Resolution Imaging 
Spectroradiometer (for more information, see Asher and Novosad 2019; 
Townshend et al. 2011). The average forest cover (in percentage) for 2006 
for the respective village is measured by dividing the total_forests value by num 
cells (Equation 1). A similar method is used to determine the average forest 
cover (in percentage) for 2012 (Equation 2). The difference in the average 
forest cover values from the year 2006 to 2012 is taken as a change in forest 
cover for the respective village, where higher difference values mean that 
compared to 2006, there are fewer forests in 2012. Therefore, higher values 
indicate more forest degradation and lower values indicate less forest 
degradation (Equation 3). The primary reason for using 2012 SHRUG 
forest cover data is so that all socio-economic and demographic variables 



[19] Chand and Behera 

 

 
 

used in the estimation of the model are from the same period because all 
the independent variables used in the model are taken from Census 2011. 
The average forest cover change (%) has been calculated using the 
following formulas: 

The average forest cover (%) in 2006 = Value of total_forests for 2006/Value of 
num_cells for 2006.                                                                                                 (1) 

The average forest cover (%) in 2012 = Value of total_forests for 2012/Value of 
num_cells for 2012.                                                                                                 (2) 

Change in forest cover (%) = The average forest cover (%) in 2006 − The average 

forest cover (%) in 2012.                                                                                      (3) 

3.3.2. Independent Variables and Their Hypothesized Effects 

It is expected that the assignment of IFRs to forest dwellers may positively 
influence forest conservation outcomes as people enjoy de jure rights over 
the forest, and this may act as an incentive for further investment in forest 
lands to enhance productivity. It may also remove the fear of eviction 
resulting from insecure forest land tenure, which is often found to have a 
substantial effect on forest degradation (Datta and Sarkar 2012). Therefore, 
the assignment of IFRs, in our case, having a patta, could be a powerful 
incentive for the majority of traditional forest landholders to adopt more 
sustainable forest land management methods and, thus, contribute to forest 
growth (Kothari, Pathak, and Bose 2011). As such, there is no record in the 
official data regarding the types of IFR land; we assume that the larger IFR 
lands are agricultural land and could be backyard plantations or orchard 
land, whereas the smaller lands are mostly residential land. So, it is expected 
that villages with a larger average per capita size of IFR land and a higher 
proportion of IFR land to the total forest area are more likely to undertake 
better forest conservation measures, resulting in less forest degradation. 
The existence of FPCs under joint forest management in the village is 
another important variable that significantly influences forest conservation 
outcomes. Some studies have empirically shown improved conservation 
outcomes in forests managed and protected by FPCs (Ballabh et al. 2002; 
Behera 2008). Therefore, it is expected that the existence of FPCs would be 
negatively related to forest degradation. 

Many researchers have suggested that user group characteristics tend to 
influence forest conservation management outcomes (Behera 2009; 
Agrawal 2001). Since free-riding issues are easier to resolve in smaller 
groups, smaller communities are more likely to be effective in the 
management and protection of forests than larger ones (Behera 2009). 



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Hence, in accordance with inferences in the literature, the size of the user 
group is hypothesized to be negatively associated with forest cover change. 
Here, the population size of the selected village is considered as the user 
group, and increased population is expected to have a positive relationship 
with forest degradation (Wade 1987; Heltberg 2001). 

The tribal population in the village is another important variable in forest 
conservation outcomes. It is commonly understood that indigenous tribal 
people and their lives and livelihoods are intricately connected with forests. 
For this reason, it is expected that they will take good care of forests, aiding 
conservation. Hence, it is hypothesized that the higher the proportion of 
tribal people in the village, the lower the forest degradation. 

The level of education is another important variable, which can directly 
and/or indirectly influence forest conservation outcomes. According to 
several studies, higher education levels among forest dwellers could increase 
their opportunity costs of pursuing traditional forest-based livelihood 
activities; hence, they may seek better off-farm employment opportunities, 
which could in turn minimize the burden on forest resources and increase 
forest cover in the village (Gunatilake 1998; Adhikari, Di Falco, and Lovett 
2004). In addition, it is also observed that respondents with higher 
education levels tend to have a more positive attitude towards forest and 
biodiversity conservation in their private land than respondents with a 
lower education level (Baranovskis et al. 2022). Hence, the literacy rate of 
villagers is hypothesized to have a negative effect on forest degradation. 

Marginal agricultural labourers form a sizeable population and rely on the 
forest and other common-pool resources for their daily livelihood activities, 
which is another key variable that is likely to influence forest conservation 
outcomes. It is observed in typical Indian villages that household reliance 
on forest resources is substantially linked to land holding size, as landless or 
marginal farmers often largely rely on forest resources for their livelihoods 
(Fernandes and Menon 1987). Therefore, the presence of a large population 
of marginal agricultural labourers is hypothesized to be positively associated 
with forest degradation. 

The distance of the village to the nearest market is used as a proxy for 
market access, which can significantly influence forest conservation 
outcomes. The effect of market access on forest conservation outcomes is a 
priori ambiguous (Behera 2009). Some authors argue that easy access to 
markets can have a negative impact on outcomes by raising the demand for 
forest resources, which may lead to increased harvesting and depletion of 
these resources (Sundar 2000; Behera 2009). However, others argue that  



[21] Chand and Behera 

 

 
 

Table 6: Dependent and Independent Variables and Their Expected Effects on 
the Model 

Category Variable Definition Expected 
Effects 

Forest 
conservation 
outcome 

ln of change in 
forest cover area 

ln of change in percentage 
of forest cover from 2006 
to 2012 

Dependent 
variable 

Institutional 
variables 
(assignment of 
property 
rights) 

ln of IFR land 
size 

ln of per capita average size 
of IFR land distributed to 
the villagers 

— 

ln of Percentage 
of IFR land to 
total forest area 

ln of percentage of IFR 
land to total forest area of 
the village 

− 

Existence of FPC Dummy variable = 1, if the 
village has FPC; 0, 
otherwise 

− 

Demographic 
variables 

ln of population ln of population in the 
village 

+ 

Socio-
economic 
variable 

ln of Schedule 
Tribe population 

ln of percentage of the 
scheduled tribe population 
to the total population of 
the village 

− 

ln of literacy rate ln of percentage of literate 
population to total 
population of the village 

− 

Economic 
variables 

ln of marginal 
agricultural 
labour 

ln of percentage of marginal 
agricultural labourer to total 
working population of the 
village 

+ 

Distance to 
market 

Distance to the nearest 
market: categorical variable 
= 1 if the market is 
available within the village; 
if not available within the 
village, the distance range 
code depending on where it 
is available—namely, 2 for 
<5 km, 3 for >5 km 

− 

External 
environment 

Existence of 
pucca roads 

Dummy variable = 1, if the 
village has a pucca road; 0, 
otherwise 

? 

Source: Authors 



Ecology, Economy and Society–the INSEE Journal [22] 

 

 

access to markets facilitates more agricultural activity and diversifications of 
livelihood to non-farm activity (Agrawal and Chhatre 2006). This can 
minimize land-use pressure on forest resources, which may decrease forest 
degradation. However, the literature on the direction of effect is conflicting. 

The existence of pucca roads is used as a proxy for forest monitoring, 
which can influence forest conservation outcomes. Pucca roads can 
contribute towards better outcomes because travelling becomes easier for 
monitoring authorities (Gautam, Shivakoti, and Webb 2004; Agrawal and 
Chhatre 2006; Behera 2009). On the other hand, it is also hypothesized that 
extension of pucca roads can have a negative impact on forest cover 
because of various development-related changes linked to land-use pressure 
and easy access to the forest, which can aid in the transportation of wood 
(Haq et al. 2022; Li et al. 2015). Hence, the effect of the existence of pucca 
roads on forest degradation is ambiguous. 

Table 7: Summary of Variables Used in the Model 

Variable N Minimum Maximum Mean Standard 
Deviation 

ln of Change in 
percentage of 
forest cover 

563 −0.94 1.29 0.68 0.27 

ln of Size of 
IFR land 

563 −3.07 0.67 −0.39 0.66 

ln of percentage 
of IFR land to 
total forest area 

563 −1.47 1.79 0.22 0.49 

FPC 563 0 1 0.89 0.31 

ln of ST 
population 

563 −1.11 2.00 1.31 0.71 

ln of Population 563 0.78 3.87 2.66 0.45 

ln of Literacy 
rate 

563 1.02 1.93 1.76 0.09 

ln of Marginal 
agricultural 
labourer 

563 −0.49 2.00 1.41 0.56 

Pucca road 563 0 1 0.28 0.45 

Distance to 
market 

563 1.00 3.00 2.80 0.54 

Source: Authors 

Table 6 summarizes the measurements and definitions of the hypothesized 
variables as well as their expected influence on forest conservation 



[23] Chand and Behera 

 

 
 

outcomes. Since the dependent variable is measured in terms of percentage 
change, the relationships between the dependent variable and the 
aforementioned independent variables are measured using OLS regression. 
This regression equation takes the following form: 

ln of Change in forest cover =  + 1 (ln of IFR land size) + 2 (ln of Percentage 

of IFR land to total forest) + 3 (ln of ST population) + 4 (ln of Population) + 

5 (ln of Literacy rate) + 6 (ln of Marginal agricultural labourer) + 7 (Existence 

of pucca road) + 8 (Distance to market) + 8 (FPC) + 1.                                 (4)                                                                                                            

Where,  is the intercept,  represents the vector of parameters to be 

estimated, and  is the error term. 

Table 7 reports the descriptive statistics of all the variables used in the OLS 
model. It is observed that there is considerable variation in the change in 
forest cover across villages. To explain this heterogeneity in forest cover 
change, the above-mentioned socio-economic, demographic, and 
institutional variables at the village level have been included in the model 
estimation. 

4. RESULTS AND DISCUSSION 

Table 8 reports the results of the OLS regression model, which estimates 
the determinants of forest conservation outcomes across the villages. The 
model is seen to be significant at the 1% level. Two important violations of 
the assumptions of the OLS regression model were tested—namely, 
multicollinearity and heteroscedasticity. Multicollinearity is tested for the 
explanatory variables by checking the variance of the inflation factor (VIF). 
The value of the mean VIF is 1.08, and all the VIF values of individual 
variables are less than 1.50, which indicates that there are no 
multicollinearity problems among the variables. The result of the Breusch-
Pagan/Cook-Weisberg test for heteroscedasticity indicates that the 
variances are constant. With regard to the individual variables used in the 
model, most of them showed the expected sign except three variables. The 
percentage of IFR land to total forest is negatively and significantly (at the 
1% level) associated with forest degradation, which indicates that villages 
with a larger percentage share of IFR land are less likely to contribute to 
forest degradation. This may be because the larger percentage of IFR land 
indicates larger assignments of individual property rights over forest lands, 
which may have incentivized the local villagers to protect the assigned land. 

The variable existence of FPCs in the village is found to be positively and 
significantly (at the 1% level) associated with forest degradation, which 
implies that villages having FPCs are less likely to contribute to forest 



Ecology, Economy and Society–the INSEE Journal [24] 

 

 

degradation. This could be due to better management and protection of 
forest resources by the FPCs. 

Table 8: Ordinary Least-Squares Regression Result of Determinants of Forest 
Degradation (Percentage Change in Forest Cover) 

Average 
Percentage of 
Forest Cover 
Change 

Coefficients Robust 
Standard 
Error 

t Value Significance VIF 

ln of Size of IFR 
land 

−0.025 0.016 −1.620 0.107 1.14 

ln of percentage 
of IFR land to 
total forest 

−0.107 0.023 −4.680 0.000 1.06 

FPC −0.105 0.032 −3.290 0.001 1.04 

ln of Tribal (ST) 
population  

0.084 0.014 5.850 0.000 1.10 

ln of Population 0.127 0.022 5.690 0.000 1.10 

ln of Literacy 
rate 

0.281 0.106 2.650 0.008 1.06 

ln of Marginal 
agricultural 
labourer 

−0.022 0.019 −1.130 0.257 1.06 

Pucca road −0.039 0.023 −1.690 0.092 1.07 

Distance to 
market 

−0.061 0.016 −3.800 0.000 1.07 

(Constant) 0.059 0.195 0.300 0.762  

Number of 
observations 

563 

F(9, 554) 18.75 

R2 0.1753 

Root MSE  0.24487 

Mean VIF 1.08 

Source: Authors 

The variable tribal (ST) population is found to be positively and 
significantly (at the 1% level) associated with forest degradation, which 
implies that villages with a higher percentage of this population are more 
likely to be associated with forest degradation. This is contrary to the 
hypothesis above. This may be related to the absence of any significant 
diversification of livelihood systems away from forest-related activities 
and/or a breakdown of local traditional institutions and collective action in 
tribal society. This needs further investigation using primary data. 



[25] Chand and Behera 

 

 
 

The variable population is found to be positively and significantly (at the 
1% level) associated with forest degradation, which implies that villages 
with a larger population are more likely to experience more forest 
degradation. This could be because of the higher pressure on forest 
resources from the large population. The variable literacy rate is also 
positively and significantly (at the 1% level) related to forest degradation, 
which means that villages with a higher literacy rate are likely to register 
more forest degradation. This is contrary to our expectations as 
hypothesized above. This could be because educated people engage in 
intensive agricultural practices in the forest lands allotted to them in order 
to boost their income quickly. 

The variable existence of pucca roads is found to be negatively and 
significantly (at the 10% level) associated with forest degradation, meaning 
that villages connected by pucca roads are less likely to experience forest 
degradation. This could be because roads enable monitoring authorities to 
travel easily inside the forest, thus enhancing forest protection. The distance 
to the nearest market is also negatively and significantly (at the 1% level) 
associated with forest degradation, meaning that villages that are located far 
away from markets are less likely to experience forest degradation. This is 
because better access to markets could incentivize forest dwellers to extract 
more forest produce and sell them in the market, which may result in the 
degradation of forest cover. Similar results are reported by Sundar (2000). 

5. CONCLUSION AND POLICY IMPLICATIONS 

This study attempted to identify and analyse the factors that could explain 
the differential forest cover change across the villages where the FRA has 
been implemented—where individual property rights have been assigned to 
improve forest conservation outcomes. The study was carried out within a 
well-defined framework of property rights, resource user characteristics, 
and the external environment and their relation to the percentage change in 
forest cover in Bankura district in West Bengal. The study clearly identifies 
the differences between a private property regime and individual property 
rights in the context of the FRA. It is observed that the pattas distributed 
among forest dwellers under the FRA do not conform with all the 
requirements for an efficient private property rights regime, such as 
exclusivity and transferability and/or tradability. However, even with ill-
defined individual property rights over forest lands, the empirical results 
provide strong evidence that the key hypothesized factors explain different 
aspects of forest management outcomes under the IFRs. 



Ecology, Economy and Society–the INSEE Journal [26] 

 

 

It appears that villages having a higher percentage of IFR land are likely to 
experience less forest degradation, which essentially suggests that the 
distribution of IFRs (patta) for forest lands under the FRA has contributed 
to increased forest cover. As discussed above, the assignment of individual 
property rights over forest land may have helped in reducing fear of 
eviction among forest dwellers, encouraging them to follow pro-
conservation measures. In addition to permanent tenurial right, the forest 
dwellers also receive other support services from the forest department; 
more importantly, fostering mutual trust between the forest dwellers and 
the forest department may have contributed to the increased forest cover. 
The findings of this paper are confirmed by an extensive body of literature 
on the importance of tenurial security for better resource management 
(Datta and Sarkar 2012). Hence, it may be suggested that more forest 
dwellers be given IFRs for better management of resources. An important 
institutional finding is the existence of FPCs in villages. The variable FPC is 
negatively and significantly associated with forest degradation, indicating 
that the FPCs may have better management and protection practices that 
help reduce forest degradation. 

Another interesting finding, which has important policy implications, is the 
association between the distance to the nearest market and forest 
conservation outcomes. In villages that are located closer to markets, more 
incentives are needed to arrest forest degradation. Villages with a higher 
literacy rate are also likely to experience more forest degradation. This could 
be because more educated people may have greater aspirations to improve 
their living conditions and, hence, may engage in intensive agricultural 
practices on the forest lands allotted to them; they may also indulge in 
selling forest produce, especially high-value wood. Hence, there is a need to 
create awareness among educated people in the community to protect 
forest resources. There is also a need for adequate non-farm employment 
opportunities in forest communities, which could go a long way in 
improving forest conservation outcomes. 

The study also found that villages with a higher percentage of ST people are 
likely to experience more forest degradation. It may be noted that in the 
absence of diversification of livelihood systems other than forest-related 
activities, population growth can put pressure on existing forest resources. 
The other probable reason for this result could be a breakdown of innate 
local traditional institutions and collective action in tribal society. However, 
it should be noted that the above findings are based on secondary data, 
which may have some limitations, and in-depth studies using primary data 
are needed to verify these findings. 

Ethics Statement: I hereby confirm that this study complies with 



[27] Chand and Behera 

 

 
 

requirements of ethical approvals from the institutional ethics committee 
for the conduct of this research.  

Data Availability Statement: The data used in this paper is not provided 
in a repository as the study is secondary in nature and the data can be 
downloaded from the original sources mentioned in the paper. 

Conflict of Interest Statement: No potential conflict of interest was 
reported by the author. 

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