Livelihood Diversification of Forest Dwellers

Abstract

The forest as an ecosystem plays a vital role in protecting the environment and meeting indispensable human needs. Restrictions on forestry activities for safeguarding the environment and seasonality in collection often count for vulnerable livelihood of the forest-fringe dwellers. This paper is an attempt to assess the existing opportunities for livelihood diversification of the forest dwellers residing at the Simlipal National Park region, India, through the Herfindahl–Hirschman Index; and to determine the key factors responsible for the scopes and realization of livelihood diversification through econometric modeling. The analysis is done across blocks with having different forest-zonal geographies and human characteristics to comprehend and ensure sustainable livelihood for a better future.

JEL Classification: C80, Q23, Q56, C50, Q01, Q2

Keywords

Livelihood diversification Sustainable livelihood Natural resource management Sustainable development

Introduction

Livelihood is a means of making a living which encompasses people’s capabilities, assets, income and activities required to secure the necessities of life. Sometimes livelihood can be best defined as the set of activities for making a living on this earth; it’s not concerned with earning income, rather linked with sustenance. In the forest areas, though, people living in and around the forest rely on forest products, both as the means and ends to livelihood, but the reliance often ensures a vulnerable livelihood.

There are basically two approaches to study the behaviour of people for livelihood diversification based on economic literature – the household economic approach (Singh et al., 1986; Taylor and Adelman, 2003) and the livelihood approach (Ashley and Carney, 1999). The household economic approach assists a framework to assess food security, livelihood and poverty. The livelihood approach commonly utilizes the Sustainable Livelihoods Framework (SLF) to weigh up people’s present livelihood assets and assess how the external environment, like social norms and relations, role of institutions, government policies, persisting trends and sudden shocks, modifies access to and the ability to convert livelihood assets into livelihood outcomes (Ansoms and McKay, 2010; Vedeld et al., 2012).

The term ‘diversification’ refers to the processes which take place at different levels of the economy aiming towards societal development. The processes are usually, but not always, directly linked with each other (Start, 2001). Therefore, it is an infinitely heterogeneous process differentiated in its causes and effects (Ellis, 1998). People diversify their portfolio of earning based on their needs and circumstances. An individual’s or household’s diversification of livelihood in rural areas is considered as income strategies by which the number of activities might be expanded regardless of the location or sector (Loison, 2015). Diversification in the context of rural economy is generally a shift from farm activities to off-farm activities.

In our study, we attempt to analyse the existing opportunities and further scopes of livelihood diversification for the people residing in the selected blocks at Simlipal National Park region, India, so as to ensure economic, social and environmental sustainability following the framework of the livelihood approach. Though some works on livelihood have been done in this region, our study differs from previous research in several points. In our study, we have collected data at household level covering the blocks with having different forest-zonal characteristics. The study attempts to capture the fact that zonal geographies as well as the socio-economic characteristics of the household members account for livelihood diversification. The use of the Herfindahl–Hirschman Index (HHI) to score livelihood diversification at household level is also an addition to the forestry sector. Moreover, the study attempts to compare the opportunities for livelihood diversification block-wise subject to forest-zonal geographies and tries to find out the most significant demographic cum socio-economic factors affecting livelihood diversification of people through a regression analysis. In the next section, the methodology used for the study is discussed. Then the following two sections deal with data analysis, findings and related discussions. Finally, some conclusions have been made to ensure economic, social and environmental justice in the study area.

Methodology

Conceptual framework of the study

In studying livelihood, the household economic approach shows a methodological framework to resolve whether households have enough food and cash to live. The development and acceptance of this thought is linked with Sen’s (1989) theory of exchange entitlements, which suggests that ‘famines occur not from an absolute lack of food, but from people’s inability to obtain access to that food’ (Mattila-Wiro, 1999). This ‘access’ largely depends on food supply, which is subjected to data on food production and price. Therefore, knowing whether households have enough resources to meet their needs requires establishing a quantifiable threshold against which their access can be evaluated. However, a pertinent question is to understand how poor people might gain access to food and income such that they can get rid of their vulnerable economic situations. In this regard, the livelihood approach or the SLF improves understanding of the livelihoods of the poor. It directs the way of thinking to facilitate the poor through development policies and institutions. Ashley and Carney (1999) defined unsustainable livelihood as a thought process forming aims and objectives, scope and precedence for development in order to augment improvement in poverty eradication. Keeping in view the above issues, the livelihood approach or SLF is an approach towards development which has also been adopted by many development organizations and non-government organizations (NGOs) as a tool for monitoring livelihoods and their transformation.

Livelihood diversification, as discussed, is a process by which households construct a diverse portfolio of activities and social support capabilities for survival in order to maintain better lives. It forms the basis for building assets which permits individuals and households to construct their own exit routes from poverty. It can even improve the quality and sustainability of natural resources that constitute key assets of rural livelihoods (Ellis and Allison, 2004). Ellis (2000) has categorized diversification as of necessity and diversification by choice. According to the author, livelihood diversification of the individuals may be captured through assessing seasonality and risk in employment opportunities, scenario of labour market and credit market, status of asset holding and ability to cope in sudden shocks.

Diverse rural livelihoods are considered as less vulnerable than undiversified ones in the study. Given the framework of SLF, our study attempts to analyse whether livelihood diversification of the forest-fringe dwellers comes from necessity or choice.

Loison (2015) shows the benefits of livelihood diversification as a safety net for the rural poor and sometimes offering means for upward mobility in rural Sub-Saharan Africa. Further, economic diversification has been found to be positively correlated with the rate of deforestation in a study conducted in two villages of Indonesia (Dewi et al., 2005). At the national level, Suryaprakash (1999) analysed diversification considering engagement of people in NTFP collection for the tribal economy at the Western Ghats region of Karnataka. Sarkar (2011) conducted a study in the Southern Forest Division of Bankura District of West Bengal, to get an idea of survival needs and socio-economic dynamics of the households, and analysed the impact of those on common property outcome. Therefore, the literature survey explores that livelihood diversification is beneficial in reducing rural poverty for every single case, but at the same time it questions whether or not it always improves the rural natural resource base and environmental quality.

Data collection and sampling procedure

Our study is restricted to some selected blocks in the Simlipal National Park region of the state of Odisha, India. The research work uses primary data along with secondary information of the study area to furnish an overall idea of the study area.

The entire Simlipal National Park region is divided into three zones – core zone, buffer zone and transitional zone. In our study, the four blocks from this region have been selected in such a way so that one (Bisoi) comes entirely under the buffer zone, one (Shamakhunta) comes entirely under the transitional zone and two (Jashipur and Bangriposi) come partly under the core and partly under the buffer zones in order to capture the existing and potential livelihood opportunities in the entire region, which vary across forest zones. In the core zone, human settlement and entry are restricted from a biological perspective. It is likely that for the changes in socio-economic-demographic characteristics of the people residing at different forest zones, the level of livelihood diversification will also fluctuate. All seven villages from the four blocks are purposively (non-probable method) selected for the study based on the list produced by the officials of STR. A proportionate sampling method is used to collect information at household level from the blocks. The procedure of sample collection is shown in Table 1.

Table 1.

Procedure of sample collection from the four blocks in the study region.

Blocks	Villages	Number of households (village-wise)	Percentage of households surveyed (block-wise)	Number of households surveyed (block-wise)
Bisoi	Hatichhad	98	33	44
Bisoi	Duarsuni	53	33	44
Bangiriposi	Charabandh	100	33	41
Bangiriposi	Dantiakacha	20	33	41
Jashipur	Bareipani	129	33	42
Shamakhunta	Jogendradasjot	67	33	35
Shamakhunta	Lulung	15	33	35
Total households				162

Source: Authors.

Around 162 households from the seven villages had been interviewed for the study. In case of choosing households from the blocks, a systematic random sampling method has been followed. Starting from a particular household in a block, every third household has been interviewed. The sample captures the population characteristics and also is based on the objectives of the study.

In order to collect the primary data, at first a semi-structured questionnaire was prepared. After having focus group discussions in the villages, the final questionnaire was designed and administered through direct personal interview in order to collect data at the household level. The questionnaire was prepared as per the requisite of the study, including demographic characteristics and socio-economic profile of the households, like age, sex, religion, ethnic composition, level of literacy, occupational structure, income earned through diverse approaches adopted by the individuals of the family, asset and land holdings, liabilities, extent of dependence on forests, perception for natural resource conservation, etc.

Compilation of data and methods used for analysis

After collecting the primary data, compilation of data has been done to assess the status quo situation in the study area. Descriptive statistics are used in this regard to analyse the socio-economic data of the studied households. After evaluating the existing livelihood opportunities in the study area, the score of livelihood diversification for each household has been calculated following Herfindahl–Hirschman Index, and block-wise comparison of the scores of livelihood diversification has been made. Comparative analysis has been done in the study area aiming at reducing forest dependency and protecting the natural resource base through finding out the scopes for further livelihood diversification. It is an acceptable fact that opportunities for livelihood diversification will vary across regions for having different zonal geographies.

The Herfindahl Index (also known as the Herfindahl–Hirschman Index, HHI, or sometimes HHI-score, named after economists Orris C. Herfindahl and Albert O. Hirschman) is a measure of the size of firms in relation to the industry and an indicator of the amount of competition among them; it is a concept widely applied in economics. The study of Ayantoye et al. (2017) is based on determining livelihood diversification among the rural households in Kwara state of Nigeria after collection of data from the 132 households through an interview schedule using multi-stage random sampling techniques. Distributive statistics, the Herfindahl Index and Tobit regression model were used for analysis. In the work of Datta and Singh (2011), the authors studied survival strategies adopted by the people living in the two backward regions of West Bengal, India, following the livelihood approach and using the Herfindahl Index. The work was carried out considering access to off-farm activities where agriculture was the main occupation of people. The authors revealed that access to diverse form of assets is subjected to socio-economic conditions of the residents as well as the geographical environment in the concerned region.

HHI is essentially equivalent to the Simpson Diversity Index, which is a diversity index used in ecology taking into account the number of species present and the relative abundance of each species. Sujithkumar (2007) collected primary data of 194 households from the three villages in Vellore district of Tamil Nadu, India (where the rural households were not only dependent on agriculture, but they used to earn from non-farm sources as well), to measure livelihood diversity using the Inverse Simpson Diversity Index. But all these studies have been done targeting the scope of livelihood diversification in off-farm jobs where people are primarily engaged in agriculture. In the context of forestry, since the reliance continues on non-timber forest products (NTFP) for sustenance and the collection is seasonal for the dwellers, diversification of livelihood on other than forestry activities should be focused upon. Seasonality is also considered as an inherent feature of the rural livelihoods in the work of Agarwal (1990).

Dash and Behera (2013) made a comparative analysis among 136 sampled villages located in the core and surrounding areas of Simlipal Biosphere Reserve, in connection with determining the factors having impact on natural resource dependence for the forest dwellers. Further, Dash and Behera (2016) suggested for alternative livelihood opportunities in order to protect the forest resource base at Simlipal Tiger Reserve (STR). Mishra (2020) reveals that forest is the source of security against sudden shortfalls of consumption during the period of droughts for sustenance through surveying three villages at Bargarh district of Odisha. Although these studies reflect the need of livelihood diversification on Odisha’s forest, livelihood analysis using a sophisticated tool like HHI in the field of forestry is rare in literature, which the present study attempts to highlight.

HHI helps to throw light on how individuals of a particular household may diversify their portfolio of earning for their sustenance. Hence, it is possible to get a score of livelihood diversification per household in the study area. HHI is given by,

H H I = \sum {A_{i j}}^{2}

A_ij represents the proportional contribution or share of livelihood activity ‘j’ to household ‘i’ aggregate income and it appears in a fraction. By squaring the share of each activity to the household’s aggregate income and summing up for all the activities where the working individuals are engaged in a particular household, the score of HHI for that household can be determined. Therefore, HHI is considered as a measure of concentration. The major benefit of the HHI in comparison to such other measures is that the concentration ratio can put more weight to the major livelihood activities. The HHI ranges from 0 to 1.

Since HHI is a measure of concentration, so the inverse of HHI is likely to show the relative spread of activities in contributing total income of a household or the degree of scatteredness of any household’s income. Forestry activities being seasonal in nature, the members of a particular household always remain engaged in several works throughout the year in the study area. Hence, it is justified to use this particular index which may truly capture the livelihood diversification. Since the Inverse of HHI = 1/ ∑ A_ij ² indicates the relative spread of activities in contributing to total income of any household, the lower the value of HHI, the greater will be the measure of diversification and vice versa.

Finally, a regression analysis has been done to capture the strength of association between the response variable (score of Inverse of HHI, as an indicator of livelihood diversification) and predictor variables (household size, average age and education level, holding of agricultural land and domestic animal, loan and level of forest dependence at household level) for determining the factors affecting livelihood diversification of the forest dwellers in the study area.

Description and importance of the study area

Simlipal National Park is located in Mayurbhanj district of Odisha, in the biotic province of Chhotnagpur plateau Deccan Peninsula. In Simlipal, the Reserve’s core zone area of around 1194.75 square kilometres has been accorded National Park status by the State Government and the buffer zone area is around 1555.25 square kilometres. The STR along with the transitional zone of 2250 square kilometres has been included as a part of the World Network of Biosphere Reserves by UNESCO in 2009. The STR is a rare protected area as it has been declared as Biosphere Reserve, Wildlife Sanctuary, Designated National Park, and runs two Flagship Conservation Programmes of the Federal Government, namely ‘Project Tiger’ and ‘Project Elephant’. Simlipal is the sixth largest Biosphere Reserve and a major Biodiversity Hotspot in the eastern India. The entire region is rich for the existence of diversified, even endangered, species of flora and fauna, and is considered as the home of indigenous tribal population. The location map of Simlipal Tiger Reserve in India is shown in Figure 1.

Figure 1.

Map of Simlipal Tiger Reserve.

Forest provides several ecosystem services, like (a) provisioning, (b) regulating, (c) supporting, and (d) cultural (TEEB, 2020). The main ecosystem services that arise from STR include provisioning of water (Rs. 70.33 billion per year), water purification (Rs. 29.20 billion per year) and climate regulation (Rs. 34.82 billion per year). As per the Millennium Ecosystem Assessment framework, the values of provisioning, regulating and supporting services were Rs. 0.69 billion, Rs. 158.95 billion and Rs. 0.66 billion per year respectively at STR (Verma et al., 2019). Therefore, the national importance of Simlipal National Park in providing livelihood to a great number of people residing in the fringe areas and global significance to preserve biological diversity have indulged us to choose this region as our study area.

Data analysis and findings

In analysing the data, block-wise analysis in the study area has been done first to find out the different existing livelihood opportunities and the practices adopted by the forest dwellers for sustenance in addition to forestry activities. Lives of the forest dwellers have become miserable in India following the order of Supreme Court dated 14 February 2000, as collection of forest products including NTFPs has been restricted. The authorities in the study area have also imposed such restrictions on collection and sale of forest products by the forest-fringe dwellers. In many of the cases despite restrictions, the forest dwellers are left with no means of survival other than collecting the forest products illegally, either for self-consumption or sale. Extraction of forest resources varies with the strictness followed during the entry-exit in the buffer and transitional zones at the Simlipal National Park. The major NTFPs available in and around the Simlipal National Park are sal leaves, sal resin, kusum seeds, mushrooms, honey, arrow root, bamboo, wild vegetables and fruits, wild date palm, etc. The NTFPs are used for making finished products like mats, brooms, badhuni, plates and bowls, etc. The products are also used for preparing oil for cooking and medicinal use, preparing soap, wine, and handicrafts. Fish is found in abundance in all the rivers flowing in this region. In the study area almost all the households have been found as engaged with collection of dry leaves and twigs for fuel use. Therefore, the extent of forest dependence of the surveyed households has been determined on the basis of the nature of collection of NTFPs. The households who collect NTFPs only for fuel use have been characterized as forest dependent as usual. Alternatively, the households who collect NTFPs in addition to fuel use have been characterized as mostly dependent on forest in the study. The block-wise dependency on the forest of the surveyed households is shown in Table 2.

Table 2.

Extent of households’ dependency on forest, block-wise.

Blocks	Number of surveyed households	Number of households mostly dependent on forests with their percentages (%)
Bangiriposi	41	36 (87.80)
Bisoi	44	32 (72.73)
Jashipur	42	7 (16.67)
Shamakhunta	35	32 (91.43)
Total	162	107 (66.05)

Source: Estimated by authors from the field survey data.

The study reflects that other than the forestry activities, the surveyed households have different livelihood options which vary across blocks. The block-wise engagement in different livelihood options is explained as follows.

Bangriposi block: In Bangriposi block the selected villages, Charabandha and Dantiakacha, are situated at very remote areas with an average distance of 35 kilometres from the Bangriposi town and thus have poor communication facilities. The majority of the households come under the BPL (below poverty level) category and possess a low level of education. It becomes tough for the villagers to acquire skill for remoteness and ignorance. Therefore, most of the surveyed households, of around 72.26 per cent, rely on NTFPs for their livelihood, which is followed by the wage labour (69.54 per cent). Around 59.66 per cent of households depends on agriculture for sustenance; that is followed by livestock rearing, of around 19.32 per cent. The other sources of livelihood are the employment provided by the forest department, income derived as ASHA (Accredited Social Health Activist) workers and from AWCs (Anganwadi Centers).

Bisoi block: In Bisoi block, the selected villages, Duarsini and Hatichhad, have an average distance of 10 kilometres from the town and this has resulted in market accessibility and other work opportunities for the residents. In this block, 81 per cent of households earn as wage labour, which is followed by 67.76 per cent of engagement in agricultural activities. Further, 55.37 per cent of households earn through collection and sale of NTFPs. The other modes of earning are through livestock rearing (13.22 per cent) and the employment provided by the forest department.

Jashipur block: In Jashipur block, the selected village Bareipani has an average distance of 40 kilometres from the town. The pathway to the town is hilly terrain, which increases the complexities of people living there. The people residing in this block collect NTFPs for self-consumption. Therefore, based upon the survival needs, the collection of NTFPs is less. Further, in this block, predominantly the people get involved in agricultural activities for self-consumption. The inaccessibility of the market actually indulges the dwellers to remain confined in their residence area. In Jashipur, apart from the agriculture activities (64.76 per cent) and working as wage labour (62.85 per cent), around 17.14 per cent of households earn livings through the sale of livestock. The other sources of livelihood are the employment provided by the forest department, like Green brigade (Sabuja Vahini) and NGOs; and income derived as ASHA workers or from AWCs.

Shamakhunta block: In the selected villages of Shamakhunta block, Jogendradasjot and Lulung, people are mostly deprived of agricultural land; and being illiterate, they live in a distressful condition. Mostly the people residing in this block work as manual labour; without having alternative livelihood opportunities, they rely on NTFPs as a means of survival. The largest share of the studied households, of around 71.13 per cent, is engaged in collection of NTFPs, and that is followed by 61.85 per cent as wage labour. Further, around 18.56 per cent of studied households have been found engaged in livestock rearing and 11.34 per cent in agriculture.

The block-wise dependence of households in different livelihood activities is shown in Figure 2.

Figure 2.

Graphical representation of the dependence of households on different livelihood options (in percentage), block-wise.

A spatial analysis of average monthly income from different sources for a representative household is depicted in Table 3.

Table 3.

Average monthly income (Rs) for a representative household from different sources, block-wise.

Sources of income	Blocks
Sources of income	Bangriposi	Bisoi	Jashipur	Shamakhunta
NTFP collection	290.45	234.19	1076.67	365.47
Agriculture	878.57	433.33	300	208.33
Livestock	900	950	750	738.10
Wage labour	2022.20	2481.15	1372.43	1688.98
Others*	1447.97	1737.31	2481.75	2667.14

Source: Estimated by authors from the field survey data.

Others include working as health worker, engagement in Green Brigade and NGOs, government jobs, etc.

After analysing different engagements of the surveyed households, the scores of livelihood diversification have been calculated at household level through the Herfindahl–Hirschman Index (HHI). The level of concentration, HHI (A) and livelihood diversification, inverse of HHI (Inverse of A), are then evaluated block-wise in Table 4.

Table 4.

Distribution of households by the level of livelihood diversification, block-wise.

Level of livelihood diversification	Value of A (HHI)	Number of households with their percentages (block-wise)
Level of livelihood diversification	Value of A (HHI)	Bangriposi	Bisoi	Jashipur	Shamakhunta
Not diversified	A = 1	4 (9.75)	7 (15.90)	19 (45.23)	3 (8.57)
Moderately diversified	0.50<A<1	29 (70.73)	35 (79.54)	20 (47.62)	29 (82.85)
Highly diversified	A⩽0.50	8 (19.51)	2 (4.54)	3 (7.14)	3 (8.57)

Source: Estimated by authors from the field survey data.

Figures in parenthesis indicate percentages.

The value of A=1 indicates that the aggregate income of any household is a contribution from a single activity. On the contrary, the households who are engaged in more than one mode of earning possess the value in between 0 and 1. In our study, we have considered that the households possessing the value of A in between 0.50 and 1 (0.50<A<1) are moderately diversified and the households possessing the value of A less than 0.50 (A⩽ 0.50) are highly diversified in terms of livelihood opportunities. Table 3 has been constituted accordingly.

It is evident from Table 3 that the majority of households who have been interviewed for the purpose of our study are engaged in more than one mode of earning for sustenance. This is only exceptional for the block Jashipur. In Jashipur, around 45 per cent of households are engaged in a single economic activity. It is because of the fact that for geographical cum geological consideration (stated earlier), the people residing there need to rely upon agricultural activities. Forestry activities are minimal as the collection is only for self-consumption. The percentage of moderately diversified households has been found to be highest in Shamakhunta block, followed by Bisoi, Bangriposi and Jashipur blocks respectively. The reason behind this is that Shamakhunta block comes under the transitional zone and higher diversification of livelihood is possible for its specific zonal characteristics. Transition zone or ‘Area of Cooperation’ refers to the large outer area of a reserve where people live and work using the natural resources of the area in a sustainable manner. There the local communities, conservation agencies, scientists, civil associations, cultural groups, businesses and other stakeholders agree to work together to manage and use the area in a sustainable way so that it may benefit the people living there. Therefore, for the better scope of non-farm livelihood opportunities in the transitional zone, the greater diversification of livelihood along with the forestry activities is possible. Bisoi block comes under the buffer zone. Buffer zones are the areas created to enhance the protection of a specific conservation area, often peripheral to it. Within the buffer zones, resource use is legally or customarily restricted, often to a lesser degree than the adjacent transitional zone. Hence, in buffer zones livelihood diversification seems low compared with transitional zones and people living there possess fewer employment opportunities other than forestry.

The blocks Bangriposi and Jashipur in the study area come under partly core and partly buffer zones, as discussed. Core area includes protected areas, as they act as reference points on the natural state of the ecosystems represented by the biosphere reserves. The villages surveyed for our study from these two blocks come under the buffer zones. The percentage of highly diversified households has been found to be highest in Bangriposi block, though it does not belong to the transitional zone because for the most part this block is rain fed and scarcity of water makes agricultural work difficult. The farmers in this block can earn only the subsistence income, and thus they are mandated to engage in forestry activities and work as wage labour in several existing livelihood alternatives for survival. Overall, our study discloses moderate livelihood diversification in the studied region and establishes the fact that livelihood diversification varies across forest zones following the zonal geographies which are beyond the control of human beings. To be more specific, the study explores that livelihood diversification not only comes from the people’s choice, but also from the necessities for sustenance where they reside.

However, livelihood diversification also depends upon the demographic and socio-economic features of the studied population residing in a particular region. Therefore, to capture those stimuli, a regression analysis has been done. The regression model shows the extent of association between the outcome variable and the explanatory variables. In our model, the outcome variable is the score of livelihood diversification (Inverse of Herfindahl–Hirschman Index) of the household. The descriptions of the explanatory variables used in the study are presented in Table 5.

Table 5.

List of explanatory variables.

Variables	Descriptions
Average age	Average age of the working group population (14–60 years) of a household (in year)
Average education level	Level of average education in a household [Variants – Illiterate: (code 0), pre-primary (3–6 years children): (code 1), primary (classes 1–5): (code 2), medium (classes 6–8): (code 3), secondary (classes 9–10): (code 4), higher secondary and graduate (above class 10): (code 5), technical: (code 6)]
Household size	Number of members in a household
Agricultural land holding	If Yes = 1, No = 0
Domestic animal	If Yes = 1, No = 0
Loan	If Yes = 1, No = 0
Extent of forest dependence	If high = 1; otherwise 0

Source: Authors.

The explanatory variables in our study have been taken in line with the work of (Oluwatayo, 2009), where the author states that the major determinants of livelihood diversification may be gender, household size, years of formal education, status of asset holding and primary occupation. In the study area, the data reveal that the average age of the working group population is around 28 years. Average education level is very low; mostly the household members are pre-primary educated. The mean household size is almost 4.6. Around 74 per cent of households possess agricultural land, though 50 per cent of them possess less than one acre. A large number of households hold domestic animals, around 89 per cent. The mean monthly income for a household is around Rs 4274.03. In the study area, it is found that the persistent vulnerable economic situation frequently indulges the dwellers to take loans either for any business or productive work. But the circumstance often forces them to spend the money for unproductive use, which leads to getting trapped in debt. Our study exposes that around 25 per cent of the surveyed households have taken a loan from formal sources, being members of self-help groups (SHGs). Further, Table 2 depicts that around 66 per cent of households are highly dependent on the collection of NTFPs. Descriptive statistics of the explanatory variables used in the study are depicted in Table 6.

Table 6.

Descriptive statistics of the explanatory variables.

Explanatory variables	Min	Max	Mean	Standard deviation
Household size	1	11	4.59	1.82
Average age	10.83	62.5	28.22	11.54
Average education level	0	5	1.51	1.04
Agricultural land holding	0	1	0.74	0.44
Loan	0	1	0.25	0.44
Domestic animal	0	1	0.89	0.32
Extent of forest dependence	0	1	0.66	0.48

Source: Estimated by authors from the field survey data.

At Simlipal region, around 90.12 per cent of dwellers belong to the tribal population. Most of the people follow the religion Hinduism. Composition of sex reflects male:female = 53.10:46.90. The estimated regression result of our study is shown in Table 7.

Table 7.

Estimated result of the regression model.

Variables	Coefficient
Constant	0.559 (0.171)
Household size	0.065 (.019)***
Average age	0.010 (0.003)***
Average education level	0.035 (0.033)
Agricultural land	−0.087 (0.075)
Loan	0.012 (0.074)
Domestic animal	0.168 (0.102)*
Extent of forest dependency	0.217 (0.065)***
N = 162
R² = 0.211
Adjusted R² = 0.175
F = 5.886, Prob = 0.000

Source: Estimated by authors from the field survey data.

Figures in parenthesis indicate standard error.

Denotes significance at 10% level.

***

Denotes significance at 1% level.

It is evident from Table 7 that the R² value for this cross-section data is 0.211. R² measures the goodness-of-fit for linear regression models. The model shows that 21 per cent of the variation in the data of outcome variable is due to the variations in the data of explanatory variables collectively. From the ANOVA table, it is found that the value of F-statistic is 5.886 and the P-value associated with it is 0.000. The probability value of less than 0.01 (P < 0.01) indicates that overall the regression result statistically significantly predicts the outcome variable. Therefore, it is a good fit for the data.

The fitted model shows that the independent variables like household size, average age, average education level, possession of domestic animals, liability and extent of forest dependence have positive coefficients and the possession of agricultural land has a negative coefficient. Regression coefficients represent the mean change in the response variable for one unit change in the predictor variable while holding other predictors in the model constant. The signs of the coefficients are as per expectation in the study.

It is quite pertinent that with the increase in household size, livelihood diversification will increase so as to feed all the household members. Age is a prime factor in determining the level of livelihood diversification. With the increase in age, people acquire much skill and environmental awareness (Cavendish, 2000). Thus the higher average age in a household (which indicates the presence of aged members) reflects diversified livelihoods of the members, which help reduce the forest dependence. Education reflects an individual’s understanding and willingness to accept new challenges that come forefront in their life, leading them to choose a number of activities for survival. A positive relationship is found between the average increase in education level (which reflects the presence of educated members) of a household and livelihood diversification in the study. Further, with the increase in forest dependence, livelihood diversification increases. This is because most of the household members in the study area are found to be engaged in collection of intermediate products from the forests. After processing the intermediate products, they sell the final products in the market and earn revenue. This forest dependence leads towards engagement in market activities, which often helps ensure diversified livelihoods.

Loans boost the households to start up businesses or modes to earn at local level irrespective of whether it is availed from a formal or informal source. SHGs are quite active in provisioning loans to the people in the study area. People even take loans to purchase livestock or for agricultural purposes. Generally, the relationship between loans and livelihood diversification in case of non-farm jobs is ambiguous (Datta and Singh, 2011). But in our study, household members availing loans are found to diversify their livelihood so as to clear the debt. Livestock often is considered as an asset for the rural households. Domestic animals are utilized in agricultural work and as a source of earning directly by selling them at maturity. The earnings from livestock rearing are usually allocated in the non-farm livelihood diversification activities. Therefore, the relationship between the holding of livestock and livelihood diversification is positive in the study, though some studies also show the ambiguous or negative relation between them (like Datta and Singh, 2011; Kassie et al., 2017).

Conversely, a negative relationship has been found between the holding of agricultural land and livelihood diversification. This is because of the fact that the number of households having agricultural land are nominal in most of the cases and the household members associated with agricultural activities mostly produce for self-consumption with low expectation of earnings. However, cooperative farming may increase the profitability of production and reduce the searching of alternative livelihood opportunities. In the study, the explanatory variables like household size, average age and extent of forest dependence are significant at the 1 per cent level; and domestic animal is significant at the 10 per cent level.

In addition, the tolerance level and the variance inflation factor (VIF) of the explanatory variables in the model have been estimated; the mean VIF is found as 1.230 (within the critical value of 10), which indicates that the model does not suffer from any severe or even moderate multi-collinearity problem. In statistics, VIF quantifies the severity of multi-collinearity and provides an index that measures how much the variance of an estimated regression coefficient is increased because of collinearity. Finally, we have gone for the test of heteroskedasticity in the data. Nature of heteroskedasticity refers to unequal variances of the error for different observations. Thus the Normal P-P plot of Regression Standardized Residual has been checked to make sure that the error term is normally distributed. The validity of the model is tested vigilantly so as to resolve the key factors responsible for livelihood diversification in the study area.

Discussion

Livelihood diversification in a broader sense refers to structural transformation (Timmer, 2009). Therefore, livelihood diversification as an instrument of earning counts for empowerment of people along with crafting environmental awareness. Government should design policies in such a way so as to enhance the livelihood opportunities. The findings of our research work reflect that scores of livelihood diversification of the households are subjected to forest-zonal characteristics. The socio-economic factors like household size, average age, holding of livestock and extent of forest dependence significantly affect the ability of forest dwellers to diversify their livelihoods. It is also found that livelihood diversification is moderate in the Simlipal region due to remoteness, which resembles the findings of another such study by Datta and Singh (2011).

Diversification gives individuals and families wider experiences, like strengthening of human capital and generation of cash resources, which can be used for further investments. It reduces stress on sensitive natural resources and provides alternative income streams (Ellis and Allison, 2004). Our study is significant as it focuses on the need of strong flow of non-farm income sources (as agricultural production is not profit-oriented and forestry activities are seasonal). A similar view is presented in the study of Kassie et al. (2017), where the authors state that due to low agricultural productivity, off-farm livelihood diversification strategies should be adopted in Ethiopia. Further, the authors place importance on livelihood diversification practices of farm households also during the slack periods, which may help boost their income. In our study, it is found that people who are engaged in agriculture produce mainly for self-consumption and a minimal surplus amount is marketed. Since the agricultural production is not profit-oriented, it hardly leads to generate income for the farmers for future investment in off-farm activities. Government should take many more initiatives to make agricultural production lucrative through providing credit facilities at affordable means and conditions, insurance at the time of crop failure, increasing the market accessibility, etc. A similar view is reflected in the work of Kimengsi et al. (2019), where access to training and credit facilities have been found as significantly affecting the livelihood of people dependent on forests. In the work of Tiffen et al. (1994), the authors give emphasis to investment for improvement of soil and water management to raise yields and better manage of natural resources. But local people’s awareness is important to maintain the natural resource base while getting engaged in different economic activities. As per the study of Singh (1995), forest protection is a win-win state of affairs, where forest dwellers are the protectors of the forest. Hence, provision of supplementary employment by the government in forest conservation (as Sabuja Vahini/Green Brigade) might be a help for the dwellers. Simlipal National Park, being a tourist spot, possesses ample opportunities to provide employment to the rural people in tourism activities. Promotion of ecotourism might be a good initiative by the government for livelihood support.

However, government initiative for livelihood promotion may not be sufficient, as access and ability to cope with changes are equally important for the dwellers to grasp the facilities. According to Sen (1989), capacities and capabilities enhanced through education enable the rural poor to discover the other avenues for livelihood support. Therefore, stepping towards elevating the educational standard at Simlipal, where a large section of people is ignorant, might create environmental responsiveness and reduce forest dependence. This view goes with the work of Fisher (2004), where the author discusses the relationship between boost in education level for exposure to employment opportunities and reduction in forest dependence. Further, Kimengsi et al. (2019) expresses that education and training for skill enhancement may significantly reduce reliance on the forest. Scientific collection of NTFPs due to growing environmental concern of the forest dwellers may help use of forest resources within a safe limit, though the expansion of livelihood diversification is closely linked with forestry activities in the forest-fringe areas.

Conclusion

The study exhibits that instead of a general policy, block-wise different strategies might be useful for realizing livelihood diversification. Ellis (2000) states that diversification of rural livelihoods, which has positive attributes for livelihood security, is important to reduce poverty in low-income developing countries. Therefore, policy should be directed towards facilitating diversity of livelihood opportunities. In Simlipal, access to different livelihood opportunities might not be sufficient and hence development policies and institutional back-up are needed to ensure that households are engaged in different livelihood activities.

The study aims to analyse whether livelihood diversification of the forest dwellers is a necessity or choice under the SLF. Restrictions from the government level over the natural resource use and seasonality in NTFP collection have made the lives of the dwellers vulnerable. Due to vulnerable economic conditions, forest dwellers are forced to engage themselves in several livelihood options throughout the year. Their involvement in different economic activities justifies livelihood diversification from necessities rather than choice.

The study may be useful for strategic planning towards ensuring sustainable livelihood while reducing the dependence on forests. However, more exhaustive analysis with an increased spatial and intertemporal sample size may project the trend of livelihood diversification of the forest-fringe dwellers and assess whether the initiatives taken by the government are pragmatic in reducing forest dependence and economic vulnerability of the forest dwellers.

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Debolina Saha

Author biographies

Shradha Agarwalla is associated with the Department of Economics, Rabindra Bharati University, India, as a Research Scholar. She is a State Junior Research Fellow at the aforesaid department of the university. She has keen interest in the research work related to environment, resource and development economics. She has publications in reputed journals as well.

Dr Debolina Saha is affiliated to the Department of Economics, Rabindra Bharati University, India. She teaches Economics as an Assistant Professor to the postgraduate students and assists research work as research supervisor. Her work focuses on environment and resource economics and development economics. She has authored and co-authored several national and international publications and has an active association with several societies and academies nationwide.

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Livelihood Diversification of Forest Dwellers – A Necessity or Choice?

Abstract

Keywords

Introduction

Methodology

Conceptual framework of the study

Data collection and sampling procedure

Compilation of data and methods used for analysis

Description and importance of the study area

Data analysis and findings

Discussion

Conclusion

Footnotes

Funding

ORCID iD

Author biographies

References