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Conservation and Society 11(4): 375-390, 2013
Article
Tiger, Lion, and Human Life in the Heart of Wilderness: Impacts of Institutional
Tourism on Development and Conservation in East Africa and India
Nilanjan Ghosha,# and Emil Uddhammarb
Multi Commodity Exchange of India Limited, Mumbai, India
a
Department of Government, Uppsala University, Uppsala, Sweden
b
#
Corresponding author. E-mail: [email protected]
Abstract
This article tests the hypothesis on whether tourism is an important institutional factor in reconciling the conflicting
goals of conservation and development. The study entails data from field surveys across protected areas including
the Serengeti National Park and the Ngorongoro Conservation Area in northern Tanzania, and the Corbett National
Park in northern India. With human development defined in terms of ‘stages of progress’ (SOP) delineated by
the respondents themselves, the study finds indicative evidences of the validity of the posed hypothesis in the
two nations, in varying proportions. Factors not related to tourism, like incomes from livestock, have affected
development in Tanzania, though not in India.
Keywords: human development, stages of progress, conservation, tourism, community, Serengeti National
Park, Ngorongoro Conservation Area, Corbett Tiger Reserve
INTRODUCTION
The apparent conflict between conservation and development in
and around the protected areas of the developing world arises as
the poor in those areas are reliant on forest resources (Dewi et al.
2005; Chan et al. 2007; Torri and Herrmann 2010). This leads
to a decline in forests, much to the detriment of both flora and
fauna. Man-animal conflict is also a special feature in these parts
of the world. Wild animals cause losses to property, cattle, and
even human life. Hence, in most cases, the human habitat in and
around wilderness does not hold a very kind opinion about the
wild predators. In most cases in the developing nations, protecting
biodiversity has resulted in a shrinkage of traditional economic
opportunities for the local population due to ensuing restrictions
on cattle ranching, farming or fuel wood collection. People
have often been evicted altogether from the protected areas
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(Uddhammar 2006; Schmidt-Soltau 2010), thereby aggravating
the conflict between conservation and traditional economic
activities (Uddhammar and Ghosh 2009). This necessitates
innovative thinking on new institutional arrangements that
could reconcile conservation and development, and, in the best
of worlds, make them benefit from each other.
However, the possible existence of a symbiotic relationship
between humans and forests has been a matter of debate among
scholars. One school strongly believes that forest resources
can be put to use to help improve the livelihoods of the poor
(Scherr et al. 2002; Dewi et al. 2005). There are others who
believe that forests can provide only limited opportunity
to contribute to poverty reduction (Wunder 2001). Part of
the discrepancy between the conflicting views originates
from the difference in assumptions about the institutional
mechanisms for creating new opportunities for rural people
to take advantage of forest resources (e.g., Sunderlin et al.
2005). Publications by several researchers like Agrawal and
Clark (2001), Anuradha et al. (2001), Borrini-Feyerabend et
al. (2003), and Greiber (2009) advocate specific institutional
mechanisms to reverse the trade-off between conservation and
development. An important entry-point of this article lies with
an attempt to understand the nature of the impact of such a
specific institutional mechanism as the exogenous stimulus on
the endogenous conservation-development dynamics.
Copyright: © Ghosh and Uddhammar 2013. This is an open access article distributed under the terms of the Creative Commons Attribution License, which
permits unrestricted use and distribution of the article, provided the original work is cited.
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376 / Ghosh and Uddhammar
In this article, tourism is hypothesised as an important
institutional variable affecting the trade-off between conservation
and development. Duffy (2002) and Vanasselt (2003) feel that
unregulated tourism can bring about major environmental losses,
with marginal financial gains. Fennell (1999), Wearing and Neil
(1999), and Ulfstrand (2003) are, however, optimistic. Kiss
(2004), Zapata et al. (2011), and Uddhammar (2006) emphasise
the need for necessary institutional structures for success of
community-based tourism1 in promoting the dual objectives.
We draw our hypothesis from this ongoing debate in
international literature, and pose it as: tourism as an institutional
intervention can reverse the trade-off between conservation and
development, thereby generating employment and income in
the sector. In order to test this hypothesis, we have conducted
surveys in Serengeti National Park (NP) and the Ngorongoro
Conservation Area (CA) in northern Tanzania, and the Corbett
Tiger Reserve in northern India. Eventual analysis has been
carried out on the basis of primary data (mostly based on
perception), as also some related secondary information. So
far, despite the raging international debate, there are hardly any
studies that test such a hypothesis for the developing world in
two completely contrasting settings, which would lend further
applicability to the posed hypothesis. An important aspect is
the methodological issue, where we define development from
a local well-being perspective, following Krishna (2004a, b),
and conservation on the basis of a composite sighting index.
Such a methodology has not been adopted so far in order to
test this hypothesis—this is an important contribution of this
article to the literature base.
Apart from the methodological perspective, the contribution
of this article to the literature is also the perspective it provides
from its departure from neoclassical valuation frameworks based
on which often such analyses are carried out (e.g., Beharry
and Scarpa 2009; Guha and Ghosh 2009; Lange and Jiddawi
2009, among others). Here, the assessments of two comparable
institutional frameworks have been conducted taking into
consideration how institutional arrangements and tourism as a
critical variable affect two target variables like conservation and
development, in a social-ecological system (SES).
Selection of the study areas
The idea here was to find well-known and frequently visited
tourist destinations in developing countries with prevalent
nature-related tourism. If tourism benefits generated by
global nature-related tourism trickle down to the local human
population, it should be visible in these areas. Secondly, in
many of these areas the pressure from a growing human
population constantly threatens the biotopes and species of
remaining wildlife. By covering protected areas in settings
with different cultural and institutional backgrounds, the aim
here was to reveal patterns that are of general applicability.
East Africa and India have some of the most widely known
and precious inheritance of biodiversity on Earth. The unique
fauna of India includes elephant, tiger, gaur, and other large
mammals, while the unique variety of large mammals in East
Africa (lion, elephant, zebra, etc.) is no less renowned. At the
same time, the poverty of the rural human population adjacent
to the protected areas in these regions is often striking. Selecting
protected areas in these countries provides the opportunity
to examine whether protected areas with a strong capacity
for tourism really can make a difference to the well-being of
the people surrounding them, and also study the consequent
impact of wildlife on human economy. Tourism in East Africa
fluctuates between being the most important and the secondmost important export product, and the safari destinations in the
region are world-renowned. Around 90 % of travellers to East
Africa are foreign tourists. The Serengeti-Ngorongoro zone in
Tanzania is a typical case representing this phenomenon of high
international tourism, as also the case of critical livelihoods of
locals being linked to the tourism economy.
India is interesting not because international tourism to
protected areas is conspicuous—international tourists comprise
only 20 % of the total number of visitors; 80 % is domestic
tourism, mostly from urban elites (Uddhammar 2006)—but
because it has a conspicuous biodiversity that is both well
known, to a large extent red-listed, and under severe pressure. In
India, Corbett Tiger Reserve in the northern part of the country
was chosen for the study. Interestingly, although most tourists
come from within the country, more than half the total revenue
derives from foreign tourists. Thus, the global connection
with the Corbett park is quite strong (Uddhammar 2006).
Again, with a majority of employees in the camps and the park
being recruited locally from the region, the local connection
is also highly prevalent. Tourism, as such, is still emerging
in the region, and has advanced only in the new millennium
(Uddhammar and Ghosh 2009). Therefore, the two cases
from developing economies offer some interesting features to
compare and contrast in the context of the hypothesis posed.
The article is divided into seven sections. In the second
section, the hypothesis is explained in the context of socialecological systems (SES) (Ostrom 2005, 2007). In the third
section, the study sites are described in light of the variables
described in SES. The fourth section briefly talks about the
methodology used. In the following section, we present some
descriptive statistics on the ‘stages of progress’ (SOP) which
delineate development in this context. Since development
and poverty have been defined by the respondent community,
this also speaks a lot about the existing culture, tradition, and
social norms of the community under consideration. It is in
this context that we would like to declare that no gender-based
distinction has been made in this article, and the information
has been reported as obtained from the field. The sixth and
the seventh sections report the results of the Indian case and
the Tanzanian cases, respectively. Finally, we end with the
concluding remarks.
A SOCIAL-ECOLOGICAL SYSTEM: HUMAN
WELL-BEING AND WILDLIFE CONSERVATION
The hypothesis can be posed in the framework of the socialecological system (SES), to better understand the interactions
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Tiger, lion, and human life in wilderness / 377
between the properties of the ecosystems and the actions of
human societies. In a given social-ecological system, one can
identify a number of variables (Ostrom 2009), as presented
in Figure 1.
In Figure 1, we can see that in the formalised flow of
influence and use, the users’ use of resource units is the core
activity affecting the outcomes, which provides feedback to
the resource system (the ecosystem) via the outcomes. The
dynamic part, i.e., the ‘interactive process,’ is represented
by the arrow going from the users through the resource units
resulting in the outcomes. By internalising the SES presented
in Figure 1 in the context of this study, it may be noted that
a special feature of the resource systems studied here is that
they are inhabited by dangerous wildlife, that every year kill
a number of people, cause damage to livestock, and destroy
crops, and hence are not popular among the local human
population. This makes these social-ecological systems unique
and critical in the sense that institutions need to be developed
to protect human lives and livelihoods.
From the SES perspective, the resource units in the
Serengeti regions are characteristically almost identical to
those of the Corbett Tiger Reserve. Typically, they involve
humans, wildlife, tourists, and NGO groups. The Serengeti
and Ngorongoro conservation zone is one of the earliest
established national parks in sub-Saharan Africa. The late
1950s witnessed the excising of Ngorongoro from Serengeti
National Park, proposed as a measure to accommodate the
interests of the Maasai pastoralists. From an ecosystemic
perspective, however, they can be considered to be integral
components of the same ecosystem.
The dynamic interactive processes of the users (which,
in this case, are the communities and the tourists), with the
governance systems and the fauna species, result in outcomes
related to the dynamic interrelationship between the dependent
variables, fauna conservation, and human development. An
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Figure 1
A formalised social ecological system (SES)
The main dependent variables are found in the ‘Outcome’ square, while the
‘Governance system’ and the ‘Resource system’ squares contain the main
independent variables. The ‘Resource units’ are the units to be measured,
and the ‘Users’ are the stakeholders involved. The resource system (a
local ecosystem) influences the resource units (the kind of units, such as
farm products, wildlife, etc.) that can be used. Dotted arrows represent
indirect influence (or feedback), while the solid arrows represent causal
mechanisms.
important part of these is the exchange between users, resulting
in effective selling of resource units. This market exchange is
essentially tourism, where tourism service providers have to
‘sell’ the services along with the sightings of wild animals,
which are a major attraction of these protected areas.
The interactive processes are affected by governance
systems. In the core zone of the Corbett Tiger Reserve,
mandatory tourist guides are recruited locally from among
villagers, and this arrangement has many advantages. While
on the one hand, local people get employment and training, on
the other, the community also gets a clear signal that wildlife is
an asset to be conserved. Uddhammar (2006: 672) also notes
that further efforts by the Ramnagar municipality and mayor
to create the image of ‘tiger city’ have played a big role in
increasing local awareness and appreciation of the park.
In Tanzania, access to the protected areas is curtailed,
and penal clauses exist on infringement (Robinson 2011).
However, there are game reserves where licensed hunting
takes place. Tourist hunting in Tanzania is regulated by the
central government with little local input into quota-setting,
block allocation, or management (Leader-Williams et al. 1996).
Revenues go to the central government with a proportion
(approximately 20%) returned to the district councils in areas
where hunting occurs.
Governance systems, on the other hand, have affected the
interactive processes between resource units. Though humanwildlife conflicts in the Serengeti have been a traditional
phenomenon, communities feel that most of these conflicts
emerged as a result of wild animals being accorded a higher
priority than human beings (Kideghesho 2010). However,
as reported by Robinson (2011), that perception has been
changing over the last few decades. While local communities
have been actively involved in providing tourism services,
there has also been a recent plan to establish Wildlife
Management Areas (WMAs) in the buffer zones surrounding
Serengeti National Park, out of which numerous benefits for
the local communities can be envisaged in the form of tourism
incomes and conservation (Kideghesho 2010: 240). This
adds a distinctive dimension to the interactive process at the
‘resource system,’ where a ‘conflictual’ interaction between
two critical resource units has been attempted to be transformed
to a ‘symbiotic’ interaction, through conscious government
policy measures.
Our interest here is primarily to explore if the interactions
between resource units are mainly ‘symbiotic’ or ‘competitive’
(Ostrom 2007, 2009). For this purpose, ‘output’ (in terms of
the SES) has been measured in four different ways. They
are: 1) ‘stages of progress’ (SOP) out of (or into) poverty for
communities around conservation areas based on primary
data (Tables 2 and 3; regressions looking at specific factors
behind this movement presented in the section ‘Measurement
of conservation’ below; and in equations 4–10); 2) employment
in the tourism sector based on primary data (Tables 4 and 7);
3) biodiversity (Tables 5 and 6); and 4) the coexistence of
tourism and wildlife (Tables 5 and 6) based on secondary and
primary data collected by us.
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378 / Ghosh and Uddhammar
Table 1
Overview of definitions of poverty and definitions of stages out of poverty as defined in group discussions in villages and towns in Tanzania and India
Poverty
First stage out of poverty
Second stage out of poverty
Tanzania
Low income, i.e., less than
Land; ability to feed family; ability
2 tractors; more than 4 ha of land,
Serengeti National
1,000 Tanzanian Shillings (TSH)
to send children to school; house
brick house; 50 livestock; 5-7 wives;
Park and Ngorongoro
per day (0.84 USD); bad
with corrugated iron sheets for roof;
more than 40 children; (particularly
Conservation Area
housing (grass roof); none or less
4 oxen; 5-10 diary cows; one wife;
daughters, who have value, and can be
than 0.4 ha of land; no children;
few children (5-10); radio (in the
‘sold’ to purchase cows); ability to buy
no wife; no livestock
case of towns)
clothes (in the case of towns); enough
capital to start a small business (in the
case of towns)
India
No livestock or only one; no land;
Can afford to hire tractor; owns a
Electricity in house; sends children for
Corbett National Park
no house; no electricity; no job or
few milch animals; owns bullocks;
higher education; owns a television
income of INR 60 per day (USD
earns between INR 70 and 135 per
set; owns a dish antenna; has water
tank in house; has solar panels; has
1.33); no medical service
day as income (USD 1.5-3); 2-3
a pucca (brick) house; has 2-4 ha
members of family are government
employed; possesses a house;
of land; can repay debts; owns a
possesses land<2 ha; can afford food
motorcycle
for three meals per day; can provide
for school
Source: Primary survey
Table 2
‘Stages of progress’ for households around Serengeti NP and
Ngorongoro CA in 2007 as compared to 1997
Stages of
Stages of progress for households in Total (%)
progress for
2007 (%)
households in
Low
Middle
High
1997 (%)
Low (63.1)
43 (24.2)
132 (74.1)
3 (1.7)
178 (100)
Middle (36.2)
37 (36.3)
51 (50.0)
14 (13.7)
102 (100)
High (0.7)
0 (0)
2 (100)
0 (0)
2 (100)
Total (100)
80 (28.4)
185 (65/6)
17 (6.0)
282 (100)
Percentages of each row within brackets. In the far left column, column
percentages are presented within brackets
N.B.:
1. Low refers to ‘poverty’; Middle refers to ‘first stage out of poverty’; High
refers to ‘second stage out of poverty.’
2. Out of the 300 respondents, 282 respondents provided valid responses
(responses like “cannot answer” are not valid responses) of their present
state, and the state that they were in around 10 years ago.
Source: Primary survey
Table 3
‘Stages of progress’ for households around Corbett NP in 2007, as
compared to 1997
Stages of
Stages of progress for household in Total (%)
progress for
2007 (%)
household in
Low
Middle
High
1997 (%)
Low (30.9)
8 (13.6)
15 (25.4)
36 (61.0)
59 (100)
Middle (51.8)
1 (1.0)
65 (65.7)
33 (33.3)
99 (100)
High (17.3)
1 (3.0)
8 (24.2)
24 (72.7)
33 (100)
Total (100)
10 (5.2)
88 (46.1)
93 (48.7)
191 (100)
Percentages of each row within brackets. In the far left column, column
percentages are presented within brackets
N.B.
1. Low refers to ‘poverty’; Middle refers to ‘first stage out of poverty’; High
refers to ‘second stage out of poverty’.
2. Out of the 196 respondents, 191 respondents provided valid responses of
their present state, and the state that they were in around 10 years ago.
Source: Primary survey
Table 4
Socio‑economic profile of lodges in and around Corbett National
Park, India in 2007
Socio‑economic features of lodges
Stratified sample
15 (population size=25)
Number of people employed in tourism
570
sector in area
Number of people earning livelihood
2,964
from tourism sector in the area*
Percentage of the employed belonging to
59
the region
Percentage of managers belonging to the
52
region
Percentage of foreign tourists
7-10
Source: Survey results; *World Bank 2006
Table 5
Changes in wildlife and local human and tourism populations in the
Corbett NP area
Factor change
Correlation
1987-2007
with tourist
visitors to
Corbett NP
1987-2006
Human population*
1.2
Tourist visitors
4.5
Elephant (Elephas maximus)
3.9
Tiger (Panthera tigris)
2.1***
0.738**
Sambar deer (Cervus
2.0
unicolor)
Cheetal deer (Axis axis)
1.7
*Garhwal district, Uttarakhand; average change 1981-1991 and 1991-2001;
**Significant at 0.01 level;
***1987-2006; Source: WII 1999; Jhala et al. 2008; NP=National park
A BRIEF DESCRIPTION OF THE STUDY AREAS
The Serengeti ecosystem encompasses the 14,800 sq. km
Serengeti National Park as well as game reserves surrounding
the NP such as Grumeti, Maswa, Ikorongo, and Kijereshi, and
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Table 6
Changes in wildlife, livestock, local human, and tourist
populations in the Serengeti‑Ngorongoro ecosystem, and their
correlations (Pearson’s r)
Population
Factor
Correlation with
change
tourist visitors to
1997-2006
area 1988-2004
Local human
1.52
population (Ngorongoro)
Livestock (Ngorongoro)
1.35
Tourist visitors (Ngorongoro)
1.23*
Tourist visitors (Serengeti)
1.01*
Elephant (Loxodonta
1.81
0.826**
Africana) (Serengeti)
Buffalo (Syncerus
1.0
caffer) (Serengeti)
Lion (Panthera leo) (Serengeti)
1.41
0.658***
N.B: correlations were calculated for 1988 to 2004, during which period
wildlife numbers have fluctuated;
*Between 1994 and 2004, the factor change is merely 1.07 due to a sharp
drop in visitor numbers after terror bombings in the US in 2001. However,
a long‑term trend from 1966 to 2004 shows a factor change of almost 5 in
Serengeti, and almost 7 in Ngorongoro.;
**Significant at 0.05 level;
***Significant at 0.01 level; Source: Ottichilo 1999; Reid et al. 2003
Table 7
Profiles of lodges surveyed
Socio‑economic features of lodges
Serengeti NP/
Ngorongoro CA
Stratified sample
13 (population size=23)
Number of people employed in sector
1,650
in area
Number of people earning livelihood
8,085
from sector in area*
Percentage of the employed belonging
64
to the region
Percentage of managers belonging to
20
the region
Percentage foreign tourists
87
Source: Survey results; *World Bank 2006
NP=National park; CA=Conservation area
open areas/community lands. The Ngorongoro Conservation
Area covers an area of 8,300 sq. km. The Maasai Mara
National Reserve in Kenya is also a part of this ecosystem
(Figure 2). Many villages outside the Serengeti National Park
participate in the community-based conservation programmes.
The National Park allocates up to 7% of its budget to support
projects identified by villagers surrounding national parks.
This offers good opportunities to study the long-term effects
on biodiversity as well as on human development in the area.
The Corbett NP (Figure 3) is located 250 kilometres
northeast of Delhi and close to the city of Ramnagar in the state
of Uttarakhand (formerly called Uttaranchal, when the survey
was conducted). The park was created in 1936 and today has
a total area—including buffer zones—of about 1,318 sq. km.
Human habitation is not allowed in the major core zone but
some settlements exist in the surrounding buffer zone. The park
is owned by the Uttarakhand state government and managed
by the Uttarakhand Forest Department.
Source: Emerton and Mfunda 1999
Figure 2
Map of the Serengeti–Mara ecosystem, including the Tanzanian game
reserves where, except in Ikorongo and Grumeti, licensed hunting
takes place
Situated on the foothills of the Himalayas, Corbett NP is
widely renowned as a tiger reserve with a rather successful
history of conservation and natural resource management. The
national park’s institutional history draws from varied sources:
the legacy of the colonial forester and conservationist Jim
Corbett, international initiatives to save the tiger in the 1970s,
the Indian government’s national level conservation programme
through Project Tiger, the history of the Forestry Civil Service,
and the interventions of various NGOs. The Corbett National
Park and the Sonanadi area were included in the Corbett Tiger
Reserve in 1991 (WII 1999). While community-based tourism
initiatives and lodges were developed in the zone, most of the
developments have occurred in the new millennium.
Corbett TR presents a unique case where the community’s
relationship with the government (or forest department) has not
been uniform. During our study, we found that in some of the
villages where tourism had developed (e.g., Bhakrakot), the
relationship seemed quite cordial, while tensions were prevalent
in others (e.g., in Laldhang, with respect to relocation).
AN OVERVIEW OF THE METHODS
A strategic method was used in selecting the villages for
interviews and data collection. For each area, we selected some
(two or three) villages close to the protected areas within the
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380 / Ghosh and Uddhammar
Source: http://www.corbett-national-park.co.in/corbett_national_park_map.html
Figure 3
A map of the Corbett Tiger Reserve
tourism ‘circuit’ (zone where tourism was more prevalent than
in others), and some a considerable distance away from the
protected area. We also selected two neighbouring towns, one
within and one outside the tourism circuit. The differentiation
between towns and villages was done based on the definition
provided by Census of India 2001. In Tanzania, the definition
of a town, as distinguished from a village, was obtained from
the 2002 Population and Housing Census. Generally, towns
are distinguished from villages on the basis of administrative,
demographic, and infrastructural characteristics, and hardly
on the basis of occupational patterns or dominance of the
agricultural sector. The control cases for towns and villages
were provided by those that were outside the tourism circuit.
This helped us to compare and contrast between regions with
and without tourism and determine the exact impact of tourism
on conservation and human development2.
Selection of households within each zone of villages/towns
was done on the basis of complete enumeration or random
(or systematic) sampling in cases where the total number of
households was not too large. In cases of very large populations
across large areas, stratification in terms of localities was created,
and then random (or systematic) samples were drawn (see
Appendix 2 for details). In total, we interviewed 300 households
in Tanzania, and 196 households in India. In both places, the
data were collected during January-March 2007. We developed
various indices as and when required, and econometric techniques
were used to test for the relationship between variables.
Development defined in terms of ‘stages of progress’
matrix
Human development has been defined in the analysis through
data collected from a ‘quasi-longitudinal’ survey, following the
method used by Krishna (2004a) in villages in Rajasthan, India,
to assess who escaped poverty, who became poor, and why.
Part of the method was to let the villagers themselves define
poverty in preliminary discussions of ‘stages of progress.’ This
was delineated by change in living conditions, and was defined
in a two-step process. In the first stage, in each of the villages
and towns selected around the protected areas, we assembled
around 8–10 people for a presentation of our purposes and to
provide them with information about the subsequent surveys.
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Tiger, lion, and human life in wilderness / 381
We also had a focus group discussion with these respondents
to find out their definition of poverty, and the first stage out
of poverty. Then, we enquired about the next step ‘upwards’
in development.
With these results as a base, we formed three Weberian ideal
types3 (but with mutually excluding categories for each item)
that were presented to the informants as part of the survey
conducted a few months later in the respective sites. This
entailed the second stage of the field survey. Even though the
definitions differed somewhat, the important thing here is that
each community had the opportunity to define poverty and the
steps for moving out of poverty themselves.
During the field survey that followed, we asked each
respondent which of the three stages fitted his/her family’s
living conditions: 1) 10 years ago; and 2) at the time of the
interview. In doing so, we created what we call a quasilongitudinal measure of the respondent’s living conditions.
From this data, a matrix of various positions of development
could be constructed with movements from the possible
positions 10 years ago to the present (see Appendix 1 for
details).
Measurement of conservation
To measure conservation, on the other hand, a composite
index was devised. This index measures wildlife sightings
by respondents. To examine whether the sightings of some
critical fauna had changed, respondents were asked whether
the sightings of certain species (decided in consultation with
the forest department, existing literature, and knowledgeable
persons from the field) had increased, remained the same,
or had diminished over time. A rating of +1 was given if the
sighting had ‘increased’, 0 for ‘no change’, and -1 if the sighting
had ‘decreased’.
We constructed a ‘fauna sighting change index’ based on
weights given to each of the species and the score given by a
respondent in terms of change in sighting. The weights were
decided in consultation with the abovementioned stakeholders,
taking into consideration the ‘rarity’ aspect of the species, and
their importance in the context of tourism. In that sense, this
could be considered as ‘informed arbitrariness’ with which
the weights in India (as also in Tanzania) were assigned
(see Appendix 3). Hence, a positive value of the index is an
indicator of the increase in sighting, while a negative value
is an indicator of a decline in the same. This measure was
complemented by measuring the factor change in species,
which was obtained based on secondary data.
Regression analysis
The hypothesis was simple here. We tried to determine
which factors lead to coexistence between conservation and
development goals. These institutional factors emerged from
tourism and other sources of change. We determined the
influence of these factors in the two study sites. The regression
equations used were as follows:
Y = α1 + β1X1 + β2X2 + β3X3 + β4X4 + u (1)
Z = α2 + β5X5 + β6X6 + β7X7 +ε (2)
Y = α3 + β8D1 + β9D2 + Ω
(3)
The following are the interpretations of the symbols used:
Y ≡ Stages of progress movement, as will be defined in
course of this analysis and further explained in Appendix 1;
X1 ≡ Difference in income from sale of livestock between
1997 and 2007;
X2 ≡ Difference in income from sale of agricultural products
between 1997 and 2007;
X3 ≡ Difference in income from park and tourism between
1997 and 2007;
X4 ≡ Difference in income by working in large cities between
1997 and 2007;
Z ≡ The fauna sighting change index;
X5 ≡ Change in importance of income from livestock4;
X 6 ≡ Change in importance of income from sale of
agricultural products;
X6 ≡ Change in importance of income from working in
major cities;
X6 ≡ Change in importance of income from tourism;
D1 ≡ Dummy variable for towns related to tourism;
D2 ≡ Dummy variable for villages related to tourism.
All these variables, which are perception-based observations
of the community, were obtained from the two rounds of primary
surveys, the first one being unstructured and the second one
consisting of a structured questionnaire. For most of the variables
(except for stages of progress, whose estimation has been
explained in Appendix 1), the respondents were asked about
their perception of whether a particular variable had changed,
as mentioned above. The changes were ‘increase’ (denoted by
+1), ‘decrease’ (denoted by -1), and ‘no change’ (denoted by 0).
In that sense, we are not looking for actual (absolute or relative)
figures of change, but for perceptions of change.
Equation (1) attempts to find out the community’s perceptions
of what sources of income have contributed to the change in
their overall poverty status. This is a reflection of the changing
relative importance of a source of income in determining the
changes in the community’s developmental status. In equation
(2), we intend to test whether change in relative importance of
a particular source of income (particularly from tourism) has an
impact on ‘fauna sighting change index,’ which is assumed to
be a proxy of fauna conservation according to the community’s
perception5. One needs to keep in mind that even if income
from a source might have increased, it is not necessary that
the relative importance of that source of income has increased
vis-à-vis other sources. Fauna sighting might be more affected
by the change in relative importance of a particular source of
income rather than the change in income6.
Equation (3) tests whether communities close to tourism sites
have witnessed better development defined in terms of ‘stages
of progress,’ as compared to those far from tourism sites. The
idea here is to examine the differences in the developments
that have been encountered in zones that are associated with
tourism against those that are not associated with the same. This
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382 / Ghosh and Uddhammar
is done by considering two tourism dummy variables (one for
towns, and the other for villages) as explanatory variables for
‘stages of progress.’ In order to vindicate the impact of tourismrelated zones on development, by controlling the impacts
of all other factors, and also removing any possibility of
multicollinearity in the model (that may destroy our objective),
all other explanatory variables are deliberately excluded. If
the estimates of the coefficients of the dummy variables are
found to be statistically significant, then one may safely argue
the existence of indicative evidences on development being
associated with tourism. In that case, equation (3), combined
with equation (1), will further buttress the contention that
tourism can be an enabling factor of development and can
enable moving the community up the ladder of ‘stages of
progress.’
DESCRIPTION OF RESULTS FOR
‘STAGES OF PROGRESS’
As we understand here, the most critical variable in this context
is the ‘stages of progress.’ The results of the first and second
rounds of discussions in the two cases are given in Tables 1,
2, and 3.
As can be seen in Table 1, the definition of ‘stages of
progress’ from poverty hinge more on family relations in
the context of Tanzania than in India. As an exposition,
‘no wife, no children’ is one of the indicators of poverty in
Tanzania. In the Tanzanian case, 5–7 wives and 40 children
was defined as part of the second step out of poverty. This
indicates that family relations in Maasai communities are
not only confined to social relations, but that they also have
a clear material content for the men. This is, of course, not
the case in India.
In the Indian case, government employment was mentioned
as one of the components of the first stage out of poverty. It
is interesting to note that in both the cases, being able to send
children to school is an item in the first stage out of poverty,
as is also the ownership of cattle. In the Serengeti/Ngorongoro
case in Tanzania as well as in the Corbett case in India, the
possibility of hiring a tractor is also a component of the first
stage out of poverty. The second stage out of poverty, in both
the cases, includes owning a brick house. Again, possession
of about 4 ha of land also stands as a condition in both cases.
A description of the ‘quasi-longitudinal’ data produced by the
surveys in the two countries is presented in the cross tabulation
in Tables 2 and 3.
As can be seen from Tables 2 and 3, the upward movement
has been more modest in the areas around the Ngorongoro
CA and Serengeti NP in Tanzania, as compared to the Indian
case. In Table 2, we see in the first row, that 74.2% of those in
poverty in 1997 had moved up to the middle level ten years
later, while only 1.7% had moved to the high level. In the Indian
case, 25.4% moved from poverty to middle level, but 61%
moved two steps up. Also the reverse movement—falling into
poverty—occurred to an extent in the Tanzanian cases; 36.3 %
of those at the middle level in 1997 had fallen to poverty ten
years later. This has not happened to any considerable extent
in India. However, 24.2% of those at the high level had fallen
one step down to the middle level in the Indian case.
Also in the Tanzanian case, about 63% were in poverty ten
years ago, while only 31% of those in the Indian case around
Corbett NP were in that position at the time. The difference
is also striking in 2007, where almost 50% in the Indian
sample considered themselves to be placed in the ‘high’ living
conditions category, while only 6% of the respondents in the
Tanzanian case classified themselves thus. More than 28% in
the Tanzanian sample considered themselves poor in 2007,
while only 5% in the Indian sample did so.
As a more general observation, a large number of those in
poverty in 1997 escaped from poverty in 2007 in both the cases.
In Tanzania, this figure is 75.9%, while in India this figure is
86.4%. One needs to bear in mind that the leap out of poverty
is a big achievement in itself, and both regions have achieved
it. An overview of how ‘stages of progress’ was quantified is
given in Appendix 1. The matrix in Appendix 1 shows that
the nine possible positions of development can be constructed
with movements from possible positions 10 years ago to those
of the present.
RESULTS IN INDIA
Factors affecting ‘stages of progress’ in India
The regression results assessing the factors for ‘stages of
progress’ movements in India are as given in the following
equation.
Y = 0.0244 X 1 + 0.085 X 2 + 0.095 . X 3 + 0.02. X 4 + 0.05618
( 0.422 )
2
( 0.001)
( 0.002 )
2
n = 187, R = 0.12, adj. R = 0.09
( 0.442 )
( 0.00 )
(4)
The figures within parentheses are the p-values of regression.
The result in equation (4) shows that the ‘difference in incomes
from the sale of agricultural products’ and the ‘difference in
incomes from tourism’ are statistically significant factors
affecting ‘stages of progress’ (SOP) (at 5% levels). The
difference in incomes from sale of agricultural products 7
has essentially resulted from developments in agricultural
marketing facilities and better infrastructure in and around the
area, as also the processes of urbanisation affecting adjoining
urban agglomerations like the town of Ramnagar. The growth
of tourism has also been a prime factor in this context, as hotels,
lodges, and eco-tourism initiatives have provided for a ‘ready’
market for agricultural products.
On the other hand, another important driver of the SOP
has been income from tourism. We surveyed around 15 of
the existing 25 lodges and found that more than 80% of the
lodges surveyed came up after 2003. The lodges were mostly
owned by urban residents of large cities like Delhi, or at times
residents of the nearby town of Ramnagar. The lodges provided
employment to the local population directly and indirectly. As
a result, there was a decline in the population migrating away
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Tiger, lion, and human life in wilderness / 383
from home to large cities in search of employment. Hence, the
‘difference in incomes earned by working in large cities’ has
not made any significant contribution to SOP. A better profile
of the lodges is provided in Table 4.
With nearly 59% of the overall employment in lodges coming
from within the zone, and 52% of the managers being local
inhabitants, it clearly goes to show that the lodges have primarily
been run by the local population, as compared to Tanzania,
where only 20% of the managers were local (as will be described
later)—this is an interesting phenomenon to be noted. The other
interesting feature to be noted here is that only 7–10% of the total
number of tourists were of foreign origin, which is miniscule
as compared to 90% of the same in Tanzania (Uddhammar and
Ghosh 2009). This further justifies the contention that trained
personnel who are employable as managers might not be in
high demand in the Corbett zone since it as yet does not cater
to international tourists to the same degree that Tanzania does.
Local educated people can serve the purpose of managing the
pattern of tourism that is domestic economy-centric.
As is evident from this discussion, a positive ‘difference in
income from tourism’ has definitely resulted in an increase in
SOP. As stated earlier, most of the lodges came up after 2003,
and has resulted in a significant change in the standards of
living of those employed by them. This has also substituted
for incomes from other sources (like incomes from large
cities, as was evident from our interviews), and has helped in
supplementing other local sources of income (e.g., agriculture).
Therefore, one of the major drivers that led to ‘escape from
poverty’ in 2007, as is noted from the results in Table 3, is
development of tourism in the Corbett zone.
Changes in fauna sightings in Corbett Reserve: is
tourism a determinant?
The critical species considered here include: tiger, elephant,
barking deer, sambar, chital, leopard, nilgai, and wild boar,
among others (Appendix 3; Table A.3.1).
Of all the 191 respondents, an overall negative value of the
composite index was estimated for only two respondents, while
all others reported a positive value. Interestingly, for the tiger,
which is considered an ‘umbrella species’ in the zone, 188
respondents reported that the sightings had increased, while
three respondents felt that the sightings had remained the same.
The respondents were also asked to state whether the
importance of income sources had changed (increased,
remained the same, or diminished). The results obtained were
as follows:
Z = −0.033. X 5 = 0.0194. X 6 −0.0397. X 7 + 0.0299. X 8 + 0.62
( 0.12 )
2
( 0.314 )
2
( 0.064 )
R = 0.055, adj. R = 0.0344, n = 191
( 0.016 )
( 0.00 )
(5)
The regression equation (5) finds ‘change in importance of
income from tourism’ as a significant variable, contributing
positively to fauna sighting. The implication can be drawn
in the following manner. Households, which have been
increasingly exposed to the tourism industry over time with
the development of the sector in the Corbett NP, are exposed
to higher sightings of species, as compared to those less
exposed to the tourism industry. During the interactive sessions
with the local people during both phases of interviews, the
communities associated with tourism revealed having adopted
a more positive outlook towards wildlife as animal sightings
was what was driving the tourism industry. The increase in
‘fauna sighting change index’ is an indicator that animals
were not treated with a negative mind set, as they used to be.
Rather, their presence was a welcome feature that helped the
cause of tourism, thereby helping the community to generate
more income out of tourism. This is where one might state that
the perception of conservation (if a positive ‘fauna sighting
change index’ is an indication) has only got better in the zones
associated with tourism.
On the other hand, the variable ‘change in importance
of income from working in major cities’ is a significant
explanatory variable. The negative sign8 associated with this
indicates that fauna sighting had generally diminished for
those households who had an increased reliance on income
from employment in major cities. A possible explanation of
this can be that the ‘city-centric’ nature of these households
make them less suitable for the natural species of the zone.
Hence, to summarise, while increased income from tourism
implies higher fauna sightings, an increase in alternative
income (from working in cities), implies a decrease in fauna
sightings.
Are sightings higher in tourism-related villages?
We ran another regression with sighting index as the dependent
variable, and with the two dummy variables related to tourism
sites; one of these was for towns that supported tourism, and the
other was for villages that supported tourism. The regression
results are as follows:
Z = 0.0109 D1 + 0.106. D2 + 0.69
( 0.2 )
2
( 0.00 )
( 0.00 )
2
n = 191, R = 0.11, adj. R = 0.09
(6)
Our results show that indeed the sightings are higher in and
around tourism-related villages, but not significantly so in
tourism-related towns. This buttresses the results obtained in
regression equation (5).
Perception-based results: verification with secondary
observations
Most of what we have observed until now in the Indian case
is based on perception, and this deserves to be verified with
secondary level information as obtained from various other
sources. The secondary information was obtained from WII
(1999) and Jhala et al. (2008), analysed through ‘factor change’
in the various variables under consideration, and presented
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384 / Ghosh and Uddhammar
in Table 5.
Table 5 shows that in Corbett NP in India, wildlife populations
have expanded significantly, including that of the tiger. The
annual number of tourist visitors has also increased considerably,
albeit from relatively low levels. It is noteworthy that the annual
number of visitors in the park has increased from 29,000 in
1986–1987 to 52,000 in 1997–1998, and finally to 120,000
in 2006–2007. The correlation between tiger—an important
umbrella species of this ecosystem—and tourist numbers was
0.738, during the 1987–2006 period (Pearson’s r). Increased
tourism and increased local human population did not hinder
the increase in wildlife numbers. In fact, we find indicative
evidence of better quality of park management leading to an
increase in wildlife numbers. This, in turn, has led to the area
becoming more attractive for tourists to visit, which, again in a
circular turn of events, has led to better monitoring of wildlife
by putting pressure on the park management to perform well
and keep wildlife well protected.9
Thus, combining the perception-based survey and also
these secondary observations that buttress the survey analysis
results, we can draw the conclusion that for this protected
area, efficient wildlife protection has worked side by side with
tourism, resulting in the well-being of the surrounding local
human population.
RESULTS IN TANZANIA
Drivers of ‘stages of progress’
In Tanzania, an identical regression was run with SOP as the
dependent variable, with the same explanatory variables as
shown in equation 1 for the Indian case. The results are as
follows:
This is prevalent for both villages and towns. However, in our
previous reporting based on regression result (7), we did not find
that tourism income is an important determinant of the ‘stages of
progress.’ This may be because ‘livestock’ generally has evolved
as an important component for income generation in the region,
particularly after 1997, while tourism income (though prominent)
might be concentrated in only a few villages and towns.
Drivers of fauna sightings
The critical species here are: lion, elephant, buffalo, wild dog,
rhino, zebra, warthog, monkey, and fish, and the ‘fauna sighting
change index’ has been constructed based on respective weights.
Here, the lion has emerged as the ‘umbrella species’ and has
been given a weight of 0.25, while considering the ‘rarity’ aspect
of zebra and rhino across space and time, both of them have
been assigned a weight of 0.15 each (Appendix 3; Table A.3.2).
Interestingly, in Tanzania, out of 293 valid responses for
changes in sightings, around 65 reported a negative ‘fauna
sighting index’ value, reflecting a perception of decline in
fauna sightings during the 10-year period, while 24 respondents
revealed a score of ‘zero’ implying a state of no change in
sightings. Two hundred and four respondents, i.e. 70% of the
sample, reported an increase in sightings.
In fact, to find whether the importance of income from
tourism has resulted in such a change, we attempted to run
an identical regression as was attempted in equation (4). In
the results, as given in equation (9), none of the variables are
significant at 5% levels, though the tourism-related variable
can be stated to be significant at 10% level of significance.
Z = 0.079 . X 5 −0.0018 . X 6 + 0.068 . X 7 + 0.041 . X 8 + 0.43
( 0.12 )
Y = 0.047 X 1 + 0.0314 X 2 + 0.057 . X 3 + 0.035 . X 4 + 0.029
( 0.013)
( 0.1)
( 0.1)
n = 282, R 2 = 0.065, adj. R 2 = 0.051
( 0.13)
( 0.00 )
(7)
In this case, ‘change in income from sale of livestock’ is the
only statistically significant variable. It was also revealed from
conversations during the initial pilot surveys that ‘livestock’
is an extremely valuable possession for inhabitants of the
Ngorongoro area. The importance of livestock could be gauged
from statements like “girls are important—they [can] be sold
and I could get a cow.”10
However, we ran another regression to check whether the
SOP movement is significantly better in villages where tourism
is prevalent. The regression analysis gave a positive response
to this concern.
Y = 0.01.D1 + 0.087.D 2 + 0.046
( 0.05)
2
( 0.00 )
( 0.00 )
2
n = 282, R = 0.03, adj. R = 0.02
(8)
There is a clear indication that the ‘stages of progress’
movement has been positive in zones where tourism exists.
( 0.533)
( 0.1)
( 0.078)
R 2 = 0.043, adj. R 2 = 0.03, n = 293
( 0.00 )
(9)
The regression results, here, weakly exhibit some evidence
of ‘change in importance of income from tourism,’ contributing
positively to fauna sighting.
Fauna sightings in tourism-related areas
It has also been observed that fauna sighting is more in areas
that are associated with tourism. This is shown in the following
regression results.
Z = 0.363.D1 + 0.422.D2 + 0.29
(0.00)
(0.00)
(0.00)
n = 293, R 2 = 0.13, adj. R 2 = 0.12
(10)
Now, if we combine the results obtained in equations (8)
and (10), we find that there is clear indication that tourismrelated areas reveal better animal sightings than other areas.
The results have also revealed a better movement in SOP than
in areas not related to tourism.
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Tiger, lion, and human life in wilderness / 385
What does the secondary data reveal?
In East Africa, significant population fluctuations have occurred
in most species between the first and last measured figures.
However, we ignore that fluctuation and report on the overall
trend during the period 1997–2006. As shown in Table 6, there
has been a considerable increase in predators like lions over
the period, while the numbers of elephants and buffalos have
stayed more or less constant. All these species are important
for tourism. Visitor numbers in the Serengeti-Ngorongoro
area have not increased much during the 1997–2007 period
(Table 6). However, a longer term trend reflects a large factor
change in the number of tourists (see Table 6). On the other
hand, the number of livestock owned in the Ngorongoro CA
has increased sharply, as also the human population in the
region. The increasing importance of livestock in the Tanzanian
economy is recognised and was enhanced by the Agricultural
and Livestock Policy 1997, where a host of incentives for
livestock was provided. This policy shift might have been
a driver of the livestock economy. With increasing human
settlements around forest areas, the demand for manure, hides,
and skins has been increasing. Apart from that, livestock is
also a potential source of draught power for transport and
cultivation activities. Interestingly, some respondents also felt
that livestock are a potential guard against price rise.
In Table 6, we further find that during this period there was
also a sharp increase in wildlife. The number of tourists visiting
this site also increased. Therefore, we find a positive, high, and
statistically significant correlation of tourist visitors with the
elephant and lion population (Table 6). Although considerable
fluctuations have occurred within the period, the figures give
an indication of the long-term trends (Packer et al. 2005).
Hence, with tourism already at a very high level, with more
than half a million tourists visiting the protected areas of East
Africa every year, the importance of tourism has increased
over the last two decades. This is almost five times that of the
number of tourists visiting Corbett NP annually. It has remained
at a very high level during the period 1997–2007. We may thus
argue that with reference to the base period, the importance of
tourism income has not changed; neither has it been responsible
for changing the ‘stages of progress’ for the entire area as a
whole during this phase. But, again, the regions associated
with tourism have benefited more in terms of SOP, than other
regions. There is no doubt that there has been a simultaneous
expansion of wildlife, local human, and livestock populations.
Table 6 clearly reveals the positive correlation between wildlife
populations and the number of tourists visiting the NP/ CA.
Again, we detect a strong possibility of a causal factor from good
wildlife management leading to increased tourism visits, leading
to better monitoring of wildlife, and also to increased pressure
on wildlife authorities to maintain high standards of wildlife
protection. This observation is supported by other research
findings that concur that tourism in this area has positively
affected wildlife (NINA 2007).
It further needs to be noted that tourism has had a sustained
impact on the standards of living, while the domestic economy
has also benefited from it. Profiles of the lodges surveyed
reveals this to a certain extent (Table 7).
The total number of people working for and earning
a livelihood from the tourism sector in Serengeti NP/
Ngorongoro CA is almost three times that of those in Corbett
NP in India. While a large proportion of employed personnel
in the lodges come from the local area only, quite unlike in
Corbett NP, only 20% of the managers are locals. The demand
for more trained personnel from outside the region in Tanzania
is prevalent primarily to cater to the international nature of
tourism in the zone.
CONCLUDING REMARKS
The results of the analyses of the data in the two cases presented
here have some differences and some similarities, though there
seem to be indications of broad similarities in terms of the
conclusions that we may draw. From the secondary data, we
find that in both the cases, an increase in the number of key
species such as lion, tiger, buffalo, and elephant has occurred
parallel to a similar increase in tourist visitors. Factors such as
the expansion of tourism and an increased human population in
general (as shown in Tables 5 and 6 in terms of factor change)
around protected areas have not affected wildlife negatively.
Rather, wildlife and tourism have expanded simultaneously.
In both the cases, respondents showed more awareness of the
opportunities that tourism had created for them in terms of
income and employment. As is evident from our regression
results, we find that sightings generally have increased in
regions where importance of tourism and tourism-related
income are more or have increased over time.
The change in the standards of living (as reflected in the SOP
movement) because of changes in incomes from tourism is more
prominent in the Indian case, and the causality is not so prominent
in the case of Tanzania, where lately, income from livestock has
emerged as an important determinant for change in economic
status. However, SOP movement in the Serengeti-Ngorongoro
region has revealed an interesting characteristic of being more
positively related to tourism-affected areas than other areas. On
the other hand, one may even note that livestock herding has not
affected conservation efforts in Tanzania, as is often expected.
In Tanzania, the other important aspect to be noted is that
in our reference period, tourism was already at a high level
of development, and not much factor change was noted even
in the secondary data. But, the long-term changes definitely
show that tourism has developed over time in a big way. In any
case, these are general causal links that can be noted here. One
plausible mechanism at work is that a rise in species count, or
more specifically, sightings, might have attracted more visitors
to the sites. Word of mouth information also quickly spreads
via electronic media from those who visit the sites.
Another causal link at work is that of alternative land-use
and biodiversity—a relationship not in the ambit of this article.
Only in parts of the Serengeti-Ngorongoro where low-yield
cattle herding is practiced, wildlife and the local rural economy
coexist. With farming, the human-wildlife conflict increases
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386 / Ghosh and Uddhammar
significantly (Uddhammar and Ghosh 2009). In such cases,
tourism could be an alternative economic activity and can
also be ecologically robust. Yet, there are ecological limits
to tourism, and institutional and governance systems are
particularly important for positive outcomes.
In this context, it is important to highlight a few limitations
of this study. We confess that the results are largely indicative
of the critical role of tourism in promoting conservation and
development. There are many other factors in force that are
not really ‘tourism-related’ (like livestock policy). Some of
these factors have been considered here, but definitely not all,
and this has resulted in the low explanatory power of some of
our regression models. Secondly, the data based on which the
analyses have been conducted and conclusions drawn are mostly
based on perceptions of the community. Such an approach has its
strengths and weaknesses. This approach marks a departure from
the traditional approach of dealing with secondary data pertaining
to various neoclassical or often agency-defined delineations
of conservation and development, thereby serving the critical
purpose of providing interesting insights about how communities
are likely to perceive the relationship between conservation
and development. On the other hand, one may not be able to
completely rule out the possibility of the randomness of the ways
in which the human mind works. Therefore, we have attempted
to buttress our conclusions with some secondary observations.
While we do not completely rule out these weaknesses, yet,
with the evidences presented here, we can definitely conclude
that human development can co-exist with institutionalised
conservation in the presence of community-based tourism, and
in no way is tourism a deterrent to this coexistence, rather it
can potentially play the role of a facilitator. In the context of
the SES, therefore, institutionalised conservation mechanisms
in the form of protected area management on part of forest
departments create interactive processes with resource systems
(protected areas) as well as resource units (wildlife, local human
population, and tourists). Of utmost importance for the emergence
of a symbiosis between development and conservation is that
there is a governance system in place that regulates land-use
in appropriate ways. Detailed descriptions of land-use changes
have been kept out of the scope of this analysis. Yet, one may
note that in both countries, there are clear restrictions on grazing
and agriculture inside the park areas, and collection of firewood
is allowed only in the buffer zones. While there is a fee for
land used for tourism close to the park in Tanzania, there is no
such fee in Corbett NP. However, in both the cases, there are
common restrictions like limiting visiting hours for tourists, as
stated earlier. Given this governance system, what we find is
that there might be a ‘symbiotic’ interaction between the two
important resource units, namely human and wildlife, when
there is an important intervening factor like community-based
tourism. Thus, the ‘competitive interaction’ between resource
units can be transformed to ‘symbiotic existence’ with forces of
tourism in vogue.
In the context of the growing debate in international
literature on the roles of tourism, therefore, our findings have
some interesting implications. Our results are in conformity
with Zapata et al. (2011) who reflect on how bottom-up
community-based tourism, borne as a result of a local initiative,
demonstrates longer life expectancy, faster growth, and more
positive impacts on the local economy. Yet, we are not really
in a position to claim the sustainability of such arrangements—
and in India, the initiatives are of recent origin. Again, though
it is generally hypothesised that tourism can reconcile the
differences between conservation and development, it has been
argued that such formulation resides on certain assumptions
that are questionable (Butcher 2011). However, our results
(both perception-based primary data and secondary data) from
the field suggest that even in two diverse settings, there are
indications of positive outcomes from tourism. Further, when
we talk of a rights-based approach to conservation as a means
to ensure conservation with justice, tourism should be seriously
considered (Greiber 2009). The need to plug-in social concerns
in conservation goals as stressed by Chan et al. (2007) may be
made possible with tourism.
The next step in this area of research would be a more
detailed study on the costs and benefits of different institutional
conservation and tourism practices for the people living adjacent
to these protected areas and their impacts on biodiversity. Such
costs and benefits are important for evaluating the sustainability
of important interventions in an SES, as emphasised by Ostrom
(2009). Ostrom (2009: 420) states “...when expected benefits
of managing a resource exceed the perceived costs of investing
in better rules and norms for most users and their leaders, the
probability of users’ self-organizing is high.” Again, a detailed
analysis of land-use patterns will help in the emergence of a
more sharpened response to our hypothesis. Human rights
issues, including institutional measures to address and resolve
human-wildlife conflict, is also an important field for future
research. This will help us in emerging with more meaningful
inferences on our posed hypothesis.
Notes
1.
Tourism may also help in monitoring ecosystems. After several
private visits to Sariska National Park in Rajasthan, Indian
conservationist Valmik Thapar found out in 2005 that Sariska
had no tigers—despite optimistic reporting to the contrary by the
state forest department. He blew the whistle and exposed a major
scandal, which eventually resulted in the adoption of a new, more
scientific tiger census method (with international peer review)
introduced by the Wildlife Institute of India (The Telegraph 2005).
2. In India the villages chosen were Chhoti Haldwani (which
is around 25 km to the east of the core zone of Dhikala and
obtains tourism benefits because of the Corbett Museum, etc.),
Laldhang (located at the southern edge of Corbett National
Park, with enraging disputes about relocation), Bhakrakot (a
tourism-related village located in the northeastern periphery
of the tiger reserve, well-known for its Camp Fortail Creek,
a nature-based tourism initiative, and also for its homestays),
and Ramnagar, (a town marking the entry point to most of the
tourism activities in Corbett). The control cases were Kunkhet
(a village far from the tiger reserve without tourism), Baluli, and
Jamariya (which is close to the reserve, but without tourism).
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Tiger, lion, and human life in wilderness / 387
In the Serengeti-Ngorongoro region, the chosen villages were
Oloirobi (a tourism-related village and within the Ngorongo
reserve), Karatu (a town 15 km from the eastern park border
and an entry point for the tourism circuit by road), Musati (a
village 15 km to the west of the park), and Natta Mbiso (also to
the west of the park), both engaged in community-based tourism
projects. The control cases were Mugumu (a town located to
the west of the park border but outside the tourism circuit) and
Upper Kitete (a village just outside the Ngorongoro reserve on
the eastern side, but not connected to the tourism circuit).
3.
4.
Weberian ideal types are typical representations of the empirical
reality, where each ideal type is distinct in a number of relevant
factors. In this case, three stages of poverty (or out of poverty)
were presented to the respondents: 1) a typical condition of
poverty; 2) a typical condition for a household in the first step
out of poverty; and 3) a typical condition for a household at the
second step out of poverty. These conditions and ideal types were
modelled from the results of the group interviews conducted in
the first phase of the field research. Table 1 presents a concrete
ideal type.
It was observed in the first round of the pilot survey in both
settings that respondents perceive ‘difference in income’ and
‘change in importance of income’ differently. It needs to be
noted here that both are perception-based measures. Although
respondents may feel that income from a particular source
might have increased, yet they themselves may sometimes
reveal its diminishing relative importance. For example, under
certain conditions, a respondent may express that income
from agriculture has increased, but its relative importance
has diminished because of changing occupational pattern
in the family. This importance is purely expressed from the
respondent’s perspective that could also be in the form of
the respondent’s own ranking of importance, and not from
the perspective of the impact of the income source on any
target variable (say, a developmental indicator like ‘stages of
progress’). Both the variables are measured by the (+1, 0, -1)
scheme, as mentioned in the text above.
5. The fundamental assumption behind this hypothesis is that
an increasing ‘fauna sighting index’ is indicative of better
fauna conservation efforts. This assumption seems a fair one,
considering the fact that human-wildlife interactions in the zone
have a long tradition in both the protected areas, and human
communities traditionally have revealed good knowledge about
the fauna in the zone (Uddhammar 2006; Kideghesho 2010).
More importantly, the possibilities of increasing intrusion/
encroachment of animals into human habitats, or vice versa,
are remote. This is because in both places, conservation norms
have become stricter over time, and enforcements have become
very rigorous, as reported by Uddhammar (2006) and Robinson
(2011).
6.
Since the analysis is mostly based on perception-based data from
the respondents, it is not expected that the regression models will
have a very high explanatory power, in terms of the R-square and
adjusted R-square values. The other important factor that will be
responsible for low explanatory power is that for explaining a
dependent variable, we do not consider a host of variables that
explain the dependent one. Rather, we only consider those that are
of relevance to the context of this article. Hence, a low explanatory
power of the models is not of much relevance here. Table 1 is a
typical representation of the concrete ideal types.
7.
The main crops produced are wheat, rice, mustard, sugarcane,
maize, soybean, gram, arhar, moong, masoor, etc. A variety
of fruits like mango, litchi, papaya, guava, banana, etc., and
vegetables like potato, cauliflower, tomato, cabbage, peas, beans,
brinjal, gourds, etc., and spices and herbs such as coriander,
turmeric, ginger, mustard, etc. are also grown in the region. In
Kaladhungi, where the hamlet of Chhoti Haldwani is located,
Jim Corbett had experimented with various agricultural
initiatives for food self-sufficiency in the region. However, the
process of urbanisation led to further demand of food items, and
over time, markets developed from two sources—the growth of
the local town of Ramnagar, and the growth of tourism.
8. Its level of significance from the statistical perspective is
marginally higher than the usually accepted 5% levels, but the
variable is definitely significant at 10% levels.
9.
The reverse process is exemplified by the episode mentioned
in endnote 1 regarding the tiger population in Sariska NP and
the alarm sounded by the well-known conservationist Valmik
Thapar.
10. Such a statement was recorded in the first phase of the primary
survey through a focus group discussion. This statement is
the view of the study population, and is a reflection of the
community-specific socio-cultural characteristics. The authors
have merely reported it here; it neither it reflects on any genderspecific views of authors, nor is this view endorsed by the
authors.
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Received: April 2011; Accepted: May 2012
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Appendix 1: Measuring ‘stages of progress’
While we have defined three ‘stages of progress,’ namely 1, 2 and 3, there are nine possible movements from one stage to another
over time, even considering the stagnancy at one stage over time. The possible movements are: 1 to 2, 2 to 3, 1 to 3, 2 to 1, 3
to 2, 3 to 1, 1 to 1, 2 to 2, and 3 to 3. Stage 1 signifies poverty and deprivation, and hence movement from 1 to 2 entails a big
jump—more significant than the movement from 2 to 3. This cannot be captured if we simply take linear differences like ‘(3-2)=1’,
which is again equal to ‘(2-1)’. Hence, this is not conducive enough to be placed in a formal quantitative framework. In order to
capture these differences, we first take the anti-log of each of 1, 2, and 3. Then, the reciprocal of the differences is considered as
the ‘stages of progress’ (SOP) coefficient. In other words, if the movement is from stage x to stage y, the SOP coefficient will be:
SOP =
1
(A.1.1)
exp ( x ) − exp ( y )
Where exp(i) refers to exponential or anti-logarithmic value of i (i=x,y). The above formula is, however, not valid in case
there has not been any movement. The details are provided below and in Table A.1.
The movement from x to y is simply the mirror image of the movement from y to x. The criticality, however, arises when we
arrive to define stagnancy, i.e., a state of no movement (say 1 to 1, 2 to 2, etc.). A situation of 3 to 3 is definitely better than 1
to 1. We assume that the stagnancy of 3 to 3 is simply neutral movement, and hence it is ‘0’. On the other hand, being at stage
2 in both periods is a situation worse than moving up from 2 to 3, but definitely better than moving down from 2 (middle) to
1 (poor). Hence, this value should be an average of the values obtained from the movement of 2 to 3, and the movement of
2 to 1. Being at 1 for both stages implies being better off than falling from 2 to 1 (you are used to poverty), but worse than
falling from 3 to 2 (you fall modestly), and hence in this case as well, the value should be an average of [2 to 1] and [3 to 2].
The jump from 1 to 3 eventually emerges as an aggregate of the movements from 1 to 2 and from 2 to 3.
Stage movement
Table A.1
Summarises the methodology of measuring SOP index
SOP index
Rationale
1 to 2
2 to 3
1 to 3
0.2141
0.0788
0.2929
Reciprocal of the difference of antilogarithmic values
Reciprocal of the difference of antilogarithmic values
Reciprocal of the difference of antilogarithmic values
2
3
3
1
2
3
−0.2141
−0.0788
−0.2929
−0.1464
−0.0677
0.0000
Mirror image of 1 to 2
Mirror image of 2 to 3
Mirror image of 1 to 3
Average of [2 to 1] and [3 to 2]
Average of [2 to 3] and [2 to 1]
Average of [3 to 2] and [2 to 3]
to
to
to
to
to
to
1
2
1
1
2
3
SOP=Stages of progress
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390 / Ghosh and Uddhammar
Appendix 2: Sampling method
Village/town
Table A.2
Samples drawn from the villages
Corbett NP, India
Sample drawing method
Total number of
households
30
Sample
size
24
Chhoti Haldwani
100
42
Every second household was selected
Jameria
30
24
Complete enumeration
Laldhang
150
25
Every fifth household was selected
Kunkhet
Baluli
90
12
30
8
Every third household was selected
Complete enumeration
Population of
~46,000
43
Upper Kitete
664
53
Oloirobi
487
47
Every tenth household was selected
Natta‑Mbisso
Musati
498
543
50
50
Every tenth household was selected
Every tenth household was selected
Karatu
Population>17,000
50
Mugumu
Population>16,000
50
Being large in size, the town was stratified by
localities and then random sampling was done.
55 households were selected, 50 available
Being large in size, the town was stratified by
localities and then random sampling was done by
selecting 58 households
Bhakrakot
Ramnagar
Complete enumeration
Being large in size, the town was stratified by
localities and then random sampling was done by
selecting a total of 50 households
Ngorongoro‑Serengeti CA, Tanzania
Every tenth household was selected
Comments
6 households
for response
8 households
for response
6 households
for response
5 households
for response
were not available
were not available
were not available
were not available
4 households were not available
for response
7 households were not available
for response
13 households were not available
for response
2 households were not available
for response
4 households were not available
for response
5 households were not available
for response
8 households were not available
for response
NP=National park; CA=Conservation area
Appendix 3: Fauna sighting index
Fauna sighting index (FSI) for the ith household is
m
FSI i = ∑ λ j .Z ji (A.3.1)
j =1
where m refers to the total number of animals sighted, j refers to the animal sighted variable, λ refers to the associated weight
of sub-component of the number of sightings, z.
Table A.3.1
Weights for Corbett NP, India
Animal
Tiger
Elephant
Barking deer
Sambar
Spotted deer
Leopard
Nilgai
Wild boar
Mahseer fish
Others
Weights
0.2
0.1
0.1
0.075
0.075
0.15
0.075
0.075
0.125
0.025
Table A.3.2
Weights for Serengeti‑Ngorongoro region, Tanzania
Animal
Weights
Lion
0.25
Elephant
0.1
Buffalo
0.1
Wild dog
0.075
Rhino
0.075
Zebra
0.15
Warthog
0.075
Monkey
0.075
Fish
0.1