1

Socio- economic Determinants of Non-Timber Forest 1

Products Collection. A case study among indigenous 2

people in Karnataka, India 3

4

5

Francisco Zorondo-Rodríguez 6

7

Directed by: 8

Dr. Victoria Reyes-García 9

10

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Máster Oficial en Ciencias Ambientales 12

Institut de Ciència i Tecnología (ICTA) 13

Universitat Autònoma de Barcelona, 14

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08193, Bellaterra, Barcelona, Spain. 16

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September 2007 18

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2

Summary 20

Non timber forest products (NTFPs) contribute to rural livelihoods through 21

cash income generation and direct consumption. Previous research has 22

studied the socio-economic attributes of NTFPs collectors to identify 23

individuals and households that highly rely on NTFPs. Understanding the 24

socio-economic attributes associated to the collection of NTFPs is a 25

relevant in the debate on the role of NTFPs on rural poverty alleviation and 26

biological conservation. This research would improve our understanding of 27

NTFPs contribution to rural livelihoods and would provide insights into 28

factors that regulate NTFPs extraction. Here, I use multivariate regression 29

analysis to evaluate the associations of the consumption and sale of NTFPs 30

with a set of socio-economic attributes at the individual and household 31

level. I used data collected among the Soligas, an indigenous tribe that 32

inhabit in Biligiri Rangaswamy Temple Wildlife Sanctuary, southern of India. 33

Results show that the probability to engage in the collection of NTFPs is 34

associated to some socio-economic variables such as age, environmental 35

employment, being unemployed, cattle ownership, time of residency, and 36

others. I also found that the influence of these associations changes 37

between the NTFPs collection for sale and consumption. Analyses with the 38

sample of individuals that sell or consume NTFPs show that NTFPs income 39

by sale and consumption is associated of some collector’s attributes such 40

as age, sex, environmental employment, time of residency, non-NTFP 41

3

individual income. Results also show that association of some variables 42

with NTFPs individual income change between NTFPs sale and 43

consumption. At the household level, I also found that NTFPs cash income 44

and consumption is associated to household size, and number of adults, 45

men and educated individuals, time of residency and economic 46

contribution. In sum, this study identifies some variables with a high 47

potential for predicting NTFPs sale and consumption pattern at individual 48

and household level, and it demonstrates that collector’s socio-economic 49

attributes influencing on different way the NTFPs sale and consumption. 50

The different association patterns would identify different social sectors 51

dependent by NTFPs, those with an economic reliance and those 52

dependents on consumption. 53

54

Key words: socio-economic attributes; non timber forest product (NTFP); 55

livelihood; Soligas; Biligiri Rangaswamy Temple Wildlife Sanctuary. 56

57

4

Introduction 58

Non-Timber Forest Products (NTFPs) refer to all biological materials found 59

in the forest except timber. NTFPs are the base of the livelihood of many 60

rural families in developing countries. NTFPs support rural livelihoods 61

through the generation of cash income with the sale of NTFP (Godoy et al. 62

1995; Hedge et al. 1996; Hedge & Enters 2000; Narendran et al. 2001; 63

Neumann & Hirsh 2001) and through the provision of essential goods –like 64

food and medicine- for household consumption (Uma Shankar et al. 1998a; 65

Narendran et al. 2001; Lacuna-Richman 2002; Delang 2006). Income 66

generated through the sale of NTFPs represents almost the 100% of the 67

cash income earned by some rural households (Godoy et al. 1995). 68

Household consumption of NTFPs also plays an important economic role in 69

rural families because the consumption of NTFPs decreases rural 70

households´s dependency on cash income (Delang 2006) 71

72

Several factors affect household’s or individual’s decisions to engage in 73

NTFPs collection. Previous research has studied the individual and 74

household characteristics associated to the collection of NTFPs. This 75

research has found that age, household size, and time of residency are 76

positively associated with cash income generated through the sale of NTFPs 77

(Lacuna-Richman 2002; Hedge & Enters 2000). Researchers have also 78

found that education and cash income generated through activities other 79

5

than NTFPs collection are negatively associated with cash income 80

generated through the sale of NTFPs (e.g Hedge et al. 1996; Uma Shaankar 81

et al 2004; Shone & Caviglia-Harris 2006). Despite the importance of 82

previous research in our understanding of the socio-economic 83

determinants of NTFPs collection for sale, researchers have paid scant 84

attention to the individual and household characteristics that affect the 85

collection of NTFPs for household consumption. Here, I contribute the 86

literature on the determinants of NTFPs collection by comparing the 87

different individual and household socio-economic attributes associated to 88

the sale and consumption of NTFPs. For the empirical analysis, I use a rich 89

body of primary data collected among the Soligas, an indigenous group 90

living in the State of Karnataka (South India). 91

92

This research is important for theoretical and practical reasons. At the 93

theoretical level, previous research suggest that a complex web of relations 94

exits between socio-economic attributes of people and NTFPs consumption 95

and sale. Those relations may be influenced by other factors, such as forest 96

resources availability, institutional framework, and social development 97

(Janse & Ottitsh 2005). Relations might vary between communities and 98

through time. Because of these complex relations, it is important to 99

compare the determinants of NTFPs consumption and sale in the same 100

place and time. The complex web of relations also implies the need to 101

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develop multiple comparisons between attributes, so multiple analyses are 102

need as a strategy to evaluate the influence of key socio-economic 103

attributes (e.g Gavin & Anderson 2007). My research contributes to that 104

goal. 105

106

At the practical level, the identification of attributes associate to NTFPs 107

consumption and sale may be pivotal on current debates about the role of 108

NTFPs in achieving sustainable development (Hedge et al. 1996; Arnold & 109

Ruiz-Pérez 2001; Gavin & Gregory 2007). The collection of NTFPs has been 110

proposed both as a sustainable strategy to alleviate rural poverty and as a 111

management tool to achieve biological conservation (Neumann & Hirsh 112

2000). However, the claims have yet to be put to empirical test (Arnold & 113

Ruiz-Pérez 2001; Belcher & Schreckenberg 2007). By examining the 114

associations between NTFPs sale and consumption and household and 115

individual socio-economic attributes, this research can 1) provide insights 116

into factors that regulate NTFPs extraction and 2) improve our 117

understanding of NTFPs contribution to rural livelihoods (Hedge et al. 118

1996; Janse & Ottitsch 2005; Gavin & Anderson 2007). As Hedge and 119

colleagues (1996) pointed out, NTFPs research would help to integrate 120

NTFPs use and biological conservation into a coherent policy for 121

management of NTFPs For instance, NTFPs research might help 122

7

management programs to identify and target sectors of rural population 123

more dependent on the extraction and sale of NTFPs. 124

125

Individual and household determinants of NTFPs collection 126

127

Most previous research on the individual and household determinants of 128

NTFPs collection has focused on the collection of NTFPs for sale, not for 129

household consumption. Researchers have proposed that dependency on 130

the sale of NTFPs might help to identify households with a greater 131

livelihood risk. Godoy and colleagues (1995) suggest that dependency on 132

NTFPs might resemble a parabola: due to lack of opportunities to 133

commercialize, poor communities are more dependent of NTFPs 134

consumption than on NTFPs sale; richer rural communities depend highly 135

on the commercialization of NTFPs; and in the richest rural communities 136

NTFPs have a less significant role as livelihood source because other 137

economic activities are available. Indeed, the situation may be the result of 138

a change in people’s socio-economic attributes. For instance, a greater 139

education level would allow people to get jobs and, therefore, to depend 140

less of NTFPs. The same effect might be due to other socio-economic 141

attributes such as cattle and land tenancy, property rights, or proximity to 142

markets and cities. 143

144

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Previous research has identified several socio-economic attributes of 145

collectors as determinants for the contribution of NTFPs to household 146

livelihood (e.g. Hedge & Enters 2000; Lacuna-Richman 2002; Janse & 147

Ottitsch 2005; Shone & Caviglia-Harris 2006). For instance, in a study on 148

Southern India Hedge & Enters (2000) found a negative association 149

between household cash income from NTFPs and i) the number of educated 150

persons in a household and ii) the cash income generated through non-151

NTFPs activities. Other researchers (Hedge et al. 1996; Hedge & Enters 152

2000; Shone & Caviglia-Harris 2006) have found a positive association 153

between cash income from NTFP and collector´s age, household size, and 154

time of residency in the village. The associations just outlined have been 155

studied in relation to cash income generated through the sale of NTFPs, 156

but researchers have overlooked the socio-economic determinants of the 157

consumption of NTFPs (Delang 2006; Galvin & Anderson 2007). 158

159

When examining the determinants of NTFPs collection it is important to 160

differentiate between collection for consumption and for sale. Attributes 161

that influence the decision to collect NTFPs for sale might not be relevant 162

for the collection of NTFPS for household consumption, and viceversa. For 163

example, men might be more devoted to the collection of NTFPs for sale, 164

but women might have a more important role in the collection of NTFPs for 165

household consumption. So, comparative analyses about socio-economic 166

9

attributes that influence both NTFPs sale and consumption should be 167

developed. 168

169

The scant research on the co-variates of the consumption of NTPFs 170

suggests that the socio-economic determinants of collection of NTFPs for 171

consumption might resemble the socio-economic determinants of 172

collection of NTFPs for sale (Lacuna-Richman 2002; Gavin & Anderson 173

2007). For example, Gavin & Anderson (2007) examined the determinants 174

of household consumption of NTFPs in the Peruvian Amazon They found 175

that time of residency was positively associated with resource use and 176

could be a good predictor of forest resources use. Families with a longer 177

history of time of residency in the area use more intensively forest 178

resources than families of more recent arrival. They also found that the 179

socio-economic variables that have been commonly associated to the sale 180

of NTFPs –e.g., age, household size, and place of residency– were also 181

predictors for resources use. Despite those previous findings, the topic has 182

received scarce attention. So, we not much understand about consumption 183

patterns and the social sectors who reliance on NTFPs consumption. Still 184

more, because to that socio-economic are immersed in a complex web of 185

interactions, research strategies should be based in multiple regression 186

analyses. Here, I present the results of multiple comparative analyses 187

between NTFPs sale and consumption. 188

10

The Environment and the People 189

190

The Biligiri Rangaswamy Temple Wildlife Sanctuary (BRT) is settle in a series 191

of hills located between 11–13°N and 77–78°E in the Biligiri Rangan (BR) 192

Hills, State of Karnataka in the southwest of India (Figure 1). The Wildlife 193

Sanctuary was established in 1974, has an extension of 540 km2, and is 194

under the administration of the Karnataka Forest Department (KFD). The 195

administration of the KFD include management plans for the prevention of 196

encroachments, illicit cutting of timber, curtailment of fire, eradication of 197

weeds, and the maintenance of wild populations of large mammals. 198

INSERT FIGURE 1 ABOUT HERE 199

200

The BRT Sanctuary straddles the Western and Eastern Ghats with an altitude 201

ranking from 600 to 1800 m.a.s.l. The climate in the area is tropical 202

monsoonal with three seasons: rainy season from June to November, winter 203

from December to February, and summer from March to May. The BRT 204

receives both southwest monsoon from the west coast and the retreating 205

northeast monsoon from the east coast. The first monsoon peak is 206

observed in May and the second in September. 207

208

The BRT has a rich biological diversity and is considered as a world level 209

conservation priority, forming part of the Western Ghats and Sri Lanka 210

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hotspots (Myers et al., 2000). The natural vegetation is composed by wet 211

evergreen, dry deciduous, scrub, grasslands, and stunted montane forests 212

(see Murali et al., 1996; Uma shankar et al., 1996). Dry deciduous forest is 213

the he most extensive vegetation in the area. Other systems existing in BRT 214

are ecosystems of shallow water and agroecosystems such as coffee and 215

tree plantations (Figure 1, Krishnaswamy et al. 2004). BRT supports a high 216

diversity and endemism of animals and plants, including significant 217

populations of spectacular species such Asiatic elephant (Elephas 218

maximus), tiger (Panthera tigris) and gaur (Bos gaurus). 219

220

The BRT also concentrates a high anthropic pressure which has decreased 221

the populations of many species (Barve et al. 2005). Researchers have 222

argued that collection of NTFPs is a major threat for biodiversity 223

conservation at the BRT. NTFPs collection might affect the vegetation 224

composition and structure and, consequently, the sustainable regeneration 225

of the vegetation (Murali et al., 1996; Uma Shankar et al., 1996; Uma 226

Shankar et al., 1998b; Barve et al., 2005). 227

228

The Soligas, tribal inhabitants of the BRT 229

230

Social Organization 231

232

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The Soligas are an indigenous group that inhabit in the BR Hills. 233

Approximately, 4000 Soligas live within the BRT wildlife sanctuary and 234

11000 live in other parts of the BR Hills. Soligas within the BRT wildlife 235

sanctuary live in 25 settlements -known as “Podu”- scattered into the forest. 236

Podus are composed by a few clusters of houses that act as a unit in 237

relation to other podus. Previous research conducted in the area (Morab, 238

1977) suggests that the social organization arise from the natural 239

conditions surrounding the Podus such as water sources, hill-ridge, or 240

bamboo groves. Podus are immersed in an upper governmental 241

administrative level, called Taluka, which in turns are grouped into a larger 242

District within a State (in this case, State of Karnataka). 243

244

Soligas Traditional Economy 245

246

Soligas traditional economy was based on shifting cultivation 247

complemented with other activities dependant on the natural environment 248

such as hunting, trapping, fishing, and honey collection. Yams, wild edible 249

fruits, honey, and wild game and fish formed the core of Soligas 250

subsistence (Morab, 1977). For the Soligas, the unit of production was the 251

nuclear family, and their economic system was characterized by the 252

absence of private appropriation of social labour. Soligas cultivated 253

agricultural plots opened in decidious forest and mainly localized on hill 254

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slopes and mountain ridges in the forest, permitting refuge of the menace 255

from wild animals. Soliga shifting agriculture consisted in cultivating about 256

two acres of land per family for about three consecutive years and then 257

abandoning the land to clear a new plot (Morab, 1977). Soligas considered 258

the forest into the Podu limits as communal property of the Podu. 259

Members of a Podu collected natural products such as firewood or honey, 260

from the forest into the Podu limits (Morab, 1977; Rajan 1983). Both the 261

collection of natural goods and the products of agriculture fields were 262

individual efforts for household subsistence and not a collective effort by 263

the local community (Morab, 1977). 264

265

Soligas typically collected a wide range of forest goods for household 266

consumption. The most commonly collected forest goods were amla 267

(Phyllanthus emblica), gallnut (Terminalia chebula), taarekai (Terminalia 268

bellirica), soapnut (Sapindus emarginatus), shikekai (Acacia concinna), 269

lichens, and wild honey –primarily from wild rock bees (Apis dorsata)– (See 270

Murali et al., 1996; Hedge et al., 1996). The collection of NTFPs provided 271

daily household consumption goods, such as combustible and construction 272

materials and ensured Soligas’ health and nutritional well being (Murali et 273

al., 1996; Uma Shankar et al., 1998a). 274

275

New economic activities and NTFPs commercialization 276

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277

In 1974, the government of India declared the BR Hills area as a Wildlife 278

Sanctuary and introduced regulations on the use of the forest and forest 279

products. The new status of the area inhabited by the Soligas generated 280

many changes in the economic activities and affected Soligas livelihood, 281

culture, and lifestyle (Rajan, 1983). The Forest Department banned timber 282

logging, hunting of all wild animals, and shifting agriculture. The Forest 283

Department gave each Soliga family about two acres of land to practice 284

settled agriculture. Additionally, the Forest Department allowed the 285

collection of firewood for household consumption and gave the Soliga 286

usufruct rights to harvest NTFPs (e.g., honey, broomstick grass) for 287

consumption and sale. At the same time, the Government established 288

cooperative societies to help the Soliga market NTFPs (Sinha and Bawa, 289

2002). 290

291

Nowdays, Soliga income and consumption depend on a variety of 292

subsistence and market activities, including settled agriculture, gathering 293

of timber and NTFPs for consumption, gathering of NTFPs for sale, and 294

wage labor in coffee plantations and for the Forest Department (Hedge et 295

al., 1996; Murali et al., 1996; Uma Shaanker et al., 2004). Settled 296

agriculture is a subsistence activity and farm products are not 297

commercialized. Livestock is a new economic activity, started by 298

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developmental support programs implemented by the Karnataka state. 299

Livestock trade is very scarce too. NTFPs play an important role in 300

household consumption and income. NTFPs provide 50% of annual cash 301

income for some families and almost 100% for a minority of Soligas within 302

the BRT (Hedge et al., 1996). Soligas also engage in other economic 303

activities such as plough cultivation, agricultural labour, and other wage 304

labour occupations and salaried jobs (Morab, 1977; Hedge et al., 1996), 305

which in some cases have discouraged the extraction and sale of NTFPs. 306

307

At the time when data were collected, in the study area the 308

commercialization of NTFPs was led by cooperative marketing societies, 309

called Large-scale Advise Multi-Purpose Societies (LAMPS). The government 310

set up LAMPS in regions with significant tribal population to promote 311

integrated tribal development. LAMPS held the harvesting rights on lease 312

from the Forest Department. In theory, Soligas sell their NTFPs through the 313

LAMPS. LAMPS process and market the products so as to capture the 314

highest possible fraction of the final consumer price. But, their “ideal” 315

functioning has been obstructed by many factors (Lélé et al., 1996), which 316

in turn affect Soligas income. In practice, Soligas obtain a lower income 317

than the potential cash income that they could receive from the 318

commercialization of NTFPs (Hedge et al., 1996; Uma Shankar et al., 1996). 319

320

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Other actors involved in the commercialization of NTFPs in the area include 321

the Karnataka Forest Department (KFD), Vivekananda Girijana Kalyana 322

Kendra (VGKK) –a local non-governmental organization–, private traders, 323

and other stakeholders (see Lélé et al., 1996). The KFD controls NTFPs 324

collection and grants license, permits, and passes to tribal and non-tribal 325

people. VGKK supports sustainable commercialization of NTFPs as a way to 326

enhance income without depleting resources. The private traders 327

commercialize NTFPs gathered by non-tribal collectors, and occasionally 328

also commercialize -illegaly- NTFPs collected by the Soligas (Lélé et al., 329

1996). 330

331

Estimation strategy 332

333

The main aim of this work is to estimate the associations between sale and 334

consumption of NTFPs and individual and household level socio-economic 335

characteristics. For the empirical analysis, I assess the association between 336

(a) individual and household level attributtes and (b) four outcome 337

variables (cash income, monetary equivalent of NTFPs consumed, total 338

income from NTFPs –sale and consumption-, and contribution of NTFPs to 339

total household cash). I use the following expression to model the 340

association between income from NTFPs (Y) and covariates: 341

342

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[1] Yihy

= α + γIihv

+ λHhv + ψC

v + ε

ihv 343

344

To fix ideas, assume, first, that Y captures the cash income generated by 345

the sale of NTFPs, where i is the subject, h the household, and v the village. 346

I use cash income for ease of exposition, but the expression also applies to 347

the other income variables. Iihv

refers to a vector of variables that capture 348

individual characteristics -such as age, sex, and literacy- where the 349

subscripts are the same. Hhv

captures household characteristics –such as 350

household size, time of residency, and land ownership-. Cv is a vector of 351

dummy variables for the podus or communities. εihv

refers to unexplained 352

value or random error term. α, γ, λ, and ψ are the coefficients estimated 353

with the regression model. 354

355

I analyzed the data using two different multivariable regression models. 356

First, I used a probit regression model to evaluate the probability that a 357

given individual would engage in the collection of NTFPs. The sample for 358

this analysis included all the participants in the study. Second, I used 359

Ordinary Least Square (OLS) regressions to evaluate the association 360

between income and consumption of NTFPs and a set of individual and 361

household explanatory variables. The sample for this analysis includes only 362

participants that reported having a positive annual income of NTFPs (i.e. 363

higher than zero). OLS were substituted by Tobit regressions when the 364

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outcome variable was censored at the 20% level or more. For consistence, I 365

used the same or equivalent explanatory variables for the individual and 366

the household level models. 367

368

Potential biases in the estimations relate to 1) random measurement error 369

of dependent and explanatory variables, 2) omitted variables, and 3) 370

possible reverse causality. First, I might have random measurement error in 371

the outcome variables. For example, as the research unfolded people´s 372

fatigue might have increased, thus reducing precision in answers 373

(Michielsen et al., 2003). Random measurement error in the outcome 374

variable would inflate standard errors. Explanatory variables might also 375

have random measurement error. For example, people do not know their 376

exact age and might guess when answering. Random measurement error in 377

the explanatory variables would produce an attenuation bias and make the 378

estimates more conservative. 379

380

Second, estimations might be biased by the role of omitted variables. For 381

example, the sale of NTFPs might be dependant on having a relative in the 382

LAMPS. If so, people with relatives on the LAMPS might sale more NTFPs. 383

Failure to control for presence of relatives in the LAMPS might bias my 384

estimations. To deal with possible biases from omitted variables, I used 385

dummy variables for the podus in the regression model. The procedure 386

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should allow me to control for the confounding role of village-level 387

attributes, such as access to markets, or village endowments (e.g., soil 388

fertility). I did not have enough degrees of freedom to run a household 389

fixed-effect model, so the estimated parameters I present might be biased 390

by unobserved household attributes. 391

392

Last, I do not have convincing instrumental variables to control for the 393

potential endogeneity of my explanatory variables. It is possible that 394

education contributes to lower collection of NTFPs, but causality could also 395

run the other way. Therefore, I cannot speak about causality but about 396

associations between the variables explored. 397

398

Methods 399

400

The data for this article were collected through a baseline and a panel 401

survey consisting on fortnightly interviews to the same individuals. The 402

baseline survey was conducted in February 1996, and the panel lasted from 403

February 1996 to February 1997 for a total of 24 rounds of data collection. 404

Data were collected by researchers from Ashoka Trust for Research in 405

Ecology and the Environment (ATREE), a local ONG working in the BRT since 406

1994. 407

408

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Sample:

416

Fortnightly interviews were administrated to a sample of Soligas 409

living within the BRT boundaries. The total sample of the study was of 410

2513 individuals belonging to 595 households from 15 podu and three 411

taluka. A total of 1467 respondents (or 58.4% of the sample) were adults, 412

defined as people over 15 years of age, from which 751 or 51.2% were men 413

and 716 or 48.8% were women. 1045 of the respondents or 41.6% of the 414

sample were between 8 and 15 years of age (51.3% boys and 48.7% girls). 415

Respondents belong to three of the four talukas in the BRT. A total of 1487 417

individuals from 357 households belong to the Yelandur taluka, which 418

included the podus of Kalyani, Manjigundi, Sigebetta, Yerakanagadde, 419

Muthagadagadde, Banglepodu, Hosapodu, Purani, and Devarahalli podus. 420

The Chamarajnagar taluka was formed by Budhipadga and Kanneri-Colony 421

podus, where 464 individuals from 100 households were part of sample. 422

Last, 264 individuals from 57 households in four podus (Gombegallu, 423

keredimba, Ganigamangala, and Chikkallur) from the Kollegal taluka 424

participated in the survey. Panjur taluka was not included in the sample. 425

426

Income from NTFPs (dependent variables): The information from the 427

fortnight survey included questions on 1) the type of NTFPs collected, 2) 428

the amount of each NTFPs collected devoted to consumption and sale, 3) 429

the cash income generated by the sale of NTFPs, and 4) the selling price of 430

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NTFPs. I used the survey information to construct eight different outcome 431

variables, four at the individual and four at the household level: 432

433

NTFPs cash income refers to the direct monetary earnings obtained thought 434

the sale of NTFPs. I constructed a variable for individual NTFPs cash 435

income, aggregating individual level responses through the year, and a 436

variable for household NTFPs cash income adding responses from all the 437

members of a household. 438

439

NTFPs consumption refers to the monetary equivalence of NTFPs destined 440

to household consumption. To calculate the monetary equivalent of NTFPs 441

consumed, I multiplied the amount of NTFPs destined to consumption 442

times the average price of the sample product sold by the household the 443

same fortnight. If the household did not report any sale for a NTFP 444

consumed, I imputed the fortnightly average village price for the product. 445

Previous research has used the selling price to evaluate and compare the 446

value of NTFPs consumed with the value of NTFPs sold (Godoy et al., 2000). 447

As with NTFPs cash income, I calculated NTFPs consumption at the 448

individual and household levels. 449

450

NTFP total income refers to the sum of NTFPs cash income and the 451

monetary value of NTFPs consumption. Last, NTFP Income ratio refers to 452

22

the ratio between cash income from NTFPs and total income, i.e. income 453

generated by all economic activities. These variables were also calculated at 454

the individual and household levels. 455

456

Socio- economic attributes (explanatory variables): The baseline survey 457

gathered information on the socio-economic characteristics which have 458

been shown to influence NTFPs collection (e.g Hedge et al., 1996, Hedge & 459

Enters, 2000; Lacuna-Richman, 2002; Shone & Caviglia-Harris, 2006; Gavin 460

& Anderson, 2007). Household level information includes household size 461

(number of people living in the household, including adults and children), 462

size of land holding (measured in acres), number of cows own by the 463

family, time of residency (years living in the current podu), title of land 464

tenancy (measured with the presence of patta), and residence localization 465

(podu and taluka). Individual level information included socio-demographic 466

characteristics of the individual such as age, sex, literacy level, and 467

principal occupation of informants. 631 individuals said they were 468

students, 561 said their main occupation was domestic labor, 413 said it 469

was agriculture, and 218 said it was NTFPs collection. Other main 470

occupations were sericulture, labor employment, pensioner, and LAMPS 471

agent. To facilitate the analysis, I grouped informants according to their 472

main occupation into three groups: 1) informants devoted to environmental 473

employment, i.e. those working in NTFPs collection, agriculture, 474

23

sericulture, or livestock rearing; 2) informants with a non environmental 475

employment, such as salaried jobs, LAMPS agent, construction wage 476

labour, and others; 3) informants without employment, or individuals who 477

did not have a formal occupation, such as retired people; and 4) students. 478

According to this classification, 663 individuals from the sample had an 479

environmental employment, 278 a non environmental employment, and 480

565 did not have any employment. 481

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Results 482

483

Collection of NTFPs 484

485

Soligas reported to extract 15 different NTFPs from the forest. NTFPs 486

extracted by Soligas include fruits, tubers, seeds, lichen, fuelwood, and 487

honey. The products most commonly extracted were Nelli´s fruits (198.9 488

kg hh-1 yr-1), lichens (2285.2 kg hh-1 yr-1), Magaliberu (847.9 kg hh-1 yr-1), and 489

fish (4258.5 kg hh-1 yr-1 ). Table 1 shows the diversity of NTFPs gathered by 490

Soligas, the quantity of products gathered through the year, the number of 491

people dedicated to the collection of the product, the monetary value 492

generated by the sale or consumption of the product, and its contribution 493

to Soligas economy. 494

INSERT TABLE 1 ABOUT HERE 495

496

From all the products commercialized by Soligas, fish, lichens, Magaliberu, 497

honey, and Nelli´s fruits contribute most to the generation of cash income. 498

Commercialization of fish represent the 28% of Soligas cash income 499

generated by NTFPS and commercialization of lichens represent 27% of 500

Soligas cash income from NTFPs. Fuelwood and honey were the principal 501

products consumed by Soligas households. In equivalent income, fuelwood 502

would represent a 6% of total cash income and honey a 3.2%. 503

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504

Descriptive statistics 505

506

Tables 2, 3, and 4 contain definition and summary statistics of the 507

outcome and explanatory variables used in multivariate analysis. About 508

13.1% (328 of 2513) of the individuals and a 24% (143 of 595) of the 509

households in the sample reported the collection of NTFPs through the 510

year. Cash income obtained through the sale of NTFPs represented the 511

19.8% of the individual cash income and the 24.8 % of the household cash 512

income (Table 2). 513

INSERT TABLE 2, TABLE 3, and TABLE 4 ABOUT HERE 514

515

The socio-economic predictors of NTFPs collection 516

517

I start the multivariate analysis by examining the socio-economic predictors 518

of the collection of NTFPs for sale (Table 5, Column a), for consumption 519

(Table 5, Column b), and in total income (Table 5, Column c). The probit 520

regression models presented in Table 5 show that some socio-economic 521

variables were equally associated with the collection of NTFPs for 522

consumption and sale, but some differed. For example, the variables age, 523

environmental employment, unemployed, cattle presence, and time of 524

residency were all associated in a positive and statistically significant way 525

26

with the collection of NTFPs for consumption and sale. One additional year 526

of age over the sample average increases the probability that a person 527

collects NTFPs for sale by a 0.8% (p<0.10) and the probability that the same 528

person collects NTFPs for consumption by a 1% (p<0.05). People who 529

reported mainly engaging in environmental employment (versus people 530

who reported having a non-environmental employment, the omitted 531

category) had a 56% higher probability of collecting NTFPs for sale, and a 532

65% higher probability of collecting NTFPs for consumption (p<0.001) than 533

people without those characteristics. Each additional cow in a household 534

respect to sample average increases the probability that an individual will 535

collect NTFPs for consumption by a 25% (p<0.05). Cattle presence is also 536

associated to collection of NTFPs for sale, but the result is not statistically 537

significant. Each additional year of residency increase in 2% the probability 538

that an individual collects NTFPs for sale and in a 5% the probability that 539

the same individual collects NTFPs for consumption. Individual income 540

from non-NTFPs activitites and household income are also associated to 541

collection of NTFPs both for sale and consumption, but their coefficient is 542

very low. 543

544

I also found a negative association between the collection of NTFPs for 545

consumption and sale and several socio-economic variables. Students have 546

an 81% lower probability of collecting NTFPs for sale and a 100% 547

27

probability of not collecting NTFPs for consumption than people with other 548

occupations. Each additional individual in a household respect to sample 549

average decreases the probability that an individual will collect NTFPs for 550

sale by a 10% (p<0.05), and for consumption by a 13% (p<0.001). Land 551

tenancy –land patta presence- decreases the probability that an individual 552

would collect NTFPs for consumption by an 83%. Land tenancy is also 553

negatively associated to the collection of NTFPs for sale, but the result is 554

not statistically significant. Household income from non-NTFPs is also 555

negatively associated to the collection of NTFPs, but the coefficient of the 556

association is very low. 557

558

Table 5 also shows that some socio-economic variables are differently 559

associated with the collection of NTFPs for consumption and sale. For 560

example, being a man increases by 34% the probability that a person 561

collects NTFPs for sale (p<0.10) (Table 5, column [a]), whereas the 562

collection of NTFPs for consumption was not affected by sex (Table 5, 563

column [b]). People without employment have a 49% higher probability of 564

collecting NTFP for consumption (p<0.10) than people reported having a 565

non-environmental employment (omitted variable), but employment was 566

not associated to the sale of NTFPs. 567

568

INSERT TABLE 5 ABOUT HERE 569

28

570

In sum, results from probit regression shows that NTFPs collection for sale 571

would be more probably fulfilled by male adults with environmental 572

employment –agriculture, sericulture, or livestock rearing-, from smaller 573

families and shorter time of residency. NTFPs collection for consumption 574

would be more probably fulfilled by adults –women and men- with 575

environmental employment or unemployment, from smaller families, with 576

cattle, and without property rights –land patta. 577

578

The socio-economic predictors of income from NTFP sale and consumption 579

580

Individual level 581

Table 6 shows the results of OLS regressions of individual level variables 582

related to income obtained from NTFPs and socio-economic variables. I 583

found that cash income generated by NTFPs sale (Table 6, column a) is 584

positively associated to sex, environmental employment, and household 585

income. An average man in the sample obtained Rs. 1153.6 more from the 586

sale of NTFPs than an average woman (p<0.001, Table 6, row 2). People 587

with environmental employment earned Rs. 887.5 more than people with 588

non-environmental employment from the sale of NTFPs (Table 6, column 589

[a], row 4). Each additional Rupee of household income increased by Rs 590

0.58 the cash income from NTFPs (p<0.001; Table 6, column [a], row 15). 591

29

NTFPs cash income was also negatively associated to unemployment and 592

household income from non-NTFPs activities. People who reported to be 593

unemployed earned Rs. 1683.7 less by the sale of NTFPs than those people 594

with a non-environmental employment –omitted variable for the analysis- 595

(p<0.001; Table 6, column [a], row 6). Each additional rupee of household 596

cash income from non-NTFPs activities decreases the income from NTFPs 597

sale by Rs. 0.61 (p<0.001; Table 6, column [a], row 14). 598

599

INSERT TABLE 6 ABOUT HERE 600

601

Table 6, column [b], shows the relationships between NTFPs consumption 602

and people’s socio-economic attributes. I found that NTFPs consumption is 603

positively associated to age, sex, environmental employment, and Non-604

NTFP individual income. One additional year of age was associated to Rs 605

2.04 more in the value of NTFPs consumed by an individual (p<0.05; Table 606

6, column [b], row 1). An average man obtains Rs. 147.1 more than an 607

average woman from the consumption of NTFPs (p<0.001, Table 6, column 608

[b], row 2). People with environmental employment earn Rs. 120.3 more 609

than people with non-environmental employment (p<0.001; Table 6, 610

column [b], row 4). Each additional rupee of individual income from non-611

NTFP activities increases the NTFPs consumption in Rs. 0.01 (p<0.05; Table 612

6, column [b], row 8). 613

30

614

NTFPs consumption was negatively associated to time of residency. Each 615

additional year living in the same podu decreases the collection of NTFPs 616

for consumption by Rs. 3.11 (p<0.001; Table 6, column [b], row 12). 617

618

In sum, I found differences in the socio-economic attributes associated to 619

the collection of NTFPs for sale and consumption of NTFPs. Unemployment, 620

household income from non-NTFP activities, and total household income 621

are only associated to NTFPs sale, but not to NTFPs consumption. Age, 622

non-NTFP Individual Income and time of residency were only associated to 623

NTFPs consumption, but not to NTFPs sale. 624

625

In Table 6, column [d], I analyze the associations between the same socio-626

economic attributes and the ratio between cash income from NTFPs and all 627

sources of cash income, as and indicator of NTFPs contribution to 628

individual economy. I found that sex, environmental employment, and time 629

of residency were associated in a statistically significant way with income 630

ratio. For example, men have a 19% higher economic contribution of NTFP 631

in total cash income than women (p < 0.05). People with environmental 632

employment have a 19% higher NTFPs contribution than people with non-633

environmental employment (p<0.10). The model also suggests that an 634

31

increase of one year in the time of residency decreases the NTFPs 635

contribution to individual economy by 1% (p <0.001). 636

637

Household level 638

639

Table 7 shows the results of OLS regressions similar to those presented in 640

Table 6, but at the household level. I found that NTFPs cash income, 641

column [a], was only associated in a statistically significant way to time of 642

residency, total household cash income, and non-NTFP income ratio. One 643

additional year of residency over sample average decreases the NTFPs 644

contribution to familiar economy by a Rs. 25.7 (p<0.10, Table 6, column 645

[a], row 6). An increase of Rupee on the household income increases the 646

NTFPs contribution in Rs. 0.04 (p<0.001, column [a], Table 6, column [a], 647

row 10). The model also suggests that income from non-NTFP activities 648

decreases the NTFPs cash income by Rs. 8091.2 (p<0.001; Table 6, column 649

[a], row 11). 650

INSERT TABLE 7 ABOUT HERE 651

652

Table 7, column [b], shows association between NTFPs consumption and 653

socio-economic variables. I found that NTFPs consumption was only 654

positively associated to household size. An additional individual in the 655

household increases NTFPs consumption by Rs. 88.32. I also found that 656

32

NTFPs consumption were negative associated with age, sex and literacy 657

ratio, and household income. One more woman in the household decreases 658

the NTFPs consumption by Rs. 73.76. An increase in adults in the 659

household decreases the income from NTFPs consumption in Rs 195.43. 660

An increasing in educated adults over sample average decreases the NTFPs 661

consumption by Rs 73.45. 662

663

In sum, Table 7 also shows that none of the socio-economic variable 664

analysed was associated to household NTFPs sale and consumption in the 665

same way. Non-NTFP income ratio is associated only to NTFPs sale. 666

Household income is negatively associated to NTFPs sale and positively to 667

NTFPs consumption. Last, sex, age and literacy ratio were only associated 668

to NTFPs consumption and not to NTFPs sale. 669

670

Table 7, column [d], shows the relationships between contribution of NTFPs 671

to familiar economy and socio-economic attributes. I found that ownership 672

of an additional cow average decreases the contribution of NTFPs to 673

familiar economy by a 10% (p<0.001; Table 7, column [d], row 8). Also, an 674

increase on household income decreases the NTFPs contribution to familiar 675

economy, but coefficient of the association was low. 676

677

33

Discussion and Conclusion 678

679

The associations between people´s socio-economic attributes and the 680

collection of NTFPs offer an interesting framework to understand the 681

consumption and commercialization of NTFPs by rural communities (Hedge 682

et al., 1996; Janse & Ottitsch, 2005; Uma Shaankar et al., 2004; Gavin & 683

Anderson, 2007). Several studies (e.g Hedge et al, 1996; Hedge & Enters, 684

2000; Lacuna-Richman, 2002) have emphasized the need to understand the 685

specific socio-economic conditions of NTFPs collectors to identify the social 686

sectors most dependent on NTFPs. Previous literature had pointed out a set 687

of attributes that influence NTFPs collection for sale, but the attributes 688

associated to NTFPs consumption have been less studied. My study aimed 689

at finding a set of predictors for NTFP collection by Soligas and at 690

uncovering relevant differences between socio-economic attributes 691

influencing the consumption and the sale of NTFPs. 692

693

My findings about people's attributes associated to NTFPs collection mesh 694

with findings from previous research. The main difference between my 695

findings and those of previous literature relate to literacy. A possible 696

explanation for the different finding is that the measure used in this study 697

to proxy literacy –Know how to read- might not be a good approximation to 698

measure education level. 699

34

700

As previous research, I found that Soliga´s participation in NTFPs collection 701

was positively associated with the age and the sex of the individual. Men 702

and older people were more likely to engage in NTFPs collection. I also 703

found that people with jobs related to the environment and unemployed 704

people extracted more NTFPs, as well as people who lived for longer time 705

in the podu. I found a negatively association between NTFPs collection and 706

being student, household size, and land tenancy. The relations found 707

between NTFPs collection and age and being a student suggest that the 708

NTFPs collection among Soligas is principally fulfilled by adults. I also 709

found that men collect NTFPs for commercialization but both, men and 710

women, collect NTFPs for consumption. A possible explanation for that 711

finding relates with the risk associated to the collection of some NTFPs. 712

Morab (1977) explained that Soligas take the risk involved in the collection 713

of some NTFPs (such as honey) because those NTFPs have high prices. Only 714

Soliga men collect products with high risks. Others NTFPs that imply a 715

minor risk and less income are collected by both sexes. Another possible 716

explanation for the finding is simply Soligas division of labor, where men 717

are in charged on NTFps commercialization and woman of domestic labour. 718

Further research about sexual division of labor is necessary for 719

understanding the role of sex in NTFPs collection and, therefore, reliance 720

on NTFPs. 721

35

722

I also found that time of residency is one of two household level attributes 723

positively associated to individual collection of NTFPs. The finding suggests 724

that NTFPs collectors come from families with a larger time in the zone. I 725

can think of two possible explanations for this finding. First, families living 726

for long time in the area might possess a greater ecological knowledge of 727

the zone. For example, families with a long history in the area might know 728

relevant aspects that can improve their effectiveness to collect NTFPs, such 729

as resource sources, access, harvest periods and uses. Therefore, families 730

with a longer history in the area might have a greater availability to collect 731

NTFPs than families of recent arrival (Ladio & Lozada, 2001; Gavin & 732

Anderson, 2007). In the case of Soligas, this interpretation may be 733

supported by research of Uma Shaanker et al. (2004), who found a positive 734

association between local ecological knowledge and the dependence to 735

forest resources. The second plausible explanation for the association 736

between NTFPs collection and time of residency is that families with a 737

longer history of occupation in the area migh rely more in Soliga traditional 738

economy -including reliance on NTFPs collection (Morab, 1977). Those 739

families might have a limited capacity to adopt the new sources of income 740

and goods into their economic system, and might continue with their 741

traditional economy based in the collection –and now commercialization- of 742

NTFPs. 743

36

744

Nevertheless, time of residency had a negative association with the value of 745

NTFPs collected by individuals and household. Keeping in mind that the 746

practice of shifting agriculture and Soligas nomadic behavior were banned 747

when the area was declared as Wildlife Sanctuary in 1972 (Morab, 1977, 748

Murali et al., 1996), possibly Soligas live more time in the same place. So, 749

time of residency value might represent a period of continuous 750

consumption of NTFPs in the immediate surroundings. In this study, NTFPs 751

more consumed by Soligas were fuelwood and honey. Uma Shankar et al., 752

(1998a), in a study focused in fuelwood in BR Hills, points out that most 753

likely the forest land in close proximity to the settlement experiences 754

maximum intensity of collection, with a progressive decline as one moves 755

away from the settlements to the forest. Thus, the negative association 756

between time of residency and consumption of NTFPs might just indicate 757

that families living longer have less NTFPs available to collect. 758

759

Several authors (e.g Hedge & Enters, 2000; Uma Shaanker et al., 2004; 760

Shone & Caviglia-Harris, 2006) have found negative associations between 761

NTFPs collection and other income sources –salaried and wage 762

employment-. The finding has been interpreted as that people preferred 763

other jobs to NTFP collection. Particularly among the Soligas, Hedge et al. 764

(1996) found that an increase in income from non-NTFP sources would 765

37

decrease the income from NTFPs. My analyses indicate a similar pattern 766

with exception of environmental employment. I found that environmental 767

employment influences positively NTFPs collection both for sale and 768

consumption. When Hedge and collaborators analyzed the effect of other 769

sources of income, they grouped all non-NTFP income sources in one 770

category. Contrary to them, I divided between sources of income related to 771

the environment and other sources of income -such as salaried jobs, wage 772

labour, etc-. The activities of the first group imply a greater contact with 773

the environment, and therefore with NTFPs, than the activities in other 774

categories. So, my results suggest that individuals with environmental 775

employment would have more opportunities to collect NTFPs than 776

individuals with jobs unrelated to environment. In fact, in the sample for 777

this study, 88% of the informants that reported collecting NTFPs as 778

secondary occupation, had reported agriculture as their main occupation. 779

780

My results also show that socio-economic attributes of people and 781

households play a different role between NTFPs sale and consumption. At 782

the individual level, being unemployed, non-NTFP household income, and 783

total household income are associated to the cash income from the 784

commercialization of NTFPs, whereas age, Non-NTFP individual income, 785

and time of residency are only associated with NTFPs consumption. The 786

finding suggest that men with environmental employment –agriculture, 787

38

sericulture, or livestock rearing- and with low income from non-NTFPs 788

activities have higher cash income from the sale of NTFPs; whereas the 789

value of NTFPs consumed is higher among elder men, with environmental 790

employment, non-NTFP individual income, and less time on the zone. 791

792

The differences between consumption and sale of NTFPs are clearer at the 793

household level. I found that most of the attributes analyzed affected only 794

the consumption, not the sale, of NTFPs. NTFPs. household cash income 795

was only associated to income from non-NTFP activities. The similar result 796

was found by Hedge et al (1996) in the area. But when the focus was the 797

consumption of NTFPs, I found that households with more adult men, with 798

more educated members, with more members, and with less household 799

income had higher consumption of NTFPs. 800

801

This study confirms that age, sex, literacy, time of residency, employment, 802

and cash income from non-NTFP activities have a high potential for 803

predicting the collection, sale, and consumption of NTFPs. Besides, this 804

study also shows that some of these variables are differently associated to 805

the sale and consumption of NTFPs. These associations are important to 806

understand the different patterns of sale and consumption of NTFPs and, 807

therefore, to understand whether NTFPs may help to achieve both rural 808

livelihood and conservation of biodiversity. The analyses performed here 809

39

allow us to identify the social sectors with a higher economic reliance on 810

NTFPs and those that consume more NTFPs. Indeed, this information would 811

be very useful to strategies for poverty alleviation, social politicies, and 812

management and conservation programs. 813

814

Last, the attributes analyzed here are immersed in a complex web of social 815

interactions. Certainly, other factors are also important in to determine 816

NTFPs collection, such as legal conditions, presence of local institutions 817

that intermediate between collectors and market, social developments, 818

people’s attitudes and values (Janse & Ottitsch, 2005) and, even, ethnicity 819

(Uma Shaankar et al., 2004). So, further research is need about the factors 820

that influence reliance of NTFPs and how these factors are immersed in the 821

web of interactions. 822

823

40

Acknowledgements 824

Data for this paper was gently provided by Dr. Kamal Bawa and Dr. Sharad 825

Lele and was collected as part of a collaborative project at ATREE (Web 826

page, India). I am very gratefull to all them. I am also grateful to Victoria 827

Reyes-García for her valuable comments and academic support. I am 828

grateful to Loreto Fuenzalida for her comments. I thank financial support 829

for the scholarship “Presidente de la República” from Chilean state. 830

41

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927

46

Legend Figure 928

Figure 1. Study site, Bigiliri Rangaswamy Temple (BRT) Wildlife Sanctuary. 929

Modified from Krishnaswamy et al., 2004. 930

931

47

Figure 1 932

933

48

Tables 934

Table 1. Non timber Forest Products collected by Soligas’ households at BR Hills (Feb 1996-Feb 1997). Monetary units

in Indian rupees (US $1= Rs 35.7 in 1996)

NTFP Scientific name Utilization N Selling

unit

Annual

Quantity

(X±SD)

Selling

Price

(X±SD)

Annual Income

(X±SD)

Annual

Contribution

to income, %

(X±SD)

Dhoopa Botswelli serrata Sale 1 Kg

10 10 100 1.2

consume - - - -

Nelli’s fruit Phyllanthus

embica

Sale 64 Kg

198.9±205.2 3.1±0.6

545.2±441.3 6.6±5.0

Consume - - - -

Antuvala Sapindus

laurifolius

Sale 24 Kg

23.25±18.5 7.5±5.1

162.0±99.2 1.8±1.6

Consume 4 2.5±1.3 10.0±5.1 0.1±0.1

Broom Sale 5 Unit

44±20.7 1.4±0.4

59.6±27.2 0.75±0.43

Consume - - - -

49

Honey Apis dorsata, A.

floria and

Serana indica

Sale 57

Kg

35.7±27.9

24.4±1.7

866.9±674.9 10.6±6.9

Consume 6 6.3±9.2 156.4±230.9 3.2±4.6

Lichens Sale 72 Kg

107.4±57.8 21.4±1.9

2285.2±1229.1 27.4±16.4

Consume 1 3 67.5 1.2

Sige Acacia sinuata Sale 3 Kg

8.6±11.5 3.6±0.6

27.3±33.4 0.4±0.5

Consume - - - -

Fuelwood - Sale 19 Nro of

headloads

16.5±14.9 13.9±3.8

240±241 2.5±2.9

Consume 154 33.4±15.4 458.0±236.9 5.9±5.2

Seed - Sale 2 Kg

3.5±0.7 4 14.0±2.8 0.2±0.0

Consume - - - -

Tubers - Sale 1 Kg

20 4 80 0.3

Consume 17 5.8±2.8 14.6±8.3 0.1±0.1

Barks Sale 8 Kg

44.5±31.0 3.7±5.1

121.3±111.0 1.2±1.5

Consume - - - -

50

Magaliberu Decalepis

hamiltonii

Sale 18 Kg

220.1±138.8 3.8±0.5

847.9±582.6 7.2±5.9

Consume - - - -

Ginger Sale 5 Kg

6.4±7.7 3.8±0.7

19.8±17.3 0.2±0.2

Consume - - - -

Fish - Sale 11 Kg

345.4±445.8 12.8±5.5

4258.5±5345.2 28.3±29.8

Consume - - - -

Others - Sale 4 -

37.5±28.4 2.5±0.01

93.5±71.2 1.1±0.7

Consume - - - -

935

51

Table 2 Dependent variables used in the regression analysis. Income in Indian rupees (US $1=35.7 Rs. at 1996

year)

Individual Household

(n=143)** Variable Definition All participants

(n=2513)*

NTFP collector

(n=328)**

NTFPs Cash

Income

Monetary earning by sale of NTFPs 146.3 ± 840.9 837.7±1670.6 1925.4±2229.6

NTFPs

Consumption

Monetary equivalence of consumed

NTFPs.

186.4 ± 938.9 197.2±189.1 453.3±234.2

NTFP total Income Total earning by sale and

consumption of NTFPs

31.9 ± 107.9 1083.9 ±

1849.6

2491.1±2389.4

NTFP Income Ratio Ratio between NTFP cash income and

total income by all economic activities

0.198±0.303 0.248±0.265

Notes: *includes all participants in the study. **Includes only the sample of people who reported the collection

of some NTFPs through the year.

936

52

Table 3 Definition and summary statistics of explanatory variables to individual level used in regression analysis.

Mean± SD

Variable Definition All participants Collectors

Age Age of subject in years 25.2± 15.9 32.6± 13.4

Sex Sex of subject (1= male; 0= female) 0.51± 0.49 0.55± 0.49

Literacy Know how to read (1=yes; 0=not) 0.57± 0.49 0.45± 0.49

Non-NTFP

Individual Income

Individual’s monetary earning by non-NTFP

activities, in indean rupees

496.1± 2063.3 2527.4± 3427.4

Employ Occupation of subject

Environmental Occupation related with environment (1=yes;

0=not)

0.29± 0.46 0.56± 0.49

Non

environmental

Occupation unrelated with environment (1=yes;

0=not)

0.12± 0.33 0.07± 0.26

Unemployed Subject without employ (1=yes; 0=not) 0.25± 0.43 0.30± 0.45

Student Student subject (1=yes; 0=not) 0.28± 0.45 0.06± 0.23

53

Table 4 Definition and summary statistics of explanatory variables to household level used in regression

analysis.

Variable Definition Mean± SD

Age Ratio Young and adult ratio 0.43± 0.49

Sex Ratio Male and female ratio 1.42± 0.97

Literacy Ratio Ratio of adults with and without literacy 0.61± 0.72

Non-NTFP Income ratio Non-NTFP and total income ratio 0.75± 0.26

Household income Household’s total income by all economic activities, in indean

rupees

7252.7± 6502.2

Salaried Adults Number of salaried adults 0.06± 0.27

Household size Number of members in the house 4.48± 1.45

Time of residency Time of the household in the podu, in years 35.0± 13.8

Land ownership Presence of legal document that certifies land ownerships land in

the ownership (1=yes; 0=not)

0.85± 0.35

Cattle presence Presence of cattle in the ownership (1=yes; 0=not) 0.55± 0.49

54

Land tenancy Property right title (Patta), (1=yes; 0=not) 0.51± 0.50

Land holding Surface of land holding, in acres. 1.72± 2.08

Cattle Number of cow 1.94± 2.83

937

938

55

Table 5 Socio-economic predictors of NTFP collection. Values are Maximum

Likelihood coefficient estimates.

[a] [b] [c]

Explanatory variables Cash income

(R2=0.47)

NTFP

consumption

(R2=0.57)

NTFP total

Income

(R2=0.58)

Individual

Age 0.008* (0.01)

0.01** (0.004)

0.01** (0.004)

Sex 0.34* (0.19)

-0.07 (0.16)

-0.023 (0.15)

Literacy -0.127 (0.15)

-0.18 (0.14)

-0.16 (0.14)

Employment

Environmental 0.56*** (0.21)

0.65*** (0.21)

0.60*** (0.21)

Unemployed -0.09 (0.28)

0.49* (0.27)

0.49** (0.25)

Student -0.81*** (0.29)

-1.08*** (0.27)

-0.90*** (0.58)

Non-NTFP Individual Income <0.00*** (0.00)

<0.00*** (0.00)

<0.001*** (0.00)

Household

Household size -0.10** (0.04)

-0.13*** (0.03)

-0.123*** (0.06)

Land ownership -0.04 (0.24)

0.16 (0.23)

0.08 (0.23)

Cattle presence 0.10 (0.12)

0.25** (0.11)

0.34*** (0.11)

Time of residency 0.02*** (0.01)

0.05*** (0.01)

0.05*** (0.02)

Land tenancy -0.22 (0.28)

-0.83*** (0.25)

-0.70*** (0.50)

56

Non-NTFP household income -<0.00*** (0.00)

-<0.00*** (0.00)

-<0.001*** (0.00)

Total Household income <0.00*** (0.00)

<0.00*** (0.00)

<0.001*** (0.00)

Notes: Standard errors are in parenthesis. *, **, and *** Significant at 90, 95,

and 99% confidence level. Regression contains a set of variables for podus

and a constant (not shown). For definition of variables see Tables 3 and 4.

939

57

Table 6 Socio-economic predictors of individual income from NTFPs sale, consumption, total income, and

income ratio. Regressions are Ordinary Least Square.

[a] [b] [c] [d]

Cash income NTFP consumption NTFP total

Income

Income ratio

Explanatory variables R2=0.10 R2=0.53 R2=0.65 R2=0.32

Individual

Age 14.09 (9.08)

2.04** (0.84)

11.09* (6.55)

0.00 (0.00)

Sex 1153.6*** (345.4)

147.1*** (22.5)

938.5*** (168.53)

0.19** (0.09)

Literacy -269.6 (243.7)

-25.1 (21.1)

-303.6 (193.02)

-0.09 (0.06)

Employment

Environmental 887.5** (351.3)

120.3*** (32.4)

1157.5*** (310.6)

0.19* (0.10)

Non Environmental 67.5 (47.5)

131.4 (354.0)

Unemployed -1683.7*** (498.3)

58.4 (41.2)

-619.2* (357.1)

-0.05 (0.14)

Student -838.0 (570.6)

0.24 (0.15)

58

Non-NTFP Individual Income -0.03 (0.03)

0.01** (0.01)

-0.05** (0.02)

Household

Households size -39.0 (76.1)

1.34 (5.9)

-37.1 (51.7)

-0.01 (0.01)

Land holding -13.1 (98.0)

-0.37 (7.6)

13.0 (50.4)

-0.01 (0.02)

Cattle -37.5 (39.0)

-2.32 (1.93)

-7.69 (12.7)

-0.01 (0.01)

Time of residency -16.6 (12.7)

-3.11*** (1.13)

-11.6 (19.92)

-0.01*** (0.00)

Land tenancy -184.0 (385.2)

-33.23 (39.0)

-557.8 (401.1)

0.02 (0.10)

household Non-NTFP income -0.61*** (0.05)

-0.01 (0.01)

-0.47*** (0.11)

Household income 0.58*** (0.05)

-0.01 (0.01)

0.46*** (0.11)

59

Table 7 Socio-economic predictors of household income from NTFPs sale, consumption –equivalent

income-, total income, and income ratio. Values are Ordinary Least Square.

[a[ [b] [c] [d]

Cash income NTFP consumption NTFP total Income Income ratio

Explanatory variables R2=0.80 R2=0.47 R2=0.82 R2=0.57

Sex Ratio -54.3 (91.99)

-73.76*** (22.7)

-132.7 (94.9)

0.02 (0.01)

Age Ratio 52.8 (198.4)

-195.43*** (49.1)

-159.1 (193.1)

-0.00 (0.03)

Literacy Ratio 43.9 (121.9)

-73.45*** (22.9)

-28.8 (128.0)

-0.00 (0.02)

Household size 55.2 (58.6)

88.32*** (16.2)

148.3** (62.1)

0.01 (0.01)

Salaried Adults -80.8 (374.8)

65.72 (83.5)

-20.2 (411.6)

-0.06 (0.08)

Time of residency -25.7* (13.9)

-0.78 (1.8)

-28.0** (13.5)

0.00 (0.00)

Land ownership -314.8 (409.3)

-90.37 (55.9)

-385.8 (419.2)

0.07 (0.06)

Cattle presence -42.6 (182.6)

-28.12 (38.4)

-94.3 (194.8)

-0.10*** (0.03)

Land tenancy 132.0 (261.4)

11.68 (52.0)

149.8 (272.5)

-0.10* (0.05)

Household income 0.04*** (0.01)

-0.01*** (0.00)

0.04** (0.02)

-0.00*** (3.0e-06)

60

Non-NTFP Income ratio -8091.2*** (775.0)

37.81 (106.2)

-8351.8*** (750.7)

940